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amir9979 / HungaBunga

HungaBunga: Brute-Force all sklearn models with all parameters using .fit .predict!
MIT License
1 stars 1 forks source link

continuum #10

Closed amir9979 closed 3 years ago

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- AdaBoostClassifier

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

AdaBoostClassifier -inf inf

The winner is: AdaBoostClassifier with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- LinearSVC

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

LinearSVC -inf inf

The winner is: LinearSVC with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- NuSVC

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

NuSVC -inf inf

The winner is: NuSVC with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- LabelPropagation

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

LabelPropagation -inf inf

The winner is: LabelPropagation with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- ComplementNB

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

ComplementNB -inf inf

The winner is: ComplementNB with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- LabelSpreading

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

LabelSpreading -inf inf

The winner is: LabelSpreading with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- PassiveAggressiveClassifier

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

PassiveAggressiveClassifier -inf inf

The winner is: PassiveAggressiveClassifier with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- GaussianNB best score: 0.4720953995777307 time/clf: 0.000 seconds best params: {'priors': None, 'var_smoothing': 1e-09}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

GaussianNB 0.472 0

The winner is: GaussianNB with score 0.472.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- LinearDiscriminantAnalysis best score: 0.4173553011585723 time/clf: 0.000 seconds best params: {'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.01}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

LinearDiscriminantAnalysis 0.417 0

The winner is: LinearDiscriminantAnalysis with score 0.417.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- MultinomialNB

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

MultinomialNB -inf inf

The winner is: MultinomialNB with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- GaussianMixture

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

GaussianMixture -inf inf

The winner is: GaussianMixture with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- Perceptron

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

Perceptron -inf inf

The winner is: Perceptron with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- BernoulliNB best score: 0.4913262567767024 time/clf: 0.000 seconds best params: {'alpha': 10, 'binarize': 0.0, 'class_prior': None, 'fit_prior': True}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

BernoulliNB 0.491 0

The winner is: BernoulliNB with score 0.491.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- NuSVC

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

NuSVC -inf inf

The winner is: NuSVC with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- SGDClassifier

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

SGDClassifier -inf inf

The winner is: SGDClassifier with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- BayesianGaussianMixture

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

BayesianGaussianMixture -inf inf

The winner is: BayesianGaussianMixture with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- DummyClassifier best score: 0.36910632730978554 time/clf: 0.000 seconds best params: {'constant': None, 'random_state': None, 'strategy': 'warn'}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

DummyClassifier 0.369 0

The winner is: DummyClassifier with score 0.369.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- DecisionTreeClassifier best score: 0.457094743560489 time/clf: 0.000 seconds best params: {'max_depth': None, 'max_features': None, 'min_samples_leaf': 2, 'min_samples_split': 5}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

DecisionTreeClassifier 0.457 0

The winner is: DecisionTreeClassifier with score 0.457.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- RadiusNeighborsClassifier

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

RadiusNeighborsClassifier -inf inf

The winner is: RadiusNeighborsClassifier with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- NoSampleWeightWrapper

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

NoSampleWeightWrapper -inf inf

The winner is: NoSampleWeightWrapper with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- CategoricalNB

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

CategoricalNB -inf inf

The winner is: CategoricalNB with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- SGDClassifier best score: 0.588046441723934 time/clf: 0.000 seconds best params: {'alpha': 0.1, 'loss': 'log', 'penalty': 'l2'}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

SGDClassifier 0.588 0

The winner is: SGDClassifier with score 0.588.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- QuadraticDiscriminantAnalysis best score: 0.5781647058823529 time/clf: 0.000 seconds best params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

QuadraticDiscriminantAnalysis 0.578 0

The winner is: QuadraticDiscriminantAnalysis with score 0.578.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- ExtraTreeClassifier best score: 0.49510969627848833 time/clf: 0.000 seconds best params: {'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 0.1, 'min_weight_fraction_leaf': 0.0, 'random_state': None, 'splitter': 'random'}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

ExtraTreeClassifier 0.495 0

The winner is: ExtraTreeClassifier with score 0.495.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- GaussianProcessClassifier best score: 0.6080778374338849 time/clf: 0.000 seconds best params: {'copy_X_train': True, 'kernel': None, 'max_iter_predict': 100, 'multi_class': 'one_vs_rest', 'n_jobs': None, 'n_restarts_optimizer': 0, 'optimizer': 'fmin_l_bfgs_b', 'random_state': None, 'warm_start': False}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

GaussianProcessClassifier 0.608 0

The winner is: GaussianProcessClassifier with score 0.608.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- CalibratedClassifierCV best score: 0.5554557772147397 time/clf: 0.000 seconds best params: {'base_estimator': None, 'cv': None, 'method': 'sigmoid'}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

CalibratedClassifierCV 0.555 0

The winner is: CalibratedClassifierCV with score 0.555.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- DecisionTreeClassifier best score: 0.4662615242431464 time/clf: 0.000 seconds best params: {'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': 5, 'max_features': 10, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': None, 'splitter': 'best'}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

DecisionTreeClassifier 0.466 0

The winner is: DecisionTreeClassifier with score 0.466.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- MLPClassifier best score: 0.5784719268951262 time/clf: 0.000 seconds best params: {'activation': 'relu', 'batch_size': 'auto', 'early_stopping': True, 'hidden_layer_sizes': (64,), 'learning_rate': 'invscaling', 'max_iter': 500}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

MLPClassifier 0.578 0

The winner is: MLPClassifier with score 0.578.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- KMeans

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

KMeans -inf inf

The winner is: KMeans with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- SVC

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

SVC -inf inf

The winner is: SVC with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- _ConstantPredictor

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

_ConstantPredictor -inf inf

The winner is: _ConstantPredictor with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- NearestCentroid

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

NearestCentroid -inf inf

The winner is: NearestCentroid with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- _BinaryGaussianProcessClassifierLaplace best score: 0.6053253179619473 time/clf: 0.000 seconds best params: {'copy_X_train': True, 'kernel': None, 'max_iter_predict': 100, 'n_restarts_optimizer': 0, 'optimizer': 'fmin_l_bfgs_b', 'random_state': None, 'warm_start': False}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

_BinaryGaussianProcessClassifierLaplace 0.605 0

The winner is: _BinaryGaussianProcessClassifierLaplace with score 0.605.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- LogisticRegression best score: 0.618684763163826 time/clf: 0.000 seconds best params: {'C': 0.1, 'max_iter': 100, 'penalty': 'l1', 'solver': 'liblinear', 'tol': 0.0001, 'warm_start': True}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

LogisticRegression 0.619 0

The winner is: LogisticRegression with score 0.619.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- SVC

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

SVC -inf inf

The winner is: SVC with score -inf.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- BaggingClassifier best score: 0.5120329837196853 time/clf: 0.000 seconds best params: {'base_estimator': None, 'bootstrap': True, 'bootstrap_features': False, 'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 10, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

BaggingClassifier 0.512 0

The winner is: BaggingClassifier with score 0.512.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- ExtraTreesClassifier best score: 0.6035207815210455 time/clf: 0.000 seconds best params: {'max_depth': None, 'max_features': 5, 'min_samples_leaf': 2, 'min_samples_split': 0.1, 'n_estimators': 100}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

ExtraTreesClassifier 0.604 0

The winner is: ExtraTreesClassifier with score 0.604.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- RandomForestClassifier best score: 0.5543373430839564 time/clf: 0.000 seconds best params: {'max_depth': 10, 'max_features': None, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 10}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

RandomForestClassifier 0.554 0

The winner is: RandomForestClassifier with score 0.554.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- RadiusNeighborsClassifier best score: 0.5680000000000001 time/clf: 0.000 seconds best params: {'leaf_size': 1, 'metric': 'cityblock', 'outlier_label': -1, 'p': 1, 'radius': 0.01, 'weights': 'uniform'}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

RadiusNeighborsClassifier 0.568 0

The winner is: RadiusNeighborsClassifier with score 0.568.

github-actions[bot] commented 3 years ago

Scoring criteria: make_scorer(pr_auc_score, needs_proba=True) --------------- model 1/1 --------------- LogisticRegression best score: 0.4905290211823976 time/clf: 0.000 seconds best params: {'C': 0.01, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.01, 'verbose': 0, 'warm_start': False}

Model make_scorer(pr_auc_score, needs_proba=True) Time/clf (s)

LogisticRegression 0.491 0

The winner is: LogisticRegression with score 0.491.