本文整理汇总了Python中sklearn.svm.SVC属性的典型用法代码示例。如果您正苦于以下问题:Python svm.SVC属性的具体用法?Python svm.SVC怎么用?Python svm.SVC使用的例子?那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类sklearn.svm
的用法示例。
在下文中一共展示了svm.SVC属性的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _build_model
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def _build_model(self,model_name,params=None):
if params==None:
if model_name=='xgb':
self.model=XGBClassifier(n_estimators=100,learning_rate=0.02)
elif model_name=='svm':
kernel_function=chi2_kernel if not (self.model_kernel=='linear' or self.model_kernel=='rbf') else self.model_kernel
self.model=SVC(C=1,kernel=kernel_function,gamma=1,probability=True)
elif model_name=='lr':
self.model=LR(C=1,penalty='l1',tol=1e-6)
else:
if model_name=='xgb':
self.model=XGBClassifier(n_estimators=1000,learning_rate=0.02,**params)
elif model_name=='svm':
self.model=SVC(C=1,kernel=kernel_function,gamma=1,probability=True)
elif model_name=='lr':
self.model=LR(C=1,penalty='l1',tol=1e-6)
log.l.info('=======> built the model {} done'.format(self.model_name))
示例2: test_svm
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def test_svm(self):
svc_clf = SVC(gamma="auto")
svc_clf.fit(self.X_train, self.y_train)
svm = SVM()
svm.train(Dataset(self.X_train, self.y_train))
assert_array_equal(
svc_clf.predict(self.X_train), svm.predict(self.X_train))
assert_array_equal(
svc_clf.predict(self.X_test), svm.predict(self.X_test))
self.assertEqual(
svc_clf.score(self.X_train, self.y_train),
svm.score(Dataset(self.X_train, self.y_train)))
self.assertEqual(
svc_clf.score(self.X_test, self.y_test),
svm.score(Dataset(self.X_test, self.y_test)))
示例3: create_pandas_only_svm_classifier
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def create_pandas_only_svm_classifier(X, y, probability=True):
class PandasOnlyEstimator(TransformerMixin):
def fit(self, X, y=None, **fitparams):
return self
def transform(self, X, **transformparams):
dataset_is_df = isinstance(X, pd.DataFrame)
if not dataset_is_df:
raise Exception("Dataset must be a pandas dataframe!")
return X
pandas_only = PandasOnlyEstimator()
clf = svm.SVC(gamma=0.001, C=100.0, probability=probability, random_state=777)
pipeline = Pipeline([("pandas_only", pandas_only), ("clf", clf)])
return pipeline.fit(X, y)
示例4: multi_class_classification
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def multi_class_classification(data_X,data_Y):
'''
calculate multi-class classification and return related evaluation metrics
'''
svc = svm.SVC(C=1, kernel='linear')
# X_train, X_test, y_train, y_test = train_test_split( data_X, data_Y, test_size=0.4, random_state=0)
clf = svc.fit(data_X, data_Y) #svm
# array = svc.coef_
# print array
predicted = cross_val_predict(clf, data_X, data_Y, cv=2)
print "accuracy",metrics.accuracy_score(data_Y, predicted)
print "f1 score macro",metrics.f1_score(data_Y, predicted, average='macro')
print "f1 score micro",metrics.f1_score(data_Y, predicted, average='micro')
print "precision score",metrics.precision_score(data_Y, predicted, average='macro')
print "recall score",metrics.recall_score(data_Y, predicted, average='macro')
print "hamming_loss",metrics.hamming_loss(data_Y, predicted)
print "classification_report", metrics.classification_report(data_Y, predicted)
print "jaccard_similarity_score", metrics.jaccard_similarity_score(data_Y, predicted)
# print "log_loss", metrics.log_loss(data_Y, predicted)
print "zero_one_loss", metrics.zero_one_loss(data_Y, predicted)
# print "AUC&ROC",metrics.roc_auc_score(data_Y, predicted)
# print "matthews_corrcoef", metrics.matthews_corrcoef(data_Y, predicted)
示例5: __init__
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def __init__(self,
learner=SVC(C=1000),
multiclass_strategy='ova',
verbose=False,
max_iter=1000,
learning_rate=0.01,
tolerance=1e-7,
callbacks=[],
scheduler=None ):
super().__init__(
learner=learner,
multiclass_strategy=multiclass_strategy,
max_iter=max_iter,
verbose=verbose,
tolerance=tolerance,
callbacks=callbacks,
scheduler=scheduler,
direction='min',
learning_rate=learning_rate,
)
self.func_form = summation
示例6: compute_accuracy_svc
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def compute_accuracy_svc(
data_train,
labels_train,
data_test,
labels_test,
param_grid=None,
verbose=0,
max_iter=-1,
):
if param_grid is None:
param_grid = [
{"C": [1, 10, 100, 1000], "kernel": ["linear"]},
{"C": [1, 10, 100, 1000], "gamma": [0.001, 0.0001], "kernel": ["rbf"]},
]
svc = SVC(max_iter=max_iter)
clf = GridSearchCV(svc, param_grid, verbose=verbose, cv=3)
return compute_accuracy_classifier(
clf, data_train, labels_train, data_test, labels_test
)
示例7: __init__
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def __init__(self, *args, **kwargs):
super(MaximumLossReductionMaximalConfidence, self).__init__(*args, **kwargs)
# self.n_labels = len(self.dataset.get_labeled_entries()[0][1])
self.n_labels = len(self.dataset.get_labeled_entries()[1][0])
random_state = kwargs.pop('random_state', None)
self.random_state_ = seed_random_state(random_state)
self.logreg_param = kwargs.pop('logreg_param',
{'multi_class': 'multinomial',
'solver': 'newton-cg',
'random_state': random_state})
self.logistic_regression_ = LogisticRegression(**self.logreg_param)
self.br_base = kwargs.pop('br_base',
SklearnProbaAdapter(SVC(kernel='linear',
probability=True,
gamma="auto",
random_state=random_state)))
示例8: objective_function
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def objective_function(x, s):
# Start the clock to determine the cost of this function evaluation
start_time = time.time()
# Shuffle the data and split up the request subset of the training data
s_max = y_train.shape[0]
shuffle = np.random.permutation(np.arange(s_max))
train_subset = X_train[shuffle[:s]]
train_targets_subset = y_train[shuffle[:s]]
# Train the SVM on the subset set
C = np.exp(float(x[0]))
gamma = np.exp(float(x[1]))
clf = svm.SVC(gamma=gamma, C=C)
clf.fit(train_subset, train_targets_subset)
# Validate this hyperparameter configuration on the full validation data
y = 1 - clf.score(X_val, y_val)
c = time.time() - start_time
return y, c
# Load the data
示例9: buildModel
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def buildModel(dataset, method, parameters):
"""
Build final model for predicting real testing data
"""
features = dataset.columns[0:-1]
if method == 'RNN':
clf = performRNNlass(dataset[features], dataset['UpDown'])
return clf
elif method == 'RF':
clf = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
elif method == 'KNN':
clf = neighbors.KNeighborsClassifier()
elif method == 'SVM':
c = parameters[0]
g = parameters[1]
clf = SVC(C=c, gamma=g)
elif method == 'ADA':
clf = AdaBoostClassifier()
return clf.fit(dataset[features], dataset['UpDown'])
示例10: sk_svm_train
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def sk_svm_train(intr, labeltr, inte, labelte, kener):
clf = svm.SVC(kernel=kener)
# 开始训练
clf.fit(intr, labeltr)
# 绘图的标识
figsign = kener
# 训练精确度
acc_train = clf.score(intr, labeltr)
# 测试精确度
acc_test = clf.score(inte, labelte)
# 支持向量的个数
vec_count = sum(clf.n_support_)
# 支持向量
vectors = clf.support_vectors_
return acc_train, acc_test, vec_count, vectors, figsign
# 结果输出函数
示例11: _prepare_classifier
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def _prepare_classifier(self, params, n_jobs=1):
X_train, y_train = params
tuned_parameters = [{
'kernel': ['rbf'],
'gamma': [1e-4,1e-3,1e-2,1e-1,1e+0,1e+1,1e+2,1e+3,1e+4],
'C': [1e+0,1e+1,1e+2,1e+3,1e+4,1e+5,1e+6,1e+7,1e+8,1e+9]
}]
clf=RandomizedSearchCV(svm.SVC(random_state=self.random_state),
tuned_parameters[0],
n_iter=self.n_randomized_search_iter,
n_jobs=n_jobs, random_state=self.random_state)
clf.fit(X_train, y_train)
params=clf.best_params_
clf=svm.SVC(kernel=params['kernel'], C=params['C'],
gamma=params['gamma'], probability=True,
random_state=self.random_state)
clf.fit(X_train, y_train)
return clf
示例12: SVM
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def SVM():
'''data1——线性分类'''
data1 = spio.loadmat('data1.mat')
X = data1['X']
y = data1['y']
y = np.ravel(y)
plot_data(X, y)
model = svm.SVC(C=1.0, kernel='linear').fit(X, y) # 指定核函数为线性核函数
plot_decisionBoundary(X, y, model) # 画决策边界
'''data2——非线性分类'''
data2 = spio.loadmat('data2.mat')
X = data2['X']
y = data2['y']
y = np.ravel(y)
plt = plot_data(X, y)
plt.show()
model = svm.SVC(gamma=100).fit(X, y) # gamma为核函数的系数,值越大拟合的越好
plot_decisionBoundary(X, y, model, class_='notLinear') # 画决策边界
# 作图
示例13: run_svms
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def run_svms():
svm_training_data, svm_validation_data, svm_test_data \
= mnist_loader.load_data()
accuracies = []
for size in SIZES:
print "\n\nTraining SVM with data set size %s" % size
clf = svm.SVC()
clf.fit(svm_training_data[0][:size], svm_training_data[1][:size])
predictions = [int(a) for a in clf.predict(svm_validation_data[0])]
accuracy = sum(int(a == y) for a, y in
zip(predictions, svm_validation_data[1])) / 100.0
print "Accuracy was %s percent" % accuracy
accuracies.append(accuracy)
f = open("more_data_svm.json", "w")
json.dump(accuracies, f)
f.close()
示例14: __call__
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def __call__(self, estimator):
fitted_estimator = estimator.fit(self.X_train, self.y_train)
if isinstance(estimator, (LinearClassifierMixin, SVC, NuSVC,
LightBaseClassifier)):
y_pred = estimator.decision_function(self.X_test)
elif isinstance(estimator, DecisionTreeClassifier):
y_pred = estimator.predict_proba(self.X_test.astype(np.float32))
elif isinstance(
estimator,
(ForestClassifier, XGBClassifier, LGBMClassifier)):
y_pred = estimator.predict_proba(self.X_test)
else:
y_pred = estimator.predict(self.X_test)
return self.X_test, y_pred, fitted_estimator
示例15: test_linear_kernel
# 需要导入模块: from sklearn import svm [as 别名]
# 或者: from sklearn.svm import SVC [as 别名]
def test_linear_kernel():
estimator = svm.SVC(kernel="linear", random_state=1)
estimator.fit([[1], [2]], [1, 2])
assembler = assemblers.SklearnSVMModelAssembler(estimator)
actual = assembler.assemble()
def kernel_ast(sup_vec_value):
return ast.BinNumExpr(
ast.NumVal(sup_vec_value),
ast.FeatureRef(0),
ast.BinNumOpType.MUL)
expected = _create_expected_single_output_ast(
estimator.dual_coef_, estimator.intercept_,
[kernel_ast(1.0), kernel_ast(2.0)])
assert utils.cmp_exprs(actual, expected)