search

openml / OpenML

Open Machine Learning
https://openml.org
BSD 3-Clause "New" or "Revised" License
664 stars 90 forks source link

Running complex pipelines #963

Open FelixNeutatz opened 5 years ago

FelixNeutatz commented 5 years ago

Dear all,

I am a great fan of OpenML! It's great :) Currently, I am looking into more complex custom generated Pipelines and unfortunately, I run into exceptions, e.g.:

  File "/home/felix/FastFeatures/new_project/fastsklearnfeature/feature_selection/ComplexityDrivenFeatureConstruction.py", line 497, in run
    my_run = openml.runs.run_model_on_task(my_pipeline, self.reader.task)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/functions.py", line 44, in run_model_on_task
    flow = sklearn_to_flow(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 393, in _serialize_model
    _extract_information_from_model(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 501, in _extract_information_from_model
    rval = sklearn_to_flow(v, model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 393, in _serialize_model
    _extract_information_from_model(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 501, in _extract_information_from_model
    rval = sklearn_to_flow(v, model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 393, in _serialize_model
    _extract_information_from_model(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 501, in _extract_information_from_model
    rval = sklearn_to_flow(v, model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 397, in _serialize_model
    _check_multiple_occurence_of_component_in_flow(model, sub_components)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 479, in _check_multiple_occurence_of_component_in_flow
    'trying to serialize %s.' % (visitee.name, model))
ValueError: Found a second occurence of component sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer) when trying to serialize FeatureUnion(n_jobs=None,
       transformer_list=[('deficit1557515560.9003851', Pipeline(memory=None,
     steps=[('deficit', ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,
         transformer_weights=None,
         transformers=[('identity', FunctionTransformer(accept_sparse=False, check_inverse=True,
   ...        inverse_func=None, kw_args=None, pass_y='deprecated',
          validate=False), [0])]))]))],
       transformer_weights=None).

Is this a bug or a missing feature?

Best regards, Felix

mfeurer commented 5 years ago

It's the latter: https://github.com/openml/OpenML/issues/340

One way to work around this is to write wrapper subclasses and use them instead.

FelixNeutatz commented 5 years ago

I see. Is there any example of how to write such as wrapper subclass?

Thank you for your help, Felix

mfeurer commented 5 years ago

No, sorry. But it's basically as simple as:

class wrapper(sklearn.ensemble.RandomForestClassifier):
    pass
FelixNeutatz commented 5 years ago

Ah, ok. Thank you for your quick help :)

FelixNeutatz commented 5 years ago

Dear Matthias,

I implemented a simple wrapper class:

from sklearn.base import BaseEstimator
from sklearn.pipeline import Pipeline

class ComplexPipelineWrapper(BaseEstimator):

    def __init__(self, my_pipeline: Pipeline):
        self.my_pipeline = my_pipeline

    def fit(self, X, y=None, **fit_params):
        self.my_pipeline.fit(X, y, **fit_params)
        self.classes_ = self.my_pipeline.named_steps['classifier'].classes_
        return self

    def predict(self, X, **predict_params):
        return self.my_pipeline.predict(X, **predict_params)

and I call it like this

from fastsklearnfeature.feature_selection.openml_wrapper.ComplexPipelineWrapper import ComplexPipelineWrapper

my_pipeline = Pipeline([('features', max_feature.pipeline),
                                    ('classifier', my_globale_module.classifier_global(**max_feature.runtime_properties['hyperparameters']))
                                    ])

my_wrapper = ComplexPipelineWrapper(my_pipeline)
my_run = openml.runs.run_model_on_task(my_wrapper, self.reader.task, avoid_duplicate_runs=False)

Unfortunately, I still get an exception:

  File "/home/felix/FastFeatures/new_project/fastsklearnfeature/feature_selection/ComplexityDrivenFeatureConstruction.py", line 553, in run
    my_run = openml.runs.run_model_on_task(my_wrapper, self.reader.task, avoid_duplicate_runs=False)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/functions.py", line 44, in run_model_on_task
    flow = sklearn_to_flow(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 393, in _serialize_model
    _extract_information_from_model(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 501, in _extract_information_from_model
    rval = sklearn_to_flow(v, model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 393, in _serialize_model
    _extract_information_from_model(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 501, in _extract_information_from_model
    rval = sklearn_to_flow(v, model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 393, in _serialize_model
    _extract_information_from_model(model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 501, in _extract_information_from_model
    rval = sklearn_to_flow(v, model)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in sklearn_to_flow
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 46, in <listcomp>
    rval = [sklearn_to_flow(element, parent_model) for element in o]
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 43, in sklearn_to_flow
    rval = _serialize_model(o)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 397, in _serialize_model
    _check_multiple_occurence_of_component_in_flow(model, sub_components)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/sklearn_converter.py", line 479, in _check_multiple_occurence_of_component_in_flow
    'trying to serialize %s.' % (visitee.name, model))
ValueError: Found a second occurence of component sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)) when trying to serialize FeatureUnion(n_jobs=None,
       transformer_list=[('Temperature1557772718.8705542', Pipeline(memory=None,
     steps=[('Temperature', ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,
         transformer_weights=None,
         transformers=[('identity', FunctionTransformer(accept_sparse=False, check_inverse=T...        inverse_func=None, kw_args=None, pass_y='deprecated',
          validate=False), [0])]))]))],
       transformer_weights=None).

Did I implement the wrapper incorrectly?

Best regards, Felix

mfeurer commented 5 years ago

Sorry for not being clear enough. You need to write a wrapper class for every occurence of a method beyond the first and need to make sure that every wrapper class is used only once.

FelixNeutatz commented 5 years ago

Thank you for the hint. I implemented as you suggested:

def replaceColumnTransformer(value: ColumnTransformer):
    NewClass = type(value.__class__.__name__ + str(time.time()).replace('.', ''), value.__class__.__bases__, dict(value.__class__.__dict__))
    return NewClass(value.transformers, value.remainder, value.sparse_threshold, value.n_jobs, value.transformer_weights)

def replaceFeatureUnion(value: FeatureUnion):
    NewClass = type(value.__class__.__name__ + str(time.time()).replace('.', ''), value.__class__.__bases__, dict(value.__class__.__dict__))
    return NewClass(value.transformer_list, value.n_jobs, value.transformer_weights)

def replacePipeline(value: Pipeline):
    NewClass = type(value.__class__.__name__ + str(time.time()).replace('.', ''), value.__class__.__bases__, dict(value.__class__.__dict__))
    return NewClass(value.steps, value.memory)

def replaceFunctionTransformer(value: FunctionTransformer):
    NewClass = type(value.__class__.__name__ + str(time.time()).replace('.', ''), value.__class__.__bases__, dict(value.__class__.__dict__))
    return NewClass(value.func, value.inverse_func, value.validate, value.accept_sparse, value.pass_y, value.check_inverse, value.kw_args, value.inv_kw_args)

def replaceIdentityTransformation(value: IdentityTransformation):
    NewClass = type(value.__class__.__name__ + str(time.time()).replace('.', ''), value.__class__.__bases__, dict(value.__class__.__dict__))
    return NewClass(value.number_parent_features)

def replace_with_new_wrapper(pip):
    my_class = pip
    if isinstance(pip, Pipeline):
        for step_counter in range(len(pip.steps)):
            pip.steps[step_counter] = (pip.steps[step_counter][0], replace_with_new_wrapper(pip.steps[step_counter][1]))
        my_class = replacePipeline(pip)
    elif isinstance(pip, FeatureUnion):
        for step_counter in range(len(pip.transformer_list)):
            pip.transformer_list[step_counter] = (pip.transformer_list[step_counter][0], replace_with_new_wrapper(pip.transformer_list[step_counter][1]))
        my_class = replaceFeatureUnion(pip)
    elif isinstance(pip, ColumnTransformer):
        for step_counter in range(len(pip.transformers)):
            pip.transformers[step_counter] = (pip.transformers[step_counter][0], replace_with_new_wrapper(pip.transformers[step_counter][1]), pip.transformers[step_counter][2])
        my_class = replaceColumnTransformer(pip)
    elif isinstance(pip, FunctionTransformer):
        my_class = replaceFunctionTransformer(pip)
    elif isinstance(pip, IdentityTransformation):
        my_class = replaceIdentityTransformation(pip)

    return my_class

...

TASK_ID = 10101
task = openml.tasks.get_task(TASK_ID)
my_run = openml.runs.run_model_on_task(my_wrapped_pipeline, task, avoid_duplicate_runs=False)
my_run.publish()

However, now I get the following exception:

Traceback (most recent call last):
  File "/home/felix/FastFeatures/new_project/fastsklearnfeature/test/openml/generate_openml_pipeline.py", line 140, in <module>
    my_run = openml.runs.run_model_on_task(my_pipeline, task, avoid_duplicate_runs=False)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/functions.py", line 49, in run_model_on_task
    add_local_measures=add_local_measures)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/functions.py", line 131, in run_flow_on_task
    _publish_flow_if_necessary(flow)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/functions.py", line 199, in _publish_flow_if_necessary
    raise e
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/functions.py", line 186, in _publish_flow_if_necessary
    flow.publish()
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/flows/flow.py", line 334, in publish
    file_elements=file_elements,
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/_api_calls.py", line 41, in _perform_api_call
    return _read_url_files(url, data=data, file_elements=file_elements)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/_api_calls.py", line 74, in _read_url_files
    raise _parse_server_exception(response, url=url)
openml.exceptions.OpenMLServerException: https://www.openml.org/api/v1/xml/flow/ returned code 163: Problem validating uploaded description file
<oml:error xmlns:oml="http://openml.org/openml">
    <oml:code>163</oml:code>
    <oml:message>Problem validating uploaded description file</oml:message>
        <oml:additional_information>XML does not correspond to XSD schema. </oml:additional_information>
    </oml:error>
mfeurer commented 5 years ago

Could you please add a print statement to File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/_api_calls.py", line 41, in _perform_api_call and print the url, data and file_elements, and then post the xml which it tries to upload?

FelixNeutatz commented 5 years ago

Sure:

url: https://www.openml.org/api/v1/xml/flow/
data: None
file elements: {'description': '<oml:flow xmlns:oml="http://openml.org/openml">\n\t<oml:name>sklearn.pipeline.Pipeline(features=sklearn.pipeline.Pipeline15591400193736384(parents=sklearn.pipeline.FeatureUnion15591400193734686({V1, V4}1559109118.490439=sklearn.pipeline.Pipeline1559140019373153(parents=sklearn.pipeline.FeatureUnion15591400193729897(V11559109116.7628496=sklearn.pipeline.Pipeline1559140019372707(V1=sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282)),V41559109116.7630394=sklearn.pipeline.Pipeline1559140019372885(V4=sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094))),identity=fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation15591400193731127),V21559109118.4906723=sklearn.pipeline.Pipeline15591400193733547(V2=sklearn.compose._column_transformer.ColumnTransformer1559140019373304(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696))),identity=fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation1559140019373596),classifier=sklearn.linear_model.logistic.LogisticRegression)</oml:name>\n\t<oml:class_name>sklearn.pipeline.Pipeline</oml:class_name>\n\t<oml:external_version>fastsklearnfeature==0.20.3,openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t<oml:language>English</oml:language>\n\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t<oml:parameter>\n\t\t<oml:name>memory</oml:name>\n\t\t<oml:default_value>null</oml:default_value>\n\t</oml:parameter>\n\t<oml:parameter>\n\t\t<oml:name>steps</oml:name>\n\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "features", "step_name": "features"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]</oml:default_value>\n\t</oml:parameter>\n\t<oml:component>\n\t\t<oml:identifier>features</oml:identifier>\n\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t<oml:name>sklearn.pipeline.Pipeline15591400193736384(parents=sklearn.pipeline.FeatureUnion15591400193734686({V1, V4}1559109118.490439=sklearn.pipeline.Pipeline1559140019373153(parents=sklearn.pipeline.FeatureUnion15591400193729897(V11559109116.7628496=sklearn.pipeline.Pipeline1559140019372707(V1=sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282)),V41559109116.7630394=sklearn.pipeline.Pipeline1559140019372885(V4=sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094))),identity=fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation15591400193731127),V21559109118.4906723=sklearn.pipeline.Pipeline15591400193733547(V2=sklearn.compose._column_transformer.ColumnTransformer1559140019373304(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696))),identity=fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation1559140019373596)</oml:name>\n\t\t\t<oml:class_name>sklearn.pipeline.Pipeline15591400193736384</oml:class_name>\n\t\t\t<oml:external_version>fastsklearnfeature==0.20.3,openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t<oml:language>English</oml:language>\n\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>memory</oml:name>\n\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>steps</oml:name>\n\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "parents", "step_name": "parents"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "identity", "step_name": "identity"}}]</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:component>\n\t\t\t\t<oml:identifier>parents</oml:identifier>\n\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t<oml:name>sklearn.pipeline.FeatureUnion15591400193734686({V1, V4}1559109118.490439=sklearn.pipeline.Pipeline1559140019373153(parents=sklearn.pipeline.FeatureUnion15591400193729897(V11559109116.7628496=sklearn.pipeline.Pipeline1559140019372707(V1=sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282)),V41559109116.7630394=sklearn.pipeline.Pipeline1559140019372885(V4=sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094))),identity=fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation15591400193731127),V21559109118.4906723=sklearn.pipeline.Pipeline15591400193733547(V2=sklearn.compose._column_transformer.ColumnTransformer1559140019373304(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696)))</oml:name>\n\t\t\t\t\t<oml:class_name>sklearn.pipeline.FeatureUnion15591400193734686</oml:class_name>\n\t\t\t\t\t<oml:external_version>fastsklearnfeature==0.20.3,openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t<oml:name>n_jobs</oml:name>\n\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t<oml:name>transformer_list</oml:name>\n\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "{V1, V4}1559109118.490439", "step_name": "{V1, V4}1559109118.490439"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "V21559109118.4906723", "step_name": "V21559109118.4906723"}}]</oml:default_value>\n\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t<oml:name>transformer_weights</oml:name>\n\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t<oml:identifier>{V1, V4}1559109118.490439</oml:identifier>\n\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t<oml:name>sklearn.pipeline.Pipeline1559140019373153(parents=sklearn.pipeline.FeatureUnion15591400193729897(V11559109116.7628496=sklearn.pipeline.Pipeline1559140019372707(V1=sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282)),V41559109116.7630394=sklearn.pipeline.Pipeline1559140019372885(V4=sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094))),identity=fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation15591400193731127)</oml:name>\n\t\t\t\t\t\t\t<oml:class_name>sklearn.pipeline.Pipeline1559140019373153</oml:class_name>\n\t\t\t\t\t\t\t<oml:external_version>fastsklearnfeature==0.20.3,openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t<oml:name>memory</oml:name>\n\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t<oml:name>steps</oml:name>\n\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "parents", "step_name": "parents"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "identity", "step_name": "identity"}}]</oml:default_value>\n\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t<oml:identifier>parents</oml:identifier>\n\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t<oml:name>sklearn.pipeline.FeatureUnion15591400193729897(V11559109116.7628496=sklearn.pipeline.Pipeline1559140019372707(V1=sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282)),V41559109116.7630394=sklearn.pipeline.Pipeline1559140019372885(V4=sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094)))</oml:name>\n\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.pipeline.FeatureUnion15591400193729897</oml:class_name>\n\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>n_jobs</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>transformer_list</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "V11559109116.7628496", "step_name": "V11559109116.7628496"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "V41559109116.7630394", "step_name": "V41559109116.7630394"}}]</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>transformer_weights</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t<oml:identifier>V11559109116.7628496</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.pipeline.Pipeline1559140019372707(V1=sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282))</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.pipeline.Pipeline1559140019372707</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>memory</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>steps</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "V1", "step_name": "V1"}}]</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:identifier>V1</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.compose._column_transformer.ColumnTransformer15591400193726637(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282)</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.compose._column_transformer.ColumnTransformer15591400193726637</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>n_jobs</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>remainder</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>"drop"</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>sparse_threshold</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>0.3</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>transformer_weights</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>transformers</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "identity", "step_name": "identity", "argument_1": [0]}}]</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:identifier>identity</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.preprocessing._function_transformer.FunctionTransformer15591400193726282</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>accept_sparse</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>check_inverse</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>true</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>func</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>inv_kw_args</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>inverse_func</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>kw_args</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>pass_y</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>"deprecated"</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>validate</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t<oml:identifier>V41559109116.7630394</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.pipeline.Pipeline1559140019372885(V4=sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094))</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.pipeline.Pipeline1559140019372885</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>memory</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>steps</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "V4", "step_name": "V4"}}]</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:identifier>V4</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.compose._column_transformer.ColumnTransformer155914001937284(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094)</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.compose._column_transformer.ColumnTransformer155914001937284</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>n_jobs</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>remainder</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>"drop"</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>sparse_threshold</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>0.3</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>transformer_weights</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>transformers</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "identity", "step_name": "identity", "argument_1": [3]}}]</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:identifier>identity</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.preprocessing._function_transformer.FunctionTransformer15591400193728094</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>accept_sparse</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>check_inverse</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>true</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>func</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>inv_kw_args</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>inverse_func</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>kw_args</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>pass_y</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>"deprecated"</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>validate</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t<oml:identifier>identity</oml:identifier>\n\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t<oml:name>fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation15591400193731127</oml:name>\n\t\t\t\t\t\t\t\t\t<oml:class_name>fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation15591400193731127</oml:class_name>\n\t\t\t\t\t\t\t\t\t<oml:external_version>fastsklearnfeature==0.20.3,openml==0.8.0</oml:external_version>\n\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>number_parent_features</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t</oml:component>\n\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t<oml:identifier>V21559109118.4906723</oml:identifier>\n\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t<oml:name>sklearn.pipeline.Pipeline15591400193733547(V2=sklearn.compose._column_transformer.ColumnTransformer1559140019373304(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696))</oml:name>\n\t\t\t\t\t\t\t<oml:class_name>sklearn.pipeline.Pipeline15591400193733547</oml:class_name>\n\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t<oml:name>memory</oml:name>\n\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t<oml:name>steps</oml:name>\n\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "V2", "step_name": "V2"}}]</oml:default_value>\n\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t<oml:identifier>V2</oml:identifier>\n\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t<oml:name>sklearn.compose._column_transformer.ColumnTransformer1559140019373304(identity=sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696)</oml:name>\n\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.compose._column_transformer.ColumnTransformer1559140019373304</oml:class_name>\n\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>n_jobs</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>remainder</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>"drop"</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>sparse_threshold</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>0.3</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>transformer_weights</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t<oml:name>transformers</oml:name>\n\t\t\t\t\t\t\t\t\t\t<oml:default_value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "identity", "step_name": "identity", "argument_1": [1]}}]</oml:default_value>\n\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t<oml:component>\n\t\t\t\t\t\t\t\t\t\t<oml:identifier>identity</oml:identifier>\n\t\t\t\t\t\t\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t\t\t\t\t\t\t<oml:name>sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t<oml:class_name>sklearn.preprocessing._function_transformer.FunctionTransformer15591400193732696</oml:class_name>\n\t\t\t\t\t\t\t\t\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t\t\t\t\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t\t\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t\t\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>accept_sparse</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>check_inverse</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>true</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>func</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>inv_kw_args</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>inverse_func</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>kw_args</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>pass_y</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>"deprecated"</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:name>validate</oml:name>\n\t\t\t\t\t\t\t\t\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t\t\t\t\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t\t\t</oml:component>\n\t\t\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t\t\t</oml:flow>\n\t\t\t\t\t</oml:component>\n\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t</oml:flow>\n\t\t\t</oml:component>\n\t\t\t<oml:component>\n\t\t\t\t<oml:identifier>identity</oml:identifier>\n\t\t\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t\t\t<oml:name>fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation1559140019373596</oml:name>\n\t\t\t\t\t<oml:class_name>fastsklearnfeature.transformations.IdentityTransformation.IdentityTransformation1559140019373596</oml:class_name>\n\t\t\t\t\t<oml:external_version>fastsklearnfeature==0.20.3,openml==0.8.0</oml:external_version>\n\t\t\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t\t\t<oml:language>English</oml:language>\n\t\t\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t\t\t<oml:parameter>\n\t\t\t\t\t\t<oml:name>number_parent_features</oml:name>\n\t\t\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t\t\t</oml:parameter>\n\t\t\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t\t\t<oml:tag>python</oml:tag>\n\t\t\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t\t\t</oml:flow>\n\t\t\t</oml:component>\n\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t<oml:tag>python</oml:tag>\n\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t</oml:flow>\n\t</oml:component>\n\t<oml:component>\n\t\t<oml:identifier>classifier</oml:identifier>\n\t\t<oml:flow xmlns:oml="http://openml.org/openml">\n\t\t\t<oml:name>sklearn.linear_model.logistic.LogisticRegression</oml:name>\n\t\t\t<oml:class_name>sklearn.linear_model.logistic.LogisticRegression</oml:class_name>\n\t\t\t<oml:external_version>openml==0.8.0,sklearn==0.20.3</oml:external_version>\n\t\t\t<oml:description>Automatically created scikit-learn flow.</oml:description>\n\t\t\t<oml:language>English</oml:language>\n\t\t\t<oml:dependencies>sklearn==0.20.3\nnumpy&gt;=1.6.1\nscipy&gt;=0.9</oml:dependencies>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>C</oml:name>\n\t\t\t\t<oml:default_value>0.001</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>class_weight</oml:name>\n\t\t\t\t<oml:default_value>"balanced"</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>dual</oml:name>\n\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>fit_intercept</oml:name>\n\t\t\t\t<oml:default_value>true</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>intercept_scaling</oml:name>\n\t\t\t\t<oml:default_value>1</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>max_iter</oml:name>\n\t\t\t\t<oml:default_value>10000</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>multi_class</oml:name>\n\t\t\t\t<oml:default_value>"auto"</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>n_jobs</oml:name>\n\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>penalty</oml:name>\n\t\t\t\t<oml:default_value>"l2"</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>random_state</oml:name>\n\t\t\t\t<oml:default_value>null</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>solver</oml:name>\n\t\t\t\t<oml:default_value>"lbfgs"</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>tol</oml:name>\n\t\t\t\t<oml:default_value>0.0001</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>verbose</oml:name>\n\t\t\t\t<oml:default_value>0</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:parameter>\n\t\t\t\t<oml:name>warm_start</oml:name>\n\t\t\t\t<oml:default_value>false</oml:default_value>\n\t\t\t</oml:parameter>\n\t\t\t<oml:tag>openml-python</oml:tag>\n\t\t\t<oml:tag>sklearn</oml:tag>\n\t\t\t<oml:tag>scikit-learn</oml:tag>\n\t\t\t<oml:tag>python</oml:tag>\n\t\t\t<oml:tag>sklearn_0.20.3</oml:tag>\n\t\t</oml:flow>\n\t</oml:component>\n\t<oml:tag>openml-python</oml:tag>\n\t<oml:tag>sklearn</oml:tag>\n\t<oml:tag>scikit-learn</oml:tag>\n\t<oml:tag>python</oml:tag>\n\t<oml:tag>sklearn_0.20.3</oml:tag>\n</oml:flow>'}

Do you need something else?

mfeurer commented 5 years ago

No, that's sufficient. I checked the xml against the xsd definition and it turns out that it is illegal for the following reasons:

Ideally, we'd catch these in the Python API, but we currently don't use an XSD checker there. @janvanrijn would it be possible to return the error message from the XSD checker via the API so that the user knows why the upload is failing?

As the generated pipeline name is only a little bit too long you could get around this issue by using a shorter hexadecimal string instead of a long integer value. @joaquinvanschoren is there anything else which could be done here? I assume this would be resolved if we update the flow description at some point in the future?

FelixNeutatz commented 5 years ago

Thank you @mfeurer :)

Yes, the XSD format was the problem. It would be great if we could put the format checker, such as the following in the client:

import xmlschema
my_schema = xmlschema.XMLSchema('openml.implementation.upload.xsd')
print(my_schema.validate('my_config.xml'))

Best regards, Felix

FelixNeutatz commented 5 years ago

I encountered one more exception:

  File "/home/felix/FastFeatures/new_project/fastsklearnfeature/feature_selection/OpenMlFeatures.py", line 296, in <module>
    selector.run()
  File "/home/felix/FastFeatures/new_project/fastsklearnfeature/feature_selection/OpenMlFeatures.py", line 238, in run
    candidate2openmltest(results[0], self.classifier, self.reader.task, 'Test')
  File "/home/felix/FastFeatures/new_project/fastsklearnfeature/feature_selection/openml_wrapper/pipeline2openml.py", line 171, in candidate2openmltest
    my_run.publish()
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/runs/run.py", line 323, in publish
    return_value = openml._api_calls._perform_api_call("/run/", file_elements=file_elements)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/_api_calls.py", line 47, in _perform_api_call
    return _read_url_files(url, data=data, file_elements=file_elements)
  File "/home/felix/FastFeatures/new_project/venv/lib/python3.7/site-packages/openml/_api_calls.py", line 80, in _read_url_files
    raise _parse_server_exception(response, url=url)
openml.exceptions.OpenMLServerException: https://www.openml.org/api/v1/xml//run/ returned code 215: Database error. Setup search query failed

I guess it's another XSD error. Can you direct me to xsd file for https://www.openml.org/api/v1/xml//run/?

mfeurer commented 5 years ago

All XSDs are available here. However, it seems that the issue is that the function in question (https://www.openml.org/api/v1/xml//run/) is not valid. Therefore, it would be best to see what's happening if you can track down why the python connector tries to open a run without an ID.

FelixNeutatz commented 5 years ago

I think https://www.openml.org/api/v1/xml//run/ is fine because in other runs that are uploaded successfully, I do see the same url. Also, if we run https://www.openml.org/api/v1/xml/flow/, you get the same message even though it is the correct url: image

Furthermore, I checked the description against openml.run.upload.xsd and the validation does not cause any issues. Any other ideas?

url = https://www.openml.org/api/v1/xml//run/
data = None
file_elements = {'description': ('description.xml', '<?xml version="1.0" encoding="utf-8"?>\n<oml:run xmlns:oml="http://openml.org/openml">\n\t<oml:task_id>31</oml:task_id>\n\t<oml:flow_id>11594</oml:flow_id>\n\t<oml:parameter_setting>\n\t\t<oml:name>memory</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11594</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>steps</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n1", "step_name": "n1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n11", "step_name": "n11"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]</oml:value>\n\t\t<oml:component>11594</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>n_jobs</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11595</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformer_list</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n2", "step_name": "n2"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n5", "step_name": "n5"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n8", "step_name": "n8"}}]</oml:value>\n\t\t<oml:component>11595</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformer_weights</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11595</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>memory</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11596</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>steps</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n3", "step_name": "n3"}}]</oml:value>\n\t\t<oml:component>11596</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>n_jobs</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11597</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>remainder</oml:name>\n\t\t<oml:value>"drop"</oml:value>\n\t\t<oml:component>11597</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>sparse_threshold</oml:name>\n\t\t<oml:value>0.3</oml:value>\n\t\t<oml:component>11597</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformer_weights</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11597</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformers</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n4", "step_name": "n4", "argument_1": [0]}}]</oml:value>\n\t\t<oml:component>11597</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>accept_sparse</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>check_inverse</oml:name>\n\t\t<oml:value>true</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>func</oml:name>\n\t\t<oml:value>{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>inv_kw_args</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>inverse_func</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>kw_args</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>pass_y</oml:name>\n\t\t<oml:value>"deprecated"</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>validate</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>11598</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>memory</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11599</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>steps</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n6", "step_name": "n6"}}]</oml:value>\n\t\t<oml:component>11599</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>n_jobs</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11600</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>remainder</oml:name>\n\t\t<oml:value>"drop"</oml:value>\n\t\t<oml:component>11600</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>sparse_threshold</oml:name>\n\t\t<oml:value>0.3</oml:value>\n\t\t<oml:component>11600</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformer_weights</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11600</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformers</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n7", "step_name": "n7", "argument_1": [5]}}]</oml:value>\n\t\t<oml:component>11600</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>accept_sparse</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>check_inverse</oml:name>\n\t\t<oml:value>true</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>func</oml:name>\n\t\t<oml:value>{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>inv_kw_args</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>inverse_func</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>kw_args</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>pass_y</oml:name>\n\t\t<oml:value>"deprecated"</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>validate</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>11601</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>memory</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11602</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>steps</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n9", "step_name": "n9"}}]</oml:value>\n\t\t<oml:component>11602</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>n_jobs</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11603</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>remainder</oml:name>\n\t\t<oml:value>"drop"</oml:value>\n\t\t<oml:component>11603</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>sparse_threshold</oml:name>\n\t\t<oml:value>0.3</oml:value>\n\t\t<oml:component>11603</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformer_weights</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11603</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>transformers</oml:name>\n\t\t<oml:value>[{"oml-python:serialized_object": "component_reference", "value": {"key": "n10", "step_name": "n10", "argument_1": [7]}}]</oml:value>\n\t\t<oml:component>11603</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>accept_sparse</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>check_inverse</oml:name>\n\t\t<oml:value>true</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>func</oml:name>\n\t\t<oml:value>{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>inv_kw_args</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>inverse_func</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>kw_args</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>pass_y</oml:name>\n\t\t<oml:value>"deprecated"</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>validate</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>11604</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>number_parent_features</oml:name>\n\t\t<oml:value>3</oml:value>\n\t\t<oml:component>11605</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>C</oml:name>\n\t\t<oml:value>1.0</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>class_weight</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>dual</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>fit_intercept</oml:name>\n\t\t<oml:value>true</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>intercept_scaling</oml:name>\n\t\t<oml:value>1</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>max_iter</oml:name>\n\t\t<oml:value>100</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>multi_class</oml:name>\n\t\t<oml:value>"warn"</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>n_jobs</oml:name>\n\t\t<oml:value>null</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>penalty</oml:name>\n\t\t<oml:value>"l2"</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>random_state</oml:name>\n\t\t<oml:value>52757</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>solver</oml:name>\n\t\t<oml:value>"warn"</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>tol</oml:name>\n\t\t<oml:value>0.0001</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>verbose</oml:name>\n\t\t<oml:value>0</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:parameter_setting>\n\t\t<oml:name>warm_start</oml:name>\n\t\t<oml:value>false</oml:value>\n\t\t<oml:component>9801</oml:component>\n\t</oml:parameter_setting>\n\t<oml:tag>openml-python</oml:tag>\n\t<oml:tag>Sklearn_0.20.3.</oml:tag>\n\t<oml:output_data>\n\t\t<oml:evaluation repeat="0" fold="0">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>4.990468999999997</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="1">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.919104000000061</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="2">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.3602079999998082</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="3">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.2695109999997243</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="4">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.101375000000072</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="5">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.155416999999993</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="6">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.1586509999997894</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="7">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.1308289999999452</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="8">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.1264780000000982</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="9">\n\t\t\t<oml:name>usercpu_time_millis_training</oml:name>\n\t\t\t<oml:value>2.144874999999935</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="0">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>1.7175489999998739</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="1">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>1.158862999999677</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="2">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>1.0408630000000585</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="3">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>0.9335520000002262</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="4">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>1.1668570000002987</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="5">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>0.9331080000003489</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="6">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>0.9287259999997133</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="7">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>1.020532000000074</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="8">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>0.9601309999998087</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="9">\n\t\t\t<oml:name>usercpu_time_millis_testing</oml:name>\n\t\t\t<oml:value>0.9117960000000203</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="0">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>6.708017999999871</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="1">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>4.077966999999738</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="2">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.4010709999998667</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="3">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.2030629999999505</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="4">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.2682320000003706</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="5">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.0885250000003417</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="6">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.0873769999995027</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="7">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.151361000000019</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="8">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.086608999999907</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="9">\n\t\t\t<oml:name>usercpu_time_millis</oml:name>\n\t\t\t<oml:value>3.0566709999999553</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="0">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.77</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="1">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.72</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="2">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.65</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="3">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.66</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="4">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.72</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="5">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.73</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="6">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.73</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="7">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.72</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="8">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.71</oml:value>\n\t\t</oml:evaluation>\n\t\t<oml:evaluation repeat="0" fold="9">\n\t\t\t<oml:name>predictive_accuracy</oml:name>\n\t\t\t<oml:value>0.76</oml:value>\n\t\t</oml:evaluation>\n\t</oml:output_data>\n</oml:run>'), 'predictions': ('predictions.arff', '% Python_3.7.3.\n% Sklearn_0.20.3.\n% NumPy_1.16.3.\n% SciPy_1.2.1.\n% Sat Jun  1 14:10:56 2019\n% Created by run_task()\n@RELATION openml_task_31_predictions\n\n@ATTRIBUTE repeat NUMERIC\n@ATTRIBUTE fold NUMERIC\n@ATTRIBUTE sample NUMERIC\n@ATTRIBUTE row_id NUMERIC\n@ATTRIBUTE confidence.good NUMERIC\n@ATTRIBUTE confidence.bad NUMERIC\n@ATTRIBUTE prediction {good, bad}\n@ATTRIBUTE correct {good, bad}\n\n@DATA\n0,0,0,490,0.8637485312948323,0.13625146870516774,good,good\n0,0,0,406,0.9012455591576974,0.09875444084230259,good,good\n0,0,0,139,0.7736627480491372,0.22633725195086288,good,good\n0,0,0,482,0.545757020939364,0.45424297906063604,good,good\n0,0,0,178,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,440,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,387,0.568917976003045,0.4310820239969551,good,good\n0,0,0,530,0.6093162726013838,0.39068372739861623,good,good\n0,0,0,64,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,976,0.6482692928326474,0.35173070716735255,good,good\n0,0,0,264,0.8428751635804103,0.15712483641958974,good,good\n0,0,0,971,0.8822363860788386,0.1177636139211614,good,good\n0,0,0,280,0.9005679854573237,0.09943201454267624,good,good\n0,0,0,807,0.9012455591576974,0.09875444084230259,good,good\n0,0,0,681,0.9012455591576974,0.09875444084230259,good,good\n0,0,0,314,0.8903650665093442,0.1096349334906558,good,good\n0,0,0,434,0.45680036454969064,0.5431996354503094,bad,good\n0,0,0,533,0.8438777262440738,0.1561222737559262,good,good\n0,0,0,50,0.7262745004004842,0.2737254995995157,good,good\n0,0,0,484,0.86552514517291,0.13447485482709,good,good\n0,0,0,327,0.8845815300648548,0.11541846993514518,good,good\n0,0,0,499,0.8027705238379688,0.19722947616203118,good,good\n0,0,0,251,0.8015660578454137,0.19843394215458632,good,good\n0,0,0,8,0.9145540647554791,0.08544593524452095,good,good\n0,0,0,617,0.5401066497081639,0.45989335029183603,good,good\n0,0,0,27,0.8300359343639496,0.16996406563605032,good,good\n0,0,0,293,0.7099430448509796,0.2900569551490204,good,good\n0,0,0,324,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,663,0.568917976003045,0.4310820239969551,good,good\n0,0,0,33,0.9012455591576974,0.09875444084230259,good,good\n0,0,0,926,0.4157532014966282,0.5842467985033718,bad,good\n0,0,0,912,0.7874804762954953,0.2125195237045047,good,good\n0,0,0,205,0.45680036454969064,0.5431996354503094,bad,good\n0,0,0,30,0.7247630472096203,0.2752369527903797,good,good\n0,0,0,43,0.5830637263433742,0.4169362736566257,good,good\n0,0,0,664,0.7736627480491372,0.22633725195086288,good,good\n0,0,0,695,0.8998862792147119,0.10011372078528809,good,good\n0,0,0,498,0.568917976003045,0.4310820239969551,good,good\n0,0,0,140,0.852316086914068,0.14768391308593204,good,good\n0,0,0,285,0.5401066497081639,0.45989335029183603,good,good\n0,0,0,735,0.7581939915228096,0.24180600847719033,good,good\n0,0,0,746,0.5401066497081639,0.45989335029183603,good,good\n0,0,0,532,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,86,0.568917976003045,0.4310820239969551,good,good\n0,0,0,994,0.9012455591576974,0.09875444084230259,good,good\n0,0,0,177,0.5022376136414448,0.4977623863585552,good,good\n0,0,0,417,0.7024801884414864,0.29751981155851365,good,good\n0,0,0,777,0.4157532014966282,0.5842467985033718,bad,good\n0,0,0,839,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,311,0.8438777262440738,0.1561222737559262,good,good\n0,0,0,433,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,770,0.66644173454183,0.33355826545817,good,good\n0,0,0,428,0.8822363860788386,0.1177636139211614,good,good\n0,0,0,877,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,99,0.7581939915228096,0.24180600847719033,good,good\n0,0,0,889,0.9151453113211984,0.08485468867880168,good,good\n0,0,0,637,0.8637485312948323,0.13625146870516774,good,good\n0,0,0,851,0.9151453113211984,0.08485468867880168,good,good\n0,0,0,644,0.4157532014966282,0.5842467985033718,bad,good\n0,0,0,635,0.8194723948234907,0.18052760517650931,good,good\n0,0,0,685,0.9012455591576974,0.09875444084230259,good,good\n0,0,0,969,0.5401066497081639,0.45989335029183603,good,good\n0,0,0,224,0.8428751635804103,0.15712483641958974,good,good\n0,0,0,806,0.6093162726013838,0.39068372739861623,good,good\n0,0,0,141,0.568917976003045,0.4310820239969551,good,good\n0,0,0,518,0.5003401614479792,0.49965983855202084,good,good\n0,0,0,965,0.7262745004004842,0.2737254995995157,good,good\n0,0,0,553,0.6111215339095801,0.38887846609041987,good,good\n0,0,0,760,0.9377395041473733,0.06226049585262666,good,good\n0,0,0,247,0.8438777262440738,0.1561222737559262,good,good\n0,0,0,252,0.568917976003045,0.4310820239969551,good,bad\n0,0,0,761,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,858,0.45680036454969064,0.5431996354503094,bad,bad\n0,0,0,598,0.8637485312948323,0.13625146870516774,good,bad\n0,0,0,922,0.45680036454969064,0.5431996354503094,bad,bad\n0,0,0,809,0.6482692928326474,0.35173070716735255,good,bad\n0,0,0,127,0.568917976003045,0.4310820239969551,good,bad\n0,0,0,775,0.5885883392672283,0.4114116607327717,good,bad\n0,0,0,832,0.49844269945692476,0.5015573005430752,bad,bad\n0,0,0,783,0.6111215339095801,0.38887846609041987,good,bad\n0,0,0,166,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,631,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,827,0.8637485312948323,0.13625146870516774,good,bad\n0,0,0,116,0.5885883392672283,0.4114116607327717,good,bad\n0,0,0,381,0.6093162726013838,0.39068372739861623,good,bad\n0,0,0,862,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,405,0.6853441384593484,0.3146558615406516,good,bad\n0,0,0,507,0.7217246613188408,0.2782753386811592,good,bad\n0,0,0,170,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,570,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,180,0.8822363860788386,0.1177636139211614,good,bad\n0,0,0,424,0.8140896526646787,0.18591034733532133,good,bad\n0,0,0,76,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,759,0.4157532014966282,0.5842467985033718,bad,bad\n0,0,0,291,0.6853441384593484,0.3146558615406516,good,bad\n0,0,0,10,0.6093162726013838,0.39068372739861623,good,bad\n0,0,0,265,0.568917976003045,0.4310820239969551,good,bad\n0,0,0,337,0.5885883392672283,0.4114116607327717,good,bad\n0,0,0,614,0.8194723948234907,0.18052760517650931,good,bad\n0,0,0,29,0.45680036454969064,0.5431996354503094,bad,bad\n0,1,0,539,0.7898977402625033,0.21010225973749674,good,good\n0,1,0,577,0.9186276360399334,0.08137236396006661,good,good\n0,1,0,619,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,937,0.5756864246447917,0.42431357535520825,good,good\n0,1,0,75,0.42065958853814545,0.5793404114618546,bad,good\n0,1,0,608,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,142,0.45296144459313703,0.547038555406863,bad,good\n0,1,0,641,0.5756864246447917,0.42431357535520825,good,good\n0,1,0,336,0.6382550189215466,0.36174498107845343,good,good\n0,1,0,893,0.6074114762115763,0.39258852378842374,good,good\n0,1,0,60,0.6382550189215466,0.36174498107845343,good,good\n0,1,0,96,0.9082532443963671,0.09174675560363292,good,good\n0,1,0,165,0.9103576359832685,0.08964236401673152,good,good\n0,1,0,709,0.6382550189215466,0.36174498107845343,good,good\n0,1,0,66,0.860339400119857,0.13966059988014304,good,good\n0,1,0,737,0.49888989643859427,0.5011101035614057,bad,good\n0,1,0,260,0.48566436944162006,0.5143356305583799,bad,good\n0,1,0,803,0.9082532443963671,0.09174675560363292,good,good\n0,1,0,393,0.5184905112187901,0.48150948878120986,good,good\n0,1,0,348,0.8941393146017035,0.10586068539829654,good,good\n0,1,0,891,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,690,0.48566436944162006,0.5143356305583799,bad,good\n0,1,0,841,0.8437972948572502,0.15620270514274984,good,good\n0,1,0,527,0.860339400119857,0.13966059988014304,good,good\n0,1,0,254,0.7866561135860906,0.2133438864139094,good,good\n0,1,0,676,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,329,0.5756864246447917,0.42431357535520825,good,good\n0,1,0,373,0.9279220247792807,0.07207797522071932,good,good\n0,1,0,20,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,343,0.6679998349001598,0.3320001650998402,good,good\n0,1,0,781,0.8916994848931389,0.1083005151068611,good,good\n0,1,0,550,0.9082532443963671,0.09174675560363292,good,good\n0,1,0,791,0.9186276360399334,0.08137236396006661,good,good\n0,1,0,221,0.6027702073120256,0.39722979268797437,good,good\n0,1,0,185,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,743,0.5121169770119464,0.48788302298805364,good,good\n0,1,0,785,0.7022229561415783,0.29777704385842174,good,good\n0,1,0,340,0.6382550189215466,0.36174498107845343,good,good\n0,1,0,196,0.8753883812676221,0.12461161873237796,good,good\n0,1,0,876,0.45296144459313703,0.547038555406863,bad,good\n0,1,0,679,0.8437972948572502,0.15620270514274984,good,good\n0,1,0,469,0.8437972948572502,0.15620270514274984,good,good\n0,1,0,371,0.8437972948572502,0.15620270514274984,good,good\n0,1,0,350,0.8753883812676221,0.12461161873237796,good,good\n0,1,0,519,0.9082532443963671,0.09174675560363292,good,good\n0,1,0,442,0.6382550189215466,0.36174498107845343,good,good\n0,1,0,696,0.5756864246447917,0.42431357535520825,good,good\n0,1,0,883,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,330,0.48566436944162006,0.5143356305583799,bad,good\n0,1,0,299,0.7289417248974102,0.27105827510258984,good,good\n0,1,0,319,0.5184905112187901,0.48150948878120986,good,good\n0,1,0,575,0.860339400119857,0.13966059988014304,good,good\n0,1,0,448,0.8579892095402005,0.14201079045979947,good,good\n0,1,0,756,0.7898977402625033,0.21010225973749674,good,good\n0,1,0,703,0.6199561755573691,0.38004382444263085,good,good\n0,1,0,238,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,132,0.860339400119857,0.13966059988014304,good,good\n0,1,0,79,0.6382550189215466,0.36174498107845343,good,good\n0,1,0,34,0.7171254009051016,0.2828745990948984,good,good\n0,1,0,493,0.7392699908922826,0.26073000910771743,good,good\n0,1,0,78,0.9279220247792807,0.07207797522071932,good,good\n0,1,0,804,0.8078716986766311,0.19212830132336892,good,good\n0,1,0,492,0.8941393146017035,0.10586068539829654,good,good\n0,1,0,536,0.6027702073120256,0.39722979268797437,good,good\n0,1,0,977,0.7392699908922826,0.26073000910771743,good,good\n0,1,0,713,0.42065958853814545,0.5793404114618546,bad,good\n0,1,0,269,0.9082532443963671,0.09174675560363292,good,good\n0,1,0,798,0.9082532443963671,0.09174675560363292,good,good\n0,1,0,206,0.8256928250991642,0.17430717490083586,good,good\n0,1,0,167,0.7022229561415783,0.29777704385842174,good,good\n0,1,0,35,0.5756864246447917,0.42431357535520825,good,bad\n0,1,0,602,0.5756864246447917,0.42431357535520825,good,bad\n0,1,0,443,0.8256928250991642,0.17430717490083586,good,bad\n0,1,0,495,0.6636782481461865,0.3363217518538135,good,bad\n0,1,0,751,0.5184905112187901,0.48150948878120986,good,bad\n0,1,0,37,0.7171254009051016,0.2828745990948984,good,bad\n0,1,0,555,0.6382550189215466,0.36174498107845343,good,bad\n0,1,0,722,0.42065958853814545,0.5793404114618546,bad,bad\n0,1,0,334,0.45296144459313703,0.547038555406863,bad,bad\n0,1,0,651,0.5756864246447917,0.42431357535520825,good,bad\n0,1,0,184,0.5756864246447917,0.42431357535520825,good,bad\n0,1,0,321,0.42065958853814545,0.5793404114618546,bad,bad\n0,1,0,966,0.6623164101627012,0.33768358983729885,good,bad\n0,1,0,998,0.42065958853814545,0.5793404114618546,bad,bad\n0,1,0,948,0.8256928250991642,0.17430717490083586,good,bad\n0,1,0,763,0.9082532443963671,0.09174675560363292,good,bad\n0,1,0,68,0.8256928250991642,0.17430717490083586,good,bad\n0,1,0,454,0.48566436944162006,0.5143356305583799,bad,bad\n0,1,0,188,0.4661034586935143,0.5338965413064857,bad,bad\n0,1,0,796,0.6923402798991323,0.3076597201008678,good,bad\n0,1,0,771,0.45296144459313703,0.547038555406863,bad,bad\n0,1,0,357,0.8256928250991642,0.17430717490083586,good,bad\n0,1,0,601,0.5756864246447917,0.42431357535520825,good,bad\n0,1,0,980,0.6382550189215466,0.36174498107845343,good,bad\n0,1,0,849,0.42065958853814545,0.5793404114618546,bad,bad\n0,1,0,335,0.5184905112187901,0.48150948878120986,good,bad\n0,1,0,409,0.7856313964377253,0.21436860356227466,good,bad\n0,1,0,900,0.48566436944162006,0.5143356305583799,bad,bad\n0,1,0,118,0.6088639750098914,0.3911360249901086,good,bad\n0,1,0,640,0.42065958853814545,0.5793404114618546,bad,bad\n0,2,0,694,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,967,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,154,0.6120738972768226,0.3879261027231774,good,good\n0,2,0,288,0.558629427805351,0.44137057219464904,good,good\n0,2,0,916,0.9066838206467236,0.09331617935327637,good,good\n0,2,0,745,0.4367026489118283,0.5632973510881717,bad,good\n0,2,0,261,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,525,0.638897240776827,0.3611027592231731,good,good\n0,2,0,407,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,985,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,149,0.9013401162323652,0.09865988376763479,good,good\n0,2,0,275,0.8862870547133991,0.1137129452866009,good,good\n0,2,0,612,0.5595325241404911,0.4404674758595088,good,good\n0,2,0,305,0.9013401162323652,0.09865988376763479,good,good\n0,2,0,929,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,225,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,654,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,897,0.9152657079941544,0.08473429200584555,good,good\n0,2,0,990,0.9409008069454234,0.05909919305457654,good,good\n0,2,0,151,0.8862870547133991,0.1137129452866009,good,good\n0,2,0,658,0.638897240776827,0.3611027592231731,good,good\n0,2,0,316,0.4782474035807365,0.5217525964192635,bad,good\n0,2,0,854,0.868284354988925,0.13171564501107502,good,good\n0,2,0,744,0.6129434076188243,0.38705659238117573,good,good\n0,2,0,432,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,512,0.7735081880890577,0.22649181191094223,good,good\n0,2,0,3,0.4782474035807365,0.5217525964192635,bad,good\n0,2,0,784,0.8347699483171185,0.16523005168288157,good,good\n0,2,0,997,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,939,0.9409008069454234,0.05909919305457654,good,good\n0,2,0,453,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,689,0.6773749989972806,0.32262500100271946,good,good\n0,2,0,69,0.9192827101934784,0.08071728980652161,good,good\n0,2,0,848,0.4367026489118283,0.5632973510881717,bad,good\n0,2,0,879,0.9409008069454234,0.05909919305457654,good,good\n0,2,0,61,0.7534964845858237,0.24650351541417626,good,good\n0,2,0,123,0.7095599728993912,0.2904400271006088,good,good\n0,2,0,219,0.868284354988925,0.13171564501107502,good,good\n0,2,0,686,0.8550776870031757,0.14492231299682437,good,good\n0,2,0,298,0.8479205798590626,0.1520794201409375,good,good\n0,2,0,710,0.8799300165526454,0.1200699834473546,good,good\n0,2,0,214,0.9192827101934784,0.08071728980652161,good,good\n0,2,0,716,0.930869989335767,0.06913001066423302,good,good\n0,2,0,638,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,73,0.638897240776827,0.3611027592231731,good,good\n0,2,0,730,0.6765738567109463,0.3234261432890538,good,good\n0,2,0,122,0.8479205798590626,0.1520794201409375,good,good\n0,2,0,944,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,121,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,567,0.9273840921571646,0.0726159078428354,good,good\n0,2,0,823,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,208,0.4782474035807365,0.5217525964192635,bad,good\n0,2,0,699,0.7095599728993912,0.2904400271006088,good,good\n0,2,0,144,0.8250428613320651,0.17495713866793497,good,good\n0,2,0,202,0.8479205798590626,0.1520794201409375,good,good\n0,2,0,189,0.6765738567109463,0.3234261432890538,good,good\n0,2,0,684,0.7228228783330375,0.2771771216669625,good,good\n0,2,0,657,0.9192827101934784,0.08071728980652161,good,good\n0,2,0,896,0.39602752928970275,0.6039724707102972,bad,good\n0,2,0,217,0.7735081880890577,0.22649181191094223,good,good\n0,2,0,309,0.558629427805351,0.44137057219464904,good,good\n0,2,0,386,0.9192827101934784,0.08071728980652161,good,good\n0,2,0,802,0.5200952774558703,0.47990472254412975,good,good\n0,2,0,423,0.8546231112005451,0.14537688879945482,good,good\n0,2,0,524,0.558629427805351,0.44137057219464904,good,good\n0,2,0,259,0.8546231112005451,0.14537688879945482,good,good\n0,2,0,93,0.8550776870031757,0.14492231299682437,good,good\n0,2,0,272,0.7534964845858237,0.24650351541417626,good,good\n0,2,0,128,0.558629427805351,0.44137057219464904,good,good\n0,2,0,255,0.8347699483171185,0.16523005168288157,good,good\n0,2,0,887,0.638897240776827,0.3611027592231731,good,bad\n0,2,0,414,0.6129434076188243,0.38705659238117573,good,bad\n0,2,0,780,0.638897240776827,0.3611027592231731,good,bad\n0,2,0,757,0.8746248133415293,0.12537518665847064,good,bad\n0,2,0,700,0.8799300165526454,0.1200699834473546,good,bad\n0,2,0,978,0.8250428613320651,0.17495713866793497,good,bad\n0,2,0,191,0.6120738972768226,0.3879261027231774,good,bad\n0,2,0,814,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,199,0.558629427805351,0.44137057219464904,good,bad\n0,2,0,674,0.8799300165526454,0.1200699834473546,good,bad\n0,2,0,80,0.9495550194216699,0.05044498057833018,good,bad\n0,2,0,840,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,227,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,313,0.638897240776827,0.3611027592231731,good,bad\n0,2,0,979,0.6880498100497494,0.31195018995025064,good,bad\n0,2,0,429,0.4782474035807365,0.5217525964192635,bad,bad\n0,2,0,561,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,707,0.558629427805351,0.44137057219464904,good,bad\n0,2,0,981,0.8479205798590626,0.1520794201409375,good,bad\n0,2,0,634,0.599430147520807,0.40056985247919297,good,bad\n0,2,0,186,0.638897240776827,0.3611027592231731,good,bad\n0,2,0,596,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,124,0.7534964845858237,0.24650351541417626,good,bad\n0,2,0,359,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,677,0.6880498100497494,0.31195018995025064,good,bad\n0,2,0,353,0.39602752928970275,0.6039724707102972,bad,bad\n0,2,0,542,0.6129434076188243,0.38705659238117573,good,bad\n0,2,0,304,0.9110681339200728,0.08893186607992724,good,bad\n0,2,0,647,0.9110681339200728,0.08893186607992724,good,bad\n0,2,0,192,0.558629427805351,0.44137057219464904,good,bad\n0,3,0,646,0.4828591439849872,0.5171408560150128,bad,good\n0,3,0,824,0.8422246299599458,0.15777537004005415,good,good\n0,3,0,795,0.8859469129364066,0.11405308706359342,good,good\n0,3,0,110,0.719812778350452,0.28018722164954807,good,good\n0,3,0,153,0.7669414614727978,0.23305853852720218,good,good\n0,3,0,28,0.5988954051780719,0.4011045948219281,good,good\n0,3,0,560,0.6050579257239185,0.3949420742760815,good,good\n0,3,0,762,0.4004261187066662,0.5995738812933338,bad,good\n0,3,0,753,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,148,0.4412377767478378,0.5587622232521622,bad,good\n0,3,0,982,0.8292806946997511,0.17071930530024895,good,good\n0,3,0,993,0.4004261187066662,0.5995738812933338,bad,good\n0,3,0,843,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,418,0.918722252866163,0.08127774713383698,good,good\n0,3,0,284,0.608469574073026,0.39153042592697396,good,good\n0,3,0,71,0.9119435218243579,0.08805647817564209,good,good\n0,3,0,529,0.4412377767478378,0.5587622232521622,bad,good\n0,3,0,734,0.8818403206825021,0.11815967931749796,good,good\n0,3,0,702,0.7460942002921096,0.2539057997078904,good,good\n0,3,0,460,0.5247197076040744,0.47528029239592556,good,good\n0,3,0,341,0.5247197076040744,0.47528029239592556,good,good\n0,3,0,377,0.9245020292348951,0.07549797076510495,good,good\n0,3,0,764,0.8474699026567866,0.1525300973432134,good,good\n0,3,0,258,0.9018138385518766,0.09818616144812341,good,good\n0,3,0,886,0.7433759906986941,0.25662400930130586,good,good\n0,3,0,161,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,354,0.8724127432683496,0.12758725673165036,good,good\n0,3,0,905,0.4828591439849872,0.5171408560150128,bad,good\n0,3,0,890,0.4004261187066662,0.5995738812933338,bad,good\n0,3,0,263,0.8632351682041345,0.1367648317958655,good,good\n0,3,0,458,0.4004261187066662,0.5995738812933338,bad,good\n0,3,0,369,0.6383972608203083,0.3616027391796916,good,good\n0,3,0,173,0.5580655661018932,0.44193443389810677,good,good\n0,3,0,865,0.9053008050043561,0.09469919499564394,good,good\n0,3,0,489,0.8632351682041345,0.1367648317958655,good,good\n0,3,0,797,0.8632351682041345,0.1367648317958655,good,good\n0,3,0,72,0.4412377767478378,0.5587622232521622,bad,good\n0,3,0,955,0.5545362579566699,0.44546374204333,good,good\n0,3,0,204,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,773,0.8474699026567866,0.1525300973432134,good,good\n0,3,0,176,0.4828591439849872,0.5171408560150128,bad,good\n0,3,0,211,0.9353963407904037,0.0646036592095963,good,good\n0,3,0,210,0.944812451832629,0.055187548167371085,good,good\n0,3,0,811,0.5988954051780719,0.4011045948219281,good,good\n0,3,0,534,0.9119435218243579,0.08805647817564209,good,good\n0,3,0,477,0.7048122332418516,0.29518776675814845,good,good\n0,3,0,626,0.7694874624023402,0.23051253759765977,good,good\n0,3,0,959,0.608469574073026,0.39153042592697396,good,good\n0,3,0,164,0.8724127432683496,0.12758725673165036,good,good\n0,3,0,400,0.8422246299599458,0.15777537004005415,good,good\n0,3,0,438,0.4004261187066662,0.5995738812933338,bad,good\n0,3,0,698,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,513,0.5580655661018932,0.44193443389810677,good,good\n0,3,0,32,0.6848137796159599,0.3151862203840401,good,good\n0,3,0,171,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,358,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,704,0.5580655661018932,0.44193443389810677,good,good\n0,3,0,328,0.7048122332418516,0.29518776675814845,good,good\n0,3,0,455,0.8186643793053872,0.1813356206946128,good,good\n0,3,0,984,0.8632351682041345,0.1367648317958655,good,good\n0,3,0,999,0.6475833780912568,0.35241662190874323,good,good\n0,3,0,14,0.4828591439849872,0.5171408560150128,bad,good\n0,3,0,515,0.4004261187066662,0.5995738812933338,bad,good\n0,3,0,872,0.4511262610061715,0.5488737389938285,bad,good\n0,3,0,145,0.608469574073026,0.39153042592697396,good,good\n0,3,0,867,0.8632351682041345,0.1367648317958655,good,good\n0,3,0,160,0.867891505737032,0.13210849426296803,good,good\n0,3,0,830,0.8724127432683496,0.12758725673165036,good,good\n0,3,0,323,0.5545362579566699,0.44546374204333,good,good\n0,3,0,48,0.8818403206825021,0.11815967931749796,good,good\n0,3,0,169,0.5580655661018932,0.44193443389810677,good,bad\n0,3,0,578,0.5580655661018932,0.44193443389810677,good,bad\n0,3,0,861,0.8186643793053872,0.1813356206946128,good,bad\n0,3,0,466,0.4004261187066662,0.5995738812933338,bad,bad\n0,3,0,63,0.6383972608203083,0.3616027391796916,good,bad\n0,3,0,212,0.4828591439849872,0.5171408560150128,bad,bad\n0,3,0,4,0.4412377767478378,0.5587622232521622,bad,bad\n0,3,0,972,0.5247197076040744,0.47528029239592556,good,bad\n0,3,0,95,0.5988954051780719,0.4011045948219281,good,bad\n0,3,0,545,0.4004261187066662,0.5995738812933338,bad,bad\n0,3,0,583,0.5580655661018932,0.44193443389810677,good,bad\n0,3,0,610,0.4511262610061715,0.5488737389938285,bad,bad\n0,3,0,129,0.4412377767478378,0.5587622232521622,bad,bad\n0,3,0,504,0.4004261187066662,0.5995738812933338,bad,bad\n0,3,0,332,0.608469574073026,0.39153042592697396,good,bad\n0,3,0,240,0.6050579257239185,0.3949420742760815,good,bad\n0,3,0,557,0.944812451832629,0.055187548167371085,good,bad\n0,3,0,457,0.4412377767478378,0.5587622232521622,bad,bad\n0,3,0,62,0.5580655661018932,0.44193443389810677,good,bad\n0,3,0,213,0.7048122332418516,0.29518776675814845,good,bad\n0,3,0,846,0.9353963407904037,0.0646036592095963,good,bad\n0,3,0,398,0.6761127443070345,0.32388725569296556,good,bad\n0,3,0,137,0.6566616379586381,0.34333836204136187,good,bad\n0,3,0,378,0.5580655661018932,0.44193443389810677,good,bad\n0,3,0,374,0.6475833780912568,0.35241662190874323,good,bad\n0,3,0,355,0.5580655661018932,0.44193443389810677,good,bad\n0,3,0,517,0.8632351682041345,0.1367648317958655,good,bad\n0,3,0,538,0.4004261187066662,0.5995738812933338,bad,bad\n0,3,0,44,0.4004261187066662,0.5995738812933338,bad,bad\n0,3,0,435,0.8019758610295411,0.19802413897045884,good,bad\n0,4,0,150,0.8814639071358452,0.11853609286415479,good,good\n0,4,0,768,0.6573313638278684,0.34266863617213167,good,good\n0,4,0,815,0.6300976397229516,0.36990236027704837,good,good\n0,4,0,506,0.8296275279383186,0.17037247206168138,good,good\n0,4,0,666,0.7154567208170819,0.2845432791829181,good,good\n0,4,0,109,0.7444845480630644,0.2555154519369356,good,good\n0,4,0,318,0.826324201715749,0.173675798284251,good,good\n0,4,0,25,0.5047636401915807,0.49523635980841924,good,good\n0,4,0,551,0.9016202998358369,0.09837970016416303,good,good\n0,4,0,325,0.5047636401915807,0.49523635980841924,good,good\n0,4,0,868,0.9251559170678143,0.07484408293218565,good,good\n0,4,0,715,0.9400336722054744,0.05996632779452561,good,good\n0,4,0,526,0.8543070431953402,0.1456929568046598,good,good\n0,4,0,718,0.8678620153701972,0.1321379846298027,good,good\n0,4,0,991,0.8543070431953402,0.1456929568046598,good,good\n0,4,0,655,0.5047636401915807,0.49523635980841924,good,good\n0,4,0,410,0.573234070155997,0.426765929844003,good,good\n0,4,0,21,0.5788980386152573,0.4211019613847427,good,good\n0,4,0,156,0.49761759370796566,0.5023824062920343,bad,good\n0,4,0,23,0.6508640751843071,0.3491359248156929,good,good\n0,4,0,91,0.41646287203333276,0.5835371279666672,bad,good\n0,4,0,111,0.7165531204628435,0.2834468795371565,good,good\n0,4,0,945,0.7444845480630644,0.2555154519369356,good,good\n0,4,0,568,0.7560285955695254,0.24397140443047466,good,good\n0,4,0,697,0.8717099203697203,0.12829007963027972,good,good\n0,4,0,248,0.7622409449778045,0.23775905502219552,good,good\n0,4,0,705,0.9116659208939651,0.08833407910603487,good,good\n0,4,0,691,0.6508640751843071,0.3491359248156929,good,good\n0,4,0,347,0.7444845480630644,0.2555154519369356,good,good\n0,4,0,913,0.9271113039576478,0.0728886960423522,good,good\n0,4,0,808,0.6573313638278684,0.34266863617213167,good,good\n0,4,0,215,0.8156868158553305,0.18431318414466952,good,good\n0,4,0,838,0.41646287203333276,0.5835371279666672,bad,good\n0,4,0,767,0.9400336722054744,0.05996632779452561,good,good\n0,4,0,362,0.7622409449778045,0.23775905502219552,good,good\n0,4,0,820,0.8427171362791097,0.1572828637208903,good,good\n0,4,0,404,0.7560285955695254,0.24397140443047466,good,good\n0,4,0,344,0.7830948312010144,0.21690516879898564,good,good\n0,4,0,740,0.5272875940655819,0.47271240593441805,good,good\n0,4,0,628,0.8784444941355039,0.12155550586449614,good,good\n0,4,0,209,0.9093367004540454,0.0906632995459546,good,good\n0,4,0,390,0.8578289886610893,0.14217101133891072,good,good\n0,4,0,473,0.9165039148851485,0.08349608511485147,good,good\n0,4,0,904,0.826324201715749,0.173675798284251,good,good\n0,4,0,441,0.4750872725115287,0.5249127274884713,bad,good\n0,4,0,383,0.7165531204628435,0.2834468795371565,good,good\n0,4,0,283,0.9165039148851485,0.08349608511485147,good,good\n0,4,0,950,0.6020099761106461,0.3979900238893539,good,good\n0,4,0,623,0.41646287203333276,0.5835371279666672,bad,good\n0,4,0,239,0.5047636401915807,0.49523635980841924,good,good\n0,4,0,769,0.8578289886610893,0.14217101133891072,good,good\n0,4,0,662,0.826324201715749,0.173675798284251,good,good\n0,4,0,683,0.8427171362791097,0.1572828637208903,good,good\n0,4,0,920,0.826324201715749,0.173675798284251,good,good\n0,4,0,158,0.6773517840170932,0.3226482159829068,good,good\n0,4,0,115,0.9165039148851485,0.08349608511485147,good,good\n0,4,0,55,0.9400336722054744,0.05996632779452561,good,good\n0,4,0,874,0.4750872725115287,0.5249127274884713,bad,good\n0,4,0,671,0.8814639071358452,0.11853609286415479,good,good\n0,4,0,413,0.8905693888067722,0.10943061119322785,good,good\n0,4,0,818,0.4750872725115287,0.5249127274884713,bad,good\n0,4,0,322,0.4750872725115287,0.5249127274884713,bad,good\n0,4,0,5,0.9329769472276499,0.06702305277235009,good,good\n0,4,0,869,0.41646287203333276,0.5835371279666672,bad,good\n0,4,0,146,0.5047636401915807,0.49523635980841924,good,good\n0,4,0,415,0.8578289886610893,0.14217101133891072,good,good\n0,4,0,82,0.8684791205947983,0.1315208794052017,good,good\n0,4,0,22,0.5047636401915807,0.49523635980841924,good,good\n0,4,0,847,0.826324201715749,0.173675798284251,good,good\n0,4,0,625,0.9165039148851485,0.08349608511485147,good,good\n0,4,0,89,0.41646287203333276,0.5835371279666672,bad,bad\n0,4,0,639,0.44558582698422133,0.5544141730157787,bad,bad\n0,4,0,552,0.5047636401915807,0.49523635980841924,good,bad\n0,4,0,983,0.4750872725115287,0.5249127274884713,bad,bad\n0,4,0,826,0.5047636401915807,0.49523635980841924,good,bad\n0,4,0,747,0.44558582698422133,0.5544141730157787,bad,bad\n0,4,0,485,0.573234070155997,0.426765929844003,good,bad\n0,4,0,237,0.6234108289761585,0.3765891710238415,good,bad\n0,4,0,242,0.44558582698422133,0.5544141730157787,bad,bad\n0,4,0,543,0.7622409449778045,0.23775905502219552,good,bad\n0,4,0,105,0.6300976397229516,0.36990236027704837,good,bad\n0,4,0,597,0.6573313638278684,0.34266863617213167,good,bad\n0,4,0,301,0.573234070155997,0.426765929844003,good,bad\n0,4,0,652,0.41646287203333276,0.5835371279666672,bad,bad\n0,4,0,500,0.41646287203333276,0.5835371279666672,bad,bad\n0,4,0,627,0.6773517840170932,0.3226482159829068,good,bad\n0,4,0,197,0.6234108289761585,0.3765891710238415,good,bad\n0,4,0,622,0.9165039148851485,0.08349608511485147,good,bad\n0,4,0,470,0.6508640751843071,0.3491359248156929,good,bad\n0,4,0,56,0.7971547487505067,0.20284525124949324,good,bad\n0,4,0,645,0.9165039148851485,0.08349608511485147,good,bad\n0,4,0,917,0.5047636401915807,0.49523635980841924,good,bad\n0,4,0,805,0.4750872725115287,0.5249127274884713,bad,bad\n0,4,0,919,0.41646287203333276,0.5835371279666672,bad,bad\n0,4,0,649,0.41646287203333276,0.5835371279666672,bad,bad\n0,4,0,853,0.41646287203333276,0.5835371279666672,bad,bad\n0,4,0,402,0.8578289886610893,0.14217101133891072,good,bad\n0,4,0,302,0.8536346482649679,0.14636535173503204,good,bad\n0,4,0,175,0.9165039148851485,0.08349608511485147,good,bad\n0,4,0,431,0.6300976397229516,0.36990236027704837,good,bad\n0,5,0,970,0.6158787144598048,0.3841212855401952,good,good\n0,5,0,957,0.6504673242745647,0.3495326757254354,good,good\n0,5,0,47,0.6290555103067772,0.3709444896932228,good,good\n0,5,0,573,0.4184725041774964,0.5815274958225036,bad,good\n0,5,0,52,0.8450403891053462,0.15495961089465377,good,good\n0,5,0,486,0.8654427153319763,0.13455728466802372,good,good\n0,5,0,726,0.9023287394781188,0.09767126052188119,good,good\n0,5,0,159,0.9399428910138143,0.06005710898618567,good,good\n0,5,0,520,0.8450403891053462,0.15495961089465377,good,good\n0,5,0,459,0.8450403891053462,0.15495961089465377,good,good\n0,5,0,987,0.8851684162316513,0.1148315837683487,good,good\n0,5,0,616,0.7933658762000863,0.20663412379991375,good,good\n0,5,0,609,0.9051178390911457,0.09488216090885428,good,good\n0,5,0,547,0.8450403891053462,0.15495961089465377,good,good\n0,5,0,859,0.9009069450347045,0.09909305496529551,good,good\n0,5,0,391,0.790725790455287,0.20927420954471299,good,good\n0,5,0,933,0.8691323659686239,0.13086763403137613,good,good\n0,5,0,988,0.4184725041774964,0.5815274958225036,bad,good\n0,5,0,408,0.8867876509100767,0.11321234908992334,good,good\n0,5,0,508,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,909,0.819115447496344,0.180884552503656,good,good\n0,5,0,267,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,942,0.9051178390911457,0.09488216090885428,good,good\n0,5,0,782,0.6504673242745647,0.3495326757254354,good,good\n0,5,0,725,0.917164622722572,0.082835377277428,good,good\n0,5,0,367,0.5414165567085732,0.45858344329142675,good,good\n0,5,0,84,0.4590877208486953,0.5409122791513047,bad,good\n0,5,0,708,0.5722486445797844,0.4277513554202156,good,good\n0,5,0,801,0.6120797392231594,0.38792026077684066,good,good\n0,5,0,650,0.4184725041774964,0.5815274958225036,bad,good\n0,5,0,12,0.6869987455706421,0.31300125442935794,good,good\n0,5,0,923,0.6120797392231594,0.38792026077684066,good,good\n0,5,0,345,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,138,0.7340506744767501,0.26594932552324996,good,good\n0,5,0,397,0.4590877208486953,0.5409122791513047,bad,good\n0,5,0,516,0.5002538594478596,0.49974614055214045,good,good\n0,5,0,119,0.6938505366911669,0.3061494633088331,good,good\n0,5,0,326,0.9299220088551765,0.07007799114482353,good,good\n0,5,0,962,0.9183742856600978,0.08162571433990214,good,good\n0,5,0,860,0.8883869318754899,0.11161306812451013,good,good\n0,5,0,511,0.8654427153319763,0.13455728466802372,good,good\n0,5,0,253,0.8851684162316513,0.1148315837683487,good,good\n0,5,0,673,0.9159386739126042,0.08406132608739576,good,good\n0,5,0,873,0.9051178390911457,0.09488216090885428,good,good\n0,5,0,903,0.9051178390911457,0.09488216090885428,good,good\n0,5,0,297,0.9051178390911457,0.09488216090885428,good,good\n0,5,0,17,0.6737634183213079,0.3262365816786921,good,good\n0,5,0,487,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,162,0.8691323659686239,0.13086763403137613,good,good\n0,5,0,399,0.8883869318754899,0.11161306812451013,good,good\n0,5,0,717,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,6,0.8867876509100767,0.11321234908992334,good,good\n0,5,0,665,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,222,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,910,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,193,0.8654427153319763,0.13455728466802372,good,good\n0,5,0,243,0.8450403891053462,0.15495961089465377,good,good\n0,5,0,385,0.8654427153319763,0.13455728466802372,good,good\n0,5,0,198,0.7340506744767501,0.26594932552324996,good,good\n0,5,0,857,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,379,0.8471277436770588,0.15287225632294119,good,good\n0,5,0,719,0.6541028113273275,0.34589718867267255,good,good\n0,5,0,605,0.5082675757259779,0.4917324242740221,good,good\n0,5,0,229,0.4184725041774964,0.5815274958225036,bad,good\n0,5,0,134,0.8851684162316513,0.1148315837683487,good,good\n0,5,0,67,0.6972448161939291,0.3027551838060709,good,good\n0,5,0,816,0.9159386739126042,0.08406132608739576,good,good\n0,5,0,845,0.7933658762000863,0.20663412379991375,good,good\n0,5,0,100,0.8471277436770588,0.15287225632294119,good,good\n0,5,0,133,0.8221801632056862,0.17781983679431385,good,good\n0,5,0,351,0.6869987455706421,0.31300125442935794,good,bad\n0,5,0,349,0.6972448161939291,0.3027551838060709,good,bad\n0,5,0,973,0.4184725041774964,0.5815274958225036,bad,bad\n0,5,0,131,0.4184725041774964,0.5815274958225036,bad,bad\n0,5,0,949,0.8851684162316513,0.1148315837683487,good,bad\n0,5,0,375,0.5002538594478596,0.49974614055214045,good,bad\n0,5,0,307,0.46307065295944316,0.5369293470405568,bad,bad\n0,5,0,315,0.4184725041774964,0.5815274958225036,bad,bad\n0,5,0,273,0.5722486445797844,0.4277513554202156,good,bad\n0,5,0,842,0.8221801632056862,0.17781983679431385,good,bad\n0,5,0,739,0.6158787144598048,0.3841212855401952,good,bad\n0,5,0,812,0.4184725041774964,0.5815274958225036,bad,bad\n0,5,0,333,0.8851684162316513,0.1148315837683487,good,bad\n0,5,0,603,0.8221801632056862,0.17781983679431385,good,bad\n0,5,0,446,0.4184725041774964,0.5815274958225036,bad,bad\n0,5,0,580,0.6120797392231594,0.38792026077684066,good,bad\n0,5,0,642,0.8672984016496108,0.13270159835038925,good,bad\n0,5,0,226,0.790725790455287,0.20927420954471299,good,bad\n0,5,0,120,0.4590877208486953,0.5409122791513047,bad,bad\n0,5,0,755,0.5975323498268648,0.40246765017313524,good,bad\n0,5,0,607,0.6158787144598048,0.3841212855401952,good,bad\n0,5,0,1,0.6504673242745647,0.3495326757254354,good,bad\n0,5,0,368,0.4184725041774964,0.5815274958225036,bad,bad\n0,5,0,884,0.6120797392231594,0.38792026077684066,good,bad\n0,5,0,566,0.5002538594478596,0.49974614055214045,good,bad\n0,5,0,878,0.4590877208486953,0.5409122791513047,bad,bad\n0,5,0,439,0.7132272669181097,0.2867727330818904,good,bad\n0,5,0,236,0.819115447496344,0.180884552503656,good,bad\n0,5,0,535,0.7757680549125119,0.22423194508748806,good,bad\n0,5,0,475,0.5414165567085732,0.45858344329142675,good,bad\n0,6,0,461,0.5001587947348924,0.4998412052651076,good,good\n0,6,0,494,0.45484018046083763,0.5451598195391624,bad,good\n0,6,0,16,0.9052070717397516,0.09479292826024849,good,good\n0,6,0,246,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,856,0.9052070717397516,0.09479292826024849,good,good\n0,6,0,339,0.5630036762931664,0.4369963237068336,good,good\n0,6,0,392,0.5001587947348924,0.4998412052651076,good,good\n0,6,0,277,0.6837843210259669,0.3162156789740331,good,good\n0,6,0,810,0.6070978656036363,0.3929021343963637,good,good\n0,6,0,632,0.7357401762721341,0.264259823727866,good,good\n0,6,0,81,0.8665944636491714,0.13340553635082858,good,good\n0,6,0,31,0.5001587947348924,0.4998412052651076,good,good\n0,6,0,223,0.9052070717397516,0.09479292826024849,good,good\n0,6,0,934,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,901,0.9321380265013826,0.06786197349861739,good,good\n0,6,0,678,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,974,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,772,0.8840331586934543,0.11596684130654568,good,good\n0,6,0,792,0.9427715656347853,0.05722843436521473,good,good\n0,6,0,968,0.9321380265013826,0.06786197349861739,good,good\n0,6,0,817,0.8840331586934543,0.11596684130654568,good,good\n0,6,0,218,0.5001587947348924,0.4998412052651076,good,good\n0,6,0,729,0.8594944049336369,0.14050559506636304,good,good\n0,6,0,19,0.886244912795886,0.11375508720411397,good,good\n0,6,0,245,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,892,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,821,0.7410420710865431,0.25895792891345687,good,good\n0,6,0,870,0.9033237832901134,0.09667621670988656,good,good\n0,6,0,825,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,587,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,356,0.9427715656347853,0.05722843436521473,good,good\n0,6,0,786,0.886244912795886,0.11375508720411397,good,good\n0,6,0,384,0.8851436687214645,0.11485633127853546,good,good\n0,6,0,774,0.8345814329730145,0.16541856702698557,good,good\n0,6,0,234,0.8640592484200246,0.13594075157997546,good,good\n0,6,0,411,0.8412628048966935,0.15873719510330653,good,good\n0,6,0,425,0.6896889658535577,0.3103110341464423,good,good\n0,6,0,130,0.827676442624949,0.17232355737505103,good,good\n0,6,0,615,0.8001896953526297,0.19981030464737032,good,good\n0,6,0,481,0.5630036762931664,0.4369963237068336,good,good\n0,6,0,98,0.5630036762931664,0.4369963237068336,good,good\n0,6,0,90,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,738,0.8840331586934543,0.11596684130654568,good,good\n0,6,0,497,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,427,0.8873369420674282,0.11266305793257185,good,good\n0,6,0,996,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,793,0.7410420710865431,0.25895792891345687,good,good\n0,6,0,256,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,294,0.9052070717397516,0.09479292826024849,good,good\n0,6,0,266,0.9052070717397516,0.09479292826024849,good,good\n0,6,0,688,0.8653318933940629,0.13466810660593712,good,good\n0,6,0,880,0.8412628048966935,0.15873719510330653,good,good\n0,6,0,346,0.5630036762931664,0.4369963237068336,good,good\n0,6,0,152,0.704667395076029,0.29533260492397095,good,good\n0,6,0,723,0.6544488619395978,0.3455511380604022,good,good\n0,6,0,680,0.8840331586934543,0.11596684130654568,good,good\n0,6,0,376,0.8640592484200246,0.13594075157997546,good,good\n0,6,0,692,0.6920121617664275,0.30798783823357245,good,good\n0,6,0,286,0.41025750595412425,0.5897424940458758,bad,good\n0,6,0,104,0.9321380265013826,0.06786197349861739,good,good\n0,6,0,898,0.9427715656347853,0.05722843436521473,good,good\n0,6,0,306,0.9321380265013826,0.06786197349861739,good,good\n0,6,0,40,0.8665944636491714,0.13340553635082858,good,good\n0,6,0,207,0.5630036762931664,0.4369963237068336,good,good\n0,6,0,881,0.9321380265013826,0.06786197349861739,good,good\n0,6,0,584,0.9052070717397516,0.09479292826024849,good,good\n0,6,0,310,0.7695394120764718,0.23046058792352814,good,good\n0,6,0,85,0.8154599890651251,0.18454001093487493,good,good\n0,6,0,748,0.9427715656347853,0.05722843436521473,good,good\n0,6,0,875,0.696629425060755,0.303370574939245,good,good\n0,6,0,668,0.45484018046083763,0.5451598195391624,bad,bad\n0,6,0,249,0.8412628048966935,0.15873719510330653,good,bad\n0,6,0,522,0.45484018046083763,0.5451598195391624,bad,bad\n0,6,0,914,0.41025750595412425,0.5897424940458758,bad,bad\n0,6,0,54,0.5630036762931664,0.4369963237068336,good,bad\n0,6,0,295,0.6896889658535577,0.3103110341464423,good,bad\n0,6,0,935,0.6096894655875413,0.3903105344124587,good,bad\n0,6,0,505,0.9052070717397516,0.09479292826024849,good,bad\n0,6,0,828,0.643238932544199,0.356761067455801,good,bad\n0,6,0,611,0.8330742466204273,0.16692575337957266,good,bad\n0,6,0,87,0.7293468520783084,0.27065314792169154,good,bad\n0,6,0,268,0.54547480012734,0.45452519987266,good,bad\n0,6,0,724,0.8412628048966935,0.15873719510330653,good,bad\n0,6,0,589,0.45484018046083763,0.5451598195391624,bad,bad\n0,6,0,203,0.41025750595412425,0.5897424940458758,bad,bad\n0,6,0,257,0.41025750595412425,0.5897424940458758,bad,bad\n0,6,0,588,0.41025750595412425,0.5897424940458758,bad,bad\n0,6,0,721,0.696629425060755,0.303370574939245,good,bad\n0,6,0,569,0.45484018046083763,0.5451598195391624,bad,bad\n0,6,0,18,0.5630036762931664,0.4369963237068336,good,bad\n0,6,0,194,0.6096894655875413,0.3903105344124587,good,bad\n0,6,0,190,0.9188899391290622,0.08111006087093772,good,bad\n0,6,0,11,0.45484018046083763,0.5451598195391624,bad,bad\n0,6,0,831,0.41025750595412425,0.5897424940458758,bad,bad\n0,6,0,540,0.6896889658535577,0.3103110341464423,good,bad\n0,6,0,938,0.5630036762931664,0.4369963237068336,good,bad\n0,6,0,924,0.5001587947348924,0.4998412052651076,good,bad\n0,6,0,899,0.5001587947348924,0.4998412052651076,good,bad\n0,6,0,155,0.5001587947348924,0.4998412052651076,good,bad\n0,6,0,528,0.41025750595412425,0.5897424940458758,bad,bad\n0,7,0,776,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,452,0.8542020765352227,0.1457979234647772,good,good\n0,7,0,70,0.8724158126258028,0.1275841873741972,good,good\n0,7,0,636,0.8666255364617338,0.13337446353826624,good,good\n0,7,0,41,0.6996893511198972,0.3003106488801028,good,good\n0,7,0,200,0.8666255364617338,0.13337446353826624,good,good\n0,7,0,401,0.750983140927528,0.24901685907247195,good,good\n0,7,0,262,0.4994225111493441,0.5005774888506559,bad,good\n0,7,0,103,0.5692246428804193,0.4307753571195807,good,good\n0,7,0,464,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,125,0.42277379066088494,0.5772262093391151,bad,good\n0,7,0,147,0.8409284854480962,0.1590715145519038,good,good\n0,7,0,659,0.6428545641303177,0.3571454358696823,good,good\n0,7,0,960,0.8542020765352227,0.1457979234647772,good,good\n0,7,0,338,0.42277379066088494,0.5772262093391151,bad,good\n0,7,0,361,0.8811153660390794,0.11888463396092067,good,good\n0,7,0,675,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,102,0.8338832714156129,0.16611672858438706,good,good\n0,7,0,741,0.5692246428804193,0.4307753571195807,good,good\n0,7,0,742,0.9075460294076004,0.09245397059239953,good,good\n0,7,0,906,0.6948439974894671,0.3051560025105329,good,good\n0,7,0,571,0.8338832714156129,0.16611672858438706,good,good\n0,7,0,244,0.8666255364617338,0.13337446353826624,good,good\n0,7,0,51,0.6775027364359041,0.3224972635640959,good,good\n0,7,0,572,0.9304175054593876,0.06958249454061237,good,good\n0,7,0,26,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,420,0.9158172534364183,0.08418274656358167,good,good\n0,7,0,541,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,604,0.7044904888423502,0.29550951115764973,good,good\n0,7,0,523,0.8605296755238191,0.13947032447618093,good,good\n0,7,0,271,0.8834989478304144,0.1165010521695856,good,good\n0,7,0,888,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,232,0.8605296755238191,0.13947032447618093,good,good\n0,7,0,954,0.42277379066088494,0.5772262093391151,bad,good\n0,7,0,895,0.9197220774044264,0.08027792259557369,good,good\n0,7,0,179,0.42277379066088494,0.5772262093391151,bad,good\n0,7,0,855,0.8409284854480962,0.1590715145519038,good,good\n0,7,0,45,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,312,0.7645629560176951,0.23543704398230492,good,good\n0,7,0,465,0.46086683393938754,0.5391331660606125,bad,good\n0,7,0,462,0.6066451577722227,0.39335484222777733,good,good\n0,7,0,24,0.9304175054593876,0.06958249454061237,good,good\n0,7,0,396,0.46086683393938754,0.5391331660606125,bad,good\n0,7,0,216,0.46086683393938754,0.5391331660606125,bad,good\n0,7,0,932,0.8338832714156129,0.16611672858438706,good,good\n0,7,0,36,0.8724158126258028,0.1275841873741972,good,good\n0,7,0,468,0.8542020765352227,0.1457979234647772,good,good\n0,7,0,947,0.9075460294076004,0.09245397059239953,good,good\n0,7,0,537,0.6066451577722227,0.39335484222777733,good,good\n0,7,0,49,0.8409284854480962,0.1590715145519038,good,good\n0,7,0,382,0.9075460294076004,0.09245397059239953,good,good\n0,7,0,394,0.8542020765352227,0.1457979234647772,good,good\n0,7,0,479,0.42277379066088494,0.5772262093391151,bad,good\n0,7,0,317,0.8274259395457341,0.17257406045426593,good,good\n0,7,0,836,0.9075460294076004,0.09245397059239953,good,good\n0,7,0,943,0.8542020765352227,0.1457979234647772,good,good\n0,7,0,388,0.7311298493545437,0.2688701506454562,good,good\n0,7,0,833,0.8042307587644746,0.19576924123552542,good,good\n0,7,0,94,0.7104496867352559,0.2895503132647441,good,good\n0,7,0,370,0.9075460294076004,0.09245397059239953,good,good\n0,7,0,488,0.8605296755238191,0.13947032447618093,good,good\n0,7,0,778,0.8887207078058973,0.1112792921941027,good,good\n0,7,0,964,0.6066451577722227,0.39335484222777733,good,good\n0,7,0,975,0.8078196240299725,0.19218037597002746,good,good\n0,7,0,53,0.9304175054593876,0.06958249454061237,good,good\n0,7,0,871,0.7912380610906993,0.20876193890930075,good,good\n0,7,0,794,0.8666255364617338,0.13337446353826624,good,good\n0,7,0,852,0.8113581299654182,0.18864187003458183,good,good\n0,7,0,779,0.6428545641303177,0.3571454358696823,good,good\n0,7,0,549,0.9304175054593876,0.06958249454061237,good,good\n0,7,0,701,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,790,0.6428545641303177,0.3571454358696823,good,bad\n0,7,0,449,0.7411803628878633,0.2588196371121368,good,bad\n0,7,0,835,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,556,0.654646184901946,0.34535381509805396,good,bad\n0,7,0,13,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,648,0.7044904888423502,0.29550951115764973,good,bad\n0,7,0,531,0.6887043508279617,0.31129564917203834,good,bad\n0,7,0,182,0.6256915228135995,0.37430847718640053,good,bad\n0,7,0,9,0.5692246428804193,0.4307753571195807,good,bad\n0,7,0,563,0.8274259395457341,0.17257406045426593,good,bad\n0,7,0,59,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,278,0.8724158126258028,0.1275841873741972,good,bad\n0,7,0,416,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,113,0.8113581299654182,0.18864187003458183,good,bad\n0,7,0,594,0.6256915228135995,0.37430847718640053,good,bad\n0,7,0,789,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,754,0.8887207078058973,0.1112792921941027,good,bad\n0,7,0,548,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,474,0.5692246428804193,0.4307753571195807,good,bad\n0,7,0,444,0.6428545641303177,0.3571454358696823,good,bad\n0,7,0,885,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,706,0.654646184901946,0.34535381509805396,good,bad\n0,7,0,74,0.6256915228135995,0.37430847718640053,good,bad\n0,7,0,656,0.5692246428804193,0.4307753571195807,good,bad\n0,7,0,731,0.4994225111493441,0.5005774888506559,bad,bad\n0,7,0,925,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,364,0.42277379066088494,0.5772262093391151,bad,bad\n0,7,0,585,0.5379850572753218,0.4620149427246783,good,bad\n0,7,0,195,0.6428545641303177,0.3571454358696823,good,bad\n0,8,0,940,0.9276696490077807,0.07233035099221934,good,good\n0,8,0,363,0.8843565161030713,0.11564348389692872,good,good\n0,8,0,46,0.8738742232084344,0.1261257767915656,good,good\n0,8,0,83,0.4201734700987917,0.5798265299012083,bad,good\n0,8,0,576,0.647096736682454,0.35290326331754596,good,good\n0,8,0,672,0.8689213335338587,0.13107866646614127,good,good\n0,8,0,451,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,447,0.647096736682454,0.35290326331754596,good,good\n0,8,0,562,0.7771056435882456,0.22289435641175437,good,good\n0,8,0,687,0.6190480084339083,0.3809519915660917,good,good\n0,8,0,183,0.8909664408815399,0.10903355911846016,good,good\n0,8,0,456,0.490932309079698,0.509067690920302,bad,good\n0,8,0,630,0.490932309079698,0.509067690920302,bad,good\n0,8,0,282,0.8352179078120507,0.1647820921879493,good,good\n0,8,0,136,0.9157872410659079,0.08421275893409211,good,good\n0,8,0,187,0.647096736682454,0.35290326331754596,good,good\n0,8,0,372,0.8843565161030713,0.11564348389692872,good,good\n0,8,0,483,0.9040922262961367,0.0959077737038633,good,good\n0,8,0,592,0.8909664408815399,0.10903355911846016,good,good\n0,8,0,941,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,157,0.4201734700987917,0.5798265299012083,bad,good\n0,8,0,752,0.6838011883351867,0.3161988116648134,good,good\n0,8,0,606,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,290,0.8689213335338587,0.13107866646614127,good,good\n0,8,0,733,0.9157872410659079,0.08421275893409211,good,good\n0,8,0,296,0.9366909520946581,0.06330904790534185,good,good\n0,8,0,163,0.647096736682454,0.35290326331754596,good,good\n0,8,0,117,0.65106007988298,0.34893992011701996,good,good\n0,8,0,467,0.9174760985833391,0.08252390141666086,good,good\n0,8,0,168,0.8689213335338587,0.13107866646614127,good,good\n0,8,0,279,0.8545400513940153,0.1454599486059846,good,good\n0,8,0,421,0.7272058584350337,0.2727941415649663,good,good\n0,8,0,389,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,660,0.7513798511533822,0.24862014884661773,good,good\n0,8,0,620,0.647096736682454,0.35290326331754596,good,good\n0,8,0,732,0.5794498148911693,0.42055018510883074,good,good\n0,8,0,114,0.5376337953449462,0.4623662046550539,good,good\n0,8,0,712,0.9059915470423021,0.09400845295769784,good,good\n0,8,0,366,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,921,0.8738742232084344,0.1261257767915656,good,good\n0,8,0,501,0.490932309079698,0.509067690920302,bad,good\n0,8,0,509,0.8545400513940153,0.1454599486059846,good,good\n0,8,0,995,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,930,0.4201734700987917,0.5798265299012083,bad,good\n0,8,0,844,0.8328091268899902,0.1671908731100098,good,good\n0,8,0,956,0.8352179078120507,0.1647820921879493,good,good\n0,8,0,101,0.5794498148911693,0.42055018510883074,good,good\n0,8,0,599,0.8843565161030713,0.11564348389692872,good,good\n0,8,0,669,0.723740408137493,0.27625959186250704,good,good\n0,8,0,564,0.7272058584350337,0.2727941415649663,good,good\n0,8,0,866,0.4201734700987917,0.5798265299012083,bad,good\n0,8,0,403,0.8328091268899902,0.1671908731100098,good,good\n0,8,0,758,0.8689213335338587,0.13107866646614127,good,good\n0,8,0,800,0.8328091268899902,0.1671908731100098,good,good\n0,8,0,65,0.9059915470423021,0.09400845295769784,good,good\n0,8,0,2,0.8689213335338587,0.13107866646614127,good,good\n0,8,0,478,0.6521272041929416,0.34787279580705843,good,good\n0,8,0,750,0.490932309079698,0.509067690920302,bad,good\n0,8,0,586,0.490932309079698,0.509067690920302,bad,good\n0,8,0,565,0.6521272041929416,0.34787279580705843,good,good\n0,8,0,629,0.9366909520946581,0.06330904790534185,good,good\n0,8,0,270,0.9059915470423021,0.09400845295769784,good,good\n0,8,0,837,0.8843565161030713,0.11564348389692872,good,good\n0,8,0,667,0.8843565161030713,0.11564348389692872,good,good\n0,8,0,882,0.7272058584350337,0.2727941415649663,good,good\n0,8,0,907,0.7801050623818441,0.21989493761815593,good,good\n0,8,0,787,0.9059915470423021,0.09400845295769784,good,good\n0,8,0,502,0.8043760375283824,0.19562396247161767,good,good\n0,8,0,961,0.6790032928308869,0.32099670716911305,good,good\n0,8,0,241,0.8517712927467971,0.14822870725320286,good,good\n0,8,0,331,0.8545400513940153,0.1454599486059846,good,bad\n0,8,0,172,0.613822462177408,0.38617753782259207,good,bad\n0,8,0,510,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,181,0.647096736682454,0.35290326331754596,good,bad\n0,8,0,228,0.8328091268899902,0.1671908731100098,good,bad\n0,8,0,496,0.6190480084339083,0.3809519915660917,good,bad\n0,8,0,633,0.8689213335338587,0.13107866646614127,good,bad\n0,8,0,412,0.8328091268899902,0.1671908731100098,good,bad\n0,8,0,720,0.723740408137493,0.27625959186250704,good,bad\n0,8,0,521,0.490932309079698,0.509067690920302,bad,bad\n0,8,0,850,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,618,0.6838011883351867,0.3161988116648134,good,bad\n0,8,0,936,0.7513798511533822,0.24862014884661773,good,bad\n0,8,0,736,0.6790032928308869,0.32099670716911305,good,bad\n0,8,0,727,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,864,0.8689213335338587,0.13107866646614127,good,bad\n0,8,0,661,0.5836841052432981,0.41631589475670194,good,bad\n0,8,0,471,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,788,0.5794498148911693,0.42055018510883074,good,bad\n0,8,0,819,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,951,0.490932309079698,0.509067690920302,bad,bad\n0,8,0,711,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,174,0.490932309079698,0.509067690920302,bad,bad\n0,8,0,953,0.8328091268899902,0.1671908731100098,good,bad\n0,8,0,931,0.5794498148911693,0.42055018510883074,good,bad\n0,8,0,235,0.4201734700987917,0.5798265299012083,bad,bad\n0,8,0,419,0.7272058584350337,0.2727941415649663,good,bad\n0,8,0,274,0.5266294163965077,0.4733705836034923,good,bad\n0,8,0,915,0.6790032928308869,0.32099670716911305,good,bad\n0,8,0,106,0.8689213335338587,0.13107866646614127,good,bad\n0,9,0,579,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,57,0.8649192998662469,0.1350807001337531,good,good\n0,9,0,799,0.722109834166632,0.277890165833368,good,good\n0,9,0,908,0.8827030812523267,0.11729691874767328,good,good\n0,9,0,591,0.722109834166632,0.277890165833368,good,good\n0,9,0,476,0.8664458607110179,0.1335541392889821,good,good\n0,9,0,7,0.6469502557870893,0.3530497442129107,good,good\n0,9,0,749,0.8649192998662469,0.1350807001337531,good,good\n0,9,0,544,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,287,0.6843296861436163,0.3156703138563836,good,good\n0,9,0,365,0.8649192998662469,0.1350807001337531,good,good\n0,9,0,829,0.722109834166632,0.277890165833368,good,good\n0,9,0,693,0.6929504390965389,0.3070495609034611,good,good\n0,9,0,97,0.6108076271611969,0.38919237283880315,good,good\n0,9,0,928,0.9007919080159862,0.09920809198401379,good,good\n0,9,0,300,0.8827030812523267,0.11729691874767328,good,good\n0,9,0,88,0.45640016820008644,0.5435998317999136,bad,good\n0,9,0,112,0.6092459890689438,0.3907540109310562,good,good\n0,9,0,670,0.8656844125447436,0.13431558745525637,good,good\n0,9,0,463,0.722109834166632,0.277890165833368,good,good\n0,9,0,38,0.7740306558885689,0.2259693441114311,good,good\n0,9,0,480,0.6829099077907084,0.31709009220929163,good,good\n0,9,0,292,0.4950273509589851,0.5049726490410149,bad,good\n0,9,0,422,0.6469502557870893,0.3530497442129107,good,good\n0,9,0,42,0.6469502557870893,0.3530497442129107,good,good\n0,9,0,554,0.5701902256056918,0.42980977439430823,good,good\n0,9,0,352,0.9261559929456403,0.0738440070543597,good,good\n0,9,0,643,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,590,0.4150992843295068,0.5849007156704932,bad,good\n0,9,0,135,0.9007919080159862,0.09920809198401379,good,good\n0,9,0,600,0.6829099077907084,0.31709009220929163,good,good\n0,9,0,574,0.5701902256056918,0.42980977439430823,good,good\n0,9,0,582,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,250,0.5800150508469601,0.41998494915303985,good,good\n0,9,0,39,0.5701902256056918,0.42980977439430823,good,good\n0,9,0,108,0.6929504390965389,0.3070495609034611,good,good\n0,9,0,863,0.8847267753683615,0.11527322463163849,good,good\n0,9,0,765,0.6092459890689438,0.3907540109310562,good,good\n0,9,0,450,0.9007919080159862,0.09920809198401379,good,good\n0,9,0,342,0.7508852117531047,0.24911478824689523,good,good\n0,9,0,395,0.6843296861436163,0.3156703138563836,good,good\n0,9,0,514,0.9007919080159862,0.09920809198401379,good,good\n0,9,0,220,0.5701902256056918,0.42980977439430823,good,good\n0,9,0,233,0.5701902256056918,0.42980977439430823,good,good\n0,9,0,902,0.9261559929456403,0.0738440070543597,good,good\n0,9,0,126,0.4150992843295068,0.5849007156704932,bad,good\n0,9,0,445,0.9007919080159862,0.09920809198401379,good,good\n0,9,0,303,0.4150992843295068,0.5849007156704932,bad,good\n0,9,0,276,0.8649192998662469,0.1350807001337531,good,good\n0,9,0,682,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,201,0.4150992843295068,0.5849007156704932,bad,good\n0,9,0,426,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,77,0.6469502557870893,0.3530497442129107,good,good\n0,9,0,107,0.6469502557870893,0.3530497442129107,good,good\n0,9,0,380,0.581613285501744,0.41838671449825604,good,good\n0,9,0,437,0.8847267753683615,0.11527322463163849,good,good\n0,9,0,986,0.7740306558885689,0.2259693441114311,good,good\n0,9,0,581,0.6829099077907084,0.31709009220929163,good,good\n0,9,0,546,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,989,0.5701902256056918,0.42980977439430823,good,good\n0,9,0,231,0.8996124466208211,0.10038755337917889,good,good\n0,9,0,436,0.8996124466208211,0.10038755337917889,good,good\n0,9,0,58,0.7445390171229542,0.2554609828770457,good,good\n0,9,0,430,0.8827030812523267,0.11729691874767328,good,good\n0,9,0,992,0.6575584661409253,0.3424415338590748,good,good\n0,9,0,0,0.581613285501744,0.41838671449825604,good,good\n0,9,0,894,0.9007919080159862,0.09920809198401379,good,good\n0,9,0,613,0.5353472662187515,0.46465273378124844,good,good\n0,9,0,281,0.8225515039786175,0.17744849602138252,good,good\n0,9,0,360,0.722109834166632,0.277890165833368,good,good\n0,9,0,472,0.4150992843295068,0.5849007156704932,bad,bad\n0,9,0,813,0.4547720106177464,0.5452279893822536,bad,bad\n0,9,0,927,0.4150992843295068,0.5849007156704932,bad,bad\n0,9,0,595,0.7181408024919955,0.2818591975080044,good,bad\n0,9,0,593,0.6092459890689438,0.3907540109310562,good,bad\n0,9,0,559,0.6469502557870893,0.3530497442129107,good,bad\n0,9,0,946,0.4983094095407027,0.5016905904592973,bad,bad\n0,9,0,491,0.6469502557870893,0.3530497442129107,good,bad\n0,9,0,624,0.4150992843295068,0.5849007156704932,bad,bad\n0,9,0,92,0.9007919080159862,0.09920809198401379,good,bad\n0,9,0,834,0.7740306558885689,0.2259693441114311,good,bad\n0,9,0,911,0.6469502557870893,0.3530497442129107,good,bad\n0,9,0,230,0.7126273858067755,0.2873726141932245,good,bad\n0,9,0,143,0.4950273509589851,0.5049726490410149,bad,bad\n0,9,0,289,0.4150992843295068,0.5849007156704932,bad,bad\n0,9,0,958,0.4547720106177464,0.5452279893822536,bad,bad\n0,9,0,15,0.45640016820008644,0.5435998317999136,bad,bad\n0,9,0,963,0.8840556070935313,0.1159443929064688,good,bad\n0,9,0,621,0.8449129801854118,0.15508701981458822,good,bad\n0,9,0,918,0.576813573915153,0.42318642608484697,good,bad\n0,9,0,952,0.6857460327417132,0.3142539672582868,good,bad\n0,9,0,653,0.6843296861436163,0.3156703138563836,good,bad\n0,9,0,728,0.5701902256056918,0.42980977439430823,good,bad\n0,9,0,766,0.4950273509589851,0.5049726490410149,bad,bad\n0,9,0,308,0.6092459890689438,0.3907540109310562,good,bad\n0,9,0,320,0.5701902256056918,0.42980977439430823,good,bad\n0,9,0,503,0.6108076271611969,0.38919237283880315,good,bad\n0,9,0,822,0.4950273509589851,0.5049726490410149,bad,bad\n0,9,0,558,0.4547720106177464,0.5452279893822536,bad,bad\n0,9,0,714,0.5701902256056918,0.42980977439430823,good,bad\n')}
mfeurer commented 5 years ago

Okay, I now get the error calling https://www.openml.org/api/v1/xml//run/. I assume the browser issues a get request, but server expects a push request. Now, I'm afraid that we hit a server issue with which I cannot help you any more. @janvanrijn @joaquinvanschoren could you please have a look at this?

FelixNeutatz commented 5 years ago

I have one more additional question: It would be great if we could upload longer pipeline names because since the name always contains the entire path to the given class, e.g. "sklearn.linear_model.logistic.LogisticRegression" or "sklearn.pipeline.Pipeline", we quickly reach the limit of 1024 characters. Of course, one can start to hack names to be smaller but this does not really help readability for other people who are interested in the corresponding flows.

Therefore, I would be super happy if we could loosen the name length limit here: https://github.com/openml/OpenML/blob/7499851f73fc0433d21fd4e156f09dd9fa231921/openml_OS/views/pages/api_new/v1/xsd/openml.implementation.upload.xsd#L13 I guess that this change would also propagate to the underlying database schema. Should I start a new issue for this question or can we discuss it here as well?