"""
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
This example presents how to estimate and visualize the variance of the Receiver
Operating Characteristic (ROC) metric using cross-validation.
ROC curves typically feature true positive rate (TPR) on the Y axis, and false
positive rate (FPR) on the X axis. This means that the top left corner of the
plot is the "ideal" point - a FPR of zero, and a TPR of one. This is not very
realistic, but it does mean that a larger Area Under the Curve (AUC) is usually
better. The "steepness" of ROC curves is also important, since it is ideal to
maximize the TPR while minimizing the FPR.
This example shows the ROC response of different datasets, created from K-fold
cross-validation. Taking all of these curves, it is possible to calculate the
mean AUC, and see the variance of the curve when the
training set is split into different subsets. This roughly shows how the
classifier output is affected by changes in the training data, and how different
the splits generated by K-fold cross-validation are from one another.
.. note::
See :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` for a
complement of the present example explaining the averaging strategies to
generalize the metrics for multiclass classifiers.
"""
# %%
# Load and prepare data
# =====================
#
# We import the :ref:`iris_dataset` which contains 3 classes, each one
# corresponding to a type of iris plant. One class is linearly separable from
# the other 2; the latter are **not** linearly separable from each other.
#
# In the following we binarize the dataset by dropping the "virginica" class
# (`class_id=2`). This means that the "versicolor" class (`class_id=1`) is
# regarded as the positive class and "setosa" as the negative class
# (`class_id=0`).
import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
target_names = iris.target_names
X, y = iris.data, iris.target
X, y = X[y != 2], y[y != 2]
n_samples, n_features = X.shape
# %%
# We also add noisy features to make the problem harder.
random_state = np.random.RandomState(0)
X = np.concatenate([X, random_state.randn(n_samples, 200 * n_features)], axis=1)
# %%
# Classification and ROC analysis
# -------------------------------
#
# Here we run a :class:`~sklearn.svm.SVC` classifier with cross-validation and
# plot the ROC curves fold-wise. Notice that the baseline to define the chance
# level (dashed ROC curve) is a classifier that would always predict the most
# frequent class.
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.metrics import RocCurveDisplay, auc
from sklearn.model_selection import StratifiedKFold
n_splits = 6
cv = StratifiedKFold(n_splits=n_splits)
classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state)
tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 100)
fig, ax = plt.subplots(figsize=(6, 6))
for fold, (train, test) in enumerate(cv.split(X, y)):
classifier.fit(X[train], y[train])
viz = RocCurveDisplay.from_estimator(
classifier,
X[test],
y[test],
name=f"ROC fold {fold}",
alpha=0.3,
lw=1,
ax=ax,
plot_chance_level=(fold == n_splits - 1),
)
interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
interp_tpr[0] = 0.0
tprs.append(interp_tpr)
aucs.append(viz.roc_auc)
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
std_auc = np.std(aucs)
ax.plot(
mean_fpr,
mean_tpr,
color="b",
label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc),
lw=2,
alpha=0.8,
)
std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
ax.fill_between(
mean_fpr,
tprs_lower,
tprs_upper,
color="grey",
alpha=0.2,
label=r"$\pm$ 1 std. dev.",
)
ax.set(
xlabel="False Positive Rate",
ylabel="True Positive Rate",
title=f"Mean ROC curve with variability\n(Positive label '{target_names[1]}')",
)
ax.legend(loc="lower right")
plt.show()