Add get_anomalies_isolation_forest method

[alex-hse-repository]

Before submitting (must do checklist)

• Did you read the contribution guide?
• Did you update the docs? We use Numpy format for all the methods and classes.
• Did you write any new necessary tests?
• Did you update the CHANGELOG?

Proposed Changes

To Discuss

1. Signature of this method differs form the others(we don't have in_column here), what should we return in case of index_only=False? -- Add in_column, use this column to populate the output series values
2. The current implementation might work quite slow on large datasets, do we want to make any optimizations now? - No

Performance Benchmark

Looks like we can try to speed up computation by parallelizing this procedure by segments, dataframe operations are quite lightweight compering to isolation forest itself

• periods = 100, n_segments = 100 -- most of the time on isolation forest(about 2 min)

• periods = 100, n_segments = 10000 + 50 features + 1 tree in forest -- most of the time on isolation forest(about 45 s), about 15-20 s on dataframe operations

from etna.analysis.outliers import get_anomalies_isolation_forest
import numpy as np
import pandas as pd

from etna.datasets import TSDataset, generate_ar_df
from omegaconf import OmegaConf, DictConfig
import hydra


@hydra.main(version_base="1.3.2", config_path=".", config_name="config")
def main(cfg: DictConfig):
    cfg = OmegaConf.to_container(cfg, resolve=True)

    def add_nans(df: pd.DataFrame, nans_frac: float):
        nan_mask = np.random.choice([True, False], size=df.shape[0], p=[1-nans_frac, nans_frac])
        df.loc[nan_mask, "target"] = np.NAN
        return df

    df = add_nans(df=generate_ar_df(n_segments=cfg["n_segments"], periods=cfg["periods"], start_time="2000-01-01"), nans_frac=cfg["nans_frac"])
    for i in range(cfg["n_features"]):
        df[f"exog_{i}"] = np.random.normal(size=len(df))
    ts = TSDataset(df=df, freq="D")

    get_anomalies_isolation_forest(
        ts=ts,
        features_to_use=[f"exog_{i}" for i in range(cfg["features_to_use"])],
        features_to_ignore=cfg["features_to_ignore"],
        ignore_missing=cfg["ignore_missing"],
        index_only=cfg["index_only"],
        n_estimators=1,
    )


if __name__ == "__main__":
    main()

Closing issues

closes #356

[github-actions]

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[brsnw250]

1. Let's add in_column, which would be mandatory for the case index only=False. And use this column to populate the output series values.
2. It is better to do optimization here.

[codecov]

Codecov Report

Attention: Patch coverage is 0% with 59 lines in your changes missing coverage. Please review.

Project coverage is 9.70%. Comparing base (5cb8485) to head (12b3fa8).
Report is 1 commits behind head on master.

Files	Patch %	Lines
...tna/analysis/outliers/isolation_forest_outliers.py	0.00%	57 Missing ⚠️
etna/analysis/__init__.py	0.00%	1 Missing ⚠️
etna/analysis/outliers/__init__.py	0.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #375       +/-   ##
==========================================
- Coverage   88.88%   9.70%   -79.19%     
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  Files         224     225        +1     
  Lines       15381   15431       +50     
==========================================
- Hits        13672    1498    -12174     
- Misses       1709   13933    +12224     

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