learning curve, kfold and gridsearch

laerning_curve

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import GridSearchCV, StratifiedKFold, learning_curve
from sklearn.ensemble import GradientBoostingClassifier


def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):
    """Generate a simple plot of the test and training learning curve"""
    plt.figure()
    plt.title(title)
    if ylim is not None:
        plt.ylim(*ylim)
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()

    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color="r")
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.1, color="g")
    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
             label="Training score")
    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
             label="Cross-validation score")

    plt.legend(loc="best")
    return plt


# Cross validate model with Kfold stratified cross val
kfold = StratifiedKFold(n_splits=10)

# Gradient boosting tunning
GBC = GradientBoostingClassifier()
gb_param_grid = {'loss': ["deviance"]}

gsGBC = GridSearchCV(GBC, param_grid=gb_param_grid, cv=kfold, scoring="accuracy", n_jobs=4, verbose=1)

gsGBC.fit(X_train,Y_train)

GBC_best = gsGBC.best_estimator_

# Best score
gsGBC.best_score_

g = plot_learning_curve(gsGBC.best_estimator_,"GradientBoosting learning curves",X_train,Y_train,cv=kfold)