#Three lines to make our compiler able to draw:

import sys

import matplotlib

matplotlib.use('Agg')

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import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

from sklearn.ensemble import BaggingClassifier

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data = datasets.load_wine()

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X = data.data

y = data.target

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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 22)

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estimator_range = [2,4,6,8,10,12,14,16]

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models = []

scores = []

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for n_estimators in estimator_range:

    # Create bagging classifier

    clf = BaggingClassifier(n_estimators = n_estimators, random_state = 22)

    # Fit the model

    clf.fit(X_train, y_train)

    # Append the model and score to their respective list

    models.append(clf)

    scores.append(accuracy_score(y_true = y_test, y_pred = clf.predict(X_test)))

# Generate the plot of scores against number of estimators

plt.figure(figsize=(9,6))

plt.plot(estimator_range, scores)

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# Adjust labels and font (to make visable)

plt.xlabel("n_estimators", fontsize = 18)

plt.ylabel("score", fontsize = 18)

plt.tick_params(labelsize = 16)

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