96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
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"""
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===============================
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Plot classification probability
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===============================
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Plot the classification probability for different classifiers. We use a 3 class
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dataset, and we classify it with a Support Vector classifier, L1 and L2
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penalized logistic regression (multinomial multiclass), a One-Vs-Rest version with
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logistic regression, and Gaussian process classification.
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Linear SVC is not a probabilistic classifier by default but it has a built-in
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calibration option enabled in this example (`probability=True`).
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The logistic regression with One-Vs-Rest is not a multiclass classifier out of
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the box. As a result it has more trouble in separating class 2 and 3 than the
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other estimators.
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"""
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import cm
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from sklearn import datasets
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.gaussian_process.kernels import RBF
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.svm import SVC
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iris = datasets.load_iris()
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X = iris.data[:, 0:2] # we only take the first two features for visualization
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y = iris.target
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n_features = X.shape[1]
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C = 10
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kernel = 1.0 * RBF([1.0, 1.0]) # for GPC
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# Create different classifiers.
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classifiers = {
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"L1 logistic": LogisticRegression(C=C, penalty="l1", solver="saga", max_iter=10000),
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"L2 logistic (Multinomial)": LogisticRegression(
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C=C, penalty="l2", solver="saga", max_iter=10000
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),
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"L2 logistic (OvR)": OneVsRestClassifier(
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LogisticRegression(C=C, penalty="l2", solver="saga", max_iter=10000)
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),
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"Linear SVC": SVC(kernel="linear", C=C, probability=True, random_state=0),
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"GPC": GaussianProcessClassifier(kernel),
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}
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n_classifiers = len(classifiers)
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fig, axes = plt.subplots(
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nrows=n_classifiers,
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ncols=len(iris.target_names),
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figsize=(3 * 2, n_classifiers * 2),
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)
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for classifier_idx, (name, classifier) in enumerate(classifiers.items()):
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y_pred = classifier.fit(X, y).predict(X)
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accuracy = accuracy_score(y, y_pred)
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print(f"Accuracy (train) for {name}: {accuracy:0.1%}")
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for label in np.unique(y):
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# plot the probability estimate provided by the classifier
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disp = DecisionBoundaryDisplay.from_estimator(
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classifier,
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X,
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response_method="predict_proba",
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class_of_interest=label,
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ax=axes[classifier_idx, label],
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vmin=0,
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vmax=1,
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)
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axes[classifier_idx, label].set_title(f"Class {label}")
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# plot data predicted to belong to given class
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mask_y_pred = y_pred == label
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axes[classifier_idx, label].scatter(
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X[mask_y_pred, 0], X[mask_y_pred, 1], marker="o", c="w", edgecolor="k"
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)
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axes[classifier_idx, label].set(xticks=(), yticks=())
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axes[classifier_idx, 0].set_ylabel(name)
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ax = plt.axes([0.15, 0.04, 0.7, 0.02])
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plt.title("Probability")
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_ = plt.colorbar(
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cm.ScalarMappable(norm=None, cmap="viridis"), cax=ax, orientation="horizontal"
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)
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plt.show()
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