class FeatureSet(object):
"""
Encapsulation of all of the features, values, and metadata about a given
set of data.
This replaces ``ExamplesTuple`` from older versions.
:param name: The name of this feature set.
:type name: str
:param ids: Example IDs for this set.
:type ids: np.array
:param labels: labels for this set.
:type labels: np.array
:param features: The features for each instance represented as either a
list of dictionaries or an array-like (if `vectorizer` is
also specified).
:type features: list of dict or array-like
:param vectorizer: Vectorizer that created feature matrix.
:type vectorizer: DictVectorizer or FeatureHasher
.. note::
If ids, labels, and/or features are not None, the number of rows in
each array must be equal.
"""
def __init__(self, name, ids, labels=None, features=None,
vectorizer=None):
super(FeatureSet, self).__init__()
self.name = name
if isinstance(ids, list):
ids = np.array(ids)
self.ids = ids
if isinstance(labels, list):
labels = np.array(labels)
self.labels = labels
self.features = features
self.vectorizer = vectorizer
# Convert list of dicts to numpy array
if isinstance(self.features, list):
if self.vectorizer is None:
self.vectorizer = NewDictVectorizer(sparse=True)
self.features = self.vectorizer.fit_transform(self.features)
if self.features is not None:
num_feats = self.features.shape[0]
if self.ids is None:
raise ValueError('A list of IDs is required')
num_ids = self.ids.shape[0]
if num_feats != num_ids:
raise ValueError(('Number of IDs (%s) does not equal '
'number of feature rows (%s)') % (num_ids,
num_feats))
if self.labels is None:
self.labels = np.empty(num_feats)
self.labels.fill(None)
num_labels = self.labels.shape[0]
if num_feats != num_labels:
raise ValueError(('Number of labels (%s) does not equal '
'number of feature rows (%s)') % (num_labels,
num_feats))
def __contains__(self, value):
"""
Check if example ID is in set
"""
return value in self.ids
def __eq__(self, other):
"""
Check whether two featuresets are the same.
.. note::
We consider feature values to be equal if any differences are in the
sixth decimal place or higher.
"""
# We need to sort the indices for the underlying
# feature sparse matrix in case we haven't done
# so already.
if not self.features.has_sorted_indices:
self.features.sort_indices()
if not other.features.has_sorted_indices:
other.features.sort_indices()
return (self.ids.shape == other.ids.shape and
self.labels.shape == other.labels.shape and
self.features.shape == other.features.shape and
(self.ids == other.ids).all() and
(self.labels == other.labels).all() and
np.allclose(self.features.data, other.features.data,
rtol=1e-6) and
(self.features.indices == other.features.indices).all() and
(self.features.indptr == other.features.indptr).all() and
self.vectorizer == other.vectorizer)
def __iter__(self):
"""
Iterate through (ID, label, feature_dict) tuples in feature set.
"""
if self.features is not None:
if not isinstance(self.vectorizer, DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if '
'they use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels,
self.features):
# When calling inverse_transform we have to add [0] to get the
# results for the current instance because it always returns a
# 2D array
yield (id_, label_,
self.vectorizer.inverse_transform(feats)[0])
else:
return
def __len__(self):
return self.features.shape[0]
def __add__(self, other):
"""
Combine two feature sets to create a new one. This is done assuming
they both have the same instances with the same IDs in the same order.
"""
# Check that the sets of IDs are equal
if set(self.ids) != set(other.ids):
raise ValueError('IDs are not in the same order in each '
'feature set')
# Compute the relative ordering of IDs for merging the features
# and labels.
ids_indices = dict((y, x) for x, y in enumerate(other.ids))
relative_order = [ids_indices[self_id] for self_id in self.ids]
# Initialize the new feature set with a name and the IDs.
new_set = FeatureSet('+'.join(sorted([self.name, other.name])),
deepcopy(self.ids))
# Combine feature matrices and vectorizers.
if not isinstance(self.vectorizer, type(other.vectorizer)):
raise ValueError('Cannot combine FeatureSets because they are '
'not both using the same type of feature '
'vectorizer (e.g., DictVectorizer, '
'FeatureHasher)')
uses_feature_hasher = isinstance(self.vectorizer, FeatureHasher)
if uses_feature_hasher:
if (self.vectorizer.n_features !=
other.vectorizer.n_features):
raise ValueError('Cannot combine FeatureSets that uses '
'FeatureHashers with different values of '
'n_features setting.')
else:
# Check for duplicate feature names.
if (set(self.vectorizer.feature_names_) &
set(other.vectorizer.feature_names_)):
raise ValueError('Cannot combine FeatureSets because they '
'have duplicate feature names.')
num_feats = self.features.shape[1]
new_set.features = sp.hstack([self.features,
other.features[relative_order]],
'csr')
new_set.vectorizer = deepcopy(self.vectorizer)
if not uses_feature_hasher:
for feat_name, index in other.vectorizer.vocabulary_.items():
new_set.vectorizer.vocabulary_[feat_name] = (index +
num_feats)
other_names = other.vectorizer.feature_names_
new_set.vectorizer.feature_names_.extend(other_names)
# If either set has labels, check that they don't conflict.
if self.has_labels:
# labels should be the same for each FeatureSet, so store once.
if other.has_labels and \
not np.all(self.labels == other.labels[relative_order]):
raise ValueError('Feature sets have conflicting labels for '
'examples with the same ID.')
new_set.labels = deepcopy(self.labels)
else:
new_set.labels = deepcopy(other.labels[relative_order])
return new_set
def filter(self, ids=None, labels=None, features=None, inverse=False):
"""
Removes or keeps features and/or examples from the Featureset depending
on the passed in parameters.
:param ids: Examples to keep in the FeatureSet. If `None`, no ID
filtering takes place.
:type ids: list of str/float
:param labels: labels that we want to retain examples for. If `None`,
no label filtering takes place.
:type labels: list of str/float
:param features: Features to keep in the FeatureSet. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
:type features: list of str
:param inverse: Instead of keeping features and/or examples in lists,
remove them.
:type inverse: bool
"""
# Construct mask that indicates which examples to keep
mask = np.ones(len(self), dtype=bool)
if ids is not None:
mask = np.logical_and(mask, np.in1d(self.ids, ids))
if labels is not None:
mask = np.logical_and(mask, np.in1d(self.labels, labels))
if inverse and (labels is not None or ids is not None):
mask = np.logical_not(mask)
# Remove examples not in mask
self.ids = self.ids[mask]
self.labels = self.labels[mask]
self.features = self.features[mask, :]
# Filter features
if features is not None:
if isinstance(self.vectorizer, FeatureHasher):
raise ValueError('FeatureSets with FeatureHasher vectorizers'
' cannot be filtered by feature.')
columns = np.array(sorted({feat_num for feat_name, feat_num in
iteritems(self.vectorizer.vocabulary_)
if (feat_name in features or
feat_name.split('=', 1)[0] in
features)}))
if inverse:
all_columns = np.arange(self.features.shape[1])
columns = all_columns[np.logical_not(np.in1d(all_columns,
columns))]
self.features = self.features[:, columns]
self.vectorizer.restrict(columns, indices=True)
def filtered_iter(self, ids=None, labels=None, features=None,
inverse=False):
"""
A version of ``__iter__`` that retains only the specified features
and/or examples from the output.
:param ids: Examples in the FeatureSet to keep. If `None`, no ID
filtering takes place.
:type ids: list of str/float
:param labels: labels that we want to retain examples for. If `None`,
no label filtering takes place.
:type labels: list of str/float
:param features: Features in the FeatureSet to keep. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
:type features: list of str
:param inverse: Instead of keeping features and/or examples in lists,
remove them.
:type inverse: bool
"""
if self.features is not None and not isinstance(self.vectorizer,
DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if they'
' use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels, self.features):
# Skip instances with IDs not in filter
if ids is not None and (id_ in ids) == inverse:
continue
# Skip instances with labels not in filter
if labels is not None and (label_ in labels) == inverse:
continue
feat_dict = self.vectorizer.inverse_transform(feats)[0]
if features is not None:
feat_dict = {name: value for name, value in
iteritems(feat_dict) if
(inverse != (name in features or
name.split('=', 1)[0] in features))}
elif not inverse:
feat_dict = {}
yield id_, label_, feat_dict
def __sub__(self, other):
"""
:returns: a copy of ``self`` with all features in ``other`` removed.
"""
new_set = deepcopy(self)
new_set.filter(features=other.vectorizer.feature_names_,
inverse=True)
return new_set
@property
def has_labels(self):
"""
:returns: Whether or not this FeatureSet has any finite labels.
"""
if self.labels is not None:
return not (np.issubdtype(self.labels.dtype, float) and
np.isnan(np.min(self.labels)))
else:
return False
def __str__(self):
"""
:returns: a string representation of FeatureSet
"""
return str(self.__dict__)
def __repr__(self):
"""
:returns: a string representation of FeatureSet
"""
return repr(self.__dict__)
def __getitem__(self, value):
"""
:returns: A specific example by row number, or if given a slice,
a new FeatureSet containing a subset of the data.
"""
# Check if we're slicing
if isinstance(value, slice):
sliced_ids = self.ids[value]
sliced_feats = (self.features[value] if self.features is not None
else None)
sliced_labels = (self.labels[value] if self.labels is not None
else None)
return FeatureSet('{}_{}'.format(self.name, value), sliced_ids,
features=sliced_feats, labels=sliced_labels,
vectorizer=self.vectorizer)
else:
label = self.labels[value] if self.labels is not None else None
feats = self.features[value, :]
features = (self.vectorizer.inverse_transform(feats)[0] if
self.features is not None else {})
return self.ids[value], label, features
class FeatureSet(object):
"""
Encapsulation of all of the features, values, and metadata about a given
set of data.
This replaces ``ExamplesTuple`` from older versions.
:param name: The name of this feature set.
:type name: str
:param ids: Example IDs for this set.
:type ids: np.array
:param labels: labels for this set.
:type labels: np.array
:param features: The features for each instance represented as either a
list of dictionaries or an array-like (if `vectorizer` is
also specified).
:type features: list of dict or array-like
:param vectorizer: Vectorizer that created feature matrix.
:type vectorizer: DictVectorizer or FeatureHasher
.. note::
If ids, labels, and/or features are not None, the number of rows in
each array must be equal.
"""
def __init__(self, name, ids, labels=None, features=None,
vectorizer=None):
super(FeatureSet, self).__init__()
self.name = name
if isinstance(ids, list):
ids = np.array(ids)
self.ids = ids
if isinstance(labels, list):
labels = np.array(labels)
self.labels = labels
self.features = features
self.vectorizer = vectorizer
# Convert list of dicts to numpy array
if isinstance(self.features, list):
if self.vectorizer is None:
self.vectorizer = NewDictVectorizer(sparse=True)
self.features = self.vectorizer.fit_transform(self.features)
if self.features is not None:
num_feats = self.features.shape[0]
if self.ids is None:
raise ValueError('A list of IDs is required')
num_ids = self.ids.shape[0]
if num_feats != num_ids:
raise ValueError(('Number of IDs (%s) does not equal '
'number of feature rows (%s)') % (num_ids,
num_feats))
if self.labels is None:
self.labels = np.empty(num_feats)
self.labels.fill(None)
num_labels = self.labels.shape[0]
if num_feats != num_labels:
raise ValueError(('Number of labels (%s) does not equal '
'number of feature rows (%s)') % (num_labels,
num_feats))
def __contains__(self, value):
"""
Check if example ID is in set
"""
return value in self.ids
def __eq__(self, other):
"""
Check whether two featuresets are the same.
.. note::
We consider feature values to be equal if any differences are in the
sixth decimal place or higher.
"""
# We need to sort the indices for the underlying
# feature sparse matrix in case we haven't done
# so already.
if not self.features.has_sorted_indices:
self.features.sort_indices()
if not other.features.has_sorted_indices:
other.features.sort_indices()
return (self.ids.shape == other.ids.shape and
self.labels.shape == other.labels.shape and
self.features.shape == other.features.shape and
(self.ids == other.ids).all() and
(self.labels == other.labels).all() and
np.allclose(self.features.data, other.features.data,
rtol=1e-6) and
(self.features.indices == other.features.indices).all() and
(self.features.indptr == other.features.indptr).all() and
self.vectorizer == other.vectorizer)
def __iter__(self):
"""
Iterate through (ID, label, feature_dict) tuples in feature set.
"""
if self.features is not None:
if not isinstance(self.vectorizer, DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if '
'they use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels,
self.features):
# When calling inverse_transform we have to add [0] to get the
# results for the current instance because it always returns a
# 2D array
yield (id_, label_,
self.vectorizer.inverse_transform(feats)[0])
else:
return
def __len__(self):
return self.features.shape[0]
def __add__(self, other):
"""
Combine two feature sets to create a new one. This is done assuming
they both have the same instances with the same IDs in the same order.
"""
# Check that the sets of IDs are equal
if set(self.ids) != set(other.ids):
raise ValueError('IDs are not in the same order in each '
'feature set')
# Compute the relative ordering of IDs for merging the features
# and labels.
ids_indices = dict((y, x) for x, y in enumerate(other.ids))
relative_order = [ids_indices[self_id] for self_id in self.ids]
# Initialize the new feature set with a name and the IDs.
new_set = FeatureSet('+'.join(sorted([self.name, other.name])),
deepcopy(self.ids))
# Combine feature matrices and vectorizers.
if not isinstance(self.vectorizer, type(other.vectorizer)):
raise ValueError('Cannot combine FeatureSets because they are '
'not both using the same type of feature '
'vectorizer (e.g., DictVectorizer, '
'FeatureHasher)')
uses_feature_hasher = isinstance(self.vectorizer, FeatureHasher)
if uses_feature_hasher:
if (self.vectorizer.n_features !=
other.vectorizer.n_features):
raise ValueError('Cannot combine FeatureSets that uses '
'FeatureHashers with different values of '
'n_features setting.')
else:
# Check for duplicate feature names.
if (set(self.vectorizer.feature_names_) &
set(other.vectorizer.feature_names_)):
raise ValueError('Cannot combine FeatureSets because they '
'have duplicate feature names.')
num_feats = self.features.shape[1]
new_set.features = sp.hstack([self.features,
other.features[relative_order]],
'csr')
new_set.vectorizer = deepcopy(self.vectorizer)
if not uses_feature_hasher:
for feat_name, index in other.vectorizer.vocabulary_.items():
new_set.vectorizer.vocabulary_[feat_name] = (index +
num_feats)
other_names = other.vectorizer.feature_names_
new_set.vectorizer.feature_names_.extend(other_names)
# If either set has labels, check that they don't conflict.
if self.has_labels:
# labels should be the same for each FeatureSet, so store once.
if other.has_labels and \
not np.all(self.labels == other.labels[relative_order]):
raise ValueError('Feature sets have conflicting labels for '
'examples with the same ID.')
new_set.labels = deepcopy(self.labels)
else:
new_set.labels = deepcopy(other.labels[relative_order])
return new_set
def filter(self, ids=None, labels=None, features=None, inverse=False):
"""
Removes or keeps features and/or examples from the Featureset depending
on the passed in parameters.
:param ids: Examples to keep in the FeatureSet. If `None`, no ID
filtering takes place.
:type ids: list of str/float
:param labels: labels that we want to retain examples for. If `None`,
no label filtering takes place.
:type labels: list of str/float
:param features: Features to keep in the FeatureSet. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
:type features: list of str
:param inverse: Instead of keeping features and/or examples in lists,
remove them.
:type inverse: bool
"""
# Construct mask that indicates which examples to keep
mask = np.ones(len(self), dtype=bool)
if ids is not None:
mask = np.logical_and(mask, np.in1d(self.ids, ids))
if labels is not None:
mask = np.logical_and(mask, np.in1d(self.labels, labels))
if inverse and (labels is not None or ids is not None):
mask = np.logical_not(mask)
# Remove examples not in mask
self.ids = self.ids[mask]
self.labels = self.labels[mask]
self.features = self.features[mask, :]
# Filter features
if features is not None:
if isinstance(self.vectorizer, FeatureHasher):
raise ValueError('FeatureSets with FeatureHasher vectorizers'
' cannot be filtered by feature.')
columns = np.array(sorted({feat_num for feat_name, feat_num in
iteritems(self.vectorizer.vocabulary_)
if (feat_name in features or
feat_name.split('=', 1)[0] in
features)}))
if inverse:
all_columns = np.arange(self.features.shape[1])
columns = all_columns[np.logical_not(np.in1d(all_columns,
columns))]
self.features = self.features[:, columns]
self.vectorizer.restrict(columns, indices=True)
def filtered_iter(self, ids=None, labels=None, features=None,
inverse=False):
"""
A version of ``__iter__`` that retains only the specified features
and/or examples from the output.
:param ids: Examples in the FeatureSet to keep. If `None`, no ID
filtering takes place.
:type ids: list of str/float
:param labels: labels that we want to retain examples for. If `None`,
no label filtering takes place.
:type labels: list of str/float
:param features: Features in the FeatureSet to keep. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
:type features: list of str
:param inverse: Instead of keeping features and/or examples in lists,
remove them.
:type inverse: bool
"""
if self.features is not None and not isinstance(self.vectorizer,
DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if they'
' use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels, self.features):
# Skip instances with IDs not in filter
if ids is not None and (id_ in ids) == inverse:
continue
# Skip instances with labels not in filter
if labels is not None and (label_ in labels) == inverse:
continue
feat_dict = self.vectorizer.inverse_transform(feats)[0]
if features is not None:
feat_dict = {name: value for name, value in
iteritems(feat_dict) if
(inverse != (name in features or
name.split('=', 1)[0] in features))}
elif not inverse:
feat_dict = {}
yield id_, label_, feat_dict
def __sub__(self, other):
"""
:returns: a copy of ``self`` with all features in ``other`` removed.
"""
new_set = deepcopy(self)
new_set.filter(features=other.vectorizer.feature_names_,
inverse=True)
return new_set
@property
def has_labels(self):
"""
:returns: Whether or not this FeatureSet has any finite labels.
"""
if self.labels is not None:
return not (np.issubdtype(self.labels.dtype, float) and
np.isnan(np.min(self.labels)))
else:
return False
def __str__(self):
"""
:returns: a string representation of FeatureSet
"""
return str(self.__dict__)
def __repr__(self):
"""
:returns: a string representation of FeatureSet
"""
return repr(self.__dict__)
def __getitem__(self, value):
"""
:returns: A specific example by row number, or if given a slice,
a new FeatureSet containing a subset of the data.
"""
# Check if we're slicing
if isinstance(value, slice):
sliced_ids = self.ids[value]
sliced_feats = (self.features[value] if self.features is not None
else None)
sliced_labels = (self.labels[value] if self.labels is not None
else None)
return FeatureSet('{}_{}'.format(self.name, value), sliced_ids,
features=sliced_feats, labels=sliced_labels,
vectorizer=self.vectorizer)
else:
label = self.labels[value] if self.labels is not None else None
feats = self.features[value, :]
features = (self.vectorizer.inverse_transform(feats)[0] if
self.features is not None else {})
return self.ids[value], label, features
class FeatureSet(object):
"""
Encapsulation of all of the features, values, and metadata about a given
set of data.
This replaces ExamplesTuple in older versions.
:param name: The name of this feature set.
:type name: str
:param ids: Example IDs for this set. If
:type ids: np.array
:param classes: Classes for this set.
:type classes: np.array
:param features: The features for each instance represented as either a
list of dictionaries or an array-like (if
`feat_vectorizer` is also specified).
:type features: list of dict or array-like
:param vectorizer: Vectorizer that created feature matrix.
:type vectorizer: DictVectorizer or FeatureHasher
.. note::
If ids, classes, and/or features are not None, the number of rows in
each array must be equal.
"""
def __init__(self, name, ids=None, classes=None, features=None,
vectorizer=None):
super(FeatureSet, self).__init__()
self.name = name
if isinstance(ids, list):
ids = np.array(ids)
self.ids = ids
if isinstance(classes, list):
classes = np.array(classes)
self.classes = classes
self.features = features
self.vectorizer = vectorizer
# Convert list of dicts to numpy array
if isinstance(self.features, list):
if self.vectorizer is None:
self.vectorizer = NewDictVectorizer(sparse=True)
self.features = self.vectorizer.fit_transform(self.features)
if self.features is not None:
num_feats = self.features.shape[0]
if self.ids is None:
self.ids = np.empty(num_feats)
self.ids.fill(None)
num_ids = self.ids.shape[0]
if num_feats != num_ids:
raise ValueError(('Number of IDs (%s) does not equal '
'number of feature rows (%s)') % (num_ids,
num_feats))
if self.classes is None:
self.classes = np.empty(num_feats)
self.classes.fill(None)
num_classes = self.classes.shape[0]
if num_feats != num_classes:
raise ValueError(('Number of classes ({}) does not equal '
'number of feature rows({})') % (num_classes,
num_feats))
def __contains__(self, value):
pass
def __iter__(self):
'''
Iterate through (ID, class, feature_dict) tuples in feature set.
'''
if self.features is not None:
if not isinstance(self.vectorizer, DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if '
'they use a DictVectorizer for their feature '
'vectorizer.')
for id_, class_, feats in zip(self.ids, self.classes,
self.features):
# When calling inverse_transform we have to add [0] to get the
# results for the current instance because it always returns a
# 2D array
yield (id_, class_,
self.vectorizer.inverse_transform(feats)[0])
else:
return
def __len__(self):
return self.features.shape[1]
def __add__(self, other):
'''
Combine two feature sets to create a new one. This is done assuming
they both have the same instances with the same IDs in the same order.
'''
new_set = FeatureSet('+'.join(sorted([self.name, other.name])))
# Combine feature matrices and vectorizers
if self.features is not None:
if not isinstance(self.vectorizer, type(other.vectorizer)):
raise ValueError('Cannot combine FeatureSets because they are '
'not both using the same type of feature '
'vectorizer (e.g., DictVectorizer, '
'FeatureHasher)')
feature_hasher = isinstance(self.vectorizer, FeatureHasher)
if feature_hasher:
if (self.vectorizer.n_features !=
other.vectorizer.n_features):
raise ValueError('Cannot combine FeatureSets that uses '
'FeatureHashers with different values of '
'n_features setting.')
else:
# Check for duplicate feature names
if (set(self.vectorizer.feature_names_) &
set(other.vectorizer.feature_names_)):
raise ValueError('Cannot combine FeatureSets because they '
'have duplicate feature names.')
num_feats = self.features.shape[1]
new_set.features = sp.hstack([self.features, other.features],
'csr')
new_set.vectorizer = deepcopy(self.vectorizer)
if not feature_hasher:
for feat_name, index in other.vectorizer.vocabulary_.items():
new_set.vectorizer.vocabulary_[feat_name] = (index +
num_feats)
other_names = other.vectorizer.feature_names_
new_set.vectorizer.feature_names_.extend(other_names)
else:
new_set.features = deepcopy(other.features)
new_set.vectorizer = deepcopy(other.vectorizer)
# Check that IDs are in the same order
if self.has_ids:
if other.has_ids and not np.all(self.ids == other.ids):
raise ValueError('IDs are not in the same order in each '
'feature set')
else:
new_set.ids = deepcopy(self.ids)
else:
new_set.ids = deepcopy(other.ids)
# If either set has labels, check that they don't conflict
if self.has_classes:
# Classes should be the same for each ExamplesTuple, so store once
if other.has_classes and not np.all(self.classes == other.classes):
raise ValueError('Feature sets have conflicting labels for '
'examples with the same ID.')
else:
new_set.classes = deepcopy(self.classes)
else:
new_set.classes = deepcopy(other.classes)
return new_set
def filter(self, ids=None, classes=None, features=None, inverse=False):
'''
Removes or keeps features and/or examples from the Featureset depending
on the passed in parameters.
:param ids: Examples to keep in the FeatureSet. If `None`, no ID
filtering takes place.
:type ids: list of str/float
:param classes: Classes that we want to retain examples for. If `None`,
no class filtering takes place.
:type classes: list of str/float
:param features: Features to keep in the FeatureSet. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
:type features: list of str
:param inverse: Instead of keeping features and/or examples in lists,
remove them.
:type inverse: bool
'''
# Construct mask that indicates which examples to keep
mask = np.ones(len(self), dtype=bool)
if ids is not None:
mask = np.logical_and(mask, np.logical_not(np.in1d(self.ids, ids)))
if classes is not None:
mask = np.logical_and(mask, np.logical_not(np.in1d(self.classes,
classes)))
if inverse:
mask = np.logical_not(mask)
# Remove examples not in mask
self.ids = self.ids[mask]
self.classes = self.classes[mask]
self.features = self.features[mask, :]
# Filter features
if features is not None:
if isinstance(self.vectorizer, FeatureHasher):
raise ValueError('FeatureSets with FeatureHasher vectorizers'
' cannot be filtered by feature.')
columns = np.array(sorted({feat_num for feat_name, feat_num in
iteritems(self.vectorizer.vocabulary_)
if (feat_name in features or
feat_name.split('=', 1)[0] in
features)}))
if inverse:
columns = ~columns
self.features = self.features[:, columns]
self.vectorizer.restrict(columns)
def filtered_iter(self, ids=None, classes=None, features=None,
inverse=False):
'''
A version of ``__iter__`` that retains only the specified features
and/or examples from the output.
:param ids: Examples in the FeatureSet to keep. If `None`, no ID
filtering takes place.
:type ids: list of str/float
:param classes: Classes that we want to retain examples for. If `None`,
no class filtering takes place.
:type classes: list of str/float
:param features: Features in the FeatureSet to keep. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
:type features: list of str
:param inverse: Instead of keeping features and/or examples in lists,
remove them.
:type inverse: bool
'''
if self.features is not None and not isinstance(self.vectorizer,
DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if they'
' use a DictVectorizer for their feature '
'vectorizer.')
for id_, class_, feats in zip(self.ids, self.classes, self.features):
# Skip instances with IDs not in filter
if ids is not None and (id_ in ids) == inverse:
continue
# Skip instances with classes not in filter
if classes is not None and (class_ in classes) == inverse:
continue
feat_dict = self.vectorizer.inverse_transform(feats)[0]
if features is not None:
feat_dict = {name: value for name, value in
iteritems(feat_dict) if
(inverse != (name in features) or
(name.split('=', 1)[0] in features))}
elif not inverse:
feat_dict = {}
yield id_, class_, feat_dict
def __sub__(self, other):
'''
Return a copy of ``self`` with all features in ``other`` removed.
'''
new_set = deepcopy(self)
new_set.filter(features=other.features, inverse=True)
return new_set
@property
def has_classes(self):
'''
Whether or not this FeatureSet has any finite classes.
'''
if self.classes is not None:
return not (np.issubdtype(self.classes.dtype, float) and
np.isnan(np.min(self.classes)))
else:
return False
@property
def has_ids(self):
'''
Whether or not this FeatureSet has any finite IDs.
'''
if self.ids is not None:
return not (np.issubdtype(self.ids.dtype, float) and
np.isnan(np.min(self.ids)))
else:
return False
@property
def feat_vectorizer(self):
''' Backward compatible name for vectorizer '''
warn('FeatureSet.feat_vectorizer will be removed in SKLL 1.0.0. '
'Please switch to using FeatureSet.vectorizer to access the '
'feature vectorizer.', DeprecationWarning)
return self.vectorizer
def __str__(self):
''' Return a string representation of FeatureSet '''
return str(self.__dict__)
def __repr__(self):
''' Return a string representation of FeatureSet '''
return repr(self.__dict__)
class FeatureSet(object):
"""
Encapsulation of all of the features, values, and metadata about a given
set of data. This replaces `ExamplesTuple` from older versions of SKLL.
Parameters
----------
name : str
The name of this feature set.
ids : np.array
Example IDs for this set.
labels : np.array, optional
labels for this set.
Defaults to ``None``.
feature : list of dict or array-like, optional
The features for each instance represented as either a
list of dictionaries or an array-like (if `vectorizer` is
also specified).
Defaults to ``None``.
vectorizer : DictVectorizer or FeatureHasher, optional
Vectorizer which will be used to generate the feature matrix.
Defaults to ``None``.
Warnings
--------
FeatureSets can only be equal if the order of the instances is
identical because these are stored as lists/arrays. Since scikit-learn's
`DictVectorizer` automatically sorts the underlying feature matrix
if it is sparse, we do not do any sorting before checking for equality.
This is not a problem because we _always_ use sparse matrices with
`DictVectorizer` when creating FeatureSets.
Notes
-----
If ids, labels, and/or features are not None, the number of rows in
each array must be equal.
"""
def __init__(self, name, ids, labels=None, features=None, vectorizer=None):
super(FeatureSet, self).__init__()
self.name = name
if isinstance(ids, list):
ids = np.array(ids)
self.ids = ids
if isinstance(labels, list):
labels = np.array(labels)
self.labels = labels
self.features = features
self.vectorizer = vectorizer
# Convert list of dicts to numpy array
if isinstance(self.features, list):
if self.vectorizer is None:
self.vectorizer = NewDictVectorizer(sparse=True)
self.features = self.vectorizer.fit_transform(self.features)
if self.features is not None:
num_feats = self.features.shape[0]
if self.ids is None:
raise ValueError('A list of IDs is required')
num_ids = self.ids.shape[0]
if num_feats != num_ids:
raise ValueError(
('Number of IDs (%s) does not equal '
'number of feature rows (%s)') % (num_ids, num_feats))
if self.labels is None:
self.labels = np.empty(num_feats)
self.labels.fill(None)
num_labels = self.labels.shape[0]
if num_feats != num_labels:
raise ValueError(
('Number of labels (%s) does not equal '
'number of feature rows (%s)') % (num_labels, num_feats))
def __contains__(self, value):
"""
Check if example ID is in the FeatureSet.
Parameters
----------
value
The value to check.
"""
return value in self.ids
def __eq__(self, other):
"""
Check whether two featuresets are the same.
Parameters
----------
other : skll.FeatureSet
The other ``FeatureSet`` to check equivalence with.
Note
----
We consider feature values to be equal if any differences are in the
sixth decimal place or higher.
"""
return (self.ids.shape == other.ids.shape
and self.labels.shape == other.labels.shape
and self.features.shape == other.features.shape
and (self.ids == other.ids).all()
and (self.labels == other.labels).all() and np.allclose(
self.features.data, other.features.data, rtol=1e-6)
and (self.features.indices == other.features.indices).all()
and (self.features.indptr == other.features.indptr).all()
and self.vectorizer == other.vectorizer)
def __iter__(self):
"""
Iterate through (ID, label, feature_dict) tuples in feature set.
"""
if self.features is not None:
if not isinstance(self.vectorizer, DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if '
'they use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels,
self.features):
# reshape to a 2D matrix if we are not using a sparse matrix
# to store the features
feats = feats.reshape(1,
-1) if not sp.issparse(feats) else feats
# When calling inverse_transform we have to add [0] to get the
# results for the current instance because it always returns a
# 2D array
yield (id_, label_,
self.vectorizer.inverse_transform(feats)[0])
else:
return
def __len__(self):
"""
The number of rows in the ``FeatureSet`` instance.
"""
return self.features.shape[0]
def __add__(self, other):
"""
Combine two feature sets to create a new one. This is done assuming
they both have the same instances with the same IDs in the same order.
Parameters
----------
other : skll.FeatureSet
The other ``FeatureSet`` to add to this one.
Raises
------
ValueError
If IDs are not in the same order in each ``FeatureSet`` instance.
ValueError
If vectorizers are different between the two ``FeatureSet`` instances.
ValueError
If there are duplicate feature names.
ValueError
If there are conflicting labels.
"""
# Check that the sets of IDs are equal
if set(self.ids) != set(other.ids):
raise ValueError('IDs are not in the same order in each '
'feature set')
# Compute the relative ordering of IDs for merging the features
# and labels.
ids_indices = dict((y, x) for x, y in enumerate(other.ids))
relative_order = [ids_indices[self_id] for self_id in self.ids]
# Initialize the new feature set with a name and the IDs.
new_set = FeatureSet('+'.join(sorted([self.name, other.name])),
deepcopy(self.ids))
# Combine feature matrices and vectorizers.
if not isinstance(self.vectorizer, type(other.vectorizer)):
raise ValueError('Cannot combine FeatureSets because they are '
'not both using the same type of feature '
'vectorizer (e.g., DictVectorizer, '
'FeatureHasher)')
uses_feature_hasher = isinstance(self.vectorizer, FeatureHasher)
if uses_feature_hasher:
if (self.vectorizer.n_features != other.vectorizer.n_features):
raise ValueError('Cannot combine FeatureSets that uses '
'FeatureHashers with different values of '
'n_features setting.')
else:
# Check for duplicate feature names.
if (set(self.vectorizer.feature_names_)
& set(other.vectorizer.feature_names_)):
raise ValueError('Cannot combine FeatureSets because they '
'have duplicate feature names.')
num_feats = self.features.shape[1]
new_set.features = sp.hstack(
[self.features, other.features[relative_order]], 'csr')
new_set.vectorizer = deepcopy(self.vectorizer)
if not uses_feature_hasher:
for feat_name, index in other.vectorizer.vocabulary_.items():
new_set.vectorizer.vocabulary_[feat_name] = (index + num_feats)
other_names = other.vectorizer.feature_names_
new_set.vectorizer.feature_names_.extend(other_names)
# If either set has labels, check that they don't conflict.
if self.has_labels:
# labels should be the same for each FeatureSet, so store once.
if other.has_labels and \
not np.all(self.labels == other.labels[relative_order]):
raise ValueError('Feature sets have conflicting labels for '
'examples with the same ID.')
new_set.labels = deepcopy(self.labels)
else:
new_set.labels = deepcopy(other.labels[relative_order])
return new_set
def filter(self, ids=None, labels=None, features=None, inverse=False):
"""
Removes or keeps features and/or examples from the `Featureset` depending
on the parameters. Filtering is done in-place.
Parameters
----------
ids : list of str/float, optional
Examples to keep in the FeatureSet. If `None`, no ID
filtering takes place.
Defaults to ``None``.
labels : list of str/float, optional
Labels that we want to retain examples for. If `None`,
no label filtering takes place.
Defaults to ``None``.
features : list of str, optional
Features to keep in the FeatureSet. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
Defaults to ``None``.
inverse : bool, optional
Instead of keeping features and/or examples in lists,
remove them.
Defaults to ``False``.
Raises
------
ValueError
If attempting to use features to filter a ``FeatureSet`` that
uses a ``FeatureHasher`` vectorizer.
"""
# Construct mask that indicates which examples to keep
mask = np.ones(len(self), dtype=bool)
if ids is not None:
mask = np.logical_and(mask, np.in1d(self.ids, ids))
if labels is not None:
mask = np.logical_and(mask, np.in1d(self.labels, labels))
if inverse and (labels is not None or ids is not None):
mask = np.logical_not(mask)
# Remove examples not in mask
self.ids = self.ids[mask]
self.labels = self.labels[mask]
self.features = self.features[mask, :]
# Filter features
if features is not None:
if isinstance(self.vectorizer, FeatureHasher):
raise ValueError('FeatureSets with FeatureHasher vectorizers'
' cannot be filtered by feature.')
columns = np.array(
sorted({
feat_num
for feat_name, feat_num in
self.vectorizer.vocabulary_.items()
if (feat_name in features
or feat_name.split('=', 1)[0] in features)
}))
if inverse:
all_columns = np.arange(self.features.shape[1])
columns = all_columns[np.logical_not(
np.in1d(all_columns, columns))]
self.features = self.features[:, columns]
self.vectorizer.restrict(columns, indices=True)
def filtered_iter(self,
ids=None,
labels=None,
features=None,
inverse=False):
"""
A version of `__iter__` that retains only the specified features
and/or examples from the output.
Parameters
----------
ids : list of str/float, optional
Examples to keep in the ``FeatureSet``. If ``None``, no ID
filtering takes place.
Defaults to ``None``.
labels : list of str/float, optional
Labels that we want to retain examples for. If ``None``,
no label filtering takes place.
Defaults to ``None``.
features : list of str, optional
Features to keep in the ``FeatureSet``. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the ``FeatureSet`` that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in ``features``.
If `None`, no feature filtering takes place.
Cannot be used if ``FeatureSet`` uses a FeatureHasher for
vectorization.
Defaults to ``None``.
inverse : bool, optional
Instead of keeping features and/or examples in lists,
remove them.
Defaults to ``False``.
Yields
------
id_ : str
The ID of the example.
label_ : str
The label of the example.
feat_dict : dict
The feature dictionary, with feature name as the key
and example value as the value.
Raises
------
ValueError
If the vectorizer is not a `DictVectorizer`.
"""
if self.features is not None and not isinstance(
self.vectorizer, DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if they'
' use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels, self.features):
# Skip instances with IDs not in filter
if ids is not None and (id_ in ids) == inverse:
continue
# Skip instances with labels not in filter
if labels is not None and (label_ in labels) == inverse:
continue
# reshape to a 2D matrix if we are not using a sparse matrix
# to store the features
feats = feats.reshape(1, -1) if not sp.issparse(feats) else feats
feat_dict = self.vectorizer.inverse_transform(feats)[0]
if features is not None:
feat_dict = {
name: value
for name, value in feat_dict.items() if (inverse != (
name in features or name.split('=', 1)[0] in features))
}
elif not inverse:
feat_dict = {}
yield id_, label_, feat_dict
def __sub__(self, other):
"""
Subset ``FeatureSet`` instance by removing all the features from the
other ``FeatureSet`` instance.
Parameters
----------
other : skll.FeatureSet
The other ``FeatureSet`` containing the features that should
be removed from this ``FeatureSet``.
Returns
-------
A copy of `self` with all features in `other` removed.
"""
new_set = deepcopy(self)
new_set.filter(features=other.vectorizer.feature_names_, inverse=True)
return new_set
@property
def has_labels(self):
"""
Check if ``FeatureSet`` has finite labels.
Returns
-------
has_labels : bool
Whether or not this FeatureSet has any finite labels.
"""
# make sure that labels is not None or a list of Nones
if self.labels is not None and not all(label is None
for label in self.labels):
# then check that they are not a list of NaNs
return not (np.issubdtype(self.labels.dtype, np.floating)
and np.isnan(np.min(self.labels)))
else:
return False
def __str__(self):
"""
Returns
-------
A string representation of ``FeatureSet``.
"""
return str(self.__dict__)
def __repr__(self):
"""
Returns
-------
A string representation of ``FeatureSet``.
"""
return repr(self.__dict__)
def __getitem__(self, value):
"""
Parameters
----------
value
The value to retrieve.
Returns
-------
A specific example by row number or, if given a slice,
a new ``FeatureSet`` instance containing a subset of the data.
"""
# Check if we're slicing
if isinstance(value, slice):
sliced_ids = self.ids[value]
sliced_feats = (self.features[value]
if self.features is not None else None)
sliced_labels = (self.labels[value]
if self.labels is not None else None)
return FeatureSet('{}_{}'.format(self.name, value),
sliced_ids,
features=sliced_feats,
labels=sliced_labels,
vectorizer=self.vectorizer)
else:
label = self.labels[value] if self.labels is not None else None
feats = self.features[value, :]
features = (self.vectorizer.inverse_transform(feats)[0]
if self.features is not None else {})
return self.ids[value], label, features
@staticmethod
def split_by_ids(fs, ids_for_split1, ids_for_split2=None):
"""
Split the ``FeatureSet`` into two new ``FeatureSet`` instances based on
the given IDs for the two splits.
Parameters
----------
fs : skll.FeatureSet
The ``FeatureSet`` instance to split.
ids_for_split1 : list of int
A list of example IDs which will be split out into
the first ``FeatureSet`` instance. Note that the
FeatureSet instance will respect the order of the
specified IDs.
ids_for_split2 : list of int, optional
An optional ist of example IDs which will be
split out into the second ``FeatureSet`` instance.
Note that the ``FeatureSet`` instance will respect
the order of the specified IDs. If this is
not specified, then the second ``FeatureSet``
instance will contain the complement of the
first set of IDs sorted in ascending order.
Defaults to ``None``.
Returns
-------
fs1 : skll.FeatureSet
The first ``FeatureSet``.
fs2 : skll.FeatureSet
The second ``FeatureSet``.
"""
# Note: an alternative way to implement this is to make copies
# of the given FeatureSet instance and then use the `filter()`
# method but that wastes too much memory since it requires making
# two copies of the original FeatureSet which may be huge. With
# the current implementation, we are creating new objects but
# they should be much smaller than the original FeatureSet.
ids1 = fs.ids[ids_for_split1]
labels1 = fs.labels[ids_for_split1]
features1 = fs.features[ids_for_split1]
if ids_for_split2 is None:
ids2 = fs.ids[~np.in1d(fs.ids, ids_for_split1)]
labels2 = fs.labels[~np.in1d(fs.ids, ids_for_split1)]
features2 = fs.features[~np.in1d(fs.ids, ids_for_split1)]
else:
ids2 = fs.ids[ids_for_split2]
labels2 = fs.labels[ids_for_split2]
features2 = fs.features[ids_for_split2]
fs1 = FeatureSet('{}_1'.format(fs.name),
ids1,
labels=labels1,
features=features1,
vectorizer=fs.vectorizer)
fs2 = FeatureSet('{}_2'.format(fs.name),
ids2,
labels=labels2,
features=features2,
vectorizer=fs.vectorizer)
return fs1, fs2
@staticmethod
def from_data_frame(df, name, labels_column=None, vectorizer=None):
"""
Helper function to create a ``FeatureSet`` instance from a `pandas.DataFrame`.
Will raise an Exception if pandas is not installed in your environment.
The ``ids`` in the ``FeatureSet`` will be the index from the given frame.
Parameters
----------
df : pd.DataFrame
The pandas.DataFrame object to use as a ``FeatureSet``.
name : str
The name of the output ``FeatureSet`` instance.
labels_column : str, optional
The name of the column containing the labels (data to predict).
Defaults to ``None``.
vectorizer : DictVectorizer or FeatureHasher, optional
Vectorizer which will be used to generate the feature matrix.
Defaults to ``None``.
Returns
-------
feature_set : skll.FeatureSet
A ``FeatureSet`` instance generated from from the given data frame.
"""
if labels_column:
feature_columns = [
column for column in df.columns if column != labels_column
]
labels = df[labels_column].tolist()
else:
feature_columns = df.columns
labels = None
features = df[feature_columns].to_dict(orient='records')
return FeatureSet(name,
ids=df.index.tolist(),
labels=labels,
features=features,
vectorizer=vectorizer)
class FeatureSet(object):
"""
Encapsulation of all of the features, values, and metadata about a given
set of data. This replaces `ExamplesTuple` from older versions of SKLL.
Parameters
----------
name : str
The name of this feature set.
ids : np.array
Example IDs for this set.
labels : np.array, optional
labels for this set.
Defaults to ``None``.
feature : list of dict or array-like, optional
The features for each instance represented as either a
list of dictionaries or an array-like (if `vectorizer` is
also specified).
Defaults to ``None``.
vectorizer : DictVectorizer or FeatureHasher, optional
Vectorizer which will be used to generate the feature matrix.
Defaults to ``None``.
Warnings
--------
FeatureSets can only be equal if the order of the instances is
identical because these are stored as lists/arrays. Since scikit-learn's
`DictVectorizer` automatically sorts the underlying feature matrix
if it is sparse, we do not do any sorting before checking for equality.
This is not a problem because we _always_ use sparse matrices with
`DictVectorizer` when creating FeatureSets.
Notes
-----
If ids, labels, and/or features are not None, the number of rows in
each array must be equal.
"""
def __init__(self, name, ids, labels=None, features=None,
vectorizer=None):
super(FeatureSet, self).__init__()
self.name = name
if isinstance(ids, list):
ids = np.array(ids)
self.ids = ids
if isinstance(labels, list):
labels = np.array(labels)
self.labels = labels
self.features = features
self.vectorizer = vectorizer
# Convert list of dicts to numpy array
if isinstance(self.features, list):
if self.vectorizer is None:
self.vectorizer = NewDictVectorizer(sparse=True)
self.features = self.vectorizer.fit_transform(self.features)
if self.features is not None:
num_feats = self.features.shape[0]
if self.ids is None:
raise ValueError('A list of IDs is required')
num_ids = self.ids.shape[0]
if num_feats != num_ids:
raise ValueError(('Number of IDs (%s) does not equal '
'number of feature rows (%s)') % (num_ids,
num_feats))
if self.labels is None:
self.labels = np.empty(num_feats)
self.labels.fill(None)
num_labels = self.labels.shape[0]
if num_feats != num_labels:
raise ValueError(('Number of labels (%s) does not equal '
'number of feature rows (%s)') % (num_labels,
num_feats))
def __contains__(self, value):
"""
Check if example ID is in the FeatureSet.
Parameters
----------
value
The value to check.
"""
return value in self.ids
def __eq__(self, other):
"""
Check whether two featuresets are the same.
Parameters
----------
other : skll.FeatureSet
The other ``FeatureSet`` to check equivalence with.
Note
----
We consider feature values to be equal if any differences are in the
sixth decimal place or higher.
"""
return (self.ids.shape == other.ids.shape and
self.labels.shape == other.labels.shape and
self.features.shape == other.features.shape and
(self.ids == other.ids).all() and
(self.labels == other.labels).all() and
np.allclose(self.features.data, other.features.data,
rtol=1e-6) and
(self.features.indices == other.features.indices).all() and
(self.features.indptr == other.features.indptr).all() and
self.vectorizer == other.vectorizer)
def __iter__(self):
"""
Iterate through (ID, label, feature_dict) tuples in feature set.
"""
if self.features is not None:
if not isinstance(self.vectorizer, DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if '
'they use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels, self.features):
# reshape to a 2D matrix if we are not using a sparse matrix
# to store the features
feats = feats.reshape(1, -1) if not sp.issparse(feats) else feats
# When calling inverse_transform we have to add [0] to get the
# results for the current instance because it always returns a
# 2D array
yield (id_, label_, self.vectorizer.inverse_transform(feats)[0])
else:
return
def __len__(self):
"""
The number of rows in the ``FeatureSet`` instance.
"""
return self.features.shape[0]
def __add__(self, other):
"""
Combine two feature sets to create a new one. This is done assuming
they both have the same instances with the same IDs in the same order.
Parameters
----------
other : skll.FeatureSet
The other ``FeatureSet`` to add to this one.
Raises
------
ValueError
If IDs are not in the same order in each ``FeatureSet`` instance.
ValueError
If vectorizers are different between the two ``FeatureSet`` instances.
ValueError
If there are duplicate feature names.
ValueError
If there are conflicting labels.
"""
# Check that the sets of IDs are equal
if set(self.ids) != set(other.ids):
raise ValueError('IDs are not in the same order in each '
'feature set')
# Compute the relative ordering of IDs for merging the features
# and labels.
ids_indices = dict((y, x) for x, y in enumerate(other.ids))
relative_order = [ids_indices[self_id] for self_id in self.ids]
# Initialize the new feature set with a name and the IDs.
new_set = FeatureSet('+'.join(sorted([self.name, other.name])),
deepcopy(self.ids))
# Combine feature matrices and vectorizers.
if not isinstance(self.vectorizer, type(other.vectorizer)):
raise ValueError('Cannot combine FeatureSets because they are '
'not both using the same type of feature '
'vectorizer (e.g., DictVectorizer, '
'FeatureHasher)')
uses_feature_hasher = isinstance(self.vectorizer, FeatureHasher)
if uses_feature_hasher:
if (self.vectorizer.n_features !=
other.vectorizer.n_features):
raise ValueError('Cannot combine FeatureSets that uses '
'FeatureHashers with different values of '
'n_features setting.')
else:
# Check for duplicate feature names.
if (set(self.vectorizer.feature_names_) &
set(other.vectorizer.feature_names_)):
raise ValueError('Cannot combine FeatureSets because they '
'have duplicate feature names.')
num_feats = self.features.shape[1]
new_set.features = sp.hstack([self.features,
other.features[relative_order]],
'csr')
new_set.vectorizer = deepcopy(self.vectorizer)
if not uses_feature_hasher:
for feat_name, index in other.vectorizer.vocabulary_.items():
new_set.vectorizer.vocabulary_[feat_name] = (index +
num_feats)
other_names = other.vectorizer.feature_names_
new_set.vectorizer.feature_names_.extend(other_names)
# If either set has labels, check that they don't conflict.
if self.has_labels:
# labels should be the same for each FeatureSet, so store once.
if other.has_labels and \
not np.all(self.labels == other.labels[relative_order]):
raise ValueError('Feature sets have conflicting labels for '
'examples with the same ID.')
new_set.labels = deepcopy(self.labels)
else:
new_set.labels = deepcopy(other.labels[relative_order])
return new_set
def filter(self, ids=None, labels=None, features=None, inverse=False):
"""
Removes or keeps features and/or examples from the `Featureset` depending
on the parameters. Filtering is done in-place.
Parameters
----------
ids : list of str/float, optional
Examples to keep in the FeatureSet. If `None`, no ID
filtering takes place.
Defaults to ``None``.
labels : list of str/float, optional
Labels that we want to retain examples for. If `None`,
no label filtering takes place.
Defaults to ``None``.
features : list of str, optional
Features to keep in the FeatureSet. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the FeatureSet that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in `features`.
If `None`, no feature filtering takes place.
Cannot be used if FeatureSet uses a FeatureHasher for
vectorization.
Defaults to ``None``.
inverse : bool, optional
Instead of keeping features and/or examples in lists,
remove them.
Defaults to ``False``.
Raises
------
ValueError
If attempting to use features to filter a ``FeatureSet`` that
uses a ``FeatureHasher`` vectorizer.
"""
# Construct mask that indicates which examples to keep
mask = np.ones(len(self), dtype=bool)
if ids is not None:
mask = np.logical_and(mask, np.in1d(self.ids, ids))
if labels is not None:
mask = np.logical_and(mask, np.in1d(self.labels, labels))
if inverse and (labels is not None or ids is not None):
mask = np.logical_not(mask)
# Remove examples not in mask
self.ids = self.ids[mask]
self.labels = self.labels[mask]
self.features = self.features[mask, :]
# Filter features
if features is not None:
if isinstance(self.vectorizer, FeatureHasher):
raise ValueError('FeatureSets with FeatureHasher vectorizers'
' cannot be filtered by feature.')
columns = np.array(sorted({feat_num for feat_name, feat_num in
iteritems(self.vectorizer.vocabulary_)
if (feat_name in features or
feat_name.split('=', 1)[0] in
features)}))
if inverse:
all_columns = np.arange(self.features.shape[1])
columns = all_columns[np.logical_not(np.in1d(all_columns,
columns))]
self.features = self.features[:, columns]
self.vectorizer.restrict(columns, indices=True)
def filtered_iter(self, ids=None, labels=None, features=None,
inverse=False):
"""
A version of `__iter__` that retains only the specified features
and/or examples from the output.
Parameters
----------
ids : list of str/float, optional
Examples to keep in the ``FeatureSet``. If ``None``, no ID
filtering takes place.
Defaults to ``None``.
labels : list of str/float, optional
Labels that we want to retain examples for. If ``None``,
no label filtering takes place.
Defaults to ``None``.
features : list of str, optional
Features to keep in the ``FeatureSet``. To help with
filtering string-valued features that were converted
to sequences of boolean features when read in, any
features in the ``FeatureSet`` that contain a `=` will be
split on the first occurrence and the prefix will be
checked to see if it is in ``features``.
If `None`, no feature filtering takes place.
Cannot be used if ``FeatureSet`` uses a FeatureHasher for
vectorization.
Defaults to ``None``.
inverse : bool, optional
Instead of keeping features and/or examples in lists,
remove them.
Defaults to ``False``.
Yields
------
id_ : str
The ID of the example.
label_ : str
The label of the example.
feat_dict : dict
The feature dictionary, with feature name as the key
and example value as the value.
Raises
------
ValueError
If the vectorizer is not a `DictVectorizer`.
"""
if self.features is not None and not isinstance(self.vectorizer,
DictVectorizer):
raise ValueError('FeatureSets can only be iterated through if they'
' use a DictVectorizer for their feature '
'vectorizer.')
for id_, label_, feats in zip(self.ids, self.labels, self.features):
# Skip instances with IDs not in filter
if ids is not None and (id_ in ids) == inverse:
continue
# Skip instances with labels not in filter
if labels is not None and (label_ in labels) == inverse:
continue
# reshape to a 2D matrix if we are not using a sparse matrix
# to store the features
feats = feats.reshape(1, -1) if not sp.issparse(feats) else feats
feat_dict = self.vectorizer.inverse_transform(feats)[0]
if features is not None:
feat_dict = {name: value for name, value in
iteritems(feat_dict) if
(inverse != (name in features or
name.split('=', 1)[0] in features))}
elif not inverse:
feat_dict = {}
yield id_, label_, feat_dict
def __sub__(self, other):
"""
Subset ``FeatureSet`` instance by removing all the features from the
other ``FeatureSet`` instance.
Parameters
----------
other : skll.FeatureSet
The other ``FeatureSet`` containing the features that should
be removed from this ``FeatureSet``.
Returns
-------
A copy of `self` with all features in `other` removed.
"""
new_set = deepcopy(self)
new_set.filter(features=other.vectorizer.feature_names_,
inverse=True)
return new_set
@property
def has_labels(self):
"""
Check if ``FeatureSet`` has finite labels.
Returns
-------
has_labels : bool
Whether or not this FeatureSet has any finite labels.
"""
# make sure that labels is not None or a list of Nones
if self.labels is not None and not all(label is None for label in self.labels):
# then check that they are not a list of NaNs
return not (np.issubdtype(self.labels.dtype, np.floating) and
np.isnan(np.min(self.labels)))
else:
return False
def __str__(self):
"""
Returns
-------
A string representation of ``FeatureSet``.
"""
return str(self.__dict__)
def __repr__(self):
"""
Returns
-------
A string representation of ``FeatureSet``.
"""
return repr(self.__dict__)
def __getitem__(self, value):
"""
Parameters
----------
value
The value to retrieve.
Returns
-------
A specific example by row number or, if given a slice,
a new ``FeatureSet`` instance containing a subset of the data.
"""
# Check if we're slicing
if isinstance(value, slice):
sliced_ids = self.ids[value]
sliced_feats = (self.features[value] if self.features is not None
else None)
sliced_labels = (self.labels[value] if self.labels is not None
else None)
return FeatureSet('{}_{}'.format(self.name, value), sliced_ids,
features=sliced_feats, labels=sliced_labels,
vectorizer=self.vectorizer)
else:
label = self.labels[value] if self.labels is not None else None
feats = self.features[value, :]
features = (self.vectorizer.inverse_transform(feats)[0] if
self.features is not None else {})
return self.ids[value], label, features
@staticmethod
def split_by_ids(fs, ids_for_split1, ids_for_split2=None):
"""
Split the ``FeatureSet`` into two new ``FeatureSet`` instances based on
the given IDs for the two splits.
Parameters
----------
fs : skll.FeatureSet
The ``FeatureSet`` instance to split.
ids_for_split1 : list of int
A list of example IDs which will be split out into
the first ``FeatureSet`` instance. Note that the
FeatureSet instance will respect the order of the
specified IDs.
ids_for_split2 : list of int, optional
An optional ist of example IDs which will be
split out into the second ``FeatureSet`` instance.
Note that the ``FeatureSet`` instance will respect
the order of the specified IDs. If this is
not specified, then the second ``FeatureSet``
instance will contain the complement of the
first set of IDs sorted in ascending order.
Defaults to ``None``.
Returns
-------
fs1 : skll.FeatureSet
The first ``FeatureSet``.
fs2 : skll.FeatureSet
The second ``FeatureSet``.
"""
# Note: an alternative way to implement this is to make copies
# of the given FeatureSet instance and then use the `filter()`
# method but that wastes too much memory since it requires making
# two copies of the original FeatureSet which may be huge. With
# the current implementation, we are creating new objects but
# they should be much smaller than the original FeatureSet.
ids1 = fs.ids[ids_for_split1]
labels1 = fs.labels[ids_for_split1]
features1 = fs.features[ids_for_split1]
if ids_for_split2 is None:
ids2 = fs.ids[~np.in1d(fs.ids, ids_for_split1)]
labels2 = fs.labels[~np.in1d(fs.ids, ids_for_split1)]
features2 = fs.features[~np.in1d(fs.ids, ids_for_split1)]
else:
ids2 = fs.ids[ids_for_split2]
labels2 = fs.labels[ids_for_split2]
features2 = fs.features[ids_for_split2]
fs1 = FeatureSet('{}_1'.format(fs.name),
ids1,
labels=labels1,
features=features1,
vectorizer=fs.vectorizer)
fs2 = FeatureSet('{}_2'.format(fs.name),
ids2,
labels=labels2,
features=features2,
vectorizer=fs.vectorizer)
return fs1, fs2
@staticmethod
def from_data_frame(df, name, labels_column=None, vectorizer=None):
"""
Helper function to create a ``FeatureSet`` instance from a `pandas.DataFrame`.
Will raise an Exception if pandas is not installed in your environment.
The ``ids`` in the ``FeatureSet`` will be the index from the given frame.
Parameters
----------
df : pd.DataFrame
The pandas.DataFrame object to use as a ``FeatureSet``.
name : str
The name of the output ``FeatureSet`` instance.
labels_column : str, optional
The name of the column containing the labels (data to predict).
Defaults to ``None``.
vectorizer : DictVectorizer or FeatureHasher, optional
Vectorizer which will be used to generate the feature matrix.
Defaults to ``None``.
Returns
-------
feature_set : skll.FeatureSet
A ``FeatureSet`` instance generated from from the given data frame.
"""
if labels_column:
feature_columns = [column for column in df.columns if column != labels_column]
labels = df[labels_column].tolist()
else:
feature_columns = df.columns
labels = None
features = df[feature_columns].to_dict(orient='records')
return FeatureSet(name,
ids=df.index.tolist(),
labels=labels,
features=features,
vectorizer=vectorizer)
