from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Boolean, BooleanNullable
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, BooleanNullable))
for f in (self, other)
]
):
return Feature([self, other], primitive=primitives.MultiplyBoolean)
if (
"numeric" in self.column_schema.semantic_tags
and isinstance(
other.column_schema.logical_type, (Boolean, BooleanNullable)
)
or "numeric" in other.column_schema.semantic_tags
and isinstance(
self.column_schema.logical_type, (Boolean, BooleanNullable)
)
):
return Feature(
[self, other], primitive=primitives.MultiplyNumericBoolean
)
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 "%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 "%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 = " 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 = ", Last {}".format(self.use_previous.get_name())
else:
use_prev_str = ""
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):
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 "{} 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")