"""
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective kernel which corresponds to a non-linear function in
the original space. They differ in the loss functions (ridge versus
epsilon-insensitive loss). In contrast to SVR, fitting a KRR can be done in
closed-form and is typically faster for medium-sized datasets. On the other
hand, the learned model is non-sparse and thus slower than SVR at
prediction-time.
This example illustrates both methods on an artificial dataset, which
consists of a sinusoidal target function and strong noise added to every fifth
datapoint.
"""
# %%
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# %%
# Generate sample data
# --------------------
import numpy as np
rng = np.random.RandomState(42)
X = 5 * rng.rand(10000, 1)
y = np.sin(X).ravel()
# Add noise to targets
y[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))
X_plot = np.linspace(0, 5, 100000)[:, None]
# %%
# Construct the kernel-based regression models
# --------------------------------------------
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVR
train_size = 100
svr = GridSearchCV(
SVR(kernel="rbf", gamma=0.1),
param_grid={"C": [1e0, 1e1, 1e2, 1e3], "gamma": np.logspace(-2, 2, 5)},
)
kr = GridSearchCV(
KernelRidge(kernel="rbf", gamma=0.1),
param_grid={"alpha": [1e0, 0.1, 1e-2, 1e-3], "gamma": np.logspace(-2, 2, 5)},
)
# %%
# Compare times of SVR and Kernel Ridge Regression
# ------------------------------------------------
import time
t0 = time.time()
svr.fit(X[:train_size], y[:train_size])
svr_fit = time.time() - t0
print(f"Best SVR with params: {svr.best_params_} and R2 score: {svr.best_score_:.3f}")
print("SVR complexity and bandwidth selected and model fitted in %.3f s" % svr_fit)
t0 = time.time()
kr.fit(X[:train_size], y[:train_size])
kr_fit = time.time() - t0
print(f"Best KRR with params: {kr.best_params_} and R2 score: {kr.best_score_:.3f}")
print("KRR complexity and bandwidth selected and model fitted in %.3f s" % kr_fit)
sv_ratio = svr.best_estimator_.support_.shape[0] / train_size
print("Support vector ratio: %.3f" % sv_ratio)
t0 = time.time()
y_svr = svr.predict(X_plot)
svr_predict = time.time() - t0
print("SVR prediction for %d inputs in %.3f s" % (X_plot.shape[0], svr_predict))
t0 = time.time()
y_kr = kr.predict(X_plot)
kr_predict = time.time() - t0
print("KRR prediction for %d inputs in %.3f s" % (X_plot.shape[0], kr_predict))
# %%
# Look at the results
# -------------------
import matplotlib.pyplot as plt
sv_ind = svr.best_estimator_.support_
plt.scatter(
X[sv_ind],
y[sv_ind],
c="r",
s=50,
label="SVR support vectors",
zorder=2,
edgecolors=(0, 0, 0),
)
plt.scatter(X[:100], y[:100], c="k", label="data", zorder=1, edgecolors=(0, 0, 0))
plt.plot(
X_plot,
y_svr,
c="r",
label="SVR (fit: %.3fs, predict: %.3fs)" % (svr_fit, svr_predict),
)
plt.plot(
X_plot, y_kr, c="g", label="KRR (fit: %.3fs, predict: %.3fs)" % (kr_fit, kr_predict)
)
plt.xlabel("data")
plt.ylabel("target")
plt.title("SVR versus Kernel Ridge")
_ = plt.legend()
# %%
# The previous figure compares the learned model of KRR and SVR when both
# complexity/regularization and bandwidth of the RBF kernel are optimized using
# grid-search. The learned functions are very similar; however, fitting KRR is
# approximately 3-4 times faster than fitting SVR (both with grid-search).
#
# Prediction of 100000 target values could be in theory approximately three
# times faster with SVR since it has learned a sparse model using only
# approximately 1/3 of the training datapoints as support vectors. However, in
# practice, this is not necessarily the case because of implementation details
# in the way the kernel function is computed for each model that can make the
# KRR model as fast or even faster despite computing more arithmetic
# operations.
# %%
# Visualize training and prediction times
# ---------------------------------------
plt.figure()
sizes = np.logspace(1, 3.8, 7).astype(int)
for name, estimator in {
"KRR": KernelRidge(kernel="rbf", alpha=0.01, gamma=10),
"SVR": SVR(kernel="rbf", C=1e2, gamma=10),
}.items():
train_time = []
test_time = []
for train_test_size in sizes:
t0 = time.time()
estimator.fit(X[:train_test_size], y[:train_test_size])
train_time.append(time.time() - t0)
t0 = time.time()
estimator.predict(X_plot[:1000])
test_time.append(time.time() - t0)
plt.plot(
sizes,
train_time,
"o-",
color="r" if name == "SVR" else "g",
label="%s (train)" % name,
)
plt.plot(
sizes,
test_time,
"o--",
color="r" if name == "SVR" else "g",
label="%s (test)" % name,
)
plt.xscale("log")
plt.yscale("log")
plt.xlabel("Train size")
plt.ylabel("Time (seconds)")
plt.title("Execution Time")
_ = plt.legend(loc="best")
# %%
# This figure compares the time for fitting and prediction of KRR and SVR for
# different sizes of the training set. Fitting KRR is faster than SVR for
# medium-sized training sets (less than a few thousand samples); however, for
# larger training sets SVR scales better. With regard to prediction time, SVR
# should be faster than KRR for all sizes of the training set because of the
# learned sparse solution, however this is not necessarily the case in practice
# because of implementation details. Note that the degree of sparsity and thus
# the prediction time depends on the parameters epsilon and C of the SVR.
# %%
# Visualize the learning curves
# -----------------------------
from sklearn.model_selection import LearningCurveDisplay
_, ax = plt.subplots()
svr = SVR(kernel="rbf", C=1e1, gamma=0.1)
kr = KernelRidge(kernel="rbf", alpha=0.1, gamma=0.1)
common_params = {
"X": X[:100],
"y": y[:100],
"train_sizes": np.linspace(0.1, 1, 10),
"scoring": "neg_mean_squared_error",
"negate_score": True,
"score_name": "Mean Squared Error",
"score_type": "test",
"std_display_style": None,
"ax": ax,
}
LearningCurveDisplay.from_estimator(svr, **common_params)
LearningCurveDisplay.from_estimator(kr, **common_params)
ax.set_title("Learning curves")
ax.legend(handles=ax.get_legend_handles_labels()[0], labels=["SVR", "KRR"])
plt.show()