Python学习----不同分类器的Calibration比较

Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that for the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class.

In this example we will compare the calibration of four different models: Logistic regression, Gaussian Naive Bayes, Random Forest Classifier and Linear SVM.

Author: Jan Hendrik Metzen License: BSD 3 clause.

 数据集

We will use a synthetic binary classification dataset with 100,000 samples and 20 features. Of the 20 features, only 2 are informative, 2 are redundant (random combinations of the informative features) and the remaining 16 are uninformative (random numbers). Of the 100,000 samples, 100 will be used for model fitting and the remaining for testing.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(
    n_samples=100_000, n_features=20, n_informative=2, n_redundant=2, random_state=42
)

train_samples = 100  # Samples used for training the models
X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    shuffle=False,
    test_size=100_000 - train_samples,
)

校准曲线

Below, we train each of the four models with the small training dataset, then plot calibration curves (also known as reliability diagrams) using predicted probabilities of the test dataset. Calibration curves are created by binning predicted probabilities, then plotting the mean predicted probability in each bin against the observed frequency (‘fraction of positives’). Below the calibration curve, we plot a histogram showing the distribution of the predicted probabilities or more specifically, the number of samples in each predicted probability bin.

import numpy as np

from sklearn.svm import LinearSVC


class NaivelyCalibratedLinearSVC(LinearSVC):
    """LinearSVC with `predict_proba` method that naively scales
    `decision_function` output."""

    def fit(self, X, y):
        super().fit(X, y)
        df = self.decision_function(X)
        self.df_min_ = df.min()
        self.df_max_ = df.max()

    def predict_proba(self, X):
        """Min-max scale output of `decision_function` to [0,1]."""
        df = self.decision_function(X)
        calibrated_df = (df - self.df_min_) / (self.df_max_ - self.df_min_)
        proba_pos_class = np.clip(calibrated_df, 0, 1)
        proba_neg_class = 1 - proba_pos_class
        proba = np.c_[proba_neg_class, proba_pos_class]
        return proba

from sklearn.calibration import CalibrationDisplay
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB

# Create classifiers
lr = LogisticRegression()
gnb = GaussianNB()
svc = NaivelyCalibratedLinearSVC(C=1.0)
rfc = RandomForestClassifier()

clf_list = [
    (lr, "Logistic"),
    (gnb, "Naive Bayes"),
    (svc, "SVC"),
    (rfc, "Random forest"),
]

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(10, 10))
gs = GridSpec(4, 2)
colors = plt.cm.get_cmap("Dark2")

ax_calibration_curve = fig.add_subplot(gs[:2, :2])
calibration_displays = {}
for i, (clf, name) in enumerate(clf_list):
    clf.fit(X_train, y_train)
    display = CalibrationDisplay.from_estimator(
        clf,
        X_test,
        y_test,
        n_bins=10,
        name=name,
        ax=ax_calibration_curve,
        color=colors(i),
    )
    calibration_displays[name] = display

ax_calibration_curve.grid()
ax_calibration_curve.set_title("Calibration plots")

# Add histogram
grid_positions = [(2, 0), (2, 1), (3, 0), (3, 1)]
for i, (_, name) in enumerate(clf_list):
    row, col = grid_positions[i]
    ax = fig.add_subplot(gs[row, col])

    ax.hist(
        calibration_displays[name].y_prob,
        range=(0, 1),
        bins=10,
        label=name,
        color=colors(i),
    )
    ax.set(title=name, xlabel="Mean predicted probability", ylabel="Count")

plt.tight_layout()
plt.show()

examples-calibration-plot-compare-calibration-pyhttps://scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py