Package: predictit
predictitLibrary/framework for making time series predictions. Choose the data, choose the models (ARIMA, regressions, LSTM...) from libraries like statsmodels, scikit-learn, tensorflow. Do the setup (it's not necessary of course, you can use some preset) and predict.Library contain model Hyperparameter optimization as well as option variable optimization. That means, that library can find optimal preprocessing (smoothing, dropping non correlated columns, standardization) and on top of that it can find optimal hyperparameters such as number of neuron layers.OutputMost common output is plotly interactive graph and object with results array, results with history etc...LinksRepo on githubOfficial readthedocs documentationInstallationPython >=3.6 (Python 2 is not supported).Install just withpip install predictitSometimes you can have issues with installing some libraries from requirements (e.g. numpy because not BLAS / LAPACK). There are also two libraries - Tensorflow and pyodbc not in requirements, because not necessary, but troublesome. If library not installed with pip, check which library don't work, install manually with stackoverflow and repeat...There are some libraries that not every user will be using (e.g. Tensorflow or libraries for some data inputs). If you want to be sure to have all libraries, you can downloadrequirements_advanced.txtand then install advanced requirements withpip install -r requirements_advanced.txt.Library was developed during 2020 and structure and even API (configuration) changed a lot. From version 2.0 it's considered to be stable and following semantic versioning.How toSoftware can be used as a python library or with command line arguments or as normal python script. Main function ispredictinmain.pyscript. There is alsopredict_multiple_columnsfunction if you want to predict more at once (columns or time frequencies) and alsocompare_modelsfunction that tell you which models are best. It evaluates error criterion on out of sample test data instead of predict (which use as much data as possible). Some models, for example decision trees just assign input from learning set, so error in predict is 0, in compare_models its accurate. So first is optimal to usecompare_models, find best models and then use it in predict.Try live demo - playground onbinderConfigImport librariesimportpredictitimportnumpyasnpimportpandasaspdfrompredictitimportconfigand typeconfig., then, if not automatically, use ctrl + spacebar to see all subcategories and in subcategories, you can see description in the docstrings for all the configurable values.You can edit config in two waysAs object attributesYou can use subcategories likegeneral,data_input,outputconfig.data_input.data='https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv'You can also use config instance directly for all the attributes and omit the subcategories (though without intellisense help).config.datetime_column='Date'# Will be used for resampling and result plot descriptionconfig.freq="D"# One day - one value resamplingMultiple parameters at once with dictionary and update functionconfig.update({'datalength':300,# Used datalength'predicts':14,# Number of predicted values'default_n_steps_in':12# Value of recursive inputs in model (do not use too high - slower and worse predictions)})# After if you setup prediction as needed, it's simplepredictions=predictit.predict()If you need create more configurations and don't want to override its values, you can create multiple instances, but you need to insert this new config as function parameterother_config=config.copy()# or predictit.configuration.Config()other_config.predicts=30# This will not affect config for other examplespredictions_3=predictit.predict(config=other_config)Simple example of using predictit as a python library and function argumentsAlthough there are many config variables, defaults should be enough.predictions_1=predictit.predict(data=np.random.randn(100,2),predicted_column=1,predicts=3)There are only two positional argumentsdataandpredicted_column(because, there is more than a hundred configurable values). So you can use alsomy_data=pd.DataFrame(np.random.randn(100,2),columns=['a','b'])predictions_1_positional=predictit.predict(my_data,'b')Simple example of usingmain.pyas a scriptOpenconfiguration.py(only script you need to edit (very simple)), do the setup. Mainly used_function and data or data_source and path. Then just runmain.py.Simple example of using command line argumentsRun code below in terminal in predictit repository folder.
Usepython predictit/main.py --helpfor more parameters' info.python predictit/main.py --used_function predict --data 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv' --predicted_column 'Temp'Example of compare_models functionYou can compare it on same data in various parts or on different data (check configuration on how to insert dictionary with data names)my_data_array=np.random.randn(200,2)# Define your data hereconfig.update({'data_all':{'First part':(my_data_array[:100],0),'Second part':(my_data_array[100:],1)},'predicted_column':0})compared_models=predictit.compare_models()Example of predict_multiple functionconfig.data=np.random.randn(120,3)config.predicted_columns=['*']# Define list of columns or '*' for predicting all of the numeric columnsconfig.used_models=['Conjugate gradient','Decision tree regression']# Use just few models to be fastermultiple_columns_prediction=predictit.predict_multiple_columns()Example of config variable optimizationconfig.update({'data':"https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv",'predicted_column':'Temp','datalength':120,'optimization':True,'optimization_variable':'default_n_steps_in','optimization_values':[4,6,8],'plot_all_optimized_models':False,'print_table':'detailed',# Print detailed table'print_result_details':True,'used_models':['AR','Sklearn regression']})predictions_optimized_config=predictit.predict()Hyperparameters tuningTo optimize hyperparameters, just setoptimizeit: 1,and model parameters limits. It is commented in it's docstrings how to use it. It's not grid bruteforce. It is a heuristic method based on halving interval is used, but still it can be time-consuming. It is recommended only to tune parameters worth of it. Or tune it by parts.GUIIt is possible to use basic GUI. But only with CSV data source. Just rungui_start.pyif you have downloaded software or callpredictit.gui_start.run_gui()if you are importing via PyPI. Screenshot of such a GUIBetter GUI with fully customizable settings will be shipped next year, hopefully.Categorical embeddingsIt is also possible to use string values in predictions. You can choose config values 'embedding' 'label' and every unique string will be assigned unique number, 'one-hot' create new column for every unique string (can be time-consuming).Feature engineeringFor feature derivation, you can use difference transformations, first and second order differences, multiplications of columns, rolling mean, rolling standard deviations and also rolling fourier transform.Feature selection is under development right now :[Data preprocessing, plotting and other FunctionsYou can use any library functions separately for your needs of course. mydatapreprocessing, mylogging and mypythontools are my other projects, which are used heavily. Example is hereimportmydatapreprocessingasmdpfrommypythontools.plotsimportplotfrompredictit.analyzeimportanalyze_columndata="https://blockchain.info/unconfirmed-transactions?format=json"# Load data from file or URLdata_loaded=mdp.load_data.load_data(data,request_datatype_suffix=".json",predicted_table='txs',data_orientation="index")# Transform various data into defined format - pandas dataframe - convert to numeric if possible, keep# only numeric data and resample ifg configured.data_consolidated=mdp.preprocessing.data_consolidation(data_loaded,predicted_column="weight",remove_nans_threshold=0.9,remove_nans_or_replace='interpolate')# Predicted column is on index 0 after consolidation)analyze_column(data_consolidated.iloc[:,0])# Preprocess data. It return preprocessed data, but also last undifferenced value and scaler for inverse# transformation, so unpack it with _data_preprocessed,_,_=mdp.preprocessing.preprocess_data(data_consolidated,remove_outliers=True,smoothit=False,correlation_threshold=False,data_transform=False,standardizeit='standardize')# Plot inserted dataplot(data_preprocessed)Using just one model apart main functionMain benefit is performance boost. You can have code under the  much simpler (much less code), but no features from configuration available.importmydatapreprocessingasmdpdata=mdp.generate_data.sin(1000)test=data[-7:]data=data[:-7]data=mdp.preprocessing.data_consolidation(data)# First tuple, because some models use raw data - one argument, e.g. [1, 2, 3...](X,y),x_input,_=mdp.create_model_inputs.create_inputs(data.values,'batch',input_type_params={'n_steps_in':6})trained_model=predictit.models.sklearn_regression.train((X,y),model='BayesianRidge')predictions_one_model=predictit.models.sklearn_regression.predict(x_input,trained_model,predicts=7)predictions_one_model_error=predictit.evaluate_predictions.compare_predicted_to_test(predictions_one_model,test,error_criterion='mape')# , plot=1Example of using library as a pro with deeper editing configconfig.update({"data":r"https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv",# Full CSV path with suffix"predicted_column":"Temp",# Column name that we want to predict"datalength":200,"predicts":7,# Number of predicted values - 7 by default"repeatit":50,# Repeat calculation times on shifted data to evaluate error criterion"other_columns":False,# Whether use other columns or not# Chose models that will be computed - remove if you want to use all the models"used_models":["AR","ARIMA","LNU","Conjugate gradient","Sklearn regression","Bayes ridge regression one column one step","Decision tree regression",],# Define parameters of models"models_parameters":{"AR":{"used_model":"ar","method":"cmle","trend":"nc","solver":"lbfgs",},"ARIMA":{"used_model":"arima","p":6,"d":0,"q":0,},"LNU":{"learning_rate":"infer","epochs":10,"w_predict":0,"normalize_learning_rate":False,},"Conjugate gradient":{"epochs":200},"Bayes ridge regression":{"model":"BayesianRidge","n_iter":300,"alpha_1":1.0e-6,"alpha_2":1.0e-6,"lambda_1":1.0e-6,"lambda_2":1.0e-6,},},})predictions_configured=predictit.predict()Performance - How to scaleTime series prediction is very different from image recognition and more data doesn't necessarily mean better prediction. If you're issuing performance problems, try fast preset (turn off optimizations, make less recurrent values, choose only few models, threshold datalength etc.) you can edit preset if you need. If you still have performance troubles, and you have too much data, use resampling and select only valuable columns - for example correlation_threshold and do not derive extra columns. If you are interested mostly in predictions and not in the plot, turn the plot off.Future workIt's planned to do real GUI and possibility to serve web app as well as desktop. Scalability can be solved two ways. First is incremental learning (not every model supports today). Second is virtualisation (processes running in cluster separately).There is very big todo list on root calledTODO.md.For developersAny help from other developers very appreciated... :D
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