def _set_up_funcs(funcs, metas_ordered, Ks, dim, X_ns=None, Y_ns=None):
# replace functions with partials of args
def replace_func(func, info):
needs_alpha = getattr(func, 'needs_alpha', False)
new = None
args = (Ks, dim)
if needs_alpha:
args = (info.alphas, ) + args
if hasattr(func, 'chooser_fn'):
args += (X_ns, Y_ns)
if (getattr(func, 'needs_all_ks', False)
and getattr(func.chooser_fn, 'returns_ks', False)):
new, K = func.chooser_fn(*args)
new.K_needed = K
else:
new = func.chooser_fn(*args)
else:
new = partial(func, *args)
for attr in dir(func):
if not (attr.startswith('__') or attr.startswith('func_')):
setattr(new, attr, getattr(func, attr))
return new
rep_funcs = dict(
(replace_func(f, info), info) for f, info in iteritems(funcs))
rep_metas_ordered = OrderedDict(
(replace_func(f, info), info) for f, info in iteritems(metas_ordered))
return rep_funcs, rep_metas_ordered
def _clone_h2o_obj(estimator, ignore=False, **kwargs):
# do initial clone
est = clone(estimator)
# set kwargs:
if kwargs:
for k, v in six.iteritems(kwargs):
setattr(est, k, v)
# check on h2o estimator
if isinstance(estimator, H2OPipeline):
# the last step from the original estimator
e = estimator.steps[-1][1]
if isinstance(e, H2OEstimator):
last_step = est.steps[-1][1]
# so it's the last step
for k, v in six.iteritems(e._parms):
k, v = _kv_str(k, v)
# if (not k in PARM_IGNORE) and (not v is None):
# e._parms[k] = v
last_step._parms[k] = v
# otherwise it's an BaseH2OFunctionWrapper
return est
def _clone_h2o_obj(estimator, ignore=False, **kwargs):
# do initial clone
est = clone(estimator)
# set kwargs:
if kwargs:
for k, v in six.iteritems(kwargs):
setattr(est, k, v)
# check on h2o estimator
if isinstance(estimator, H2OPipeline):
# the last step from the original estimator
e = estimator.steps[-1][1]
if isinstance(e, H2OEstimator):
last_step = est.steps[-1][1]
# so it's the last step
for k, v in six.iteritems(e._parms):
k, v = _kv_str(k, v)
# if (not k in PARM_IGNORE) and (not v is None):
# e._parms[k] = v
last_step._parms[k] = v
# otherwise it's an BaseH2OFunctionWrapper
return est
def _set_up_funcs(funcs, metas_ordered, Ks, dim, X_ns=None, Y_ns=None):
# replace functions with partials of args
def replace_func(func, info):
needs_alpha = getattr(func, 'needs_alpha', False)
new = None
args = (Ks, dim)
if needs_alpha:
args = (info.alphas,) + args
if hasattr(func, 'chooser_fn'):
args += (X_ns, Y_ns)
if (getattr(func, 'needs_all_ks', False) and
getattr(func.chooser_fn, 'returns_ks', False)):
new, K = func.chooser_fn(*args)
new.K_needed = K
else:
new = func.chooser_fn(*args)
else:
new = partial(func, *args)
for attr in dir(func):
if not (attr.startswith('__') or attr.startswith('func_')):
setattr(new, attr, getattr(func, attr))
return new
rep_funcs = dict(
(replace_func(f, info), info) for f, info in iteritems(funcs))
rep_metas_ordered = OrderedDict(
(replace_func(f, info), info) for f, info in iteritems(metas_ordered))
return rep_funcs, rep_metas_ordered
def get_params(self, deep=True):
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
if not deep:
return super(EnsembleClassifier, self).get_params(deep=False)
else:
out = self.named_clfs.copy()
for name, step in six.iteritems(self.named_clfs):
for k, v in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, k)] = v
return out
def get_params(self, deep=True):
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(self.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
if not deep:
return super(Pipeline, self).get_params(deep=False)
else:
out = self.named_steps.copy()
for name, step in six.iteritems(self.named_steps):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""Get classifier parameter names for GridSearch"""
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['{0}__{1}'.format(name, key)] = value
return out
def get_params(self, deep=True):
""" Return estimator parameter names for GridSearch support"""
if not deep:
return super(EnsembleClassifier, self).get_params(deep=False)
else:
out = self.named_clfs.copy()
for name, step in six.iteritems(self.named_clfs):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
""" Get classifier parameter names for GridSearch"""
if not deep:
return super(FWLS_Classifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""Return estimator parameter names for GridSearch support."""
if not deep:
return super(EnsembleVoteClassifier, self).get_params(deep=False)
else:
out = self.named_clfs.copy()
for name, step in six.iteritems(self.named_clfs):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
'''Получить имена параметров классификатора для GridSearch"'''
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out=self.named_classifirs.copy()
for name, step in six.iteritems(self.named_classifirs): #six.iteritems-Возвращает итератор по элементам словаря.
for key, value in six.iteritems(step.get_parms(deep=True)):
out['%s_%s' % (name, key)] = value
return out
def get_params(self, deep=True):
""" Get classifier parameter names for GridSearch"""
if not deep:
return super(SLS_Classifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""Return estimator parameter names for GridSearch support"""
if not deep:
return super(HybridFeatureVotingClassifier, self).get_params(deep=False)
else:
out = super(HybridFeatureVotingClassifier, self).get_params(deep=False)
out.update(self.named_estimators.copy())
for name, step in six.iteritems(self.named_estimators):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""Получить имена парметров классификатора для GridSearch"""
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s %s' % (name, key)] = value
return out
def get_params(self, deep=True):
if not deep:
return super(SparkPipeline, self).get_params(deep=False)
else:
out = self.named_steps.copy()
for name, step in six.iteritems(self.named_steps):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
out.update(super(SparkPipeline, self).get_params(deep=False))
return out
def get_params(self, deep=True): """Return estimator parameter names for GridSearch support""" if not deep: return super(MultiLabelVotingClassifier, self).get_params(deep=False) else: out = super(MultiLabelVotingClassifier, self).get_params(deep=False) out.update(self.named_estimators.copy()) for name, step in six.iteritems(self.named_estimators): for key, value in six.iteritems(step.get_params(deep=True)): out['%s__%s' % (name, key)] = value return out
def get_params(self, deep=True):
""" Get classifier parameter names for GridSearch"""
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for k, v in six.iteritems(step.get_params(deep=True)):
out["%s__%s" % (name, k)] = v
return out
def get_params(self, deep=True):
""" GridSearchの実行時に分類金パラメータ名を取得 """
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
# キューを"分類器の名前__パラメータ名",
# バリューをパラメータの値とするディクショナリを生成
out = self.name_classifiers.copy()
for name, step in six.iteritems(self.name_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""
get classifier parameter names for GridSearch
useful for grid search for hyperparameter-tuning
access the parameters of individual classifiers in the ensemble
"""
if not deep:
return super(MajorityVoteClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=False):
"""Return estimator parameter names for GridSearch support"""
if not deep:
return super(StackingRegressor, self).get_params(deep=False)
else:
# TODO: this will not work, need to implement `named_estimators`
raise NotImplementedError("`deep` attribute not yet supported.")
out = super(StackingRegressor, self).get_params(deep=False)
out.update(self.named_estimators.copy())
for name, step in six.iteritems(self.named_estimators):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""Return estimator parameter names for GridSearch support."""
if not deep:
return super(StackingCVClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
out.update(self.named_meta_classifier.copy())
for name, step in six.iteritems(self.named_meta_classifier):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_params(self, deep=True):
"""Return estimator parameter names for GridSearch support."""
if not deep:
return super(StackingCVClassifier, self).get_params(deep=False)
else:
out = self.named_classifiers.copy()
for name, step in six.iteritems(self.named_classifiers):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
out.update(self.named_meta_classifier.copy())
for name, step in six.iteritems(self.named_meta_classifier):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def get_feature_names(self):
"""Array mapping from feature integer indices to feature name"""
if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0:
raise ValueError("Vocabulary wasn't fitted or is empty!")
return [t for t, i in sorted(six.iteritems(self.vocabulary_),
key=itemgetter(1))]
def mypp(params, offset=0, printer=repr):
# Do a multi-line justified repr:
options = np.get_printoptions()
np.set_printoptions(precision=5, threshold=64, edgeitems=2)
params_list = list()
this_line_length = offset
line_sep = ',\n' + (1 + offset // 2) * ' '
for i, (k, v) in enumerate(sorted(six.iteritems(params))):
if type(v) is float:
# use str for representing floating point numbers
# this way we get consistent representation across
# architectures and versions.
this_repr = '%s=%s' % (k, str(v))
else:
# use repr of the rest
this_repr = '%s=%s' % (k, printer(v))
if len(this_repr) > 500000000:
this_repr = this_repr[:300] + '...' + this_repr[-100:]
if i > 0:
if (this_line_length + len(this_repr) >= 75 or '\n' in this_repr):
params_list.append(line_sep)
this_line_length = len(line_sep)
else:
params_list.append(', ')
this_line_length += 2
params_list.append(this_repr)
this_line_length += len(this_repr)
np.set_printoptions(**options)
lines = ''.join(params_list)
# Strip trailing space to avoid nightmare in doctests
lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n'))
return lines
def transform(self, raw_documents):
"""Extract token counts out of raw text documents using the vocabulary
fitted with fit or the one provided in the constructor.
Parameters
----------
raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns
-------
vectors : sparse matrix, [n_samples, n_features]
"""
if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0:
raise ValueError("Vocabulary wasn't fitted or is empty!")
# raw_documents can be an iterable so we don't know its size in
# advance
# result of document conversion to term count arrays
i_indices = _make_int_array()
j_indices = _make_int_array()
values = _make_int_array()
analyze = self.build_analyzer()
for n_doc, doc in enumerate(raw_documents):
term_counts = Counter(analyze(doc))
for term, count in six.iteritems(term_counts):
if term in self.vocabulary_:
i_indices.append(n_doc)
j_indices.append(self.vocabulary_[term])
values.append(count)
n_doc += 1
return self._term_counts_to_matrix(n_doc, i_indices, j_indices, values)
def _pprint(params, offset=0, printer=repr):
"""
From: https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/base.py
With line 142-143 removed
"""
# Do a multi-line justified repr:
options = np.get_printoptions()
np.set_printoptions(precision=5, threshold=64, edgeitems=2)
params_list = list()
this_line_length = offset
line_sep = ',\n' + (1 + offset // 2) * ' '
for i, (k, v) in enumerate(sorted(six.iteritems(params))):
if type(v) is float:
# use str for representing floating point numbers
# this way we get consistent representation across
# architectures and versions.
this_repr = '%s=%s' % (k, str(v))
else:
# use repr of the rest
this_repr = '%s=%s' % (k, printer(v))
if i > 0:
if (this_line_length + len(this_repr) >= 75 or '\n' in this_repr):
params_list.append(line_sep)
this_line_length = len(line_sep)
else:
params_list.append(', ')
this_line_length += 2
params_list.append(this_repr)
this_line_length += len(this_repr)
np.set_printoptions(**options)
lines = ''.join(params_list)
# Strip trailing space to avoid nightmare in doctests
lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n'))
return lines
def _new_base_estimator(est, clonable_kwargs):
"""When the grid searches are pickled, the estimator
has to be dropped out. When we load it back in, we have
to reinstate a new one, since the fit is predicated on
being able to clone a base estimator, we've got to have
an estimator to clone and fit.
Parameters
----------
est : str
The type of model to build
Returns
-------
estimator : H2OEstimator
The cloned base estimator
"""
est_map = {
'dl': H2ODeepLearningEstimator,
'gbm': H2OGradientBoostingEstimator,
'glm': H2OGeneralizedLinearEstimator,
# 'glrm': H2OGeneralizedLowRankEstimator,
# 'km' : H2OKMeansEstimator,
'nb': H2ONaiveBayesEstimator,
'rf': H2ORandomForestEstimator
}
estimator = est_map[est]() # initialize the new ones
for k, v in six.iteritems(clonable_kwargs):
k, v = _kv_str(k, v)
estimator._parms[k] = v
return estimator
def test_type_of_target():
for group, group_examples in iteritems(EXAMPLES):
for example in group_examples:
assert_equal(type_of_target(example),
group,
msg=('type_of_target(%r) should be %r, got %r' %
(example, group, type_of_target(example))))
for example in NON_ARRAY_LIKE_EXAMPLES:
msg_regex = 'Expected array-like \(array or non-string sequence\).*'
assert_raises_regex(ValueError, msg_regex, type_of_target, example)
for example in MULTILABEL_SEQUENCES:
msg = ('You appear to be using a legacy multi-label data '
'representation. Sequence of sequences are no longer supported;'
' use a binary array or sparse matrix instead.')
assert_raises_regex(ValueError, msg, type_of_target, example)
try:
from pandas import SparseSeries
except ImportError:
pass
y = SparseSeries([1, 0, 0, 1, 0])
msg = "y cannot be class 'SparseSeries'."
assert_raises_regex(ValueError, msg, type_of_target, y)
def get_feature_names(self):
"""Array mapping from feature integer indices to feature name"""
if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0:
raise ValueError("Vocabulary wasn't fitted or is empty!")
return [t for t, i in sorted(six.iteritems(self.vocabulary_),
key=itemgetter(1))]
def _fit(self, X, y=None, **fit_params):
# self._validate_steps()
fit_params_steps = dict(
(name, {}) for name, step in self.steps if step is not None)
for pname, pval in six.iteritems(fit_params):
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval
Xt = X
for name, transform in self.steps[:-1]:
start_time = time.time()
if transform is None:
pass
elif hasattr(transform, "fit_transform"):
Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
else:
Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
.transform(Xt)
self.pipeline_info.add_preprocessor_timing(
name,
time.time() - start_time)
if self._final_estimator is None:
return Xt, {}
return Xt, fit_params_steps[self.steps[-1][0]]
def test_type_of_target():
for group, group_examples in iteritems(EXAMPLES):
for example in group_examples:
assert_equal(type_of_target(example), group,
msg=('type_of_target(%r) should be %r, got %r'
% (example, group, type_of_target(example))))
for example in NON_ARRAY_LIKE_EXAMPLES:
msg_regex = 'Expected array-like \(array or non-string sequence\).*'
assert_raises_regex(ValueError, msg_regex, type_of_target, example)
for example in MULTILABEL_SEQUENCES:
msg = ('You appear to be using a legacy multi-label data '
'representation. Sequence of sequences are no longer supported;'
' use a binary array or sparse matrix instead.')
assert_raises_regex(ValueError, msg, type_of_target, example)
try:
from pandas import SparseSeries
except ImportError:
raise SkipTest("Pandas not found")
y = SparseSeries([1, 0, 0, 1, 0])
msg = "y cannot be class 'SparseSeries'."
assert_raises_regex(ValueError, msg, type_of_target, y)
def transform(self, X):
"""Apply the schema normalization.
Parameters
----------
X : pd.DataFrame, shape=(n_samples, n_features)
The Pandas frame to transform. The operation will
be applied to a copy of the input data, and the result
will be returned.
Returns
-------
X : pd.DataFrame or np.ndarray, shape=(n_samples, n_features)
The operation is applied to a copy of ``X``,
and the result set is returned.
"""
check_is_fitted(self, "fit_cols_")
X, _ = check_dataframe(X, cols=self.cols)
# validate that fit cols in test set
cols = self.fit_cols_
validate_test_set_columns(cols, X.columns)
# normalize
for k, v in six.iteritems(self.schema):
X[k] = X[k].astype(v)
return X # DataFrame
def _fit_transform(self, X, y=None, **fit_params):
""" fit and transform X by transforming it by every step in sequence """
# shallow copy of steps - this should really be steps_
self.steps = list(self.steps)
self._validate_steps()
# Setup the memory
memory = check_memory(self.memory)
fit_transform_one_cached = memory.cache(_fit_transform_one)
fit_params_steps = dict(
(name, {}) for name, step in self.steps if step is not None)
for pname, pval in six.iteritems(fit_params):
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval
Xt = X
for step_idx, (name, transformer) in enumerate(self.steps):
if transformer is None:
pass
else:
if hasattr(memory, 'cachedir') and memory.cachedir is None:
# we do not clone when caching is disabled to preserve
# backward compatibility
cloned_transformer = transformer
else:
cloned_transformer = clone(transformer)
# Fit or load from cache the current transfomer
Xt, fitted_transformer = fit_transform_one_cached(
cloned_transformer, None, Xt, y, **fit_params_steps[name])
# Replace the transformer of the step with the fitted
# transformer. This is necessary when loading the transformer
# from the cache.
self.steps[step_idx] = (name, fitted_transformer)
return Xt
def get_grid_results_table(search):
"""Get the grid results from a fit ``RandomizedSearchCV``.
Parameters
----------
search : RandomizedSearchCV
The pre-fit grid search.
Returns
-------
res : pd.DataFrame
The results dataframe
"""
# the search results
res = search.cv_results_
# unpack the dict
dct = {
k: res[k]
for k in ('mean_fit_time', 'std_fit_time', 'mean_score_time',
'std_score_time', 'mean_test_score', 'std_test_score')
}
prefix = "param_"
for k, v in six.iteritems(res):
if k.startswith(prefix):
key = k.split(prefix)[-1]
dct[key] = v.data
return pd.DataFrame.from_dict(dct)
def restrict(self, support, indices=False):
"""Restrict the features to those in support.
Parameters
----------
support : array-like
Boolean mask or list of indices (as returned by the get_support
member of feature selectors).
indices : boolean, optional
Whether support is a list of indices.
"""
if not indices:
support = np.where(support)[0]
names = self.feature_names_
new_vocab = {}
for i in support:
new_vocab[names[i]] = len(new_vocab)
self.vocabulary_ = new_vocab
self.feature_names_ = [
f for f, i in sorted(six.iteritems(new_vocab), key=itemgetter(1))
]
return self
def set_params(self, **params):
if not params:
# Simple optimisation to gain speed (inspect is slow)
return self
# gmm_0
gmm_0_param_dict = {}
gmm_1_param_dict = {}
valid_params = self.get_params(deep=True)
for key, value in six.iteritems(params):
split = key.split('__',1)
if len(split) > 1:
# combined key name
prefix, name = split
if prefix.find('gmm_0') >= 0:
gmm_0_param_dict[name] = value
elif prefix.find('gmm_1') >= 0:
gmm_1_param_dict[name] = value
else:
raise ValueError('Invalid parameter %s ' 'for estimator %s'
% (key, self.__class__.__name__))
else:
# simple objects case
if not key in valid_params:
raise ValueError('Invalid parameter %s ' 'for estimator %s'
% (key, self.__class__.__name__))
setattr(self, key, value)
self.gmm_0.set_params(params=gmm_0_param_dict)
self.gmm_1.set_params(params=gmm_0_param_dict)
## self.gmm_1.set_params(gmm_1_param_dict)
return self
def fit(self, X, y=None):
"""Learn a list of feature name -> indices mappings.
Parameters
----------
X : Mapping or iterable over Mappings
Dict(s) or Mapping(s) from feature names (arbitrary Python
objects) to feature values (strings or convertible to dtype).
y : (ignored)
Returns
-------
self
"""
# collect all the possible feature names
feature_names = set()
for x in X:
for f, v in six.iteritems(x):
if isinstance(v, six.string_types):
f_v = "%s%s%s" % (f, self.separator, v)
if f_v not in self._onehot_dict:
self._onehot_dict[f_v] = [f, v]
f = f_v
feature_names.add(f)
# sort the feature names to define the mapping
feature_names = sorted(feature_names)
self.vocabulary_ = dict((f, i) for i, f in enumerate(feature_names))
self.feature_names_ = feature_names
return self
def _remove_highandlow(self, cscmatrix, feature_to_pos, high, low):
"""Remove too rare or too common features.
Prune features that are non zero in more samples than high or less
documents than low.
This does not prune samples with zero features.
"""
kept_indices = []
removed_indices = set()
for colptr in xrange(len(cscmatrix.indptr) - 1):
len_slice = cscmatrix.indptr[colptr + 1] - cscmatrix.indptr[colptr]
if len_slice <= high and len_slice >= low:
kept_indices.append(colptr)
else:
removed_indices.add(colptr)
s_kept_indices = set(kept_indices)
new_mapping = dict((v, i) for i, v in enumerate(kept_indices))
feature_to_pos = dict((k, new_mapping[v])
for k, v in six.iteritems(feature_to_pos)
if v in s_kept_indices)
return cscmatrix[:, kept_indices], feature_to_pos, removed_indices
def transform(self, X):
"""Transform a test matrix given the already-fit transformer.
Parameters
----------
X : Pandas ``DataFrame``
The Pandas frame to transform. The operation will
be applied to a copy of the input data, and the result
will be returned.
Returns
-------
X : Pandas ``DataFrame``
The operation is applied to a copy of ``X``,
and the result set is returned.
"""
check_is_fitted(self, 'sq_nms_')
# check on state of X and cols
X, _ = validate_is_pd(X, self.cols)
sq_nms_ = self.sq_nms_
# scale by norms
for nm, the_norm in six.iteritems(sq_nms_):
X[nm] /= the_norm
return X if self.as_df else X.as_matrix()
def restrict(self, support, indices=False):
"""Restrict the features to those in support using feature selection.
This function modifies the estimator in-place.
"""
if self.has_been_restricted == True:
return self
if not indices:
support = np.where(support)[0]
names = self.feature_names_
new_vocab = {}
for i in support:
new_vocab[names[i]] = len(new_vocab)
self.vocabulary_ = new_vocab
self.feature_names_ = [
f for f, i in sorted(six.iteritems(new_vocab), key=itemgetter(1))
]
self.has_been_restricted = True
return self
def test_paired_distances():
# Test the pairwise_distance helper function.
rng = np.random.RandomState(0)
# Euclidean distance should be equivalent to calling the function.
X = rng.random_sample((5, 4))
# Euclidean distance, with Y != X.
Y = rng.random_sample((5, 4))
for metric, func in iteritems(PAIRED_DISTANCES):
S = paired_distances(X, Y, metric=metric)
S2 = func(X, Y)
assert_array_almost_equal(S, S2)
S3 = func(csr_matrix(X), csr_matrix(Y))
assert_array_almost_equal(S, S3)
if metric in PAIRWISE_DISTANCE_FUNCTIONS:
# Check the pairwise_distances implementation
# gives the same value
distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)
distances = np.diag(distances)
assert_array_almost_equal(distances, S)
# Check the callable implementation
S = paired_distances(X, Y, metric='manhattan')
S2 = paired_distances(X, Y, metric=lambda x, y: np.abs(x - y).sum(axis=0))
assert_array_almost_equal(S, S2)
# Test that a value error is raised when the lengths of X and Y should not
# differ
Y = rng.random_sample((3, 4))
assert_raises(ValueError, paired_distances, X, Y)
def set_params(self, **params):
"""Set the parameters of this solver.
:returns self : self
Returns self
"""
if not params:
# Simple optimization to gain speed(inspect is slow)
return self
valid_params = self.get_params(deep=True)
from sklearn.externals import six
for key, value in six.iteritems(params):
split = key.split('__', 1)
if len(split) > 1:
# nested objects case
name, sub_name = split
if name not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(name, self))
sub_object = valid_params[name]
sub_object.set_params(**{sub_name: value})
else:
# simple objects case
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self.__class__.__name__))
setattr(self, key, value)
return self
def fit(self, X, y=None):
"""Learn a list of feature name -> indices mappings.
Parameters
----------
X : Mapping or iterable over Mappings
Dict(s) or Mapping(s) from feature names (arbitrary Python
objects) to feature values (strings or convertible to dtype).
y : (ignored)
Returns
-------
self
"""
# collect all the possible feature names
feature_names = set()
for x in X:
for f, v in six.iteritems(x):
if isinstance(v, six.string_types):
f_v = "%s%s%s" % (f, self.separator, v)
if f_v not in self._onehot_dict:
self._onehot_dict[f_v] = [f, v]
f = f_v
feature_names.add(f)
# sort the feature names to define the mapping
feature_names = sorted(feature_names)
self.vocabulary_ = dict((f, i) for i, f in enumerate(feature_names))
self.feature_names_ = feature_names
return self
def most_common(d):
'''
Items of a defaultdict(int) with the heighest values.
Like Counter.most_common in Python >= 2.7.
'''
return sorted(six.iteritems(d), key=operator.itemgetter(1), reverse=True)
def __init__(self, cols=None, as_df=True, trans_col_name=None, **kwargs):
super(_SelectiveTransformerWrapper, self).__init__(
cols=cols, as_df=as_df)
# this is a STATIC attribute of subclasses
try:
cls = self._cls
except AttributeError:
raise DeveloperError("_SelectiveTransformerWrapper subclasses "
"must contain a static _cls attribute that "
"maps to a sklearn type!")
# get the (default) parameters for the estimator in question
# and initialize to default
self.estimator_ = cls()
default_est_parms = self.estimator_.get_params(deep=True)
# set the attributes in the estimator AND in the constructor so this
# class behaves like sklearn in grid search
self.estimator_.set_params(**kwargs)
# set the kwargs here to behave like sklearn
for k, v in six.iteritems(default_est_parms):
if kwargs:
v = kwargs.get(k, v) # try get from kwargs, fail w def. value
setattr(self, k, v)
self.trans_col_name = trans_col_name
def test_paired_distances():
""" Test the pairwise_distance helper function. """
rng = np.random.RandomState(0)
# Euclidean distance should be equivalent to calling the function.
X = rng.random_sample((5, 4))
# Euclidean distance, with Y != X.
Y = rng.random_sample((5, 4))
for metric, func in iteritems(PAIRED_DISTANCES):
S = paired_distances(X, Y, metric=metric)
S2 = func(X, Y)
assert_array_almost_equal(S, S2)
if metric in PAIRWISE_DISTANCE_FUNCTIONS:
# Check the the pairwise_distances implementation
# gives the same value
distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)
distances = np.diag(distances)
assert_array_almost_equal(distances, S)
# Check the callable implementation
S = paired_distances(X, Y, metric='manhattan')
S2 = paired_distances(X, Y,
metric=lambda x, y: np.abs(x -y).sum(axis=0))
assert_array_almost_equal(S, S2)
# Test that a value error is raised when the lengths of X and Y should not
# differ
Y = rng.random_sample((3, 4))
assert_raises(ValueError, paired_distances, X, Y)
def _move_available():
to_delete = []
for n, parents in iteritems(deps):
if not parents:
available.add(n)
to_delete.append(n)
for n in to_delete:
del deps[n]
def mapper(X, separator=self.separator):
feature_names = []
for x in X:
for f, v in six.iteritems(x):
if isinstance(v, six.string_types):
f = "%s%s%s" % (f, self.separator, v)
feature_names.append(f)
accum.add(set(feature_names))
def update(self, other):
"""Adds counts for elements in other"""
if isinstance(other, self.__class__):
for x, n in six.iteritems(other):
self[x] += n
else:
for x in other:
self[x] += 1
def test_is_multilabel():
for group, group_examples in iteritems(EXAMPLES):
if group.startswith("multilabel"):
assert_, exp = assert_true, "True"
else:
assert_, exp = assert_false, "False"
for example in group_examples:
assert_(is_multilabel(example), msg="is_multilabel(%r) should be %s" % (example, exp))
def get_params(self, deep=True):
if not deep:
return super(FeatureUnion, self).get_params(deep=False)
else:
out = dict(self.transformer_list)
for name, trans in self.transformer_list:
for key, value in iteritems(trans.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
def test_is_multilabel():
for group, group_examples in iteritems(EXAMPLES):
if group.startswith('multilabel'):
assert_, exp = assert_true, 'True'
else:
assert_, exp = assert_false, 'False'
for example in group_examples:
assert_(is_multilabel(example),
msg='is_multilabel(%r) should be %s' % (example, exp))
def test_type_of_target():
for group, group_examples in iteritems(EXAMPLES):
for example in group_examples:
assert_equal(type_of_target(example), group,
msg='type_of_target(%r) should be %r, got %r'
% (example, group, type_of_target(example)))
for example in NON_ARRAY_LIKE_EXAMPLES:
assert_raises(ValueError, type_of_target, example)
def set_params(self, **params):
if not params:
return self
for key, value in six.iteritems(params):
split = key.split('__', 1)
if len(split) > 1:
print("length is greter than one ", split, value)
else:
print("length is one ", split, value)
setattr(self, key, value)
def test_is_label_indicator_matrix():
for group, group_examples in iteritems(EXAMPLES):
if group == 'multilabel-indicator':
assert_, exp = assert_true, 'True'
else:
assert_, exp = assert_false, 'False'
for example in group_examples:
assert_(is_label_indicator_matrix(example),
msg='is_label_indicator_matrix(%r) should be %s'
% (example, exp))
def test_is_sequence_of_sequences():
for group, group_examples in iteritems(EXAMPLES):
if group == 'multilabel-sequences':
assert_, exp = assert_true, 'True'
else:
assert_, exp = assert_false, 'False'
for example in group_examples:
assert_(is_sequence_of_sequences(example),
msg='is_sequence_of_sequences(%r) should be %s'
% (example, exp))
