回答編集履歴
4
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@@ -111,4 +111,7 @@
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111
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局在したガウシアンの場合(gamma=10000.)
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114
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115
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CVしたガウシアンの場合
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117
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3
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@@ -110,5 +110,5 @@
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110
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広がったガウシアンの場合(gamma=0.000001)
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局在したガウシアン(gamma=10000.)
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局在したガウシアンの場合(gamma=10000.)
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114
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2
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@@ -99,4 +99,16 @@
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CV(get_OvO, *load())
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print('OneHot')
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CV(get_OneHot, *load(fonehot=True))
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```
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```
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---
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追記
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IrisのXに2変数を使って平面上に射影したときの境界線。
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広がったガウシアンの場合(gamma=0.000001)
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112
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局在したガウシアン(gamma=10000.)
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114
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1
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@@ -2,4 +2,101 @@
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2
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3
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http://scikit-learn.org/stable/modules/svm.html
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sklearnのSVCはOne-vs-Oneで実装されていませんか？
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5
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sklearnのSVCはOne-vs-Oneで実装されていませんか？
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---
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8
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9
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簡単なIrisに対してのコード。
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10
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SVCをもうすでに使っているのならマルチクラスを分類できない理由は特にないように思いましたが…
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13
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```python
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import numpy as np
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15
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import RandomizedSearchCV
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from sklearn.svm import SVC
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from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier
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from sklearn.multioutput import MultiOutputClassifier
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def load(fonehot=False):
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data = load_iris()
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x = data['data']
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y = data['target']
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hs = y
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if fonehot:
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en = OneHotEncoder()
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y = en.fit_transform(y.reshape(-1, 1)).toarray()
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return x, y, hs
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def get_SVC():
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clf = SVC()
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param_grid = {'kernel': ['rbf', 'linear'],
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'C': np.logspace(-10, 1, 1000),
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'gamma': np.logspace(-10, 1, 1000)}
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clf = RandomizedSearchCV(clf, param_grid, cv=5, n_iter=100, random_state=2018)
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return clf
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def get_OvR():
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clf = SVC()
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clf = OneVsRestClassifier(clf)
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param_grid = {'estimator__kernel': ['rbf', 'linear'],
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'estimator__C': np.logspace(-10, 1, 1000),
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'estimator__gamma': np.logspace(-10, 1, 1000)}
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clf = RandomizedSearchCV(clf, param_grid, cv=5, n_iter=100, random_state=2018)
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return clf
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def get_OvO():
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clf = SVC()
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clf = OneVsOneClassifier(clf)
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param_grid = {'estimator__kernel': ['rbf', 'linear'],
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'estimator__C': np.logspace(-10, 1, 1000),
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'estimator__gamma': np.logspace(-10, 1, 1000)}
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63
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clf = RandomizedSearchCV(clf, param_grid, cv=5, n_iter=100, random_state=2018)
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return clf
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def get_OneHot():
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67
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clf = SVC()
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clf = MultiOutputClassifier(clf)
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param_grid = {'estimator__kernel': ['rbf', 'linear'],
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70
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'estimator__C': np.logspace(-10, 1, 1000),
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71
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'estimator__gamma': np.logspace(-10, 1, 1000)}
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72
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clf = RandomizedSearchCV(clf, param_grid, cv=5, n_iter=100, random_state=2018)
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return clf
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74
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75
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def CV(get_clf, x, y, hs, n_splits=3):
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76
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kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2018)
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s_s = []
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78
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pss = []
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79
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for tr, ts in kf.split(x, hs):
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80
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x_ = x[tr]
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81
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y_ = y[tr]
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82
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px = x[ts]
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83
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py = y[ts]
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84
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clf = get_clf()
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85
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clf.fit(x_, y_)
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86
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s_ = accuracy_score(y_, clf.predict(x_))
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87
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ps = accuracy_score(py, clf.predict(px))
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s_s.append(s_)
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pss.append(ps)
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90
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print('train: {0:7.4f} {1:7.4f}'.format(np.mean(s_s), np.std(s_s)))
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print('test: {0:7.4f} {1:7.4f}'.format(np.mean(pss), np.std(pss)))
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92
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93
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if __name__ == '__main__':
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94
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print('SVC(Implemented with One-vs-one)')
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95
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CV(get_SVC, *load())
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96
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print('OneVsRest')
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97
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CV(get_OvR, *load())
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98
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print('OneVsOne')
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99
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CV(get_OvO, *load())
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100
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print('OneHot')
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101
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CV(get_OneHot, *load(fonehot=True))
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102
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```
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