featuretools.primitives.RollingTrend#
- class featuretools.primitives.RollingTrend(window_length=3, gap=1, min_periods=0)[source]#
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]
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