本文整理汇总了Python中sklearn.compose.ColumnTransformer.transform方法的典型用法代码示例。如果您正苦于以下问题：Python ColumnTransformer.transform方法的具体用法？Python ColumnTransformer.transform怎么用？Python ColumnTransformer.transform使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.compose.ColumnTransformer的用法示例。
在下文中一共展示了ColumnTransformer.transform方法的3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: test_column_transformer_sparse_stacking
# 需要导入模块: from sklearn.compose import ColumnTransformer [as 别名]
# 或者: from sklearn.compose.ColumnTransformer import transform [as 别名]
def test_column_transformer_sparse_stacking():
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
col_trans = ColumnTransformer([('trans1', Trans(), [0]),
('trans2', SparseMatrixTrans(), 1)])
col_trans.fit(X_array)
X_trans = col_trans.transform(X_array)
assert_true(sparse.issparse(X_trans))
assert_equal(X_trans.shape, (X_trans.shape[0], X_trans.shape[0] + 1))
assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0]))示例2: test_column_transformer_sparse_stacking
# 需要导入模块: from sklearn.compose import ColumnTransformer [as 别名]
# 或者: from sklearn.compose.ColumnTransformer import transform [as 别名]
def test_column_transformer_sparse_stacking():
X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
col_trans = ColumnTransformer([('trans1', Trans(), [0]),
('trans2', SparseMatrixTrans(), 1)],
sparse_threshold=0.8)
col_trans.fit(X_array)
X_trans = col_trans.transform(X_array)
assert sparse.issparse(X_trans)
assert_equal(X_trans.shape, (X_trans.shape[0], X_trans.shape[0] + 1))
assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0]))
assert len(col_trans.transformers_) == 2
assert col_trans.transformers_[-1][0] != 'remainder'
col_trans = ColumnTransformer([('trans1', Trans(), [0]),
('trans2', SparseMatrixTrans(), 1)],
sparse_threshold=0.1)
col_trans.fit(X_array)
X_trans = col_trans.transform(X_array)
assert not sparse.issparse(X_trans)
assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1)
assert_array_equal(X_trans[:, 1:], np.eye(X_trans.shape[0]))示例3: StratifiedKFold
# 需要导入模块: from sklearn.compose import ColumnTransformer [as 别名]
# 或者: from sklearn.compose.ColumnTransformer import transform [as 别名]
###############################################################################
# We will perform a 10-fold cross-validation and train the neural-network with
# the two different strategies previously presented.
from sklearn.model_selection import StratifiedKFold
skf = StratifiedKFold(n_splits=10)
cv_results_imbalanced = []
cv_time_imbalanced = []
cv_results_balanced = []
cv_time_balanced = []
for train_idx, valid_idx in skf.split(X_train, y_train):
X_local_train = preprocessor.fit_transform(X_train.iloc[train_idx])
y_local_train = y_train.iloc[train_idx].values.ravel()
X_local_test = preprocessor.transform(X_train.iloc[valid_idx])
y_local_test = y_train.iloc[valid_idx].values.ravel()
elapsed_time, roc_auc = fit_predict_imbalanced_model(
X_local_train, y_local_train, X_local_test, y_local_test)
cv_time_imbalanced.append(elapsed_time)
cv_results_imbalanced.append(roc_auc)
elapsed_time, roc_auc = fit_predict_balanced_model(
X_local_train, y_local_train, X_local_test, y_local_test)
cv_time_balanced.append(elapsed_time)
cv_results_balanced.append(roc_auc)
###############################################################################
# Plot of the results and computation time
###############################################################################