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automatic detection of multioutput datasets #1001

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automatic detection of multioutput datasets #1001

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3dd6475
automatic detection of multioutput datasets
jhmenke Jan 14, 2020
e017fbe
Merge branch 'master' of https://github.com/EpistasisLab/tpot into de…
jhmenke Jan 20, 2020
81c0079
bugfix in operator_utils
jhmenke Jan 20, 2020
ae72c92
enable automatic multioutput for default regressors/classifiers
jhmenke Jan 20, 2020
2f5140a
Merge branch 'development' into development
jhmenke May 9, 2020
124ec4b
Update operator_utils.py
weixuanfu May 13, 2020
a86fdf9
Update operator_utils.py
weixuanfu May 13, 2020
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43 changes: 36 additions & 7 deletions tpot/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -491,8 +491,7 @@ def _setup_toolbox(self):
self._toolbox.register('expr_mut', self._gen_grow_safe, min_=self._min, max_=self._max)
self._toolbox.register('mutate', self._random_mutation_operator)


def _fit_init(self):
def _fit_init(self, multi_output_target: bool = False):
# initialization for fit function
if not self.warm_start or not hasattr(self, '_pareto_front'):
self._pop = []
Expand All @@ -501,6 +500,35 @@ def _fit_init(self):
self._last_optimized_pareto_front_n_gens = 0
self._setup_config(self.config_dict)

if multi_output_target:
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Modifying _config_dict may not work in the situation that use use a customized configurations instead of default one. So, I think a practical way is to modify the _compile_to_sklearn function (here). If multi_output_target is True, then
sklearn_pipeline=MultiOutputClassifier(estimator=sklearn_pipeline) or sklearn_pipeline=MultiOutputRegessor(estimator=sklearn_pipeline) . I think it maybe a more general solution for multioutput dataset.

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This seems better, to be honest i didn't find a good place where to put my code and only settled on the _fit_init function therefore.

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Okay i looked into it, but the code would be a mess. Several functions would have to take multi_output_target as a new argument (most of them in export_utils.py), since they don't have access to the data or the TPOT Object.

Imo _fit_init seems to be the least intrusive point to include the checks

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Thank you for looking into. You are right. I think TPOT exported codes should also include MultiOutputRegessor/MultiOutputClassifier, which should change a lot of codes in TPOT. I will look into it when I get some time next week.

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any updates?

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@jhmenke I am sorry for overlooking this. I did not get a chance to look into this issue those days due to my busy schedule. I agree that TPOT need some major changes for including MultiOutputRegessor/MultiOutputClassifier. I may get some time in March to add those changes. You are welcome to push any changes meanwhile.

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Could we use my PR as a temporary fix until you have time to thoroughly refactor the code? I can prepare an update with the current development branch.

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Sorry for the delay. I think we can use it for a temporary solution with a minor release.

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Okay i merged the current master/development into this. Should be good to go as an interim solution then.

single_output_classifiers = [
'sklearn.naive_bayes.MultinomialNB',
'sklearn.svm.LinearSVC',
'xgboost.XGBClassifier'
]
single_output_regressors = [
'sklearn.ensemble.AdaBoostRegressor',
'sklearn.linear_model.LassoLarsCV',
'sklearn.linear_model.ElasticNetCV',
'sklearn.svm.LinearSVR',
'xgboost.XGBRegressor',
'sklearn.linear_model.SGDRegressor'
]
for model in list(self._config_dict.keys()):
if model in single_output_classifiers:
if 'sklearn.multioutput.MultiOutputClassifier' not in self._config_dict.keys():
self._config_dict['sklearn.multioutput.MultiOutputClassifier'] = {"estimator": {}}
self._config_dict['sklearn.multioutput.MultiOutputClassifier']['estimator'][model] = self._config_dict[model]
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There is only one sklearn.multioutput.MultiOutputClassifier in self._config_dict and estimator is keeping updated until the last model in single_output_classifiers so that the rest models should be removed.

self._config_dict.pop(model, None)
elif model in single_output_regressors:
if 'sklearn.multioutput.MultiOutputRegressor' not in self._config_dict.keys():
self._config_dict['sklearn.multioutput.MultiOutputRegressor'] = {"estimator": {}}
if model == 'sklearn.linear_model.ElasticNetCV':
self._config_dict['sklearn.linear_model.MultiTaskElasticNetCV'] = self._config_dict[model]
else:
self._config_dict['sklearn.multioutput.MultiOutputRegressor']['estimator'][model] = self._config_dict[model]
self._config_dict.pop(model, None)

self._setup_template(self.template)

self.operators = []
Expand Down Expand Up @@ -622,7 +650,7 @@ def fit(self, features, target, sample_weight=None, groups=None):
Returns a copy of the fitted TPOT object

"""
self._fit_init()
self._fit_init(multi_output_target=len(target.shape) > 1 and target.shape[1] > 1)
features, target = self._check_dataset(features, target, sample_weight)


Expand Down Expand Up @@ -792,10 +820,11 @@ def _update_top_pipeline(self):
if not self._optimized_pipeline:
raise RuntimeError('There was an error in the TPOT optimization '
'process. This could be because the data was '
'not formatted properly, or because data for '
'not formatted properly, because data for '
'a regression problem was provided to the '
'TPOTClassifier object. Please make sure you '
'passed the data to TPOT correctly.')
'TPOTClassifier object, or an error in a '
'custom scoring function. Please make sure '
'you passed the data to TPOT correctly.')
else:
pareto_front_wvalues = [pipeline_scores.wvalues[1] for pipeline_scores in self._pareto_front.keys]
if not self._last_optimized_pareto_front:
Expand Down Expand Up @@ -1157,7 +1186,7 @@ def _check_dataset(self, features, target, sample_weight=None):

try:
if target is not None:
X, y = check_X_y(features, target, accept_sparse=True, dtype=None)
X, y = check_X_y(features, target, accept_sparse=True, dtype=None, multi_output=len(target.shape) > 1 and target.shape[1] > 1)
if self._imputed:
return X, y
else:
Expand Down
2 changes: 1 addition & 1 deletion tpot/operator_utils.py
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Expand Up @@ -212,7 +212,7 @@ def op_type(cls):
for dkey, dval in prange.items():
dep_import_str, dep_op_str, dep_op_obj = source_decode(dkey, verbose=verbose)
if dep_import_str in import_hash:
import_hash[import_str].append(dep_op_str)
import_hash[dep_import_str].append(dep_op_str)
else:
import_hash[dep_import_str] = [dep_op_str]
dep_op_list[pname] = dep_op_str
Expand Down