Skip to content

DOC modified the graph for better readability #25644

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 3 commits into from
Feb 20, 2023
Merged

DOC modified the graph for better readability #25644

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
8e29578
DOC modified the graph for better readability
star1327p Feb 19, 2023
3aa4f97
Merge branch 'main' into linear_classification
star1327p Feb 20, 2023
ec1951d
make space for the legend on the lower left
star1327p Feb 20, 2023
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
Diff view
27 changes: 14 additions & 13 deletions examples/classification/plot_lda.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -47,8 +47,8 @@ def generate_data(n_samples, n_features):
for _ in range(n_averages):
X, y = generate_data(n_train, n_features)

clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
oa = OAS(store_precision=False, assume_centered=False)
clf3 = LinearDiscriminantAnalysis(solver="lsqr", covariance_estimator=oa).fit(
X, y
Expand All @@ -69,36 +69,37 @@ def generate_data(n_samples, n_features):
features_samples_ratio,
acc_clf1,
linewidth=2,
label="Linear Discriminant Analysis with Ledoit Wolf",
color="navy",
linestyle="dashed",
label="LDA",
color="gold",
linestyle="solid",
)
plt.plot(
features_samples_ratio,
acc_clf2,
linewidth=2,
label="Linear Discriminant Analysis",
color="gold",
linestyle="solid",
label="LDA with Ledoit Wolf",
color="navy",
linestyle="dashed",
)
plt.plot(
features_samples_ratio,
acc_clf3,
linewidth=2,
label="Linear Discriminant Analysis with OAS",
label="LDA with OAS",
color="red",
linestyle="dotted",
)

plt.xlabel("n_features / n_samples")
plt.ylabel("Classification accuracy")

plt.legend(loc=3, prop={"size": 12})
plt.legend(loc="lower left")
plt.ylim((0.65, 1.0))
plt.suptitle(
"Linear Discriminant Analysis vs. "
"LDA (Linear Discriminant Analysis) vs. "
+ "\n"
+ "Shrinkage Linear Discriminant Analysis vs. "
+ "LDA with Ledoit Wolf vs. "
+ "\n"
+ "OAS Linear Discriminant Analysis (1 discriminative feature)"
+ "LDA with OAS (1 discriminative feature)"
)
plt.show()