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DOC fix deprecation warning in plot_oneclass #27452

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Merged
merged 4 commits into from
Sep 25, 2023
Merged

DOC fix deprecation warning in plot_oneclass #27452

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72634be
DOC fix deprecation warning in plot_oneclass
glemaitre Sep 22, 2023
b61d64d
use DecisionBoundaryDisplay
glemaitre Sep 22, 2023
76c6ac8
Merge branch 'main' into fix_plot_oneclass
ArturoAmorQ Sep 25, 2023
10bf6da
Update examples/svm/plot_oneclass.py
glemaitre Sep 25, 2023
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71 changes: 51 additions & 20 deletions examples/svm/plot_oneclass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,13 +11,11 @@

"""

import matplotlib.font_manager
import matplotlib.pyplot as plt
# %%
import numpy as np

from sklearn import svm

xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
# Generate train data
X = 0.3 * np.random.randn(100, 2)
X_train = np.r_[X + 2, X - 2]
Expand All @@ -37,24 +35,52 @@
n_error_test = y_pred_test[y_pred_test == -1].size
n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size

# plot the line, the points, and the nearest vectors to the plane
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# %%
import matplotlib.font_manager
import matplotlib.lines as mlines
import matplotlib.pyplot as plt

from sklearn.inspection import DecisionBoundaryDisplay

plt.title("Novelty Detection")
plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="darkred")
plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors="palevioletred")
_, ax = plt.subplots()

# generate grid for the boundary display
xx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))
X = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)], axis=1)
DecisionBoundaryDisplay.from_estimator(
clf,
X,
response_method="decision_function",
plot_method="contourf",
ax=ax,
cmap="PuBu",
)
DecisionBoundaryDisplay.from_estimator(
clf,
X,
response_method="decision_function",
plot_method="contourf",
ax=ax,
levels=[0, 10000],
colors="palevioletred",
)
DecisionBoundaryDisplay.from_estimator(
clf,
X,
response_method="decision_function",
plot_method="contour",
ax=ax,
levels=[0],
colors="darkred",
linewidths=2,
)

s = 40
b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
plt.axis("tight")
plt.xlim((-5, 5))
plt.ylim((-5, 5))
b1 = ax.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
b2 = ax.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
c = ax.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
plt.legend(
[a.collections[0], b1, b2, c],
[mlines.Line2D([], [], color="darkred"), b1, b2, c],
[
"learned frontier",
"training observations",
Expand All @@ -64,8 +90,13 @@
loc="upper left",
prop=matplotlib.font_manager.FontProperties(size=11),
)
plt.xlabel(
"error train: %d/200 ; errors novel regular: %d/40 ; errors novel abnormal: %d/40"
% (n_error_train, n_error_test, n_error_outliers)
ax.set(
xlabel=(
f"error train: {n_error_train}/200 ; errors novel regular: {n_error_test}/40 ;"
f" errors novel abnormal: {n_error_outliers}/40"
),
title="Novelty Detection",
xlim=(-5, 5),
ylim=(-5, 5),
)
plt.show()