Feat/show_anomalies for multivariate

[cnhwl]

Checklist before merging this PR:

• Mentioned all issues that this PR fixes or addresses.
• Summarized the updates of this PR under Summary.
• Added an entry under Unreleased in the Changelog.

Fixes #2114.

Summary

I determine whether this feature is enabled by adding parameter multivariate_plot: bool = False to show_anomalies(), which is implemented in the show_anomalies_from_scores function.

My general idea is to iterate through the components in the series and separately plot each component (including series, pred_series, pred_scores and anomalies). The following is a simple example, with the output shown below:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from darts import TimeSeries
from darts.ad.utils import (
    eval_metric_from_scores,
    show_anomalies_from_scores,
)
from darts.ad import (
    ForecastingAnomalyModel,
    NormScorer,
    WassersteinScorer,
)
from darts.models import RegressionModel

def generate_data_ex1(random_state: int):
    np.random.seed(random_state)

    # create the train set using standard normal distribution
    comp1 = np.expand_dims(np.random.normal(loc=0, scale=1, size=200), axis=1)
    comp2 = np.expand_dims(np.random.normal(loc=0, scale=1, size=200), axis=1)
    
    # Calculate means and standard deviations
    mean1, std1 = np.mean(comp1), np.std(comp1)
    mean2, std2 = np.mean(comp2), np.std(comp2)
    
    # Identify anomalies
    anomalies1 = (comp1 > mean1 + 2 * std1).astype(int)
    anomalies2 = (comp2 > mean2 + 2 * std2).astype(int)
    
    # Concatenate anomalies
    anomalies = np.concatenate([anomalies1, anomalies2], axis=1)
    
    # Concatenate the original values
    vals = np.concatenate([comp1, comp2], axis=1)
    
    return vals, anomalies

# Example usage
data, anomalies = generate_data_ex1(random_state=42)

series = TimeSeries.from_values(data, columns=["comp1", "comp2"])
series_train = series[:120]
series_test = series[120:]
anomalies_series = TimeSeries.from_values(anomalies, columns=["comp1_anomalies", "comp2_anomalies"])
anomalies_series_test = anomalies_series[120:]

anomaly_model = ForecastingAnomalyModel(
    model=RegressionModel(lags=10),
    scorer=[
        NormScorer(component_wise=True),
        WassersteinScorer(component_wise=True)
    ],
)

anomaly_model.fit(series_train, allow_model_training=True, verbose=True)

anomaly_model.show_anomalies(
    series=series_test,
    anomalies=anomalies_series_test,
    metric="AUC_ROC",
    multivariate_plot=True
)

I would appreciate more input on how to further improve this feature, thank you very much!

[codecov]

Codecov Report

Attention: Patch coverage is 39.36170% with 57 lines in your changes missing coverage. Please review.

Project coverage is 93.51%. Comparing base (b41be28) to head (e504930).
Report is 1 commits behind head on master.

Files with missing lines	Patch %	Lines
darts/ad/utils.py	39.36%	57 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2544      +/-   ##
==========================================
- Coverage   93.86%   93.51%   -0.35%     
==========================================
  Files         139      139              
  Lines       14855    14895      +40     
==========================================
- Hits        13943    13929      -14     
- Misses        912      966      +54     

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