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    "info": {
        "author": "Daniel Malachov",
        "author_email": "malachovd@seznam.cz",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Environment :: Other Environment",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries :: Application Frameworks",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# predictit\nLibrary/framework for making predictions. Choose best of 20 models (ARIMA, regressions, LSTM...) from libraries like statsmodels, sci-kit, tensorflow and some own models. Library also automaticcaly preprocess data and chose optimal parameters of predictions.\n## Output\nMost common output is plotly interactive graph, deploying to database and list of results.\nPrintscreen of graph\n![Printscreen of output HTML graph](https://raw.githubusercontent.com/Malachov/predictit/master/output_example.png)\n\n## How to use\n```Python \npip install predictit\n```\n\n### Simple example with Pypi\n```Python\nimport predictit\n\npredictions = predictit.main.predict()  # Make prediction on test data\n```\n\n### Example with own data from CSV and config\n```Python\nimport predictit\n\npredictit.config.predicts = 12  # Create 30 predictions\npredictit.config.data_source = 'csv'  # Define that we load data from CSV\npredictit.config.csv_adress = r'E:\\VSCODE\\Diplomka\\test_data\\daily-minimum-temperatures.csv'  # Load CSV file with data\npredictit.config.datalength = 1000  # Consider only last 1000 data points  \npredictit.config.predicted_columns_names = 'Temp'  # Column name that we want to predict\npredictit.config.optimizeit = 0  # Find or not best parameters for models\npredictit.config.compareit = 6  # Visualize 6 best models\npredictit.config.repeatit = 4  # Repeat calculation 4x times on shifted data to reduce chance\npredictit.config.other_columns = 0  # Whether use other columns or not\n\n# Chose models that will be computed\nused_models = {\n            \"AR (Autoregression)\": predictit.models.ar,\n\n            \"ARIMA (Autoregression integrated moving average)\": predictit.models.arima,\n\n            \"Autoregressive Linear neural unit\": predictit.models.autoreg_LNU,\n            \"Conjugate gradient\": predictit.models.cg,\n\n            \"Extreme learning machine\": predictit.models.elm,\n\n            \"Sklearn universal\": predictit.models.sklearn_universal,\n\n            \"Bayes Ridge Regression\": predictit.models.regression_bayes_ridge,\n            \"Hubber regression\": predictit.models.regression_hubber,\n            \"Lasso Regression\": predictit.models.regression_lasso,\n           }\n           \n# Define parameters of models\n\nn_steps_in = 50  # How many lagged values in models\noutput_shape = 'batch'  # Whether batch or one-step models\n\nmodels_parameters = {\n\n\n        \"AR (Autoregression)\": {\"plot\": 0, 'method': 'cmle', 'ic': 'aic', 'trend': 'nc', 'solver': 'lbfgs'},\n        \"ARMA\": {\"plot\": 0, \"p\": 3, \"q\": 0, 'method': 'mle', 'ic': 'aic', 'trend': 'nc', 'solver': 'lbfgs', 'forecast_type': 'in_sample'},\n        \"ARIMA (Autoregression integrated moving average)\": {\"p\": 12, \"d\": 0, \"q\": 1, \"plot\": 0, 'method': 'css', 'ic': 'aic', 'trend': 'nc', 'solver': 'nm', 'forecast_type': 'out_of_sample'},\n\n        \"Autoregressive Linear neural unit\": {\"plot\": 0, \"lags\": n_steps_in, \"mi\": 1, \"minormit\": 0, \"tlumenimi\": 1},\n        \"Conjugate gradient\": {\"n_steps_in\": 30, \"epochs\": 5, \"constant\": 1, \"other_columns_lenght\": None, \"constant\": None},\n\n        \"Extreme learning machine\": {\"n_steps_in\": 20, \"output_shape\": 'one_step', \"other_columns_lenght\": None, \"constant\": None, \"n_hidden\": 20, \"alpha\": 0.3, \"rbf_width\": 0, \"activation_func\": 'selu'},\n\n        \"Sklearn universal\": {\"n_steps_in\": n_steps_in, \"output_shape\": \"one_step\", \"model\": predictit.models.default_regressor, \"constant\": None},\n\n        \"Bayes Ridge Regression\": {\"n_steps_in\": n_steps_in, \"output_shape\": output_shape, \"other_columns_lenght\": None, \"constant\": None, \"alpha_1\": 1.e-6, \"alpha_2\": 1.e-6, \"lambda_1\": 1.e-6, \"lambda_2\": 1.e-6},\n        \"Hubber regression\": {\"n_steps_in\": n_steps_in, \"output_shape\": output_shape, \"other_columns_lenght\": None, \"constant\": None, \"epsilon\": 1.35, \"alpha\": 0.0001},\n        \"Lasso Regression\": {\"n_steps_in\": n_steps_in, \"output_shape\": output_shape, \"other_columns_lenght\": None, \"constant\": None, \"alpha\": 0.6}\n        \n        }\n        \npredictions = predictit.main.predict()\n```",
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