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

import numpy as np
import pandas as pd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Datetime, Double

from featuretools.primitives.base import AggregationPrimitive


[docs]class TimeSinceLastMax(AggregationPrimitive): """Calculates the time since the maximum value occurred. Description: Given a list of numbers, and a corresponding index of datetimes, find the time of the maximum value, and return the time elapsed since it occured. This calculation is done using an instance id's cutoff time. If multiple values equal the maximum, use the first occuring maximum. Examples: >>> from datetime import datetime >>> time_since_last_max = TimeSinceLastMax() >>> cutoff_time = datetime(2010, 1, 1, 12, 0, 0) >>> times = [datetime(2010, 1, 1, 11, 45, 0), ... datetime(2010, 1, 1, 11, 55, 15), ... datetime(2010, 1, 1, 11, 57, 30)] >>> time_since_last_max(times, [1, 3, 2], time=cutoff_time) 285.0 """ name = "time_since_last_max" input_types = [ ColumnSchema(logical_type=Datetime, semantic_tags={"time_index"}), ColumnSchema(semantic_tags={"numeric"}), ] return_type = ColumnSchema(logical_type=Double, semantic_tags={"numeric"}) uses_calc_time = True stack_on_self = False default_value = 0 def get_function(self): def time_since_last_max(datetime_col, numeric_col, time=None): df = pd.DataFrame( { "datetime": datetime_col, "numeric": numeric_col, }, ).dropna() if df.empty: return np.nan max_row = df.loc[df["numeric"].idxmax()] max_time = max_row["datetime"] time_since = time - max_time return time_since.total_seconds() return time_since_last_max