mydatapreprocessing 1.1.26

Library/framework for making predictions.

mydatapreprocessing

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.

First - preprocessing load data and preprocess it. It contains functions like load_data, data_consolidation, preprocess_data, preprocess_data_inverse, add_frequency_columns, rolling_windows, add_derived_columns etc.

Examples:

>>> 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')

>>> # 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')

>>> # 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')


>>> # Allowed data formats for load_data are examples

>>> # 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, 'col_2': 'a'}, {'col_1': 0, 'col_2': 'd'}] # List of records
>>> # {'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"]

Second module is inputs. It take tabular time series data (usually processed by module preprocessing) and put it into format 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

Examples:

>>> import mydatapreprocessing as mdp

>>> data = np.array([[1, 2, 3, 4, 5, 6, 7, 8], [9, 10, 11, 12 ,13, 14 ,15, 16], [17 ,18 ,19, 20, 21, 22, 23, 24]]).T
>>> X, y, x_input, _ = mdp.inputs.make_sequences(data, n_steps_in= 2, n_steps_out=3)

>>> # This example create from such a array:

>>> # data = array([[1, 9, 17],
>>> #               [2, 10, 18],
>>> #               [3, 11, 19],
>>> #               [4, 12, 20],
>>> #               [5, 13, 21],
>>> #               [6, 14, 22],
>>> #               [7, 15, 23],
>>> #               [8, 16, 24]])

>>> # Such a results (data are serialized).

>>> # X = array([[1, 2, 3, 9, 10, 11, 17, 18, 19],
>>> #            [2, 3, 4, 10, 11, 12, 18, 19, 20],
>>> #            [3, 4, 5, 11, 12, 13, 19, 20, 21],
>>> #            [4, 5, 6, 12, 13, 14, 20, 21, 22]])

>>> # y = array([[4, 5],
>>> #            [5, 6],
>>> #            [6, 7],
>>> #            [7, 8]]

>>> # x_input = array([[ 6,  7,  8, 14, 15, 16, 22, 23, 24]])

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.

Examples:

>>> import mydatapreprocessing as mdp

>>> data = mdp.generatedata.gen_sin(1000)