Source code for featuretools.primitives.standard.aggregation.num_consecutive_greater_mean

import numpy as np
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import IntegerNullable

from featuretools.primitives.base import AggregationPrimitive

[docs]class NumConsecutiveGreaterMean(AggregationPrimitive): """Determines the length of the longest subsequence above the mean. Description: Given a list of numbers, find the longest subsequence of numbers larger than the mean of the entire sequence. Return the length of the longest subsequence. Args: skipna (bool): If this is False and any value in x is `NaN`, then the result will be `NaN`. If True, `NaN` values are skipped. Default is True. Examples: >>> num_consecutive_greater_mean = NumConsecutiveGreaterMean() >>> num_consecutive_greater_mean([1, 2, 3, 4, 5, 6]) 3.0 We can also control the way `NaN` values are handled. >>> num_consecutive_greater_mean = NumConsecutiveGreaterMean(skipna=False) >>> num_consecutive_greater_mean([1, 2, 3, 4, 5, 6, None]) nan """ name = "num_consecutive_greater_mean" input_types = [ColumnSchema(semantic_tags={"numeric"})] return_type = ColumnSchema(logical_type=IntegerNullable, semantic_tags={"numeric"}) stack_on_self = False default_value = 0
[docs] def __init__(self, skipna=True): self.skipna = skipna
def get_function(self): def num_consecutive_greater_mean(x): # check for NaN cases if x.isnull().all(): return np.nan if not self.skipna and x.isnull().values.any(): return np.nan x_mean = x.mean() # In some cases, the mean of x may be NaN # (such as when x has both inf and -inf values) if np.isnan(x.mean()): return np.nan # Find indices of points at or below mean x = x.dropna().reset_index(drop=True) below_mean_indices = x[x <= x_mean].index.to_series() # If none of x is below the mean, return the length of x if below_mean_indices.empty: return len(x) # Pad index with start/end values, in case the longest # sequence occurs at the beginning or end of x below_mean_indices[-1] = -1 below_mean_indices[len(x)] = len(x) below_mean_indices = below_mean_indices.sort_index() # Calculate gaps between points below mean below_mean_indices_shifted = below_mean_indices.shift(1) diffs = below_mean_indices - below_mean_indices_shifted # Take biggest gap, and subtract 1 to get result max_gap = (diffs).max() - 1 return max_gap return num_consecutive_greater_mean