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| # -*- coding: utf-8 -*- | |
| """Lab2222.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1OUGOeTdmMbccW_st3Ao8nHDR5wm_VUNg | |
| from google.colab import drive | |
| drive.mount("/content/ML_Course") | |
| cd /content/ML_Course/MyDrive/ML_Course | |
| """ | |
| pip3 install -U scikit-learn scipy matplotlib | |
| import pandas as pd | |
| housing = pd.read_csv("housing.csv") | |
| housing.head(n = 5) | |
| housing.columns | |
| housing.describe() | |
| housing.info() | |
| # Commented out IPython magic to ensure Python compatibility. | |
| # %matplotlib inline | |
| import matplotlib.pyplot as plt | |
| housing.hist(bins=50, figsize=(20,15)) | |
| plt.show() | |
| # to make this notebook's output identical at every run | |
| import numpy as np | |
| np.random.seed(10) | |
| # For illustration only. Sklearn has train_test_split() | |
| def split_train_test(data, test_ratio): | |
| shuffled_indices = np.random.permutation(len(data)) | |
| test_set_size = int(len(data) * test_ratio) | |
| test_indices = shuffled_indices[:test_set_size] | |
| train_indices = shuffled_indices[test_set_size:] | |
| return data.iloc[train_indices], data.iloc[test_indices] | |
| # run the function to get the train & test set | |
| train_set, test_set = split_train_test(housing, 0.2) | |
| train_set.info() | |
| test_set.info() | |
| from sklearn.model_selection import train_test_split | |
| train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10) | |
| train_set.info() | |
| test_set.info() | |
| test_set.to_csv('blind_test.csv', index = False) | |
| train_set.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, | |
| s=train_set["population"]/100, label="population", figsize=(10,7), | |
| c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True, | |
| sharex=False) | |
| plt.legend() | |
| plt.show() | |
| train_set.info() | |
| train_set[train_set.isna().any(axis=1)] | |
| train_set_clean = train_set.dropna(subset=["total_bedrooms"]) | |
| train_set_clean | |
| train_set_clean.info() | |
| train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y | |
| train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set | |
| train_features.info() | |
| train_features.head() | |
| train_features.columns | |
| train_features.info() | |
| train_features.describe() | |
| train_labels | |
| train_features.hist(bins=50, figsize=(12,9)) | |
| train_features.describe() | |
| from sklearn.preprocessing import MinMaxScaler | |
| scaler = MinMaxScaler() ## define the transformer | |
| scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset | |
| print("Min of each column: ",scaler.data_min_) | |
| print("Max of each column: ",scaler.data_max_) | |
| train_features.describe() | |
| train_features_normalized = scaler.transform(train_features) | |
| train_features_normalized | |
| pd.DataFrame(train_features_normalized).hist(bins=50, figsize=(12,9)) | |
| plt.show() | |
| ## 1. split data to get train and test set | |
| from sklearn.model_selection import train_test_split | |
| train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10) | |
| ## 2. clean the missing values | |
| train_set_clean = train_set.dropna(subset=["total_bedrooms"]) | |
| train_set_clean | |
| ## 2. derive training features and training labels | |
| train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y | |
| train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set | |
| ## 4. scale the numeric features in training set | |
| from sklearn.preprocessing import MinMaxScaler | |
| scaler = MinMaxScaler() ## define the transformer | |
| scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset | |
| train_features_normalized = scaler.transform(train_features) | |
| train_features_normalized | |
| from sklearn.linear_model import LinearRegression ## import the LinearRegression Function | |
| lin_reg = LinearRegression() ## Initialize the class | |
| lin_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning | |
| # feed the training data X, and label Y for supervised learning | |
| training_predictions = lin_reg.predict(train_features_normalized) | |
| training_predictions.shape | |
| train_labels | |
| ## plot scatter plot | |
| import matplotlib.pyplot as plt | |
| plt.scatter(training_predictions, train_labels ) | |
| plt.xlabel('training_predictions', fontsize=15,color="red") | |
| plt.ylabel('train_label', fontsize=15,color="green") | |
| plt.title('Scatter plot for training_predictions and train_label', fontsize=15) | |
| plt.xlim(0,np.max(training_predictions)) # remove the predictions that have negative prices | |
| plt.show() | |
| import numpy as np | |
| np.corrcoef(training_predictions, train_labels) | |
| import pandas as pd | |
| prediction_summary = pd.DataFrame({'predicted_label':training_predictions, 'actual_label':train_labels}) | |
| prediction_summary | |
| prediction_summary['error'] = prediction_summary['actual_label'] - prediction_summary['predicted_label'] | |
| prediction_summary | |
| from sklearn.metrics import mean_squared_error | |
| lin_mse = mean_squared_error(train_labels, training_predictions) | |
| lin_rmse = np.sqrt(lin_mse) | |
| lin_rmse | |
| ## Step 1: training the data using decision tree algorithm | |
| from sklearn.tree import DecisionTreeRegressor ## import the DecisionTree Function | |
| tree_reg = DecisionTreeRegressor(random_state=10) ## Initialize the class | |
| tree_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning | |
| ### Step 2: make a prediction using tree model | |
| training_predictions_trees = tree_reg.predict(train_features_normalized) | |
| training_predictions_trees | |
| ## Step 3: visualize the scatter plot between predictions and actual labels | |
| import matplotlib.pyplot as plt | |
| plt.scatter(training_predictions_trees, train_labels ) | |
| plt.xlabel('training_predictions_trees', fontsize=15,color="red") | |
| plt.ylabel('train_label', fontsize=15,color="green") | |
| plt.title('Scatter plot for training_predictions_trees and train_label', fontsize=15) | |
| plt.xlim(0,np.max(training_predictions_trees)) # remove the predictions that have negative prices | |
| plt.show() | |
| from sklearn.metrics import mean_squared_error | |
| tree_mse = mean_squared_error(train_labels, training_predictions_trees) | |
| tree_rmse = np.sqrt(tree_mse) | |
| tree_rmse | |
| ## 1. clean the missing values in test set | |
| test_set_clean = test_set.dropna(subset=["total_bedrooms"]) | |
| test_set_clean | |
| ## 2. derive test features and test labels. In this case, test labels are only used for evaluation | |
| test_labels = test_set_clean["median_house_value"].copy() # get labels for output label Y | |
| test_features = test_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set | |
| ## 4. scale the numeric features in test set. | |
| ## important note: do not apply fit function on the test set, using same scalar from training set | |
| test_features_normalized = scaler.transform(test_features) | |
| test_features_normalized | |
| ### Step 5: make a prediction using tree model | |
| test_predictions_trees = tree_reg.predict(test_features_normalized) | |
| test_predictions_trees | |
| from sklearn.metrics import mean_squared_error | |
| test_tree_mse = mean_squared_error(test_labels, test_predictions_trees) | |
| test_tree_rmse = np.sqrt(test_tree_mse) | |
| test_tree_rmse | |
| # Step 1: install Gradio | |
| #!pip install --quiet gradio | |
| # Step 2: import library | |
| #import gradio as gr | |
| #print(gr.__version__) | |
| # Step 3.1: Define a simple "Hello World" function | |
| # requirement: input is text, output is text | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| # Step 3.2: Define the input component (text style) and output component (text style) to create a simple GUI | |
| import gradio as gr | |
| input_module = gr.inputs.Textbox(label = "Input Text") | |
| output_module = gr.outputs.Textbox(label = "Output Text") | |
| # Step 3.3: Put all three component together into the gradio's interface function | |
| gr.Interface(fn=greet, inputs=input_module, outputs=output_module).launch() | |
| # Step 5.1: Define a simple "image-to-text" function | |
| # requirement: input is text, output is text | |
| def caption(image): | |
| return "Image is processed!!" | |
| # Step 5.2: Define the input component (image style) and output component (text style) to create a simple GUI | |
| import gradio as gr | |
| input_module = gr.inputs.Image(label = "Input Image") | |
| output_module = gr.outputs.Textbox(label = "Output Text") | |
| # Step 5.3: Put all three component together into the gradio's interface function | |
| gr.Interface(fn=caption, inputs=input_module, outputs=output_module).launch() | |
| # Step 6.1: Define different input components | |
| import gradio as gr | |
| # a. define text data type | |
| input_module1 = gr.inputs.Textbox(label = "Input Text") | |
| # b. define image data type | |
| input_module2 = gr.inputs.Image(label = "Input Image") | |
| # c. define Number data type | |
| input_module3 = gr.inputs.Number(label = "Input Number") | |
| # d. define Slider data type | |
| input_module4 = gr.inputs.Slider(1, 100, step=5, label = "Input Slider") | |
| # e. define Checkbox data type | |
| input_module5 = gr.inputs.Checkbox(label = "Does it work?") | |
| # f. define Radio data type | |
| input_module6 = gr.inputs.Radio(choices=["park", "zoo", "road"], label = "Input Radio") | |
| # g. define Dropdown data type | |
| input_module7 = gr.inputs.Dropdown(choices=["park", "zoo", "road"], label = "Input Dropdown") | |
| # Step 6.2: Define different output components | |
| # a. define text data type | |
| output_module1 = gr.outputs.Textbox(label = "Output Text") | |
| # b. define image data type | |
| output_module2 = gr.outputs.Image(label = "Output Image") | |
| # you can define more output components | |
| # Step 6.3: Define a new function that accommodates the input modules. | |
| def multi_inputs(input1, input2, input3, input4, input5, input6, input7 ): | |
| import numpy as np | |
| ## processing inputs | |
| ## return outputs | |
| output1 = "Processing inputs and return outputs" # text output example | |
| output2 = np.random.rand(6,6) # image-like array output example | |
| return output1,output2 | |
| # Step 6.4: Put all three component together into the gradio's interface function | |
| gr.Interface(fn=multi_inputs, | |
| inputs=[input_module1, input_module2, input_module3, | |
| input_module4, input_module5, input_module6, | |
| input_module7], | |
| outputs=[output_module1, output_module2] | |
| ).launch() | |
| # Step 6.1: Define different input components | |
| import gradio as gr | |
| # a. define text data type | |
| input_module1 = gr.inputs.Slider(-124.35,-114.35, step =0.5,label = "Longitude") | |
| # b. define image data type | |
| input_module2 = gr.inputs.Slider(32,41, step =0.5,label = "Latitude") | |
| # c. define Number data type | |
| input_module3 = gr.inputs.Slider(1,52, step = 1,label = "Housing_median_age(Year)") | |
| # d. define Slider data type | |
| input_module4 = gr.inputs.Slider(1, 40000, step=1, label = "Total_rooms") | |
| # e. define Checkbox data type | |
| input_module5 = gr.inputs.Slider(1, 6441,label = "Total_bedrooms") | |
| # f. define Radio data type | |
| input_module6 = gr.inputs.Slider(1,6441,step = 1,label = "Population") | |
| # g. define Dropdown data type | |
| input_module7 = gr.inputs.Slider(1,6081,step = 1,label = "Households") | |
| input_module8 = gr.inputs.Slider(0,15,step = 1,label = "Median_income") | |
| # Step 6.2: Define different output components | |
| # a. define text data type | |
| output_module1 = gr.outputs.Textbox(label = "Predicted Housing Prices") | |
| # b. define image data type | |
| output_module2 = gr.outputs.Image(label = "Output Image") | |
| # you can define more output components | |
| train_set.columns | |
| #save machinel earning model to local drive | |
| import pickle | |
| #save | |
| with open('tree_reg.pkl','wb') as f: | |
| pickle.dump(tree_reg,f) | |
| ls | |
| # Step 6.3: Define a new function that accommodates the input modules. | |
| def machine_learning_model(input1, input2, input3, input4, input5, input6, input7, input8): | |
| print('Start ML process') | |
| import numpy as np | |
| import pandas as pd | |
| print(input1, input2, input3, input4, input5, input6, input7, input8) | |
| #1. process the user submission | |
| new_feature = np.array([[input1, input2, input3, input4, input5, input6, input7, input8]]) | |
| print(new_feature) | |
| test_set = pd.DataFrame(new_feature, columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms', | |
| 'total_bedrooms', 'population', 'households', 'median_income']) | |
| ## 1. clean the missing values in test set | |
| test_set_clean = test_set.dropna(subset=["total_bedrooms"]) | |
| test_set_clean | |
| ## 2. derive test features and test labels. In this case, test labels are only used for evaluation | |
| #test_labels = test_set_clean["median_house_value"].copy() # get labels for output label Y | |
| #test_features = test_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set | |
| test_features_normalized = scaler.transform(test_set_clean) | |
| print("test_features_normalized: ", test_features_normalized) | |
| with open('tree_reg.pkl','rb') as f: | |
| tree_reg = pickle.load(f) | |
| print("Start processing") | |
| output1 = 'This is the output' | |
| output2 = np.random.rand(28,28) | |
| #2. follow the data preprocessing steps as we have done in the test data | |
| #2.2 Check missing values in total_bedrroms | |
| # 2.2 feature normalization | |
| #3. load pre trained machine learning | |
| #4 apply loaded modeld | |
| test_predictions_trees = tree_reg.predict(test_features_normalized) | |
| print("Predicition is :",test_predictions_trees) | |
| import matplotlib.pyplot as plt | |
| train_set.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, | |
| s=train_set["population"]/100, label="population", figsize=(10,7), | |
| c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True, | |
| sharex=False) | |
| plt.legend() | |
| #plt.show() | |
| plt.xlim(-124.35,-114.35) | |
| plt.ylim(32,41) | |
| plt.plot([input1],[input2],marker = "X",markersize = 20, markeredgecolor="yellow", markerfacecolor="black") | |
| plt.savefig('test.png') | |
| #5 send back the prediciton | |
| return test_predictions_trees,'test.png' | |
| gr.Interface(fn=machine_learning_model, | |
| inputs=[input_module1, input_module2, input_module3, | |
| input_module4, input_module5, input_module6, | |
| input_module7, input_module8], | |
| outputs=[output_module1, output_module2] | |
| ).launch(debug = True) | |