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[ [ [ "[this doc on github](https://github.com/dotnet/interactive/tree/master/samples/notebooks/fsharp/Samples)\n\n# Machine Learning over House Prices with ML.NET\n\n### Reference the packages", "_____no_output_____" ] ], [ [ "#r \"nuget:Microsoft.ML,1.4.0\"\r\n#r \"nuget:Microsoft.ML.AutoML,0.16.0\"\r\n#r \"nuget:Microsoft.Data.Analysis,0.2.0\"\r\n#r \"nuget: XPlot.Plotly.Interactive, 4.0.6\"\r\n \r\nopen Microsoft.Data.Analysis\r\nopen XPlot.Plotly", "_____no_output_____" ] ], [ [ "### Adding better default formatting for data frames\n\nRegister a formatter for data frames and data frame rows.\n", "_____no_output_____" ] ], [ [ "module DateFrameFormatter = \r\n \r\n // Locally open the F# HTML DSL.\r\n open Html\r\n\r\n let maxRows = 20\r\n\r\n Formatter.Register<DataFrame>((fun (df: DataFrame) (writer: TextWriter) ->\r\n\r\n let take = 20\r\n table [] [\r\n thead [] [\r\n th [] [ str \"Index\" ]\r\n for c in df.Columns do\r\n th [] [ str c.Name]\r\n ]\r\n tbody [] [\r\n for i in 0 .. min maxRows (int df.Rows.Count - 1) do\r\n tr [] [\r\n td [] [ i ]\r\n for o in df.Rows.[int64 i] do\r\n td [] [ o ]\r\n ]\r\n ]\r\n ]\r\n |> writer.Write\r\n\r\n ), mimeType = \"text/html\")\r\n \r\n Formatter.Register<DataFrameRow>((fun (row: DataFrameRow) (writer: TextWriter) ->\r\n\r\n table [] [\r\n tbody [] [\r\n tr [] [\r\n for o in row do\r\n td [] [ o ] \r\n ]\r\n ]\r\n ]\r\n |> writer.Write\r\n\r\n ), mimeType = \"text/html\")\r\n ", "_____no_output_____" ] ], [ [ "### Download the data", "_____no_output_____" ] ], [ [ "open System.Net.Http\nlet housingPath = \"housing.csv\"\nif not(File.Exists(housingPath)) then\n let contents = HttpClient().GetStringAsync(\"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\").Result\n File.WriteAllText(\"housing.csv\", contents)", "_____no_output_____" ] ], [ [ "### Add the data to the data frame", "_____no_output_____" ] ], [ [ "let housingData = DataFrame.LoadCsv(housingPath)\nhousingData", "_____no_output_____" ], [ "housingData.Description()", "_____no_output_____" ] ], [ [ "### Display the data", "_____no_output_____" ] ], [ [ "let graph =\n Histogram(x = housingData.[\"median_house_value\"],\n nbinsx = 20)\ngraph |> Chart.Plot", "_____no_output_____" ], [ "let graph =\r\n Scattergl(\r\n x = housingData.[\"longitude\"],\r\n y = housingData.[\"latitude\"],\r\n mode = \"markers\",\r\n marker =\r\n Marker(\r\n color = housingData.[\"median_house_value\"],\r\n colorscale = \"Jet\"))\r\n\r\nlet plot = Chart.Plot(graph)\r\nplot.Width <- 600\r\nplot.Height <- 600\r\ndisplay(plot)", "_____no_output_____" ] ], [ [ "### Prepare the training and validation sets", "_____no_output_____" ] ], [ [ "module Array = \n let shuffle (arr: 'T[]) =\n let rnd = Random()\n let arr = Array.copy arr\n for i in 0 .. arr.Length - 1 do\n let r = i + rnd.Next(arr.Length - i)\n let temp = arr.[r]\n arr.[r] <- arr.[i]\n arr.[i] <- temp\n arr\n\nlet randomIndices = [| 0 .. int housingData.Rows.Count - 1 |] |> Array.shuffle\n\nlet testSize = int (float (housingData.Rows.Count) * 0.1)\nlet trainRows = randomIndices.[testSize..]\nlet testRows = randomIndices.[..testSize - 1]\n\nlet housing_train = housingData.[trainRows]\nlet housing_test = housingData.[testRows]\n\ndisplay(housing_train.Rows.Count)\ndisplay(housing_test.Rows.Count)", "_____no_output_____" ] ], [ [ "### Create the regression model and train it", "_____no_output_____" ] ], [ [ "#!time\n\nopen Microsoft.ML\nopen Microsoft.ML.Data\nopen Microsoft.ML.AutoML\n\nlet mlContext = MLContext()\n\nlet experiment = mlContext.Auto().CreateRegressionExperiment(maxExperimentTimeInSeconds = 15u)\nlet result = experiment.Execute(housing_train, labelColumnName = \"median_house_value\")", "_____no_output_____" ] ], [ [ "### Display the training results", "_____no_output_____" ] ], [ [ "let scatters =\r\n result.RunDetails\r\n |> Seq.filter (fun d -> not (isNull d.ValidationMetrics))\r\n |> Seq.groupBy (fun r -> r.TrainerName)\r\n |> Seq.map (fun (name, details) ->\r\n Scattergl(\r\n name = name,\r\n x = (details |> Seq.map (fun r -> r.RuntimeInSeconds)),\r\n y = (details |> Seq.map (fun r -> r.ValidationMetrics.MeanAbsoluteError)),\r\n mode = \"markers\",\r\n marker = Marker(size = 12)))\r\n\r\nlet chart = Chart.Plot(scatters)\r\nchart.WithXTitle(\"Training Time\")\r\nchart.WithYTitle(\"Error\")\r\ndisplay(chart)\r\n\r\nConsole.WriteLine(\"Best Trainer:{0}\", result.BestRun.TrainerName);", "_____no_output_____" ] ], [ [ "### Validate and display the results ", "_____no_output_____" ] ], [ [ "let testResults = result.BestRun.Model.Transform(housing_test)\r\n\r\nlet trueValues = testResults.GetColumn<float32>(\"median_house_value\")\r\nlet predictedValues = testResults.GetColumn<float32>(\"Score\")\r\n\r\nlet predictedVsTrue =\r\n Scattergl(\r\n x = trueValues,\r\n y = predictedValues,\r\n mode = \"markers\")\r\n\r\nlet maximumValue = Math.Max(Seq.max trueValues, Seq.max predictedValues)\r\n\r\nlet perfectLine =\r\n Scattergl(\r\n x = [| 0.0f; maximumValue |],\r\n y = [| 0.0f; maximumValue |],\r\n mode = \"lines\")\r\n\r\nlet chart = Chart.Plot([| predictedVsTrue; perfectLine |])\r\nchart.WithXTitle(\"True Values\")\r\nchart.WithYTitle(\"Predicted Values\")\r\nchart.WithLegend(false)\r\nchart.Width = 600\r\nchart.Height = 600\r\ndisplay(chart)", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "ML Course, Bogotá, Colombia (&copy; Josh Bloom; June 2019)", "_____no_output_____" ] ], [ [ "%run ../talktools.py", "_____no_output_____" ] ], [ [ "# Featurization and Dirty Data (and NLP)", "_____no_output_____" ], [ "<img src=\"imgs/workflow.png\">\nSource: [V. Singh](https://www.slideshare.net/hortonworks/data-science-workshop)\n\n### Notes:\n\nBiased workflow :)\n\n* Labels → Answer\n* Feature extraction. Taking images/data and manipulate it, and do a feature matrix, each feature is a number (measurable). Domain-specific.\n* Run a ML model on the featurized data. Split in validation and test sets.\n* After predicting and validating results, the new results can become new training data. But you have to beware of skewing the results or the training data → introducing bias. One way to fight this is randomize sampling/results? (I didn't understand how to do this tbh).", "_____no_output_____" ], [ "<img src=\"imgs/feature.png\">\nSource: Lightsidelabs\n", "_____no_output_____" ], [ "<img src=\"imgs/feature2.png\">", "_____no_output_____" ], [ "# Featurization Examples\n\nIn the real world, we are very rarely presented with a clean feature matrix. Raw data are missing, noisy, ugly and unfiltered. And sometimes we dont even have the data we need to make models and predictions. Indeed the conversion of raw data to data that's suitable for learning on is time consuming, difficult, and where a lot of the domain understanding is required.\n\nWhen we extract features from raw data (say PDF documents) we often are presented with a variety of data types:\n<img src=\"imgs/feat.png\">", "_____no_output_____" ], [ "# Categorical & Missing Features\n\n\nOften times, we might be presented with raw data (say from an Excel spreadsheet) that looks like:\n\n| eye color | height | country of origin | gender |\n| ------------| ---------| ---------------------| ------- |\n| brown | 1.85 | Colombia | M |\n| brown | 1.25 | USA | |\n| blonde | 1.45 | Mexico | F |\n| red | 2.01 | Mexico | F |\n| | | Chile | F |\n| Brown | 1.02 | Colombia | | \n\nWhat do you notice in this dataset? \n\nSince many ML learn algorithms require, as we'll see, a full matrix of numerical input features, there's often times a lot of preprocessing work that is needed before we can learn.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\n\ndf = pd.DataFrame({\"eye color\": [\"brown\", \"brown\", \"blonde\", \"red\", None, \"Brown\"],\n \"height\": [1.85, 1.25, 1.45, 2.01, None, 1.02],\n \"country of origin\": [\"Colombia\", \"USA\", \"Mexico\", \"Mexico\", \"Chile\", \"Colombia\"],\n \"gender\": [\"M\", None, \"F\", \"F\",\"F\", None]})\ndf", "_____no_output_____" ] ], [ [ "Let's first normalize the data so it's all lower case. This will handle the \"Brown\" and \"brown\" issue.", "_____no_output_____" ] ], [ [ "df_new = df.copy()\ndf_new[\"eye color\"] = df_new[\"eye color\"].str.lower()\ndf_new", "_____no_output_____" ] ], [ [ "Let's next handle the NaN in the height. What should we use here?", "_____no_output_____" ] ], [ [ "# mean of everyone?\nnp.nanmean(df_new[\"height\"].values)", "_____no_output_____" ], [ "# mean of just females?\nnp.nanmean(df_new[df_new[\"gender\"] == 'F'][\"height\"]) ", "_____no_output_____" ], [ "df_new1 = df_new.copy()\ndf_new1.at[4, \"height\"] = np.nanmean(df_new[df_new[\"gender\"] == 'F'][\"height\"]) \ndf_new1", "_____no_output_____" ] ], [ [ "Let's next handle the eye color. What should we use?", "_____no_output_____" ] ], [ [ "df_new1[\"eye color\"].mode()", "_____no_output_____" ], [ "df_new2 = df_new1.copy()\ndf_new2.at[4, \"eye color\"] = df_new1[\"eye color\"].mode().values[0]\ndf_new2", "_____no_output_____" ] ], [ [ "How should we handle the missing gender entries?", "_____no_output_____" ] ], [ [ "df_new3 = df_new2.fillna(\"N/A\")\ndf_new3", "_____no_output_____" ] ], [ [ "We're done, right? No. We fixed the dirty, missing data problem but we still dont have a numerical feature matrix.\n\nWe could do a mapping such that \"Colombia\" -> 1, \"USA\" -> 2, ... etc. but then that would imply an ordering between what is fundamentally categories (without ordering). Instead we want to do `one-hot encoding`, where every unique value gets its own column. `pandas` as a method on DataFrames called `get_dummies` which does this for us.\n\n### Notes:\n\nMany algorithms/methods cannot handle cathegorical data, that's why you have to do the `one-hot encoding` \"trick\". An example of one that can handle it is Random forest.", "_____no_output_____" ] ], [ [ "pd.get_dummies(df_new3, prefix=['eye color', 'country of origin', 'gender'])", "_____no_output_____" ] ], [ [ "Note: depending on the learning algorithm you use, you may want to do `drop_first=True` in `get_dummies`.", "_____no_output_____" ], [ "Of course there are helpful tools that exist for us to deal with dirty, missing data.", "_____no_output_____" ] ], [ [ "%run transform", "_____no_output_____" ], [ "bt = BasicTransformer(return_df=True)\nbt.fit_transform(df_new)", "_____no_output_____" ] ], [ [ "## Time series\n\nThe [wafer dataset](http://www.timeseriesclassification.com/description.php?Dataset=Wafer) is a set of timeseries capturing sensor measurements (1000 training examples, 6164 test examples) of one silicon wafer during the manufacture of semiconductors. Each wafer has a classification of normal or abnormal. The abnormal wafers are representative of a range of problems commonly encountered during semiconductor manufacturing.", "_____no_output_____" ] ], [ [ "import requests\nfrom io import StringIO\ndat_file = requests.get(\"https://github.com/zygmuntz/time-series-classification/blob/master/data/wafer/Wafer.csv?raw=true\")\ndata = StringIO(dat_file.text)", "_____no_output_____" ], [ "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\ndata.seek(0)\ndf = pd.read_csv(data, header=None)", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ] ], [ [ "0 to 151 time measurements, the latest colum is the label we are trying to predict.", "_____no_output_____" ] ], [ [ "df[152].value_counts()", "_____no_output_____" ], [ "## save the data as numpy arrays\ntarget = df.values[:,152].astype(int)\ntime_series = df.values[:,0:152]", "_____no_output_____" ], [ "normal_inds = np.argwhere(target == 1) ; np.random.shuffle(normal_inds)\nabnormal_inds = np.argwhere(target == -1); np.random.shuffle(abnormal_inds)\n\nnum_to_plot = 3\nfig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(12,6))\n\nfor i in range(num_to_plot):\n ax1.plot(time_series[normal_inds[i][0],:], label=f\"#{normal_inds[i][0]}: {target[normal_inds[i][0]]}\")\n ax2.plot(time_series[abnormal_inds[i][0],:], label=f\"#{abnormal_inds[i][0]}: {target[abnormal_inds[i][0]]}\")\n\nax1.legend()\nax2.legend()\nax1.set_title(\"Normal\") ; ax2.set_title(\"Abnormal\") \nax1.set_xlabel(\"time\") ; ax2.set_xlabel(\"time\")\nax1.set_ylabel(\"Value\")", "_____no_output_____" ] ], [ [ "What would be good features here?", "_____no_output_____" ] ], [ [ "f1 = np.mean(time_series, axis=1) # how about the mean?\nf1.shape", "_____no_output_____" ], [ "import seaborn as sns, numpy as np\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nax = sns.distplot(f1)", "_____no_output_____" ], [ "# Plotting differences of means between normal and abnormal\nax = sns.distplot(f1[normal_inds], kde_kws={\"label\": \"normal\"})\nsns.distplot(f1[abnormal_inds], ax=ax, kde_kws={\"label\": \"abnormal\"})", "_____no_output_____" ], [ "f2 = np.min(time_series, axis=1) # how about the mean?\nf2.shape", "_____no_output_____" ], [ "# Differences in the minimum - Not much difference, just a small subset at the \"end that can tell something interesting\nax = sns.distplot(f2[normal_inds], kde_kws={\"label\": \"normal\"})\nsns.distplot(f2[abnormal_inds], ax=ax, kde_kws={\"label\": \"abnormal\"})", "_____no_output_____" ] ], [ [ "Often there are entire python packages devoted to help us build features from certain types of datasets (timeseries, text, images, movies, etc.). In the case of timeseries, a popular package is `tsfresh` (*\"It automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.\"*). See the [tsfresh docs](https://tsfresh.readthedocs.io/en/latest/) and the [list of features generated](https://tsfresh.readthedocs.io/en/latest/text/list_of_features.html).", "_____no_output_____" ] ], [ [ "# !pip install tsfresh", "_____no_output_____" ], [ "dfc = df.copy()\ndel dfc[152]\nd = dfc.stack()\nd = d.reset_index()\nd = d.rename(columns={\"level_0\": \"id\", \"level_1\": \"time\", 0: \"value\"})\ny = df[152]", "_____no_output_____" ], [ "from tsfresh import extract_features\n\nmax_num=300\n\nfrom tsfresh import extract_relevant_features\n\nfeatures_filtered_direct = extract_relevant_features(d[d[\"id\"] < max_num], y.iloc[0:max_num],\n column_id='id', column_sort='time', n_jobs=4)\n#extracted_features = extract_features(, column_id=\"id\", \n # column_sort=\"time\", disable_progressbar=False, n_jobs=3)", "Feature Extraction: 100%|██████████| 20/20 [00:19<00:00, 1.01it/s]\nWARNING:tsfresh.utilities.dataframe_functions:The columns 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'value__fft_coefficient__coeff_86__attr_\"imag\"'\n 'value__fft_coefficient__coeff_86__attr_\"real\"'\n 'value__fft_coefficient__coeff_87__attr_\"abs\"'\n 'value__fft_coefficient__coeff_87__attr_\"angle\"'\n 'value__fft_coefficient__coeff_87__attr_\"imag\"'\n 'value__fft_coefficient__coeff_87__attr_\"real\"'\n 'value__fft_coefficient__coeff_88__attr_\"abs\"'\n 'value__fft_coefficient__coeff_88__attr_\"angle\"'\n 'value__fft_coefficient__coeff_88__attr_\"imag\"'\n 'value__fft_coefficient__coeff_88__attr_\"real\"'\n 'value__fft_coefficient__coeff_89__attr_\"abs\"'\n 'value__fft_coefficient__coeff_89__attr_\"angle\"'\n 'value__fft_coefficient__coeff_89__attr_\"imag\"'\n 'value__fft_coefficient__coeff_89__attr_\"real\"'\n 'value__fft_coefficient__coeff_90__attr_\"abs\"'\n 'value__fft_coefficient__coeff_90__attr_\"angle\"'\n 'value__fft_coefficient__coeff_90__attr_\"imag\"'\n 'value__fft_coefficient__coeff_90__attr_\"real\"'\n 'value__fft_coefficient__coeff_91__attr_\"abs\"'\n 'value__fft_coefficient__coeff_91__attr_\"angle\"'\n 'value__fft_coefficient__coeff_91__attr_\"imag\"'\n 'value__fft_coefficient__coeff_91__attr_\"real\"'\n 'value__fft_coefficient__coeff_92__attr_\"abs\"'\n 'value__fft_coefficient__coeff_92__attr_\"angle\"'\n 'value__fft_coefficient__coeff_92__attr_\"imag\"'\n 'value__fft_coefficient__coeff_92__attr_\"real\"'\n 'value__fft_coefficient__coeff_93__attr_\"abs\"'\n 'value__fft_coefficient__coeff_93__attr_\"angle\"'\n 'value__fft_coefficient__coeff_93__attr_\"imag\"'\n 'value__fft_coefficient__coeff_93__attr_\"real\"'\n 'value__fft_coefficient__coeff_94__attr_\"abs\"'\n 'value__fft_coefficient__coeff_94__attr_\"angle\"'\n 'value__fft_coefficient__coeff_94__attr_\"imag\"'\n 'value__fft_coefficient__coeff_94__attr_\"real\"'\n 'value__fft_coefficient__coeff_95__attr_\"abs\"'\n 'value__fft_coefficient__coeff_95__attr_\"angle\"'\n 'value__fft_coefficient__coeff_95__attr_\"imag\"'\n 'value__fft_coefficient__coeff_95__attr_\"real\"'\n 'value__fft_coefficient__coeff_96__attr_\"abs\"'\n 'value__fft_coefficient__coeff_96__attr_\"angle\"'\n 'value__fft_coefficient__coeff_96__attr_\"imag\"'\n 'value__fft_coefficient__coeff_96__attr_\"real\"'\n 'value__fft_coefficient__coeff_97__attr_\"abs\"'\n 'value__fft_coefficient__coeff_97__attr_\"angle\"'\n 'value__fft_coefficient__coeff_97__attr_\"imag\"'\n 'value__fft_coefficient__coeff_97__attr_\"real\"'\n 'value__fft_coefficient__coeff_98__attr_\"abs\"'\n 'value__fft_coefficient__coeff_98__attr_\"angle\"'\n 'value__fft_coefficient__coeff_98__attr_\"imag\"'\n 'value__fft_coefficient__coeff_98__attr_\"real\"'\n 'value__fft_coefficient__coeff_99__attr_\"abs\"'\n 'value__fft_coefficient__coeff_99__attr_\"angle\"'\n 'value__fft_coefficient__coeff_99__attr_\"imag\"'\n 'value__fft_coefficient__coeff_99__attr_\"real\"'] did not have any finite values. Filling with zeros.\nWARNING:tsfresh.feature_selection.relevance:Infered classification as machine learning task\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__autocorrelation__lag_0 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.2 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.4 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.6 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.2 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.2 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.8 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.4 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.6 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.4__ql_0.8 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.4 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__fft_coefficient__coeff_0__attr_\"imag\" is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.6 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.4 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.6 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.2__ql_0.8 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.6__ql_0.8 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.6 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.8 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.4 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.8 is constant\nWARNING:tsfresh.feature_selection.relevance:[test_feature_significance] Feature value__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.4__ql_0.4 is constant\n" ], [ "feats = features_filtered_direct[features_filtered_direct.columns[0:4]].rename(lambda x: x[0:14], axis='columns')\nfeats[\"target\"] = y.iloc[0:max_num]\nsns.pairplot(feats, hue=\"target\")", "_____no_output_____" ] ], [ [ "# Text Data\n\nMany applications involve parsing and understanding something about natural language, ie. speech or text data. Categorization is a classic usage of Natural Language Processing (NLP): what bucket does this text belong to? \n\nQuestion: **What are some examples where learning on text has commerical or industrial applications?**", "_____no_output_____" ], [ "A classic dataset in text processing is the [20,000+ newsgroup documents corpus](http://qwone.com/~jason/20Newsgroups/). These texts taken from old discussion threads in 20 different [newgroups](https://en.wikipedia.org/wiki/Usenet_newsgroup):\n\n<pre>\ncomp.graphics\ncomp.os.ms-windows.misc\ncomp.sys.ibm.pc.hardware\ncomp.sys.mac.hardware\ncomp.windows.x\t\nrec.autos\nrec.motorcycles\nrec.sport.baseball\nrec.sport.hockey\t\nsci.crypt\nsci.electronics\nsci.med\nsci.space\nmisc.forsale\t\ntalk.politics.misc\ntalk.politics.guns\ntalk.politics.mideast\t\ntalk.religion.misc\nalt.atheism\nsoc.religion.christian\n</pre>\nOne of the tasks is to assign a document to the correct group, ie. classify which group this belongs to. `sklearn` has a download facility for this dataset:", "_____no_output_____" ] ], [ [ "from sklearn.datasets import fetch_20newsgroups\nnews_train = fetch_20newsgroups(subset='train', categories=['sci.space','rec.autos'], data_home='datatmp/')", "Downloading 20news dataset. This may take a few minutes.\nINFO:sklearn.datasets.twenty_newsgroups:Downloading 20news dataset. This may take a few minutes.\nDownloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\nINFO:sklearn.datasets.twenty_newsgroups:Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n" ], [ "news_train.target_names", "_____no_output_____" ], [ "print(news_train.data[1])", "From: smith@ctron.com (Lawrence C Smith)\nSubject: Re: MR2 - noisy engine.\nOrganization: Cabletron Systems, Inc.\nLines: 16\nDistribution: world\nReply-To: smith@ctron.com\nNNTP-Posting-Host: glinda.ctron.com\n\nIn article <Apr21.053718.19765@engr.washington.edu>, eliot@lanmola.engr.washington.edu (eliot) writes:\n\n>if the noise really bugs you, there is nothing else that you can do\n>except to sell it and get a V6.\n\nPerhaps a nice used '88 Pontiac Fiero GT? 2.8 liters.\n\nDoes anyone know if the motor mounts for the 2.8 and the twin-dual-cam 3.4\nliter match? The 3.4 is supposedly derived from the pushrod 3.1, which was\na punched out 2.8 liter. Should be a drop-in replacement, eh? 205 horses in\na mid-engine the size of a Fiero?\n\nLarry Smith (smith@ctron.com) No, I don't speak for Cabletron. Need you ask?\n-\nLiberty is not the freedom to do whatever we want,\nit is the freedom to do whatever we are able.\n\n" ], [ "news_train.target_names[news_train.target[1]]", "_____no_output_____" ], [ "autos = np.argwhere(news_train.target == 1) \nsci = np.argwhere(news_train.target == 0)", "_____no_output_____" ] ], [ [ "**How do you (as a human) classify text? What do you look for? How might we make these features?**", "_____no_output_____" ] ], [ [ "# total character count?\nf1 = np.array([len(x) for x in news_train.data])\nf1", "_____no_output_____" ], [ "ax = sns.distplot(f1[autos], kde_kws={\"label\": \"autos\"})\nsns.distplot(f1[sci], ax=ax, kde_kws={\"label\": \"sci\"})\nax.set_xscale(\"log\")\nax.set_xlabel(\"number of charaters\")", "_____no_output_____" ], [ "# total character words?\nf2 = np.array([len(x.split(\" \")) for x in news_train.data])\nf2", "_____no_output_____" ], [ "ax = sns.distplot(f2[autos], kde_kws={\"label\": \"autos\"})\nsns.distplot(f2[sci], ax=ax, kde_kws={\"label\": \"sci\"})\nax.set_xscale(\"log\")\nax.set_xlabel(\"number of words\")", "_____no_output_____" ], [ "# number of questions asked or exclaimations?\nf3 = np.array([x.count(\"?\") + x.count(\"!\") for x in news_train.data])\nf3", "_____no_output_____" ], [ "ax = sns.distplot(f3[autos], kde_kws={\"label\": \"autos\"})\nsns.distplot(f3[sci], ax=ax, kde_kws={\"label\": \"sci\"})\nax.set_xlabel(\"number of questions asked\")", "_____no_output_____" ] ], [ [ "We've got three fairly uninformative features now. We should be able to do better. \nUnsurprisingly, what matters most in NLP is the content: the words used, the tone, the meaning from the ordering of those words. The basic components of NLP are:\n\n * Tokenization - intelligently splitting up words in sentences, paying attention to conjunctions, punctuation, etc.\n * Lemmization - reducing a word to its base form\n * Entity recognition - finding proper names, places, etc. in documents\n \nThere a many Python packages that help with NLP, including `nltk`, `textblob`, `gensim`, etc. Here we'll use the fairly modern and battletested [`spaCy`](https://spacy.io/).\n\n### Notes:\n\n* For tokenization you need to know the language you are dealing with.\n* Lemmization idea is to kind of normalize the whole text content, and maybe reduce words, such as adverbs to adjectives or plurals to singular, etc.\n* For lemmization not only needs the language but also the kind of content you are dealing with.", "_____no_output_____" ] ], [ [ "#!pip install spacy", "_____no_output_____" ], [ "#!python -m spacy download en", "_____no_output_____" ], [ "#!python -m spacy download es", "_____no_output_____" ], [ "import spacy\n\n# Load English tokenizer, tagger, parser, NER and word vectors\nnlp = spacy.load(\"en\")\n\n# the spanish model is\n# nlp = spacy.load(\"es\")\n\ndoc = nlp(u\"Guido said that 'Python is one of the best languages for doing Data Science.' \"\n \"Why he said that should be clear to anyone who knows Python.\")\nen_doc = doc", "_____no_output_____" ] ], [ [ "`doc` is now an `iterable ` with each word/item properly tokenized and tagged. This is done by applying rules specific to each language. Linguistic annotations are available as Token attributes.", "_____no_output_____" ] ], [ [ "for token in doc:\n print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,\n token.shape_, token.is_alpha, token.is_stop)", "Guido Guido PROPN NNP nsubj Xxxxx True False\nsaid say VERB VBD ROOT xxxx True False\nthat that ADP IN mark xxxx True True\n' ' PUNCT `` punct ' False False\nPython Python PROPN NNP nsubj Xxxxx True False\nis be VERB VBZ ccomp xx True True\none one NUM CD attr xxx True True\nof of ADP IN prep xx True True\nthe the DET DT det xxx True True\nbest good ADJ JJS amod xxxx True False\nlanguages language NOUN NNS pobj xxxx True False\nfor for ADP IN prep xxx True True\ndoing do VERB VBG pcomp xxxx True True\nData Data PROPN NNP compound Xxxx True False\nScience Science PROPN NNP dobj Xxxxx True False\n. . PUNCT . punct . False False\n' ' PUNCT '' punct ' False False\nWhy why ADV WRB advmod Xxx True True\nhe -PRON- PRON PRP nsubj xx True True\nsaid say VERB VBD ROOT xxxx True False\nthat that DET DT nsubj xxxx True True\nshould should VERB MD aux xxxx True True\nbe be VERB VB ccomp xx True True\nclear clear ADJ JJ acomp xxxx True False\nto to ADP IN prep xx True True\nanyone anyone NOUN NN pobj xxxx True True\nwho who PRON WP nsubj xxx True True\nknows know VERB VBZ relcl xxxx True False\nPython Python PROPN NNP dobj Xxxxx True False\n. . PUNCT . punct . False False\n" ], [ "from spacy import displacy\n\ndisplacy.serve(doc, style=\"dep\")", "_____no_output_____" ], [ "displacy.render(doc, style = \"ent\", jupyter = True)", "_____no_output_____" ], [ "nlp = spacy.load(\"es\")", "_____no_output_____" ], [ "# https://www.elespectador.com/noticias/ciencia/decenas-de-nuevas-supernovas-ayudaran-medir-la-expansion-del-universo-articulo-863683\ndoc = nlp(u'En los últimos años, los investigadores comenzaron a'\n 'informar un nuevo tipo de supernovas de cinco a diez veces'\n 'más brillantes que las supernovas de Tipo \"IA\". ')", "_____no_output_____" ], [ "for token in doc:\n print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,\n token.shape_, token.is_alpha, token.is_stop)", "En En ADP ADP__AdpType=Prep case Xx True True\nlos lo DET DET__Definite=Def|Gender=Masc|Number=Plur|PronType=Art det xxx True True\núltimos último ADJ ADJ__Gender=Masc|Number=Plur|NumType=Ord amod xxxx True True\naños año NOUN NOUN__Gender=Masc|Number=Plur obl xxxx True False\n, , PUNCT PUNCT__PunctType=Comm punct , False False\nlos lo DET DET__Definite=Def|Gender=Masc|Number=Plur|PronType=Art det xxx True True\ninvestigadores investigador NOUN NOUN__Gender=Masc|Number=Plur nsubj xxxx True False\ncomenzaron comenzar AUX AUX__Mood=Ind|Number=Plur|Person=3|Tense=Past|VerbForm=Fin aux xxxx True False\nainformar ainformar VERB VERB__VerbForm=Inf ROOT xxxx True False\nun uno DET DET__Definite=Ind|Gender=Masc|Number=Sing|PronType=Art det xx True True\nnuevo nuevo ADJ ADJ__Gender=Masc|Number=Sing amod xxxx True True\ntipo tipo NOUN NOUN__Gender=Masc|Number=Sing obj xxxx True False\nde de ADP ADP__AdpType=Prep case xx True True\nsupernovas supernova NOUN NOUN__Gender=Fem|Number=Plur nmod xxxx True False\nde de ADP ADP__AdpType=Prep case xx True True\ncinco cincar NUM NUM__Number=Plur|NumType=Card nummod xxxx True True\na a ADP ADP__AdpType=Prep case x True False\ndiez diez NUM NUM__Number=Plur|NumType=Card obj xxxx True False\nvecesmás vecesmás AUX AUX__Mood=Ind|Number=Plur|Person=3|Tense=Pres|VerbForm=Fin amod xxxx True False\nbrillantes brillante ADJ ADJ__Number=Plur obj xxxx True False\nque que PRON PRON__PronType=Rel mark xxx True True\nlas los DET DET__Definite=Def|Gender=Fem|Number=Plur|PronType=Art det xxx True True\nsupernovas supernova NOUN NOUN__Gender=Fem|Number=Plur nmod xxxx True False\nde de ADP ADP__AdpType=Prep case xx True True\nTipo Tipo PROPN PROPN___ nmod Xxxx True False\n\" \" PUNCT PUNCT__PunctType=Quot punct \" False False\nIA IA PROPN PROPN___ flat XX True False\n\" \" PUNCT PUNCT__PunctType=Quot punct \" False False\n. . PUNCT PUNCT__PunctType=Peri punct . False False\n" ], [ "from spacy import displacy\n\ndisplacy.serve(doc, style=\"dep\")", "_____no_output_____" ], [ "[i for i in doc.sents]", "_____no_output_____" ] ], [ [ "One very powerful way to featurize text/documents is to count the frequency of words---this is called **bag of words**. Each individual token occurrence frequency is used to generate a feature. So the two sentences become:\n\n```json\n{\"Guido\": 1,\n \"said\": 2,\n \"that\": 2,\n \"Python\": 2,\n \"is\": 1,\n \"one\": 1,\n \"of\": 1,\n \"best\": 1,\n \"languages\": 1,\n \"for\": 1,\n \"Data\": 1,\n \"Science\": 1,\n \"Why\", 1,\n \"he\": 1,\n \"should\": 1,\n \"be\": 1,\n \"anyone\": 1,\n \"who\": 1\n }\n ```\n", "_____no_output_____" ], [ "A corpus of documents can be represented as a matrix with one row per document and one column per token.\n\nQuestion: **What are some challenges you see with brute force BoW?**", "_____no_output_____" ] ], [ [ "from spacy.lang.en.stop_words import STOP_WORDS\nSTOP_WORDS", "_____no_output_____" ] ], [ [ "`sklearn` has a number of helper functions, include the [`CountVectorizer`](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html):\n\n> Convert a collection of text documents to a matrix of token counts. This implementation produces a sparse representation of the counts using `scipy.sparse.csr_matrix`.", "_____no_output_____" ] ], [ [ "# the following is from https://www.dataquest.io/blog/tutorial-text-classification-in-python-using-spacy/\nimport pandas as pd\nfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\nfrom sklearn.base import TransformerMixin\nfrom sklearn.pipeline import Pipeline\nimport string\nfrom spacy.lang.en.stop_words import STOP_WORDS\nfrom spacy.lang.en import English\n\n# Create our list of punctuation marks\npunctuations = string.punctuation\n\n# Create our list of stopwords\nnlp = spacy.load('en')\nstop_words = spacy.lang.en.stop_words.STOP_WORDS\n\n# Load English tokenizer, tagger, parser, NER and word vectors\nparser = English()\n\n# Creating our tokenizer function\ndef spacy_tokenizer(sentence):\n # Creating our token object, which is used to create documents with linguistic annotations.\n mytokens = parser(sentence)\n\n # Lemmatizing each token and converting each token into lowercase\n mytokens = [ word.lemma_.lower().strip() if word.lemma_ != \"-PRON-\" else word.lower_ for word in mytokens ]\n\n # Removing stop words\n mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]\n\n # return preprocessed list of tokens\n return mytokens\n\n# Custom transformer using spaCy\nclass predictors(TransformerMixin):\n def transform(self, X, **transform_params):\n # Cleaning Text\n return [clean_text(text) for text in X]\n\n def fit(self, X, y=None, **fit_params):\n return self\n\n def get_params(self, deep=True):\n return {}\n\n# Basic function to clean the text\ndef clean_text(text):\n # Removing spaces and converting text into lowercase\n return text.strip().lower()", "_____no_output_____" ], [ "bow_vector = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1))", "_____no_output_____" ], [ "X = bow_vector.fit_transform([x.text for x in en_doc.sents])", "_____no_output_____" ], [ "X", "_____no_output_____" ], [ "bow_vector.get_feature_names()", "_____no_output_____" ] ], [ [ "Why did we get `datum` as one of our feature names?", "_____no_output_____" ] ], [ [ "X.toarray()", "_____no_output_____" ], [ "doc.text", "_____no_output_____" ] ], [ [ "Let's try a bigger corpus (the newsgroups):", "_____no_output_____" ] ], [ [ "news_train = fetch_20newsgroups(subset='train', \n remove=('headers', 'footers', 'quotes'),\n categories=['sci.space','rec.autos'], data_home='datatmp/')", "_____no_output_____" ], [ "%time X = bow_vector.fit_transform(news_train.data)", "CPU times: user 1.76 s, sys: 2.98 ms, total: 1.76 s\nWall time: 1.76 s\n" ], [ "X", "_____no_output_____" ], [ "bow_vector.get_feature_names()", "_____no_output_____" ] ], [ [ "Most of those features will only appear once and we might not want to include them (as they add noise). In order to reweight the count features into floating point values suitable for usage by a classifier it is very common to use the *tf–idf* transform. \n\nFrom [`sklearn`](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer): \n\n> Tf means term-frequency while tf-idf means term-frequency times inverse document-frequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification.\nThe goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus.\n\n\nLet's keep only those terms that show up in at least 3% of the docs, but not those that show up in more than 90%.", "_____no_output_____" ] ], [ [ "tfidf_vector = TfidfVectorizer(tokenizer = spacy_tokenizer, min_df=0.03, max_df=0.9, max_features=1000)", "_____no_output_____" ], [ "%time X = tfidf_vector.fit_transform(news_train.data)", "CPU times: user 1.27 s, sys: 1.23 ms, total: 1.27 s\nWall time: 1.28 s\n" ], [ "tfidf_vector.get_feature_names()", "_____no_output_____" ], [ "X", "_____no_output_____" ], [ "print(X[1,:])", " (0, 29)\t0.3815483132390864\n (0, 179)\t0.3115081012023542\n (0, 242)\t0.44371077138978526\n (0, 83)\t0.3573749506148357\n (0, 138)\t0.25772936628163756\n (0, 74)\t0.31784760138938223\n (0, 182)\t0.44889378780494893\n (0, 282)\t0.25264664433284\n" ], [ "y = news_train.target\nnp.savez(\"tfidf.npz\", X=X.todense(), y=y)", "_____no_output_____" ] ], [ [ "One of the challenges with BoW and TF-IDF is that we lose context. \"Me gusta esta clase, no\" is the same as \"No me gusta esta clase\". \n\nOne way to handle this is with N-grams -- not just frequencies of individual words but of groupings of n-words. Eg. \"Me gusta\", \"gusta esta\", \"esta clase\", \"clase no\", \"no me\" (bigrams). ", "_____no_output_____" ] ], [ [ "bow_vector = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,2))\nX = bow_vector.fit_transform([x.text for x in en_doc.sents])\nbow_vector.get_feature_names()", "_____no_output_____" ] ], [ [ "As we'll see later in the week, while bigram TF-IDF certainly works to capture some small scale meaning, `word embeddings` tend to do very well.", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Calibration of non-isoplanatic low frequency data\n\nThis uses an implementation of the SageCAL algorithm to calibrate a simulated SKA1LOW observation in which sources inside the primary beam have one set of calibration errors and sources outside have different errors.\n\nIn this example, the peeler sources are held fixed in strength and location and only the gains solved. The other sources, inside the primary beam, are partitioned into weak (<5Jy) and strong (>5Jy). The weak sources are processed collectively as an image. The bright sources are processed individually.\n", "_____no_output_____" ] ], [ [ "% matplotlib inline\n\nimport os\nimport sys\n\nsys.path.append(os.path.join('..', '..'))\n\nfrom data_models.parameters import arl_path\n\nresults_dir = arl_path('test_results')\n\nimport numpy\n\nfrom astropy.coordinates import SkyCoord\nfrom astropy import units as u\nfrom astropy.wcs.utils import pixel_to_skycoord\n\nfrom matplotlib import pyplot as plt\n\nfrom data_models.memory_data_models import SkyModel\nfrom data_models.polarisation import PolarisationFrame\n\nfrom workflows.arlexecute.execution_support.arlexecute import arlexecute\n\nfrom processing_components.skycomponent.operations import find_skycomponents\nfrom processing_components.calibration.calibration import solve_gaintable\nfrom processing_components.calibration.operations import apply_gaintable, create_gaintable_from_blockvisibility\nfrom processing_components.visibility.base import create_blockvisibility, copy_visibility\nfrom processing_components.image.deconvolution import restore_cube\nfrom processing_components.skycomponent.operations import select_components_by_separation, insert_skycomponent, \\\n select_components_by_flux\nfrom processing_components.image.operations import show_image, qa_image, copy_image, create_empty_image_like\nfrom processing_components.simulation.testing_support import create_named_configuration, create_low_test_beam, \\\n simulate_gaintable, create_low_test_skycomponents_from_gleam\nfrom processing_components.skycomponent.operations import apply_beam_to_skycomponent, find_skycomponent_matches\nfrom processing_components.imaging.base import create_image_from_visibility, advise_wide_field, \\\n predict_skycomponent_visibility\nfrom processing_components.imaging.imaging_functions import invert_function\n\nfrom workflows.arlexecute.calibration.calskymodel_workflows import calskymodel_solve_workflow\n\nfrom processing_components.image.operations import export_image_to_fits\n\nimport logging\n\ndef init_logging():\n log = logging.getLogger()\n logging.basicConfig(filename='%s/skymodel_cal.log' % results_dir,\n filemode='a',\n format='%(thread)s %(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',\n datefmt='%H:%M:%S',\n level=logging.INFO)\nlog = logging.getLogger()\nlogging.info(\"Starting skymodel_cal\")\n", "_____no_output_____" ] ], [ [ "Use Dask throughout", "_____no_output_____" ] ], [ [ "arlexecute.set_client(use_dask=True)\narlexecute.run(init_logging)", "_____no_output_____" ] ], [ [ "We make the visibility. The parameter rmax determines the distance of the furthest antenna/stations used. All over parameters are determined from this number.\n\nWe set the w coordinate to be zero for all visibilities so as not to have to do full w-term processing. This speeds up the imaging steps.", "_____no_output_____" ] ], [ [ "nfreqwin = 1\nntimes = 1\nrmax = 750\nfrequency = numpy.linspace(0.8e8, 1.2e8, nfreqwin)\nif nfreqwin > 1:\n channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]])\nelse:\n channel_bandwidth = [0.4e8]\ntimes = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes)\n\nphasecentre=SkyCoord(ra=+30.0 * u.deg, dec=-45.0 * u.deg, frame='icrs', equinox='J2000')\nlowcore = create_named_configuration('LOWBD2', rmax=rmax)\n\nblock_vis = create_blockvisibility(lowcore, times, frequency=frequency,\n channel_bandwidth=channel_bandwidth, weight=1.0, phasecentre=phasecentre,\n polarisation_frame=PolarisationFrame(\"stokesI\"), zerow=True)", "_____no_output_____" ], [ "wprojection_planes=1\nadvice=advise_wide_field(block_vis, guard_band_image=5.0, delA=0.02, \n wprojection_planes=wprojection_planes)\n\nvis_slices = advice['vis_slices']\nnpixel=advice['npixels2']\ncellsize=advice['cellsize']", "_____no_output_____" ] ], [ [ "Generate the model from the GLEAM catalog, including application of the primary beam.", "_____no_output_____" ] ], [ [ "beam = create_image_from_visibility(block_vis, npixel=npixel, frequency=frequency,\n nchan=nfreqwin, cellsize=cellsize, phasecentre=phasecentre)\n\noriginal_gleam_components = create_low_test_skycomponents_from_gleam(flux_limit=1.0,\n phasecentre=phasecentre, frequency=frequency, \n polarisation_frame=PolarisationFrame('stokesI'),\n radius=npixel * cellsize/2.0)\n\nbeam = create_low_test_beam(beam)\npb_gleam_components = apply_beam_to_skycomponent(original_gleam_components, beam, \n flux_limit=0.5)\nfrom matplotlib import pylab\npylab.rcParams['figure.figsize'] = (12.0, 12.0)\npylab.rcParams['image.cmap'] = 'rainbow'\n\n\nshow_image(beam, components=pb_gleam_components, cm='Greys', title='Primary beam plus GLEAM components')\nprint(\"Number of components %d\" % len(pb_gleam_components))", "_____no_output_____" ] ], [ [ "Generate the template image", "_____no_output_____" ] ], [ [ "model = create_image_from_visibility(block_vis, npixel=npixel, \n frequency=[numpy.average(frequency)], \n nchan=1,\n channel_bandwidth=[numpy.sum(channel_bandwidth)], \n cellsize=cellsize, phasecentre=phasecentre)", "_____no_output_____" ] ], [ [ "Create sources to be peeled", "_____no_output_____" ] ], [ [ "peel_distance = 0.16\npeelers = select_components_by_separation(phasecentre, pb_gleam_components, \n min=peel_distance)\ngleam_components = select_components_by_separation(phasecentre, pb_gleam_components, \n max=peel_distance)\nprint(\"There are %d sources inside the primary beam and %d sources outside\"\n % (len(gleam_components), len(peelers)))", "_____no_output_____" ] ], [ [ "Create the model visibilities, applying a different gain table for peeled sources and other components", "_____no_output_____" ] ], [ [ "corrupted_vis = copy_visibility(block_vis, zero=True)\ngt = create_gaintable_from_blockvisibility(block_vis, timeslice='auto')\n\ncomponents_errors = [(p, 1.0) for p in peelers]\ncomponents_errors.append((pb_gleam_components, 0.1))\n\nfor sc, phase_error in components_errors:\n component_vis = copy_visibility(block_vis, zero=True)\n gt = simulate_gaintable(gt, amplitude_error=0.0, phase_error=phase_error)\n component_vis = predict_skycomponent_visibility(component_vis, sc)\n component_vis = apply_gaintable(component_vis, gt)\n corrupted_vis.data['vis'][...]+=component_vis.data['vis'][...]\n \ndirty, sumwt = invert_function(corrupted_vis, model, context='2d')\nqa=qa_image(dirty)\nvmax=qa.data['medianabs']*20.0\nvmin=-qa.data['medianabs']\nprint(qa)\nexport_image_to_fits(dirty, '%s/calskymodel_before_dirty.fits' % results_dir)\nshow_image(dirty, cm='Greys', components=peelers, vmax=vmax, vmin=vmin, title='Peelers')\nshow_image(dirty, cm='Greys', components=gleam_components, vmax=vmax, vmin=vmin, title='Targets')\nplt.show()", "_____no_output_____" ] ], [ [ "Find the components above the threshold", "_____no_output_____" ] ], [ [ "qa = qa_image(dirty)\nvmax=qa.data['medianabs']*20.0\nvmin=-qa.data['medianabs']*2.0\nprint(qa)\nthreshold = 10.0*qa.data['medianabs']\nprint(\"Selecting sources brighter than %f\" % threshold)\ninitial_found_components= find_skycomponents(dirty, threshold=threshold)\nshow_image(dirty, components=initial_found_components, cm='Greys', vmax=vmax, vmin=vmin,\n title='Dirty image plus found components')\nplt.show()", "_____no_output_____" ], [ "peel_distance = 0.16\nflux_threshold=5.0\npeelers = select_components_by_separation(phasecentre, initial_found_components, \n min=peel_distance)\n\ninbeam_components = select_components_by_separation(phasecentre, initial_found_components, \n max=peel_distance)\n\nbright_components = select_components_by_flux(inbeam_components, fmin=flux_threshold)\nfaint_components = select_components_by_flux(inbeam_components, fmax=flux_threshold)\n\nprint(\"%d sources will be peeled (i.e. held fixed but gain solved)\" % len(peelers))\nprint(\"%d bright sources will be processed as components (solved both as component and for gain)\" % len(bright_components))\nprint(\"%d faint sources will be processed collectively as a fixed image and gain solved\" % len(faint_components))\n\nfaint_model = create_empty_image_like(model)\nfaint_model = insert_skycomponent(faint_model, faint_components, insert_method='Lanczos')\n\nshow_image(faint_model, cm='Greys', title='Model for faint sources', vmax=0.3, vmin=-0.03)\nplt.show()\n \ncalskymodel_graph = [arlexecute.execute(SkyModel, nout=1)(components=[p], fixed=True) for p in peelers] \\\n + [arlexecute.execute(SkyModel, nout=1)(components=[b], fixed=False) for b in bright_components] \\\n + [arlexecute.execute(SkyModel, nout=1)(images=[faint_model], fixed=True)]", "_____no_output_____" ] ], [ [ "Run skymodel_cal using dask", "_____no_output_____" ] ], [ [ "corrupted_vis = arlexecute.scatter(corrupted_vis)\ngraph = calskymodel_solve_workflow(corrupted_vis, calskymodel_graph, niter=30, gain=0.25, tol=1e-8)\ncalskymodel, residual_vis = arlexecute.compute(graph, sync=True)", "_____no_output_____" ] ], [ [ "Combine all components for display", "_____no_output_____" ] ], [ [ "skymodel_components = list()\nfor csm in calskymodel:\n skymodel_components += csm[0].components", "_____no_output_____" ] ], [ [ "Check that the peeled sources are not altered", "_____no_output_____" ] ], [ [ "recovered_peelers = find_skycomponent_matches(peelers, skymodel_components, 1e-5)\nok = True\nfor p in recovered_peelers:\n ok = ok and numpy.abs(peelers[p[0]].flux[0,0] - skymodel_components[p[1]].flux[0,0]) < 1e-7\nprint(\"Peeler sources flux unchanged: %s\" % ok)\nok = True\nfor p in recovered_peelers:\n ok = ok and peelers[p[0]].direction.separation(skymodel_components[p[1]].direction).rad < 1e-15\nprint(\"Peeler sources directions unchanged: %s\" % ok)", "_____no_output_____" ] ], [ [ "Now we find the components in the residual image and add those to the existing model", "_____no_output_____" ] ], [ [ "residual, sumwt = invert_function(residual_vis, model, context='2d')\nqa = qa_image(residual)\nvmax=qa.data['medianabs']*30.0\nvmin=-qa.data['medianabs']*3.0\nprint(qa)\nthreshold = 20.0*qa.data['medianabs']\nprint(\"Selecting sources brighter than %f\" % threshold)\n\nfinal_found_components = find_skycomponents(residual, threshold=threshold)\nshow_image(residual, components=final_found_components, cm='Greys', \n title='Residual image after Sagecal with newly identified components', vmax=vmax, vmin=vmin)\n\nplt.show()\n\nfinal_components= skymodel_components + final_found_components", "_____no_output_____" ] ], [ [ "Make a restored image", "_____no_output_____" ] ], [ [ "psf, _ = invert_function(residual_vis, model, dopsf=True, context='2d')\n\ncomponent_image = copy_image(faint_model)\ncomponent_image = insert_skycomponent(component_image, final_components)\nrestored = restore_cube(component_image, psf, residual)\nexport_image_to_fits(restored, '%s/calskymodel_restored.fits' % results_dir)\n\nqa=qa_image(restored, context='Restored image after SageCal')\nprint(qa)\n\nshow_image(restored, components=final_components, cm='Greys', \n title='Restored image after SageCal', vmax=vmax, vmin=vmin)\nplt.show()", "_____no_output_____" ] ], [ [ "Now match the recovered components to the originals", "_____no_output_____" ] ], [ [ "original_bright_components = peelers + bright_components\nmatches = find_skycomponent_matches(final_components, original_bright_components, 3*cellsize)", "_____no_output_____" ] ], [ [ "Look at the range of separations found", "_____no_output_____" ] ], [ [ "separations = [match[2] for match in matches]\nplt.clf()\nplt.hist(separations/cellsize, bins=50)\nplt.title('Separation between input and recovered source in pixels')\nplt.xlabel('Separation in cells (cellsize = %g radians)' % cellsize)\nplt.ylabel('Number')\nplt.show()", "_____no_output_____" ] ], [ [ "Now look at the matches between the original components and those recovered.", "_____no_output_____" ] ], [ [ "totalfluxin = numpy.sum([c.flux[0,0] for c in pb_gleam_components]) \ntotalfluxout = numpy.sum([c.flux[0,0] for c in final_components]) + numpy.sum(faint_model.data)\nprint(\"Recovered %.3f (Jy) of original %.3f (Jy)\" % (totalfluxout, totalfluxin))\nfound = [match[1] for match in matches]\nnotfound = list()\nfor c in range(len(original_bright_components)):\n if c not in found:\n notfound.append(c)\n \nprint(\"The following original components were not found\", notfound)", "_____no_output_____" ] ], [ [ "Look at the recovered flux and the location of the unmatched components. From the image display these seem to be blends of close components.", "_____no_output_____" ] ], [ [ "fluxin = [original_bright_components[match[1]].flux[0,0] for match in matches]\nfluxout = [final_components[match[0]].flux[0,0] for match in matches]\nmissed_components = [original_bright_components[c] for c in notfound]\nmissed_flux = [match.flux[0,0] for match in missed_components]\n \nplt.clf()\nplt.plot(fluxin, fluxout, '.', color='blue')\nplt.plot(missed_flux, len(missed_flux)*[0.0], '.', color='red')\n\nplt.title('Recovered flux')\nplt.xlabel('Component input flux')\nplt.ylabel('Component recovered flux')\nplt.show()\n\nshow_image(restored, components=missed_components, cm='Greys', \n title='Restored original model with missing components', vmax=vmax, vmin=vmin)\nplt.show()\n", "_____no_output_____" ], [ "arlexecute.close()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## Multiple linear regression\n\n**For Table 3 of the paper**\n\nCell-based QUBICC R2B5 model", "_____no_output_____" ] ], [ [ "from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.regularizers import l1_l2\nimport tensorflow as tf\nimport tensorflow.nn as nn\nimport gc\nimport numpy as np\nimport pandas as pd\nimport importlib\nimport os\nimport sys\n\n#Import sklearn before tensorflow (static Thread-local storage)\nfrom sklearn.preprocessing import StandardScaler\n\nfrom tensorflow.keras.optimizers import Nadam\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, BatchNormalization\n\npath = '/pf/b/b309170'\npath_data = path + '/my_work/icon-ml_data/cloud_cover_parameterization/grid_cell_based_QUBICC_R02B05/based_on_var_interpolated_data'\n\n# Add path with my_classes to sys.path\nsys.path.insert(0, path + '/workspace_icon-ml/cloud_cover_parameterization/')\n\nimport my_classes\nimportlib.reload(my_classes)\nfrom my_classes import simple_sundqvist_scheme\nfrom my_classes import write_infofile\nfrom my_classes import load_data\n\nimport matplotlib.pyplot as plt\nimport time\n\nNUM = 1", "_____no_output_____" ], [ "# Prevents crashes of the code\ngpus = tf.config.list_physical_devices('GPU')\ntf.config.set_visible_devices(gpus[0], 'GPU')", "_____no_output_____" ], [ "# Allow the growth of memory Tensorflow allocates (limits memory usage overall)\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)", "_____no_output_____" ], [ "scaler = StandardScaler()", "_____no_output_____" ], [ "# Data is not yet normalized\ninput_data = np.load(path_data + '/cloud_cover_input_qubicc.npy', mmap_mode='r')\noutput_data = np.load(path_data + '/cloud_cover_output_qubicc.npy', mmap_mode='r')", "_____no_output_____" ], [ "(samples_total, no_of_features) = input_data.shape\nassert no_of_features < samples_total # Making sure there's no mixup", "_____no_output_____" ], [ "# Scale the input data = (samples_total, no_of_features)\nscaler.fit(input_data)\nassert len(scaler.mean_) == no_of_features # Every feature has its own mean and std and we scale accordingly\ninput_data_scaled = scaler.transform(input_data)", "_____no_output_____" ] ], [ [ "### Training the multiple linear model on the entire data set", "_____no_output_____" ] ], [ [ "t0 = time.time()\n\n# The optimal multiple linear regression model\nlin_reg = LinearRegression()\nlin_reg.fit(input_data_scaled, output_data)\n\nprint(time.time() - t0)", "114.00159311294556\n" ], [ "# Loss of this optimal multiple linear regression model\nclc_predictions = lin_reg.predict(input_data_scaled)\nlin_mse = mean_squared_error(output_data, clc_predictions)\nprint('The mean squared error of the linear model is %.2f.'%lin_mse) ", "The mean squared error of the linear model is 401.47.\n" ] ], [ [ "### Zero Output Model", "_____no_output_____" ] ], [ [ "np.mean(output_data**2, dtype=np.float64)", "_____no_output_____" ] ], [ [ "### Constant Output Model", "_____no_output_____" ] ], [ [ "mean = np.mean(output_data, dtype=np.float64)\nnp.mean((output_data-mean)**2, dtype=np.float64)", "_____no_output_____" ] ], [ [ "### Randomly initialized neural network", "_____no_output_____" ] ], [ [ "# Create the model\nmodel = Sequential()\n\n# First hidden layer\nmodel.add(Dense(units=64, activation='tanh', input_dim=no_of_features, \n kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\n\n# Second hidden layer\nmodel.add(Dense(units=64, activation=nn.leaky_relu, kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\n# model.add(Dropout(0.221)) # We drop 18% of the hidden nodes\nmodel.add(BatchNormalization())\n\n# Third hidden layer\nmodel.add(Dense(units=64, activation='tanh', kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\n# model.add(Dropout(0.221)) # We drop 18% of the hidden nodes\n\n# Output layer\nmodel.add(Dense(1, activation='linear', kernel_regularizer=l1_l2(l1=0.004749, l2=0.008732)))\nmodel.compile(loss='mse', optimizer=Nadam())", "_____no_output_____" ], [ "# model_fold_3 is implemented in ICON-A\nbatch_size = 2**20\n\nfor i in range(1 + input_data_scaled.shape[0]//batch_size):\n if i == 0:\n clc_predictions = model.predict_on_batch(input_data_scaled[i*batch_size:(i+1)*batch_size])\n else:\n clc_predictions = np.concatenate((clc_predictions, model.predict_on_batch(input_data_scaled[i*batch_size:(i+1)*batch_size])), axis=0)\n K.clear_session()\n gc.collect()", "_____no_output_____" ], [ "lin_mse = mean_squared_error(output_data, clc_predictions[:, 0])\nprint('The mean squared error of the randomly initialized neural network is %.2f.'%lin_mse) ", "The mean squared error of the randomly initialized neural network is 913.91.\n" ] ], [ [ "### Simplified Sundqvist function\n\ninput_data is unscaled", "_____no_output_____" ] ], [ [ "qv = input_data[:, 0]\ntemp = input_data[:, 3]\npres = input_data[:, 4]", "_____no_output_____" ], [ "t0 = time.time()\n\n# 0.001% of the data\nind = np.random.randint(0, samples_total, samples_total//10**5)\n\n# Entries will be in [0, 1]\nsundqvist = []\nfor i in ind:\n sundqvist.append(simple_sundqvist_scheme(qv[i], temp[i], pres[i], ps=101325))\n \ntime.time() - t0", "_____no_output_____" ], [ "np.mean((output_data[ind] - 100*np.array(sundqvist))**2, dtype=np.float64)", "_____no_output_____" ] ] ]

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[ "CC-BY-3.0" ]

[ "CC-BY-3.0" ]

[ "CC-BY-3.0" ]

[ [ [ "empty" ] ] ]

[ "empty" ]

[ [ "empty" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "!git -C /home/ec2-user/SageMaker/burgos pull\n!/home/ec2-user/SageMaker/rgs/miniconda/envs/rgsutils/bin/pip install -e /home/ec2-user/SageMaker/burgos --upgrade", "remote: Enumerating objects: 9, done.\u001b[K\nremote: Counting objects: 100% (9/9), done.\u001b[K\nremote: Compressing objects: 100% (1/1), done.\u001b[K\nremote: Total 5 (delta 4), reused 5 (delta 4), pack-reused 0\u001b[K\nUnpacking objects: 100% (5/5), done.\nFrom https://github.com/zach-data/burgos\n 0555fd7..5fbd382 development -> origin/development\nUpdating 0555fd7..5fbd382\nFast-forward\n burgos/db/_clients.py | 15 \u001b[32m++++++++\u001b[m\u001b[31m-------\u001b[m\n 1 file changed, 8 insertions(+), 7 deletions(-)\nObtaining file:///home/ec2-user/SageMaker/burgos\nInstalling collected packages: burgos\n Found existing installation: burgos 0.0.4\n Uninstalling burgos-0.0.4:\n Successfully uninstalled burgos-0.0.4\n Running setup.py develop for burgos\nSuccessfully installed burgos\n" ], [ "import burgos\nprint(burgos.__version__)\ncontext = burgos.ConfigurationManager()\nredshift = burgos.RedshiftManager(context)", "0.0.4\n" ], [ "redshift.load_table('tpch','10gb','nation')", "\n COPY nation \n FROM 's3://redshift-managed-loads-datasets-us-east-1/dataset=tpch/size=10GB/table=nation/nation.manifest' \n iam_role 'arn:aws:iam::029328890439:role/rgs-5d224842-IamRoleStack-17AR6NN4I0B-RedshiftRole-TLNR44OVSWTQ' \n gzip \n delimiter '|' \n COMPUPDATE OFF \n MANIFEST\n \nRow(tablename='\"tpch_10gb\".\"nation\"', rows=50)\nRow(tablename='\"tpch_10gb\".\"nation\"', rows=280)\n" ], [ "tables = ['nation', 'region', 'part', 'supplier', 'partsupp', 'customer', 'orders', 'lineitem']\nfor table in tables:\nredshift.load_table('tpch','1tb','nation')", "['arn', 'aws', 'sagemaker', 'us-east-1', '029328890439', 'notebook-instance/rgs-5d224842-notebook']\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "### **<font color='blue'> Artistic Colorizer </font>**", "_____no_output_____" ], [ "#◢ DeOldify - Colorize your own photos!\n\n####**Credits:**\n\nSpecial thanks to:\n\nMatt Robinson and María Benavente for pioneering the DeOldify image colab notebook. \n\nDana Kelley for doing things, breaking stuff & having an opinion on everything.", "_____no_output_____" ], [ "\n\n---\n\n\n#◢ Verify Correct Runtime Settings\n\n**<font color='#FF000'> IMPORTANT </font>**\n\nIn the \"Runtime\" menu for the notebook window, select \"Change runtime type.\" Ensure that the following are selected:\n* Runtime Type = Python 3\n* Hardware Accelerator = GPU \n", "_____no_output_____" ], [ "#◢ Git clone and install DeOldify", "_____no_output_____" ] ], [ [ "!git clone https://github.com/jantic/DeOldify.git DeOldify ", "_____no_output_____" ], [ "cd DeOldify", "_____no_output_____" ] ], [ [ "#◢ Setup", "_____no_output_____" ] ], [ [ "#NOTE: This must be the first call in order to work properly!\nfrom deoldify import device\nfrom deoldify.device_id import DeviceId\n#choices: CPU, GPU0...GPU7\ndevice.set(device=DeviceId.GPU0)\n\nimport torch\n\nif not torch.cuda.is_available():\n print('GPU not available.')", "_____no_output_____" ], [ "!pip install -r colab_requirements.txt", "_____no_output_____" ], [ "import fastai\nfrom deoldify.visualize import *", "_____no_output_____" ], [ "!mkdir 'models'\n!wget https://www.dropbox.com/s/zkehq1uwahhbc2o/ColorizeArtistic_gen.pth?dl=0 -O ./models/ColorizeArtistic_gen.pth", "_____no_output_____" ], [ "!wget https://media.githubusercontent.com/media/jantic/DeOldify/master/resource_images/watermark.png -O ./resource_images/watermark.png", "_____no_output_____" ], [ "colorizer = get_image_colorizer(artistic=True)", "_____no_output_____" ] ], [ [ "#◢ Instructions", "_____no_output_____" ], [ "### source_url\nType in a url to a direct link of an image. Usually that means they'll end in .png, .jpg, etc. NOTE: If you want to use your own image, upload it first to a site like Imgur. \n\n### render_factor\nThe default value of 35 has been carefully chosen and should work -ok- for most scenarios (but probably won't be the -best-). This determines resolution at which the color portion of the image is rendered. Lower resolution will render faster, and colors also tend to look more vibrant. Older and lower quality images in particular will generally benefit by lowering the render factor. Higher render factors are often better for higher quality images, but the colors may get slightly washed out. \n\n### watermarked\nSelected by default, this places a watermark icon of a palette at the bottom left corner of the image. This is intended to be a standard way to convey to others viewing the image that it is colorized by AI. We want to help promote this as a standard, especially as the technology continues to improve and the distinction between real and fake becomes harder to discern. This palette watermark practice was initiated and lead by the company MyHeritage in the MyHeritage In Color feature (which uses a newer version of DeOldify than what you're using here).\n\n#### How to Download a Copy\nSimply right click on the displayed image and click \"Save image as...\"!\n\n## Pro Tips\n\nYou can evaluate how well the image is rendered at each render_factor by using the code at the bottom (that cell under \"See how well render_factor values perform on a frame here\"). ", "_____no_output_____" ], [ "#◢ Colorize!!", "_____no_output_____" ] ], [ [ "source_url = '' #@param {type:\"string\"}\nrender_factor = 35 #@param {type: \"slider\", min: 7, max: 40}\nwatermarked = True #@param {type:\"boolean\"}\n\nif source_url is not None and source_url !='':\n image_path = colorizer.plot_transformed_image_from_url(url=source_url, render_factor=render_factor, compare=True, watermarked=watermarked)\n show_image_in_notebook(image_path)\nelse:\n print('Provide an image url and try again.')", "_____no_output_____" ] ], [ [ "## See how well render_factor values perform on the image here", "_____no_output_____" ] ], [ [ "for i in range(10,40,2):\n colorizer.plot_transformed_image('test_images/image.png', render_factor=i, display_render_factor=True, figsize=(8,8))", "_____no_output_____" ] ], [ [ "---\n#⚙ Recommended image sources \n* [/r/TheWayWeWere](https://www.reddit.com/r/TheWayWeWere/)", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown", "markdown", "markdown", "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<div style=\"width: 100%; clear: both;\">\n<div style=\"float: left; width: 50%;\">\n<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\" align=\"left\">\n</div>\n<div style=\"float: right; width: 50%;\">\n<p style=\"margin: 0; padding-top: 22px; text-align:right;\">M2.859 · Visualización de datos · Práctica, Parte 2</p>\n<p style=\"margin: 0; text-align:right;\">2021-1 · Máster universitario en Ciencia de datos (Data science)</p>\n<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n</div>\n</div>\n<div style=\"width:100%;\">&nbsp;</div>\n\n\n# A9: Práctica Final (parte 2) - Wrangling data\n\n\nEl [**wrangling data**](https://en.wikipedia.org/wiki/Data_wrangling) es el proceso de transformar y mapear datos de un formulario de datos \"sin procesar\" a otro formato con la intención de hacerlo más apropiado y valioso para una variedad de propósitos posteriores, como el análisis. El objetivo del wrangling data es garantizar la calidad y la utilidad de los datos. Los analistas de datos suelen pasar la mayor parte de su tiempo en el proceso de disputa de datos en comparación con el análisis real de los datos.\n\nEl proceso de wrangling data puede incluir más manipulación, visualización de datos, agregación de datos, entrenamiento de un modelo estadístico, así como muchos otros usos potenciales. El wrangling data normalmente sigue un conjunto de pasos generales que comienzan con la extracción de los datos sin procesar de la fuente de datos, \"removiendo\" los datos sin procesar (por ejemplo, clasificación) o analizando los datos en estructuras de datos predefinidas y, finalmente, depositando el contenido resultante en un sumidero de datos para almacenamiento y uso futuro.\n \nPara ello vamos a necesitar las siguientes librerías:", "_____no_output_____" ] ], [ [ "from six import StringIO\n\nfrom IPython.display import Image \nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nimport pydotplus\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport pandas as pd\npd.set_option('display.max_columns', None)", "_____no_output_____" ] ], [ [ "# 1. Carga del conjunto de datos (1 punto)\n\nSe ha seleccionado un conjunto de datos desde el portal Stack Overflow Annual Developer Survey, que examina todos los aspectos de la experiencia de los programadores de la comunidad (Stack Overflow), desde la satisfacción profesional y la búsqueda de empleo hasta la educación y las opiniones sobre el software de código abierto; y los resultados se publican en la siguiente URL: https://insights.stackoverflow.com/survey.\n\nEn este portal se encuentran publicados los resultados de los últimos 11 años. Para los fines de la práctica final de esta asignatura se usará el dataset del año 2021, cuyo link de descarga es: https://info.stackoverflowsolutions.com/rs/719-EMH-566/images/stack-overflow-developer-survey-2021.zip.", "_____no_output_____" ] ], [ [ "so2021_df = pd.read_csv('survey_results_public.csv', header=0)\nso2021_df.sample(5)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Selección de variables:</strong> se realiza la selección de todas las variables del dataset que servirán para responder a todas las cuestiones planteadas en la primera parte de la práctica:\n</div>", "_____no_output_____" ] ], [ [ "so2021_data = so2021_df[['MainBranch', 'Employment', 'Country', 'EdLevel', 'Age1stCode', 'YearsCode', 'YearsCodePro', 'DevType', 'CompTotal', 'LanguageHaveWorkedWith', 'DatabaseHaveWorkedWith', 'PlatformHaveWorkedWith', 'WebframeHaveWorkedWith', 'MiscTechHaveWorkedWith', 'ToolsTechHaveWorkedWith', 'NEWCollabToolsHaveWorkedWith', 'OpSys', 'Age', 'Gender', 'Trans', 'Ethnicity', 'MentalHealth', 'ConvertedCompYearly']]\nso2021_data.head(5)", "_____no_output_____" ], [ "so2021_data.shape", "_____no_output_____" ], [ "so2021_data.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 83439 entries, 0 to 83438\nData columns (total 23 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 MainBranch 83439 non-null object \n 1 Employment 83323 non-null object \n 2 Country 83439 non-null object \n 3 EdLevel 83126 non-null object \n 4 Age1stCode 83243 non-null object \n 5 YearsCode 81641 non-null object \n 6 YearsCodePro 61216 non-null object \n 7 DevType 66484 non-null object \n 8 CompTotal 47183 non-null float64\n 9 LanguageHaveWorkedWith 82357 non-null object \n 10 DatabaseHaveWorkedWith 69546 non-null object \n 11 PlatformHaveWorkedWith 52135 non-null object \n 12 WebframeHaveWorkedWith 61707 non-null object \n 13 MiscTechHaveWorkedWith 47055 non-null object \n 14 ToolsTechHaveWorkedWith 72537 non-null object \n 15 NEWCollabToolsHaveWorkedWith 81234 non-null object \n 16 OpSys 83294 non-null object \n 17 Age 82407 non-null object \n 18 Gender 82286 non-null object \n 19 Trans 80678 non-null object \n 20 Ethnicity 79464 non-null object \n 21 MentalHealth 76920 non-null object \n 22 ConvertedCompYearly 46844 non-null float64\ndtypes: float64(2), object(21)\nmemory usage: 14.6+ MB\n" ], [ "so2021_data.isnull().values.any() #valores perdidos en dataset", "_____no_output_____" ], [ "so2021_data.isnull().any() # valores perdidos por columnas en el dataset", "_____no_output_____" ], [ "data = so2021_data.dropna()", "_____no_output_____" ], [ "data.isnull().any() # valores perdidos por columnas en el dataset", "_____no_output_____" ], [ "data.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 15173 entries, 45 to 83437\nData columns (total 23 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 MainBranch 15173 non-null object \n 1 Employment 15173 non-null object \n 2 Country 15173 non-null object \n 3 EdLevel 15173 non-null object \n 4 Age1stCode 15173 non-null object \n 5 YearsCode 15173 non-null object \n 6 YearsCodePro 15173 non-null object \n 7 DevType 15173 non-null object \n 8 CompTotal 15173 non-null float64\n 9 LanguageHaveWorkedWith 15173 non-null object \n 10 DatabaseHaveWorkedWith 15173 non-null object \n 11 PlatformHaveWorkedWith 15173 non-null object \n 12 WebframeHaveWorkedWith 15173 non-null object \n 13 MiscTechHaveWorkedWith 15173 non-null object \n 14 ToolsTechHaveWorkedWith 15173 non-null object \n 15 NEWCollabToolsHaveWorkedWith 15173 non-null object \n 16 OpSys 15173 non-null object \n 17 Age 15173 non-null object \n 18 Gender 15173 non-null object \n 19 Trans 15173 non-null object \n 20 Ethnicity 15173 non-null object \n 21 MentalHealth 15173 non-null object \n 22 ConvertedCompYearly 15173 non-null float64\ndtypes: float64(2), object(21)\nmemory usage: 2.8+ MB\n" ], [ "data.head()", "_____no_output_____" ], [ "data.to_csv('data.csv', index=False)", "_____no_output_____" ], [ "data_test = data.copy()", "_____no_output_____" ], [ "data_test.to_csv('data_test.csv', index=False)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable Ethnicity:</strong>.\n</div>", "_____no_output_____" ] ], [ [ "from re import search\n\ndef choose_ethnia(cell_ethnia):\n val_ethnia_exceptions = [\"I don't know\", \"Or, in your own words:\"]\n \n if cell_ethnia == \"I don't know;Or, in your own words:\":\n return val_ethnia_exceptions[0]\n \n if search(\";\", cell_ethnia):\n row_ethnia_values = cell_ethnia.split(';', 5)\n first_val = row_ethnia_values[0]\n\n if first_val not in val_ethnia_exceptions:\n return first_val\n\n if len(row_ethnia_values) > 1:\n if row_ethnia_values[1] not in val_ethnia_exceptions:\n return row_ethnia_values[1]\n\n if len(row_ethnia_values) > 2:\n if row_ethnia_values[2] not in val_ethnia_exceptions:\n return row_ethnia_values[2]\n else:\n return cell_ethnia", "_____no_output_____" ], [ "data_test['Ethnicity'] = data_test['Ethnicity'].apply(choose_ethnia)", "_____no_output_____" ], [ "data_test.drop(index=data_test[data_test['Ethnicity'] == 'Or, in your own words:'].index, inplace=True)", "_____no_output_____" ], [ "data_test.drop(index=data_test[data_test['Ethnicity'] == 'Prefer not to say'].index, inplace=True)", "_____no_output_____" ], [ "data_test['Ethnicity'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['Ethnicity'] = data_test['Ethnicity'].replace(['Black or of African descent'], 'Negro')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['East Asian'], 'Asiatico del este')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['Hispanic or Latino/a/x'], 'Latino')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace([\"I don't know\"], 'No Definido')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['Indigenous (such as Native American, Pacific Islander, or Indigenous Australian)'], 'Indigena')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['Middle Eastern'], 'Medio Oriente')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['South Asian'], 'Asiatico del Sur')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['Southeast Asian'], 'Asiatico del Sudeste')\ndata_test['Ethnicity'] = data_test['Ethnicity'].replace(['White or of European descent'], 'Blanco o Europeo')", "_____no_output_____" ], [ "data_test['Ethnicity'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test.to_csv('data_test.csv', index=False)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable Employment:</strong>.\n</div>", "_____no_output_____" ] ], [ [ "data_test['Employment'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['Employment'] = data_test['Employment'].replace(['Employed full-time'], 'Tiempo completo')\ndata_test['Employment'] = data_test['Employment'].replace(['Employed part-time'], 'Tiempo parcial')\ndata_test['Employment'] = data_test['Employment'].replace(['Independent contractor, freelancer, or self-employed'], 'Independiete')", "_____no_output_____" ], [ "data_test['Employment'].drop_duplicates().sort_values()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable EdLevel:</strong>.\n</div>", "_____no_output_____" ] ], [ [ "data_test['EdLevel'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['EdLevel'] = data_test['EdLevel'].replace(['Associate degree (A.A., A.S., etc.)'], 'Grado Asociado')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Bachelor’s degree (B.A., B.S., B.Eng., etc.)'], 'Licenciatura')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Master’s degree (M.A., M.S., M.Eng., MBA, etc.)'], 'Master')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Other doctoral degree (Ph.D., Ed.D., etc.)'], 'Doctorado')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Primary/elementary school'], 'Primaria')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Professional degree (JD, MD, etc.)'], 'Grado Profesional')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)'], 'Secundaria')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Some college/university study without earning a degree'], 'Estudios sin grado')\ndata_test['EdLevel'] = data_test['EdLevel'].replace(['Something else'], 'Otro')", "_____no_output_____" ], [ "data_test['EdLevel'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test.to_csv('data_test.csv', index=False)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable DevType:</strong>.\n</div>", "_____no_output_____" ] ], [ [ "data_test['DevType'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "from re import search\n\ndef choose_devtype(cell_devtype):\n val_devtype_exceptions = [\"Other (please specify):\"]\n \n if cell_devtype == \"Other (please specify):\":\n return val_devtype_exceptions[0]\n \n if search(\";\", cell_devtype):\n row_devtype_values = cell_devtype.split(';', 10)\n first_val = row_devtype_values[0]\n \n if first_val not in val_devtype_exceptions:\n return first_val\n\n if len(row_devtype_values) > 1:\n if row_devtype_values[1] not in val_devtype_exceptions:\n return row_devtype_values[1] \n\n else:\n return cell_devtype", "_____no_output_____" ], [ "data_test['DevType'] = data_test['DevType'].apply(choose_devtype)", "_____no_output_____" ], [ "data_test['DevType'].head()", "_____no_output_____" ], [ "data_test['DevType'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['DevType'].value_counts()", "_____no_output_____" ], [ "data_test['DevType'] = data_test['DevType'].replace(['Developer, full-stack'], 'Desarrollador full-stack')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, front-end'], 'Desarrollador front-end')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, mobile'], 'Desarrollador móvil')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, back-end'], 'Desarrollador back-end')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, desktop or enterprise applications'], 'Desarrollador Escritorio')\ndata_test['DevType'] = data_test['DevType'].replace(['Engineer, data'], 'Ingeniero de datos')\ndata_test['DevType'] = data_test['DevType'].replace(['Data scientist or machine learning specialist'], 'Cientifico de datos')\ndata_test['DevType'] = data_test['DevType'].replace(['Other (please specify):'], 'Otro')\ndata_test['DevType'] = data_test['DevType'].replace(['Engineering manager'], 'Manager de Ingeniería')\ndata_test['DevType'] = data_test['DevType'].replace(['DevOps specialist'], 'Especialista en DevOps')\ndata_test['DevType'] = data_test['DevType'].replace(['Senior Executive (C-Suite, VP, etc.)'], 'Ejecutivo Senior')\ndata_test['DevType'] = data_test['DevType'].replace(['Academic researcher'], 'Investigador Académico')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, QA or test'], 'Desarrollador de QA o Test')\ndata_test['DevType'] = data_test['DevType'].replace(['Data or business analyst'], 'Analista de datos o negocio')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, embedded applications or devices'], 'Desarrollador de aplicaciones embebidas')\ndata_test['DevType'] = data_test['DevType'].replace(['System administrator'], 'Administrador de sistemas')\ndata_test['DevType'] = data_test['DevType'].replace(['Engineer, site reliability'], 'Ingeniero de confiabilidad del sitio')\ndata_test['DevType'] = data_test['DevType'].replace(['Product manager'], 'Gerente de producto')\ndata_test['DevType'] = data_test['DevType'].replace(['Database administrator'], 'Administrador de base de datos')\ndata_test['DevType'] = data_test['DevType'].replace(['Student'], 'Estudiante')\ndata_test['DevType'] = data_test['DevType'].replace(['Developer, game or graphics'], 'Desarrollador de juegos o gráfico')\ndata_test['DevType'] = data_test['DevType'].replace(['Scientist'], 'Científico')\ndata_test['DevType'] = data_test['DevType'].replace(['Designer'], 'Diseñador')\ndata_test['DevType'] = data_test['DevType'].replace(['Educator'], 'Educador')\ndata_test['DevType'] = data_test['DevType'].replace(['Marketing or sales professional'], 'Profesional en Marketing o ventas')", "_____no_output_____" ], [ "data_test['DevType'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['DevType'].value_counts()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable MainBranch:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['MainBranch'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['MainBranch'] = data_test['MainBranch'].replace(['I am a developer by profession'], 'Desarrollador Profesional')\ndata_test['MainBranch'] = data_test['MainBranch'].replace(['I am not primarily a developer, but I write code sometimes as part of my work'], 'Desarrollador ocasional')", "_____no_output_____" ], [ "data_test['MainBranch'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test.to_csv('data_test.csv', index=False)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable Age1stCode:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['Age1stCode'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "data_test['Age1stCode'].value_counts()", "_____no_output_____" ], [ "data_test['Age1stCode'] = data_test['Age1stCode'].replace(['11 - 17 years'], '11-17')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['18 - 24 years'], '18-24')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['25 - 34 years'], '25-34')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['35 - 44 years'], '35-44')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['45 - 54 years'], '45-54')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['5 - 10 years'], '5-10')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['55 - 64 years'], '55-64')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['Older than 64 years'], '> 64')\ndata_test['Age1stCode'] = data_test['Age1stCode'].replace(['Younger than 5 years'], '< 5')", "_____no_output_____" ], [ "data_test['Age1stCode'].value_counts()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable YearsCode:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['YearsCode'] = data_test['YearsCode'].replace(['More than 50 years'], 50)\ndata_test['YearsCode'] = data_test['YearsCode'].replace(['Less than 1 year'], 1)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable YearsCodePro:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['YearsCodePro'] = data_test['YearsCodePro'].replace(['More than 50 years'], 50)\ndata_test['YearsCodePro'] = data_test['YearsCodePro'].replace(['Less than 1 year'], 1)", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable OpSys:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['OpSys'].value_counts()", "_____no_output_____" ], [ "data_test['OpSys'] = data_test['OpSys'].replace(['Windows Subsystem for Linux (WSL)'], 'Windows')\ndata_test['OpSys'] = data_test['OpSys'].replace(['Linux-based'], 'Linux')\ndata_test['OpSys'] = data_test['OpSys'].replace(['Other (please specify)'], 'Otro')", "_____no_output_____" ], [ "data_test['OpSys'].value_counts()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable Age:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['Age'].value_counts()", "_____no_output_____" ], [ "data_test['Age'] = data_test['Age'].replace(['25-34 years old'], '25-34')\ndata_test['Age'] = data_test['Age'].replace(['35-44 years old'], '35-44')\ndata_test['Age'] = data_test['Age'].replace(['18-24 years old'], '18-24')\ndata_test['Age'] = data_test['Age'].replace(['45-54 years old'], '45-54')\ndata_test['Age'] = data_test['Age'].replace(['55-64 years old'], '55-64')\ndata_test['Age'] = data_test['Age'].replace(['Under 18 years old'], '< 18')\ndata_test['Age'] = data_test['Age'].replace(['65 years or older'], '>= 65')\ndata_test['Age'] = data_test['Age'].replace(['Prefer not to say'], 'No definido')", "_____no_output_____" ], [ "data_test['Age'] = data_test['Age'].replace(['25-34 years old'], '25-34')", "_____no_output_____" ], [ "data_test['Age'].value_counts()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable Gender:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['Gender'].value_counts()", "_____no_output_____" ], [ "data_test['Gender'] = data_test['Gender'].replace(['Man'], 'Hombre')\ndata_test['Gender'] = data_test['Gender'].replace(['Woman'], 'Mujer')\ndata_test['Gender'] = data_test['Gender'].replace(['Non-binary, genderqueer, or gender non-conforming'], 'No binario u otro')\ndata_test['Gender'] = data_test['Gender'].replace(['Man;Non-binary, genderqueer, or gender non-conforming'], 'No binario u otro')\ndata_test['Gender'] = data_test['Gender'].replace(['Man;Or, in your own words:'], 'Hombre')\ndata_test['Gender'] = data_test['Gender'].replace(['Or, in your own words:'], 'No definido')\ndata_test['Gender'] = data_test['Gender'].replace(['Woman;Non-binary, genderqueer, or gender non-conforming'], 'No binario u otro')\ndata_test['Gender'] = data_test['Gender'].replace(['Man;Woman'], 'No definido')\ndata_test['Gender'] = data_test['Gender'].replace(['Man;Woman;Non-binary, genderqueer, or gender non-conforming;Or, in your own words:'], 'No binario u otro')\ndata_test['Gender'] = data_test['Gender'].replace(['Non-binary, genderqueer, or gender non-conforming;Or, in your own words:'], 'No binario u otro')\ndata_test['Gender'] = data_test['Gender'].replace(['Man;Woman;Non-binary, genderqueer, or gender non-conforming'], 'No binario u otro')", "_____no_output_____" ], [ "data_test['Gender'] = data_test['Gender'].replace(['Prefer not to say'], 'No definido')", "_____no_output_____" ], [ "data_test['Gender'].value_counts()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable Trans:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['Trans'].value_counts()", "_____no_output_____" ], [ "data_test['Trans'] = data_test['Trans'].replace(['Yes'], 'Si')\ndata_test['Trans'] = data_test['Trans'].replace(['Prefer not to say'], 'No definido')\ndata_test['Trans'] = data_test['Trans'].replace(['Or, in your own words:'], 'No definido')", "_____no_output_____" ], [ "data_test['Trans'].value_counts()", "_____no_output_____" ] ], [ [ "<div style=\"background-color: #EDF7FF; border-color: #7C9DBF; border-left: 5px solid #7C9DBF; padding: 0.5em;\">\n<strong>Variable MentalHealth:</strong>\n</div>", "_____no_output_____" ] ], [ [ "data_test['MentalHealth'].value_counts()", "_____no_output_____" ], [ "from re import search\n\ndef choose_mental_health(cell_mental_health):\n val_mental_health_exceptions = [\"Or, in your own words:\"]\n \n if cell_mental_health == \"Or, in your own words:\":\n return val_mental_health_exceptions[0]\n \n if search(\";\", cell_mental_health):\n row_mental_health_values = cell_mental_health.split(';', 10)\n first_val = row_mental_health_values[0]\n \n return first_val\n else:\n return cell_mental_health", "_____no_output_____" ], [ "data_test['MentalHealth'] = data_test['MentalHealth'].apply(choose_mental_health)", "_____no_output_____" ], [ "data_test['MentalHealth'].value_counts()", "_____no_output_____" ], [ "data_test['MentalHealth'] = data_test['MentalHealth'].replace(['None of the above'], 'Ninguna de las mencionadas')\ndata_test['MentalHealth'] = data_test['MentalHealth'].replace(['I have a concentration and/or memory disorder (e.g. ADHD)'], 'Desorden de concentración o memoria')\ndata_test['MentalHealth'] = data_test['MentalHealth'].replace(['I have a mood or emotional disorder (e.g. depression, bipolar disorder)'], 'Desorden emocional')\ndata_test['MentalHealth'] = data_test['MentalHealth'].replace(['I have an anxiety disorder'], 'Desorden de ansiedad')\ndata_test['MentalHealth'] = data_test['MentalHealth'].replace(['Prefer not to say'], 'No definido')\ndata_test['MentalHealth'] = data_test['MentalHealth'].replace([\"I have autism / an autism spectrum disorder (e.g. Asperger's)\"], 'Tipo de autismo')\ndata_test['MentalHealth'] = data_test['MentalHealth'].replace(['Or, in your own words:'], 'No definido')", "_____no_output_____" ], [ "data_test['MentalHealth'].value_counts()", "_____no_output_____" ] ], [ [ "# 2. Selección de campos para subdatasets\n\nSe seleccionarán los campos adecuados para responder a cada una de las cuestiones que se plantearon en la primera parte de la práctica.", "_____no_output_____" ], [ "### 2.1. Según la autodeterminación de la etnia, ¿Qué etnia tiene un mayor sueldo anual?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_etnia = data_test[['Country', 'Ethnicity', 'ConvertedCompYearly']]\ndata_etnia.head()", "_____no_output_____" ], [ "df_data_etnia = data_etnia.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_etnia['ConvertedCompYearly'], 0.1)\n\nprint(df_data_etnia[mask])", " Country Ethnicity ConvertedCompYearly\n45 Brazil Blanco o Europeo 60480.0\n50 Greece Blanco o Europeo 25944.0\n58 Russian Federation Blanco o Europeo 22644.0\n76 Poland Blanco o Europeo 45564.0\n77 Canada Blanco o Europeo 151263.0\n... ... ... ...\n83425 Finland Blanco o Europeo 19452.0\n83428 Brazil Latino 41232.0\n83431 Pakistan Asiatico del Sudeste 11676.0\n83432 Canada Asiatico del este 80169.0\n83436 United States of America Blanco o Europeo 90000.0\n\n[11611 rows x 3 columns]\n" ], [ "df_data_etnia_no_outliers = df_data_etnia[mask]", "_____no_output_____" ], [ "df_data_etnia_no_outliers = df_data_etnia_no_outliers.copy()", "_____no_output_____" ], [ "df_data_etnia_no_outliers['ConvertedCompYearlyCategorical'] = 'ALTO'\ndf_data_etnia_no_outliers.loc[(df_data_etnia_no_outliers['ConvertedCompYearly'] >= 0) & (df_data_etnia_no_outliers['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_data_etnia_no_outliers.loc[(df_data_etnia_no_outliers['ConvertedCompYearly'] > 32747) & (df_data_etnia_no_outliers['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_data_etnia_no_outliers)", " Country Ethnicity ConvertedCompYearly \\\n45 Brazil Blanco o Europeo 60480.0 \n50 Greece Blanco o Europeo 25944.0 \n58 Russian Federation Blanco o Europeo 22644.0 \n76 Poland Blanco o Europeo 45564.0 \n77 Canada Blanco o Europeo 151263.0 \n... ... ... ... \n83425 Finland Blanco o Europeo 19452.0 \n83428 Brazil Latino 41232.0 \n83431 Pakistan Asiatico del Sudeste 11676.0 \n83432 Canada Asiatico del este 80169.0 \n83436 United States of America Blanco o Europeo 90000.0 \n\n ConvertedCompYearlyCategorical \n45 MEDIO \n50 BAJO \n58 BAJO \n76 MEDIO \n77 ALTO \n... ... \n83425 BAJO \n83428 MEDIO \n83431 BAJO \n83432 MEDIO \n83436 MEDIO \n\n[11611 rows x 4 columns]\n" ], [ "df_data_etnia_alto = df_data_etnia_no_outliers[df_data_etnia_no_outliers['ConvertedCompYearlyCategorical'] == 'ALTO']", "_____no_output_____" ], [ "df_data_etnia_alto = df_data_etnia_alto[['Ethnicity', 'ConvertedCompYearlyCategorical']]", "_____no_output_____" ], [ "df_flourish = df_data_etnia_alto['Ethnicity'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "df_flourish", "_____no_output_____" ], [ "df_flourish.to_csv('001_df_flourish.csv', index=False)", "_____no_output_____" ], [ "df_data_etnia_alto.to_csv('001_df_data_etnia_alto.csv', index=False)", "_____no_output_____" ], [ "df_data_etnia.to_csv('001_data_etnia_categorical.csv', index=False)", "_____no_output_____" ], [ "data_etnia.to_csv('001_data_etnia.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.2. ¿Cuáles son los porcentajes de programadores que trabajan a tiempo completo, medio tiempo o freelance?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_time_work_dev = data_test[['Country', 'Employment', 'ConvertedCompYearly', 'EdLevel', 'Age']]\ndata_time_work_dev.head()", "_____no_output_____" ], [ "df_flourish_002 = data_time_work_dev['Employment'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "df_flourish_002", "_____no_output_____" ], [ "df_flourish_002['counts'] = (df_flourish_002['counts'] * 100 ) / data_time_work_dev.shape[0]", "_____no_output_____" ], [ "df_flourish_002", "_____no_output_____" ], [ "df_flourish_002['counts'] = df_flourish_002['counts'].round(2)", "_____no_output_____" ], [ "df_flourish_002", "_____no_output_____" ], [ "df_flourish_002.to_csv('002_df_flourish.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.3. ¿Cuáles son los países con mayor número de programadores profesionales que son activos en la comunidad Stack Overflow?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_pro_dev_active_so = data_test[['Country', 'Employment', 'MainBranch', 'EdLevel', 'DevType', 'Age']]\ndata_pro_dev_active_so.head()", "_____no_output_____" ], [ "df_flourish_003 = data_pro_dev_active_so['Country'].value_counts().sort_values(ascending=False).head(10)", "_____no_output_____" ], [ "df_flourish_003 = df_flourish_003.to_frame()", "_____no_output_____" ], [ "df_flourish_003 = df_flourish_003.reset_index()\ndf_flourish_003.columns = [\"País\", \"# Programadores Profesionales\"]", "_____no_output_____" ], [ "df_flourish_003.to_csv('003_df_flourish_003.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.4. ¿Cuál es el nivel educativo que mayores ingresos registra entre los encuestados?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_edlevel_income = data_test[['ConvertedCompYearly', 'EdLevel']]\ndata_edlevel_income.head()", "_____no_output_____" ], [ "df_data_edlevel_income = data_edlevel_income.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_edlevel_income['ConvertedCompYearly'], 0.1)\n\nprint(df_data_edlevel_income[mask])", " ConvertedCompYearly EdLevel\n45 60480.0 Licenciatura\n50 25944.0 Licenciatura\n58 22644.0 Grado Profesional\n76 45564.0 Licenciatura\n77 151263.0 Doctorado\n... ... ...\n83425 19452.0 Secundaria\n83428 41232.0 Master\n83431 11676.0 Licenciatura\n83432 80169.0 Licenciatura\n83436 90000.0 Secundaria\n\n[11611 rows x 2 columns]\n" ], [ "df_data_edlevel_income = df_data_edlevel_income[mask]", "_____no_output_____" ], [ "df_data_edlevel_income['ConvertedCompYearlyCategorical'] = 'ALTO'\ndf_data_edlevel_income.loc[(df_data_edlevel_income['ConvertedCompYearly'] >= 0) & (df_data_edlevel_income['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_data_edlevel_income.loc[(df_data_edlevel_income['ConvertedCompYearly'] > 32747) & (df_data_edlevel_income['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_data_edlevel_income)", " ConvertedCompYearly EdLevel ConvertedCompYearlyCategorical\n45 60480.0 Licenciatura MEDIO\n50 25944.0 Licenciatura BAJO\n58 22644.0 Grado Profesional BAJO\n76 45564.0 Licenciatura MEDIO\n77 151263.0 Doctorado ALTO\n... ... ... ...\n83425 19452.0 Secundaria BAJO\n83428 41232.0 Master MEDIO\n83431 11676.0 Licenciatura BAJO\n83432 80169.0 Licenciatura MEDIO\n83436 90000.0 Secundaria MEDIO\n\n[11611 rows x 3 columns]\n" ], [ "df_data_edlevel_income = df_data_edlevel_income[df_data_edlevel_income['ConvertedCompYearlyCategorical'] == 'ALTO']", "_____no_output_____" ], [ "df_data_edlevel_income = df_data_edlevel_income[['EdLevel', 'ConvertedCompYearlyCategorical']]", "_____no_output_____" ], [ "df_flourish_004 = df_data_edlevel_income['EdLevel'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "df_flourish_004", "_____no_output_____" ], [ "df_flourish_004.to_csv('004_df_flourish.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.5. ¿Existe brecha salarial entre hombres y mujeres u otros géneros?, y de ¿Cuánto es la diferencia? ¿Cuáles son los peores países en cuanto a brecha salarial? ¿Cuáles son los países que han reducido esta brecha salarial entre programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_wage_gap = data_test[['Country', 'ConvertedCompYearly', 'Gender']]\ndata_wage_gap.head()", "_____no_output_____" ], [ "df_data_wage_gap = data_wage_gap.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_wage_gap['ConvertedCompYearly'], 0.1)\n\nprint(df_data_wage_gap[mask])", " Country ConvertedCompYearly Gender\n45 Brazil 60480.0 Hombre\n50 Greece 25944.0 Hombre\n58 Russian Federation 22644.0 Hombre\n76 Poland 45564.0 Hombre\n77 Canada 151263.0 Hombre\n... ... ... ...\n83425 Finland 19452.0 Hombre\n83428 Brazil 41232.0 Hombre\n83431 Pakistan 11676.0 Hombre\n83432 Canada 80169.0 Mujer\n83436 United States of America 90000.0 Hombre\n\n[11611 rows x 3 columns]\n" ], [ "df_data_wage_gap = df_data_wage_gap[mask]", "_____no_output_____" ], [ "df_data_wage_gap['ConvertedCompYearlyCategorical'] = 'ALTO'\ndf_data_wage_gap.loc[(df_data_wage_gap['ConvertedCompYearly'] >= 0) & (df_data_wage_gap['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_data_wage_gap.loc[(df_data_wage_gap['ConvertedCompYearly'] > 32747) & (df_data_wage_gap['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_data_wage_gap)", " Country ConvertedCompYearly Gender \\\n45 Brazil 60480.0 Hombre \n50 Greece 25944.0 Hombre \n58 Russian Federation 22644.0 Hombre \n76 Poland 45564.0 Hombre \n77 Canada 151263.0 Hombre \n... ... ... ... \n83425 Finland 19452.0 Hombre \n83428 Brazil 41232.0 Hombre \n83431 Pakistan 11676.0 Hombre \n83432 Canada 80169.0 Mujer \n83436 United States of America 90000.0 Hombre \n\n ConvertedCompYearlyCategorical \n45 MEDIO \n50 BAJO \n58 BAJO \n76 MEDIO \n77 ALTO \n... ... \n83425 BAJO \n83428 MEDIO \n83431 BAJO \n83432 MEDIO \n83436 MEDIO \n\n[11611 rows x 4 columns]\n" ], [ "df_data_wage_gap = df_data_wage_gap[df_data_wage_gap['ConvertedCompYearlyCategorical'].isin(['ALTO', 'MEDIO'])]", "_____no_output_____" ], [ "df_data_wage_gap = df_data_wage_gap[['Country', 'Gender', 'ConvertedCompYearlyCategorical']]", "_____no_output_____" ], [ "df_data_wage_gap.to_csv('005_df_data_wage_gap.csv', index=False)", "_____no_output_____" ], [ "df_data_wage_gap['ConvertedCompYearlyCategorical'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "df_data_wage_gap['Gender'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "df_data_wage_gap['Country'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "df_data_wage_gap1 = df_data_wage_gap.copy()", "_____no_output_____" ], [ "df_flourish_005 = df_data_wage_gap1.groupby(['Country', 'Gender']).size().unstack(fill_value=0).sort_values('Hombre')", "_____no_output_____" ], [ "df_flourish_005 = df_flourish_005.apply(lambda x: pd.concat([x.head(40), x.tail(5)]))", "_____no_output_____" ], [ "df_flourish_005.to_csv('005_flourish_data.csv', index=True)", "_____no_output_____" ] ], [ [ "### 2.6. ¿Cuáles son los ingresos promedios según los rangos de edad? ¿Cuál es el rango de edad con el mejor y peor ingreso?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_age_income = data_test[['ConvertedCompYearly', 'Age']]\ndata_age_income.head()", "_____no_output_____" ], [ "df_data_age_income = data_age_income.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_age_income['ConvertedCompYearly'], 0.1)\n\nprint(df_data_age_income[mask])", " ConvertedCompYearly Age\n45 60480.0 35-44\n50 25944.0 25-34\n58 22644.0 25-34\n76 45564.0 25-34\n77 151263.0 35-44\n... ... ...\n83425 19452.0 18-24\n83428 41232.0 25-34\n83431 11676.0 18-24\n83432 80169.0 18-24\n83436 90000.0 25-34\n\n[11611 rows x 2 columns]\n" ], [ "df_data_age_income = df_data_age_income[mask]", "_____no_output_____" ], [ "df_data_age_income1 = df_data_age_income.copy()", "_____no_output_____" ], [ "df_data_age_income1.to_csv('006_df_data_age_income1.csv', index=False)", "_____no_output_____" ], [ "grouped_df = df_data_age_income1.groupby(\"Age\")\n\naverage_df = grouped_df.mean()", "_____no_output_____" ], [ "average_df", "_____no_output_____" ], [ "df_flourish_006 = average_df.copy()", "_____no_output_____" ], [ "df_flourish_006.to_csv('006_df_flourish_006.csv', index=True)", "_____no_output_____" ] ], [ [ "### 2.7. ¿Cuáles son las tecnologías que permiten tener un mejor ingreso salarial anual?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_techs_best_income1 = data_test[['ConvertedCompYearly', 'LanguageHaveWorkedWith', 'DatabaseHaveWorkedWith', 'PlatformHaveWorkedWith', 'WebframeHaveWorkedWith', 'MiscTechHaveWorkedWith', 'ToolsTechHaveWorkedWith', 'NEWCollabToolsHaveWorkedWith']]\ndata_techs_best_income1.head()", "_____no_output_____" ], [ "data_techs_best_income1['AllTechs'] = data_techs_best_income1['LanguageHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['DatabaseHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['PlatformHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['WebframeHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['MiscTechHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['ToolsTechHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['NEWCollabToolsHaveWorkedWith'].map(str)\nprint (data_techs_best_income1)", " ConvertedCompYearly LanguageHaveWorkedWith \\\n45 60480.0 C#;C++;JavaScript;PowerShell;SQL;TypeScript \n50 25944.0 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 22644.0 Bash/Shell;HTML/CSS;JavaScript;Python;SQL \n64 500000.0 HTML/CSS;JavaScript;Python \n76 45564.0 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n... ... ... \n83428 41232.0 Bash/Shell;Node.js;TypeScript \n83431 11676.0 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 80169.0 Ruby \n83436 90000.0 Groovy;Java;Python \n83437 816816.0 Bash/Shell;JavaScript;Node.js;Python \n\n DatabaseHaveWorkedWith \\\n45 Microsoft SQL Server;PostgreSQL;Redis \n50 Couchbase;MariaDB;Microsoft SQL Server;MongoDB... \n58 Oracle \n64 MySQL \n76 Firebase;Microsoft SQL Server;MongoDB;MySQL;Po... \n... ... \n83428 Elasticsearch;MongoDB;PostgreSQL;Redis \n83431 Firebase;MySQL;SQLite \n83432 MySQL;PostgreSQL \n83436 DynamoDB;Elasticsearch;MongoDB;PostgreSQL;Redis \n83437 Cassandra;Elasticsearch;MongoDB;PostgreSQL;Redis \n\n PlatformHaveWorkedWith \\\n45 Heroku;Microsoft Azure \n50 AWS;DigitalOcean;Microsoft Azure \n58 Heroku \n64 AWS \n76 Google Cloud Platform;Microsoft Azure \n... ... \n83428 AWS;Google Cloud Platform \n83431 Google Cloud Platform \n83432 Google Cloud Platform;Heroku \n83436 AWS;Google Cloud Platform \n83437 Heroku \n\n WebframeHaveWorkedWith \\\n45 ASP.NET Core ;React.js \n50 Angular;ASP.NET;ASP.NET Core ;Express;Svelte \n58 Django;FastAPI;Vue.js \n64 Flask \n76 Angular;Angular.js;ASP.NET;ASP.NET Core ;Djang... \n... ... \n83428 React.js \n83431 Flask;jQuery \n83432 Flask;React.js;Ruby on Rails;Vue.js \n83436 FastAPI;Flask \n83437 Django;Express;Flask;React.js \n\n MiscTechHaveWorkedWith \\\n45 .NET Core / .NET 5 \n50 .NET Framework;.NET Core / .NET 5 \n58 NumPy;Pandas;Torch/PyTorch \n64 Pandas \n76 .NET Framework;.NET Core / .NET 5;Apache Spark... \n... ... \n83428 React Native \n83431 Flutter \n83432 NumPy;Pandas;TensorFlow;Torch/PyTorch \n83436 Hadoop;Keras;NumPy;Pandas \n83437 NumPy;Pandas;TensorFlow;Torch/PyTorch \n\n ToolsTechHaveWorkedWith \\\n45 Docker;Git;Kubernetes \n50 Docker;Kubernetes \n58 Docker;Git \n64 Git \n76 Docker;Git;Unity 3D \n... ... \n83428 Docker;Git;Terraform;Yarn \n83431 Git \n83432 Docker;Git;Kubernetes;Yarn \n83436 Ansible;Docker;Git;Terraform \n83437 Ansible;Docker;Git;Terraform \n\n NEWCollabToolsHaveWorkedWith \\\n45 Notepad++;Visual Studio;Visual Studio Code \n50 Notepad++;Visual Studio;Visual Studio Code \n58 IPython/Jupyter;Visual Studio Code \n64 Notepad++;PyCharm;Sublime Text \n76 Android Studio;Eclipse;NetBeans;Notepad++;Visu... \n... ... \n83428 Visual Studio Code;Webstorm \n83431 Android Studio;IntelliJ;IPython/Jupyter;Notepa... \n83432 Atom;IPython/Jupyter;Vim;Visual Studio Code \n83436 Android Studio;Eclipse;IntelliJ;IPython/Jupyte... \n83437 PyCharm;Sublime Text \n\n AllTechs \n45 C#;C++;JavaScript;PowerShell;SQL;TypeScript;Mi... \n50 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 Bash/Shell;HTML/CSS;JavaScript;Python;SQL;Orac... \n64 HTML/CSS;JavaScript;Python;MySQL;AWS;Flask;Pan... \n76 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n... ... \n83428 Bash/Shell;Node.js;TypeScript;Elasticsearch;Mo... \n83431 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 Ruby;MySQL;PostgreSQL;Google Cloud Platform;He... \n83436 Groovy;Java;Python;DynamoDB;Elasticsearch;Mong... \n83437 Bash/Shell;JavaScript;Node.js;Python;Cassandra... \n\n[14517 rows x 9 columns]\n" ], [ "df_data_techs_best_income = data_techs_best_income1[['ConvertedCompYearly', 'AllTechs']].copy()", "_____no_output_____" ], [ "df_data_techs_best_income1 = df_data_techs_best_income.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_techs_best_income1['ConvertedCompYearly'], 0.1)\n\nprint(df_data_techs_best_income1[mask])", " ConvertedCompYearly AllTechs\n45 60480.0 C#;C++;JavaScript;PowerShell;SQL;TypeScript;Mi...\n50 25944.0 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type...\n58 22644.0 Bash/Shell;HTML/CSS;JavaScript;Python;SQL;Orac...\n76 45564.0 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav...\n77 151263.0 HTML/CSS;Python;R;DynamoDB;AWS;Flask;Keras;Num...\n... ... ...\n83425 19452.0 HTML/CSS;JavaScript;Node.js;TypeScript;DynamoD...\n83428 41232.0 Bash/Shell;Node.js;TypeScript;Elasticsearch;Mo...\n83431 11676.0 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j...\n83432 80169.0 Ruby;MySQL;PostgreSQL;Google Cloud Platform;He...\n83436 90000.0 Groovy;Java;Python;DynamoDB;Elasticsearch;Mong...\n\n[11611 rows x 2 columns]\n" ], [ "df_data_techs_best_income1 = df_data_techs_best_income1[mask]", "_____no_output_____" ], [ "df_data_techs_best_income1['ConvertedCompYearlyCategorical'] = 'ALTO'\ndf_data_techs_best_income1.loc[(df_data_techs_best_income1['ConvertedCompYearly'] >= 0) & (df_data_techs_best_income1['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_data_techs_best_income1.loc[(df_data_techs_best_income1['ConvertedCompYearly'] > 32747) & (df_data_techs_best_income1['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_data_techs_best_income1)", " ConvertedCompYearly AllTechs \\\n45 60480.0 C#;C++;JavaScript;PowerShell;SQL;TypeScript;Mi... \n50 25944.0 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 22644.0 Bash/Shell;HTML/CSS;JavaScript;Python;SQL;Orac... \n76 45564.0 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n77 151263.0 HTML/CSS;Python;R;DynamoDB;AWS;Flask;Keras;Num... \n... ... ... \n83425 19452.0 HTML/CSS;JavaScript;Node.js;TypeScript;DynamoD... \n83428 41232.0 Bash/Shell;Node.js;TypeScript;Elasticsearch;Mo... \n83431 11676.0 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 80169.0 Ruby;MySQL;PostgreSQL;Google Cloud Platform;He... \n83436 90000.0 Groovy;Java;Python;DynamoDB;Elasticsearch;Mong... \n\n ConvertedCompYearlyCategorical \n45 MEDIO \n50 BAJO \n58 BAJO \n76 MEDIO \n77 ALTO \n... ... \n83425 BAJO \n83428 MEDIO \n83431 BAJO \n83432 MEDIO \n83436 MEDIO \n\n[11611 rows x 3 columns]\n" ], [ "df_data_techs_best_income1 = df_data_techs_best_income1[df_data_techs_best_income1['ConvertedCompYearlyCategorical'].isin(['ALTO', 'MEDIO'])]", "_____no_output_____" ], [ "df_data_techs_best_income1['AllTechs'] = df_data_techs_best_income1['AllTechs'].str.replace(' ', '')", "_____no_output_____" ], [ "df_data_techs_best_income1['AllTechs'] = df_data_techs_best_income1['AllTechs'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts = df_data_techs_best_income1['AllTechs'].str.split(expand=True).stack().value_counts().rename_axis('Tech').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts.head(10)", "_____no_output_____" ], [ "df_data_techs_best_income_007 = df_counts.head(10)", "_____no_output_____" ], [ "df_data_techs_best_income_007.to_csv('007_df_data_techs_best_income.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.8. ¿Cuántas tecnologías en promedio domina un programador profesional?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_techs_dev_pro1 = data_test[['DevType', 'LanguageHaveWorkedWith', 'DatabaseHaveWorkedWith', 'PlatformHaveWorkedWith', 'WebframeHaveWorkedWith', 'MiscTechHaveWorkedWith', 'ToolsTechHaveWorkedWith', 'NEWCollabToolsHaveWorkedWith']]\ndata_techs_dev_pro1.head()", "_____no_output_____" ], [ "data_techs_dev_pro1['AllTechs'] = data_techs_dev_pro1['LanguageHaveWorkedWith'].map(str) + ';' + data_techs_dev_pro1['DatabaseHaveWorkedWith'].map(str) + ';' + data_techs_dev_pro1['PlatformHaveWorkedWith'].map(str) + ';' + data_techs_dev_pro1['WebframeHaveWorkedWith'].map(str) + ';' + data_techs_dev_pro1['MiscTechHaveWorkedWith'].map(str) + ';' + data_techs_best_income1['ToolsTechHaveWorkedWith'].map(str) + ';' + data_techs_dev_pro1['NEWCollabToolsHaveWorkedWith'].map(str)\nprint (data_techs_dev_pro1)", " DevType \\\n45 Desarrollador Escritorio \n50 Desarrollador full-stack \n58 Desarrollador full-stack \n64 Desarrollador front-end \n76 Desarrollador front-end \n... ... \n83428 Ejecutivo Senior \n83431 Desarrollador móvil \n83432 Desarrollador back-end \n83436 Cientifico de datos \n83437 Desarrollador back-end \n\n LanguageHaveWorkedWith \\\n45 C#;C++;JavaScript;PowerShell;SQL;TypeScript \n50 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 Bash/Shell;HTML/CSS;JavaScript;Python;SQL \n64 HTML/CSS;JavaScript;Python \n76 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n... ... \n83428 Bash/Shell;Node.js;TypeScript \n83431 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 Ruby \n83436 Groovy;Java;Python \n83437 Bash/Shell;JavaScript;Node.js;Python \n\n DatabaseHaveWorkedWith \\\n45 Microsoft SQL Server;PostgreSQL;Redis \n50 Couchbase;MariaDB;Microsoft SQL Server;MongoDB... \n58 Oracle \n64 MySQL \n76 Firebase;Microsoft SQL Server;MongoDB;MySQL;Po... \n... ... \n83428 Elasticsearch;MongoDB;PostgreSQL;Redis \n83431 Firebase;MySQL;SQLite \n83432 MySQL;PostgreSQL \n83436 DynamoDB;Elasticsearch;MongoDB;PostgreSQL;Redis \n83437 Cassandra;Elasticsearch;MongoDB;PostgreSQL;Redis \n\n PlatformHaveWorkedWith \\\n45 Heroku;Microsoft Azure \n50 AWS;DigitalOcean;Microsoft Azure \n58 Heroku \n64 AWS \n76 Google Cloud Platform;Microsoft Azure \n... ... \n83428 AWS;Google Cloud Platform \n83431 Google Cloud Platform \n83432 Google Cloud Platform;Heroku \n83436 AWS;Google Cloud Platform \n83437 Heroku \n\n WebframeHaveWorkedWith \\\n45 ASP.NET Core ;React.js \n50 Angular;ASP.NET;ASP.NET Core ;Express;Svelte \n58 Django;FastAPI;Vue.js \n64 Flask \n76 Angular;Angular.js;ASP.NET;ASP.NET Core ;Djang... \n... ... \n83428 React.js \n83431 Flask;jQuery \n83432 Flask;React.js;Ruby on Rails;Vue.js \n83436 FastAPI;Flask \n83437 Django;Express;Flask;React.js \n\n MiscTechHaveWorkedWith \\\n45 .NET Core / .NET 5 \n50 .NET Framework;.NET Core / .NET 5 \n58 NumPy;Pandas;Torch/PyTorch \n64 Pandas \n76 .NET Framework;.NET Core / .NET 5;Apache Spark... \n... ... \n83428 React Native \n83431 Flutter \n83432 NumPy;Pandas;TensorFlow;Torch/PyTorch \n83436 Hadoop;Keras;NumPy;Pandas \n83437 NumPy;Pandas;TensorFlow;Torch/PyTorch \n\n ToolsTechHaveWorkedWith \\\n45 Docker;Git;Kubernetes \n50 Docker;Kubernetes \n58 Docker;Git \n64 Git \n76 Docker;Git;Unity 3D \n... ... \n83428 Docker;Git;Terraform;Yarn \n83431 Git \n83432 Docker;Git;Kubernetes;Yarn \n83436 Ansible;Docker;Git;Terraform \n83437 Ansible;Docker;Git;Terraform \n\n NEWCollabToolsHaveWorkedWith \\\n45 Notepad++;Visual Studio;Visual Studio Code \n50 Notepad++;Visual Studio;Visual Studio Code \n58 IPython/Jupyter;Visual Studio Code \n64 Notepad++;PyCharm;Sublime Text \n76 Android Studio;Eclipse;NetBeans;Notepad++;Visu... \n... ... \n83428 Visual Studio Code;Webstorm \n83431 Android Studio;IntelliJ;IPython/Jupyter;Notepa... \n83432 Atom;IPython/Jupyter;Vim;Visual Studio Code \n83436 Android Studio;Eclipse;IntelliJ;IPython/Jupyte... \n83437 PyCharm;Sublime Text \n\n AllTechs \n45 C#;C++;JavaScript;PowerShell;SQL;TypeScript;Mi... \n50 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 Bash/Shell;HTML/CSS;JavaScript;Python;SQL;Orac... \n64 HTML/CSS;JavaScript;Python;MySQL;AWS;Flask;Pan... \n76 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n... ... \n83428 Bash/Shell;Node.js;TypeScript;Elasticsearch;Mo... \n83431 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 Ruby;MySQL;PostgreSQL;Google Cloud Platform;He... \n83436 Groovy;Java;Python;DynamoDB;Elasticsearch;Mong... \n83437 Bash/Shell;JavaScript;Node.js;Python;Cassandra... \n\n[14517 rows x 9 columns]\n" ], [ "df_data_techs_dev_pro = data_techs_dev_pro1[['DevType', 'AllTechs']].copy()", "_____no_output_____" ], [ "df_data_techs_dev_pro = df_data_techs_dev_pro[df_data_techs_dev_pro['DevType'].isin(['Desarrollador full-stack', 'Desarrollador front-end', 'Desarrollador móvil', 'Desarrollador back-end', 'Desarrollador Escritorio', 'Desarrollador de QA o Test', 'Desarrollador de aplicaciones embebidas', 'Administrador de base de datos', 'Desarrollador de juegos o gráfico'])]", "_____no_output_____" ], [ "df_data_techs_dev_pro.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 12832 entries, 45 to 83437\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 DevType 12832 non-null object\n 1 AllTechs 12832 non-null object\ndtypes: object(2)\nmemory usage: 300.8+ KB\n" ], [ "df_data_techs_dev_pro1 = df_data_techs_dev_pro.copy()", "_____no_output_____" ], [ "df_data_techs_dev_pro1.to_csv('008_df_data_techs_dev_pro1.csv', index=True)", "_____no_output_____" ], [ "def convert_row_to_list(lst):\n return lst.split(';')", "_____no_output_____" ], [ "df_data_techs_dev_pro1['ListTechs'] = df_data_techs_dev_pro1['AllTechs'].apply(convert_row_to_list)", "_____no_output_____" ], [ "df_data_techs_dev_pro1['LenListTechs'] = df_data_techs_dev_pro1['ListTechs'].map(len)", "_____no_output_____" ], [ "df_flourish_008 = df_data_techs_dev_pro1[['DevType', 'LenListTechs']].copy()\ndf_flourish_008", "_____no_output_____" ], [ "grouped_df = df_flourish_008.groupby(\"DevType\")\n\naverage_df_008 = round(grouped_df.mean())", "_____no_output_____" ], [ "df_flourish_008 = average_df_008.copy()", "_____no_output_____" ], [ "df_flourish_008.to_csv('008_df_flourish_008.csv', index=True)", "_____no_output_____" ] ], [ [ "### 2.9. ¿En qué rango de edad se inició la mayoría de los programadores en la programación?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_age1stcode_dev_pro1 = data_test[['Age1stCode']]\ndata_age1stcode_dev_pro1.head()", "_____no_output_____" ], [ "data_age1stcode_dev_pro1 = data_age1stcode_dev_pro1['Age1stCode'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "data_age1stcode_dev_pro1.to_csv('009_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.10. ¿Cuántos años como programadores se requiere para obtener un ingreso salarial alto?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_yearscode_high_income1 = data_test[['ConvertedCompYearly', 'YearsCode']]\ndata_yearscode_high_income1.head()", "_____no_output_____" ], [ "df_data_yearscode_high_income = data_yearscode_high_income1.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_yearscode_high_income['ConvertedCompYearly'], 0.1)\n\nprint(df_data_yearscode_high_income[mask])", " ConvertedCompYearly YearsCode\n45 60480.0 22\n50 25944.0 12\n58 22644.0 5\n76 45564.0 12\n77 151263.0 10\n... ... ...\n83425 19452.0 5\n83428 41232.0 12\n83431 11676.0 9\n83432 80169.0 5\n83436 90000.0 10\n\n[11611 rows x 2 columns]\n" ], [ "df_data_yearscode_high_income = df_data_yearscode_high_income[mask]", "_____no_output_____" ], [ "df_data_yearscode_high_income['ConvertedCompYearlyCategorical'] = 'ALTO'\n\ndf_data_yearscode_high_income.loc[(df_data_yearscode_high_income['ConvertedCompYearly'] >= 0) & (df_data_yearscode_high_income['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_data_yearscode_high_income.loc[(df_data_yearscode_high_income['ConvertedCompYearly'] > 32747) & (df_data_yearscode_high_income['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_data_yearscode_high_income)", " ConvertedCompYearly YearsCode ConvertedCompYearlyCategorical\n45 60480.0 22 MEDIO\n50 25944.0 12 BAJO\n58 22644.0 5 BAJO\n76 45564.0 12 MEDIO\n77 151263.0 10 ALTO\n... ... ... ...\n83425 19452.0 5 BAJO\n83428 41232.0 12 MEDIO\n83431 11676.0 9 BAJO\n83432 80169.0 5 MEDIO\n83436 90000.0 10 MEDIO\n\n[11611 rows x 3 columns]\n" ], [ "df_data_yearscode_high_income.to_csv('010_df_flourish.csv', index=False)", "_____no_output_____" ], [ "df_data_yearscode_high_income['ConvertedCompYearlyCategorical'].value_counts()", "_____no_output_____" ], [ "df_flourish_010 = df_data_yearscode_high_income[['YearsCode', 'ConvertedCompYearlyCategorical']].copy()\n\ndf_flourish_010.head()", "_____no_output_____" ], [ "df_flourish_010['YearsCode'] = pd.to_numeric(df_flourish_010['YearsCode'])", "_____no_output_____" ], [ "df_flourish_010.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 11611 entries, 45 to 83436\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 YearsCode 11611 non-null int64 \n 1 ConvertedCompYearlyCategorical 11611 non-null object\ndtypes: int64(1), object(1)\nmemory usage: 530.2+ KB\n" ], [ "grouped_df_010 = df_flourish_010.groupby(\"ConvertedCompYearlyCategorical\")\n\naverage_df_010 = round(grouped_df_010.mean())", "_____no_output_____" ], [ "average_df_010", "_____no_output_____" ], [ "average_df_010.to_csv('010_flourish_data.csv', index=True)", "_____no_output_____" ] ], [ [ "### 2.11. ¿Cuáles son los perfiles que registran los mejores ingresos?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_profiles_dev_high_income1 = data_test[['ConvertedCompYearly', 'DevType']].copy()\ndata_profiles_dev_high_income1.head()", "_____no_output_____" ], [ "df_data_profiles_dev_high_income = data_profiles_dev_high_income1.copy()", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_data_profiles_dev_high_income['ConvertedCompYearly'], 0.1)\n\nprint(df_data_profiles_dev_high_income[mask])", " ConvertedCompYearly DevType\n45 60480.0 Desarrollador Escritorio\n50 25944.0 Desarrollador full-stack\n58 22644.0 Desarrollador full-stack\n76 45564.0 Desarrollador front-end\n77 151263.0 Cientifico de datos\n... ... ...\n83425 19452.0 Desarrollador full-stack\n83428 41232.0 Ejecutivo Senior\n83431 11676.0 Desarrollador móvil\n83432 80169.0 Desarrollador back-end\n83436 90000.0 Cientifico de datos\n\n[11611 rows x 2 columns]\n" ], [ "df_data_profiles_dev_high_income = df_data_profiles_dev_high_income[mask]", "_____no_output_____" ], [ "df_data_profiles_dev_high_income['ConvertedCompYearlyCategorical'] = 'ALTO'\n\ndf_data_profiles_dev_high_income.loc[(df_data_profiles_dev_high_income['ConvertedCompYearly'] >= 0) & (df_data_profiles_dev_high_income['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_data_profiles_dev_high_income.loc[(df_data_profiles_dev_high_income['ConvertedCompYearly'] > 32747) & (df_data_profiles_dev_high_income['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_data_profiles_dev_high_income)", " ConvertedCompYearly DevType \\\n45 60480.0 Desarrollador Escritorio \n50 25944.0 Desarrollador full-stack \n58 22644.0 Desarrollador full-stack \n76 45564.0 Desarrollador front-end \n77 151263.0 Cientifico de datos \n... ... ... \n83425 19452.0 Desarrollador full-stack \n83428 41232.0 Ejecutivo Senior \n83431 11676.0 Desarrollador móvil \n83432 80169.0 Desarrollador back-end \n83436 90000.0 Cientifico de datos \n\n ConvertedCompYearlyCategorical \n45 MEDIO \n50 BAJO \n58 BAJO \n76 MEDIO \n77 ALTO \n... ... \n83425 BAJO \n83428 MEDIO \n83431 BAJO \n83432 MEDIO \n83436 MEDIO \n\n[11611 rows x 3 columns]\n" ], [ "df_data_profiles_dev_high_income['ConvertedCompYearlyCategorical'].value_counts()", "_____no_output_____" ], [ "df_flourish_011 = df_data_profiles_dev_high_income[['DevType', 'ConvertedCompYearlyCategorical']].copy()", "_____no_output_____" ], [ "df_flourish_011 = df_flourish_011[df_flourish_011['ConvertedCompYearlyCategorical'].isin(['ALTO'])]", "_____no_output_____" ], [ "df_flourish_011.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2892 entries, 77 to 83372\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 DevType 2892 non-null object\n 1 ConvertedCompYearlyCategorical 2892 non-null object\ndtypes: object(2)\nmemory usage: 67.8+ KB\n" ], [ "df_data_flourish_011 = df_flourish_011['DevType'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "df_data_flourish_011 = df_data_flourish_011.head(10)", "_____no_output_____" ], [ "df_data_flourish_011", "_____no_output_____" ], [ "df_data_flourish_011.to_csv('011_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.12. ¿Cuáles son las 10 tecnologías más usadas entre los programadores por países?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_10_techs_popular_dev_countries = data_test[['Country', 'LanguageHaveWorkedWith', 'DatabaseHaveWorkedWith', 'PlatformHaveWorkedWith', 'WebframeHaveWorkedWith', 'MiscTechHaveWorkedWith', 'ToolsTechHaveWorkedWith', 'NEWCollabToolsHaveWorkedWith']]\ndata_10_techs_popular_dev_countries.head()", "_____no_output_____" ], [ "data_10_techs_popular_dev_countries['AllTechs'] = data_10_techs_popular_dev_countries['LanguageHaveWorkedWith'].map(str) + ';' + data_10_techs_popular_dev_countries['DatabaseHaveWorkedWith'].map(str) + ';' + data_10_techs_popular_dev_countries['PlatformHaveWorkedWith'].map(str) + ';' + data_10_techs_popular_dev_countries['WebframeHaveWorkedWith'].map(str) + ';' + data_10_techs_popular_dev_countries['MiscTechHaveWorkedWith'].map(str) + ';' + data_10_techs_popular_dev_countries['ToolsTechHaveWorkedWith'].map(str) + ';' + data_10_techs_popular_dev_countries['NEWCollabToolsHaveWorkedWith'].map(str)\nprint (data_10_techs_popular_dev_countries)", " Country \\\n45 Brazil \n50 Greece \n58 Russian Federation \n64 United States of America \n76 Poland \n... ... \n83428 Brazil \n83431 Pakistan \n83432 Canada \n83436 United States of America \n83437 Canada \n\n LanguageHaveWorkedWith \\\n45 C#;C++;JavaScript;PowerShell;SQL;TypeScript \n50 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 Bash/Shell;HTML/CSS;JavaScript;Python;SQL \n64 HTML/CSS;JavaScript;Python \n76 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n... ... \n83428 Bash/Shell;Node.js;TypeScript \n83431 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 Ruby \n83436 Groovy;Java;Python \n83437 Bash/Shell;JavaScript;Node.js;Python \n\n DatabaseHaveWorkedWith \\\n45 Microsoft SQL Server;PostgreSQL;Redis \n50 Couchbase;MariaDB;Microsoft SQL Server;MongoDB... \n58 Oracle \n64 MySQL \n76 Firebase;Microsoft SQL Server;MongoDB;MySQL;Po... \n... ... \n83428 Elasticsearch;MongoDB;PostgreSQL;Redis \n83431 Firebase;MySQL;SQLite \n83432 MySQL;PostgreSQL \n83436 DynamoDB;Elasticsearch;MongoDB;PostgreSQL;Redis \n83437 Cassandra;Elasticsearch;MongoDB;PostgreSQL;Redis \n\n PlatformHaveWorkedWith \\\n45 Heroku;Microsoft Azure \n50 AWS;DigitalOcean;Microsoft Azure \n58 Heroku \n64 AWS \n76 Google Cloud Platform;Microsoft Azure \n... ... \n83428 AWS;Google Cloud Platform \n83431 Google Cloud Platform \n83432 Google Cloud Platform;Heroku \n83436 AWS;Google Cloud Platform \n83437 Heroku \n\n WebframeHaveWorkedWith \\\n45 ASP.NET Core ;React.js \n50 Angular;ASP.NET;ASP.NET Core ;Express;Svelte \n58 Django;FastAPI;Vue.js \n64 Flask \n76 Angular;Angular.js;ASP.NET;ASP.NET Core ;Djang... \n... ... \n83428 React.js \n83431 Flask;jQuery \n83432 Flask;React.js;Ruby on Rails;Vue.js \n83436 FastAPI;Flask \n83437 Django;Express;Flask;React.js \n\n MiscTechHaveWorkedWith \\\n45 .NET Core / .NET 5 \n50 .NET Framework;.NET Core / .NET 5 \n58 NumPy;Pandas;Torch/PyTorch \n64 Pandas \n76 .NET Framework;.NET Core / .NET 5;Apache Spark... \n... ... \n83428 React Native \n83431 Flutter \n83432 NumPy;Pandas;TensorFlow;Torch/PyTorch \n83436 Hadoop;Keras;NumPy;Pandas \n83437 NumPy;Pandas;TensorFlow;Torch/PyTorch \n\n ToolsTechHaveWorkedWith \\\n45 Docker;Git;Kubernetes \n50 Docker;Kubernetes \n58 Docker;Git \n64 Git \n76 Docker;Git;Unity 3D \n... ... \n83428 Docker;Git;Terraform;Yarn \n83431 Git \n83432 Docker;Git;Kubernetes;Yarn \n83436 Ansible;Docker;Git;Terraform \n83437 Ansible;Docker;Git;Terraform \n\n NEWCollabToolsHaveWorkedWith \\\n45 Notepad++;Visual Studio;Visual Studio Code \n50 Notepad++;Visual Studio;Visual Studio Code \n58 IPython/Jupyter;Visual Studio Code \n64 Notepad++;PyCharm;Sublime Text \n76 Android Studio;Eclipse;NetBeans;Notepad++;Visu... \n... ... \n83428 Visual Studio Code;Webstorm \n83431 Android Studio;IntelliJ;IPython/Jupyter;Notepa... \n83432 Atom;IPython/Jupyter;Vim;Visual Studio Code \n83436 Android Studio;Eclipse;IntelliJ;IPython/Jupyte... \n83437 PyCharm;Sublime Text \n\n AllTechs \n45 C#;C++;JavaScript;PowerShell;SQL;TypeScript;Mi... \n50 C#;HTML/CSS;JavaScript;Node.js;PowerShell;Type... \n58 Bash/Shell;HTML/CSS;JavaScript;Python;SQL;Orac... \n64 HTML/CSS;JavaScript;Python;MySQL;AWS;Flask;Pan... \n76 Bash/Shell;C#;Dart;Delphi;Go;HTML/CSS;Java;Jav... \n... ... \n83428 Bash/Shell;Node.js;TypeScript;Elasticsearch;Mo... \n83431 C#;Dart;HTML/CSS;Java;JavaScript;Kotlin;Node.j... \n83432 Ruby;MySQL;PostgreSQL;Google Cloud Platform;He... \n83436 Groovy;Java;Python;DynamoDB;Elasticsearch;Mong... \n83437 Bash/Shell;JavaScript;Node.js;Python;Cassandra... \n\n[14517 rows x 9 columns]\n" ], [ "df_data_10_techs_popular_dev_countries = data_10_techs_popular_dev_countries[['Country', 'AllTechs']].copy()", "_____no_output_____" ], [ "df_data_10_techs_popular_dev_countries.head()", "_____no_output_____" ], [ "df_data_10_techs_popular_dev_countries['AllTechs'] = df_data_10_techs_popular_dev_countries['AllTechs'].str.replace(' ', '')", "_____no_output_____" ], [ "df_data_10_techs_popular_dev_countries['AllTechs'] = df_data_10_techs_popular_dev_countries['AllTechs'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts = df_data_10_techs_popular_dev_countries['AllTechs'].str.split(expand=True).stack().value_counts().rename_axis('Tech').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts", "_____no_output_____" ], [ "data_10_techs_popular_dev_countries.to_csv('012_data_10_techs_popular_dev_countries.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.13. ¿Cuáles el sistema operativo más usado entre los encuestados?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_data_so_devs = data_test[['OpSys']].copy()", "_____no_output_____" ], [ "df_data_so_devs.tail()", "_____no_output_____" ], [ "df_data_so_devs['OpSys'].drop_duplicates().sort_values()", "_____no_output_____" ], [ "df_data_so_devs['OpSys'] = df_data_so_devs['OpSys'].replace(['Other (please specify):'], 'Otro')", "_____no_output_____" ], [ "df_data_so_devs['OpSys'].value_counts()", "_____no_output_____" ], [ "df_counts = df_data_so_devs['OpSys'].str.split(expand=True).stack().value_counts().rename_axis('OS').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts", "_____no_output_____" ], [ "df_counts.to_csv('013_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.14. ¿Qué proporción de programadores tiene algún desorden mental por país?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "data_devs_mental_health_countries = data_test[['Country', 'MentalHealth']]\ndata_devs_mental_health_countries.head()", "_____no_output_____" ], [ "data_devs_mental_health_countries['MentalHealth'].value_counts()", "_____no_output_____" ], [ "df_data_devs_mental_health_countries = data_devs_mental_health_countries.copy()", "_____no_output_____" ], [ "df_data_devs_mental_health_countries = df_data_devs_mental_health_countries[df_data_devs_mental_health_countries['MentalHealth'].isin(['Desorden de concentración o memoria', 'Desorden emocional', 'Desorden de ansiedad', 'Tipo de autismo'])]", "_____no_output_____" ], [ "df_data_devs_mental_health_countries.head()", "_____no_output_____" ], [ "df_data_flourish_014 = df_data_devs_mental_health_countries['Country'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "df_data_flourish_014 = df_data_flourish_014.head(10)\ndf_data_flourish_014", "_____no_output_____" ], [ "df_data_flourish_014_best_ten = df_data_devs_mental_health_countries[df_data_devs_mental_health_countries['Country'].isin(['United States of America', 'United Kingdom of Great Britain and Northern Ireland', 'Brazil', 'Canada', 'India', 'Germany', 'Australia', 'Netherlands', 'Poland', 'Turkey'])]", "_____no_output_____" ], [ "df = df_data_flourish_014_best_ten.copy()", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "df1 = pd.crosstab(df['Country'], df['MentalHealth'])\ndf1", "_____no_output_____" ], [ "(df_data_devs_mental_health_countries.groupby(['Country', 'MentalHealth']).size() \n .sort_values(ascending=False) \n .reset_index(name='count') \n .drop_duplicates(subset='Country'))", "_____no_output_____" ], [ "df_flourish_data_014 = (df_data_devs_mental_health_countries.groupby(['Country', 'MentalHealth']).size() \n .sort_values(ascending=False) \n .reset_index(name='count'))", "_____no_output_____" ], [ "df_flourish_data_014 = df_flourish_data_014.sort_values('Country')", "_____no_output_____" ], [ "df_data_flourish_014.head(10).to_csv('014_flourish_data_014.csv', index=False)", "_____no_output_____" ], [ "df1.to_csv('014_flourish_data_014.csv', index=True)", "_____no_output_____" ] ], [ [ "### 2.15. ¿Cuáles son los países que tienen los mejores sueldos entre los programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_best_incomes_countries = data_test[['Country', 'ConvertedCompYearly']].copy()", "_____no_output_____" ], [ "df_best_incomes_countries", "_____no_output_____" ], [ "def remove_outliers(df, q=0.05):\n upper = df.quantile(1-q)\n lower = df.quantile(q)\n mask = (df < upper) & (df > lower)\n return mask\n\nmask = remove_outliers(df_best_incomes_countries['ConvertedCompYearly'], 0.1)\n\nprint(df_best_incomes_countries[mask])", " Country ConvertedCompYearly\n45 Brazil 60480.0\n50 Greece 25944.0\n58 Russian Federation 22644.0\n76 Poland 45564.0\n77 Canada 151263.0\n... ... ...\n83425 Finland 19452.0\n83428 Brazil 41232.0\n83431 Pakistan 11676.0\n83432 Canada 80169.0\n83436 United States of America 90000.0\n\n[11611 rows x 2 columns]\n" ], [ "df_best_incomes_countries_no_outliers = df_best_incomes_countries[mask]", "_____no_output_____" ], [ "df_best_incomes_countries_no_outliers1 = df_best_incomes_countries_no_outliers.copy()", "_____no_output_____" ], [ "df_best_incomes_countries_no_outliers1['ConvertedCompYearlyCategorical'] = 'ALTO'\ndf_best_incomes_countries_no_outliers1.loc[(df_best_incomes_countries_no_outliers1['ConvertedCompYearly'] >= 0) & (df_best_incomes_countries_no_outliers1['ConvertedCompYearly'] <= 32747), 'ConvertedCompYearlyCategorical'] = 'BAJO'\ndf_best_incomes_countries_no_outliers1.loc[(df_best_incomes_countries_no_outliers1['ConvertedCompYearly'] > 32747) & (df_best_incomes_countries_no_outliers1['ConvertedCompYearly'] <= 90000), 'ConvertedCompYearlyCategorical'] = 'MEDIO'\n\nprint(df_best_incomes_countries_no_outliers1)", " Country ConvertedCompYearly \\\n45 Brazil 60480.0 \n50 Greece 25944.0 \n58 Russian Federation 22644.0 \n76 Poland 45564.0 \n77 Canada 151263.0 \n... ... ... \n83425 Finland 19452.0 \n83428 Brazil 41232.0 \n83431 Pakistan 11676.0 \n83432 Canada 80169.0 \n83436 United States of America 90000.0 \n\n ConvertedCompYearlyCategorical \n45 MEDIO \n50 BAJO \n58 BAJO \n76 MEDIO \n77 ALTO \n... ... \n83425 BAJO \n83428 MEDIO \n83431 BAJO \n83432 MEDIO \n83436 MEDIO \n\n[11611 rows x 3 columns]\n" ], [ "df_best_incomes_countries_no_outliers1['ConvertedCompYearlyCategorical'].value_counts()", "_____no_output_____" ], [ "df_best_incomes_countries_alto = df_best_incomes_countries_no_outliers1[df_best_incomes_countries_no_outliers1['ConvertedCompYearlyCategorical'] == 'ALTO']", "_____no_output_____" ], [ "df_alto = df_best_incomes_countries_alto[['Country', 'ConvertedCompYearlyCategorical']].copy()", "_____no_output_____" ], [ "df_flourish_015 = df_alto['Country'].value_counts().to_frame('counts').reset_index()", "_____no_output_____" ], [ "df_flourish_015.head(10)", "_____no_output_____" ], [ "df_flourish_015.head(10).to_csv('015_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.16. ¿Cuáles son los 10 lenguajes de programación más usados entre los programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_10_prog_languages_devs = data_test[['LanguageHaveWorkedWith']].copy()\ndf_10_prog_languages_devs.head()", "_____no_output_____" ], [ "df_10_prog_languages_devs['LanguageHaveWorkedWith'] = df_10_prog_languages_devs['LanguageHaveWorkedWith'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts_016 = df_10_prog_languages_devs['LanguageHaveWorkedWith'].str.split(expand=True).stack().value_counts().rename_axis('Languages').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts_016.head(10)", "_____no_output_____" ], [ "df_counts_016.head(10).to_csv('016_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.17. ¿Cuáles son las bases de datos más usadas entre los programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_10_databases = data_test[['DatabaseHaveWorkedWith']].copy()\ndf_10_databases.head()", "_____no_output_____" ], [ "df_10_databases['DatabaseHaveWorkedWith'] = df_10_databases['DatabaseHaveWorkedWith'].str.replace(' ', '')", "_____no_output_____" ], [ "df_10_databases['DatabaseHaveWorkedWith'] = df_10_databases['DatabaseHaveWorkedWith'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts_017 = df_10_databases['DatabaseHaveWorkedWith'].str.split(expand=True).stack().value_counts().rename_axis('Databases').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts_017.head(10)", "_____no_output_____" ], [ "df_counts_017.head(10).to_csv('017_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.18. ¿Cuáles son las plataformas más usadas entre los programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_10_platforms = data_test[['PlatformHaveWorkedWith']].copy()\ndf_10_platforms.head()", "_____no_output_____" ], [ "df_10_platforms['PlatformHaveWorkedWith'] = df_10_platforms['PlatformHaveWorkedWith'].str.replace(' ', '')", "_____no_output_____" ], [ "df_10_platforms['PlatformHaveWorkedWith'] = df_10_platforms['PlatformHaveWorkedWith'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts_018 = df_10_platforms['PlatformHaveWorkedWith'].str.split(expand=True).stack().value_counts().rename_axis('Platform').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts_018.head(10)", "_____no_output_____" ], [ "df_counts_018.to_csv('018_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.19. ¿Cuáles son los frameworks web más usados entre los programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_10_web_frameworks = data_test[['WebframeHaveWorkedWith']].copy()\ndf_10_web_frameworks.head()", "_____no_output_____" ], [ "df_10_web_frameworks['WebframeHaveWorkedWith'] = df_10_web_frameworks['WebframeHaveWorkedWith'].str.replace(' ', '')", "_____no_output_____" ], [ "df_10_web_frameworks['WebframeHaveWorkedWith'] = df_10_web_frameworks['WebframeHaveWorkedWith'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts_019 = df_10_web_frameworks['WebframeHaveWorkedWith'].str.split(expand=True).stack().value_counts().rename_axis('Web framework').reset_index(name='Count')", "_____no_output_____" ], [ "df_counts_019.head(10)", "_____no_output_____" ], [ "df_counts_019.to_csv('019_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.20. ¿Cuáles son las herramientas tecnológicas más usadas entre los programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_10_data_misc_techs = data_test[['MiscTechHaveWorkedWith', 'ToolsTechHaveWorkedWith']].copy()\ndf_10_data_misc_techs.head()", "_____no_output_____" ], [ "df_10_data_misc_techs['AllMiscTechs'] = df_10_data_misc_techs['MiscTechHaveWorkedWith'].map(str) + ';' + df_10_data_misc_techs['ToolsTechHaveWorkedWith'].map(str)", "_____no_output_____" ], [ "df_10_data_misc_techs.head()", "_____no_output_____" ], [ "df_10_data_misc_techs['AllMiscTechs'] = df_10_data_misc_techs['AllMiscTechs'].str.replace(' ', '')", "_____no_output_____" ], [ "df_10_data_misc_techs['AllMiscTechs'] = df_10_data_misc_techs['AllMiscTechs'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts_020 = df_10_data_misc_techs['AllMiscTechs'].str.split(expand=True).stack().value_counts().rename_axis('Tecnología').reset_index(name='# Programadores')", "_____no_output_____" ], [ "df_counts_020.head(10)", "_____no_output_____" ], [ "df_counts_020.head(10).to_csv('020_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.21. ¿Cuáles son las herramientas colaborativas más usadas entre programadores?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_10_colab = data_test[['NEWCollabToolsHaveWorkedWith']].copy()\ndf_10_colab.head()", "_____no_output_____" ], [ "df_10_colab['NEWCollabToolsHaveWorkedWith'] = df_10_colab['NEWCollabToolsHaveWorkedWith'].str.replace(' ', '')", "_____no_output_____" ], [ "df_10_colab['NEWCollabToolsHaveWorkedWith'] = df_10_colab['NEWCollabToolsHaveWorkedWith'].str.replace(';', ' ')", "_____no_output_____" ], [ "df_counts_021 = df_10_colab['NEWCollabToolsHaveWorkedWith'].str.split(expand=True).stack().value_counts().rename_axis('Herramienta Colaborativa').reset_index(name='# Programadores')", "_____no_output_____" ], [ "df_counts_021.head(10)", "_____no_output_____" ], [ "df_counts_021.head(10).to_csv('021_flourish_data.csv', index=False)", "_____no_output_____" ] ], [ [ "### 2.22. ¿Cuáles son los países con mayor número de programadores trabajando a tiempo completo?\n\nSe seleccionarán los campos adecuados para responder a esta pregunta", "_____no_output_____" ] ], [ [ "df_fulltime_employment = data_test[['Country', 'Employment']].copy()\ndf_fulltime_employment.head()", "_____no_output_____" ], [ "df_fulltime_employment.info()", "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 14517 entries, 45 to 83437\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Country 14517 non-null object\n 1 Employment 14517 non-null object\ndtypes: object(2)\nmemory usage: 856.3+ KB\n" ], [ "df_fulltime_only = df_fulltime_employment[df_fulltime_employment['Employment'] == 'Tiempo completo']", "_____no_output_____" ], [ "df_fulltime_only.head()", "_____no_output_____" ], [ "df_flourish_022 = df_fulltime_only['Country'].value_counts().to_frame('# Programadores').reset_index()", "_____no_output_____" ], [ "df_flourish_022.head(10)", "_____no_output_____" ], [ "df_flourish_022.head(10).to_csv('022_flourish_data.csv', index=False)", "_____no_output_____" ] ] ]

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[ [ [ "# This Python file uses the following encoding: utf-8\n\n# Analise.ipynb\n# Github:@WeDias\n\n# MIT License\n#\n# Copyright (c) 2020 Wesley R. Dias\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n\nfrom time import time\nfrom random import randint\n\n\nclass Analisador:\n \"\"\"Classe criada para obter os tempos de execução\n dos seguintes algoritmos de ordenação:\n 1.Built-in Python\n 2.Quicksort\n 3.Mergesort\n 4.Seleção\n \"\"\"\n _tempo_total = 0\n _resultados = {\n 'nativo': {},\n 'quicksort': {},\n 'mergesort': {},\n 'selecao': {}\n }\n\n # noinspection PyUnusedLocal\n def __init__(self, inicio: int = 2000, fim: int = 22000,\n passo: int = 2000, intervalo: tuple = (0, 20000)):\n \"\"\"Serve para iniciar o processo de obtenção dos tempos de execução de cada algoritmo\n :param inicio: int, número inicial de elementos aleatórios da lista, padrão 2000\n :param fim: int, número final de elementos aleatórios da lista, padrão 22000\n :param passo: int, número de elementos incrementados a cada teste, padrão 2000\n :param intervalo: tuple, intervalo de números aleatórios, padrão (0, 20000)\n \"\"\"\n inicio_exec = time()\n algoritmos = {\n 'nativo': self._nativo,\n 'quicksort': self._quicksort,\n 'mergesort': self._mergesort,\n 'selecao': self._selecao\n }\n for amostra in range(inicio, fim + 1, passo):\n array = [randint(intervalo[0], intervalo[1]) for a in range(amostra)]\n for nome_algo, algoritmo in algoritmos.items():\n self._temporizador(nome_algo, algoritmo, array, amostra)\n self._tempo_total = time() - inicio_exec\n print(f'Tempo total de execução: {self._tempo_total:.2f}s\\n')\n\n def resultado(self) -> dict:\n \"\"\"Serve para retornar um dicionário com os dados de tempo de\n execução de cada algoritmo em cada caso de teste\n :return: dict, dados do tempo de execução de cada algoritmo\n \"\"\"\n return self._resultados\n\n @staticmethod\n def _temporizador(nome_algo: str, algoritmo, array: list, amostra: int) -> None:\n \"\"\"Serve para registrar o tempo gasto para um algoritmo de\n ordenação ser executado e ordenar uma determinada lista\n :param nome_algo: str, nome do algoritmo\n :param algoritmo: function, algoritmo a ser executado\n :param array: list, lista de elementos a ser ordenada\n :param amostra: int, número de elementos da lista\n :return: None\n \"\"\"\n inicio_exec_algo = time()\n algoritmo(array)\n Analisador._resultados[nome_algo][amostra] = round(time() - inicio_exec_algo, 3)\n\n @staticmethod\n def _nativo(array: list) -> list:\n \"\"\"Algoritmo de ordenação Built-in do Python, serve para ordenar listas\n :param array: list, lista de elementos a ser ordenada\n :return: list, lista de elementos ordenados\n \"\"\"\n return sorted(array)\n\n @staticmethod\n def _quicksort(array: list) -> list:\n \"\"\"Algoritmo de ordenação Quicksort, serve para ordenar listas\n :param array: list, lista de elementos a ser ordenada\n :return: list, lista de elementos ordenados\n \"\"\"\n if len(array) <= 1:\n return array\n m = array[0]\n return Analisador._quicksort(\n [x for x in array if x < m]) + \\\n [x for x in array if x == m] + \\\n Analisador._quicksort([x for x in array if x > m])\n\n @staticmethod\n def _selecao(array: list) -> list:\n \"\"\"Algoritmo de ordenação Seleção, serve para ordenar listas\n :param array: list, lista de elementos a ser ordenada\n :return: list, lista de elementos ordenados\n \"\"\"\n r = []\n while array:\n m = min(array)\n r.append(m)\n array.remove(m)\n return r\n\n @staticmethod\n def _mergesort(array: list) -> list:\n \"\"\"Algoritmo de ordenação Mergesort, serve para ordenar listas\n :param array: list, lista de elementos a ser ordenada\n :return: list, lista de elementos ordenados\n \"\"\"\n if len(array) <= 1:\n return array\n else:\n m = len(array) // 2\n e = Analisador._mergesort(array[:m])\n d = Analisador._mergesort(array[m:])\n return Analisador._merge(e, d)\n\n @staticmethod\n def _merge(e:list , d: list) -> list:\n \"\"\"Serve para auxiliar no Mergesort\n :param e: list, Lista da esquerda\n :param d: list, Lista da direita\n :return: list, lista de elementos ordenados\n \"\"\"\n r = []\n i, j = 0, 0\n while i < len(e) and j < len(d):\n if e[i] <= d[j]:\n r.append(e[i])\n i += 1\n else:\n r.append(d[j])\n j += 1\n r += e[i:]\n r += d[j:]\n return r", "_____no_output_____" ], [ "# Obtenção dos dados de execução dos algoritmos\nfrom pandas import DataFrame\n\nanalise = Analisador()\ndf = DataFrame.from_dict(analise.resultado())\nprint(df)", "Tempo total de execução: 30.01s\n\n nativo quicksort mergesort selecao\n2000 0.000 0.008 0.014 0.052\n4000 0.000 0.018 0.028 0.223\n6000 0.000 0.035 0.053 0.479\n8000 0.001 0.044 0.075 0.852\n10000 0.001 0.043 0.092 1.351\n12000 0.002 0.065 0.125 1.976\n14000 0.002 0.059 0.116 2.651\n16000 0.003 0.069 0.137 3.470\n18000 0.003 0.091 0.177 4.422\n20000 0.004 0.127 0.214 5.439\n22000 0.004 0.100 0.191 6.954\n" ], [ "# Geração do gráfico comparativo\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(16, 9))\nplt.plot(df, marker='.')\nplt.title('Comparação entre algoritmos de ordenação', size=16)\nplt.legend(('Nativo (Python)', 'Quicksort', 'Mergesort', 'Seleção'), fontsize=14)\nplt.xlabel('Número de elementos', size=14)\nplt.ylabel('Tempo em segundos', size=14)\nplt.grid()\nplt.show()", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Pyber Analysis", "_____no_output_____" ], [ "### 4.3 Loading and Reading CSV files", "_____no_output_____" ] ], [ [ "# Add Matplotlib inline magic command\n%matplotlib inline\n# Dependencies and Setup\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport matplotlib.dates as mdates\n\n# File to Load (Remember to change these)\ncity_data_to_load = \"Resources/city_data.csv\"\nride_data_to_load = \"Resources/ride_data.csv\"\n\n# Read the City and Ride Data\ncity_data_df = pd.read_csv(city_data_to_load)\nride_data_df = pd.read_csv(ride_data_to_load)", "_____no_output_____" ] ], [ [ "### Merge the DataFrames", "_____no_output_____" ] ], [ [ "# Combine the data into a single dataset\npyber_data_df = pd.merge(ride_data_df, city_data_df, how=\"left\", on=[\"city\", \"city\"])\n\n# Display the data table for preview\npyber_data_df", "_____no_output_____" ] ], [ [ "## Deliverable 1: Get a Summary DataFrame ", "_____no_output_____" ] ], [ [ "# 1. Get the total rides for each city type\ntot_rides_by_type = pyber_data_df.groupby([\"type\"]).count()[\"ride_id\"]\ntot_rides_by_type", "_____no_output_____" ], [ "# 2. Get the total drivers for each city type\ntot_drivers_by_type = city_data_df.groupby([\"type\"]).sum()[\"driver_count\"]\ntot_drivers_by_type", "_____no_output_____" ], [ "# 3. Get the total amount of fares for each city type\ntot_fares_by_type = pyber_data_df.groupby([\"type\"]).sum()[\"fare\"]\ntot_fares_by_type", "_____no_output_____" ], [ "# 4. Get the average fare per ride for each city type. \navg_fare_by_type = round((tot_fares_by_type / tot_rides_by_type), 2)\navg_fare_by_type", "_____no_output_____" ], [ "# 5. Get the average fare per driver for each city type. \navg_fare_per_driver_by_type = round((tot_fares_by_type / tot_drivers_by_type), 2)\navg_fare_per_driver_by_type ", "_____no_output_____" ], [ "# 6. Create a PyBer summary DataFrame. \npyber_summary_df = pd.DataFrame({\n \"Total Rides\": tot_rides_by_type,\n \"Total Drivers\": tot_drivers_by_type,\n \"Total Fares\": tot_fares_by_type,\n \"Average Fare per Ride\": avg_fare_by_type,\n \"Average Fare per Driver\": avg_fare_per_driver_by_type \n })\npyber_summary_df.dtypes", "_____no_output_____" ], [ "# 7. Cleaning up the DataFrame. Delete the index name\npyber_summary_df.index.name = None\npyber_summary_df", "_____no_output_____" ], [ "# 8. Format the columns.\npyber_summary_df['Total Rides'] = pyber_summary_df['Total Rides'].map('{:,}'.format)\npyber_summary_df['Total Drivers'] = pyber_summary_df['Total Drivers'].map('{:,}'.format)\npyber_summary_df['Total Fares'] = pyber_summary_df['Total Fares'].map('${:,}'.format)\npyber_summary_df['Average Fare per Ride'] = pyber_summary_df['Average Fare per Ride'].map('${:,}'.format)\npyber_summary_df['Average Fare per Driver'] = pyber_summary_df['Average Fare per Driver'].map('${:,}'.format)\npyber_summary_df", "_____no_output_____" ] ], [ [ "## Deliverable 2. Create a multiple line plot that shows the total weekly of the fares for each type of city.", "_____no_output_____" ] ], [ [ "# 1. Read the merged DataFrame\npyber_data_df", "_____no_output_____" ], [ "# 2. Using groupby() to create a new DataFrame showing the sum of the fares \n# for each date where the indices are the city type and date.\ntot_fares_by_date_df = pd.DataFrame(pyber_data_df.groupby([\"type\", \"date\"]).sum()[\"fare\"])\ntot_fares_by_date_df", "_____no_output_____" ], [ "# 3. Reset the index on the DataFrame you created in #1. This is needed to use the 'pivot()' function.\n# df = df.reset_index()\ntot_fares_by_date_df = tot_fares_by_date_df.reset_index()\ntot_fares_by_date_df", "_____no_output_____" ], [ "# 4. Create a pivot table with the 'date' as the index, the columns ='type', and values='fare' \n# to get the total fares for each type of city by the date. \npyber_pivot = tot_fares_by_date_df.pivot(index=\"date\", columns=\"type\", values=\"fare\")\npyber_pivot", "_____no_output_____" ], [ "# 5. Create a new DataFrame from the pivot table DataFrame using loc on the given dates, '2019-01-01':'2019-04-29'.\npyber_pivot_df = pyber_pivot.loc['2019-01-01':'2019-04-29']\npyber_pivot_df", "_____no_output_____" ], [ "# 6. Set the \"date\" index to datetime datatype. This is necessary to use the resample() method in Step 8.\npyber_pivot_df.index = pd.to_datetime(pyber_pivot_df.index)", "_____no_output_____" ], [ "# 7. Check that the datatype for the index is datetime using df.info()\npyber_pivot_df.info()", "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 2196 entries, 2019-01-01 00:08:16 to 2019-04-28 19:35:03\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Rural 114 non-null float64\n 1 Suburban 573 non-null float64\n 2 Urban 1509 non-null float64\ndtypes: float64(3)\nmemory usage: 68.6 KB\n" ], [ "# 8. Create a new DataFrame using the \"resample()\" function by week 'W' and get the sum of the fares for each week.\ntot_fares_by_week_df = pyber_pivot_df.resample('W').sum()\ntot_fares_by_week_df", "_____no_output_____" ], [ "# 8. Using the object-oriented interface method, plot the resample DataFrame using the df.plot() function. \n\n# Import the style from Matplotlib.\nfrom matplotlib import style\n# Use the graph style fivethirtyeight.\nstyle.use('fivethirtyeight')\n\nfig, ax = plt.subplots()\ntot_fares_by_week_df.plot(figsize=(20,7), ax=ax)\n\nax.set_title(\"Total Fares by City Type\")\nax.set_ylabel(\"Fares($USD)\")\nax.set_xlabel(\"Month(Weekly Fare Totals)\")\nax.legend(labels=[\"Rural\", \"Suburban\", \"Urban\"],\n loc=\"center\")\n\nplt.savefig(\"analysis/PyBer_fare_summary.png\")\n\nplt.show()", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "!pip install ndfinance #install ndfinance", "Requirement already satisfied: ndfinance in /home/bellmanlabs/anaconda3/envs/BellmanFinance/lib/python3.7/site-packages (0.0.2)\n" ], [ "EXPORT_PATH = \"./bt_results/all_weather_portfolio/\"", "_____no_output_____" ] ], [ [ "# Import Packages", "_____no_output_____" ] ], [ [ "from ndfinance.brokers.backtest import *\nfrom ndfinance.core import BacktestEngine\nfrom ndfinance.analysis.backtest import BacktestAnalyzer\nfrom ndfinance.strategies import PeriodicRebalancingStrategy\nfrom ndfinance.visualizers.backtest_visualizer import BasicVisualizer\n%matplotlib inline\nimport matplotlib.pyplot as plt", "2020-10-10 13:22:13,815\tINFO resource_spec.py:212 -- Starting Ray with 15.38 GiB memory available for workers and up to 7.7 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).\n2020-10-10 13:22:14,051\tWARNING services.py:923 -- Redis failed to start, retrying now.\n2020-10-10 13:22:14,252\tINFO services.py:1165 -- View the Ray dashboard at \u001b[1m\u001b[32mlocalhost:8265\u001b[39m\u001b[22m\n" ] ], [ [ "# build strategy", "_____no_output_____" ] ], [ [ "class AllWeatherPortfolio(PeriodicRebalancingStrategy):\n def __init__(self, weight_dict, rebalance_period):\n super(AllWeatherPortfolio, self).__init__(rebalance_period)\n self.weight_dict = weight_dict\n \n \n def _logic(self):\n self.broker.order(Rebalance(self.weight_dict.keys(), self.weight_dict.values()))", "_____no_output_____" ] ], [ [ "# set portfolio elements, weights, rebalance period\nyou can adjust and play in your own!", "_____no_output_____" ] ], [ [ "PORTFOLIO = {\n \"GLD\" : 0.05, \n \"SPY\" : 0.5,\n \"SPTL\" : 0.15,\n \"BWZ\" : 0.15,\n \"SPHY\": 0.15,\n}", "_____no_output_____" ], [ "REBALANCE_PERIOD = TimeFrames.day * 365", "_____no_output_____" ] ], [ [ "# Make data provider", "_____no_output_____" ] ], [ [ "dp = BacktestDataProvider()\ndp.add_yf_tickers(*PORTFOLIO.keys())", "_____no_output_____" ] ], [ [ "# Make time indexer", "_____no_output_____" ] ], [ [ "indexer = TimeIndexer(dp.get_shortest_timestamp_seq())\ndp.set_indexer(indexer)\ndp.cut_data()", "_____no_output_____" ] ], [ [ "# Make broker and add assets", "_____no_output_____" ] ], [ [ "brk = BacktestBroker(dp, initial_margin=10000)\n_ = [brk.add_asset(Asset(ticker=ticker)) for ticker in PORTFOLIO.keys()]", "_____no_output_____" ] ], [ [ "# Initialize strategy", "_____no_output_____" ] ], [ [ "strategy = AllWeatherPortfolio(PORTFOLIO, rebalance_period=REBALANCE_PERIOD)", "_____no_output_____" ] ], [ [ "# Initialize backtest engine", "_____no_output_____" ] ], [ [ "engine = BacktestEngine()\nengine.register_broker(brk)\nengine.register_strategy(strategy)", "_____no_output_____" ] ], [ [ "# run", "_____no_output_____" ] ], [ [ "log = engine.run()", "[ENGINE]: 100%|██████████| 2090/2090 [00:00<00:00, 11637.76it/s]\n" ] ], [ [ "# run analysis", "_____no_output_____" ] ], [ [ "analyzer = BacktestAnalyzer(log)\nanalyzer.print()", "\n\n-------------------------------------------------- [BACKTEST RESULT] --------------------------------------------------\nCAGR:10.644\nMDD:19.49\nCAGR_MDD_ratio:0.546\nwin_trade_count:24\nlose_trade_count:11\ntotal_trade_count:75\nwin_rate_percentage:32.0\nlose_rate_percentage:14.667\nsharpe_ratio:0.279\nsortino_ratio:0.361\npnl_ratio_sum:13.677\npnl_ratio:6.269\naverage_realized_pnl:90.262\nmax_realized_pnl:1255.897\nmin_realized_pnl:-232.054\naverage_realized_pnl_percentage:2.427\nmax_realized_pnl_percentage:26.859\nmin_realized_pnl_percentage:-13.873\naverage_realized_pnl_percentage_weighted:0.661\nmax_realized_pnl_percentage_weighted:9.432\nmin_realized_pnl_percentage_weighted:-1.999\naverage_portfolio_value_total:12706.284\nmax_portfolio_value_total:18406.706\nmin_portfolio_value_total:9613.284\naverage_portfolio_value:12706.284\nmax_portfolio_value:18406.706\nmin_portfolio_value:9613.284\naverage_leverage:0.88\nmax_leverage:1.0\nmin_leverage:0.0\naverage_leverage_total:0.88\nmax_leverage_total:1.0\nmin_leverage_total:0.0\naverage_cash_weight_percentage:11.962\nmax_cash_weight_percentage:100.0\nmin_cash_weight_percentage:0.0\naverage_cash_weight_percentage_total:11.962\nmax_cash_weight_percentage_total:100.0\nmin_cash_weight_percentage_total:0.0\naverage_unrealized_pnl_percentage:2.343\nmax_unrealized_pnl_percentage:10.526\nmin_unrealized_pnl_percentage:-11.134\naverage_unrealized_pnl_percentage_total:2.343\nmax_unrealized_pnl_percentage_total:10.526\nmin_unrealized_pnl_percentage_total:-11.134\naverage_1M_pnl_percentage:0.59\nmax_1M_pnl_percentage:8.165\nmin_1M_pnl_percentage:-6.083\naverage_1D_pnl_percentage:0.0\nmax_1D_pnl_percentage:0.0\nmin_1D_pnl_percentage:0.0\naverage_1W_pnl_percentage:0.107\nmax_1W_pnl_percentage:5.255\nmin_1W_pnl_percentage:-9.881\n" ] ], [ [ "# visualize", "_____no_output_____" ] ], [ [ "visualizer = BasicVisualizer()\nvisualizer.plot_log(log)", "_____no_output_____" ] ], [ [ "# Export", "_____no_output_____" ] ], [ [ "visualizer.export(EXPORT_PATH)\nanalyzer.export(EXPORT_PATH)", "\n\n-------------------------------------------------- [EXPORTING FIGURES] --------------------------------------------------\nexporting figure to: ./bt_results/all_weather_portfolio/plot/mdd.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/cagr.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/sharpe.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/sortino.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/portfolio_value.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/portfolio_value_cum_pnl_perc.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/portfolio_value_total.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/portfolio_value_total_cum_pnl_perc.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/realized_pnl_percentage_hist.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/realized_pnl_percentage_weighted_hist.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/1M_pnl_hist.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/1D_pnl_hist.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/1W_pnl_hist.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/1M_pnl_bar.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/1D_pnl_bar.png\nexporting figure to: ./bt_results/all_weather_portfolio/plot/1W_pnl_bar.png\n\n\n-------------------------------------------------- [EXPORTING RESULT/LOG] --------------------------------------------------\nsaving log: ./bt_results/all_weather_portfolio/broker_log.csv\nsaving log: ./bt_results/all_weather_portfolio/portfolio_log.csv\nsaving result to: ./bt_results/all_weather_portfolio/result.json\n" ] ] ]

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[ [ [ "# Estatísticas descritivas e Visualização de Dados\nEste notebook é responsável por mostrar as estatíscas descritivas da base dados com visualizações.\nSerá analisado o comportamento de algumas características que são cruciais na compra/venda de veículos usados.", "_____no_output_____" ] ], [ [ "from Utils import *\nfrom tqdm import tqdm\nfrom matplotlib import pyplot as plt\nimport seaborn as sns", "_____no_output_____" ], [ "pd.set_option('display.max_colwidth', 100)\nDATASET = \"../datasets/clean_vehicles_2.csv\"\ndf = pd.read_csv(DATASET)\n\ndf.describe()", "_____no_output_____" ] ], [ [ "## Estatísticas Univariadas\nAqui vamos análisar o comportamento de alguns dados em relação a sua distribuição.\n### Ano de fabricação\n\n", "_____no_output_____" ] ], [ [ "##Análise de média, desvio padrão, mediana e moda do Ano de fabricação\nprint(\n\"Ano do veículo:\\n\"\n\"Média: \"+floatStr(df['year'].mean())+\"\\n\"+\n\"Desvio padrão: \"+floatStr(df['year'].std())+\"\\n\"+ \n\"Mediana: \"+floatStr(df['year'].median())+\"\\n\"+\n\"IQR: \"+floatStr(df['year'].describe()[6] - df['year'].describe()[4])+\"\\n\"+\n\"Moda: \"+floatStr(df['year'].mode().loc[0]) \n)", "Ano do veículo:\nMédia: 2010.26\nDesvio padrão: 8.67\nMediana: 2012.0\nIQR: 9.0\nModa: 2017.0\n" ] ], [ [ "Aqui notamos uma mediana maior do que a média. O que nos levar a imaginar que esta grandeza não segue uma distribuição normal.\nIsto indica que deve haver alguns carros muito antigos sendo vendidos, gerando uma caractéristica de assimetria na curva.\n\n\nPara verifcarmos isso, vamos gerar o histograma", "_____no_output_____" ] ], [ [ "##Plotar o histograma da distribuição em relação ao ano de fabricação do veículo\nbars = df[df['year']> 0].year.max() - df[df['year']> 0].year.min()\ndf[df['year']> 0].year.hist(bins = int(bars))", "_____no_output_____" ] ], [ [ "Porém, este plotting não nos dá uma boa visualização. Nesta lista há alguns carros voltados para colecionadores, que não é o perfil que queremos estudar. \nEntão, tomando o ano de 1985 como limiar, analisamos o histograma da distribuição de carros comercializáveis \"para uso normal\".\n\nAgora conseguimos perceber que a maior parte dos carros vendidos são fábricados depois de 2000. ", "_____no_output_____" ] ], [ [ "##Plot do histograma dos anos de fabricação do carro limitando à 1985\nbars = df['year'].max() - 1985\ndf[df['year']> 1985].year.hist(bins = int(bars))\n", "_____no_output_____" ] ], [ [ "### Preço de revenda do veículo", "_____no_output_____" ] ], [ [ "##Análise de estatísticas univariadas dos valores de preço do veículo\nprint(\n\"Preço do veículo:\\n\"\n\"Média: \"+floatStr(df[df['price'] > 0].price.mean())+\"\\n\"+\n\"Desvio padrão: \"+floatStr(df[df['price'] > 0].price.std())+\"\\n\"+ \n\"Mediana: \"+floatStr(df[df['price'] > 0].price.median())+\"\\n\"+\n\"IQR: \"+floatStr(df['price'].describe()[6] - df['price'].describe()[4])+\"\\n\"+\n\"Moda: \"+floatStr(df[df['price'] > 0].price.mode().loc[0]) \n)", "Preço do veículo:\nMédia: 36809.65\nDesvio padrão: 6571953.45\nMediana: 11495.0\nIQR: 13000.0\nModa: 7995\n" ] ], [ [ "Aqui encontramos uma diferença muito grandes nestes dados. O que nos faz pensar que temos uma distribuição muito variada e assimétrica de preços.\n\n\nDevido a esta característica, não conseguiremos ver um histograma com todos os dados. Podemos contornar isto de 2 maneiras:\n\n * Poderíamos usar o log10 para ter uma noção da ordem de grandeza, mas não conseguiríamos extrair muita informação, pois a maioria se encaixariam em log10(x) = 4.\n * Outra alternativa seria plotar um subconjunto dos preços. \nEntão, depois de algumas análises, protaremos apenas valores de 0 a $ 100.000.\n", "_____no_output_____" ] ], [ [ "sns.distplot(df[(df['price'] > 0) & (df['price'] < 100000)].price, bins = 100,norm_hist = False, hist=True, kde=False)", "_____no_output_____" ] ], [ [ "### Leitura atual do Odômetro (Milhas percorrida pelo veículo)", "_____no_output_____" ] ], [ [ "##Análise de estatísticas univariadas dos valores de leitura do Odômetro.\n##Note que estamos descartando valores nulos para fazer esta análise\nprint(\n\"Odômetro do veículo:\\n\"\n\"Média: \"+floatStr(df[df['odometer'] > 0].odometer.mean())+\"\\n\"+\n\"Desvio padrão: \"+floatStr(df[df['odometer'] > 0].odometer.std())+\"\\n\"+ \n\"Mediana: \"+floatStr(df[df['odometer'] > 0].odometer.median())+\"\\n\"+\n\"IQR: \"+floatStr(df['odometer'].describe()[6] - df['odometer'].describe()[4])+\"\\n\"+\n\"Moda: \"+floatStr(df[df['odometer'] > 0].odometer.mode().loc[0]) \n)", "Odômetro do veículo:\nMédia: 99705.09\nDesvio padrão: 111570.94\nMediana: 92200.0\nIQR: 92054.0\nModa: 150000.0\n" ] ], [ [ "Aqui também temos uma grande varidade de valores. Apenas 492 deles estão acima de 800.000 de milhas registradas. Para fim de análise, iremos utilizar este intervalo.\n", "_____no_output_____" ] ], [ [ "sns.distplot(df[(df['odometer'] > 0) & (df['odometer'] < 400000)].odometer, bins = 100,norm_hist = False, hist=True, kde=False)", "_____no_output_____" ] ], [ [ "### Visualização de quantidade de anúncios por fabricantes de veículos\nFaremos uma análise visual para tentar perceber quais as marcas mais populares no mercado de seminovos.", "_____no_output_____" ] ], [ [ "## Plotar a divisão de mercas que são mais anunciadas\nmanufacturers = df['manufacturer'].value_counts().drop(df['manufacturer'].value_counts().index[8]).drop(df['manufacturer'].value_counts().index[13:])\nsns.set()\nplt.figure(figsize=(10,5))\nsns.barplot(x=manufacturers.index, y=manufacturers)\nprint(\"As 3 marcas mais anunciadas (Ford, chevrolet, toyota) equivalem a \"\n +str(round(sum(df['manufacturer'].value_counts().values[0:3])/df['manufacturer'].count()*100,2))\n +\"% deste mercado.\")\n", "As 3 marcas mais anunciadas (Ford, chevrolet, toyota) equivalem a 40.15% deste mercado.\n" ], [ "filter_list = ['ford', 'chevrolet', 'toyota', 'nissan', 'honda']\nfiltereddf = df[df.manufacturer.isin(filter_list)]\n\nax = sns.boxplot(x=\"manufacturer\", y=\"price\", data= filtereddf[filtereddf['price']< 40000])", "_____no_output_____" ] ], [ [ "### Visualização da relação entre preço de carros classificados por tração\n\nAqui podemos comparar como o preço variam de acordo com a tração do veículo \n\n\n * 4wd: 4x4\n * rwd: tração traseira \n * fwd: tração dianteira\n \n \nComparamos a média, mediana e quantidade. Porém, já percebemos de análises anteriores que a mediana nos dá um valor mais razoável, por isto ordenamos baseado nela\n", "_____no_output_____" ] ], [ [ "df[df['drive'] != 'undefined'].groupby(['drive']).agg(['mean','median','count'])['price'].sort_values(by='median', ascending=False)", "_____no_output_____" ] ], [ [ "## Estatísticas Bivariadas\nAqui vamos tentar encontrar se as grandezas numéricas possuem algum tipo de correlação. Primeiro analisaremos o método de spearman, depois de pearson. Em seguida tentaremos utilizar ", "_____no_output_____" ] ], [ [ "##Aplicando-se alguns limitadores para analisar correlações entre as variáveis\ncar = df[(df['odometer']> 0) & (df['odometer']<400000)]\ncar = car[(car['price']>0) & (car['price']<100000)]\ncar = car[car['year']>=1985]\ncar = car.drop(['lat','long'], axis=1)\ncar.cov()", "_____no_output_____" ], [ "car.corr(method='spearman')", "_____no_output_____" ], [ "car.corr(method='pearson')", "_____no_output_____" ], [ "##Relaçãoo de preço x milhas rodadas entre as 3 marcas mais populares\nfilter_list = ['ford', 'chevrolet', 'toyota']\ncar[car['manufacturer'].isin(filter_list)].plot.scatter(x='odometer',y='price')", "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n" ], [ "g = sns.FacetGrid(car[car['manufacturer']!='undefined'], col=\"manufacturer\", hue='drive')\ng.map(sns.scatterplot, \"year\", \"price\")\ng.add_legend()\n\n#Clique na imagem pequena para expandir", "_____no_output_____" ] ] ]

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[ [ [ "Welcome in the introductory template of the python graph gallery. Here is how to proceed to add a new `.ipynb` file that will be converted to a blogpost in the gallery!", "_____no_output_____" ], [ "## Notebook Metadata", "_____no_output_____" ], [ "It is very important to add the following fields to your notebook. It helps building the page later on:\n- **slug**: the URL of the blogPost. It should be exactly the same as the file title. Example: `70-basic-density-plot-with-seaborn`\n- **chartType**: the chart type like density or heatmap. For a complete list see [here](https://github.com/holtzy/The-Python-Graph-Gallery/blob/master/src/util/sectionDescriptions.js), it must be one of the `id` options.\n- **title**: what will be written in big on top of the blogpost! use html syntax there.\n- **description**: what will be written just below the title, centered text.\n- **keyword**: list of keywords related with the blogpost\n- **seoDescription**: a description for the bloppost meta. Should be a bit shorter than the description and must not contain any html syntax.", "_____no_output_____" ], [ "## Add a chart description", "_____no_output_____" ], [ "A chart example always come with some explanation. It must:\n\ncontain keywords\nlink to related pages like the parent page (graph section)\ngive explanations. In depth for complicated charts. High level for beginner level charts", "_____no_output_____" ], [ "## Add a chart", "_____no_output_____" ] ], [ [ "import seaborn as sns, numpy as np\nnp.random.seed(0)\nx = np.random.randn(100)\nax = sns.distplot(x)", "_____no_output_____" ] ] ]

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[ [ [ "# Airbnb - Rio de Janeiro\n* Download [data](http://insideairbnb.com/get-the-data.html)\n* We downloaded `listings.csv` from all monthly dates available\n\n## Questions\n1. What was the price and supply behavior before and during the pandemic?\n2. Does a title in English or Portuguese impact the price?\n3. What features correlate with the price? Can we predict a price? Which features matters?", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport glob\nimport re\nimport pendulum\nimport tqdm\n\nimport matplotlib.pyplot as plt\n\nimport langid\nlangid.set_languages(['en','pt'])", "_____no_output_____" ] ], [ [ "### Read files\nRead all 30 files and get their date", "_____no_output_____" ] ], [ [ "files = sorted(glob.glob('data/listings*.csv'))\n\ndf = []\nfor f in files:\n date = pendulum.from_format(re.findall(r\"\\d{4}_\\d{2}_\\d{2}\", f)[0], fmt=\"YYYY_MM_DD\").naive()\n csv = pd.read_csv(f)\n csv[\"date\"] = date\n df.append(csv)\ndf = pd.concat(df)\ndf", "_____no_output_____" ] ], [ [ "### Deal with NaNs\n* Drop `neighbourhood_group` as it is all NaNs;\n* Fill `reviews_per_month` with zeros (if there is no review, then reviews per month are zero)\n* Keep `name` for now\n* Drop `host_name` rows, as there is not any null host_id\n* Keep `last_review` too, as there are rooms with no review", "_____no_output_____" ] ], [ [ "df.isna().any()", "_____no_output_____" ], [ "df = df.drop([\"host_name\", \"neighbourhood_group\"], axis=1)\ndf[\"reviews_per_month\"] = df[\"reviews_per_month\"].fillna(0.)\ndf.head()", "_____no_output_____" ] ], [ [ "### Detect `name` language\n* Clean strings for evaluation\n* Remove common neighbourhoods name in Portuguese from `name` column to diminish misprediction\n* Remove several non-alphanumeric characters\n* Detect language using [langid](https://github.com/saffsd/langid.py)\n* I restricted between pt, en. There are very few rooms listed in other languages.\n* Drop `name` column", "_____no_output_____" ] ], [ [ "import unicodedata", "_____no_output_____" ], [ "stopwords = pd.unique(df[\"neighbourhood\"])\nstopwords = [re.sub(r\"[\\(\\)]\", \"\", x.lower().strip()).split() for x in stopwords]\nstopwords = [x for item in stopwords for x in item]\nstopwords += [unicodedata.normalize(\"NFKD\", x).encode('ASCII', 'ignore').decode() for x in stopwords]\nstopwords += [\"rio\", \"janeiro\", \"copa\", \"arpoador\", \"pepê\", \"pepe\", \"lapa\", \"morro\", \"corcovado\"]\nstopwords = set(stopwords)", "_____no_output_____" ], [ "docs = [re.sub(r\"[\\-\\_\\\\\\/\\,\\;\\:\\!\\+\\’\\%\\&\\d\\*\\#\\\"\\´\\`\\.\\|\\(\\)\\[\\]\\@\\'\\»\\«\\>\\<\\❤️\\…]\", \" \", str(x)) for x in df[\"name\"].tolist()]\ndocs = [\" \".join(x.lower().strip().split()) for x in docs]\ndocs = [\"\".join(e for e in x if (e.isalnum() or \" \")) for x in docs]", "_____no_output_____" ], [ "ndocs = []\nfor doc in tqdm.tqdm(docs):\n ndocs.append(\" \".join([x for x in doc.split() if x not in stopwords]))\ndocs = ndocs", "100%|██████████| 1047691/1047691 [00:01<00:00, 649856.94it/s]\n" ], [ "results = []\nfor d in tqdm.tqdm(docs):\n results.append(langid.classify(d)[0])", "100%|██████████| 1047691/1047691 [01:10<00:00, 14788.52it/s]\n" ], [ "df[\"language\"] = results\n\n# Because we transformed NaNs into string, fill those detection with nans too\ndf.loc[df[\"name\"].isna(), \"language\"] = pd.NA", "_____no_output_____" ] ], [ [ "* Test accuracy, manually label 383 out of 88191 (95% conf. interval, 5% margin of error)", "_____no_output_____" ] ], [ [ "df.loc[~df[\"name\"].isna()].drop_duplicates(\"name\").shape", "_____no_output_____" ], [ "df.loc[~df[\"name\"].isna()].drop_duplicates(\"name\")[[\"name\", \"language\"]].sample(n=383, random_state=42).to_csv(\"lang_pred_1.csv\")", "_____no_output_____" ], [ "lang_pred = pd.read_csv(\"lang_pred.csv\", index_col=0)\nlang_pred.head()", "_____no_output_____" ], [ "overall_accuracy = (lang_pred[\"pred\"] == lang_pred[\"true\"]).sum() / lang_pred.shape[0]\npt_accuracy = (lang_pred[lang_pred[\"true\"] == \"pt\"][\"true\"] == lang_pred[lang_pred[\"true\"] == \"pt\"][\"pred\"]).sum() / lang_pred[lang_pred[\"true\"] == \"pt\"].shape[0]\nen_accuracy = (lang_pred[lang_pred[\"true\"] == \"en\"][\"true\"] == lang_pred[lang_pred[\"true\"] == \"en\"][\"pred\"]).sum() / lang_pred[lang_pred[\"true\"] == \"en\"].shape[0]\nprint(f\"Overall accuracy: {overall_accuracy*100}%\")\nprint(f\"Portuguese accuracy: {pt_accuracy*100}%\")\nprint(f\"English accuracy: {en_accuracy*100}%\")", "Overall accuracy: 92.95039164490862%\nPortuguese accuracy: 92.67241379310344%\nEnglish accuracy: 95.27027027027027%\n" ], [ "df = df.drop(\"name\", axis=1)\ndf.head()", "_____no_output_____" ], [ "df[\"language\"].value_counts()", "_____no_output_____" ] ], [ [ "### Calculate how many times a room appeared\n* There are 30 months of data, and rooms appear multiple times\n* Calculate for a specific date, how many times the same room appeared up to that date", "_____no_output_____" ] ], [ [ "df = df.set_index([\"id\", \"date\"])\ndf[\"appearances\"] = df.groupby([\"id\", \"date\"])[\"host_id\"].count().unstack().cumsum(axis=1).stack()\ndf = df.reset_index()\ndf.head()", "_____no_output_____" ] ], [ [ "### Days since last review\n* Calculate days since last review\n* Then categorize them by the length of the days", "_____no_output_____" ] ], [ [ "df.loc[:, \"last_review\"] = pd.to_datetime(df[\"last_review\"], format=\"%Y/%m/%d\")", "_____no_output_____" ], [ "# For each scraping date, consider the last date to serve as comparision as the maximum date\nlast_date = df.groupby(\"date\")[\"last_review\"].max()\ndf[\"last_date\"] = df.apply(lambda row: last_date.loc[row[\"date\"]], axis=1)", "_____no_output_____" ], [ "df[\"days_last_review\"] = (df[\"last_date\"] - df[\"last_review\"]).dt.days", "_____no_output_____" ], [ "df = df.drop(\"last_date\", axis=1)\ndf.head()", "_____no_output_____" ], [ "df[\"days_last_review\"].describe()", "_____no_output_____" ], [ "def categorize_last_review(days_last_review):\n \"\"\"Transform days since last review into categories\n \n Transform days since last review into one of those categories:\n last_week, last_month, last_half_year, last_year, last_two_years, \n long_time_ago, or never\n\n Args:\n days_last_review (int): Days since the last review\n\n Returns:\n str: A string with the category name. \n \n \"\"\"\n if days_last_review <= 7:\n return \"last_week\"\n elif days_last_review <= 30:\n return \"last_month\"\n elif days_last_review <= 182:\n return \"last_half_year\"\n elif days_last_review <= 365:\n return \"last_year\"\n elif days_last_review <= 730:\n return \"last_two_years\"\n elif days_last_review > 730:\n return \"long_time_ago\"\n else:\n return \"never\"\n \ndf.loc[:, \"last_review\"] = df.apply(lambda row: categorize_last_review(row[\"days_last_review\"]), axis=1)", "_____no_output_____" ], [ "df = df.drop([\"days_last_review\"], axis=1)\ndf.head()", "_____no_output_____" ], [ "df = df.set_index([\"id\", \"date\"])\n\ndf.loc[:, \"appearances\"] = df[\"appearances\"].astype(int)\n\ndf.loc[:, \"host_id\"] = df[\"host_id\"].astype(\"category\")\ndf.loc[:, \"neighbourhood\"] = df[\"neighbourhood\"].astype(\"category\")\ndf.loc[:, \"room_type\"] = df[\"room_type\"].astype(\"category\")\ndf.loc[:, \"last_review\"] = df[\"last_review\"].astype(\"category\")\ndf.loc[:, \"language\"] = df[\"language\"].astype(\"category\")", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "df.to_pickle(\"data.pkl\")", "_____no_output_____" ] ], [ [ "### Distributions\n* Check the distribution of features", "_____no_output_____" ] ], [ [ "df = pd.read_pickle(\"data.pkl\")\ndf.head()", "_____no_output_____" ], [ "df[\"latitude\"].hist(bins=250)", "_____no_output_____" ], [ "df[\"longitude\"].hist(bins=250)", "_____no_output_____" ], [ "df[\"price\"].hist(bins=250)", "_____no_output_____" ], [ "df[\"minimum_nights\"].hist(bins=250)", "_____no_output_____" ], [ "df[\"number_of_reviews\"].hist()", "_____no_output_____" ], [ "df[\"reviews_per_month\"].hist(bins=250)", "_____no_output_____" ], [ "df[\"calculated_host_listings_count\"].hist(bins=250)", "_____no_output_____" ], [ "df[\"availability_365\"].hist()", "_____no_output_____" ], [ "df[\"appearances\"].hist(bins=29)", "_____no_output_____" ], [ "df.describe()", "_____no_output_____" ] ], [ [ "### Limits\n* We are analising mostly for touristic purpose, so get the short-term rentals only\n* Prices between 10 and 10000 (The luxury Copacabana Palace Penthouse at 8000 for example)\n* Short-term rentals (minimum_nights < 31)\n* Impossibility of more than 31 reviews per month", "_____no_output_____" ] ], [ [ "df = pd.read_pickle(\"data.pkl\")\ntotal_records = len(df)", "_____no_output_____" ], [ "outbound_values = (df[\"price\"] < 10) | (df[\"price\"] > 10000)\ndf = df[~outbound_values]\n\nprint(f\"Removed values {outbound_values.sum()}, {outbound_values.sum()*100/total_records}%\")", "Removed values 5069, 0.48382586086928303%\n" ], [ "long_term = df[\"minimum_nights\"] >= 31\ndf = df[~long_term]\n\nprint(f\"Removed values {long_term.sum()}, {long_term.sum()*100/total_records}%\")", "Removed values 5742, 0.54806235808077%\n" ], [ "reviews_limit = df[\"reviews_per_month\"] > 31\ndf = df[~reviews_limit]\n\nprint(f\"Removed values {reviews_limit.sum()}, {reviews_limit.sum()*100/total_records}%\")", "Removed values 2, 0.00019089597982611286%\n" ] ], [ [ "### Log skewed variables\n* Most numerical values are skewed, so log them", "_____no_output_____" ] ], [ [ "df.describe()", "_____no_output_____" ], [ "# number_of_reviews, reviews_per_month, availability_365 have zeros, thus sum one to all\ndf[\"number_of_reviews\"] = np.log(df[\"number_of_reviews\"] + 1)\ndf[\"reviews_per_month\"] = np.log(df[\"reviews_per_month\"] + 1)\ndf[\"availability_365\"] = np.log(df[\"availability_365\"] + 1)", "_____no_output_____" ], [ "df[\"price\"] = np.log(df[\"price\"])\ndf[\"minimum_nights\"] = np.log(df[\"minimum_nights\"])\ndf[\"calculated_host_listings_count\"] = np.log(df[\"calculated_host_listings_count\"])\ndf[\"appearances\"] = np.log(df[\"appearances\"])", "_____no_output_____" ], [ "df.describe()", "_____no_output_____" ] ], [ [ "### Extreme outliers\n* Most outliers are clearly mistyped values (one can check these rooms ids in their website)\n* Remove extreme outliers first from large deviations within the same `id` (eliminate rate jumps of same room)\n* Then remove those from same scraping `date`, `neighbourhood` and `room_type`", "_____no_output_____" ] ], [ [ "df = df.reset_index()", "_____no_output_____" ], [ "q25 = df.groupby([\"id\"])[\"price\"].quantile(0.25)\nq75 = df.groupby([\"id\"])[\"price\"].quantile(0.75)\n\next = q75 + 3 * (q75 - q25)\next = ext[(q75 - q25) > 0.]", "_____no_output_____" ], [ "affected_rows = []\nmultiple_id = df[df[\"id\"].isin(ext.index)]\nfor row in tqdm.tqdm(multiple_id.itertuples(), total=len(multiple_id)):\n if row.price >= ext.loc[row.id]:\n affected_rows.append(row.Index)", "100%|██████████| 1023406/1023406 [00:14<00:00, 70302.71it/s]\n" ], [ "df = df.drop(affected_rows)\nprint(f\"Removed values {len(affected_rows)}, {len(affected_rows)*100/total_records}%\")", "Removed values 21078, 2.0118527313874033%\n" ], [ "# Remove extreme outliers per neighbourhood, room_type and scraping date\nq25 = df.groupby([\"date\", \"neighbourhood\", \"room_type\"])[\"price\"].quantile(0.25)\nq75 = df.groupby([\"date\", \"neighbourhood\", \"room_type\"])[\"price\"].quantile(0.75)\next = q75 + 3 * (q75 - q25)\next", "_____no_output_____" ], [ "affected_rows = []\nfor row in tqdm.tqdm(df.itertuples(), total=len(df)):\n if row.price >= ext.loc[(row.date, row.neighbourhood, row.room_type)]:\n affected_rows.append(row.Index)", "100%|██████████| 1015800/1015800 [01:30<00:00, 11223.94it/s]\n" ], [ "df = df.drop(affected_rows)\nprint(f\"Removed values {len(affected_rows)}, {len(affected_rows)*100/total_records}%\")", "Removed values 3528, 0.3367405084132631%\n" ], [ "df.describe()", "_____no_output_____" ], [ "df[\"price\"].hist()", "_____no_output_____" ], [ "df.to_pickle(\"treated_data.pkl\")", "_____no_output_____" ] ] ]

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[ [ [ "[](https://colab.research.google.com/github/real-itu/modern-ai-course/blob/master/lecture-04/lab.ipynb)", "_____no_output_____" ], [ "# Lab 4 - Math\n\n", "_____no_output_____" ], [ "### Stats\n\nYou're given the following dataset:\n\n", "_____no_output_____" ] ], [ [ "import random\r\nrandom.seed(0)\r\nx = [random.gauss(0, 1)**2 for _ in range(20)]\r\nprint(x)", "[0.8868279034128675, 1.9504304025306558, 0.46201173092655257, 0.13727289350107572, 1.0329650747173147, 0.00520129768651921, 0.032111381051647944, 0.6907259056240523, 1.713578821550704, 0.037592456206679545, 0.9865449735727571, 0.418585230265908, 0.11133432341026718, 2.7082355435792898, 0.3123577703699347, 0.26435707416151544, 5.779789763931721, 2.344213906200638, 0.6343578347545124, 4.014607380283022]\n" ] ], [ [ "> Compute the min, max, mean, median, standard deviation and variance of x\n", "_____no_output_____" ] ], [ [ "# Your code here\r\nimport math\r\n# min\r\nmi = min(x)\r\nprint(\"min: \" + str(mi))\r\n\r\n#max\r\nma = max(x)\r\nprint(\"max: \" + str(ma))\r\n\r\n#mean\r\nmean = sum(x)/len(x)\r\nprint(\"mean: \" + str(mean))\r\n\r\n#median \r\nmedian = sorted(x)[int(len(x)/2)]\r\nprint(\"median: \" + str(median))\r\n\r\n#stddv\r\n#variance\r\nlars = 0\r\nfor v in x:\r\n lars += math.pow(v - mean, 2)\r\nvariance = lars / len(x)\r\nstddv = math.sqrt(variance)\r\nprint(\"standard deviation: \" + str(stddv))\r\nprint(\"variance: \" + str(variance))", "min: 0.00520129768651921\nmax: 5.779789763931721\nmean: 1.2261550833868817\nmedian: 0.6907259056240523\nstandard deviation: 1.4717408201314568\nvariance: 2.166021041641213\n" ] ], [ [ "### Vectors\n\nYou're given the two 3 dimensional vectors a and b below.", "_____no_output_____" ] ], [ [ "a = [1, 3, 5]\r\nb = [2, 9, 13]", "_____no_output_____" ] ], [ [ "> Compute\n 1. $a + b$\n 2. $2a-3b$\n 3. $ab$ - the inner product\n\n", "_____no_output_____" ] ], [ [ "# Your code here\r\nfirst = list(map(lambda t: t[0]+t[1], list(zip(a,b))))\r\nprint(first)\r\n\r\nsecond = list(map(lambda t: t[0] - t[1], list(zip(list(map(lambda x: x*2, a)), list(map(lambda x: x*3, b))))))\r\nprint(second)\r\n\r\nthird = sum(list(map(lambda t: t[0] * t[1], list(zip(a,b)))))\r\nprint(third)", "[3, 12, 18]\n[-4, -21, -29]\n94\n" ] ], [ [ "### Gradients", "_____no_output_____" ], [ "Given the function $f(x,y) = 3x^2 + 6y$\r\n\r\n> Compute the partial gradients $\\frac{df}{dx}$ and $\\frac{df}{dy}$", "_____no_output_____" ], [ "Your answer here\r\n\r\n$\\frac{df}{dx} = 6x$\r\n\r\n\r\n$\\frac{df}{dy} = 6$", "_____no_output_____" ], [ "The function above corresponds to the following computational graph", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "> Denote each arrow with the corresponding partial gradient, e.g. $\\frac{df}{dc} = 1$ between $f$ and $c$, and use the generalized chain rule on graphs to compute the gradients $\\frac{df}{dc}$, $\\frac{df}{db}$, $\\frac{df}{da}$, $\\frac{df}{dx}$, $\\frac{df}{dy}$.", "_____no_output_____" ], [ "Your answer here\r\n\r\n$\\frac{df}{dc} = 1$\r\n\r\n$\\frac{dc}{d6} = y$\r\n\r\n$\\frac{dc}{dy} = 6$\r\n\r\n\r\n$\\frac{df}{db} = 1$\r\n\r\n$\\frac{db}{d3} = a$\r\n\r\n$\\frac{db}{da} = 3$\r\n\r\n$\\frac{da}{dx} = 2x$\r\n\r\n----------------------------------\r\n\r\n$\\frac{df}{da} = \\frac{df}{db} * \\frac{db}{da} = 1 * 3 = 3$\r\n\r\n$\\frac{df}{dx} = \\frac{df}{db} * \\frac{db}{da} * \\frac{da}{dx} = 1 * 3 * 2x = 6x$\r\n\r\n$\\frac{df}{dy} = \\frac{df}{dc} * \\frac{dc}{dy} = 1 * 6 = 6$", "_____no_output_____" ], [ "### Autodiff\n\nThis exercise is quite hard. It's OK if you don't finish it, but you should try your best!", "_____no_output_____" ], [ "You are given the following function (pseudo-code):", "_____no_output_____" ] ], [ [ "def parents_grads(node):\r\n \"\"\" \r\n returns parents of node and the gradients of node w.r.t each parent\r\n e.g. in the example graph above parents_grads(f) would return: [(b, df/db), (c, df/dc)]\r\n \"\"\"", "_____no_output_____" ] ], [ [ "> Complete the `backprop` method below to create a recursive algorithm such that calling `backward(node)` computes the gradient of `node` w.r.t. every (upstream - to the left) node in the computational graph. Every node has a `node.grad` attribute that is initialized to `0.0`, it's numerical gradient. The algorithm should modify this property directly, it should not return anything. Assume the gradients from `parents_grads` can be treated like real numbers, so you can e.g. multiply and add them.", "_____no_output_____" ] ], [ [ "def backprop(node, df_dnode):\r\n node.grad += df_dnode\r\n # Your code here\r\n parents = parents_grads(node)\r\n for parent, grad in parents:\r\n backprop(parent, grad + df_dnode) \r\n\r\n\r\ndef backward(node):\r\n \"\"\"\r\n Computes the gradient of every (upstream) node in the computational graph w.r.t. node.\r\n \"\"\"\r\n backprop(node, 1.0) # The gradient of a node w.r.t. itself is 1 by definition.", "_____no_output_____" ] ], [ [ "Ok, now let's try to actually make it work! We'll define a class `Node` which contains the node value, gradient and parents and their gradients", "_____no_output_____" ] ], [ [ "from typing import Sequence, Tuple\r\n\r\nclass Node:\r\n def __init__(self, value: float, parents_grads: Sequence[Tuple['Node', float]]):\r\n self.value = value\r\n self.grad = 0.0\r\n self.parents_grads = parents_grads \r\n \r\n def __repr__(self):\r\n return \"Node(value=%.4f, grad=%.4f)\"%(self.value, self.grad)", "_____no_output_____" ] ], [ [ "So far no magic. We still havn't defined how we get the `parents_grads`, but we'll get there. Now move the `backprop` and `grad` function into the class, and modify it so it works with the class. ", "_____no_output_____" ] ], [ [ "# Your code here\r\nfrom typing import Sequence, Tuple\r\n\r\nclass Node:\r\n def __init__(self, value: float, parents_grads: Sequence[Tuple['Node', float]]):\r\n self.value = value\r\n self.grad = 0.0\r\n self.parents_grads = parents_grads \r\n \r\n def __repr__(self):\r\n return \"Node(value=%.4f, grad=%.4f)\"%(self.value, self.grad)\r\n\r\n def backprop(self, df_dnode):\r\n self.grad += df_dnode\r\n # Your code here\r\n for parent, grad in self.parents_grads:\r\n parent.backprop(grad * df_dnode) \r\n\r\n\r\n def backward(self):\r\n \"\"\"\r\n Computes the gradient of every (upstream) node in the computational graph w.r.t. node.\r\n \"\"\"\r\n self.backprop(1.0) # The gradient of a node w.r.t. itself is 1 by definition.", "_____no_output_____" ] ], [ [ "Now let's create a simple graph: $y = x^2$, and compute it for $x=2$. We'll set the parent_grads directly based on our knowledge that $\\frac{dx^2}{dx}=2x$", "_____no_output_____" ] ], [ [ "x = Node(2.0, [])\r\ny = Node(x.value**2, parents_grads=[(x, 2*x.value)])", "_____no_output_____" ] ], [ [ "And print the two nodes", "_____no_output_____" ] ], [ [ "print(\"x\", x, \"y\", y)", "x Node(value=2.0000, grad=0.0000) y Node(value=4.0000, grad=0.0000)\n" ] ], [ [ "> Verify that the `y.backward()` call below computes the correct gradients", "_____no_output_____" ] ], [ [ "y.backward()\r\nprint(\"x\", x, \"y\", y)", "x Node(value=2.0000, grad=4.0000) y Node(value=4.0000, grad=1.0000)\n" ] ], [ [ "$\\frac{dy}{dx}$ should be 4 and $\\frac{dy}{dy}$ should be 1", "_____no_output_____" ], [ "Ok, so it seems to work, but it's not very easy to use, since you have to define all the `parents_grads` whenever you're creating new nodes. **Here's the trick.** We can make a function `square(node:Node)->Node` which can square any Node. See below", "_____no_output_____" ] ], [ [ "def square(node: Node) -> Node:\r\n return Node(node.value**2, [(node, 2*node.value)])", "_____no_output_____" ] ], [ [ "Let's verify that it works", "_____no_output_____" ] ], [ [ "x = Node(3.0, [])\r\ny = square(x)\r\nprint(\"x\", x, \"y\", y)\r\ny.backward()\r\nprint(\"x\", x, \"y\", y)", "x Node(value=3.0000, grad=0.0000) y Node(value=9.0000, grad=0.0000)\nx Node(value=3.0000, grad=6.0000) y Node(value=9.0000, grad=1.0000)\n" ] ], [ [ "Now we're getting somewhere. These calls to square can of course be chained", "_____no_output_____" ] ], [ [ "x = Node(3.0, [])\r\ny = square(x)\r\nz = square(y)\r\nprint(\"x\", x, \"y\", y, \"z\", z)\r\nz.backward()\r\nprint(\"x\", x, \"y\", y,\"z\", z)", "x Node(value=3.0000, grad=0.0000) y Node(value=9.0000, grad=0.0000) z Node(value=81.0000, grad=0.0000)\nx Node(value=3.0000, grad=108.0000) y Node(value=9.0000, grad=18.0000) z Node(value=81.0000, grad=1.0000)\n" ] ], [ [ "> Compute the $\\frac{dz}{dx}$ gradient by hand and verify that it's correct", "_____no_output_____" ], [ "Your answer here\r\n\r\n$\\frac{dz}{dx} = \\frac{dz}{dy} * \\frac{dy}{dx} = 2y * 2x = 2 (x^2) * 2x = 2 * 3^2 * 2 * 3 = 108$", "_____no_output_____" ], [ "Similarly we can create functions like this for all the common operators, plus, minus, multiplication, etc. With enough base operators like this we can create any computation we want, and compute the gradients automatically with `.backward()`\n\n> Finish the plus function below and verify that it works", "_____no_output_____" ] ], [ [ "def plus(a: Node, b:Node)->Node:\r\n \"\"\"\r\n Computes a+b\r\n \"\"\"\r\n # Your code here\r\n return Node(a.value + b.value, [(a, 1), (b, 1)])", "_____no_output_____" ], [ "x = Node(4.0, [])\r\ny = Node(5.0, [])\r\nz = plus(x, y)\r\nprint(\"x\", x, \"y\", y, \"z\", z)\r\nz.backward()\r\nprint(\"x\", x, \"y\", y,\"z\", z)", "x Node(value=4.0000, grad=0.0000) y Node(value=5.0000, grad=0.0000) z Node(value=9.0000, grad=0.0000)\nx Node(value=4.0000, grad=1.0000) y Node(value=5.0000, grad=1.0000) z Node(value=9.0000, grad=1.0000)\n" ] ], [ [ "> Finish the multiply function below and verify that it works:", "_____no_output_____" ] ], [ [ "def multiply(a: Node, b:Node)->Node:\r\n \"\"\"\r\n Computes a*b\r\n \"\"\"\r\n # Your code hre\r\n return Node(a.value*b.value, [(a,b.value),(b,a.value)])", "_____no_output_____" ], [ "x = Node(4.0, [])\r\ny = Node(5.0, [])\r\nz = multiply(x, y)\r\nprint(\"x\", x, \"y\", y, \"z\", z)\r\nz.backward()\r\nprint(\"x\", x, \"y\", y,\"z\", z)", "x Node(value=4.0000, grad=0.0000) y Node(value=5.0000, grad=0.0000) z Node(value=20.0000, grad=0.0000)\nx Node(value=4.0000, grad=5.0000) y Node(value=5.0000, grad=4.0000) z Node(value=20.0000, grad=1.0000)\n" ] ], [ [ "We'll stop here, but with just a few more functions we could compute a lot of common computations, and get their gradients automatically!\n\nThis is super nice, but it's kind of annoying having to write `plus(a,b)`. Wouldn't it be nice if we could just write `a+b`? With python operator overloading we can! If we define the `__add__` method on `Node`, this will be executed instead of the regular plus operation when we add something to a `Node`. \n\n> Modify the `Node` class so that it overload the plus, `__add__(self, other)`, and multiplication, `__mul__(self, other)`, operators and run the code below to verify that it works.", "_____no_output_____" ] ], [ [ "# Your code here\r\nclass Node:\r\n def __init__(self, value: float, parents_grads: Sequence[Tuple['Node', float]]):\r\n self.value = value\r\n self.grad = 0.0\r\n self.parents_grads = parents_grads \r\n \r\n def __repr__(self):\r\n return \"Node(value=%.4f, grad=%.4f)\"%(self.value, self.grad)\r\n\r\n def backprop(self, df_dnode):\r\n self.grad += df_dnode\r\n # Your code here\r\n for parent, grad in self.parents_grads:\r\n parent.backprop(grad * df_dnode) \r\n\r\n\r\n def backward(self):\r\n \"\"\"\r\n Computes the gradient of every (upstream) node in the computational graph w.r.t. node.\r\n \"\"\"\r\n self.backprop(1.0) # The gradient of a node w.r.t. itself is 1 by definition.\r\n \r\n def __add__(self, other):\r\n return Node(self.value + other.value, [(self, 1), (other, 1)])\r\n\r\n def __mul__(self, other):\r\n return Node(self.value * other.value, [(self, other.value), (other, self.value)])", "_____no_output_____" ], [ "a = Node(2.0, [])\r\nb = Node(3.0, [])\r\nc = Node(4.0, [])\r\nd = a*b + c # Behold the magic of operator overloading!\r\nprint(\"a\", a, \"b\", b, \"c\", c, \"d\", d)\r\nd.backward()\r\nprint(\"a\", a, \"b\", b, \"c\", c, \"d\", d)", "a Node(value=2.0000, grad=0.0000) b Node(value=3.0000, grad=0.0000) c Node(value=4.0000, grad=0.0000) d Node(value=10.0000, grad=0.0000)\na Node(value=2.0000, grad=3.0000) b Node(value=3.0000, grad=2.0000) c Node(value=4.0000, grad=1.0000) d Node(value=10.0000, grad=1.0000)\n" ] ], [ [ "Congratulations, you've made your own tiny library for autodiff! That's really an awesome achievement!\n\nNow, I wouldn't recommend using your library for anything other than a tool to understand the inner workings of autodiff. Extremely good libraries already exist, which has a lot of functions defined and are super-duper optimized and can even run on specialized hardware like GPUs. Some of those libraries are [PyTorch](https://pytorch.org/), [Tensorflow](https://www.tensorflow.org/) and [Jax](https://github.com/google/jax). ", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import syft as sy\nimport numpy as np\nfrom syft.core.adp.entity import DataSubject", "_____no_output_____" ] ], [ [ "## To Do\n\nDownload a dataset from Domain\n\nConvert all string columns to unique integers ---> could use hashes\n", "_____no_output_____" ] ], [ [ "domain_node = sy.login(email=\"info@openmined.org\", password=\"changethis\", port=8081)", "Connecting to http://localhost:8081... done! \t Logging into adp... done!\n" ], [ "domain_node.store.pandas", "_____no_output_____" ], [ "import pandas as pd", "_____no_output_____" ], [ "canada = pd.read_csv(\"../../trade_demo/datasets/ca - feb 2021.csv\")", "/Users/ishanmishra/PycharmProjects/PySyft/.tox/syft.jupyter/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (14) have mixed types.Specify dtype option on import or set low_memory=False.\n exec(code_obj, self.user_global_ns, self.user_ns)\n" ], [ "canada.head()", "_____no_output_____" ], [ "import hashlib", "_____no_output_____" ], [ "hashlib.algorithms_available", "_____no_output_____" ], [ "test_string = \"February 2021\"\nhashlib.md5(test_string.encode(\"utf-8\"))", "<md5 _hashlib.HASH object @ 0x143f50f10>\n" ], [ "int(hashlib.sha256(test_string.encode(\"utf-8\")).hexdigest(), 16) % 10**8", "_____no_output_____" ], [ "def convert_string(s: str, digits: int=15):\n \"\"\"Maps a string to a unique hash using SHA, converts it to a hash or an int\"\"\"\n if to_int:\n return int(hashlib.sha256(s.encode(\"utf-8\")).hexdigest(), 16) % 10**digits\n else:\n return hashlib.sha256(s.encode(\"utf-8\")).hexdigest()", "_____no_output_____" ], [ "convert_string(\"Canada\", to_int=False)", "_____no_output_____" ], [ "convert_string(\"Canada\", to_int=True, digits=10)", "_____no_output_____" ], [ "convert_string(\"Canada\", to_int=True, digits=260)", "_____no_output_____" ], [ "canada.columns", "_____no_output_____" ], [ "#domain_node.load_dataset(canada)\ncanada.shape", "_____no_output_____" ], [ "domain_node.datasets.pandas", "_____no_output_____" ], [ "canada['Trade Flow']", "_____no_output_____" ], [ "domain_node.store.pandas", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from nltk.book import *", "*** Introductory Examples for the NLTK Book ***\nLoading text1, ..., text9 and sent1, ..., sent9\nType the name of the text or sentence to view it.\nType: 'texts()' or 'sents()' to list the materials.\ntext1: Moby Dick by Herman Melville 1851\ntext2: Sense and Sensibility by Jane Austen 1811\ntext3: The Book of Genesis\ntext4: Inaugural Address Corpus\ntext5: Chat Corpus\ntext6: Monty Python and the Holy Grail\ntext7: Wall Street Journal\ntext8: Personals Corpus\ntext9: The Man Who Was Thursday by G . K . Chesterton 1908\n" ], [ "text1", "_____no_output_____" ], [ "text1.concordance(\"monstrous\") # use to search for word 'monstrous'", "Displaying 11 of 11 matches:\nong the former , one was of a most monstrous size . ... This came towards us , \nON OF THE PSALMS . \" Touching that monstrous bulk of the whale or ork we have r\nll over with a heathenish array of monstrous clubs and spears . Some were thick\nd as you gazed , and wondered what monstrous cannibal and savage could ever hav\nthat has survived the flood ; most monstrous and most mountainous ! That Himmal\nthey might scout at Moby Dick as a monstrous fable , or still worse and more de\nth of Radney .'\" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l\ning Scenes . In connexion with the monstrous pictures of whales , I am strongly\nere to enter upon those still more monstrous stories of them which are to be fo\nght have been rummaged out of this monstrous cabinet there is no telling . But \nof Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u\n" ], [ "text1.similar(\"monstrous\")", "true contemptible christian abundant few part mean careful puzzled\nmystifying passing curious loving wise doleful gamesome singular\ndelightfully perilous fearless\n" ], [ "text2.similar(\"monstrous\")", "very so exceedingly heartily a as good great extremely remarkably\nsweet vast amazingly\n" ], [ "text2.common_contexts(['monstrous','very'])", "a_pretty am_glad a_lucky is_pretty be_glad\n" ], [ "%matplotlib inline\nimport seaborn as sns", "_____no_output_____" ], [ "text4.dispersion_plot(['citizens','democracy','freedom','duties','America'])", "_____no_output_____" ], [ "text4", "_____no_output_____" ], [ "#text3.generate('day')", "_____no_output_____" ], [ "len(text3)", "_____no_output_____" ], [ "sorted(set(text3))", "_____no_output_____" ], [ "len(set(text3)) # this calculates the amonut of used words in the text3", "_____no_output_____" ], [ "from __future__ import division", "_____no_output_____" ], [ "len(text3)/len(set(text3)) # the average used time of each word", "_____no_output_____" ], [ "text3.count(\"smote\") # to calculate how many used times of 'smote'", "_____no_output_____" ], [ "text2[len(text2)-20:]", "_____no_output_____" ], [ "sent = ['word1', 'word2', 'word3', 'word4', 'word5',\\\n 'word6', 'word7', 'word8', 'word9', 'word10']\n#Now modify the sent list content\nsent[0] = 'First'\nsent[9] = 'Last'\nsent[1:9] = ['Second','Third']\nsent", "_____no_output_____" ], [ "sent[9] # size of sent will automatically reshaped \\\n #into (1,4) after you modify its values", "_____no_output_____" ], [ "len(set(text5))", "_____no_output_____" ], [ "'a' * 2", "_____no_output_____" ], [ "'a' * 'a'", "_____no_output_____" ], [ "saying = ['After', 'all', 'is', 'said', 'and', 'done',\\\n 'more', 'is', 'said', 'than', 'done']\ntokens = set(saying) # choose each word once\ntokens = sorted(tokens) # then sorted the word list via alphabet order\n#tokens\ntokens[-2:]", "_____no_output_____" ], [ "fdist1 = FreqDist(text1)\nfdist1.items()", "_____no_output_____" ], [ "fdist1.plot(50, cumulative=True)", "_____no_output_____" ], [ "fdist1.hapaxes()", "_____no_output_____" ], [ "V = set(text1)\nlong_words = [w for w in V if len(w) > 15]\nsorted(long_words)", "_____no_output_____" ], [ "fdist5 = FreqDist(text5)\nsorted([w for w in set(text5) if len(w)>7 and fdist5[w] > 7])", "_____no_output_____" ], [ "text4.collocations() #print the most frequent bigrams(two words) from the text", "United States; fellow citizens; four years; years ago; Federal\nGovernment; General Government; American people; Vice President; Old\nWorld; Almighty God; Fellow citizens; Chief Magistrate; Chief Justice;\nGod bless; every citizen; Indian tribes; public debt; one another;\nforeign nations; political parties\n" ], [ "text8.collocations() # not so make sense....", "would like; medium build; social drinker; quiet nights; non smoker;\nlong term; age open; Would like; easy going; financially secure; fun\ntimes; similar interests; Age open; weekends away; poss rship; well\npresented; never married; single mum; permanent relationship; slim\nbuild\n" ], [ "fdist = FreqDist([len(w) for w in text1])", "_____no_output_____" ], [ "fdist.plot();fdist.plot(cumulative = True)", "_____no_output_____" ], [ "sent7", "_____no_output_____" ], [ "from nltk.book import *\nbabelize_shell()", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from pyspark import SparkContext\nsc = SparkContext()\nfrom pyspark.sql import SparkSession\n\nspark = SparkSession \\\n .builder \\\n .appName(\"Python Spark regression example\") \\\n .config(\"spark.some.config.option\", \"some-value\") \\\n .getOrCreate()", "_____no_output_____" ], [ "df = spark.read.csv('titanic.csv',header=True, inferSchema = True)", "_____no_output_____" ], [ "df.show()", "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n|PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked|\n+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n| 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S|\n| 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C|\n| 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S|\n| 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S|\n| 5| 0| 3|Allen, Mr. Willia...| male|35.0| 0| 0| 373450| 8.05| null| S|\n| 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q|\n| 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S|\n| 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S|\n| 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742|11.1333| null| S|\n| 10| 1| 2|Nasser, Mrs. Nich...|female|14.0| 1| 0| 237736|30.0708| null| C|\n| 11| 1| 3|Sandstrom, Miss. ...|female| 4.0| 1| 1| PP 9549| 16.7| G6| S|\n| 12| 1| 1|Bonnell, Miss. El...|female|58.0| 0| 0| 113783| 26.55| C103| S|\n| 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S|\n| 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S|\n| 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S|\n| 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706| 16.0| null| S|\n| 17| 0| 3|Rice, Master. Eugene| male| 2.0| 4| 1| 382652| 29.125| null| Q|\n| 18| 1| 2|Williams, Mr. Cha...| male|null| 0| 0| 244373| 13.0| null| S|\n| 19| 0| 3|Vander Planke, Mr...|female|31.0| 1| 0| 345763| 18.0| null| S|\n| 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C|\n+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\nonly showing top 20 rows\n\n" ], [ "df.describe()", "_____no_output_____" ], [ "# Shape of the DateFrame\nprint((df.count(), len(df.columns)))", "(891, 12)\n" ], [ "df.dtypes", "_____no_output_____" ], [ "df.columns", "_____no_output_____" ], [ "# Number of Male Passengers and Number of Female Passengers\nprint(df.filter(df['Sex'] == 'male').count())\nprint(df.filter(df['Sex'] == 'female').count())", "577\n314\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nfrom IPython.display import display, HTML\n\ndf = pd.DataFrame({\"sku\": ['325','436','772'], \"price\": ['$100', '$124', '$99'] } )\n\n# remove/replace the dollar sign\ndf['price'] = df['price'].str.replace('$', '')\n\n# convert from str to int, raise an error if it can't convert\ndf['price'] = pd.to_numeric(df['price'], errors='raise')\n\n# assign df the view returned with only the rows from the price column with \n# price greater than 100\ndf = df.loc[ df['price'] >= 100 ]\n\n# rename the column headers\ndf = df.rename({'sku': 'Product #Ref', 'price': 'Retail Price'}, axis=1)\n\n# convert the price column back to string\ndf['Retail Price'] = df['Retail Price'].astype(str)\n\n# add a dollar sign in front of the price\ndf['Retail Price'] = '$' + df['Retail Price']\n\ndf", "_____no_output_____" ], [ "display( HTML('<h1>Table Showing products with prices of $100 or more.</h1>' + df.to_html()) )", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "%pylab inline\nfrom ipyparallel import Client, error\ncluster=Client(profile=\"mpi\")\nview=cluster[:]\nview.block=True\n\ntry:\n from openmdao.utils.notebook_utils import notebook_mode\nexcept ImportError:\n !python -m pip install openmdao[notebooks]", "_____no_output_____" ] ], [ [ "```{note}\nThis feature requires MPI, and may not be able to be run on Colab.\n```", "_____no_output_____" ], [ "# Distributed Variables\n\nAt times when you need to perform a computation using large input arrays, you may want to perform that computation in multiple processes, where each process operates on some subset of the input values. This may be done purely for performance reasons, or it may be necessary because the entire input will not fit in the memory of a single machine. In any case, this can be accomplished in OpenMDAO by declaring those inputs and outputs as distributed. By definition, a variable is distributed if each process contains only a part of the whole variable. Conversely, when a variable is not distributed (i.e., serial), each process contains a copy of the entire variable. A component that has at least one distributed variable can also be called a distributed component.\n\nWe’ve already seen that by using [src_indices](connect-with-src-indices), we can connect an input to only a subset of an output variable. By giving different values for src_indices in each MPI process, we can distribute computations on a distributed output across the processes. All of the scenarios that involve connecting distributed and serial variables are detailed in [Connections involving distributed variables](../working_with_groups/dist_serial.ipynb).", "_____no_output_____" ], [ "\n## Example: Simple Component with Distributed Input and Output\n\nThe following example shows how to create a simple component, *SimpleDistrib*, that takes a distributed variable as an input and computes a distributed output. The calculation is divided across the available processes, but the details of that division are not contained in the component. In fact, the input is sized based on it's connected source using the \"shape_by_conn\" argument.", "_____no_output_____" ] ], [ [ "%%px \n\nimport numpy as np\n\nimport openmdao.api as om\n\n\nclass SimpleDistrib(om.ExplicitComponent):\n\n def setup(self):\n\n # Distributed Input\n self.add_input('in_dist', shape_by_conn=True, distributed=True)\n\n # Distributed Output\n self.add_output('out_dist', copy_shape='in_dist', distributed=True)\n\n def compute(self, inputs, outputs):\n x = inputs['in_dist']\n\n # \"Computationally Intensive\" operation that we wish to parallelize.\n f_x = x**2 - 2.0*x + 4.0\n\n outputs['out_dist'] = f_x", "_____no_output_____" ] ], [ [ "In the next part of the example, we take the `SimpleDistrib` component, place it into a model, and run it. Suppose the vector of data we want to process has 7 elements. We have 4 processors available for computation, so if we distribute them as evenly as we can, 3 procs can handle 2 elements each, and the 4th processor can pick up the last one. OpenMDAO's utilities includes the `evenly_distrib_idxs` function which computes the sizes and offsets for all ranks. The sizes are used to determine how much of the array to allocate on any specific rank. The offsets are used to figure out where the local portion of the array starts, and in this example, is used to set the initial value properly. In this case, the initial value for the full distributed input \"in_dist\" is a vector of 7 values between 3.0 and 9.0, and each processor has a 1 or 2 element piece of it.", "_____no_output_____" ] ], [ [ "%%px\n\nfrom openmdao.utils.array_utils import evenly_distrib_idxs\nfrom openmdao.utils.mpi import MPI\n\nsize = 7\n\nif MPI:\n comm = MPI.COMM_WORLD\n rank = comm.rank\n sizes, offsets = evenly_distrib_idxs(comm.size, size)\nelse:\n # When running in serial, the entire variable is on rank 0.\n rank = 0\n sizes = {rank : size}\n offsets = {rank : 0}\n\nprob = om.Problem()\nmodel = prob.model\n\n# Create a distributed source for the distributed input.\nivc = om.IndepVarComp()\nivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)\n\nmodel.add_subsystem(\"indep\", ivc)\nmodel.add_subsystem(\"D1\", SimpleDistrib())\n\nmodel.connect('indep.x_dist', 'D1.in_dist')\n\nprob.setup()\n\n# Set initial values of distributed variable.\nx_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]\nprob.set_val('indep.x_dist', x_dist_init)\n\nprob.run_model()\n\n# Values on each rank.\nfor var in ['indep.x_dist', 'D1.out_dist']:\n print(var, prob.get_val(var))\n \n# Full gathered values.\nfor var in ['indep.x_dist', 'D1.out_dist']:\n print(var, prob.get_val(var, get_remote=True))\nprint('')", "_____no_output_____" ], [ "%%px\nfrom openmdao.utils.assert_utils import assert_near_equal\n\nassert_near_equal(prob.get_val(var, get_remote=True), np.array([7., 12., 19., 28., 39., 52., 67.]))", "_____no_output_____" ] ], [ [ "Note that we created a connection source 'x_dist' that passes its value to 'D1.in_dist'. OpenMDAO requires a source for non-constant inputs, and usually creates one automatically as an output of a component referred to as an 'Auto-IVC'. However, the automatic creation is not supported for distributed variables. We must manually create an `IndepVarComp` and connect it to our input. \n\nWhen using distributed variables, OpenMDAO can't always size the component inputs based on the shape of the connected source. In this example, the component determines its own split using `evenly_distrib_idxs`. This requires that the component know the full vector size, which is passed in via the option 'vec_size'.", "_____no_output_____" ] ], [ [ "%%px\n\nimport numpy as np\n\nimport openmdao.api as om\nfrom openmdao.utils.array_utils import evenly_distrib_idxs\nfrom openmdao.utils.mpi import MPI\n\nclass SimpleDistrib(om.ExplicitComponent):\n\n def initialize(self):\n self.options.declare('vec_size', types=int, default=1,\n desc=\"Total size of vector.\")\n\n def setup(self):\n comm = self.comm\n rank = comm.rank\n\n size = self.options['vec_size']\n sizes, _ = evenly_distrib_idxs(comm.size, size)\n mysize = sizes[rank]\n\n # Distributed Input\n self.add_input('in_dist', np.ones(mysize, float), distributed=True)\n\n # Distributed Output\n self.add_output('out_dist', np.ones(mysize, float), distributed=True)\n\n def compute(self, inputs, outputs):\n x = inputs['in_dist']\n\n # \"Computationally Intensive\" operation that we wish to parallelize.\n f_x = x**2 - 2.0*x + 4.0\n\n outputs['out_dist'] = f_x\n\n\nsize = 7\n\nif MPI:\n comm = MPI.COMM_WORLD\n rank = comm.rank\n sizes, offsets = evenly_distrib_idxs(comm.size, size)\nelse:\n # When running in serial, the entire variable is on rank 0.\n rank = 0\n sizes = {rank : size}\n offsets = {rank : 0}\n\nprob = om.Problem()\nmodel = prob.model\n\n# Create a distributed source for the distributed input.\nivc = om.IndepVarComp()\nivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)\n\nmodel.add_subsystem(\"indep\", ivc)\nmodel.add_subsystem(\"D1\", SimpleDistrib(vec_size=size))\n\nmodel.connect('indep.x_dist', 'D1.in_dist')\n\nprob.setup()\n\n# Set initial values of distributed variable.\nx_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]\nprob.set_val('indep.x_dist', x_dist_init)\n\nprob.run_model()\n\n# Values on each rank.\nfor var in ['indep.x_dist', 'D1.out_dist']:\n print(var, prob.get_val(var))\n\n# Full gathered values.\nfor var in ['indep.x_dist', 'D1.out_dist']:\n print(var, prob.get_val(var, get_remote=True))\nprint('')", "_____no_output_____" ], [ "%%px\nfrom openmdao.utils.assert_utils import assert_near_equal\n\nassert_near_equal(prob.get_val(var, get_remote=True), np.array([7., 12., 19., 28., 39., 52., 67.]))", "_____no_output_____" ] ], [ [ "\n## Example: Distributed I/O and a Serial Input\n\nOpenMDAO supports both serial and distributed I/O on the same component, so in this example, we expand the problem to include a serial input. In this case, the serial input also has a vector width of 7, but those values will be the same on each processor. This serial input is included in the computation by taking the vector sum and adding it to the distributed output.", "_____no_output_____" ] ], [ [ "%%px \n\nimport numpy as np\n\nimport openmdao.api as om\nfrom openmdao.utils.array_utils import evenly_distrib_idxs\nfrom openmdao.utils.mpi import MPI\n\n\nclass MixedDistrib1(om.ExplicitComponent):\n\n def setup(self):\n\n # Distributed Input\n self.add_input('in_dist', shape_by_conn=True, distributed=True)\n\n # Serial Input\n self.add_input('in_serial', shape_by_conn=True)\n\n # Distributed Output\n self.add_output('out_dist', copy_shape='in_dist', distributed=True)\n\n def compute(self, inputs, outputs):\n x = inputs['in_dist']\n y = inputs['in_serial']\n\n # \"Computationally Intensive\" operation that we wish to parallelize.\n f_x = x**2 - 2.0*x + 4.0\n\n # This operation is repeated on all procs.\n f_y = y ** 0.5\n \n outputs['out_dist'] = f_x + np.sum(f_y)\n \nsize = 7\n\nif MPI:\n comm = MPI.COMM_WORLD\n rank = comm.rank\n sizes, offsets = evenly_distrib_idxs(comm.size, size)\nelse:\n # When running in serial, the entire variable is on rank 0.\n rank = 0\n sizes = {rank : size}\n offsets = {rank : 0}\n\nprob = om.Problem()\nmodel = prob.model\n\n# Create a distributed source for the distributed input.\nivc = om.IndepVarComp()\nivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)\nivc.add_output('x_serial', np.zeros(size))\n\nmodel.add_subsystem(\"indep\", ivc)\nmodel.add_subsystem(\"D1\", MixedDistrib1())\n\nmodel.connect('indep.x_dist', 'D1.in_dist')\nmodel.connect('indep.x_serial', 'D1.in_serial')\n\nprob.setup()\n\n# Set initial values of distributed variable.\nx_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]\nprob.set_val('indep.x_dist', x_dist_init)\n\n# Set initial values of serial variable.\nx_serial_init = 1.0 + 2.0*np.arange(size)\nprob.set_val('indep.x_serial', x_serial_init)\n\nprob.run_model()\n\n# Values on each rank.\nfor var in ['indep.x_dist', 'indep.x_serial', 'D1.out_dist']:\n print(var, prob.get_val(var))\n \n# Full gathered values.\nfor var in ['indep.x_dist', 'indep.x_serial', 'D1.out_dist']:\n print(var, prob.get_val(var, get_remote=True))\nprint('')", "_____no_output_____" ], [ "%%px\n\nassert_near_equal(prob.get_val(var, get_remote=True), np.array([24.53604616, 29.53604616, 36.53604616, 45.53604616, 56.53604616, 69.53604616, 84.53604616]), 1e-6)", "_____no_output_____" ] ], [ [ "## Example: Distributed I/O and a Serial Ouput\n\nYou can also create a component with a serial output and distributed outputs and inputs. This situation tends to be more tricky and usually requires you to performe some MPI operations in your component's `run` method. If the serial output is only a function of the serial inputs, then you can handle that variable just like you do on any other component. However, this example extends the previous component to include a serial output that is a function of both the serial and distributed inputs. In this case, it's a function of the sum of the square root of each element in the full distributed vector. Since the data is not all on any local processor, we use an MPI operation, in this case `Allreduce`, to make a summation across the distributed vector, and gather the answer back to each processor. The MPI operation and your implementation will vary, but consider this to be a general example.", "_____no_output_____" ] ], [ [ "%%px\n\nimport numpy as np\n\nimport openmdao.api as om\nfrom openmdao.utils.array_utils import evenly_distrib_idxs\nfrom openmdao.utils.mpi import MPI\n\n\nclass MixedDistrib2(om.ExplicitComponent):\n\n def setup(self):\n\n # Distributed Input\n self.add_input('in_dist', shape_by_conn=True, distributed=True)\n\n # Serial Input\n self.add_input('in_serial', shape_by_conn=True)\n\n # Distributed Output\n self.add_output('out_dist', copy_shape='in_dist', distributed=True)\n \n # Serial Output\n self.add_output('out_serial', copy_shape='in_serial')\n\n def compute(self, inputs, outputs):\n x = inputs['in_dist']\n y = inputs['in_serial']\n\n # \"Computationally Intensive\" operation that we wish to parallelize.\n f_x = x**2 - 2.0*x + 4.0\n\n # These operations are repeated on all procs.\n f_y = y ** 0.5\n g_y = y**2 + 3.0*y - 5.0\n \n # Compute square root of our portion of the distributed input.\n g_x = x ** 0.5\n \n # Distributed output\n outputs['out_dist'] = f_x + np.sum(f_y)\n \n # Serial output\n if MPI and comm.size > 1:\n\n # We need to gather the summed values to compute the total sum over all procs.\n local_sum = np.array(np.sum(g_x))\n total_sum = local_sum.copy()\n self.comm.Allreduce(local_sum, total_sum, op=MPI.SUM)\n \n outputs['out_serial'] = g_y + total_sum\n else:\n # Recommended to make sure your code can run in serial too, for testing.\n outputs['out_serial'] = g_y + np.sum(g_x)\n \nsize = 7\n\nif MPI:\n comm = MPI.COMM_WORLD\n rank = comm.rank\n sizes, offsets = evenly_distrib_idxs(comm.size, size)\nelse:\n # When running in serial, the entire variable is on rank 0.\n rank = 0\n sizes = {rank : size}\n offsets = {rank : 0}\n\nprob = om.Problem()\nmodel = prob.model\n\n# Create a distributed source for the distributed input.\nivc = om.IndepVarComp()\nivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)\nivc.add_output('x_serial', np.zeros(size))\n\nmodel.add_subsystem(\"indep\", ivc)\nmodel.add_subsystem(\"D1\", MixedDistrib2())\n\nmodel.connect('indep.x_dist', 'D1.in_dist')\nmodel.connect('indep.x_serial', 'D1.in_serial')\n\nprob.setup()\n\n# Set initial values of distributed variable.\nx_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]\nprob.set_val('indep.x_dist', x_dist_init)\n\n# Set initial values of serial variable.\nx_serial_init = 1.0 + 2.0*np.arange(size)\nprob.set_val('indep.x_serial', x_serial_init)\n\nprob.run_model()\n\n# Values on each rank.\nfor var in ['indep.x_dist', 'indep.x_serial', 'D1.out_dist', 'D1.out_serial']:\n print(var, prob.get_val(var))\n \n# Full gathered values.\nfor var in ['indep.x_dist', 'indep.x_serial', 'D1.out_dist', 'D1.out_serial']:\n print(var, prob.get_val(var, get_remote=True))\nprint('')", "_____no_output_____" ], [ "%%px\n\nassert_near_equal(prob.get_val(var, get_remote=True), np.array([15.89178696, 29.89178696, 51.89178696, 81.89178696, 119.89178696, 165.89178696, 219.89178696]), 1e-6)", "_____no_output_____" ] ], [ [ "```{note}\nIn this example, we introduce a new component called an [IndepVarComp](indepvarcomp.ipynb). If you used OpenMDAO prior to version 3.2, then you are familiar with this component. It is used to define an independent variable.\n\nYou usually do not have to define these because OpenMDAO defines and uses them automatically for all unconnected inputs in your model. This automatically-created `IndepVarComp` is called an Auto-IVC.\n\nHowever, when we define a distributed input, we often use the “src_indices” attribute to determine the allocation of that input to the processors that the component sees. For some sets of these indices, it isn’t possible to easily determine the full size of the corresponding independent variable, and the *IndepVarComp* cannot be created automatically. So, for unconnected inputs on a distributed component, you must manually create one, as we did in this example.\n```\n\n# Derivatives with Distributed Variables\n\nIn the following examples, we show how to add analytic derivatives to the distributed examples given above. In most cases it is straighforward, but when you have a serial output and a distributed input, the [matrix-free](matrix-free-api) format is required.\n\n## Derivatives: Distributed I/O and a Serial Input\n\nIn this example, we have a distributed input, a distributed output, and a serial input. The derivative of 'out_dist' with respect to 'in_dict' has a diagonal Jacobian, so we use sparse declaration and each processor gives `declare_partials` the local number of rows and columns. The derivatives are verified against complex step using `check_totals` since our component is complex-safe.", "_____no_output_____" ] ], [ [ "%%px \n\nimport numpy as np\n\nimport openmdao.api as om\nfrom openmdao.utils.array_utils import evenly_distrib_idxs\nfrom openmdao.utils.mpi import MPI\n\n\nclass MixedDistrib1(om.ExplicitComponent):\n\n def setup(self):\n\n # Distributed Input\n self.add_input('in_dist', shape_by_conn=True, distributed=True)\n\n # Serial Input\n self.add_input('in_serial', shape_by_conn=True)\n\n # Distributed Output\n self.add_output('out_dist', copy_shape='in_dist', distributed=True)\n\n def setup_partials(self):\n meta = self.get_io_metadata(metadata_keys=['shape'])\n local_size = meta['in_dist']['shape'][0]\n\n row_col_d = np.arange(local_size)\n\n self.declare_partials('out_dist', 'in_dist', rows=row_col_d, cols=row_col_d)\n self.declare_partials('out_dist', 'in_serial')\n\n def compute(self, inputs, outputs):\n x = inputs['in_dist']\n y = inputs['in_serial']\n\n # \"Computationally Intensive\" operation that we wish to parallelize.\n f_x = x**2 - 2.0*x + 4.0\n\n # This operation is repeated on all procs.\n f_y = y ** 0.5\n\n outputs['out_dist'] = f_x + np.sum(f_y)\n\n def compute_partials(self, inputs, partials):\n x = inputs['in_dist']\n y = inputs['in_serial']\n size = len(y)\n local_size = len(x)\n\n partials['out_dist', 'in_dist'] = 2.0 * x - 2.0\n\n df_dy = 0.5 / y ** 0.5\n partials['out_dist', 'in_serial'] = np.tile(df_dy, local_size).reshape((local_size, size))\n\n\nsize = 7\n\nif MPI:\n comm = MPI.COMM_WORLD\n rank = comm.rank\n sizes, offsets = evenly_distrib_idxs(comm.size, size)\nelse:\n # When running in serial, the entire variable is on rank 0.\n rank = 0\n sizes = {rank : size}\n offsets = {rank : 0}\n\nprob = om.Problem()\nmodel = prob.model\n\n# Create a distributed source for the distributed input.\nivc = om.IndepVarComp()\nivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)\nivc.add_output('x_serial', np.zeros(size))\n\nmodel.add_subsystem(\"indep\", ivc)\nmodel.add_subsystem(\"D1\", MixedDistrib1())\n\nmodel.connect('indep.x_dist', 'D1.in_dist')\nmodel.connect('indep.x_serial', 'D1.in_serial')\n\nmodel.add_design_var('indep.x_serial')\nmodel.add_design_var('indep.x_dist')\nmodel.add_objective('D1.out_dist')\n\nprob.setup(force_alloc_complex=True)\n\n# Set initial values of distributed variable.\nx_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]\nprob.set_val('indep.x_dist', x_dist_init)\n\n# Set initial values of serial variable.\nx_serial_init = 1.0 + 2.0*np.arange(size)\nprob.set_val('indep.x_serial', x_serial_init)\n\nprob.run_model()\n\nif rank > 0:\n prob.check_totals(method='cs', out_stream=None)\nelse:\n prob.check_totals(method='cs')\n", "_____no_output_____" ], [ "%%px\n\ntotals = prob.check_totals(method='cs', out_stream=None)\nfor key, val in totals.items():\n assert_near_equal(val['rel error'][0], 0.0, 1e-6)", "_____no_output_____" ] ], [ [ "## Derivatives: Distributed I/O and a Serial Output\n\nIf you have a component with distributed inputs and a serial output, then the standard `compute_partials` API will not work for specifying the derivatives. You will need to use the matrix-free API with `compute_jacvec_product`, which is described in the feature document for [ExplicitComponent](explicit_component.ipynb)\n\nComputing the matrix-vector product for the derivative of the serial output with respect to a distributed input will require you to use MPI operations to gather the required parts of the Jacobian to all processors. The following example shows how to implement derivatives on the earlier `MixedDistrib2` component.", "_____no_output_____" ] ], [ [ "%%px\n\n\nimport numpy as np\n\nimport openmdao.api as om\nfrom openmdao.utils.array_utils import evenly_distrib_idxs\nfrom openmdao.utils.mpi import MPI\n\n\nclass MixedDistrib2(om.ExplicitComponent):\n\n def setup(self):\n\n # Distributed Input\n self.add_input('in_dist', shape_by_conn=True, distributed=True)\n\n # Serial Input\n self.add_input('in_serial', shape_by_conn=True)\n\n # Distributed Output\n self.add_output('out_dist', copy_shape='in_dist', distributed=True)\n\n # Serial Output\n self.add_output('out_serial', copy_shape='in_serial')\n\n def compute(self, inputs, outputs):\n x = inputs['in_dist']\n y = inputs['in_serial']\n\n # \"Computationally Intensive\" operation that we wish to parallelize.\n f_x = x**2 - 2.0*x + 4.0\n\n # These operations are repeated on all procs.\n f_y = y ** 0.5\n g_y = y**2 + 3.0*y - 5.0\n\n # Compute square root of our portion of the distributed input.\n g_x = x ** 0.5\n\n # Distributed output\n outputs['out_dist'] = f_x + np.sum(f_y)\n\n # Serial output\n if MPI and comm.size > 1:\n\n # We need to gather the summed values to compute the total sum over all procs.\n local_sum = np.array(np.sum(g_x))\n total_sum = local_sum.copy()\n self.comm.Allreduce(local_sum, total_sum, op=MPI.SUM)\n outputs['out_serial'] = g_y + total_sum\n else:\n # Recommended to make sure your code can run in serial too, for testing.\n outputs['out_serial'] = g_y + np.sum(g_x)\n\n def compute_jacvec_product(self, inputs, d_inputs, d_outputs, mode):\n x = inputs['in_dist']\n y = inputs['in_serial']\n\n df_dx = 2.0 * x - 2.0\n df_dy = 0.5 / y ** 0.5\n dg_dx = 0.5 / x ** 0.5\n dg_dy = 2.0 * y + 3.0\n\n local_size = len(x)\n size = len(y)\n\n if mode == 'fwd':\n if 'out_dist' in d_outputs:\n if 'in_dist' in d_inputs:\n d_outputs['out_dist'] += df_dx * d_inputs['in_dist']\n if 'in_serial' in d_inputs:\n d_outputs['out_dist'] += np.tile(df_dy, local_size).reshape((local_size, size)).dot(d_inputs['in_serial'])\n if 'out_serial' in d_outputs:\n if 'in_dist' in d_inputs:\n if MPI and comm.size > 1:\n deriv = np.tile(dg_dx, size).reshape((size, local_size)).dot(d_inputs['in_dist'])\n deriv_sum = np.zeros(deriv.size)\n self.comm.Allreduce(deriv, deriv_sum, op=MPI.SUM)\n d_outputs['out_serial'] += deriv_sum\n else:\n # Recommended to make sure your code can run in serial too, for testing.\n d_outputs['out_serial'] += np.tile(dg_dx, local_size).reshape((local_size, size)).dot(d_inputs['in_dist'])\n if 'in_serial' in d_inputs:\n d_outputs['out_serial'] += dg_dy * d_inputs['in_serial']\n\n else:\n if 'out_dist' in d_outputs:\n if 'in_dist' in d_inputs:\n d_inputs['in_dist'] += df_dx * d_outputs['out_dist']\n if 'in_serial' in d_inputs:\n d_inputs['in_serial'] += np.tile(df_dy, local_size).reshape((local_size, size)).dot(d_outputs['out_dist'])\n if 'out_serial' in d_outputs:\n if 'out_serial' in d_outputs:\n if 'in_dist' in d_inputs:\n if MPI and comm.size > 1:\n deriv = np.tile(dg_dx, size).reshape((size, local_size)).dot(d_outputs['out_serial'])\n deriv_sum = np.zeros(deriv.size)\n self.comm.Allreduce(deriv, deriv_sum, op=MPI.SUM)\n d_inputs['in_dist'] += deriv_sum\n else:\n # Recommended to make sure your code can run in serial too, for testing.\n d_inputs['in_dist'] += np.tile(dg_dx, local_size).reshape((local_size, size)).dot(d_outputs['out_serial']) \n if 'in_serial' in d_inputs:\n d_inputs['in_serial'] += dg_dy * d_outputs['out_serial']\n\n\nsize = 7\n\nif MPI:\n comm = MPI.COMM_WORLD\n rank = comm.rank\n sizes, offsets = evenly_distrib_idxs(comm.size, size)\nelse:\n # When running in serial, the entire variable is on rank 0.\n rank = 0\n sizes = {rank : size}\n offsets = {rank : 0}\n\nprob = om.Problem()\nmodel = prob.model\n\n# Create a distributed source for the distributed input.\nivc = om.IndepVarComp()\nivc.add_output('x_dist', np.zeros(sizes[rank]), distributed=True)\nivc.add_output('x_serial', np.zeros(size))\n\nmodel.add_subsystem(\"indep\", ivc)\nmodel.add_subsystem(\"D1\", MixedDistrib2())\n\nmodel.connect('indep.x_dist', 'D1.in_dist')\nmodel.connect('indep.x_serial', 'D1.in_serial')\n\nmodel.add_design_var('indep.x_serial')\nmodel.add_design_var('indep.x_dist')\nmodel.add_constraint('D1.out_dist', lower=0.0)\nmodel.add_constraint('D1.out_serial', lower=0.0)\n\nprob.setup(force_alloc_complex=True)\n\n# Set initial values of distributed variable.\nx_dist_init = 3.0 + np.arange(size)[offsets[rank]:offsets[rank] + sizes[rank]]\nprob.set_val('indep.x_dist', x_dist_init)\n\n# Set initial values of serial variable.\nx_serial_init = 1.0 + 2.0*np.arange(size)\nprob.set_val('indep.x_serial', x_serial_init)\n\nprob.run_model()\n\nif rank > 0:\n prob.check_totals(method='cs', out_stream=None)\nelse:\n prob.check_totals(method='cs')", "_____no_output_____" ], [ "%%px\n\ntotals = prob.check_totals(method='cs', out_stream=None)\nfor key, val in totals.items():\n assert_near_equal(val['rel error'][0], 0.0, 1e-6)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ [ [ "Lambda School Data Science\n\n*Unit 2, Sprint 1, Module 4*\n\n---\n\n# Logistic Regression\n- do train/validate/test split\n- begin with baselines for classification\n- express and explain the intuition and interpretation of Logistic Regression\n- use sklearn.linear_model.LogisticRegression to fit and interpret Logistic Regression models\n\nLogistic regression is the baseline for classification models, as well as a handy way to predict probabilities (since those too live in the unit interval). While relatively simple, it is also the foundation for more sophisticated classification techniques such as neural networks (many of which can effectively be thought of as networks of logistic models).", "_____no_output_____" ], [ "### Setup\n\nRun the code cell below. You can work locally (follow the [local setup instructions](https://lambdaschool.github.io/ds/unit2/local/)) or on Colab.\n\nLibraries:\n- category_encoders\n- numpy\n- pandas\n- scikit-learn", "_____no_output_____" ] ], [ [ "%%capture\nimport sys\n\n# If you're on Colab:\nif 'google.colab' in sys.modules:\n DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Linear-Models/master/data/'\n !pip install category_encoders==2.*\n\n# If you're working locally:\nelse:\n DATA_PATH = '../data/'", "_____no_output_____" ] ], [ [ "# Do train/validate/test split", "_____no_output_____" ], [ "## Overview", "_____no_output_____" ], [ "### Predict Titanic survival 🚢\n\nKaggle is a platform for machine learning competitions. [Kaggle has used the Titanic dataset](https://www.kaggle.com/c/titanic/data) for their most popular \"getting started\" competition. \n\nKaggle splits the data into train and test sets for participants. Let's load both:", "_____no_output_____" ] ], [ [ "import pandas as pd\ntrain = pd.read_csv(DATA_PATH+'titanic/train.csv')\ntest = pd.read_csv(DATA_PATH+'titanic/test.csv')", "_____no_output_____" ] ], [ [ "Notice that the train set has one more column than the test set:", "_____no_output_____" ] ], [ [ "train.shape, test.shape", "_____no_output_____" ] ], [ [ "Which column is in train but not test? The target!", "_____no_output_____" ] ], [ [ "set(train.columns) - set(test.columns)", "_____no_output_____" ] ], [ [ "### Why doesn't Kaggle give you the target for the test set?\n\n#### Rachel Thomas, [How (and why) to create a good validation set](https://www.fast.ai/2017/11/13/validation-sets/)\n\n> One great thing about Kaggle competitions is that they force you to think about validation sets more rigorously (in order to do well). For those who are new to Kaggle, it is a platform that hosts machine learning competitions. Kaggle typically breaks the data into two sets you can download:\n>\n> 1. a **training set**, which includes the _independent variables,_ as well as the _dependent variable_ (what you are trying to predict).\n>\n> 2. a **test set**, which just has the _independent variables._ You will make predictions for the test set, which you can submit to Kaggle and get back a score of how well you did.\n>\n> This is the basic idea needed to get started with machine learning, but to do well, there is a bit more complexity to understand. **You will want to create your own training and validation sets (by splitting the Kaggle “training” data). You will just use your smaller training set (a subset of Kaggle’s training data) for building your model, and you can evaluate it on your validation set (also a subset of Kaggle’s training data) before you submit to Kaggle.**\n>\n> The most important reason for this is that Kaggle has split the test data into two sets: for the public and private leaderboards. The score you see on the public leaderboard is just for a subset of your predictions (and you don’t know which subset!). How your predictions fare on the private leaderboard won’t be revealed until the end of the competition. The reason this is important is that you could end up overfitting to the public leaderboard and you wouldn’t realize it until the very end when you did poorly on the private leaderboard. Using a good validation set can prevent this. You can check if your validation set is any good by seeing if your model has similar scores on it to compared with on the Kaggle test set. ...\n>\n> Understanding these distinctions is not just useful for Kaggle. In any predictive machine learning project, you want your model to be able to perform well on new data.", "_____no_output_____" ], [ "### 2-way train/test split is not enough\n\n#### Hastie, Tibshirani, and Friedman, [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/), Chapter 7: Model Assessment and Selection\n\n> If we are in a data-rich situation, the best approach is to randomly divide the dataset into three parts: a training set, a validation set, and a test set. The training set is used to fit the models; the validation set is used to estimate prediction error for model selection; the test set is used for assessment of the generalization error of the final chosen model. Ideally, the test set should be kept in a \"vault,\" and be brought out only at the end of the data analysis. Suppose instead that we use the test-set repeatedly, choosing the model with the smallest test-set error. Then the test set error of the final chosen model will underestimate the true test error, sometimes substantially.\n\n#### Andreas Mueller and Sarah Guido, [Introduction to Machine Learning with Python](https://books.google.com/books?id=1-4lDQAAQBAJ&pg=PA270)\n\n> The distinction between the training set, validation set, and test set is fundamentally important to applying machine learning methods in practice. Any choices made based on the test set accuracy \"leak\" information from the test set into the model. Therefore, it is important to keep a separate test set, which is only used for the final evaluation. It is good practice to do all exploratory analysis and model selection using the combination of a training and a validation set, and reserve the test set for a final evaluation - this is even true for exploratory visualization. Strictly speaking, evaluating more than one model on the test set and choosing the better of the two will result in an overly optimistic estimate of how accurate the model is.\n\n#### Hadley Wickham, [R for Data Science](https://r4ds.had.co.nz/model-intro.html#hypothesis-generation-vs.hypothesis-confirmation)\n\n> There is a pair of ideas that you must understand in order to do inference correctly:\n>\n> 1. Each observation can either be used for exploration or confirmation, not both.\n>\n> 2. You can use an observation as many times as you like for exploration, but you can only use it once for confirmation. As soon as you use an observation twice, you’ve switched from confirmation to exploration.\n>\n> This is necessary because to confirm a hypothesis you must use data independent of the data that you used to generate the hypothesis. Otherwise you will be over optimistic. There is absolutely nothing wrong with exploration, but you should never sell an exploratory analysis as a confirmatory analysis because it is fundamentally misleading.\n>\n> If you are serious about doing an confirmatory analysis, one approach is to split your data into three pieces before you begin the analysis.\n\n\n#### Sebastian Raschka, [Model Evaluation](https://sebastianraschka.com/blog/2018/model-evaluation-selection-part4.html)\n\n> Since “a picture is worth a thousand words,” I want to conclude with a figure (shown below) that summarizes my personal recommendations ...\n\n<img src=\"https://sebastianraschka.com/images/blog/2018/model-evaluation-selection-part4/model-eval-conclusions.jpg\" width=\"600\">\n\nUsually, we want to do **\"Model selection (hyperparameter optimization) _and_ performance estimation.\"** (The green box in the diagram.)\n\nTherefore, we usually do **\"3-way holdout method (train/validation/test split)\"** or **\"cross-validation with independent test set.\"**", "_____no_output_____" ], [ "### What's the difference between Training, Validation, and Testing sets?\n\n#### Brandon Rohrer, [Training, Validation, and Testing Data Sets](https://end-to-end-machine-learning.teachable.com/blog/146320/training-validation-testing-data-sets)\n\n> The validation set is for adjusting a model's hyperparameters. The testing data set is the ultimate judge of model performance.\n>\n> Testing data is what you hold out until very last. You only run your model on it once. You don’t make any changes or adjustments to your model after that. ...", "_____no_output_____" ], [ "## Follow Along\n\n> You will want to create your own training and validation sets (by splitting the Kaggle “training” data).\n\nDo this, using the [sklearn.model_selection.train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function:", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import train_test_split", "_____no_output_____" ], [ "train.shape, test.shape", "_____no_output_____" ], [ "train, val = train_test_split(train, random_state=28)", "_____no_output_____" ], [ "train.shape, val.shape, test.shape", "_____no_output_____" ] ], [ [ "## Challenge", "_____no_output_____" ], [ "For your assignment, you'll do a 3-way train/validate/test split.\n\nThen next sprint, you'll begin to participate in a private Kaggle challenge, just for your cohort! \n\nYou will be provided with data split into 2 sets: training and test. You will create your own training and validation sets, by splitting the Kaggle \"training\" data, so you'll end up with 3 sets total.", "_____no_output_____" ], [ "# Begin with baselines for classification", "_____no_output_____" ], [ "## Overview", "_____no_output_____" ], [ "We'll begin with the **majority class baseline.**\n\n[Will Koehrsen](https://twitter.com/koehrsen_will/status/1088863527778111488)\n\n> A baseline for classification can be the most common class in the training dataset.\n\n[*Data Science for Business*](https://books.google.com/books?id=4ZctAAAAQBAJ&pg=PT276), Chapter 7.3: Evaluation, Baseline Performance, and Implications for Investments in Data\n\n> For classification tasks, one good baseline is the _majority classifier,_ a naive classifier that always chooses the majority class of the training dataset (see Note: Base rate in Holdout Data and Fitting Graphs). This may seem like advice so obvious it can be passed over quickly, but it is worth spending an extra moment here. There are many cases where smart, analytical people have been tripped up in skipping over this basic comparison. For example, an analyst may see a classification accuracy of 94% from her classifier and conclude that it is doing fairly well—when in fact only 6% of the instances are positive. So, the simple majority prediction classifier also would have an accuracy of 94%. ", "_____no_output_____" ], [ "## Follow Along", "_____no_output_____" ], [ "Determine majority class", "_____no_output_____" ] ], [ [ "target = 'Survived'\ny_train = train[target]\ny_train.value_counts()", "_____no_output_____" ] ], [ [ "What if we guessed the majority class for every prediction?", "_____no_output_____" ] ], [ [ "y_pred = y_train.apply(lambda x : 0)", "_____no_output_____" ] ], [ [ "#### Use a classification metric: accuracy\n\n[Classification metrics are different from regression metrics!](https://scikit-learn.org/stable/modules/model_evaluation.html)\n- Don't use _regression_ metrics to evaluate _classification_ tasks.\n- Don't use _classification_ metrics to evaluate _regression_ tasks.\n\n[Accuracy](https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score) is a common metric for classification. Accuracy is the [\"proportion of correct classifications\"](https://en.wikipedia.org/wiki/Confusion_matrix): the number of correct predictions divided by the total number of predictions.", "_____no_output_____" ], [ "What is the baseline accuracy if we guessed the majority class for every prediction?", "_____no_output_____" ] ], [ [ "from sklearn.metrics import accuracy_score", "_____no_output_____" ], [ "accuracy_score(y_train, y_pred)", "_____no_output_____" ], [ "train[target].value_counts(normalize=True)", "_____no_output_____" ], [ "y_val = val[target]\ny_val\ny_pred = [0] * len(y_val)\naccuracy_score(y_val, y_pred)", "_____no_output_____" ] ], [ [ "## Challenge", "_____no_output_____" ], [ "In your assignment, your Sprint Challenge, and your upcoming Kaggle challenge, you'll begin with the majority class baseline. How quickly can you beat this baseline?", "_____no_output_____" ], [ "# Express and explain the intuition and interpretation of Logistic Regression\n", "_____no_output_____" ], [ "## Overview\n\nTo help us get an intuition for *Logistic* Regression, let's start by trying *Linear* Regression instead, and see what happens...", "_____no_output_____" ], [ "## Follow Along", "_____no_output_____" ], [ "### Linear Regression?", "_____no_output_____" ] ], [ [ "train.describe()", "_____no_output_____" ], [ "# 1. Import estimator class\nfrom sklearn.linear_model import LinearRegression\n\n# 2. Instantiate this class\nlinear_reg = LinearRegression()\n\n# 3. Arrange X feature matrices (already did y target vectors)\nfeatures = ['Pclass', 'Age', 'Fare']\nX_train = train[features]\nX_val = val[features]\n\n# Impute missing values\nfrom sklearn.impute import SimpleImputer\nimputer = SimpleImputer()\nX_train_imputed = imputer.fit_transform(X_train)\nX_val_imputed = imputer.transform(X_val)\n\n# 4. Fit the model\nlinear_reg.fit(X_train_imputed, y_train)\n\n# 5. Apply the model to new data.\n# The predictions look like this ...\nlinear_reg.predict(X_val_imputed)", "_____no_output_____" ], [ "# Get coefficients\npd.Series(linear_reg.coef_, features)", "_____no_output_____" ], [ "test_case = [[1, 5, 500]] # 1st class, 5-year old, Rich\nlinear_reg.predict(test_case)", "_____no_output_____" ] ], [ [ "### Logistic Regression!", "_____no_output_____" ] ], [ [ "from sklearn.linear_model import LogisticRegression\n\nlog_reg = LogisticRegression(solver='lbfgs')\nlog_reg.fit(X_train_imputed, y_train)\nprint('Validation Accuracy', log_reg.score(X_val_imputed, y_val))", "Validation Accuracy 0.7219730941704036\n" ], [ "# The predictions look like this\nlog_reg.predict(X_val_imputed)", "_____no_output_____" ], [ "log_reg.predict(test_case)", "_____no_output_____" ], [ "log_reg.predict_proba(test_case)", "_____no_output_____" ], [ "# What's the math?\nlog_reg.coef_", "_____no_output_____" ], [ "log_reg.intercept_", "_____no_output_____" ], [ "# The logistic sigmoid \"squishing\" function, implemented to accept numpy arrays\nimport numpy as np\n\ndef sigmoid(x):\n return 1 / (1 + np.e**(-x))", "_____no_output_____" ], [ "sigmoid(log_reg.intercept_ + np.dot(log_reg.coef_, np.transpose(test_case)))", "_____no_output_____" ], [ "log_reg.coef_", "_____no_output_____" ], [ "test_case", "_____no_output_____" ], [ "sigmoid()", "_____no_output_____" ] ], [ [ "So, clearly a more appropriate model in this situation! For more on the math, [see this Wikipedia example](https://en.wikipedia.org/wiki/Logistic_regression#Probability_of_passing_an_exam_versus_hours_of_study).", "_____no_output_____" ], [ "# Use sklearn.linear_model.LogisticRegression to fit and interpret Logistic Regression models", "_____no_output_____" ], [ "## Overview\n\nNow that we have more intuition and interpretation of Logistic Regression, let's use it within a realistic, complete scikit-learn workflow, with more features and transformations.", "_____no_output_____" ], [ "## Follow Along\n\nSelect these features: `['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']`\n\n(Why shouldn't we include the `Name` or `Ticket` features? What would happen here?) \n\nFit this sequence of transformers & estimator:\n\n- [category_encoders.one_hot.OneHotEncoder](https://contrib.scikit-learn.org/categorical-encoding/onehot.html)\n- [sklearn.impute.SimpleImputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html)\n- [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n- [sklearn.linear_model.LogisticRegressionCV](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html)\n\nGet validation accuracy.", "_____no_output_____" ] ], [ [ "features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\ntarget = 'Survived'\n\nX_train = train[features]\ny_train = train[target]\nX_val = val[features]\ny_val = val[target]\n\nX_train.shape, y_train.shape, X_val.shape, y_val.shape", "_____no_output_____" ], [ "import category_encoders as ce\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegressionCV", "_____no_output_____" ], [ "encoder = ce.OneHotEncoder(use_cat_names=True)\nX_train_encoded = encoder.fit_transform(X_train)", "_____no_output_____" ], [ "X_val_encoded = encoder.transform(X_val)", "_____no_output_____" ], [ "imputer = SimpleImputer(strategy='mean')\nX_train_imputed = imputer.fit_transform(X_train_encoded)\nX_val_imputed = imputer.transform(X_val_encoded)", "_____no_output_____" ], [ "scaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train_imputed)\nX_val_scaled = scaler.transform(X_val_imputed)", "_____no_output_____" ], [ "model = LogisticRegressionCV()\nmodel.fit(X_train_scaled, y_train)", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n warnings.warn(CV_WARNING, FutureWarning)\n" ], [ "y_pred = model.predict(X_val_scaled)\naccuracy_score(y_val, y_pred)", "_____no_output_____" ], [ "print('Validation Accuracy:', model.score(X_val_scaled, y_val))", "Validation Accuracy: 0.7802690582959642\n" ] ], [ [ "Plot coefficients:", "_____no_output_____" ] ], [ [ "%matplotlib inline\ncoefficients = pd.Series(model.coef_[0], X_train_encoded.columns)\ncoefficients.sort_values().plot.barh();", "_____no_output_____" ] ], [ [ "Generate [Kaggle](https://www.kaggle.com/c/titanic) submission:", "_____no_output_____" ], [ "## Challenge\n\nYou'll use Logistic Regression for your assignment, your Sprint Challenge, and optionally for your first model in our Kaggle challenge!", "_____no_output_____" ], [ "# Review\n\nFor your assignment, you'll use a [**dataset of 400+ burrito reviews**](https://srcole.github.io/100burritos/). How accurately can you predict whether a burrito is rated 'Great'?\n\n> We have developed a 10-dimensional system for rating the burritos in San Diego. ... Generate models for what makes a burrito great and investigate correlations in its dimensions.\n\n- Do train/validate/test split. Train on reviews from 2016 & earlier. Validate on 2017. Test on 2018 & later.\n- Begin with baselines for classification.\n- Use scikit-learn for logistic regression.\n- Get your model's validation accuracy. (Multiple times if you try multiple iterations.)\n- Get your model's test accuracy. (One time, at the end.)\n- Commit your notebook to your fork of the GitHub repo.\n- Watch Aaron's [video #1](https://www.youtube.com/watch?v=pREaWFli-5I) (12 minutes) & [video #2](https://www.youtube.com/watch?v=bDQgVt4hFgY) (9 minutes) to learn about the mathematics of Logistic Regression.", "_____no_output_____" ], [ "# Sources\n- Brandon Rohrer, [Training, Validation, and Testing Data Sets](https://end-to-end-machine-learning.teachable.com/blog/146320/training-validation-testing-data-sets)\n- Hadley Wickham, [R for Data Science](https://r4ds.had.co.nz/model-intro.html#hypothesis-generation-vs.hypothesis-confirmation), Hypothesis generation vs. hypothesis confirmation\n- Hastie, Tibshirani, and Friedman, [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/), Chapter 7: Model Assessment and Selection\n- Mueller and Guido, [Introduction to Machine Learning with Python](https://books.google.com/books?id=1-4lDQAAQBAJ&pg=PA270), Chapter 5.2.2: The Danger of Overfitting the Parameters and the Validation Set\n- Provost and Fawcett, [Data Science for Business](https://books.google.com/books?id=4ZctAAAAQBAJ&pg=PT276), Chapter 7.3: Evaluation, Baseline Performance, and Implications for Investments in Data\n- Rachel Thomas, [How (and why) to create a good validation set](https://www.fast.ai/2017/11/13/validation-sets/)\n- Sebastian Raschka, [Model Evaluation](https://sebastianraschka.com/blog/2018/model-evaluation-selection-part4.html)\n- Will Koehrsen, [\"A baseline for classification can be the most common class in the training dataset.\"](https://twitter.com/koehrsen_will/status/1088863527778111488)", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/harvardnlp/pytorch-struct/blob/master/notebooks/Unsupervised_CFG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ] ], [ [ "!pip install -qqq torchtext -qqq pytorch-transformers dgl\n!pip install -qqqU git+https://github.com/harvardnlp/pytorch-struct", " Building wheel for torch-struct (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ], [ "import torchtext\nimport torch\nfrom torch_struct import SentCFG\nfrom torch_struct.networks import NeuralCFG\nimport torch_struct.data", "_____no_output_____" ], [ "# Download and the load default data.\nWORD = torchtext.data.Field(include_lengths=True)\nUD_TAG = torchtext.data.Field(init_token=\"<bos>\", eos_token=\"<eos>\", include_lengths=True)\n\n# Download and the load default data.\ntrain, val, test = torchtext.datasets.UDPOS.splits(\n fields=(('word', WORD), ('udtag', UD_TAG), (None, None)), \n filter_pred=lambda ex: 5 < len(ex.word) < 30\n)\n\nWORD.build_vocab(train.word, min_freq=3)\nUD_TAG.build_vocab(train.udtag)\ntrain_iter = torch_struct.data.TokenBucket(train, \n batch_size=200,\n device=\"cuda:0\")\n", "_____no_output_____" ], [ "H = 256\nT = 30\nNT = 30\nmodel = NeuralCFG(len(WORD.vocab), T, NT, H)\nmodel.cuda()\nopt = torch.optim.Adam(model.parameters(), lr=0.001, betas=[0.75, 0.999])", "_____no_output_____" ], [ "def train():\n #model.train()\n losses = []\n for epoch in range(10):\n for i, ex in enumerate(train_iter):\n opt.zero_grad()\n words, lengths = ex.word \n N, batch = words.shape\n words = words.long()\n params = model(words.cuda().transpose(0, 1))\n dist = SentCFG(params, lengths=lengths)\n loss = dist.partition.mean()\n (-loss).backward()\n losses.append(loss.detach())\n torch.nn.utils.clip_grad_norm_(model.parameters(), 3.0)\n opt.step()\n\n if i % 100 == 1: \n print(-torch.tensor(losses).mean(), words.shape)\n losses = []", "_____no_output_____" ], [ "train()", "tensor(52.3219) torch.Size([29, 18])\n" ], [ "for i, ex in enumerate(train_iter):\n opt.zero_grad()\n words, lengths = ex.word \n\n N, batch = words.shape\n words = words.long()\n params = terms(words.transpose(0, 1)), rules(batch), roots(batch)\n\n tree = CKY(MaxSemiring).marginals(params, lengths=lengths, _autograd=True)\n print(tree)\n break", "_____no_output_____" ], [ "def split(spans):\n batch, N = spans.shape[:2]\n splits = []\n for b in range(batch):\n cover = spans[b].nonzero()\n left = {i: [] for i in range(N)}\n right = {i: [] for i in range(N)}\n batch_split = {}\n for i in range(cover.shape[0]):\n i, j, A = cover[i].tolist()\n left[i].append((A, j, j - i + 1))\n right[j].append((A, i, j - i + 1))\n for i in range(cover.shape[0]):\n i, j, A = cover[i].tolist()\n B = None\n for B_p, k, a_span in left[i]:\n for C_p, k_2, b_span in right[j]:\n if k_2 == k + 1 and a_span + b_span == j - i + 1:\n B, C = B_p, C_p\n k_final = k\n break\n if j > i:\n batch_split[(i, j)] =k\n splits.append(batch_split)\n return splits ", "_____no_output_____" ], [ "splits = split(spans)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Sklearn", "_____no_output_____" ], [ "## sklearn.linear_model", "_____no_output_____" ] ], [ [ "from matplotlib.colors import ListedColormap\nfrom sklearn import cross_validation, datasets, linear_model, metrics\n\nimport numpy as np", "D:\\Education\\GitHub\\Coursera\\MachineLearningAndDataAnalysisCoursera\\venv\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n \"This module will be removed in 0.20.\", DeprecationWarning)\n" ], [ "%pylab inline", "Populating the interactive namespace from numpy and matplotlib\n" ] ], [ [ "### Линейная регрессия", "_____no_output_____" ], [ "#### Генерация данных", "_____no_output_____" ] ], [ [ "data, target, coef = datasets.make_regression(n_features = 2, n_informative = 1, n_targets = 1, \n noise = 5., coef = True, random_state = 2)", "_____no_output_____" ], [ "pylab.scatter(list(map(lambda x:x[0], data)), target, color='r')\npylab.scatter(list(map(lambda x:x[1], data)), target, color='b')", "_____no_output_____" ], [ "train_data, test_data, train_labels, test_labels = cross_validation.train_test_split(data, target, \n test_size = 0.3)", "_____no_output_____" ] ], [ [ "#### LinearRegression", "_____no_output_____" ] ], [ [ "linear_regressor = linear_model.LinearRegression()\nlinear_regressor.fit(train_data, train_labels)\npredictions = linear_regressor.predict(test_data)", "_____no_output_____" ], [ "print(test_labels)", "[-36.69728864 -91.477377 11.96165156 -0.74051877 24.47584129\n 12.42286854 -57.46293828 28.15553021 -10.27758354 -80.80239408\n 22.2276832 64.70214251 -22.33224966 -84.32102748 -44.51417742\n -18.57607726 -76.75213382 -18.86438755 -40.84204295 -63.4056294\n -35.32062686 -10.06708677 21.20540389 78.24817537 -24.77820218\n -26.87743177 -12.0017312 64.19559505 -16.79027112 -71.3715844 ]\n" ], [ "print(predictions)", "[-27.37351175 -93.7792872 12.92330013 0.89313596 22.43864415\n 5.91564656 -54.96106527 22.07112775 -8.09116828 -78.72050659\n 18.40889843 67.58130785 -29.40807671 -82.64062439 -55.06907019\n -25.93566194 -69.15559338 -18.53616803 -47.76985106 -59.5373645\n -43.0530469 -9.24228731 15.45126983 65.6471009 -26.49152707\n -28.21874519 -8.03490249 69.06852394 -15.19504878 -72.42753591]\n" ], [ "metrics.mean_absolute_error(test_labels, predictions)", "_____no_output_____" ], [ "linear_scoring = cross_validation.cross_val_score(linear_regressor, data, target, scoring = 'mean_absolute_error', \n cv=10)\nprint('mean: {}, std: {}'.format(linear_scoring.mean(), linear_scoring.std()))", "mean: -4.070071498779696, std: 1.07371044928902\n" ], [ "scorer = metrics.make_scorer(metrics.mean_absolute_error, greater_is_better=True)", "_____no_output_____" ], [ "linear_scoring = cross_validation.cross_val_score(linear_regressor, data, target, scoring=scorer, \n cv = 10)\nprint('mean: {}, std: {}'.format(linear_scoring.mean(), linear_scoring.std()))", "mean: 4.070071498779696, std: 1.07371044928902\n" ], [ "coef", "_____no_output_____" ], [ "linear_regressor.coef_", "_____no_output_____" ], [ "# в лекции не указано, что в уравнении обученной модели также участвует свободный член\nlinear_regressor.intercept_", "_____no_output_____" ], [ "print(\"y = {:.2f}*x1 + {:.2f}*x2\".format(coef[0], coef[1]))", "y = 38.08*x1 + 0.00*x2\n" ], [ "print(\"y = {:.2f}*x1 + {:.2f}*x2 + {:.2f}\".format(linear_regressor.coef_[0], \n linear_regressor.coef_[1], \n linear_regressor.intercept_))", "y = 38.15*x1 + -0.16*x2 + -0.88\n" ] ], [ [ "#### Lasso", "_____no_output_____" ] ], [ [ "lasso_regressor = linear_model.Lasso(random_state = 3)\nlasso_regressor.fit(train_data, train_labels)\nlasso_predictions = lasso_regressor.predict(test_data)", "_____no_output_____" ], [ "lasso_scoring = cross_validation.cross_val_score(lasso_regressor, data, target, scoring = scorer, cv = 10)\nprint('mean: {}, std: {}'.format(lasso_scoring.mean(), lasso_scoring.std()))", "mean: 4.1544782466663985, std: 1.0170354384993352\n" ], [ "print(lasso_regressor.coef_)", "[37.31062919 -0. ]\n" ], [ "print(\"y = {:.2f}*x1 + {:.2f}*x2\".format(coef[0], coef[1]))", "y = 38.08*x1 + 0.00*x2\n" ], [ "print(\"y = {:.2f}*x1 + {:.2f}*x2\".format(lasso_regressor.coef_[0], lasso_regressor.coef_[1]))", "y = 37.31*x1 + -0.00*x2\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import matplotlib.pyplot as plt\nimport numpy as np\nimport functools\nimport time", "_____no_output_____" ] ], [ [ "# Questão 1", "_____no_output_____" ], [ "Resolva o sistema linear $Ax = b$ em que\n\n\n$\nA =\n\\begin{bmatrix}\n9. & −4. & 1. & 0. & 0. & 0. & 0. \\\\\n−4. & 6. & −4. & 1. & 0. & 0. & 0. \\\\\n1. & −4. & 6. & −4. & 1. & 0. & 0. \\\\\n0. & 1. & −4. & 6. & −4. & 1. & 0. \\\\\n\\vdots & \\ddots & \\ddots & \\ddots & \\ddots & \\ddots & \\vdots \\\\\n0. & 0. & 1. & −4. & 6. & −4. & 1. \\\\\n0. & 0. & 0. & 1. & −4. & 5. & −2. \\\\\n0. & 0. & 0. & 0. & 1. & −2. & 1.\n\\end{bmatrix} \\in R^{\\; 200 \\times 200}\n$\n\n\n\ne \n\n$\nb =\n\\begin{bmatrix}\n1 \\\\\n1\\\\\n1\\\\\n1\\\\\n\\vdots\\\\\n1\\\\\n1\\\\\n1\\\\\n\\end{bmatrix} \\in\nR^{\\; 200}\n$\n\nusando o método da Eliminação de Gauss (com pivoteamento parcial) e os métodos iterativos de Gauss-Jacobi e Gauss-Seidel, se possível. Compare o desempenho dos métodos para a resolução do sistema linear em termos do tempo de execução.", "_____no_output_____" ] ], [ [ "def timer(f):\n @functools.wraps(f)\n def wrapper_timer(*args, **kwargs):\n tempo_inicio = time.perf_counter()\n retorno = f(*args, **kwargs)\n tempo_fim = time.perf_counter()\n tempo_exec = tempo_fim - tempo_inicio\n print(f\"Tempo de Execução: {tempo_exec:0.4f} segundos\")\n return retorno\n return wrapper_timer", "_____no_output_____" ], [ "def FatoracaoLUPivot(A):\n U = np.copy(A)\n n = A.shape[0]\n L = np.zeros_like(A)\n P = np.eye(n)\n\n m = 0\n\n for j in range(n):\n\n #Pivoteamento Parcial\n\n k = np.argmax(np.abs(U[j:,j])) + j\n U[j], U[k] = np.copy(U[k]), np.copy(U[j])\n P[j], P[k] = np.copy(P[k]), np.copy(P[j])\n\n L[j], L[k] = np.copy(L[k]), np.copy(L[j])\n\n m += 1\n\n for i in range(j + 1, n):\n L[i][j] = U[i][j]/U[j][j]\n for k in range(j + 1, n):\n U[i][k] -= L[i][j] * U[j][k]\n U[i][j] = 0\n m += 1\n L += np.eye(n)\n return L, U, P", "_____no_output_____" ], [ "def SubstituicaoRegressiva(U, c): # U triangular superior\n x = np.copy(c)\n n = U.shape[0]\n\n for i in range(n-1, -1, -1):\n for j in range(i + 1, n):\n x[i] -= (U[i,j] * x[j])\n x[i] /= U[i,i]\n return x", "_____no_output_____" ], [ "def SubstituicaoDireta(U, c): #U triangular inferior\n x = np.copy(c)\n n = U.shape[0]\n\n for i in range(n):\n for j in range(i):\n x[i] -= (U[i,j] * x[j])\n x[i] /= U[i,i]\n return x", "_____no_output_____" ], [ "@timer\ndef EliminacaoGaussLUPivot(A, b):\n L, U, P = FatoracaoLUPivot(A)\n # Resolver Ly = b e Ux = y\n y = SubstituicaoDireta(L, b)\n x = SubstituicaoRegressiva(U, y)\n\n return P, x", "_____no_output_____" ], [ "def buildA():\n A = np.zeros((200, 200))\n A[0,0:3] = np.array([9, -4, 1])\n A[1,0:4] = np.array([-4, 6, -4, 1])\n A[198,196:200] = np.array([1, -4, 5, -2])\n A[199,197:200] = np.array([1, -2, 1])\n\n for i in range(2, 198):\n A[i, i-2:i+3] = np.array([1, -4, 6, -4, 1])\n \n return A", "_____no_output_____" ], [ "A = buildA()\nA", "_____no_output_____" ], [ "b = np.ones((200,1))\nb", "_____no_output_____" ] ], [ [ "## Solução por Eliminação de Gauss com Pivoteamento Parcial", "_____no_output_____" ] ], [ [ "P, x = EliminacaoGaussLUPivot(A, b)", "Tempo de Execução: 4.0179 segundos\n" ], [ "x", "_____no_output_____" ], [ "print(np.max(np.abs(P @ A @ x - b)))", "2.384185791015625e-07\n" ] ], [ [ "## Método Gauss-Jacobi", "_____no_output_____" ] ], [ [ "@timer\ndef GaussJacobi(A, b):\n n = A.shape[0]\n x_history = list()\n x_old = np.zeros(n)\n x_new = np.zeros(n)\n k_limite = 200\n k = 0\n tau = 1E-4\n Dr = 1\n\n while (k < k_limite and Dr > tau):\n for i in range(n):\n soma = 0\n for j in range(n):\n if (i == j):\n continue\n soma += A[i,j]*x_old[j]\n x_new[i] = (b[i] - soma) / A[i,i]\n \n k += 1\n Dr = np.max(np.abs(x_new - x_old)) / np.max(np.abs(x_new))\n x_history.append(x_old)\n x_old = np.copy(x_new)\n \n return x_history, x_new", "_____no_output_____" ], [ "history, x = GaussJacobi(A, b)", "Tempo de Execução: 4.8295 segundos\n" ], [ "erros = []\nfor i in range(len(history)):\n erro = np.max(np.abs(A @ history[i] - b))\n if (erro != np.inf):\n erros.append(erro)", "_____no_output_____" ], [ "plt.semilogy(erros)", "_____no_output_____" ] ], [ [ "## Método Gauss-Seidel", "_____no_output_____" ] ], [ [ "@timer\ndef GaussSeidel(A, b, k_limite=200):\n n = A.shape[0]\n x_history = list()\n x_old = np.zeros(n)\n x_new = np.zeros(n)\n k = 0\n tau = 1E-4\n Dr = 1\n\n while (k < k_limite and Dr > tau):\n for i in range(n):\n soma = 0\n for j in range(n):\n if (i == j):\n continue\n soma += A[i,j]*x_new[j]\n x_new[i] = (b[i] - soma) / A[i,i]\n \n \n Dr = np.max(np.abs(x_new - x_old)) / np.max(np.abs(x_new))\n x_history.append(x_old)\n x_old = np.copy(x_new)\n k += 1\n\n if (Dr > tau):\n print(\"NÃO CONVERGIU!\")\n \n return x_history, x_new", "_____no_output_____" ], [ "history, x = GaussSeidel(A, b)", "NÃO CONVERGIU!\nTempo de Execução: 4.7215 segundos\n" ], [ "print(np.max(np.abs(A @ x - b)))", "1.4693583616174237\n" ], [ "erros = []\nfor i in range(len(history)):\n erro = np.max(np.abs(A @ history[i] - b))\n if (erro != np.inf):\n erros.append(erro)", "_____no_output_____" ], [ "plt.semilogy(erros)", "_____no_output_____" ] ], [ [ "## Análise dos Resultados", "_____no_output_____" ], [ "Além de reutilizar as funções criadas para a Eliminação de Gauss com Pivoteamento Parcial, escrevi rotinas para implementar os métodos de Gauss-Jacobi e Gauss-Seidel. \n\nAinda, criei uma função decoradora `timer` para envolver as rotinas de cada método de resolução de sistemas lineares para contabilizar e imprimir o respectivo tempo de execução.\n\nAo longo da análise, considerei o erro absoluto máximo como a componente de maior diferença, em módulo, entre $Ax$ e $b$.\n\nO método de Eliminação de Gauss demorou $4,0178$ segundos e obteve a resposta correta, com erro absoluto máximo de aproximadamente $2,4E-7$.\n\nO método de Gauss-Jacobi executou em $4,8295$ segundos e divergiu, com erro crescente a cada iteração.\n\nO método de Gauss-Seidel executou em $4,7215$ segundos e apresentou uma condição peculiar. O resultado não divergiu, mas converge lentamente $-$ de forma que $200$ iterações não foram suficientes para obter a resposta correta e o erro absoluto máximo da última iteração permaneceu próximo de $1,5$.\n\nPortanto, o método de melhor performance no caso analisado foi a Eliminação de Gauss com Pivoteamento Parcial, pois ela obteve a melhor resposta e executou em menos tempo.", "_____no_output_____" ], [ "# Questão 2", "_____no_output_____" ], [ "Determine valores de $\\beta$ que garantem a convergência dos métodos de Gauss-Jacobi e Gauss-Seidel quando\naplicados para resolução do sistema linear $Ax = b$ em que\n\n\n$A =\n\\begin{bmatrix}\n−10 & 2 \\\\\n\\beta & 5\n\\end{bmatrix}$\ne\n$b =\n\\begin{bmatrix}\n1 \\\\\n1\n\\end{bmatrix}$", "_____no_output_____" ], [ "## Gauss-Jacobi\n\n#### $i=1$:\n\n$\\sum_{j \\ne i} |a_{ij}| = |a_{12}| = 2 < 10 = |a_{11}|$\n\n#### $i=2$:\n\n$\\sum_{j \\ne i} |a_{ij}| = |a_{21}| = \\beta < 5 = |a_{22}|$\n\nLogo, a matriz é diagonalmente estritamente crescente para $\\beta < 5$ e o conjunto de matrizes que satisfazem essa condição garante a convergência do Método de Gauss-Jacobi.", "_____no_output_____" ], [ "## Gauss-Seidel\n\n### Critério das Linhas\n\nConforme vimos no critério de convergência para Gauss-Jacobi, a matriz é diagonalmente estritalmente crescente $-$ e, consequentemente o método de Gauss-Seidel converge $-$ para $\\beta < 5$.\n\n### Critério de Sassenfeld\n\n#### $i=1$:\n\n$\\beta_1 = \\frac{1}{|a_{11}|} \\left( \\sum_{j = 2}^2 |a_{1j}|\\right) = \\frac{|a_{12}|}{|a_{11}|} = \\frac{2}{10} = \\frac{1}{5} < 1$\n\n#### $i=2$:\n\n$\\beta_2 = \\frac{1}{|a_{22}|} \\left( \\sum_{j = 1}^1 |a_{2j}| \\beta_j\\right) = \\frac{|a_{21}| \\beta_1}{|a_{22}|} = \\frac{\\beta \\times (1/5)}{5} = \\frac{\\beta}{25} < 1 \\iff \\beta < 25$\n\nDessa forma, como os dois critérios são independentemente suficientes para determinar a convergência do método, realizei a união dos intervalos de $\\beta$.\n\nPortanto, valores de $\\beta$ no intervalo $\\beta < 25$ garantem a convergência de Gauss-Seidel.", "_____no_output_____" ] ] ]

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[ [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown", "markdown", "markdown", "markdown", "markdown", "markdown" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# default_exp core", "_____no_output_____" ] ], [ [ "# hello\n\n> API details.\n", "_____no_output_____" ] ], [ [ "#hide\nfrom nbdev.showdoc import *\nfrom nbdev_tutorial.core import *\n%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "#export\nclass HelloSayer:\n \"Say hello to `to` using `say_hello`\"\n def __init__(self, to): self.to = to\n\n def say(self):\n \"Do the saying\"\n return say_hello(self.to)", "_____no_output_____" ], [ "show_doc(HelloSayer)", "_____no_output_____" ], [ "Card(suit=2, rank=11)", "_____no_output_____" ], [ "show_doc(Card)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Assignment 9: Implement Dynamic Programming\n\nIn this exercise, we will begin to explore the concept of dynamic programming and how it related to various object containers with respect to computational complexity. ", "_____no_output_____" ], [ "## Deliverables:\n\n \n\n 1) Choose and implement a Dynamic Programming algorithm in Python, make sure you are using a Dynamic Programming solution (not another one).\n\n 2) Use the algorithm to solve a range of scenarios. \n \n 3) Explain what is being done in the implementation. That is, write up a walk through of the algorithm and explain how it is a Dynamic Programming solution.\n\n\n\n \n### Prepare an executive summary of your results, referring to the table and figures you have generated. Explain how your results relate to big O notation. Describe your results in language that management can understand. This summary should be included as text paragraphs in the Jupyter notebook. Explain how the algorithm works and why it is a useful to data engineers.", "_____no_output_____" ], [ "# A. The Dynamic programming problem: Longest Increasing Sequence\n\n\n### The Longest Increasing Subsequence (LIS) problem is to find the length of the longest subsequence of a given sequence such that all elements of the subsequence are sorted in increasing order. For example, the length of LIS for {10, 22, 9, 33, 21, 50, 41, 60, 80} is 6 and LIS is {10, 22, 33, 50, 60, 80}. ", "_____no_output_____" ], [ "# A. Setup: Library imports and Algorithm", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\n\nimport seaborn as sns\nimport time\n#import itertools\n\nimport random\nimport matplotlib.pyplot as plt\n#import networkx as nx\n#import pydot\n#from networkx.drawing.nx_pydot import graphviz_layout\n\n#from collections import deque\n", "_____no_output_____" ], [ "# Dynamic Programming Approach of Finding LIS by reducing the problem to longest common Subsequence\n \ndef lis(a):\n n=len(a) #get the length of the list\n \n b=sorted(list(set(a))) #removes duplicates, and sorts list\n m=len(b) #gets the length of the truncated and sorted list\n \n dp=[[-1 for i in range(m+1)] for j in range(n+1)] #instantiates a list of lists filled with -1 columns are indicies of the sorted array; rows the original array\n \n for i in range(n+1): # for every column in the table at each row:\n \n for j in range(m+1):\n if i==0 or j==0: #if at first element in either a row or column set the table row,index to zero\n dp[i][j]=0 \n elif a[i-1]==b[j-1]: #else if the sorted array value matches the original array:\n dp[i][j]=1+dp[i-1][j-1]#sets dp[i][j] to 1+prveious cell of the dyanmic table\n else:\n dp[i][j]=max(dp[i-1][j],dp[i][j-1]) #else record the max of the row or column for that cell in the cell\n return dp[-1][-1] # This will return the max running sequence.\n \n# Driver program to test above function\narr1 = [10, 22, 9, 33, 21, 50, 41, 60]\nlen_arr1 = len(arr1)\nprint(\"Longest increaseing sequence has a length of:\", lis(arr1))\n# addtional comments included from the original code contributed by Dheeraj Khatri (https://www.geeksforgeeks.org/longest-increasing-subsequence-dp-3/) \n\n\ndef Container(arr, fun): ### I'm glad I was able to reuse this from assignment 3 and 4. Useful function.\n objects = [] #instantiates an empty list to collect the returns\n times = [] #instantiates an empty list to collect times for each computation\n for t in arr:\n start= time.perf_counter() #collects the start time\n obj = fun(t) # applies the function to the arr object\n end = time.perf_counter() # collects end time\n duration = (end-start)* 1E3 #converts to milliseconds\n objects.append(obj)# adds the returns of the functions to the objects list\n times.append(duration) # adds the duration for computation to list\n return objects, times\n\n\n", "Longest increaseing sequence has a length of: 5\n" ] ], [ [ "# B. Test Array Generation", "_____no_output_____" ] ], [ [ "RANDOM_SEED = 300\n\nnp.random.seed(RANDOM_SEED) \narr100 = list(np.random.randint(low=1, high= 5000, size=100))\n\nnp.random.seed(RANDOM_SEED) \narr200 = list(np.random.randint(low=1, high= 5000, size=200))\n\n\nnp.random.seed(RANDOM_SEED) \narr400 = list(np.random.randint(low=1, high= 5000, size=400))\n\n\nnp.random.seed(RANDOM_SEED) \narr600 = list(np.random.randint(low=1, high= 5000, size=600))\n\nnp.random.seed(RANDOM_SEED) \narr800 = list(np.random.randint(low=1, high= 5000, size=800))\n\nprint(len(arr100), len(arr200), len(arr400), len(arr600), len(arr800))\n\n\n\n", "100 200 400 600 800\n" ], [ "arr_list = [arr100, arr200, arr400, arr600, arr800]\n\nmetrics = Container(arr_list, lis)", "_____no_output_____" ] ], [ [ "### Table1. Performance Summary", "_____no_output_____" ] ], [ [ "summary = {\n 'ArraySize' : [len(arr100), len(arr200), len(arr400), len(arr600), len(arr800)], \n 'SequenceLength' : [metrics[0][0],metrics[0][1], metrics[0][2], metrics[0][3], metrics[0][4]],\n 'Time(ms)' : [metrics[1][0],metrics[1][1], metrics[1][2], metrics[1][3], metrics[1][4]]\n}\n\ndf =pd.DataFrame(summary)\ndf", "_____no_output_____" ] ], [ [ "### Figure 1. Performance", "_____no_output_____" ] ], [ [ "sns.scatterplot(data=df, x='Time(ms)', y='ArraySize')\n", "_____no_output_____" ] ], [ [ "# Discussion\n\n Explain what is being done in the implementation. That is, write up a walk through of the algorithm and explain how it is a Dynamic Programming solution.\n", "_____no_output_____" ], [ "The dyanamic programming problem above finds the length of the longest incrementing sequence of values in a list. The defined function makes a sorted copy of the list containing only unique values and also creates a dynamic table (in the form of a list of lists) using a nested list comprehension. This table contains the incidices of the sorted array as columns and the indicies of the original array as rows. To begin, the table is instantiated with values of -1. The value of zero indicies are set to zero in the dynamic table and if a given index in the original array is found to be increasing the dyanamic table is incremented. until all positions are assessed. The funciton then returns the maximum value of the increments which will be the length of the longest running sequence. This is a dynamic progromming problem because the solution builds on a smaller subset problems. \n\nDyanmic programming is an important concept for developers and engineers. Functions and programs that use dynamic programming help solve problems which present themselves as factorial time complexity in a more efficient way. At face value, it appears that this problem of the longest incrementing sequence will have to compare all values in a given array to all previous values in the array. Dyanmic programming allows for a shortcut in a sense. We can compare the given array with a sorted version of that array and at the intersection of the sorted and unsorted arrays we can determine if we need to make an additon to our incrementing sequence tally. \n\nShown above in table and figure 1 is the time required for the algorithm to tally the longest running sequence for various array sizes. Because the algorithm utilizes a nested for loop it is the expeictation that the time will grow as a function of the square of the original array length. This is confirmed when inspecting the scatterplot in figure 1. Thus, the developed algorithm in big O notation is O(n^2) time complexity which is much more efficient than factorial time.", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Chapter 3 Questions", "_____no_output_____" ], [ "#### 3.1 Form dollar bars for E-mini S&P 500 futures:\n1. Apply a symmetric CUSUM filter (Chapter 2, Section 2.5.2.1) where the threshold is the standard deviation of daily returns (Snippet 3.1).\n2. Use Snippet 3.4 on a pandas series t1, where numDays=1.\n3. On those sampled features, apply the triple-barrier method, where ptSl=[1,1] and t1 is the series you created in point 1.b.\n4. Apply getBins to generate the labels.", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport timeit\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_curve, classification_report, confusion_matrix\n\nfrom mlfinlab.corefns.core_functions import CoreFunctions\nfrom mlfinlab.fracdiff.fracdiff import frac_diff_ffd\n\nimport matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ], [ "# Read in data\ndata = pd.read_csv('official_data/dollar_bars.csv', nrows=40000)\ndata.index = pd.to_datetime(data['date_time'])\ndata = data.drop('date_time', axis=1)", "_____no_output_____" ], [ "data.head()", "_____no_output_____" ] ], [ [ "**Apply a symmetric CUSUM filter (Chapter 2, Section 2.5.2.1) where the threshold is the standard deviation of daily returns (Snippet 3.1).**", "_____no_output_____" ] ], [ [ "# Compute daily volatility\nvol = CoreFunctions.get_daily_vol(close=data['close'], lookback=50)", "Calculating daily volatility for dynamic thresholds\n" ], [ "vol.plot(figsize=(14, 7), title='Volatility as caclulated by de Prado')\nplt.show()", "_____no_output_____" ], [ "# Apply Symmetric CUSUM Filter and get timestamps for events\n# Note: Only the CUSUM filter needs a point estimate for volatility\ncusum_events = CoreFunctions.get_t_events(data['close'], threshold=vol.mean())", " 1%|▏ | 592/39998 [00:00<00:06, 5912.41it/s]" ] ], [ [ "**Use Snippet 3.4 on a pandas series t1, where numDays=1.**", "_____no_output_____" ] ], [ [ "# Compute vertical barrier\nvertical_barriers = CoreFunctions.add_vertical_barrier(cusum_events, data['close'])\nvertical_barriers.head()", "_____no_output_____" ] ], [ [ "**On those sampled features, apply the triple-barrier method, where ptSl=[1,1] and t1 is the series you created in point 1.b.**", "_____no_output_____" ] ], [ [ "triple_barrier_events = CoreFunctions.get_events(close=data['close'],\n t_events=cusum_events,\n pt_sl=[1, 1],\n target=vol,\n min_ret=0.01,\n num_threads=1,\n vertical_barrier_times=vertical_barriers,\n side=None)", "/home/ariadne/Desktop/Research Project/research/2019-03-03_AS_JJ_Chapter3/mlfinlab/corefns/core_functions.py:205: FutureWarning: \nPassing list-likes to .loc or [] with any missing label will raise\nKeyError in the future, you can use .reindex() as an alternative.\n\nSee the documentation here:\nhttps://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n target = target.loc[t_events]\n" ], [ "triple_barrier_events.head()", "_____no_output_____" ], [ "labels = CoreFunctions.get_bins(triple_barrier_events, data['close'])", "_____no_output_____" ], [ "labels.head()", "_____no_output_____" ], [ "labels['bin'].value_counts()", "_____no_output_____" ] ], [ [ "---\n#### 3.2 From exercise 1, use Snippet 3.8 to drop rare labels.", "_____no_output_____" ] ], [ [ "clean_labels = CoreFunctions.drop_labels(labels)", "_____no_output_____" ], [ "print(labels.shape)\nprint(clean_labels.shape)", "(660, 3)\n(660, 3)\n" ] ], [ [ "---\n#### 3.3 Adjust the getBins function (Snippet 3.5) to return a 0 whenever the vertical barrier is the one touched first.\nThis change was made inside the module CoreFunctions.", "_____no_output_____" ], [ "---\n#### 3.4 Develop a trend-following strategy based on a popular technical analysis statistic (e.g., crossing moving averages). For each observation, themodel suggests a side, but not a size of the bet.\n\n1. Derive meta-labels for pt_sl = [1,2] and t1 where num_days=1. Use as trgt the daily standard deviation as computed by Snippet 3.1.\n2. Train a random forest to decide whether to trade or not. Note: The decision is whether to trade or not, {0,1}, since the underllying model (the crossing moveing average has decided the side{-1, 1})", "_____no_output_____" ] ], [ [ "# This question is answered in the notebook: 2019-03-06_JJ_Trend-Following-Question", "_____no_output_____" ] ], [ [ "----\n#### 3.5 Develop a mean-reverting strategy based on Bollinger bands. For each observation, the model suggests a side, but not a size of the bet.\n\n* (a) Derive meta-labels for ptSl = [0, 2] and t1 where numDays = 1. Use as trgt the daily standard deviation as computed by Snippet 3.1.\n* (b) Train a random forest to decide whether to trade or not. Use as features: volatility, seial correlation, and teh crossinmg moving averages.\n* (c) What is teh accuracy of prediction from the primary model? (i.e. if the secondary model does not filter the bets) What are the precision, recall and FI-scores?\n* (d) What is teh accuracy of prediction from the primary model? What are the precision, recall and FI-scores?\n", "_____no_output_____" ] ], [ [ "# This question is answered in the notebook: 2019-03-07_BBand-Question", "_____no_output_____" ] ] ]

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[ "CNRI-Python", "CECILL-B" ]

[ "CNRI-Python", "CECILL-B" ]

[ "CNRI-Python", "CECILL-B" ]

[ [ [ "", "_____no_output_____" ], [ "# If I have seen further it is by standing on the shoulders of Giants\n(Newton??)", "_____no_output_____" ], [ "\n(https://www.openhub.net/)", "_____no_output_____" ], [ "\n(https://www.openhub.net/)", "_____no_output_____" ], [ "\n(https://www.openhub.net/)", "_____no_output_____" ], [ "\n(https://www.openhub.net/)", "_____no_output_____" ], [ "\n(https://www.openhub.net/)", "_____no_output_____" ], [ "### Pero, ¿qué es lo que hace fuertes a estos proyectos?", "_____no_output_____" ], [ "\n(https://medium.com/@sailorhg/coding-like-a-girl-595b90791cce)", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "## Codigo de conducta", "_____no_output_____" ], [ "### PyCon 2016 Code Of Conduct\nHarassment includes offensive communication related to gender, sexual orientation, disability, physical appearance, body size, race, religion, sexual images in public spaces, deliberate intimidation, stalking, following, harassing photography or recording, sustained disruption of talks or other events, inappropriate physical contact, and unwelcome sexual attention.", "_____no_output_____" ], [ "Participants asked to stop any harassing behavior are expected to comply immediately.", "_____no_output_____" ], [ "Exhibitors in the expo hall, sponsor or vendor booths, or similar activities are also subject to the anti-harassment policy. In particular, exhibitors should not use sexualized images, activities, or other material. Booth staff (including volunteers) should not use sexualized clothing/uniforms/costumes, or otherwise create a sexualized environment.", "_____no_output_____" ], [ "Be careful in the words that you choose. Remember that sexist, racist, and other exclusionary jokes can be offensive to those around you. Excessive swearing and offensive jokes are not appropriate for PyCon.", "_____no_output_____" ], [ "If a participant engages in behavior that violates this code of conduct, the conference organizers may take any action they deem appropriate, including warning the offender or expulsion from the conference with no refund.", "_____no_output_____" ], [ "## Políticas de inclusión", "_____no_output_____" ], [ "\n\n__The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers.__\n\nThe Python Software Foundation (PSF) is a 501(c)(3) non-profit corporation that holds the intellectual property rights behind the Python programming language. We manage the open source licensing for Python version 2.1 and later and own and protect the trademarks associated with Python. We also run the North American PyCon conference annually, support other Python conferences around the world, and fund Python related development with our grants program and by funding special projects.\n\n(https://www.python.org/psf/)", "_____no_output_____" ], [ "\n\n__We inspire women to fall in love with programming.__\n\n_Django Girls organize free Python and Django workshops, create open sourced online tutorials and curate amazing first experiences with technology._\n\n(https://djangogirls.org/)", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "\n\nWe are an international mentorship group with a focus on helping more women become active participants and leaders in the Python open-source community. Our mission is to promote, educate and advance a diverse Python community through outreach, education, conferences, events and social gatherings.\n\nPyLadies also aims to provide a friendly support network for women and a bridge to the larger Python world. Anyone with an interest in Python is encouraged to participate!\n\n(http://www.pyladies.com/)", "_____no_output_____" ], [ "\n(https://www.slideshare.net/fmasanori/import-community-62142823)", "_____no_output_____" ], [ "# ¡Gracias!\n\n<center>David Manuel Ochoa González<br>\ncorreos: ochoadavid at gmail.com - dochoa at iteso.mx<br>\ngithub: https://github.com/ochoadavid<br>\nmaterial de apoyo en: https://github.com/ochoadavid/TallerDePython</center>", "_____no_output_____" ] ] ]

[ "markdown" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# T1566 - Phishing\nAdversaries may send phishing messages to elicit sensitive information and/or gain access to victim systems. All forms of phishing are electronically delivered social engineering. Phishing can be targeted, known as spearphishing. In spearphishing, a specific individual, company, or industry will be targeted by the adversary. More generally, adversaries can conduct non-targeted phishing, such as in mass malware spam campaigns.\n\nAdversaries may send victim’s emails containing malicious attachments or links, typically to execute malicious code on victim systems or to gather credentials for use of [Valid Accounts](https://attack.mitre.org/techniques/T1078). Phishing may also be conducted via third-party services, like social media platforms.", "_____no_output_____" ], [ "## Atomic Tests:\nCurrently, no tests are available for this technique.", "_____no_output_____" ], [ "## Detection\nNetwork intrusion detection systems and email gateways can be used to detect phishing with malicious attachments in transit. Detonation chambers may also be used to identify malicious attachments. Solutions can be signature and behavior based, but adversaries may construct attachments in a way to avoid these systems.\n\nURL inspection within email (including expanding shortened links) can help detect links leading to known malicious sites. Detonation chambers can be used to detect these links and either automatically go to these sites to determine if they're potentially malicious, or wait and capture the content if a user visits the link.\n\nBecause most common third-party services used for phishing via service leverage TLS encryption, SSL/TLS inspection is generally required to detect the initial communication/delivery. With SSL/TLS inspection intrusion detection signatures or other security gateway appliances may be able to detect malware.\n\nAnti-virus can potentially detect malicious documents and files that are downloaded on the user's computer. Many possible detections of follow-on behavior may take place once [User Execution](https://attack.mitre.org/techniques/T1204) occurs.", "_____no_output_____" ], [ "## Shield Active Defense\n### Email Manipulation \n Modify the flow or contents of email. \n\n Email flow manipulation includes changing which mail appliances process mail flows, to which systems they forward mail, or moving mail after it arrives in an inbox. Email content manipulation includes altering the contents of an email message.\n#### Opportunity\nA phishing email can be detected and blocked from arriving at the intended recipient. \n#### Use Case\nA defender can intercept emails that are detected as suspicious or malicious by email detection tools and prevent deliver to the intended target. \n#### Procedures\nModify the destination of inbound email to facilitate the collection of inbound spearphishing messages.\nModify the contents of an email message to maintain continuity when it is used for adversary engagement purposes.", "_____no_output_____" ] ] ]

[ "markdown" ]

[ [ "markdown", "markdown", "markdown", "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Import Libraries", "_____no_output_____" ] ], [ [ "from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms", "_____no_output_____" ] ], [ [ "## Data Transformations\n\nWe first start with defining our data transformations. We need to think what our data is and how can we augment it to correct represent images which it might not see otherwise. \n", "_____no_output_____" ] ], [ [ "# Train Phase transformations\ntrain_transforms = transforms.Compose([\n # transforms.Resize((28, 28)),\n # transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\n transforms.RandomRotation((-7.0, 7.0), fill=(1,)),\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,)) # The mean and std have to be sequences (e.g., tuples), therefore you should add a comma after the values. \n # Note the difference between (0.1307) and (0.1307,)\n ])\n\n# Test Phase transformations\ntest_transforms = transforms.Compose([\n # transforms.Resize((28, 28)),\n # transforms.ColorJitter(brightness=0.10, contrast=0.1, saturation=0.10, hue=0.1),\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])\n", "_____no_output_____" ] ], [ [ "# Dataset and Creating Train/Test Split", "_____no_output_____" ] ], [ [ "train = datasets.MNIST('./data', train=True, download=True, transform=train_transforms)\ntest = datasets.MNIST('./data', train=False, download=True, transform=test_transforms)", "_____no_output_____" ] ], [ [ "# Dataloader Arguments & Test/Train Dataloaders\n", "_____no_output_____" ] ], [ [ "SEED = 1\n\n# CUDA?\ncuda = torch.cuda.is_available()\nprint(\"CUDA Available?\", cuda)\n\n# For reproducibility\ntorch.manual_seed(SEED)\n\nif cuda:\n torch.cuda.manual_seed(SEED)\n\n# dataloader arguments - something you'll fetch these from cmdprmt\ndataloader_args = dict(shuffle=True, batch_size=128, num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n\n# train dataloader\ntrain_loader = torch.utils.data.DataLoader(train, **dataloader_args)\n\n# test dataloader\ntest_loader = torch.utils.data.DataLoader(test, **dataloader_args)", "CUDA Available? True\n" ] ], [ [ "# The model\nLet's start with the model we first saw", "_____no_output_____" ] ], [ [ "import torch.nn.functional as F\ndropout_value = 0.1\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n # Input Block\n self.convblock1 = nn.Sequential(\n nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n nn.ReLU(),\n nn.BatchNorm2d(16),\n nn.Dropout(dropout_value)\n ) # output_size = 26\n\n # CONVOLUTION BLOCK 1\n self.convblock2 = nn.Sequential(\n nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(3, 3), padding=0, bias=False),\n nn.ReLU(),\n nn.BatchNorm2d(32),\n nn.Dropout(dropout_value)\n ) # output_size = 24\n\n # TRANSITION BLOCK 1\n self.convblock3 = nn.Sequential(\n nn.Conv2d(in_channels=32, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n ) # output_size = 24\n self.pool1 = nn.MaxPool2d(2, 2) # output_size = 12\n\n # CONVOLUTION BLOCK 2\n self.convblock4 = nn.Sequential(\n nn.Conv2d(in_channels=10, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n nn.ReLU(), \n nn.BatchNorm2d(16),\n nn.Dropout(dropout_value)\n ) # output_size = 10\n self.convblock5 = nn.Sequential(\n nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n nn.ReLU(), \n nn.BatchNorm2d(16),\n nn.Dropout(dropout_value)\n ) # output_size = 8\n self.convblock6 = nn.Sequential(\n nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),\n nn.ReLU(), \n nn.BatchNorm2d(16),\n nn.Dropout(dropout_value)\n ) # output_size = 6\n self.convblock7 = nn.Sequential(\n nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), padding=1, bias=False),\n nn.ReLU(), \n nn.BatchNorm2d(16),\n nn.Dropout(dropout_value)\n ) # output_size = 6\n \n # OUTPUT BLOCK\n self.gap = nn.Sequential(\n nn.AvgPool2d(kernel_size=6)\n ) # output_size = 1\n\n self.convblock8 = nn.Sequential(\n nn.Conv2d(in_channels=16, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),\n # nn.BatchNorm2d(10),\n # nn.ReLU(),\n # nn.Dropout(dropout_value)\n ) \n\n\n self.dropout = nn.Dropout(dropout_value)\n\n def forward(self, x):\n x = self.convblock1(x)\n x = self.convblock2(x)\n x = self.convblock3(x)\n x = self.pool1(x)\n x = self.convblock4(x)\n x = self.convblock5(x)\n x = self.convblock6(x)\n x = self.convblock7(x)\n x = self.gap(x) \n x = self.convblock8(x)\n\n x = x.view(-1, 10)\n return F.log_softmax(x, dim=-1)", "_____no_output_____" ] ], [ [ "# Model Params\nCan't emphasize on how important viewing Model Summary is. \nUnfortunately, there is no in-built model visualizer, so we have to take external help", "_____no_output_____" ] ], [ [ "!pip install torchsummary\nfrom torchsummary import summary\nuse_cuda = torch.cuda.is_available()\ndevice = torch.device(\"cuda\" if use_cuda else \"cpu\")\nprint(device)\nmodel = Net().to(device)\nsummary(model, input_size=(1, 28, 28))", "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\ncuda\n----------------------------------------------------------------\n Layer (type) Output Shape Param #\n================================================================\n Conv2d-1 [-1, 16, 26, 26] 144\n ReLU-2 [-1, 16, 26, 26] 0\n BatchNorm2d-3 [-1, 16, 26, 26] 32\n Dropout-4 [-1, 16, 26, 26] 0\n Conv2d-5 [-1, 32, 24, 24] 4,608\n ReLU-6 [-1, 32, 24, 24] 0\n BatchNorm2d-7 [-1, 32, 24, 24] 64\n Dropout-8 [-1, 32, 24, 24] 0\n Conv2d-9 [-1, 10, 24, 24] 320\n MaxPool2d-10 [-1, 10, 12, 12] 0\n Conv2d-11 [-1, 16, 10, 10] 1,440\n ReLU-12 [-1, 16, 10, 10] 0\n BatchNorm2d-13 [-1, 16, 10, 10] 32\n Dropout-14 [-1, 16, 10, 10] 0\n Conv2d-15 [-1, 16, 8, 8] 2,304\n ReLU-16 [-1, 16, 8, 8] 0\n BatchNorm2d-17 [-1, 16, 8, 8] 32\n Dropout-18 [-1, 16, 8, 8] 0\n Conv2d-19 [-1, 16, 6, 6] 2,304\n ReLU-20 [-1, 16, 6, 6] 0\n BatchNorm2d-21 [-1, 16, 6, 6] 32\n Dropout-22 [-1, 16, 6, 6] 0\n Conv2d-23 [-1, 16, 6, 6] 2,304\n ReLU-24 [-1, 16, 6, 6] 0\n BatchNorm2d-25 [-1, 16, 6, 6] 32\n Dropout-26 [-1, 16, 6, 6] 0\n AvgPool2d-27 [-1, 16, 1, 1] 0\n Conv2d-28 [-1, 10, 1, 1] 160\n================================================================\nTotal params: 13,808\nTrainable params: 13,808\nNon-trainable params: 0\n----------------------------------------------------------------\nInput size (MB): 0.00\nForward/backward pass size (MB): 1.06\nParams size (MB): 0.05\nEstimated Total Size (MB): 1.12\n----------------------------------------------------------------\n" ] ], [ [ "# Training and Testing\n\nAll right, so we have 24M params, and that's too many, we know that. But the purpose of this notebook is to set things right for our future experiments. \n\nLooking at logs can be boring, so we'll introduce **tqdm** progressbar to get cooler logs. \n\nLet's write train and test functions", "_____no_output_____" ] ], [ [ "from tqdm import tqdm\n\ntrain_losses = []\ntest_losses = []\ntrain_acc = []\ntest_acc = []\n\ndef train(model, device, train_loader, optimizer, epoch):\n model.train()\n pbar = tqdm(train_loader)\n correct = 0\n processed = 0\n for batch_idx, (data, target) in enumerate(pbar):\n # get samples\n data, target = data.to(device), target.to(device)\n\n # Init\n optimizer.zero_grad()\n # In PyTorch, we need to set the gradients to zero before starting to do backpropragation because PyTorch accumulates the gradients on subsequent backward passes. \n # Because of this, when you start your training loop, ideally you should zero out the gradients so that you do the parameter update correctly.\n\n # Predict\n y_pred = model(data)\n\n # Calculate loss\n loss = F.nll_loss(y_pred, target)\n train_losses.append(loss)\n\n # Backpropagation\n loss.backward()\n optimizer.step()\n\n # Update pbar-tqdm\n \n pred = y_pred.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n processed += len(data)\n\n pbar.set_description(desc= f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}')\n train_acc.append(100*correct/processed)\n\ndef test(model, device, test_loader):\n model.eval()\n test_loss = 0\n correct = 0\n with torch.no_grad():\n for data, target in test_loader:\n data, target = data.to(device), target.to(device)\n output = model(data)\n test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n test_loss /= len(test_loader.dataset)\n test_losses.append(test_loss)\n\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n test_loss, correct, len(test_loader.dataset),\n 100. * correct / len(test_loader.dataset)))\n \n test_acc.append(100. * correct / len(test_loader.dataset))", "_____no_output_____" ], [ "from torch.optim.lr_scheduler import StepLR\n\nmodel = Net().to(device)\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n# scheduler = StepLR(optimizer, step_size=6, gamma=0.1)\n\n\nEPOCHS = 20\nfor epoch in range(EPOCHS):\n print(\"EPOCH:\", epoch)\n train(model, device, train_loader, optimizer, epoch)\n # scheduler.step()\n test(model, device, test_loader)", "\r 0%| | 0/469 [00:00<?, ?it/s]" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Document AI Specialized Parser with HITL\nThis notebook shows you how to use Document AI's specialized parsers ex. Invoice, Receipt, W2, W9, etc. and also shows Human in the Loop (HITL) output for supported parsers.", "_____no_output_____" ] ], [ [ "# Install necessary Python libraries and restart your kernel after.\n!python -m pip install -r ../requirements.txt", "_____no_output_____" ], [ "from google.cloud import documentai_v1beta3 as documentai\nfrom PIL import Image, ImageDraw\n\nimport os\nimport pandas as pd", "_____no_output_____" ] ], [ [ "## Set your processor variables ", "_____no_output_____" ] ], [ [ "# TODO(developer): Fill these variables with your values before running the sample\nPROJECT_ID = \"YOUR_PROJECT_ID_HERE\"\nLOCATION = \"us\" # Format is 'us' or 'eu'\nPROCESSOR_ID = \"PROCESSOR_ID\" # Create processor in Cloud Console\nPDF_PATH = \"../resources/procurement/invoices/invoice.pdf\" # Update to path of target document", "_____no_output_____" ] ], [ [ "The following code calls the synchronous API and parses the form fields and values.", "_____no_output_____" ] ], [ [ "def process_document_sample():\n # Instantiates a client\n client_options = {\"api_endpoint\": \"{}-documentai.googleapis.com\".format(LOCATION)}\n client = documentai.DocumentProcessorServiceClient(client_options=client_options)\n\n # The full resource name of the processor, e.g.:\n # projects/project-id/locations/location/processor/processor-id\n # You must create new processors in the Cloud Console first\n name = f\"projects/{PROJECT_ID}/locations/{LOCATION}/processors/{PROCESSOR_ID}\"\n\n with open(PDF_PATH, \"rb\") as image:\n image_content = image.read()\n\n # Read the file into memory\n document = {\"content\": image_content, \"mime_type\": \"application/pdf\"}\n\n # Configure the process request\n request = {\"name\": name, \"document\": document}\n\n # Recognizes text entities in the PDF document\n result = client.process_document(request=request)\n document = result.document\n entities = document.entities\n print(\"Document processing complete.\\n\\n\")\n\n # For a full list of Document object attributes, please reference this page: https://googleapis.dev/python/documentai/latest/_modules/google/cloud/documentai_v1beta3/types/document.html#Document \n types = []\n values = []\n confidence = []\n \n # Grab each key/value pair and their corresponding confidence scores.\n for entity in entities:\n types.append(entity.type_)\n values.append(entity.mention_text)\n confidence.append(round(entity.confidence,4))\n \n # Create a Pandas Dataframe to print the values in tabular format. \n df = pd.DataFrame({'Type': types, 'Value': values, 'Confidence': confidence})\n display(df)\n \n if result.human_review_operation:\n print (\"Triggered HITL long running operation: {}\".format(result.human_review_operation))\n\n return document\n\n\ndef get_text(doc_element: dict, document: dict):\n \"\"\"\n Document AI identifies form fields by their offsets\n in document text. This function converts offsets\n to text snippets.\n \"\"\"\n response = \"\"\n # If a text segment spans several lines, it will\n # be stored in different text segments.\n for segment in doc_element.text_anchor.text_segments:\n start_index = (\n int(segment.start_index)\n if segment in doc_element.text_anchor.text_segments\n else 0\n )\n end_index = int(segment.end_index)\n response += document.text[start_index:end_index]\n return response", "_____no_output_____" ], [ "doc = process_document_sample()", "_____no_output_____" ] ], [ [ "## Draw the bounding boxes\nWe will now use the spatial data returned by the processor to mark our values on the invoice pdf file that we first converted into a jpg.", "_____no_output_____" ] ], [ [ "JPG_PATH = \"../resources/procurement/invoices/invoice.jpg\" # Update to path of a jpg of your sample document.", "_____no_output_____" ], [ "document_image = Image.open(JPG_PATH)\ndraw = ImageDraw.Draw(document_image)\nfor entity in doc.entities:\n # Draw the bounding box around the entities\n vertices = []\n for vertex in entity.page_anchor.page_refs[0].bounding_poly.normalized_vertices:\n vertices.append({'x': vertex.x * document_image.size[0], 'y': vertex.y * document_image.size[1]})\n draw.polygon([\n vertices[0]['x'], vertices[0]['y'],\n vertices[1]['x'], vertices[1]['y'],\n vertices[2]['x'], vertices[2]['y'],\n vertices[3]['x'], vertices[3]['y']], outline='blue')\ndocument_image", "_____no_output_____" ] ], [ [ "# Human in the loop (HITL) Operation", "_____no_output_____" ], [ "**Only complete this section if a HITL Operation is triggered.** </br>", "_____no_output_____" ] ], [ [ "lro = \"LONG_RUNNING_OPERATION\" # LRO printed in the previous cell ex. projects/660199673046/locations/us/operations/174674963333130330", "_____no_output_____" ], [ "client = documentai.DocumentProcessorServiceClient()\noperation = client._transport.operations_client.get_operation(lro)\nif operation.done:\n print(\"HITL location: {} \".format(str(operation.response.value)[5:-1]))\nelse:\n print('Waiting on human review.')", "_____no_output_____" ], [ "!gsutil cp \"HITL_LOCATION\" response.json # Location printed above ex. gs://gcs_bucket/receipt-output/174674963333130330/data-00001-of-00001.json", "_____no_output_____" ], [ "with open(\"response.json\", \"r\") as file:\n import json\n entities = {}\n data = json.load(file)\n for entity in data['entities']:\n if 'mentionText' in entity:\n entities[entity['type']] = entity['mentionText']\n else:\n entities[entity['type']] = \"\"\n \n for t in entities:\n print(\"{} : {}\\n \".format(t, entities[t]))", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "Notebook which focuses on the randomly generated data sets and the performance comparison of algorithms on it", "_____no_output_____" ] ], [ [ "from IPython.core.display import display, HTML\ndisplay(HTML('<style>.container {width:100% !important;}</style>'))", "_____no_output_____" ], [ "%matplotlib notebook\nimport matplotlib.pyplot as plt \nimport numpy as np\nimport torch\nfrom itertools import product, chain\n\nimport nmf.mult\nimport nmf.pgrad\nimport nmf.nesterov\n\nimport nmf_torch.mult\nimport nmf_torch.pgrad\nimport nmf_torch.nesterov\nimport nmf_torch.norms\n\nimport matplotlib\nimport pickle\n\nfrom performance.performance_eval_func import get_random_lowrank_matrix, get_time_ratio,\\\n compare_performance, plot_errors_dict,\\\n torch_algo_wrapper,\\\n plot_ratios_gpu_algo, plot_ratios_cpu_gpu, plot_ratios_cpu_algo,\\\n plot_errors_dict, errors_at_time_t_over_inner_dim\n ", "_____no_output_____" ], [ "algo_dict_to_test = {\n \"mult\": nmf.mult.factorize_Fnorm,\n \"pgrad\": nmf.pgrad.factorize_Fnorm_subproblems,\n \"nesterov\": nmf.nesterov.factorize_Fnorm,\n\n \"mult_torch\": torch_algo_wrapper(nmf_torch.mult.factorize_Fnorm, \n device=\"cuda\"),\n \"pgrad_torch\": torch_algo_wrapper(nmf_torch.pgrad.factorize_Fnorm_subproblems, \n device=\"cuda\"),\n \"nesterov_torch\": torch_algo_wrapper(nmf_torch.nesterov.factorize_Fnorm, \n device=\"cuda\")\n}", "_____no_output_____" ], [ "f, ax = plt.subplots()\nplot_errors_dict(errors_over_r_random, ax, log=True, x_lbl=\"Inner dim\", title=\"site3\")\n\nf, ax = plt.subplots()\nplot_errors_dict(errors_over_r_random, ax, log=False, x_lbl=\"Inner dim\", title=\"site3\")", "_____no_output_____" ], [ "shapes = [(5 * a, a) for a in [30, 100, 300, 1000, 3000]]\nshapes", "_____no_output_____" ], [ "inner_dims_small = [sh[1] // 10 for sh in shapes]\ninner_dims_small", "_____no_output_____" ], [ "inner_dims_big = [8 * sh[1] // 10 for sh in shapes]\ninner_dims_big", "_____no_output_____" ], [ "shapes_all = shapes + shapes\ninner_dims = inner_dims_small + inner_dims_big", "_____no_output_____" ], [ "times = [5, 25, 200, 1200, 8000]\ntimes = times + [t * 2 for t in times]", "_____no_output_____" ], [ "print(len(shapes_all))", "10\n" ], [ "errors_dict = pickle.load(open(\"random_data_errors_dict.pkl\",\"rb\"))", "_____no_output_____" ], [ "del errors_dict[(3, (150, 30))]", "_____no_output_____" ], [ "errors_dict = {}", "_____no_output_____" ], [ "for inner_dim, shape, t in zip(inner_dims, shapes_all, times):\n print((inner_dim, shape))\n if (inner_dim, shape) in errors_dict.keys():\n continue\n \n V = get_random_lowrank_matrix(shape[0], inner_dim, shape[1]) + np.random.rand(*shape) * 0.1\n\n W_init = np.random.rand(shape[0], inner_dim)\n H_init = np.random.rand(inner_dim, shape[1])\n\n errors = compare_performance(V=V, inner_dim=inner_dim, time_limit=t,\n W_init=W_init, H_init=H_init, \n algo_dict_to_test=algo_dict_to_test)\n errors_dict[(inner_dim, shape)] = errors\n pickle.dump(errors_dict, open(\"random_data_errors_dict.pkl\",\"wb\"))", "(3, (150, 30))\nStarting mult\nStarting pgrad\nStarting nesterov\nStarting mult_torch\nStarting pgrad_torch\nStarting nesterov_torch\n(10, (500, 100))\n(30, (1500, 300))\n(100, (5000, 1000))\n(300, (15000, 3000))\n(24, (150, 30))\n(80, (500, 100))\n(240, (1500, 300))\n(800, (5000, 1000))\n(2400, (15000, 3000))\n" ], [ "pickle.dump(errors_dict, open(\"random_data_errors_dict.pkl\",\"wb\"))", "_____no_output_____" ], [ "keys = zip(inner_dims, shapes_all)\nkeys = sorted(keys, key=lambda k: k[0])\nkeys = sorted(keys, key=lambda k: k[1][0])", "_____no_output_____" ], [ "keys", "_____no_output_____" ], [ "for k in keys:\n r, shape = k\n M = np.random.rand(shape[0], r) @ np.random.rand(r, shape[1])\n\n errros_dict_particular_data = errors_dict[k]\n\n f, axes = plt.subplots(3, 2, figsize=(8, 7), dpi=100, gridspec_kw=dict(hspace=0.45, top=0.92, bottom=0.08, \n left=0.08, right=0.99))\n f.suptitle(\"Comparison, time ratio for {}, {:.2f}KB, {:.2f}MB\".format(k, M.nbytes / 2**10, M.nbytes / 2**20))\n\n plot_errors_dict(errros_dict_particular_data, axes[0, 0], log=True, title=\"Objective function\", x_lbl=\"time [s]\")\n\n plot_ratios_cpu_gpu(errros_dict_particular_data, axes[0, 1])\n plot_ratios_cpu_algo(errros_dict_particular_data, axes[1:, 0], selected_algs=[\"mult\", \"pgrad\", \"nesterov\"])\n plot_ratios_gpu_algo(errros_dict_particular_data, axes[1:, 1],\n selected_algs=[\"mult_torch\", \"pgrad_torch\", \"nesterov_torch\"])", "_____no_output_____" ], [ "font = {'family' : 'normal',\n 'weight' : 'normal',\n 'size' : 14}\n\nmatplotlib.rc('font', **font)", "_____no_output_____" ], [ "figsize = (9, 10)\n\ngridspec_kw = dict(wspace=0.4, hspace=0.9,\n top=0.85,\n bottom=0.1,\n left=0.1, right=0.95)", "_____no_output_____" ], [ "plt.close(\"all\")\n\n\nf, axes1 = plt.subplots(3, 2, figsize=figsize, dpi=100, \n gridspec_kw=gridspec_kw)\nf.suptitle(\"Ratio between time required\\nto reach particular cost function value on CPU and on GPU\")\n\nf, axes2 = plt.subplots(3, 2, figsize=figsize, dpi=100, \n gridspec_kw=gridspec_kw)\nf.suptitle(\"Ratio between time required\\nto reach particular cost function value on CPU and on GPU\")\n\naxes1[0,0].get_shared_y_axes().join(*axes1[0, :], *axes1[1, :], *axes1[2, :])\naxes2[0,0].get_shared_y_axes().join(*axes2[0, :], *axes2[1, :], *axes2[2, :])\n\naxes1[2,1].set_axis_off()\naxes2[2,1].set_axis_off()\n\naxes1 = list(axes1.ravel()) \naxes2 = list(axes2.ravel())\n\n\nlegend_is = False\nfor k, a in zip(keys, chain.from_iterable(zip(axes1, axes2))):\n print(a)\n r, shape = k\n plot_ratios_cpu_gpu(errors_dict[k], a)\n \n M = np.random.rand(shape[0], r) @ np.random.rand(r, shape[1])\n kb = M.nbytes / 2**10 \n mb = M.nbytes / 2**20\n \n if mb < 1:\n size = \"{:.1f}KB\".format(kb) \n else:\n size = \"{:.1f}MB\".format(mb) \n \n a.set_title(\"Factorization of size {}\\nmat. dim. {}, {}\".format(k[0], k[1], size))\n \n if legend_is:\n a.get_legend().remove()\n else:\n legend_is = True", "_____no_output_____" ], [ "plt.close(\"all\")\n\nf, axes1 = plt.subplots(3, 2, figsize=figsize, dpi=100, \n gridspec_kw=gridspec_kw)\n\nf.suptitle(\"Ratio between time required to reach a particular\"+\n \"cost \\n function value for multiplicative algorithms and gradient algorithms\")\n\n\nf, axes2 = plt.subplots(3, 2, figsize=figsize, dpi=100, \n gridspec_kw=gridspec_kw)\n\n\nf.suptitle(\"Ratio between time required to reach a particular cost \\n function value for projecitve and Nesterov gradient algorithms\")\n\n\naxes1 = list(axes1.ravel())\naxes2 = list(axes2.ravel())\n\naxes1[-1].set_axis_off()\naxes2[-1].set_axis_off()\n\n\n# axes1[0].get_shared_y_axes().join(*axes1)\naxes2[0].get_shared_y_axes().join(*axes2)\n\nprint(keys)\nprint(len(axes1))\nprint(len(axes2))\n\nlegend_is = False\nfor k, a1, a2 in zip(keys[::2], axes1, axes2):\n r, shape = k\n \n if r != 0.1 * shape[1]:\n print(k)\n continue\n \n plot_ratios_gpu_algo(errors_dict[k], [a1, a2],\n selected_algs=[\"mult_torch\", \n \"pgrad_torch\",\n \"nesterov_torch\"])\n \n M = np.random.rand(shape[0], r) @ np.random.rand(r, shape[1])\n kb = M.nbytes / 2**10 \n mb = M.nbytes / 2**20\n \n if mb < 1:\n size = \"{:.2f}KB\".format(kb) \n else:\n size = \"{:.2f}MB\".format(mb) \n \n a1.set_title(\"factorization of size {}\\nmat. shape {} {}\".format(k[0], k[1], size))\n a2.set_title(\"factorization of size {}\\nmat. shape {} {}\".format(k[0], k[1], size))\n \n if legend_is:\n a1.get_legend().remove()\n a2.get_legend().remove()\n else:\n legend_is = True", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "%matplotlib inline", "_____no_output_____" ] ], [ [ "\n02: Fitting Power Spectrum Models\n=================================\n\nIntroduction to the module, beginning with the FOOOF object.\n", "_____no_output_____" ] ], [ [ "# Import the FOOOF object\nfrom fooof import FOOOF\n\n# Import utility to download and load example data\nfrom fooof.utils.download import load_fooof_data", "_____no_output_____" ], [ "# Download examples data files needed for this example\nfreqs = load_fooof_data('freqs.npy', folder='data')\nspectrum = load_fooof_data('spectrum.npy', folder='data')", "_____no_output_____" ] ], [ [ "FOOOF Object\n------------\n\nAt the core of the module, which is object oriented, is the :class:`~fooof.FOOOF` object,\nwhich holds relevant data and settings as attributes, and contains methods to run the\nalgorithm to parameterize neural power spectra.\n\nThe organization is similar to sklearn:\n\n- A model object is initialized, with relevant settings\n- The model is used to fit the data\n- Results can be extracted from the object\n\n\n", "_____no_output_____" ], [ "Calculating Power Spectra\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe :class:`~fooof.FOOOF` object fits models to power spectra. The module itself does not\ncompute power spectra, and so computing power spectra needs to be done prior to using\nthe FOOOF module.\n\nThe model is broadly agnostic to exactly how power spectra are computed. Common\nmethods, such as Welch's method, can be used to compute the spectrum.\n\nIf you need a module in Python that has functionality for computing power spectra, try\n`NeuroDSP <https://neurodsp-tools.github.io/neurodsp/>`_.\n\nNote that FOOOF objects require frequency and power values passed in as inputs to\nbe in linear spacing. Passing in non-linear spaced data (such logged values) may\nproduce erroneous results.\n\n\n", "_____no_output_____" ], [ "Fitting an Example Power Spectrum\n---------------------------------\n\nThe following example demonstrates fitting a power spectrum model to a single power spectrum.\n\n\n", "_____no_output_____" ] ], [ [ "# Initialize a FOOOF object\nfm = FOOOF()\n\n# Set the frequency range to fit the model\nfreq_range = [2, 40]\n\n# Report: fit the model, print the resulting parameters, and plot the reconstruction\nfm.report(freqs, spectrum, freq_range)", "_____no_output_____" ] ], [ [ "Fitting Models with 'Report'\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe above method 'report', is a convenience method that calls a series of methods:\n\n- :meth:`~fooof.FOOOF.fit`: fits the power spectrum model\n- :meth:`~fooof.FOOOF.print_results`: prints out the results\n- :meth:`~fooof.FOOOF.plot`: plots to data and model fit\n\nEach of these methods can also be called individually.\n\n\n", "_____no_output_____" ] ], [ [ "# Alternatively, just fit the model with FOOOF.fit() (without printing anything)\nfm.fit(freqs, spectrum, freq_range)\n\n# After fitting, plotting and parameter fitting can be called independently:\n# fm.print_results()\n# fm.plot()", "_____no_output_____" ] ], [ [ "Model Parameters\n~~~~~~~~~~~~~~~~\n\nOnce the power spectrum model has been calculated, the model fit parameters are stored\nas object attributes that can be accessed after fitting.\n\nFollowing the sklearn convention, attributes that are fit as a result of\nthe model have a trailing underscore, for example:\n\n- ``aperiodic_params_``\n- ``peak_params_``\n- ``error_``\n- ``r2_``\n- ``n_peaks_``\n\n\n", "_____no_output_____" ], [ "Access model fit parameters from FOOOF object, after fitting:\n\n\n", "_____no_output_____" ] ], [ [ "# Aperiodic parameters\nprint('Aperiodic parameters: \\n', fm.aperiodic_params_, '\\n')\n\n# Peak parameters\nprint('Peak parameters: \\n', fm.peak_params_, '\\n')\n\n# Goodness of fit measures\nprint('Goodness of fit:')\nprint(' Error - ', fm.error_)\nprint(' R^2 - ', fm.r_squared_, '\\n')\n\n# Check how many peaks were fit\nprint('Number of fit peaks: \\n', fm.n_peaks_)", "_____no_output_____" ] ], [ [ "Selecting Parameters\n~~~~~~~~~~~~~~~~~~~~\n\nYou can also select parameters using the :meth:`~fooof.FOOOF.get_params`\nmethod, which can be used to specify which parameters you want to extract.\n\n\n", "_____no_output_____" ] ], [ [ "# Extract a model parameter with `get_params`\nerr = fm.get_params('error')\n\n# Extract parameters, indicating sub-selections of parameter\nexp = fm.get_params('aperiodic_params', 'exponent')\ncfs = fm.get_params('peak_params', 'CF')\n\n# Print out a custom parameter report\ntemplate = (\"With an error level of {error:1.2f}, FOOOF fit an exponent \"\n \"of {exponent:1.2f} and peaks of {cfs:s} Hz.\")\nprint(template.format(error=err, exponent=exp,\n cfs=' & '.join(map(str, [round(cf, 2) for cf in cfs]))))", "_____no_output_____" ] ], [ [ "For a full description of how you can access data with :meth:`~fooof.FOOOF.get_params`,\ncheck the method's documentation.\n\nAs a reminder, you can access the documentation for a function using '?' in a\nJupyter notebook (ex: `fm.get_params?`), or more generally with the `help` function\nin general Python (ex: `help(get_params)`).\n\n\n", "_____no_output_____" ], [ "Notes on Interpreting Peak Parameters\n-------------------------------------\n\nPeak parameters are labeled as:\n\n- CF: center frequency of the extracted peak\n- PW: power of the peak, over and above the aperiodic component\n- BW: bandwidth of the extracted peak\n\nNote that the peak parameters that are returned are not exactly the same as the\nparameters of the Gaussians used internally to fit the peaks.\n\nSpecifically:\n\n- CF is the exact same as mean parameter of the Gaussian\n- PW is the height of the model fit above the aperiodic component [1],\n which is not necessarily the same as the Gaussian height\n- BW is 2 * the standard deviation of the Gaussian [2]\n\n[1] Since the Gaussians are fit together, if any Gaussians overlap,\nthan the actual height of the fit at a given point can only be assessed\nwhen considering all Gaussians. To be better able to interpret heights\nfor single peak fits, we re-define the peak height as above, and label it\nas 'power', as the units of the input data are expected to be units of power.\n\n[2] Gaussian standard deviation is '1 sided', where as the returned BW is '2 sided'.\n\n\n", "_____no_output_____" ], [ "The underlying gaussian parameters are also available from the FOOOF object,\nin the ``gaussian_params_`` attribute.\n\n\n", "_____no_output_____" ] ], [ [ "# Compare the 'peak_params_' to the underlying gaussian parameters\nprint(' Peak Parameters \\t Gaussian Parameters')\nfor peak, gauss in zip(fm.peak_params_, fm.gaussian_params_):\n print('{:5.2f} {:5.2f} {:5.2f} \\t {:5.2f} {:5.2f} {:5.2f}'.format(*peak, *gauss))", "_____no_output_____" ] ], [ [ "FOOOFResults\n~~~~~~~~~~~~\n\nThere is also a convenience method to return all model fit results:\n:func:`~fooof.FOOOF.get_results`.\n\nThis method returns all the model fit parameters, including the underlying Gaussian\nparameters, collected together into a FOOOFResults object.\n\nThe FOOOFResults object, which in Python terms is a named tuple, is a standard data\nobject used with FOOOF to organize and collect parameter data.\n\n\n", "_____no_output_____" ] ], [ [ "# Grab each model fit result with `get_results` to gather all results together\n# Note that this returns a FOOOFResult object\nfres = fm.get_results()\n\n# You can also unpack all fit parameters when using `get_results`\nap_params, peak_params, r_squared, fit_error, gauss_params = fm.get_results()", "_____no_output_____" ], [ "# Print out the FOOOFResults\nprint(fres, '\\n')\n\n# From FOOOFResults, you can access the different results\nprint('Aperiodic Parameters: \\n', fres.aperiodic_params)\n\n# Check the r^2 and error of the model fit\nprint('R-squared: \\n {:5.4f}'.format(fm.r_squared_))\nprint('Fit error: \\n {:5.4f}'.format(fm.error_))", "_____no_output_____" ] ], [ [ "Conclusion\n----------\n\nIn this tutorial, we have explored the basics of the :class:`~fooof.FOOOF` object,\nfitting power spectrum models, and extracting parameters.\n\nBefore we move on to controlling the fit procedure, and interpreting the results,\nin the next tutorial, we will first explore how this model is actually fit.\n\n\n", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

[ [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "## Discretisation\n\nDiscretisation is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that span the range of the variable's values. Discretisation is also called **binning**, where bin is an alternative name for interval.\n\n\n### Discretisation helps handle outliers and may improve value spread in skewed variables\n\nDiscretisation helps handle outliers by placing these values into the lower or higher intervals, together with the remaining inlier values of the distribution. Thus, these outlier observations no longer differ from the rest of the values at the tails of the distribution, as they are now all together in the same interval / bucket. In addition, by creating appropriate bins or intervals, discretisation can help spread the values of a skewed variable across a set of bins with equal number of observations.\n\n\n### Discretisation approaches\n\nThere are several approaches to transform continuous variables into discrete ones. Discretisation methods fall into 2 categories: **supervised and unsupervised**. Unsupervised methods do not use any information, other than the variable distribution, to create the contiguous bins in which the values will be placed. Supervised methods typically use target information in order to create the bins or intervals.\n\n\n#### Unsupervised discretisation methods\n\n- Equal width discretisation\n- Equal frequency discretisation\n- K-means discretisation\n\n#### Supervised discretisation methods\n\n- Discretisation using decision trees\n\n\nIn this lecture, I will describe **equal width discretisation**.\n\n\n## Equal width discretisation\n\nEqual width discretisation divides the scope of possible values into N bins of the same width.The width is determined by the range of values in the variable and the number of bins we wish to use to divide the variable:\n\nwidth = (max value - min value) / N\n\nwhere N is the number of bins or intervals.\n\nFor example if the values of the variable vary between 0 and 100, we create 5 bins like this: width = (100-0) / 5 = 20. The bins thus are 0-20, 20-40, 40-60, 80-100. The first and final bins (0-20 and 80-100) can be expanded to accommodate outliers (that is, values under 0 or greater than 100 would be placed in those bins as well).\n\nThere is no rule of thumb to define N, that is something to determine experimentally.\n\n## In this demo\n\nWe will learn how to perform equal width binning using the Titanic dataset with\n\n- pandas and NumPy\n- Feature-engine\n- Scikit-learn", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.preprocessing import KBinsDiscretizer\n\nfrom feature_engine.discretisers import EqualWidthDiscretiser", "_____no_output_____" ], [ "# load the numerical variables of the Titanic Dataset\n\ndata = pd.read_csv('../titanic.csv',\n usecols=['age', 'fare', 'survived'])\n\ndata.head()", "_____no_output_____" ], [ "# Let's separate into train and test set\n\nX_train, X_test, y_train, y_test = train_test_split(\n data[['age', 'fare']],\n data['survived'],\n test_size=0.3,\n random_state=0)\n\nX_train.shape, X_test.shape", "_____no_output_____" ] ], [ [ "The variables Age and fare contain missing data, that I will fill by extracting a random sample of the variable.", "_____no_output_____" ] ], [ [ "def impute_na(data, variable):\n\n df = data.copy()\n\n # random sampling\n df[variable + '_random'] = df[variable]\n\n # extract the random sample to fill the na\n random_sample = X_train[variable].dropna().sample(\n df[variable].isnull().sum(), random_state=0)\n\n # pandas needs to have the same index in order to merge datasets\n random_sample.index = df[df[variable].isnull()].index\n df.loc[df[variable].isnull(), variable + '_random'] = random_sample\n\n return df[variable + '_random']", "_____no_output_____" ], [ "# replace NA in both train and test sets\n\nX_train['age'] = impute_na(data, 'age')\nX_test['age'] = impute_na(data, 'age')\n\nX_train['fare'] = impute_na(data, 'fare')\nX_test['fare'] = impute_na(data, 'fare')", "_____no_output_____" ], [ "# let's explore the distribution of age\n\ndata[['age', 'fare']].hist(bins=30, figsize=(8,4))\nplt.show()", "_____no_output_____" ] ], [ [ "## Equal width discretisation with pandas and NumPy\n\nFirst we need to determine the intervals' edges or limits.", "_____no_output_____" ] ], [ [ "# let's capture the range of the variable age\n\nage_range = X_train['age'].max() - X_train['age'].min()\n\nage_range", "_____no_output_____" ], [ "# let's divide the range into 10 equal width bins\n\nage_range / 10", "_____no_output_____" ] ], [ [ "The range or width of our intervals will be 7 years.", "_____no_output_____" ] ], [ [ "# now let's capture the lower and upper boundaries\n\nmin_value = int(np.floor( X_train['age'].min()))\nmax_value = int(np.ceil( X_train['age'].max()))\n\n# let's round the bin width\ninter_value = int(np.round(age_range / 10))\n\nmin_value, max_value, inter_value", "_____no_output_____" ], [ "# let's capture the interval limits, so we can pass them to the pandas cut \n# function to generate the bins\n\nintervals = [i for i in range(min_value, max_value+inter_value, inter_value)]\n\nintervals", "_____no_output_____" ], [ "# let's make labels to label the different bins\n\nlabels = ['Bin_' + str(i) for i in range(1, len(intervals))]\n\nlabels", "_____no_output_____" ], [ "# create binned age / discretise age\n\n# create one column with labels\nX_train['Age_disc_labels'] = pd.cut(x=X_train['age'],\n bins=intervals,\n labels=labels,\n include_lowest=True)\n\n# and one with bin boundaries\nX_train['Age_disc'] = pd.cut(x=X_train['age'],\n bins=intervals,\n include_lowest=True)\n\nX_train.head(10)", "_____no_output_____" ] ], [ [ "We can see in the above output how by discretising using equal width, we placed each Age observation within one interval / bin. For example, age=13 was placed in the 7-14 interval, whereas age 30 was placed into the 28-35 interval.\n\nWhen performing equal width discretisation, we guarantee that the intervals are all of the same lenght, however there won't necessarily be the same number of observations in each of the intervals. See below:", "_____no_output_____" ] ], [ [ "X_train.groupby('Age_disc')['age'].count()", "_____no_output_____" ], [ "X_train.groupby('Age_disc')['age'].count().plot.bar()\nplt.xticks(rotation=45)\nplt.ylabel('Number of observations per bin')", "_____no_output_____" ] ], [ [ "The majority of people on the Titanic were between 14-42 years of age.\n\nNow, we can discretise Age in the test set, using the same interval boundaries that we calculated for the train set:", "_____no_output_____" ] ], [ [ "X_test['Age_disc_labels'] = pd.cut(x=X_test['age'],\n bins=intervals,\n labels=labels,\n include_lowest=True)\n\nX_test['Age_disc'] = pd.cut(x=X_test['age'],\n bins=intervals,\n include_lowest=True)\n\nX_test.head()", "_____no_output_____" ], [ "# if the distributions in train and test set are similar, we should expect similar propotion of\n# observations in the different intervals in the train and test set\n# let's see that below\n\nt1 = X_train.groupby(['Age_disc'])['age'].count() / len(X_train)\nt2 = X_test.groupby(['Age_disc'])['age'].count() / len(X_test)\n\ntmp = pd.concat([t1, t2], axis=1)\ntmp.columns = ['train', 'test']\ntmp.plot.bar()\nplt.xticks(rotation=45)\nplt.ylabel('Number of observations per bin')", "_____no_output_____" ] ], [ [ "## Equal width discretisation with Feature-Engine", "_____no_output_____" ] ], [ [ "# Let's separate into train and test set\n\nX_train, X_test, y_train, y_test = train_test_split(\n data[['age', 'fare']],\n data['survived'],\n test_size=0.3,\n random_state=0)\n\nX_train.shape, X_test.shape", "_____no_output_____" ], [ "# replace NA in both train and test sets\n\nX_train['age'] = impute_na(data, 'age')\nX_test['age'] = impute_na(data, 'age')\n\nX_train['fare'] = impute_na(data, 'fare')\nX_test['fare'] = impute_na(data, 'fare')", "_____no_output_____" ], [ "# with feature engine we can automate the process for many variables\n# in one line of code\n\ndisc = EqualWidthDiscretiser(bins=10, variables = ['age', 'fare'])\n\ndisc.fit(X_train)", "_____no_output_____" ], [ "# in the binner dict, we can see the limits of the intervals. For age\n# the value increases aproximately 7 years from one bin to the next.\n\n# for fare it increases in around 50 dollars from one interval to the \n# next, but it increases always the same value, aka, same width.\n\ndisc.binner_dict_", "_____no_output_____" ], [ "# transform train and text\n\ntrain_t = disc.transform(X_train)\ntest_t = disc.transform(X_test)", "_____no_output_____" ], [ "train_t.head()", "_____no_output_____" ], [ "t1 = train_t.groupby(['age'])['age'].count() / len(train_t)\nt2 = test_t.groupby(['age'])['age'].count() / len(test_t)\n\ntmp = pd.concat([t1, t2], axis=1)\ntmp.columns = ['train', 'test']\ntmp.plot.bar()\nplt.xticks(rotation=0)\nplt.ylabel('Number of observations per bin')", "_____no_output_____" ], [ "t1 = train_t.groupby(['fare'])['fare'].count() / len(train_t)\nt2 = test_t.groupby(['fare'])['fare'].count() / len(test_t)\n\ntmp = pd.concat([t1, t2], axis=1)\ntmp.columns = ['train', 'test']\ntmp.plot.bar()\nplt.xticks(rotation=0)\nplt.ylabel('Number of observations per bin')", "_____no_output_____" ] ], [ [ "We can see quite clearly, that equal width discretisation does not improve the value spread. The original variable Fare was skewed, and the discrete variable is also skewed.\n\n## Equal width discretisation with Scikit-learn", "_____no_output_____" ] ], [ [ "# Let's separate into train and test set\n\nX_train, X_test, y_train, y_test = train_test_split(\n data[['age', 'fare']],\n data['survived'],\n test_size=0.3,\n random_state=0)\n\nX_train.shape, X_test.shape", "_____no_output_____" ], [ "# replace NA in both train and test sets\n\nX_train['age'] = impute_na(data, 'age')\nX_test['age'] = impute_na(data, 'age')\n\nX_train['fare'] = impute_na(data, 'fare')\nX_test['fare'] = impute_na(data, 'fare')", "_____no_output_____" ], [ "disc = KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')\n\ndisc.fit(X_train[['age', 'fare']])", "_____no_output_____" ], [ "disc.bin_edges_", "_____no_output_____" ], [ "train_t = disc.transform(X_train[['age', 'fare']])\n\ntrain_t = pd.DataFrame(train_t, columns = ['age', 'fare'])\n\ntrain_t.head()", "_____no_output_____" ], [ "test_t = disc.transform(X_test[['age', 'fare']])\n\ntest_t = pd.DataFrame(test_t, columns = ['age', 'fare'])", "_____no_output_____" ], [ "t1 = train_t.groupby(['age'])['age'].count() / len(train_t)\nt2 = test_t.groupby(['age'])['age'].count() / len(test_t)\n\ntmp = pd.concat([t1, t2], axis=1)\ntmp.columns = ['train', 'test']\ntmp.plot.bar()\nplt.xticks(rotation=0)\nplt.ylabel('Number of observations per bin')", "_____no_output_____" ], [ "t1 = train_t.groupby(['fare'])['fare'].count() / len(train_t)\nt2 = test_t.groupby(['fare'])['fare'].count() / len(test_t)\n\ntmp = pd.concat([t1, t2], axis=1)\ntmp.columns = ['train', 'test']\ntmp.plot.bar()\nplt.xticks(rotation=0)\nplt.ylabel('Number of observations per bin')", "_____no_output_____" ] ] ]

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[ [ [ "# MCIS6273 Data Mining (Prof. Maull) / Fall 2021 / HW0\n\n**This assignment is worth up to 20 POINTS to your grade total if you complete it on time.**\n\n| Points <br/>Possible | Due Date | Time Commitment <br/>(estimated) |\n|:---------------:|:--------:|:---------------:|\n| 20 | Wednesday, Sep 1 @ Midnight | _up to_ 20 hours |\n\n\n* **GRADING:** Grading will be aligned with the completeness of the objectives.\n\n* **INDEPENDENT WORK:** Copying, cheating, plagiarism and academic dishonesty _are not tolerated_ by University or course policy. Please see the syllabus for the full departmental and University statement on the academic code of honor.\n\n## OBJECTIVES\n* Familiarize yourself with the JupyterLab environment, Markdown and Python\n\n* Familiarize yourself with Github and basic git\n\n* Explore JupyterHub Linux console integrating what you learned in the prior parts of this homework\n\n* Listen to the Talk Python['Podcast'] from June 25, 2021: A Path to Data Science Interview with Sanyam Bhutani\n\n* Explore Python for data munging and analysis, with an introduction to CSV and Pandas\n\n## WHAT TO TURN IN\nYou are being encouraged to turn the assignment in using the provided\nJupyter Notebook. To do so, make a directory in your Lab environment called\n`homework/hw0`. Put all of your files in that directory. Then zip that directory,\nrename it with your name as the first part of the filename (e.g. `maull_hw0_files.zip`), then\ndownload it to your local machine, then upload the `.zip` to Blackboard.\n\nIf you do not know how to do this, please ask, or visit one of the many tutorials out there\non the basics of using zip in Linux.\n\nIf you choose not to use the provided notebook, you will still need to turn in a\n`.ipynb` Jupyter Notebook and corresponding files according to the instructions in\nthis homework.\n\n\n## ASSIGNMENT TASKS\n### (0%) Familiarize yourself with the JupyterLab environment, Markdown and Python \n\nAs stated in the course announcement [Jupyter (https://jupyter.org)](https://jupyter.org) is the\ncore platform we will be using in this course and\nis a popular platform for data scientists around the world. We have a JupyterLab\nsetup for this course so that we can operate in a cloud-hosted environment, free from\nsome of the resource constraints of running Jupyter on your local machine (though you are free to set\nit up on your own and seek my advice if you desire).\n\nYou have been given the information about the Jupyter environment we have setup for our course, and\nthe underlying Python environment will be using is the [Anaconda (https://anaconda.com)](https://anaconda.com)\ndistribution. It is not necessary for this assignment, but you are free to look at the multitude\nof packages installed with Anaconda, though we will not use the majority of them explicitly.\n\nAs you will soon find out, Notebooks are an incredibly effective way to mix code with narrative\nand you can create cells that are entirely code or entirely Markdown. Markdown (MD or `md`) is\na highly readable text format that allows for easy documentation of text files, while allowing\nfor HTML-based rendering of the text in a way that is style-independent.\n\nWe will be using Markdown frequently in this course, and you will learn that there are many different\n\"flavors\" or Markdown. We will only be using the basic flavor, but you will benefit from exploring\nthe \"Github flavored\" Markdown, though you will not be responsible for using it in this course -- only the\n\"basic\" flavor. Please refer to the original course announcement about Markdown.\n\n&#167; **THERE IS NOTHING TO TURN IN FOR THIS PART.** Play with and become familiar with the basic functions of\nthe Lab environment given to you online in the course Blackboard.\n\n\n&#167; **PLEASE _CREATE A MARKDOWN DOCUMENT_ CALLED `semester_goals.md` WITH 3 SENTENCES/FRAGMENTS THAT\nANSWER THE FOLLOWING QUESTION:**\n\n* **What do you wish to accomplish this semester in Data Mining?**\n\nRead the documentation for basic Markdown [here](https://www.markdownguide.org/basic-syntax). \nTurn in the text `.md` file *not* the processed `.html`. In whatever you turn in, \nyou must show the use of *ALL* the following:\n\n* headings (one level is fine),\n* bullets,\n* bold and italics\n\nAgain, the content of your document needs to address the question above and it should live\nin the top level directory of your assignment submission. This part will be graded but no\npoints are awarded for your answer.\n\n\n\n### (0%) Familiarize yourself with Github and basic git \n\n[Github (https://github.com)](https://github.com) is the _de facto_ platform for open source software in the world based\non the very popular [git (https://git-scm.org)](https://git-scm.org) version control system. Git has a sophisticated set\nof tools for version control based on the concept of local repositories for fast commits and remote\nrepositories only when collaboration and remote synchronization is necessary. Github enhances git by providing\ntools and online hosting of public and private repositories to encourage and promote sharing and collaboration.\nGithub hosts some of the world's most widely used open source software.\n\n**If you are already familiar with git and Github, then this part will be very easy!**\n\n&#167; **CREATE A PUBLIC GITHUB REPO NAMED `\"mcis6273-F21-datamining\"` AND PLACE A README.MD FILE IN IT.**\nCreate your first file called\n`README.md` at the top level of the repository. You can put whatever text you like in the file \n(If you like, use something like [lorem ipsum](https://lipsum.com/)\nto generate random sentences to place in the file.).\nPlease include the link to **your** Github repository that now includes the minimal `README.md`. \nYou don't have to have anything elaborate in that file or the repo. \n\n\n\n### (0%) Explore JupyterHub Linux console integrating what you learned in the prior parts of this homework \n\nThe Linux console in JupyterLab is a great way to perform command-line tasks and is an essential tool\nfor basic scripting that is part of a data scientist's toolkit. Open a console in the lab environment\nand familiarize yourself with your files and basic commands using git as indicated below.\n\n1. In a new JupyterLab command line console, run the `git clone` command to clone the new\n repository you created in the prior part.\n You will want to read the documentation on this \n command (try here [https://www.git-scm.com/docs/git-clone](https://www.git-scm.com/docs/git-clone) to get a good\n start).\n2. Within the same console, modify your `README.md` file, check it in and push it back to your repository, using\n `git push`. Read the [documentation about `git push`](https://git-scm.com/docs/git-push).\n3. The commands `wget` and `curl` are useful for grabbing data and files from remote resources off the web.\n Read the documentation on each of these commands by typing `man wget` or `man curl` in the terminal.\n Make sure you pipe the output to a file or use the proper flags to do so.\n\n&#167; **THERE IS NOTHING TO TURN IN FOR THIS PART.**\n\n\n\n### (30%) Listen to the Talk Python['Podcast'] from June 25, 2021: A Path to Data Science Interview with Sanyam Bhutani \n\nData science is one of the most important and \"hot\" disciplines today\nand there is a lot going on from data engineering to modeling and\nanalysis.\n\nBhutani is one of the top [Kaggle]() leaders and in this interview\nshares his experience from computer science to data science, \ndocumenting some of the lessons he learned along the way.\n\nPlease listen to this one hour podcast and answer some of the questions below.\nYou can listen to it from one of the two links below:\n\n* [Talk Python['Podcast'] landing page](https://talkpython.fm/episodes/transcript/322/a-path-into-data-science)\n* [direct link to mp3 file](https://downloads.talkpython.fm/podcasts/talkpython/322-starting-in-data-sci.mp3)\n\n&#167; **PLEASE ANSWER THE FOLLOWING QUESTIONS AFTER LISTENING TO THE PODCAST:**\n\n1. List 3 things that you learned from this podcast?\n\n2. What is your reaction to the podcast? Pick at least one point Sanyam brought up in the interview that you agree with and list your reason why.\n\n3. After listening to the podcast, do you think you are more interested or less interested in a career in Data Science?\n\n\n\n### (70%) Explore Python for data munging and analysis, with an introduction to CSV and Pandas \n\n\nPython's strengths shine when tasked with data munging and analysis. As we will learn throughout\nthe course, there are a number of excellent data sources for open data of all kinds now\navailable for the public. These open data sources are heralding the new era of transparency\nfrom all levels from small municipal data to big government data, from transportation, to science,\nto education.\n\nTo warm up to such datasets, we will be working with an interesting\ndataset from the US Fish and Wildlife Service (FWS). This is a \nwater quality data set taken from a managed national refuge in\nVirginia called Back Bay National Wildlife Refuge, which was \nestablished in 1938. As a function of being managed by the FWS, \nwater quality samples are taken regularly from the marshes within \nthe refuge.\n\nYou can (and should) learn a little more about Back Bay from\nthis link, since it has an interesting history, features and wildlife.\n\n* [https://www.fws.gov/refuge/Back_Bay/about.html](https://www.fws.gov/refuge/Back_Bay/about.html)\n\n\nThe data we will be looking at can be found as a direct download \nfrom data.gov, the US data repository where many datasets from\na variety of sources can be found -- mostly related to the \nmultitude of US government agencies.\n\nThe dataset is a small water quality dataset with several decades\nof water quality data from Back Bay. We will be warming up\nto this dataset with a basic investigation into the shape, content\nand context of the data contained therein.\n\n\nIn this part of the assignment, we will make use of Python libraries to pull the data from the\nendpoint and use [Pandas](https://pandas.pydata.org) to plot the data. The raw CSV data is\nreadily imported into Pandas from the following URL:\n\n* [FWS Water Quality Data 12/20/2020](https://catalog.data.gov/dataset/water-quality-data/resource/f4d736fd-ade9-4e3f-b8e0-ae7fd98b2f87)\n\nPlease take a look at the page, on it you will notice a link \nto the raw CSV file:\n\n* [https://ecos.fws.gov/ServCat/DownloadFile/173741?Reference=117348](https://ecos.fws.gov/ServCat/DownloadFile/173741?Reference=117348)\n\nWe are going to explore this dataset to learn a bit more about the \nwater quality characteristics of Bay Bay over the past couple decades\nor so.\n\n&#167; **WRITE THE CODE IN YOUR NOTEBOOK TO LOAD AND RESHAPE THE COMPLETE CSV WATER QUALITY DATASET**:\n\nYou will need to perform the following steps:\n\n1. **use [`pandas.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) method to load the dataset** into a Pandas DataFrame;\n2. **clean the data so that the range of years is restricted to the 20 year period from 1999 to 2018**\n5. **store the entire dataset back into a new CSV** file called `back_bay_1998-2018_clean.csv`.\n\n**HINTS:** _Here are some a code hints you might like to study and use to craft a solution:_\n\n* study [`pandas.DataFrame.query()]`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html?highlight=query#pandas.DataFrame.query) to learn how to filter and query year ranges\n* study [`pandas.DataFrame.groupby()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html?highlight=groupby#pandas.DataFrame.groupby) to understand how to group data\n\n\n&#167; **USE PANDAS TO LOAD THE CSV DATA TO A DATAFRAME AND ANSWER THE FOLLOWING QUESTIONS:**\n\n1. How many and what are the names of the columns in this dataset?\n2. What is the mean `Dissolved Oxygen (mg/L)` over the entire dataset?\n3. Which year were the highest number of `AirTemp (C)` data points collected?\n4. Which year were the least number of `AirTemp (C)` data points collected?\n\n\nTo answer these questions, you'll need to dive further into Pandas, which is\nthe standard tool in the Python data science stack for loading, manipulating,\ntransforming, analyzing and preparing data as input to other tools such as\n[Numpy (http://www.numpy.org/)](http://www.numpy.org/), \n[SciKitLearn (http://scikit-learn.org/stable/index.html)](http://scikit-learn.org/stable/index.html), \n[NLTK (http://www.nltk.org/)](http://www.nltk.org/) and others.\n\nFor this assignment, you will only need to learn how to load and select data using Pandas.\n\n* **LOADING DATA**\nThe core data structure in Pandas is the `DataFrame`. You will need to visit\nthe Pandas documentation [(https://pandas.pydata.org/pandas-docs/stable/reference/)](https://pandas.pydata.org/pandas-docs/stable/reference/)\nto learn more about the library, but to help you along with a hint, read the\ndocumentation on the [`pandas.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) method.\n\n* **SELECTING DATA**\nThe [tutorial here on indexing and selecting](http://pandas.pydata.org/pandas-docs/stable/indexing.html)\nshould be of great use in understanding how to index and select subsets of\nthe data to answer the questions.\n\n* **GROUPING DATA** You may use [`DataFrame.value_counts()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.value_counts.html?highlight=value_counts#pandas.DataFrame.value_counts) or [`DataFrame.groupby()`](https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html) to group\nthe data you need for these questons. You will also find [`DataFrame.groupby()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html?highlight=groupby#pandas.DataFrame.groupby) and [`DataFrame.describe()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html?highlight=describe#pandas.DataFrame.describe) very useful.\n\n**CODE HINTS**\n\nHere is example code that should give you clues about the structure\nof your code for this part.\n\n```python\n import pandas as pd\n\n df = pd.read_csv('your_json_file.csv')\n\n # code for question 1 ... and so on\n```\n\n\n&#167; **EXPLORING WATER SALINITY IN THE DATA**\n\nThe Back Bay refuge is on the eastern coast of Virginia and to\nthe east is the Atlantic Ocean. Salinity is a measure of\nthe salt concentration of water, and you can learn a little more\nabout salinity in water [here](https://www.usgs.gov/special-topic/water-science-school/science/saline-water-and-salinity?qt-science_center_objects=0#qt-science_center_objects).\n\nYou will notice that there is a `Site_Id` variable in the data, which \nwe will find refers to the five sampling locations (see the [documentation here](https://ecos.fws.gov/ServCat/Reference/Profile/117348))\nof (1) the Bay, (2) D-Pool (fishing pond), (3) C-Pool, (4) B-Pool and (5) A-Pool.\n\nThe ppt in Salinity is the percent salinity, and so 1 ppt is equivalent to 10000 ppm salinity. Use this information to answer \nthe following questions.\n\n1. Which sampling location has the highest mean ppt? What is the equivalent ppm?\n2. When looking at the mean ppt, which location would you infer is furthest from the influence of ocean water inflows? \n (Assume that higher salinity correlates to closer proximity to the ocean.)\n3. Dig a little deeper into #2, and write why there may be some uncertainty in your answer? (hint: certainty is improved by consistency in data)\n4. Use the data to determine the correlation between `Salinity (ppt)` and `pH (standard units)`. Use the [DataFrame.corr()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.corr.html?highlight=correlate). You just need to report the correlation value. \n\n\n\n", "_____no_output_____" ] ] ]

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[ [ "markdown" ] ]

[ "CC-BY-4.0" ]

[ "CC-BY-4.0" ]

[ "CC-BY-4.0" ]

[ [ [ "## Obligatory imports", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn\nimport matplotlib\n%matplotlib inline\nmatplotlib.rcParams['figure.figsize'] = (12,8)\nmatplotlib.rcParams['font.size']=20\nmatplotlib.rcParams['lines.linewidth']=4\nmatplotlib.rcParams['xtick.major.size'] = 10\nmatplotlib.rcParams['ytick.major.size'] = 10\nmatplotlib.rcParams['xtick.major.width'] = 2\nmatplotlib.rcParams['ytick.major.width'] = 2", "_____no_output_____" ] ], [ [ "# We use the MNIST Dataset again", "_____no_output_____" ] ], [ [ "import IPython\nurl = 'http://yann.lecun.com/exdb/mnist/'\niframe = '<iframe src=' + url + ' width=80% height=400px></iframe>'\nIPython.display.HTML(iframe)", "_____no_output_____" ] ], [ [ "## Fetch the data", "_____no_output_____" ] ], [ [ "from sklearn.datasets import fetch_mldata", "_____no_output_____" ], [ "mnist = fetch_mldata('MNIST original', data_home='../day4/data/')", "_____no_output_____" ], [ "allimages = mnist.data\nallimages.shape", "_____no_output_____" ], [ "all_image_labels = mnist.target\nset(all_image_labels)", "_____no_output_____" ] ], [ [ "## check out the data", "_____no_output_____" ] ], [ [ "digit1 = mnist.data[0,:].reshape(28,-1) # arr.reshape(4, -1) is equivalent to arr.reshape(4, 7), is arr has size 28", "_____no_output_____" ], [ "fig, ax = plt.subplots(figsize=(1.5, 1.5))\nax.imshow(digit1, vmin=0, vmax=1)", "_____no_output_____" ] ], [ [ "# Theoretical background\n**Warning: math ahead**\n<img src=\"images/logreg_schematics.svg\" alt=\"logreg-schematics\" style=\"width: 50%;\"/>", "_____no_output_____" ], [ "## Taking logistic regression a step further: neural networks\n<img src=\"images/mlp_schematics.svg\" alt=\"nn-schematics\" style=\"width: 50%;\"/>", "_____no_output_____" ], [ "### How (artificial) neural networks predict a label from features?\n* The *input layer* has **dimention = number of features.**\n* For each training example, each feature value is \"fed\" into the input layer. \n* Each \"neuron\" in the hidden layer receives a weighted sum of the features: the weight is initialized to a random value in the beginning, and the network \"learns\" from the datasetsand tunes these weights. Each hidden neuron, based on its input, and an \"activation function\", e.g.: the logistic function\n\n* The output is again, a weighted sum of the values at each hidden neuron. \n* There can be *more than one hidden layer*, in which case the output of the first hidden layer becomes the input of the second hidden layer. \n\n### Regularization\nLike Logistic regression and SVM, neural networks also can be improved with regularization.\nFot scikit-learn, the relevant tunable parameter is `alpha` (as opposed to `gamma` for LR and SVM). \nFurthermore, it has default value 0.0001, unlike gamma, for which it is 1. ", "_____no_output_____" ], [ "### Separate the data into training data and test data", "_____no_output_____" ] ], [ [ "len(allimages)", "_____no_output_____" ] ], [ [ "### Sample the data, 70000 is too many images to handle on a single PC", "_____no_output_____" ] ], [ [ "len(allimages)", "_____no_output_____" ], [ "size_desired_dataset = 2000", "_____no_output_____" ], [ "sample_idx = np.random.choice(len(allimages), size_desired_dataset)\nimages = allimages[sample_idx, :]\nimage_labels = all_image_labels[sample_idx]", "_____no_output_____" ], [ "set(image_labels)", "_____no_output_____" ], [ "image_labels.shape", "_____no_output_____" ] ], [ [ "### Partition into training and test set *randomly*", "_____no_output_____" ], [ "**As a rule of thumb, 80/20 split between training/test dataset is often recommended.**\nSee below for cross validation and how that changes this thumbrule.\n", "_____no_output_____" ] ], [ [ "from scipy.stats import itemfreq", "_____no_output_____" ], [ "from sklearn.model_selection import train_test_split", "_____no_output_____" ], [ "training_data, test_data, training_labels, test_labels = train_test_split(images, image_labels, train_size=0.8)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n FutureWarning)\n" ] ], [ [ "** Importance of normalization** \nIf Feature A is in the range [0,1] and Feature B is in [10000,50000], SVM (in fact, most of the classifiers) will suffer inaccuracy.\nThe solution is to *normalize* (AKA \"feature scaling\") each feature to the same interval e.g. [0,1] or [-1, 1].\n\n**scipy provides a standard function for this:**", "_____no_output_____" ] ], [ [ "from sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\n# Fit only to the training data: IMPORTANT\nscaler.fit(training_data)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "from sklearn.neural_network import MLPClassifier", "_____no_output_____" ], [ "clf = MLPClassifier(hidden_layer_sizes=(50,), max_iter = 5000)\nclf.fit(scaler.transform(training_data), training_labels)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "clf.score(scaler.transform(training_data), training_labels), clf.score(scaler.transform(test_data), test_labels)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ] ], [ [ "### Visualize the hidden layer:", "_____no_output_____" ] ], [ [ "# source: \n# \n#http://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html\nfig, axes = plt.subplots(4, 4, figsize=(15,15))\n# use global min / max to ensure all weights are shown on the same scale\nvmin, vmax = clf.coefs_[0].min(), clf.coefs_[0].max()\nfor coef, ax in zip(clf.coefs_[0].T, axes.ravel()):\n ax.matshow(coef.reshape(28, 28), cmap=plt.cm.gray, vmin=.5 * vmin,\n vmax=.5 * vmax)\n ax.set_xticks(())\n ax.set_yticks(())\n\nplt.show()", "_____no_output_____" ] ], [ [ "Not bad, but is it better than Logistic regression? Check out with Learning curves:", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import learning_curve\nimport pandas as pd", "_____no_output_____" ], [ "curve = learning_curve(clf, scaler.transform(images), image_labels)\ntrain_sizes, train_scores, test_scores = curve\n\ntrain_scores = pd.DataFrame(train_scores)\ntrain_scores.loc[:,'train_size'] = train_sizes\ntest_scores = pd.DataFrame(test_scores)\ntest_scores.loc[:,'train_size'] = train_sizes\n\ntrain_scores = pd.melt(train_scores, id_vars=['train_size'], value_name = 'CrossVal score')\ntest_scores = pd.melt(test_scores, id_vars=['train_size'], value_name = 'CrossVal score')", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "matplotlib.rcParams['figure.figsize'] = (12,8)\nsns.tsplot(train_scores, time = 'train_size', unit='variable', value = 'CrossVal score')\nsns.tsplot(test_scores, time = 'train_size', unit='variable', value = 'CrossVal score', color='g')\nplt.ylim(0,1.1)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/seaborn/timeseries.py:183: UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.\n warnings.warn(msg, UserWarning)\n" ] ], [ [ "Not really, we can try to improve it with parameter space search.", "_____no_output_____" ], [ "## Parameter space search with `GridSearchCV`", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import GridSearchCV", "_____no_output_____" ], [ "clr = MLPClassifier()", "_____no_output_____" ], [ "clf = GridSearchCV(clr, {'alpha':np.logspace(-8, -1, 2)})", "_____no_output_____" ], [ "clf.fit(scaler.transform(images), image_labels)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "clf.best_params_", "_____no_output_____" ], [ "clf.best_score_", "_____no_output_____" ], [ "nn_tuned = clf.best_estimator_\nnn_tuned.fit(scaler.transform(training_data), training_labels)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "curve = learning_curve(nn_tuned, scaler.transform(images), image_labels)\ntrain_sizes, train_scores, test_scores = curve\n\ntrain_scores = pd.DataFrame(train_scores)\ntrain_scores.loc[:,'train_size'] = train_sizes\ntest_scores = pd.DataFrame(test_scores)\ntest_scores.loc[:,'train_size'] = train_sizes\n\ntrain_scores = pd.melt(train_scores, id_vars=['train_size'], value_name = 'CrossVal score')\ntest_scores = pd.melt(test_scores, id_vars=['train_size'], value_name = 'CrossVal score')", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n % self.max_iter, ConvergenceWarning)\n/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n % self.max_iter, ConvergenceWarning)\n/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n % self.max_iter, ConvergenceWarning)\n/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n % self.max_iter, ConvergenceWarning)\n/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n % self.max_iter, ConvergenceWarning)\n/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n % self.max_iter, ConvergenceWarning)\n" ], [ "matplotlib.rcParams['figure.figsize'] = (12,8)\nsns.tsplot(train_scores, time = 'train_size', unit='variable', value = 'CrossVal score')\nsns.tsplot(test_scores, time = 'train_size', unit='variable', value = 'CrossVal score', color='g')\nplt.ylim(0,1.1)\n\nplt.legend()", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/seaborn/timeseries.py:183: UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.\n warnings.warn(msg, UserWarning)\nNo handles with labels found to put in legend.\n" ] ], [ [ "The increase in accuracy is miniscule.", "_____no_output_____" ], [ "## Multi layered NN's", "_____no_output_____" ] ], [ [ "from sklearn.preprocessing import StandardScaler \nscaler = StandardScaler() \nscaler.fit(images)\n\nimages_normed = scaler.transform(images)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "clr = MLPClassifier(hidden_layer_sizes=(25,25))\nclf = GridSearchCV(clr, {'alpha':np.logspace(-80, -1, 3)})\nclf.fit(images_normed, image_labels)\nclf.best_score_", "_____no_output_____" ], [ "clf.best_params_", "_____no_output_____" ], [ "nn_tuned = clf.best_estimator_\nnn_tuned.fit(scaler.transform(training_data), training_labels)", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n warnings.warn(msg, DataConversionWarning)\n" ], [ "curve = learning_curve(nn_tuned, images_normed, image_labels)\ntrain_sizes, train_scores, test_scores = curve\n\ntrain_scores = pd.DataFrame(train_scores)\ntrain_scores.loc[:,'train_size'] = train_sizes\ntest_scores = pd.DataFrame(test_scores)\ntest_scores.loc[:,'train_size'] = train_sizes\n\ntrain_scores = pd.melt(train_scores, id_vars=['train_size'], value_name = 'CrossVal score')\ntest_scores = pd.melt(test_scores, id_vars=['train_size'], value_name = 'CrossVal score')", "_____no_output_____" ], [ "matplotlib.rcParams['figure.figsize'] = (12, 8)\nsns.tsplot(train_scores, time = 'train_size', unit='variable', value = 'CrossVal score')\nsns.tsplot(test_scores, time = 'train_size', unit='variable', value = 'CrossVal score', color='g')\nplt.ylim(0,1.1)\n\nplt.legend()", "/home/dmanik/venvs/teaching/lib/python3.5/site-packages/seaborn/timeseries.py:183: UserWarning: The tsplot function is deprecated and will be removed or replaced (in a substantially altered version) in a future release.\n warnings.warn(msg, UserWarning)\nNo handles with labels found to put in legend.\n" ] ], [ [ "Hmm... multi-hidden layer NN's seem to be much harder to tune.\nMaybe we need to try with wider range of parameters for Gridsearch?", "_____no_output_____" ], [ "Finding optimum parameters for advanced classifiers is not always so straightforward, and quite often the most time consuming part. This so-called **Hyperparameter optimization** is a topic in itself, and has numerous approaches and libraries. \n\n* [http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html](http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html)\n* [Practical Bayesian Optimization of Machine Learning Algorithms](https://dash.harvard.edu/handle/1/11708816)", "_____no_output_____" ], [ "**sklearn's neural network functionality is rather limited.** More advanced toolboxes for neural networks:\n* [keras](https://keras.io/)\n* [tensorflow](https://www.tensorflow.org/)\n* [Theano](http://deeplearning.net/software/theano/)", "_____no_output_____" ], [ "# Exercise\n\n## iris dataset\nTrain a neural network on the `iris` dataset and run cross validation. Do not forget to normalize the featurs. \nCompare the results against LogisticRegression.\n\nUse Grid search to tune the NN further.", "_____no_output_____" ], [ "## Further reading\n* http://www.ritchieng.com/applying-machine-learning/\n ", "_____no_output_____" ] ] ]

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[ [ [ "#Import Libraries\nfrom sklearn.model_selection import train_test_split\n#----------------------------------------------------\n\n#Splitting data\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=44, shuffle =True)\n\n#Splitted Data\nprint('X_train shape is ' , X_train.shape)\nprint('X_test shape is ' , X_test.shape)\nprint('y_train shape is ' , y_train.shape)\nprint('y_test shape is ' , y_test.shape)", "_____no_output_____" ], [ "import numpy as np\nfrom sklearn.model_selection import train_test_split\n\nX, y = np.arange(10).reshape((5, 2)), range(5)\n\nX_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state=40, shuffle = True)\n\n\nprint(X_train)\nprint('<<<<<<>>>>>>>')\nprint(y_train)\nprint('<<<<<<>>>>>>>')\nprint(X_test)\nprint('<<<<<<>>>>>>>')\nprint(y_test)", "[[0 1]\n [2 3]\n [8 9]\n [6 7]]\n<<<<<<>>>>>>>\n[0, 1, 4, 3]\n<<<<<<>>>>>>>\n[[4 5]]\n<<<<<<>>>>>>>\n[2]\n" ] ] ]

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[ [ [ "<center> <h1>Numerical Methods -- Assignment 5</h1> </center>", "_____no_output_____" ], [ "## Problem1 -- Energy density", "_____no_output_____" ], [ "The matter and radiation density of the universe at redshift $z$ is\n$$\\Omega_m(z) = \\Omega_{m,0}(1+z)^3$$\n$$\\Omega_r(z) = \\Omega_{r,0}(1+z)^4$$\nwhere $\\Omega_{m,0}=0.315$ and $\\Omega_r = 9.28656 \\times 10^{-5}$", "_____no_output_____" ], [ "### (a) Plot", "_____no_output_____" ] ], [ [ "%config InlineBackend.figure_format = 'retina' \nimport matplotlib.pyplot as plt\nimport numpy as np\n\nz = np.linspace(-1000,4000,10000)\nO_m0 = 0.315\nO_r0 = 9.28656e-5\nO_m = O_m0*np.power(z+1,3)\nO_r = O_r0*np.power(z+1,4)\n#define where the roots are\nx1 = -1; x2 = O_m0/O_r0\ny1 = O_m0*np.power(x1+1,3)\ny2 = O_m0*np.power(x2+1,3)\nx = np.array([x1,x2])\ny = np.array([y1,y2])\n#plot the results\nplt.figure(figsize=(8,8))\nplt.plot(z,O_m,'-',label=\"matter density\")\nplt.plot(z,O_r,'-',label=\"radiation density\")\nplt.plot(x,y,'h',label=r\"$z_{eq}$\")\nplt.xlabel(\"redshift(z)\")\nplt.ylabel(\"energy density\")\nplt.legend()\nplt.show()", "_____no_output_____" ] ], [ [ "### (b) Analytical solution", "_____no_output_____" ], [ "An analytical solution can be found by equating the two equations. Since $z$ denotes for the redshift and it has a physical meaning, so it must take a real value for it to have a meaning. Thus\n\\begin{align*}\n\\Omega_m(z) &= \\Omega_r(z)\\\\\n\\Omega_{m,0}(1+z)^3 &= \\Omega_{r,0}(1+z)^4\\\\\n(1+z)^3(0.315-9.28656 \\times 10^{-5} z)&=0\\\\\n(1+z)^3 &= 0\\\\\nor \\ (0.315-9.28656 \\times 10^{-5} (z+1))&=0\\\\\n\\end{align*}\n$z_1 = -1$ or $z_2 = 3391.0$", "_____no_output_____" ], [ "### (c) Bisection method", "_____no_output_____" ], [ "The bisection method in mathematics is a root-finding method that repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing. It is a very simple and robust method, but it is also relatively slow. scipy.optimize.bisect calculates the roots for a given function, but for it to work $f(a)$ and $f(b)$ must take different signs (so that there exists a root $\\in [a,b]$).", "_____no_output_____" ] ], [ [ "from scipy.optimize import bisect\n\ndef f(z):\n O_m0 = 0.315\n O_r0 = 9.28656e-5\n O_m = O_m0*np.power(z+1,3)\n O_r = O_r0*np.power(z+1,4)\n return O_m -O_r\nz1 = bisect(f,-1000,0,xtol=1e-10)\nz2 = bisect(f,0,4000,xtol=1e-10)\nprint \"The roots are found to be:\",z1,z2", "The roots are found to be: -1.00000000003 3390.9987595\n" ] ], [ [ "### (d) Secant method", "_____no_output_____" ], [ "The $\\textit{secant method}$ uses secant lines to find the root. A secant line is a straight line that intersects two points of a curve. In the secant method, a line is drawn between two points on the continuous function such that it extends and intersects the $x$ axis. A secant line $y$ is drawn from $f(b)$ to $f(a)$ and intersects at point $c$ on the $x$ axis such that\n$$y = \\frac{f(b)-f(a)}{b-a}(c-b)+f(b)$$\nThe solution is therefore\n$$c = b-f(b)\\frac{b-a}{f(b)-f(a)}$$", "_____no_output_____" ] ], [ [ "\ndef secant(f, x0, x1, eps):\n f_x0 = f(x0)\n f_x1 = f(x1)\n iteration_counter = 0\n while abs(f_x1) > eps and iteration_counter < 100:\n try:\n denominator = float(f_x1 - f_x0)/(x1 - x0)\n x = x1 - float(f_x1)/denominator\n except ZeroDivisionError:\n print \"Error! - denominator zero for x = \", x\n sys.exit(1) # Abort with error\n x0 = x1\n x1 = x\n f_x0 = f_x1\n f_x1 = f(x1)\n iteration_counter += 1\n # Here, either a solution is found, or too many iterations\n if abs(f_x1) > eps:\n iteration_counter = -1\n return x, iteration_counter\n#find the roots in the nearby region, with an accuracy of 1e-10\nz1 = secant(f,-10,-0.5,1e-10)[0]\nz2 = secant(f,3000,4000,1e-10)[0]\n\nprint \"The roots are found to be:\",z1,z2\n", "The roots are found to be: -0.999466618551 3390.9987595\n" ] ], [ [ "### (e) Newton-Raphson method", "_____no_output_____" ], [ "In numerical methods, $\\textit{Newton-Raphson method}$ is a method for finding successively better approximations to the roots of a real-valued function. The algorithm is as follows:\n* Starting with a function $f$ defined over the real number $x$, the function's derivative $f'$, and an initial guess $x_0$ for a root of the fucntion $f$, then a better approximation $x_1$ is:\n$$x_1 = x_0 -\\frac{f(x_0)}{f'(x_0)}$$\n* The process is then repeated as\n$$x_{n+1} = x_n-\\frac{f(x_n)}{f'(x_n)}$$\nuntil a sufficiently satisfactory value is reached.", "_____no_output_____" ] ], [ [ "def fprime(z):\n O_m0 = 0.315\n O_r0 = 9.28656e-5\n O_m = O_m0*np.power(z+1,2)\n O_r = O_r0*np.power(z+1,3)\n return 3*O_m -4*O_r\n\ndef Newton(f, dfdx, x, eps):\n f_value = f(x)\n iteration_counter = 0\n while abs(f_value) > eps and iteration_counter < 100:\n try:\n x = x - float(f_value)/dfdx(x)\n except ZeroDivisionError:\n print \"Error! - derivative zero for x = \", x\n sys.exit(1) # Abort with error\n\n f_value = f(x)\n iteration_counter += 1\n\n # Here, either a solution is found, or too many iterations\n if abs(f_value) > eps:\n iteration_counter = -1\n return x, iteration_counter\n\nz1 = Newton(f,fprime,0,1e-10)[0]\nz2 = Newton(f,fprime,3000,1e-10)[0]\nprint \"The roots are found to be:\",z1,z2\n ", "The roots are found to be: -0.9993234602 3390.9987595\n" ] ], [ [ "Now, change the initial guess far from the values obtained from (b). And test how the three algorithms perform respectively.", "_____no_output_____" ] ], [ [ "#test how the bisection method perform\nimport time\nstart1 = time.time()\nz1 = bisect(f,-1000,1000,xtol=1e-10)\nend1 = time.time()\nstart2 = time.time()\nz2 = bisect(f,3000,10000,xtol=1e-10)\nend2 = time.time()\nerr1 = abs((z1-(-1))/(-1))\nerr2 = abs((z2-(O_m0/O_r0-1))/(O_m0/O_r0-1))\nprint \"The roots are found to be:\",z1,z2\nprint \"With a deviation of:\",err1,err2\nprint \"Time used are:\",end1-start1,end2-start2", "The roots are found to be: -1.00000000003 3390.9987595\nWith a deviation of: 3.31965566147e-11 1.11306528845e-14\nTime used are: 0.00043797492981 0.000349998474121\n" ], [ "#test how the secant method perform\nstart1 = time.time()\nz1 = secant(f,-1000,1000,1e-10)[0]\nend1 = time.time()\nstart2 = time.time()\nz2 = secant(f,3000,10000,1e-10)[0]\nend2 = time.time()\nerr1 = abs((z1-(-1))/(-1))\nerr2 = abs((z2-(O_m0/O_r0-1))/(O_m0/O_r0-1))\nprint \"The roots are found to be:\",z1,z2\nprint \"With a deviation of:\",err1,err2\nprint \"Time used are:\",end1-start1,end2-start2\nprint \"Roots found after\",secant(f,-10,-0.5,1e-10)[1],\"and\",secant(f,3000,4000,1e-10)[1],\"loops\"\n", "The roots are found to be: -0.999414972528 3390.9987595\nWith a deviation of: 0.000585027471743 0.0\nTime used are: 0.000823020935059 0.000194072723389\nRoots found after 25 and 8 loops\n" ], [ "#test how the newton-Raphson method perform\nstart1 = time.time()\nz1 = Newton(f,fprime,-1000,1e-10)[0]\nend1 = time.time()\nstart2 = time.time()\nz2 = Newton(f,fprime,10000,1e-10)[0]\nend2 = time.time()\nerr1 = abs((z1-(-1))/(-1))\nerr2 = abs((z2-(O_m0/O_r0-1))/(O_m0/O_r0-1))\nprint \"The roots are found to be:\",z1,z2\nprint \"With a deviation of:\",err1,err2\nprint \"Time used are:\",end1-start1,end2-start2\nprint \"Roots found after\",Newton(f,fprime,0,1e-10)[1],\"and\",Newton(f,fprime,3000,1e-10)[1],\"loops\"", "The roots are found to be: -1.00051824126 3390.9987595\nWith a deviation of: 0.000518241260632 0.0\nTime used are: 0.000991821289062 0.000278949737549\nRoots found after 18 and 7 loops\n" ] ], [ [ "It is not difficult to find out that tested with the function given, bisection method is the fastest and the most reliable method in finding the first root; however, in determining the second root, both the secant method and Newton's method showed better performance, with zero deviation from the actual value, and a much faster run time. But in general, when dealing with more complicated calculations, bisection method is relatively slow. But within a given tolerance Newton's method and secant method may probably show better performance.", "_____no_output_____" ], [ "## Problem 2 -- Potential", "_____no_output_____" ], [ "$\\textit{Navarro-Frenk-White}$ and $\\textit{Hernquist}$ potential can be expressed as the following equations:\n$$\\Phi_{NFW}(r) = \\Phi_0\\frac{r_s}{r}\\,ln(1+r/r_s)$$\n$$\\Phi_{Hernquist}(r) = -\\Phi_0\\,\\frac{1}{2(1+r/r_s)}$$\nwith $\\Phi_0 = 1.659 \\times 10^4 \\ km^2/s^2$ and $r_s = 15.61 \\ kpc$.\nThe apocentre and pericentre can be found by solving the following equation:\n\\begin{align*}\nE_{tot} &= \\frac{1}{2}\\left(v_t^2+v_r^2\\right)+\\Phi\\\\\n\\end{align*}\nwhere $L = J_r=rv_r$ is the angular momentum in the radial direction, and $E_{tot}$ is the total energy of the elliptical orbit and can be found by $(r,v_t,v_r)$ of a given star.\nDefine the residue function\n$$R\\equiv E_{tot} - \\frac{1}{2}\\left(\\frac{L}{r}\\right)^2-\\Phi$$\nso that the percenter and apocenter can be found when $R=0$.\nThen, the radial action $J_r$ is defined as $\\textit{(Jason L. Sanders,2015)}$\n$$J_r = \\frac{1}{\\pi}\\int_{r_p}^{r_a}dr\\sqrt{2E-2\\Phi-\\frac{L^2}{r^2}}$$\nwhere $r_p$ is the pericentric radius and $r_a$ is the apocentric radius.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.optimize import newton\nfrom scipy.integrate import quad\nfrom math import * \n\nr = np.array([7.80500, 15.6100,31.2200,78.0500,156.100]) #r in kpc\nvt = np.array([139.234,125.304,94.6439,84.5818,62.8640]) # vt in km/s\nvr = np.array([-15.4704,53.7018,-283.932,-44.5818,157.160]) # vr in km/s\n#NFW profile potential\ndef NFW(r):\n phi0 = 1.659e4\n rs = 15.61\n ratio = rs/r\n return -phi0*ratio*np.log(1+1/ratio)\n#Hernquist profile potential\ndef H(r):\n phi0 = 1.659e4\n rs = 15.61\n ratio = r/rs\n return -phi0/(2*(1+ratio))\n#1st derivative of Hernquist profile potential\ndef H_d(r):\n phi0 = 1.659e4\n rs = 15.61\n ratio = r/rs\n return phi0*0.5/rs*((1+ratio)**(-2)) \n#1st derivative of NFW profile potential\ndef NFW_d(r):\n phi0 = 1.659e4\n rs = 15.61\n ratio = rs/r\n return -phi0*rs*((-1/r**2)*np.log(1+1/ratio)+1/(r*rs)*(1+1/ratio)**(-1))\n#total energy, NFW profile\ndef E_NFW(r,vt,vr):\n E = 0.5*(vt**2+vr**2)+NFW(r)\n return E\n#total energy, Hernquist profile\ndef E_H(r,vt,vr):\n E = 0.5*(vt**2+vr**2)+H(r)\n return E\n#Residue function\ndef Re(r,Energy,momentum,p):\n return Energy - 0.5*(momentum/r)**2-p\n\n#Residue function for NFW profile\ndef R_NFW(r,Energy,momentum):\n return Energy - 0.5*(momentum/r)**2-NFW(r)\n#Residue function for Hernquist profile\ndef R_H(r,Energy,momentum):\n return Energy - 0.5*(momentum/r)**2-H(r)\n#derivative of residue of NFW profile\ndef R_dNFW(r,Energy,momentum):\n return Energy*0+momentum**2*r**(-3)-NFW_d(r)\n#derivative of residue of Hernquist profile\ndef R_dH(r,Energy,momentum):\n return Energy*0+momentum**2*r**(-3)-H_d(r)\n\n#second derivative of residue of Hernquist profile, come handy if the \n#calculated value for pericentre for Hernquist profile is too far off\n#from the value calculated for NFW profile\ndef R_ddH(r,Energy,momentum):\n phi0 = 1.659e4\n rs = 15.61\n ratio = r/rs\n return Energy*0-3*momentum**2*r**(-4)+phi0*0.5/rs**2*((1+ratio)**(-3)) \n#function that defines the radial action\ndef r_actionNFW(r,Energy,momentum):\n return np.sqrt(2*(Energy-NFW(r))-(momentum/r)**2)/pi\ndef r_actionH(r,Energy,momentum):\n return np.sqrt(2*(Energy-H(r))-(momentum/r)**2)/pi\n\nR1 = np.linspace(7,400,1000)\nR2 = np.linspace(10,500,1000)\nR3 = np.linspace(7,600,1000)\nR4 = np.linspace(50,800,1000)\nR5 = np.linspace(50,1500,1000)\nMomentum = r*vt\nEnergy_nfw = E_NFW(r,vt,vr)\nEnergy_h = E_H(r,vt,vr)\n#plot results for 5 stars\n#1st star\ni = 0\nR_nfw = Re(R1,Energy_nfw[i],Momentum[i],NFW(R1))\nR_h = Re(R1,Energy_h[i],Momentum[i],H(R1))\nplt.figure(figsize=(15,10))\nplt.plot(R1,R_nfw,ls='-',label=\"NFW\",color='#9370db',lw=2)\nplt.plot(R1,R_h,ls='--',label=\"Hernquist\",color='salmon',alpha=0.75,lw=2)\nplt.rc('xtick', labelsize=15) # fontsize of the tick labels\nplt.rc('ytick', labelsize=15)\nplt.axhline(y=0,color='#b22222',lw=3)\nplt.title(r\"1st star, $R= E_{tot} - \\frac{1}{2}\\left(\\frac{L}{r}\\right)^2-\\Phi$\",fontsize=20)\nplt.xlabel(\"r(kpc)\",fontsize=15)\nplt.ylabel(\"Residue\",fontsize=15)\nz1 = newton(R_NFW,10,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\nz2 = newton(R_NFW,100,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\ne1 = R_NFW(z1,Energy_nfw[i],Momentum[i])\ne2 = R_NFW(z2,Energy_nfw[i],Momentum[i])\nprint \"The pericentre and apocentre are found to be:\",z1,\"kpc\",\"and\",z2,\"kpc\",\"for the NFW profile\"\nplt.plot(z1,e1,marker='d',label='pericentre-NFW')\nplt.plot(z2,e2,marker='d',label='apocentre-NFW')\nz3 = newton(R_H,10,args=(Energy_h[i],Momentum[i]),fprime=R_dH)\ne3 = Re(z1,Energy_h[i],Momentum[i],H(z1))\nprint \"The pericentre is found to be:\",z3,\"kpc\",\"for the Hernquist profile\"\nplt.plot(z1,e1,marker='o',label='pericentre-H')\nplt.legend(fontsize=15)\nplt.show()\n\nJ_NFW = quad(r_actionNFW,z1,z2,args=(Energy_nfw[i],Momentum[i]))\nprint \"The radial action for NFW profile is:\",J_NFW[0],\"km/s kpc\"\nJ_H = quad(r_actionH,z3,np.inf,args=(Energy_h[i],Momentum[i]))\nprint \"The radial action for Hernquist profile is:\",J_H[0],\"km/s kpc\"", "The pericentre and apocentre are found to be: 7.75067639682 kpc and 178.316271432 kpc for the NFW profile\nThe pericentre is found to be: 7.75234061029 kpc for the Hernquist profile\n" ], [ "#2nd star\ni = 1\nR_nfw = Re(R2,Energy_nfw[i],Momentum[i],NFW(R2))\nR_h = Re(R2,Energy_h[i],Momentum[i],H(R2))\nplt.figure(figsize=(15,10))\nplt.plot(R2,R_nfw,ls='-',label=\"NFW\",color='#9370db',lw=2)\nplt.plot(R2,R_h,ls='--',label=\"Hernquist\",color='salmon',alpha=0.75,lw=2)\nplt.rc('xtick', labelsize=15) # fontsize of the tick labels\nplt.rc('ytick', labelsize=15)\nplt.axhline(y=0,color='#b22222',lw=3)\nplt.title(r\"2nd star, $R= E_{tot} - \\frac{1}{2}\\left(\\frac{L}{r}\\right)^2-\\Phi$\",fontsize=20)\nplt.xlabel(\"r(kpc)\",fontsize=15)\nplt.ylabel(\"Residue\",fontsize=15)\nz1 = newton(R_NFW,10,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\nz2 = newton(R_NFW,400,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\ne1 = R_NFW(z1,Energy_nfw[i],Momentum[i])\ne2 = R_NFW(z2,Energy_nfw[i],Momentum[i])\nprint \"The pericentre and apocentre are found to be:\",z1,\"kpc\",\"and\",z2,\"kpc\",\"for the NFW profile\"\nplt.plot(z1,e1,marker='d',label='pericentre-NFW')\nplt.plot(z2,e2,marker='d',label='apocentre-NFW')\nz3 = newton(R_H,10,args=(Energy_h[i],Momentum[i]),fprime=R_dH)\ne3 = Re(z1,Energy_h[i],Momentum[i],H(z1))\nprint \"The pericentre is found to be:\",z3,\"kpc\",\"for the Hernquist profile\"\nplt.plot(z3,e3,marker='o',label='pericentre-H')\nplt.legend(fontsize=15)\nplt.show()\n\nJ_NFW = quad(r_actionNFW,z1,z2,args=(Energy_nfw[i],Momentum[i]))\nprint \"The radial action for NFW profile is:\",J_NFW[0],\"km/s kpc\"\nJ_H = quad(r_actionH,z3,np.inf,args=(Energy_h[i],Momentum[i]))\nprint \"The radial action for Hernquist profile is:\",J_H[0],\"km/s kpc\"", "The pericentre and apocentre are found to be: 14.1075227275 kpc and 375.764622203 kpc for the NFW profile\nThe pericentre is found to be: 14.1986051415 kpc for the Hernquist profile\n" ], [ "#3rd star\ni = 2\nR_nfw = Re(R3,Energy_nfw[i],Momentum[i],NFW(R3))\nR_h = Re(R3,Energy_h[i],Momentum[i],H(R3))\nplt.figure(figsize=(15,10))\nplt.plot(R3,R_nfw,ls='-',label=\"NFW\",color='#9370db',lw=2)\nplt.plot(R3,R_h,ls='--',label=\"Hernquist\",color='salmon',alpha=0.75,lw=2)\nplt.rc('xtick', labelsize=15) # fontsize of the tick labels\nplt.rc('ytick', labelsize=15)\nplt.axhline(y=0,color='#b22222',lw=3)\nplt.title(r\"3rd star, $R= E_{tot} - \\frac{1}{2}\\left(\\frac{L}{r}\\right)^2-\\Phi$\",fontsize=20)\nplt.xlabel(\"r(kpc)\",fontsize=15)\nplt.ylabel(\"Residue\",fontsize=15)\nz1 = newton(R_NFW,10,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\ne1 = R_NFW(z1,Energy_nfw[i],Momentum[i])\nprint \"The pericentre is found to be:\",z1,\"kpc\",\"for the NFW profile\"\nplt.plot(z1,e1,marker='d',label='pericentre-NFW')\nz2 = newton(R_H,10,args=(Energy_h[i],Momentum[i]),fprime=R_dH,fprime2=R_ddH)\ne2 = R_H(z2,Energy_h[i],Momentum[i])\nprint \"The pericentre is found to be:\",z2,\"kpc\",\"for the Hernquist profile\"\nplt.plot(z2,e2,marker='o',label='pericentre-H')\nplt.legend(fontsize=15)\nplt.show()\n\nJ_NFW = quad(r_actionNFW,z1,np.inf,args=(Energy_nfw[i],Momentum[i]))\nprint \"The radial action for NFW profile is:\",J_NFW[0],\"km/s kpc\"\nJ_H = quad(r_actionH,z2,np.inf,args=(Energy_h[i],Momentum[i]))\nprint \"The radial action for Hernquist profile is:\",J_H[0],\"km/s kpc\"", "The pericentre is found to be: 9.47357617853 kpc for the NFW profile\nThe pericentre is found to be: 9.62166037758 kpc for the Hernquist profile\n" ], [ "#4th star\ni = 3\nR_nfw = Re(R4,Energy_nfw[i],Momentum[i],NFW(R4))\nR_h = Re(R4,Energy_h[i],Momentum[i],H(R4))\nplt.figure(figsize=(15,10))\nplt.plot(R4,R_nfw,ls='-',label=\"NFW\",color='#9370db',lw=2)\nplt.plot(R4,R_h,ls='--',label=\"Hernquist\",color='salmon',alpha=0.75,lw=2)\nplt.rc('xtick', labelsize=15) # fontsize of the tick labels\nplt.rc('ytick', labelsize=15)\nplt.axhline(y=0,color='#b22222',lw=3)\nplt.title(r\"4th star, $R= E_{tot} - \\frac{1}{2}\\left(\\frac{L}{r}\\right)^2-\\Phi$\",fontsize=20)\nplt.xlabel(\"r(kpc)\",fontsize=15)\nplt.ylabel(\"Residue\",fontsize=15)\nz1 = newton(R_NFW,10,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\nz2 = newton(R_NFW,400,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\ne1 = R_NFW(z1,Energy_nfw[i],Momentum[i])\ne2 = R_NFW(z2,Energy_nfw[i],Momentum[i])\nprint \"The pericentre and apocentre are found to be:\",z1,\"kpc\",\"and\",z2,\"kpc\",\"for the NFW profile\"\nplt.plot(z1,e1,marker='d',label='pericentre-NFW')\nplt.plot(z2,e2,marker='d',label='apocentre-NFW')\nz3 = newton(R_H,50,args=(Energy_h[i],Momentum[i]),fprime=R_dH,fprime2=R_ddH)\ne3 = R_H(z3,Energy_h[i],Momentum[i])\nprint \"The pericentre is found to be:\",z3,\"kpc\",\"for the Hernquist profile\"\nplt.plot(z1,e1,marker='o',label='pericentre-H')\nplt.legend(fontsize=15)\nplt.show()\n\nJ_NFW = quad(r_actionNFW,z1,z2,args=(Energy_nfw[i],Momentum[i]))\nprint \"The radial action for NFW profile is:\",J_NFW[0],\"km/s kpc\"\nJ_H = quad(r_actionH,z3,np.inf,args=(Energy_h[i],Momentum[i]))\nprint \"The radial action for Hernquist profile is:\",J_H[0],\"km/s kpc\"", "The pericentre and apocentre are found to be: 64.9161691596 kpc and 697.429352749 kpc for the NFW profile\nThe pericentre is found to be: 67.7971003933 kpc for the Hernquist profile\n" ], [ "#5th star\ni = 4\nR_nfw = Re(R5,Energy_nfw[i],Momentum[i],NFW(R5))\nR_h = Re(R5,Energy_h[i],Momentum[i],H(R5))\nplt.figure(figsize=(15,10))\nplt.plot(R5,R_nfw,ls='-',label=\"NFW\",color='#9370db',lw=2)\nplt.plot(R5,R_h,ls='--',label=\"Hernquist\",color='salmon',alpha=0.75,lw=2)\nplt.rc('xtick', labelsize=15) # fontsize of the tick labels\nplt.rc('ytick', labelsize=15)\nplt.axhline(y=0,color='#b22222',lw=3)\nplt.title(r\"5th star, $R= E_{tot} - \\frac{1}{2}\\left(\\frac{L}{r}\\right)^2-\\Phi$\",fontsize=20)\nplt.xlabel(\"r(kpc)\",fontsize=15)\nplt.ylabel(\"Residue\",fontsize=15)\nz1 = newton(R_NFW,10,args=(Energy_nfw[i],Momentum[i]),fprime=R_dNFW)\ne1 = R_NFW(z1,Energy_nfw[i],Momentum[i])\nprint \"The pericentre is found to be:\",z1,\"kpc\",\"for the NFW profile\"\nplt.plot(z1,e1,marker='d',label='pericentre-NFW')\nz2 = newton(R_H,50,args=(Energy_h[i],Momentum[i]),fprime=R_dH,fprime2=R_ddH)\ne2 = R_H(z2,Energy_h[i],Momentum[i])\nprint \"The pericentre is found to be:\",z2,\"kpc\",\"for the Hernquist profile\"\nplt.plot(z1,e1,marker='o',label='pericentre-H')\nplt.legend(fontsize=15)\nplt.show()\n\nJ_NFW = quad(r_actionNFW,z1,np.inf,args=(Energy_nfw[i],Momentum[i]))\nprint \"The radial action for NFW profile is:\",J_NFW[0],\"km/s kpc\"\nJ_H = quad(r_actionH,z2,np.inf,args=(Energy_h[i],Momentum[i]))\nprint \"The radial action for Hernquist profile is:\",J_H[0],\"km/s kpc\"", "The pericentre is found to be: 52.2586723359 kpc for the NFW profile\nThe pericentre is found to be: 55.9497763757 kpc for the Hernquist profile\n" ] ], [ [ "The table below lists all the parameters of the five stars.", "_____no_output_____" ], [ "<img src=\"Desktop/table.png\">", "_____no_output_____" ], [ "## Problem 3 -- System of equations", "_____no_output_____" ], [ "$$f(x,y) = x^2+y^2-50=0$$\n$$g(x,y) = x \\times y -25 = 0$$", "_____no_output_____" ], [ "### (a) Analytical solution", "_____no_output_____" ], [ "First $f(x,y)-2g(x,y)$,we find:\n\\begin{align*}\nx^2+y^2-2xy &=0\\\\\n(x-y)^2 &= 0\\\\\nx&=y\n\\end{align*}\nThen $f(x,y)+2g(x,y)$,we find:\n\\begin{align*}\nx^2+y^2+2xy &=100\\\\\n(x+y)^2 &= 100\\\\\nx,y = 5,5 \\ &or -5,-5\n\\end{align*}", "_____no_output_____" ], [ "### (b) Newton's method", "_____no_output_____" ], [ "Newton-Raphson method can also be applied to solve multivariate systems. The algorithm is simply as follows:\n* Suppose we have an N-D multivariate system of the form:\n\\begin{cases}\nf_1(x_1,...,x_N)=f_1(\\mathbf{x})=0\\\\\nf_2(x_1,...,x_N)=f_2(\\mathbf{x})=0\\\\\n...... \\\\\nf_N(x_1,...,x_N)=f_N(\\mathbf{x})=0\\\\\n\\end{cases}\nwhere we have defined \n$$\\mathbf{x}=[x_1,...,x_N]^T$$\nDefine a vector function\n$$\\mathbf{f}(\\mathbf{x})=[f_1(\\mathbf{x}),...,f_N(\\mathbf{x})]^T$$\nSo that the equation system above can be written as\n$$\\mathbf{f}(\\mathbf{x})=\\mathbf{0}$$\n* $\\mathbf{J}_{\\mathbf{f}}(\\mathbf{x})$ is the $\\textit{Jacobian matrix}$ over the function vector $\\mathbf{f}(\\mathbf{x})$ \n$$\\mathbf{J}_{\\mathbf{f}}(\\mathbf{x})=\\begin{bmatrix}\n\\frac{\\partial f_1}{\\partial x_1} & \\dots & \\frac{\\partial f_1}{\\partial x_N} \\\\\n\\vdots & \\ddots & \\vdots \\\\\n\\frac{\\partial f_N}{\\partial x_1} & \\dots & \\frac{\\partial f_N}{\\partial x_N}\n\\end{bmatrix}$$\n* If all equations are linear we have\n$$\\mathbf{f}(\\mathbf{x}+\\delta \\mathbf{x})=\\mathbf{f}(\\mathbf{x})+\\mathbf{J}(\\mathbf{x})\\delta\\mathbf{x}$$\n* by assuming $\\mathbf{f}(\\mathbf{x}+\\delta \\mathbf{x})=0$, we can find the roots as $\\mathbf{x}+\\delta \\mathbf{x}$, where\n$$\\delta \\mathbf{x} = -\\mathbf{J}(\\mathbf{x})^{-1}\\mathbf{f}(\\mathbf{x})$$\n* The approximation can be improved iteratively\n$$\\mathbf{x}_{n+1} = \\mathbf{x}_n +\\delta \\mathbf{x}_n = \\mathbf{x}_n-\\mathbf{J}(\\mathbf{x}_n)^{-1}\\mathbf{f}(\\mathbf{x}_n)$$", "_____no_output_____" ] ], [ [ "from scipy.optimize import fsolve\nimport numpy as np\n\nf1 = lambda x: [x[0]**2+x[1]**2-50,x[0]*x[1]-25]\n#the Jacobian needed to implement Newton's method\nfd = lambda x: np.array([[2*x[0],2*x[1]],[x[1],x[0]]]).reshape(2,2)\n#define the domain where we want to find the solution (x,y)\na = np.linspace(-10,10,100)\nb = a\n#for every point (a,b), pass on to fsolve and append the result \n#then round the result and see how many pairs of solutions there are\ni = 0\nresult = np.array([[5,5]])\n#print result\nfor a,b in zip(a,b):\n x = fsolve(f1,[a,b],fprime=fd)\n x = np.round(x)\n result = np.append(result,[x],axis=0)\n\nprint \"The sets of solutions are found to be:\",np.unique(result,axis=0)\n", "The sets of solutions are found to be: [[-5. -5.]\n [ 5. 5.]]\n" ] ], [ [ "From above we learn that the solutions are indeed left with $(x,y) = (5,5)$ or $(x,y) = (-5,-5)$ ", "_____no_output_____" ], [ "### (c) Convergence", "_____no_output_____" ] ], [ [ "%config InlineBackend.figure_format = 'retina' \nimport numpy as np\nfrom scipy.optimize import fsolve\n\nimport matplotlib.pyplot as plt\n\ndef f(x, y):\n return x**2+y**2-50;\n\ndef g(x, y):\n return x*y-25\n\nx = np.linspace(-6, 6, 500)\n\n@np.vectorize\ndef fy(x):\n x0 = 0.0\n def tmp(y):\n return f(x, y)\n y1, = fsolve(tmp, x0)\n return y1\n\n@np.vectorize\ndef gy(x):\n x0 = 0.0\n def tmp(y):\n return g(x, y)\n y1, = fsolve(tmp, x0)\n return y1\n\n\nplt.plot(x, fy(x), x, gy(x))\nplt.xlabel('x')\nplt.ylabel('y')\nplt.rc('xtick', labelsize=10) # fontsize of the tick labels\nplt.rc('ytick', labelsize=10)\nplt.legend(['fy', 'gy'])\nplt.show()\n#print fy(x)", "_____no_output_____" ], [ "i =1\nI = np.array([])\nF = np.array([])\nG = np.array([])\nX_std = np.array([])\nY_std = np.array([])\nwhile i<50:\n x_result = fsolve(f1,[-100,-100],maxfev=i)\n f_result = f(x_result[0],x_result[1])\n g_result = g(x_result[0],x_result[1])\n x1_std = abs(x_result[0]+5.0)\n x2_std = abs(x_result[1]+5.0)\n F = np.append(F,f_result)\n G = np.append(G,g_result)\n I = np.append(I,i)\n X_std = np.append(X_std,x1_std)\n Y_std = np.append(Y_std,x2_std)\n i+=1\nxtol = 1.49012e-08\nplt.loglog(I,np.abs(F),I,np.abs(G))\nplt.title(\"converge of f and g\")\nplt.xlabel(\"iterations\")\nplt.ylabel(\"function values\")\nplt.legend(['f','g'])\nplt.show()", "_____no_output_____" ], [ "plt.loglog(I,X_std,I,Y_std)\nplt.axhline(y=xtol,color='#b22222',lw=3)\nplt.title(r\"$converge \\ of \\ \\Delta_x \\ and \\ \\Delta_y$\")\nplt.xlabel(\"iterations\")\nplt.ylabel(\"Deviation values\")\nplt.legend([r'$\\Delta x$',r'$\\Delta y$','tolerance'])\nplt.show()", "_____no_output_____" ] ], [ [ "### (d) Maximum iterations", "_____no_output_____" ], [ "Now also apply the Jacobian. The jacobian of the system of equation is simply as follows\n$$\\mathbf{J} = \\begin{bmatrix}\n\\frac{\\partial f}{\\partial x} & \\frac{\\partial f}{\\partial y} \\\\\n\\frac{\\partial g}{\\partial x} & \\frac{\\partial g}{\\partial y}\n\\end{bmatrix}$$\n\n$$=\\begin{bmatrix}\n2x & 2y \\\\\ny & x\n\\end{bmatrix}$$", "_____no_output_____" ] ], [ [ "fd = lambda x: np.array([[2*x[0],2*x[1]],[x[1],x[0]]]).reshape(2,2)\n\n\ni =1\nI = np.array([])\nF = np.array([])\nG = np.array([])\nX_std = np.array([])\nY_std = np.array([])\nwhile i<50:\n x_result = fsolve(f1,[-100,-100],fprime=fd,maxfev=i)\n f_result = f(x_result[0],x_result[1])\n g_result = g(x_result[0],x_result[1])\n x1_std = abs(x_result[0]+5.0)\n x2_std = abs(x_result[1]+5.0)\n F = np.append(F,f_result)\n G = np.append(G,g_result)\n I = np.append(I,i)\n X_std = np.append(X_std,x1_std)\n Y_std = np.append(Y_std,x2_std)\n i+=1\nxtol = 1.49012e-08\nplt.loglog(I,np.abs(F),I,np.abs(G))\nplt.title(\"converge of f and g\")\nplt.xlabel(\"iterations\")\nplt.ylabel(\"function values\")\nplt.legend(['f','g'])\nplt.show()", "_____no_output_____" ], [ "plt.loglog(I,X_std,I,Y_std)\nplt.axhline(y=xtol,color='#b22222',lw=3)\nplt.title(r\"$converge \\ of \\ \\Delta_x \\ and \\ \\Delta_y$\")\nplt.xlabel(\"iterations\")\nplt.ylabel(\"Deviation values\")\nplt.legend([r'$\\Delta x$',r'$\\Delta y$','tolerance'])\nplt.show()", "_____no_output_____" ] ], [ [ "Now that we have applied the Jacobian and it can be seen directly from the two plots above that they both drop more quickly to zero, and f and g (also $\\Delta x$ and $\\Delta y$) started to converge more quickly in the case when the Jacobian is applied, and is approaching the tolerance much faster ($\\Delta x$ and $\\Delta y$). This happens because when the comupter is trying to build up the Jacobian, it needs multiple sets of solutions to estimate the partial derivatives, and the derivatives are all just calculated from $\\frac{f(x+\\Delta x)}{\\Delta x}$ and its accuracy will only increase with the cumulation of sets of solutions. But in the case of the Jacobian is applied, the function for the first derivative is then already known, so even if we start very far off we can still have an exponential increase in accuracy (the increase in accuracy in the case without Jacobian is also exponential but much slower, approximately 10 times slower).", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import matplotlib\nmatplotlib.use('nbagg')\nimport matplotlib.animation as anm\nimport matplotlib.pyplot as plt\nimport math\nimport matplotlib.patches as patches\nimport numpy as np", "_____no_output_____" ], [ "class World: ### fig:world_init_add_timespan (1-5行目)\n def __init__(self, time_span, time_interval, debug=False):\n self.objects = [] \n self.debug = debug\n self.time_span = time_span # 追加\n self.time_interval = time_interval # 追加\n \n def append(self,obj): # オブジェクトを登録するための関数\n self.objects.append(obj)\n \n def draw(self): ### fig:world_draw_with_timesapn (1, 10, 21-26, 28-34行目)\n fig = plt.figure(figsize=(4,4)) # 8x8 inchの図を準備\n ax = fig.add_subplot(111) # サブプロットを準備\n ax.set_aspect('equal') # 縦横比を座標の値と一致させる\n ax.set_xlim(-5,5) # X軸を-5m x 5mの範囲で描画\n ax.set_ylim(-5,5) # Y軸も同様に\n ax.set_xlabel(\"X\",fontsize=10) # X軸にラベルを表示\n ax.set_ylabel(\"Y\",fontsize=10) # 同じくY軸に\n \n elems = []\n \n if self.debug: \n for i in range(int(self.time_span/self.time_interval)): self.one_step(i, elems, ax) \n else:\n self.ani = anm.FuncAnimation(fig, self.one_step, fargs=(elems, ax),\n frames=int(self.time_span/self.time_interval)+1, interval=int(self.time_interval*1000), repeat=False)\n plt.show()\n \n def one_step(self, i, elems, ax):\n while elems: elems.pop().remove()\n time_str = \"t = %.2f[s]\" % (self.time_interval*i) # 時刻として表示する文字列\n elems.append(ax.text(-4.4, 4.5, time_str, fontsize=10))\n for obj in self.objects:\n obj.draw(ax, elems)\n if hasattr(obj, \"one_step\"): obj.one_step(self.time_interval) # 変更", "_____no_output_____" ], [ "class IdealRobot: ### fig:robot_camera（1,2,8,28-29行目,49-53行目）\n def __init__(self, pose, agent=None, sensor=None, color=\"black\"): # 引数を追加\n self.pose = pose \n self.r = 0.2 \n self.color = color \n self.agent = agent\n self.poses = [pose]\n self.sensor = sensor # 追加\n \n def draw(self, ax, elems):\n x, y, theta = self.pose \n xn = x + self.r * math.cos(theta) \n yn = y + self.r * math.sin(theta)\n elems += ax.plot([x,xn], [y,yn], color=self.color)\n c = patches.Circle(xy=(x, y), radius=self.r, fill=False, color=self.color) \n elems.append(ax.add_patch(c)) \n self.poses.append(self.pose)\n elems += ax.plot([e[0] for e in self.poses], [e[1] for e in self.poses], linewidth=0.5, color=\"black\")\n if self.sensor and len(self.poses) > 1: #追加\n self.sensor.draw(ax, elems, self.poses[-2]) #追加\n \n @classmethod \n def state_transition(self, nu, omega, time, pose):\n t0 = pose[2]\n if math.fabs(omega) < 1e-10:\n return pose + np.array( [nu*math.cos(t0), \n nu*math.sin(t0),\n omega ] ) * time\n else:\n return pose + np.array( [nu/omega*(math.sin(t0 + omega*time) - math.sin(t0)), \n nu/omega*(-math.cos(t0 + omega*time) + math.cos(t0)),\n omega*time ] )\n\n def one_step(self, time_interval):\n if not self.agent: return\n obs = self.sensor.data(self.pose) if self.sensor else None #追加\n nu, omega = self.agent.decision(obs) #引数追加\n self.pose = self.state_transition(nu, omega, time_interval, self.pose)", "_____no_output_____" ], [ "class Agent:\n def __init__(self, nu, omega):\n self.nu = nu\n self.omega = omega\n \n def decision(self, observation=None):\n return self.nu, self.omega", "_____no_output_____" ], [ "class Landmark:\n def __init__(self, x, y):\n self.pos = np.array([x, y]).T\n self.id = None\n \n def draw(self, ax, elems):\n c = ax.scatter(self.pos[0], self.pos[1], s=100, marker=\"*\", label=\"landmarks\", color=\"orange\")\n elems.append(c)\n elems.append(ax.text(self.pos[0], self.pos[1], \"id:\" + str(self.id), fontsize=10))", "_____no_output_____" ], [ "class Map:\n def __init__(self): # 空のランドマークのリストを準備\n self.landmarks = []\n \n def append_landmark(self, landmark): # ランドマークを追加\n landmark.id = len(self.landmarks) # 追加するランドマークにIDを与える\n self.landmarks.append(landmark)\n\n def draw(self, ax, elems): # 描画（Landmarkのdrawを順に呼び出し）\n for lm in self.landmarks: lm.draw(ax, elems)", "_____no_output_____" ], [ "class IdealCamera: ### fig:camera2（1-4行目、6, 12-13行目, 26-32行目）\n def __init__(self, env_map):\n self.map = env_map\n self.lastdata = [] # 追加\n \n def data(self, cam_pose):\n observed = []\n for lm in self.map.landmarks:\n z = self.observation_function(cam_pose, lm.pos)\n observed.append((z, lm.id))\n \n self.lastdata = observed # 追加\n return observed \n \n @classmethod\n def observation_function(cls, cam_pose, obj_pos):\n diff = obj_pos - cam_pose[0:2]\n phi = math.atan2(diff[1], diff[0]) - cam_pose[2]\n while phi >= np.pi: phi -= 2*np.pi\n while phi < -np.pi: phi += 2*np.pi\n return np.array( [np.hypot(*diff), phi ] ).T\n \n def draw(self, ax, elems, cam_pose): # 追加 ###camera2_1\n for lm in self.lastdata:\n x, y, theta = cam_pose\n distance, direction = lm[0][0], lm[0][1]\n lx = x + distance * math.cos(direction + theta)\n ly = y + distance * math.sin(direction + theta)\n elems += ax.plot([x,lx], [y,ly], color=\"pink\")", "_____no_output_____" ], [ "world = World(10, 0.1, debug=False) ### fig:sensor_drawing (10-19行目)\n\n### 地図を生成して3つランドマークを追加 ###\nm = Map() \nm.append_landmark(Landmark(2,-2))\nm.append_landmark(Landmark(-1,-3))\nm.append_landmark(Landmark(3,3))\nworld.append(m) \n\n### ロボットを作る ###\nstraight = Agent(0.2, 0.0) \ncircling = Agent(0.2, 10.0/180*math.pi) \nrobot1 = IdealRobot( np.array([ 2, 3, math.pi/6]).T, sensor=IdealCamera(m), agent=straight ) # 引数にcameraを追加、整理\nrobot2 = IdealRobot( np.array([-2, -1, math.pi/5*6]).T, sensor=IdealCamera(m), agent=circling, color=\"red\") # robot3は消しました\nworld.append(robot1)\nworld.append(robot2)\n\n### アニメーション実行 ###\nworld.draw()", "_____no_output_____" ], [ "cam = IdealCamera(m) \np = cam.data(robot2.pose)\nprint(p)", "[(array([4.12310563, 2.26829546]), 0), (array([2.23606798, 1.40612541]), 1), (array([ 6.40312424, -3.09517024]), 2)]\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Exploratory Data Analysis", "_____no_output_____" ] ], [ [ "from pyspark import SparkContext, SparkConf\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\nfrom pyspark.sql import functions as F", "_____no_output_____" ], [ "spark = SparkSession.builder.master('local[1]').appName(\"Jupyter\").getOrCreate()\nsc = spark.sparkContext", "_____no_output_____" ], [ "#test if this works\n\nimport pandas as pd \nimport numpy as np\nimport matplotlib.pyplot as plt \nimport scipy\nimport datetime\n\n# the more advanced python visualization library\nimport seaborn as sns\n\n# apply style to all the charts\nsns.set_style('whitegrid')\n\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)", "_____no_output_____" ], [ "# create sparksession\n\nspark = SparkSession \\\n .builder \\\n .appName(\"Pysparkexample\") \\\n .config(\"spark.some.config.option\", \"some-value\") \\\n .getOrCreate()", "_____no_output_____" ] ], [ [ "# Load Data", "_____no_output_____" ] ], [ [ "#collisions = spark.read.csv('data/accidents.csv', header='true', inferSchema = True)\n#collisions.show(2)\n\ndf_new = spark.read.csv('data/accidents_new.csv', header='true', inferSchema = True)", "_____no_output_____" ] ], [ [ "# Data Perspective\n_____", "_____no_output_____" ], [ "* One variable\n * Numeric variables:\n * continuous\n * discrete\n * Categorical variables:\n * ordinal\n * nominal\n\n* Multiple variables:\n * Numeric x Numeric\n * Categorical x Numeric\n * Categorical x Categorical\n____________________", "_____no_output_____" ], [ "# Overview", "_____no_output_____" ] ], [ [ "print('The total number of rows : ', df_new.count(),\n '\\nThe total number of columns :', len(df_new.columns))", "The total number of rows : 128647 \nThe total number of columns : 40\n" ] ], [ [ "# Data Schema\nPrint the data schema for our dataset - SAAQ Accident Information", "_____no_output_____" ] ], [ [ "df_new.printSchema()", "root\n |-- ID: string (nullable = true)\n |-- DATE: timestamp (nullable = true)\n |-- WEEK_DAY: string (nullable = true)\n |-- REG: string (nullable = true)\n |-- MUNCP: string (nullable = true)\n |-- STREET: string (nullable = true)\n |-- TYPE_ACCDN: integer (nullable = true)\n |-- SURFACE: integer (nullable = true)\n |-- LIGHT: integer (nullable = true)\n |-- STR_ASPCT: integer (nullable = true)\n |-- STR_CONFIG: integer (nullable = true)\n |-- METEO: integer (nullable = true)\n |-- GRAVITE: string (nullable = true)\n |-- NB_VEH_IMPLIQUES_ACCDN: integer (nullable = true)\n |-- NB_VICTIMES_TOTAL: integer (nullable = true)\n |-- NB_MORTS: integer (nullable = true)\n |-- NB_BLESSES_GRAVES: integer (nullable = true)\n |-- NB_BLESS_LEGERS: integer (nullable = true)\n |-- NB_DECES_PIETON: integer (nullable = true)\n |-- NB_BLESSES_PIETON: integer (nullable = true)\n |-- NB_VICTIMES_PIETON: integer (nullable = true)\n |-- NB_DECES_MOTO: integer (nullable = true)\n |-- NB_BLESSES_MOTO: integer (nullable = true)\n |-- NB_VICTIMES_MOTO: integer (nullable = true)\n |-- NB_DECES_VELO: integer (nullable = true)\n |-- NB_BLESSES_VELO: integer (nullable = true)\n |-- NB_VICTIMES_VELO: integer (nullable = true)\n |-- nb_automobile_camion_leger: integer (nullable = true)\n |-- nb_camionLourd_tractRoutier: integer (nullable = true)\n |-- nb_outil_equipement: integer (nullable = true)\n |-- nb_tous_autobus_minibus: integer (nullable = true)\n |-- nb_bicyclette: integer (nullable = true)\n |-- nb_cyclomoteur: integer (nullable = true)\n |-- nb_motocyclette: integer (nullable = true)\n |-- nb_taxi: integer (nullable = true)\n |-- nb_urgence: integer (nullable = true)\n |-- nb_motoneige: integer (nullable = true)\n |-- nb_VHR: integer (nullable = true)\n |-- nb_autres_types: integer (nullable = true)\n |-- nb_veh_non_precise: integer (nullable = true)\n\n" ], [ "# Create temporary table query with SQL\n\ndf_new.createOrReplaceTempView('AccidentData')\naccidents_limit_10 = spark.sql(\n'''\nSELECT * FROM AccidentData\nLIMIT 10\n'''\n).toPandas()\n\naccidents_limit_10", "_____no_output_____" ] ], [ [ "# One Variable\n__________", "_____no_output_____" ], [ "## a. Numeric - Data Totals\nTotals for various accident records", "_____no_output_____" ] ], [ [ "from pyspark.sql import functions as func\n\n#df_new.agg(func.sum(\"NB_BLESSES_VELO\").alias('Velo'),func.sum(\"NB_VICTIMES_MOTO\"),func.sum(\"NB_VEH_IMPLIQUES_ACCDN\")).show()\n\ndf_new.agg(func.sum(\"NB_VEH_IMPLIQUES_ACCDN\").alias('Ttl Cars In Accidents')).show()\ndf_new.agg(func.sum(\"NB_VICTIMES_TOTAL\").alias('Ttl Victims')).show()\ndf_new.agg(func.sum(\"NB_MORTS\").alias('Ttl Deaths')).show()\ndf_new.agg(func.sum(\"NB_BLESSES_GRAVES\").alias('Ttl Severe Injuries')).show()\ndf_new.agg(func.sum(\"NB_BLESS_LEGERS\").alias('Ttl Light Injuries')).show()\ndf_new.agg(func.sum(\"NB_DECES_PIETON\").alias('Ttl Pedestrian Deaths')).show()\ndf_new.agg(func.sum(\"NB_BLESSES_PIETON\").alias('Ttl Pedestrian Injuries')).show()\ndf_new.agg(func.sum(\"NB_VICTIMES_PIETON\").alias('Ttl Pedestrian Victims')).show()\ndf_new.agg(func.sum(\"NB_DECES_MOTO\").alias('Ttl Moto Deaths')).show()\ndf_new.agg(func.sum(\"NB_BLESSES_MOTO\").alias('Ttl Moto Injuries')).show()\ndf_new.agg(func.sum(\"NB_VICTIMES_MOTO\").alias('Ttl Moto Victims')).show()\ndf_new.agg(func.sum(\"NB_DECES_VELO\").alias('Ttl Bike Deaths')).show()\ndf_new.agg(func.sum(\"NB_BLESSES_VELO\").alias('Ttl Bike Injuries')).show()\ndf_new.agg(func.sum(\"NB_VICTIMES_VELO\").alias('Ttl Bike Victims')).show()\ndf_new.agg(func.sum(\"nb_automobile_camion_leger\").alias('Ttl Car - Light Trucks')).show()\ndf_new.agg(func.sum(\"nb_camionLourd_tractRoutier\").alias('Ttl Heavy Truck - Tractor')).show()\ndf_new.agg(func.sum(\"nb_outil_equipement\").alias('Ttl Equipment - Tools')).show()\ndf_new.agg(func.sum(\"nb_tous_autobus_minibus\").alias('Ttl Bus')).show()\ndf_new.agg(func.sum(\"nb_bicyclette\").alias('Ttl Bikes')).show()\ndf_new.agg(func.sum(\"nb_cyclomoteur\").alias('Ttl Motorized Bike')).show()\ndf_new.agg(func.sum(\"nb_motocyclette\").alias('Ttl Motorcycle')).show()\ndf_new.agg(func.sum(\"nb_taxi\").alias('Ttl Taxi')).show()\ndf_new.agg(func.sum(\"nb_urgence\").alias('Ttl Emergency')).show()\ndf_new.agg(func.sum(\"nb_motoneige\").alias('Ttl Snowmobile')).show()\ndf_new.agg(func.sum(\"nb_VHR\").alias('Ttl Motorhome')).show()\ndf_new.agg(func.sum(\"nb_autres_types\").alias('Ttl Other Types')).show()\ndf_new.agg(func.sum(\"nb_veh_non_precise\").alias('Ttl Non Specified Vehicles')).show()", "+---------------------+\n|Ttl Cars In Accidents|\n+---------------------+\n| 251681|\n+---------------------+\n\n+-----------+\n|Ttl Victims|\n+-----------+\n| 39532|\n+-----------+\n\n+----------+\n|Ttl Deaths|\n+----------+\n| 170|\n+----------+\n\n+-------------------+\n|Ttl Severe Injuries|\n+-------------------+\n| 1339|\n+-------------------+\n\n+------------------+\n|Ttl Light Injuries|\n+------------------+\n| 38023|\n+------------------+\n\n+---------------------+\n|Ttl Pedestrian Deaths|\n+---------------------+\n| 97|\n+---------------------+\n\n+-----------------------+\n|Ttl Pedestrian Injuries|\n+-----------------------+\n| 7255|\n+-----------------------+\n\n+----------------------+\n|Ttl Pedestrian Victims|\n+----------------------+\n| 7352|\n+----------------------+\n\n+---------------+\n|Ttl Moto Deaths|\n+---------------+\n| 11|\n+---------------+\n\n+-----------------+\n|Ttl Moto Injuries|\n+-----------------+\n| 1159|\n+-----------------+\n\n+----------------+\n|Ttl Moto Victims|\n+----------------+\n| 1170|\n+----------------+\n\n+---------------+\n|Ttl Bike Deaths|\n+---------------+\n| 24|\n+---------------+\n\n+-----------------+\n|Ttl Bike Injuries|\n+-----------------+\n| 4447|\n+-----------------+\n\n+----------------+\n|Ttl Bike Victims|\n+----------------+\n| 4471|\n+----------------+\n\n+----------------------+\n|Ttl Car - Light Trucks|\n+----------------------+\n| 196290|\n+----------------------+\n\n+-------------------------+\n|Ttl Heavy Truck - Tractor|\n+-------------------------+\n| 13762|\n+-------------------------+\n\n+---------------------+\n|Ttl Equipment - Tools|\n+---------------------+\n| 1789|\n+---------------------+\n\n+-------+\n|Ttl Bus|\n+-------+\n| 3664|\n+-------+\n\n+---------+\n|Ttl Bikes|\n+---------+\n| 5861|\n+---------+\n\n+------------------+\n|Ttl Motorized Bike|\n+------------------+\n| 884|\n+------------------+\n\n+--------------+\n|Ttl Motorcycle|\n+--------------+\n| 1933|\n+--------------+\n\n+--------+\n|Ttl Taxi|\n+--------+\n| 5104|\n+--------+\n\n+-------------+\n|Ttl Emergency|\n+-------------+\n| 4167|\n+-------------+\n\n+--------------+\n|Ttl Snowmobile|\n+--------------+\n| 5|\n+--------------+\n\n+-------------+\n|Ttl Motorhome|\n+-------------+\n| 7|\n+-------------+\n\n+---------------+\n|Ttl Other Types|\n+---------------+\n| 694|\n+---------------+\n\n+--------------------------+\n|Ttl Non Specified Vehicles|\n+--------------------------+\n| 17521|\n+--------------------------+\n\n" ], [ "df_totals = pd.DataFrame(columns=['Attr','Total'])\n\n#df_totals.append({'Attr':'NB_VEH_IMPLIQUES_ACCDN','Total':df_new.agg(func.sum(\"NB_VEH_IMPLIQUES_ACCDN\"))},ignore_index=True)\n#df_totals", "_____no_output_____" ] ], [ [ "## b. Categorical", "_____no_output_____" ], [ "### GRAVITE - severity of the accident", "_____no_output_____" ] ], [ [ "gravite_levels = spark.sql(\n'''\nSELECT GRAVITE, COUNT(*) as Total FROM AccidentData\nGROUP BY GRAVITE\nORDER BY Total DESC\n'''\n).toPandas()\n\ngravite_levels", "_____no_output_____" ], [ "# Pie Chart\n\nfig,ax = plt.subplots(1,1,figsize=(12,6))\n\nwedges, texts, autotexts = ax.pie(gravite_levels['Total'], radius=2, #labeldistance=2, pctdistance=1.1,\n autopct='%1.2f%%', startangle=90)\n\n\nax.legend(wedges, gravite_levels['GRAVITE'],\n title=\"GRAVITE\",\n loc=\"center left\",\n bbox_to_anchor=(1, 0, 0.5, 1))\n\nplt.setp(autotexts, size=12, weight=\"bold\")\nax.axis('equal') \nplt.tight_layout()\n\nplt.savefig('figures/gravite_levels.png')\nplt.show()", "_____no_output_____" ] ], [ [ "### METEO - Weather Conditions", "_____no_output_____" ] ], [ [ "meteo_conditions = spark.sql(\n'''\nSELECT METEO, COUNT(*) as Total FROM AccidentData\nGROUP BY METEO\nORDER BY Total DESC\n'''\n).toPandas()\n\nmeteo_conditions['METEO'] = meteo_conditions['METEO'].replace( {11:'Clear',12:'Overcast: cloudy/dark',13:'Fog/mist',\n 14:'Rain/bruine',15:'Heavy rain',16:'Strong wind',\n 17:'Snow/storm',18:'Blowing snow/blizzard',\n 19:'Ice',99:'Other..'})\n\nmeteo_conditions", "_____no_output_____" ], [ "fig,ax = plt.subplots(1,1,figsize=(10,6))\n\nplt.bar(meteo_conditions['METEO'], meteo_conditions['Total'],\n align='center', alpha=0.7, width=0.7, color='purple')\n\nplt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')\nfig.tight_layout()\n\nplt.savefig('figures/meteo_conditions.png')\nplt.show()", "_____no_output_____" ] ], [ [ "# Multiple Variables\n____________\n## Numeric X Categorical", "_____no_output_____" ], [ "### 1. Accident Victims by Municipality", "_____no_output_____" ] ], [ [ "victims_by_municipality = spark.sql(\n'''\nSELECT MUNCP, SUM(NB_VICTIMES_TOTAL) as Total FROM AccidentData\nGROUP BY MUNCP\nORDER BY Total DESC\n\n'''\n).toPandas()\n\nvictims_by_municipality", "_____no_output_____" ], [ "fig,ax = plt.subplots(1,1,figsize=(10,6))\n\nvictims_by_municipality.plot(x = 'MUNCP', y = 'Total', kind = 'barh', color = 'C0', ax = ax, legend = False)\n\nax.set_xlabel('Total Victims', size = 16)\nax.set_ylabel('Municipality', size = 16)\n\nplt.savefig('figures/victims_by_municipality.png')\nplt.show()", "_____no_output_____" ], [ "\nvictims_by_region = spark.sql(\n'''\nSELECT MUNCP, SUM(NB_VICTIMES_TOTAL) as Total FROM AccidentData\nGROUP BY MUNCP\n\n'''\n).toPandas()\n\nplt.figure(figsize = (10,6))\nsns.distplot(np.log(victims_by_region['Total']))\n\nplt.title('Total Victims Histogram by Region', size = 16)\nplt.ylabel('Density', size = 16)\nplt.xlabel('Log Total', size = 16)\n\nplt.savefig('figures/distplot.png')\nplt.show()", "_____no_output_____" ] ], [ [ "### 2. Total Collisions by Day of Week", "_____no_output_____" ] ], [ [ "collisions_by_day = spark.sql(\n'''\nSELECT WEEK_DAY, COUNT(WEEK_DAY) as Number_of_Collisions FROM AccidentData\nGROUP BY WEEK_DAY\nORDER BY Number_of_Collisions DESC\n\n'''\n).toPandas()\n\ncollisions_by_day", "_____no_output_____" ], [ "fig,ax = plt.subplots(1,1,figsize=(10,6))\n\ncollisions_by_day.plot(x = 'WEEK_DAY', y = 'Number_of_Collisions', kind = 'barh', color = 'C0', ax = ax, legend = False)\n\nax.set_xlabel('Number_of_Collisions', size = 16)\nax.set_ylabel('WEEK_DAY', size = 16)\n\nplt.savefig('figures/collisions_by_day.png')\nplt.show()", "_____no_output_____" ] ], [ [ "#### \"VE\", Friday has the highest number of collisions.", "_____no_output_____" ], [ "### 3. Top 10 Accidents by street", "_____no_output_____" ] ], [ [ "accidents_by_street = spark.sql(\n'''\nSELECT STREET, COUNT(STREET) as Number_of_Accidents FROM AccidentData\nGROUP BY STREET\nORDER BY Number_of_Accidents DESC\nLIMIT 10\n'''\n).toPandas()", "_____no_output_____" ], [ "fig,ax = plt.subplots(1,1,figsize=(10,6))\n\n#accidents_by_street.plot(x = 'STREET', y = 'Number_of_Accidents', kind = 'barh', color = 'C0', ax = ax, legend = False)\nsns.barplot(x=accidents_by_street['Number_of_Accidents'], y=accidents_by_street['STREET'], orient='h')\n\nax.set_xlabel('Number of Accidents', size = 16)\nax.set_ylabel('Street', size = 16)\n\nplt.savefig('figures/accidents_by_street.png')\nplt.show()", "_____no_output_____" ] ], [ [ "## Numeric X Numeric", "_____no_output_____" ], [ "### Correlation Heatmap\nIllustrates the corellation between numeric variables of the dataset.", "_____no_output_____" ] ], [ [ "plot_df = spark.sql(\n'''\nSELECT METEO, SURFACE, LIGHT, TYPE_ACCDN, \nNB_MORTS, NB_BLESSES_GRAVES, NB_VEH_IMPLIQUES_ACCDN, NB_VICTIMES_TOTAL\nFROM AccidentData\n\n'''\n).toPandas()\n\n\ncorrmat = plot_df.corr()\nf, ax = plt.subplots(figsize=(10, 7))\nsns.heatmap(corrmat, vmax=.8, square=True)\nplt.savefig('figures/heatmap.png')\nplt.show()\n", "_____no_output_____" ] ], [ [ "## Categorical X Categorical", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport os\nimport csv\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "data_dir = '/home/steffi/dev/data/ExpW/ExpwCleaned'\nlabels_csv = '/home/steffi/dev/data/ExpW/labels_clean.csv'", "_____no_output_____" ], [ "expw = pd.read_csv(labels_csv, delimiter=',')", "_____no_output_____" ], [ "expw.head()", "_____no_output_____" ], [ "expressions = expw.iloc[:, 2:]", "_____no_output_____" ], [ "expression_mapping = {\n 0: \"angry\",\n 1: \"disgust\",\n 2: \"fear\",\n 3: \"happy\",\n 4: \"sad\",\n 5: \"surprise\",\n 6: \"neutral\"\n}", "_____no_output_____" ] ], [ [ "## Hist plot for all values (even though 0 is actually useless)", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots(figsize=(10,10))\nax.hist(expressions, alpha=0.5, label=expressions.columns)\nax.legend()", "_____no_output_____" ] ], [ [ "#### Filter out all values that are equal to 0", "_____no_output_____" ] ], [ [ "expressions.value_counts(sort=True)", "_____no_output_____" ] ], [ [ "#### ===> columns contempt, unkown and NF can be dropped ", "_____no_output_____" ] ], [ [ "expressions_drop = expressions.drop(columns=[\"unknown\", \"contempt\", \"NF\"])", "_____no_output_____" ], [ "exp_nan = expressions_drop.replace(0, np.NaN)", "_____no_output_____" ], [ "exp_stacked = exp_nan.stack(dropna=True)", "_____no_output_____" ], [ "exp_unstacked = exp_stacked.reset_index(level=1)\nexpressions_single = exp_unstacked.rename(columns={\"level_1\": \"expression\"}).drop(columns=[0])", "_____no_output_____" ], [ "expressions_single.head()", "_____no_output_____" ] ], [ [ "### Append expressions to expw", "_____no_output_____" ] ], [ [ "expw[\"expression\"] = expressions_single[\"expression\"]", "_____no_output_____" ], [ "expw.head()", "_____no_output_____" ] ], [ [ "### Remove unnecessary columns", "_____no_output_____" ] ], [ [ "expw_minimal = expw.drop(expw.columns[1:-1], axis=1)", "_____no_output_____" ], [ "expw_minimal.loc[:, \"Image name\"] = data_dir + \"/\" + expw_minimal[\"Image name\"].astype(str)", "_____no_output_____" ], [ "expw_minimal.shape", "_____no_output_____" ] ], [ [ "### Histogram of expression distribution", "_____no_output_____" ] ], [ [ "x_ticks = [f\"{idx} = {expr}, count: {count}\" for idx, (expr, count) in enumerate(zip(list(expressions_single.value_counts().index.get_level_values(0)), expressions_single.value_counts().values))]", "_____no_output_____" ], [ "x_ticks", "_____no_output_____" ], [ "ax = expressions_single.value_counts().plot(kind='barh')\nax.set_yticklabels(x_ticks)", "_____no_output_____" ] ], [ [ "### Create a csv file with all absolute image paths for annotating with FairFace", "_____no_output_____" ] ], [ [ "col_name = \"img_path\"", "_____no_output_____" ], [ "image_names = expw[[\"Image name\"]]", "_____no_output_____" ], [ "image_names.head()", "_____no_output_____" ], [ "image_names.rename(columns={\"Image name\": \"img_path\"}, inplace=True)", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/frame.py:4296: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n return super().rename(\n" ], [ "image_names.loc[:, \"img_path\"] = data_dir + \"/\" + image_names[\"img_path\"].astype(str)", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/indexing.py:1783: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n self.obj[item_labels[indexer[info_axis]]] = value\n" ], [ "save_path = \"/home/steffi/dev/independent_study/FairFace/expw_image_paths.csv\"", "_____no_output_____" ], [ "image_names.to_csv(save_path, index=False)", "_____no_output_____" ] ], [ [ "### Filter only img_paths which contain \"black\", \"African\", \"chinese\", \"asian\"", "_____no_output_____" ] ], [ [ "black = image_names.loc[image_names.img_path.str.contains('(black)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "african = image_names.loc[image_names.img_path.str.contains('(African)'), :]", "_____no_output_____" ], [ "asian = image_names.loc[image_names.img_path.str.contains('(asian)'), :]", "_____no_output_____" ], [ "chinese = image_names.loc[image_names.img_path.str.contains('(chinese)'), :]", "_____no_output_____" ], [ "filtered = pd.concat([black, african, asian, chinese])", "_____no_output_____" ] ], [ [ "### Filter and save subgroups", "_____no_output_____" ], [ "##### Anger", "_____no_output_____" ] ], [ [ "black_angry_annoyed = black.loc[image_names.img_path.str.contains('(angry)|(annoyed)'), :]", "_____no_output_____" ], [ "black_angry_annoyed.to_csv(\"/home/steffi/dev/independent_study/FairFace/black_angry_annoyed.csv\", index=False)", "_____no_output_____" ], [ "black_angry_annoyed.head()", "_____no_output_____" ], [ "african_angry_annoyed = african.loc[image_names.img_path.str.contains('(angry)|(annoyed)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "african_angry_annoyed.to_csv(\"/home/steffi/dev/independent_study/FairFace/african_angry_annoyed.csv\", index=False)", "_____no_output_____" ], [ "african_angry_annoyed.shape", "_____no_output_____" ], [ "asian_angry_annoyed = asian.loc[image_names.img_path.str.contains('(angry)|(annoyed)'), :]", "_____no_output_____" ], [ "asian_angry_annoyed", "_____no_output_____" ], [ "asian_angry_annoyed.to_csv(\"/home/steffi/dev/independent_study/FairFace/asian_angry_annoyed.csv\", index=False)", "_____no_output_____" ], [ "chinese_angry_annoyed = chinese.loc[image_names.img_path.str.contains('(angry)|(annoyed)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "chinese_angry_annoyed.head()", "_____no_output_____" ], [ "chinese_angry_annoyed.to_csv(\"/home/steffi/dev/independent_study/FairFace/chinese_angry_annoyed.csv\", index=False)", "_____no_output_____" ] ], [ [ "##### Surprise", "_____no_output_____" ] ], [ [ "black_awe_astound_amazed = black.loc[image_names.img_path.str.contains('(awe)|(astound)|(amazed)'), :]", "_____no_output_____" ], [ "black_awe_astound_amazed", "_____no_output_____" ], [ "black_awe_astound_amazed.to_csv(\"/home/steffi/dev/independent_study/FairFace/black_awe_astound_amazed.csv\", index=False)", "_____no_output_____" ], [ "african_awe = african.loc[image_names.img_path.str.contains('(awe)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "african_awe", "_____no_output_____" ], [ "african_awe.to_csv(\"/home/steffi/dev/independent_study/FairFace/african_awe.csv\", index=False)", "_____no_output_____" ], [ "asian_astound = asian.loc[image_names.img_path.str.contains('(astound)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "asian_astound.to_csv(\"/home/steffi/dev/independent_study/FairFace/asian_astound.csv\", index=False)", "_____no_output_____" ], [ "asian_astound", "_____no_output_____" ], [ "chinese_astound = chinese.loc[image_names.img_path.str.contains('(astound)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "chinese_astound.to_csv(\"/home/steffi/dev/independent_study/FairFace/chinese_astound.csv\", index=False)", "_____no_output_____" ], [ "chinese_astound", "_____no_output_____" ] ], [ [ "#### Fear", "_____no_output_____" ] ], [ [ "black_fear = black.loc[image_names.img_path.str.contains('(fear)|(frightened)|(anxious)|(shocked)'), :]", "_____no_output_____" ], [ "black_fear.shape", "_____no_output_____" ], [ "african_fear = african.loc[image_names.img_path.str.contains('(fear)|(frightened)|(anxious)|(shocked)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "black_african_fear = pd.concat([african_fear, black_fear])", "_____no_output_____" ], [ "black_african_fear.shape", "_____no_output_____" ], [ "black_african_fear.to_csv(\"/home/steffi/dev/independent_study/FairFace/black_african_fear.csv\", index=False)", "_____no_output_____" ] ], [ [ "#### Disgust", "_____no_output_____" ] ], [ [ "black_disgust = black.loc[image_names.img_path.str.contains('(distaste)|(disgust)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "african_digsust = african.loc[image_names.img_path.str.contains('(distaste)|(disgust)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "african_digsust.shape", "_____no_output_____" ], [ "black_african_disgust = pd.concat([black_disgust, african_digsust])", "_____no_output_____" ], [ "pd.set_option('display.max_colwidth', -1)", "<ipython-input-55-0891b765a168>:1: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.\n pd.set_option('display.max_colwidth', -1)\n" ], [ "black_african_disgust", "_____no_output_____" ], [ "black_african_disgust.to_csv(\"/home/steffi/dev/independent_study/FairFace/black_african_disgust.csv\", index=False)", "_____no_output_____" ], [ "disgust_all = image_names.loc[image_names.img_path.str.contains('(distaste)|(disgust)'), :]", "/home/steffi/.conda/envs/InferFace/lib/python3.8/site-packages/pandas/core/strings.py:2001: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n return func(self, *args, **kwargs)\n" ], [ "disgust_all.shape", "_____no_output_____" ], [ "disgust_all", "_____no_output_____" ] ], [ [ "#### Saving all filtered to csv", "_____no_output_____" ] ], [ [ "filtered_save_path = \"/home/steffi/dev/independent_study/FairFace/filtered_expw_image_paths.csv\"", "_____no_output_____" ], [ "filtered.to_csv(filtered_save_path, index=False)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport numpy as np\nimport random\nimport os\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, classification_report\n%matplotlib inline", "_____no_output_____" ], [ "!fasttext", "usage: fasttext <command> <args>\n\nThe commands supported by fasttext are:\n\n supervised train a supervised classifier\n quantize quantize a model to reduce the memory usage\n test evaluate a supervised classifier\n predict predict most likely labels\n predict-prob predict most likely labels with probabilities\n skipgram train a skipgram model\n cbow train a cbow model\n print-word-vectors print word vectors given a trained model\n print-sentence-vectors print sentence vectors given a trained model\n print-ngrams print ngrams given a trained model and word\n nn query for nearest neighbors\n analogies query for analogies\n dump dump arguments,dictionary,input/output vectors\n\n" ], [ "def fasttext_predict_prob(model_file_name,query_list,encoding='utf-8'):\n cmd = 'fastText.exe'\n fp = open('temp.txt','wt',encoding=encoding)\n fp.writelines([x+'\\n' for x in query_list])\n fp.close()\n commands = ' predict-prob '+model_file_name+' '+'temp.txt '+str(1)\n print('Executing : ' ,cmd+commands)\n out = os.popen(cmd+commands).read()\n out = out.splitlines()\n return [item.split(' ') for item in out]", "_____no_output_____" ], [ "fp = open('dbpedia_csv/classes.txt','rt')\nclass_names = fp.readlines()\nfp.close()\nclass_names = [x.strip() for x in class_names]\nclass_names", "_____no_output_____" ], [ "fp = open('dbpedia.test','rt',encoding='utf-8')\nlines = fp.readlines()\nfp.close()", "_____no_output_____" ], [ "labels = []\nqlist = []\nfor line in lines:\n i = line.index(',')\n labels.append(line[:i])\n qlist.append(line[i+1:].strip())", "_____no_output_____" ], [ "labels = [x.split('__')[-1].strip() for x in labels]", "_____no_output_____" ], [ "out = fasttext_predict_prob('models/dbpedia_1.bin',qlist)", "Executing : fastText.exe predict-prob models/dbpedia_1.bin temp.txt 1\n" ], [ "predicted = [x[0] for x in out]\npredicted = [x.split('__')[-1].strip() for x in predicted]", "_____no_output_____" ], [ "y_true = [class_names.index(x) for x in labels]\ny_pred = [class_names.index(x) for x in predicted]", "_____no_output_____" ], [ "cm = confusion_matrix(labels, predicted, labels=class_names)\nprint (\"Confusion matrix \\n\",cm)", "Confusion matrix \n [[4767 39 8 4 14 34 50 3 2 0 7 12 9 51]\n [ 33 4924 0 0 5 1 30 1 1 0 0 0 2 3]\n [ 18 3 4772 13 84 0 8 1 0 0 0 27 17 57]\n [ 3 1 18 4960 14 0 0 1 0 2 0 0 1 0]\n [ 5 3 47 10 4923 2 4 0 2 1 0 0 1 2]\n [ 24 1 0 0 4 4957 8 1 0 0 2 0 1 2]\n [ 46 25 1 0 6 7 4880 21 7 2 0 1 1 3]\n [ 2 1 0 0 1 0 14 4973 9 0 0 0 0 0]\n [ 0 0 1 0 1 0 8 11 4978 0 0 0 0 1]\n [ 2 0 0 0 0 1 0 6 0 4940 50 0 0 1]\n [ 11 1 0 0 0 0 1 0 0 8 4978 0 0 1]\n [ 1 0 10 1 0 0 1 0 0 0 0 4956 19 12]\n [ 5 1 3 1 1 2 1 1 0 0 1 14 4937 33]\n [ 36 4 15 2 8 3 4 1 1 0 3 5 36 4882]]\n" ], [ "print (\" Classification Report \\n\",classification_report(y_true, y_pred, target_names=class_names,digits=3))", " Classification Report \n precision recall f1-score support\n\n Company 0.962 0.953 0.958 5000\nEducationalInstitution 0.984 0.985 0.985 5000\n Artist 0.979 0.954 0.966 5000\n Athlete 0.994 0.992 0.993 5000\n OfficeHolder 0.973 0.985 0.979 5000\n MeanOfTransportation 0.990 0.991 0.991 5000\n Building 0.974 0.976 0.975 5000\n NaturalPlace 0.991 0.995 0.993 5000\n Village 0.996 0.996 0.996 5000\n Animal 0.997 0.988 0.993 5000\n Plant 0.988 0.996 0.992 5000\n Album 0.988 0.991 0.990 5000\n Film 0.983 0.987 0.985 5000\n WrittenWork 0.967 0.976 0.972 5000\n\n accuracy 0.983 70000\n macro avg 0.983 0.983 0.983 70000\n weighted avg 0.983 0.983 0.983 70000\n\n" ] ] ]

[ "code" ]

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[ "CC-BY-4.0" ]

[ "CC-BY-4.0" ]

[ "CC-BY-4.0" ]

[ [ [ "# T M V A_Tutorial_Classification_Tmva_App\nTMVA example, for classification\n with following objectives:\n * Apply a BDT with TMVA\n\n\n\n\n**Author:** Lailin XU \n<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository on Tuesday, April 27, 2021 at 01:21 AM.</small></i>", "_____no_output_____" ] ], [ [ "from ROOT import TMVA, TFile, TTree, TCut, TH1F, TCanvas, gROOT, TLegend\nfrom subprocess import call\nfrom os.path import isfile\nfrom array import array\n \ngROOT.SetStyle(\"ATLAS\")", "Welcome to JupyROOT 6.22/07\n" ] ], [ [ "Setup TMVA", "_____no_output_____" ] ], [ [ "TMVA.Tools.Instance()", "_____no_output_____" ] ], [ [ "Reader. One reader for each application.", "_____no_output_____" ] ], [ [ "reader = TMVA.Reader(\"Color:!Silent\")\nreader_S = TMVA.Reader(\"Color:!Silent\")\nreader_B = TMVA.Reader(\"Color:!Silent\")\n ", "_____no_output_____" ] ], [ [ "Inputs\n=============\nLoad data\nAn unknown sample", "_____no_output_____" ] ], [ [ "trfile = \"Zp2TeV_ttbar.root\"\ndata = TFile.Open(trfile)\ntree = data.Get('tree')", "_____no_output_____" ] ], [ [ "Known signal", "_____no_output_____" ] ], [ [ "trfile_S = \"Zp1TeV_ttbar.root\"\ndata_S = TFile.Open(trfile_S)\ntree_S = data_S.Get('tree')", "_____no_output_____" ] ], [ [ "Known background", "_____no_output_____" ] ], [ [ "trfile_B = \"SM_ttbar.root\"\ndata_B = TFile.Open(trfile_B)\ntree_B = data_B.Get('tree')\n ", "_____no_output_____" ] ], [ [ "Set input variables. Do this for each reader", "_____no_output_____" ] ], [ [ "branches = {}\nfor branch in tree.GetListOfBranches():\n branchName = branch.GetName()\n branches[branchName] = array('f', [-999])\n tree.SetBranchAddress(branchName, branches[branchName])\n if branchName not in [\"mtt_truth\", \"weight\", \"nlep\", \"njets\"]:\n reader.AddVariable(branchName, branches[branchName])\n\nbranches_S = {}\nfor branch in tree_S.GetListOfBranches():\n branchName = branch.GetName()\n branches_S[branchName] = array('f', [-999])\n tree_S.SetBranchAddress(branchName, branches_S[branchName])\n if branchName not in [\"mtt_truth\", \"weight\", \"nlep\", \"njets\"]:\n reader_S.AddVariable(branchName, branches_S[branchName])\n\nbranches_B = {}\nfor branch in tree_B.GetListOfBranches():\n branchName = branch.GetName()\n branches_B[branchName] = array('f', [-999])\n tree_B.SetBranchAddress(branchName, branches_B[branchName])\n if branchName not in [\"mtt_truth\", \"weight\", \"nlep\", \"njets\"]:\n reader_B.AddVariable(branchName, branches_B[branchName])\n ", "_____no_output_____" ] ], [ [ "Book method(s)\n=============\nBDT", "_____no_output_____" ] ], [ [ "methodName1 = \"BDT\"\nweightfile = 'dataset/weights/TMVAClassification_{0}.weights.xml'.format(methodName1)\nreader.BookMVA( methodName1, weightfile )\nreader_S.BookMVA( methodName1, weightfile )\nreader_B.BookMVA( methodName1, weightfile )", "_____no_output_____" ] ], [ [ "BDTG", "_____no_output_____" ] ], [ [ "methodName2 = \"BDTG\"\nweightfile = 'dataset/weights/TMVAClassification_{0}.weights.xml'.format(methodName2)\nreader.BookMVA( methodName2, weightfile )\nreader_S.BookMVA( methodName2, weightfile )\nreader_B.BookMVA( methodName2, weightfile )", "_____no_output_____" ] ], [ [ "Loop events for evaluation\n================", "_____no_output_____" ], [ "Book histograms", "_____no_output_____" ] ], [ [ "nbins, xmin, xmax=20, -1, 1", "_____no_output_____" ] ], [ [ "Signal", "_____no_output_____" ] ], [ [ "tag = \"S\"\nhname=\"BDT_{0}\".format(tag)\nh1 = TH1F(hname, hname, nbins, xmin, xmax)\nh1.Sumw2()\nhname=\"BDTG_{0}\".format(tag)\nh2 = TH1F(hname, hname, nbins, xmin, xmax)\nh2.Sumw2()\n\nnevents = tree_S.GetEntries()\nfor i in range(nevents):\n tree_S.GetEntry(i)\n\n BDT = reader_S.EvaluateMVA(methodName1)\n BDTG = reader_S.EvaluateMVA(methodName2)\n h1.Fill(BDT)\n h2.Fill(BDTG)", "_____no_output_____" ] ], [ [ "Background", "_____no_output_____" ] ], [ [ "tag = \"B\"\nhname=\"BDT_{0}\".format(tag)\nh3 = TH1F(hname, hname, nbins, xmin, xmax)\nh3.Sumw2()\nhname=\"BDTG_{0}\".format(tag)\nh4 = TH1F(hname, hname, nbins, xmin, xmax)\nh4.Sumw2()\n\nnevents = tree_B.GetEntries()\nfor i in range(nevents):\n tree_B.GetEntry(i)\n\n BDT = reader_B.EvaluateMVA(methodName1)\n BDTG = reader_B.EvaluateMVA(methodName2)\n h3.Fill(BDT)\n h4.Fill(BDTG)", "_____no_output_____" ] ], [ [ "New sample", "_____no_output_____" ] ], [ [ "tag = \"N\"\nhname=\"BDT_{0}\".format(tag)\nh5 = TH1F(hname, hname, nbins, xmin, xmax)\nh5.Sumw2()\nhname=\"BDTG_{0}\".format(tag)\nh6 = TH1F(hname, hname, nbins, xmin, xmax)\nh6.Sumw2()\n\nnevents = tree.GetEntries()\nfor i in range(nevents):\n tree.GetEntry(i)\n\n BDT = reader.EvaluateMVA(methodName1)\n BDTG = reader.EvaluateMVA(methodName2)\n h5.Fill(BDT)\n h6.Fill(BDTG)", "_____no_output_____" ] ], [ [ "Helper function to normalize hists", "_____no_output_____" ] ], [ [ "def norm_hists(h):\n\n h_new = h.Clone()\n hname = h.GetName() + \"_normalized\"\n h_new.SetName(hname)\n h_new.SetTitle(hname)\n ntot = h.Integral()\n if ntot!=0:\n h_new.Scale(1./ntot)\n\n return h_new", "_____no_output_____" ] ], [ [ "Plotting", "_____no_output_____" ] ], [ [ "myc = TCanvas(\"c\", \"c\", 800, 600)\nmyc.SetFillColor(0)\nmyc.cd()", "_____no_output_____" ] ], [ [ "Compare the performance for BDT", "_____no_output_____" ] ], [ [ "nh1 = norm_hists(h1)\nnh1.GetXaxis().SetTitle(\"BDT\")\nnh1.GetYaxis().SetTitle(\"A.U.\")\nnh1.Draw(\"hist\")\nnh3 = norm_hists(h3)\nnh3.SetLineColor(2)\nnh3.SetMarkerColor(2)\nnh3.Draw(\"same hist\")\nnh5 = norm_hists(h5)\nnh5.SetLineColor(4)\nnh5.SetMarkerColor(4)\nnh5.Draw(\"same\")\n\nymin = 0\nymax = max(nh1.GetMaximum(), nh3.GetMaximum(), nh5.GetMaximum())\nnh1.GetYaxis().SetRangeUser(ymin, ymax*1.5)", "_____no_output_____" ] ], [ [ "Draw legends", "_____no_output_____" ] ], [ [ "lIy = 0.92\nlg = TLegend(0.60, lIy-0.25, 0.85, lIy)\nlg.SetBorderSize(0)\nlg.SetFillStyle(0)\nlg.SetTextFont(42)\nlg.SetTextSize(0.04)\nlg.AddEntry(nh1, \"Signal 1 TeV\", \"l\")\nlg.AddEntry(nh3, \"Background\", \"l\")\nlg.AddEntry(nh5, \"Signal 2 TeV\", \"l\")\nlg.Draw()\n\nmyc.Draw()\nmyc.SaveAs(\"TMVA_tutorial_cla_app_1.png\")", "Info in <TCanvas::Print>: png file TMVA_tutorial_cla_app_1.png has been created\n" ] ], [ [ "Compare the performance for BDTG", "_____no_output_____" ] ], [ [ "nh1 = norm_hists(h2)\nnh1.GetXaxis().SetTitle(\"BDTG\")\nnh1.GetYaxis().SetTitle(\"A.U.\")\nnh1.Draw(\"hist\")\nnh3 = norm_hists(h4)\nnh3.SetLineColor(2)\nnh3.SetMarkerColor(2)\nnh3.Draw(\"same hist\")\nnh5 = norm_hists(h6)\nnh5.SetLineColor(4)\nnh5.SetMarkerColor(4)\nnh5.Draw(\"same\")\n\nymin = 0\nymax = max(nh1.GetMaximum(), nh3.GetMaximum(), nh5.GetMaximum())\nnh1.GetYaxis().SetRangeUser(ymin, ymax*1.5)", "_____no_output_____" ] ], [ [ "Draw legends", "_____no_output_____" ] ], [ [ "lIy = 0.92\nlg = TLegend(0.60, lIy-0.25, 0.85, lIy)\nlg.SetBorderSize(0)\nlg.SetFillStyle(0)\nlg.SetTextFont(42)\nlg.SetTextSize(0.04)\nlg.AddEntry(nh1, \"Signal 1 TeV\", \"l\")\nlg.AddEntry(nh3, \"Background\", \"l\")\nlg.AddEntry(nh5, \"Signal 2 TeV\", \"l\")\nlg.Draw()\n\nmyc.Draw()\nmyc.SaveAs(\"TMVA_tutorial_cla_app_2.png\")", "Info in <TCanvas::Print>: png file TMVA_tutorial_cla_app_2.png has been created\n" ] ], [ [ "Draw all canvases ", "_____no_output_____" ] ], [ [ "from ROOT import gROOT \ngROOT.GetListOfCanvases().Draw()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Data Processing ", "_____no_output_____" ] ], [ [ "%pylab inline\nmatplotlib.rcParams['figure.figsize'] = [20, 10]\nimport pandas as pd\nimport numpy as np\nimport warnings\nwarnings.filterwarnings(\"ignore\")", "Populating the interactive namespace from numpy and matplotlib\n" ], [ "# All variables we concern about\ncolumnNames1 = [\"releaseNum\", \"1968ID\", \"personNumber\", \"gender\", \"marriage\", \"familyNumber\", \"sequenceNum\", \n \"relationToHead\", \"age\", 'employmentStatus', \"education\", \"nonHeadlaborIncome\"]\n\ncolumnNames2 = [\"releaseNum\", \"1968ID\", \"personNumber\", \"gender\", \"marriage\", \"familyNumber\", \"sequenceNum\", \n \"relationToHead\", \"age\", 'employmentStatus', \"education\"]\n\nFcolumnNames1999_2001 = ['releaseNum', 'familyID', 'composition', 'headCount', 'ageHead', 'maritalStatus', 'employmentStatus', \n 'liquidWealth', 'race', 'industry' ,'geoCode','incomeHead', \"incomeWife\", \n 'foodCost', 'houseCost', 'transCost', 'educationCost', 'childCost', 'healthCost', 'education', \n 'participation', 'investmentAmount', 'annuityIRA', 'wealthWithoutHomeEquity', \"wealthWithHomeEquity\"]\n\nFcolumnNames2003_2007 = ['releaseNum', 'familyID', 'composition', 'headCount', 'ageHead', 'maritalStatus', 'employmentStatus', \n 'liquidWealth', 'race', 'industry', 'incomeHead', \"incomeWife\", \n 'foodCost', 'houseCost', 'transCost', 'educationCost', 'childCost', 'healthCost', 'geoCode', 'education', \n 'participation', 'investmentAmount', 'annuityIRA', 'wealthWithoutHomeEquity', \"wealthWithHomeEquity\"]\n\nFcolumnNames2019 = ['releaseNum', 'familyID', 'composition', 'headCount', 'ageHead', 'maritalStatus', 'employmentStatus', \n 'liquidWealth', 'race', 'industry' ,'incomeHead', 'incomeWife', \n 'participation', 'investmentAmount', 'annuityIRA', 'wealthWithoutHomeEquity', 'wealthWithHomeEquity',\n 'foodCost', 'houseCost', 'transCost', 'educationCost', 'childCost', 'healthCost', 'geoCode', 'education']\n# The timeline we care about\nyears = [1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017]\n\n# The function used to complile all years data into one dataFrame, \n# the input \"features\" is a list of features.\ndef compile_data_with_features(features, years):\n df = pd.DataFrame()\n # Loading the data through years\n for year in years:\n df_sub = pd.read_excel(\"individual/\" + str(year) + \".xlsx\")\n if year >= 2005:\n df_sub.columns = columnNames1\n df_sub['year'] = year\n df = pd.concat([df, df_sub[['year'] + features + [\"nonHeadlaborIncome\"]]])\n else:\n df_sub.columns = columnNames2\n df_sub['year'] = year\n df = pd.concat([df, df_sub[['year'] + features]])\n df = df.reset_index(drop = True)\n return df\n\ndef Fcompile_data_with_features(features, years):\n df = pd.DataFrame()\n # Loading the data through years\n for year in years:\n df_sub = pd.read_excel(\"family/\" + str(year) + \".xlsx\")\n if year >= 1999 and year <= 2001:\n df_sub.columns = FcolumnNames1999_2001\n elif year >= 2003 and year <= 2007:\n df_sub.columns = FcolumnNames2003_2007\n else:\n df_sub.columns = FcolumnNames2019\n df_sub['year'] = year\n df = pd.concat([df, df_sub[['familyID','year'] + features]])\n df = df.reset_index(drop = True)\n return df\n\n# The function is used to drop the values we do not like in the dataFrame, \n# the input \"features\" and \"values\" are both list\ndef drop_values(features, values, df): \n for feature in features:\n for value in values:\n df = df[df[feature] != value]\n df = df.reset_index(drop = True)\n return df", "_____no_output_____" ] ], [ [ "### Individual Data", "_____no_output_____" ] ], [ [ "Idf = compile_data_with_features([\"1968ID\", \"personNumber\", \"familyNumber\",\"gender\", \"marriage\", \n \"age\", 'employmentStatus', \"education\", \"relationToHead\"], years)\nIdf[\"ID\"] = Idf[\"1968ID\"]* 1000 + Idf[\"personNumber\"]\n# pick out the head in the individual\ndf_head = Idf[Idf[\"relationToHead\"] == 10]\ndf_head = df_head.reset_index(drop = True)\n# compile individuals with all 10 years data.\ncompleteIndividualData = []\nfor ID, value in df_head.groupby(\"ID\"):\n if len(value) == len(years):\n completeIndividualData.append(value)\nprint(\"Number of heads with complete data: \", len(completeIndividualData))", "Number of heads with complete data: 3074\n" ], [ "# prepare the combined dataset and set up dummy variables for qualitative data\ndf = Fcompile_data_with_features(['composition', 'headCount', 'ageHead', 'maritalStatus', 'employmentStatus', \n 'liquidWealth', 'race', 'industry' ,'geoCode','incomeHead', \"incomeWife\", \n 'foodCost', 'houseCost', 'transCost', 'educationCost', 'childCost', \n 'healthCost', 'education', 'participation', 'investmentAmount', 'annuityIRA', \n 'wealthWithoutHomeEquity', \"wealthWithHomeEquity\"], years)", "_____no_output_____" ] ], [ [ "### Family Data", "_____no_output_____" ] ], [ [ "# prepare the combined dataset and set up dummy variables for qualitative data\ndf = Fcompile_data_with_features(['composition', 'headCount', 'ageHead', 'maritalStatus', 'employmentStatus', \n 'liquidWealth', 'race', 'industry' ,'geoCode','incomeHead', \"incomeWife\", \n 'foodCost', 'houseCost', 'transCost', 'educationCost', 'childCost', \n 'healthCost', 'education', 'participation', 'investmentAmount', 'annuityIRA', \n 'wealthWithoutHomeEquity', \"wealthWithHomeEquity\"], years)\n\ndf = drop_values([\"ageHead\"],[999], df)\ndf = drop_values([\"maritalStatus\"],[8,9], df)\ndf = drop_values([\"employmentStatus\"],[0, 22, 98, 99], df)\ndf = drop_values([\"liquidWealth\"],[999999998,999999999], df)\ndf = drop_values([\"race\"],[0,8,9], df)\ndf = drop_values([\"industry\"],[999,0], df)\ndf = drop_values([\"education\"],[99,0], df)\ndf[\"totalExpense\"] = df[['foodCost', 'houseCost', 'transCost', \n 'educationCost', 'childCost', 'healthCost']].sum(axis = 1)\ndf[\"laborIncome\"] = df[\"incomeHead\"] + df[\"incomeWife\"]\ndf[\"costPerPerson\"] = df[\"totalExpense\"]/df[\"headCount\"]\n\n\n\nmaritalStatus = [\"Married\", \"neverMarried\", \"Widowed\", \"Divorced\", \"Separated\"]\nemploymentStatus = [\"Working\", \"temporalLeave\", \"unemployed\", \"retired\", \"disabled\", \"keepHouse\", \"student\", \"other\"]\nrace = [\"White\", \"Black\",\"AmericanIndian\",\"Asian\",\"Latino\",\"otherBW\",\"otherRace\"]\n# Education\n# < 8th grade: middle school\n# >= 8 and < 12: high scho0l\n# >=12 and < 15: college\n# >= 15 post graduate\neducation = [\"middleSchool\", \"highSchool\", \"college\", \"postGraduate\"]\n# Industry\n# < 400 manufacturing\n# >= 400 and < 500 publicUtility\n# >= 500 and < 680 retail \n# >= 680 and < 720 finance\n# >= 720 and < 900 service\n# >= 900 otherIndustry\nindustry = [\"manufacturing\", \"publicUtility\", \"retail\", \"finance\", \"service\", \"otherIndustry\"]\ndata = []\nfor i in range(len(df)):\n dataCollect = []\n # marital status\n dataCollect.append(maritalStatus[int(df.iloc[i][\"maritalStatus\"]-1)])\n # employment\n dataCollect.append(employmentStatus[int(df.iloc[i][\"employmentStatus\"]-1)])\n # race\n dataCollect.append(race[int(df.iloc[i][\"race\"] - 1)])\n # Education variable \n if df.iloc[i][\"education\"] < 8:\n dataCollect.append(education[0])\n elif df.iloc[i][\"education\"] >= 8 and df.iloc[i][\"education\"] < 12:\n dataCollect.append(education[1])\n elif df.iloc[i][\"education\"] >= 12 and df.iloc[i][\"education\"] < 15:\n dataCollect.append(education[2])\n else:\n dataCollect.append(education[3])\n # industry variable \n if df.iloc[i][\"industry\"] < 400:\n dataCollect.append(industry[0])\n elif df.iloc[i][\"industry\"] >= 400 and df.iloc[i][\"industry\"] < 500:\n dataCollect.append(industry[1])\n elif df.iloc[i][\"industry\"] >= 500 and df.iloc[i][\"industry\"] < 680:\n dataCollect.append(industry[2])\n elif df.iloc[i][\"industry\"] >= 680 and df.iloc[i][\"industry\"] < 720:\n dataCollect.append(industry[3])\n elif df.iloc[i][\"industry\"] >= 720 and df.iloc[i][\"industry\"] < 900:\n dataCollect.append(industry[4])\n else:\n dataCollect.append(industry[5])\n data.append(dataCollect)\n# Categorical dataFrame\ndf_cat = pd.DataFrame(data, columns = [\"maritalStatus\", \"employmentStatus\", \"race\", \"education\", \"industry\"])", "_____no_output_____" ], [ "Fdf = pd.concat([df[[\"familyID\", \"year\",'composition', 'headCount', 'ageHead', 'liquidWealth', 'laborIncome', \n \"costPerPerson\",\"totalExpense\", 'participation', 'investmentAmount', 'annuityIRA', \n 'wealthWithoutHomeEquity', \"wealthWithHomeEquity\"]], \n df_cat[[\"maritalStatus\", \"employmentStatus\", \"education\",\"race\", \"industry\"]]], axis=1)\n# Adjust for inflation. \nyears = [1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017]\nvalues_at2020 = np.array([1.55, 1.46, 1.40, 1.32, 1.24, 1.20, 1.15, 1.11, 1.09, 1.05])\nvalues_at2005 = values_at2020/1.32\nvalues_at2005\nquantVariables = ['annuityIRA', 'investmentAmount', 'liquidWealth', 'laborIncome', 'costPerPerson','costPerPerson',\n 'totalExpense', 'wealthWithoutHomeEquity', 'wealthWithHomeEquity']\nfor i in range(len(Fdf)):\n for variable in quantVariables:\n Fdf.at[i, variable] = round(Fdf.at[i, variable] * values_at2005[years.index(Fdf.at[i,\"year\"])], 2)", "_____no_output_____" ] ], [ [ "### Link Family Data with Individual Head Data", "_____no_output_____" ] ], [ [ "completeFamilyData = []\nfor individual in completeIndividualData:\n idf = pd.DataFrame()\n for i in range(len(individual)): \n idf = pd.concat([idf, Fdf[(Fdf.year == individual.iloc[i].year)&\n (Fdf.familyID == individual.iloc[i].familyNumber)]])\n completeFamilyData.append(idf.set_index(\"year\", drop = True))", "_____no_output_____" ], [ "FamilyData = [f for f in completeFamilyData if len(f) == len(years)]\nlen(FamilyData)", "_____no_output_____" ], [ "# skilled definition with college and postGraduate\nskilled_index = []\nfor i in range(1973):\n if \"postGraduate\" in FamilyData[i].education.values or \"college\" in FamilyData[i].education.values:\n skilled_index.append(i)\nlen(skilled_index)", "_____no_output_____" ], [ "# skilled definition with postGraduate\nskilled_index = []\nfor i in range(1973):\n if \"postGraduate\" in FamilyData[i].education.values:\n skilled_index.append(i)\nlen(skilled_index)", "_____no_output_____" ], [ "# working in the finance industry\nfinance_index = []\nfor i in range(1973):\n if \"finance\" in FamilyData[i].industry.values:\n finance_index.append(i)\nlen(finance_index)", "_____no_output_____" ], [ "a = FamilyData[randint(0, 1973)]\na", "_____no_output_____" ] ], [ [ "# Individual plot", "_____no_output_____" ] ], [ [ "def inFeaturePlot(FamilyData, feature, n):\n plt.figure()\n for i in range(n[0],n[1]):\n FamilyData[i][feature].plot(marker='o')\n plt.show()\n\ndef plotFeatureVsAge(FamilyData, feature, n):\n plt.figure()\n for i in range(n[0],n[1]):\n plt.plot(FamilyData[i].ageHead, FamilyData[i][feature], marker = 'o')\n plt.show()", "_____no_output_____" ], [ "inFeaturePlot(FamilyData,\"laborIncome\" , [1,100])", "_____no_output_____" ] ], [ [ "# Average variable plot", "_____no_output_____" ] ], [ [ "def plotFeature(FamilyData, feature):\n df = FamilyData[0][feature] * 0\n for i in range(len(FamilyData)):\n df = df + FamilyData[i][feature]\n df = df/len(FamilyData)\n df.plot(marker='o')\n print(df)", "_____no_output_____" ], [ "# laborIncome\nplotFeature(FamilyData, \"laborIncome\")", "year\n1999 58515.849468\n2001 62096.450076\n2003 61631.421186\n2005 62050.746072\n2007 60616.224024\n2009 61723.606183\n2011 55187.245819\n2013 55558.055246\n2015 51013.996452\n2017 48526.321338\nName: laborIncome, dtype: float64\n" ], [ "# laborIncome\nplotFeature(FamilyData, \"investmentAmount\")", "year\n1999 44860.393310\n2001 62282.585910\n2003 58126.453117\n2005 55852.234668\n2007 62082.949823\n2009 50626.348201\n2011 56326.484034\n2013 72089.909782\n2015 74328.152053\n2017 85350.660922\nName: investmentAmount, dtype: float64\n" ], [ "# Expenditure\nplotFeature(FamilyData, \"totalExpense\")", "year\n1999 32567.763675\n2001 34693.636589\n2003 34174.804709\n2005 40804.406837\n2007 41616.631510\n2009 39253.224445\n2011 38401.690223\n2013 37231.670071\n2015 36753.446087\n2017 35466.241521\nName: totalExpense, dtype: float64\n" ], [ "# wealthWithoutHomeEquity\nplotFeature(FamilyData, \"wealthWithoutHomeEquity\")", "year\n1999 189996.335023\n2001 209022.036493\n2003 215746.606690\n2005 240508.410542\n2007 294690.792701\n2009 265977.239736\n2011 264675.113533\n2013 263506.552458\n2015 319621.413077\n2017 325448.128738\nName: wealthWithoutHomeEquity, dtype: float64\n" ], [ "# wealthWithHomeEquity\nplotFeature(FamilyData, \"wealthWithHomeEquity\")", "year\n1999 249291.759757\n2001 280795.618855\n2003 299767.171313\n2005 348341.237202\n2007 414308.082108\n2009 361313.661936\n2011 357152.367968\n2013 354735.116067\n2015 423869.457172\n2017 440618.187532\nName: wealthWithHomeEquity, dtype: float64\n" ], [ "plotFeature(FamilyData, \"annuityIRA\")", "year\n1999 31462.509377\n2001 34869.478459\n2003 31720.994932\n2005 40220.458186\n2007 50619.510897\n2009 41815.880892\n2011 62674.657881\n2013 65190.544349\n2015 78211.521034\n2017 84070.374050\nName: annuityIRA, dtype: float64\n" ] ], [ [ "## Compare The Distribution Over Age", "_____no_output_____" ] ], [ [ "df = Fdf[(Fdf[\"ageHead\"]>=20) & (Fdf[\"ageHead\"]<=80)]\ndf[['liquidWealth', 'laborIncome', 'costPerPerson', 'totalExpense','investmentAmount', 'annuityIRA',\n 'wealthWithoutHomeEquity', 'wealthWithHomeEquity']] = df[['liquidWealth', 'laborIncome', 'costPerPerson', 'totalExpense','investmentAmount', 'annuityIRA',\n 'wealthWithoutHomeEquity', 'wealthWithHomeEquity']]/1000\ndf.shape", "_____no_output_____" ], [ "df.columns", "_____no_output_____" ], [ "ww = df.groupby(\"ageHead\")[\"liquidWealth\"].mean()\nnn = df.groupby(\"ageHead\")[\"annuityIRA\"].mean()\ncc = df.groupby(\"ageHead\")[\"totalExpense\"].mean()\nkk = df.groupby(\"ageHead\")[\"investmentAmount\"].mean()\nytyt = df.groupby(\"ageHead\")[\"laborIncome\"].mean()", "_____no_output_____" ], [ "plt.figure(figsize = [14,8])\nplt.plot(ww, label = \"wealth\")\nplt.plot(cc, label = \"Consumption\")\nplt.plot(kk, label = \"Stock\")\nplt.legend()", "_____no_output_____" ], [ "plt.plot(nn, label = \"IRA\")", "_____no_output_____" ], [ "np.save('nn',nn)", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from google.colab import drive\ndrive.mount('/content/drive')", "_____no_output_____" ], [ "from fastai import *\nfrom fastai.datasets import *\nfrom fastai.text import *\nimport pandas as pd\nfrom tqdm import tqdm", "_____no_output_____" ] ], [ [ "# Data", "_____no_output_____" ] ], [ [ "path = Path('/content/drive/My Drive/Archieve/ValueLabs'); path.ls()", "_____no_output_____" ], [ "train_df = pd.read_csv(path/'Train.csv'); train_df.head()", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "bs = 24", "_____no_output_____" ] ], [ [ "# LM", "_____no_output_____" ] ], [ [ "data_lm = (TextList\n .from_df(train_df,cols=['question', 'answer_text', 'distractor'])\n .split_by_rand_pct(0.1)\n .label_for_lm()\n .databunch(bs=bs)\n )", "_____no_output_____" ], [ "data_lm.save(path/'lm.pkl')", "_____no_output_____" ], [ "data_lm = load_data(path, 'lm.pkl', bs=bs)\ndata_lm.show_batch()", "_____no_output_____" ], [ "learn = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.3)", "_____no_output_____" ], [ "learn.lr_find()", "_____no_output_____" ], [ "learn.recorder.plot(skip_end=10)", "_____no_output_____" ], [ "learn.fit_one_cycle(1, 1e-2, moms=(0.8,0.7))", "_____no_output_____" ], [ "learn.save(path/'fit_head')", "_____no_output_____" ], [ "learn.load(path/'fit_head')", "_____no_output_____" ], [ "#@title Default title text\nvariable_name = \"hello\" #@param {type:\"string\"}", "_____no_output_____" ], [ "learn.fit_one_cycle(10, 1e-3, moms=(0.8,0.7))", "_____no_output_____" ], [ "learn.save(path/'fine_tuned')", "_____no_output_____" ], [ "learn.load(path/'fine_tuned')", "_____no_output_____" ], [ "learn.predict(\"hi what is the problem\",20)", "_____no_output_____" ], [ "test_df = pd.read_csv(path/'Test.csv');test_df.head()", "_____no_output_____" ], [ "combined = test_df['question']+test_df['answer_text']", "_____no_output_____" ], [ "combined.head()\ncombined.shape", "_____no_output_____" ], [ "output = []\nfor index, value in combined.iteritems():\n if index % 100 == 0: print(index)\n output.append(learn.predict(value))", "_____no_output_____" ], [ "import pickle", "_____no_output_____" ], [ "with open(path/'output_list','w') as f:\n pickle.dumps(output, f)", "_____no_output_____" ], [ "output[:5]", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# RadarCOVID-Report", "_____no_output_____" ], [ "## Data Extraction", "_____no_output_____" ] ], [ [ "import datetime\nimport json\nimport logging\nimport os\nimport shutil\nimport tempfile\nimport textwrap\nimport uuid\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker\nimport numpy as np\nimport pandas as pd\nimport retry\nimport seaborn as sns\n\n%matplotlib inline", "_____no_output_____" ], [ "current_working_directory = os.environ.get(\"PWD\")\nif current_working_directory:\n os.chdir(current_working_directory)\n\nsns.set()\nmatplotlib.rcParams[\"figure.figsize\"] = (15, 6)\n\nextraction_datetime = datetime.datetime.utcnow()\nextraction_date = extraction_datetime.strftime(\"%Y-%m-%d\")\nextraction_previous_datetime = extraction_datetime - datetime.timedelta(days=1)\nextraction_previous_date = extraction_previous_datetime.strftime(\"%Y-%m-%d\")\nextraction_date_with_hour = datetime.datetime.utcnow().strftime(\"%Y-%m-%d@%H\")\ncurrent_hour = datetime.datetime.utcnow().hour\nare_today_results_partial = current_hour != 23", "_____no_output_____" ] ], [ [ "### Constants", "_____no_output_____" ] ], [ [ "from Modules.ExposureNotification import exposure_notification_io\n\nspain_region_country_code = \"ES\"\ngermany_region_country_code = \"DE\"\n\ndefault_backend_identifier = spain_region_country_code\n\nbackend_generation_days = 7 * 2\ndaily_summary_days = 7 * 4 * 3\ndaily_plot_days = 7 * 4\ntek_dumps_load_limit = daily_summary_days + 1", "_____no_output_____" ] ], [ [ "### Parameters", "_____no_output_____" ] ], [ [ "environment_backend_identifier = os.environ.get(\"RADARCOVID_REPORT__BACKEND_IDENTIFIER\")\nif environment_backend_identifier:\n report_backend_identifier = environment_backend_identifier\nelse:\n report_backend_identifier = default_backend_identifier\nreport_backend_identifier", "_____no_output_____" ], [ "environment_enable_multi_backend_download = \\\n os.environ.get(\"RADARCOVID_REPORT__ENABLE_MULTI_BACKEND_DOWNLOAD\")\nif environment_enable_multi_backend_download:\n report_backend_identifiers = None\nelse:\n report_backend_identifiers = [report_backend_identifier]\n\nreport_backend_identifiers", "_____no_output_____" ], [ "environment_invalid_shared_diagnoses_dates = \\\n os.environ.get(\"RADARCOVID_REPORT__INVALID_SHARED_DIAGNOSES_DATES\")\nif environment_invalid_shared_diagnoses_dates:\n invalid_shared_diagnoses_dates = environment_invalid_shared_diagnoses_dates.split(\",\")\nelse:\n invalid_shared_diagnoses_dates = []\n\ninvalid_shared_diagnoses_dates", "_____no_output_____" ] ], [ [ "### COVID-19 Cases", "_____no_output_____" ] ], [ [ "report_backend_client = \\\n exposure_notification_io.get_backend_client_with_identifier(\n backend_identifier=report_backend_identifier)", "_____no_output_____" ], [ "@retry.retry(tries=10, delay=10, backoff=1.1, jitter=(0, 10))\ndef download_cases_dataframe_from_ecdc():\n return pd.read_csv(\n \"https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/data.csv\")\n\nconfirmed_df_ = download_cases_dataframe_from_ecdc()", "_____no_output_____" ], [ "confirmed_df = confirmed_df_.copy()\nconfirmed_df = confirmed_df[[\"dateRep\", \"cases\", \"geoId\"]]\nconfirmed_df.rename(\n columns={\n \"dateRep\":\"sample_date\",\n \"cases\": \"new_cases\",\n \"geoId\": \"country_code\",\n },\n inplace=True)\nconfirmed_df[\"sample_date\"] = pd.to_datetime(confirmed_df.sample_date, dayfirst=True)\nconfirmed_df[\"sample_date\"] = confirmed_df.sample_date.dt.strftime(\"%Y-%m-%d\")\nconfirmed_df.sort_values(\"sample_date\", inplace=True)\nconfirmed_df.tail()", "_____no_output_____" ], [ "def sort_source_regions_for_display(source_regions: list) -> list:\n if report_backend_identifier in source_regions:\n source_regions = [report_backend_identifier] + \\\n list(sorted(set(source_regions).difference([report_backend_identifier])))\n else:\n source_regions = list(sorted(source_regions))\n return source_regions", "_____no_output_____" ], [ "confirmed_days = pd.date_range(\n start=confirmed_df.iloc[0].sample_date,\n end=extraction_datetime)\nsource_regions_at_date_df = pd.DataFrame(data=confirmed_days, columns=[\"sample_date\"])\nsource_regions_at_date_df[\"source_regions_at_date\"] = \\\n source_regions_at_date_df.sample_date.apply(\n lambda x: report_backend_client.source_regions_for_date(date=x))\nsource_regions_at_date_df.sort_values(\"sample_date\", inplace=True)\nsource_regions_at_date_df[\"_source_regions_group\"] = source_regions_at_date_df. \\\n source_regions_at_date.apply(lambda x: \",\".join(sort_source_regions_for_display(x)))\nsource_regions_at_date_df[\"sample_date_string\"] = \\\n source_regions_at_date_df.sample_date.dt.strftime(\"%Y-%m-%d\")\nsource_regions_at_date_df.tail()", "_____no_output_____" ], [ "source_regions_for_summary_df = \\\n source_regions_at_date_df[[\"sample_date\", \"_source_regions_group\"]].copy()\nsource_regions_for_summary_df.rename(columns={\"_source_regions_group\": \"source_regions\"}, inplace=True)\nsource_regions_for_summary_df.head()", "_____no_output_____" ], [ "confirmed_output_columns = [\"sample_date\", \"new_cases\", \"covid_cases\"]\nconfirmed_output_df = pd.DataFrame(columns=confirmed_output_columns)\n\nfor source_regions_group, source_regions_group_series in \\\n source_regions_at_date_df.groupby(\"_source_regions_group\"):\n source_regions_set = set(source_regions_group.split(\",\"))\n confirmed_source_regions_set_df = \\\n confirmed_df[confirmed_df.country_code.isin(source_regions_set)].copy()\n confirmed_source_regions_group_df = \\\n confirmed_source_regions_set_df.groupby(\"sample_date\").new_cases.sum() \\\n .reset_index().sort_values(\"sample_date\")\n confirmed_source_regions_group_df[\"covid_cases\"] = \\\n confirmed_source_regions_group_df.new_cases.rolling(7, min_periods=0).mean().round()\n confirmed_source_regions_group_df = \\\n confirmed_source_regions_group_df[confirmed_output_columns]\n confirmed_source_regions_group_df.fillna(method=\"ffill\", inplace=True)\n confirmed_source_regions_group_df = \\\n confirmed_source_regions_group_df[\n confirmed_source_regions_group_df.sample_date.isin(\n source_regions_group_series.sample_date_string)]\n confirmed_output_df = confirmed_output_df.append(confirmed_source_regions_group_df)\n\nconfirmed_df = confirmed_output_df.copy()\nconfirmed_df.tail()", "_____no_output_____" ], [ "confirmed_df.rename(columns={\"sample_date\": \"sample_date_string\"}, inplace=True)\nconfirmed_df.sort_values(\"sample_date_string\", inplace=True)\nconfirmed_df.tail()", "_____no_output_____" ], [ "confirmed_df[[\"new_cases\", \"covid_cases\"]].plot()", "_____no_output_____" ] ], [ [ "### Extract API TEKs", "_____no_output_____" ] ], [ [ "raw_zip_path_prefix = \"Data/TEKs/Raw/\"\nfail_on_error_backend_identifiers = [report_backend_identifier]\nmulti_backend_exposure_keys_df = \\\n exposure_notification_io.download_exposure_keys_from_backends(\n backend_identifiers=report_backend_identifiers,\n generation_days=backend_generation_days,\n fail_on_error_backend_identifiers=fail_on_error_backend_identifiers,\n save_raw_zip_path_prefix=raw_zip_path_prefix)\nmulti_backend_exposure_keys_df[\"region\"] = multi_backend_exposure_keys_df[\"backend_identifier\"]\nmulti_backend_exposure_keys_df.rename(\n columns={\n \"generation_datetime\": \"sample_datetime\",\n \"generation_date_string\": \"sample_date_string\",\n },\n inplace=True)\nmulti_backend_exposure_keys_df.head()", "WARNING:root:NoKeysFoundException(\"No exposure keys found on endpoint 'https://radarcovidpre.covid19.gob.es/dp3t/v1/gaen/exposed/1604707200000' (parameters: {'generation_date': '2020-11-07', 'endpoint_identifier_components': ['2020-11-07'], 'backend_identifier': 'ES@PRE', 'server_endpoint_url': 'https://radarcovidpre.covid19.gob.es/dp3t'}).\")\n" ], [ "early_teks_df = multi_backend_exposure_keys_df[\n multi_backend_exposure_keys_df.rolling_period < 144].copy()\nearly_teks_df[\"rolling_period_in_hours\"] = early_teks_df.rolling_period / 6\nearly_teks_df[early_teks_df.sample_date_string != extraction_date] \\\n .rolling_period_in_hours.hist(bins=list(range(24)))", "_____no_output_____" ], [ "early_teks_df[early_teks_df.sample_date_string == extraction_date] \\\n .rolling_period_in_hours.hist(bins=list(range(24)))", "_____no_output_____" ], [ "multi_backend_exposure_keys_df = multi_backend_exposure_keys_df[[\n \"sample_date_string\", \"region\", \"key_data\"]]\nmulti_backend_exposure_keys_df.head()", "_____no_output_____" ], [ "active_regions = \\\n multi_backend_exposure_keys_df.groupby(\"region\").key_data.nunique().sort_values().index.unique().tolist()\nactive_regions", "_____no_output_____" ], [ "multi_backend_summary_df = multi_backend_exposure_keys_df.groupby(\n [\"sample_date_string\", \"region\"]).key_data.nunique().reset_index() \\\n .pivot(index=\"sample_date_string\", columns=\"region\") \\\n .sort_index(ascending=False)\nmulti_backend_summary_df.rename(\n columns={\"key_data\": \"shared_teks_by_generation_date\"},\n inplace=True)\nmulti_backend_summary_df.rename_axis(\"sample_date\", inplace=True)\nmulti_backend_summary_df = multi_backend_summary_df.fillna(0).astype(int)\nmulti_backend_summary_df = multi_backend_summary_df.head(backend_generation_days)\nmulti_backend_summary_df.head()", "_____no_output_____" ], [ "def compute_keys_cross_sharing(x):\n teks_x = x.key_data_x.item()\n common_teks = set(teks_x).intersection(x.key_data_y.item())\n common_teks_fraction = len(common_teks) / len(teks_x)\n return pd.Series(dict(\n common_teks=common_teks,\n common_teks_fraction=common_teks_fraction,\n ))\n\nmulti_backend_exposure_keys_by_region_df = \\\n multi_backend_exposure_keys_df.groupby(\"region\").key_data.unique().reset_index()\nmulti_backend_exposure_keys_by_region_df[\"_merge\"] = True\nmulti_backend_exposure_keys_by_region_combination_df = \\\n multi_backend_exposure_keys_by_region_df.merge(\n multi_backend_exposure_keys_by_region_df, on=\"_merge\")\nmulti_backend_exposure_keys_by_region_combination_df.drop(\n columns=[\"_merge\"], inplace=True)\nif multi_backend_exposure_keys_by_region_combination_df.region_x.nunique() > 1:\n multi_backend_exposure_keys_by_region_combination_df = \\\n multi_backend_exposure_keys_by_region_combination_df[\n multi_backend_exposure_keys_by_region_combination_df.region_x !=\n multi_backend_exposure_keys_by_region_combination_df.region_y]\nmulti_backend_exposure_keys_cross_sharing_df = \\\n multi_backend_exposure_keys_by_region_combination_df \\\n .groupby([\"region_x\", \"region_y\"]) \\\n .apply(compute_keys_cross_sharing) \\\n .reset_index()\nmulti_backend_cross_sharing_summary_df = \\\n multi_backend_exposure_keys_cross_sharing_df.pivot_table(\n values=[\"common_teks_fraction\"],\n columns=\"region_x\",\n index=\"region_y\",\n aggfunc=lambda x: x.item())\nmulti_backend_cross_sharing_summary_df", "<ipython-input-22-4e21708c19d8>:2: FutureWarning: `item` has been deprecated and will be removed in a future version\n teks_x = x.key_data_x.item()\n<ipython-input-22-4e21708c19d8>:3: FutureWarning: `item` has been deprecated and will be removed in a future version\n common_teks = set(teks_x).intersection(x.key_data_y.item())\n" ], [ "multi_backend_without_active_region_exposure_keys_df = \\\n multi_backend_exposure_keys_df[multi_backend_exposure_keys_df.region != report_backend_identifier]\nmulti_backend_without_active_region = \\\n multi_backend_without_active_region_exposure_keys_df.groupby(\"region\").key_data.nunique().sort_values().index.unique().tolist()\nmulti_backend_without_active_region", "_____no_output_____" ], [ "exposure_keys_summary_df = multi_backend_exposure_keys_df[\n multi_backend_exposure_keys_df.region == report_backend_identifier]\nexposure_keys_summary_df.drop(columns=[\"region\"], inplace=True)\nexposure_keys_summary_df = \\\n exposure_keys_summary_df.groupby([\"sample_date_string\"]).key_data.nunique().to_frame()\nexposure_keys_summary_df = \\\n exposure_keys_summary_df.reset_index().set_index(\"sample_date_string\")\nexposure_keys_summary_df.sort_index(ascending=False, inplace=True)\nexposure_keys_summary_df.rename(columns={\"key_data\": \"shared_teks_by_generation_date\"}, inplace=True)\nexposure_keys_summary_df.head()", "/opt/hostedtoolcache/Python/3.8.6/x64/lib/python3.8/site-packages/pandas/core/frame.py:4110: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n return super().drop(\n" ] ], [ [ "### Dump API TEKs", "_____no_output_____" ] ], [ [ "tek_list_df = multi_backend_exposure_keys_df[\n [\"sample_date_string\", \"region\", \"key_data\"]].copy()\ntek_list_df[\"key_data\"] = tek_list_df[\"key_data\"].apply(str)\ntek_list_df.rename(columns={\n \"sample_date_string\": \"sample_date\",\n \"key_data\": \"tek_list\"}, inplace=True)\ntek_list_df = tek_list_df.groupby(\n [\"sample_date\", \"region\"]).tek_list.unique().reset_index()\ntek_list_df[\"extraction_date\"] = extraction_date\ntek_list_df[\"extraction_date_with_hour\"] = extraction_date_with_hour\n\ntek_list_path_prefix = \"Data/TEKs/\"\ntek_list_current_path = tek_list_path_prefix + f\"/Current/RadarCOVID-TEKs.json\"\ntek_list_daily_path = tek_list_path_prefix + f\"Daily/RadarCOVID-TEKs-{extraction_date}.json\"\ntek_list_hourly_path = tek_list_path_prefix + f\"Hourly/RadarCOVID-TEKs-{extraction_date_with_hour}.json\"\n\nfor path in [tek_list_current_path, tek_list_daily_path, tek_list_hourly_path]:\n os.makedirs(os.path.dirname(path), exist_ok=True)\n\ntek_list_df.drop(columns=[\"extraction_date\", \"extraction_date_with_hour\"]).to_json(\n tek_list_current_path,\n lines=True, orient=\"records\")\ntek_list_df.drop(columns=[\"extraction_date_with_hour\"]).to_json(\n tek_list_daily_path,\n lines=True, orient=\"records\")\ntek_list_df.to_json(\n tek_list_hourly_path,\n lines=True, orient=\"records\")\ntek_list_df.head()", "_____no_output_____" ] ], [ [ "### Load TEK Dumps", "_____no_output_____" ] ], [ [ "import glob\n\ndef load_extracted_teks(mode, region=None, limit=None) -> pd.DataFrame:\n extracted_teks_df = pd.DataFrame(columns=[\"region\"])\n file_paths = list(reversed(sorted(glob.glob(tek_list_path_prefix + mode + \"/RadarCOVID-TEKs-*.json\"))))\n if limit:\n file_paths = file_paths[:limit]\n for file_path in file_paths:\n logging.info(f\"Loading TEKs from '{file_path}'...\")\n iteration_extracted_teks_df = pd.read_json(file_path, lines=True)\n extracted_teks_df = extracted_teks_df.append(\n iteration_extracted_teks_df, sort=False)\n extracted_teks_df[\"region\"] = \\\n extracted_teks_df.region.fillna(spain_region_country_code).copy()\n if region:\n extracted_teks_df = \\\n extracted_teks_df[extracted_teks_df.region == region]\n return extracted_teks_df", "_____no_output_____" ], [ "daily_extracted_teks_df = load_extracted_teks(\n mode=\"Daily\",\n region=report_backend_identifier,\n limit=tek_dumps_load_limit)\ndaily_extracted_teks_df.head()", "_____no_output_____" ], [ "exposure_keys_summary_df_ = daily_extracted_teks_df \\\n .sort_values(\"extraction_date\", ascending=False) \\\n .groupby(\"sample_date\").tek_list.first() \\\n .to_frame()\nexposure_keys_summary_df_.index.name = \"sample_date_string\"\nexposure_keys_summary_df_[\"tek_list\"] = \\\n exposure_keys_summary_df_.tek_list.apply(len)\nexposure_keys_summary_df_ = exposure_keys_summary_df_ \\\n .rename(columns={\"tek_list\": \"shared_teks_by_generation_date\"}) \\\n .sort_index(ascending=False)\nexposure_keys_summary_df = exposure_keys_summary_df_\nexposure_keys_summary_df.head()", "_____no_output_____" ] ], [ [ "### Daily New TEKs", "_____no_output_____" ] ], [ [ "tek_list_df = daily_extracted_teks_df.groupby(\"extraction_date\").tek_list.apply(\n lambda x: set(sum(x, []))).reset_index()\ntek_list_df = tek_list_df.set_index(\"extraction_date\").sort_index(ascending=True)\ntek_list_df.head()", "_____no_output_____" ], [ "def compute_teks_by_generation_and_upload_date(date):\n day_new_teks_set_df = tek_list_df.copy().diff()\n try:\n day_new_teks_set = day_new_teks_set_df[\n day_new_teks_set_df.index == date].tek_list.item()\n except ValueError:\n day_new_teks_set = None\n if pd.isna(day_new_teks_set):\n day_new_teks_set = set()\n day_new_teks_df = daily_extracted_teks_df[\n daily_extracted_teks_df.extraction_date == date].copy()\n day_new_teks_df[\"shared_teks\"] = \\\n day_new_teks_df.tek_list.apply(lambda x: set(x).intersection(day_new_teks_set))\n day_new_teks_df[\"shared_teks\"] = \\\n day_new_teks_df.shared_teks.apply(len)\n day_new_teks_df[\"upload_date\"] = date\n day_new_teks_df.rename(columns={\"sample_date\": \"generation_date\"}, inplace=True)\n day_new_teks_df = day_new_teks_df[\n [\"upload_date\", \"generation_date\", \"shared_teks\"]]\n day_new_teks_df[\"generation_to_upload_days\"] = \\\n (pd.to_datetime(day_new_teks_df.upload_date) -\n pd.to_datetime(day_new_teks_df.generation_date)).dt.days\n day_new_teks_df = day_new_teks_df[day_new_teks_df.shared_teks > 0]\n return day_new_teks_df\n\nshared_teks_generation_to_upload_df = pd.DataFrame()\nfor upload_date in daily_extracted_teks_df.extraction_date.unique():\n shared_teks_generation_to_upload_df = \\\n shared_teks_generation_to_upload_df.append(\n compute_teks_by_generation_and_upload_date(date=upload_date))\nshared_teks_generation_to_upload_df \\\n .sort_values([\"upload_date\", \"generation_date\"], ascending=False, inplace=True)\nshared_teks_generation_to_upload_df.tail()", "<ipython-input-30-827222b35590>:4: FutureWarning: `item` has been deprecated and will be removed in a future version\n day_new_teks_set = day_new_teks_set_df[\n" ], [ "today_new_teks_df = \\\n shared_teks_generation_to_upload_df[\n shared_teks_generation_to_upload_df.upload_date == extraction_date].copy()\ntoday_new_teks_df.tail()", "_____no_output_____" ], [ "if not today_new_teks_df.empty:\n today_new_teks_df.set_index(\"generation_to_upload_days\") \\\n .sort_index().shared_teks.plot.bar()", "_____no_output_____" ], [ "generation_to_upload_period_pivot_df = \\\n shared_teks_generation_to_upload_df[\n [\"upload_date\", \"generation_to_upload_days\", \"shared_teks\"]] \\\n .pivot(index=\"upload_date\", columns=\"generation_to_upload_days\") \\\n .sort_index(ascending=False).fillna(0).astype(int) \\\n .droplevel(level=0, axis=1)\ngeneration_to_upload_period_pivot_df.head()", "_____no_output_____" ], [ "new_tek_df = tek_list_df.diff().tek_list.apply(\n lambda x: len(x) if not pd.isna(x) else None).to_frame().reset_index()\nnew_tek_df.rename(columns={\n \"tek_list\": \"shared_teks_by_upload_date\",\n \"extraction_date\": \"sample_date_string\",}, inplace=True)\nnew_tek_df.tail()", "_____no_output_____" ], [ "shared_teks_uploaded_on_generation_date_df = shared_teks_generation_to_upload_df[\n shared_teks_generation_to_upload_df.generation_to_upload_days == 0] \\\n [[\"upload_date\", \"shared_teks\"]].rename(\n columns={\n \"upload_date\": \"sample_date_string\",\n \"shared_teks\": \"shared_teks_uploaded_on_generation_date\",\n })\nshared_teks_uploaded_on_generation_date_df.head()", "_____no_output_____" ], [ "estimated_shared_diagnoses_df = shared_teks_generation_to_upload_df \\\n .groupby([\"upload_date\"]).shared_teks.max().reset_index() \\\n .sort_values([\"upload_date\"], ascending=False) \\\n .rename(columns={\n \"upload_date\": \"sample_date_string\",\n \"shared_teks\": \"shared_diagnoses\",\n })\ninvalid_shared_diagnoses_dates_mask = \\\n estimated_shared_diagnoses_df.sample_date_string.isin(invalid_shared_diagnoses_dates)\nestimated_shared_diagnoses_df[invalid_shared_diagnoses_dates_mask] = 0\nestimated_shared_diagnoses_df.head()", "_____no_output_____" ] ], [ [ "### Hourly New TEKs", "_____no_output_____" ] ], [ [ "hourly_extracted_teks_df = load_extracted_teks(\n mode=\"Hourly\", region=report_backend_identifier, limit=25)\nhourly_extracted_teks_df.head()", "_____no_output_____" ], [ "hourly_new_tek_count_df = hourly_extracted_teks_df \\\n .groupby(\"extraction_date_with_hour\").tek_list. \\\n apply(lambda x: set(sum(x, []))).reset_index().copy()\nhourly_new_tek_count_df = hourly_new_tek_count_df.set_index(\"extraction_date_with_hour\") \\\n .sort_index(ascending=True)\n\nhourly_new_tek_count_df[\"new_tek_list\"] = hourly_new_tek_count_df.tek_list.diff()\nhourly_new_tek_count_df[\"new_tek_count\"] = hourly_new_tek_count_df.new_tek_list.apply(\n lambda x: len(x) if not pd.isna(x) else 0)\nhourly_new_tek_count_df.rename(columns={\n \"new_tek_count\": \"shared_teks_by_upload_date\"}, inplace=True)\nhourly_new_tek_count_df = hourly_new_tek_count_df.reset_index()[[\n \"extraction_date_with_hour\", \"shared_teks_by_upload_date\"]]\nhourly_new_tek_count_df.head()", "_____no_output_____" ], [ "hourly_summary_df = hourly_new_tek_count_df.copy()\nhourly_summary_df.set_index(\"extraction_date_with_hour\", inplace=True)\nhourly_summary_df = hourly_summary_df.fillna(0).astype(int).reset_index()\nhourly_summary_df[\"datetime_utc\"] = pd.to_datetime(\n hourly_summary_df.extraction_date_with_hour, format=\"%Y-%m-%d@%H\")\nhourly_summary_df.set_index(\"datetime_utc\", inplace=True)\nhourly_summary_df = hourly_summary_df.tail(-1)\nhourly_summary_df.head()", "_____no_output_____" ] ], [ [ "### Data Merge", "_____no_output_____" ] ], [ [ "result_summary_df = exposure_keys_summary_df.merge(\n new_tek_df, on=[\"sample_date_string\"], how=\"outer\")\nresult_summary_df.head()", "_____no_output_____" ], [ "result_summary_df = result_summary_df.merge(\n shared_teks_uploaded_on_generation_date_df, on=[\"sample_date_string\"], how=\"outer\")\nresult_summary_df.head()", "_____no_output_____" ], [ "result_summary_df = result_summary_df.merge(\n estimated_shared_diagnoses_df, on=[\"sample_date_string\"], how=\"outer\")\nresult_summary_df.head()", "_____no_output_____" ], [ "result_summary_df = confirmed_df.tail(daily_summary_days).merge(\n result_summary_df, on=[\"sample_date_string\"], how=\"left\")\nresult_summary_df.head()", "_____no_output_____" ], [ "result_summary_df[\"sample_date\"] = pd.to_datetime(result_summary_df.sample_date_string)\nresult_summary_df = result_summary_df.merge(source_regions_for_summary_df, how=\"left\")\nresult_summary_df.set_index([\"sample_date\", \"source_regions\"], inplace=True)\nresult_summary_df.drop(columns=[\"sample_date_string\"], inplace=True)\nresult_summary_df.sort_index(ascending=False, inplace=True)\nresult_summary_df.head()", "_____no_output_____" ], [ "with pd.option_context(\"mode.use_inf_as_na\", True):\n result_summary_df = result_summary_df.fillna(0).astype(int)\n result_summary_df[\"teks_per_shared_diagnosis\"] = \\\n (result_summary_df.shared_teks_by_upload_date / result_summary_df.shared_diagnoses).fillna(0)\n result_summary_df[\"shared_diagnoses_per_covid_case\"] = \\\n (result_summary_df.shared_diagnoses / result_summary_df.covid_cases).fillna(0)\n\nresult_summary_df.head(daily_plot_days)", "_____no_output_____" ], [ "weekly_result_summary_df = result_summary_df \\\n .sort_index(ascending=True).fillna(0).rolling(7).agg({\n \"covid_cases\": \"sum\",\n \"shared_teks_by_generation_date\": \"sum\",\n \"shared_teks_by_upload_date\": \"sum\",\n \"shared_diagnoses\": \"sum\"\n}).sort_index(ascending=False)\n\nwith pd.option_context(\"mode.use_inf_as_na\", True):\n weekly_result_summary_df = weekly_result_summary_df.fillna(0).astype(int)\n weekly_result_summary_df[\"teks_per_shared_diagnosis\"] = \\\n (weekly_result_summary_df.shared_teks_by_upload_date / weekly_result_summary_df.shared_diagnoses).fillna(0)\n weekly_result_summary_df[\"shared_diagnoses_per_covid_case\"] = \\\n (weekly_result_summary_df.shared_diagnoses / weekly_result_summary_df.covid_cases).fillna(0)\n\nweekly_result_summary_df.head()", "_____no_output_____" ], [ "last_7_days_summary = weekly_result_summary_df.to_dict(orient=\"records\")[1]\nlast_7_days_summary", "_____no_output_____" ] ], [ [ "## Report Results", "_____no_output_____" ] ], [ [ "display_column_name_mapping = {\n \"sample_date\": \"Sample\\u00A0Date\\u00A0(UTC)\",\n \"source_regions\": \"Source Countries\",\n \"datetime_utc\": \"Timestamp (UTC)\",\n \"upload_date\": \"Upload Date (UTC)\",\n \"generation_to_upload_days\": \"Generation to Upload Period in Days\",\n \"region\": \"Backend\",\n \"region_x\": \"Backend\\u00A0(A)\",\n \"region_y\": \"Backend\\u00A0(B)\",\n \"common_teks\": \"Common TEKs Shared Between Backends\",\n \"common_teks_fraction\": \"Fraction of TEKs in Backend (A) Available in Backend (B)\",\n \"covid_cases\": \"COVID-19 Cases in Source Countries (7-day Rolling Average)\",\n \"shared_teks_by_generation_date\": \"Shared TEKs by Generation Date\",\n \"shared_teks_by_upload_date\": \"Shared TEKs by Upload Date\",\n \"shared_diagnoses\": \"Shared Diagnoses (Estimation)\",\n \"teks_per_shared_diagnosis\": \"TEKs Uploaded per Shared Diagnosis\",\n \"shared_diagnoses_per_covid_case\": \"Usage Ratio (Fraction of Cases in Source Countries Which Shared Diagnosis)\",\n \"shared_teks_uploaded_on_generation_date\": \"Shared TEKs Uploaded on Generation Date\",\n}", "_____no_output_____" ], [ "summary_columns = [\n \"covid_cases\",\n \"shared_teks_by_generation_date\",\n \"shared_teks_by_upload_date\",\n \"shared_teks_uploaded_on_generation_date\",\n \"shared_diagnoses\",\n \"teks_per_shared_diagnosis\",\n \"shared_diagnoses_per_covid_case\",\n]", "_____no_output_____" ] ], [ [ "### Daily Summary Table", "_____no_output_____" ] ], [ [ "result_summary_df_ = result_summary_df.copy()\nresult_summary_df = result_summary_df[summary_columns]\nresult_summary_with_display_names_df = result_summary_df \\\n .rename_axis(index=display_column_name_mapping) \\\n .rename(columns=display_column_name_mapping)\nresult_summary_with_display_names_df", "_____no_output_____" ] ], [ [ "### Daily Summary Plots", "_____no_output_____" ] ], [ [ "result_plot_summary_df = result_summary_df.head(daily_plot_days)[summary_columns] \\\n .droplevel(level=[\"source_regions\"]) \\\n .rename_axis(index=display_column_name_mapping) \\\n .rename(columns=display_column_name_mapping)\nsummary_ax_list = result_plot_summary_df.sort_index(ascending=True).plot.bar(\n title=f\"Daily Summary\",\n rot=45, subplots=True, figsize=(15, 22), legend=False)\nax_ = summary_ax_list[-1]\nax_.get_figure().tight_layout()\nax_.get_figure().subplots_adjust(top=0.95)\nax_.yaxis.set_major_formatter(matplotlib.ticker.PercentFormatter(1.0))\n_ = ax_.set_xticklabels(sorted(result_plot_summary_df.index.strftime(\"%Y-%m-%d\").tolist()))", "_____no_output_____" ] ], [ [ "### Daily Generation to Upload Period Table", "_____no_output_____" ] ], [ [ "display_generation_to_upload_period_pivot_df = \\\n generation_to_upload_period_pivot_df \\\n .head(backend_generation_days)\ndisplay_generation_to_upload_period_pivot_df \\\n .head(backend_generation_days) \\\n .rename_axis(columns=display_column_name_mapping) \\\n .rename_axis(index=display_column_name_mapping)", "_____no_output_____" ], [ "fig, generation_to_upload_period_pivot_table_ax = plt.subplots(\n figsize=(12, 1 + 0.6 * len(display_generation_to_upload_period_pivot_df)))\ngeneration_to_upload_period_pivot_table_ax.set_title(\n \"Shared TEKs Generation to Upload Period Table\")\nsns.heatmap(\n data=display_generation_to_upload_period_pivot_df\n .rename_axis(columns=display_column_name_mapping)\n .rename_axis(index=display_column_name_mapping),\n fmt=\".0f\",\n annot=True,\n ax=generation_to_upload_period_pivot_table_ax)\ngeneration_to_upload_period_pivot_table_ax.get_figure().tight_layout()", "_____no_output_____" ] ], [ [ "### Hourly Summary Plots ", "_____no_output_____" ] ], [ [ "hourly_summary_ax_list = hourly_summary_df \\\n .rename_axis(index=display_column_name_mapping) \\\n .rename(columns=display_column_name_mapping) \\\n .plot.bar(\n title=f\"Last 24h Summary\",\n rot=45, subplots=True, legend=False)\nax_ = hourly_summary_ax_list[-1]\nax_.get_figure().tight_layout()\nax_.get_figure().subplots_adjust(top=0.9)\n_ = ax_.set_xticklabels(sorted(hourly_summary_df.index.strftime(\"%Y-%m-%d@%H\").tolist()))", "_____no_output_____" ] ], [ [ "### Publish Results", "_____no_output_____" ] ], [ [ "def get_temporary_image_path() -> str:\n return os.path.join(tempfile.gettempdir(), str(uuid.uuid4()) + \".png\")\n\ndef save_temporary_plot_image(ax):\n if isinstance(ax, np.ndarray):\n ax = ax[0]\n media_path = get_temporary_image_path()\n ax.get_figure().savefig(media_path)\n return media_path\n\ndef save_temporary_dataframe_image(df):\n import dataframe_image as dfi\n media_path = get_temporary_image_path()\n dfi.export(df, media_path)\n return media_path", "_____no_output_____" ], [ "github_repository = os.environ.get(\"GITHUB_REPOSITORY\")\nif github_repository is None:\n github_repository = \"pvieito/Radar-STATS\"\n\ngithub_project_base_url = \"https://github.com/\" + github_repository\n\ndisplay_formatters = {\n display_column_name_mapping[\"teks_per_shared_diagnosis\"]: lambda x: f\"{x:.2f}\",\n display_column_name_mapping[\"shared_diagnoses_per_covid_case\"]: lambda x: f\"{x:.2%}\",\n}\ndaily_summary_table_html = result_summary_with_display_names_df \\\n .head(daily_plot_days) \\\n .rename_axis(index=display_column_name_mapping) \\\n .rename(columns=display_column_name_mapping) \\\n .to_html(formatters=display_formatters)\nmulti_backend_summary_table_html = multi_backend_summary_df \\\n .head(daily_plot_days) \\\n .rename_axis(columns=display_column_name_mapping) \\\n .rename(columns=display_column_name_mapping) \\\n .rename_axis(index=display_column_name_mapping) \\\n .to_html(formatters=display_formatters)\n\ndef format_multi_backend_cross_sharing_fraction(x):\n if pd.isna(x):\n return \"-\"\n elif round(x * 100, 1) == 0:\n return \"\"\n else:\n return f\"{x:.1%}\"\n\nmulti_backend_cross_sharing_summary_table_html = multi_backend_cross_sharing_summary_df \\\n .rename_axis(columns=display_column_name_mapping) \\\n .rename(columns=display_column_name_mapping) \\\n .rename_axis(index=display_column_name_mapping) \\\n .to_html(\n classes=\"table-center\",\n formatters=display_formatters,\n float_format=format_multi_backend_cross_sharing_fraction)\nmulti_backend_cross_sharing_summary_table_html = \\\n multi_backend_cross_sharing_summary_table_html \\\n .replace(\"<tr>\",\"<tr style=\\\"text-align: center;\\\">\")\n\nextraction_date_result_summary_df = \\\n result_summary_df[result_summary_df.index.get_level_values(\"sample_date\") == extraction_date]\nextraction_date_result_hourly_summary_df = \\\n hourly_summary_df[hourly_summary_df.extraction_date_with_hour == extraction_date_with_hour]\n\ncovid_cases = \\\n extraction_date_result_summary_df.covid_cases.sum()\nshared_teks_by_generation_date = \\\n extraction_date_result_summary_df.shared_teks_by_generation_date.sum()\nshared_teks_by_upload_date = \\\n extraction_date_result_summary_df.shared_teks_by_upload_date.sum()\nshared_diagnoses = \\\n extraction_date_result_summary_df.shared_diagnoses.sum()\nteks_per_shared_diagnosis = \\\n extraction_date_result_summary_df.teks_per_shared_diagnosis.sum()\nshared_diagnoses_per_covid_case = \\\n extraction_date_result_summary_df.shared_diagnoses_per_covid_case.sum()\n\nshared_teks_by_upload_date_last_hour = \\\n extraction_date_result_hourly_summary_df.shared_teks_by_upload_date.sum().astype(int)\n\nreport_source_regions = extraction_date_result_summary_df.index \\\n .get_level_values(\"source_regions\").item().split(\",\")\n\ndisplay_source_regions = \", \".join(report_source_regions)\nif len(report_source_regions) == 1:\n display_brief_source_regions = report_source_regions[0]\nelse:\n display_brief_source_regions = f\"{len(report_source_regions)} 🇪🇺\"", "<ipython-input-56-300f594104ba>:64: FutureWarning: `item` has been deprecated and will be removed in a future version\n report_source_regions = extraction_date_result_summary_df.index \\\n" ], [ "summary_plots_image_path = save_temporary_plot_image(\n ax=summary_ax_list)\nsummary_table_image_path = save_temporary_dataframe_image(\n df=result_summary_with_display_names_df)\nhourly_summary_plots_image_path = save_temporary_plot_image(\n ax=hourly_summary_ax_list)\nmulti_backend_summary_table_image_path = save_temporary_dataframe_image(\n df=multi_backend_summary_df)\ngeneration_to_upload_period_pivot_table_image_path = save_temporary_plot_image(\n ax=generation_to_upload_period_pivot_table_ax)", "_____no_output_____" ] ], [ [ "### Save Results", "_____no_output_____" ] ], [ [ "report_resources_path_prefix = \"Data/Resources/Current/RadarCOVID-Report-\"\nresult_summary_df.to_csv(\n report_resources_path_prefix + \"Summary-Table.csv\")\nresult_summary_df.to_html(\n report_resources_path_prefix + \"Summary-Table.html\")\nhourly_summary_df.to_csv(\n report_resources_path_prefix + \"Hourly-Summary-Table.csv\")\nmulti_backend_summary_df.to_csv(\n report_resources_path_prefix + \"Multi-Backend-Summary-Table.csv\")\nmulti_backend_cross_sharing_summary_df.to_csv(\n report_resources_path_prefix + \"Multi-Backend-Cross-Sharing-Summary-Table.csv\")\ngeneration_to_upload_period_pivot_df.to_csv(\n report_resources_path_prefix + \"Generation-Upload-Period-Table.csv\")\n_ = shutil.copyfile(\n summary_plots_image_path,\n report_resources_path_prefix + \"Summary-Plots.png\")\n_ = shutil.copyfile(\n summary_table_image_path,\n report_resources_path_prefix + \"Summary-Table.png\")\n_ = shutil.copyfile(\n hourly_summary_plots_image_path,\n report_resources_path_prefix + \"Hourly-Summary-Plots.png\")\n_ = shutil.copyfile(\n multi_backend_summary_table_image_path,\n report_resources_path_prefix + \"Multi-Backend-Summary-Table.png\")\n_ = shutil.copyfile(\n generation_to_upload_period_pivot_table_image_path,\n report_resources_path_prefix + \"Generation-Upload-Period-Table.png\")", "_____no_output_____" ] ], [ [ "### Publish Results as JSON", "_____no_output_____" ] ], [ [ "summary_results_api_df = result_summary_df.reset_index()\nsummary_results_api_df[\"sample_date_string\"] = \\\n summary_results_api_df[\"sample_date\"].dt.strftime(\"%Y-%m-%d\")\nsummary_results_api_df[\"source_regions\"] = \\\n summary_results_api_df[\"source_regions\"].apply(lambda x: x.split(\",\"))\n\ntoday_summary_results_api_df = \\\n summary_results_api_df.to_dict(orient=\"records\")[0]\n\nsummary_results = dict(\n backend_identifier=report_backend_identifier,\n source_regions=report_source_regions,\n extraction_datetime=extraction_datetime,\n extraction_date=extraction_date,\n extraction_date_with_hour=extraction_date_with_hour,\n last_hour=dict(\n shared_teks_by_upload_date=shared_teks_by_upload_date_last_hour,\n shared_diagnoses=0,\n ),\n today=today_summary_results_api_df,\n last_7_days=last_7_days_summary,\n daily_results=summary_results_api_df.to_dict(orient=\"records\"))\nsummary_results = \\\n json.loads(pd.Series([summary_results]).to_json(orient=\"records\"))[0]\n\nwith open(report_resources_path_prefix + \"Summary-Results.json\", \"w\") as f:\n json.dump(summary_results, f, indent=4)", "_____no_output_____" ] ], [ [ "### Publish on README", "_____no_output_____" ] ], [ [ "with open(\"Data/Templates/README.md\", \"r\") as f:\n readme_contents = f.read()\n\nreadme_contents = readme_contents.format(\n extraction_date_with_hour=extraction_date_with_hour,\n github_project_base_url=github_project_base_url,\n daily_summary_table_html=daily_summary_table_html,\n multi_backend_summary_table_html=multi_backend_summary_table_html,\n multi_backend_cross_sharing_summary_table_html=multi_backend_cross_sharing_summary_table_html,\n display_source_regions=display_source_regions)\n\nwith open(\"README.md\", \"w\") as f:\n f.write(readme_contents)", "_____no_output_____" ] ], [ [ "### Publish on Twitter", "_____no_output_____" ] ], [ [ "enable_share_to_twitter = os.environ.get(\"RADARCOVID_REPORT__ENABLE_PUBLISH_ON_TWITTER\")\ngithub_event_name = os.environ.get(\"GITHUB_EVENT_NAME\")\n\nif enable_share_to_twitter and github_event_name == \"schedule\" and \\\n (shared_teks_by_upload_date_last_hour or not are_today_results_partial):\n import tweepy\n\n twitter_api_auth_keys = os.environ[\"RADARCOVID_REPORT__TWITTER_API_AUTH_KEYS\"]\n twitter_api_auth_keys = twitter_api_auth_keys.split(\":\")\n auth = tweepy.OAuthHandler(twitter_api_auth_keys[0], twitter_api_auth_keys[1])\n auth.set_access_token(twitter_api_auth_keys[2], twitter_api_auth_keys[3])\n\n api = tweepy.API(auth)\n\n summary_plots_media = api.media_upload(summary_plots_image_path)\n summary_table_media = api.media_upload(summary_table_image_path)\n generation_to_upload_period_pivot_table_image_media = api.media_upload(generation_to_upload_period_pivot_table_image_path)\n media_ids = [\n summary_plots_media.media_id,\n summary_table_media.media_id,\n generation_to_upload_period_pivot_table_image_media.media_id,\n ]\n\n if are_today_results_partial:\n today_addendum = \" (Partial)\"\n else:\n today_addendum = \"\"\n\n status = textwrap.dedent(f\"\"\"\n #RadarCOVID – {extraction_date_with_hour}\n\n Source Countries: {display_brief_source_regions}\n\n Today{today_addendum}:\n - Uploaded TEKs: {shared_teks_by_upload_date:.0f} ({shared_teks_by_upload_date_last_hour:+d} last hour)\n - Shared Diagnoses: ≤{shared_diagnoses:.0f}\n - Usage Ratio: ≤{shared_diagnoses_per_covid_case:.2%}\n\n Last 7 Days:\n - Shared Diagnoses: ≤{last_7_days_summary[\"shared_diagnoses\"]:.0f}\n - Usage Ratio: ≤{last_7_days_summary[\"shared_diagnoses_per_covid_case\"]:.2%}\n\n Info: {github_project_base_url}#documentation\n \"\"\")\n status = status.encode(encoding=\"utf-8\")\n api.update_status(status=status, media_ids=media_ids)", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# Custom conversions\n\nHere we show how custom conversions can be passed to OpenSCM-Units' `ScmUnitRegistry`.", "_____no_output_____" ] ], [ [ "# NBVAL_IGNORE_OUTPUT\nimport traceback\n\nimport pandas as pd\n\nfrom openscm_units import ScmUnitRegistry", "_____no_output_____" ] ], [ [ "## Custom conversions DataFrame\n\nOn initialisation, a `pd.DataFrame` can be provided which contains the custom conversions. This `pd.DataFrame` should be formatted as shown below, with an index that contains the different species and columns which contain the conversion for different metrics.", "_____no_output_____" ] ], [ [ "metric_conversions_custom = pd.DataFrame([\n {\n \"Species\": \"CH4\",\n \"Custom1\": 20,\n \"Custom2\": 25,\n },\n {\n \"Species\": \"N2O\",\n \"Custom1\": 341,\n \"Custom2\": 300,\n },\n]).set_index(\"Species\")\nmetric_conversions_custom", "_____no_output_____" ] ], [ [ "With such a `pd.DataFrame`, we can use custom conversions in our unit registry as shown.", "_____no_output_____" ] ], [ [ "# initialise the unit registry with custom conversions\nunit_registry = ScmUnitRegistry(metric_conversions=metric_conversions_custom)\n# add standard conversions before moving on\nunit_registry.add_standards()\n\n# start with e.g. N2O\nnitrous_oxide = unit_registry(\"N2O\")\ndisplay(f\"N2O: {nitrous_oxide}\")\n\n# our unit registry allows us to make conversions using the \n# conversion factors we previously defined\nwith unit_registry.context(\"Custom1\"):\n display(f\"N2O in CO2-equivalent: {nitrous_oxide.to('CO2')}\")", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import tmqa12 as tmqa1", "_____no_output_____" ], [ "answer = tmqa1.answer_question(\"Who is the wife of Barack Obama?\", verbose=True)\n\nprint(\"\")\nprint(answer)\nprint(\"\")\n \nif answer: \n print(\"Answer:\",tmqa1.get_wd_label(answer[0][0]), \"(\"+str(answer[0][0])+\")\")\n print(\"Paths:\",[[tmqa1.get_wd_label(e) for e in row] for row in answer[1:]])", "-> q_nlp: Who is the wife of Barack Obama?\n-> q_themes: ([(Barack Obama, ['Q47513588', 'Q76', 'Q13202704', 'Q47513588', 'Q76', 'Q13202704', 'Q47513588', 'Q76', 'Q13202704', 'Q76']), (The Wife, ['Q19102288', 'Q16205566', 'Q23936005', 'Q3273509'])], [the wife, the wife, barack obama, the wife])\n-> q_themes_enhanced: [('The Wife', ['Q19102288', 'Q16205566', 'Q23936005', 'Q3273509']), ('wife', ['P26', 'Q188830', 'Q24039104']), ('Wife', ['Q11447160', 'Q7999459', 'Q28318271', 'Q7774795', 'Q7999457']), ('Barack Obama', ['Q47513588', 'Q76', 'Q13202704']), ('Barack', ['Q18643532', 'Q76', 'Q37011990', 'Q380650']), ('Obama', ['Q223850', 'Q15259951', 'Q33687029', 'Q76', 'Q18355807'])]\n-> q_predicates: [(is, ['P31', 'P433', 'P131'])]\n-> q_focused_parts: [(is, ['Q189', 'Q294'])]\n-> Building the graph with k_deep 50 ... (could be long)\n--> 305 nodes and 304 edges\n-> predicates_dict: {'P2842': 1, 'P580': 4, 'P26': 5, 'P279': 2, 'P1476': 4, 'P805': 2, 'P1343': 2, 'P527': 2, 'P460': 4, 'P1013': 4, 'P1889': 3, 'http://www.w3.org/2002/07/owl#sameAs': 2, 'P155': 4, 'P585': 2, 'P166': 1, 'P1552': 1, 'P1533': 1, 'P407': 2, 'P1686': 1, 'P1411': 1, 'P361': 1, 'P571': 1, 'P1429': 1, 'P25': 1, 'P108': 3, 'P582': 2, 'P106': 4, 'P19': 2, 'P31': 20, 'P1245': 1, 'P22': 1, 'P1424': 1, 'P282': 2, 'P734': 1, 'P1039': 3, 'P3373': 2, 'P136': 4, 'P1299': 1, 'P276': 2, 'P102': 1, 'P1038': 2, 'P461': 1, 'P17': 1, 'P495': 2, 'P1225': 1, 'P21': 1, 'P272': 1, 'P171': 2, 'P462': 1, 'P161': 1, 'P18': 1, 'P195': 2, 'P217': 2, 'P2959': 1, 'P2676': 2, 'P1657': 1, 'P1705': 1, 'P3834': 1, 'P1319': 1, 'P570': 1, 'P1562': 1, 'P243': 2, 'P162': 1, 'P1545': 1, 'P179': 1, 'P1258': 2, 'P105': 1, 'P364': 1, 'P1006': 2, 'P2581': 2, 'P1003': 1, 'P123': 1, 'P646': 2, 'P1014': 2, 'P170': 1, 'P227': 2, 'P131': 1}\n-> paths_keywords: (['is', 'wife', 'barack obama', 'the wife', 'iceland', 'icelandic language'], {'spouse': [spouse, ['P26']], 'wife': [spouse, ['P26']]}, [Who])\n-> Computing possible paths... (could be long)\n--> len(path_nodes): 3594\n-> Filtering paths... (could be long)\n--> len(paths_nodes_filtered): 845\n-> Computing hypothesises...\n--> hypothesises: [['Q13133', 1322.0332404641126], ['Q157084', 450.76978892943373], ['Q18531596', 34.619210543955575], ['Q3155966', 17.075340204616296], ['Q766106', 15.781747038102182], ['Q10703919', 7.563230523356064], ['Q18643532', 4.2906634691330856e-05], ['Q37011990', 4.484738372623802e-11]]\n-> Computing golden paths...\n--> len(golden_paths): 5\n--> len(cleared_golden_paths): 3\n->\tRunning time is 252.94s\n\n[['Q13133', 'Q157084', 'Q18531596', 'Q3155966', 'Q766106', 'Q10703919', 'Q18643532', 'Q37011990'], ['Q13133', 'P26', 'Q76', 'P31', 'Q5', 'P31', 'Q24039104', 'P21', 'Q6581072', 'P1552', 'Q188830', 'P26', 'Q18531596'], ['Q13133', 'P26', 'Q76', 'P31', 'Q5', 'P31', 'Q24039104', 'P21', 'Q6581072', 'P1552', 'Q188830', 'P279', 'Q1196129']]\n\nAnswer: Michelle Obama (Q13133)\nPaths: [['Michelle Obama', 'spouse', 'Barack Obama', 'instance of', 'human', 'instance of', 'wife', 'sex or gender', 'female', 'has quality', 'wife', 'spouse', 'Billy Strachan'], ['Michelle Obama', 'spouse', 'Barack Obama', 'instance of', 'human', 'instance of', 'wife', 'sex or gender', 'female', 'has quality', 'wife', 'subclass of', 'spouse']]\n" ], [ "questions = (\n \"Which actor voiced the Unicorn in The Last Unicorn?\",\n \"what city was alex golfis born in\",\n \"what was the cause of death of yves klein\",\n \"Who is the president of the United States?\",\n \"When was produced the first Matrix movie?\",\n \"Who made the soundtrack of the The Last Unicorn movie?\",\n \"Who is the author of Le Petit Prince?\",\n \"how is called the rabbit in Alice in Wonderland?\",\n \"what's akbar tandjung's ethnicity\"\n )\n\nfor question in questions:\n print(\"Question:\",question)\n answer = tmqa1.answer_question(question)\n if answer: \n print(\"Answer:\",tmqa1.get_wd_label(answer[0][0]), \"(\"+str(answer[0][0])+\")\")\n print(\"Paths:\",[[tmqa1.get_wd_label(e) for e in row] for row in answer[1:]])\n else:\n print(\"I don't know\")\n print(\"\\n\")", "Question: Which actor voiced the Unicorn in The Last Unicorn?\nAnswer: Mia Farrow (Q202725)\nPaths: [['Mia Farrow', 'voice actor', 'The Last Unicorn', 'character role', 'The Unicorn', 'instance of', 'unicorn in a fictional work', 'different from', 'Unicorn', 'named after', 'Unicorn'], ['Mia Farrow', 'voice actor', 'The Last Unicorn', 'character role', 'The Unicorn', 'of', 'The Last Unicorn'], ['Mia Farrow', 'voice actor', 'The Last Unicorn', 'character role', 'The Unicorn', 'instance of', 'unicorn in a fictional work', 'different from', 'Unicorn', 'depicts', 'unicorn'], ['Mia Farrow', 'voice actor', 'The Last Unicorn', 'character role', 'The Unicorn', 'instance of', 'unicorn in a fictional work', 'subclass of', 'unicorn'], ['Mia Farrow', 'voice actor', 'The Last Unicorn', 'character role', 'The Unicorn', 'instance of', 'unicorn in a fictional work', 'different from', 'Unicorn', 'described by source', 'Paulys Realenzyklopädie der klassischen Altertumswissenschaft', 'described by source', 'Actor', 'has part', 'Actor'], ['Mia Farrow', 'voice actor', 'The Last Unicorn', 'character role', 'The Unicorn', 'instance of', 'unicorn in a fictional work', 'different from', 'Unicorn', 'described by source', 'Paulys Realenzyklopädie der klassischen Altertumswissenschaft', 'described by source', 'Actor', 'has part', 'Actor']]\n\n\nQuestion: what city was alex golfis born in\nAnswer: The City (Q18148906)\nPaths: [['The City', 'country of origin', 'United States of America', 'country', 'Alex', 'point in time', ''], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Glen Cove'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Saratoga Springs'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Syracuse'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Yonkers'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Canandaigua'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Geneva'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Glens Falls'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Rye'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Salamanca (city), New York'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Norwich'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Cohoes'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Watervliet'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Tonawanda (city), New York'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Cortland'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Batavia'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Fulton'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Binghamton'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Ithaca'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Auburn'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Utica'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Schenectady'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Long Beach'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Buffalo'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Farrukhabad'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'White Plains'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Amsterdam'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Corning'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Troy'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Sherrill'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Beacon'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Dunkirk'], ['The City', 'country of origin', 'United States of America', 'country', 'city', 'instance of', 'Elmira'], ['list of cities in New York', 'is a list of', 'city', 'country', 'United States of America', 'country of origin', 'The City', 'instance of', 'film'], ['town of the United States', 'instance of', 'Alex', 'country', 'United States of America', 'country of origin', 'The City', 'instance of', 'film'], ['city of the United States', 'subclass of', 'city', 'country', 'United States of America', 'country of origin', 'The City', 'instance of', 'film'], ['', 'point in time', 'Alex', 'country', 'United States of America', 'country of origin', 'The City'], ['Glen Cove', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Saratoga Springs', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Syracuse', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Yonkers', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Canandaigua', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Geneva', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Glens Falls', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Rye', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Salamanca (city), New York', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Norwich', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Cohoes', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Watervliet', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Tonawanda (city), New York', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Cortland', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Batavia', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Fulton', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Binghamton', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Ithaca', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Auburn', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Utica', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Schenectady', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Long Beach', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Buffalo', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Farrukhabad', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['White Plains', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Amsterdam', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Corning', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Troy', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Sherrill', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Beacon', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Dunkirk', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['Elmira', 'instance of', 'city', 'country', 'United States of America', 'country of origin', 'The City'], ['film', 'instance of', 'The City', 'country of origin', 'United States of America', 'country', 'city', 'is a list of', 'list of cities in New York'], ['film', 'instance of', 'The City', 'country of origin', 'United States of America', 'country', 'Alex', 'instance of', 'town of the United States'], ['film', 'instance of', 'The City', 'country of origin', 'United States of America', 'country', 'city', 'subclass of', 'city of the United States']]\n\n\nQuestion: what was the cause of death of yves klein\n" ] ] ]

[ "code" ]

[ [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\nimport seaborn as sns\n", "_____no_output_____" ], [ "%matplotlib inline\nrcParams['figure.figsize'] = 10, 8\nsns.set_style('whitegrid')", "_____no_output_____" ], [ "num = 50\nxv = np.linspace(-500,400,num)\nyv = np.linspace(-500,400,num)\n\nX,Y = np.meshgrid(xv,yv)\n\n\n# frist X,Y\n\n\na = 8.2\nintervalo = 10\nvalor = 100\nke = 1/(4*np.pi*8.85418e-12)\n\nV = np.zeros((num,num))\nprint(V)\n# v = np.zeros((2,10)) \nkl = 1e-12\n\n# x -> i\n# y -> j\n# x = a + intervalo/2 \ni = j = 0\nfor xi in xv:\n for yj in yv:\n x = a + intervalo/2 \n for k in range(100):\n #print(k,x)\n # calcula o valor da carga do intevalor de linha\n Q = ( (kl*x) / ((x**2) + (a**2)) ) * intervalo # pL * dx\n d = np.sqrt((x-xi)**2 + yj**2)\n if d<0.01:\n d == 0.01\n V[j][i] += ke*(Q/d)\n x = x + intervalo\n # print(i,j) \n j += 1 \n i += 1 \n j = 0 \n \nfig = plt.figure()\nax = plt.axes(projection='3d')\nax.plot_wireframe(X, Y, V, color='black') \nplt.show()\n\nprint(V[0][0])\nprint(V[0][1])\n", "[[0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n" ], [ "num = 50\nxv = np.linspace(-500,400,num)\nyv = np.linspace(-500,400,num)\n\n\ni = j = 0\nfor xi in xv:\n \n i += 1 \nprint(i)\n\nprint(\"50 ou\", (900/50))", "50\n50 ou %d 18.0\n" ], [ "%% Descobrir o E1 - Campo eletrico dentro do cilindro\n%tem que ser por gauss pq é um fio infinito\n \n\n%% Variaveis Dadas\nclc\nclear all\nclose all\n\n%% variaveis do problema\ne0 = 8.854*10^-12;\nkc = 8.8*10^-12;\na = 10;\n\nconst=1/(4*pi*e0); %Constante\n\n%% Variaveis Criadas\n\npasse = 1;\nlimites = 20;\n%Onde o campo sera medido:\nx= -limites:passe:limites; %vetor na coordenada x onde será calculado E\ny= -limites:passe:limites; %vetor na coordenada z onde será calculado E\nz= -limites:passe:limites; %vetor na coordenada z onde será calculado E\n\n%Gerador do campo:\nxl= a:passe:(5*limites); % variação da coordenada x onde está a carga \n% yl= -passe:passe:passe; % variação da coordenada y onde está a carga \n\ndL = passe; %tamanho de cada segmento\n\n%inicializa o campo elétrico:\nV(:,:,:) = zeros (length(x),length(y),length(z)); \n\n%% Desenvolvimento\nfor i = 1:length(x)% varre a coordenada x onde E será calculado\n disp(i)\n for j = 1: length(y) % varre a coordenada y onde E será calculado\n for k = 1: length(z) % varre a coordenada z onde E será calculado\n\n for m = 1:length(xl) % varre a coordenada x da carga\n% #for n = 1:length(yl) % varre a coordenada y da carga\n \n r = [x(i),y(j),z(k)]; %vetor posição apontando para onde estamos calculando E\n rl= [xl(m),0,0];% vetor posição apontando para a carga\n\n if ((r-rl)*(r-rl)'>0.00000001)\n V(i,j,k) = V(i,j,k) + const*((((kc.*xl(m))/(xl(m).^2 + a^2)).*dL)/(sqrt((r-rl)*(r-rl)'))');\n\n Q = ke/sqrt((x-xi)**2 + yj**2)*( (kl*x) / ((x**2) + (a**2)) ) * intervalo # pL * dx\n \n end \n \n% # end \n \n end\n end\n end\nend\n%% Grafico\n% xd = linspace(-limites,limites);\n% yd = linspace(-limites,limites);\n% zd = linspace(-limites,limites);\n% [X,Y] = meshgrid(xd,yd);\n% \n% figure(1)\n% surf(x,y,V(:,:,0));\n% xlabel('x')\n% ylabel('y')\n% zlabel('z')\n% axis([-5 20 -20 20 -inf inf])\n% grid on\n% colormap(jet(20))\n% colorbar\n%% prof\n[X,Z] = meshgrid(x,z);\nfigure\n[C,h] = contour(x,z,squeeze(V(:,3,:)),20);%faz o gráfico das curvas de nível para o potencial\nset(h,'ShowText','on','TextStep',get(h,'LevelStep'))\nxlabel('eixo x (m)')\nylabel('eixo z (m)')\n\n\n\n%% Print resultado\nmax = int64(length(x));\nV0 = V(max/2,max/2);\nVinf = 0;\ndisp(0-V0)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import sys\nsys.path.append(\"../\")", "_____no_output_____" ], [ "from loss import compute_loss\nimport networks as net\nfrom data_sampler import data_sampler\n\nimport functools\nimport numpy as np\nimport pandas as pd\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F", "_____no_output_____" ], [ "train_pull = pd.read_csv(\"../../../data/fashion_mnisit/train_pull.csv\", header=None).values\ntrain_top = pd.read_csv(\"../../../data/fashion_mnisit/train_top.csv\", header=None).values\n# test_pull = pd.read_csv(\"../../data/fashion_mnisit/test_pull.csv\", header=None).values\n# test_top = pd.read_csv(\"../../data/fashion_mnisit/test_top.csv\", header=None).values", "_____no_output_____" ], [ "input_nc = 1\noutput_nc = 1\ndiscr_filters = 8\nmax_power = 8\nn_layers = 3\nnorm_lay = nn.BatchNorm2d\nstart_size = 28\ngen_filters = 16\ndropout = None\nn_blocks = 1", "_____no_output_____" ], [ "discr_a = net.Discrimanator(input_nc=input_nc,\n discr_filters=discr_filters,\n max_power=max_power,\n n_layers=n_layers,\n norm_lay=norm_lay,\n start_size=start_size)\n\ndiscr_b = net.Discrimanator(input_nc=input_nc,\n discr_filters=discr_filters,\n max_power=max_power,\n n_layers=n_layers,\n norm_lay=norm_lay,\n start_size=start_size)\n\ngener_a = net.ResnetGenerator(\n input_nc = input_nc,\n output_nc = output_nc,\n gen_filters = gen_filters,\n norm_lay = norm_lay,\n dropout = dropout,\n n_blocks = n_blocks\n)\n\ngener_b = net.ResnetGenerator(\n input_nc = input_nc,\n output_nc = output_nc,\n gen_filters = gen_filters,\n norm_lay = norm_lay,\n dropout = dropout,\n n_blocks = n_blocks\n)", "_____no_output_____" ], [ "batch_a, batch_b = data_sampler(10, train_pull, train_top)\nbatch_a = batch_a.view(-1, 1, 28, 28).float()\nbatch_b = batch_b.view(-1, 1, 28, 28).float()", "_____no_output_____" ], [ "compute_loss(\n gener_a = gener_a,\n gener_b = gener_b,\n discr_a = discr_a,\n discr_b = discr_b,\n batch_a = batch_a,\n batch_b = batch_b,\n alpha = 10\n)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n%matplotlib inline", "_____no_output_____" ], [ "aa = pd.read_hdf('results/noise_type_exp_res_06_27_12.h5')\nbb = pd.read_hdf('results/noise_type_exp_res_08_13_53.h5')\ndf = pd.concat((aa, bb))\ndf.reset_index()\nprint(\"Done reading\")", "Done reading\n" ], [ "df.columns", "_____no_output_____" ], [ "df['n_classes'].unique()", "_____no_output_____" ], [ "df['noise type'].unique()", "_____no_output_____" ], [ "ff = sns.barplot(x=\"noise type\", y=\"accuracy\", hue=\"approach\", data=df)\nff.set_xlabel(\"Noise Type\")\nff.set_ylabel(\"Mean Accuracy\")\nff.set_title(\"Effect of Noise Type on Classifier Accuracy\")\nff.set_xticklabels(\n [\"Add White\\nNoise Signal\", \"Add Shot\\nNoise\", \"Insert\\nOffset\", \"Filter with\\nLow Pass\", \"Add White\\nNoise\"]\n)\nleg = ff.legend(title=\"Approach\", frameon=True, fancybox=True, shadow=True, framealpha=1, loc=\"upper left\")\nfl = leg.get_frame()\nfl.set_facecolor('white')\nfl.set_edgecolor('black')\n\nfig = ff.get_figure()\nfig.subplots_adjust(bottom=0.15)\nfig.savefig(\"noise_type.pdf\", format=\"pdf\")", "_____no_output_____" ], [ "type(ff)", "_____no_output_____" ] ], [ [ "Interesting to note that the loss of high-frequency information had almost no effect on the SVM classifier.\nAlso interesting that shot noise was more harmful to the vRNN than to SNN methods.\nNot surprising that additive white noise had the greatest effect on accuracy for all classification methods.", "_____no_output_____" ] ], [ [ "mf1 = pd.read_hdf('results/whitenoise_mag_exp_res_11_03_42.h5')\nmf2 = pd.read_hdf('results/whitenoise_mag_exp_res_10_35_19.h5')\nmf = pd.concat((mf1, mf2))", "_____no_output_____" ], [ "mf.columns\nmf_filt = mf[mf['noise magnitude'] < 0.15]", "_____no_output_____" ], [ "ff = sns.lmplot(\"noise magnitude\", \"accuracy\", hue=\"approach\", data=mf_filt, x_estimator=np.mean,\n scatter_kws={\"alpha\": 0.5}, fit_reg=False, legend=False)\n\nax = ff.axes[0][0]\nax.set_title(\"Effect of White Noise Magnitude on Accuracy\")\nax.set_xlabel(\"Noise Magnitude\")\nax.set_ylabel(\"Accuracy\")\nleg = ax.legend(title=\"Approach\", bbox_to_anchor=(1, 0.6), frameon=True, fancybox=True, shadow=True, framealpha=1)\nfl = leg.get_frame()\nfl.set_facecolor('white')\nfl.set_edgecolor('black')\nax.set_xlim((0.0, 0.11))\n\nff.fig.savefig(\"noise_mag.pdf\", format=\"pdf\")", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Tower-of-Hanoi\" data-toc-modified-id=\"Tower-of-Hanoi-1\">Tower of Hanoi</a></span></li><li><span><a href=\"#Learning-Outcomes\" data-toc-modified-id=\"Learning-Outcomes-2\">Learning Outcomes</a></span></li><li><span><a href=\"#Demo:-How-to-Play-Tower-of-Hanoi\" data-toc-modified-id=\"Demo:-How-to-Play-Tower-of-Hanoi-3\">Demo: How to Play Tower of Hanoi</a></span></li><li><span><a href=\"#Definitions\" data-toc-modified-id=\"Definitions-4\">Definitions</a></span></li><li><span><a href=\"#Demo:-How-to-Play-Tower-of-Hanoi\" data-toc-modified-id=\"Demo:-How-to-Play-Tower-of-Hanoi-5\">Demo: How to Play Tower of Hanoi</a></span></li><li><span><a href=\"#Student-Activity\" data-toc-modified-id=\"Student-Activity-6\">Student Activity</a></span></li><li><span><a href=\"#Reflection\" data-toc-modified-id=\"Reflection-7\">Reflection</a></span></li><li><span><a href=\"#Did-any-group-derive-formula-for-minimum-number-of-moves?\" data-toc-modified-id=\"Did-any-group-derive-formula-for-minimum-number-of-moves?-8\">Did any group derive formula for minimum number of moves?</a></span></li><li><span><a href=\"#Questions?\" data-toc-modified-id=\"Questions?-9\">Questions?</a></span></li><li><span><a href=\"#-Tower-of-Hanoi-as-RL-problem\" data-toc-modified-id=\"-Tower-of-Hanoi-as-RL-problem-10\"> Tower of Hanoi as RL problem</a></span></li><li><span><a href=\"#-Tower-of-Hanoi-Solutions\" data-toc-modified-id=\"-Tower-of-Hanoi-Solutions-11\"> Tower of Hanoi Solutions</a></span></li><li><span><a href=\"#Greedy-Tower-of-Hanoi\" data-toc-modified-id=\"Greedy-Tower-of-Hanoi-12\">Greedy Tower of Hanoi</a></span></li><li><span><a href=\"#-Tower-of-Hanoi-Solutions\" data-toc-modified-id=\"-Tower-of-Hanoi-Solutions-13\"> Tower of Hanoi Solutions</a></span></li><li><span><a href=\"#THERE-MUST-BE-A-BETTER-WAY!\" data-toc-modified-id=\"THERE-MUST-BE-A-BETTER-WAY!-14\">THERE MUST BE A BETTER WAY!</a></span></li><li><span><a href=\"#RECURSION\" data-toc-modified-id=\"RECURSION-15\">RECURSION</a></span></li><li><span><a href=\"#2-Requirements-for-Recursion\" data-toc-modified-id=\"2-Requirements-for-Recursion-16\">2 Requirements for Recursion</a></span></li><li><span><a href=\"#Think,-Pair,-&amp;-Share\" data-toc-modified-id=\"Think,-Pair,-&amp;-Share-17\">Think, Pair, &amp; Share</a></span></li><li><span><a href=\"#Recursion-Steps-to-Solve-Tower-of-Hanoi\" data-toc-modified-id=\"Recursion-Steps-to-Solve-Tower-of-Hanoi-18\">Recursion Steps to Solve Tower of Hanoi</a></span></li><li><span><a href=\"#Illustrated-Recursive-Steps-to-Solve-Tower-of-Hanoi\" data-toc-modified-id=\"Illustrated-Recursive-Steps-to-Solve-Tower-of-Hanoi-19\">Illustrated Recursive Steps to Solve Tower of Hanoi</a></span></li><li><span><a href=\"#Check-for-understanding\" data-toc-modified-id=\"Check-for-understanding-20\">Check for understanding</a></span></li><li><span><a href=\"#Check-for-understanding\" data-toc-modified-id=\"Check-for-understanding-21\">Check for understanding</a></span></li><li><span><a href=\"#Check-for-understanding\" data-toc-modified-id=\"Check-for-understanding-22\">Check for understanding</a></span></li><li><span><a href=\"#Takeaways\" data-toc-modified-id=\"Takeaways-23\">Takeaways</a></span></li><li><span><a href=\"#Bonus-Material\" data-toc-modified-id=\"Bonus-Material-24\">Bonus Material</a></span></li><li><span><a href=\"#Dynamic-Programming\" data-toc-modified-id=\"Dynamic-Programming-25\">Dynamic Programming</a></span></li><li><span><a href=\"#What-would-Dynamic-Programming-look-like-for-Tower-of-Hanoi?\" data-toc-modified-id=\"What-would-Dynamic-Programming-look-like-for-Tower-of-Hanoi?-26\">What would Dynamic Programming look like for Tower of Hanoi?</a></span></li><li><span><a href=\"#Tower-of-Hanoi-for-Final-Project\" data-toc-modified-id=\"Tower-of-Hanoi-for-Final-Project-27\">Tower of Hanoi for Final Project</a></span></li><li><span><a href=\"#Further-Study\" data-toc-modified-id=\"Further-Study-28\">Further Study</a></span></li></ul></div>", "_____no_output_____" ], [ "<center><h2>Tower of Hanoi</h2></center>\n\n<center><img src=\"images/tower_of_hanoi.jpg\" width=\"75%\"/></center>", "_____no_output_____" ], [ "<center><h2>Learning Outcomes</h2></center>\n\n__By the end of this session, you should be able to__:\n\n- Solve Tower of Hanoi by hand.\n- Explain how to Tower of Hanoi with recursion in your words.", "_____no_output_____" ], [ "<center><h2>Demo: How to Play Tower of Hanoi</h2></center>\n\n<center><img src=\"images/tower_of_hanoi.jpg\" width=\"35%\"/></center>\n\nThe Goal: Move all disks from start to finish.\n\nRules: \n\n1. Only one disk may be moved at a time.\n2. Each move consists of taking the upper disk from one of the rods and sliding it onto another rod, on top of the other disks that may already be present on that rod.\n3. No disk may be placed on top of a smaller disk.", "_____no_output_____" ], [ "__My nephew enjoys the Tower of Hanoi.__\n\n", "_____no_output_____" ], [ "Definitions\n-----\n\n- Rod: The vertical shaft\n- Disks: The items on the rod", "_____no_output_____" ], [ "<center><h2>Demo: How to Play Tower of Hanoi</h2></center>\n\n1) Solve with 1 Disc", "_____no_output_____" ], [ "2) Solve with 2 Discs", "_____no_output_____" ], [ "<center><img src=\"http://hangaroundtheweb.com/wp-content/uploads/2018/07/write-down-english.jpg\" width=\"20%\"/></center>", "_____no_output_____" ], [ "> If you can’t write it down in English, you can’t code it. \n> — Peter Halpern", "_____no_output_____" ], [ "<center><h2>Student Activity</h2></center>\n\nIn small groups, solve Tower of Hanoi: \nhttps://www.mathsisfun.com/games/towerofhanoi-flash.html\n\nRecord the minimal number of steps for each number of discs:\n\n1. 3 discs\n1. 4 discs\n1. 5 discs\n1. 6 discs\n\nIf someone has never solved the puzzle, they should lead. If you have solved it, only give hints when the team is stuck.", "_____no_output_____" ], [ "<center><h2>Reflection</h2></center>\n\nHow difficult was each version? \n\nHow many more steps were needed for each increase in disk? \nCould you write a formula to model the minimum of numbers as number of disks increase?", "_____no_output_____" ] ], [ [ "reset -fs", "_____no_output_____" ], [ "from IPython.display import YouTubeVideo\n\n# 3 rings\nYouTubeVideo('S4HOSbrS4bY')", "_____no_output_____" ], [ "# 6 rings\nYouTubeVideo('iFV821yY7Ns')", "_____no_output_____" ] ], [ [ "<center><h2>Did any group derive formula for minimum number of moves?</h2></center>", "_____no_output_____" ] ], [ [ "# Calculate the optimal number of moves\n\nprint(f\"{'# disks':>7} | {'# moves':>10}\")\n \nfor n_disks in range(1, 21): \n n_moves = (2 ** n_disks)-1\n print(f\"{n_disks:>7} {n_moves:>10,}\")", "# disks | # moves\n 1 1\n 2 3\n 3 7\n 4 15\n 5 31\n 6 63\n 7 127\n 8 255\n 9 511\n 10 1,023\n 11 2,047\n 12 4,095\n 13 8,191\n 14 16,383\n 15 32,767\n 16 65,535\n 17 131,071\n 18 262,143\n 19 524,287\n 20 1,048,575\n" ] ], [ [ "<center><h2>Questions?</h2></center>", "_____no_output_____" ], [ "<center><h2> Tower of Hanoi as RL problem</h2></center>\n\nElements of Reinforcement Learning Problem:\n\n1. Is it sequential?\n1. What is the environment? What are the parameters?\n1. Who is the agent?\n1. What are the states?\n1. What are the rewards?", "_____no_output_____" ], [ "1. Sequential - Yes! By definition moves are one-at-a-time\n1. Environment - The number of rods and disks.\n1. Agent - The mover of disks. You right now. The function you are about to write.\n1. State - Location of disks on rods at a given step\n1. Rewards:\n 1. End - All disks on last rod\n 1. Intermediate - Disks closer to solution state", "_____no_output_____" ], [ "<center><h2> Tower of Hanoi Solutions</h2></center>\n\nWhat would a greedy approach look like?\n\nWould it solve the problem?", "_____no_output_____" ], [ "By greedy, I mean an agent can only move directly towards the goal (of course must follow the rules of the game)", "_____no_output_____" ], [ "<center><h2>Greedy Tower of Hanoi</h2></center>\n\n1. Move the smallest disk to the end.\n2. Move the next disk to the middle.\n3. Then get stuck!", "_____no_output_____" ], [ "<center><h2> Tower of Hanoi Solutions</h2></center>\n\nWhat would a random approach look like?\n\nWould it solve the problem?", "_____no_output_____" ], [ "Find all valid moves, select one.\n\nYes, it would solve. It would take a looooong time.", "_____no_output_____" ], [ "<center><h2>THERE MUST BE A BETTER WAY!</h2></center>", "_____no_output_____" ], [ "<center><h2>RECURSION</h2></center>", "_____no_output_____" ], [ "<center><h2>2 Requirements for Recursion</h2></center>\n\n1. Must have a base case\n1. Move towards the base case by calling the function itself", "_____no_output_____" ], [ "<center><h2>Think, Pair, & Share</h2></center>\n\nHow does the 2 requirements of recursion apply to Tower of Hanoi?\n\nWhat is the base case?\n\nHow can we move towards base case?\n\nHow can we generalize to any number of discs?", "_____no_output_____" ], [ "<center><h2>Recursion Steps to Solve Tower of Hanoi</h2></center>\n\n1. Base case: Move the largest disk to rightmost tower. \n2. Move towards the base case by defining:\n - `from_rod`\n - `to_rod`\n - `aux_rod`\n3. Call the function: Solving the same problem with one less disk.", "_____no_output_____" ], [ "<center><h2>Illustrated Recursive Steps to Solve Tower of Hanoi</h2></center>\n\n<center><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/20/Tower_of_Hanoi_recursion_SMIL.svg/512px-Tower_of_Hanoi_recursion_SMIL.svg.png\" width=\"75%\"/></center>", "_____no_output_____" ], [ "<center><h2>Check for understanding</h2></center>\n\nIs the recursion solution deterministic or schocastic?", "_____no_output_____" ], [ "Deterministic - Every time its plays, it will always play the same", "_____no_output_____" ], [ "<center><h2>Check for understanding</h2></center>\n\nDoes recursion solution learn? Will it get better the more it plays?", "_____no_output_____" ], [ "No - It does not learn.", "_____no_output_____" ], [ "<center><h2>Check for understanding</h2></center>\n\nIs the recursive solution optimal?", "_____no_output_____" ], [ "Yes - It will always play the minum number of moves.", "_____no_output_____" ], [ "<center><h2>Takeaways</h2></center>\n\n- Tower of Hanoi is a game to test the cognitive abilities, most often of small children.\n- Tower of Hanoi has the elements of Reinforcement Learning:\n 1. Sequential \n 1. Environment\n 1. Agent\n 1. State\n 1. Rewards\n- Recursion is a deterministic, non-learning, and optimal solution for Tower of Hanoi.", "_____no_output_____" ], [ " ", "_____no_output_____" ], [ "-----\nBonus Material\n-----", "_____no_output_____" ], [ "<center><h2>Dynamic Programming</h2></center>\n\nWhat is it?", "_____no_output_____" ], [ "Look up previous solutions (rather than re-compute them) to overlapping subproblems.\n\nRequires a cache to store solutions.", "_____no_output_____" ], [ "<center><h2>What would Dynamic Programming look like for Tower of Hanoi?</h2></center>", "_____no_output_____" ], [ "Track optimal solutions to subproblems. \n\nUse those solutions instead of recomputing them.", "_____no_output_____" ], [ "However, the recursive solution is optimal for Tower of Hanoi so there is no reason to add dynamic programming.", "_____no_output_____" ], [ "Tower of Hanoi for Final Project\n------\n\nIf you are interesting in extending Tower of Hanoi as your Final Projects, here ideas to consider:\n\n- Frame it as Markov decision process (MDP)\n- Can you make it stochastic?\n- How can you reformulate the problem that a learned solution would perform better than a deterministic solution?", "_____no_output_____" ], [ "Further Study\n------\n\n- [Q-learning for Tower of Hanoi](https://github.com/khpeek/Q-learning-Hanoi)\n- [RL take for Tower of Hanoi](https://kenandeen.wordpress.com/2015/08/22/tower-of-hanoi-reinforcement-learning/)", "_____no_output_____" ], [ " ", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Auto detection to main + 4 cropped images\n**Pipeline:**\n\n1. Load cropped image csv file\n2. Apply prediction\n3. Save prediction result back to csv file\n* pred_value\n* pred_cat\n* pred_bbox", "_____no_output_____" ] ], [ [ "# Import libraries\n%matplotlib inline\nfrom pycocotools.coco import COCO\nfrom keras.models import load_model\n# from utils.utils import *\n# from utils.bbox import *\n# from utils.image import load_image_pixels\nfrom keras.preprocessing.image import load_img\nfrom keras.preprocessing.image import img_to_array\nimport numpy as np\nimport pandas as pd\nimport skimage.io as io\nimport matplotlib.pyplot as plt\nimport pylab\nimport torchvision.transforms.functional as TF\nimport PIL\nimport os\nimport json \nfrom urllib.request import urlretrieve\npylab.rcParams['figure.figsize'] = (8.0, 10.0)", "Using TensorFlow backend.\n" ], [ "# Define image directory\nprojectDir=os.getcwd()\ndataDir='.'\ndataType='val2017'\nimageDir='{}/images/'.format(dataDir)\nannFile='{}/images/{}_selected/annotations/instances_{}.json'.format(dataDir,dataType,dataType)", "_____no_output_____" ] ], [ [ "## Utilities", "_____no_output_____" ] ], [ [ "class BoundBox:\n def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):\n self.xmin = xmin\n self.ymin = ymin\n self.xmax = xmax\n self.ymax = ymax\n self.objness = objness\n self.classes = classes\n self.label = -1\n self.score = -1\n\n def get_label(self):\n if self.label == -1:\n self.label = np.argmax(self.classes)\n\n return self.label\n\n def get_score(self):\n if self.score == -1:\n self.score = self.classes[self.get_label()]\n\n return self.score\n\ndef _sigmoid(x):\n return 1. / (1. + np.exp(-x))\n\ndef decode_netout(netout, anchors, obj_thresh, net_h, net_w):\n grid_h, grid_w = netout.shape[:2]\n nb_box = 3\n netout = netout.reshape((grid_h, grid_w, nb_box, -1))\n nb_class = netout.shape[-1] - 5\n boxes = []\n netout[..., :2] = _sigmoid(netout[..., :2])\n netout[..., 4:] = _sigmoid(netout[..., 4:])\n netout[..., 5:] = netout[..., 4][..., np.newaxis] * netout[..., 5:]\n netout[..., 5:] *= netout[..., 5:] > obj_thresh\n\n for i in range(grid_h*grid_w):\n row = i // grid_w\n col = i % grid_w\n for b in range(nb_box):\n # 4th element is objectness score\n objectness = netout[int(row)][int(col)][b][4]\n if(objectness.all() <= obj_thresh): continue\n # first 4 elements are x, y, w, and h\n x, y, w, h = netout[int(row)][int(col)][b][:4]\n x = (col + x) / grid_w # center position, unit: image width\n y = (row + y) / grid_h # center position, unit: image height\n w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width\n h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height\n # last elements are class probabilities\n classes = netout[int(row)][col][b][5:]\n box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)\n boxes.append(box)\n return boxes\n\ndef correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):\n new_w, new_h = net_w, net_h\n for i in range(len(boxes)):\n x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w\n y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h\n boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)\n boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)\n boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)\n boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)\n\ndef _interval_overlap(interval_a, interval_b):\n x1, x2 = interval_a\n x3, x4 = interval_b\n if x3 < x1:\n if x4 < x1:\n return 0\n else:\n return min(x2,x4) - x1\n else:\n if x2 < x3:\n return 0\n else:\n return min(x2,x4) - x3\n\ndef bbox_iou(box1, box2):\n intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n intersect = intersect_w * intersect_h\n w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n union = w1*h1 + w2*h2 - intersect\n return float(intersect) / union\n\ndef do_nms(boxes, nms_thresh):\n if len(boxes) > 0:\n nb_class = len(boxes[0].classes)\n else:\n return\n for c in range(nb_class):\n sorted_indices = np.argsort([-box.classes[c] for box in boxes])\n for i in range(len(sorted_indices)):\n index_i = sorted_indices[i]\n if boxes[index_i].classes[c] == 0: continue\n for j in range(i+1, len(sorted_indices)):\n index_j = sorted_indices[j]\n if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:\n boxes[index_j].classes[c] = 0\n\n# load and prepare an image\ndef load_image_pixels(filename, shape):\n # load the image to get its shape\n image = load_img(filename)\n width, height = image.size\n # load the image with the required size\n image = load_img(filename, target_size=shape)\n # convert to numpy array\n image = img_to_array(image)\n # scale pixel values to [0, 1]\n image = image.astype('float32')\n image /= 255.0\n # add a dimension so that we have one sample\n image = np.expand_dims(image, 0)\n return image, width, height\n\n# get all of the results above a threshold\ndef get_boxes(boxes, labels, thresh):\n v_boxes, v_labels, v_scores = list(), list(), list()\n # enumerate all boxes\n for box in boxes:\n # enumerate all possible labels\n for i in range(len(labels)):\n # check if the threshold for this label is high enough\n if box.classes[i] > thresh:\n v_boxes.append(box)\n v_labels.append(labels[i])\n v_scores.append(box.classes[i]*100)\n # don't break, many labels may trigger for one box\n return v_boxes, v_labels, v_scores\n\n# draw all results\ndef draw_boxes(filename, v_boxes, v_labels, v_scores):\n # load the image\n data = plt.imread(filename)\n # plot the image\n plt.imshow(data)\n # get the context for drawing boxes\n ax = plt.gca()\n # plot each box\n for i in range(len(v_boxes)):\n box = v_boxes[i]\n # get coordinates\n y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n # calculate width and height of the box\n width, height = x2 - x1, y2 - y1\n # create the shape\n rect = plt.Rectangle((x1, y1), width, height, fill=False, color='white')\n # draw the box\n ax.add_patch(rect)\n # draw text and score in top left corner\n label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n plt.text(x1, y1, label, color='white')\n # show the plot\n plt.show()", "_____no_output_____" ] ], [ [ "## Load model", "_____no_output_____" ] ], [ [ "# load yolov3 model\nmodel = load_model('yolov3_model.h5')\n# define the expected input shape for the model\ninput_w, input_h = 416, 416\n# define the anchors\nanchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]\n# define the probability threshold for detected objects\nclass_threshold = 0.6\n# define the labels\nlabels = [\"person\", \"bicycle\", \"car\", \"motorbike\", \"airplane\", \"bus\", \"train\", \"truck\",\n \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\",\n \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\",\n \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\",\n \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\", \"knife\", \"spoon\", \"bowl\", \"banana\",\n \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\",\n \"chair\", \"couch\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\",\n \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\",\n \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]", "WARNING:tensorflow:From /Users/haiho/PycharmProjects/yolov3_huynhngocanh/venv/lib/python3.5/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\nInstructions for updating:\nIf using Keras pass *_constraint arguments to layers.\n" ] ], [ [ "## Gather & concatenate all csv files", "_____no_output_____" ] ], [ [ "all_files = []\ncat = 'book'\nfor subdir, dirs, files in os.walk(os.path.join(imageDir,cat)):\n for filename in files:\n filepath = subdir + os.sep + filename\n if filepath.endswith(\".csv\"):\n all_files.append(filepath)\n print(filepath)", "./images/book/529148/1139919.csv\n./images/book/344621/1139063.csv\n./images/book/112798/1985721.csv\n./images/book/385719/1139451.csv\n./images/book/389315/1652379.csv\n./images/book/368684/1145116.csv\n./images/book/506933/1147645.csv\n./images/book/166478/1138070.csv\n./images/book/206579/1986194.csv\n./images/book/96001/1137334.csv\n./images/book/167159/1137818.csv\n./images/book/551439/1140019.csv\n./images/book/16958/1144744.csv\n./images/book/458255/1141588.csv\n./images/book/172617/1140850.csv\n./images/book/334399/1648816.csv\n./images/book/14038/2197005.csv\n./images/book/250901/1986136.csv\n./images/book/25603/1144688.csv\n./images/book/172595/1139612.csv\n./images/book/421923/1137283.csv\n./images/book/222299/1648151.csv\n./images/book/413247/1139762.csv\n./images/book/398377/1138534.csv\n./images/book/509699/1141405.csv\n./images/book/415741/1648882.csv\n./images/book/472678/1648320.csv\n./images/book/575187/1145126.csv\n./images/book/55528/1647877.csv\n./images/book/467176/908400467176.csv\n./images/book/573094/1985088.csv\n./images/book/539883/1138176.csv\n./images/book/553664/1138185.csv\n./images/book/540280/1650976.csv\n./images/book/115870/1141268.csv\n./images/book/479248/1662457.csv\n./images/book/340175/908400340175.csv\n./images/book/400082/1143266.csv\n./images/book/632/1987085.csv\n./images/book/416343/1985126.csv\n./images/book/93353/1137053.csv\n./images/book/71226/1138892.csv\n./images/book/213086/1654376.csv\n./images/book/416451/1658919.csv\n./images/book/194724/1137084.csv\n./images/book/455301/1138815.csv\n./images/book/176232/2143009.csv\n./images/book/24610/1144582.csv\n./images/book/455937/1138668.csv\n./images/book/154705/1649151.csv\n./images/book/123633/1146234.csv\n./images/book/45229/1650257.csv\n./images/book/147518/1985748.csv\n./images/book/136915/1987277.csv\n./images/book/215778/1140731.csv\n./images/book/387098/1983947.csv\n./images/book/134882/1146908.csv\n./images/book/466125/1985068.csv\n./images/book/451308/1984216.csv\n./images/book/58111/1141657.csv\n./images/book/482319/1147472.csv\n./images/book/104666/1140282.csv\n./images/book/121586/1139801.csv\n./images/book/179898/1650038.csv\n./images/book/214224/1654951.csv\n./images/book/473219/1139961.csv\n./images/book/316666/1989379.csv\n./images/book/77396/1136959.csv\n./images/book/547336/1146671.csv\n./images/book/89648/1651552.csv\n./images/book/125129/1983875.csv\n./images/book/36936/2141551.csv\n./images/book/480936/1653080.csv\n./images/book/309938/1139039.csv\n./images/book/323202/1649362.csv\n./images/book/84674/1137165.csv\n./images/book/535253/1985407.csv\n./images/book/255165/1142550.csv\n./images/book/384527/1144024.csv\n./images/book/128148/908400128148.csv\n./images/book/125062/1648577.csv\n./images/book/476810/1147902.csv\n./images/book/567886/1137471.csv\n./images/book/379441/1662278.csv\n./images/book/135872/1138037.csv\n./images/book/179392/1655523.csv\n./images/book/199771/1143506.csv\n./images/book/507575/1140358.csv\n./images/book/39477/1140893.csv\n./images/book/31248/1143403.csv\n./images/book/542776/2196961.csv\n./images/book/308430/1648499.csv\n./images/book/317999/1137617.csv\n./images/book/316015/1657179.csv\n./images/book/262227/1648770.csv\n./images/book/200839/1984132.csv\n./images/book/97994/1142182.csv\n./images/book/205514/1138665.csv\n./images/book/565877/1648063.csv\n./images/book/529568/2141653.csv\n" ], [ "li = []\nfor filename in all_files:\n df = pd.read_csv(filename, index_col=None, header=0)\n li.append(df)\ndf_images = pd.concat(li, axis=0, ignore_index=True)\ndf_images.head()", "_____no_output_____" ] ], [ [ "## Apply prediction to multiple images", "_____no_output_____" ] ], [ [ "df_pred = pd.DataFrame(columns=['pred','pred_cat','pred_bbox'])\niou_threshold = 0.5\nfor idx, item in df_images.iterrows():\n file_path = os.path.join(item['path'], item['filename'])\n image, image_w, image_h = load_image_pixels(file_path, (input_w, input_h))\n yhat = model.predict(image)\n boxes = list()\n for i in range(len(yhat)):\n # decode the output of the network\n boxes += decode_netout(yhat[i][0], anchors[i], class_threshold, input_h, input_w)\n # correct the sizes of the bounding boxes for the shape of the image\n correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)\n # suppress non-maximal boxes\n do_nms(boxes, 0.5)\n # get the details of the detected objects\n v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)\n \n ##########\n # summarize what we found\n # for i in range(len(v_boxes)):\n # print(v_labels[i], v_scores[i])\n # draw what we found\n # draw_boxes(file_path, v_boxes, v_labels, v_scores)\n\n ##########\n boxes = item['bbox'].lstrip(\"[\")\n boxes = boxes.rstrip(\"]\")\n boxes = boxes.strip()\n x, y, w, h = list(map(int,boxes.split(\",\")))\n _box = BoundBox(x, y, x+w, y+h)\n is_detected = False\n for i, box in enumerate(v_boxes): # y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n # print(bbox_iou(box, _box))\n # print(bbox_iou(_box, box))\n iou = bbox_iou(box, _box)\n if iou > iou_threshold:\n df_pred = df_pred.append({\n 'pred': v_scores[i],\n 'pred_cat': v_labels[i],\n 'pred_bbox': [box.xmin, box.ymin, box.xmax-box.xmin, box.ymax-box.ymin]\n }, ignore_index=True)\n is_detected=True\n break\n if not is_detected:\n df_pred = df_pred.append({\n 'pred': np.nan,\n 'pred_cat': np.nan,\n 'pred_bbox': np.nan\n }, ignore_index=True)", "_____no_output_____" ], [ "df = pd.concat([df_images, df_pred], axis=1)\ndf.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 500 entries, 0 to 499\nData columns (total 9 columns):\nbbox 500 non-null object\ncategory 500 non-null object\nfilename 500 non-null object\nheight 500 non-null int64\npath 500 non-null object\nwidth 500 non-null int64\npred 56 non-null float64\npred_cat 56 non-null object\npred_bbox 56 non-null object\ndtypes: float64(1), int64(2), object(6)\nmemory usage: 35.3+ KB\n" ], [ "df.head()", "_____no_output_____" ], [ "df.to_csv(imageDir+cat+\"/prediction_results.csv\", index=False)", "_____no_output_____" ] ] ]

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[ [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from egocom import audio\nfrom egocom.multi_array_alignment import gaussian_kernel\nfrom egocom.transcription import async_srt_format_timestamp\nfrom scipy.io import wavfile\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score\nfrom egocom.transcription import write_subtitles", "_____no_output_____" ], [ "def gaussian_smoothing(arr, samplerate = 44100, window_size = 0.1):\n '''Returns a locally-normalized array by dividing each point by a the \n sum of the points around it, with greater emphasis on the points \n nearest (using a Guassian convolution)\n \n Parameters\n ----------\n arr : np.array\n samplerate : int\n window_size : float (in seconds)\n \n Returns\n -------\n A Guassian smoothing of the input arr'''\n \n kern = gaussian_kernel(kernel_length=int(samplerate * window_size), nsigma=3)\n return np.convolve(arr, kern, 'same')", "_____no_output_____" ], [ "# Tests for audio.avg_pool_1d\n\ndef test_exact_recoverability(\n arr = range(10),\n pool_size = 4,\n weights = [0.2,0.3,0.3,0.2],\n): \n '''Verify that downsampled signal can be fully recovered exactly.'''\n complete_result = audio.avg_pool_1d(range(10), pool_size, filler = True, weights = weights)\n downsampled_result = audio.avg_pool_1d(range(10), pool_size, filler = False, weights = weights)\n # Try to recover filled_pooled_mags using the downsampled pooled_mags\n upsampled_result = audio.upsample_1d(downsampled_result, len(arr), pool_size)\n assert(np.all(upsampled_result == complete_result))\n \n \ndef test_example(\n arr = range(10),\n pool_size = 4,\n weights = [0.2,0.3,0.3,0.2],\n):\n '''Verify that avg_pool_1d produces the result we expect.'''\n result = audio.avg_pool_1d(range(10), pool_size, weights = weights)\n expected = np.array([1.5, 1.5, 1.5, 1.5, 5.5, 5.5, 5.5, 5.5, 8.5, 8.5])\n assert(np.all(result - expected < 1e-6))\n \ntest_exact_recoverability()\ntest_example()", "_____no_output_____" ] ], [ [ "# Generate speaker labels from max raw audio magnitudes", "_____no_output_____" ] ], [ [ "data_dir = '/Users/cgn/Dropbox (Facebook)/EGOCOM/raw_audio/wav/'\n\nfn_dict = {}\nfor fn in sorted(os.listdir(data_dir)):\n key = fn[9:23] + fn[32:37] if 'part' in fn else fn[9:21]\n fn_dict[key] = fn_dict[key] + [fn] if key in fn_dict else [fn]", "_____no_output_____" ], [ "samplerate = 44100\nwindow = 1 # Averages signals with windows of N seconds.\nwindow_length = int(samplerate * window)\n\nlabels = {}\nfor key in list(fn_dict.keys()):\n print(key, end = \" | \")\n fns = fn_dict[key]\n wavs = [wavfile.read(data_dir + fn)[1] for fn in fns]\n duration = min(len(w) for w in wavs)\n wavs = np.stack([w[:duration] for w in wavs])\n \n # Only use the magnitudes of both left and right for each audio wav.\n mags = abs(wavs).sum(axis = 2) \n\n # DOWNSAMPLED (POOLED) Discretized/Fast (no overlap) gaussian smoothing with one-second time window.\n kwargs = {\n 'pool_size': window_length, \n 'weights': gaussian_kernel(kernel_length=window_length),\n 'filler': False,\n }\n pooled_mags = np.apply_along_axis(audio.avg_pool_1d, 1, mags, **kwargs) \n\n # Create noisy speaker labels\n threshold = np.percentile(pooled_mags, 10, axis = 1)\n no_one_speaking = (pooled_mags > np.expand_dims(threshold, axis = 1)).sum(axis = 0) == 0\n speaker_labels = np.argmax(pooled_mags, axis = 0)\n speaker_labels[no_one_speaking] = -1\n \n # User 1-based indexing for speaker labels (ie increase by 1)\n speaker_labels = [z if z < 0 else z + 1 for z in speaker_labels]\n \n # Store results\n labels[key] = speaker_labels", "day_1__con_1__part1 | day_1__con_1__part2 | day_1__con_1__part3 | day_1__con_1__part4 | day_1__con_1__part5 | day_1__con_2__part1 | day_1__con_2__part2 | day_1__con_2__part3 | day_1__con_2__part4 | day_1__con_2__part5 | day_1__con_3__part1 | day_1__con_3__part2 | day_1__con_3__part3 | day_1__con_3__part4 | day_1__con_4__part1 | day_1__con_4__part2 | day_1__con_4__part3 | day_1__con_4__part4 | day_1__con_5__part1 | day_1__con_5__part2 | day_1__con_5__part3 | day_1__con_5__part4 | day_1__con_5__part5 | day_2__con_1__part1 | day_2__con_1__part2 | day_2__con_1__part3 | day_2__con_1__part4 | day_2__con_1__part5 | day_2__con_2__part1 | day_2__con_2__part2 | day_2__con_2__part3 | day_2__con_2__part4 | day_2__con_3 | day_2__con_4 | day_2__con_5 | day_2__con_6 | day_2__con_7 | day_3__con_1 | day_3__con_2 | day_3__con_3 | day_3__con_4 | day_3__con_5 | day_3__con_6 | day_4__con_1 | day_4__con_2 | day_4__con_3 | day_4__con_4 | day_4__con_5 | day_4__con_6 | day_5__con_1 | day_5__con_2 | day_5__con_3 | day_5__con_4 | day_5__con_5 | day_5__con_6 | day_5__con_7 | day_5__con_8 | day_6__con_1 | day_6__con_2 | day_6__con_3 | day_6__con_4 | day_6__con_5 | day_6__con_6 | " ], [ "# Write result to file\nloc = '/Users/cgn/Dropbox (Facebook)/EGOCOM/raw_audio_speaker_labels_{}.json'.format(str(window))\ndef default(o):\n if isinstance(o, np.int64): return int(o) \n raise TypeError\n \nimport json\nwith open(loc, 'w') as fp:\n json.dump(labels, fp, default = default)\nfp.close()", "_____no_output_____" ], [ "# Read result into a dict\nimport json\nwith open(loc, 'r') as fp:\n labels = json.load(fp)\nfp.close()", "_____no_output_____" ] ], [ [ "## Generate ground truth speaker labels", "_____no_output_____" ] ], [ [ "def create_gt_speaker_labels(\n df_times_speaker,\n duration_in_seconds,\n time_window_seconds = 0.5,\n):\n stack = rev_times[::-1]\n stack_time = stack.pop()\n label_times = np.arange(0, duration_in_seconds, time_window_seconds)\n result = [-1] * len(label_times)\n\n for i, t in enumerate(label_times):\n while stack_time['endTime'] > t and stack_time['endTime'] <= t + time_window_seconds:\n result[i] = stack_time['speaker']\n if len(stack) == 0:\n break\n stack_time = stack.pop()\n \n return result", "_____no_output_____" ], [ "df = pd.read_csv(\"/Users/cgn/Dropbox (Facebook)/EGOCOM/ground_truth_transcriptions.csv\")[\n [\"key\", \"endTime\", \"speaker\", ]\n].dropna()", "_____no_output_____" ], [ "gt_speaker_labels = {}\nfor key, sdf in df.groupby('key'):\n print(key, end = \" | \")\n wavs = [wavfile.read(data_dir + fn)[1] for fn in fn_dict[key]]\n duration = min(len(w) for w in wavs)\n DL = sdf[[\"endTime\", \"speaker\"]].to_dict('list')\n rev_times = [dict(zip(DL,t)) for t in zip(*DL.values())]\n duration_in_seconds = np.ceil(duration / float(samplerate))\n gt_speaker_labels[key] = create_gt_speaker_labels(rev_times, duration_in_seconds, window)", "day_1__con_1__part1 | day_1__con_1__part2 | day_1__con_1__part3 | day_1__con_1__part4 | day_1__con_1__part5 | day_1__con_2__part1 | day_1__con_2__part2 | day_1__con_2__part3 | day_1__con_2__part4 | day_1__con_2__part5 | day_1__con_3__part1 | day_1__con_3__part2 | day_1__con_3__part3 | day_1__con_3__part4 | day_1__con_4__part1 | day_1__con_4__part2 | day_1__con_4__part3 | day_1__con_4__part4 | day_1__con_5__part1 | day_1__con_5__part2 | day_1__con_5__part3 | day_1__con_5__part4 | day_1__con_5__part5 | day_2__con_1__part1 | day_2__con_1__part2 | day_2__con_1__part3 | day_2__con_1__part4 | day_2__con_1__part5 | day_2__con_2__part1 | day_2__con_2__part2 | day_2__con_2__part3 | day_2__con_2__part4 | day_2__con_3 | day_2__con_4 | day_2__con_5 | day_2__con_6 | day_2__con_7 | day_3__con_1 | day_3__con_2 | day_3__con_3 | day_3__con_4 | day_3__con_5 | day_3__con_6 | day_4__con_1 | day_4__con_2 | day_4__con_3 | day_4__con_4 | day_4__con_5 | day_4__con_6 | day_5__con_1 | day_5__con_2 | day_5__con_3 | day_5__con_4 | day_5__con_5 | day_5__con_6 | day_5__con_7 | day_5__con_8 | day_6__con_1 | day_6__con_2 | day_6__con_3 | day_6__con_4 | day_6__con_5 | day_6__con_6 | " ], [ "# Write result to file\nloc = '/Users/cgn/Dropbox (Facebook)/EGOCOM/rev_ground_truth_speaker_labels_{}.json'.format(str(window))\nwith open(loc, 'w') as fp:\n json.dump(gt_speaker_labels, fp, default = default)\nfp.close()", "_____no_output_____" ], [ "# Read result into a dict\nwith open(loc, 'r') as fp:\n gt_speaker_labels = json.load(fp)\nfp.close()", "_____no_output_____" ], [ "scores = []\nfor key in labels.keys():\n true = gt_speaker_labels[key]\n pred = labels[key]\n if len(true) > len(pred):\n true = true[:-1]\n# diff = round(accuracy_score(true[:-1], pred) - accuracy_score(true[1:], pred), 3)\n# scores.append(diff)\n# print(key, accuracy_score(true[1:], pred), accuracy_score(true[:-1], pred), diff)\n score = accuracy_score(true, pred)\n scores.append(score)\n print(key, np.round(score, 3))", "day_1__con_1__part1 0.724\nday_1__con_1__part2 0.763\nday_1__con_1__part3 0.702\nday_1__con_1__part4 0.773\nday_1__con_1__part5 0.867\nday_1__con_2__part1 0.68\nday_1__con_2__part2 0.742\nday_1__con_2__part3 0.792\nday_1__con_2__part4 0.869\nday_1__con_2__part5 0.85\nday_1__con_3__part1 0.822\nday_1__con_3__part2 0.735\nday_1__con_3__part3 0.758\nday_1__con_3__part4 0.867\nday_1__con_4__part1 0.859\nday_1__con_4__part2 0.833\nday_1__con_4__part3 0.77\nday_1__con_4__part4 0.72\nday_1__con_5__part1 0.666\nday_1__con_5__part2 0.607\nday_1__con_5__part3 0.637\nday_1__con_5__part4 0.67\nday_1__con_5__part5 0.6\nday_2__con_1__part1 0.699\nday_2__con_1__part2 0.743\nday_2__con_1__part3 0.723\nday_2__con_1__part4 0.68\nday_2__con_1__part5 0.714\nday_2__con_2__part1 0.715\nday_2__con_2__part2 0.68\nday_2__con_2__part3 0.677\nday_2__con_2__part4 0.633\nday_2__con_3 0.623\nday_2__con_4 0.754\nday_2__con_5 0.842\nday_2__con_6 0.728\nday_2__con_7 0.663\nday_3__con_1 0.868\nday_3__con_2 0.787\nday_3__con_3 0.769\nday_3__con_4 0.793\nday_3__con_5 0.685\nday_3__con_6 0.741\nday_4__con_1 0.784\nday_4__con_2 0.669\nday_4__con_3 0.736\nday_4__con_4 0.777\nday_4__con_5 0.423\nday_4__con_6 0.708\nday_5__con_1 0.731\nday_5__con_2 0.693\nday_5__con_3 0.731\nday_5__con_4 0.723\nday_5__con_5 0.727\nday_5__con_6 0.783\nday_5__con_7 0.682\nday_5__con_8 0.612\nday_6__con_1 0.78\nday_6__con_2 0.686\nday_6__con_3 0.678\nday_6__con_4 0.558\nday_6__con_5 0.636\nday_6__con_6 0.598\n" ], [ "print('Average accuracy:', str(np.round(np.mean(scores), 3)* 100) + '%')", "Average accuracy: 72.3%\n" ], [ "loc = '/Users/cgn/Dropbox (Facebook)/EGOCOM/subtitles/'\nfor key in labels.keys():\n gt = gt_speaker_labels[key]\n est = labels[key]\n with open(loc + \"speaker_\" + key + '.srt', 'w') as f:\n print(key, end = \" | \")\n for t, s_est in enumerate(est):\n s_gt = gt[t]\n print(t + 1, file = f)\n print(async_srt_format_timestamp(t*window), end = \"\", file = f)\n print(' --> ', end = '', file = f)\n print(async_srt_format_timestamp(t*window+window), file = f)\n print('Rev.com Speaker:', end = \" \", file = f)\n if s_gt == -1:\n print('No one is speaking', file = f)\n elif s_gt == 1:\n print('Curtis', file = f)\n else:\n print('Speaker ' + str(s_gt), file = f)\n print('MaxMag Speaker:', end = \" \", file = f)\n if s_est == -1:\n print('No one is speaking', file = f)\n elif s_est == 1:\n print('Curtis', file = f)\n else:\n print('Speaker ' + str(s_est), file = f)\n print(file = f)", "day_1__con_1__part1 | day_1__con_1__part2 | day_1__con_1__part3 | day_1__con_1__part4 | day_1__con_1__part5 | day_1__con_2__part1 | day_1__con_2__part2 | day_1__con_2__part3 | day_1__con_2__part4 | day_1__con_2__part5 | day_1__con_3__part1 | day_1__con_3__part2 | day_1__con_3__part3 | day_1__con_3__part4 | day_1__con_4__part1 | day_1__con_4__part2 | day_1__con_4__part3 | day_1__con_4__part4 | day_1__con_5__part1 | day_1__con_5__part2 | day_1__con_5__part3 | day_1__con_5__part4 | day_1__con_5__part5 | day_2__con_1__part1 | day_2__con_1__part2 | day_2__con_1__part3 | day_2__con_1__part4 | day_2__con_1__part5 | day_2__con_2__part1 | day_2__con_2__part2 | day_2__con_2__part3 | day_2__con_2__part4 | day_2__con_3 | day_2__con_4 | day_2__con_5 | day_2__con_6 | day_2__con_7 | day_3__con_1 | day_3__con_2 | day_3__con_3 | day_3__con_4 | day_3__con_5 | day_3__con_6 | day_4__con_1 | day_4__con_2 | day_4__con_3 | day_4__con_4 | day_4__con_5 | day_4__con_6 | day_5__con_1 | day_5__con_2 | day_5__con_3 | day_5__con_4 | day_5__con_5 | day_5__con_6 | day_5__con_7 | day_5__con_8 | day_6__con_1 | day_6__con_2 | day_6__con_3 | day_6__con_4 | day_6__con_5 | day_6__con_6 | " ] ], [ [ "## Generate subtitles", "_____no_output_____" ] ], [ [ "for key in labels.keys():\n gt = labels[key]\n with open(\"subtitles/est_\" + key + '.srt', 'w') as f:\n for t, s in enumerate(gt):\n print(t + 1, file = f)\n print(async_srt_format_timestamp(t*window), end = \"\", file = f)\n print(' --> ', end = '', file = f)\n print(async_srt_format_timestamp(t*window+window), file = f)\n print('Max mag of wavs speaker id', file = f)\n if s == -1:\n print('No one is speaking', file = f)\n elif s == 1:\n print('Curtis', file = f)\n else:\n print('Speaker ' + str(s), file = f)\n print(file = f)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "from torchvision.models import *\nimport wandb\nfrom sklearn.model_selection import train_test_split\nimport os,cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom torch.nn import *\nimport torch,torchvision\nfrom tqdm import tqdm\ndevice = 'cuda'\nPROJECT_NAME = 'Intel-Image-Classification-V2'", "_____no_output_____" ], [ "def load_data():\n data = []\n labels = {}\n labels_r = {}\n idx = 0\n for label in os.listdir('./data/'):\n idx += 1\n labels[label] = idx\n labels_r[idx] = label\n for folder in os.listdir('./data/'):\n for file in os.listdir(f'./data/{folder}/')[:1000]:\n img = cv2.imread(f'./data/{folder}/{file}')\n img = cv2.resize(img,(56,56))\n img = img / 255.0\n data.append([\n img,\n np.eye(labels[foder],len(labels))[labels[folder]-1]\n ])\n X = []\n y = []\n for d in data:\n X.append(d[0])\n y.append(d[1])\n X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.5,shuffle=False)\n X_train = torch.from_numpy(np.array(X_train)).to(device).view(-1,3,56,56).float()\n y_train = torch.from_numpy(np.array(y_train)).to(device).float()\n X_test = torch.from_numpy(np.array(X_test)).to(device).view(-1,3,56,56).float()\n y_test = torch.from_numpy(np.array(y_test)).to(device).float()\n return X,y,X_train,X_test,y_train,y_test,labels,labels_r,idx,data", "_____no_output_____" ], [ "X,y,X_train,X_test,y_train,y_test,labels,labels_r,idx,data = load_data()", "_____no_output_____" ], [ "def load_data():\n data = []\n labels = {}\n labels_r = {}\n idx = 0\n for label in os.listdir('./data/'):\n idx += 1\n labels[label] = idx\n labels_r[idx] = label\n for folder in os.listdir('./data/'):\n for file in os.listdir(f'./data/{folder}/')[:1000]:\n img = cv2.imread(f'./data/{folder}/{file}')\n img = cv2.resize(img,(56,56))\n img = img / 255.0\n data.append([\n img,\n np.eye(labels[labels_r],len(labels))[labels[folder]-1]\n ])\n X = []\n y = []\n for d in data:\n X.append(d[0])\n y.append(d[1])\n X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.5,shuffle=False)\n X_train = torch.from_numpy(np.array(X_train)).to(device).view(-1,3,56,56).float()\n y_train = torch.from_numpy(np.array(y_train)).to(device).float()\n X_test = torch.from_numpy(np.array(X_test)).to(device).view(-1,3,56,56).float()\n y_test = torch.from_numpy(np.array(y_test)).to(device).float()\n return X,y,X_train,X_test,y_train,y_test,labels,labels_r,idx,data", "_____no_output_____" ], [ "def load_data():\n data = []\n labels = {}\n labels_r = {}\n idx = 0\n for label in os.listdir('./data/'):\n idx += 1\n labels[label] = idx\n labels_r[idx] = label\n for folder in os.listdir('./data/'):\n for file in os.listdir(f'./data/{folder}/')[:1000]:\n img = cv2.imread(f'./data/{folder}/{file}')\n img = cv2.resize(img,(56,56))\n img = img / 255.0\n data.append([\n img,\n np.eye(labels[folder],len(labels))[labels[folder]-1]\n ])\n X = []\n y = []\n for d in data:\n X.append(d[0])\n y.append(d[1])\n X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.5,shuffle=False)\n X_train = torch.from_numpy(np.array(X_train)).to(device).view(-1,3,56,56).float()\n y_train = torch.from_numpy(np.array(y_train)).to(device).float()\n X_test = torch.from_numpy(np.array(X_test)).to(device).view(-1,3,56,56).float()\n y_test = torch.from_numpy(np.array(y_test)).to(device).float()\n return X,y,X_train,X_test,y_train,y_test,labels,labels_r,idx,data", "_____no_output_____" ], [ "X,y,X_train,X_test,y_train,y_test,labels,labels_r,idx,data = load_data()", "_____no_output_____" ], [ "torch.save(X_train,'X_train.pt')\ntorch.save(y_train,'y_train.pt')\ntorch.save(X_test,'X_test.pt')\ntorch.save(y_test,'y_test.pt')\ntorch.save(labels_r,'labels_r.pt')\ntorch.save(labels,'labels.pt')\ntorch.save(X_train,'X_train.pth')\ntorch.save(y_train,'y_train.pth')\ntorch.save(X_test,'X_test.pth')\ntorch.save(y_test,'y_test.pth')\ntorch.save(labels_r,'labels_r.pth')\ntorch.save(labels,'labels.pth')", "_____no_output_____" ], [ "def get_loss(model,X,y,criterion):\n preds = model(X)\n loss = criterion(preds,y)\n return loss.item()", "_____no_output_____" ], [ "def get_acuracy(model,X,y):\n preds = model(X)\n correct = 0\n total = 0\n for pred,yb in tqdm(zip(preds,y)):\n pred = int(torch.argmax(pred))\n yb = int(torch.argmax(yb))\n if pred == yb:\n correct += 1\n total += 1\n acc = round(correct/total,3)*100\n return acc", "_____no_output_____" ], [ "model = resnet18().to(device)\nmodel.fc = Linear(512,len(labels))\ncriterion = MSELoss()\noptimizer = Adam(model.parameters(),lr=0.001)\nepochs = 100\nbathc_size = 32", "_____no_output_____" ], [ "model = resnet18().to(device)\nmodel.fc = Linear(512,len(labels))\ncriterion = MSELoss()\noptimizer = optim.Adam(model.parameters(),lr=0.001)\nepochs = 100\nbathc_size = 32", "_____no_output_____" ], [ "model = resnet18().to(device)\nmodel.fc = Linear(512,len(labels))\ncriterion = MSELoss()\noptimizer = torch.optim.Adam(model.parameters(),lr=0.001)\nepochs = 100\nbathc_size = 32", "_____no_output_____" ], [ "wandb.init(project=PROJECT_NAME,name='baseline-TL')\nfor _ in tqdm(range(epochs)):\n for i in range(0,len(X_train),batch_size):\n X_batch = X_train[i:i+batch_size]\n y_batch = y_train[i:i+batch_size]\n model.to(device)\n preds = model(X_batch)\n loss = criterion(preds,y_batch)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n model.eval()\n torch.cuda.empty_cache()\n wandb.log({'Loss':(get_loss(model,X_train,y_train,criterion)+get_loss(model,X_batch,y_batch,criterion))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val Loss':get_loss(model,X_test,y_test,criterion)})\n torch.cuda.empty_cache()\n wandb.log({'Acc':(get_accuracy(model,X_train,y_train)+get_accuracy(model,X_batch,y_batch))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val ACC':get_accuracy(model,X_test,y_test)})\n torch.cuda.empty_cache()\n model.train()\nwandb.finish()", "_____no_output_____" ], [ "model = resnet18().to(device)\nmodel.fc = Linear(512,len(labels))\ncriterion = MSELoss()\noptimizer = torch.optim.Adam(model.parameters(),lr=0.001)\nepochs = 100\nbatch_size = 32", "_____no_output_____" ], [ "wandb.init(project=PROJECT_NAME,name='baseline-TL')\nfor _ in tqdm(range(epochs)):\n for i in range(0,len(X_train),batch_size):\n X_batch = X_train[i:i+batch_size]\n y_batch = y_train[i:i+batch_size]\n model.to(device)\n preds = model(X_batch)\n loss = criterion(preds,y_batch)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n model.eval()\n torch.cuda.empty_cache()\n wandb.log({'Loss':(get_loss(model,X_train,y_train,criterion)+get_loss(model,X_batch,y_batch,criterion))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val Loss':get_loss(model,X_test,y_test,criterion)})\n torch.cuda.empty_cache()\n wandb.log({'Acc':(get_accuracy(model,X_train,y_train)+get_accuracy(model,X_batch,y_batch))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val ACC':get_accuracy(model,X_test,y_test)})\n torch.cuda.empty_cache()\n model.train()\nwandb.finish()", "_____no_output_____" ], [ "def get_accuracy(model,X,y):\n preds = model(X)\n correct = 0\n total = 0\n for pred,yb in tqdm(zip(preds,y)):\n pred = int(torch.argmax(pred))\n yb = int(torch.argmax(yb))\n if pred == yb:\n correct += 1\n total += 1\n acc = round(correct/total,3)*100\n return acc", "_____no_output_____" ], [ "model = resnet18().to(device)\nmodel.fc = Linear(512,len(labels))\ncriterion = MSELoss()\noptimizer = torch.optim.Adam(model.parameters(),lr=0.001)\nepochs = 100\nbatch_size = 32", "_____no_output_____" ], [ "wandb.init(project=PROJECT_NAME,name='baseline-TL')\nfor _ in tqdm(range(epochs)):\n for i in range(0,len(X_train),batch_size):\n X_batch = X_train[i:i+batch_size]\n y_batch = y_train[i:i+batch_size]\n model.to(device)\n preds = model(X_batch)\n loss = criterion(preds,y_batch)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n model.eval()\n torch.cuda.empty_cache()\n wandb.log({'Loss':(get_loss(model,X_train,y_train,criterion)+get_loss(model,X_batch,y_batch,criterion))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val Loss':get_loss(model,X_test,y_test,criterion)})\n torch.cuda.empty_cache()\n wandb.log({'Acc':(get_accuracy(model,X_train,y_train)+get_accuracy(model,X_batch,y_batch))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val ACC':get_accuracy(model,X_test,y_test)})\n torch.cuda.empty_cache()\n model.train()\nwandb.finish()", "_____no_output_____" ], [ "class Model(Module):\n def __init__(self):\n super().__init__()\n self.max_pool2d = MaxPool2d((2,2),(2,2))\n self.activation = ReLU()\n self.conv1 = Conv2d(3,7,(5,5))\n self.conv2 = Conv2d(7,14,(5,5))\n self.conv2bn = BatchNorm2d(14)\n self.conv3 = Conv2d(14,21,(5,5))\n self.linear1 = Linear(21*5*5,256)\n self.linear2 = Linear(256,512)\n self.linear2bn = BatchNorm1d(512)\n self.linear3 = Linear(512,256)\n self.output = Linear(256,len(labels))\n \n def forward(self,X):\n preds = self.max_pool2d(self.activation(self.conv1(X)))\n preds = self.max_pool2d(self.activation(self.conv2bn(self.conv2(preds))))\n preds = self.max_pool2d(self.activation(self.conv3(preds)))\n preds = preds.view(-1,21*5*5)\n preds = self.activation(self.linear1(preds))\n preds = self.activation(self.linear2bn(self.linear2(preds)))\n preds = self.activation(self.linear3(preds))\n preds = self.output(preds)\n return preds", "_____no_output_____" ], [ "model = Model().to(device)\ncriterion = MSELoss()\noptimizer = Adam(model.parameters(),lr=0.001)\nepochs = 100\nbathc_size = 32", "_____no_output_____" ], [ "model = Model().to(device)\ncriterion = MSELoss()\noptimizer = torch.optim.Adam(model.parameters(),lr=0.001)\nepochs = 100\nbathc_size = 32", "_____no_output_____" ], [ "wandb.init(project=PROJECT_NAME,name='baseline-CNN')\nfor _ in tqdm(range(epochs)):\n for i in range(0,len(X_train),batch_size):\n X_batch = X_train[i:i+batch_size]\n y_batch = y_train[i:i+batch_size]\n model.to(device)\n preds = model(X_batch)\n loss = criterion(preds,y_batch)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n model.eval()\n torch.cuda.empty_cache()\n wandb.log({'Loss':(get_loss(model,X_train,y_train,criterion)+get_loss(model,X_batch,y_batch,criterion))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val Loss':get_loss(model,X_test,y_test,criterion)})\n torch.cuda.empty_cache()\n wandb.log({'Acc':(get_accuracy(model,X_train,y_train)+get_accuracy(model,X_batch,y_batch))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val ACC':get_accuracy(model,X_test,y_test)})\n torch.cuda.empty_cache()\n model.train()\nwandb.finish()", "_____no_output_____" ], [ "class Model(Module):\n def __init__(self):\n super().__init__()\n self.max_pool2d = MaxPool2d((2,2),(2,2))\n self.activation = ReLU()\n self.conv1 = Conv2d(3,7,(5,5))\n self.conv2 = Conv2d(7,14,(5,5))\n self.conv2bn = BatchNorm2d(14)\n self.conv3 = Conv2d(14,21,(5,5))\n self.linear1 = Linear(21*5*5,256)\n self.linear2 = Linear(256,512)\n self.linear2bn = BatchNorm1d(512)\n self.linear3 = Linear(512,256)\n self.output = Linear(256,len(labels))\n \n def forward(self,X):\n preds = self.max_pool2d(self.activation(self.conv1(X)))\n preds = self.max_pool2d(self.activation(self.conv2bn(self.conv2(preds))))\n preds = self.max_pool2d(self.activation(self.conv3(preds)))\n preds = preds.view(-1,21*3*3)\n preds = self.activation(self.linear1(preds))\n preds = self.activation(self.linear2bn(self.linear2(preds)))\n preds = self.activation(self.linear3(preds))\n preds = self.output(preds)\n return preds", "_____no_output_____" ], [ "model = Model().to(device)\ncriterion = MSELoss()\noptimizer = torch.optim.Adam(model.parameters(),lr=0.001)\nepochs = 100\nbathc_size = 32", "_____no_output_____" ], [ "wandb.init(project=PROJECT_NAME,name='baseline-CNN')\nfor _ in tqdm(range(epochs)):\n for i in range(0,len(X_train),batch_size):\n X_batch = X_train[i:i+batch_size]\n y_batch = y_train[i:i+batch_size]\n model.to(device)\n preds = model(X_batch)\n loss = criterion(preds,y_batch)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n model.eval()\n torch.cuda.empty_cache()\n wandb.log({'Loss':(get_loss(model,X_train,y_train,criterion)+get_loss(model,X_batch,y_batch,criterion))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val Loss':get_loss(model,X_test,y_test,criterion)})\n torch.cuda.empty_cache()\n wandb.log({'Acc':(get_accuracy(model,X_train,y_train)+get_accuracy(model,X_batch,y_batch))/2})\n torch.cuda.empty_cache()\n wandb.log({'Val ACC':get_accuracy(model,X_test,y_test)})\n torch.cuda.empty_cache()\n model.train()\nwandb.finish()", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "Caníbales y misioneros\nmediante búsqueda primero en anchura.", "_____no_output_____" ] ], [ [ "from copy import deepcopy\nfrom collections import deque\nimport sys\n\n# (m, c, b) hace referencia a el número de misioneros, canibales y el bote\nclass Estado(object):\n def __init__(self, misioneros, canibales, bote):\n self.misioneros = misioneros\n self.canibales = canibales\n self.bote = bote\n #se establecen los movimientos que estos tendran \n def siguientes(self):\n if self.bote == 1:\n signo = -1\n direction = \"Ida\"\n else:\n signo = 1\n direction = \"Vuelta\"\n for m in range(3):\n for c in range(3):\n nuevo_Estado = Estado(self.misioneros+signo*m, self.canibales+signo*c, self.bote+signo*1);\n if m+c >= 1 and m+c <= 2 and nuevo_Estado.validar(): # comprobar si la acción y el estado resultante son válidos\n accion = \" %d misioneros y %d canibales %s. %r\" % ( m, c, direction, nuevo_Estado) \n yield accion, nuevo_Estado\n \n def validar(self):\n # validacion inicial\n if self.misioneros < 0 or self.canibales < 0 or self.misioneros > 3 or self.canibales > 3 or (self.bote != 0 and self.bote != 1):\n return False \n # luego verifica si los misioneros superan en número a los canibales\n\n # más canibales que misioneros en la orilla original\n if self.canibales > self.misioneros and self.misioneros > 0: \n return False\n # más canibales que misioneros en otra orilla\n if self.canibales < self.misioneros and self.misioneros < 3: \n return False\n return True\n\n# valida estado objetivo\n def estado_final(self):\n return self.canibales == 0 and self.misioneros == 0 and self.bote == 0\n\n\n# funcion para devolver los estados en los que se encuentra\n def __repr__(self):\n return \"< Estado (%d, %d, %d) >\" % (self.misioneros, self.canibales, self.bote)\n\n\n# clase nodo\nclass Nodo(object): #se crea la clase nodo para hacer la relacion con sus adyacencias \n def __init__(self, nodo_pariente, estado, accion, profundidad):\n self.nodo_pariente = nodo_pariente\n self.estado = estado\n self.accion = accion\n self.profundidad = profundidad\n \n\n# metodo expandir\n#Busca lo nodos adyacentes \n def expandir(self):\n for (accion, estado_sig) in self.estado.siguientes():\n nodo_sig = Nodo(\n nodo_pariente=self,\n estado=estado_sig,\n accion=accion,\n profundidad=self.profundidad + 1)\n yield nodo_sig\n \n\n# funcion para guardar y devolver la solucion del problema\n def devolver_solucion(self):\n solucion = []\n nodo = self\n while nodo.nodo_pariente is not None:\n solucion.append(nodo.accion)\n nodo = nodo.nodo_pariente\n solucion.reverse()\n return solucion\n\n\n# metodo BFS - busqueda por anchura\n\ndef BFS(Estado_inicial):\n nodo_inicial = Nodo(\n nodo_pariente=None,\n estado=Estado_inicial,\n accion=None,\n profundidad=0) # Se establece la conexion con los nodos hijos\n \n cola = deque([nodo_inicial]) #se crea la cola\n profundidad_maxima = -1\n while True:\n if not cola:\n return None\n nodo = cola.popleft() \n if nodo.profundidad > profundidad_maxima: #se defienen los nuevos nodos \n profundidad_maxima = nodo.profundidad\n if nodo.estado.estado_final():\n solucion = nodo.devolver_solucion()\n return solucion\n cola.extend(nodo.expandir())\n\n\ndef main(): #Caso prueba \n Estado_inicial = Estado(3,3,1)\n solucion = BFS(Estado_inicial)\n if solucion is None:\n print(\"no tiene solucion\")\n else:\n for pasos in solucion:\n print(\"%s\" % pasos)\n\n\nif __name__ == \"__main__\":\n main()", " 0 misioneros y 2 canibales Ida. < Estado (3, 1, 0) >\n 0 misioneros y 1 canibales Vuelta. < Estado (3, 2, 1) >\n 0 misioneros y 2 canibales Ida. < Estado (3, 0, 0) >\n 0 misioneros y 1 canibales Vuelta. < Estado (3, 1, 1) >\n 2 misioneros y 0 canibales Ida. < Estado (1, 1, 0) >\n 1 misioneros y 1 canibales Vuelta. < Estado (2, 2, 1) >\n 2 misioneros y 0 canibales Ida. < Estado (0, 2, 0) >\n 0 misioneros y 1 canibales Vuelta. < Estado (0, 3, 1) >\n 0 misioneros y 2 canibales Ida. < Estado (0, 1, 0) >\n 0 misioneros y 1 canibales Vuelta. < Estado (0, 2, 1) >\n 0 misioneros y 2 canibales Ida. < Estado (0, 0, 0) >\n" ] ], [ [ "Función en Python que reciba un grafo, un nodo inicial y un nodo final y devuelva la ruta del nodo inicial al nodo final utilizando búsqueda primero en profundidad. Se deben crear las clases Grafo y Nodo con sus respectivos métodos y atributos. La función debe retornar None en caso de que no haya ninguna ruta posible.\n", "_____no_output_____" ] ], [ [ "class Enlace:\n #pendientes\n def __init__(self, a=None, b=None):\n self.a = a\n self.b = b\n\n def __eq__(self, other):\n return self.a == other.a and self.b == other.b\n\n def __str__(self):\n return \"(\" + str(self.a) + \",\" + str(self.b) + \")\"\n\n def __repr__(self):\n return self.__str__()\n\nclass Nodo:\n\n def __init__(self, padre=None, nombre=\"\"):\n self.padre = padre\n self.nombre = nombre\n\n def __eq__(self, other):\n return self.nombre == other.nombre\n\n def __str__(self):\n return self.nombre\n\n def __repr__(self):\n return self.__str__()\n\nclass Grafo: #se crea el grafo con los nodos y enlaces entre si \n def __init__(self, nodos=[], enlaces=[]):\n self.nodos = nodos\n self.enlaces = enlaces\n\n def __str__(self):\n return \"Nodos : \" + str(self.nodos) + \" Enlaces : \" + str(self.enlaces)\n\n def __repr__(self):\n return self.__str__()\n\ndef hallarRuta(grafo, nodoInicial, nodoFinal): #Se halla el recorrido final del grafo \n actual = nodoInicial\n p=[actual]\n estadoFinal = nodoFinal\n \n visitados = [actual]\n rutaFinal=[]\n\n while len(p) > 0: # se verifica que el grafo no este vacio, que a este llegen los nodos adyacentes\n if (actual!=estadoFinal):\n siguiente = generar_siguiente(actual, grafo, visitados)\n if siguiente != Nodo(nombre=\"\"):\n p.append(siguiente)\n actual = p[len(p)-1]\n visitados.append(actual)#se toma cómo agrega a la \"ruta\" de visitados el nodo en el que nos hallamos\n else:\n p.pop()\n actual = p[len(p)-1]\n else:\n while len(p) > 0:\n \n rutaFinal.insert(0,p.pop())#finalmente se le insertan todos todos los visitados a rutaFinal \n break\n \n print(\"La ruta final es\")\n print(rutaFinal)\n\n\ndef generar_siguiente(actual, grafo, visitados):# una ves visitado uno de los nodos, pasa al siguiente nodo, al siguiente hijo\n #comprueba si este nodo ya fue visitado o no\n for i in range(len(grafo.enlaces)):\n if actual.nombre == grafo.enlaces[i].a:\n\n nodoSiguiente = Nodo(padre=actual.nombre, nombre=grafo.enlaces[i].b)\n if nodoSiguiente not in visitados:\n \n return nodoSiguiente\n break\n return Nodo(nombre=\"\")\n\n#EJEMPLO\n#acontinuación se definen las adyacencia del grafo a consultar el recorrrido\nnodos = [Nodo(nombre=\"A\"),Nodo(nombre=\"B\"),Nodo(nombre=\"C\"),Nodo(nombre=\"D\"),Nodo(nombre=\"F\"),Nodo(nombre=\"E\")]\nE1 = Enlace(a = \"A\", b = \"C\" )\nE2 = Enlace(a = \"A\", b = \"D\" )\nE3 = Enlace(a = \"A\", b = \"F\" )\nE4 = Enlace(a = \"B\", b = \"E\" )\nE5 = Enlace(a = \"C\", b = \"F\" )\nE6 = Enlace(a = \"D\", b = \"E\" )\nE7 = Enlace(a = \"E\", b = \"F\" )\nenlaces = [E1,E2,E3,E4,E5,E6,E7]\ngrafo = Grafo(nodos=nodos,enlaces=enlaces)\nnodoA = Nodo(nombre=\"A\")\nnodoB = Nodo(nombre=\"F\")\n\nruta = hallarRuta(grafo, nodoA, nodoB)\n\nprint(ruta)", "La ruta final es\n[A, C, F]\nNone\n" ] ], [ [ "\nDesarrollar un programa en Python que solucione el problema del rompecabezas des-\nlizante para 8 números utilizando búsqueda en anchura . El programa debe leer el\n\nestado inicial desde un archivo. Algunas configuraciones no tienen solución.", "_____no_output_____" ] ], [ [ "class Estados():\n def __init__(self,Mat,Npadre=None):\n self.Npadre=Npadre\n self.Mat=Mat\n \n #Mat=[[0 1 2],[3 4 5],[6 7 8]]\n \n def BuscZ(self): #Buscar el 0 dentro de la matriz\n itera=0\n Pos=[]\n for y,i in enumerate(self.Mat):\n if 0 in i:\n itera+=1\n Pos.append(y)\n Pos.append(i.index(0))\n#Funcion Busca 0\n return Pos\n#prueba=Estados([[1,2,0],[3,4,5],[6,7,8]])\n#prueba.BuscZ()\n\n\n#Buscar Hijos- Movimientos\n def BuscaH():\n #arriba\n \n #abajo\n\n #derecha\n\n #izquierda", "_____no_output_____" ] ], [ [ "\nDesarrollar un programa en Python que encuentre la ruta de salida en un laberinto representado por una matriz de 0 y 1. Un 0 significa que se puede pasar por esa casilla un 1 representa que hay pared en dicha casilla y 2 que es la salida. El programa debe leer la configuración del laberinto desde un archivo, solicitar al usuario el estado inicial y dibujar el laberinto con la ruta de salida. Se debe utilizar búsqueda primero en profundidad.\n", "_____no_output_____" ] ], [ [ "\n#creacion de ambiente\nfrom IPython.display import display\nimport ipywidgets as widgets\nimport time\nimport random\n\n# se crea el tablero\nclass Tablero:\n def __init__(self, tamanoCelda=(40, 40), nCeldas=(5,5)): #dimensiones del tablero\n self.out = widgets.HTML()\n display(self.out)\n self.tamanoCelda = tamanoCelda\n self.nCeldas = nCeldas\n\n def dibujar(self, agente , trashes, obstaculos,pila=[]):\n \n tablero = \"<h1 style='color:green'>Encuentra la salida </h1>\"\n tablero+=\"<br>\"\n tablero += \"<table border='1' >{}</table>\"\n \n filas = \"\"\n for i in range(self.nCeldas[0]):\n s = \"\"\n for j in range(self.nCeldas[1]):\n \n agenteaux = Agente(x = j, y = i)\n \n if agente == agenteaux:\n contenido = \\\n \"<span style='font-size:{tamanoEmoticon}px;'>{emoticon}</span>\".\\\n format(tamanoEmoticon=agente.tamanoEmoticon, emoticon=agente.emoticon)\n\n elif agenteaux in trashes:\n index = trashes.index(agenteaux)\n contenido = \\\n \"<span style='font-size:{tamanoEmoticon}px;'>{emoticon}</span>\".\\\n format(tamanoEmoticon=trashes[index].tamanoEmoticon, emoticon=trashes[index].emoticon)\n elif agenteaux in obstaculos:\n index = obstaculos.index(agenteaux)\n contenido = \\\n \"<span style='font-size:{tamanoEmoticon}px;'>{emoticon}</span>\".\\\n format(tamanoEmoticon=obstaculos[index].tamanoEmoticon, emoticon=obstaculos[index].emoticon)\n elif Nodo(x=j,y=i) in pila:\n contenido = \\\n \"<span style='font-size:{tamanoEmoticon}px;'>{emoticon}</span>\".\\\n format(tamanoEmoticon=30, emoticon=\"👣\")\n \n else:\n contenido=\"\"\n \n \n s += \"<td style='height:{alto}px;width:{ancho}px'>{contenido}</td>\".\\\n format(alto=self.tamanoCelda[0], ancho=self.tamanoCelda[1], \n contenido=contenido)\n filas += \"<tr>{}</tr>\".format(s)\n tablero = tablero.format(filas)\n \n self.out.value=tablero\n return trashes\n\n#Agente\n#se crea la clase agente y los movimientos que este tendra\nclass Agente:\n def __init__(self, x=2, y=2, emoticon=\"🧍‍♂️\", tamanoEmoticon=30): #estado inicial del agente \n self.x = x\n self.y = y\n self.emoticon = emoticon\n self.tamanoEmoticon = tamanoEmoticon\n\n def __eq__(self, other):\n return self.x == other.x and self.y == other.y\n\n def __str__(self):\n return \"(\"+str(self.x)+\",\"+str(self.y)+\",\"+self.emoticon+\")\"\n\n def __repr__(self):\n return self.__str__()\n#se definen los movimientos que tendra el agente \n#movimientos en \"X\" y en \"Y\" respectivamente\n\n def Abajo(self): # movimiento hacia abajo\n self.y += 1\n\n def Arriba(self): #movimiento hacia arriba\n self.y -= 1\n\n def Derecha(self): #movimiento hacia la derecha \n self.x += 1\n\n def Izquierda(self): #Movimiento hacia la izquierda\n self.x -= 1\n\nclass Nodo: # se define la clase nodo \n\n def __init__(self, x=0, y=0):\n self.x = x\n self.y = y\n \n def __eq__(self, other):\n return self.x == other.x and self.y==other.y\n\n def __str__(self):\n return str(\"(posX: \"+str(self.x)+\", posY: \"+str(self.y)+\")\")\n\n def __repr__(self):\n return self.__str__()\n\ndef generar_siguiente(actual, visitados): #se comprueba si la posicion ya fue visitado o no, si ya lo fue busca otro nodo\n posx=int(actual.x)\n posy=int(actual.y)\n if posy < len(nombres)-1:\n #Movimiento hacia abajo\n Abajo=int(arreglo[posy+1][posx])\n if Abajo==0 or Abajo==2:\n Siguiente=Nodo(x=posx,y=posy+1)\n if Siguiente not in visitados:\n return Siguiente\n #Movimiento hacia la derecha \n if posx < len(nombres)-1 :\n Derecha=int(arreglo[posy][posx+1])\n if Derecha==0 or Derecha==2:\n Siguiente=Nodo(x=posx+1,y=posy)\n if Siguiente not in visitados:\n return Siguiente\n if posy > 0 :\n #MOVIMIENTO HACIA ARRIBA, MIENTRAS QUE NI SE SAlGA SE LAS DIMENSIONES \n Arriba=int(arreglo[posy-1][posx])\n if Arriba==0 or Arriba==2:\n Siguiente=Nodo(x=posx,y=posy-1)\n if Siguiente not in visitados:\n return Siguiente\n if posx>0:\n #MOVIMIENTO HACIA LA IZQUIERDA, HASTA EL BORDE DEL ESCENARIO \n Izq=int(arreglo[posy][posx-1])\n if Izq==0 or Izq==2:\n Siguiente=Nodo(x=posx-1,y=posy)\n if Siguiente not in visitados:\n return Siguiente\n\n\n return Nodo(x=-1,y=-1)\n\ndef HallarRuta(agente , elementos): # SE DEFINE LA FUNCION, LA CUAL NOS AYUDARA A BUSCAR LA SALIDA DEL LABERINTO\n escenario=elementos[0]\n # print(arreglo)\n for i in range(len(arreglo)):# se recorre \n nombres.append(i)\n \n #rSE RECORRE TODO EL GRAFO PARA SABER EL NODO FINAL \n\n estadoFinal=Nodo() \n for i in range(len(nombres)):\n arreglito=arreglo[i]\n for j in range(len(arreglito)):\n if int(arreglito[j])==2:\n estadoFinal=Nodo(x=i,y=j)\n \n actual = Nodo(x=agente.x,y=agente.y)\n visitados = [actual]\n pila=[actual]\n\n while len(pila)!=0 and actual != estadoFinal: # MIENTRAS LA PILA EN LA QUE SE ALMACENAN LOS NO CONSULTADOS NO SEA 0\n \n#Se busca el siguiente nodo, ignorando los ya visitados\n\n siguienteNodo=generar_siguiente(actual,visitados)\n if siguienteNodo != Nodo(x=-1,y=-1):\n pila.append(siguienteNodo)# el nodo actual pasa a ser visitado \n actual=siguienteNodo # se busca un nuevo nodo \n agente.x=int(actual.x)\n agente.y=int(actual.y)\n visitados.append(actual)\n else:\n pila.pop()\n actual=pila[len(pila)-1]\n agente.x=int(actual.x)\n agente.y=int(actual.y)\n\n escenario.dibujar(agente, elementos[1], elementos[2],pila) # se dibuja el escenario \n time.sleep(1) # tiempo de retraso\n\n\n#Importar para leer archivo csv\nfrom google.colab import files\nfiles.upload()\nimport csv, operator\n\ndef obtenerArregloArchivo():\n Trashes=[]\n Obstaculos=[]\n cont=0\n with open('EscenarioPunto#4.csv') as csvarchivo:\n entrada = csv.reader(csvarchivo)\n arreglo= []\n for reg in entrada:\n arreglo.append(reg)\n if cont == 0:\n cantfilas = len(reg)\n for i in range(len(reg)):\n if len(reg) == 1:\n agente.energia = int(reg[0])\n else:\n if reg[i] == '1':\n Obstaculos.append(Agente(x=i , y=cont , emoticon=\"⛓️\" ))\n if reg[i] == '2':\n Trashes.append(Agente(x=i , y=cont , emoticon=\"🥇\" ))\n cont+=1\n escenario = Tablero(nCeldas=(cont,cantfilas)) \n elementos = [escenario , Trashes, Obstaculos, agente, arreglo]\n \n return elementos\n \n \n#crearEscenarioArchivo()\n\n#Posicion inicil del agente \nposx = input(\" X: \")\nposy = input(\" Y: \")\n\nnombres=[]\nagente = Agente(x=int(posx), y = int(posy))\nelementos = obtenerArregloArchivo()\narreglo=elementos[4]\nruta = HallarRuta(agente , elementos)", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/MishcaGestoso/Linear-Algebra-58019/blob/main/Prelim_Exam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "###Prelim Exam", "_____no_output_____" ], [ "Question 1.\nA 4 x 4 matrix whose diagonal elements are all one (1's)", "_____no_output_____" ] ], [ [ "import numpy as np\nA = np.array([1,1,1,1,])\nC = np.diag(A)\nprint (C)", "[[1 0 0 0]\n [0 1 0 0]\n [0 0 1 0]\n [0 0 0 1]]\n" ] ], [ [ "Question 2. doubles all the values of each element.", "_____no_output_____" ] ], [ [ "import numpy as np\nA = np.array([1, 1, 1, 1])\nB = np.diag(A)\nprint (C*2) #To double the value of array C\n", "[[2 0 0 0]\n [0 2 0 0]\n [0 0 2 0]\n [0 0 0 2]]\n" ] ], [ [ "Question 3. The cross-product of matrices, A = [2,7,4] and B = [3,9,8]\n\n", "_____no_output_____" ] ], [ [ "import numpy as np\nA = np.array([2, 7, 4])\nB = np.array([3, 9, 8])\n#To compute the cross of arrays A and B\ncross = np.cross(A,B)\nprint(cross)", "[20 -4 -3]\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# SkyScan Config\nMake temporary changes to a running SkyScan instance. It will revert back to the values in the environment file when restart.", "_____no_output_____" ] ], [ [ "broker=\"192.168.1.47\" # update with the IP for the Raspberry PI", "_____no_output_____" ], [ "!pip install paho-mqtt", "Requirement already satisfied: paho-mqtt in /Users/lberndt/opt/anaconda3/lib/python3.8/site-packages (1.5.0)\r\n" ], [ "import paho.mqtt.client as mqtt\nimport json", "_____no_output_____" ], [ "client = mqtt.Client(\"notebook-config\")", "_____no_output_____" ] ], [ [ "## Camera Zoom\nThis is how much the camera is zoomed in. It is an Int number between 0-9999 (max)", "_____no_output_____" ] ], [ [ "client.connect(broker)\ndata = {}\ndata['cameraZoom'] = 9999 # Update this Value\njson_data = json.dumps(data)\nclient.publish(\"skyscan/config/json\",json_data)", "_____no_output_____" ] ], [ [ "## Camera Delay\nFloat value for the number of seconds to wait after sending the camera move API command, before sending the take picture API command.", "_____no_output_____" ] ], [ [ "client.connect(broker)\ndata = {}\ndata['cameraDelay'] = 0.25 # Update this Value\njson_data = json.dumps(data)\nclient.publish(\"skyscan/config/json\",json_data)", "_____no_output_____" ] ], [ [ "## Camera Move Speed\nThis is how fast the camea will move. It is an Int number between 0-99 (max)", "_____no_output_____" ] ], [ [ "client.connect(broker)\ndata = {}\ndata['cameraMoveSpeed'] = 99 # Update this Value\njson_data = json.dumps(data)\nclient.publish(\"skyscan/config/json\",json_data)", "_____no_output_____" ] ], [ [ "## Camera Lead\nThis is how far the tracker should move the center point in front of the currently tracked plane. It is a float, and is measured in seconds, example: 0.25 . It is based on the planes current heading and how fast it is going. ", "_____no_output_____" ] ], [ [ "client.connect(broker)\ndata = {}\ndata['cameraLead'] = 0.45 # Update this Value\njson_data = json.dumps(data)\nclient.publish(\"skyscan/config/json\",json_data)", "_____no_output_____" ] ], [ [ "## Camera Bearing\nThis is a float to correct the cameras heading to help it better align with True North. It can be from -180 to 180. ", "_____no_output_____" ] ], [ [ "client.connect(broker)\ndata = {}\ndata['cameraBearing'] = -2 # Update this Value\njson_data = json.dumps(data)\nclient.publish(\"skyscan/config/json\",json_data)", "_____no_output_____" ] ], [ [ "## Minimum Elevation\nThe minimum elevation above the horizon which the Tracker will follow an airplane. Int value between 0-90 degrees.", "_____no_output_____" ] ], [ [ "client.connect(broker)\ndata = {}\ndata['minElevation'] = 45 # Update this Value\njson_data = json.dumps(data)\nclient.publish(\"skyscan/config/json\",json_data)", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd \nfrom pprint import pprint ", "_____no_output_____" ], [ "database_location = \"/Users/pyuvraj/CCPP/data_for_profit_from_stock/stock_per_category\"\ndatabase_name = \"all_stocks_data_with_ranking.csv\"", "_____no_output_____" ], [ "database = pd.read_csv(database_location + \"/\" + database_name)", "_____no_output_____" ], [ "list(database)", "_____no_output_____" ], [ "del database['Unnamed: 0']", "_____no_output_____" ], [ "print(database)", " Symbol Name Last Sale \\\n0 A Agilent Technologies Inc. Common Stock $155.70 \n1 AA Alcoa Corporation Common Stock $40.71 \n2 AAL American Airlines Group Inc. Common Stock $21.02 \n3 AAMC Altisource Asset Management Corp Com $15.64 \n4 AAME Atlantic American Corporation Common Stock $4.53 \n... ... ... ... \n3690 ZNH China Southern Airlines Company Limited Common... $25.78 \n3691 ZSAN Zosano Pharma Corporation Common Stock $0.7736 \n3692 ZTR Virtus Total Return Fund Inc. $9.84 \n3693 ZUO Zuora Inc. Class A Common Stock $16.18 \n3694 ZYNE Zynerba Pharmaceuticals Inc. Common Stock $4.44 \n\n Net Change % Change Market Cap Country IPO Year Volume \\\n0 0.6600 0.426% 4.724611e+10 United States 1999.0 1209255 \n1 1.9600 5.058% 7.607321e+09 NaN 2016.0 6141478 \n2 0.1100 0.526% 1.360956e+10 United States NaN 28254416 \n3 0.6000 3.989% 3.203571e+07 United States NaN 9312 \n4 0.2900 6.84% 9.248349e+07 United States NaN 28547 \n... ... ... ... ... ... ... \n3690 0.2800 1.098% 7.903819e+09 China 1997.0 13875 \n3691 0.0235 3.133% 8.256507e+07 United States 2015.0 658697 \n3692 0.1200 1.235% 2.426802e+08 United States 1988.0 85161 \n3693 -0.2900 -1.761% 1.978814e+09 NaN 2018.0 1646779 \n3694 -0.0900 -1.987% 1.831568e+08 United States 2015.0 332826 \n\n Sector Industry stock_name stock_growth \n0 Capital Goods Electrical Products A 68.540905 \n1 Basic Industries Aluminum AA -12.964433 \n2 Transportation Air Freight/Delivery Services AAL -40.173583 \n3 Finance Investment Managers AAMC 175.465860 \n4 Finance Life Insurance AAME -54.365260 \n... ... ... ... ... \n3690 Transportation Air Freight/Delivery Services ZNH 1.850460 \n3691 Health Care Major Pharmaceuticals ZSAN -209.998420 \n3692 Finance Investment Managers ZTR -14.345879 \n3693 Technology EDP Services ZUO 159.067875 \n3694 Health Care Major Pharmaceuticals ZYNE -33.255809 \n\n[3695 rows x 13 columns]\n" ], [ "g = database.groupby(['Industry'])", "_____no_output_____" ], [ "dir(g.Industry)", "_____no_output_____" ], [ "database.sort_values(['stock_growth'], ascending=True).groupby(['Sector']).head(3)\n", "_____no_output_____" ], [ "pprint(set(database['Industry']))", "{'Accident &Health Insurance',\n 'Advertising',\n 'Aerospace',\n 'Agricultural Chemicals',\n 'Air Freight/Delivery Services',\n 'Aluminum',\n 'Apparel',\n 'Assisted Living Services',\n 'Auto Manufacturing',\n 'Auto Parts:O.E.M.',\n 'Automotive Aftermarket',\n 'Banks',\n 'Beverages (Production/Distribution)',\n 'Biotechnology: Biological Products (No Diagnostic Substances)',\n 'Biotechnology: Commercial Physical & Biological Resarch',\n 'Biotechnology: Electromedical & Electrotherapeutic Apparatus',\n 'Biotechnology: In Vitro & In Vivo Diagnostic Substances',\n 'Biotechnology: Laboratory Analytical Instruments',\n 'Books',\n 'Broadcasting',\n 'Building Materials',\n 'Building Products',\n 'Building operators',\n 'Business Services',\n 'Catalog/Specialty Distribution',\n 'Clothing/Shoe/Accessory Stores',\n 'Coal Mining',\n 'Commercial Banks',\n 'Computer Communications Equipment',\n 'Computer Manufacturing',\n 'Computer Software: Prepackaged Software',\n 'Computer Software: Programming Data Processing',\n 'Computer peripheral equipment',\n 'Construction/Ag Equipment/Trucks',\n 'Consumer Electronics/Appliances',\n 'Consumer Electronics/Video Chains',\n 'Consumer Specialties',\n 'Containers/Packaging',\n 'Department/Specialty Retail Stores',\n 'Diversified Commercial Services',\n 'Diversified Financial Services',\n 'Diversified Manufacture',\n 'EDP Peripherals',\n 'EDP Services',\n 'Electric Utilities: Central',\n 'Electrical Products',\n 'Electronic Components',\n 'Electronics Distribution',\n 'Engineering & Construction',\n 'Environmental Services',\n 'Farming/Seeds/Milling',\n 'Finance Companies',\n 'Finance/Investors Services',\n 'Finance: Consumer Services',\n 'Fluid Controls',\n 'Food Chains',\n 'Food Distributors',\n 'Forest Products',\n 'Home Furnishings',\n 'Homebuilding',\n 'Hospital/Nursing Management',\n 'Hotels/Resorts',\n 'Industrial Machinery/Components',\n 'Industrial Specialties',\n 'Integrated oil Companies',\n 'Internet and Information Services',\n 'Investment Bankers/Brokers/Service',\n 'Investment Managers',\n 'Life Insurance',\n 'Major Banks',\n 'Major Chemicals',\n 'Major Pharmaceuticals',\n 'Managed Health Care',\n 'Marine Transportation',\n 'Meat/Poultry/Fish',\n 'Medical Electronics',\n 'Medical Specialities',\n 'Medical/Dental Instruments',\n 'Medical/Nursing Services',\n 'Metal Fabrications',\n 'Military/Government/Technical',\n 'Mining & Quarrying of Nonmetallic Minerals (No Fuels)',\n 'Motor Vehicles',\n 'Movies/Entertainment',\n 'Multi-Sector Companies',\n 'Natural Gas Distribution',\n 'Newspapers/Magazines',\n 'Office Equipment/Supplies/Services',\n 'Oil & Gas Production',\n 'Oil Refining/Marketing',\n 'Oil/Gas Transmission',\n 'Oilfield Services/Equipment',\n 'Ophthalmic Goods',\n 'Ordnance And Accessories',\n 'Other Consumer Services',\n 'Other Metals and Minerals',\n 'Other Pharmaceuticals',\n 'Other Specialty Stores',\n 'Package Goods/Cosmetics',\n 'Packaged Foods',\n 'Paints/Coatings',\n 'Paper',\n 'Plastic Products',\n 'Pollution Control Equipment',\n 'Power Generation',\n 'Precious Metals',\n 'Professional Services',\n 'Property-Casualty Insurers',\n 'Publishing',\n 'RETAIL: Building Materials',\n 'Radio And Television Broadcasting And Communications Equipment',\n 'Railroads',\n 'Real Estate',\n 'Real Estate Investment Trusts',\n 'Recreational Products/Toys',\n 'Rental/Leasing Companies',\n 'Restaurants',\n 'Retail: Computer Software & Peripheral Equipment',\n 'Savings Institutions',\n 'Semiconductors',\n 'Service to the Health Industry',\n 'Services-Misc. Amusement & Recreation',\n 'Shoe Manufacturing',\n 'Specialty Chemicals',\n 'Specialty Foods',\n 'Specialty Insurers',\n 'Steel/Iron Ore',\n 'Telecommunications Equipment',\n 'Television Services',\n 'Textiles',\n 'Tobacco',\n 'Tools/Hardware',\n 'Transportation Services',\n 'Trucking Freight/Courier Services',\n 'Trusts Except Educational Religious and Charitable',\n 'Water Supply',\n 'Wholesale Distributors'}\n" ], [ "growth_per_Industry = database.groupby('Industry').mean()", "_____no_output_____" ], [ "growth_per_Industry.sort_values(by=['stock_growth'], ascending=False).head(10)", "_____no_output_____" ], [ "database.sort_values(['stock_growth'], ascending=False).head(40)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Running and Plotting Coeval Cubes", "_____no_output_____" ], [ "The aim of this tutorial is to introduce you to how `21cmFAST` does the most basic operations: producing single coeval cubes, and visually verifying them. It is a great place to get started with `21cmFAST`.", "_____no_output_____" ] ], [ [ "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport os\n# We change the default level of the logger so that\n# we can see what's happening with caching.\nimport logging, sys, os\nlogger = logging.getLogger('21cmFAST')\nlogger.setLevel(logging.INFO)\n\nimport py21cmfast as p21c\n\n# For plotting the cubes, we use the plotting submodule:\nfrom py21cmfast import plotting\n\n# For interacting with the cache\nfrom py21cmfast import cache_tools\n\n", "_____no_output_____" ], [ "print(f\"Using 21cmFAST version {p21c.__version__}\")", "Using 21cmFAST version 3.0.2\n" ] ], [ [ "Clear the cache so that we get the same results for the notebook every time (don't worry about this for now). Also, set the default output directory to `_cache/`:", "_____no_output_____" ] ], [ [ "if not os.path.exists('_cache'):\n os.mkdir('_cache')\n \np21c.config['direc'] = '_cache'\ncache_tools.clear_cache(direc=\"_cache\")", "2020-10-02 09:51:10,651 | INFO | Removed 0 files from cache.\n" ] ], [ [ "## Basic Usage", "_____no_output_____" ], [ "The simplest (and typically most efficient) way to produce a coeval cube is simply to use the `run_coeval` method. This consistently performs all steps of the calculation, re-using any data that it can without re-computation or increased memory overhead.", "_____no_output_____" ] ], [ [ "coeval8, coeval9, coeval10 = p21c.run_coeval(\n redshift = [8.0, 9.0, 10.0],\n user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100, \"USE_INTERPOLATION_TABLES\": True},\n cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n astro_params = p21c.AstroParams({\"HII_EFF_FACTOR\":20.0}),\n random_seed=12345\n)", "_____no_output_____" ] ], [ [ "There are a number of possible inputs for `run_coeval`, which you can check out either in the [API reference](../reference/py21cmfast.html) or by calling `help(p21c.run_coeval)`. Notably, the `redshift` must be given: it can be a single number, or a list of numbers, defining the redshift at which the output coeval cubes will be defined. \n\nOther params we've given here are `user_params`, `cosmo_params` and `astro_params`. These are all used for defining input parameters into the backend C code (there's also another possible input of this kind; `flag_options`). These can be given either as a dictionary (as `user_params` has been), or directly as a relevant object (like `cosmo_params` and `astro_params`). If creating the object directly, the parameters can be passed individually or via a single dictionary. So there's a lot of flexibility there! Nevertheless we *encourage* you to use the basic dictionary. The other ways of passing the information are there so we can use pre-defined objects later on. For more information about these \"input structs\", see the [API docs](../reference/_autosummary/py21cmfast.inputs.html).\n\nWe've also given a `direc` option: this is the directory in which to search for cached data (and also where cached data should be written). Throughout this notebook we're going to set this directly to the `_cache` folder, which allows us to manage it directly. By default, the cache location is set in the global configuration in `~/.21cmfast/config.yml`. You'll learn more about caching further on in this tutorial. \n\nFinally, we've given a random seed. This sets all the random phases for the simulation, and ensures that we can exactly reproduce the same results on every run. \n\nThe output of `run_coeval` is a list of `Coeval` instances, one for each input redshift (it's just a single object if a single redshift was passed, not a list). They store *everything* related to that simulation, so that it can be completely compared to other simulations. \n\nFor example, the input parameters:", "_____no_output_____" ] ], [ [ "print(\"Random Seed: \", coeval8.random_seed)\nprint(\"Redshift: \", coeval8.redshift)\nprint(coeval8.user_params)", "Random Seed: 12345\nRedshift: 8.0\nUserParams(BOX_LEN:100, DIM:300, HII_DIM:100, HMF:1, POWER_SPECTRUM:0, USE_FFTW_WISDOM:False, USE_RELATIVE_VELOCITIES:False)\n" ] ], [ [ "This is where the utility of being able to pass a *class instance* for the parameters arises: we could run another iteration of coeval cubes, with the same user parameters, simply by doing `p21c.run_coeval(user_params=coeval8.user_params, ...)`.\n\nAlso in the `Coeval` instance are the various outputs from the different steps of the computation. You'll see more about what these steps are further on in the tutorial. But for now, we show that various boxes are available:", "_____no_output_____" ] ], [ [ "print(coeval8.hires_density.shape)\nprint(coeval8.brightness_temp.shape)", "(300, 300, 300)\n(100, 100, 100)\n" ] ], [ [ "Along with these, full instances of the output from each step are available as attributes that end with \"struct\". These instances themselves contain the `numpy` arrays of the data cubes, and some other attributes that make them easier to work with:", "_____no_output_____" ] ], [ [ "coeval8.brightness_temp_struct.global_Tb", "_____no_output_____" ] ], [ [ "By default, each of the components of the cube are cached to disk (in our `_cache/` folder) as we run it. However, the `Coeval` cube itself is _not_ written to disk by default. Writing it to disk incurs some redundancy, since that data probably already exists in the cache directory in seperate files. \n\nLet's save to disk. The save method by default writes in the current directory (not the cache!):", "_____no_output_____" ] ], [ [ "filename = coeval8.save(direc='_cache')", "_____no_output_____" ] ], [ [ "The filename of the saved file is returned:", "_____no_output_____" ] ], [ [ "print(os.path.basename(filename))", "Coeval_z8.0_a3c7dea665420ae9c872ba2fab1b3d7d_r12345.h5\n" ] ], [ [ "Such files can be read in:", "_____no_output_____" ] ], [ [ "new_coeval8 = p21c.Coeval.read(filename, direc='.')", "_____no_output_____" ] ], [ [ "Some convenient plotting functions exist in the `plotting` module. These can work directly on `Coeval` objects, or any of the output structs (as we'll see further on in the tutorial). By default the `coeval_sliceplot` function will plot the `brightness_temp`, using the standard traditional colormap:", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots(1,3, figsize=(14,4))\nfor i, (coeval, redshift) in enumerate(zip([coeval8, coeval9, coeval10], [8,9,10])):\n plotting.coeval_sliceplot(coeval, ax=ax[i], fig=fig);\n plt.title(\"z = %s\"%redshift)\nplt.tight_layout()", "_____no_output_____" ] ], [ [ "Any 3D field can be plotted, by setting the `kind` argument. For example, we could alternatively have plotted the dark matter density cubes perturbed to each redshift:", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots(1,3, figsize=(14,4))\nfor i, (coeval, redshift) in enumerate(zip([coeval8, coeval9, coeval10], [8,9,10])):\n plotting.coeval_sliceplot(coeval, kind='density', ax=ax[i], fig=fig);\n plt.title(\"z = %s\"%redshift)\nplt.tight_layout()", "_____no_output_____" ] ], [ [ "To see more options for the plotting routines, see the [API Documentation](../reference/_autosummary/py21cmfast.plotting.html).", "_____no_output_____" ], [ "`Coeval` instances are not cached themselves -- they are containers for data that is itself cached (i.e. each of the `_struct` attributes of `Coeval`). See the [api docs](../reference/_autosummary/py21cmfast.outputs.html) for more detailed information on these. \n\nYou can see the filename of each of these structs (or the filename it would have if it were cached -- you can opt to *not* write out any given dataset):", "_____no_output_____" ] ], [ [ "coeval8.init_struct.filename", "_____no_output_____" ] ], [ [ "You can also write the struct anywhere you'd like on the filesystem. This will not be able to be automatically used as a cache, but it could be useful for sharing files with colleagues.", "_____no_output_____" ] ], [ [ "coeval8.init_struct.save(fname='my_init_struct.h5')", "_____no_output_____" ] ], [ [ "This brief example covers most of the basic usage of `21cmFAST` (at least with `Coeval` objects -- there are also `Lightcone` objects for which there is a separate tutorial). \n\nFor the rest of the tutorial, we'll cover a more advanced usage, in which each step of the calculation is done independently.", "_____no_output_____" ], [ "## Advanced Step-by-Step Usage", "_____no_output_____" ], [ "Most users most of the time will want to use the high-level `run_coeval` function from the previous section. However, there are several independent steps when computing the brightness temperature field, and these can be performed one-by-one, adding any other effects between them if desired. This means that the new `21cmFAST` is much more flexible. In this section, we'll go through in more detail how to use the lower-level methods.\n\nEach step in the chain will receive a number of input-parameter classes which define how the calculation should run. These are the `user_params`, `cosmo_params`, `astro_params` and `flag_options` that we saw in the previous section.\n\nConversely, each step is performed by running a function which will return a single object. Every major function returns an object of the same fundamental class (an ``OutputStruct``) which has various methods for reading/writing the data, and ensuring that it's in the right state to receive/pass to and from C.\nThese are the objects stored as `init_box_struct` etc. in the `Coeval` class.\n\nAs we move through each step, we'll outline some extra details, hints and tips about using these inputs and outputs.", "_____no_output_____" ], [ "### Initial Conditions", "_____no_output_____" ], [ "The first step is to get the initial conditions, which defines the *cosmological* density field before any redshift evolution is applied.", "_____no_output_____" ] ], [ [ "initial_conditions = p21c.initial_conditions(\n user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100},\n cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n random_seed=54321\n)", "_____no_output_____" ] ], [ [ "We've already come across all these parameters as inputs to the `run_coeval` function. Indeed, most of the steps have very similar interfaces, and are able to take a random seed and parameters for where to look for the cache. We use a different seed than in the previous section so that all our boxes are \"fresh\" (we'll show how the caching works in a later section).\n\nThese initial conditions have 100 cells per side, and a box length of 100 Mpc. Note again that they can either be passed as a dictionary containing the input parameters, or an actual instance of the class. While the former is the suggested way, one benefit of the latter is that it can be queried for the relevant parameters (by using ``help`` or a post-fixed ``?``), or even queried for defaults:", "_____no_output_____" ] ], [ [ "p21c.CosmoParams._defaults_", "_____no_output_____" ] ], [ [ "(these defaults correspond to the Planck15 cosmology contained in Astropy).", "_____no_output_____" ], [ "So what is in the ``initial_conditions`` object? It is what we call an ``OutputStruct``, and we have seen it before, as the `init_box_struct` attribute of `Coeval`. It contains a number of arrays specifying the density and velocity fields of our initial conditions, as well as the defining parameters. For example, we can easily show the cosmology parameters that are used (note the non-default $\\sigma_8$ that we passed):", "_____no_output_____" ] ], [ [ "initial_conditions.cosmo_params", "_____no_output_____" ] ], [ [ "A handy tip is that the ``CosmoParams`` class also has a reference to a corresponding Astropy cosmology, which can be used more broadly:", "_____no_output_____" ] ], [ [ "initial_conditions.cosmo_params.cosmo", "_____no_output_____" ] ], [ [ "Merely printing the initial conditions object gives a useful representation of its dependent parameters:", "_____no_output_____" ] ], [ [ "print(initial_conditions)", "InitialConditions(UserParams(BOX_LEN:100, DIM:300, HII_DIM:100, HMF:1, POWER_SPECTRUM:0, USE_FFTW_WISDOM:False, USE_RELATIVE_VELOCITIES:False);\n\tCosmoParams(OMb:0.04897468161869667, OMm:0.30964144154550644, POWER_INDEX:0.9665, SIGMA_8:0.8, hlittle:0.6766);\n\trandom_seed:54321)\n" ] ], [ [ "(side-note: the string representation of the object is used to uniquely define it in order to save it to the cache... which we'll explore soon!).\n\nTo see which arrays are defined in the object, access the ``fieldnames`` (this is true for *all* `OutputStruct` objects):", "_____no_output_____" ] ], [ [ "initial_conditions.fieldnames", "_____no_output_____" ] ], [ [ "The `coeval_sliceplot` function also works on `OutputStruct` objects (as well as the `Coeval` object as we've already seen). It takes the object, and a specific field name. By default, the field it plots is the _first_ field in `fieldnames` (for any `OutputStruct`).", "_____no_output_____" ] ], [ [ "plotting.coeval_sliceplot(initial_conditions, \"hires_density\");", "_____no_output_____" ] ], [ [ "### Perturbed Field", "_____no_output_____" ], [ "After obtaining the initial conditions, we need to *perturb* the field to a given redshift (i.e. the redshift we care about). This step clearly requires the results of the previous step, which we can easily just pass in. Let's do that:", "_____no_output_____" ] ], [ [ "perturbed_field = p21c.perturb_field(\n redshift = 8.0,\n init_boxes = initial_conditions\n)", "_____no_output_____" ] ], [ [ "Note that we didn't need to pass in any input parameters, because they are all contained in the `initial_conditions` object itself. The random seed is also taken from this object.\n\nAgain, the output is an `OutputStruct`, so we can view its fields:", "_____no_output_____" ] ], [ [ "perturbed_field.fieldnames", "_____no_output_____" ] ], [ [ "This time, it has only density and velocity (the velocity direction is chosen without loss of generality). Let's view the perturbed density field:", "_____no_output_____" ] ], [ [ "plotting.coeval_sliceplot(perturbed_field, \"density\");", "_____no_output_____" ] ], [ [ "It is clear here that the density used is the *low*-res density, but the overall structure of the field looks very similar.", "_____no_output_____" ], [ "### Ionization Field", "_____no_output_____" ], [ "Next, we need to ionize the box. This is where things get a little more tricky. In the simplest case (which, let's be clear, is what we're going to do here) the ionization occurs at the *saturated limit*, which means we can safely ignore the contribution of the spin temperature. This means we can directly calculate the ionization on the density/velocity fields that we already have. A few more parameters are needed here, and so two more \"input parameter dictionaries\" are available, ``astro_params`` and ``flag_options``. Again, a reminder that their parameters can be viewed by using eg. `help(p21c.AstroParams)`, or by looking at the [API docs](../reference/_autosummary/py21cmfast.inputs.html).", "_____no_output_____" ], [ "For now, let's leave everything as default. In that case, we can just do:", "_____no_output_____" ] ], [ [ "ionized_field = p21c.ionize_box(\n perturbed_field = perturbed_field\n)", "2020-02-29 15:10:43,902 | INFO | Existing init_boxes found and read in (seed=54321).\n" ] ], [ [ "That was easy! All the information required by ``ionize_box`` was given directly by the ``perturbed_field`` object. If we had _also_ passed a redshift explicitly, this redshift would be checked against that from the ``perturbed_field`` and an error raised if they were incompatible:", "_____no_output_____" ], [ "Let's see the fieldnames:", "_____no_output_____" ] ], [ [ "ionized_field.fieldnames", "_____no_output_____" ] ], [ [ "Here the ``first_box`` field is actually just a flag to tell the C code whether this has been *evolved* or not. Here, it hasn't been, it's the \"first box\" of an evolutionary chain. Let's plot the neutral fraction:", "_____no_output_____" ] ], [ [ "plotting.coeval_sliceplot(ionized_field, \"xH_box\");", "_____no_output_____" ] ], [ [ "### Brightness Temperature", "_____no_output_____" ], [ "Now we can use what we have to get the brightness temperature:", "_____no_output_____" ] ], [ [ "brightness_temp = p21c.brightness_temperature(ionized_box=ionized_field, perturbed_field=perturbed_field)", "_____no_output_____" ] ], [ [ "This has only a single field, ``brightness_temp``:", "_____no_output_____" ] ], [ [ "plotting.coeval_sliceplot(brightness_temp);", "_____no_output_____" ] ], [ [ "### The Problem", "_____no_output_____" ], [ "And there you have it -- you've computed each of the four steps (there's actually another, `spin_temperature`, that you require if you don't assume the saturated limit) individually. \n\nHowever, some problems quickly arise. What if you want the `perturb_field`, but don't care about the initial conditions? We know how to get the full `Coeval` object in one go, but it would seem that the sub-boxes have to _each_ be computed as the input to the next. \n\nA perhaps more interesting problem is that some quantities require *evolution*: i.e. a whole bunch of simulations at a string of redshifts must be performed in order to obtain the current redshift. This is true when not in the saturated limit, for example. That means you'd have to manually compute each redshift in turn, and pass it to the computation at the next redshift. While this is definitely possible, it becomes difficult to set up manually when all you care about is the box at the final redshift.\n\n`py21cmfast` solves this by making each of the functions recursive: if `perturb_field` is not passed the `init_boxes` that it needs, it will go and compute them, based on the parameters that you've passed it. If the previous `spin_temp` box required for the current redshift is not passed -- it will be computed (and if it doesn't have a previous `spin_temp` *it* will be computed, and so on).\n\nThat's all good, but what if you now want to compute another `perturb_field`, with the same fundamental parameters (but at a different redshift)? Since you didn't ever see the `init_boxes`, they'll have to be computed all over again. That's where the automatic caching comes in, which is where we turn now...", "_____no_output_____" ], [ "## Using the Automatic Cache", "_____no_output_____" ], [ "To solve all this, ``21cmFAST`` uses an on-disk caching mechanism, where all boxes are saved in HDF5 format in a default location. The cache allows for reading in previously-calculated boxes automatically if they match the parameters that are input. The functions used at every step (in the previous section) will try to use a cached box instead of calculating a new one, unless its explicitly asked *not* to. \n\nThus, we could do this:", "_____no_output_____" ] ], [ [ "perturbed_field = p21c.perturb_field(\n redshift = 8.0,\n user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100},\n cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n)\nplotting.coeval_sliceplot(perturbed_field, \"density\");", "2020-02-29 15:10:45,367 | INFO | Existing z=8.0 perturb_field boxes found and read in (seed=12345).\n" ] ], [ [ "Note that here we pass exactly the same parameters as were used in the previous section. It gives a message that the full box was found in the cache and immediately returns. However, if we change the redshift:", "_____no_output_____" ] ], [ [ "perturbed_field = p21c.perturb_field(\n redshift = 7.0,\n user_params = {\"HII_DIM\": 100, \"BOX_LEN\": 100},\n cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n)\nplotting.coeval_sliceplot(perturbed_field, \"density\");", "2020-02-29 15:10:45,748 | INFO | Existing init_boxes found and read in (seed=12345).\n" ] ], [ [ "Now it finds the initial conditions, but it must compute the perturbed field at the new redshift. If we had changed the initial parameters as well, it would have to calculate everything:", "_____no_output_____" ] ], [ [ "perturbed_field = p21c.perturb_field(\n redshift = 8.0,\n user_params = {\"HII_DIM\": 50, \"BOX_LEN\": 100},\n cosmo_params = p21c.CosmoParams(SIGMA_8=0.8),\n)\n\nplotting.coeval_sliceplot(perturbed_field, \"density\");", "_____no_output_____" ] ], [ [ "This shows that we don't need to perform the *previous* step to do any of the steps, they will be calculated automatically.\n\nNow, let's get an ionized box, but this time we won't assume the saturated limit, so we need to use the spin temperature. We can do this directly in the ``ionize_box`` function, but let's do it explicitly. We will use the auto-generation of the initial conditions and perturbed field. However, the spin temperature is an *evolved* field, i.e. to compute the field at $z$, we need to know the field at $z+\\Delta z$. This continues up to some redshift, labelled ``z_heat_max``, above which the spin temperature can be defined directly from the perturbed field. \n\nThus, one option is to pass to the function a *previous* spin temperature box, to evolve to *this* redshift. However, we don't have a previous spin temperature box yet. Of course, the function itself will go and calculate that box if it's not given (or read it from cache if it's been calculated before!). When it tries to do that, it will go to the one before, and so on until it reaches ``z_heat_max``, at which point it will calculate it directly. \n\nTo facilitate this recursive progression up the redshift ladder, there is a parameter, ``z_step_factor``, which is a multiplicate factor that determines the previous redshift at each step. \n\nWe can also pass the dependent boxes explicitly, which provides the parameters necessary.\n\n**WARNING: THIS IS THE MOST TIME-CONSUMING STEP OF THE CALCULATION!**", "_____no_output_____" ] ], [ [ "spin_temp = p21c.spin_temperature(\n perturbed_field = perturbed_field,\n zprime_step_factor=1.05,\n)", "2020-02-29 15:11:38,347 | INFO | Existing init_boxes found and read in (seed=521414794440).\n" ], [ "plotting.coeval_sliceplot(spin_temp, \"Ts_box\");", "_____no_output_____" ] ], [ [ "Let's note here that each of the functions accepts a few of the same arguments that modifies how the boxes are cached. There is a ``write`` argument, which if set to ``False``, will disable writing that box to cache (and it is passed through the recursive heirarchy). There is also ``regenerate``, which if ``True``, forces this box and all its predecessors to be re-calculated even if they exist in the cache. Then there is ``direc``, which we have seen before.\n\nFinally note that by default, ``random_seed`` is set to ``None``. If this is the case, then any cached dataset matching all other parameters will be read in, and the ``random_seed`` will be set based on the file read in. If it is set to an integer number, then the cached dataset must also match the seed. If it is ``None``, and no matching dataset is found, a random seed will be autogenerated.", "_____no_output_____" ], [ "Now if we calculate the ionized box, ensuring that it uses the spin temperature, then it will also need to be evolved. However, due to the fact that we cached each of the spin temperature steps, these should be read in accordingly:", "_____no_output_____" ] ], [ [ "ionized_box = p21c.ionize_box(\n spin_temp = spin_temp,\n zprime_step_factor=1.05,\n)", "2020-02-29 15:12:55,794 | INFO | Existing init_boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,814 | INFO | Existing z=34.2811622461279 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,827 | INFO | Existing z=34.2811622461279 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,865 | INFO | Existing z=32.60110690107419 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,880 | INFO | Existing z=32.60110690107419 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,906 | INFO | Existing z=31.00105419149923 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,919 | INFO | Existing z=31.00105419149923 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,948 | INFO | Existing z=29.4771944680945 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,963 | INFO | Existing z=29.4771944680945 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:55,991 | INFO | Existing z=28.02589949342333 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,005 | INFO | Existing z=28.02589949342333 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,033 | INFO | Existing z=26.643713803260315 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,051 | INFO | Existing z=26.643713803260315 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,079 | INFO | Existing z=25.32734647929554 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,094 | INFO | Existing z=25.32734647929554 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,127 | INFO | Existing z=24.073663313614798 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,141 | INFO | Existing z=24.073663313614798 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,168 | INFO | Existing z=22.879679346299806 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,182 | INFO | Existing z=22.879679346299806 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,205 | INFO | Existing z=21.742551758380767 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,219 | INFO | Existing z=21.742551758380767 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,403 | INFO | Existing z=20.659573103219778 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,418 | INFO | Existing z=20.659573103219778 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,620 | INFO | Existing z=19.62816486020931 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,635 | INFO | Existing z=19.62816486020931 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,784 | INFO | Existing z=18.645871295437438 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,793 | INFO | Existing z=18.645871295437438 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,931 | INFO | Existing z=17.71035361470232 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:56,941 | INFO | Existing z=17.71035361470232 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,085 | INFO | Existing z=16.81938439495459 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,095 | INFO | Existing z=16.81938439495459 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,243 | INFO | Existing z=15.970842280909132 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,254 | INFO | Existing z=15.970842280909132 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,399 | INFO | Existing z=15.162706934199171 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,408 | INFO | Existing z=15.162706934199171 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,544 | INFO | Existing z=14.393054223046828 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,554 | INFO | Existing z=14.393054223046828 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,691 | INFO | Existing z=13.66005164099698 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,700 | INFO | Existing z=13.66005164099698 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,832 | INFO | Existing z=12.961953943806646 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,840 | INFO | Existing z=12.961953943806646 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,970 | INFO | Existing z=12.297098994101567 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:57,978 | INFO | Existing z=12.297098994101567 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,106 | INFO | Existing z=11.663903803906255 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,114 | INFO | Existing z=11.663903803906255 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,244 | INFO | Existing z=11.060860765625003 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,254 | INFO | Existing z=11.060860765625003 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,394 | INFO | Existing z=10.486534062500002 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,402 | INFO | Existing z=10.486534062500002 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,529 | INFO | Existing z=9.939556250000003 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,538 | INFO | Existing z=9.939556250000003 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,674 | INFO | Existing z=9.418625000000002 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,682 | INFO | Existing z=9.418625000000002 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,810 | INFO | Existing z=8.922500000000001 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,819 | INFO | Existing z=8.922500000000001 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,947 | INFO | Existing z=8.450000000000001 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:12:58,956 | INFO | Existing z=8.450000000000001 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:12:59,086 | INFO | Existing z=8.0 perturb_field boxes found and read in (seed=521414794440).\n" ], [ " plotting.coeval_sliceplot(ionized_box, \"xH_box\");", "_____no_output_____" ] ], [ [ "Great! So again, we can just get the brightness temp:", "_____no_output_____" ] ], [ [ "brightness_temp = p21c.brightness_temperature(\n ionized_box = ionized_box,\n perturbed_field = perturbed_field,\n spin_temp = spin_temp\n)", "_____no_output_____" ] ], [ [ "Now lets plot our brightness temperature, which has been evolved from high redshift with spin temperature fluctuations:", "_____no_output_____" ] ], [ [ "plotting.coeval_sliceplot(brightness_temp);", "_____no_output_____" ] ], [ [ "We can also check what the result would have been if we had limited the maximum redshift of heating. Note that this *recalculates* all previous spin temperature and ionized boxes, because they depend on both ``z_heat_max`` and ``zprime_step_factor``.", "_____no_output_____" ] ], [ [ "ionized_box = p21c.ionize_box(\n spin_temp = spin_temp,\n zprime_step_factor=1.05,\n z_heat_max = 20.0\n)\n\nbrightness_temp = p21c.brightness_temperature(\n ionized_box = ionized_box,\n perturbed_field = perturbed_field,\n spin_temp = spin_temp\n)\n\nplotting.coeval_sliceplot(brightness_temp);", "2020-02-29 15:13:08,824 | INFO | Existing init_boxes found and read in (seed=521414794440).\n2020-02-29 15:13:08,840 | INFO | Existing z=19.62816486020931 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:11,438 | INFO | Existing z=18.645871295437438 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:11,447 | INFO | Existing z=19.62816486020931 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:14,041 | INFO | Existing z=17.71035361470232 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:14,050 | INFO | Existing z=18.645871295437438 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:16,667 | INFO | Existing z=16.81938439495459 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:16,675 | INFO | Existing z=17.71035361470232 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:19,213 | INFO | Existing z=15.970842280909132 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:19,222 | INFO | Existing z=16.81938439495459 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:21,756 | INFO | Existing z=15.162706934199171 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:21,764 | INFO | Existing z=15.970842280909132 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:24,409 | INFO | Existing z=14.393054223046828 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:24,417 | INFO | Existing z=15.162706934199171 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:26,938 | INFO | Existing z=13.66005164099698 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:26,947 | INFO | Existing z=14.393054223046828 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:29,504 | INFO | Existing z=12.961953943806646 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:29,517 | INFO | Existing z=13.66005164099698 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:32,163 | INFO | Existing z=12.297098994101567 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:32,171 | INFO | Existing z=12.961953943806646 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:34,704 | INFO | Existing z=11.663903803906255 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:34,712 | INFO | Existing z=12.297098994101567 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:37,257 | INFO | Existing z=11.060860765625003 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:37,266 | INFO | Existing z=11.663903803906255 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:39,809 | INFO | Existing z=10.486534062500002 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:39,817 | INFO | Existing z=11.060860765625003 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:42,378 | INFO | Existing z=9.939556250000003 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:42,387 | INFO | Existing z=10.486534062500002 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:44,941 | INFO | Existing z=9.418625000000002 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:44,950 | INFO | Existing z=9.939556250000003 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:47,518 | INFO | Existing z=8.922500000000001 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:47,528 | INFO | Existing z=9.418625000000002 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:50,077 | INFO | Existing z=8.450000000000001 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:50,086 | INFO | Existing z=8.922500000000001 spin_temp boxes found and read in (seed=521414794440).\n2020-02-29 15:13:52,626 | INFO | Existing z=8.0 perturb_field boxes found and read in (seed=521414794440).\n2020-02-29 15:13:52,762 | INFO | Existing brightness_temp box found and read in (seed=521414794440).\n" ] ], [ [ "As we can see, it's very similar!", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import gym\nimport numpy as np\nfrom collections import defaultdict\nfrom functools import partial ", "_____no_output_____" ], [ "env = gym.make('Blackjack-v0')", "_____no_output_____" ], [ "def policy_blackjack_game(state):\n player_score, dealer_score, usable_ace = state \n if (player_score >= 17):\n return 0 # don't take any cards, stick\n else:\n return 1 # take additional cards, hit", "_____no_output_____" ], [ "def generate_blackjack_episode():\n \n #initalizing the value of episode, states, actions, rewards\n episode = []\n states = []\n actions = []\n rewards = []\n \n #starting the environment\n state = env.reset()\n \n #settng the state value to player_score, dealer_score and usable_ace\n player_score, dealer_score, usable_ace = state\n\n while (True): \n \n #finding the action by passing on the state\n action = policy_blackjack_game(state) \n next_state, reward, done, info = env.step(action)\n \n #creating a list of episodes, states, actions, rewards\n episode.append((state, action, reward))\n states.append(state)\n actions.append(action)\n rewards.append(reward)\n if done:\n break\n state = next_state\n \n return episode, states, actions, rewards ", "_____no_output_____" ], [ "def black_jack_every_visit_prediction(policy, env, num_episodes):\n \n #initializing the value of total_rewards, number of states, and value_function\n total_rewards = 0\n num_states = defaultdict(float)\n value_function = defaultdict(float)\n \n for k in range (0, num_episodes):\n episode, states, actions, rewards = generate_blackjack_episode() \n total_rewards = 0\n for i in range(len(states)-1, -1,-1):\n current_state = states[i]\n #finding the sum of rewards\n total_rewards += rewards[i]\n #all the state values of every visit are considered\n #increasing the count of states by 1\n num_states[current_state] += 1\n \n #finding the value_function by incremental method\n value_function[current_state] += (total_rewards - value_function[current_state]) / (num_states[current_state])\n \n return value_function", "_____no_output_____" ], [ "black_jack_every_visit_prediction(policy_blackjack_game, env, 10000)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "import numpy as np\nimport xarray as xr\nimport scipy.ndimage as ndimage\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport metpy.calc as mpcalc\nfrom metpy.units import units\nfrom datetime import datetime", "/usr/lib/python3/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.2) or chardet (3.0.4) doesn't match a supported version!\n RequestsDependencyWarning)\n" ], [ "# Get dataset from NOMADS Server\nds = xr.open_dataset('http://nomads.ncep.noaa.gov:80/dods/gfs_0p25_1hr/gfs20201121/gfs_0p25_1hr_12z')\n\n# Select desired vars\nds = ds[['hgtprs', 'tmpprs', 'ugrdprs', 'vgrdprs']]\n\n# Select time\nds = ds.sel(time=ds.time[0])\n\n# Select level\nds = ds.sel(lev=850)\n\n# Select lat/lon slice\nds = ds.sel(lon=slice(220, 310), lat=slice(15, 65))\n\nds", "_____no_output_____" ], [ "# Calcualte dx and dy \ndx, dy = mpcalc.lat_lon_grid_deltas(ds.lon.values, ds.lat.values)\n\n# Set calculation units \nT, u, v = ds.tmpprs.values * units('K'), ds.ugrdprs.values * units('m/s'), ds.vgrdprs.values * units('m/s')\n\n# Calculate temperature advection\nT_adv = mpcalc.advection(scalar=T, wind=[u, v], deltas=(dx, dy), dim_order='yx')\n\n# Smooth data\nz = ndimage.gaussian_filter(ds.hgtprs, sigma=3, order=0)\nT_adv = ndimage.gaussian_filter(T_adv, sigma=3, order=0) * units('K / s')\n\n# Set plot units\nu = u.to('kt')\nv = v.to('kt')\nT = T.to('degC')\nT_adv = T_adv.to('degC/hr') * 3", "_____no_output_____" ], [ "# Set Projection of Data\ndatacrs = ccrs.PlateCarree()\n\n# Set Projection of Plot\nplotcrs = ccrs.LambertConformal(central_latitude=[30, 60], central_longitude=-100)\n\n# Create new figure\nfig = plt.figure(figsize=(15, 12.5))\ngs = gridspec.GridSpec(2, 1, height_ratios=[1, .02], bottom=.07, top=.99, hspace=0.01, wspace=0.01)\n\n# Add the map and set the extent\nax = plt.subplot(gs[0], projection=plotcrs)\nax.set_extent([235, 290, 20, 55])\n\n# Add state/country boundaries to plot\ncountry_borders=cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries', scale='10m', facecolor='none')\nax.add_feature(country_borders, edgecolor='black', linewidth=1.0)\nstate_borders=cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lakes', scale='10m', facecolor='none')\nax.add_feature(state_borders, edgecolor='black', linewidth=0.5)\n\n# Plot Height Contours\nclev = np.arange(900, 3000, 30)\ncs = ax.contour(ds.lon, ds.lat, z, clev, colors='black', linewidths=2, transform=datacrs)\nplt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i', rightside_up=True, use_clabeltext=True)\n\n# Plot Temperature Contours\nclevtemp850 = np.arange(-20, 20, 2)\ncs2 = ax.contour(ds.lon, ds.lat, T, clevtemp850, colors='grey', linewidths=1.25, linestyles='--', transform=datacrs)\nplt.clabel(cs2, fontsize=10, inline=1, inline_spacing=10, fmt='%i', rightside_up=True, use_clabeltext=True)\n\n# Plot Colorfill of Temperature Advection\ncint = np.arange(-8, 9)\ncf = ax.contourf(ds.lon, ds.lat, T_adv, cint[cint != 0], extend='both', cmap='bwr', transform=datacrs)\ncb = plt.colorbar(cf, ax=ax, pad=0, aspect=50, orientation='horizontal', extendrect=True, ticks=cint)\ncb.set_label(r'$^{\\circ}$C 3h$^{-1}$', size='large')\n\n# Plot Wind Barbs\nax.barbs(ds.lon, ds.lat, u.magnitude, v.magnitude, length=6, regrid_shape=20, pivot='middle', transform=datacrs)\n\n# Change datetiem64 to datetime\nvalid = datetime.utcfromtimestamp(ds.time.values.astype('O')/1e9)\n\n# Add plot headers\nplt.title(f'GFS 850mb Temperature Advection', loc='left')\nplt.title(f'Run: {valid.strftime(\"%a %Y-%m-%d %H:%M\")} UTC\\nValid: {valid.strftime(\"%a %Y-%m-%d %H:%M\")} UTC', loc='right')\n\n# Add title\nplt.suptitle(f'weather.carterhumphreys.com', fontsize=16, x=0.50, y=0.90)\n \n# Export plot and close\nplt.show()", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# Copyright 2019 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================", "_____no_output_____" ] ], [ [ "# Composing a pipeline from reusable, pre-built, and lightweight components\n\nThis tutorial describes how to build a Kubeflow pipeline from reusable, pre-built, and lightweight components. The following provides a summary of the steps involved in creating and using a reusable component:\n\n- Write the program that contains your component’s logic. The program must use files and command-line arguments to pass data to and from the component.\n- Containerize the program.\n- Write a component specification in YAML format that describes the component for the Kubeflow Pipelines system.\n- Use the Kubeflow Pipelines SDK to load your component, use it in a pipeline and run that pipeline.\n\nThen, we will compose a pipeline from a reusable component, a pre-built component, and a lightweight component. The pipeline will perform the following steps:\n- Train an MNIST model and export it to Google Cloud Storage.\n- Deploy the exported TensorFlow model on AI Platform Prediction service.\n- Test the deployment by calling the endpoint with test data.", "_____no_output_____" ], [ "Note: Ensure that you have Docker installed, if you want to build the image locally, by running the following command:\n \n`which docker`\n \nThe result should be something like:\n\n`/usr/bin/docker`", "_____no_output_____" ] ], [ [ "import kfp\nimport kfp.gcp as gcp\nimport kfp.dsl as dsl\nimport kfp.compiler as compiler\nimport kfp.components as comp\nimport datetime\n\nimport kubernetes as k8s", "_____no_output_____" ], [ "# Required Parameters\nPROJECT_ID='<ADD GCP PROJECT HERE>'\nGCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'", "_____no_output_____" ] ], [ [ "## Create client\n\nIf you run this notebook **outside** of a Kubeflow cluster, run the following command:\n- `host`: The URL of your Kubeflow Pipelines instance, for example \"https://`<your-deployment>`.endpoints.`<your-project>`.cloud.goog/pipeline\"\n- `client_id`: The client ID used by Identity-Aware Proxy\n- `other_client_id`: The client ID used to obtain the auth codes and refresh tokens.\n- `other_client_secret`: The client secret used to obtain the auth codes and refresh tokens.\n\n```python\nclient = kfp.Client(host, client_id, other_client_id, other_client_secret)\n```\n\nIf you run this notebook **within** a Kubeflow cluster, run the following command:\n```python\nclient = kfp.Client()\n```\n\nYou'll need to create OAuth client ID credentials of type `Other` to get `other_client_id` and `other_client_secret`. Learn more about [creating OAuth credentials](\nhttps://cloud.google.com/iap/docs/authentication-howto#authenticating_from_a_desktop_app)", "_____no_output_____" ] ], [ [ "# Optional Parameters, but required for running outside Kubeflow cluster\n\n# The host for 'AI Platform Pipelines' ends with 'pipelines.googleusercontent.com'\n# The host for pipeline endpoint of 'full Kubeflow deployment' ends with '/pipeline'\n# Examples are:\n# https://7c021d0340d296aa-dot-us-central2.pipelines.googleusercontent.com\n# https://kubeflow.endpoints.kubeflow-pipeline.cloud.goog/pipeline\nHOST = '<ADD HOST NAME TO TALK TO KUBEFLOW PIPELINE HERE>'\n\n# For 'full Kubeflow deployment' on GCP, the endpoint is usually protected through IAP, therefore the following \n# will be needed to access the endpoint.\nCLIENT_ID = '<ADD OAuth CLIENT ID USED BY IAP HERE>'\nOTHER_CLIENT_ID = '<ADD OAuth CLIENT ID USED TO OBTAIN AUTH CODES HERE>'\nOTHER_CLIENT_SECRET = '<ADD OAuth CLIENT SECRET USED TO OBTAIN AUTH CODES HERE>'", "_____no_output_____" ], [ "# This is to ensure the proper access token is present to reach the end point for 'AI Platform Pipelines'\n# If you are not working with 'AI Platform Pipelines', this step is not necessary\n! gcloud auth print-access-token", "_____no_output_____" ], [ "# Create kfp client\nin_cluster = True\ntry:\n k8s.config.load_incluster_config()\nexcept:\n in_cluster = False\n pass\n\nif in_cluster:\n client = kfp.Client()\nelse:\n if HOST.endswith('googleusercontent.com'):\n CLIENT_ID = None\n OTHER_CLIENT_ID = None\n OTHER_CLIENT_SECRET = None\n\n client = kfp.Client(host=HOST, \n client_id=CLIENT_ID,\n other_client_id=OTHER_CLIENT_ID, \n other_client_secret=OTHER_CLIENT_SECRET)", "_____no_output_____" ] ], [ [ "# Build reusable components", "_____no_output_____" ], [ "## Writing the program code", "_____no_output_____" ], [ "The following cell creates a file `app.py` that contains a Python script. The script downloads MNIST dataset, trains a Neural Network based classification model, writes the training log and exports the trained model to Google Cloud Storage.\n\nYour component can create outputs that the downstream components can use as inputs. Each output must be a string and the container image must write each output to a separate local text file. For example, if a training component needs to output the path of the trained model, the component writes the path into a local file, such as `/output.txt`.", "_____no_output_____" ] ], [ [ "%%bash\n\n# Create folders if they don't exist.\nmkdir -p tmp/reuse_components_pipeline/mnist_training\n\n# Create the Python file that lists GCS blobs.\ncat > ./tmp/reuse_components_pipeline/mnist_training/app.py <<HERE\nimport argparse\nfrom datetime import datetime\nimport tensorflow as tf\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\n '--model_path', type=str, required=True, help='Name of the model file.')\nparser.add_argument(\n '--bucket', type=str, required=True, help='GCS bucket name.')\nargs = parser.parse_args()\n\nbucket=args.bucket\nmodel_path=args.model_path\n\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Flatten(input_shape=(28, 28)),\n tf.keras.layers.Dense(512, activation=tf.nn.relu),\n tf.keras.layers.Dropout(0.2),\n tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n])\n\nmodel.compile(optimizer='adam',\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\nprint(model.summary()) \n\nmnist = tf.keras.datasets.mnist\n(x_train, y_train),(x_test, y_test) = mnist.load_data()\nx_train, x_test = x_train / 255.0, x_test / 255.0\n\ncallbacks = [\n tf.keras.callbacks.TensorBoard(log_dir=bucket + '/logs/' + datetime.now().date().__str__()),\n # Interrupt training if val_loss stops improving for over 2 epochs\n tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),\n]\n\nmodel.fit(x_train, y_train, batch_size=32, epochs=5, callbacks=callbacks,\n validation_data=(x_test, y_test))\n\nfrom tensorflow import gfile\n\ngcs_path = bucket + \"/\" + model_path\n# The export require the folder is new\nif gfile.Exists(gcs_path):\n gfile.DeleteRecursively(gcs_path)\ntf.keras.experimental.export_saved_model(model, gcs_path)\n\nwith open('/output.txt', 'w') as f:\n f.write(gcs_path)\nHERE", "_____no_output_____" ] ], [ [ "## Create a Docker container\nCreate your own container image that includes your program. ", "_____no_output_____" ], [ "### Creating a Dockerfile", "_____no_output_____" ], [ "Now create a container that runs the script. Start by creating a Dockerfile. A Dockerfile contains the instructions to assemble a Docker image. The `FROM` statement specifies the Base Image from which you are building. `WORKDIR` sets the working directory. When you assemble the Docker image, `COPY` copies the required files and directories (for example, `app.py`) to the file system of the container. `RUN` executes a command (for example, install the dependencies) and commits the results. ", "_____no_output_____" ] ], [ [ "%%bash\n\n# Create Dockerfile.\n# AI platform only support tensorflow 1.14\ncat > ./tmp/reuse_components_pipeline/mnist_training/Dockerfile <<EOF\nFROM tensorflow/tensorflow:1.14.0-py3\nWORKDIR /app\nCOPY . /app\nEOF", "_____no_output_____" ] ], [ [ "### Build docker image", "_____no_output_____" ], [ "Now that we have created our Dockerfile for creating our Docker image. Then we need to build the image and push to a registry to host the image. There are three possible options:\n- Use the `kfp.containers.build_image_from_working_dir` to build the image and push to the Container Registry (GCR). This requires [kaniko](https://cloud.google.com/blog/products/gcp/introducing-kaniko-build-container-images-in-kubernetes-and-google-container-builder-even-without-root-access), which will be auto-installed with 'full Kubeflow deployment' but not 'AI Platform Pipelines'.\n- Use [Cloud Build](https://cloud.google.com/cloud-build), which would require the setup of GCP project and enablement of corresponding API. If you are working with GCP 'AI Platform Pipelines' with GCP project running, it is recommended to use Cloud Build.\n- Use [Docker](https://www.docker.com/get-started) installed locally and push to e.g. GCR.", "_____no_output_____" ], [ "**Note**:\nIf you run this notebook **within Kubeflow cluster**, **with Kubeflow version >= 0.7** and exploring **kaniko option**, you need to ensure that valid credentials are created within your notebook's namespace.\n- With Kubeflow version >= 0.7, the credential is supposed to be copied automatically while creating notebook through `Configurations`, which doesn't work properly at the time of creating this notebook. \n- You can also add credentials to the new namespace by either [copying credentials from an existing Kubeflow namespace, or by creating a new service account](https://www.kubeflow.org/docs/gke/authentication/#kubeflow-v0-6-and-before-gcp-service-account-key-as-secret).\n- The following cell demonstrates how to copy the default secret to your own namespace.\n\n```bash\n%%bash\n\nNAMESPACE=<your notebook name space>\nSOURCE=kubeflow\nNAME=user-gcp-sa\nSECRET=$(kubectl get secrets \\${NAME} -n \\${SOURCE} -o jsonpath=\"{.data.\\${NAME}\\.json}\" | base64 -D)\nkubectl create -n \\${NAMESPACE} secret generic \\${NAME} --from-literal=\"\\${NAME}.json=\\${SECRET}\"\n```", "_____no_output_____" ] ], [ [ "IMAGE_NAME=\"mnist_training_kf_pipeline\"\nTAG=\"latest\" # \"v_$(date +%Y%m%d_%H%M%S)\"\n\nGCR_IMAGE=\"gcr.io/{PROJECT_ID}/{IMAGE_NAME}:{TAG}\".format(\n PROJECT_ID=PROJECT_ID,\n IMAGE_NAME=IMAGE_NAME,\n TAG=TAG\n)\n\nAPP_FOLDER='./tmp/reuse_components_pipeline/mnist_training/'", "_____no_output_____" ], [ "# In the following, for the purpose of demonstration\n# Cloud Build is choosen for 'AI Platform Pipelines'\n# kaniko is choosen for 'full Kubeflow deployment'\n\nif HOST.endswith('googleusercontent.com'):\n # kaniko is not pre-installed with 'AI Platform Pipelines'\n import subprocess\n # ! gcloud builds submit --tag ${IMAGE_NAME} ${APP_FOLDER}\n cmd = ['gcloud', 'builds', 'submit', '--tag', GCR_IMAGE, APP_FOLDER]\n build_log = (subprocess.run(cmd, stdout=subprocess.PIPE).stdout[:-1].decode('utf-8'))\n print(build_log)\n \nelse:\n if kfp.__version__ <= '0.1.36':\n # kfp with version 0.1.36+ introduce broken change that will make the following code not working\n import subprocess\n \n builder = kfp.containers._container_builder.ContainerBuilder(\n gcs_staging=GCS_BUCKET + \"/kfp_container_build_staging\"\n )\n\n kfp.containers.build_image_from_working_dir(\n image_name=GCR_IMAGE,\n working_dir=APP_FOLDER,\n builder=builder\n )\n else:\n raise(\"Please build the docker image use either [Docker] or [Cloud Build]\")", "_____no_output_____" ] ], [ [ "#### If you want to use docker to build the image\nRun the following in a cell\n```bash\n%%bash -s \"{PROJECT_ID}\"\n\nIMAGE_NAME=\"mnist_training_kf_pipeline\"\nTAG=\"latest\" # \"v_$(date +%Y%m%d_%H%M%S)\"\n\n# Create script to build docker image and push it.\ncat > ./tmp/components/mnist_training/build_image.sh <<HERE\nPROJECT_ID=\"${1}\"\nIMAGE_NAME=\"${IMAGE_NAME}\"\nTAG=\"${TAG}\"\nGCR_IMAGE=\"gcr.io/\\${PROJECT_ID}/\\${IMAGE_NAME}:\\${TAG}\"\ndocker build -t \\${IMAGE_NAME} .\ndocker tag \\${IMAGE_NAME} \\${GCR_IMAGE}\ndocker push \\${GCR_IMAGE}\ndocker image rm \\${IMAGE_NAME}\ndocker image rm \\${GCR_IMAGE}\nHERE\n\ncd tmp/components/mnist_training\nbash build_image.sh\n```", "_____no_output_____" ] ], [ [ "image_name = GCR_IMAGE", "_____no_output_____" ] ], [ [ "## Writing your component definition file\nTo create a component from your containerized program, you must write a component specification in YAML that describes the component for the Kubeflow Pipelines system.\n\nFor the complete definition of a Kubeflow Pipelines component, see the [component specification](https://www.kubeflow.org/docs/pipelines/reference/component-spec/). However, for this tutorial you don’t need to know the full schema of the component specification. The notebook provides enough information to complete the tutorial.\n\nStart writing the component definition (component.yaml) by specifying your container image in the component’s implementation section:", "_____no_output_____" ] ], [ [ "%%bash -s \"{image_name}\"\n\nGCR_IMAGE=\"${1}\"\necho ${GCR_IMAGE}\n\n# Create Yaml\n# the image uri should be changed according to the above docker image push output\n\ncat > mnist_pipeline_component.yaml <<HERE\nname: Mnist training\ndescription: Train a mnist model and save to GCS\ninputs:\n - name: model_path\n description: 'Path of the tf model.'\n type: String\n - name: bucket\n description: 'GCS bucket name.'\n type: String\noutputs:\n - name: gcs_model_path\n description: 'Trained model path.'\n type: GCSPath\nimplementation:\n container:\n image: ${GCR_IMAGE}\n command: [\n python, /app/app.py,\n --model_path, {inputValue: model_path},\n --bucket, {inputValue: bucket},\n ]\n fileOutputs:\n gcs_model_path: /output.txt\nHERE", "_____no_output_____" ], [ "import os\nmnist_train_op = kfp.components.load_component_from_file(os.path.join('./', 'mnist_pipeline_component.yaml')) ", "_____no_output_____" ], [ "mnist_train_op.component_spec", "_____no_output_____" ] ], [ [ "# Define deployment operation on AI Platform", "_____no_output_____" ] ], [ [ "mlengine_deploy_op = comp.load_component_from_url(\n 'https://raw.githubusercontent.com/kubeflow/pipelines/1.1.2/components/gcp/ml_engine/deploy/component.yaml')\n\ndef deploy(\n project_id,\n model_uri,\n model_id,\n runtime_version,\n python_version):\n \n return mlengine_deploy_op(\n model_uri=model_uri,\n project_id=project_id, \n model_id=model_id, \n runtime_version=runtime_version, \n python_version=python_version,\n replace_existing_version=True, \n set_default=True)", "_____no_output_____" ] ], [ [ "Kubeflow serving deployment component as an option. **Note that, the deployed Endppoint URI is not availabe as output of this component.**\n```python\nkubeflow_deploy_op = comp.load_component_from_url(\n 'https://raw.githubusercontent.com/kubeflow/pipelines/1.1.2/components/gcp/ml_engine/deploy/component.yaml')\n\ndef deploy_kubeflow(\n model_dir,\n tf_server_name):\n return kubeflow_deploy_op(\n model_dir=model_dir,\n server_name=tf_server_name,\n cluster_name='kubeflow', \n namespace='kubeflow',\n pvc_name='', \n service_type='ClusterIP')\n```", "_____no_output_____" ], [ "# Create a lightweight component for testing the deployment", "_____no_output_____" ] ], [ [ "def deployment_test(project_id: str, model_name: str, version: str) -> str:\n\n model_name = model_name.split(\"/\")[-1]\n version = version.split(\"/\")[-1]\n \n import googleapiclient.discovery\n \n def predict(project, model, data, version=None):\n \"\"\"Run predictions on a list of instances.\n\n Args:\n project: (str), project where the Cloud ML Engine Model is deployed.\n model: (str), model name.\n data: ([[any]]), list of input instances, where each input instance is a\n list of attributes.\n version: str, version of the model to target.\n\n Returns:\n Mapping[str: any]: dictionary of prediction results defined by the model.\n \"\"\"\n\n service = googleapiclient.discovery.build('ml', 'v1')\n name = 'projects/{}/models/{}'.format(project, model)\n\n if version is not None:\n name += '/versions/{}'.format(version)\n\n response = service.projects().predict(\n name=name, body={\n 'instances': data\n }).execute()\n\n if 'error' in response:\n raise RuntimeError(response['error'])\n\n return response['predictions']\n\n import tensorflow as tf\n import json\n \n mnist = tf.keras.datasets.mnist\n (x_train, y_train),(x_test, y_test) = mnist.load_data()\n x_train, x_test = x_train / 255.0, x_test / 255.0\n\n result = predict(\n project=project_id,\n model=model_name,\n data=x_test[0:2].tolist(),\n version=version)\n print(result)\n \n return json.dumps(result)", "_____no_output_____" ], [ "# # Test the function with already deployed version\n# deployment_test(\n# project_id=PROJECT_ID,\n# model_name=\"mnist\",\n# version='ver_bb1ebd2a06ab7f321ad3db6b3b3d83e6' # previous deployed version for testing\n# )", "_____no_output_____" ], [ "deployment_test_op = comp.func_to_container_op(\n func=deployment_test, \n base_image=\"tensorflow/tensorflow:1.15.0-py3\",\n packages_to_install=[\"google-api-python-client==1.7.8\"])", "_____no_output_____" ] ], [ [ "# Create your workflow as a Python function", "_____no_output_____" ], [ "Define your pipeline as a Python function. ` @kfp.dsl.pipeline` is a required decoration, and must include `name` and `description` properties. Then compile the pipeline function. After the compilation is completed, a pipeline file is created.", "_____no_output_____" ] ], [ [ "# Define the pipeline\n@dsl.pipeline(\n name='Mnist pipeline',\n description='A toy pipeline that performs mnist model training.'\n)\ndef mnist_reuse_component_deploy_pipeline(\n project_id: str = PROJECT_ID,\n model_path: str = 'mnist_model', \n bucket: str = GCS_BUCKET\n):\n train_task = mnist_train_op(\n model_path=model_path, \n bucket=bucket\n ).apply(gcp.use_gcp_secret('user-gcp-sa'))\n \n deploy_task = deploy(\n project_id=project_id,\n model_uri=train_task.outputs['gcs_model_path'],\n model_id=\"mnist\", \n runtime_version=\"1.14\",\n python_version=\"3.5\"\n ).apply(gcp.use_gcp_secret('user-gcp-sa')) \n \n deploy_test_task = deployment_test_op(\n project_id=project_id,\n model_name=deploy_task.outputs[\"model_name\"], \n version=deploy_task.outputs[\"version_name\"],\n ).apply(gcp.use_gcp_secret('user-gcp-sa'))\n \n return True", "_____no_output_____" ] ], [ [ "### Submit a pipeline run", "_____no_output_____" ] ], [ [ "pipeline_func = mnist_reuse_component_deploy_pipeline", "_____no_output_____" ], [ "experiment_name = 'minist_kubeflow'\n\narguments = {\"model_path\":\"mnist_model\",\n \"bucket\":GCS_BUCKET}\n\nrun_name = pipeline_func.__name__ + ' run'\n\n# Submit pipeline directly from pipeline function\nrun_result = client.create_run_from_pipeline_func(pipeline_func, \n experiment_name=experiment_name, \n run_name=run_name, \n arguments=arguments)", "_____no_output_____" ] ], [ [ "**As an alternative, you can compile the pipeline into a package.** The compiled pipeline can be easily shared and reused by others to run the pipeline.\n\n```python\npipeline_filename = pipeline_func.__name__ + '.pipeline.zip'\ncompiler.Compiler().compile(pipeline_func, pipeline_filename)\n\nexperiment = client.create_experiment('python-functions-mnist')\n\nrun_result = client.run_pipeline(\n experiment_id=experiment.id, \n job_name=run_name, \n pipeline_package_path=pipeline_filename, \n params=arguments)\n```", "_____no_output_____" ] ] ]

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[ "Apache-2.0", "CC-BY-4.0" ]

[ "Apache-2.0", "CC-BY-4.0" ]

[ "Apache-2.0", "CC-BY-4.0" ]

[ [ [ "<table> <tr>\n <td style=\"background-color:#ffffff;\">\n <a href=\"http://qworld.lu.lv\" target=\"_blank\"><img src=\"..\\images\\qworld.jpg\" width=\"25%\" align=\"left\"> </a></td>\n <td style=\"background-color:#ffffff;vertical-align:bottom;text-align:right;\">\n prepared by <a href=\"http://abu.lu.lv\" target=\"_blank\">Abuzer Yakaryilmaz</a> (<a href=\"http://qworld.lu.lv/index.php/qlatvia/\" target=\"_blank\">QLatvia</a>)\n </td> \n</tr></table>", "_____no_output_____" ], [ "<table width=\"100%\"><tr><td style=\"color:#bbbbbb;background-color:#ffffff;font-size:11px;font-style:italic;text-align:right;\">This cell contains some macros. If there is a problem with displaying mathematical formulas, please run this cell to load these macros. </td></tr></table>\n$ \\newcommand{\\bra}[1]{\\langle #1|} $\n$ \\newcommand{\\ket}[1]{|#1\\rangle} $\n$ \\newcommand{\\braket}[2]{\\langle #1|#2\\rangle} $\n$ \\newcommand{\\dot}[2]{ #1 \\cdot #2} $\n$ \\newcommand{\\biginner}[2]{\\left\\langle #1,#2\\right\\rangle} $\n$ \\newcommand{\\mymatrix}[2]{\\left( \\begin{array}{#1} #2\\end{array} \\right)} $\n$ \\newcommand{\\myvector}[1]{\\mymatrix{c}{#1}} $\n$ \\newcommand{\\myrvector}[1]{\\mymatrix{r}{#1}} $\n$ \\newcommand{\\mypar}[1]{\\left( #1 \\right)} $\n$ \\newcommand{\\mybigpar}[1]{ \\Big( #1 \\Big)} $\n$ \\newcommand{\\sqrttwo}{\\frac{1}{\\sqrt{2}}} $\n$ \\newcommand{\\dsqrttwo}{\\dfrac{1}{\\sqrt{2}}} $\n$ \\newcommand{\\onehalf}{\\frac{1}{2}} $\n$ \\newcommand{\\donehalf}{\\dfrac{1}{2}} $\n$ \\newcommand{\\hadamard}{ \\mymatrix{rr}{ \\sqrttwo & \\sqrttwo \\\\ \\sqrttwo & -\\sqrttwo }} $\n$ \\newcommand{\\vzero}{\\myvector{1\\\\0}} $\n$ \\newcommand{\\vone}{\\myvector{0\\\\1}} $\n$ \\newcommand{\\vhadamardzero}{\\myvector{ \\sqrttwo \\\\ \\sqrttwo } } $\n$ \\newcommand{\\vhadamardone}{ \\myrvector{ \\sqrttwo \\\\ -\\sqrttwo } } $\n$ \\newcommand{\\myarray}[2]{ \\begin{array}{#1}#2\\end{array}} $\n$ \\newcommand{\\X}{ \\mymatrix{cc}{0 & 1 \\\\ 1 & 0} } $\n$ \\newcommand{\\Z}{ \\mymatrix{rr}{1 & 0 \\\\ 0 & -1} } $\n$ \\newcommand{\\Htwo}{ \\mymatrix{rrrr}{ \\frac{1}{2} & \\frac{1}{2} & \\frac{1}{2} & \\frac{1}{2} \\\\ \\frac{1}{2} & -\\frac{1}{2} & \\frac{1}{2} & -\\frac{1}{2} \\\\ \\frac{1}{2} & \\frac{1}{2} & -\\frac{1}{2} & -\\frac{1}{2} \\\\ \\frac{1}{2} & -\\frac{1}{2} & -\\frac{1}{2} & \\frac{1}{2} } } $\n$ \\newcommand{\\CNOT}{ \\mymatrix{cccc}{1 & 0 & 0 & 0 \\\\ 0 & 1 & 0 & 0 \\\\ 0 & 0 & 0 & 1 \\\\ 0 & 0 & 1 & 0} } $\n$ \\newcommand{\\norm}[1]{ \\left\\lVert #1 \\right\\rVert } $\n$ \\newcommand{\\pstate}[1]{ \\lceil \\mspace{-1mu} #1 \\mspace{-1.5mu} \\rfloor } $", "_____no_output_____" ], [ "<h2>Visualization of a (Real-Valued) Qubit</h2>", "_____no_output_____" ], [ "_We use certain tools from python library \"<b>matplotlib.pyplot</b>\" for drawing. Check the notebook [Python: Drawing](../python/Python06_Drawing.ipynb) for the list of these tools._", "_____no_output_____" ], [ "Suppose that we have a single qubit. \n\nEach possible (real-valued) quantum state of this qubit is a point on 2-dimensional space.\n\nIt can also be represented as a vector from origin to that point.\n\nWe start with the visual representation of the following quantum states: \n\n$$ \\ket{0} = \\myvector{1\\\\0}, ~~ \\ket{1} = \\myvector{0\\\\1} , ~~ -\\ket{0} = \\myrvector{-1\\\\0}, ~~\\mbox{and}~~ -\\ket{1} = \\myrvector{0\\\\-1}. $$", "_____no_output_____" ], [ "We draw these quantum states as points.\n\nWe use one of our predefined functions for drawing axes: \"draw_axes()\". We include our predefined functions with the following line of code:\n\n %run qlatvia.py", "_____no_output_____" ] ], [ [ "# import the drawing methods\nfrom matplotlib.pyplot import plot, figure, show\n\n# draw a figure\nfigure(figsize=(6,6), dpi=80)\n\n# include our predefined functions\n%run qlatvia.py\n\n# draw the axes\ndraw_axes()\n\n# draw the origin\nplot(0,0,'ro') # a point in red color\n\n# draw these quantum states as points (in blue color)\nplot(1,0,'bo') \nplot(0,1,'bo')\nplot(-1,0,'bo')\nplot(0,-1,'bo')\n\nshow()", "_____no_output_____" ] ], [ [ "Now, we draw the quantum states as arrows (vectors):", "_____no_output_____" ] ], [ [ "# import the drawing methods\nfrom matplotlib.pyplot import figure, arrow, show\n\n# draw a figure\nfigure(figsize=(6,6), dpi=80)\n\n# include our predefined functions\n%run qlatvia.py\n\n# draw the axes\ndraw_axes()\n\n# draw the quantum states as vectors (in blue color)\narrow(0,0,0.92,0,head_width=0.04, head_length=0.08, color=\"blue\")\narrow(0,0,0,0.92,head_width=0.04, head_length=0.08, color=\"blue\")\narrow(0,0,-0.92,0,head_width=0.04, head_length=0.08, color=\"blue\")\narrow(0,0,0,-0.92,head_width=0.04, head_length=0.08, color=\"blue\")\n\nshow()", "_____no_output_____" ] ], [ [ "<h3> Task 1 </h3>\n\nWrite a function that returns a randomly created 2-dimensional (real-valued) quantum state.\n\n_You can use your code written for [a task given in notebook \"Quantum State](B28_Quantum_State.ipynb#task2)._\n\nCreate 100 random quantum states by using your function and then draw all of them as points.\n\nCreate 1000 random quantum states by using your function and then draw all of them as points.\n\nThe different colors can be used when drawing the points ([matplotlib.colors](https://matplotlib.org/2.0.2/api/colors_api.html)).", "_____no_output_____" ] ], [ [ "# randomly creating a 2-dimensional quantum state\nfrom random import randrange\ndef random_quantum_state():\n first_entry = randrange(-100,101)\n second_entry = randrange(-100,101)\n length_square = first_entry**2+second_entry**2\n while length_square == 0:\n first_entry = randrange(-100,101)\n second_entry = randrange(-100,101)\n length_square = first_entry**2+second_entry**2\n first_entry = first_entry / length_square**0.5\n second_entry = second_entry / length_square**0.5\n return [first_entry,second_entry]", "_____no_output_____" ], [ "# import the drawing methods\nfrom matplotlib.pyplot import plot, figure\n\n# draw a figure\nfigure(figsize=(6,6), dpi=60) \n\n# draw the origin\nplot(0,0,'ro') \n\nfrom random import randrange\ncolors = ['ro','bo','go','yo','co','mo','ko']\n\nfor i in range(100):\n # create a random quantum state\n quantum_state = random_quantum_state(); \n # draw a blue point for the random quantum state\n x = quantum_state[0];\n y = quantum_state[1];\n plot(x,y,colors[randrange(len(colors))]) \n\nshow()", "_____no_output_____" ], [ "# randomly creating a 2-dimensional quantum state\nfrom random import randrange\ndef random_quantum_state():\n first_entry = randrange(-1000,1001)\n second_entry = randrange(-1000,1001)\n length_square = first_entry**2+second_entry**2\n while length_square == 0:\n first_entry = randrange(-100,101)\n second_entry = randrange(-100,101)\n length_square = first_entry**2+second_entry**2\n first_entry = first_entry / length_square**0.5\n second_entry = second_entry / length_square**0.5\n return [first_entry,second_entry]", "_____no_output_____" ], [ "# import the drawing methods\nfrom matplotlib.pyplot import plot, figure\n\n# draw a figure\nfigure(figsize=(6,6), dpi=60) \n\n# draw the origin\nplot(0,0,'ro') \n\nfrom random import randrange\ncolors = ['ro','bo','go','yo','co','mo','ko']\n\nfor i in range(1000):\n # create a random quantum state\n quantum_state = random_quantum_state(); \n # draw a blue point for the random quantum state\n x = quantum_state[0];\n y = quantum_state[1];\n plot(x,y,colors[randrange(len(colors))]) \n\nshow()", "_____no_output_____" ] ], [ [ "<a href=\"B30_Visualization_of_a_Qubit_Solutions.ipynb#task1\">click for our solution</a>", "_____no_output_____" ], [ "<h3> Task 2 </h3>\n\nRepeat the previous task by drawing the quantum states as vectors (arrows) instead of points.\n\nThe different colors can be used when drawing the points ([matplotlib.colors](https://matplotlib.org/2.0.2/api/colors_api.html)).\n\n_Please keep the codes below for drawing axes for getting a better visual focus._", "_____no_output_____" ] ], [ [ "# randomly creating a 2-dimensional quantum state\nfrom random import randrange\ndef random_quantum_state():\n first_entry = randrange(-1000,1001)\n second_entry = randrange(-1000,1001)\n length_square = first_entry**2+second_entry**2\n while length_square == 0:\n first_entry = randrange(-100,101)\n second_entry = randrange(-100,101)\n length_square = first_entry**2+second_entry**2\n first_entry = first_entry / length_square**0.5\n second_entry = second_entry / length_square**0.5\n return [first_entry,second_entry]", "_____no_output_____" ], [ "# import the drawing methods\nfrom matplotlib.pyplot import plot, figure, arrow\n\n# draw a figure\nfigure(figsize=(6,6), dpi=60) \n\n# include our predefined functions\n%run qlatvia.py\n\n# draw the axes\ndraw_axes()\n\n# draw the origin\nplot(0,0,'ro') \n\nfrom random import randrange\ncolors = ['r','b','g','y','b','c','m']\n\nfor i in range(500):\n quantum_state = random_quantum_state(); \n x = quantum_state[0];\n y = quantum_state[1];\n x = 0.92 * x\n y = 0.92 * y\n arrow(0,0,x,y,head_width=0.04,head_length=0.08,color=colors[randrange(len(colors))])\n\nshow()", "_____no_output_____" ] ], [ [ "<a href=\"B30_Visualization_of_a_Qubit_Solutions.ipynb#task2\">click for our solution</a>", "_____no_output_____" ], [ "<h3> Unit circle </h3>\n\nAll quantum states of a qubit form the unit circle.\n\nThe length of each quantum state is 1.\n\nAll points that are 1 unit away from the origin form the circle with radius 1 unit.\n\nWe can draw the unit circle with python.\n\nWe have a predefined function for drawing the unit circle: \"draw_unit_circle()\".", "_____no_output_____" ] ], [ [ "# import the drawing methods\nfrom matplotlib.pyplot import figure\n\nfigure(figsize=(6,6), dpi=80) # size of the figure\n\n# include our predefined functions\n%run qlatvia.py\n\n# draw axes\ndraw_axes()\n\n# draw the unit circle\ndraw_unit_circle()", "_____no_output_____" ] ], [ [ "<h3>Quantum state of a qubit</h3>", "_____no_output_____" ], [ "Suppose that we have a single qubit. \n\nEach possible (real-valued) quantum state of this qubit is a point on 2-dimensional space.\n\nIt can also be represented as a vector from origin to that point.\n\nWe draw the quantum state $ \\myvector{3/5 \\\\ 4/5} $ and its elements.", "_____no_output_____" ], [ "<i style=\"font-size:10pt;\">\nOur predefined function \"draw_qubit()\" draws a figure, the origin, the axes, the unit circle, and base quantum states.\n<br>\nOur predefined function \"draw_quantum_state(x,y,name)\" draws an arrow from (0,0) to (x,y) and associates it with <u>name</u>.\n<br>\nWe include our predefined functions with the following line of code:\n \n %run qlatvia.py\n</i> ", "_____no_output_____" ] ], [ [ "%run qlatvia.py\n\ndraw_qubit()\n\ndraw_quantum_state(3/5,4/5,\"|v>\")", "_____no_output_____" ] ], [ [ "Now, we draw its angle with $ \\ket{0} $-axis and its projections on both axes.\n\n<i> For drawing the angle, we use the method \"Arc\" from library \"matplotlib.patches\". </i> ", "_____no_output_____" ] ], [ [ "%run qlatvia.py\n\ndraw_qubit()\n\ndraw_quantum_state(3/5,4/5,\"|v>\")\n\nfrom matplotlib.pyplot import arrow, text, gca\n\n# the projection on |0>-axis\narrow(0,0,3/5,0,color=\"blue\",linewidth=1.5)\narrow(0,4/5,3/5,0,color=\"blue\",linestyle='dotted')\ntext(0.1,-0.1,\"cos(a)=3/5\")\n\n# the projection on |1>-axis\narrow(0,0,0,4/5,color=\"blue\",linewidth=1.5)\narrow(3/5,0,0,4/5,color=\"blue\",linestyle='dotted')\ntext(-0.1,0.55,\"sin(a)=4/5\",rotation=\"90\")\n\n# drawing the angle with |0>-axis\nfrom matplotlib.patches import Arc\ngca().add_patch( Arc((0,0),0.4,0.4,angle=0,theta1=0,theta2=53) )\ntext(0.08,0.05,'.',fontsize=30)\ntext(0.21,0.09,'a')", "_____no_output_____" ] ], [ [ "<b> Observations: </b>\n<ul>\n <li> The angle of quantum state with state $ \\ket{0} $ is $a$.</li> \n <li> The amplitude of state $ \\ket{0} $ is $ \\cos(a) = \\frac{3}{5} $.</li>\n <li> The probability of observing state $ \\ket{0} $ is $ \\cos^2(a) = \\frac{9}{25} $.</li>\n <li> The amplitude of state $ \\ket{1} $ is $ \\sin(a) = \\frac{4}{5} $.</li>\n <li> The probability of observing state $ \\ket{1} $ is $ \\sin^2(a) = \\frac{16}{25} $.</li>\n</ul>", "_____no_output_____" ], [ "<h3> The angle of a quantum state </h3>\n\nThe angle of a vector (in radians) on the unit circle is the length of arc in counter-clockwise direction that starts from $ (1,0) $ and with the points representing the vector.\n\nWe execute the following code a couple of times to see different examples, where the angle is picked randomly in each run.\n\nYou can also set the value of \"myangle\" manually for seeing a specific angle.", "_____no_output_____" ] ], [ [ "# set the angle\n\nfrom random import randrange\nmyangle = randrange(361)\n\n################################################\n\nfrom matplotlib.pyplot import figure,gca\nfrom matplotlib.patches import Arc\nfrom math import sin,cos,pi\n\n# draw a figure\nfigure(figsize=(6,6), dpi=60)\n\n%run qlatvia.py\n\ndraw_axes()\n\nprint(\"the selected angle is\",myangle,\"degrees\")\n\nratio_of_arc = ((1000*myangle/360)//1)/1000\n\nprint(\"it is\",ratio_of_arc,\"of a full circle\")\n\nprint(\"its length is\",ratio_of_arc,\"x 2\\u03C0\",\"=\",ratio_of_arc*2*pi)\n\nmyangle_in_radian = 2*pi*(myangle/360)\n\nprint(\"its radian value is\",myangle_in_radian)\n\ngca().add_patch( Arc((0,0),0.2,0.2,angle=0,theta1=0,theta2=myangle,color=\"red\",linewidth=2) )\n\ngca().add_patch( Arc((0,0),2,2,angle=0,theta1=0,theta2=myangle,color=\"brown\",linewidth=2) )\n\nx = cos(myangle_in_radian)\ny = sin(myangle_in_radian)\n\ndraw_quantum_state(x,y,\"|v>\")\n\n# the projection on |0>-axis\narrow(0,0,x,0,color=\"blue\",linewidth=1)\narrow(0,y,x,0,color=\"blue\",linestyle='dashed')\n\n# the projection on |1>-axis\narrow(0,0,0,y,color=\"blue\",linewidth=1)\narrow(x,0,0,y,color=\"blue\",linestyle='dashed')\n\nprint()\nprint(\"the amplitude of state |0> is\",x)\nprint(\"the amplitude of state |1> is\",y)\nprint()\nprint(\"the probability of observing state |0> is\",x*x)\nprint(\"the probability of observing state |1> is\",y*y)\nprint(\"the total probability is\",round(x*x+y*y,6))", "the selected angle is 27 degrees\nit is 0.075 of a full circle\nits length is 0.075 x 2π = 0.47123889803846897\nits radian value is 0.47123889803846897\n\nthe amplitude of state |0> is 0.8910065241883679\nthe amplitude of state |1> is 0.45399049973954675\n\nthe probability of observing state |0> is 0.7938926261462367\nthe probability of observing state |1> is 0.2061073738537634\nthe total probability is 1.0\n" ] ], [ [ "<h3> Random quantum states </h3>\n\nAny quantum state of a (real-valued) qubit is a point on the unit circle.\n\nWe use this fact to create random quantum states by picking a random point on the unit circle. \n\nFor this purpose, we randomly pick an angle between zero and 360 degrees and then find the amplitudes of the quantum state by using the basic trigonometric functions.", "_____no_output_____" ], [ "<a id=\"task3\"></a>\n<h3> Task 3 </h3>\n\nDefine a function randomly creating a quantum state based on this idea.\n\nRandomly create a quantum state by using this function.\n\nDraw the quantum state on the unit circle.\n\nRepeat the task for a few times.\n\nRandomly create 100 quantum states and draw all of them.", "_____no_output_____" ], [ "<i> You can save your function for using later: comment out the first command, give an appropriate file name, and then run the cell.</i>", "_____no_output_____" ] ], [ [ "# %%writefile FILENAME.py \n# randomly creating a 2-dimensional quantum state\nfrom random import randrange\ndef random_quantum_state2():\n angle_degree = randrange(360)\n angle_radian = 2*pi*angle/360\n return [cos(angle_radian),sin(angle_radian)]", "_____no_output_____" ] ], [ [ "<i style=\"font-size:10pt;\">\nOur predefined function \"draw_qubit()\" draws a figure, the origin, the axes, the unit circle, and base quantum states.\n<br>\nOur predefined function \"draw_quantum_state(x,y,name)\" draws an arrow from (0,0) to (x,y) and associates it with <u>name</u>.\n<br>\nWe include our predefined functions with the following line of code:\n \n %run qlatvia.py\n</i> ", "_____no_output_____" ] ], [ [ "# visually test your function\n%run qlatvia.py\n\n# draw the axes\ndraw_qubit()\n\nfrom random import randrange\nfor i in range(6):\n [x,y]=random_quantum_state2()\n draw_quantum_state(x,y,\"|v\"+str(i)+\">\")", "_____no_output_____" ], [ "# include our predefined functions\n%run qlatvia.py\n\n# draw the axes\ndraw_qubit()\n\nfor i in range(100):\n [x,y]=random_quantum_state2()\n draw_quantum_state(x,y,\"\")", "_____no_output_____" ] ], [ [ "<a href=\"B30_Visualization_of_a_Qubit_Solutions.ipynb#task3\">click for our solution</a>", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ [ [ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport statsmodels.api as sm\nimport pylab", "_____no_output_____" ], [ "df1 = pd.read_csv('data/power_act_.csv') \n", "_____no_output_____" ] ], [ [ " 'power_act_.csv'\n\n In total we have 18 columns and 64328 rows \n\n Coulmns names : \n ['dt_start_utc', 'power_act_21', 'power_act_24', 'power_act_47' .....]\n\n\n date ranges from '2019-06-30' to '2021-04-30'\n\n Date seems to be recorded for every 15 minutes\n\nAll the columns contains missing values only for 'power_act_21' its 1.5% whereas >20% for other features", "_____no_output_____" ] ], [ [ "df1.tail(10)", "_____no_output_____" ], [ "df1.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 64328 entries, 0 to 64327\nData columns (total 18 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 dt_start_utc 64328 non-null object \n 1 power_act_21 63323 non-null float64\n 2 power_act_24 35870 non-null float64\n 3 power_act_47 51966 non-null float64\n 4 power_act_84 35820 non-null float64\n 5 power_act_176 21472 non-null float64\n 6 power_act_179 11574 non-null float64\n 7 power_act_183 11634 non-null float64\n 8 power_act_196 36264 non-null float64\n 9 power_act_202 32659 non-null float64\n 10 power_act_206 36155 non-null float64\n 11 power_act_222 20587 non-null float64\n 12 power_act_233 35804 non-null float64\n 13 power_act_291 6976 non-null float64\n 14 power_act_292 6324 non-null float64\n 15 power_act_293 11608 non-null float64\n 16 power_act_308 11604 non-null float64\n 17 power_act_336 11434 non-null float64\ndtypes: float64(17), object(1)\nmemory usage: 8.8+ MB\n" ], [ "df1.describe()", "_____no_output_____" ], [ "df1.isnull().sum().sort_values(ascending=False)/len(df1)*100", "_____no_output_____" ], [ "df1['dt_start_utc'] = df1['dt_start_utc'].apply(pd.to_datetime)", "_____no_output_____" ], [ "df1 = df1.set_index('dt_start_utc')", "_____no_output_____" ], [ "plt.figure(figsize=(10,6))\ndf1['power_act_21'].plot()\n", "_____no_output_____" ], [ "plt.figure(figsize=(10,6))\ndf1['power_act_47'].plot()", "_____no_output_____" ], [ "plt.figure(figsize=(10,6))\ndf1['power_act_196'].plot()", "_____no_output_____" ], [ "df2 = pd.read_csv('data/power_fc_.csv') ", "_____no_output_____" ] ], [ [ "'power_fc_.csv'\n\n In total we have 23 columns and 66020 rows \n\n Coulmns names : \n ['dt_start_utc', 'power_act_21', 'power_act_24', 'power_act_47' .....]\n\n\n date ranges from ''2019-06-13 07:00'' to ''2021-04-30 23:45''\n\n Date seems to be recorded for every 15 minutes\n\nno null values for 'power_act_21' whereas for other features >17% null values", "_____no_output_____" ] ], [ [ "df2['dt_start_utc'].max()", "_____no_output_____" ], [ "df2.head()", "_____no_output_____" ], [ "df2.shape", "_____no_output_____" ], [ "df2.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 66020 entries, 0 to 66019\nData columns (total 23 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 dt_start_utc 66020 non-null object \n 1 power_fc_21 66020 non-null float64\n 2 power_fc_24 36708 non-null float64\n 3 power_fc_47 54789 non-null float64\n 4 power_fc_48 18400 non-null float64\n 5 power_fc_51 18400 non-null float64\n 6 power_fc_176 21934 non-null float64\n 7 power_fc_179 11639 non-null float64\n 8 power_fc_183 11639 non-null float64\n 9 power_fc_196 36708 non-null float64\n 10 power_fc_202 33261 non-null float64\n 11 power_fc_206 36612 non-null float64\n 12 power_fc_222 21934 non-null float64\n 13 power_fc_233 36708 non-null float64\n 14 power_fc_291 11624 non-null float64\n 15 power_fc_292 11624 non-null float64\n 16 power_fc_293 11624 non-null float64\n 17 power_fc_308 11624 non-null float64\n 18 power_fc_322 11624 non-null float64\n 19 power_fc_325 11624 non-null float64\n 20 power_fc_327 11624 non-null float64\n 21 power_fc_336 11624 non-null float64\n 22 power_fc_339 11624 non-null float64\ndtypes: float64(22), object(1)\nmemory usage: 11.6+ MB\n" ], [ "df2.isnull().sum().sort_values(ascending=False)/len(df1)*100", "_____no_output_____" ], [ "df2['dt_start_utc'] = df2['dt_start_utc'].apply(pd.to_datetime)\n#df2 = df2.reset_index('dt_start_utc')", "_____no_output_____" ], [ "df2['dt_start_utc'] = df2['dt_start_utc'].apply(pd.to_datetime)", "_____no_output_____" ], [ "df2.head()", "_____no_output_____" ], [ "df3 = pd.read_csv('data/regelleistung_aggr_results.csv')\n", "_____no_output_____" ] ], [ [ " 'regelleistung_aggr_results.csv'\n\n In total we have 17 columns and 16068 rows \n\n Coulmns names : \n ['date_start', 'date_end', 'product', 'reserve_type', 'total_min_capacity_price_eur_mw',# \n 'total_average_capacity_price_eur_mw', 'total_marginal_capacity_price_eur_mw','total_min_energy_price_eur_mwh', 'total_average_energy_price_eur_mwh', 'total_marginal_energy_price_eur_mwh', 'germany_min_capacity_price_eur_mw',\n 'germany_average_capacity_price_eur_mw', 'germany_marginal_capacity_price_eur_mw','germany_min_energy_price_eur_mwh',\n 'germany_average_energy_price_eur_mwh', 'germany_marginal_energy_price_eur_mwh', 'germany_import_export_mw']\n\n 2 unique reserve type ['MRL', 'SRL']\n\n 12 unique product type ['NEG_00_04', 'NEG_04_08', 'NEG_08_12', 'NEG_12_16', 'NEG_16_20','NEG_20_24', 'POS_00_04', 'POS_04_08', 'POS_08_12', 'POS_12_16', 'POS_16_20', 'POS_20_24']\n\n date ranges from '2019-01-01' to '2021-03-19'\n\n Date seems to be recorded for every hours (24 values for each days)\n\n Few columns contains missing values of about 37%", "_____no_output_____" ] ], [ [ "df3.shape", "_____no_output_____" ], [ "df3.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 16068 entries, 0 to 16067\nData columns (total 17 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 date_start 16068 non-null object \n 1 date_end 16068 non-null object \n 2 product 16068 non-null object \n 3 reserve_type 16068 non-null object \n 4 total_min_capacity_price_eur_mw 16068 non-null float64\n 5 total_average_capacity_price_eur_mw 16068 non-null float64\n 6 total_marginal_capacity_price_eur_mw 16068 non-null float64\n 7 total_min_energy_price_eur_mwh 15828 non-null float64\n 8 total_average_energy_price_eur_mwh 15828 non-null float64\n 9 total_marginal_energy_price_eur_mwh 15828 non-null float64\n 10 germany_min_capacity_price_eur_mw 16068 non-null float64\n 11 germany_average_capacity_price_eur_mw 16068 non-null float64\n 12 germany_marginal_capacity_price_eur_mw 16068 non-null float64\n 13 germany_min_energy_price_eur_mwh 15828 non-null float64\n 14 germany_average_energy_price_eur_mwh 15828 non-null float64\n 15 germany_marginal_energy_price_eur_mwh 15828 non-null float64\n 16 germany_import_export_mw 16068 non-null int64 \ndtypes: float64(12), int64(1), object(4)\nmemory usage: 2.1+ MB\n" ], [ "df3.groupby(by='date_start').count().head(2)", "_____no_output_____" ], [ "df3['reserve_type'].unique()", "_____no_output_____" ], [ "df3['product'].unique()", "_____no_output_____" ], [ "#sns.pairplot(df3)", "_____no_output_____" ], [ "df3.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 16068 entries, 0 to 16067\nData columns (total 17 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 date_start 16068 non-null object \n 1 date_end 16068 non-null object \n 2 product 16068 non-null object \n 3 reserve_type 16068 non-null object \n 4 total_min_capacity_price_eur_mw 16068 non-null float64\n 5 total_average_capacity_price_eur_mw 16068 non-null float64\n 6 total_marginal_capacity_price_eur_mw 16068 non-null float64\n 7 total_min_energy_price_eur_mwh 15828 non-null float64\n 8 total_average_energy_price_eur_mwh 15828 non-null float64\n 9 total_marginal_energy_price_eur_mwh 15828 non-null float64\n 10 germany_min_capacity_price_eur_mw 16068 non-null float64\n 11 germany_average_capacity_price_eur_mw 16068 non-null float64\n 12 germany_marginal_capacity_price_eur_mw 16068 non-null float64\n 13 germany_min_energy_price_eur_mwh 15828 non-null float64\n 14 germany_average_energy_price_eur_mwh 15828 non-null float64\n 15 germany_marginal_energy_price_eur_mwh 15828 non-null float64\n 16 germany_import_export_mw 16068 non-null int64 \ndtypes: float64(12), int64(1), object(4)\nmemory usage: 2.1+ MB\n" ], [ "df3.isnull().sum().sort_values(ascending=False)/len(df1)*100", "_____no_output_____" ], [ "df3.shape", "_____no_output_____" ], [ "df4 = pd.read_csv('data/regelleistung_demand.csv')", "_____no_output_____" ], [ "df4.head()", "_____no_output_____" ] ], [ [ " 'regelleistung_demand.csv'\n\n In total we have 6 columns and 16188 rows \n\n Coulmns names : ['date_start', 'date_end', 'product', 'total_demand_mw',\n 'germany_block_demand_mw', 'reserve_type']\n\n 2 unique reserve type ['MRL', 'SRL']\n\n 12 unique product type ['NEG_00_04', 'NEG_04_08', 'NEG_08_12', 'NEG_12_16', 'NEG_16_20','NEG_20_24', 'POS_00_04', 'POS_04_08', 'POS_08_12', 'POS_12_16', 'POS_16_20', 'POS_20_24']\n\n date ranges from '2019-01-01' to '2021-03-18'\n\n Date seems to be recorded for every hours (24 values for each days)\n\nno missing values ", "_____no_output_____" ] ], [ [ "df4.isnull().sum().sort_values(ascending=False)", "_____no_output_____" ], [ "def check_unique(df):\n ''' check unique values for each columns and \n print them if they are below 15'''\n\n for col in df.columns:\n n = df[col].nunique()\n print(f'{col} has {n} unique values')\n if n < 15:\n print(df[col].unique())", "_____no_output_____" ], [ "df4.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 16188 entries, 0 to 16187\nData columns (total 6 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 date_start 16188 non-null object\n 1 date_end 16188 non-null object\n 2 product 16188 non-null object\n 3 total_demand_mw 16188 non-null int64 \n 4 germany_block_demand_mw 16188 non-null int64 \n 5 reserve_type 16188 non-null object\ndtypes: int64(2), object(4)\nmemory usage: 758.9+ KB\n" ], [ "sns.pairplot(df4)", "_____no_output_____" ] ] ]

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[ [ "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Determinant Quantum Monte Carlo", "_____no_output_____" ], [ "## 1 Hubbard model\n\nThe Hubbard model is defined as\n\n\\begin{align}\n \\label{eq:ham} \\tag{1}\n H &= -\\sum_{ij\\sigma} t_{ij} \\left( \\hat{c}_{i\\sigma}^\\dagger \\hat{c}_{j\\sigma} + hc \\right) \n + \\sum_{i\\sigma} (\\varepsilon_i - \\mu) \\hat{n}_{i\\sigma} \n + U \\sum_{i} \\left( \\hat{n}_{i\\uparrow} - \\tfrac{1}{2} \\right) \\left( \\hat{n}_{i\\downarrow} - \\tfrac{1}{2} \\right)\n\\end{align}\n\nwhere $U$ is the interaction strength, $\\varepsilon_i$ the on-site energy at site $i$ and $t_{ij}$ the hopping energy between the sites $i$ and $j$. The chemical potential is defined to be $\\mu = 0$ for a half filled Hubabrd model since the total chemical potential is given as $\\mu + \\tfrac{U}{2}$:\n\n\\begin{equation}\n H = -\\sum_{ij\\sigma} t_{ij} \\left( \\hat{c}_{i\\sigma}^\\dagger \\hat{c}_{j\\sigma} + hc \\right) \n + \\sum_{i\\sigma} \\left(\\varepsilon_i - \\mu - \\tfrac{U}{2}\\right) \\hat{n}_{i\\sigma}\n + U \\sum_{i} \\hat{n}_{i\\uparrow} \\hat{n}_{i\\downarrow}.\n\\end{equation}\n\nThe non-interacting of the Hubbard Hamiltonian is quadratic in the creation and annihilation operators,\n\n\\begin{equation}\n K = -\\sum_{ij\\sigma} t_{ij} \\left( \\hat{c}_{i\\sigma}^\\dagger \\hat{c}_{j\\sigma} + hc \\right) \n + \\sum_{i\\sigma} (\\varepsilon_i - \\mu) \\hat{n}_{i\\sigma}, \n \\label{eq:ham_kin} \\tag{2}\n\\end{equation}\n\nwhile the interaction part is quartic in the fermion operators:\n\n\\begin{equation}\n V = U \\sum_{i} \\left( \\hat{n}_{i\\uparrow} - \\tfrac{1}{2} \\right) \\left( \\hat{n}_{i\\downarrow} - \\tfrac{1}{2} \\right). \n \\label{eq:ham_inter} \\tag{3}\n\\end{equation}\n\n", "_____no_output_____" ], [ "## 2 Distribution operator\n\n\nThe expectation value of a observable $O$ is given by\n\n\\begin{equation}\n \\langle O \\rangle = \\text{Tr}(O \\mathcal{P}),\n\\end{equation}\n\nwhere $\\mathcal{P}$ is the distribution operator,\n\n\\begin{equation}\n \\mathcal{P} = \\frac{1}{\\mathcal{Z}} e^{-\\beta H},\n \\label{eq:distop} \\tag{4}\n\\end{equation}\n\n$\\mathcal{Z}$ is the partition function,\n\n\\begin{equation}\n \\mathcal{Z} = \\text{Tr}(e^{-\\beta H}),\n \\label{eq:partition}\n\\end{equation}\n\nand $\\beta = 1/k_B T$ is the inverse of the temperature. The trace is taken over the Hilbert space describing all occupied states of the system:\n\n\\begin{equation}\n \\text{Tr}(e^{-\\beta H}) = \\sum_i \\langle \\psi_i | e^{-\\beta H} | \\psi_i \\rangle.\n\\end{equation}\n\nIn order to obtain a computable approximation of the distribution operator the partition function has to be approximated. Since the quadratic term $ K $ and quartic term $ V $ of the Hubbard Hamiltonian do not commute a Trotter-Suzuki decomposition has to be used to approximate $\\mathcal{Z}$. By dividing the imaginary-time interval from $0$ to $\\beta$ into $L$ intervals of size $\\Delta \\tau = \\beta / L$, the partition function can be written as\n\n\\begin{equation}\n \\label{eq:partitionTrotterDecomp}\n \\mathcal{Z} = \\text{Tr}(e^{-\\beta H }) = \\text{Tr}( \\prod_{l=1}^{L} e^{-\\Delta \\tau H}) \n = \\text{Tr}( \\prod_{l=1}^{L} e^{-\\Delta \\tau K} e^{-\\Delta \\tau V}) + \\mathcal{O}(\\Delta \\tau^2).\n\\end{equation}\n\nThe spin-up and spin-down operators of the quadratic kinetic energy term are independent and can be written as\n\n\\begin{equation}\n e^{-\\Delta \\tau K} = e^{-\\Delta \\tau K_\\uparrow} e^{-\\Delta \\tau K_\\downarrow}.\n\\end{equation}\n\nThe particle number operators $\\hat{n}_{i\\sigma}$ in the interacting term of the Hubbard Hamiltonian are independent of the site index $i$:\n\n\\begin{equation}\n e^{-\\Delta \\tau V} = e^{- U \\Delta\\tau \\sum_{i=1}^N \n \\left( \\hat{n}_{i\\uparrow} - \\tfrac{1}{2} \\right) \\left( \\hat{n}_{i\\downarrow} - \\tfrac{1}{2} \\right)} \n = \\prod_{i=1}^N e^{- U \\Delta\\tau \\left( \\hat{n}_{i\\uparrow} - \\tfrac{1}{2} \\right) \\left( \\hat{n}_{i\\downarrow} - \\tfrac{1}{2} \\right)}\n\\end{equation}\n\nThe quartic contributions of the interacting term need to be represented in a quadratic form. This can be achieved by using the discrete \\emph{Hubbard-Stratonovich} transformation, which replaces the term $\\left( \\hat{n}_{i\\uparrow} - \\tfrac{1}{2} \\right) \\left( \\hat{n}_{i\\downarrow} - \\tfrac{1}{2} \\right)$ by a quadratic term of the form $\\left( \\hat{n}_{i\\uparrow} - \\hat{n}_{i\\downarrow} \\right)$. For $U>0$, this yields\n\n\\begin{equation}\n \\label{eq:hubbardStratanovichInteractionTerm}\n e^{- U \\Delta\\tau \\left( \\hat{n}_{i\\uparrow} - \\tfrac{1}{2} \\right) \\left( \\hat{n}_{i\\downarrow} - \\tfrac{1}{2} \\right)} = C \\sum_{h_i = \\pm 1} e^{\\nu h_i \\left( \\hat{n}_{i\\uparrow} - \\hat{n}_{i\\downarrow} \\right)},\n\\end{equation}\n\nwhere $C=\\frac{1}{2} e^{-\\frac{U \\Delta\\tau}{4}}$ and the constant $\\nu$ is defined by\n\\begin{equation}\n \\label{eq:lambda}\n \\cosh \\nu = e^{-\\frac{U \\Delta\\tau}{2}}.\n\\end{equation}\n\nThe set of auxiliary variables $\\lbrace h_i \\rbrace$ is called the *Hubbard-Stratonovich field* or *configuration*. The variables $h_i$ take the values $\\pm 1$ for a up- or down-spin, respectively. Using the Hubbard-Stratanovich transformation the interaction term can be formulated as\n\n\\begin{equation}\n\\begin{split}\n \\label{eq:hubbardStratanovichInteractionFull}\n e^{-\\Delta\\tau V} &= \\prod_{i=1}^N \\left(C \\sum_{h_i = \\pm 1} e^{\\nu h_i \\left( \\hat{n}_{i\\uparrow} - \\hat{n}_{i\\downarrow} \\right)}\\right) \\\\\n &= C^N \\sum_{h_i = \\pm 1} e^{\\sum_{i=1}^N \\nu h_i \\left( \\hat{n}_{i\\uparrow} - \\hat{n}_{i\\downarrow} \\right)} \\\\\n &= C^N \\text{Tr}_h e^{\\sum_{i=1}^N \\nu h_i \\left( \\hat{n}_{i\\uparrow} - \\hat{n}_{i\\downarrow} \\right)} \\\\\n &= C^N \\text{Tr}_h e^{\\sum_{i=1}^N \\nu h_i \\hat{n}_{i\\uparrow}} e^{-\\sum_{i=1}^N \\nu h_i \\hat{n}_{i\\downarrow}} \\\\\n &= C^N \\text{Tr}_h e^{V_\\uparrow} e^{V_\\downarrow}\n\\end{split}\n\\end{equation}\nwhere\n\\begin{equation}\n V_\\sigma = \\sum_{i=1}^N \\nu h_i \\hat{n}_{i\\sigma} = \\sigma \\nu \\boldsymbol{\\hat{c}}_\\sigma^\\dagger V(h) \\boldsymbol{\\hat{c}}_\\sigma\n\\end{equation}\n\n\nand $V(h)$ is a diagonal matrix of the configurations $V(h) = \\text{diag}(h_1, h_2, \\dots, h_N)$. Taking into account the $L$ imaginary time slices, the Hubbard-Stratonovich variables are expanded to have two indices $h_{i, l}$, where $i$ represents the site index and $l$ the imaginary time slice:\n\n\\begin{equation}\n h_i \\longrightarrow h_{il}, \\quad V(h) \\longrightarrow V_l(h_l), \\quad V_\\sigma \\longrightarrow V_{l\\sigma}.\n\\end{equation}\n\nThe Hubbard-Stratonovich field or configuration now is a $N \\times L$ matrix for a system of $N$ sites with $L$ time steps. \nTherefore, the partition function can be approximated by\n\n\\begin{equation}\n \\label{eq:partitionApproximation} \\tag{5}\n \\mathcal{Z} = \\eta_d \\text{Tr}_h \\text{Tr} \\left( \\prod_{l=1}^L e^{-\\Delta\\tau K_\\uparrow} e^{V_{l\\uparrow}} \\right) \\left( \\prod_{l=1}^L e^{-\\Delta\\tau K_\\downarrow} e^{V_{l\\downarrow}} \\right),\n\\end{equation}\n\nwhere $\\eta_d = C^{NL}$ is a normalization constant. At this point, all operators are quadratic in the fermion operators. For any quadratic operator\n\n\\begin{equation}\n H_l = \\sum_{ij} \\hat{c}_i^\\dagger (H_l)_{ij} \\hat{c}_j\n\\end{equation}\n\nthe trace can be computed via the a determinant:\n\n\\begin{equation}\n \\text{Tr}(e^{-H_1}e^{-H_2} \\dots e^{-H_L}) = \\det(I + e^{-H_L} e^{-H_{L-1}} \\dots e^{-H_1} )\n\\end{equation}\n\nUsing this identity, the trace in the expression of the partition function \\eqref{eq:partitionApproximation} can be turned into a computable form:\n\n\\begin{equation}\n \\label{eq:partitionComputable} \\tag{6}\n \\mathcal{Z}_h = \\eta_d \\text{Tr}_h \\det[M_\\uparrow(h)] \\det[M_\\downarrow(h)],\n\\end{equation}\n\nwhere for $\\sigma = \\pm1$ and $h=(h_1, h_2, \\dots, h_L)$ the matrix\n\n\\begin{equation}\n \\label{eq:mMatrix} \\tag{7}\n M_\\sigma(h) = I + B_{L,\\sigma}(h_L) B_{L-1,\\sigma}(h_{L-1}) \\dots B_{1,\\sigma}(h_1)\n\\end{equation}\n\nconsist of the time step matrices $B_{l,\\sigma}(h_l)$, which are associated with the operators $e^{-\\Delta\\tau K_\\sigma} e^{V_{l\\sigma}}$:\n\n\\begin{equation}\n \\label{eq:bMatrix}\n B_{l,\\sigma}(h_l) = e^{-\\Delta\\tau K_\\sigma} e^{\\sigma \\nu V_l(h_l)}.\n\\end{equation}\n\nWith the approximation \\eqref{eq:partitionComputable} the distribution operator $\\mathcal{P}$, defined in \\eqref{eq:distop}, can be expressed as the computable approximation\n\n\\begin{equation}\n \\label{eq:distopComputable} \\tag{8}\n \\mathcal{P}(h) = \\frac{\\eta_d}{\\mathcal{Z}_h} \\det[M_\\uparrow(h)] \\det[M_\\downarrow(h)].\n\\end{equation}\n\nThe Green's function $G$ associated with the configuration $h$ is defined as the inverse of the matrix $M_\\sigma(h)$:\n\n\\begin{equation}\n G_\\sigma(h) = \\left[M_\\sigma(h)\\right]^{-1}\n\\end{equation}\n\n", "_____no_output_____" ], [ "## 3 Determinant Quantum Monte Carlo algorithm\n\nThe simulation of the Hubbard model is a classical Monte Carlo problem. The Hubbard-Stratanovich variables or configurations $h$ are sampled such that the follow the probability distribution $\\mathcal{P}(h)$. The determinant QMC (DQMC) algorithm can be summarized by the following steps:\n\nFirst, the configuration $h$ is initialized with an array of randomly distributed values of $\\pm 1$. Starting from the time slice $l=1$, a change in the Hubbard-Stratanovich field on the lattice site $i=1$ is proposed:\n\\begin{equation}\n h^\\prime_{1, 1} = -h_{1, 1}.\n\\end{equation}\n\nWith the new configuration $h^\\prime$ the Metropolis ratio\n\\begin{equation}\n d_{1, 1} = \\frac{\\det[M_\\uparrow(h^\\prime)] \\det[M_\\downarrow(h^\\prime)]}{\\det[M_\\uparrow(h)] \\det[M_\\downarrow(h)]},\n\\end{equation}\n\ncan be computed. A random number generator is then used to sample a uniformly distributed random number $r$. If $r < d_{1, 1}$, the proposed update to the configuration is accepted:\n\\begin{equation}\n h = h^\\prime.\n\\end{equation}\n\nAfter all lattice sites $i = 1, \\dots, N$ of the imaginary time slice $l=1$ have been updated, the procedure is continued with the next time slice $l=2$ until all imaginary time slices $l=1, \\dots, L$ are updated. This concludes one \\emph{sweep} through the Hubbard-Stratanovich field. After a few hundred \"warmup-sweeps\" have been performed, measurements can be made after completing an entire set of updates to all space-time points of the system (see section \\ref{sec:measurements}). The measurement results have to be normalized with the number of \"measurement-sweeps\". One iteration (sweep) of the DQMC algorithm can be summarized as\n\n1. Set $l=1$ and $i=1$\n2. Propose new coonfiguration $h^\\prime$ by flipping spin:\n \\begin{equation}\n h^\\prime_{i, l} = -h_{i, l}.\n \\end{equation}\n3. Compute Metropolis ratio:\n \\begin{equation}\n d_{i, l} = \\frac{\\det[M_\\uparrow(h^\\prime)] \\det[M_\\downarrow(h^\\prime)]}{\\det[M_\\uparrow(h)] \\det[M_\\downarrow(h)]},\n \\end{equation}\n where \n \\begin{equation}\n M_\\sigma(h) = I + B_{l-1,\\sigma} \\dots B_{1, \\sigma} B_{L,\\sigma}B_{L-1,\\sigma} \\dots B_{l,\\sigma}.\n \\end{equation}\n \n4. Sample random number $r$\n5. Accept new configuration, if $r < d_{i, l}$:\n \\begin{equation}\n h_{i, l} = \\begin{cases} h^\\prime_{i, l} &\\text{if } r < d_{i, l} \\\\\n h_{i, l} &\\text{else }\n \\end{cases}\n \\end{equation}\n6. Increment site $i = i+1$ if $i < N$\n7. Increment time slice $l = l+1$ if $i=N$\n\n\n### 3.1 Rank-one update scheme\n\n\nThe one-site update of the inner DQMC loop leads to a simple rank-one update of the matrix $M_\\sigma(h)$, which leads to an efficient method of computing the Metropolis ratio $d_{i,l}$ and Green's function $G_\\sigma$.\n\nThe DQMC simulation requires the computation of $NL$ Metropolis ratios \n\n\\begin{equation}\n d = \\frac{\\det[M_\\uparrow(h^\\prime)] \\det[M_\\downarrow(h^\\prime)]}{\\det[M_\\uparrow(h)] \\det[M_\\downarrow(h)]}\n\\end{equation}\n\nfor the configurations $h = (h_1, h_2, \\dots, h_l)$ and $h^\\prime = (h^\\prime_1, h^\\prime_2, \\dots, h^\\prime_L)$ per sweep. The matrix $M_\\sigma(h)$, defined in \\eqref{eq:mMatrix}, is given by\n\n\\begin{equation}\n M_\\sigma = I + B_{L,\\sigma} B_{L-1,\\sigma} \\dots B_{1,\\sigma}.\n\\end{equation}\n\nwith the time step matrices\n\\begin{equation}\n B_{l,\\sigma} = e^{-\\Delta\\tau K_\\sigma} e^{\\sigma \\nu V_l(h_l)}.\n\\end{equation}\n\nThe Green's function of the configuration $h$ is defined as\n\\begin{equation}\n G_\\sigma(h) = M_\\sigma^{-1}.\n\\end{equation}\n\nThe elements of the configuration $h$ and $h^\\prime$ differ only by one element at a specific time slice $l$ and spatial site $i$, which gets inverted by a proposed update:\n\\begin{equation}\n h^\\prime_{i,l} = - h_{i, l}.\n\\end{equation}\n\n\nDuring one sweep of the QMC iteration the inner loops run over the $l=1, \\dots, L$ imaginary time slices and $i=1, \\dots, N$ lattice sites. Starting with the first time slice, $l=1$, the Metropolis ratio $d_{i, 1}$ for each lattice site $i$ is given by\n\\begin{equation}\n d_{i, 1} = d_\\uparrow d_\\downarrow,\n\\end{equation}\nwhere for $\\sigma = \\pm 1$\n\\begin{equation}\n d_\\sigma = 1 + \\alpha_{i,\\sigma} \\left[ 1 - \\boldsymbol{e}_i^T M_\\sigma^{-1}(h) \\boldsymbol{e}_i \\right] = 1 + \\alpha_{i, \\sigma} \\left[ 1 - G_{ii}^\\sigma(h) \\right]\n\\end{equation}\nand\n\\begin{equation}\n \\alpha_{i,\\sigma} = e^{-2\\sigma \\nu h_{i,1}} - 1.\n\\end{equation}\n\nThe Metropolis ration $d_{i, 1}$ can therefore be obtained by computing the inverse of the matrix $M_\\sigma(h)$, which corresponds to the Green's function $G_\\sigma(h)$. If $G_\\sigma(h)$ has already been computed in a previous step, then it is essentially free to compute the Metropolis ratio.\\\\\nIf the proposed update $h^\\prime$ to the configuration is accepted, the Green's function needs to be updated by a rank-1 matrix update:\n\\begin{equation}\n G_\\sigma(h^\\prime) = G_\\sigma(h) - \\frac{\\alpha_{i, \\sigma}}{d_{i, 1}} \\boldsymbol{u}_\\sigma \\boldsymbol{w}_\\sigma^T,\n\\end{equation}\nwhere \n\\begin{equation}\n \\boldsymbol{u}_\\sigma = \\left[I - G_\\sigma(h)\\right] \\boldsymbol{e}_i, \\qquad \\boldsymbol{w}_\\sigma = \\left[G_\\sigma(h)\\right]^T \\boldsymbol{e}_i.\n\\end{equation}\n\nAfter all spatial sites $i=1, \\dots, N$ have been updated, we can move to the next time slice $l=2$. The matrices $M_\\sigma(h)$ and $M_\\sigma(h^\\prime)$ can be written as\n\\begin{equation}\n\\begin{split}\n M_\\sigma(h) &= B_{1, \\sigma}^{-1}(h_1) \\hat{M}_\\sigma(h) B_{1, \\sigma}(h_1)\\\\\n M_\\sigma(h^\\prime) &= B_{1, \\sigma}^{-1}(h_1^\\prime) \\hat{M}_\\sigma(h^\\prime) B_{1, \\sigma}(h_1^\\prime)\n\\end{split}\n\\end{equation}\nwhere \n\\begin{equation}\n\\begin{split}\n \\hat{M}_\\sigma(h) &= I + B_{1, \\sigma}(h_1) B_{L, \\sigma}(h_L) B_{L-1, \\sigma}(h_{L-1}) \\dots B_{2, \\sigma}(h_2)\\\\\n \\hat{M}_\\sigma(h^\\prime) &= I + B_{1, \\sigma}(h_1^\\prime) B_{L, \\sigma}(h_L^\\prime) B_{L-1, \\sigma}(h_{L-1}^\\prime) \\dots B_{2, \\sigma}(h_2^\\prime)\n\\end{split}\n\\end{equation}\n\nare obtained by a cyclic permutation of the time step matrices $B_{l,\\sigma}(h_l)$. The Metropolis ratios $d_{i, 2}$ corresponding to the time slice $l=2$ can therefore be written as\n\\begin{equation}\n d_{i,2} = \\frac{\\det\\big[M_\\uparrow(h^\\prime)\\big] \\det\\big[M_\\downarrow(h^\\prime)\\big]}{\\det\\big[M_\\uparrow(h)\\big] \\det\\big[M_\\downarrow(h)\\big]} \n = \\frac{\\det\\big[\\hat{M}_\\uparrow(h^\\prime)\\big] \\det\\big[\\hat{M}_\\downarrow(h^\\prime)\\big]}{\\det\\big[\\hat{M}_\\uparrow(h)\\big] \\det\\big[\\hat{M}_\\downarrow(h)\\big]}.\n\\end{equation}\n\nThe associated Green's functions are given by \"wrapping\":\n\\begin{equation}\n \\hat{G}_\\sigma(h) = B^{-1}_{1,\\sigma}(h_1) G_\\sigma(h) B_{1,\\sigma}(h_1).\n\\end{equation}\n\nWrapping the Green's function ensures that the configurations $h_2$ and $h_2^\\prime$ associated with the time slice $l=2$ appear at the same location of the matrices $\\hat{M}_\\sigma(h)$ and $\\hat{M}_\\sigma(h^\\prime)$ as the configurations $h_1$ and $h_1^\\prime$ at the time slice $l=1$. Therefore the same formulation can be used for the time slice $l=2$ as for the time slice $l=1$ to compute the Metropolis ratio and update the Green's functions. This procedure can be repeated for all the remaining time slices $l=3, 4, \\dots, L$.", "_____no_output_____" ] ] ]

[ "markdown" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]