python

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score
from boruta import BorutaPy
import lightgbm as lgb
import xgboost as xgb
from sklearn.utils import check_random_state

class BorutaPyForLGB(BorutaPy):
    def __init__(self, estimator, n_estimators=1000, perc=100, alpha=0.05,
                 two_step=True, max_iter=100, random_state=None, verbose=0):
        super().__init__(estimator, n_estimators, perc, alpha,
                         two_step, max_iter, random_state, verbose)
        if random_state is None:
            self.random_state_input = np.random.randint(0, 2**64-1)
        elif isinstance(random_state, int):
            self.random_state_input = random_state
        else:
            raise TypeError('random_state must be int or None')

    def _get_tree_num(self, n_feat):
        depth = self.estimator.get_params()['max_depth']
        if (depth == None) or (depth <= 0):
            depth = 10
        f_repr = 100
        multi = ((n_feat * 2) / (np.sqrt(n_feat * 2) * depth))
        n_estimators = int(multi * f_repr)
        return n_estimators

    def _fit(self, X, y):
        # check input params
        self._check_params(X, y)
        self.random_state = check_random_state(self.random_state)
        # setup variables for Boruta
        n_sample, n_feat = X.shape
        _iter = 1
        # holds the decision about each feature:
        # 0  - default state = tentative in original code
        # 1  - accepted in original code
        # -1 - rejected in original code
        dec_reg = np.zeros(n_feat, dtype=np.int)
        # counts how many times a given feature was more important than
        # the best of the shadow features
        hit_reg = np.zeros(n_feat, dtype=np.int)
        # these record the history of the iterations
        imp_history = np.zeros(n_feat, dtype=np.float)
        sha_max_history = []

        # set n_estimators
        if self.n_estimators != 'auto':
            self.estimator.set_params(n_estimators=self.n_estimators)

        # main feature selection loop
        while np.any(dec_reg == 0) and _iter < self.max_iter:
            # find optimal number of trees and depth
            if self.n_estimators == 'auto':
                # number of features that aren't rejected
                not_rejected = np.where(dec_reg >= 0)[0].shape[0]
                n_tree = self._get_tree_num(not_rejected)
                self.estimator.set_params(n_estimators=n_tree)

            # make sure we start with a new tree in each iteration
            self.estimator.set_params(random_state=self.random_state_input)

            # add shadow attributes, shuffle them and train estimator, get imps
            cur_imp = self._add_shadows_get_imps(X, y, dec_reg)

            # get the threshold of shadow importances we will use for rejection
            imp_sha_max = np.percentile(cur_imp[1], self.perc)

            # record importance history
            sha_max_history.append(imp_sha_max)
            imp_history = np.vstack((imp_history, cur_imp[0]))

            # register which feature is more imp than the max of shadows
            hit_reg = self._assign_hits(hit_reg, cur_imp, imp_sha_max)

            # based on hit_reg we check if a feature is doing better than
            # expected by chance
            dec_reg = self._do_tests(dec_reg, hit_reg, _iter)

            # print out confirmed features
            if self.verbose > 0 and _iter < self.max_iter:
                self._print_results(dec_reg, _iter, 0)
            if _iter < self.max_iter:
                _iter += 1

        # we automatically apply R package's rough fix for tentative ones
        confirmed = np.where(dec_reg == 1)[0]
        tentative = np.where(dec_reg == 0)[0]
        # ignore the first row of zeros
        tentative_median = np.median(imp_history[1:, tentative], axis=0)
        # which tentative to keep
        tentative_confirmed = np.where(tentative_median
                                       > np.median(sha_max_history))[0]
        tentative = tentative[tentative_confirmed]

        # basic result variables
        self.n_features_ = confirmed.shape[0]
        self.support_ = np.zeros(n_feat, dtype=np.bool)
        self.support_[confirmed] = 1
        self.support_weak_ = np.zeros(n_feat, dtype=np.bool)
        self.support_weak_[tentative] = 1

        # ranking, confirmed variables are rank 1
        self.ranking_ = np.ones(n_feat, dtype=np.int)
        # tentative variables are rank 2
        self.ranking_[tentative] = 2
        # selected = confirmed and tentative
        selected = np.hstack((confirmed, tentative))
        # all rejected features are sorted by importance history
        not_selected = np.setdiff1d(np.arange(n_feat), selected)
        # large importance values should rank higher = lower ranks -> *(-1)
        imp_history_rejected = imp_history[1:, not_selected] * -1

        # update rank for not_selected features
        if not_selected.shape[0] > 0:
                # calculate ranks in each iteration, then median of ranks across feats
                iter_ranks = self._nanrankdata(imp_history_rejected, axis=1)
                rank_medians = np.nanmedian(iter_ranks, axis=0)
                ranks = self._nanrankdata(rank_medians, axis=0)

                # set smallest rank to 3 if there are tentative feats
                if tentative.shape[0] > 0:
                    ranks = ranks - np.min(ranks) + 3
                else:
                    # and 2 otherwise
                    ranks = ranks - np.min(ranks) + 2
                self.ranking_[not_selected] = ranks
        else:
            # all are selected, thus we set feature supports to True
            self.support_ = np.ones(n_feat, dtype=np.bool)

        # notify user
        if self.verbose > 0:
            self._print_results(dec_reg, _iter, 1)
        return self

Jetzt, da wir fertig sind, lassen Sie es uns unten laufen. Der Code basiert auf Folgendem. https://github.com/masakiaota/blog/blob/master/boruta/Madalon_Data_Set.ipynb

def main():
    #Lesen Sie die Daten
    data_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_train.data'
    label_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_train.labels'
    X_data = pd.read_csv(data_url, sep=" ", header=None)
    y_data = pd.read_csv(label_url, sep=" ", header=None)
    data = X_data.iloc[:,0:500]
    data['target'] = y_data[0]

    y=data['target']
    X=data.drop(columns='target')
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)


    #Lernen Sie mit ganzen Daten
    model = lgb.LGBMClassifier(objective='binary',
                                num_leaves = 23,
                                learning_rate=0.1,
                                n_estimators=100,)
    model.fit(X_train.values, y_train.values)

    y_test_pred = model.predict(X_test.values)
    print(confusion_matrix(y_test.values, y_test_pred, labels=model.classes_), '\n')
    print('SCORE with ALL Features: %1.2f\n' % accuracy_score(y_test, y_test_pred))


    #Funktionsauswahl mit Boruta(Verwenden Sie das teilweise umgeschriebene Boruta Py)
    model = lgb.LGBMClassifier(objective='binary',
                                num_leaves = 23,
                                learning_rate=0.1,
                                n_estimators=100,)
    feat_selector = BorutaPyForLGB(model, n_estimators='auto', two_step=False,verbose=2, random_state=42)
    feat_selector.fit(X_train.values, y_train.values)
    print(X_train.columns[feat_selector.support_])

    #Extrahieren Sie das ausgewählte Feature
    X_train_selected = X_train.iloc[:,feat_selector.support_]
    X_test_selected = X_test.iloc[:,feat_selector.support_]
    print(X_test_selected.head())


    #Lernen Sie mit der ausgewählten Funktion
    model = lgb.LGBMClassifier(objective='binary',
                                num_leaves = 23,
                                learning_rate=0.1,
                                n_estimators=100,)
    model.fit(X_train_selected.values, y_train.values)

    y_test_pred = model.predict(X_test_selected.values)
    print(confusion_matrix(y_test.values, y_test_pred, labels=model.classes_), '\n')
    print('SCORE with selected Features: %1.2f\n' % accuracy_score(y_test, y_test_pred))


if __name__=='__main__':
    main()

Das Ergebnis der Ausführung ist wie folgt. Es scheint, dass Sie es richtig auswählen können.

Ergebnis

[[192  57]
 [ 49 202]] 
SCORE with ALL Features: 0.79


Index([48, 105, 153, 241, 318, 336, 338, 378, 442, 453, 472, 475], dtype='object')

[[212  37]
 [ 34 217]] 
SCORE with selected Features: 0.86

Verwenden Sie xgboost mit Boruta

Das oben erstellte BorutaPyForLGB () kann auch mit xgboost verwendet werden.

python

def main():
    #Lesen Sie die Daten
    data_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_train.data'
    label_url='https://archive.ics.uci.edu/ml/machine-learning-databases/madelon/MADELON/madelon_train.labels'
    X_data = pd.read_csv(data_url, sep=" ", header=None)
    y_data = pd.read_csv(label_url, sep=" ", header=None)
    data = X_data.iloc[:,0:500]
    data['target'] = y_data[0]

    y=data['target']
    X=data.drop(columns='target')
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)


    #Lernen Sie mit ganzen Daten
    model = lgb.LGBMClassifier(objective='binary',
                                num_leaves = 23,
                                learning_rate=0.1,
                                n_estimators=100,)
    model.fit(X_train.values, y_train.values)

    y_test_pred = model.predict(X_test.values)
    print(confusion_matrix(y_test.values, y_test_pred, labels=model.classes_), '\n')
    print('SCORE with ALL Features: %1.2f\n' % accuracy_score(y_test, y_test_pred))


    #Funktionsauswahl mit Boruta(Verwenden Sie das teilweise umgeschriebene Boruta Py)
    model = lgb.LGBMClassifier(objective='binary',
                                num_leaves = 23,
                                learning_rate=0.1,
                                n_estimators=100,)
    feat_selector = BorutaPyForLGB(model, n_estimators='auto', two_step=False,verbose=2, random_state=42)
    feat_selector.fit(X_train.values, y_train.values)
    print(X_train.columns[feat_selector.support_])

    #Extrahieren Sie das ausgewählte Feature
    X_train_selected = X_train.iloc[:,feat_selector.support_]
    X_test_selected = X_test.iloc[:,feat_selector.support_]
    print(X_test_selected.head())


    #Lernen Sie mit der ausgewählten Funktion
    model = lgb.LGBMClassifier(objective='binary',
                                num_leaves = 23,
                                learning_rate=0.1,
                                n_estimators=100,)
    model.fit(X_train_selected.values, y_train.values)

    y_test_pred = model.predict(X_test_selected.values)
    print(confusion_matrix(y_test.values, y_test_pred, labels=model.classes_), '\n')
    print('SCORE with selected Features: %1.2f\n' % accuracy_score(y_test, y_test_pred))


if __name__=='__main__':
    main()

Es funktioniert, aber es funktioniert nicht gut und die Genauigkeit ist gesunken. Es scheint, dass die Anzahl der Funktionen zu stark reduziert wurde, aber warum?

Ergebnis

[[182  67]
 [ 75 176]] 
SCORE with ALL Features: 0.72


Index([28, 378, 451, 475], dtype='object')

[[148 101]
 [109 142]] 
SCORE with selected Features: 0.58

Es ist wie es ist. Bitte lassen Sie mich wissen, ob es eine Möglichkeit gibt, es kürzer zu schreiben. Lass es uns versuchen!