Add plot_residuals

[Mr-Geekman]

IMPORTANT: Please do not create a Pull Request without creating an issue first.

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?

Type of Change

• Examples / docs / tutorials / contributors update
• Bug fix (non-breaking change which fixes an issue)
• Improvement (non-breaking change which improves an existing feature)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Proposed Changes

Look #514.

Related Issue

#514.

Closing issues

Closes #514.

[Mr-Geekman]

Example of usage:

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

from etna.pipeline import Pipeline
from etna.metrics import MAE
from etna.models import LinearPerSegmentModel
from etna.transforms import LagTransform
from etna.datasets import TSDataset
from etna.analysis import plot_residuals, plot_backtest


def multitrend_df() -> pd.DataFrame:
    """Generate one segment pd.DataFrame with multiple linear trend."""
    df = pd.DataFrame({"timestamp": pd.date_range("2020-01-01", "2021-05-31")})
    ns = [100, 150, 80, 187]
    ks = [0.4, -0.3, 0.8, -0.6]
    x = np.zeros(shape=(len(df)))
    left = 0
    right = 0
    for i, (n, k) in enumerate(zip(ns, ks)):
        right += n
        x[left:right] = np.arange(0, n, 1) * k
        for _n, _k in zip(ns[:i], ks[:i]):
            x[left:right] += _n * _k
        left = right
    df["target"] = x
    df["segment"] = "segment_1"
    df = TSDataset.to_dataset(df=df)
    return df


df = multitrend_df()
df.loc[:, pd.IndexSlice["segment_2", "target"]] = np.log(df.loc[:, pd.IndexSlice["segment_1", "target"]] + 100)
df.loc[:, pd.IndexSlice["segment_3", "target"]] = np.log(df.loc[:, pd.IndexSlice["segment_1", "target"]] + 1000)
ts = TSDataset(df=df, freq="D")
transforms = [LagTransform(in_column="target", out_column="lags", lags=[5, 6, 7, 8, 9, 10])]
pipeline = Pipeline(model=LinearPerSegmentModel(), transforms=transforms, horizon=5)
metrics, forecast_df, info = pipeline.backtest(ts=ts, metrics=[MAE()], n_folds=5)

plot_backtest(forecast_df=forecast_df, ts=ts)
plt.savefig("backtest.png")

plot_residuals(forecast_df=forecast_df, ts=ts, feature="timestamp")
plt.savefig("image_timestamp.png")

plot_residuals(forecast_df=forecast_df, ts=ts, transforms=transforms, feature="lags_5")
plt.savefig("image_lags.png")

Backtest plot:

Plot against timestamp:

Plot against lag feature:

[martins0n]

👍

[codecov-commenter]

Codecov Report

Merging #539 (adadd9d) into master (95a988c) will decrease coverage by 32.86%.
The diff coverage is 15.90%.

@@             Coverage Diff             @@
##           master     #539       +/-   ##
===========================================
- Coverage   87.17%   54.30%   -32.87%     
===========================================
  Files         118      118               
  Lines        5777     5797       +20     
===========================================
- Hits         5036     3148     -1888     
- Misses        741     2649     +1908     

Impacted Files	Coverage Δ
etna/analysis/plotters.py	12.12% <13.95%> (+1.50%)	⬆️
etna/analysis/__init__.py	100.00% <100.00%> (ø)
etna/commands/__init__.py	0.00% <0.00%> (-100.00%)	⬇️
etna/commands/backtest_command.py	0.00% <0.00%> (-96.43%)	⬇️
etna/commands/forecast_command.py	0.00% <0.00%> (-92.00%)	⬇️
etna/commands/__main__.py	0.00% <0.00%> (-87.50%)	⬇️
etna/commands/resolvers.py	0.00% <0.00%> (-80.00%)	⬇️
etna/analysis/outliers/density_outliers.py	22.44% <0.00%> (-75.52%)	⬇️
etna/datasets/datasets_generation.py	26.47% <0.00%> (-73.53%)	⬇️
etna/transforms/timestamp/time_flags.py	27.02% <0.00%> (-72.98%)	⬇️
... and 68 more

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