95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
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"""
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===========================================================
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Plot class probabilities calculated by the VotingClassifier
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===========================================================
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.. currentmodule:: sklearn
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Plot the class probabilities of the first sample in a toy dataset predicted by
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three different classifiers and averaged by the
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:class:`~ensemble.VotingClassifier`.
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First, three exemplary classifiers are initialized
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(:class:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,
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and :class:`~ensemble.RandomForestClassifier`) and used to initialize a
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soft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which
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means that the predicted probabilities of the
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:class:`~ensemble.RandomForestClassifier` count 5 times as much as the weights
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of the other classifiers when the averaged probability is calculated.
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To visualize the probability weighting, we fit each classifier on the training
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set and plot the predicted class probabilities for the first sample in this
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example dataset.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.ensemble import RandomForestClassifier, VotingClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import GaussianNB
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clf1 = LogisticRegression(max_iter=1000, random_state=123)
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clf2 = RandomForestClassifier(n_estimators=100, random_state=123)
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clf3 = GaussianNB()
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X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
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y = np.array([1, 1, 2, 2])
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eclf = VotingClassifier(
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estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
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voting="soft",
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weights=[1, 1, 5],
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)
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# predict class probabilities for all classifiers
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probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]
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# get class probabilities for the first sample in the dataset
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class1_1 = [pr[0, 0] for pr in probas]
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class2_1 = [pr[0, 1] for pr in probas]
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# plotting
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N = 4 # number of groups
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ind = np.arange(N) # group positions
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width = 0.35 # bar width
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fig, ax = plt.subplots()
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# bars for classifier 1-3
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p1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color="green", edgecolor="k")
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p2 = ax.bar(
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ind + width,
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np.hstack(([class2_1[:-1], [0]])),
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width,
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color="lightgreen",
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edgecolor="k",
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)
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# bars for VotingClassifier
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p3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color="blue", edgecolor="k")
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p4 = ax.bar(
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ind + width, [0, 0, 0, class2_1[-1]], width, color="steelblue", edgecolor="k"
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)
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# plot annotations
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plt.axvline(2.8, color="k", linestyle="dashed")
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ax.set_xticks(ind + width)
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ax.set_xticklabels(
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[
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"LogisticRegression\nweight 1",
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"GaussianNB\nweight 1",
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"RandomForestClassifier\nweight 5",
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"VotingClassifier\n(average probabilities)",
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],
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rotation=40,
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ha="right",
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)
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plt.ylim([0, 1])
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plt.title("Class probabilities for sample 1 by different classifiers")
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plt.legend([p1[0], p2[0]], ["class 1", "class 2"], loc="upper left")
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plt.tight_layout()
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plt.show()
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