`TSF` cannot handle series of variable length

[sarahmish]

Environment Details

Please indicate the following details about the environment in which you found the bug:

• SDMetrics version: 0.3.2
• Python version: 3.7

Error Description

Metrics TSFClassifierEfficacy and TSFCDetection crash when we try to compute the metrics of time series with non-fixed length. That is because sktime.classification.compose.TimeSeriesForestClassifier does not handle time series of variable length. We get the following error:

ValueError: Tabularization failed, it's possible that not all series were of equal length

We could either pad or truncate the timeseries handle this situation.

Steps to reproduce

import pandas as pd
from sdmetrics.timeseries import TSFCDetection, TSFClassifierEfficacy

real = pd.DataFrame({
    "seq_index": [1, 1, 2, 2, 2],
    "dim_0": [0, 0, 0, 0, 1],
    "dim_1": [3, 4, 3, 3, 3] 
})

synth = pd.DataFrame({
    "seq_index": [1, 1, 2, 2, 2],
    "dim_0": [1, 1, 0, 0, 1],
    "dim_1": [4, 4, 3, 3, 3] 
})

TSFClassifierEfficacy.compute(real, synth, entity_columns=['seq_index'], target='dim_0')

full trace

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-23-29d0a52ef600> in <module>()
----> 1 TSFClassifierEfficacy.compute(real, synth, entity_columns=['seq_index'], target='dim_0')

/usr/local/lib/python3.7/dist-packages/sdmetrics/timeseries/efficacy/base.py in compute(cls, real_data, synthetic_data, metadata, entity_columns, target)
    107             real_data, synthetic_data, metadata, entity_columns, target)
    108 
--> 109         return cls._compute_score(real_data, synthetic_data, entity_columns, target)

/usr/local/lib/python3.7/dist-packages/sdmetrics/timeseries/efficacy/base.py in _compute_score(cls, real_data, synthetic_data, entity_columns, target)
     78 
     79         real_acc = cls._scorer(real_x_train, real_x_test, real_y_train, real_y_test)
---> 80         synt_acc = cls._scorer(synt_x, real_x_test, synt_y, real_y_test)
     81 
     82         return synt_acc / real_acc

/usr/local/lib/python3.7/dist-packages/sdmetrics/timeseries/ml_scorers.py in tsf_classifier(X_train, X_test, y_train, y_test)
     19     ]
     20     clf = Pipeline(steps)
---> 21     clf.fit(X_train, y_train)
     22     return clf.score(X_test, y_test)
     23 

/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    339         """
    340         fit_params_steps = self._check_fit_params(**fit_params)
--> 341         Xt = self._fit(X, y, **fit_params_steps)
    342         with _print_elapsed_time('Pipeline',
    343                                  self._log_message(len(self.steps) - 1)):

/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params_steps)
    305                 message_clsname='Pipeline',
    306                 message=self._log_message(step_idx),
--> 307                 **fit_params_steps[name])
    308             # Replace the transformer of the step with the fitted
    309             # transformer. This is necessary when loading the transformer

/usr/local/lib/python3.7/dist-packages/joblib/memory.py in __call__(self, *args, **kwargs)
    350 
    351     def __call__(self, *args, **kwargs):
--> 352         return self.func(*args, **kwargs)
    353 
    354     def call_and_shelve(self, *args, **kwargs):

/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    752     with _print_elapsed_time(message_clsname, message):
    753         if hasattr(transformer, 'fit_transform'):
--> 754             res = transformer.fit_transform(X, y, **fit_params)
    755         else:
    756             res = transformer.fit(X, y, **fit_params).transform(X)

/usr/local/lib/python3.7/dist-packages/sktime/transformations/base.py in fit_transform(self, Z, X)
     89         else:
     90             # Fit method of arity 2 (supervised transformation)
---> 91             return self.fit(Z, X).transform(Z)
     92 
     93     # def inverse_transform(self, Z, X=None):

/usr/local/lib/python3.7/dist-packages/sktime/transformations/panel/compose.py in transform(self, X, y)
    217         # them into a single column
    218         if isinstance(X, pd.DataFrame):
--> 219             Xt = from_nested_to_2d_array(X)
    220         else:
    221             Xt = from_3d_numpy_to_2d_array(X)

/usr/local/lib/python3.7/dist-packages/sktime/utils/data_processing.py in from_nested_to_2d_array(X, return_numpy)
    178         if Xt.ndim != 2:
    179             raise ValueError(
--> 180                 "Tabularization failed, it's possible that not "
    181                 "all series were of equal length"
    182             )

ValueError: Tabularization failed, it's possible that not all series were of equal length