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29eef20dda
* init commit * model + view * fixes plus doc strings * tests * trail map * remove ) * linting? * fix * refactor * renaming * rename v2 * final chart * linting Co-authored-by: James Maslek <jmaslek11@gmail.com>
63 lines
1.5 KiB
Python
63 lines
1.5 KiB
Python
# pylint: disable=too-many-arguments
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"""Quantile Anomaly Detection Model"""
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__docformat__ = "numpy"
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import logging
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from typing import Tuple, Union, List
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import pandas as pd
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from darts import TimeSeries
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from darts.ad import KMeansScorer
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from darts.ad import QuantileDetector
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.forecast import helpers
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def get_anomaly_detection_data(
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data: Union[pd.Series, pd.DataFrame],
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target_column: str = "close",
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train_split: float = 0.6,
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) -> Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries]]:
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"""Get Quantile Anomaly Detection Data
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Parameters
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----------
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data: Union[pd.Series, pd.DataFrame]
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Input Data
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-------
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target_column: str
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Target column to forecast. Defaults to "close".
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train_split: (float, optional)
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Train/val split. Defaults to 0.85.
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Returns
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-------
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Tuple[
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List[TimeSeries],
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List[TimeSeries],
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List[TimeSeries],
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]
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"""
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use_scalers = False
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_, ticker_series = helpers.get_series(data, target_column, is_scaler=use_scalers)
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# split dataset into train and val
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train, val = ticker_series.split_before(train_split)
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scorer = KMeansScorer(k=2, window=5)
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scorer.fit(train)
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anom_score = scorer.score(val)
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detector = QuantileDetector(high_quantile=0.99)
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detector.fit(scorer.score(train))
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binary_anom = detector.detect(anom_score)
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return ticker_series, anom_score, binary_anom
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