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

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 TimeSinceLastMin(AggregationPrimitive): """Calculates the time since the minimum value occurred. Description: Given a list of numbers, and a corresponding index of datetimes, find the time of the minimum 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 minimum, use the first occuring minimum. Examples: >>> from datetime import datetime >>> time_since_last_min = TimeSinceLastMin() >>> 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_min(times, [1, 3, 2], time=cutoff_time) 900.0 """ name = "time_since_last_min" 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_min(datetime_col, numeric_col, time=None): df = pd.DataFrame( { "datetime": datetime_col, "numeric": numeric_col, }, ).dropna() if df.empty: return np.nan min_row = df.loc[df["numeric"].idxmin()] min_time = min_row["datetime"] time_since = time - min_time return time_since.total_seconds() return time_since_last_min