.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/calibration/plot_calibration_multiclass.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py: ================================================== Probability Calibration for 3-class classification ================================================== This example illustrates how sigmoid :ref:`calibration ` changes predicted probabilities for a 3-class classification problem. Illustrated is the standard 2-simplex, where the three corners correspond to the three classes. Arrows point from the probability vectors predicted by an uncalibrated classifier to the probability vectors predicted by the same classifier after sigmoid calibration on a hold-out validation set. Colors indicate the true class of an instance (red: class 1, green: class 2, blue: class 3). .. GENERATED FROM PYTHON SOURCE LINES 17-29 Data ---- Below, we generate a classification dataset with 2000 samples, 2 features and 3 target classes. We then split the data as follows: * train: 600 samples (for training the classifier) * valid: 400 samples (for calibrating predicted probabilities) * test: 1000 samples Note that we also create `X_train_valid` and `y_train_valid`, which consists of both the train and valid subsets. This is used when we only want to train the classifier but not calibrate the predicted probabilities. .. GENERATED FROM PYTHON SOURCE LINES 29-47 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import numpy as np from sklearn.datasets import make_blobs np.random.seed(0) X, y = make_blobs( n_samples=2000, n_features=2, centers=3, random_state=42, cluster_std=5.0 ) X_train, y_train = X[:600], y[:600] X_valid, y_valid = X[600:1000], y[600:1000] X_train_valid, y_train_valid = X[:1000], y[:1000] X_test, y_test = X[1000:], y[1000:] .. GENERATED FROM PYTHON SOURCE LINES 48-54 Fitting and calibration ----------------------- First, we will train a :class:`~sklearn.ensemble.RandomForestClassifier` with 25 base estimators (trees) on the concatenated train and validation data (1000 samples). This is the uncalibrated classifier. .. GENERATED FROM PYTHON SOURCE LINES 54-60 .. code-block:: Python from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(n_estimators=25) clf.fit(X_train_valid, y_train_valid) .. raw:: html

RandomForestClassifier(n_estimators=25)

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.. GENERATED FROM PYTHON SOURCE LINES 61-65 To train the calibrated classifier, we start with the same :class:`~sklearn.ensemble.RandomForestClassifier` but train it using only the train data subset (600 samples) then calibrate, with `method='sigmoid'`, using the valid data subset (400 samples) in a 2-stage process. .. GENERATED FROM PYTHON SOURCE LINES 65-73 .. code-block:: Python from sklearn.calibration import CalibratedClassifierCV clf = RandomForestClassifier(n_estimators=25) clf.fit(X_train, y_train) cal_clf = CalibratedClassifierCV(clf, method="sigmoid", cv="prefit") cal_clf.fit(X_valid, y_valid) .. raw:: html

CalibratedClassifierCV(cv='prefit',
                           estimator=RandomForestClassifier(n_estimators=25))

.. GENERATED FROM PYTHON SOURCE LINES 74-78 Compare probabilities --------------------- Below we plot a 2-simplex with arrows showing the change in predicted probabilities of the test samples. .. GENERATED FROM PYTHON SOURCE LINES 78-184 .. code-block:: Python import matplotlib.pyplot as plt plt.figure(figsize=(10, 10)) colors = ["r", "g", "b"] clf_probs = clf.predict_proba(X_test) cal_clf_probs = cal_clf.predict_proba(X_test) # Plot arrows for i in range(clf_probs.shape[0]): plt.arrow( clf_probs[i, 0], clf_probs[i, 1], cal_clf_probs[i, 0] - clf_probs[i, 0], cal_clf_probs[i, 1] - clf_probs[i, 1], color=colors[y_test[i]], head_width=1e-2, ) # Plot perfect predictions, at each vertex plt.plot([1.0], [0.0], "ro", ms=20, label="Class 1") plt.plot([0.0], [1.0], "go", ms=20, label="Class 2") plt.plot([0.0], [0.0], "bo", ms=20, label="Class 3") # Plot boundaries of unit simplex plt.plot([0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], "k", label="Simplex") # Annotate points 6 points around the simplex, and mid point inside simplex plt.annotate( r"($\frac{1}{3}$, $\frac{1}{3}$, $\frac{1}{3}$)", xy=(1.0 / 3, 1.0 / 3), xytext=(1.0 / 3, 0.23), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) plt.plot([1.0 / 3], [1.0 / 3], "ko", ms=5) plt.annotate( r"($\frac{1}{2}$, $0$, $\frac{1}{2}$)", xy=(0.5, 0.0), xytext=(0.5, 0.1), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) plt.annotate( r"($0$, $\frac{1}{2}$, $\frac{1}{2}$)", xy=(0.0, 0.5), xytext=(0.1, 0.5), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) plt.annotate( r"($\frac{1}{2}$, $\frac{1}{2}$, $0$)", xy=(0.5, 0.5), xytext=(0.6, 0.6), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) plt.annotate( r"($0$, $0$, $1$)", xy=(0, 0), xytext=(0.1, 0.1), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) plt.annotate( r"($1$, $0$, $0$)", xy=(1, 0), xytext=(1, 0.1), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) plt.annotate( r"($0$, $1$, $0$)", xy=(0, 1), xytext=(0.1, 1), xycoords="data", arrowprops=dict(facecolor="black", shrink=0.05), horizontalalignment="center", verticalalignment="center", ) # Add grid plt.grid(False) for x in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: plt.plot([0, x], [x, 0], "k", alpha=0.2) plt.plot([0, 0 + (1 - x) / 2], [x, x + (1 - x) / 2], "k", alpha=0.2) plt.plot([x, x + (1 - x) / 2], [0, 0 + (1 - x) / 2], "k", alpha=0.2) plt.title("Change of predicted probabilities on test samples after sigmoid calibration") plt.xlabel("Probability class 1") plt.ylabel("Probability class 2") plt.xlim(-0.05, 1.05) plt.ylim(-0.05, 1.05) _ = plt.legend(loc="best") .. image-sg:: /auto_examples/calibration/images/sphx_glr_plot_calibration_multiclass_001.png :alt: Change of predicted probabilities on test samples after sigmoid calibration :srcset: /auto_examples/calibration/images/sphx_glr_plot_calibration_multiclass_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 185-211 In the figure above, each vertex of the simplex represents a perfectly predicted class (e.g., 1, 0, 0). The mid point inside the simplex represents predicting the three classes with equal probability (i.e., 1/3, 1/3, 1/3). Each arrow starts at the uncalibrated probabilities and end with the arrow head at the calibrated probability. The color of the arrow represents the true class of that test sample. The uncalibrated classifier is overly confident in its predictions and incurs a large :ref:`log loss `. The calibrated classifier incurs a lower :ref:`log loss ` due to two factors. First, notice in the figure above that the arrows generally point away from the edges of the simplex, where the probability of one class is 0. Second, a large proportion of the arrows point towards the true class, e.g., green arrows (samples where the true class is 'green') generally point towards the green vertex. This results in fewer over-confident, 0 predicted probabilities and at the same time an increase in the predicted probabilities of the correct class. Thus, the calibrated classifier produces more accurate predicted probabilities that incur a lower :ref:`log loss ` We can show this objectively by comparing the :ref:`log loss ` of the uncalibrated and calibrated classifiers on the predictions of the 1000 test samples. Note that an alternative would have been to increase the number of base estimators (trees) of the :class:`~sklearn.ensemble.RandomForestClassifier` which would have resulted in a similar decrease in :ref:`log loss `. .. GENERATED FROM PYTHON SOURCE LINES 211-221 .. code-block:: Python from sklearn.metrics import log_loss score = log_loss(y_test, clf_probs) cal_score = log_loss(y_test, cal_clf_probs) print("Log-loss of") print(f" * uncalibrated classifier: {score:.3f}") print(f" * calibrated classifier: {cal_score:.3f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Log-loss of * uncalibrated classifier: 1.327 * calibrated classifier: 0.549 .. GENERATED FROM PYTHON SOURCE LINES 222-226 Finally we generate a grid of possible uncalibrated probabilities over the 2-simplex, compute the corresponding calibrated probabilities and plot arrows for each. The arrows are colored according the highest uncalibrated probability. This illustrates the learned calibration map: .. GENERATED FROM PYTHON SOURCE LINES 226-276 .. code-block:: Python plt.figure(figsize=(10, 10)) # Generate grid of probability values p1d = np.linspace(0, 1, 20) p0, p1 = np.meshgrid(p1d, p1d) p2 = 1 - p0 - p1 p = np.c_[p0.ravel(), p1.ravel(), p2.ravel()] p = p[p[:, 2] >= 0] # Use the three class-wise calibrators to compute calibrated probabilities calibrated_classifier = cal_clf.calibrated_classifiers_[0] prediction = np.vstack( [ calibrator.predict(this_p) for calibrator, this_p in zip(calibrated_classifier.calibrators, p.T) ] ).T # Re-normalize the calibrated predictions to make sure they stay inside the # simplex. This same renormalization step is performed internally by the # predict method of CalibratedClassifierCV on multiclass problems. prediction /= prediction.sum(axis=1)[:, None] # Plot changes in predicted probabilities induced by the calibrators for i in range(prediction.shape[0]): plt.arrow( p[i, 0], p[i, 1], prediction[i, 0] - p[i, 0], prediction[i, 1] - p[i, 1], head_width=1e-2, color=colors[np.argmax(p[i])], ) # Plot the boundaries of the unit simplex plt.plot([0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], "k", label="Simplex") plt.grid(False) for x in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: plt.plot([0, x], [x, 0], "k", alpha=0.2) plt.plot([0, 0 + (1 - x) / 2], [x, x + (1 - x) / 2], "k", alpha=0.2) plt.plot([x, x + (1 - x) / 2], [0, 0 + (1 - x) / 2], "k", alpha=0.2) plt.title("Learned sigmoid calibration map") plt.xlabel("Probability class 1") plt.ylabel("Probability class 2") plt.xlim(-0.05, 1.05) plt.ylim(-0.05, 1.05) plt.show() .. image-sg:: /auto_examples/calibration/images/sphx_glr_plot_calibration_multiclass_002.png :alt: Learned sigmoid calibration map :srcset: /auto_examples/calibration/images/sphx_glr_plot_calibration_multiclass_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.320 seconds) .. _sphx_glr_download_auto_examples_calibration_plot_calibration_multiclass.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/calibration/plot_calibration_multiclass.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_calibration_multiclass.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_calibration_multiclass.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_calibration_multiclass.zip ` .. include:: plot_calibration_multiclass.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_