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 ExpandingSTD(TransformPrimitive):
"""Computes the expanding standard deviation for events over a given window.
Description:
Given a list of datetimes, returns the expanding standard deviation
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_std = ExpandingSTD()
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> ans = expanding_std(times, [5, 4, 3, 2, 1]).tolist()
>>> [round(x, 2) for x in ans]
[nan, nan, 0.71, 1.0, 1.29]
We can also control the gap before the expanding calculation.
>>> import pandas as pd
>>> expanding_std = ExpandingSTD(gap=0)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> ans = expanding_std(times, [5, 4, 3, 2, 1]).tolist()
>>> [round(x, 2) for x in ans]
[nan, 0.71, 1.0, 1.29, 1.58]
We can also control the minimum number of periods required for the rolling calculation.
>>> import pandas as pd
>>> expanding_std = ExpandingSTD(min_periods=3)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> ans = expanding_std(times, [5, 4, 3, 2, 1]).tolist()
>>> [round(x, 2) for x in ans]
[nan, nan, nan, 1.0, 1.29]
"""
name = "expanding_std"
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_std(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).std().values
return expanding_std