Metadata-Version: 2.1
Name: mydatapreprocessing
Version: 1.1.4
Summary: Library/framework for making predictions.
Home-page: https://github.com/Malachov/mydatapreprocessing
Author: Daniel Malachov
Author-email: malachovd@seznam.cz
License: mit
Description: # mydatapreprocessing
        
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        Load data from web link or local file (json, csv, excel file, parquet, h5...), consolidate it and do preprocessing like resampling, standardization, string embedding, new columns derivation, feature extraction etc. based on configuration.
        
        Library contain 3 modules.
        
        ## Preprocessing
        
        First - `preprocessing` load data, consolidate it and do the preprocessing. It contains functions like load_data, data_consolidation, preprocess_data, preprocess_data_inverse, add_frequency_columns, rolling_windows, add_derived_columns etc.
        
        ### Example
        
        ```python
        ### Preprocessing module
        
        import mydatapreprocessing.preprocessing as mdpp
        
        data = "https://blockchain.info/unconfirmed-transactions?format=json"
        
        # Load data from file or URL
        data_loaded = mdpp.load_data(data, request_datatype_suffix=".json", predicted_table='txs')
        
        
        #Some examples of other inputs to data_load function
        
        # myarray_or_dataframe # Numpy array or Pandas.DataFrame
        # r"/home/user/my.json" # Local file. The same with .parquet, .h5, .json or .xlsx. On windows it's necessary to use raw string - 'r' in front of string because of escape symbols \
        # "https://yoururl/your.csv" # Web url (with suffix). Same with json.
        # "https://blockchain.info/unconfirmed-transactions?format=json" # In this case you have to specify also 'request_datatype_suffix': "json", 'data_orientation': "index", 'predicted_table': 'txs',
        # {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']} # Dict with colums or rows (index) - necessary to setup data_orientation!
        
        
        # You can use more files in list and data will be concatenated. It can be list of paths or list of python objects. Example:
        
        # [{'col_1': 3, 'col_2': 'a'}, {'col_1': 0, 'col_2': 'd'}]  # List of records
        # [np.random.randn(20, 3), np.random.randn(25, 3)]  # Dataframe same way
        # ["https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv", "https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv"]  # List of URLs
        # ["path/to/my1.csv", "path/to/my1.csv"]
        
        
        # Transform various data into defined format - pandas dataframe - convert to numeric if possible, keep
        # only numeric data and resample ifg configured. It return array, dataframe
        data_consolidated = mdpp.data_consolidation(
            data_loaded, predicted_column="weight", data_orientation="index", remove_nans_threshold=0.9, remove_nans_or_replace='interpolate')
        
        # You can add some extra informations to the data that can help (beware it can slow down the machine learning model)
        to_be_extended = np.array([[0, 2] * 64, [0, 0, 0, 5] * 32]).T
        extended = mdpp.add_frequency_columns(to_be_extended, window=8)
        
        
        to_be_extended2 = pd.DataFrame([range(30), range(30, 60)]).T
        extended2 = mdpp.add_derived_columns(to_be_extended2, differences=True, second_differences=True, multiplications=True,
                                            rolling_means=True, rolling_stds=True, mean_distances=True, window=10)
        
        # Feature extraction is under development  :[
        
        # Preprocess data. It return preprocessed data, but also last undifferenced value and scaler for inverse
        # transformation, so unpack it with _
        data_preprocessed, _, _ = mdpp.preprocess_data(data_consolidated, remove_outliers=True, smoothit=False,
                                                      correlation_threshold=False, data_transform=False, standardizeit='standardize')
        ```
        
        ## Inputs
        
        Second module is `inputs`. It take tabular time series data and put it into format (input vector X, output vector y and input for predicted value x_input) that can be inserted into machine learning models for example on sklearn or tensorflow. It contain functions make_sequences, create_inputs and create_tests_outputs
        
        ### Example
        
        ```python
        import mydatapreprocessing.inputs as mdpi
        
        data = np.array([[1, 3, 5, 2, 3, 4, 5, 66, 3]]).T
        seqs, Y, x_input, test_inputs = mdpi.inputs.make_sequences(data, predicts=7, repeatit=3, n_steps_in=6, n_steps_out=1, constant=1)
        
        ```
        
        Third module is `generatedata`. It generate some basic data like sin, ramp random. In the future, it will also import some real datasets for models KPI.
        
Platform: any
Classifier: Programming Language :: Python
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Natural Language :: English
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Description-Content-Type: text/markdown
