SimonBlanke /
Hyperactive
| @@ 58-86 (lines=29) @@ | ||
| 55 | assert opt._config_.metric == score |
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| 56 | ||
| 57 | ||
| 58 | def test_keras_losses(): |
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| 59 | from hyperactive import RandomSearchOptimizer |
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| 60 | ||
| 61 | ml_losses = [ |
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| 62 | "mean_squared_error", |
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| 63 | "mean_absolute_error", |
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| 64 | "mean_absolute_percentage_error", |
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| 65 | "mean_squared_logarithmic_error", |
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| 66 | "squared_hinge", |
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| 67 | "hinge", |
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| 68 | # "categorical_hinge", |
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| 69 | "logcosh", |
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| 70 | "categorical_crossentropy", |
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| 71 | # "sparse_categorical_crossentropy", |
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| 72 | "binary_crossentropy", |
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| 73 | "kullback_leibler_divergence", |
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| 74 | "poisson", |
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| 75 | "cosine_proximity", |
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| 76 | ] |
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| 77 | ||
| 78 | for loss in ml_losses: |
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| 79 | opt = RandomSearchOptimizer(search_config, 1, metric=loss) |
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| 80 | assert opt._config_.metric == loss |
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| 81 | opt.fit(X, y) |
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| 82 | assert opt._config_.metric == loss |
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| 83 | opt.predict(X) |
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| 84 | assert opt._config_.metric == loss |
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| 85 | opt.score(X, y) |
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| 86 | assert opt._config_.metric == loss |
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| 87 | ||
| 88 | ||
| 89 | def test_keras_n_jobs(): |
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| @@ 50-78 (lines=29) @@ | ||
| 47 | assert opt._config_.metric == score |
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| 48 | ||
| 49 | ||
| 50 | def test_keras_losses(): |
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| 51 | from hyperactive import RandomSearchOptimizer |
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| 52 | ||
| 53 | ml_losses = [ |
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| 54 | "mean_squared_error", |
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| 55 | "mean_absolute_error", |
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| 56 | "mean_absolute_percentage_error", |
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| 57 | "mean_squared_logarithmic_error", |
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| 58 | "squared_hinge", |
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| 59 | "hinge", |
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| 60 | # "categorical_hinge", |
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| 61 | "logcosh", |
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| 62 | "categorical_crossentropy", |
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| 63 | # "sparse_categorical_crossentropy", |
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| 64 | "binary_crossentropy", |
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| 65 | "kullback_leibler_divergence", |
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| 66 | "poisson", |
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| 67 | "cosine_proximity", |
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| 68 | ] |
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| 69 | ||
| 70 | for loss in ml_losses: |
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| 71 | opt = RandomSearchOptimizer(search_config, 1, metric=loss) |
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| 72 | assert opt._config_.metric == loss |
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| 73 | opt.fit(X, y) |
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| 74 | assert opt._config_.metric == loss |
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| 75 | opt.predict(X) |
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| 76 | assert opt._config_.metric == loss |
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| 77 | opt.score(X, y) |
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| 78 | assert opt._config_.metric == loss |
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| 79 | ||
| 80 | ||
| 81 | def test_keras_n_jobs(): |
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| @@ 59-80 (lines=22) @@ | ||
| 56 | assert opt._config_.metric == score |
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| 57 | ||
| 58 | ||
| 59 | def test_lightgbm_regression(): |
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| 60 | from hyperactive import RandomSearchOptimizer |
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| 61 | ||
| 62 | ml_losses = [ |
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| 63 | "explained_variance_score", |
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| 64 | "max_error", |
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| 65 | "mean_absolute_error", |
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| 66 | "mean_squared_error", |
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| 67 | "mean_squared_log_error", |
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| 68 | "median_absolute_error", |
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| 69 | "r2_score", |
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| 70 | ] |
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| 71 | ||
| 72 | for loss in ml_losses: |
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| 73 | opt = RandomSearchOptimizer(search_config, 1, metric=loss) |
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| 74 | assert opt._config_.metric == loss |
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| 75 | opt.fit(X, y) |
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| 76 | assert opt._config_.metric == loss |
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| 77 | opt.predict(X) |
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| 78 | assert opt._config_.metric == loss |
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| 79 | opt.score(X, y) |
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| 80 | assert opt._config_.metric == loss |
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| 81 | ||
| 82 | ||
| 83 | """ |
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| @@ 57-78 (lines=22) @@ | ||
| 54 | assert opt._config_.metric == score |
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| 55 | ||
| 56 | ||
| 57 | def test_sklearn_regression(): |
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| 58 | from hyperactive import RandomSearchOptimizer |
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| 59 | ||
| 60 | ml_losses = [ |
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| 61 | "explained_variance_score", |
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| 62 | "max_error", |
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| 63 | "mean_absolute_error", |
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| 64 | "mean_squared_error", |
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| 65 | "mean_squared_log_error", |
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| 66 | "median_absolute_error", |
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| 67 | "r2_score", |
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| 68 | ] |
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| 69 | ||
| 70 | for loss in ml_losses: |
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| 71 | opt = RandomSearchOptimizer(search_config, 1, metric=loss) |
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| 72 | assert opt._config_.metric == loss |
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| 73 | opt.fit(X, y) |
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| 74 | assert opt._config_.metric == loss |
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| 75 | opt.predict(X) |
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| 76 | assert opt._config_.metric == loss |
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| 77 | opt.score(X, y) |
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| 78 | assert opt._config_.metric == loss |
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| 79 | ||
| 80 | ||
| 81 | """ |
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| @@ 57-78 (lines=22) @@ | ||
| 54 | assert opt._config_.metric == score |
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| 55 | ||
| 56 | ||
| 57 | def test_xgboost_regression(): |
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| 58 | from hyperactive import RandomSearchOptimizer |
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| 59 | ||
| 60 | ml_losses = [ |
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| 61 | "explained_variance_score", |
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| 62 | "max_error", |
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| 63 | "mean_absolute_error", |
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| 64 | "mean_squared_error", |
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| 65 | "mean_squared_log_error", |
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| 66 | "median_absolute_error", |
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| 67 | "r2_score", |
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| 68 | ] |
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| 69 | ||
| 70 | for loss in ml_losses: |
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| 71 | opt = RandomSearchOptimizer(search_config, 1, metric=loss) |
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| 72 | assert opt._config_.metric == loss |
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| 73 | opt.fit(X, y) |
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| 74 | assert opt._config_.metric == loss |
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| 75 | opt.predict(X) |
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| 76 | assert opt._config_.metric == loss |
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| 77 | opt.score(X, y) |
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| 78 | assert opt._config_.metric == loss |
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| 79 | ||
| 80 | ||
| 81 | """ |
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| @@ 57-78 (lines=22) @@ | ||
| 54 | assert opt._config_.metric == score |
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| 55 | ||
| 56 | ||
| 57 | def test_catboost_regression(): |
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| 58 | from hyperactive import RandomSearchOptimizer |
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| 59 | ||
| 60 | ml_losses = [ |
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| 61 | "explained_variance_score", |
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| 62 | "max_error", |
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| 63 | "mean_absolute_error", |
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| 64 | "mean_squared_error", |
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| 65 | "mean_squared_log_error", |
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| 66 | "median_absolute_error", |
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| 67 | "r2_score", |
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| 68 | ] |
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| 69 | ||
| 70 | for loss in ml_losses: |
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| 71 | opt = RandomSearchOptimizer(search_config, 1, metric=loss) |
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| 72 | assert opt._config_.metric == loss |
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| 73 | opt.fit(X, y) |
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| 74 | assert opt._config_.metric == loss |
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| 75 | opt.predict(X) |
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| 76 | assert opt._config_.metric == loss |
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| 77 | opt.score(X, y) |
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| 78 | assert opt._config_.metric == loss |
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| 79 | ||
| 80 | ||
| 81 | """ |
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