I am doing my master’s thesis and would not mind trying to use the functionality of this package combined with newer scikit-learn capabilities. I have added a proposed solution to the problem, merely replacing load_boston() with load_diabetes(). Hopefully the maintainers will react on the issue. Whether they accept my suggestion or do something else.

Thanks to you, it was solved.

@IanWord, you mentioned above that the issue is solved, but when I install the version that is currently in PyPI, the bug appears. Could you update the version in PyPI, please? Thank you!

I am doing my master’s thesis and would not mind trying to use the functionality of this package combined with newer scikit-learn capabilities. I have added a proposed solution to the problem, merely replacing load_boston() with load_diabetes(). Hopefully the maintainers will react on the issue. Whether they accept my suggestion or do something else.

new version 1.0.17 uploaded to PyPi with the changes discussed here.

The solution is to install the tool after cloning the repo locally.

First, git clone the repo. Then, access the main folder BorutaShap and install using pip install -e .

At that point the package works fine. I’m on an M1 mac btw and no problems so far.

이 문제는 여전히 존재합니다. 실제로 데이터 세트를 대체하는 방법은 무엇입니까?

Landword, this code is from github. During the class definition process, the Boston data part was changed to the diabets data.

!pip install shap from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, IsolationForest from sklearn.datasets import load_breast_cancer, load_diabetes from statsmodels.stats.multitest import multipletests from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.cluster import KMeans from scipy.sparse import issparse from scipy.stats import binom_test, ks_2samp import matplotlib.pyplot as plt from tqdm.auto import tqdm import random import pandas as pd import numpy as np from numpy.random import choice import seaborn as sns import shap import os import re

import warnings warnings.filterwarnings(“ignore”)

!pip install shap from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, IsolationForest from sklearn.datasets import load_breast_cancer, load_diabetes from statsmodels.stats.multitest import multipletests from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.cluster import KMeans from scipy.sparse import issparse from scipy.stats import binom_test, ks_2samp import matplotlib.pyplot as plt from tqdm.auto import tqdm import random import pandas as pd import numpy as np from numpy.random import choice import seaborn as sns import shap import os import re

import warnings warnings.filterwarnings(“ignore”)

class BorutaShap:

"""
BorutaShap is a wrapper feature selection method built on the foundations of both the SHAP and Boruta algorithms.
"""

def __init__(self, model=None, importance_measure='Shap',
            classification=True, percentile=100, pvalue=0.05):

    """
    Parameters
    ----------
    model: Model Object
        If no model specified then a base Random Forest will be returned otherwise the specifed model will
        be returned.
    importance_measure: String
        Which importance measure too use either Shap or Gini/Gain
    classification: Boolean
        if true then the problem is either a binary or multiclass problem otherwise if false then it is regression
    percentile: Int
        An integer ranging from 0-100 it changes the value of the max shadow importance values. Thus, lowering its value
        would make the algorithm more lenient.
    p_value: float
        A float used as a significance level again if the p-value is increased the algorithm will be more lenient making it smaller
        would make it more strict also by making the model more strict could impact runtime making it slower. As it will be less likley
        to reject and accept features.
    """

    self.importance_measure = importance_measure.lower()
    self.percentile = percentile
    self.pvalue = pvalue
    self.classification = classification
    self.model = model
    self.check_model()


def check_model(self):

    """
    Checks that a model object has been passed as a parameter when intiializing the BorutaShap class.
    Returns
    -------
    Model Object
        If no model specified then a base Random Forest will be returned otherwise the specifed model will
        be returned.
    Raises
    ------
    AttirbuteError
         If the model object does not have the required attributes.
    """

    check_fit = hasattr(self.model, 'fit')
    check_predict_proba = hasattr(self.model, 'predict')

    try:
        check_feature_importance = hasattr(self.model, 'feature_importances_')

    except:
        check_feature_importance = True


    if self.model is None:

        if self.classification:
            self.model = RandomForestClassifier()
        else:
            self.model = RandomForestRegressor()

    elif check_fit is False and check_predict_proba is False:
        raise AttributeError('Model must contain both the fit() and predict() methods')

    elif check_feature_importance is False and self.importance_measure == 'gini':
        raise AttributeError('Model must contain the feature_importances_ method to use Gini try Shap instead')

    else:
        pass


def check_X(self):

    """
    Checks that the data passed to the BorutaShap instance is a pandas Dataframe
    Returns
    -------
    Datframe
    Raises
    ------
    AttirbuteError
         If the data is not of the expected type.
    """

    if isinstance(self.X, pd.DataFrame) is False:
        raise AttributeError('X must be a pandas Dataframe')

    else:
        pass


def missing_values_y(self):

    """
    Checks for missing values in target variable.
    Returns
    -------
    Boolean
    Raises
    ------
    AttirbuteError
         If data is not in the expected format.
    """

    if isinstance(self.y, pd.Series):
        return self.y.isnull().any().any()

    elif isinstance(self.y, np.ndarray):
        return np.isnan(self.y).any()

    else:
        raise AttributeError('Y must be a pandas Dataframe or a numpy array')


def check_missing_values(self):

    """
    Checks for missing values in the data.
    Returns
    -------
    Boolean
    Raises
    ------
    AttirbuteError
         If there are missing values present.
    """

    X_missing = self.X.isnull().any().any()
    Y_missing = self.missing_values_y()

    models_to_check = ('xgb', 'catboost', 'lgbm', 'lightgbm')

    model_name = str(type(self.model)).lower()
    if X_missing or Y_missing:

        if any([x in model_name for x in models_to_check]):
            print('Warning there are missing values in your data !')

        else:
            raise ValueError('There are missing values in your Data')

    else:
        pass


def Check_if_chose_train_or_test_and_train_model(self):

    """
    Decides to fit the model to either the training data or the test/unseen data a great discussion on the
    differences can be found here.
    https://compstat-lmu.github.io/iml_methods_limitations/pfi-data.html#introduction-to-test-vs.training-data
    """
    if self.stratify is not None and not self.classification:
        raise ValueError('Cannot take a strtified sample from continuous variable please bucket the variable and try again !')


    if self.train_or_test.lower() == 'test':
        # keeping the same naming convenetion as to not add complexit later on
        self.X_boruta_train, self.X_boruta_test, self.y_train, self.y_test, self.w_train, self.w_test = train_test_split(self.X_boruta,
                                                                                                                            self.y,
                                                                                                                            self.sample_weight,
                                                                                                                            test_size=0.3,
                                                                                                                            random_state=self.random_state,
                                                                                                                            stratify=self.stratify)
        self.Train_model(self.X_boruta_train, self.y_train, sample_weight = self.w_train)

    elif self.train_or_test.lower() == 'train':
        # model will be trained and evaluated on the same data
        self.Train_model(self.X_boruta, self.y, sample_weight = self.sample_weight)

    else:
        raise ValueError('The train_or_test parameter can only be "train" or "test"')



def Train_model(self, X, y, sample_weight = None):

    """
    Trains Model also checks to see if the model is an instance of catboost as it needs extra parameters
    also the try except is for models with a verbose statement
    Parameters
    ----------
    X: Dataframe
        A pandas dataframe of the features.
    y: Series/ndarray
        A pandas series or numpy ndarray of the target
    sample_weight: Series/ndarray
        A pandas series or numpy ndarray of the sample weights
    Returns
    ----------
    fitted model object
    """

    if 'catboost' in str(type(self.model)).lower():
        self.model.fit(X, y, sample_weight = sample_weight, cat_features = self.X_categorical,  verbose=False)

    else:

        try:
            self.model.fit(X, y, sample_weight = sample_weight, verbose=False)

        except:
            self.model.fit(X, y, sample_weight = sample_weight)




def fit(self, X, y, sample_weight = None, n_trials = 20, random_state=0, sample=False,
        train_or_test = 'test', normalize=True, verbose=True, stratify=None):

    """
    The main body of the program this method it computes the following
    1. Extend the information system by adding copies of all variables (the information system
    is always extended by at least 5 shadow attributes, even if the number of attributes in
    the original set is lower than 5).
    2. Shuffle the added attributes to remove their correlations with the response.
    3. Run a random forest classifier on the extended information system and gather the
    Z scores computed.
    4. Find the maximum Z score among shadow attributes (MZSA), and then assign a hit to
    every attribute that scored better than MZSA.
    5. For each attribute with undetermined importance perform a two-sided test of equality
    with the MZSA.
    6. Deem the attributes which have importance significantly lower than MZSA as ‘unimportant’
    and permanently remove them from the information system.
    7. Deem the attributes which have importance significantly higher than MZSA as ‘important’.
    8. Remove all shadow attributes.
    9. Repeat the procedure until the importance is assigned for all the attributes, or the
    algorithm has reached the previously set limit of the random forest runs.
    10. Stores results.
    Parameters
    ----------
    X: Dataframe
        A pandas dataframe of the features.
    y: Series/ndarray
        A pandas series or numpy ndarray of the target
    sample_weight: Series/ndarray
        A pandas series or numpy ndarray of the sample weight of the observations (optional)
    random_state: int
        A random state for reproducibility of results
    Sample: Boolean
        if true then a rowise sample of the data will be used to calculate the feature importance values
    sample_fraction: float
        The sample fraction of the original data used in calculating the feature importance values only
        used if Sample==True.
    train_or_test: string
        Decides whether the feature importance should be calculated on out of sample data see the dicussion here.
        https://compstat-lmu.github.io/iml_methods_limitations/pfi-data.html#introduction-to-test-vs.training-data
    normalize: boolean
        if true the importance values will be normalized using the z-score formula
    verbose: Boolean
        a flag indicator to print out all the rejected or accepted features.
    stratify: array
        allows the train test splits to be stratified based on given values.
    """

    if sample_weight is None:
        sample_weight = np.ones(len(X))
    np.random.seed(random_state)
    self.starting_X = X.copy()
    self.X = X.copy()
    self.y = y.copy()
    self.sample_weight = sample_weight.copy()
    self.n_trials = n_trials
    self.random_state = random_state
    self.ncols = self.X.shape[1]
    self.all_columns = self.X.columns.to_numpy()
    self.rejected_columns = []
    self.accepted_columns = []

    self.check_X()
    self.check_missing_values()
    self.sample = sample
    self.train_or_test = train_or_test
    self.stratify = stratify

    self.features_to_remove = []
    self.hits  = np.zeros(self.ncols)
    self.order = self.create_mapping_between_cols_and_indices()
    self.create_importance_history()

    if self.sample: self.preds = self.isolation_forest(self.X, self.sample_weight)

    for trial in tqdm(range(self.n_trials)):

        self.remove_features_if_rejected()
        self.columns = self.X.columns.to_numpy()
        self.create_shadow_features()

        # early stopping
        if self.X.shape[1] == 0:
            break

        else:

            self.Check_if_chose_train_or_test_and_train_model()

            self.X_feature_import, self.Shadow_feature_import = self.feature_importance(normalize=normalize)
            self.update_importance_history()
            hits = self.calculate_hits()
            self.hits += hits
            self.history_hits = np.vstack((self.history_hits, self.hits))
            self.test_features(iteration=trial+1)

    self.store_feature_importance()
    self.calculate_rejected_accepted_tentative(verbose=verbose)


def calculate_rejected_accepted_tentative(self, verbose):

    """
    Figures out which features have been either accepted rejeected or tentative
    Returns
    -------
    3 lists
    """

    self.rejected  = list(set(self.flatten_list(self.rejected_columns))-set(self.flatten_list(self.accepted_columns)))
    self.accepted  = list(set(self.flatten_list(self.accepted_columns)))
    self.tentative = list(set(self.all_columns) - set(self.rejected + self.accepted))

    if verbose:
        print(str(len(self.accepted))  + ' attributes confirmed important: ' + str(self.accepted))
        print(str(len(self.rejected))  + ' attributes confirmed unimportant: ' + str(self.rejected))
        print(str(len(self.tentative)) + ' tentative attributes remains: ' + str(self.tentative))



def create_importance_history(self):

    """
    Creates a dataframe object to store historical feature importance scores.
    Returns
    -------
    Datframe
    """

    self.history_shadow = np.zeros(self.ncols)
    self.history_x = np.zeros(self.ncols)
    self.history_hits = np.zeros(self.ncols)


def update_importance_history(self):

    """
    At each iteration update the datframe object that stores the historical feature importance scores.
    Returns
    -------
    Datframe
    """

    padded_history_shadow  = np.full((self.ncols), np.NaN)
    padded_history_x = np.full((self.ncols), np.NaN)

    for (index, col) in enumerate(self.columns):
        map_index = self.order[col]
        padded_history_shadow[map_index] = self.Shadow_feature_import[index]
        padded_history_x[map_index] = self.X_feature_import[index]

    self.history_shadow = np.vstack((self.history_shadow, padded_history_shadow))
    self.history_x = np.vstack((self.history_x, padded_history_x))



def store_feature_importance(self):

    """
    Reshapes the columns in the historical feature importance scores object also adds the mean, median, max, min
    shadow feature scores.
    Returns
    -------
    Datframe
    """

    self.history_x = pd.DataFrame(data=self.history_x,
                             columns=self.all_columns)


    self.history_x['Max_Shadow']    =  [max(i) for i in self.history_shadow]
    self.history_x['Min_Shadow']    =  [min(i) for i in self.history_shadow]
    self.history_x['Mean_Shadow']   =  [np.nanmean(i) for i in self.history_shadow]
    self.history_x['Median_Shadow'] =  [np.nanmedian(i) for i in self.history_shadow]


def results_to_csv(self, filename='feature_importance'):

    """
    Saves the historical feature importance scores to csv.
    Parameters
    ----------
    filname : string
        used as the name for the outputed file.
    Returns
    -------
    comma delimnated file
    """

    features = pd.DataFrame(data={'Features':self.history_x.iloc[1:].columns.values,
    'Average Feature Importance':self.history_x.iloc[1:].mean(axis=0).values,
    'Standard Deviation Importance':self.history_x.iloc[1:].std(axis=0).values})

    decision_mapper = self.create_mapping_of_features_to_attribute(maps=['Tentative','Rejected','Accepted', 'Shadow'])
    features['Decision'] = features['Features'].map(decision_mapper)
    features = features.sort_values(by='Average Feature Importance',ascending=False)

    features.to_csv(filename + '.csv', index=False)


def remove_features_if_rejected(self):

    """
    At each iteration if a feature has been rejected by the algorithm remove it from the process
    """

    if len(self.features_to_remove) != 0:
        for feature in self.features_to_remove:
            try:
                self.X.drop(feature, axis = 1, inplace=True)
            except:
                pass

    else:
        pass


@staticmethod
def average_of_list(lst):
    return sum(lst) / len(lst)

@staticmethod
def flatten_list(array):
    return [item for sublist in array for item in sublist]


def create_mapping_between_cols_and_indices(self):
    return dict(zip(self.X.columns.to_list(), np.arange(self.X.shape[1])))


def calculate_hits(self):

    """
    If a features importance is greater than the maximum importance value of all the random shadow
    features then we assign it a hit.
    Parameters
    ----------
    Percentile : value ranging from 0-1
        can be used to reduce value of the maximum value of the shadow features making the algorithm
        more lenient.
    """

    shadow_threshold = np.percentile(self.Shadow_feature_import,
                                    self.percentile)

    padded_hits = np.zeros(self.ncols)
    hits = self.X_feature_import > shadow_threshold

    for (index, col) in enumerate(self.columns):
        map_index = self.order[col]
        padded_hits[map_index] += hits[index]

    return padded_hits


def create_shadow_features(self):
    """
    Creates the random shadow features by shuffling the existing columns.
    Returns:
        Datframe with random permutations of the original columns.
    """
    self.X_shadow = self.X.apply(np.random.permutation)
    
    if isinstance(self.X_shadow, pd.DataFrame):
        # append
        obj_col = self.X_shadow.select_dtypes("object").columns.tolist()
        if obj_col ==[] :
             pass
        else :
             self.X_shadow[obj_col] =self.X_shadow[obj_col].astype("category")

    self.X_shadow.columns = ['shadow_' + feature for feature in self.X.columns]
    self.X_boruta = pd.concat([self.X, self.X_shadow], axis = 1)

    col_types = self.X_boruta.dtypes
    self.X_categorical = list(col_types[(col_types=='category' ) | (col_types=='object')].index)


@staticmethod
def calculate_Zscore(array):
    """
    Calculates the Z-score of an array
    Parameters
     ----------
    array: array_like
    Returns:
        normalised array
    """
    mean_value = np.mean(array)
    std_value  = np.std(array)
    return [(element-mean_value)/std_value for element in array]


def feature_importance(self, normalize):

    """
    Caculates the feature importances scores of the model
    Parameters
    ----------
    importance_measure: string
        allows the user to choose either the Shap or Gini importance metrics
    normalize: boolean
        if true the importance values will be normalized using the z-score formula
    Returns:
        array of normalized feature importance scores for both the shadow and original features.
    Raise
    ----------
        ValueError:
            If no Importance measure was specified
    """

    if self.importance_measure == 'shap':

        self.explain()
        vals = self.shap_values

        if normalize:
            vals = self.calculate_Zscore(vals)

        X_feature_import = vals[:len(self.X.columns)]
        Shadow_feature_import = vals[len(self.X_shadow.columns):]


    elif self.importance_measure == 'gini':

            feature_importances_ =  np.abs(self.model.feature_importances_)

            if normalize:
                feature_importances_ = self.calculate_Zscore(feature_importances_)

            X_feature_import = feature_importances_[:len(self.X.columns)]
            Shadow_feature_import = feature_importances_[len(self.X.columns):]

    else:

        raise ValueError('No Importance_measure was specified select one of (shap, gini)')


    return X_feature_import, Shadow_feature_import


@staticmethod
def isolation_forest(X, sample_weight):
    '''
    fits isloation forest to the dataset and gives an anomally score to every sample
    '''
    clf = IsolationForest().fit(X, sample_weight = sample_weight)
    preds = clf.score_samples(X)
    return preds


@staticmethod
def get_5_percent(num):
    return round(5  / 100 * num)


def get_5_percent_splits(self, length):
    '''
    splits dataframe into 5% intervals
    '''
    five_percent = self.get_5_percent(length)
    return np.arange(five_percent,length,five_percent)



def find_sample(self):
    '''
    Finds a sample by comparing the distributions of the anomally scores between the sample and the original
    distribution using the KS-test. Starts of a 5% howver will increase to 10% and then 15% etc. if a significant sample can not be found
    '''
    loop = True
    iteration = 0
    size = self.get_5_percent_splits(self.X.shape[0])
    element = 1
    while loop:

        sample_indices = choice(np.arange(self.preds.size),  size=size[element], replace=False)
        sample = np.take(self.preds, sample_indices)
        if ks_2samp(self.preds, sample).pvalue > 0.95:
            break

        if iteration == 20:
            element  += 1
            iteration = 0


    return self.X_boruta.iloc[sample_indices]



def explain(self):

    """
    The shap package has numerous variants of explainers which use different assumptions depending on the model
    type this function allows the user to choose explainer
    Returns:
        shap values
    Raise
    ----------
        ValueError:
            if no model type has been specified tree as default
    """


    explainer = shap.TreeExplainer(self.model, 
                                   feature_perturbation = "tree_path_dependent",
                                   approximate = True)


    if self.sample:


        if self.classification:
            # for some reason shap returns values wraped in a list of length 1

            self.shap_values = np.array(explainer.shap_values(self.find_sample()))
            if isinstance(self.shap_values, list):

                class_inds = range(len(self.shap_values))
                shap_imp = np.zeros(self.shap_values[0].shape[1])
                for i, ind in enumerate(class_inds):
                    shap_imp += np.abs(self.shap_values[ind]).mean(0)
                self.shap_values /= len(self.shap_values)

            elif len(self.shap_values.shape) == 3:
                self.shap_values = np.abs(self.shap_values).sum(axis=0)
                self.shap_values = self.shap_values.mean(0)

            else:
                self.shap_values = np.abs(self.shap_values).mean(0)

        else:
            self.shap_values = explainer.shap_values(self.find_sample())
            self.shap_values = np.abs(self.shap_values).mean(0)

    else:

        if self.classification:
            # for some reason shap returns values wraped in a list of length 1
            self.shap_values = np.array(explainer.shap_values(self.X_boruta))
            if isinstance(self.shap_values, list):

                class_inds = range(len(self.shap_values))
                shap_imp = np.zeros(self.shap_values[0].shape[1])
                for i, ind in enumerate(class_inds):
                    shap_imp += np.abs(self.shap_values[ind]).mean(0)
                self.shap_values /= len(self.shap_values)

            elif len(self.shap_values.shape) == 3:
                self.shap_values = np.abs(self.shap_values).sum(axis=0)
                self.shap_values = self.shap_values.mean(0)

            else:
                self.shap_values = np.abs(self.shap_values).mean(0)

        else:
            self.shap_values = explainer.shap_values(self.X_boruta)
            self.shap_values = np.abs(self.shap_values).mean(0)



@staticmethod
def binomial_H0_test(array, n, p, alternative):
    """
    Perform a test that the probability of success is p.
    This is an exact, two-sided test of the null hypothesis
    that the probability of success in a Bernoulli experiment is p
    """
    return [binom_test(x, n=n, p=p, alternative=alternative) for x in array]


@staticmethod
def symetric_difference_between_two_arrays(array_one, array_two):
    set_one = set(array_one)
    set_two = set(array_two)
    return np.array(list(set_one.symmetric_difference(set_two)))


@staticmethod
def find_index_of_true_in_array(array):
    length = len(array)
    return list(filter(lambda x: array[x], range(length)))


@staticmethod
def bonferoni_corrections(pvals, alpha=0.05, n_tests=None):
    """
    used to counteract the problem of multiple comparisons.
    """
    pvals = np.array(pvals)

    if n_tests is None:
        n_tests = len(pvals)
    else:
        pass

    alphacBon = alpha / float(n_tests)
    reject = pvals <= alphacBon
    pvals_corrected = pvals * float(n_tests)
    return reject, pvals_corrected


def test_features(self, iteration):

    """
    For each feature with an undetermined importance perform a two-sided test of equality
    with the maximum shadow value to determine if it is statistcally better
    Parameters
    ----------
    hits: an array which holds the history of the number times
          this feature was better than the maximum shadow
    Returns:
        Two arrays of the names of the accepted and rejected columns at that instance
    """

    acceptance_p_values = self.binomial_H0_test(self.hits,
                                                n=iteration,
                                                p=0.5,
                                                alternative='greater')

    regect_p_values = self.binomial_H0_test(self.hits,
                                            n=iteration,
                                            p=0.5,
                                            alternative='less')

    # [1] as function returns a tuple
    modified_acceptance_p_values = self.bonferoni_corrections(acceptance_p_values,
                                                              alpha=0.05,
                                                              n_tests=len(self.columns))[1]

    modified_regect_p_values = self.bonferoni_corrections(regect_p_values,
                                                          alpha=0.05,
                                                          n_tests=len(self.columns))[1]

    # Take the inverse as we want true to keep featrues
    rejected_columns = np.array(modified_regect_p_values) < self.pvalue
    accepted_columns = np.array(modified_acceptance_p_values) < self.pvalue

    rejected_indices = self.find_index_of_true_in_array(rejected_columns)
    accepted_indices = self.find_index_of_true_in_array(accepted_columns)

    rejected_features = self.all_columns[rejected_indices]
    accepted_features = self.all_columns[accepted_indices]


    self.features_to_remove = rejected_features


    self.rejected_columns.append(rejected_features)
    self.accepted_columns.append(accepted_features)


def TentativeRoughFix(self):

    """
    Sometimes no matter how many iterations are run a feature may neither be rejected or
    accepted. This method is used in this case to make a decision on a tentative feature
    by comparing its median importance value with the median max shadow value.
    Parameters
    ----------
    tentative: an array which holds the names of the tentative attiributes.
    Returns:
        Two arrays of the names of the final decision of the accepted and rejected columns.
    """

    median_tentaive_values = self.history_x[self.tentative].median(axis=0).values
    median_max_shadow = self.history_x['Max_Shadow'].median(axis=0)


    filtered = median_tentaive_values > median_max_shadow

    self.tentative = np.array(self.tentative)
    newly_accepted = self.tentative[filtered]

    if len(newly_accepted) < 1:
        newly_rejected = self.tentative

    else:
        newly_rejected = self.symetric_difference_between_two_arrays(newly_accepted, self.tentative)

    print(str(len(newly_accepted)) + ' tentative features are now accepted: ' + str(newly_accepted))
    print(str(len(newly_rejected)) + ' tentative features are now rejected: ' + str(newly_rejected))

    self.rejected = self.rejected + newly_rejected.tolist()
    self.accepted = self.accepted + newly_accepted.tolist()



def Subset(self, tentative=False):
    """
    Returns the subset of desired features
    """
    if tentative:
        return self.starting_X[self.accepted + self.tentative.tolist()]
    else:
        return self.starting_X[self.accepted]


@staticmethod
def create_list(array, color):
    colors = [color for x in range(len(array))]
    return colors

@staticmethod
def filter_data(data, column, value):
    data = data.copy()
    return data.loc[(data[column] == value) | (data[column] == 'Shadow')]


@staticmethod
def hasNumbers(inputString):
    return any(char.isdigit() for char in inputString)


@staticmethod
def check_if_which_features_is_correct(my_string):

    my_string = str(my_string).lower()
    if my_string in ['tentative','rejected','accepted','all']:
        pass

    else:
        raise ValueError(my_string + " is not a valid value did you mean to type 'all', 'tentative', 'accepted' or 'rejected' ?")



def plot(self, X_rotation=90, X_size=8, figsize=(12,8),
        y_scale='log', which_features='all', display=True):

    """
    creates a boxplot of the feature importances
    Parameters
    ----------
    X_rotation: int
        Controls the orientation angle of the tick labels on the X-axis
    X_size: int
        Controls the font size of the tick labels
    y_scale: string
        Log transform of the y axis scale as hard to see the plot as it is normally dominated by two or three
        features.
    which_features: string
        Despite efforts if the number of columns is large the plot becomes cluttered so this parameter allows you to
        select subsets of the features like the accepted, rejected or tentative features default is all.
    Display: Boolean
    controls if the output is displayed or not, set to false when running test scripts
    """
    # data from wide to long
    data = self.history_x.iloc[1:]
    data['index'] = data.index
    data = pd.melt(data, id_vars='index', var_name='Methods')

    decision_mapper = self.create_mapping_of_features_to_attribute(maps=['Tentative','Rejected','Accepted', 'Shadow'])
    data['Decision'] = data['Methods'].map(decision_mapper)
    data.drop(['index'], axis=1, inplace=True)


    options = { 'accepted' : self.filter_data(data,'Decision', 'Accepted'),
                'tentative': self.filter_data(data,'Decision', 'Tentative'),
                'rejected' : self.filter_data(data,'Decision', 'Rejected'),
                'all' : data
                }

    self.check_if_which_features_is_correct(which_features)
    data = options[which_features.lower()]

    self.box_plot(data=data,
                  X_rotation=X_rotation,
                  X_size=X_size,
                  y_scale=y_scale,
                  figsize=figsize)
    if display:
        plt.show()
    else:
        plt.close()


def box_plot(self, data, X_rotation, X_size, y_scale, figsize):

    if y_scale=='log':
        minimum = data['value'].min()
        if minimum <= 0:
            data['value'] += abs(minimum) + 0.01

    order = data.groupby(by=["Methods"])["value"].mean().sort_values(ascending=False).index
    my_palette = self.create_mapping_of_features_to_attribute(maps= ['yellow','red','green','blue'])

    # Use a color palette
    plt.figure(figsize=figsize)
    ax = sns.boxplot(x=data["Methods"], y=data["value"],
                    order=order, palette=my_palette)

    if y_scale == 'log':ax.set(yscale="log")
    ax.set_xticklabels(ax.get_xticklabels(), rotation=X_rotation, size=X_size)
    ax.set_title('Feature Importance')
    ax.set_ylabel('Z-Score')
    ax.set_xlabel('Features')


def create_mapping_of_features_to_attribute(self, maps = []):

    rejected = list(self.rejected)
    tentative = list(self.tentative)
    accepted = list(self.accepted)
    shadow = ['Max_Shadow','Median_Shadow','Min_Shadow','Mean_Shadow']

    tentative_map = self.create_list(tentative, maps[0])
    rejected_map  = self.create_list(rejected, maps[1])
    accepted_map  = self.create_list(accepted, maps[2])
    shadow_map = self.create_list(shadow, maps[3])

    values = tentative_map + rejected_map + accepted_map + shadow_map
    keys = tentative + rejected + accepted + shadow

    return self.to_dictionary(keys, values)


@staticmethod
def to_dictionary(list_one, list_two):
    return dict(zip(list_one, list_two))

def load_data(data_type=‘classification’):

"""
Load Example datasets for the user to try out the package
"""

data_type = data_type.lower()

if data_type == 'classification':
    cancer = load_breast_cancer()
    X = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))
    y = X.pop('target')

elif data_type == 'regression':
    diabetes = load_diabetes()
    X = pd.DataFrame(np.c_[diabetes['data'], diabetes['target']], columns = np.append(diabetes['feature_names'], ['target']))
    y = X.pop('target')

else:
    raise ValueError("No data_type was specified, use either 'classification' or 'regression'")


return X, y