＜セル３＞
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import r2_score
import numpy as np
trial = 100
pred_svr_score = []; pred_svr_train_score = []
for i in range(trial):
x_train_ori, x_test_ori, y_train_ori, y_test_ori = train_test_split(x, y, test_size=0.2, train_size=0.8)
model_svr = SVR()
model_svr.fit(x_train_ori, y_train_ori)
pred_svr_ori = model_svr.predict(x_test_ori)
pred_svr_score.append(r2_score(y_test_ori, pred_svr_ori))
pred_svr_ori_train = model_svr.predict(x_train_ori)
pred_svr_train_score.append(r2_score(y_train_ori, pred_svr_ori_train))
pred_svr_ave = np.average(np.array(pred_svr_score))
pred_svr_train_ave = np.average(np.array(pred_svr_train_score))
print(pred_svr_ave, pred_svr_train_ave)
実行結果
0.22315444797654346 0.2444680512648118
SVRのスコアは結構低めの0.22315と出てきました。
とりあえず最後の学習で得られたデータをグラフにしてみます。
＜セル４＞
from matplotlib import pyplot as plt
fig=plt.figure(figsize=(8,6))
plt.clf()
plt.scatter(y_test_ori, pred_svr_ori)
実行結果
なんというかバラバラという感じで一貫性があるようには見えませんね。
それではオプションをいじることでこれがどう変わっていくのか、楽しみながいじっていきましょう。
SVRのヘルプ
ここもいつも通り先にヘルプを見て、どんなオプションがあるのか確認していきましょう。
＜セル５＞
print(help(SVR))
実行結果
Help on class SVR in module sklearn.svm._classes:
class SVR(sklearn.base.RegressorMixin, sklearn.svm._base.BaseLibSVM)
| SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)
|
| Epsilon-Support Vector Regression.
|
| The free parameters in the model are C and epsilon.
|
| The implementation is based on libsvm. The fit time complexity
| is more than quadratic with the number of samples which makes it hard
| to scale to datasets with more than a couple of 10000 samples. For large
| datasets consider using :class:`sklearn.svm.LinearSVR` or
| :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a
| :class:`sklearn.kernel_approximation.Nystroem` transformer.
|
| Read more in the :ref:`User Guide <svm_regression>`.
|
| Parameters
| ----------
| kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
| Specifies the kernel type to be used in the algorithm.
| It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
| a callable.
| If none is given, 'rbf' will be used. If a callable is given it is
| used to precompute the kernel matrix.
|
| degree : int, default=3
| Degree of the polynomial kernel function ('poly').
| Ignored by all other kernels.
|
| gamma : {'scale', 'auto'} or float, default='scale'
| Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
|
| - if ``gamma='scale'`` (default) is passed then it uses
| 1 / (n_features * X.var()) as value of gamma,
| - if 'auto', uses 1 / n_features.
|
| .. versionchanged:: 0.22
| The default value of ``gamma`` changed from 'auto' to 'scale'.
|
| coef0 : float, default=0.0
| Independent term in kernel function.
| It is only significant in 'poly' and 'sigmoid'.
|
| tol : float, default=1e-3
| Tolerance for stopping criterion.
|
| C : float, default=1.0
| Regularization parameter. The strength of the regularization is
| inversely proportional to C. Must be strictly positive.
| The penalty is a squared l2 penalty.
|
| epsilon : float, default=0.1
| Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
| within which no penalty is associated in the training loss function
| with points predicted within a distance epsilon from the actual
| value.
|
| shrinking : bool, default=True
| Whether to use the shrinking heuristic.
| See the :ref:`User Guide <shrinking_svm>`.
|
| cache_size : float, default=200
| Specify the size of the kernel cache (in MB).
|
| verbose : bool, default=False
| Enable verbose output. Note that this setting takes advantage of a
| per-process runtime setting in libsvm that, if enabled, may not work
| properly in a multithreaded context.
|
| max_iter : int, default=-1
| Hard limit on iterations within solver, or -1 for no limit.
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