%run "/Users/jsgalan/Desktop/Gama/test5.py" Using GAMA version 0.1.0. GamaClassifier(max_total_time=3600,verbosity=30,scoring=neg_log_loss,population_size=50,max_eval_time=300,regularize_length=True,keep_analysis_log=True,random_state=None,cache_dir=None,n_jobs=1) Preprocessing took 0.0027s. Moving on to search phase. Starting EA with new population. Current pareto-front updated with individual with wvalues (-0.4436561587921241, -1). Overall pareto-front updated with individual with wvalues (-0.4436561587921241, -1). Individual 4edca9a9-92e2-4d6b-ba37-e30f3872795b Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=17, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.4436561587921241, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 964, in fit return AgglomerativeClustering.fit(self, X.T, **params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 794, in fit (self.affinity, )) ValueError: cosine was provided as affinity. Ward can only work with euclidean distances. Individual 0ff95901-acd1-4b39-aea1-cd4ed9f50f83 Pipeline: BernoulliNB(FeatureAgglomeration(data, FeatureAgglomeration.affinity='cosine', FeatureAgglomeration.linkage='ward'), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -2). Individual 3463ba18-b68e-4baf-9475-c7638135a0b3 Pipeline: KNeighborsClassifier(MaxAbsScaler(Binarizer(data, Binarizer.threshold=0.75)), KNeighborsClassifier.n_neighbors=20, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.7964386506609253, -3). Individual 5324c659-bd76-410a-8875-d3dd9768dbff Pipeline: GaussianNB(data) Fitness: was evaluated. Fitness is (-0.6451920682867989, -1). Individual 73bae870-71a9-45ea-b118-0dc75b118c14 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=1.0, GradientBoostingClassifier.max_depth=1, GradientBoostingClassifier.max_features=0.05, GradientBoostingClassifier.min_samples_leaf=16, GradientBoostingClassifier.min_samples_split=10, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.55) Fitness: was evaluated. Fitness is (-5.019345262968769, -1). Current pareto-front updated with individual with wvalues (-0.1413777157929218, -1). Overall pareto-front updated with individual with wvalues (-0.1413777157929218, -1). Individual a169c6db-2cd0-408a-b787-c8a2839f91f9 Pipeline: LogisticRegression(data, LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1413777157929218, -1). Individual fcc5be1c-71a5-4af2-a6d0-eee2653fcf7f Pipeline: GaussianNB(data) Fitness: was evaluated. Fitness is (-0.6451920682867989, -1). Individual dedb09e3-7245-4bed-84e8-1bc242e8f08c Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17637608565409724, -1). Individual 59f8cfbf-8173-461b-ae41-fa46935ef4ea Pipeline: MultinomialNB(data, alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-1.3689035548582325, -1). Individual 60479645-66cd-49a2-88f3-d6a7b429a53a Pipeline: RandomForestClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.5), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=19, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.18636644832905433, -2). Individual 784c0723-a306-4c6a-ab33-8635dffa0d1c Pipeline: LogisticRegression(data, LogisticRegression.C=10.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1486602520683052, -1). Individual 7bf24bd5-d31d-4902-bb0e-38af0e18ea05 Pipeline: DecisionTreeClassifier(Binarizer(Normalizer(data, Normalizer.norm='max'), Binarizer.threshold=0.05), DecisionTreeClassifier.criterion='entropy', DecisionTreeClassifier.max_depth=9, min_samples_leaf=13, min_samples_split=18) Fitness: was evaluated. Fitness is (-0.5487048057934257, -3). Individual 39d05625-fab6-42ee-abf2-ed6d59498099 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.14546220063104273, -1). Individual e163dcb5-55bd-4ca6-97af-384673b27078 Pipeline: ExtraTreesClassifier(PCA(data, PCA.iterated_power=2, PCA.svd_solver='randomized'), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.8, min_samples_leaf=8, min_samples_split=10, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.26055310053986186, -2). Individual 1ab9ffde-70b7-46ac-ab7b-454dee3bf377 Pipeline: KNeighborsClassifier(Binarizer(data, Binarizer.threshold=0.55), KNeighborsClassifier.n_neighbors=14, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-1.0392535647656556, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual 2fae4763-0cde-4fe9-8092-19ec4db8ba3b Pipeline: LogisticRegression(RobustScaler(FastICA(data, FastICA.tol=0.2)), LogisticRegression.C=25.0, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -3). Individual efe06418-0d7e-4f6f-8398-88c39794528f Pipeline: GaussianNB(data) Fitness: was evaluated. Fitness is (-0.6451920682867989, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 1d0d37ad-6d3d-4430-84c5-9ec4b8344e86 Pipeline: MultinomialNB(RBFSampler(Normalizer(data, Normalizer.norm='l2'), RBFSampler.gamma=1.0), alpha=1.0, fit_prior=True) Fitness: was evaluated. Fitness is (-inf, -3). Current pareto-front updated with individual with wvalues (-0.10003223911893651, -1). Overall pareto-front updated with individual with wvalues (-0.10003223911893651, -1). Individual 0e3f1b38-86f7-4d25-9125-8721ed9c3f25 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.1, GradientBoostingClassifier.max_depth=3, GradientBoostingClassifier.max_features=0.7000000000000001, GradientBoostingClassifier.min_samples_leaf=5, GradientBoostingClassifier.min_samples_split=13, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.4) Fitness: was evaluated. Fitness is (-0.10003223911893651, -1). Individual 7e8df815-e27c-40bc-a9c3-7625d26f5207 Pipeline: ExtraTreesClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.35000000000000003), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.15000000000000002, min_samples_leaf=18, min_samples_split=11, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.49655610863608607, -2). Individual 637a1af9-5acf-44b4-aa4c-f7eeb8ffe310 Pipeline: BernoulliNB(Normalizer(RBFSampler(data, RBFSampler.gamma=0.15000000000000002), Normalizer.norm='max'), alpha=10.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.8461483473087155, -3). Individual 9daf6f92-ed07-4eea-b420-9aa5cf73ef33 Pipeline: ExtraTreesClassifier(Binarizer(data, Binarizer.threshold=0.7000000000000001), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=1, min_samples_split=4, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.5438995546688092, -2). Individual 1f50effd-21a8-4448-a97c-1188eae64bee Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15523771028447075, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual 553f46a2-1365-43ec-b3d1-f2d2e5f3038c Pipeline: LogisticRegression(data, LogisticRegression.C=5.0, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 964, in fit return AgglomerativeClustering.fit(self, X.T, **params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 794, in fit (self.affinity, )) ValueError: l2 was provided as affinity. Ward can only work with euclidean distances. Individual 8ad8ba8b-f094-423d-94be-94f737480974 Pipeline: RandomForestClassifier(FeatureAgglomeration(data, FeatureAgglomeration.affinity='l2', FeatureAgglomeration.linkage='ward'), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-inf, -2). Individual e542172c-353c-4b71-b77b-65807ce3ccc0 Pipeline: KNeighborsClassifier(RobustScaler(data), KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.2283541075486385, -2). Individual a3e6cd0d-80ac-414e-995a-529bd6ec5c44 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.9000000000000001, RandomForestClassifier.min_samples_leaf=11, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13982834927763638, -1). Individual 06f9dc03-5b96-4382-97d9-9785ca298cac Pipeline: ExtraTreesClassifier(MaxAbsScaler(RBFSampler(data, RBFSampler.gamma=0.75)), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.15000000000000002, min_samples_leaf=11, min_samples_split=5, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.661591469614736, -3). Individual e8ffab2f-ec0f-41a7-a6ff-e295adb86a12 Pipeline: MultinomialNB(data, alpha=100.0, fit_prior=True) Fitness: was evaluated. Fitness is (-1.3927795782581782, -1). Individual 069f6124-9127-4a5b-8d6e-bcf738900681 Pipeline: RandomForestClassifier(MinMaxScaler(RBFSampler(data, RBFSampler.gamma=0.45)), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=1.0, RandomForestClassifier.min_samples_leaf=10, RandomForestClassifier.min_samples_split=4, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.6709641466268109, -3). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 5d70ed2f-2c5b-49de-b86a-741bd78376e4 Pipeline: MultinomialNB(FastICA(FeatureAgglomeration(data, FeatureAgglomeration.affinity='precomputed', FeatureAgglomeration.linkage='average'), FastICA.tol=0.05), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual 25c45f54-0078-4567-ae34-48e89c4e2b51 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15611470962349028, -1). Individual 3bef05d4-ec0c-4276-aa72-a21b49b3cdce Pipeline: LogisticRegression(FeatureAgglomeration(data, FeatureAgglomeration.affinity='euclidean', FeatureAgglomeration.linkage='ward'), LogisticRegression.C=25.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.17802525471081077, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/feature_selection/variance_threshold.py", line 75, in fit raise ValueError(msg.format(self.threshold)) ValueError: No feature in X meets the variance threshold 0.35000 Individual 47be17f6-4935-4df2-8a90-39fe2c7094c7 Pipeline: BernoulliNB(VarianceThreshold(Nystroem(data, Nystroem.gamma=0.30000000000000004, Nystroem.kernel='cosine', Nystroem.n_components=6), VarianceThreshold.threshold=0.35000000000000003), alpha=0.001, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual 60c0559e-7c62-440d-8d82-46e6e8de5b82 Pipeline: MultinomialNB(data, alpha=0.001, fit_prior=False) Fitness: was evaluated. Fitness is (-1.5377462106794924, -1). Individual bba9f2f1-80e9-498d-881c-7034a0a9e3a3 Pipeline: GaussianNB(Nystroem(data, Nystroem.gamma=0.25, Nystroem.kernel='chi2', Nystroem.n_components=3)) Fitness: was evaluated. Fitness is (-1.839300479522536, -2). Individual 088e98e8-a0d3-4403-90b9-22c15990c909 Pipeline: GradientBoostingClassifier(Nystroem(Normalizer(data, Normalizer.norm='max'), Nystroem.gamma=0.75, Nystroem.kernel='additive_chi2', Nystroem.n_components=9), GradientBoostingClassifier.learning_rate=0.001, GradientBoostingClassifier.max_depth=7, GradientBoostingClassifier.max_features=0.4, GradientBoostingClassifier.min_samples_leaf=5, GradientBoostingClassifier.min_samples_split=15, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.15000000000000002) Fitness: was evaluated. Fitness is (-0.6008862670261906, -3). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 9918fc1f-d369-49ef-9f59-37b320d3789d Pipeline: MultinomialNB(FeatureAgglomeration(RobustScaler(data), FeatureAgglomeration.affinity='euclidean', FeatureAgglomeration.linkage='ward'), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual 76b870da-ff17-4b88-83a0-3bbcca14ee21 Pipeline: ExtraTreesClassifier(RobustScaler(data), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.1, min_samples_leaf=14, min_samples_split=18, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.3029123993598437, -2). Individual 81bf4499-ca20-4309-a7fe-d8439f360970 Pipeline: GaussianNB(Normalizer(data, Normalizer.norm='l1')) Fitness: was evaluated. Fitness is (-1.7257673517785812, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual c7891c8c-223c-4091-983a-0e0d97a1d665 Pipeline: LogisticRegression(SelectFwe(Normalizer(data, Normalizer.norm='l1'), SelectFwe.alpha=0.028, SelectFwe.score_func=f_classif), LogisticRegression.C=20.0, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -3). Individual 6f7754c9-9ab0-46e3-80be-ff080a155b5f Pipeline: GaussianNB(MaxAbsScaler(data)) Fitness: was evaluated. Fitness is (-0.7180959923816549, -2). Individual 450e90b5-1639-4805-b2a1-9b32de6df2cc Pipeline: LogisticRegression(data, LogisticRegression.C=5.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.12039817852436316, -1). Individual 0be891d6-124b-4918-9a37-54a2a6a247b5 Pipeline: DecisionTreeClassifier(FastICA(data, FastICA.tol=0.65), DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=8, min_samples_leaf=20, min_samples_split=18) Fitness: was evaluated. Fitness is (-1.9703939999671443, -2). Individual fb0944da-12df-4f83-973e-3c1a308bf056 Pipeline: GaussianNB(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False)) Fitness: was evaluated. Fitness is (-2.321658344557496, -2). Individual e612e599-1691-4b15-80a9-1e51c384976e Pipeline: BernoulliNB(data, alpha=0.01, fit_prior=True) Fitness: was evaluated. Fitness is (-0.653748633836787, -1). Individual aa88a5eb-fe73-4535-a72e-f0ca5bfa159c Pipeline: ExtraTreesClassifier(VarianceThreshold(FeatureAgglomeration(data, FeatureAgglomeration.affinity='euclidean', FeatureAgglomeration.linkage='complete'), VarianceThreshold.threshold=0.35000000000000003), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.8500000000000001, min_samples_leaf=10, min_samples_split=5, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.4251580054110882, -3). Individual b69da46f-af51-42ee-bff9-16f436a8fab7 Pipeline: GaussianNB(data) Fitness: was evaluated. Fitness is (-0.6451920682867989, -1). Individual b7ea2619-bd4c-4c4a-b9b7-6426a8c95c8a Pipeline: GaussianNB(data) Fitness: was evaluated. Fitness is (-0.6451920682867989, -1). Individual ddb37293-493b-4337-8390-ef2fbf4b740a Pipeline: GradientBoostingClassifier(MaxAbsScaler(data), GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=5, GradientBoostingClassifier.max_features=1.0, GradientBoostingClassifier.min_samples_leaf=12, GradientBoostingClassifier.min_samples_split=3, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.1) Fitness: was evaluated. Fitness is (-0.29551212400352384, -2). Individual d2c59a96-b285-4734-bd75-3455b2696dc8 Pipeline: KNeighborsClassifier(Normalizer(data, Normalizer.norm='l2'), KNeighborsClassifier.n_neighbors=17, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.23786883811545848, -2). Individual 7ceb7606-155e-4909-9c33-80fd842f4626 Pipeline: GaussianNB(data) Fitness: was evaluated. Fitness is (-0.6451920682867989, -1). Individual 5dee05d1-d7ab-4718-a672-ae8220f021e5 Pipeline: GaussianNB(SelectFwe(data, SelectFwe.alpha=0.003, SelectFwe.score_func=f_classif)) Fitness: was evaluated. Fitness is (-0.6436600811501233, -2). Individual 76455bf8-01be-4d49-bfef-cd4b467c29f1 Pipeline: KNeighborsClassifier(MaxAbsScaler(data), KNeighborsClassifier.n_neighbors=20, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.1610164002164801, -2). Individual 193decad-3868-4569-bbf8-a3655becd500 Pipeline: GaussianNB(SelectPercentile(data, SelectPercentile.percentile=18, SelectPercentile.score_func=f_classif)) Fitness: was evaluated. Fitness is (-0.29326474785979917, -2). Individual 5a31275f-3dd5-4ae1-8876-b9353489f8f4 Pipeline: MultinomialNB(data, alpha=0.1, fit_prior=True) Fitness: was evaluated. Fitness is (-1.5684728912224282, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual 603e2dfd-80d2-45ba-8b05-f635f2842a97 Pipeline: LogisticRegression(data, LogisticRegression.C=0.5, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -1). Individual b8ee0c39-4f46-4961-ac6c-34f2cb639005 Pipeline: BernoulliNB(Binarizer(data, Binarizer.threshold=0.75), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.9792562894493158, -2). Individual 6bfeca4e-68f3-4e09-92f8-a21345ef9a26 Pipeline: BernoulliNB(VarianceThreshold(data, VarianceThreshold.threshold=0.5), alpha=0.01, fit_prior=True) Fitness: was evaluated. Fitness is (-0.6603178081512427, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 964, in fit return AgglomerativeClustering.fit(self, X.T, **params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 794, in fit (self.affinity, )) ValueError: cosine was provided as affinity. Ward can only work with euclidean distances. Individual 66fdb9c8-2f2b-41b6-8750-45cf6c709052 Pipeline: BernoulliNB(MinMaxScaler(FeatureAgglomeration(data, FeatureAgglomeration.affinity='cosine', FeatureAgglomeration.linkage='ward')), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual c49ed863-73b3-4e84-aff5-1378b037c853 Pipeline: RandomForestClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.5), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1627399774072172, -2). Individual 1de38171-1ab3-41e2-9b8c-6023ba28c5b5 Pipeline: GaussianNB(StandardScaler(data)) Fitness: was evaluated. Fitness is (-0.7180959999707803, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 964, in fit return AgglomerativeClustering.fit(self, X.T, **params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 794, in fit (self.affinity, )) ValueError: cosine was provided as affinity. Ward can only work with euclidean distances. Individual cc233d47-0d64-4ab9-8435-8b2719587661 Pipeline: MultinomialNB(FeatureAgglomeration(data, FeatureAgglomeration.affinity='cosine', FeatureAgglomeration.linkage='ward'), alpha=0.1, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -2). Individual 034e4da0-5547-4ae9-9922-45109a25af04 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17418923840565934, -1). Individual 84a6617e-766d-4d16-b479-3f1d547781a5 Pipeline: LogisticRegression(RBFSampler(data, RBFSampler.gamma=0.55), LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.6764129038159197, -2). Individual 4c283ac6-e86e-4629-b3f6-f645b5506e70 Pipeline: RandomForestClassifier(Normalizer(VarianceThreshold(data, VarianceThreshold.threshold=0.5), Normalizer.norm='l2'), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=19, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1818005359790485, -3). Individual d3b305b8-5286-4591-bbea-b08e7018b445 Pipeline: BernoulliNB(Nystroem(data, Nystroem.gamma=0.45, Nystroem.kernel='linear', Nystroem.n_components=4), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.6638410605941357, -2). Individual 79b9dd25-fe3e-42c5-9e7d-c1ed06896630 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.5, GradientBoostingClassifier.max_depth=10, GradientBoostingClassifier.max_features=0.7500000000000001, GradientBoostingClassifier.min_samples_leaf=5, GradientBoostingClassifier.min_samples_split=11, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.4) Fitness: was evaluated. Fitness is (-0.16796580486701668, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 1d69b186-9d32-4632-85fe-9e2080882be0 Pipeline: MultinomialNB(PCA(data, PCA.iterated_power=2, PCA.svd_solver='randomized'), alpha=1.0, fit_prior=True) Fitness: was evaluated. Fitness is (-inf, -2). Individual 66937739-7e17-4ee7-af09-e663b88b712e Pipeline: GaussianNB(RobustScaler(data)) Fitness: was evaluated. Fitness is (-0.7180959974958447, -2). Individual ffbf5d97-0e42-4438-8bfe-78fcaf1346a9 Pipeline: LogisticRegression(data, LogisticRegression.C=15.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.142268493792663, -1). Individual c6ca0f70-7d5b-42ec-8f4c-d4a07247f9ea Pipeline: ExtraTreesClassifier(Binarizer(data, Binarizer.threshold=0.7000000000000001), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=4, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.5824130652883588, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual bb130090-7059-4643-9adc-efde6d9f5900 Pipeline: MultinomialNB(RBFSampler(Binarizer(Normalizer(data, Normalizer.norm='max'), Binarizer.threshold=0.05), RBFSampler.gamma=1.0), alpha=1.0, fit_prior=True) Fitness: was evaluated. Fitness is (-inf, -4). Individual 1ab78b9b-1680-4cac-a2e7-8698963caf31 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17266043597680103, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual bebfbda9-297d-422c-91a8-358b56dc5af2 Pipeline: MultinomialNB(RBFSampler(data, RBFSampler.gamma=1.0), alpha=1.0, fit_prior=True) Fitness: was evaluated. Fitness is (-inf, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 964, in fit return AgglomerativeClustering.fit(self, X.T, **params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 794, in fit (self.affinity, )) ValueError: l2 was provided as affinity. Ward can only work with euclidean distances. Individual 6921f0a6-bba4-4d7f-a23a-5198334f238f Pipeline: ExtraTreesClassifier(FeatureAgglomeration(data, FeatureAgglomeration.affinity='l2', FeatureAgglomeration.linkage='ward'), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.9500000000000001, min_samples_leaf=2, min_samples_split=9, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-inf, -2). Individual 87aa1c07-87ca-49c9-ac9a-b4500e0b8961 Pipeline: MultinomialNB(data, alpha=0.01, fit_prior=False) Fitness: was evaluated. Fitness is (-1.5377268203575762, -1). Individual 15c8a72b-dd21-4767-9958-fd647b211255 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.34345232931417763, -1). Individual 121a437d-f2fe-4d73-baf0-c65d6e07ef32 Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=6, min_samples_leaf=15, min_samples_split=17) Fitness: was evaluated. Fitness is (-0.5287493146122427, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 9513efcf-e613-4146-9295-b9e8bab77227 Pipeline: MultinomialNB(FastICA(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), FastICA.tol=0.05), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual bb364e41-0668-4a48-9a2b-3e3e6c9bd720 Pipeline: RandomForestClassifier(MinMaxScaler(RBFSampler(data, RBFSampler.gamma=0.45)), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=1.0, RandomForestClassifier.min_samples_leaf=10, RandomForestClassifier.min_samples_split=4, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-3.2682877046250542, -3). Individual 54a09668-7739-4187-a9c1-bb9b009b5ddc Pipeline: BernoulliNB(Normalizer(data, Normalizer.norm='l2'), alpha=10.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.654500608045733, -2). Individual 700105a4-6bf6-4fb0-abff-0e8730b860cc Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=20, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.3309638745346888, -1). Individual bdcaeeb5-83cf-4836-9c41-c07e99f82107 Pipeline: BernoulliNB(data, alpha=0.1, fit_prior=False) Fitness: was evaluated. Fitness is (-0.7082488247802313, -1). Individual 5b7713fc-7a38-4fbb-b4b5-d09575b784eb Pipeline: GaussianNB(Nystroem(data, Nystroem.gamma=0.25, Nystroem.kernel='laplacian', Nystroem.n_components=3)) Fitness: was evaluated. Fitness is (-12.030743924270904, -2). Individual 2e9f43b7-68ba-4e08-974d-6dd30a7ffe56 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.55, RandomForestClassifier.min_samples_leaf=12, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13375467112011966, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 3162ec20-421a-49d7-bab7-77200d7289f0 Pipeline: MultinomialNB(FastICA(FeatureAgglomeration(data, FeatureAgglomeration.affinity='precomputed', FeatureAgglomeration.linkage='complete'), FastICA.tol=0.05), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual ab04b9d1-8935-420e-829f-3f87932321e7 Pipeline: MultinomialNB(FeatureAgglomeration(MinMaxScaler(RobustScaler(data)), FeatureAgglomeration.affinity='euclidean', FeatureAgglomeration.linkage='ward'), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.6925801168213684, -4). Individual ca852551-fa8c-4fdc-aa81-c70477d21535 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15671362538554615, -1). Individual 2f6816cf-1c0a-4baa-9d18-9e6892c1cd81 Pipeline: RandomForestClassifier(RobustScaler(data), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.8500000000000001, RandomForestClassifier.min_samples_leaf=11, RandomForestClassifier.min_samples_split=16, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.12964572818964315, -2). Individual f8d6ae86-927f-4b8f-8be5-2a52506c5014 Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.55, min_samples_leaf=6, min_samples_split=20, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.12837146245941816, -1). Individual 1076c4e5-daa0-42ef-9079-d2dd483b4de1 Pipeline: KNeighborsClassifier(Binarizer(Binarizer(data, Binarizer.threshold=0.55), Binarizer.threshold=0.75), KNeighborsClassifier.n_neighbors=14, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-1.0392535647656556, -3). Individual 04827ff7-d196-411b-adb4-e12aaa59fb51 Pipeline: LogisticRegression(RobustScaler(FastICA(data, FastICA.tol=0.2)), LogisticRegression.C=25.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.24821515778043846, -3). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 610, in fit self._count(X, Y) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 714, in _count raise ValueError("Input X must be non-negative") ValueError: Input X must be non-negative Individual 19b7d5cb-ae9f-47ed-8156-9a37e7769813 Pipeline: MultinomialNB(PCA(data, PCA.iterated_power=2, PCA.svd_solver='randomized'), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -2). Individual 64ecaeaf-d401-4f51-a0f9-fc8ce6e1864e Pipeline: ExtraTreesClassifier(MaxAbsScaler(RBFSampler(data, RBFSampler.gamma=0.75)), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.15000000000000002, min_samples_leaf=11, min_samples_split=5, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.6654705409072871, -3). Individual 0c17cea0-18bb-4579-bc88-c555a549f045 Pipeline: RandomForestClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.7000000000000001), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.9000000000000001, RandomForestClassifier.min_samples_leaf=11, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1707429770569387, -2). Individual d89e21a9-4c44-4db8-9e2f-f6eb1bce82b3 Pipeline: LogisticRegression(FeatureAgglomeration(data, FeatureAgglomeration.affinity='euclidean', FeatureAgglomeration.linkage='ward'), LogisticRegression.C=25.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.17802525471081077, -2). Individual 38c1e184-e93a-43f5-bb24-929da009f2bf Pipeline: MultinomialNB(data, alpha=10.0, fit_prior=False) Fitness: was evaluated. Fitness is (-1.5193115536897108, -1). Individual b6f54747-d70b-46b9-b548-53a8166642ff Pipeline: KNeighborsClassifier(PolynomialFeatures(Binarizer(data, Binarizer.threshold=0.55), PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), KNeighborsClassifier.n_neighbors=14, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-1.0392535647656556, -3). Individual af581555-07b2-4745-85eb-e46b49afa9ac Pipeline: ExtraTreesClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.5), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.7500000000000001, min_samples_leaf=8, min_samples_split=7, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1758291631527151, -2). Individual d0606861-0adb-44c3-bfd9-4633816984d5 Pipeline: RandomForestClassifier(MaxAbsScaler(data), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.12230534324917641, -2). Individual 7a752c17-2ccc-4528-a19a-0127672c791b Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=7, GradientBoostingClassifier.max_features=0.9500000000000001, GradientBoostingClassifier.min_samples_leaf=19, GradientBoostingClassifier.min_samples_split=9, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.7000000000000001) Fitness: was evaluated. Fitness is (-0.2830001221693617, -1). Individual 3d2c9481-d7f6-435a-9ae2-97e61c9bcae4 Pipeline: LogisticRegression(data, LogisticRegression.C=0.01, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.16971246346553845, -1). Individual 311cdbef-e530-4e41-a307-d36dffb5d8c4 Pipeline: KNeighborsClassifier(PCA(data, PCA.iterated_power=6, PCA.svd_solver='randomized'), KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.33833896929245333, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 964, in fit return AgglomerativeClustering.fit(self, X.T, **params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py", line 794, in fit (self.affinity, )) ValueError: cosine was provided as affinity. Ward can only work with euclidean distances. Individual b4936c0b-7672-43c7-a14d-d73983d7c57e Pipeline: BernoulliNB(FeatureAgglomeration(data, FeatureAgglomeration.affinity='cosine', FeatureAgglomeration.linkage='ward'), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -2). Individual a7ee829b-0971-467a-80f8-4d369fcc8189 Pipeline: KNeighborsClassifier(Binarizer(data, Binarizer.threshold=0.7000000000000001), KNeighborsClassifier.n_neighbors=14, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.9050381829832573, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/decomposition/fastica_.py", line 536, in fit_transform return self._fit(X, compute_sources=True) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/decomposition/fastica_.py", line 505, in _fit compute_sources=compute_sources, return_n_iter=True) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/decomposition/fastica_.py", line 344, in fastica W, n_iter = _ica_par(X1, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/decomposition/fastica_.py", line 111, in _ica_par - g_wtx[:, np.newaxis] * W) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/decomposition/fastica_.py", line 58, in _sym_decorrelation s, u = linalg.eigh(np.dot(W, W.T)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/scipy/linalg/decomp.py", line 374, in eigh a1 = _asarray_validated(a, check_finite=check_finite) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/scipy/_lib/_util.py", line 239, in _asarray_validated a = toarray(a) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/numpy/lib/function_base.py", line 498, in asarray_chkfinite "array must not contain infs or NaNs") ValueError: array must not contain infs or NaNs Individual 5157cb6d-8bb5-4753-a4a9-5c8fb45730c7 Pipeline: DecisionTreeClassifier(FastICA(Binarizer(Normalizer(data, Normalizer.norm='max'), Binarizer.threshold=0.05), FastICA.tol=0.25), DecisionTreeClassifier.criterion='entropy', DecisionTreeClassifier.max_depth=9, min_samples_leaf=13, min_samples_split=18) Fitness: was evaluated. Fitness is (-inf, -4). Individual b9483a8d-2fb6-4828-9c0c-ae81c1ec9850 Pipeline: LogisticRegression(data, LogisticRegression.C=10.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1486602520683052, -1). Individual 5d1611b5-c380-4d44-a809-893e2df10dfc Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=22, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='uniform') Fitness: was evaluated. Fitness is (-0.40106314648068536, -1). Individual 6d71850b-4d31-4468-b034-72d273603179 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=8, GradientBoostingClassifier.max_features=0.7000000000000001, GradientBoostingClassifier.min_samples_leaf=14, GradientBoostingClassifier.min_samples_split=20, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.6500000000000001) Fitness: was evaluated. Fitness is (-0.276865946252567, -1). Individual 56d5a1c0-65fa-400a-8466-1ade2505dc73 Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=9, min_samples_leaf=19, min_samples_split=19) Fitness: was evaluated. Fitness is (-0.43872599456724526, -1). Individual 4cb00b01-83f3-4a92-96d5-120b2a3fc404 Pipeline: ExtraTreesClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.35000000000000003), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.15000000000000002, min_samples_leaf=18, min_samples_split=11, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.49852273141927145, -2). Individual f45b9d50-c177-411e-aa4a-f009a4592413 Pipeline: ExtraTreesClassifier(MaxAbsScaler(RBFSampler(data, RBFSampler.gamma=0.75)), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.15000000000000002, min_samples_leaf=11, min_samples_split=3, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.6650895749297269, -3). Individual c98872b8-3ada-4cff-afe5-0c80f6d35773 Pipeline: RandomForestClassifier(SelectFwe(VarianceThreshold(data, VarianceThreshold.threshold=0.5), SelectFwe.alpha=0.015, SelectFwe.score_func=f_classif), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.16104427283898973, -3). Individual 08c39d49-c7aa-46e1-bb29-de3f775d81dc Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='entropy', DecisionTreeClassifier.max_depth=4, min_samples_leaf=5, min_samples_split=7) Fitness: was evaluated. Fitness is (-1.381311476041048, -1). Individual 49195f81-c192-4917-a173-91ff7b157aac Pipeline: RandomForestClassifier(Binarizer(Normalizer(data, Normalizer.norm='max'), Binarizer.threshold=0.05), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.8, RandomForestClassifier.min_samples_leaf=1, RandomForestClassifier.min_samples_split=16, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.5459139530938318, -3). Individual 938170d7-9263-4e0f-aa17-1b5031e86ae3 Pipeline: MultinomialNB(MaxAbsScaler(data), alpha=100.0, fit_prior=True) Fitness: was evaluated. Fitness is (-0.5792436447233439, -2). Individual 5751b3bc-cd37-4ed5-bb30-9718779c0279 Pipeline: RandomForestClassifier(PCA(data, PCA.iterated_power=9, PCA.svd_solver='randomized'), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1795575456418973, -2). Individual 09a5f231-bffe-4ec1-9c5b-9c8c3e60966f Pipeline: BernoulliNB(FastICA(data, FastICA.tol=0.35000000000000003), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.489911504329837, -2). Individual 9e267efb-6bc8-4cdc-bd31-81c9ef49cf4d Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=3, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17429377391692613, -1). Individual f4ac8231-0e54-427d-8b75-931852f740ed Pipeline: GaussianNB(SelectFwe(data, SelectFwe.alpha=0.022, SelectFwe.score_func=f_classif)) Fitness: was evaluated. Fitness is (-0.6436600811501233, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/feature_selection/variance_threshold.py", line 75, in fit raise ValueError(msg.format(self.threshold)) ValueError: No feature in X meets the variance threshold 0.70000 Individual 4fa0c18c-24f2-440a-ae8e-3f0c92d67446 Pipeline: LogisticRegression(VarianceThreshold(RBFSampler(data, RBFSampler.gamma=0.55), VarianceThreshold.threshold=0.7000000000000001), LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -3). Individual c4b3d78d-6119-4af5-83b6-525896022b00 Pipeline: RandomForestClassifier(StandardScaler(data), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.14750064242684677, -2). Individual 20ba714f-348b-490a-a2df-c8c3db233511 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=20, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1498300908618102, -1). Individual 170a2eac-06f0-4a04-942a-360acdb8a9c9 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.001, GradientBoostingClassifier.max_depth=4, GradientBoostingClassifier.max_features=0.5, GradientBoostingClassifier.min_samples_leaf=6, GradientBoostingClassifier.min_samples_split=12, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.8500000000000001) Fitness: was evaluated. Fitness is (-0.5855163689554179, -1). Individual fb97c89a-467b-4272-878c-09533f6c6a9e Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=12, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1352760988014082, -1). Individual 79c90392-b508-4034-a61e-681ecdeeb1df Pipeline: GaussianNB(FeatureAgglomeration(SelectFwe(data, SelectFwe.alpha=0.003, SelectFwe.score_func=f_classif), FeatureAgglomeration.affinity='precomputed', FeatureAgglomeration.linkage='average')) Fitness: was evaluated. Fitness is (-0.29544814017935883, -3). Individual 2236fb7c-ad98-4208-af30-673e0b469f3e Pipeline: BernoulliNB(Nystroem(data, Nystroem.gamma=0.75, Nystroem.kernel='linear', Nystroem.n_components=4), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.6641938978528833, -2). Individual a0753ddf-06fe-4a53-8c85-1c365e0147ab Pipeline: GradientBoostingClassifier(RobustScaler(data), GradientBoostingClassifier.learning_rate=1.0, GradientBoostingClassifier.max_depth=1, GradientBoostingClassifier.max_features=0.05, GradientBoostingClassifier.min_samples_leaf=16, GradientBoostingClassifier.min_samples_split=10, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.55) Fitness: was evaluated. Fitness is (-0.2493992203067253, -2). Individual e4675e09-73ee-4fdd-bf2f-029936cd6ff7 Pipeline: RandomForestClassifier(SelectPercentile(data, SelectPercentile.percentile=11, SelectPercentile.score_func=f_classif), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.16383760648191859, -2). Individual 4ef9a285-5f20-41b1-a8ae-23ca71d002b7 Pipeline: RandomForestClassifier(FastICA(data, FastICA.tol=0.15000000000000002), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.3163316992525467, -2). Individual 1f552471-4eab-4ff8-aa54-1ef6c7d0fa44 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=4, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.8260931846061061, -1). Individual 06fba014-fe55-4958-8acd-7ac7b481d1e6 Pipeline: GaussianNB(Binarizer(data, Binarizer.threshold=0.8500000000000001)) Fitness: was evaluated. Fitness is (-2.3086461659615205, -2). Individual e9c2a74d-9474-48b6-a063-a30bc87ed98e Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=5, GradientBoostingClassifier.max_features=1.0, GradientBoostingClassifier.min_samples_leaf=12, GradientBoostingClassifier.min_samples_split=3, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.1) Fitness: was evaluated. Fitness is (-0.2952829525664325, -1). Individual 490b70cc-86c7-460f-bdc7-dbf836e2aae3 Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=7, min_samples_leaf=15, min_samples_split=17) Fitness: was evaluated. Fitness is (-0.5412835305907605, -1). Individual 00d22994-dd9a-41ea-92ad-82c9e16188d0 Pipeline: KNeighborsClassifier(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), KNeighborsClassifier.n_neighbors=20, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.44546313945229077, -2). Individual 85a52910-56d2-4158-b479-b84812a39c8e Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.1, min_samples_leaf=14, min_samples_split=18, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.2921074183263413, -1). Individual e02ed6a4-beda-4bb4-9a59-ba3c2b96c4ed Pipeline: LogisticRegression(SelectFwe(data, SelectFwe.alpha=0.01, SelectFwe.score_func=f_classif), LogisticRegression.C=15.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1358713874407069, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual c22ffb93-b5b9-4c17-91d3-db7eea1b8f77 Pipeline: LogisticRegression(Normalizer(data, Normalizer.norm='l2'), LogisticRegression.C=0.0001, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -2). Individual 4764c6be-bbdb-4ac8-8b90-392a9200aea8 Pipeline: GaussianNB(VarianceThreshold(data, VarianceThreshold.threshold=0.55)) Fitness: was evaluated. Fitness is (-0.6888696938178579, -2). Individual 1c734e58-4001-4d01-bf1f-ef7e6c4ea12f Pipeline: LogisticRegression(Normalizer(data, Normalizer.norm='l1'), LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.6070012123484818, -2). Individual b5699235-7098-4679-ab4d-5c9676d296f6 Pipeline: BernoulliNB(data, alpha=0.1, fit_prior=True) Fitness: was evaluated. Fitness is (-0.65346942290782, -1). Individual ce051591-5997-494f-98ad-2beca8e09f19 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.8, RandomForestClassifier.min_samples_leaf=12, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1332513371415066, -1). Individual 95333b33-deb6-44f1-bf4c-ea6cef6a0ce0 Pipeline: BernoulliNB(FastICA(Nystroem(data, Nystroem.gamma=0.45, Nystroem.kernel='linear', Nystroem.n_components=4), FastICA.tol=1.0), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.45545030614370957, -3). Individual 44156d95-e641-49e0-9407-c7104906dd81 Pipeline: ExtraTreesClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.35000000000000003), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.15000000000000002, min_samples_leaf=11, min_samples_split=11, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.48371979756147554, -2). Individual 0e680104-c21c-4c60-8535-cd394437fc52 Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15493096336432077, -1). Individual 6e164960-9619-41aa-b53e-92ce1e06ed7c Pipeline: LogisticRegression(data, LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1413777157929218, -1). Individual 5dd55a18-b833-44f0-84cf-3007b57bf34e Pipeline: RandomForestClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.5), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=19, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17700502709210061, -2). Individual 218eabb4-cd61-465e-8f85-6fd676553194 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='uniform') Fitness: was evaluated. Fitness is (-0.3511606883587052, -1). Individual 5c0aec40-226b-424d-b562-f7d9e38b12d7 Pipeline: RandomForestClassifier(MinMaxScaler(data), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.9000000000000001, RandomForestClassifier.min_samples_leaf=11, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1894210891011341, -2). Individual f441f731-4840-40c4-8110-2d31defa1645 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15272037519238216, -1). Individual b8ddbe65-b288-49a8-a8cd-3010bb36f978 Pipeline: KNeighborsClassifier(MaxAbsScaler(data), KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.17064871694629427, -2). Individual 64bbcf7d-6dc4-462c-bc24-86b13b4331f5 Pipeline: GaussianNB(MinMaxScaler(data)) Fitness: was evaluated. Fitness is (-0.7180959932393904, -2). Individual aa0ddea2-8a24-40d8-8116-3e16c5085d6d Pipeline: BernoulliNB(data, alpha=100.0, fit_prior=True) Fitness: was evaluated. Fitness is (-1.1388843071928618, -1). Individual 21299679-71b5-4b82-869b-58ba87ef404c Pipeline: MultinomialNB(data, alpha=0.01, fit_prior=True) Fitness: was evaluated. Fitness is (-1.5686729007154658, -1). Individual 884302d5-cc15-4eb5-9f73-141b927d047e Pipeline: GradientBoostingClassifier(RBFSampler(data, RBFSampler.gamma=0.05), GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=7, GradientBoostingClassifier.max_features=0.9500000000000001, GradientBoostingClassifier.min_samples_leaf=19, GradientBoostingClassifier.min_samples_split=9, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.7000000000000001) Fitness: was evaluated. Fitness is (-0.6712859233188584, -2). Individual bc47a94f-7748-4430-ba9e-5512bdb29226 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.5, GradientBoostingClassifier.max_depth=10, GradientBoostingClassifier.max_features=0.7500000000000001, GradientBoostingClassifier.min_samples_leaf=5, GradientBoostingClassifier.min_samples_split=11, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.1) Fitness: was evaluated. Fitness is (-9.105125720616622, -1). Individual 381636bb-58c7-47cf-a077-d32336a24ac2 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=17, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.4436561587921241, -1). Individual 8dc78bad-4f8f-4b49-80b5-887573094592 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.9000000000000001, RandomForestClassifier.min_samples_leaf=3, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.27058567519608895, -1). Individual 8771be91-55d8-438b-aa5a-a6dc31ce16d8 Pipeline: ExtraTreesClassifier(MaxAbsScaler(data), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.55, min_samples_leaf=6, min_samples_split=20, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1283677069395419, -2). Individual 327dec72-0a67-45d7-a980-d4bc33c31512 Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='entropy', DecisionTreeClassifier.max_depth=9, min_samples_leaf=6, min_samples_split=20) Fitness: was evaluated. Fitness is (-1.0519406173957067, -1). Individual 731ef290-24d4-4d05-be92-520485c0e4ed Pipeline: GaussianNB(Normalizer(data, Normalizer.norm='l1')) Fitness: was evaluated. Fitness is (-1.7257673517785812, -2). Individual f9842291-a403-416d-9d6a-8ebbd8ca168e Pipeline: ExtraTreesClassifier(SelectFwe(data, SelectFwe.alpha=0.023, SelectFwe.score_func=f_classif), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15489425806732632, -2). Individual f8831ef5-fcdb-4277-ade6-c8eb69fc4cb1 Pipeline: KNeighborsClassifier(RBFSampler(data, RBFSampler.gamma=0.05), KNeighborsClassifier.n_neighbors=17, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.7035992525811132, -2). Individual cdd32fa0-fe30-402b-ba2b-81b6b4e169aa Pipeline: KNeighborsClassifier(FastICA(data, FastICA.tol=0.2), KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.5571218265235915, -2). Individual 4b04a386-317b-44e5-a4d4-04c54cabc872 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.2, RandomForestClassifier.min_samples_leaf=9, RandomForestClassifier.min_samples_split=7, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13003264304779047, -1). Individual 208d6865-943b-455e-aa82-04db61df57a1 Pipeline: LogisticRegression(SelectPercentile(data, SelectPercentile.percentile=14, SelectPercentile.score_func=f_classif), LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1657127143377689, -2). Individual c451364c-5637-48a6-8949-ccacbc934d79 Pipeline: DecisionTreeClassifier(MaxAbsScaler(data), DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=9, min_samples_leaf=19, min_samples_split=19) Fitness: was evaluated. Fitness is (-0.44022038196912555, -2). Individual 1c71a6c6-63e4-4d6d-8351-7183cf53a6b9 Pipeline: DecisionTreeClassifier(StandardScaler(data), DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=6, min_samples_leaf=15, min_samples_split=17) Fitness: was evaluated. Fitness is (-0.48520393741622353, -2). Individual 0b427b63-2ed2-4b47-b448-8c2d15306a0e Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=17, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='uniform') Fitness: was evaluated. Fitness is (-0.4463506707999326, -1). Individual baa3cd27-a0c7-4d36-a12a-e7166b82d8f9 Pipeline: LogisticRegression(VarianceThreshold(data, VarianceThreshold.threshold=0.25), LogisticRegression.C=15.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.14751944722277494, -2). Individual ef2af167-fdf5-4ac7-bfae-4afe1e64c42d Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.16729538036202657, -1). Individual 6f837821-be6c-4a56-b409-ddd6bf79362e Pipeline: BernoulliNB(data, alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.9815831354721065, -1). Individual 0a6f8813-8f29-46b1-aa83-397c19a6f1f6 Pipeline: KNeighborsClassifier(SelectPercentile(data, SelectPercentile.percentile=12, SelectPercentile.score_func=f_classif), KNeighborsClassifier.n_neighbors=17, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.5784732742197416, -2). Individual df3c61bf-de29-47ce-8867-c4f96c251ae3 Pipeline: GaussianNB(RBFSampler(SelectPercentile(data, SelectPercentile.percentile=18, SelectPercentile.score_func=f_classif), RBFSampler.gamma=0.75)) Fitness: was evaluated. Fitness is (-0.6685085277957212, -3). Individual 3e83077b-4415-46b5-b3a8-57c8bb41f8bd Pipeline: BernoulliNB(PolynomialFeatures(FastICA(data, FastICA.tol=0.35000000000000003), PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-0.6496387301326293, -3). Individual 983cba08-6b11-4041-a974-dec6f128bab1 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.7500000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1749657576083425, -1). Individual 5f2e9973-6a7a-454d-acde-a05c586b4bd2 Pipeline: GradientBoostingClassifier(SelectFwe(data, SelectFwe.alpha=0.046, SelectFwe.score_func=f_classif), GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=8, GradientBoostingClassifier.max_features=0.7000000000000001, GradientBoostingClassifier.min_samples_leaf=14, GradientBoostingClassifier.min_samples_split=20, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.6500000000000001) Fitness: was evaluated. Fitness is (-0.27664757580532157, -2). Individual fdc4e91e-d1e7-47a6-82c3-4830e7333277 Pipeline: GradientBoostingClassifier(MinMaxScaler(data), GradientBoostingClassifier.learning_rate=0.1, GradientBoostingClassifier.max_depth=3, GradientBoostingClassifier.max_features=0.7000000000000001, GradientBoostingClassifier.min_samples_leaf=5, GradientBoostingClassifier.min_samples_split=13, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.4) Fitness: was evaluated. Fitness is (-0.10241254915950981, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 265, in fit Xt, fit_params = self._fit(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 230, in _fit **fit_params_steps[name]) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py", line 329, in __call__ return self.func(*args, **kwargs) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 614, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/base.py", line 465, in fit_transform return self.fit(X, y, **fit_params).transform(X) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/feature_selection/variance_threshold.py", line 75, in fit raise ValueError(msg.format(self.threshold)) ValueError: No feature in X meets the variance threshold 0.95000 Individual 92de2331-f08f-4535-a3eb-0b314880ff0b Pipeline: BernoulliNB(VarianceThreshold(FastICA(data, FastICA.tol=0.35000000000000003), VarianceThreshold.threshold=0.9500000000000001), alpha=100.0, fit_prior=False) Fitness: was evaluated. Fitness is (-inf, -3). Individual 1ba4bf42-af28-409b-b1c7-2ca780f02b43 Pipeline: ExtraTreesClassifier(RobustScaler(data), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.1, min_samples_leaf=14, min_samples_split=18, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.28927791759429466, -2). Individual a3df868b-0e97-48b1-8a85-6e4dbb31a419 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17598691242902015, -1). Individual 066b46e9-22b8-4b68-b257-227f11805e2a Pipeline: BernoulliNB(data, alpha=0.001, fit_prior=True) Fitness: was evaluated. Fitness is (-0.6537771541382482, -1). Individual 493017ea-62e3-44de-96b8-50e4e3d987d4 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.5, GradientBoostingClassifier.max_depth=10, GradientBoostingClassifier.max_features=0.5, GradientBoostingClassifier.min_samples_leaf=19, GradientBoostingClassifier.min_samples_split=8, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.1) Fitness: was evaluated. Fitness is (-13.475585869367613, -1). Individual ade20fd0-c368-4eb4-8c74-a46d64fc69ea Pipeline: DecisionTreeClassifier(SelectPercentile(data, SelectPercentile.percentile=70, SelectPercentile.score_func=f_classif), DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=9, min_samples_leaf=19, min_samples_split=19) Fitness: was evaluated. Fitness is (-0.4398457287309683, -2). Individual 60c2cc62-b70c-41f7-9b4b-ed7a0f1bb04d Pipeline: RandomForestClassifier(PCA(data, PCA.iterated_power=9, PCA.svd_solver='randomized'), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17959734364058946, -2). Individual afd92f69-94f0-4231-abbb-70b9cf3a5fe7 Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.55, min_samples_leaf=11, min_samples_split=10, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1512499251430471, -1). Individual 27979f64-875a-4080-81c1-6ab557aed9b2 Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=13, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15626519785066173, -1). Individual caa25498-1243-4b9d-a7bf-e71d07308f42 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13510600419364813, -1). Individual 503e952c-76dd-406d-bcc5-c1f43452c048 Pipeline: GradientBoostingClassifier(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=5, GradientBoostingClassifier.max_features=1.0, GradientBoostingClassifier.min_samples_leaf=12, GradientBoostingClassifier.min_samples_split=3, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.1) Fitness: was evaluated. Fitness is (-0.2609380529549082, -2). Individual 80fabacd-9a66-4575-9e46-050867654756 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=20, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15670223962390362, -1). Individual 6ef337b9-1c52-49a1-9e6e-60a282b66e4c Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.3, min_samples_leaf=4, min_samples_split=4, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1152465436576707, -1). Individual df7f0ce5-364a-4ce8-9264-26a383e94e12 Pipeline: RandomForestClassifier(FeatureAgglomeration(data, FeatureAgglomeration.affinity='l1', FeatureAgglomeration.linkage='complete'), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=3, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.37426686900179074, -2). Individual 33ad344c-3e17-4fae-ab68-a57fbc02c05b Pipeline: LogisticRegression(FeatureAgglomeration(data, FeatureAgglomeration.affinity='euclidean', FeatureAgglomeration.linkage='ward'), LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.17901415046101596, -2). Individual df680465-2c72-489b-8271-506c828b404b Pipeline: ExtraTreesClassifier(FastICA(VarianceThreshold(data, VarianceThreshold.threshold=0.5), FastICA.tol=0.15000000000000002), ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.7500000000000001, min_samples_leaf=8, min_samples_split=7, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.31298686418794375, -3). Individual 741e39bb-39c1-4fa3-97cd-39fe6fd4d6c8 Pipeline: RandomForestClassifier(MinMaxScaler(data), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13489024004135125, -2). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual 165572ae-8c2b-4a36-b2fd-d44c365c4cf8 Pipeline: LogisticRegression(data, LogisticRegression.C=10.0, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -1). Individual 4c4cb760-07cf-4c83-876e-0cec9a0acfa6 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=23, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='uniform') Fitness: was evaluated. Fitness is (-0.40209818676670744, -1). Individual d8ac1f36-02ab-4c7b-b331-1c7f20f4ef7e Pipeline: GaussianNB(SelectFwe(data, SelectFwe.alpha=0.01, SelectFwe.score_func=f_classif)) Fitness: was evaluated. Fitness is (-0.6436600811501233, -2). Individual b66f54fe-2b44-4b2e-9f6e-dacc77206b9e Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='entropy', DecisionTreeClassifier.max_depth=8, min_samples_leaf=7, min_samples_split=5) Fitness: was evaluated. Fitness is (-1.3311204456475985, -1). Individual d80f81fe-ac7e-4a3f-86c4-523d93d21cfb Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.8, RandomForestClassifier.min_samples_leaf=12, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.132091204210914, -1). Individual edf74925-b91b-472b-a5d2-403a52525c9b Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.1, RandomForestClassifier.min_samples_leaf=12, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15273819071549716, -1). Individual eb387ca5-eae4-4b0a-b57c-8318386dcb13 Pipeline: LogisticRegression(data, LogisticRegression.C=25.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.15433171393427209, -1). Individual 63f19426-2f9e-45cf-b462-ec83f1d50cec Pipeline: RandomForestClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.5), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=19, RandomForestClassifier.min_samples_split=9, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1794004278398369, -2). Individual ef3a7697-76a2-492a-bb5e-6c28f4e26fef Pipeline: KNeighborsClassifier(FastICA(MaxAbsScaler(data), FastICA.tol=1.0), KNeighborsClassifier.n_neighbors=20, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.5819627907000552, -3). Individual 0e130618-d114-44a8-9395-3aacd2f4ac46 Pipeline: KNeighborsClassifier(FeatureAgglomeration(data, FeatureAgglomeration.affinity='cosine', FeatureAgglomeration.linkage='average'), KNeighborsClassifier.n_neighbors=27, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.30256958312064614, -2). Individual f9a18c72-b216-43f5-b234-c45263224e11 Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=8, GradientBoostingClassifier.max_features=0.7000000000000001, GradientBoostingClassifier.min_samples_leaf=14, GradientBoostingClassifier.min_samples_split=11, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.6500000000000001) Fitness: was evaluated. Fitness is (-0.2767629374180636, -1). Individual 68f321a2-5726-4f4a-8614-cd18c2d8e300 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=28, KNeighborsClassifier.p=1, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.22952430732743198, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual 8b8d6422-122f-4df8-a3db-521f3f29ee6a Pipeline: LogisticRegression(data, LogisticRegression.C=0.01, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -1). Individual 8d3cf0e6-3620-4cda-ac95-3c41cd99372b Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=True, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.55, min_samples_leaf=6, min_samples_split=20, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13995623305356183, -1). Individual 0d6c216d-32c6-4877-ab42-c93650130d2f Pipeline: ExtraTreesClassifier(MaxAbsScaler(data), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.05, min_samples_leaf=6, min_samples_split=20, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.488542765516524, -2). Individual 8e1a992d-3344-4cb3-a0e7-f82f0f4c6f59 Pipeline: LogisticRegression(StandardScaler(data), LogisticRegression.C=0.01, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.18011507944729133, -2). Individual 45812522-15ea-4132-a9c4-a6bd953e92d5 Pipeline: RandomForestClassifier(MaxAbsScaler(data), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=7, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.11542235036828274, -2). Individual eb5cfe6d-a5aa-456a-908f-06e121e746d2 Pipeline: RandomForestClassifier(VarianceThreshold(data, VarianceThreshold.threshold=0.5), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.45, RandomForestClassifier.min_samples_leaf=8, RandomForestClassifier.min_samples_split=13, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1580276543336788, -2). Individual e0ddea82-c858-4b93-885c-72609a7598a3 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=41, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.18915901162735105, -1). Individual e9e7c3ef-5b6b-48d1-9641-23168cf9c35d Pipeline: ExtraTreesClassifier(SelectFwe(data, SelectFwe.alpha=0.023, SelectFwe.score_func=f_classif), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.9000000000000001, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.14299451084538853, -2). Individual cecef790-501e-46b1-ac5e-3ee4ccaf198f Pipeline: RandomForestClassifier(SelectFwe(data, SelectFwe.alpha=0.034, SelectFwe.score_func=f_classif), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15158730806500018, -2). Individual 41899eeb-3996-4b53-be48-4be887ebc162 Pipeline: ExtraTreesClassifier(SelectPercentile(data, SelectPercentile.percentile=91, SelectPercentile.score_func=f_classif), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15413625925772492, -2). Individual 77740dde-642a-4708-8312-abfd1f38b86f Pipeline: GradientBoostingClassifier(data, GradientBoostingClassifier.learning_rate=0.01, GradientBoostingClassifier.max_depth=8, GradientBoostingClassifier.max_features=0.7000000000000001, GradientBoostingClassifier.min_samples_leaf=14, GradientBoostingClassifier.min_samples_split=20, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.8) Fitness: was evaluated. Fitness is (-0.27231141408849974, -1). encountered while evaluating pipeline. Traceback (most recent call last): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 105, in evaluate_pipeline prediction, scores = cross_val_predict_score(pl, X, y_train, y_score, cv=cv, metrics=metrics) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/genetic_programming/compilers/scikitlearn.py", line 58, in cross_val_predict_score predictions = cross_val_predict(estimator, X, y_train, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 777, in cross_val_predict for train, test in cv.split(X, y, groups)) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 983, in __call__ if self.dispatch_one_batch(iterator): File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 825, in dispatch_one_batch self._dispatch(tasks) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 782, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 182, in apply_async result = ImmediateResult(func) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 545, in __init__ self.results = batch() File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in __call__ for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 261, in for func, args, kwargs in self.items] File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 850, in _fit_and_predict estimator.fit(X_train, y_train, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/pipeline.py", line 267, in fit self._final_estimator.fit(Xt, y, **fit_params) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1276, in fit solver = _check_solver(self.solver, self.penalty, self.dual) File "/Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 449, in _check_solver "dual=False, got dual=%s" % (solver, dual)) ValueError: Solver lbfgs supports only dual=False, got dual=True Individual 3f16b8ad-6257-461a-93a3-e464cbdb64b5 Pipeline: LogisticRegression(SelectPercentile(data, SelectPercentile.percentile=14, SelectPercentile.score_func=f_classif), LogisticRegression.C=0.5, LogisticRegression.dual=True, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-inf, -2). Individual 49b397e7-aa3c-4109-b9a5-73adb59ced5c Pipeline: LogisticRegression(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), LogisticRegression.C=25.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.13984237966665866, -2). Individual 730a9abb-899d-450e-8c20-e7e8ee285768 Pipeline: KNeighborsClassifier(data, KNeighborsClassifier.n_neighbors=20, KNeighborsClassifier.p=2, KNeighborsClassifier.weights='distance') Fitness: was evaluated. Fitness is (-0.39257422428568356, -1). Individual 1fbc0fa6-3fc8-4834-925f-a52e98b59e8a Pipeline: LogisticRegression(VarianceThreshold(data, VarianceThreshold.threshold=0.25), LogisticRegression.C=10.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.13827267623675257, -2). Individual f19ad24e-4170-4a52-9ca7-0e540f91ea4f Pipeline: LogisticRegression(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), LogisticRegression.C=0.01, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.13645601279826958, -2). Individual 8ac00f0b-86b6-4098-85f9-ef6ddd8fafc2 Pipeline: ExtraTreesClassifier(Binarizer(data, Binarizer.threshold=0.0), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.6603177992264594, -2). Individual 90e6d237-cd46-4b90-bfbb-3b7f89f7c6f2 Pipeline: RandomForestClassifier(Nystroem(MaxAbsScaler(data), Nystroem.gamma=0.45, Nystroem.kernel='laplacian', Nystroem.n_components=9), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.1684491184572483, -3). Individual 92739048-f6e0-49a1-abbb-a0a61c6200df Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.14873317605109115, -1). Individual 6a8074c2-d5ea-40d6-aafd-85b78613de97 Pipeline: LogisticRegression(VarianceThreshold(data, VarianceThreshold.threshold=0.5), LogisticRegression.C=25.0, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.1518686582164998, -2). Individual f00afb9a-5441-45d5-a50d-173e599f944f Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.14516154192560063, -1). Current pareto-front updated with individual with wvalues (-0.09607394639483764, -2). Overall pareto-front updated with individual with wvalues (-0.09607394639483764, -2). Individual 65deefce-4b66-46ff-b7cf-9d386413807d Pipeline: RandomForestClassifier(PolynomialFeatures(data, PolynomialFeatures.degree=2, PolynomialFeatures.include_bias=False, PolynomialFeatures.interaction_only=False), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.7500000000000001, RandomForestClassifier.min_samples_leaf=4, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.09607394639483764, -2). Individual f532c351-61c4-459c-8b73-7c99acbefb29 Pipeline: LogisticRegression(FeatureAgglomeration(data, FeatureAgglomeration.affinity='cosine', FeatureAgglomeration.linkage='average'), LogisticRegression.C=0.5, LogisticRegression.dual=False, LogisticRegression.penalty='l2', LogisticRegression.solver='lbfgs') Fitness: was evaluated. Fitness is (-0.2246934713984482, -2). Individual 12b43f3a-5e7b-493c-9c9f-0a8e2d42e7cf Pipeline: ExtraTreesClassifier(data, ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='gini', ExtraTreesClassifier.max_features=0.1, min_samples_leaf=11, min_samples_split=18, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.2661267127441992, -1). Individual 414b3d54-1d2b-4f5a-b603-b6963e345586 Pipeline: MultinomialNB(VarianceThreshold(data, VarianceThreshold.threshold=0.5), alpha=0.01, fit_prior=False) Fitness: was evaluated. Fitness is (-1.5212470103427558, -2). Individual 638b34a2-a3b3-469a-85d7-d733401a8ba5 Pipeline: RandomForestClassifier(SelectPercentile(data, SelectPercentile.percentile=11, SelectPercentile.score_func=f_classif), RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.6000000000000001, RandomForestClassifier.min_samples_leaf=18, RandomForestClassifier.min_samples_split=10, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15642869454305375, -2). Individual e6cf19c3-1e1f-4c9e-b958-0e99ad031e05 Pipeline: ExtraTreesClassifier(MinMaxScaler(data), ExtraTreesClassifier.bootstrap=False, ExtraTreesClassifier.criterion='entropy', ExtraTreesClassifier.max_features=0.45, min_samples_leaf=17, min_samples_split=6, ExtraTreesClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.15400193205212695, -2). Individual da043c5b-d432-4b4d-9354-53a44a221938 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.12311939405575383, -1). Individual e33bc670-c3f5-4a28-9b80-145f2ef830b8 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.8, RandomForestClassifier.min_samples_leaf=17, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.14477283700349766, -1). Individual 51dfbfa8-83b4-4f14-8202-c28ed09fb568 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=True, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.55, RandomForestClassifier.min_samples_leaf=3, RandomForestClassifier.min_samples_split=2, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.16634283691012397, -1). Individual 6e43cf0a-6158-4c61-8c22-502f52ca4d72 Pipeline: RandomForestClassifier(data, RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='entropy', RandomForestClassifier.max_features=0.5, RandomForestClassifier.min_samples_leaf=7, RandomForestClassifier.min_samples_split=19, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.17523769637424763, -1). Individual bd9166f4-8e08-4a42-87d4-00afcb86d8b9 Pipeline: DecisionTreeClassifier(data, DecisionTreeClassifier.criterion='gini', DecisionTreeClassifier.max_depth=6, min_samples_leaf=2, min_samples_split=3) Fitness: was evaluated. Fitness is (-2.5041582732126524, -1). Individual 8276093f-35cd-4ec4-9601-de01f524c456 Pipeline: BernoulliNB(data, alpha=1.0, fit_prior=True) Fitness: was evaluated. Fitness is (-0.6512589457055173, -1). Individual 65029f41-b24b-4866-aa3a-484b2529b9d6 Pipeline: BernoulliNB(data, alpha=0.01, fit_prior=True) Fitness: was evaluated. Fitness is (-0.653748633836787, -1). Current pareto-front updated with individual with wvalues (-0.09383085110906923, -2). Overall pareto-front updated with individual with wvalues (-0.09383085110906923, -2). Individual bfc085d7-0155-46ce-be4e-c0b212e586dd Pipeline: GradientBoostingClassifier(MinMaxScaler(data), GradientBoostingClassifier.learning_rate=0.1, GradientBoostingClassifier.max_depth=3, GradientBoostingClassifier.max_features=0.1, GradientBoostingClassifier.min_samples_leaf=5, GradientBoostingClassifier.min_samples_split=13, GradientBoostingClassifier.n_estimators=100, GradientBoostingClassifier.subsample=0.4) Fitness: was evaluated. Fitness is (-0.09383085110906923, -2). Individual 1a0d694c-066d-4aa3-86e0-f1a5aac02d3c Pipeline: RandomForestClassifier(RobustScaler(data), RandomForestClassifier.bootstrap=False, RandomForestClassifier.criterion='gini', RandomForestClassifier.max_features=0.35000000000000003, RandomForestClassifier.min_samples_leaf=12, RandomForestClassifier.min_samples_split=11, RandomForestClassifier.n_estimators=100) Fitness: was evaluated. Fitness is (-0.13669430887574996, -2). Search phase terminated because of Keyboard Interrupt. Search phase took 162.4648s. Moving on to post processing. --------------------------------------------------------------------------- TypeError Traceback (most recent call last) /Users/jsgalan/Desktop/Gama/test5.py in () 11 automl = GamaClassifier() 12 automl.evaluation_completed(process_individual) ---> 13 automl.fit(X, y) /Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/gama.py in fit(self, X, y, arff_file_path, warm_start, auto_ensemble_n, restart_, keep_cache) 309 310 with Stopwatch() as post_sw: --> 311 self._postprocess_phase(auto_ensemble_n, timeout=time_left) 312 log.info("Postprocessing took {:.4f}s.".format(post_sw.elapsed_time)) 313 log_parseable_event(log, TOKENS.POSTPROCESSING_END, post_sw.elapsed_time) /Users/jsgalan/Library/Enthought/Canopy/edm/envs/User/lib/python3.5/site-packages/gama-0.1.dev0-py3.5.egg/gama/gama.py in _postprocess_phase(self, n, timeout) 389 def _postprocess_phase(self, n, timeout=1e6): 390 """ Perform any necessary post processing, such as ensemble building. """ --> 391 self._best_pipeline = list(reversed(sorted(self._final_pop, key=lambda ind: ind.fitness.values)))[0] 392 log.info("Best pipeline has fitness of {}".format(self._best_pipeline.fitness.values)) 393 self._best_pipeline = self._operator_set.compile(self._best_pipeline) TypeError: 'NoneType' object is not iterable