Code Duplication - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 21-25 lines in 6 locations

tests/test_lightgbm.py 1 location


    opt.score(X, y)


def test_lightgbm_classification():
    from hyperactive import RandomSearchOptimizer

    ml_scores = [
        "accuracy_score",
        "balanced_accuracy_score",
        "average_precision_score",
        "brier_score_loss",
        "f1_score",
        "log_loss",
        "precision_score",
        "recall_score",
        "jaccard_score",
        "roc_auc_score",
    ]

    for score in ml_scores:
        opt = RandomSearchOptimizer(search_config, 1, metric=score)
        assert opt._config_.metric == score
        opt.fit(X, y)
        assert opt._config_.metric == score
        opt.predict(X)
        assert opt._config_.metric == score
        opt.score(X, y)
        assert opt._config_.metric == score


def test_lightgbm_regression():

tests/test_catboost.py 1 location


    opt.score(X, y)


def test_catboost_classification():
    from hyperactive import RandomSearchOptimizer

    ml_scores = [
        "accuracy_score",
        "balanced_accuracy_score",
        "average_precision_score",
        "brier_score_loss",
        "f1_score",
        "log_loss",
        "precision_score",
        "recall_score",
        "jaccard_score",
        "roc_auc_score",
    ]

    for score in ml_scores:
        opt = RandomSearchOptimizer(search_config, 1, metric=score)
        assert opt._config_.metric == score
        opt.fit(X, y)
        assert opt._config_.metric == score
        opt.predict(X)
        assert opt._config_.metric == score
        opt.score(X, y)
        assert opt._config_.metric == score


def test_catboost_regression():

tests/test_sklearn.py 1 location


    opt.score(X, y)


def test_sklearn_classification():
    from hyperactive import RandomSearchOptimizer

    ml_scores = [
        "accuracy_score",
        "balanced_accuracy_score",
        "average_precision_score",
        "brier_score_loss",
        "f1_score",
        "log_loss",
        "precision_score",
        "recall_score",
        "jaccard_score",
        "roc_auc_score",
    ]

    for score in ml_scores:
        opt = RandomSearchOptimizer(search_config, 1, metric=score)
        assert opt._config_.metric == score
        opt.fit(X, y)
        assert opt._config_.metric == score
        opt.predict(X)
        assert opt._config_.metric == score
        opt.score(X, y)
        assert opt._config_.metric == score


def test_sklearn_regression():

tests/test_xgboost.py 1 location


    opt.score(X, y)


def test_xgboost_classification():
    from hyperactive import RandomSearchOptimizer

    ml_scores = [
        "accuracy_score",
        "balanced_accuracy_score",
        "average_precision_score",
        "brier_score_loss",
        "f1_score",
        "log_loss",
        "precision_score",
        "recall_score",
        "jaccard_score",
        "roc_auc_score",
    ]

    for score in ml_scores:
        opt = RandomSearchOptimizer(search_config, 1, metric=score)
        assert opt._config_.metric == score
        opt.fit(X, y)
        assert opt._config_.metric == score
        opt.predict(X)
        assert opt._config_.metric == score
        opt.score(X, y)
        assert opt._config_.metric == score


def test_xgboost_regression():

tests/local/_test_keras_cnn.py 1 location


    opt.score(X, y)


def test_keras_scores():
    from hyperactive import RandomSearchOptimizer

    ml_scores = [
        "accuracy",
        "binary_accuracy",
        "categorical_accuracy",
        # "sparse_categorical_accuracy",
        "top_k_categorical_accuracy",
        # "sparse_top_k_categorical_accuracy",
    ]

    for score in ml_scores:
        opt = RandomSearchOptimizer(search_config, 1, metric=score)
        assert opt._config_.metric == score
        opt.fit(X, y)
        assert opt._config_.metric == score
        opt.predict(X)
        assert opt._config_.metric == score
        opt.score(X, y)
        assert opt._config_.metric == score


def test_keras_losses():

tests/test_keras_mlp.py 1 location


    opt.score(X, y)


def test_keras_scores():
    from hyperactive import RandomSearchOptimizer

    ml_scores = [
        "accuracy",
        "binary_accuracy",
        "categorical_accuracy",
        "sparse_categorical_accuracy",
        "top_k_categorical_accuracy",
        "sparse_top_k_categorical_accuracy",
    ]

    for score in ml_scores:
        opt = RandomSearchOptimizer(search_config, 1, metric=score)
        assert opt._config_.metric == score
        opt.fit(X, y)
        assert opt._config_.metric == score
        opt.predict(X)
        assert opt._config_.metric == score
        opt.score(X, y)
        assert opt._config_.metric == score


def test_keras_losses():

		@@ 32-56 (lines=25) @@
29		opt.score(X, y)
30
31
32		def test_lightgbm_classification():
33		from hyperactive import RandomSearchOptimizer
34
35		ml_scores = [
36		"accuracy_score",
37		"balanced_accuracy_score",
38		"average_precision_score",
39		"brier_score_loss",
40		"f1_score",
41		"log_loss",
42		"precision_score",
43		"recall_score",
44		"jaccard_score",
45		"roc_auc_score",
46		]
47
48		for score in ml_scores:
49		opt = RandomSearchOptimizer(search_config, 1, metric=score)
50		assert opt._config_.metric == score
51		opt.fit(X, y)
52		assert opt._config_.metric == score
53		opt.predict(X)
54		assert opt._config_.metric == score
55		opt.score(X, y)
56		assert opt._config_.metric == score
57
58
59		def test_lightgbm_regression():

		@@ 30-54 (lines=25) @@
27		opt.score(X, y)
28
29
30		def test_catboost_classification():
31		from hyperactive import RandomSearchOptimizer
32
33		ml_scores = [
34		"accuracy_score",
35		"balanced_accuracy_score",
36		"average_precision_score",
37		"brier_score_loss",
38		"f1_score",
39		"log_loss",
40		"precision_score",
41		"recall_score",
42		"jaccard_score",
43		"roc_auc_score",
44		]
45
46		for score in ml_scores:
47		opt = RandomSearchOptimizer(search_config, 1, metric=score)
48		assert opt._config_.metric == score
49		opt.fit(X, y)
50		assert opt._config_.metric == score
51		opt.predict(X)
52		assert opt._config_.metric == score
53		opt.score(X, y)
54		assert opt._config_.metric == score
55
56
57		def test_catboost_regression():

		@@ 30-54 (lines=25) @@
27		opt.score(X, y)
28
29
30		def test_sklearn_classification():
31		from hyperactive import RandomSearchOptimizer
32
33		ml_scores = [
34		"accuracy_score",
35		"balanced_accuracy_score",
36		"average_precision_score",
37		"brier_score_loss",
38		"f1_score",
39		"log_loss",
40		"precision_score",
41		"recall_score",
42		"jaccard_score",
43		"roc_auc_score",
44		]
45
46		for score in ml_scores:
47		opt = RandomSearchOptimizer(search_config, 1, metric=score)
48		assert opt._config_.metric == score
49		opt.fit(X, y)
50		assert opt._config_.metric == score
51		opt.predict(X)
52		assert opt._config_.metric == score
53		opt.score(X, y)
54		assert opt._config_.metric == score
55
56
57		def test_sklearn_regression():

		@@ 30-54 (lines=25) @@
27		opt.score(X, y)
28
29
30		def test_xgboost_classification():
31		from hyperactive import RandomSearchOptimizer
32
33		ml_scores = [
34		"accuracy_score",
35		"balanced_accuracy_score",
36		"average_precision_score",
37		"brier_score_loss",
38		"f1_score",
39		"log_loss",
40		"precision_score",
41		"recall_score",
42		"jaccard_score",
43		"roc_auc_score",
44		]
45
46		for score in ml_scores:
47		opt = RandomSearchOptimizer(search_config, 1, metric=score)
48		assert opt._config_.metric == score
49		opt.fit(X, y)
50		assert opt._config_.metric == score
51		opt.predict(X)
52		assert opt._config_.metric == score
53		opt.score(X, y)
54		assert opt._config_.metric == score
55
56
57		def test_xgboost_regression():

		@@ 35-55 (lines=21) @@
32		opt.score(X, y)
33
34
35		def test_keras_scores():
36		from hyperactive import RandomSearchOptimizer
37
38		ml_scores = [
39		"accuracy",
40		"binary_accuracy",
41		"categorical_accuracy",
42		# "sparse_categorical_accuracy",
43		"top_k_categorical_accuracy",
44		# "sparse_top_k_categorical_accuracy",
45		]
46
47		for score in ml_scores:
48		opt = RandomSearchOptimizer(search_config, 1, metric=score)
49		assert opt._config_.metric == score
50		opt.fit(X, y)
51		assert opt._config_.metric == score
52		opt.predict(X)
53		assert opt._config_.metric == score
54		opt.score(X, y)
55		assert opt._config_.metric == score
56
57
58		def test_keras_losses():

		@@ 27-47 (lines=21) @@
24		opt.score(X, y)
25
26
27		def test_keras_scores():
28		from hyperactive import RandomSearchOptimizer
29
30		ml_scores = [
31		"accuracy",
32		"binary_accuracy",
33		"categorical_accuracy",
34		"sparse_categorical_accuracy",
35		"top_k_categorical_accuracy",
36		"sparse_top_k_categorical_accuracy",
37		]
38
39		for score in ml_scores:
40		opt = RandomSearchOptimizer(search_config, 1, metric=score)
41		assert opt._config_.metric == score
42		opt.fit(X, y)
43		assert opt._config_.metric == score
44		opt.predict(X)
45		assert opt._config_.metric == score
46		opt.score(X, y)
47		assert opt._config_.metric == score
48
49
50		def test_keras_losses():

SimonBlanke / Hyperactive

Code Duplication Length = 21-25 lines in 6 locations

tests/test_lightgbm.py 1 location

tests/test_catboost.py 1 location

tests/test_sklearn.py 1 location

tests/test_xgboost.py 1 location

tests/local/_test_keras_cnn.py 1 location

tests/test_keras_mlp.py 1 location