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[ENH] classification test scenario with three classes and pd-multiindex mtype #6374

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May 29, 2024
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[ENH] classification test scenario with three classes and pd-multiindex mtype #6374

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efcdcd8
3 class
fkiraly May 1, 2024
319578e
Update scenarios_classification.py
fkiraly May 1, 2024
3d75693
Update scenarios_classification.py
fkiraly May 2, 2024
83d0d5f
Merge branch 'main' into classif-three-class
fkiraly May 27, 2024
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[AUTOMATED] update CONTRIBUTORS.md
fkiraly May 27, 2024
e7065a1
fix
fkiraly May 28, 2024
58b034f
Merge branch 'fix-classif-fit_predict' into classif-three-class
fkiraly May 28, 2024
16fba8c
remove unused imports
fkiraly May 28, 2024
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Merge branch 'fix-classif-fit_predict' into classif-three-class
fkiraly May 28, 2024
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Update test_all_classifiers.py
fkiraly May 28, 2024
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33 changes: 32 additions & 1 deletion sktime/utils/_testing/scenarios_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,11 @@
from sktime.base import BaseObject
from sktime.registry import scitype
from sktime.utils._testing.hierarchical import _make_hierarchical
from sktime.utils._testing.panel import _make_classification_y, _make_panel_X
from sktime.utils._testing.panel import (
_make_classification_y,
_make_panel,
_make_panel_X,
)
from sktime.utils._testing.scenarios import TestScenario

# random seed for generating data to keep scenarios exactly reproducible
Expand Down Expand Up @@ -117,6 +121,31 @@ def args(self):
default_arg_sequence = ["fit", "predict", "predict", "predict"]


class ClassifierFitPredictThreeClasses(ClassifierTestScenario):
"""Fit/predict with univariate panel X, pd-multiindex mtype, and three classes."""

_tags = {
"X_univariate": True,
"X_unequal_length": False,
"is_enabled": True,
"n_classes": 3,
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}

@property
def args(self):
y = _make_classification_y(n_instances=18, n_classes=3, random_state=RAND_SEED)
X = _make_panel(n_instances=18, n_timepoints=20, random_state=RAND_SEED, y=y)
X_test = _make_panel_X(n_instances=5, n_timepoints=20, random_state=RAND_SEED)

return {
"fit": {"y": y, "X": X},
"predict": {"X": X_test},
}

default_method_sequence = ["fit", "predict", "predict_proba", "decision_function"]
default_arg_sequence = ["fit", "predict", "predict", "predict"]


class ClassifierFitPredictNumpy(ClassifierTestScenario):
"""Fit/predict with univariate panel X, numpy3D mtype, and labels y."""

Expand Down Expand Up @@ -216,6 +245,7 @@ def args(self):

scenarios_classification = [
ClassifierFitPredict,
ClassifierFitPredictThreeClasses,
ClassifierFitPredictNumpy,
ClassifierFitPredictMultivariate,
ClassifierFitPredictUnequalLength,
Expand All @@ -224,6 +254,7 @@ def args(self):
# same scenarios used for early classification
scenarios_early_classification = [
ClassifierFitPredict,
ClassifierFitPredictThreeClasses,
ClassifierFitPredictNumpy,
ClassifierFitPredictMultivariate,
ClassifierFitPredictUnequalLength,
Expand Down