def test_grid_search_sparse_score_func():
X_, y_ = test_dataset_classif(n_samples=200, n_features=100, seed=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
X_ = sp.csr_matrix(X_)
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
cv.fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
def test_grid_search_sparse():
"""Test that grid search works with both dense and sparse matrices"""
X_, y_ = test_dataset_classif(n_samples=200, n_features=100, seed=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C':[0.1, 1.0]})
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
X_ = sp.csr_matrix(X_)
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C':[0.1, 1.0]})
cv.fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
assert np.mean(y_pred == y_pred2) >= .9
assert_equal(C, C2)
def test_grid_search_sparse():
"""Test that grid search works with both dense and sparse matrices"""
X_, y_ = test_dataset_classif(n_samples=200, n_features=100, seed=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
X_ = sp.csr_matrix(X_)
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
assert np.mean(y_pred == y_pred2) >= .9
assert_equal(C, C2)
def test_grid_search_sparse_score_func():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
cv.set_params(refit=False).fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
X_ = sp.csr_matrix(X_)
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
cv.set_params(refit=False).fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
def test_grid_search_sparse_score_func():
X_, y_ = test_dataset_classif(n_samples=200, n_features=100, seed=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
cv.fit(X_[:180], y_[:180], refit=False)
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
X_ = sp.csr_matrix(X_)
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
cv.fit(X_[:180], y_[:180], refit=False)
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
def train_svm_crossvalidated(X, y, tuned_parameters={'kernel': ['rbf'], 'gamma': 2.0**np.arange(-15,3), 'C': 2.0**np.arange(-5, 15)}):
"""
Performs grid search with stratified K-fold cross validation on observations X with
true labels y and returns an optimal SVM, clf
"""
k_fold = _size_dependent_k_split(np.size(X,0))
clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=recall_score)
clf.fit(X, y, cv=StratifiedKFold(y, k_fold))
y_true, y_pred = y, clf.predict(X)
#print "Classification report for the best estimator: "
#print clf.best_estimator
print "Tuned with optimal value: %0.3f" % recall_score(y_true, y_pred)
return clf
X_test_pca = pca.transform(X_test)
# Train a SVM classification model
print "Fitting the classifier to the training set"
param_grid = {"C": [1, 5, 10, 100], "gamma": [0.0001, 0.001, 0.01, 0.1]}
clf = GridSearchCV(SVC(kernel="rbf"), param_grid, fit_params={"class_weight": "auto"}, n_jobs=-1)
clf = clf.fit(X_train_pca, y_train)
print "Best estimator found by grid search:"
print clf.best_estimator
# Quantitative evaluation of the model quality on the test set
y_pred = clf.predict(X_test_pca)
print classification_report(y_test, y_pred, labels=selected_target, target_names=target_names[selected_target])
print confusion_matrix(y_test, y_pred, labels=selected_target)
# Qualitative evaluation of the predictions using matplotlib
n_row = 3
n_col = 4
def title(y_pred, y_test, target_names, i):
pred_name = target_names[y_pred[i]].rsplit("_", 1)[-1]
true_name = target_names[y_test[i]].rsplit("_", 1)[-1]
return "predicted: %s\ntrue: %s" % (pred_name, true_name)
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf'),
param_grid,
fit_params={'class_weight': 'auto'})
clf = clf.fit(X_train_pca, y_train)
print "done in %0.3fs" % (time() - t0)
print "Best estimator found by grid search:"
print clf.best_estimator
################################################################################
# Quantitative evaluation of the model quality on the test set
print "Predicting the people names on the testing set"
t0 = time()
y_pred = clf.predict(X_test_pca)
print "done in %0.3fs" % (time() - t0)
print classification_report(y_test, y_pred, target_names=target_names)
print confusion_matrix(y_test, y_pred, labels=range(n_classes))
################################################################################
# Qualitative evaluation of the predictions using matplotlib
n_row = 3
n_col = 4
def title(y_pred, y_test, target_names, i):
pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
# split the dataset in two equal part respecting label proportions
train, test = iter(StratifiedKFold(y, 2)).next()
################################################################################
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = [
('precision', precision_score),
('recall', recall_score),
]
for score_name, score_func in scores:
clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func)
clf.fit(X[train], y[train], cv=StratifiedKFold(y[train], 5))
y_true, y_pred = y[test], clf.predict(X[test])
print "Classification report for the best estimator: "
print clf.best_estimator
print "Tuned for '%s' with optimal value: %0.3f" % (
score_name, score_func(y_true, y_pred))
print classification_report(y_true, y_pred)
print "Grid scores:"
pprint(clf.grid_scores_)
print
# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf'),
param_grid,
fit_params={'class_weight': 'auto'})
#clf = SVC(kernel='rbf')
#clf = SVC(kernel='linear')
clf.fit(np.vstack([moto_vq_train, plane_vq_train]), np.array(labels))
print "Best estimator found by grid search:"
#print clf.best_estimator
###############################################################################
# Evaluation
moto_vq_eval, plane_vq_eval = [
np.load(file) for file in ['moto_vq_eval.npy', 'plane_vq_eval.npy']
]
y_name = ['moto'] * moto_vq_eval.shape[0] + ['plane'] * plane_vq_eval.shape[0]
y_test = [0] * moto_vq_eval.shape[0] + [1] * plane_vq_eval.shape[0]
y_test = np.array(y_test)
y_pred = clf.predict(np.vstack([moto_vq_eval, plane_vq_eval]))
print classification_report(y_test, y_pred, labels=labels, class_names=y_name)
print confusion_matrix(y_test, y_pred)
clf = GridSearchCV(SVC(kernel='rbf'), param_grid,
fit_params={'class_weight': 'auto'})
#clf = SVC(kernel='rbf')
#clf = SVC(kernel='linear')
clf.fit(np.vstack([moto_vq_train,plane_vq_train]),
np.array(labels))
print "Best estimator found by grid search:"
#print clf.best_estimator
###############################################################################
# Evaluation
moto_vq_eval, plane_vq_eval = [np.load(file)
for file
in ['moto_vq_eval.npy','plane_vq_eval.npy']]
y_name = ['moto']*moto_vq_eval.shape[0] + ['plane']* plane_vq_eval.shape[0]
y_test = [0]* moto_vq_eval.shape[0] + [1]* plane_vq_eval.shape[0]
y_test = np.array(y_test)
y_pred = clf.predict(np.vstack([moto_vq_eval, plane_vq_eval]))
print classification_report(y_test, y_pred, labels=labels, class_names=y_name)
print confusion_matrix(y_test, y_pred)
tuned_parameters = [{
'kernel': ['rbf'],
'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]
}, {
'kernel': ['linear'],
'C': [1, 10, 100, 1000]
}]
scores = [
('precision', precision_score),
('recall', recall_score),
]
for score_name, score_func in scores:
clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func)
clf.fit(X[train], y[train], cv=StratifiedKFold(y[train], 5))
y_true, y_pred = y[test], clf.predict(X[test])
print "Classification report for the best estimator: "
print clf.best_estimator
print "Tuned for '%s' with optimal value: %0.3f" % (
score_name, score_func(y_true, y_pred))
print classification_report(y_true, y_pred)
print "Grid scores:"
pprint(clf.grid_scores_)
print
# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality
from scikits.learn.grid_search import GridSearchCV
from scikits.learn import datasets
from scikits.learn.metrics import zero_one
################################################################################
# Loading the Digits dataset
digits = datasets.load_digits()
# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target
################################################################################
# Set the parameters by cross-validation
tuned_parameters = [{'kernel':('rbf', ), 'gamma':[1e-3, 1e-4]},
{'kernel':('linear', )}]
clf = GridSearchCV(SVC(C=1), tuned_parameters, n_jobs=2)
y_pred = []
y_true = []
for train, test in StratifiedKFold(y, 2):
clf.fit(X[train], y[train], cv=StratifiedKFold(y[train], 5))
y_pred = np.append(y_pred, clf.predict(X[test]))
y_true = np.append(y_true, y[test])
classif_rate = np.mean(y_pred == y_true) * 100
print "Classification rate : %f" % classif_rate