def fit(x, y, estimator, dataframe, params):
vectorizer = CountVectorizer(stop_words=['go', '', ' '], binary=False, lowercase=True)
vectorizer.fit(dataframe[x].values)
fresh_estimator = clone(estimator)
x_np, y_np, feature_names, selector = \
select_features(
df = dataframe,
vectorizer=vectorizer,
feature_col=x,
label_col=y,
select_method=None,
continuous_col=None
)
estimator = RandomizedSearchCV(estimator, params, n_iter=60, cv=3, n_jobs=3, refit=True)
estimator.fit(x_np, y_np)
best_params = estimator.best_params_
if method not in ['lr', 'svm']:
print("Calibrating...")
estimator = CalibratedClassifierCV(fresh_estimator.set_params(**best_params), 'isotonic', 3)
estimator.fit(x_np, y_np)
from sklearn.base import _pprint
_pprint(estimator.get_params(deep=True), offset=2)
return estimator, selector, vectorizer
def __repr__(self):
'''Pretty-print this object'''
class_name = self.__class__.__name__
return '{:s}({:s})'.format(class_name,
_pprint(self.get_params(deep=False)['params'],
offset=len(class_name),),)
def _build_repr(self):
# XXX This is copied from sklearn.BaseEstimator's get_params
cls = self.__class__
init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
init_signature = signature(init)
if init is object.__init__:
args = []
else:
args = sorted([p.name for p in init_signature.parameters.values()
if p.name != 'self' and p.kind != p.VAR_KEYWORD])
class_name = self.__class__.__name__
params = dict()
for key in args:
warnings.simplefilter("always", DeprecationWarning)
try:
with warnings.catch_warnings(record=True) as w:
value = getattr(self, key, None)
if len(w) and w[0].category == DeprecationWarning:
continue
finally:
warnings.filters.pop(0)
params[key] = value
return '%s(%s)' % (class_name, _pprint(params, offset=len(class_name)))
def run(self, *args, **kwargs):
for metric in self.metrics:
kwargs = dict(metric)
metric_name = kwargs.pop('metric')
metric_fn = getattr(sys.modules[__name__], metric_name) # TODO allow external metrics
r = metric_fn(self.inputs[0], **kwargs)
print('%s(%s): %s' % (metric_name, _pprint(kwargs, offset=len(metric_name)), r))
def __repr__(self):
from sklearn.base import _pprint
class_name = self.__class__.__name__
return '%s(%s)' % (
class_name,
_pprint(
self.get_params(deep=False),
offset=len(class_name),
),
)
def __repr__(self):
strategy_name = self.__class__.__name__
estimator_name = self.estimator.__class__.__name__
return '%s(%s(%s))' % (
strategy_name,
estimator_name,
_pprint(
self.get_params(deep=False),
offset=len(strategy_name),
),
)
def __repr__(self):
'''Pretty-print this object'''
class_name = self.__class__.__name__
return '{:s}({:s})'.format(
class_name,
_pprint(
self.get_params(deep=False)['params'],
offset=len(class_name),
),
)
def __repr__(self):
# needed to add indicators to the output
class_name = self.__class__.__name__
return '%s(%s)' % (
class_name,
_pprint(
{
**self.get_params(deep=False),
**self.indicators
},
offset=len(class_name),
),
)
def __repr__(self):
class_name = self.__class__.__name__
params = dict(vars(self), history=list(self.history))
return "{0}({1})".format(class_name,
_pprint(params, offset=len(class_name)))
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name, _pprint(self._get_params(), offset=len(class_name), ),)
def __repr__(self):
class_name = self.__class__.__name__
params = dict(vars(self), history=list(self.history))
return "{0}({1})".format(
class_name, _pprint(params, offset=len(class_name)))
def __repr__(self):
class_name = getattr(self, '_cls_name', self._cls.__class__.__name__)
return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
offset=len(class_name),),)
def __str__(self):
return "{}({})".format(self.name, _pprint(self.get_params(),
offset=len(self.name)))
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name,
_pprint(self._kwargs, offset=len(class_name)),)
def __repr__(self):
class_name = self.__class__.__name__
args = _pprint(self._kwargs, offset=len(class_name))
return '%s(%s)' % (class_name, args.replace('\\n', '\n'))
def __repr__(self) -> str:
class_name = self.__class__.__name__
param_list = skb._pprint(self.get_hyperparams(), offset=14) # 8 chars
return '%s(%s)' % (class_name, param_list)
def __str__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name,
_pprint(self.get_params(deep=True),
offset=len(class_name), printer=str,),)
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
offset=len(class_name),),)
def __repr__(self):
from sklearn.base import _pprint
class_name = self.__class__.__name__
return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
offset=len(class_name),),)
def nice_repr(est):
class_name = est.__class__.__name__
return ('%s(%s)' % (class_name, _pprint(_changed_params(est),
offset=len(class_name))))
