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Regression scores do not necessarily have to be between 0 and 1 (as opposed to classification scores).
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import pytest
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from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType
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from apt.utils.datasets import ArrayDataset
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from apt.utils import dataset_utils
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.ensemble import RandomForestClassifier
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def test_sklearn_classifier():
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(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
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underlying_model = RandomForestClassifier()
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model = SklearnClassifier(underlying_model, ModelOutputType.CLASSIFIER_VECTOR)
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train = ArrayDataset(x_train, y_train)
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test = ArrayDataset(x_test, y_test)
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model.fit(train)
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pred = model.predict(x_test)
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assert(pred.shape[0] == x_test.shape[0])
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score = model.score(test)
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assert(0.0 <= score <= 1.0)
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def test_sklearn_regressor():
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(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
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underlying_model = DecisionTreeRegressor()
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model = SklearnRegressor(underlying_model)
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train = ArrayDataset(x_train, y_train)
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test = ArrayDataset(x_test, y_test)
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model.fit(train)
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pred = model.predict(x_test)
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assert (pred.shape[0] == x_test.shape[0])
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score = model.score(test)
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