.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples_classification/1-quickstart/plot_comp_methods_on_2d_dataset.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_classification_1-quickstart_plot_comp_methods_on_2d_dataset.py: ====================================================== Comparing prediction sets on a two-dimensional dataset ====================================================== In this tutorial, we compare the prediction sets estimated by :class:`~mapie.classification.MapieClassifier` with the "lac" and "aps" on the two-dimensional dataset presented by Sadinle et al. (2019). .. GENERATED FROM PYTHON SOURCE LINES 13-43 We will use MAPIE to estimate a prediction set of several classes such that the probability that the true label of a new test point is included in the prediction set is always higher than the target confidence level : ``1 - α``. Throughout this tutorial, we compare two conformity scores : softmax score or cumulated softmax score. We start by using the softmax score or cumulated score output by the base classifier as the conformity score on a toy two-dimensional dataset. We estimate the prediction sets as follows : * First we generate a dataset with train, calibration and test, the model is fitted in the training set. * We set the conformal score ``Sᵢ = 𝑓̂(Xᵢ)ᵧᵢ`` from the softmax output of the true class or the cumulated score (by decreasing order) for each sample in the calibration set. * Then we define q̂ as being the ``(n + 1)(1 - α) / n`` previous quantile of ``S₁, ..., Sₙ`` (this is essentially the quantile α, but with a small sample correction). * Finally, for a new test data point (where ``Xₙ₊₁`` is known but ``Yₙ₊₁`` is not), create a prediction set ``C(Xₙ₊₁) = {y: 𝑓̂(Xₙ₊₁)ᵧ > q̂}`` which includes all the classes with a sufficiently high conformity score. We use a two-dimensional dataset with three labels. The distribution of the data is a bivariate normal with diagonal covariance matrices for each label. .. GENERATED FROM PYTHON SOURCE LINES 43-82 .. code-block:: default # Reference: # Mauricio Sadinle, Jing Lei, and Larry Wasserman. # "Least Ambiguous Set-Valued Classifiers With Bounded Error Levels." # Journal of the American Statistical Association, 114:525, 223-234, 2019. from typing import List import matplotlib.pyplot as plt import numpy as np from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from mapie._typing import NDArray from mapie.classification import MapieClassifier from mapie.metrics import (classification_coverage_score, classification_mean_width_score) centers = [(0, 3.5), (-2, 0), (2, 0)] covs = [np.eye(2), np.eye(2) * 2, np.diag([5, 1])] x_min, x_max, y_min, y_max, step = -6, 8, -6, 8, 0.1 n_samples = 500 n_classes = 3 np.random.seed(42) X = np.vstack( [ np.random.multivariate_normal(center, cov, n_samples) for center, cov in zip(centers, covs) ] ) y = np.hstack([np.full(n_samples, i) for i in range(n_classes)]) X_train, X_cal, y_train, y_cal = train_test_split(X, y, test_size=0.3) xx, yy = np.meshgrid( np.arange(x_min, x_max, step), np.arange(x_min, x_max, step) ) X_test = np.stack([xx.ravel(), yy.ravel()], axis=1) .. GENERATED FROM PYTHON SOURCE LINES 83-84 Let's see our training data .. GENERATED FROM PYTHON SOURCE LINES 84-101 .. code-block:: default colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728"} y_train_col = list(map(colors.get, y_train)) fig = plt.figure() plt.scatter( X_train[:, 0], X_train[:, 1], color=y_train_col, marker="o", s=10, edgecolor="k", ) plt.xlabel("X") plt.ylabel("Y") plt.show() .. image-sg:: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_001.png :alt: plot comp methods on 2d dataset :srcset: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 102-109 We fit our training data with a Gaussian Naive Base estimator. Then we apply :class:`~mapie.classification.MapieClassifier` in the calibration data with the methods ``"lac"`` and ``"aps"``` to the estimator indicating that it has already been fitted with `cv="prefit"`. We then estimate the prediction sets with differents alpha values with a ``fit`` and ``predict`` process. .. GENERATED FROM PYTHON SOURCE LINES 109-131 .. code-block:: default clf = GaussianNB().fit(X_train, y_train) y_pred = clf.predict(X_test) y_pred_proba = clf.predict_proba(X_test) y_pred_proba_max = np.max(y_pred_proba, axis=1) methods = ["lac", "aps"] mapie, y_pred_mapie, y_ps_mapie = {}, {}, {} alpha = [0.2, 0.1, 0.05] for method in methods: mapie[method] = MapieClassifier( estimator=clf, method=method, cv="prefit", random_state=42, ) mapie[method].fit(X_cal, y_cal) y_pred_mapie[method], y_ps_mapie[method] = mapie[method].predict( X_test, alpha=alpha, include_last_label=True, ) .. GENERATED FROM PYTHON SOURCE LINES 132-141 MAPIE produces two outputs: - y_pred_mapie: the prediction in the test set given by the base estimator. - y_ps_mapie: the prediction sets estimated by MAPIE using the selected method. Let's now visualize the distribution of the conformity scores with the two methods with the calculated quantiles for the three alpha values. .. GENERATED FROM PYTHON SOURCE LINES 141-178 .. code-block:: default def plot_scores( alphas: List[float], scores: NDArray, quantiles: NDArray, method: str, ax: plt.Axes, ) -> None: colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c"} ax.hist(scores, bins="auto") i = 0 for quantile in quantiles: ax.vlines( x=quantile, ymin=0, ymax=500, color=colors[i], linestyles="dashed", label=f"alpha = {alphas[i]}", ) i = i + 1 ax.set_title(f"Distribution of scores for '{method}' method") ax.legend() ax.set_xlabel("scores") ax.set_ylabel("count") fig, axs = plt.subplots(1, 2, figsize=(10, 5)) for i, method in enumerate(methods): conformity_scores = mapie[method].conformity_scores_ n = mapie[method].n_samples_ quantiles = mapie[method].conformity_score_function_.quantiles_ plot_scores(alpha, conformity_scores, quantiles, method, axs[i]) plt.show() .. image-sg:: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_002.png :alt: Distribution of scores for 'lac' method, Distribution of scores for 'aps' method :srcset: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 179-181 We will now compare the differences between the prediction sets of the different values ​​of alpha. .. GENERATED FROM PYTHON SOURCE LINES 181-233 .. code-block:: default def plot_results( alphas: List[float], y_pred_mapie: NDArray, y_ps_mapie: NDArray ) -> None: tab10 = plt.cm.get_cmap("Purples", 4) colors = { 0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728", 4: "#c896af", 5: "#94a98a", 6: "#8a94a9", 7: "#a99f8a", 8: "#1e1b16", 9: "#4a4336", } y_pred_col = list(map(colors.get, y_pred_mapie)) fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2, figsize=(10, 10)) axs = {0: ax1, 1: ax2, 2: ax3, 3: ax4} axs[0].scatter( X_test[:, 0], X_test[:, 1], color=y_pred_col, marker=".", s=10, alpha=0.4, ) axs[0].set_title("Predicted labels") for i, alpha_ in enumerate(alphas): y_pi_sums = y_ps_mapie[:, :, i].sum(axis=1) num_labels = axs[i + 1].scatter( X_test[:, 0], X_test[:, 1], c=y_pi_sums, marker="o", s=10, alpha=1, cmap=tab10, vmin=0, vmax=3, ) plt.colorbar(num_labels, ax=axs[i + 1]) axs[i + 1].set_title(f"Number of labels for alpha={alpha_}") plt.show() for method in methods: plot_results(alpha, y_pred_mapie[method], y_ps_mapie[method]) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_003.png :alt: Predicted labels, Number of labels for alpha=0.2, Number of labels for alpha=0.1, Number of labels for alpha=0.05 :srcset: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_003.png :class: sphx-glr-multi-img * .. image-sg:: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_004.png :alt: Predicted labels, Number of labels for alpha=0.2, Number of labels for alpha=0.1, Number of labels for alpha=0.05 :srcset: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_004.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 234-245 For the "lac" method, when the class coverage is not large enough, the prediction sets can be empty when the model is uncertain at the border between two labels. These null regions disappear for larger class coverages but ambiguous classification regions arise with several labels included in the prediction sets. By definition, the "aps" method does not produce empty prediction sets. However, the prediction sets tend to be slightly bigger in ambiguous regions. Let's now compare the effective coverage and the average of prediction set widths as function of the ``1 - α`` target coverage. .. GENERATED FROM PYTHON SOURCE LINES 245-289 .. code-block:: default alpha_ = np.arange(0.02, 0.98, 0.02) coverage, mean_width = {}, {} mapie, y_ps_mapie = {}, {} for method in methods: mapie[method] = MapieClassifier( estimator=clf, method=method, cv="prefit", random_state=42, ) mapie[method].fit(X_cal, y_cal) _, y_ps_mapie[method] = mapie[method].predict( X, alpha=alpha_, include_last_label="randomized" ) coverage[method] = [ classification_coverage_score(y, y_ps_mapie[method][:, :, i]) for i, _ in enumerate(alpha_) ] mean_width[method] = [ classification_mean_width_score(y_ps_mapie[method][:, :, i]) for i, _ in enumerate(alpha_) ] fig, axs = plt.subplots(1, 3, figsize=(15, 5)) axs[0].set_xlabel("1 - alpha") axs[0].set_ylabel("Quantile") for method in methods: quantiles = mapie[method].conformity_score_function_.quantiles_ axs[0].scatter(1 - alpha_, quantiles, label=method) axs[0].legend() for method in methods: axs[1].scatter(1 - alpha_, coverage[method], label=method) axs[1].set_xlabel("1 - alpha") axs[1].set_ylabel("Coverage score") axs[1].plot([0, 1], [0, 1], label="x=y", color="black") axs[1].legend() for method in methods: axs[2].scatter(1 - alpha_, mean_width[method], label=method) axs[2].set_xlabel("1 - alpha") axs[2].set_ylabel("Average size of prediction sets") axs[2].legend() plt.show() .. image-sg:: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_005.png :alt: plot comp methods on 2d dataset :srcset: /examples_classification/1-quickstart/images/sphx_glr_plot_comp_methods_on_2d_dataset_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 290-294 It is seen that both methods give coverages close to the target coverages, regardless of the ``α`` value. However, the "aps" produces slightly bigger prediction sets, but without empty regions (if the selection of the last label is not randomized). .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.517 seconds) .. _sphx_glr_download_examples_classification_1-quickstart_plot_comp_methods_on_2d_dataset.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_comp_methods_on_2d_dataset.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_comp_methods_on_2d_dataset.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_