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automl / auto-sklearn

Automated Machine Learning with scikit-learn
https://automl.github.io/auto-sklearn
BSD 3-Clause "New" or "Revised" License
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ValueError: No models fitted! #11

Closed ghost closed 8 years ago

ghost commented 9 years ago

Greetings,

I tried to run the example from the README.md. All steps run without errors, until I try to score the test set. Then I get a ValueError: No models fitted!. The console output shows various runtime warnings, among them indications about missing files. What could be the cause?

(Unfortunately, github promptly refuses to accept a text file for some reason, so I pasted the console output during fitting and the error trace when trying to score below.)

My setup is a Python 2.7.6 virtualenv, running from IPython 4.0.0. The installed packages are as follows:

argparse (1.2.1) AutoSklearn (0.0.1.dev0) cma (1.1.06) decorator (4.0.2) funcsigs (0.4) HPOlib (0.1.0) HPOlibConfigSpace (0.1dev) ipython (4.0.0) ipython-genutils (0.1.0) liac-arff (2.1.0) lockfile (0.10.2) matplotlib (1.4.3) mock (1.3.0) networkx (1.10) nose (1.3.7) numpy (1.9.0) pandas (0.16.2) ParamSklearn (0.1dev) path.py (8.1.1) pbr (1.8.0) pexpect (3.3) pickleshare (0.5) pip (1.5.4) protobuf (3.0.0-alpha-1) psutil (3.2.1) pyMetaLearn (0.1dev) pymongo (3.0.3) pyparsing (2.0.3) python-dateutil (2.4.2) pytz (2015.6) PyYAML (3.11) scikit-learn (0.15.2) scipy (0.14.0) setuptools (18.3.2) simplegeneric (0.8.1) six (1.9.0) traitlets (4.0.0) wheel (0.24.0) wsgiref (0.1.2)

Thanks for your response.

Console output: [INFO] [09-24 17:58:47:AutoML_54da6690e2c896d2d9aafe349b066645_1]: Remaining time after reading 54da6690e2c896d2d9aafe349b066645 3600.00 sec /media/selects/venv/py27/local/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:1057: RuntimeWarning: Degrees of freedom <= 0 for slice. warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning) /media/selects/venv/py27/local/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:598: RuntimeWarning: Mean of empty slice warnings.warn("Mean of empty slice", RuntimeWarning) [WARNING] [09-24 17:58:47:pyMetaLearn.input.aslib_simple]: Not found: /media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/metalearni ng/files/multiclass.classification_dense_acc_metric/ground_truth.arff (maybe you want to add it) [WARNING] [09-24 17:58:47:pyMetaLearn.input.aslib_simple]: Not found: /media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/metalearni ng/files/multiclass.classification_dense_acc_metric/citation.bib (maybe you want to add it) [WARNING] [09-24 17:58:47:pyMetaLearn.input.aslib_simple]: Not found: /media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/metalearni ng/files/multiclass.classification_dense_acc_metric/cv.arff (maybe you want to add it) [INFO] [09-24 17:58:48:autosklearn.metalearning.metalearning]: Reading meta-data took 0.59 seconds ['133', '132', '131', '130', '137', '136', '135', '134', '139', '138', '24', '25', '26', '27', '20', '21', '22', '23', '28', '29', '4', '8', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '59', '58', '55', '54', '57', '56', '51', '50', '53', '52', '115', '114', '88', '89', '111', '110', '113', '112', '82', '83', '80', '81', '119', '118', '84', '85', '3', '7', '108', '1 09', '102', '103', '100', '101', '106', '107', '104', '105', '39', '38', '33', '32', '31', '30', '37', '36', '35', '34', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '2', '6', '99', '98', '91', '90', '93', '92', '95', '94', '97', '96', '11', '10', '13', '12', '15', '14', '17', '16', '19', '18', '117', '116', '41', '48', '49', '46', '86', '44', '45', '42', '43', '40', '87', '1' , '5', '9', '142', '140', '141', '77', '76', '75', '74', '73', '72', '71', '70', '79', '78', '47'] [WARNING] [09-24 17:58:48:pyMetaLearn.optimizers.metalearn_optimizer.metalearner]: Could not find runs for instance 1118_bac [WARNING] [09-24 17:58:48:pyMetaLearn.optimizers.metalearn_optimizer.metalearner]: Could not find runs for instance 314_bac [WARNING] [09-24 17:58:48:pyMetaLearn.optimizers.metalearn_optimizer.metalearner]: Could not find runs for instance 454_bac [WARNING] [09-24 17:58:48:pyMetaLearn.optimizers.metalearn_optimizer.metalearner]: Could not find runs for instance 809_bac [WARNING] [09-24 17:58:48:pyMetaLearn.optimizers.metalearn_optimizer.metalearner]: Could not find runs for instance 948_bac [WARNING] [09-24 17:58:48:pyMetaLearn.metalearning.kNearestDatasets.kND]: Found no best configuration for instance 1118_bac [WARNING] [09-24 17:58:48:pyMetaLearn.metalearning.kNearestDatasets.kND]: Found no best configuration for instance 948_bac [WARNING] [09-24 17:58:48:pyMetaLearn.metalearning.kNearestDatasets.kND]: Found no best configuration for instance 454_bac [WARNING] [09-24 17:58:48:pyMetaLearn.metalearning.kNearestDatasets.kND]: Found no best configuration for instance 809_bac [WARNING] [09-24 17:58:48:pyMetaLearn.metalearning.kNearestDatasets.kND]: Found no best configuration for instance 314_bac [INFO] [09-24 17:58:48:AutoML_54da6690e2c896d2d9aafe349b066645_1]: Time left for 54da6690e2c896d2d9aafe349b066645 after finding initial configurations: 3598.94sec Calling: smac --numRun 1 --scenario /tmp/autosklearn_tmp_14167_1103/54da6690e2c896d2d9aafe349b066645.scenario --initial-challengers " -balancing:strategy 'weighting' -classifier 'lda' -imputation:s trategy 'median' -kernel_pca:gamma '0.0290194572424' -kernel_pca:kernel 'rbf' -kernel_pca:n_components '1971' -lda:n_components '232' -lda:tol '0.000804876897084' -preprocessor 'kernel_pca' -rescal ing:strategy 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'libsvm_svc' -imputation:strategy 'median' -liblinear_svc_preprocessor:C '18592.5543358' -liblinear_svc_p reprocessor:class_weight 'auto' -liblinear_svc_preprocessor:dual 'False' -liblinear_svc_preprocessor:fit_intercept 'True' -liblinear_svc_preprocessor:intercept_scaling '1' -liblinear_svc_preprocess or:loss 'l2' -liblinear_svc_preprocessor:multi_class 'ovr' -liblinear_svc_preprocessor:penalty 'l2' -liblinear_svc_preprocessor:tol '0.040232270855' -libsvm_svc:C '6111.7121149' -libsvm_svc:class_w eight 'None' -libsvm_svc:coef0 '0.844884936773' -libsvm_svc:degree '5' -libsvm_svc:gamma '0.117882960246' -libsvm_svc:kernel 'poly' -libsvm_svc:max_iter '-1' -libsvm_svc:shrinking 'False' -libsvm_s vc:tol '0.00109298090501' -preprocessor 'liblinear_svc_preprocessor' -rescaling:strategy 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'liblinear_svc' -imputation:s trategy 'mean' -kernel_pca:gamma '1.6331524928' -kernel_pca:kernel 'rbf' -kernel_pca:n_components '761' -liblinear_svc:C '44.5016816038' -liblinear_svc:class_weight 'auto' -liblinear_svc:dual 'Fals e' -liblinear_svc:fit_intercept 'True' -liblinear_svc:intercept_scaling '1' -liblinear_svc:loss 'l2' -liblinear_svc:multi_class 'ovr' -liblinear_svc:penalty 'l2' -liblinear_svc:tol '0.0018788986680 6' -preprocessor 'kernel_pca' -rescaling:strategy 'normalize'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'libsvm_svc' -imputation:strategy 'mean' -libsvm_svc:C '50.8707992 587' -libsvm_svc:class_weight 'auto' -libsvm_svc:gamma '4.72168867253' -libsvm_svc:kernel 'rbf' -libsvm_svc:max_iter '-1' -libsvm_svc:shrinking 'True' -libsvm_svc:tol '1.67692533041e-05' -preproces sor 'select_rates' -rescaling:strategy 'normalize' -select_rates:alpha '0.318343160914' -select_rates:mode 'fdr' -select_rates:score_func 'f_classif'" --initial-challengers " -balancing:strategy 'n one' -classifier 'ridge' -imputation:strategy 'median' -kernel_pca:gamma '2.43149422021' -kernel_pca:kernel 'rbf' -kernel_pca:n_components '1194' -preprocessor 'kernel_pca' -rescaling:strategy 'nor malize' -ridge:alpha '1.30657587648e-05' -ridge:fit_intercept 'True' -ridge:tol '0.000760986834404'" --initial-challengers " -adaboost:algorithm 'SAMME.R' -adaboost:learning_rate '0.400363929326' - adaboost:max_depth '5' -adaboost:n_estimators '319' -balancing:strategy 'none' -classifier 'adaboost' -imputation:strategy 'most_frequent' -preprocessor 'no_preprocessing' -rescaling:strategy 'min/ max'" --initial-challengers " -balancing:strategy 'none' -classifier 'qda' -imputation:strategy 'mean' -pca:keep_variance '0.748479656855' -pca:whiten 'False' -preprocessor 'pca' -qda:reg_param '3. 82874880102' -qda:tol '0.0130621640728' -rescaling:strategy 'normalize'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'libsvm_svc' -imputation:strategy 'mean' -libsvm_svc:C ' 18807.7593252' -libsvm_svc:class_weight 'None' -libsvm_svc:gamma '0.940704535703' -libsvm_svc:kernel 'rbf' -libsvm_svc:max_iter '-1' -libsvm_svc:shrinking 'True' -libsvm_svc:tol '0.00148731196993' -preprocessor 'select_rates' -rescaling:strategy 'min/max' -select_rates:alpha '0.126666738937' -select_rates:mode 'fdr' -select_rates:score_func 'f_classif'" --initial-challengers " -balancing:str ategy 'weighting' -classifier 'lda' -imputation:strategy 'mean' -kitchen_sinks:gamma '1.48108179896' -kitchen_sinks:n_components '3450' -lda:n_components '25' -lda:tol '0.0426553560955' -preprocess or 'kitchen_sinks' -rescaling:strategy 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'random_forest' -feature_agglomeration:affinity 'manhattan' -feature_agglomerat ion:linkage 'average' -feature_agglomeration:n_clusters '76' -imputation:strategy 'median' -preprocessor 'feature_agglomeration' -random_forest:bootstrap 'True' -random_forest:criterion 'entropy' - random_forest:max_depth 'None' -random_forest:max_features '1.60908385606' -random_forest:max_leaf_nodes 'None' -random_forest:min_samples_leaf '2' -random_forest:min_samples_split '12' -random_for est:n_estimators '100' -rescaling:strategy 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier 'ridge' -imputation:strategy 'mean' -nystroem_sampler:coef0 '0.476829591723' -ny stroem_sampler:degree '3' -nystroem_sampler:gamma '0.0817500204362' -nystroem_sampler:kernel 'poly' -nystroem_sampler:n_components '7840' -preprocessor 'nystroem_sampler' -rescaling:strategy 'min/m ax' -ridge:alpha '3.52478796331e-06' -ridge:fit_intercept 'True' -ridge:tol '2.63925768895e-05'" --initial-challengers " -balancing:strategy 'none' -classifier 'random_forest' -imputation:strategy 'mean' -preprocessor 'no_preprocessing' -random_forest:bootstrap 'True' -random_forest:criterion 'gini' -random_forest:max_depth 'None' -random_forest:max_features '1.0' -random_forest:max_leaf_nod es 'None' -random_forest:min_samples_leaf '1' -random_forest:min_samples_split '2' -random_forest:n_estimators '100' -rescaling:strategy 'min/max'" --initial-challengers " -balancing:strategy 'none ' -classifier 'lda' -imputation:strategy 'median' -lda:n_components '203' -lda:tol '0.0935342136025' -preprocessor 'select_rates' -rescaling:strategy 'normalize' -select_rates:alpha '0.048178281695 5' -select_rates:mode 'fwe' -select_rates:score_func 'f_classif'" --initial-challengers " -balancing:strategy 'none' -classifier 'sgd' -imputation:strategy 'mean' -preprocessor 'no_preprocessing' - rescaling:strategy 'min/max' -sgd:alpha '0.0001' -sgd:eta0 '0.01' -sgd:fit_intercept 'True' -sgd:learning_rate 'optimal' -sgd:loss 'hinge' -sgd:n_iter '20' -sgd:penalty 'l2'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'liblinear_svc' -imputation:strategy 'mean' -kitchen_sinks:gamma '1.62106650658' -kitchen_sinks:n_components '6034' -liblinear_svc:C '780.976275468' -l iblinear_svc:class_weight 'auto' -liblinear_svc:dual 'False' -liblinear_svc:fit_intercept 'True' -liblinear_svc:intercept_scaling '1' -liblinear_svc:loss 'l2' -liblinear_svc:multi_class 'ovr' -libl inear_svc:penalty 'l2' -liblinear_svc:tol '2.60869016302e-05' -preprocessor 'kitchen_sinks' -rescaling:strategy 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'libsv m_svc' -feature_agglomeration:affinity 'manhattan' -feature_agglomeration:linkage 'average' -feature_agglomeration:n_clusters '89' -imputation:strategy 'most_frequent' -libsvm_svc:C '246.452178174' -libsvm_svc:class_weight 'auto' -libsvm_svc:gamma '0.0442300193285' -libsvm_svc:kernel 'rbf' -libsvm_svc:max_iter '-1' -libsvm_svc:shrinking 'False' -libsvm_svc:tol '0.0180487670379' -preprocessor 'feature_agglomeration' -rescaling:strategy 'standard'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'passive_aggresive' -imputation:strategy 'median' -passive_aggresive:C ' 1.31125616578' -passive_aggresive:fit_intercept 'True' -passive_aggresive:loss 'hinge' -passive_aggresive:n_iter '948' -preprocessor 'select_percentile_classification' -rescaling:strategy 'min/max' -select_percentile_classification:percentile '83.3669247487' -select_percentile_classification:score_func 'chi2'" --initial-challengers " -balancing:strategy 'none' -classifier 'sgd' -imputation:s trategy 'most_frequent' -preprocessor 'no_preprocessing' -rescaling:strategy 'min/max' -sgd:alpha '0.00292211727831' -sgd:epsilon '0.0116887099622' -sgd:eta0 '0.080560671307' -sgd:fit_intercept 'Tr ue' -sgd:learning_rate 'invscaling' -sgd:loss 'modified_huber' -sgd:n_iter '754' -sgd:penalty 'l1' -sgd:power_t '0.463498329665'" --initial-challengers " -balancing:strategy 'none' -classifier 'ran dom_forest' -imputation:strategy 'mean' -preprocessor 'select_rates' -random_forest:bootstrap 'False' -random_forest:criterion 'entropy' -random_forest:max_depth 'None' -random_forest:max_features '4.67839426105' -random_forest:max_leaf_nodes 'None' -random_forest:min_samples_leaf '10' -random_forest:min_samples_split '10' -random_forest:n_estimators '100' -rescaling:strategy 'standard' -sel ect_rates:alpha '0.167486470473' -select_rates:mode 'fdr' -select_rates:score_func 'f_classif'" --initial-challengers " -balancing:strategy 'none' -classifier 'sgd' -imputation:strategy 'most_frequ ent' -preprocessor 'select_rates' -rescaling:strategy 'min/max' -select_rates:alpha '0.155334914856' -select_rates:mode 'fpr' -select_rates:score_func 'f_classif' -sgd:alpha '6.49185336268e-05' -sg d:eta0 '0.0665593974375' -sgd:fit_intercept 'True' -sgd:learning_rate 'optimal' -sgd:loss 'log' -sgd:n_iter '189' -sgd:penalty 'l2'" --initial-challengers " -balancing:strategy 'weighting' -classif ier 'sgd' -imputation:strategy 'median' -preprocessor 'no_preprocessing' -rescaling:strategy 'min/max' -sgd:alpha '0.000134377776157' -sgd:epsilon '0.000256156800074' -sgd:eta0 '0.05222815237' -sgd :fit_intercept 'True' -sgd:learning_rate 'constant' -sgd:loss 'modified_huber' -sgd:n_iter '429' -sgd:penalty 'l1'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'passive_aggr esive' -imputation:strategy 'median' -liblinear_svc_preprocessor:C '0.306520222754' -liblinear_svc_preprocessor:class_weight 'None' -liblinear_svc_preprocessor:dual 'False' -liblinear_svc_preproces sor:fit_intercept 'True' -liblinear_svc_preprocessor:intercept_scaling '1' -liblinear_svc_preprocessor:loss 'l2' -liblinear_svc_preprocessor:multi_class 'ovr' -liblinear_svc_preprocessor:penalty 'l 2' -liblinear_svc_preprocessor:tol '4.83193374386e-05' -passive_aggresive:C '0.000522592495213' -passive_aggresive:fit_intercept 'True' -passive_aggresive:loss 'hinge' -passive_aggresive:n_iter '31 3' -preprocessor 'liblinear_svc_preprocessor' -rescaling:strategy 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier 'libsvm_svc' -imputation:strategy 'median' -libsvm_svc:C '19690.0557441' -libsvm_svc:class_weight 'None' -libsvm_svc:gamma '4.89593584562e-05' -libsvm_svc:kernel 'rbf' -libsvm_svc:max_iter '-1' -libsvm_svc:shrinking 'True' -libsvm_svc:tol '0.019646836528 3' -preprocessor 'random_trees_embedding' -random_trees_embedding:max_depth '4' -random_trees_embedding:max_leaf_nodes 'None' -random_trees_embedding:min_samples_leaf '16' -random_trees_embedding:m in_samples_split '9' -random_trees_embedding:n_estimators '52' -rescaling:strategy 'standard'" --initial-challengers " -balancing:strategy 'none' -classifier 'sgd' -imputation:strategy 'mean' -prep rocessor 'no_preprocessing' -rescaling:strategy 'min/max' -sgd:alpha '0.0001' -sgd:eta0 '0.01' -sgd:fit_intercept 'True' -sgd:learning_rate 'optimal' -sgd:loss 'hinge' -sgd:n_iter '20' -sgd:penalty 'l2'" --initial-challengers " -balancing:strategy 'weighting' -classifier 'random_forest' -extra_trees_preproc_for_classification:bootstrap 'True' -extra_trees_preproc_for_classification:criterion 'entropy' -extra_trees_preproc_for_classification:max_depth 'None' -extra_trees_preproc_for_classification:max_features '3.61796566599' -extra_trees_preproc_for_classification:min_samples_leaf '6' -extra_trees_preproc_for_classification:min_samples_split '2' -extra_trees_preproc_for_classification:n_estimators '100' -imputation:strategy 'mean' -preprocessor 'extra_trees_preproc_for_classifi cation' -random_forest:bootstrap 'True' -random_forest:criterion 'entropy' -random_forest:max_depth 'None' -random_forest:max_features '0.857466092817' -random_forest:max_leafnodes 'None' -random forest:min_samples_leaf '14' -random_forest:min_samples_split '15' -random_forest:n_estimators '100' -rescaling:strategy 'normalize'" Calling: runsolver --watcher-data /dev/null -W 3598 -d 5 python /media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/ensemble_selecti on_script.py /tmp/autosklearn_tmp_14167_1103 54da6690e2c896d2d9aafe349b066645 multiclass.classification acc_metric 3593.92797899 /tmp/autosklearn_output_14167_1103 50 1 /tmp/autosklearn_tmp_14167_1 103/ensemble_indices_1

Out[16]: <AutoSklearnClassifier(AutoSklearnClassifier-1, initial)>

In [17]: >>> print automl.score(X_test, y_test)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-6a5d99e9c9c3> in <module>()
----> 1 print automl.score(X_test, y_test)

/media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/automl.pyc in score(self, X, y)
    358 
    359     def score(self, X, y):
--> 360         prediction = self.predict(X)
    361         return evaluator.calculate_score(y, prediction, self.task_,
    362                                          self.metric_, self.target_num_)

/media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/estimators.pyc in predict(self, X)
    137             The predicted classes.
    138         """
--> 139         return super(AutoSklearnClassifier, self).predict(X)
    140 
    141 

/media/selects/venv/py27/local/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/automl.pyc in predict(self, X)
    327 
    328         if len(models) == 0:
--> 329             raise ValueError("No models fitted!")
    330 
    331         if self.ohe_ is not None:

ValueError: No models fitted!
pmk2109 commented 8 years ago

Having the same issue

mfeurer commented 8 years ago

Did you also try to run the example?

pmk2109 commented 8 years ago

Yep ... I'll copy the output below (I appreciate you working on this, once I get it working I'm sure it will be awesome)

[INFO] [2016-02-02 19:03:42,316:AutoML(1):aa6aede47b243ade0492c3bbad51d1ab] Start calculating metafeatures for aa6aede47b243ade0492c3bbad51d1ab [INFO] [2016-02-02 19:03:42,321:AutoML(1):aa6aede47b243ade0492c3bbad51d1ab] Calculating Metafeatures (categorical attributes) took 0.00 [INFO] [2016-02-02 19:03:42,349:AutoML(1):aa6aede47b243ade0492c3bbad51d1ab] Calculating Metafeatures (encoded attributes) took 0.03sec [INFO] [2016-02-02 19:03:42,804:AutoML(1):aa6aede47b243ade0492c3bbad51d1ab] Time left for aa6aede47b243ade0492c3bbad51d1ab after finding initial configurations: 3598.26sec [INFO] [2016-02-02 19:03:42,809:autosklearn.util.smac] Start SMAC with 3598.25sec time left [INFO] [2016-02-02 19:03:42,809:autosklearn.util.smac] Calling: smac --numRun 1 --scenario /tmp/autosklearn_tmp_32341_2124/aa6aede47b243ade0492c3bbad51d1ab.scenario --num-ei-random 1000 --num-challengers 100 --initial-incumbent DEFAULT --retryTargetAlgorithmRunCount 0 --intensification-percentage 0.5 --validation false --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'sgd' -classifier:sgd:alpha '0.0001' -classifier:sgd:average 'True' -classifier:sgd:eta0 '0.01' -classifier:sgd:fit_intercept 'True' -classifier:sgd:learning_rate 'optimal' -classifier:sgd:loss 'hinge' -classifier:sgd:n_iter '5' -classifier:sgd:penalty 'l2' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.1' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'no_preprocessing' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'extra_trees' -classifier:extra_trees:bootstrap 'False' -classifier:extra_trees:criterion 'entropy' -classifier:extra_trees:max_depth 'None' -classifier:extra_trees:max_features '2.10843034926' -classifier:extra_trees:min_samples_leaf '5' -classifier:extra_trees:min_samples_split '6' -classifier:extra_trees:min_weight_fraction_leaf '0.0' -classifier:extra_trees:n_estimators '100' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.0139334888516' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'select_rates' -preprocessor:select_rates:alpha '0.158094692549' -preprocessor:select_rates:mode 'fwe' -preprocessor:select_rates:score_func 'chi2' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'liblinear_svc' -classifier:liblinear_svc:C '34.5233071874' -classifier:liblinear_svc:dual 'False' -classifier:liblinear_svc:fit_intercept 'True' -classifier:liblinear_svc:intercept_scaling '1' -classifier:liblinear_svc:loss 'squared_hinge' -classifier:liblinear_svc:multi_class 'ovr' -classifier:liblinear_svc:penalty 'l2' -classifier:liblinear_svc:tol '0.0103053322307' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.000124642010466' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'polynomial' -preprocessor:polynomial:degree '2' -preprocessor:polynomial:include_bias 'True' -preprocessor:polynomial:interaction_only 'False' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'libsvm_svc' -classifier:libsvm_svc:C '2099.09721534' -classifier:libsvm_svc:coef0 '0.328500980533' -classifier:libsvm_svc:degree '3' -classifier:libsvm_svc:gamma '2.80955762537' -classifier:libsvm_svc:kernel 'poly' -classifier:libsvm_svc:max_iter '-1' -classifier:libsvm_svc:shrinking 'False' -classifier:libsvm_svc:tol '0.00321372911771' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.00338399109404' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'select_rates' -preprocessor:select_rates:alpha '0.0859436710109' -preprocessor:select_rates:mode 'fpr' -preprocessor:select_rates:score_func 'chi2' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'liblinear_svc' -classifier:liblinear_svc:C '0.58158687766' -classifier:liblinear_svc:dual 'False' -classifier:liblinear_svc:fit_intercept 'True' -classifier:liblinear_svc:intercept_scaling '1' -classifier:liblinear_svc:loss 'squared_hinge' -classifier:liblinear_svc:multi_class 'ovr' -classifier:liblinear_svc:penalty 'l2' -classifier:liblinear_svc:tol '0.0526132119058' -imputation:strategy 'most_frequent' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'polynomial' -preprocessor:polynomial:degree '2' -preprocessor:polynomial:include_bias 'True' -preprocessor:polynomial:interaction_only 'False' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'proj_logit' -classifier:proj_logit:max_epochs '2' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.00118703229042' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'no_preprocessing' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'random_forest' -classifier:random_forest:bootstrap 'False' -classifier:random_forest:criterion 'gini' -classifier:random_forest:max_depth 'None' -classifier:random_forest:max_features '1.92119344674' -classifier:random_forest:max_leaf_nodes 'None' -classifier:random_forest:min_samples_leaf '3' -classifier:random_forest:min_samples_split '7' -classifier:random_forest:min_weight_fraction_leaf '0.0' -classifier:random_forest:n_estimators '100' -imputation:strategy 'median' -one_hot_encoding:minimum_fraction '0.18557940618' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'feature_agglomeration' -preprocessor:feature_agglomeration:affinity 'euclidean' -preprocessor:feature_agglomeration:linkage 'ward' -preprocessor:feature_agglomeration:n_clusters '235' -preprocessor:feature_agglomeration:pooling_func 'mean' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'lda' -classifier:lda:n_components '242' -classifier:lda:shrinkage 'None' -classifier:lda:tol '0.00113395744796' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.00488914480304' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'gem' -preprocessor:gem:N '8' -preprocessor:gem:precond '0.393445967511' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'libsvm_svc' -classifier:libsvm_svc:C '2.82095068356' -classifier:libsvm_svc:coef0 '0.752565840633' -classifier:libsvm_svc:degree '5' -classifier:libsvm_svc:gamma '1.92439641344' -classifier:libsvm_svc:kernel 'poly' -classifier:libsvm_svc:max_iter '-1' -classifier:libsvm_svc:shrinking 'True' -classifier:libsvm_svc:tol '0.000376820736906' -imputation:strategy 'mean' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'select_percentile_classification' -preprocessor:select_percentile_classification:percentile '83.6010685058' -preprocessor:select_percentile_classification:score_func 'f_classif' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'proj_logit' -classifier:proj_logit:max_epochs '11' -imputation:strategy 'median' -one_hot_encoding:minimum_fraction '0.00288336715952' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'gem' -preprocessor:gem:N '16' -preprocessor:gem:precond '0.306284393464' -rescaling:choice 'standardize'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'extra_trees' -classifier:extra_trees:bootstrap 'True' -classifier:extra_trees:criterion 'gini' -classifier:extra_trees:max_depth 'None' -classifier:extra_trees:max_features '2.37790105664' -classifier:extra_trees:min_samples_leaf '2' -classifier:extra_trees:min_samples_split '4' -classifier:extra_trees:min_weight_fraction_leaf '0.0' -classifier:extra_trees:n_estimators '100' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.00430577029047' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'select_rates' -preprocessor:select_rates:alpha '0.309340894918' -preprocessor:select_rates:mode 'fdr' -preprocessor:select_rates:score_func 'f_classif' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'libsvm_svc' -classifier:libsvm_svc:C '17.0489364595' -classifier:libsvm_svc:coef0 '0.744604016557' -classifier:libsvm_svc:degree '1' -classifier:libsvm_svc:gamma '0.0861002047329' -classifier:libsvm_svc:kernel 'poly' -classifier:libsvm_svc:max_iter '-1' -classifier:libsvm_svc:shrinking 'True' -classifier:libsvm_svc:tol '0.0236218221161' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.000186313230587' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'gem' -preprocessor:gem:N '17' -preprocessor:gem:precond '0.148774809986' -rescaling:choice 'standardize'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'gradient_boosting' -classifier:gradient_boosting:learning_rate '0.250449093686' -classifier:gradient_boosting:loss 'deviance' -classifier:gradient_boosting:max_depth '4' -classifier:gradient_boosting:max_features '3.01322770006' -classifier:gradient_boosting:max_leaf_nodes 'None' -classifier:gradient_boosting:min_samples_leaf '5' -classifier:gradient_boosting:min_samples_split '10' -classifier:gradient_boosting:min_weight_fraction_leaf '0.0' -classifier:gradient_boosting:n_estimators '100' -classifier:gradient_boosting:subsample '0.735717575373' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.278160654679' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'select_rates' -preprocessor:select_rates:alpha '0.0507415761243' -preprocessor:select_rates:mode 'fpr' -preprocessor:select_rates:score_func 'f_classif' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'sgd' -classifier:sgd:alpha '0.000645727672061' -classifier:sgd:average 'True' -classifier:sgd:epsilon '7.98141194861e-05' -classifier:sgd:eta0 '0.0636614615972' -classifier:sgd:fit_intercept 'True' -classifier:sgd:learning_rate 'invscaling' -classifier:sgd:loss 'modified_huber' -classifier:sgd:n_iter '133' -classifier:sgd:penalty 'l2' -classifier:sgd:power_t '0.198720154544' -imputation:strategy 'mean' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'liblinear_svc_preprocessor' -preprocessor:liblinear_svc_preprocessor:C '27.6468634938' -preprocessor:liblinear_svc_preprocessor:dual 'False' -preprocessor:liblinear_svc_preprocessor:fit_intercept 'True' -preprocessor:liblinear_svc_preprocessor:intercept_scaling '1' -preprocessor:liblinear_svc_preprocessor:loss 'squared_hinge' -preprocessor:liblinear_svc_preprocessor:multi_class 'ovr' -preprocessor:liblinear_svc_preprocessor:penalty 'l1' -preprocessor:liblinear_svc_preprocessor:tol '0.00186672282072' -rescaling:choice 'normalize'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'libsvm_svc' -classifier:libsvm_svc:C '295.418518308' -classifier:libsvm_svc:gamma '0.0509801555285' -classifier:libsvm_svc:kernel 'rbf' -classifier:libsvm_svc:max_iter '-1' -classifier:libsvm_svc:shrinking 'False' -classifier:libsvm_svc:tol '0.0161617567103' -imputation:strategy 'median' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'feature_agglomeration' -preprocessor:feature_agglomeration:affinity 'cosine' -preprocessor:feature_agglomeration:linkage 'complete' -preprocessor:feature_agglomeration:n_clusters '216' -preprocessor:feature_agglomeration:pooling_func 'mean' -rescaling:choice 'none'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'gradient_boosting' -classifier:gradient_boosting:learning_rate '0.200667953199' -classifier:gradient_boosting:loss 'deviance' -classifier:gradient_boosting:max_depth '7' -classifier:gradient_boosting:max_features '3.93258694897' -classifier:gradient_boosting:max_leaf_nodes 'None' -classifier:gradient_boosting:min_samples_leaf '7' -classifier:gradient_boosting:min_samples_split '11' -classifier:gradient_boosting:min_weight_fraction_leaf '0.0' -classifier:gradient_boosting:n_estimators '100' -classifier:gradient_boosting:subsample '0.997653205575' -imputation:strategy 'most_frequent' -one_hot_encoding:minimum_fraction '0.000461996907301' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'no_preprocessing' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'extra_trees' -classifier:extra_trees:bootstrap 'False' -classifier:extra_trees:criterion 'gini' -classifier:extra_trees:max_depth 'None' -classifier:extra_trees:max_features '4.27689663157' -classifier:extra_trees:min_samples_leaf '3' -classifier:extra_trees:min_samples_split '8' -classifier:extra_trees:min_weight_fraction_leaf '0.0' -classifier:extra_trees:n_estimators '100' -imputation:strategy 'mean' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'extra_trees_preproc_for_classification' -preprocessor:extra_trees_preproc_for_classification:bootstrap 'True' -preprocessor:extra_trees_preproc_for_classification:criterion 'gini' -preprocessor:extra_trees_preproc_for_classification:max_depth 'None' -preprocessor:extra_trees_preproc_for_classification:max_features '4.29160593434' -preprocessor:extra_trees_preproc_for_classification:min_samples_leaf '4' -preprocessor:extra_trees_preproc_for_classification:min_samples_split '3' -preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf '0.0' -preprocessor:extra_trees_preproc_for_classification:n_estimators '100' -rescaling:choice 'normalize'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'random_forest' -classifier:random_forest:bootstrap 'True' -classifier:random_forest:criterion 'gini' -classifier:random_forest:max_depth 'None' -classifier:random_forest:max_features '1.0' -classifier:random_forest:max_leaf_nodes 'None' -classifier:random_forest:min_samples_leaf '1' -classifier:random_forest:min_samples_split '2' -classifier:random_forest:min_weight_fraction_leaf '0.0' -classifier:random_forest:n_estimators '100' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.01' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'no_preprocessing' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'random_forest' -classifier:random_forest:bootstrap 'True' -classifier:random_forest:criterion 'gini' -classifier:random_forest:max_depth 'None' -classifier:random_forest:max_features '0.62203299707' -classifier:random_forest:max_leaf_nodes 'None' -classifier:random_forest:min_samples_leaf '6' -classifier:random_forest:min_samples_split '8' -classifier:random_forest:min_weight_fraction_leaf '0.0' -classifier:random_forest:n_estimators '100' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.000159991240057' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'feature_agglomeration' -preprocessor:feature_agglomeration:affinity 'cosine' -preprocessor:feature_agglomeration:linkage 'average' -preprocessor:feature_agglomeration:n_clusters '309' -preprocessor:feature_agglomeration:pooling_func 'mean' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'liblinear_svc' -classifier:liblinear_svc:C '0.30992815211' -classifier:liblinear_svc:dual 'False' -classifier:liblinear_svc:fit_intercept 'True' -classifier:liblinear_svc:intercept_scaling '1' -classifier:liblinear_svc:loss 'squared_hinge' -classifier:liblinear_svc:multi_class 'ovr' -classifier:liblinear_svc:penalty 'l2' -classifier:liblinear_svc:tol '0.0655697501082' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.00470215321783' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'feature_agglomeration' -preprocessor:feature_agglomeration:affinity 'euclidean' -preprocessor:feature_agglomeration:linkage 'average' -preprocessor:feature_agglomeration:n_clusters '283' -preprocessor:feature_agglomeration:pooling_func 'median' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'passive_aggressive' -classifier:passive_aggressive:C '0.736502830586' -classifier:passive_aggressive:fit_intercept 'True' -classifier:passive_aggressive:loss 'hinge' -classifier:passive_aggressive:n_iter '116' -imputation:strategy 'most_frequent' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'extra_trees_preproc_for_classification' -preprocessor:extra_trees_preproc_for_classification:bootstrap 'True' -preprocessor:extra_trees_preproc_for_classification:criterion 'gini' -preprocessor:extra_trees_preproc_for_classification:max_depth 'None' -preprocessor:extra_trees_preproc_for_classification:max_features '3.59519496236' -preprocessor:extra_trees_preproc_for_classification:min_samples_leaf '6' -preprocessor:extra_trees_preproc_for_classification:min_samples_split '5' -preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf '0.0' -preprocessor:extra_trees_preproc_for_classification:n_estimators '100' -rescaling:choice 'normalize'" --initial-challengers " -balancing:strategy 'weighting' -classifier:choice 'liblinear_svc' -classifier:liblinear_svc:C '0.273249130804' -classifier:liblinear_svc:dual 'False' -classifier:liblinear_svc:fit_intercept 'True' -classifier:liblinear_svc:intercept_scaling '1' -classifier:liblinear_svc:loss 'squared_hinge' -classifier:liblinear_svc:multi_class 'ovr' -classifier:liblinear_svc:penalty 'l2' -classifier:liblinear_svc:tol '0.00750025466836' -imputation:strategy 'median' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'no_preprocessing' -rescaling:choice 'normalize'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'adaboost' -classifier:adaboost:algorithm 'SAMME' -classifier:adaboost:learning_rate '1.00811045165' -classifier:adaboost:max_depth '6' -classifier:adaboost:n_estimators '468' -imputation:strategy 'mean' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'liblinear_svc_preprocessor' -preprocessor:liblinear_svc_preprocessor:C '1.18284317259' -preprocessor:liblinear_svc_preprocessor:dual 'False' -preprocessor:liblinear_svc_preprocessor:fit_intercept 'True' -preprocessor:liblinear_svc_preprocessor:intercept_scaling '1' -preprocessor:liblinear_svc_preprocessor:loss 'squared_hinge' -preprocessor:liblinear_svc_preprocessor:multi_class 'ovr' -preprocessor:liblinear_svc_preprocessor:penalty 'l1' -preprocessor:liblinear_svc_preprocessor:tol '0.00227926069243' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'sgd' -classifier:sgd:alpha '8.4637990261e-06' -classifier:sgd:average 'True' -classifier:sgd:eta0 '0.0299104961513' -classifier:sgd:fit_intercept 'True' -classifier:sgd:l1_ratio '0.544995215291' -classifier:sgd:learning_rate 'optimal' -classifier:sgd:loss 'log' -classifier:sgd:n_iter '370' -classifier:sgd:penalty 'elasticnet' -imputation:strategy 'most_frequent' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'select_percentile_classification' -preprocessor:select_percentile_classification:percentile '71.667144562' -preprocessor:select_percentile_classification:score_func 'chi2' -rescaling:choice 'normalize'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'random_forest' -classifier:random_forest:bootstrap 'True' -classifier:random_forest:criterion 'gini' -classifier:random_forest:max_depth 'None' -classifier:random_forest:max_features '1.29449316402' -classifier:random_forest:max_leaf_nodes 'None' -classifier:random_forest:min_samples_leaf '11' -classifier:random_forest:min_samples_split '18' -classifier:random_forest:min_weight_fraction_leaf '0.0' -classifier:random_forest:n_estimators '100' -imputation:strategy 'mean' -one_hot_encoding:minimum_fraction '0.0010924728998' -one_hot_encoding:use_minimum_fraction 'True' -preprocessor:choice 'feature_agglomeration' -preprocessor:feature_agglomeration:affinity 'cosine' -preprocessor:feature_agglomeration:linkage 'complete' -preprocessor:feature_agglomeration:n_clusters '385' -preprocessor:feature_agglomeration:pooling_func 'max' -rescaling:choice 'min/max'" --initial-challengers " -balancing:strategy 'none' -classifier:choice 'adaboost' -classifier:adaboost:algorithm 'SAMME' -classifier:adaboost:learning_rate '1.23062080068' -classifier:adaboost:max_depth '6' -classifier:adaboost:n_estimators '499' -imputation:strategy 'median' -one_hot_encoding:use_minimum_fraction 'False' -preprocessor:choice 'extra_trees_preproc_for_classification' -preprocessor:extra_trees_preproc_for_classification:bootstrap 'True' -preprocessor:extra_trees_preproc_for_classification:criterion 'entropy' -preprocessor:extra_trees_preproc_for_classification:max_depth 'None' -preprocessor:extra_trees_preproc_for_classification:max_features '3.5347851525' -preprocessor:extra_trees_preproc_for_classification:min_samples_leaf '6' -preprocessor:extra_trees_preproc_for_classification:min_samples_split '8' -preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf '0.0' -preprocessor:extra_trees_preproc_for_classification:n_estimators '100' -rescaling:choice 'standardize'" [INFO] [2016-02-02 19:03:42,828:AutoML(1):aa6aede47b243ade0492c3bbad51d1ab] Start Ensemble with 3598.23sec time left [INFO] [2016-02-02 19:03:42,829:autosklearn.util.submit_process] Calling: runsolver --watcher-data /dev/null -W 3598 -d 5 python -m autosklearn.ensemble_selection_script --auto-sklearn-tmp-directory /tmp/autosklearn_tmp_32341_2124 --basename aa6aede47b243ade0492c3bbad51d1ab --task multiclass.classification --metric acc_metric --limit 3593.23100591 --output-directory /tmp/autosklearn_output_32341_2124 --ensemble-size 50 --ensemble-nbest 50 --auto-sklearn-seed 1 --max-iterations -1 --precision 32 /home/patrick/anaconda2/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:1136: RuntimeWarning: Degrees of freedom <= 0 for slice. warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning) /home/patrick/anaconda2/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:675: RuntimeWarning: Mean of empty slice

warnings.warn("Mean of empty slice", RuntimeWarning)

ValueError Traceback (most recent call last)

in () ----> 1 get_ipython().run_cell_magic(u'time', u'', u'autoclf.fit(X_train, y_train)\nprint(automl.score(X_test,y_test))') /home/patrick/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell) 2291 magic_arg_s = self.var_expand(line, stack_depth) 2292 with self.builtin_trap: -> 2293 result = fn(magic_arg_s, cell) 2294 return result 2295 /home/patrick/anaconda2/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns) /home/patrick/anaconda2/lib/python2.7/site-packages/IPython/core/magic.pyc in (f, _a, *_k) 191 # but it's overkill for just that one bit of state. 192 def magic_deco(arg): --> 193 call = lambda f, _a, *_k: f(_a, *_k) 194 195 if callable(arg): /home/patrick/anaconda2/lib/python2.7/site-packages/IPython/core/magics/execution.pyc in time(self, line, cell, local_ns) 1165 else: 1166 st = clock2() -> 1167 exec(code, glob, local_ns) 1168 end = clock2() 1169 out = None in () /home/patrick/anaconda2/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/estimators.pyc in fit(self, X, y, metric, feat_type, dataset_name) 266 267 return super(AutoSklearnClassifier, self).fit(X, y, task, metric, --> 268 feat_type, dataset_name) 269 270 def predict(self, X): /home/patrick/anaconda2/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/automl.pyc in fit(self, X, y, task, metric, feat_type, dataset_name) 254 encode_labels=False) 255 --> 256 return self._fit(loaded_data_manager) 257 258 def fit_automl_dataset(self, dataset): /home/patrick/anaconda2/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/automl.pyc in _fit(self, datamanager) 508 509 if self._queue is None: --> 510 self._load_models() 511 512 return self /home/patrick/anaconda2/lib/python2.7/site-packages/AutoSklearn-0.0.1.dev0-py2.7-linux-x86_64.egg/autosklearn/automl.pyc in _load_models(self) 609 self.models_ = self._backend.load_all_models(seed) 610 if len(self.models_) == 0: --> 611 raise ValueError('No models fitted!') 612 613 self.ensemble_indices_ = self._backend.load_ensemble_indices_weights( ValueError: No models fitted!
pmk2109 commented 8 years ago

And this was the input (if it matters):

import autosklearn.classification import sklearn.datasets digits = sklearn.datasets.load_digits() X = digits.data y = digits.target import numpy as np

indices = np.arange(X.shape[0]) np.random.shuffle(indices) X = X[indices] y = y[indices] X_train = X[:1000] y_train = y[:1000] X_test = X[1000:] y_test = y[1000:]

automl = autosklearn.classification.AutoSklearnClassifier() automl.fit(X_train, y_train) print(automl.score(X_test,y_test))

pmk2109 commented 8 years ago

Also unknown if you are still supporting ParamSklearn but I was having some trouble there too with importing random_sampler from HPOlibConfigSpace

(but no trouble importing hyperparameter or converters... just to check if I could import anything at all)

from HPOlibConfigSpace import random_sampler

from HPOlibConfigSpace.random_sampler import RandomSampler

ImportError Traceback (most recent call last)

in () 1 #from HPOlibConfigSpace import random_sampler ----> 2 from HPOlibConfigSpace.random_sampler import RandomSampler ImportError: No module named random_sampler
pmk2109 commented 8 years ago

Ok for the first issue (no models selected)... i reinstalled anaconda and setup a new environment, purged openjdk and installed Oracle Java 1.8.0_71 and that seems to have done the trick... now I still get a runtime error that indicates mean of empty slice but the scoring function works with ~.978 accuracy

mfeurer commented 8 years ago

Good to hear that you solved it. This error can in general have two reasons:

  1. problem with the installation - this will be fixed in the near future
  2. dataset too big for the time and memory limits. Once we have worked on problem 1, we can also output a warning.

Regarding the mean of empty slice, this did not yet lead to any problems. We already have an issue on the verboseness of auto-sklearn, but right now, there are more urgent things to be done.

Finally, we do not develop ParamSklearn any more, it is completely merged into autosklearn.pipeline. If you want to have an example of how to randomly sample configurations for the pipeline, please open an issue for that.

mfeurer commented 8 years ago

The new version of auto-sklearn mitigates installation problems like this and provides better error logging - if the problem still persists, please re-open this issue.