8000 DOC use notebook-style for plot_svm_anova.py by JihaneBennis · Pull Request #22779 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

DOC use notebook-style for plot_svm_anova.py #22779

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 5 commits into from
Mar 18, 2022
Merged

DOC use notebook-style for plot_svm_anova.py #22779

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
13a4751
Fix notebook style example
JihaneBennis Mar 12, 2022
5a55a3e
lines
JihaneBennis Mar 12, 2022
5c78da0
flake8 correction
JihaneBennis Mar 12, 2022
f4757b2
Update examples/svm/plot_svm_anova.py
lesteve Mar 18, 2022
05488f1
Move imports closer to the point they are used
lesteve Mar 18, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
8000
Diff view
33 changes: 20 additions & 13 deletions examples/svm/plot_svm_anova.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,26 +10,29 @@

"""

# %%
# Load some data to play with
# ---------------------------
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectPercentile, chi2
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC


# #############################################################################
# Import some data to play with
X, y = load_iris(return_X_y=True)

# Add non-informative features
np.random.seed(0)
X = np.hstack((X, 2 * np.random.random((X.shape[0], 36))))
rng = np.random.RandomState(0)
X = np.hstack((X, 2 * rng.random((X.shape[0], 36))))

# %%
# Create the pipeline
# -------------------
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectPercentile, chi2
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

# #############################################################################
# Create a feature-selection transform, a scaler and an instance of SVM that we
# combine together to have a full-blown estimator

clf = Pipeline(
[
("anova", SelectPercentile(chi2)),
Expand All @@ -38,8 +41,12 @@
]
)

# #############################################################################
# %%
# Plot the cross-validation score as a function of percentile of features
# -----------------------------------------------------------------------
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score

score_means = list()
score_stds = list()
percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
Expand Down
31A7
0