8000 DOC: update notebook-style for plot_calibration.py by AmarCodes-22 · Pull Request #22734 · scikit-learn/scikit-learn · GitHub
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DOC: update notebook-style for plot_calibration.py #22734

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Merged
merged 3 commits into from
Mar 17, 2022
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DOC: update notebook-style for plot_calibration.py #22734

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f94479c
update notebook-style for plot_calibration.py
AmarCodes-22 Mar 8, 2022
d22a0fd
Move imports closer to their use
AmarCodes-22 Mar 13, 2022
51d24cc
Update examples/calibration/plot_calibration.py
jeremiedbb Mar 17, 2022
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41 changes: 25 additions & 16 deletions examples/calibration/plot_calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,24 +22,21 @@
Brier score.

"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Authors:
# Mathieu Blondel <mathieu@mblondel.org>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD Style.

# %%
# Generate synthetic dataset
# --------------------------
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import brier_score_loss
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import train_test_split


n_samples = 50000
n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here

Expand All @@ -58,17 +55,24 @@
X, y, sample_weight, test_size=0.9, random_state=42
)

# Gaussian Naive-Bayes with no calibration
# %%
# Gaussian Naive-Bayes
# --------------------
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import brier_score_loss
from sklearn.naive_bayes import GaussianNB

# With no calibration
clf = GaussianNB()
clf.fit(X_train, y_train) # GaussianNB itself does not support sample-weights
prob_pos_clf = clf.predict_proba(X_test)[:, 1]

# Gaussian Naive-Bayes with isotonic calibration
# With isotonic calibration
clf_isotonic = CalibratedClassifierCV(clf, cv=2, method="isotonic")
clf_isotonic.fit(X_train, y_train, sample_weight=sw_train)
prob_pos_isotonic = clf_isotonic.predict_proba(X_test)[:, 1]

# Gaussian Naive-Bayes with sigmoid calibration
# With sigmoid calibration
clf_sigmoid = CalibratedClassifierCV(clf, cv=2, method="sigmoid")
clf_sigmoid.fit(X_train, y_train, sample_weight=sw_train)
prob_pos_sigmoid = clf_sigmoid.predict_proba(X_test)[:, 1]
Expand All @@ -84,8 +88,12 @@
clf_sigmoid_score = brier_score_loss(y_test, prob_pos_sigmoid, sample_weight=sw_test)
print("With sigmoid calibration: %1.3f" % clf_sigmoid_score)

# #############################################################################
# Plot the data and the predicted probabilities
# %%
# Plot data and the predicted probabilities
# -----------------------------------------
from matplotlib import cm
import matplotlib.pyplot as plt

plt.figure()
y_unique = np.unique(y)
colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))
Expand All @@ -105,6 +113,7 @@
plt.title("Data")

plt.figure()

order = np.lexsort((prob_pos_clf,))
plt.plot(prob_pos_clf[order], "r", label="No calibration (%1.3f)" % clf_score)
plt.plot(
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