automatic import of python-mlregressionopeneuler20.03

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+/mlregression-0.1.10.tar.gz
diff --git a/python-mlregression.spec b/python-mlregression.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mlregression
+Version:	0.1.10
+Release:	1
+Summary:	Machine learning regression off-the-shelf
+License:	MIT License
+URL:		https://github.com/muhlbach/mlregression
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ec/b8/85a0e6d41da0cc6934acc4fd78d72a1b8b32f32c3e19bd584b6d99c394bb/mlregression-0.1.10.tar.gz
+BuildArch:	noarch
+
+
+%description
+
+# *** ATTENTION ***
+Don't immidiately run `pip install mlregression`. See Section _Installation_.
+
+# Machine learning regression (mlregression)
+
+Machine Learning Regression (mlregrresion) is an off-the-shelf implementation of the most popular ML methods that automatically takes care of fitting and parameter tuning.
+
+Currently, the __fully__ implemented models include:
+- Ensemble trees (Random forests, XGBoost, LightGBM, GradientBoostingRegressor, ExtraTreesRegressor)
+- Penalized regression (Ridge, Lasso, ElasticNet, Lars, LassoLars) 
+- Neural nets (Simple neural nets with 1-5 hidden layers, rely activation, and early stopping)
+
+_NB!_ When using penalized regressions, consider using the native CV-implementation from scikit-learn for speed, e.g., simply set `estimator="LassoCV"` similar to Example 1.
+
+Scikit-learn regressors (together with `XGBoost` and `LightGBM`) can be estimated by setting the `estimator`-argument equal to the name (string) as in Example 1 (`estimator="RandomForestRegressor"`).
+Alternatively, one can provide an instance of an estimator, e.g., `estimator=RandomForestRegressor()`. Again, this is fully automated for most Scikit-learn regressors, but for non-standard methods, one would have to provide a parameter grid as well, e.g., `param_grid={...}`.
+
+Please contact the authors below if you find any bugs or have any suggestions for improvement. Thank you!
+
+Author: Nicolaj Søndergaard Mühlbach (n.muhlbach at gmail dot com, muhlbach at mit dot edu) 
+
+## Code dependencies
+This code has the following dependencies:
+
+- Python >=3.6
+- numpy >=1.19
+- pandas >=1.3
+- scikit-learn >=1
+- scikit-learn-intelex >= 2021.3
+- daal >= 2021.3
+- daal4py >= 2021.3
+- tbb >= 2021.4
+- xgboost >=1.5
+- lightgbm >=3.2
+
+
+## Installation
+Before calling `pip install mlregression`, we recommend using `conda` to install the dependencies. In our experience, calling the following command works like a charm:
+```
+conda install -c conda-forge numpy">=1.19" pandas">=1.3" scikit-learn">=1" scikit-learn-intelex">=2021.3" daal">=2021.3" daal4py">=2021.3" tbb">=2021.4" xgboost">=1.5" lightgbm">=3.2" --force-reinstall
+```
+After this, install `mlregression` by calling `pip install mlregression`.
+Note that without installing the dependensies, the package will not work. As of now, it does not work when installing the dependensies via `pip install`. The reason is that we are using the Intel® Extension for Scikit-learn to massively speed up computations, but the dependensies are not properly installed via `pip install`.
+
+## Usage
+We demonstrate the use of __mlregression__ below, using random forests, xgboost, and lightGBM as underlying regressors.
+
+```python
+#------------------------------------------------------------------------------
+# Libraries
+#------------------------------------------------------------------------------
+# Standard
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+
+# This library
+from mlregression.mlreg import MLRegressor
+
+#------------------------------------------------------------------------------
+# Data
+#------------------------------------------------------------------------------
+# Generate data
+X, y = make_regression(n_samples=500,
+                       n_features=10, 
+                       n_informative=5,
+                       n_targets=1,
+                       bias=0.0,
+                       coef=False,
+                       random_state=1991)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+#------------------------------------------------------------------------------
+# Example 1: Prediction
+#------------------------------------------------------------------------------
+# Specify any of the following estimators:
+"""
+"LinearRegression",
+"RidgeCV", "LassoCV", "ElasticNetCV",
+"RandomForestRegressor","ExtraTreesRegressor", "GradientBoostingRegressor",
+"XGBRegressor", "LGBMegressor",
+"MLPRegressor",
+"""
+
+# For instance, pick "RandomForestRegressor"
+estimator = "RandomForestRegressor"
+# Note that the 'estimator' may also be an instance of a class, e.g., RandomForestRegressor(), conditional on being imported first, e.g. from sklearn.ensemble import RandomForestRegressor
+
+# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models') and the number of cross-validation folds ('n_cv_folds')
+mlreg = MLRegressor(estimator=estimator,
+                    n_cv_folds=5,
+                    max_n_models=2)
+
+# Fit
+mlreg.fit(X=X_train, y=y_train)
+
+# Predict
+y_hat = mlreg.predict(X=X_test)
+
+# Access all the usual attributes
+mlreg.best_score_
+mlreg.best_estimator_
+
+# Compute the score
+mlreg.score(X=X_test,y=y_test)
+
+#------------------------------------------------------------------------------
+# Example 2: Cross-fitting
+#------------------------------------------------------------------------------
+# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models'), the number of cross-validation folds ('n_cv_folds'), AND the number of cross-fitting folds ('n_cf_folds')
+mlreg = MLRegressor(estimator=estimator,
+                    n_cv_folds=5,
+                    max_n_models=2,
+                    n_cf_folds=2)
+
+# Cross fit
+mlreg.cross_fit(X=X_train, y=y_train)
+
+# Extract in-sample that are estimated in an out-of-sample way (e.g., via cross-fitting)
+y_hat = mlreg.y_pred_cf_
+
+# Likewise, extract the residualized outcomes used in e.g., double machine learning. This is \tilde{Y} = Y - E[Y|X=x]
+y_res = mlreg.y_res_cf_
+```
+
+<!-- ## Example
+We provide an example script in `demo.py`. -->
+
+
+
+
+%package -n python3-mlregression
+Summary:	Machine learning regression off-the-shelf
+Provides:	python-mlregression
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mlregression
+
+# *** ATTENTION ***
+Don't immidiately run `pip install mlregression`. See Section _Installation_.
+
+# Machine learning regression (mlregression)
+
+Machine Learning Regression (mlregrresion) is an off-the-shelf implementation of the most popular ML methods that automatically takes care of fitting and parameter tuning.
+
+Currently, the __fully__ implemented models include:
+- Ensemble trees (Random forests, XGBoost, LightGBM, GradientBoostingRegressor, ExtraTreesRegressor)
+- Penalized regression (Ridge, Lasso, ElasticNet, Lars, LassoLars) 
+- Neural nets (Simple neural nets with 1-5 hidden layers, rely activation, and early stopping)
+
+_NB!_ When using penalized regressions, consider using the native CV-implementation from scikit-learn for speed, e.g., simply set `estimator="LassoCV"` similar to Example 1.
+
+Scikit-learn regressors (together with `XGBoost` and `LightGBM`) can be estimated by setting the `estimator`-argument equal to the name (string) as in Example 1 (`estimator="RandomForestRegressor"`).
+Alternatively, one can provide an instance of an estimator, e.g., `estimator=RandomForestRegressor()`. Again, this is fully automated for most Scikit-learn regressors, but for non-standard methods, one would have to provide a parameter grid as well, e.g., `param_grid={...}`.
+
+Please contact the authors below if you find any bugs or have any suggestions for improvement. Thank you!
+
+Author: Nicolaj Søndergaard Mühlbach (n.muhlbach at gmail dot com, muhlbach at mit dot edu) 
+
+## Code dependencies
+This code has the following dependencies:
+
+- Python >=3.6
+- numpy >=1.19
+- pandas >=1.3
+- scikit-learn >=1
+- scikit-learn-intelex >= 2021.3
+- daal >= 2021.3
+- daal4py >= 2021.3
+- tbb >= 2021.4
+- xgboost >=1.5
+- lightgbm >=3.2
+
+
+## Installation
+Before calling `pip install mlregression`, we recommend using `conda` to install the dependencies. In our experience, calling the following command works like a charm:
+```
+conda install -c conda-forge numpy">=1.19" pandas">=1.3" scikit-learn">=1" scikit-learn-intelex">=2021.3" daal">=2021.3" daal4py">=2021.3" tbb">=2021.4" xgboost">=1.5" lightgbm">=3.2" --force-reinstall
+```
+After this, install `mlregression` by calling `pip install mlregression`.
+Note that without installing the dependensies, the package will not work. As of now, it does not work when installing the dependensies via `pip install`. The reason is that we are using the Intel® Extension for Scikit-learn to massively speed up computations, but the dependensies are not properly installed via `pip install`.
+
+## Usage
+We demonstrate the use of __mlregression__ below, using random forests, xgboost, and lightGBM as underlying regressors.
+
+```python
+#------------------------------------------------------------------------------
+# Libraries
+#------------------------------------------------------------------------------
+# Standard
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+
+# This library
+from mlregression.mlreg import MLRegressor
+
+#------------------------------------------------------------------------------
+# Data
+#------------------------------------------------------------------------------
+# Generate data
+X, y = make_regression(n_samples=500,
+                       n_features=10, 
+                       n_informative=5,
+                       n_targets=1,
+                       bias=0.0,
+                       coef=False,
+                       random_state=1991)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+#------------------------------------------------------------------------------
+# Example 1: Prediction
+#------------------------------------------------------------------------------
+# Specify any of the following estimators:
+"""
+"LinearRegression",
+"RidgeCV", "LassoCV", "ElasticNetCV",
+"RandomForestRegressor","ExtraTreesRegressor", "GradientBoostingRegressor",
+"XGBRegressor", "LGBMegressor",
+"MLPRegressor",
+"""
+
+# For instance, pick "RandomForestRegressor"
+estimator = "RandomForestRegressor"
+# Note that the 'estimator' may also be an instance of a class, e.g., RandomForestRegressor(), conditional on being imported first, e.g. from sklearn.ensemble import RandomForestRegressor
+
+# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models') and the number of cross-validation folds ('n_cv_folds')
+mlreg = MLRegressor(estimator=estimator,
+                    n_cv_folds=5,
+                    max_n_models=2)
+
+# Fit
+mlreg.fit(X=X_train, y=y_train)
+
+# Predict
+y_hat = mlreg.predict(X=X_test)
+
+# Access all the usual attributes
+mlreg.best_score_
+mlreg.best_estimator_
+
+# Compute the score
+mlreg.score(X=X_test,y=y_test)
+
+#------------------------------------------------------------------------------
+# Example 2: Cross-fitting
+#------------------------------------------------------------------------------
+# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models'), the number of cross-validation folds ('n_cv_folds'), AND the number of cross-fitting folds ('n_cf_folds')
+mlreg = MLRegressor(estimator=estimator,
+                    n_cv_folds=5,
+                    max_n_models=2,
+                    n_cf_folds=2)
+
+# Cross fit
+mlreg.cross_fit(X=X_train, y=y_train)
+
+# Extract in-sample that are estimated in an out-of-sample way (e.g., via cross-fitting)
+y_hat = mlreg.y_pred_cf_
+
+# Likewise, extract the residualized outcomes used in e.g., double machine learning. This is \tilde{Y} = Y - E[Y|X=x]
+y_res = mlreg.y_res_cf_
+```
+
+<!-- ## Example
+We provide an example script in `demo.py`. -->
+
+
+
+
+%package help
+Summary:	Development documents and examples for mlregression
+Provides:	python3-mlregression-doc
+%description help
+
+# *** ATTENTION ***
+Don't immidiately run `pip install mlregression`. See Section _Installation_.
+
+# Machine learning regression (mlregression)
+
+Machine Learning Regression (mlregrresion) is an off-the-shelf implementation of the most popular ML methods that automatically takes care of fitting and parameter tuning.
+
+Currently, the __fully__ implemented models include:
+- Ensemble trees (Random forests, XGBoost, LightGBM, GradientBoostingRegressor, ExtraTreesRegressor)
+- Penalized regression (Ridge, Lasso, ElasticNet, Lars, LassoLars) 
+- Neural nets (Simple neural nets with 1-5 hidden layers, rely activation, and early stopping)
+
+_NB!_ When using penalized regressions, consider using the native CV-implementation from scikit-learn for speed, e.g., simply set `estimator="LassoCV"` similar to Example 1.
+
+Scikit-learn regressors (together with `XGBoost` and `LightGBM`) can be estimated by setting the `estimator`-argument equal to the name (string) as in Example 1 (`estimator="RandomForestRegressor"`).
+Alternatively, one can provide an instance of an estimator, e.g., `estimator=RandomForestRegressor()`. Again, this is fully automated for most Scikit-learn regressors, but for non-standard methods, one would have to provide a parameter grid as well, e.g., `param_grid={...}`.
+
+Please contact the authors below if you find any bugs or have any suggestions for improvement. Thank you!
+
+Author: Nicolaj Søndergaard Mühlbach (n.muhlbach at gmail dot com, muhlbach at mit dot edu) 
+
+## Code dependencies
+This code has the following dependencies:
+
+- Python >=3.6
+- numpy >=1.19
+- pandas >=1.3
+- scikit-learn >=1
+- scikit-learn-intelex >= 2021.3
+- daal >= 2021.3
+- daal4py >= 2021.3
+- tbb >= 2021.4
+- xgboost >=1.5
+- lightgbm >=3.2
+
+
+## Installation
+Before calling `pip install mlregression`, we recommend using `conda` to install the dependencies. In our experience, calling the following command works like a charm:
+```
+conda install -c conda-forge numpy">=1.19" pandas">=1.3" scikit-learn">=1" scikit-learn-intelex">=2021.3" daal">=2021.3" daal4py">=2021.3" tbb">=2021.4" xgboost">=1.5" lightgbm">=3.2" --force-reinstall
+```
+After this, install `mlregression` by calling `pip install mlregression`.
+Note that without installing the dependensies, the package will not work. As of now, it does not work when installing the dependensies via `pip install`. The reason is that we are using the Intel® Extension for Scikit-learn to massively speed up computations, but the dependensies are not properly installed via `pip install`.
+
+## Usage
+We demonstrate the use of __mlregression__ below, using random forests, xgboost, and lightGBM as underlying regressors.
+
+```python
+#------------------------------------------------------------------------------
+# Libraries
+#------------------------------------------------------------------------------
+# Standard
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+
+# This library
+from mlregression.mlreg import MLRegressor
+
+#------------------------------------------------------------------------------
+# Data
+#------------------------------------------------------------------------------
+# Generate data
+X, y = make_regression(n_samples=500,
+                       n_features=10, 
+                       n_informative=5,
+                       n_targets=1,
+                       bias=0.0,
+                       coef=False,
+                       random_state=1991)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+#------------------------------------------------------------------------------
+# Example 1: Prediction
+#------------------------------------------------------------------------------
+# Specify any of the following estimators:
+"""
+"LinearRegression",
+"RidgeCV", "LassoCV", "ElasticNetCV",
+"RandomForestRegressor","ExtraTreesRegressor", "GradientBoostingRegressor",
+"XGBRegressor", "LGBMegressor",
+"MLPRegressor",
+"""
+
+# For instance, pick "RandomForestRegressor"
+estimator = "RandomForestRegressor"
+# Note that the 'estimator' may also be an instance of a class, e.g., RandomForestRegressor(), conditional on being imported first, e.g. from sklearn.ensemble import RandomForestRegressor
+
+# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models') and the number of cross-validation folds ('n_cv_folds')
+mlreg = MLRegressor(estimator=estimator,
+                    n_cv_folds=5,
+                    max_n_models=2)
+
+# Fit
+mlreg.fit(X=X_train, y=y_train)
+
+# Predict
+y_hat = mlreg.predict(X=X_test)
+
+# Access all the usual attributes
+mlreg.best_score_
+mlreg.best_estimator_
+
+# Compute the score
+mlreg.score(X=X_test,y=y_test)
+
+#------------------------------------------------------------------------------
+# Example 2: Cross-fitting
+#------------------------------------------------------------------------------
+# Instantiate model and choose the number of parametrizations to examine using cross-validation ('max_n_models'), the number of cross-validation folds ('n_cv_folds'), AND the number of cross-fitting folds ('n_cf_folds')
+mlreg = MLRegressor(estimator=estimator,
+                    n_cv_folds=5,
+                    max_n_models=2,
+                    n_cf_folds=2)
+
+# Cross fit
+mlreg.cross_fit(X=X_train, y=y_train)
+
+# Extract in-sample that are estimated in an out-of-sample way (e.g., via cross-fitting)
+y_hat = mlreg.y_pred_cf_
+
+# Likewise, extract the residualized outcomes used in e.g., double machine learning. This is \tilde{Y} = Y - E[Y|X=x]
+y_res = mlreg.y_res_cf_
+```
+
+<!-- ## Example
+We provide an example script in `demo.py`. -->
+
+
+
+
+%prep
+%autosetup -n mlregression-0.1.10
+
+%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-mlregression -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.10-1
+- Package Spec generated
diff --git a/sources b/sources
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--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8c52fe19428f99c12468e6e7c30d8bff  mlregression-0.1.10.tar.gz