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


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