featuretools.primitives.RollingTrend

class featuretools.primitives.RollingTrend(window_length=3, gap=1, min_periods=0)

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]

__init__(window_length=3, gap=1, min_periods=0)

Methods

__init__([window_length, gap, min_periods])
flatten_nested_input_types(input_types)	Flattens nested column schema inputs into a single list.
generate_name(base_feature_names)
generate_names(base_feature_names)
get_args_string()
get_arguments()
get_description(input_column_descriptions[, ...])
get_filepath(filename)
get_function()

Attributes

base_of
base_of_exclude
commutative
compatibility	Additional compatible libraries
default_value	Default value this feature returns if no data found.
description_template
input_types	woodwork.ColumnSchema types of inputs
max_stack_depth
name	Name of the primitive
number_output_features	Number of columns in feature matrix associated with this feature
return_type	ColumnSchema type of return
series_library
stack_on
stack_on_exclude
stack_on_self
uses_calc_time
uses_full_dataframe