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MNT accelerate examples/kernel_approximation/plot_scalable_poly_kernels.py (#22903) 

Co-authored-by: sveneschlbeck <sven.eschlbeck@t-online.de>
Co-authored-by: Sven Eschlbeck <66701689+sveneschlbeck@users.noreply.github.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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Jordan Silke 2022-03-29 11:49:03 -04:00 committed by GitHub
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examples/kernel_approximation/plot_scalable_poly_kernels.py
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@ -64,11 +64,10 @@ mm = make_pipeline(MinMaxScaler(), Normalizer())
X_train = mm.fit_transform(X_train)
X_test = mm.transform(X_test)
# %%
# As a baseline, train a linear SVM on the original features and print the
# accuracy. We also measure and store accuracies and training times to
# plot them latter.
# plot them later.
results = {}
@ -95,11 +94,14 @@ print(f"Linear SVM score on raw features: {lsvm_score:.2f}%")
# polynomial kernel of degree four would have approximately 8.5 million
# features (precisely, 54^4). Thanks to :class:`PolynomialCountSketch`, we can
# condense most of the discriminative information of that feature space into a
# much more compact representation. We repeat the experiment 5 times to
# compensate for the stochastic nature of :class:`PolynomialCountSketch`.
# much more compact representation. While we run the experiment only a single time
# (`n_runs` = 1) in this example, in practice one should repeat the experiment several
# times to compensate for the stochastic nature of :class:`PolynomialCountSketch`.
n_runs = 3
for n_components in [250, 500, 1000, 2000]:
n_runs = 1
N_COMPONENTS = [250, 500, 1000, 2000]
for n_components in N_COMPONENTS:
ps_lsvm_time = 0
ps_lsvm_score = 0
@ -148,7 +150,7 @@ ksvm_time = time.time() - start
ksvm_score = 100 * ksvm.score(X_test, y_test)
results["KSVM"] = {"time": ksvm_time, "score": ksvm_score}
print(f"Kernel-SVM score on raw featrues: {ksvm_score:.2f}%")
print(f"Kernel-SVM score on raw features: {ksvm_score:.2f}%")
# %%
# Finally, plot the results of the different methods against their training
@ -156,8 +158,6 @@ print(f"Kernel-SVM score on raw featrues: {ksvm_score:.2f}%")
# but its training time is much larger and, most importantly, will grow
# much faster if the number of training samples increases.
N_COMPONENTS = [250, 500, 1000, 2000]
fig, ax = plt.subplots(figsize=(7, 7))
ax.scatter(
[
@ -181,6 +181,7 @@ ax.scatter(
label="Linear SVM + PolynomialCountSketch",
c="blue",
)
for n_components in N_COMPONENTS:
ax.scatter(
[