Source code for featuretools.primitives.standard.transform.time_series.rolling_trend

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_rolling_agg_to_series,
)
from featuretools.utils import calculate_trend


[docs]class RollingTrend(TransformPrimitive): """Calculates the trend of a given window of entries of a column over time. Description: Given a list of numbers and a corresponding list of datetimes, return a rolling slope of the linear trend of values, starting at the row `gap` rows away from the current row and looking backward over the specified time window (by `window_length` and `gap`). Input datetimes should be monotonic. Args: window_length (int, string, optional): Specifies the amount of data included in each window. If an integer is provided, it will correspond to a number of rows. For data with a uniform sampling frequency, for example of one day, the window_length will correspond to a period of time, in this case, 7 days for a window_length of 7. If a string is provided, it must be one of pandas' offset alias strings ('1D', '1H', etc), and it will indicate a length of time that each window should span. The list of available offset aliases can be found at https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases. Defaults to 3. gap (int, string, optional): Specifies a gap backwards from each instance before the window of usable data begins. If an integer is provided, it will correspond to a number of rows. If a string is provided, it must be one of pandas' offset alias strings ('1D', '1H', etc), and it will indicate a length of time between a target instance and the beginning of its window. Defaults to 1. min_periods (int, optional): Minimum number of observations required for performing calculations over the window. Can only be as large as window_length when window_length is an integer. When window_length is an offset alias string, this limitation does not exist, but care should be taken to not choose a min_periods that will always be larger than the number of observations in a window. Defaults to 1. Examples: >>> import pandas as pd >>> rolling_trend = RollingTrend() >>> times = pd.date_range(start="2019-01-01", freq="1D", periods=10) >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist() [nan, nan, nan, 1.4999999999999998, 2.9999999999999996, 5.999999999999999, 7.999999999999999, 16.0, 36.0, 72.0] We can also control the gap before the rolling calculation. >>> rolling_trend = RollingTrend(gap=0) >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist() [nan, nan, 1.4999999999999998, 2.9999999999999996, 5.999999999999999, 7.999999999999999, 16.0, 36.0, 72.0, 144.0] We can also control the minimum number of periods required for the rolling calculation. >>> rolling_trend = RollingTrend(window_length=4, min_periods=4, gap=0) >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist() [nan, nan, nan, 2.299999999999999, 4.599999999999998, 6.799999999999996, 12.799999999999992, 26.399999999999984, 55.19999999999997, 110.39999999999993] We can also set the window_length and gap using offset alias strings. >>> rolling_trend = RollingTrend(window_length="4D", gap="1D") >>> rolling_trend(times, [1, 2, 4, 8, 16, 24, 48, 96, 192, 384]).tolist() [nan, nan, nan, 1.4999999999999998, 2.299999999999999, 4.599999999999998, 6.799999999999996, 12.799999999999992, 26.399999999999984, 55.19999999999997] """ name = "rolling_trend" 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, window_length=3, gap=1, min_periods=0): self.window_length = window_length self.gap = gap self.min_periods = min_periods
def get_function(self): def rolling_trend(datetime, numeric): x = pd.Series(numeric.values, index=datetime.values) return apply_rolling_agg_to_series( x, calculate_trend, self.window_length, self.gap, self.min_periods, ) return rolling_trend