GBDT-LogisticRegression

GBDT_LR.py

"""
GBDT + LogisticRegression
"""
import numpy as np

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns

from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.pipeline import Pipeline


class GBDTTransformer(TransformerMixin):

    def __init__(self, n_estimators=100):

        self.n_estimators = n_estimators
        self.gbdt = GradientBoostingClassifier(n_estimators=n_estimators)
        self.gbdt_ohe = OneHotEncoder()

    def fit(self, X, y):

        self.gbdt.fit(X, y)
        self.gbdt_ohe.fit(self.gbdt.apply(X)[:, :, 0])

        return self

    def transform(self, X):

        return self.gbdt_ohe.transform(self.gbdt.apply(X)[:, :, 0])


if __name__ == '__main__':
    X, y = make_moons(n_samples=1000, noise=0.3, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    classifiers = [
        LogisticRegression(),
        GradientBoostingClassifier(),
        Pipeline([('gbdt', GBDTTransformer()),
                  ('lr', LogisticRegression())])
    ]

    h = .02  # mesh step
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['#FF0000', '#0000FF'])

    for clf in classifiers:
        clf.fit(X_train, y_train)
        Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
        Z = Z.reshape(xx.shape)

        plt.contourf(xx, yy, Z, cmap=cm, alpha=.8)
        plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)

        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.show()