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+/hpsklearn-0.1.0.tar.gz
diff --git a/python-hpsklearn.spec b/python-hpsklearn.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-hpsklearn
+Version:	0.1.0
+Release:	1
+Summary:	Hyperparameter Optimization for sklearn
+License:	BSD
+URL:		http://hyperopt.github.com/hyperopt-sklearn/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ce/cb/61b99f73621e2692abd0e730f7888a9983d01f626868336fa1db1d57bc1e/hpsklearn-0.1.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+# hyperopt-sklearn
+
+[Hyperopt-sklearn](http://hyperopt.github.com/hyperopt-sklearn/) is
+[Hyperopt](http://hyperopt.github.com/hyperopt)-based model selection among machine learning algorithms in
+[scikit-learn](http://scikit-learn.org/).
+
+See how to use hyperopt-sklearn through [examples](http://hyperopt.github.io/hyperopt-sklearn/#documentation)
+or older
+[notebooks](http://nbviewer.ipython.org/github/hyperopt/hyperopt-sklearn/tree/master/notebooks)
+
+
+## Installation
+
+Installation from a git clone using pip is supported:
+
+    git clone git@github.com:hyperopt/hyperopt-sklearn.git
+    (cd hyperopt-sklearn && pip install -e .)
+
+## Usage
+
+If you are familiar with sklearn, adding the hyperparameter search with hyperopt-sklearn is only a one line change from the standard pipeline.
+
+```
+from hpsklearn import HyperoptEstimator, svc
+from sklearn import svm
+
+# Load Data
+# ...
+
+if use_hpsklearn:
+    estim = HyperoptEstimator(classifier=svc('mySVC'))
+else:
+    estim = svm.SVC()
+
+estim.fit(X_train, y_train)
+
+print(estim.score(X_test, y_test))
+# <<show score here>>
+```
+
+Complete example using the Iris dataset:
+
+```
+from hpsklearn import HyperoptEstimator, any_classifier
+from sklearn.datasets import load_iris
+from hyperopt import tpe
+import numpy as np
+
+# Download the data and split into training and test sets
+
+iris = load_iris()
+
+X = iris.data
+y = iris.target
+
+test_size = int(0.2 * len(y))
+np.random.seed(13)
+indices = np.random.permutation(len(X))
+X_train = X[ indices[:-test_size]]
+y_train = y[ indices[:-test_size]]
+X_test = X[ indices[-test_size:]]
+y_test = y[ indices[-test_size:]]
+
+# Instantiate a HyperoptEstimator with the search space and number of evaluations
+
+estim = HyperoptEstimator(classifier=any_classifier('my_clf'),
+                          preprocessing=any_preprocessing('my_pre'),
+                          algo=tpe.suggest,
+                          max_evals=100,
+                          trial_timeout=120)
+
+# Search the hyperparameter space based on the data
+
+estim.fit( X_train, y_train )
+
+# Show the results
+
+print( estim.score( X_test, y_test ) )
+# 1.0
+
+print( estim.best_model() )
+# {'learner': ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
+#           max_depth=3, max_features='log2', max_leaf_nodes=None,
+#           min_impurity_decrease=0.0, min_impurity_split=None,
+#           min_samples_leaf=1, min_samples_split=2,
+#           min_weight_fraction_leaf=0.0, n_estimators=13, n_jobs=1,
+#           oob_score=False, random_state=1, verbose=False,
+#           warm_start=False), 'preprocs': (), 'ex_preprocs': ()}
+```
+
+Here's an example using MNIST and being more specific on the classifier and preprocessing.
+
+```
+from hpsklearn import HyperoptEstimator, extra_trees
+from sklearn.datasets import fetch_mldata
+from hyperopt import tpe
+import numpy as np
+
+# Download the data and split into training and test sets
+
+digits = fetch_mldata('MNIST original')
+
+X = digits.data
+y = digits.target
+
+test_size = int(0.2 * len(y))
+np.random.seed(13)
+indices = np.random.permutation(len(X))
+X_train = X[ indices[:-test_size]]
+y_train = y[ indices[:-test_size]]
+X_test = X[ indices[-test_size:]]
+y_test = y[ indices[-test_size:]]
+
+# Instantiate a HyperoptEstimator with the search space and number of evaluations
+
+estim = HyperoptEstimator(classifier=extra_trees('my_clf'),
+                          preprocessing=[],
+                          algo=tpe.suggest,
+                          max_evals=10,
+                          trial_timeout=300)
+
+# Search the hyperparameter space based on the data
+
+estim.fit( X_train, y_train )
+
+# Show the results
+
+print( estim.score( X_test, y_test ) )
+# 0.962785714286 
+
+print( estim.best_model() )
+# {'learner': ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='entropy',
+#           max_depth=None, max_features=0.959202875857,
+#           max_leaf_nodes=None, min_impurity_decrease=0.0,
+#           min_impurity_split=None, min_samples_leaf=1,
+#           min_samples_split=2, min_weight_fraction_leaf=0.0,
+#           n_estimators=20, n_jobs=1, oob_score=False, random_state=3,
+#           verbose=False, warm_start=False), 'preprocs': (), 'ex_preprocs': ()}
+```
+
+## Available Components
+
+Not all of the classifiers/regressors/preprocessing from sklearn have been implemented yet.
+A list of those currently available is shown below.
+If there is something you would like that is not on the list, feel free to make an issue or a pull request!
+The source code for implementing these functions is found [here](https://github.com/hyperopt/hyperopt-sklearn/blob/master/hpsklearn/components.py)
+
+### Classifiers
+
+```
+svc
+svc_linear
+svc_rbf
+svc_poly
+svc_sigmoid
+liblinear_svc
+
+knn
+
+ada_boost
+gradient_boosting
+
+random_forest
+extra_trees
+decision_tree
+
+sgd
+
+xgboost_classification
+
+multinomial_nb
+gaussian_nb
+
+passive_aggressive
+
+linear_discriminant_analysis
+quadratic_discriminant_analysis
+
+rbm
+
+colkmeans
+
+one_vs_rest
+one_vs_one
+output_code
+
+```
+
+For a simple generic search space across many classifiers, use `any_classifier`. If your data is in a sparse matrix format, use `any_sparse_classifier`.
+
+### Regressors
+
+```
+svr
+svr_linear
+svr_rbf
+svr_poly
+svr_sigmoid
+
+knn_regression
+
+ada_boost_regression
+gradient_boosting_regression
+
+random_forest_regression
+extra_trees_regression
+
+sgd_regression
+
+xgboost_regression
+```
+
+For a simple generic search space across many regressors, use `any_regressor`. If your data is in a sparse matrix format, use `any_sparse_regressor`.
+
+### Preprocessing
+
+```
+pca
+
+one_hot_encoder
+
+standard_scaler
+min_max_scaler
+normalizer
+
+ts_lagselector
+
+tfidf
+
+```
+
+For a simple generic search space across many preprocessing algorithms, use `any_preprocessing`.
+If you are working with raw text data, use `any_text_preprocessing`.
+Currently only TFIDF is used for text, but more may be added in the future.
+Note that the `preprocessing` parameter in `HyperoptEstimator` is expecting a list, since various preprocessing steps can be chained together.
+The generic search space functions `any_preprocessing` and `any_text_preprocessing` already return a list, but the others do not so they should be wrapped in a list.
+If you do not want to do any preprocessing, pass in an empty list `[]`.
+
+%package -n python3-hpsklearn
+Summary:	Hyperparameter Optimization for sklearn
+Provides:	python-hpsklearn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-hpsklearn
+# hyperopt-sklearn
+
+[Hyperopt-sklearn](http://hyperopt.github.com/hyperopt-sklearn/) is
+[Hyperopt](http://hyperopt.github.com/hyperopt)-based model selection among machine learning algorithms in
+[scikit-learn](http://scikit-learn.org/).
+
+See how to use hyperopt-sklearn through [examples](http://hyperopt.github.io/hyperopt-sklearn/#documentation)
+or older
+[notebooks](http://nbviewer.ipython.org/github/hyperopt/hyperopt-sklearn/tree/master/notebooks)
+
+
+## Installation
+
+Installation from a git clone using pip is supported:
+
+    git clone git@github.com:hyperopt/hyperopt-sklearn.git
+    (cd hyperopt-sklearn && pip install -e .)
+
+## Usage
+
+If you are familiar with sklearn, adding the hyperparameter search with hyperopt-sklearn is only a one line change from the standard pipeline.
+
+```
+from hpsklearn import HyperoptEstimator, svc
+from sklearn import svm
+
+# Load Data
+# ...
+
+if use_hpsklearn:
+    estim = HyperoptEstimator(classifier=svc('mySVC'))
+else:
+    estim = svm.SVC()
+
+estim.fit(X_train, y_train)
+
+print(estim.score(X_test, y_test))
+# <<show score here>>
+```
+
+Complete example using the Iris dataset:
+
+```
+from hpsklearn import HyperoptEstimator, any_classifier
+from sklearn.datasets import load_iris
+from hyperopt import tpe
+import numpy as np
+
+# Download the data and split into training and test sets
+
+iris = load_iris()
+
+X = iris.data
+y = iris.target
+
+test_size = int(0.2 * len(y))
+np.random.seed(13)
+indices = np.random.permutation(len(X))
+X_train = X[ indices[:-test_size]]
+y_train = y[ indices[:-test_size]]
+X_test = X[ indices[-test_size:]]
+y_test = y[ indices[-test_size:]]
+
+# Instantiate a HyperoptEstimator with the search space and number of evaluations
+
+estim = HyperoptEstimator(classifier=any_classifier('my_clf'),
+                          preprocessing=any_preprocessing('my_pre'),
+                          algo=tpe.suggest,
+                          max_evals=100,
+                          trial_timeout=120)
+
+# Search the hyperparameter space based on the data
+
+estim.fit( X_train, y_train )
+
+# Show the results
+
+print( estim.score( X_test, y_test ) )
+# 1.0
+
+print( estim.best_model() )
+# {'learner': ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
+#           max_depth=3, max_features='log2', max_leaf_nodes=None,
+#           min_impurity_decrease=0.0, min_impurity_split=None,
+#           min_samples_leaf=1, min_samples_split=2,
+#           min_weight_fraction_leaf=0.0, n_estimators=13, n_jobs=1,
+#           oob_score=False, random_state=1, verbose=False,
+#           warm_start=False), 'preprocs': (), 'ex_preprocs': ()}
+```
+
+Here's an example using MNIST and being more specific on the classifier and preprocessing.
+
+```
+from hpsklearn import HyperoptEstimator, extra_trees
+from sklearn.datasets import fetch_mldata
+from hyperopt import tpe
+import numpy as np
+
+# Download the data and split into training and test sets
+
+digits = fetch_mldata('MNIST original')
+
+X = digits.data
+y = digits.target
+
+test_size = int(0.2 * len(y))
+np.random.seed(13)
+indices = np.random.permutation(len(X))
+X_train = X[ indices[:-test_size]]
+y_train = y[ indices[:-test_size]]
+X_test = X[ indices[-test_size:]]
+y_test = y[ indices[-test_size:]]
+
+# Instantiate a HyperoptEstimator with the search space and number of evaluations
+
+estim = HyperoptEstimator(classifier=extra_trees('my_clf'),
+                          preprocessing=[],
+                          algo=tpe.suggest,
+                          max_evals=10,
+                          trial_timeout=300)
+
+# Search the hyperparameter space based on the data
+
+estim.fit( X_train, y_train )
+
+# Show the results
+
+print( estim.score( X_test, y_test ) )
+# 0.962785714286 
+
+print( estim.best_model() )
+# {'learner': ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='entropy',
+#           max_depth=None, max_features=0.959202875857,
+#           max_leaf_nodes=None, min_impurity_decrease=0.0,
+#           min_impurity_split=None, min_samples_leaf=1,
+#           min_samples_split=2, min_weight_fraction_leaf=0.0,
+#           n_estimators=20, n_jobs=1, oob_score=False, random_state=3,
+#           verbose=False, warm_start=False), 'preprocs': (), 'ex_preprocs': ()}
+```
+
+## Available Components
+
+Not all of the classifiers/regressors/preprocessing from sklearn have been implemented yet.
+A list of those currently available is shown below.
+If there is something you would like that is not on the list, feel free to make an issue or a pull request!
+The source code for implementing these functions is found [here](https://github.com/hyperopt/hyperopt-sklearn/blob/master/hpsklearn/components.py)
+
+### Classifiers
+
+```
+svc
+svc_linear
+svc_rbf
+svc_poly
+svc_sigmoid
+liblinear_svc
+
+knn
+
+ada_boost
+gradient_boosting
+
+random_forest
+extra_trees
+decision_tree
+
+sgd
+
+xgboost_classification
+
+multinomial_nb
+gaussian_nb
+
+passive_aggressive
+
+linear_discriminant_analysis
+quadratic_discriminant_analysis
+
+rbm
+
+colkmeans
+
+one_vs_rest
+one_vs_one
+output_code
+
+```
+
+For a simple generic search space across many classifiers, use `any_classifier`. If your data is in a sparse matrix format, use `any_sparse_classifier`.
+
+### Regressors
+
+```
+svr
+svr_linear
+svr_rbf
+svr_poly
+svr_sigmoid
+
+knn_regression
+
+ada_boost_regression
+gradient_boosting_regression
+
+random_forest_regression
+extra_trees_regression
+
+sgd_regression
+
+xgboost_regression
+```
+
+For a simple generic search space across many regressors, use `any_regressor`. If your data is in a sparse matrix format, use `any_sparse_regressor`.
+
+### Preprocessing
+
+```
+pca
+
+one_hot_encoder
+
+standard_scaler
+min_max_scaler
+normalizer
+
+ts_lagselector
+
+tfidf
+
+```
+
+For a simple generic search space across many preprocessing algorithms, use `any_preprocessing`.
+If you are working with raw text data, use `any_text_preprocessing`.
+Currently only TFIDF is used for text, but more may be added in the future.
+Note that the `preprocessing` parameter in `HyperoptEstimator` is expecting a list, since various preprocessing steps can be chained together.
+The generic search space functions `any_preprocessing` and `any_text_preprocessing` already return a list, but the others do not so they should be wrapped in a list.
+If you do not want to do any preprocessing, pass in an empty list `[]`.
+
+%package help
+Summary:	Development documents and examples for hpsklearn
+Provides:	python3-hpsklearn-doc
+%description help
+# hyperopt-sklearn
+
+[Hyperopt-sklearn](http://hyperopt.github.com/hyperopt-sklearn/) is
+[Hyperopt](http://hyperopt.github.com/hyperopt)-based model selection among machine learning algorithms in
+[scikit-learn](http://scikit-learn.org/).
+
+See how to use hyperopt-sklearn through [examples](http://hyperopt.github.io/hyperopt-sklearn/#documentation)
+or older
+[notebooks](http://nbviewer.ipython.org/github/hyperopt/hyperopt-sklearn/tree/master/notebooks)
+
+
+## Installation
+
+Installation from a git clone using pip is supported:
+
+    git clone git@github.com:hyperopt/hyperopt-sklearn.git
+    (cd hyperopt-sklearn && pip install -e .)
+
+## Usage
+
+If you are familiar with sklearn, adding the hyperparameter search with hyperopt-sklearn is only a one line change from the standard pipeline.
+
+```
+from hpsklearn import HyperoptEstimator, svc
+from sklearn import svm
+
+# Load Data
+# ...
+
+if use_hpsklearn:
+    estim = HyperoptEstimator(classifier=svc('mySVC'))
+else:
+    estim = svm.SVC()
+
+estim.fit(X_train, y_train)
+
+print(estim.score(X_test, y_test))
+# <<show score here>>
+```
+
+Complete example using the Iris dataset:
+
+```
+from hpsklearn import HyperoptEstimator, any_classifier
+from sklearn.datasets import load_iris
+from hyperopt import tpe
+import numpy as np
+
+# Download the data and split into training and test sets
+
+iris = load_iris()
+
+X = iris.data
+y = iris.target
+
+test_size = int(0.2 * len(y))
+np.random.seed(13)
+indices = np.random.permutation(len(X))
+X_train = X[ indices[:-test_size]]
+y_train = y[ indices[:-test_size]]
+X_test = X[ indices[-test_size:]]
+y_test = y[ indices[-test_size:]]
+
+# Instantiate a HyperoptEstimator with the search space and number of evaluations
+
+estim = HyperoptEstimator(classifier=any_classifier('my_clf'),
+                          preprocessing=any_preprocessing('my_pre'),
+                          algo=tpe.suggest,
+                          max_evals=100,
+                          trial_timeout=120)
+
+# Search the hyperparameter space based on the data
+
+estim.fit( X_train, y_train )
+
+# Show the results
+
+print( estim.score( X_test, y_test ) )
+# 1.0
+
+print( estim.best_model() )
+# {'learner': ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
+#           max_depth=3, max_features='log2', max_leaf_nodes=None,
+#           min_impurity_decrease=0.0, min_impurity_split=None,
+#           min_samples_leaf=1, min_samples_split=2,
+#           min_weight_fraction_leaf=0.0, n_estimators=13, n_jobs=1,
+#           oob_score=False, random_state=1, verbose=False,
+#           warm_start=False), 'preprocs': (), 'ex_preprocs': ()}
+```
+
+Here's an example using MNIST and being more specific on the classifier and preprocessing.
+
+```
+from hpsklearn import HyperoptEstimator, extra_trees
+from sklearn.datasets import fetch_mldata
+from hyperopt import tpe
+import numpy as np
+
+# Download the data and split into training and test sets
+
+digits = fetch_mldata('MNIST original')
+
+X = digits.data
+y = digits.target
+
+test_size = int(0.2 * len(y))
+np.random.seed(13)
+indices = np.random.permutation(len(X))
+X_train = X[ indices[:-test_size]]
+y_train = y[ indices[:-test_size]]
+X_test = X[ indices[-test_size:]]
+y_test = y[ indices[-test_size:]]
+
+# Instantiate a HyperoptEstimator with the search space and number of evaluations
+
+estim = HyperoptEstimator(classifier=extra_trees('my_clf'),
+                          preprocessing=[],
+                          algo=tpe.suggest,
+                          max_evals=10,
+                          trial_timeout=300)
+
+# Search the hyperparameter space based on the data
+
+estim.fit( X_train, y_train )
+
+# Show the results
+
+print( estim.score( X_test, y_test ) )
+# 0.962785714286 
+
+print( estim.best_model() )
+# {'learner': ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='entropy',
+#           max_depth=None, max_features=0.959202875857,
+#           max_leaf_nodes=None, min_impurity_decrease=0.0,
+#           min_impurity_split=None, min_samples_leaf=1,
+#           min_samples_split=2, min_weight_fraction_leaf=0.0,
+#           n_estimators=20, n_jobs=1, oob_score=False, random_state=3,
+#           verbose=False, warm_start=False), 'preprocs': (), 'ex_preprocs': ()}
+```
+
+## Available Components
+
+Not all of the classifiers/regressors/preprocessing from sklearn have been implemented yet.
+A list of those currently available is shown below.
+If there is something you would like that is not on the list, feel free to make an issue or a pull request!
+The source code for implementing these functions is found [here](https://github.com/hyperopt/hyperopt-sklearn/blob/master/hpsklearn/components.py)
+
+### Classifiers
+
+```
+svc
+svc_linear
+svc_rbf
+svc_poly
+svc_sigmoid
+liblinear_svc
+
+knn
+
+ada_boost
+gradient_boosting
+
+random_forest
+extra_trees
+decision_tree
+
+sgd
+
+xgboost_classification
+
+multinomial_nb
+gaussian_nb
+
+passive_aggressive
+
+linear_discriminant_analysis
+quadratic_discriminant_analysis
+
+rbm
+
+colkmeans
+
+one_vs_rest
+one_vs_one
+output_code
+
+```
+
+For a simple generic search space across many classifiers, use `any_classifier`. If your data is in a sparse matrix format, use `any_sparse_classifier`.
+
+### Regressors
+
+```
+svr
+svr_linear
+svr_rbf
+svr_poly
+svr_sigmoid
+
+knn_regression
+
+ada_boost_regression
+gradient_boosting_regression
+
+random_forest_regression
+extra_trees_regression
+
+sgd_regression
+
+xgboost_regression
+```
+
+For a simple generic search space across many regressors, use `any_regressor`. If your data is in a sparse matrix format, use `any_sparse_regressor`.
+
+### Preprocessing
+
+```
+pca
+
+one_hot_encoder
+
+standard_scaler
+min_max_scaler
+normalizer
+
+ts_lagselector
+
+tfidf
+
+```
+
+For a simple generic search space across many preprocessing algorithms, use `any_preprocessing`.
+If you are working with raw text data, use `any_text_preprocessing`.
+Currently only TFIDF is used for text, but more may be added in the future.
+Note that the `preprocessing` parameter in `HyperoptEstimator` is expecting a list, since various preprocessing steps can be chained together.
+The generic search space functions `any_preprocessing` and `any_text_preprocessing` already return a list, but the others do not so they should be wrapped in a list.
+If you do not want to do any preprocessing, pass in an empty list `[]`.
+
+%prep
+%autosetup -n hpsklearn-0.1.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-hpsklearn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8947177
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+839ae12cc13d7d49ca511622c56bcc1d  hpsklearn-0.1.0.tar.gz