Source code for featuretools.primitives.standard.transform.exponential.exponential_weighted_average

from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Double

from featuretools.primitives.base import TransformPrimitive

[docs]class ExponentialWeightedAverage(TransformPrimitive): """Computes the exponentially weighted moving average for a series of numbers Description: Returns the exponentially weighted moving average for a series of numbers. Exactly one of center of mass (com), span, half-life, and alpha must be provided. Missing values can be ignored when calculating weights by setting 'ignore_na' to True. Args: com (float): Specify decay in terms of center of mass for com >= 0. Default is None. span (float): Specify decay in terms of span for span >= 1. Default is None. halflife (float): Specify decay in terms of half-life for halflife > 0. Default is None. alpha (float): Specify smoothing factor alpha directly. Alpha should be greater than 0 and less than or equal to 1. Default is None. ignore_na (bool): Ignore missing values when calculating weights. Default is False. Examples: >>> exponential_weighted_average = ExponentialWeightedAverage(com=0.5) >>> exponential_weighted_average([1, 2, 3, 4]).tolist() [1.0, 1.75, 2.615384615384615, 3.55] Missing values can be ignored >>> ewma_ignorena = ExponentialWeightedAverage(com=0.5, ignore_na=True) >>> ewma_ignorena([1, 2, 3, None, 4]).tolist() [1.0, 1.75, 2.615384615384615, 2.615384615384615, 3.55] """ name = "exponential_weighted_average" input_types = [ColumnSchema(semantic_tags={"numeric"})] return_type = ColumnSchema(logical_type=Double, semantic_tags={"numeric"}) uses_full_dataframe = True
[docs] def __init__(self, com=None, span=None, halflife=None, alpha=None, ignore_na=False): if all(x is None for x in [com, span, halflife, alpha]): com = 0.5 = com self.span = span self.halflife = halflife self.alpha = alpha self.ignore_na = ignore_na
def get_function(self): def exponential_weighted_average(x): return x.ewm(, span=self.span, halflife=self.halflife, alpha=self.alpha, ignore_na=self.ignore_na, ).mean() return exponential_weighted_average