Source code for featuretools.primitives.standard.transform.binary.or_primitive

import numpy as np
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Boolean, BooleanNullable

from featuretools.primitives.base.transform_primitive_base import TransformPrimitive
from featuretools.utils.gen_utils import Library

[docs]class Or(TransformPrimitive): """Performs element-wise logical OR of two lists. Description: Given a list of booleans X and a list of booleans Y, determine whether each value in X is `True`, or whether its corresponding value in Y is `True`. Examples: >>> _or = Or() >>> _or([False, True, False], [True, True, False]).tolist() [True, True, False] """ name = "or" input_types = [ [ ColumnSchema(logical_type=BooleanNullable), ColumnSchema(logical_type=BooleanNullable), ], [ColumnSchema(logical_type=Boolean), ColumnSchema(logical_type=Boolean)], [ ColumnSchema(logical_type=Boolean), ColumnSchema(logical_type=BooleanNullable), ], [ ColumnSchema(logical_type=BooleanNullable), ColumnSchema(logical_type=Boolean), ], ] return_type = ColumnSchema(logical_type=BooleanNullable) commutative = True compatibility = [Library.PANDAS, Library.DASK, Library.SPARK] description_template = "whether {} is true or {} is true" def get_function(self): return np.logical_or def generate_name(self, base_feature_names): return "OR(%s, %s)" % (base_feature_names[0], base_feature_names[1])