# Classification notebook¶

This notebook contains the simple examples of timeseries classification using ETNA library.

## Classification¶

Task formulation: Given the set of time series $$\{x_i\}_{i=1}^{N}$$ and corresponding labels $$\{y_i\}_{i=1}^{N}$$ we need to find a classifier that can learn the relationship between time series and label and accurately predict the label of new series.

Our library introduces tools for binary time series classification in experimental format. This implies that the architecture and the API of the objects from etna.experimental module might face changes in the future. To use this module, you need to install the corresponding dependencies.

:

#!pip install "etna[classification]" -q


Consider the example FordA dataset from UCR archive. Dataset consists of engine noise measurements and the problem is to diagnose whether a certain symptom exists in the engine. The comprehensive description of FirdA dataset can be found here.

To load the dataset, we will use fetch_ucr_dataset util form pyts library <https://pyts.readthedocs.io/en/stable/index.html>__.

:

from pyts.datasets.ucr import fetch_ucr_dataset
import matplotlib.pyplot as plt

:

X_train, X_test, y_train, y_test = fetch_ucr_dataset(dataset="FordA", return_X_y=True)
y_train[y_train == -1], y_test[y_test == -1] = 0, 0  # transform labels to 0,1

:

X_train.shape, X_test.shape, y_train.shape, y_test.shape

:

((3601, 500), (1320, 500), (3601,), (1320,))


Let’s visualize a sample from each class.

:

for c in [0, 1]:
class_samples = X_train[y_train == c]
plt.plot(class_samples, label="class " + str(c), c=["r", "b"][c])
plt.legend(loc="best"); ### Feature extraction¶

Raw time series values usually are not the best features for the classifier. Series length is usually much higher than the number of samples in the dataset, this case classifiers to work poorly. There exists special technique to extract more informative features from the time series, you can find a comprehensive review of them in this paper.

In our library we offer two methods for feature extraction able to work with the time series of different length 1. TSFreshFeatureExtractor - extract features using extract_features method form tsfresh

:

from etna.experimental.classification.feature_extraction import TSFreshFeatureExtractor

/Users/a.p.chikov/PycharmProjects/etna_dev/etna-ts/etna/settings.py:61: UserWarning: tsfresh is not available, to install it, run pip install tsfresh==0.19.0 && pip install protobuf==3.20.1
warnings.warn(


Using this method requires installing tsfresh separately due to the dependencies conflict.

:

!pip install tsfresh==0.19.0 -q && pip install protobuf==3.20.1 -q


Constructor expects parameters of extract_features method, see the full list here and fill_na_value parameter defines the value to fill the possible NaNs in the generated features

:

from etna.experimental.classification.feature_extraction import TSFreshFeatureExtractor
from tsfresh.feature_extraction.settings import MinimalFCParameters
from tsfresh import extract_features

:

tsfresh_feature_extractor = TSFreshFeatureExtractor(default_fc_parameters=MinimalFCParameters(), fill_na_value=-100)

1. WEASELFeatureExtractor – extract features using the WEASEL algorithm, see the original paper

This method has a long list of parameters, the most important of them are:
- padding_value – value to pad the series on test set to fit the shortest series in train set - word_size, n_bins – word size and the alphabet size to approximate the series(strongly influence on the performance) - window_sizes – sizes of the sliding windows - window_steps – steps of the windows - chi2_threshold – feature selection threshold(the greter, the fewer features are selected)
:

from etna.experimental.classification.feature_extraction import WEASELFeatureExtractor

:

weasel_feature_extractor = feature_extractor = WEASELFeatureExtractor(
word_size=4,
n_bins=4,
window_sizes=[0.2, 0.3, 0.5, 0.7, 0.9],
window_steps=[0.1, 0.15, 0.25, 0.35, 0.45],
chi2_threshold=2,
)


### Performance evaluation¶

To evaluate the performance of our feature extraction methods, we will use masked_crossval_score method of TimeSeriesBinaryClassifier.

:

from etna.experimental.classification import TimeSeriesBinaryClassifier
from sklearn.linear_model import LogisticRegression


Firstly, we need to create the instance of TimeSeriesBinaryClassifier, which requires setting the feature extractor and the classification model with sklearn interface.

:

model = LogisticRegression(max_iter=1000)
clf = TimeSeriesBinaryClassifier(feature_extractor=tsfresh_feature_extractor, classifier=model)


Then we need to prepare the fold masks

:

from sklearn.model_selection import KFold
import numpy as np

:

mask = np.zeros(len(X_train))
for fold_idx, (train_index, test_index) in enumerate(KFold(n_splits=5).split(X_train)):


Then we can run the cross validation and evaluate the performance on 5 folds.

:

metrics = clf.masked_crossval_score(x=X_train, y=y_train, mask=mask)

Feature Extraction: 100%|█████████████████| 2880/2880 [00:01<00:00, 2330.32it/s]
Feature Extraction: 100%|███████████████████| 721/721 [00:00<00:00, 2502.10it/s]
Feature Extraction: 100%|█████████████████| 2881/2881 [00:01<00:00, 2582.49it/s]
Feature Extraction: 100%|███████████████████| 720/720 [00:00<00:00, 2602.03it/s]
Feature Extraction: 100%|█████████████████| 2881/2881 [00:01<00:00, 2545.80it/s]
Feature Extraction: 100%|███████████████████| 720/720 [00:00<00:00, 2388.25it/s]
Feature Extraction: 100%|█████████████████| 2881/2881 [00:01<00:00, 2495.73it/s]
Feature Extraction: 100%|███████████████████| 720/720 [00:00<00:00, 2483.62it/s]
Feature Extraction: 100%|█████████████████| 2881/2881 [00:01<00:00, 2401.59it/s]
Feature Extraction: 100%|███████████████████| 720/720 [00:00<00:00, 1763.24it/s]


The returned metrics dict contains the set of standard classification metrics for each fold:

:

metrics

:

{'precision': [0.5383522727272727,
0.5160048049745619,
0.5422891046203586,
0.48479549208199746,
0.5564688579909953],
'recall': [0.531589156237011,
0.5139824524851263,
0.538684876779783,
0.4855088120003704,
0.5484257847863261],
'fscore': [0.511929226858391,
0.497349709114415,
0.5324451810300866,
0.4796875,
0.5365782570679103],
'AUC': [0.5555427269508459,
0.5453465132609517,
0.5570033834266291,
0.5186734158461681,
0.5629105765287568]}

:

{metric: np.mean(values) for metric, values in metrics.items()}

:

{'precision': 0.5275821064790372,
'recall': 0.5236382164577233,
'fscore': 0.5115979748141605,
'AUC': 0.5478953232026702}


This feature extraction method shows quite poor quality on this dataset, let’s try out the second one.

:

clf = TimeSeriesBinaryClassifier(feature_extractor=weasel_feature_extractor, classifier=model)

:

metrics

:

{'precision': [0.8489879944589811,
0.8723519197376037,
0.8526994807058697,
0.8640069169960474,
0.8791666666666667],
'recall': [0.8490470912421682,
0.8723059471722574,
0.8539010057371147,
0.8638383900737677,
0.8802257832388056],
'fscore': [0.848819918551392,
0.8722212362749713,
0.8526401964797381,
0.863862627821725,
0.8790824629033722],
'AUC': [0.9314875536877107,
0.945698389548657,
0.9299313249560619,
0.9476758541930307,
0.9500847267465704]}

:

{metric: np.mean(values) for metric, values in metrics.items()}

:

{'precision': 0.8634425957130338,
'recall': 0.8638636434928226,
'fscore': 0.8633252884062397,
'AUC': 0.9409755698264062}


As you can see, the feature extraction performance strongly depends on the task domain, so it is a good practice to benchmark several methods on your task.

## Predictability analysis¶

Task formulation: Given the set of time series $$\{x_i\}_{i=1}^{N}$$ we need to define whether each of the series can be forecasted with some quality threshold.

This is the extension of the classification task, which helps you to perform some kind of pre-validation on your dataset. This might help to identify the “bad” segments, which should be processed separately.

You can train the PredictabilityAnalyzer on your own, however it requires using the dataset consists of the best possible scores for the segments, which might be hard to collect. Therefore, we pretrained several instances of the analyzer on the datasets with H, D and W frequencies.

To demonstrate the usage of this tool, we will use M4 dataset.

:

import pandas as pd
from etna.datasets import TSDataset
from tqdm.notebook import tqdm

:

!curl "https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/Train/Daily-train.csv" -o m4.csv

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
100 91.3M  100 91.3M    0     0  6748k      0  0:00:13  0:00:13 --:00:01 9628k:--:-- 9564k

:

df_raw = pd.read_csv("m4.csv")

:

dfs = []
for i in tqdm(range(len(df_raw))):
segment_name = df_raw.iloc[i, 0]
segment_values = df_raw.iloc[i, 1:].dropna().astype(float).values
segment_len = len(segment_values)
timestamps = pd.date_range(
start=pd.to_datetime("2022-11-04") - pd.to_timedelta("1d") * (segment_len - 1), end="2022-11-04"
)
df_segment = pd.DataFrame({"target": segment_values, "segment": segment_name, "timestamp": timestamps})
dfs.append(df_segment)

:

df = pd.concat(dfs)
df = TSDataset.to_dataset(df)
ts = TSDataset(df=df, freq="D")


Let’s visualize several random segments from the dataset

:

print("Number of segments:", len(ts.segments))
ts.plot(n_segments=10)

Number of segments: 4227 Dataset consists of 4k segments of 1-4 years length. As the plot suggests, the behavior of the segments are different across the dataset, and it might be hard to predict all of them accurately. Let’s try to evaluate the SMAPE on the backtest using some baseline model.

:

from etna.pipeline import Pipeline
from etna.models import NaiveModel
from etna.metrics import SMAPE

/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Torchmetrics v0.9 introduced a new argument class property called full_state_update that has
not been set for this class (SMAPE). The property determines if update by
default needs access to the full metric state. If this is not the case, significant speedups can be
achieved and we recommend setting this to False.
We provide an checking function
from torchmetrics.utilities import check_forward_full_state_property
that can be used to check if the full_state_update=True (old and potential slower behaviour,
default for now) or if full_state_update=False can be used safely.

warnings.warn(*args, **kwargs)

:

pipeline = Pipeline(model=NaiveModel(), transforms=[], horizon=30)


It takes about 2 minutes even for naive model to evaluate the performance on this dataset, imagine what time it takes for more complex one.

:

metrics, _, _ = pipeline.backtest(ts, metrics=[SMAPE()], n_folds=3, aggregate_metrics=False)

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   26.3s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   53.6s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  1.4min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  1.4min finished


Let’s visualize the resulting metrics

:

from etna.analysis import metric_per_segment_distribution_plot, plot_metric_per_segment

:

metric_per_segment_distribution_plot(metrics_df=metrics, metric_name="SMAPE", per_fold_aggregation_mode="mean") :

plot_metric_per_segment(metrics_df=metrics, metric_name="SMAPE", top_k=20) Most of the segments can be forecasted with SMAPE less than 10, however there is a list of segments with SMAPE greater than 20 which we want to catch and analyze separately.

:

agg_metrics = metrics.groupby("segment").mean().reset_index()

Number of bad segments: 42


:

from etna.experimental.classification import PredictabilityAnalyzer


Let’s look at the list of available analyzers

:

PredictabilityAnalyzer.get_available_models()

:

['weasel', 'tsfresh', 'tsfresh_min']


Pertained analyzer can be loaded from the public s3 bucket by it’s name and dataset frequency

:

PredictabilityAnalyzer.download_model(model_name="weasel", dataset_freq="D", path="weasel_analyzer.pickle")


Once we loaded the analyzer, we can create an instance of it

:

weasel_analyzer = PredictabilityAnalyzer.load("weasel_analyzer.pickle")


### Analyze segments predictability¶

Now we can analyze the dataset for predictability, which might be done the two ways.

:

def metrics_for_bad_segments(predictability):
bad_segments = [segment for segment in predictability if predictability[segment] == 1]
"SMAPE", ascending=False
)

1. The short way: using analyze_predictability method.

:

%%time
predictability = weasel_analyzer.analyze_predictability(ts)

CPU times: user 11.5 s, sys: 925 ms, total: 12.4 s
Wall time: 12.4 s

:

metrics = metrics_for_bad_segments(predictability)

Number of bad segments: 138

1. The long way: using predict_proba method. This is more flexible as you can choose the threshold for predictability score.

:

%%time
series = weasel_analyzer.get_series_from_dataset(ts)
predictability_score = weasel_analyzer.predict_proba(series)

CPU times: user 11.5 s, sys: 915 ms, total: 12.5 s
Wall time: 12.5 s

:

threthold = 0.4
predictability = {segment: int(predictability_score[i] > threthold) for i, segment in enumerate(sorted(ts.segments))}

:

metrics = metrics_for_bad_segments(predictability)

Number of bad segments: 398


Let’s take a look at the segments with the bad metrics:

:

metrics.head(10)

:

segment SMAPE fold_number
412 D137 57.452473 1.0
4072 D86 52.144064 1.0
734 D166 45.620776 1.0
3387 D4047 29.501460 1.0
1310 D2178 29.205434 1.0
4061 D85 22.579621 1.0
1205 D2083 22.547771 1.0
3333 D4 20.994039 1.0
357 D132 15.903925 1.0
2778 D35 14.327464 1.0
:

ts.plot(segments=metrics.head(10)["segment"]) It took only about 15 seconds to analyze the dataset and suggest the set of possible bad segments for weasel-based analyzer, which is much faster than using any baseline pipeline.

However, there might be false-positives in the results.

:

metrics.tail(10)

:

segment SMAPE fold_number
2972 D3674 1.384460 1.0
3706 D53 1.294982 1.0
3534 D418 1.281990 1.0
2167 D295 1.258861 1.0
2457 D321 1.177998 1.0
1240 D2114 1.145384 1.0
2446 D320 1.123942 1.0
3242 D3917 1.010831 1.0
346 D131 0.487805 1.0
1348 D2211 0.000000 1.0
:

ts.plot(segments=metrics.tail(10)["segment"]) That’s all for this notebook. Remember, that this is an experimental feature, and it might change the interface in the future!

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