Python sklearn.base 模块，RegressorMixin() 实例源码
我们从Python开源项目中，提取了以下3个代码示例，用于说明如何使用sklearn.base.RegressorMixin()。
def _check_sklearn_model(model):
if not (isinstance(model, BaseEstimator) and
isinstance(model, RegressorMixin)):
raise RuntimeError('Needs to supply an instance of a scikit-learn '
'compatible regression class.')
def _generate_bases_test(est, pd_est):
def test(self):
self.assertTrue(isinstance(pd_est, FrameMixin), pd_est)
self.assertFalse(isinstance(est, FrameMixin))
self.assertTrue(isinstance(pd_est, base.BaseEstimator))
try:
mixins = [
base.ClassifierMixin,
base.ClusterMixin,
base.BiclusterMixin,
base.TransformerMixin,
base.DensityMixin,
base.MetaEstimatorMixin,
base.ClassifierMixin,
base.RegressorMixin]
except:
if _sklearn_ver > 17:
raise
mixins = [
base.ClassifierMixin,
base.RegressorMixin]
for mixin in mixins:
self.assertEqual(
isinstance(pd_est, mixin),
isinstance(est,
mixin)
return test
def evaluate(self, point):
"""
Fits model using the particular setting of hyperparameters and
evaluates the model validation data.
Parameters
----------
* `point`: dict
A mapping of parameter names to the corresponding values
Returns
-------
* `score`: float
Score (more is better!) for some specific point
"""
X_train, y_train, X_test, y_test = (
self.X_train, self.y_train, self.X_test, self.y_test)
# apply transformation to model parameters,for example exp transformation
point_mapped = {}
for param, val in point.items():
point_mapped[param] = self.space[param][1](val)
model_instance = self.model(**point_mapped)
if 'random_state' in model_instance.get_params():
model_instance.set_params(random_state=self.random_state)
min_obj_val = -5.0
# Infeasible parameters are expected to raise an exception,thus the try
# catch below,infeasible parameters yield assumed smallest objective.
try:
model_instance.fit(X_train, y_train)
if isinstance(model_instance, RegressorMixin): # r^2 metric
y_predicted = model_instance.predict(X_test)
score = r2_score(y_test, y_predicted)
elif isinstance(model_instance, ClassifierMixin): # log loss
y_predicted = model_instance.predict_proba(X_test)
score = -log_loss(y_test, y_predicted) # in the context of this function,the higher score is better
# avoid any kind of singularitites,eg probability being zero,and thus breaking the log_loss
if math.isnan(score):
score = min_obj_val
score = max(score, min_obj_val) # this is necessary to avoid -inf or NaN
except BaseException as ex:
score = min_obj_val # on error: return assumed smallest value of objective function
return score
# this is necessary to generate table for README in the end
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