scikit-learn/examples/model_selection/plot_det.py

"""
=======================================
Detection error tradeoff (DET) curve
=======================================

In this example, we compare receiver operating characteristic (ROC) and
detection error tradeoff (DET) curves for different classification algorithms
for the same classification task.

DET curves are commonly plotted in normal deviate scale.
To achieve this we transform the errors rates as returned by the
``detection_error_tradeoff_curve`` function and the axis scale using
``scipy.stats.norm``.

The point of this example is to demonstrate two properties of DET curves,
namely:

1. It might be easier to visually assess the overall performance of different
   classification algorithms using DET curves over ROC curves.
   Due to the linear scale used for plotting ROC curves, different classifiers
   usually only differ in the top left corner of the graph and appear similar
   for a large part of the plot. On the other hand, because DET curves
   represent straight lines in normal deviate scale. As such, they tend to be
   distinguishable as a whole and the area of interest spans a large part of
   the plot.
2. DET curves give the user direct feedback of the detection error tradeoff to
   aid in operating point analysis.
   The user can deduct directly from the DET-curve plot at which rate
   false-negative error rate will improve when willing to accept an increase in
   false-positive error rate (or vice-versa).

The plots in this example compare ROC curves on the left side to corresponding
DET curves on the right.
There is no particular reason why these classifiers have been chosen for the
example plot over other classifiers available in scikit-learn.

.. note::

    - See :func:`sklearn.metrics.roc_curve` for further information about ROC
      curves.

    - See :func:`sklearn.metrics.detection_error_tradeoff_curve` for further
      information about DET curves.

    - This example is loosely based on
      :ref:`sphx_glr_auto_examples_classification_plot_classifier_comparison.py`
      .

"""
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import detection_error_tradeoff_curve
from sklearn.metrics import roc_curve

from scipy.stats import norm
from matplotlib.ticker import FuncFormatter

N_SAMPLES = 1000

names = [
    "Linear SVM",
    "Random Forest",
]

classifiers = [
    SVC(kernel="linear", C=0.025),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
]

X, y = make_classification(
    n_samples=N_SAMPLES, n_features=2, n_redundant=0, n_informative=2,
    random_state=1, n_clusters_per_class=1)

# preprocess dataset, split into training and test part
X = StandardScaler().fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=.4, random_state=0)

# prepare plots
fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(10, 5))

# first prepare the ROC curve
ax_roc.set_title('Receiver Operating Characteristic (ROC) curves')
ax_roc.set_xlabel('False Positive Rate')
ax_roc.set_ylabel('True Positive Rate')
ax_roc.set_xlim(0, 1)
ax_roc.set_ylim(0, 1)
ax_roc.grid(linestyle='--')
ax_roc.yaxis.set_major_formatter(
    FuncFormatter(lambda y, _: '{:.0%}'.format(y)))
ax_roc.xaxis.set_major_formatter(
    FuncFormatter(lambda y, _: '{:.0%}'.format(y)))

# second prepare the DET curve
ax_det.set_title('Detection Error Tradeoff (DET) curves')
ax_det.set_xlabel('False Positive Rate')
ax_det.set_ylabel('False Negative Rate')
ax_det.set_xlim(-3, 3)
ax_det.set_ylim(-3, 3)
ax_det.grid(linestyle='--')

# customized ticks for DET curve plot to represent normal deviate scale
ticks = [0.001, 0.01, 0.05, 0.20, 0.5, 0.80, 0.95, 0.99, 0.999]
tick_locs = norm.ppf(ticks)
tick_lbls = [
    '{:.0%}'.format(s) if (100*s).is_integer() else '{:.1%}'.format(s)
    for s in ticks
]
plt.sca(ax_det)
plt.xticks(tick_locs, tick_lbls)
plt.yticks(tick_locs, tick_lbls)

# iterate over classifiers
for name, clf in zip(names, classifiers):
    clf.fit(X_train, y_train)

    if hasattr(clf, "decision_function"):
        y_score = clf.decision_function(X_test)
    else:
        y_score = clf.predict_proba(X_test)[:, 1]

    roc_fpr, roc_tpr, _ = roc_curve(y_test, y_score)
    det_fpr, det_fnr, _ = detection_error_tradeoff_curve(y_test, y_score)

    ax_roc.plot(roc_fpr, roc_tpr)

    # transform errors into normal deviate scale
    ax_det.plot(
        norm.ppf(det_fpr),
        norm.ppf(det_fnr)
    )

# add a single legend
plt.sca(ax_det)
plt.legend(names, loc="upper right")

# plot
plt.tight_layout()
plt.show()