featuretools.primitives.ExpandingMin

class featuretools.primitives.ExpandingMin(gap=1, min_periods=1)

计算给定窗口内事件的扩展最小值.

Description:

给定一个日期时间列表,返回从当前行开始,距离当前行 gap 行之外的扩展最小值. 扩展原语计算在给定时间点的原语值,使用所有可用的数据,直到相应的时间点.

输入的日期时间应为单调递增.

Parameters:

• gap (int, 可选) – 指定在可用数据开始之前,从每个实例向后跳过的间隔.对应于行数.默认为 1.

• min_periods (int, 可选) – 执行计算所需的最小观测数.默认为 1.

Examples

>>> import pandas as pd
>>> expanding_min = ExpandingMin()
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_min(times, [5, 4, 3, 2, 1]).tolist()
[nan, 5.0, 4.0, 3.0, 2.0]

我们也可以控制扩展计算前的间隔.

>>> import pandas as pd
>>> expanding_min = ExpandingMin(gap=0)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_min(times, [5, 4, 3, 2, 1]).tolist()
[5.0, 4.0, 3.0, 2.0, 1.0]

我们还可以控制滚动计算所需的最小周期数.

>>> import pandas as pd
>>> expanding_min = ExpandingMin(min_periods=3)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_min(times, [5, 4, 3, 2, 1]).tolist()
[nan, nan, nan, 3.0, 2.0]

__init__(gap=1, min_periods=1)

Methods

__init__([gap, min_periods])
flatten_nested_input_types(input_types)	将嵌套的列模式输入展平成一个列表.
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
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
stack_on
stack_on_exclude
stack_on_self
uses_calc_time
uses_full_dataframe