本文整理汇总了Python中tpot.TPOTClassifier.predict方法的典型用法代码示例。如果您正苦于以下问题：Python TPOTClassifier.predict方法的具体用法？Python TPOTClassifier.predict怎么用？Python TPOTClassifier.predict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tpot.TPOTClassifier的用法示例。
在下文中一共展示了TPOTClassifier.predict方法的4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_predict
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import predict [as 别名]
def test_predict():
"""Assert that the TPOT predict function raises a ValueError when no optimized pipeline exists"""
tpot_obj = TPOTClassifier()
try:
tpot_obj.predict(testing_features)
assert False # Should be unreachable
except ValueError:
pass示例2: test_predict_2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import predict [as 别名]
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(input_matrix)', tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)示例3: test_predict_2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import predict [as 别名]
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
result = tpot_obj.predict(testing_features)
assert result.shape == (testing_features.shape[0],)示例4: main
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import predict [as 别名]
def main():
# set up the path to the data sets and the data were are going to experiment
# with
base_path = '/scratch/ditzler/Git/ClassificationDatasets/csv/'
data_setz = [#'bank',
'blood',
'breast-cancer-wisc-diag',
'breast-cancer-wisc-prog',
'breast-cancer-wisc',
'breast-cancer',
'congressional-voting',
'conn-bench-sonar-mines-rocks',
'credit-approval',
'cylinder-bands',
'echocardiogram',
#'fertility',
'haberman-survival',
'heart-hungarian',
'hepatitis',
'ionosphere',
'mammographic',
'molec-biol-promoter',
'musk-1',
'oocytes_merluccius_nucleus_4d',
'oocytes_trisopterus_nucleus_2f',
'ozone',
'parkinsons',
'pima',
#'pittsburg-bridges-T-OR-D';
'planning',
'ringnorm',
#'spambase',
'spectf_train',
'statlog-australian-credit',
'statlog-german-credit',
'statlog-heart',
'titanic',
#'twonorm',
'vertebral-column-2clases']
# nsplits is like the number of cv (its bootstraps here) then set up some variales
# to save the results to.
n_splitz = 10
errors = np.zeros((len(data_setz),))
fms = np.zeros((len(data_setz),))
times = np.zeros((len(data_setz),))
m = 0
for n in range(n_splitz):
print 'Spilt ' + str(n) + ' of ' + str(n_splitz)
for i in range(len(data_setz)):
print ' ' + data_setz[i]
df = pd.read_csv(base_path + data_setz[i] + '.csv', sep=',')
data = df.as_matrix()
X = data[:, :-1]
y = data[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25, random_state=m)
m += 1
ts = time.time()
tpot = TPOTClassifier(generations=10, population_size=25, verbosity=1)
tpot.fit(X_train, y_train)
times[i] += (time.time() - ts)
errors[i] += (1-tpot.score(X_test, y_test))
yhat = tpot.predict(X_test)
fms[i] += f1_score(y_test, yhat, average='macro')
errors /= n_splitz
fms /= n_splitz
times /= n_splitz
df = pd.DataFrame({'errors': errors, 'fms': fms, 'times': times})
df.to_csv(path_or_buf='tpot-results2.csv', sep=',')
return None