diff --git a/nnetsauce.egg-info/PKG-INFO b/nnetsauce.egg-info/PKG-INFO deleted file mode 100644 index a89e758..0000000 --- a/nnetsauce.egg-info/PKG-INFO +++ /dev/null @@ -1,26 +0,0 @@ -Metadata-Version: 2.1 -Name: nnetsauce -Version: 0.25.2 -Summary: Quasi-randomized (neural) networks -Home-page: https://techtonique.github.io/nnetsauce/ -Download-URL: https://github.com/Techtonique/nnetsauce -Author: T. Moudiki -Author-email: thierry.moudiki@gmail.com -License: BSD Clause Clear -Classifier: Development Status :: 3 - Alpha -Classifier: Intended Audience :: Developers -Classifier: Programming Language :: Python :: 3 -License-File: LICENSE -Requires-Dist: joblib -Requires-Dist: matplotlib -Requires-Dist: numpy -Requires-Dist: pandas -Requires-Dist: pyvinecopulib -Requires-Dist: requests -Requires-Dist: scipy -Requires-Dist: scikit-learn -Requires-Dist: statsmodels -Requires-Dist: threadpoolctl -Requires-Dist: tqdm - -Quasi-randomized (neural) networks for regression, classification and time series forecasting diff --git a/nnetsauce.egg-info/SOURCES.txt b/nnetsauce.egg-info/SOURCES.txt deleted file mode 100644 index 421e168..0000000 --- a/nnetsauce.egg-info/SOURCES.txt +++ /dev/null @@ -1,89 +0,0 @@ -LICENSE -README.md -setup.py -nnetsauce/__init__.py -nnetsauce.egg-info/PKG-INFO -nnetsauce.egg-info/SOURCES.txt -nnetsauce.egg-info/dependency_links.txt -nnetsauce.egg-info/requires.txt -nnetsauce.egg-info/top_level.txt -nnetsauce/base/__init__.py -nnetsauce/base/base.py -nnetsauce/base/baseRegressor.py -nnetsauce/boosting/__init__.py -nnetsauce/boosting/adaBoostClassifier.py -nnetsauce/boosting/bst.py -nnetsauce/custom/__init__.py -nnetsauce/custom/custom.py -nnetsauce/custom/customClassifier.py -nnetsauce/custom/customRegressor.py -nnetsauce/datasets/__init__.py -nnetsauce/datasets/dowload.py -nnetsauce/deep/__init__.py -nnetsauce/deep/deepClassifier.py -nnetsauce/deep/deepMTS.py -nnetsauce/deep/deepRegressor.py -nnetsauce/glm/__init__.py -nnetsauce/glm/glm.py -nnetsauce/glm/glmClassifier.py -nnetsauce/glm/glmRegressor.py -nnetsauce/lazypredict/__init__.py -nnetsauce/lazypredict/config.py -nnetsauce/lazypredict/lazydeepClassifier.py -nnetsauce/lazypredict/lazydeepMTS.py -nnetsauce/lazypredict/lazydeepRegressor.py -nnetsauce/mts/__init__.py -nnetsauce/mts/classical.py -nnetsauce/mts/mts.py -nnetsauce/multitask/__init__.py -nnetsauce/multitask/multitaskClassifier.py -nnetsauce/multitask/simplemultitaskClassifier.py -nnetsauce/nonconformist/__init__.py -nnetsauce/nonconformist/acp.py -nnetsauce/nonconformist/base.py -nnetsauce/nonconformist/cp.py -nnetsauce/nonconformist/evaluation.py -nnetsauce/nonconformist/icp.py -nnetsauce/nonconformist/nc.py -nnetsauce/nonconformist/util.py -nnetsauce/optimizers/__init__.py -nnetsauce/optimizers/helpers.py -nnetsauce/optimizers/optimizer.py -nnetsauce/predictioninterval/__init__.py -nnetsauce/predictioninterval/predictioninterval.py -nnetsauce/predictionset/__init__.py -nnetsauce/predictionset/predictionset.py -nnetsauce/randombag/__init__.py -nnetsauce/randombag/bag.py -nnetsauce/randombag/helpers.py -nnetsauce/randombag/randomBagClassifier.py -nnetsauce/randombag/randomBagRegressor.py -nnetsauce/ridge2/__init__.py -nnetsauce/ridge2/ridge2.py -nnetsauce/ridge2/ridge2Classifier.py -nnetsauce/ridge2/ridge2MultitaskClassifier.py -nnetsauce/ridge2/ridge2Regressor.py -nnetsauce/rvfl/__init__.py -nnetsauce/rvfl/bayesianrvfl2Regressor.py -nnetsauce/rvfl/bayesianrvflRegressor.py -nnetsauce/sampling/__init__.py -nnetsauce/sampling/copulas.py -nnetsauce/sampling/helpers.py -nnetsauce/sampling/rowsubsampling.py -nnetsauce/simulation/__init__.py -nnetsauce/simulation/getsims.py -nnetsauce/simulation/nodesimulation.py -nnetsauce/simulation/sobol.py -nnetsauce/utils/__init__.py -nnetsauce/utils/activations.py -nnetsauce/utils/lmfuncs.py -nnetsauce/utils/matrixops.py -nnetsauce/utils/memoize.py -nnetsauce/utils/misc.py -nnetsauce/utils/model_selection.py -nnetsauce/utils/progress_bar.py -nnetsauce/utils/psdcheck.py -nnetsauce/utils/timeseries.py -nnetsauce/utils/where.py -nnetsauce/votingregressor/__init__.py -nnetsauce/votingregressor/votingregressor.py \ No newline at end of file diff --git a/nnetsauce.egg-info/dependency_links.txt b/nnetsauce.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/nnetsauce.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/nnetsauce.egg-info/requires.txt b/nnetsauce.egg-info/requires.txt deleted file mode 100644 index 57810a4..0000000 --- a/nnetsauce.egg-info/requires.txt +++ /dev/null @@ -1,11 +0,0 @@ -joblib -matplotlib -numpy -pandas -pyvinecopulib -requests -scipy -scikit-learn -statsmodels -threadpoolctl -tqdm diff --git a/nnetsauce.egg-info/top_level.txt b/nnetsauce.egg-info/top_level.txt deleted file mode 100644 index 85b8954..0000000 --- a/nnetsauce.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -nnetsauce diff --git a/nnetsauce/lazypredict/lazydeepMTS.py b/nnetsauce/lazypredict/lazydeepMTS.py index aad905e..5a9d5db 100644 --- a/nnetsauce/lazypredict/lazydeepMTS.py +++ b/nnetsauce/lazypredict/lazydeepMTS.py @@ -375,11 +375,9 @@ def fit(self, X_train, X_test, xreg=None, per_series=False, **kwargs): custom_metric = self.custom_metric(X_test, X_pred) else: custom_metric = self.custom_metric(X_test_h, X_pred) - print(f"\n\n Custom metric: {custom_metric} \n\n") CUSTOM_METRIC.append(custom_metric) except Exception as e: custom_metric = np.iinfo(np.float32).max - print(f"\n\n Custom metric: {custom_metric} \n\n") CUSTOM_METRIC.append(np.iinfo(np.float32).max) if (self.replications is not None) or (self.type_pi == "gaussian"): @@ -590,11 +588,9 @@ def fit(self, X_train, X_test, xreg=None, per_series=False, **kwargs): if self.custom_metric is not None: try: custom_metric = self.custom_metric(X_test, X_pred) - print(f"\n\n Custom metric: {custom_metric} \n\n") CUSTOM_METRIC.append(custom_metric) except Exception as e: custom_metric = np.iinfo(np.float32).max - print(f"\n\n Custom metric: {custom_metric} \n\n") CUSTOM_METRIC.append(custom_metric) if self.verbose > 0: @@ -961,11 +957,9 @@ def fit(self, X_train, X_test, xreg=None, per_series=False, **kwargs): custom_metric = self.custom_metric(X_test, X_pred) else: custom_metric = self.custom_metric(X_test_h, X_pred) - print(f"\n\n Custom metric: {custom_metric} \n\n") CUSTOM_METRIC.append(custom_metric) except Exception as e: custom_metric = np.iinfo(np.float32).max - print(f"\n\n Custom metric: {custom_metric} \n\n") CUSTOM_METRIC.append(np.iinfo(np.float32).max) if self.verbose > 0: @@ -992,9 +986,7 @@ def fit(self, X_train, X_test, xreg=None, per_series=False, **kwargs): if self.custom_metric is not None: scores_verbose["Custom metric"] = custom_metric - - print(scores_verbose) - + if self.predictions: predictions[name] = X_pred @@ -1025,12 +1017,6 @@ def fit(self, X_train, X_test, xreg=None, per_series=False, **kwargs): if self.custom_metric is not None: scores["Custom metric"] = CUSTOM_METRIC - print(f"\n\n Scores: {scores} \n\n") - for key, value in scores.items(): - print(f"\n\n Key: {key} \n\n") - print(f"\n\n Value: {value} \n\n") - print(f"\n\n len(Value): {len(value)} \n\n") - if per_series: scores = dict_to_dataframe_series(scores, self.series_names) else: diff --git a/setup.py b/setup.py index 23b4fd8..d77cad9 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from codecs import open from os import path -__version__ = '0.25.2' +__version__ = '0.25.3' # get the dependencies and installs here = path.abspath(path.dirname(__file__))