Make trend transforms work with NaNs

[alex-hse-repository]

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Related Issue

Closing issues

closes #417

[codecov-commenter]

Codecov Report

Merging #456 (09d1bec) into master (8b93c44) will decrease coverage by 0.00%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #456      +/-   ##
==========================================
- Coverage   87.98%   87.97%   -0.01%     
==========================================
  Files         115      115              
  Lines        5435     5440       +5     
==========================================
+ Hits         4782     4786       +4     
- Misses        653      654       +1     

Impacted Files	Coverage Δ
...na/transforms/decomposition/change_points_trend.py	99.06% <100.00%> (+0.01%)	⬆️
etna/transforms/decomposition/detrend.py	98.33% <100.00%> (ø)
etna/transforms/decomposition/stl.py	94.28% <100.00%> (+0.25%)	⬆️
etna/transforms/decomposition/trend.py	100.00% <100.00%> (ø)
etna/datasets/tsdataset.py	89.52% <0.00%> (-0.34%)	⬇️

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[martins0n]

It seems ok

But something like this not working anyway and we should think about it

import pandas as pd
from etna import pipeline

from etna.datasets.tsdataset import TSDataset
from etna.pipeline import Pipeline
from etna.metrics import SMAPE
from etna.transforms import TrendTransform

from etna.models.sarimax import SARIMAXModel
from sklearn.linear_model import LinearRegression
import numpy as np

from etna.transforms.missing_values import imputation

def example_df():
    df1 = pd.DataFrame()
    df1["timestamp"] = pd.date_range(start="2020-01-01", end="2020-02-01", freq="H")
    df1["segment"] = "segment_1"
    df1["target"] = np.arange(len(df1))
        #+ 2 * np.random.normal(size=len(df1)


    df2 = pd.DataFrame()
    df2["timestamp"] = pd.date_range(start="2020-01-01", end="2020-02-01", freq="H")
    df2["segment"] = "segment_2"
    df2["target"] = np.sqrt(np.arange(len(df2)) + 2 * np.cos(np.arange(len(df2))))

    return pd.concat([df1, df2], ignore_index=True)


def df_with_nans_in_tails(example_df):
    df = TSDataset.to_dataset(example_df)
    df.loc[:4, pd.IndexSlice["segment_1", "target"]] = None
    df.loc[-3:, pd.IndexSlice["segment_1", "target"]] = None
    return df
 
example_df = example_df()
df_with_nans_in_tails = df_with_nans_in_tails(example_df)       


from etna.datasets import *
from etna.models import *
from etna.transforms import *
from etna.pipeline import Pipeline
pipeline = Pipeline(model=NaiveModel(), transforms=[LinearTrendTransform("target"), TimeSeriesImputerTransform()], horizon=5)

pipeline.fit(TSDataset(df_with_nans_in_tails, freq="1H"))
print(pipeline.forecast())

[martins0n]

Before PR. Did it work with nulls in middle of TSDataset?