DI2 1.0.1

An an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions.

DI2 (Distribution Discretizer) Instructions

Data normalization:

Normalizer.min_max(pandas.Series) - Normalizes data by min_max normalization https://en.wikipedia.org/wiki/Feature_scaling#Rescaling

Normalizer.z_score(pandas.Series) - Normalizes data z-score normalization https://en.wikipedia.org/wiki/Feature_scaling#Standardization

Normalizer.mean(pandas.Series) - Normalizes data by mean normalization https://en.wikipedia.org/wiki/Feature_scaling#Mean_normalization

Returns an array of the data after normalization procedure.

Goodness of fit test

Discretizer.chi_squared_goodness_of_fit(array, string, integer) - Applies Person's chi-squared goodness of fit test https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test

Discretizer.kolmogorov_goodness_of_fit(array, string, boolean) - Applies the Kolmogorov–Smirnov goodness of fit test https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test

The chi_squared_goodness_of_fit(array, string, integer) receives the data (missings must be removed) in form an array, a string with the name of a continuous distribution, from https://docs.scipy.org/doc/scipy/reference/stats.html, and an integer with the number of bins for the test to consider. Returns a list. [0] : chi-squared statistic : float, [1] : data : array.float

The kolmogorov_goodness_of_fit(array, string, boolean) receives the data (missings must be removed) in form an array, a string with the name of a continuous distribution, from https://docs.scipy.org/doc/scipy/reference/stats.html, and a boolean indicating if outliers should be removed (if true the data returned has outliers removed) Returns a list. [0] : Kolmogorov-Smirnov statistic : float, [1] : data : array.float

Discretizer

Discretizer.distribution_discretizer(pandas.Dataframe, integer, optional:string, optional:float, optional:boolean, optional:string, optional:array) - Discretizes data according to the best fitting distribution.

The distribution_discretizer(pandas.Dataframe, integer, string, float, boolean, string, array) receives the data (pandas.Dataframe), an integer representing the number of categories for discretization, an string with the name of the main statistical hypothesis test to apply (options available: "chi2", "ks"), a float between 0 and 0.49 which indicates the width range to consider a value as being a border value, a boolean indicating if outliers should be removed, a string indicating the normalization method to be used (options available: "min_max","mean", z_score), an array of continuous distributions, from https://docs.scipy.org/doc/scipy/reference/stats.html, to be considered by the discretizer.

As an illustrative example we use the dataset available at the UCI machine learning repository https://archive.ics.uci.edu/ml/datasets/Breast+Tissue.

---> DI2 was developed by L. Alexandre ([e-mail]), R.S. Costa ([e-mail]) and R. Henriques <---