Source code for featuretools.primitives.standard.transform.time_series.rolling_outlier_count

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

from featuretools.primitives.base.transform_primitive_base import TransformPrimitive
from featuretools.primitives.standard.transform.time_series.utils import (
    apply_rolling_agg_to_series,
)


[docs]class RollingOutlierCount(TransformPrimitive): """Determines how many values are outliers over a given window. Description: Given a list of numbers and a corresponding list of datetimes, return a rolling count of outliers within the numeric values, starting at the row `gap` rows away from the current row and looking backward over the specified window (by `window_length` and `gap`). Values are deemed outliers using the IQR method, computed over the whole series. Input datetimes should be monotonic. Args: window_length (int, string, optional): Specifies the amount of data included in each window. If an integer is provided, it will correspond to a number of rows. For data with a uniform sampling frequency, for example of one day, the window_length will correspond to a period of time, in this case, 7 days for a window_length of 7. If a string is provided, it must be one of Pandas' offset alias strings ('1D', '1H', etc), and it will indicate a length of time that each window should span. The list of available offset aliases can be found at https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases. Defaults to 3. gap (int, string, optional): Specifies a gap backwards from each instance before the window of usable data begins. If an integer is provided, it will correspond to a number of rows. If a string is provided, it must be one of Pandas' offset alias strings ('1D', '1H', etc), and it will indicate a length of time between a target instance and the beginning of its window. Defaults to 1, which excludes the target instance from the window. min_periods (int, optional): Minimum number of observations required for performing calculations over the window. Can only be as large as window_length when window_length is an integer. When window_length is an offset alias string, this limitation does not exist, but care should be taken to not choose a min_periods that will always be larger than the number of observations in a window. Defaults to 1. Note: Only offset aliases with fixed frequencies can be used when defining gap and window_length. This means that aliases such as `M` or `W` cannot be used, as they can indicate different numbers of days. ('M', because different months are different numbers of days; 'W' because week will indicate a certain day of the week, like W-Wed, so that will indicate a different number of days depending on the anchoring date.) Note: When using an offset alias to define `gap`, an offset alias must also be used to define `window_length`. This limitation does not exist when using an offset alias to define `window_length`. In fact, if the data has a uniform sampling frequency, it is preferable to use a numeric `gap` as it is more efficient. Examples: >>> import pandas as pd >>> rolling_outlier_count = RollingOutlierCount(window_length=4) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=6) >>> rolling_outlier_count(times, [0, 0, 0, 0, 10, 0]).tolist() [nan, 0.0, 0.0, 0.0, 0.0, 1.0] We can also control the gap before the rolling calculation. >>> import pandas as pd >>> rolling_outlier_count = RollingOutlierCount(window_length=4, gap=0) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=6) >>> rolling_outlier_count(times, [0, 0, 0, 0, 10, 0]).tolist() [0.0, 0.0, 0.0, 0.0, 1.0, 1.0] We can also control the minimum number of periods required for the rolling calculation. >>> import pandas as pd >>> rolling_outlier_count = RollingOutlierCount(window_length=4, min_periods=3) >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=6) >>> rolling_outlier_count(times, [0, 0, 0, 0, 10, 0]).tolist() [nan, nan, nan, 0.0, 0.0, 1.0] We can also set the window_length and gap using offset alias strings. >>> import pandas as pd >>> rolling_outlier_count = RollingOutlierCount(window_length='4min', gap='1min') >>> times = pd.date_range(start='2019-01-01', freq='1min', periods=6) >>> rolling_outlier_count(times, [0, 0, 0, 0, 10, 0]).tolist() [nan, 0.0, 0.0, 0.0, 0.0, 1.0] """ name = "rolling_outlier_count" input_types = [ ColumnSchema(logical_type=Datetime, semantic_tags={"time_index"}), ColumnSchema(semantic_tags={"numeric"}), ] return_type = ColumnSchema(logical_type=Double, semantic_tags={"numeric"}) uses_full_dataframe = True
[docs] def __init__(self, window_length=3, gap=1, min_periods=0): self.window_length = window_length self.gap = gap self.min_periods = min_periods
def get_outliers_count(self, numeric_series): # We know the column is numeric, so use the Double logical type in case Woodwork's # type inference could not infer a numeric type if not len(numeric_series.dropna()): return np.nan if numeric_series.ww.schema is None: numeric_series = init_series(numeric_series, logical_type="Double") box_plot_info = numeric_series.ww.box_plot_dict() return len(box_plot_info["high_values"]) + len(box_plot_info["low_values"]) def get_function(self): def rolling_outlier_count(datetime, numeric): x = pd.Series(numeric.values, index=datetime.values) return apply_rolling_agg_to_series( series=x, agg_func=self.get_outliers_count, window_length=self.window_length, gap=self.gap, min_periods=self.min_periods, ignore_window_nans=False, ) return rolling_outlier_count