NOTICE
The upcoming release of Featuretools 1.0.0 contains several breaking changes. Users are encouraged to test this version prior to release by installing from GitHub:
pip install https://github.com/alteryx/featuretools/archive/woodwork-integration.zip
For details on migrating to the new version, refer to Transitioning to Featuretools Version 1.0. Please report any issues in the Featuretools GitHub repo or by messaging in Alteryx Open Source Slack.
from woodwork.column_schema import ColumnSchema from woodwork.logical_types import Boolean from featuretools import primitives from featuretools.entityset.relationship import Relationship, RelationshipPath from featuretools.entityset.timedelta import Timedelta from featuretools.feature_base.utils import is_valid_input from featuretools.primitives.base import ( AggregationPrimitive, PrimitiveBase, TransformPrimitive ) from featuretools.primitives.utils import serialize_primitive from featuretools.utils.wrangle import ( _check_time_against_column, _check_timedelta ) _ES_REF = {} class FeatureBase(object): def __init__(self, dataframe, base_features, relationship_path, primitive, name=None, names=None): """Base class for all features Args: entityset (EntitySet): entityset this feature is being calculated for dataframe (DataFrame): dataframe for calculating this feature base_features (list[FeatureBase]): list of base features for primitive relationship_path (RelationshipPath): path from this dataframe to the dataframe of the base features. primitive (:class:`.PrimitiveBase`): primitive to calculate. if not initialized when passed, gets initialized with no arguments """ assert all(isinstance(f, FeatureBase) for f in base_features), \ "All base features must be features" self.dataframe_name = dataframe.ww.name self.entityset = _ES_REF[dataframe.ww.metadata['entityset_id']] self.base_features = base_features # initialize if not already initialized if not isinstance(primitive, PrimitiveBase): primitive = primitive() self.primitive = primitive self.relationship_path = relationship_path self._name = name self._names = names assert self._check_input_types(), ("Provided inputs don't match input " "type requirements") def __getitem__(self, key): assert self.number_output_features > 1, \ 'can only access slice of multi-output feature' assert self.number_output_features > key, \ 'index is higher than the number of outputs' return FeatureOutputSlice(self, key) @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): raise NotImplementedError("Must define from_dictionary on FeatureBase subclass") [docs] def rename(self, name): """Rename Feature, returns copy""" feature_copy = self.copy() feature_copy._name = name feature_copy._names = None return feature_copy def copy(self): raise NotImplementedError("Must define copy on FeatureBase subclass") def get_name(self): if not self._name: self._name = self.generate_name() return self._name def get_feature_names(self): if not self._names: if self.number_output_features == 1: self._names = [self.get_name()] else: self._names = self.generate_names() if self.get_name() != self.generate_name(): self._names = [self.get_name() + '[{}]'.format(i) for i in range(len(self._names))] return self._names def get_function(self, **kwargs): return self.primitive.get_function(**kwargs) def get_dependencies(self, deep=False, ignored=None, copy=True): """Returns features that are used to calculate this feature ..note:: If you only want the features that make up the input to the feature function use the base_features attribute instead. """ deps = [] for d in self.base_features[:]: deps += [d] if hasattr(self, "where") and self.where: deps += [self.where] if ignored is None: ignored = set([]) deps = [d for d in deps if d.unique_name() not in ignored] if deep: for dep in deps[:]: # copy so we don't modify list we iterate over deep_deps = dep.get_dependencies(deep, ignored) deps += deep_deps return deps [docs] def get_depth(self, stop_at=None): """Returns depth of feature""" max_depth = 0 stop_at_set = set() if stop_at is not None: stop_at_set = set([i.unique_name() for i in stop_at]) if self.unique_name() in stop_at_set: return 0 for dep in self.get_dependencies(deep=True, ignored=stop_at_set): max_depth = max(dep.get_depth(stop_at=stop_at), max_depth) return max_depth + 1 def _check_input_types(self): if len(self.base_features) == 0: return True input_types = self.primitive.input_types if input_types is not None: if type(input_types[0]) != list: input_types = [input_types] for t in input_types: zipped = list(zip(t, self.base_features)) if all([is_valid_input(f.column_schema, t) for t, f in zipped]): return True else: return True return False @property def dataframe(self): """Dataframe this feature belongs too""" return self.entityset[self.dataframe_name] @property def number_output_features(self): return self.primitive.number_output_features def __repr__(self): return "<Feature: %s>" % (self.get_name()) def hash(self): return hash(self.get_name() + self.dataframe_name) def __hash__(self): return self.hash() @property def column_schema(self): feature = self column_schema = self.primitive.return_type while column_schema is None: # get column_schema of first base feature base_feature = feature.base_features[0] column_schema = base_feature.column_schema # only the original time index should exist # so make this feature's return type just a Datetime if 'time_index' in column_schema.semantic_tags: column_schema = ColumnSchema(logical_type=column_schema.logical_type, semantic_tags=column_schema.semantic_tags - {"time_index"}) elif 'index' in column_schema.semantic_tags: column_schema = ColumnSchema(logical_type=column_schema.logical_type, semantic_tags=column_schema.semantic_tags - {"index"}) # Need to add back in the numeric standard tag so the schema can get recognized # as a valid return type if column_schema.is_numeric: column_schema.semantic_tags.add('numeric') if column_schema.is_categorical: column_schema.semantic_tags.add('category') # direct features should keep the foreign key tag, but all other features should get converted if not isinstance(feature, DirectFeature) and 'foreign_key' in column_schema.semantic_tags: column_schema = ColumnSchema(logical_type=column_schema.logical_type, semantic_tags=column_schema.semantic_tags - {"foreign_key"}) feature = base_feature return column_schema @property def default_value(self): return self.primitive.default_value def get_arguments(self): raise NotImplementedError("Must define get_arguments on FeatureBase subclass") def to_dictionary(self): return { 'type': type(self).__name__, 'dependencies': [dep.unique_name() for dep in self.get_dependencies()], 'arguments': self.get_arguments(), } def _handle_binary_comparision(self, other, Primitive, PrimitiveScalar): if isinstance(other, FeatureBase): return Feature([self, other], primitive=Primitive) return Feature([self], primitive=PrimitiveScalar(other)) def __eq__(self, other): """Compares to other by equality""" return self._handle_binary_comparision(other, primitives.Equal, primitives.EqualScalar) def __ne__(self, other): """Compares to other by non-equality""" return self._handle_binary_comparision(other, primitives.NotEqual, primitives.NotEqualScalar) def __gt__(self, other): """Compares if greater than other""" return self._handle_binary_comparision(other, primitives.GreaterThan, primitives.GreaterThanScalar) def __ge__(self, other): """Compares if greater than or equal to other""" return self._handle_binary_comparision(other, primitives.GreaterThanEqualTo, primitives.GreaterThanEqualToScalar) def __lt__(self, other): """Compares if less than other""" return self._handle_binary_comparision(other, primitives.LessThan, primitives.LessThanScalar) def __le__(self, other): """Compares if less than or equal to other""" return self._handle_binary_comparision(other, primitives.LessThanEqualTo, primitives.LessThanEqualToScalar) def __add__(self, other): """Add other""" return self._handle_binary_comparision(other, primitives.AddNumeric, primitives.AddNumericScalar) def __radd__(self, other): return self.__add__(other) def __sub__(self, other): """Subtract other""" return self._handle_binary_comparision(other, primitives.SubtractNumeric, primitives.SubtractNumericScalar) def __rsub__(self, other): return Feature([self], primitive=primitives.ScalarSubtractNumericFeature(other)) def __div__(self, other): """Divide by other""" return self._handle_binary_comparision(other, primitives.DivideNumeric, primitives.DivideNumericScalar) def __truediv__(self, other): return self.__div__(other) def __rtruediv__(self, other): return self.__rdiv__(other) def __rdiv__(self, other): return Feature([self], primitive=primitives.DivideByFeature(other)) def __mul__(self, other): """Multiply by other""" if isinstance(other, FeatureBase): if all([isinstance(f.column_schema.logical_type, Boolean) for f in (self, other)]): return Feature([self, other], primitive=primitives.MultiplyBoolean) return self._handle_binary_comparision(other, primitives.MultiplyNumeric, primitives.MultiplyNumericScalar) def __rmul__(self, other): return self.__mul__(other) def __mod__(self, other): """Take modulus of other""" return self._handle_binary_comparision(other, primitives.ModuloNumeric, primitives.ModuloNumericScalar) def __rmod__(self, other): return Feature([self], primitive=primitives.ModuloByFeature(other)) def __and__(self, other): return self.AND(other) def __rand__(self, other): return Feature([other, self], primitive=primitives.And) def __or__(self, other): return self.OR(other) def __ror__(self, other): return Feature([other, self], primitive=primitives.Or) def __not__(self, other): return self.NOT(other) def __abs__(self): return Feature([self], primitive=primitives.Absolute) def __neg__(self): return Feature([self], primitive=primitives.Negate) def AND(self, other_feature): """Logical AND with other_feature""" return Feature([self, other_feature], primitive=primitives.And) def OR(self, other_feature): """Logical OR with other_feature""" return Feature([self, other_feature], primitive=primitives.Or) def NOT(self): """Creates inverse of feature""" return Feature([self], primitive=primitives.Not) def isin(self, list_of_output): return Feature([self], primitive=primitives.IsIn(list_of_outputs=list_of_output)) def is_null(self): """Compares feature to null by equality""" return Feature([self], primitive=primitives.IsNull) def __invert__(self): return self.NOT() def unique_name(self): return u"%s: %s" % (self.dataframe_name, self.get_name()) def relationship_path_name(self): return self.relationship_path.name class IdentityFeature(FeatureBase): """Feature for dataframe that is equivalent to underlying column""" def __init__(self, column, name=None): self.column_name = column.ww.name self.return_type = column.ww.schema metadata = column.ww.schema._metadata es = _ES_REF[metadata['entityset_id']] super(IdentityFeature, self).__init__(dataframe=es[metadata['dataframe_name']], base_features=[], relationship_path=RelationshipPath([]), primitive=PrimitiveBase, name=name) @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): dataframe_name = arguments['dataframe_name'] column_name = arguments['column_name'] column = entityset[dataframe_name].ww[column_name] return cls(column=column, name=arguments['name']) def copy(self): """Return copy of feature""" return IdentityFeature(self.entityset[self.dataframe_name].ww[self.column_name]) def generate_name(self): return self.column_name def get_depth(self, stop_at=None): return 0 def get_arguments(self): return { 'name': self._name, 'column_name': self.column_name, 'dataframe_name': self.dataframe_name, } @property def column_schema(self): return self.return_type class DirectFeature(FeatureBase): """Feature for child dataframe that inherits a feature value from a parent dataframe""" input_types = [ColumnSchema()] return_type = None def __init__(self, base_feature, child_dataframe_name, relationship=None, name=None): base_feature = _validate_base_features(base_feature)[0] self.parent_dataframe_name = base_feature.dataframe_name relationship = self._handle_relationship(base_feature.entityset, child_dataframe_name, relationship) child_dataframe = base_feature.entityset[child_dataframe_name] super(DirectFeature, self).__init__(dataframe=child_dataframe, base_features=[base_feature], relationship_path=RelationshipPath([(True, relationship)]), primitive=PrimitiveBase, name=name) def _handle_relationship(self, entityset, child_dataframe_name, relationship): child_dataframe = entityset[child_dataframe_name] if relationship: relationship_child = relationship.child_dataframe assert child_dataframe.ww.name == relationship_child.ww.name, \ 'child_dataframe must be the relationship child dataframe' assert self.parent_dataframe_name == relationship.parent_dataframe.ww.name, \ 'Base feature must be defined on the relationship parent dataframe' else: child_relationships = entityset.get_forward_relationships(child_dataframe.ww.name) possible_relationships = (r for r in child_relationships if r.parent_dataframe.ww.name == self.parent_dataframe_name) relationship = next(possible_relationships, None) if not relationship: raise RuntimeError('No relationship from "%s" to "%s" found.' % (child_dataframe.ww.name, self.parent_dataframe_name)) # Check for another path. elif next(possible_relationships, None): message = "There are multiple relationships to the base dataframe. " \ "You must specify a relationship." raise RuntimeError(message) return relationship @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): base_feature = dependencies[arguments['base_feature']] relationship = Relationship.from_dictionary(arguments['relationship'], entityset) child_dataframe_name = relationship.child_dataframe.ww.name return cls(base_feature=base_feature, child_dataframe_name=child_dataframe_name, relationship=relationship, name=arguments['name']) @property def number_output_features(self): return self.base_features[0].number_output_features @property def default_value(self): return self.base_features[0].default_value def copy(self): """Return copy of feature""" _is_forward, relationship = self.relationship_path[0] return DirectFeature(self.base_features[0], self.dataframe_name, relationship=relationship) @property def column_schema(self): return self.base_features[0].column_schema def generate_name(self): return self._name_from_base(self.base_features[0].get_name()) def generate_names(self): return [self._name_from_base(base_name) for base_name in self.base_features[0].get_feature_names()] def get_arguments(self): _is_forward, relationship = self.relationship_path[0] return { 'name': self._name, 'base_feature': self.base_features[0].unique_name(), 'relationship': relationship.to_dictionary(), } def _name_from_base(self, base_name): return u"%s.%s" % (self.relationship_path_name(), base_name) class AggregationFeature(FeatureBase): # Feature to condition this feature by in # computation (e.g. take the Count of products where the product_id is # "basketball".) where = None #: (str or :class:`.Timedelta`): Use only some amount of previous data from # each time point during calculation use_previous = None def __init__(self, base_features, parent_dataframe_name, primitive, relationship_path=None, use_previous=None, where=None, name=None): base_features = _validate_base_features(base_features) for bf in base_features: if bf.number_output_features > 1: raise ValueError("Cannot stack on whole multi-output feature.") self.child_dataframe_name = base_features[0].dataframe_name entityset = base_features[0].entityset relationship_path, self._path_is_unique = \ self._handle_relationship_path(entityset, parent_dataframe_name, relationship_path) self.parent_dataframe_name = parent_dataframe_name if where is not None: self.where = _validate_base_features(where)[0] msg = "Where feature must be defined on child dataframe {}".format( self.child_dataframe_name) assert self.where.dataframe_name == self.child_dataframe_name, msg if use_previous: assert entityset[self.child_dataframe_name].ww.time_index is not None, ( "Applying function that requires time index to dataframe that " "doesn't have one") self.use_previous = _check_timedelta(use_previous) assert len(base_features) > 0 time_index = base_features[0].dataframe.ww.time_index time_col = base_features[0].dataframe.ww[time_index] assert time_index is not None, ("Use previous can only be defined " "on dataframes with a time index") assert _check_time_against_column(self.use_previous, time_col) super(AggregationFeature, self).__init__(dataframe=entityset[parent_dataframe_name], base_features=base_features, relationship_path=relationship_path, primitive=primitive, name=name) def _handle_relationship_path(self, entityset, parent_dataframe_name, relationship_path): parent_dataframe = entityset[parent_dataframe_name] child_dataframe = entityset[self.child_dataframe_name] if relationship_path: assert all(not is_forward for is_forward, _r in relationship_path), \ 'All relationships in path must be backward' _is_forward, first_relationship = relationship_path[0] first_parent = first_relationship.parent_dataframe assert parent_dataframe.ww.name == first_parent.ww.name, \ 'parent_dataframe must match first relationship in path.' _is_forward, last_relationship = relationship_path[-1] assert child_dataframe.ww.name == last_relationship.child_dataframe.ww.name, \ 'Base feature must be defined on the dataframe at the end of relationship_path' path_is_unique = entityset \ .has_unique_forward_path(child_dataframe.ww.name, parent_dataframe.ww.name) else: paths = entityset \ .find_backward_paths(parent_dataframe.ww.name, child_dataframe.ww.name) first_path = next(paths, None) if not first_path: raise RuntimeError('No backward path from "%s" to "%s" found.' % (parent_dataframe.ww.name, child_dataframe.ww.name)) # Check for another path. elif next(paths, None): message = "There are multiple possible paths to the base dataframe. " \ "You must specify a relationship path." raise RuntimeError(message) relationship_path = RelationshipPath([(False, r) for r in first_path]) path_is_unique = True return relationship_path, path_is_unique @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): base_features = [dependencies[name] for name in arguments['base_features']] relationship_path = [Relationship.from_dictionary(r, entityset) for r in arguments['relationship_path']] parent_dataframe_name = relationship_path[0].parent_dataframe.ww.name relationship_path = RelationshipPath([(False, r) for r in relationship_path]) primitive = primitives_deserializer.deserialize_primitive(arguments['primitive']) use_previous_data = arguments['use_previous'] use_previous = use_previous_data and Timedelta.from_dictionary(use_previous_data) where_name = arguments['where'] where = where_name and dependencies[where_name] return cls(base_features=base_features, parent_dataframe_name=parent_dataframe_name, primitive=primitive, relationship_path=relationship_path, use_previous=use_previous, where=where, name=arguments['name']) def copy(self): return AggregationFeature(self.base_features, parent_dataframe_name=self.parent_dataframe_name, relationship_path=self.relationship_path, primitive=self.primitive, use_previous=self.use_previous, where=self.where) def _where_str(self): if self.where is not None: where_str = u" WHERE " + self.where.get_name() else: where_str = '' return where_str def _use_prev_str(self): if self.use_previous is not None and hasattr(self.use_previous, 'get_name'): use_prev_str = u", Last {}".format(self.use_previous.get_name()) else: use_prev_str = u'' return use_prev_str def generate_name(self): return self.primitive.generate_name(base_feature_names=[bf.get_name() for bf in self.base_features], relationship_path_name=self.relationship_path_name(), parent_dataframe_name=self.parent_dataframe_name, where_str=self._where_str(), use_prev_str=self._use_prev_str()) def generate_names(self): return self.primitive.generate_names(base_feature_names=[bf.get_name() for bf in self.base_features], relationship_path_name=self.relationship_path_name(), parent_dataframe_name=self.parent_dataframe_name, where_str=self._where_str(), use_prev_str=self._use_prev_str()) def get_arguments(self): return { 'name': self._name, 'base_features': [feat.unique_name() for feat in self.base_features], 'relationship_path': [r.to_dictionary() for _, r in self.relationship_path], 'primitive': serialize_primitive(self.primitive), 'where': self.where and self.where.unique_name(), 'use_previous': self.use_previous and self.use_previous.get_arguments(), } def relationship_path_name(self): if self._path_is_unique: return self.child_dataframe_name else: return self.relationship_path.name class TransformFeature(FeatureBase): def __init__(self, base_features, primitive, name=None): # Any edits made to this method should also be made to the # new_class_init method in make_trans_primitive base_features = _validate_base_features(base_features) for bf in base_features: if bf.number_output_features > 1: raise ValueError("Cannot stack on whole multi-output feature.") dataframe = base_features[0].entityset[base_features[0].dataframe_name] super(TransformFeature, self).__init__(dataframe=dataframe, base_features=base_features, relationship_path=RelationshipPath([]), primitive=primitive, name=name) @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): base_features = [dependencies[name] for name in arguments['base_features']] primitive = primitives_deserializer.deserialize_primitive(arguments['primitive']) return cls(base_features=base_features, primitive=primitive, name=arguments['name']) def copy(self): return TransformFeature(self.base_features, self.primitive) def generate_name(self): return self.primitive.generate_name(base_feature_names=[bf.get_name() for bf in self.base_features]) def generate_names(self): return self.primitive.generate_names(base_feature_names=[bf.get_name() for bf in self.base_features]) def get_arguments(self): return { 'name': self._name, 'base_features': [feat.unique_name() for feat in self.base_features], 'primitive': serialize_primitive(self.primitive) } class GroupByTransformFeature(TransformFeature): def __init__(self, base_features, primitive, groupby, name=None): if not isinstance(groupby, FeatureBase): groupby = IdentityFeature(groupby) assert len({"category", "foreign_key"} - groupby.column_schema.semantic_tags) < 2 self.groupby = groupby base_features = _validate_base_features(base_features) base_features.append(groupby) super(GroupByTransformFeature, self).__init__(base_features=base_features, primitive=primitive, name=name) @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): base_features = [dependencies[name] for name in arguments['base_features']] primitive = primitives_deserializer.deserialize_primitive(arguments['primitive']) groupby = dependencies[arguments['groupby']] return cls(base_features=base_features, primitive=primitive, groupby=groupby, name=arguments['name']) def copy(self): # the groupby feature is appended to base_features in the __init__ # so here we separate them again return GroupByTransformFeature(self.base_features[:-1], self.primitive, self.groupby) def generate_name(self): # exclude the groupby feature from base_names since it has a special # place in the feature name base_names = [bf.get_name() for bf in self.base_features[:-1]] _name = self.primitive.generate_name(base_names) return u"{} by {}".format(_name, self.groupby.get_name()) def generate_names(self): base_names = [bf.get_name() for bf in self.base_features[:-1]] _names = self.primitive.generate_names(base_names) names = [name + " by {}".format(self.groupby.get_name()) for name in _names] return names def get_arguments(self): # Do not include groupby in base_features. feature_names = [feat.unique_name() for feat in self.base_features if feat.unique_name() != self.groupby.unique_name()] return { 'name': self._name, 'base_features': feature_names, 'primitive': serialize_primitive(self.primitive), 'groupby': self.groupby.unique_name(), } class Feature(object): """ Alias to create feature. Infers the feature type based on init parameters. """ def __new__(self, base, dataframe_name=None, groupby=None, parent_dataframe_name=None, primitive=None, use_previous=None, where=None): # either direct or identity if primitive is None and dataframe_name is None: return IdentityFeature(base) elif primitive is None and dataframe_name is not None: return DirectFeature(base, dataframe_name) elif primitive is not None and parent_dataframe_name is not None: assert isinstance(primitive, AggregationPrimitive) or issubclass(primitive, AggregationPrimitive) return AggregationFeature(base, parent_dataframe_name=parent_dataframe_name, use_previous=use_previous, where=where, primitive=primitive) elif primitive is not None: assert (isinstance(primitive, TransformPrimitive) or issubclass(primitive, TransformPrimitive)) if groupby is not None: return GroupByTransformFeature(base, primitive=primitive, groupby=groupby) return TransformFeature(base, primitive=primitive) raise Exception("Unrecognized feature initialization") class FeatureOutputSlice(FeatureBase): """ Class to access specific multi output feature column """ def __init__(self, base_feature, n, name=None): base_features = [base_feature] self.num_output_parent = base_feature.number_output_features msg = "cannot access slice from single output feature" assert(self.num_output_parent > 1), msg msg = "cannot access column that is not between 0 and " + str(self.num_output_parent - 1) assert(n < self.num_output_parent), msg self.n = n self._name = name self._names = [name] if name else None self.base_features = base_features self.base_feature = base_features[0] self.dataframe_name = base_feature.dataframe_name self.entityset = base_feature.entityset self.primitive = base_feature.primitive self.relationship_path = base_feature.relationship_path def __getitem__(self, key): raise ValueError("Cannot get item from slice of multi output feature") def generate_name(self): return self.base_feature.get_feature_names()[self.n] @property def number_output_features(self): return 1 def get_arguments(self): return { 'name': self._name, 'base_feature': self.base_feature.unique_name(), 'n': self.n } @classmethod def from_dictionary(cls, arguments, entityset, dependencies, primitives_deserializer): base_feature_name = arguments['base_feature'] base_feature = dependencies[base_feature_name] n = arguments['n'] name = arguments['name'] return cls(base_feature=base_feature, n=n, name=name) def copy(self): return FeatureOutputSlice(self.base_feature, self.n) def _validate_base_features(feature): if 'Series' == type(feature).__name__: return [IdentityFeature(feature)] elif hasattr(feature, '__iter__'): features = [_validate_base_features(f)[0] for f in feature] msg = "all base features must share the same dataframe" assert len(set([bf.dataframe_name for bf in features])) == 1, msg return features elif isinstance(feature, FeatureBase): return [feature] else: raise Exception("Not a feature")