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ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
"""Quantile Anomaly Detector View"""
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__docformat__ = "numpy"
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import logging
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from datetime import datetime
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from typing import List, Optional, Union
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from openbb_terminal.config_plot import PLOT_DPI
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from openbb_terminal.config_terminal import theme
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.forecast import anom_model, helpers
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from openbb_terminal.helper_funcs import (
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plot_autoscale,
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)
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from openbb_terminal.rich_config import console
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logger = logging.getLogger(__name__)
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# pylint: disable=too-many-arguments
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@log_start_end(log=logger)
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def display_anomaly_detection(
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data: Union[pd.Series, pd.DataFrame],
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dataset_name="",
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target_column: str = "close",
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train_split: float = 0.6,
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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external_axes: Optional[List[plt.axes]] = None,
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):
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"""Display Quantile Anomaly Detection
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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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forecast_horizon: (int, optional)
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Forecast horizon. Defaults to 5.
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export: (str, optional)
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Export data to csv. Defaults to "".
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start_date: (Optional[datetime], optional)
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Start date. Defaults to None.
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end_date: (Optional[datetime], optional)
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End date. Defaults to None.
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external_axes: (Optional[List[plt.axes]], optional)
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External axes. Defaults to None.
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Returns
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-------
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None
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"""
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data = helpers.clean_data(data, start_date, end_date, target_column)
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if not helpers.check_data(data, target_column):
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return
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(ticker_series, _, binary_anom) = anom_model.get_anomaly_detection_data(
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data=data,
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target_column=target_column,
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train_split=train_split,
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)
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if not external_axes:
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_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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else:
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ax = external_axes[0]
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ticker_series.plot(label=target_column, ax=ax)
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ticker_max = np.max(ticker_series.values())
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(binary_anom * int(ticker_max)).plot(
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label="detected binary anomaly", c="red", ax=ax
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)
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# set min and max for y axis
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ax.set_ylim([-1, ticker_max * 1.1])
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ax.set_title(f"Quantile Anomaly Detection for {dataset_name}")
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ax.set_xlabel("Date")
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console.print(
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f" [green]Quantile Anomaly Detection for {dataset_name} calculated.[/green]"
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)
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theme.style_primary_axis(ax)
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if external_axes is None:
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theme.visualize_output()
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