Source code for featuretools.primitives.standard.transform.time_series.expanding.expanding_mean

import pandas as pd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Datetime, Double

from featuretools.primitives.base.transform_primitive_base import TransformPrimitive
from featuretools.primitives.standard.transform.time_series.utils import (
    _apply_gap_for_expanding_primitives,
)


[docs]class ExpandingMean(TransformPrimitive): """Computes the expanding mean of events over a given window. Description: Given a list of datetimes, returns an expanding mean starting at the row `gap` rows away from the current row. An expanding primitive calculates the value of a primitive for a given time with all the data available up to the corresponding point in time. Input datetimes should be monotonic. Args: gap (int, optional): Specifies a gap backwards from each instance before the usable data begins. Corresponds to number of rows. Defaults to 1. min_periods (int, optional): Minimum number of observations required for performing calculations over the window. Defaults to 1. Examples: >>> import pandas as pd >>> expanding_mean = ExpandingMean() >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> expanding_mean(times, [5, 4, 3, 2, 1]).tolist() [nan, 5.0, 4.5, 4.0, 3.5] We can also control the gap before the expanding calculation. >>> import pandas as pd >>> expanding_mean = ExpandingMean(gap=0) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> expanding_mean(times, [5, 4, 3, 2, 1]).tolist() [5.0, 4.5, 4.0, 3.5, 3.0] We can also control the minimum number of periods required for the rolling calculation. >>> import pandas as pd >>> expanding_mean = ExpandingMean(min_periods=3) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5) >>> expanding_mean(times, [5, 4, 3, 2, 1]).tolist() [nan, nan, nan, 4.0, 3.5] """ name = "expanding_mean" input_types = [ ColumnSchema(logical_type=Datetime, semantic_tags={"time_index"}), ColumnSchema(semantic_tags={"numeric"}), ] return_type = ColumnSchema(logical_type=Double, semantic_tags={"numeric"}) uses_full_dataframe = True
[docs] def __init__(self, gap=1, min_periods=1): self.gap = gap self.min_periods = min_periods
def get_function(self): def expanding_mean(datetime, numeric): x = pd.Series(numeric.values, index=datetime) x = _apply_gap_for_expanding_primitives(x, self.gap) return x.expanding(min_periods=self.min_periods).mean().values return expanding_mean