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@@ -0,0 +1 @@ +/random_forest_mc-1.0.3.tar.gz diff --git a/python-random-forest-mc.spec b/python-random-forest-mc.spec new file mode 100644 index 0000000..7953456 --- /dev/null +++ b/python-random-forest-mc.spec @@ -0,0 +1,774 @@ +%global _empty_manifest_terminate_build 0 +Name: python-random-forest-mc +Version: 1.0.3 +Release: 1 +Summary: This project is about use Random Forest approach using a dynamic tree selection Monte Carlo based. +License: MIT +URL: https://github.com/ysraell/random-forest-mc +Source0: https://mirrors.aliyun.com/pypi/web/packages/59/a6/ab3063d395dcf2c0d39ed3d3d7ac44cfe09a63595833443c32212b49ac9b/random_forest_mc-1.0.3.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-tqdm + +%description +# Random Forest with Tree Selection Monte Carlo Based (RF-TSMC) + + +<a href="https://pypi.org/project/random-forest-mc"><img src="https://img.shields.io/pypi/pyversions/random-forest-mc" alt="Python versions"></a> +<a href="https://pypi.org/project/random-forest-mc"><img src="https://img.shields.io/pypi/v/random-forest-mc?color=blue" alt="PyPI version"></a> + + + +[](https://lgtm.com/projects/g/ysraell/random-forest-mc/alerts/) +[](https://lgtm.com/projects/g/ysraell/random-forest-mc/context:python) +[](https://github.com/psf/black) + +This project is about use Random Forest approach for *multiclass classification* using a dynamic tree selection Monte Carlo based. The first implementation is found in [2] (using Common Lisp). + +## Install: + +Install using `pip`: + +```bash +$ pip3 install random-forest-mc +``` + +Install from this repo: + +```bash +$ git clone https://github.com/ysraell/random-forest-mc.git +$ cd random-forest-mc +$ pip3 install . +``` + +## Usage: + +Example of a full cycle using `titanic.csv`: + +```python +import numpy as np +import pandas as pd + +from random_forest_mc.model import RandomForestMC +from random_forest_mc.utils import LoadDicts, load_file_json, dump_file_json + +dicts = LoadDicts("tests/") +dataset_dict = dicts.datasets_metadata +ds_name = "titanic" +params = dataset_dict[ds_name] +target_col = params["target_col"] +dataset = ( + pd.read_csv(params["csv_path"])[params["ds_cols"] + [params["target_col"]]] + .dropna() + .reset_index(drop=True) +) +dataset["Age"] = dataset["Age"].astype(np.uint8) +dataset["SibSp"] = dataset["SibSp"].astype(np.uint8) +dataset["Pclass"] = dataset["Pclass"].astype(str) +dataset["Fare"] = dataset["Fare"].astype(np.uint32) +cls = RandomForestMC( + n_trees=8, target_col=target_col, max_discard_trees=4 +) +cls.process_dataset(dataset) +cls.fit() # or with cls.fitParallel(max_workers=8) +y_test = dataset[params["target_col"]].to_list() +cls.setWeightedTrees(True) # predictions weighted by survive scores +y_pred = cls.testForest(dataset) +accuracy_hard = sum([v == p for v, p in zip(y_test, y_pred)]) / len(y_pred) +cls.setSoftVoting(True) # for predicitons using soft voting strategy +y_pred = cls.testForest(dataset) +accuracy_soft = sum([v == p for v, p in zip(y_test, y_pred)]) / len(y_pred) + +# Simply predictions: + +# One row +row = dataset.loc[0] +cls.predict(row) +{'0': 0.75, '1': 0.25} + +# Multiple rows (dataset) +cls.predict(dataset.sample(n=10)) +['0', '1', ...] + +# Get the probabilities: +cls.predict_proba(dataset.sample(n=10)) +[ + {'0': 0.75, '1': 0.25}, + {'0': 1.0, '1': 0.0}, + ... + {'0': 0.625, '1': 0.375} +] + +# Works with missing values: + +cols = list(dataset.columns) +cols.pop(cols.index('Class')) +ds = dataset[cols[:10]+['Class']] + +row = ds.loc[0] +cls.predict(row) +{'0': 0.75, '1': 0.25} + +cls.predict(dataset.sample(n=10)) +['0', '1', ...] + +# Saving model: +ModelDict = cls.model2dict() +dump_file_json(path_dict, ModelDict) +del ModelDict + +# Loading model +ModelDict = load_file_json(path_dict) +cls = RandomForestMC() +cls.dict2model(ModelDict) +# Before run fit again, load dataset. Check if the features are the same! +cls.process_dataset(dataset) + +row = dataset.loc[0] +# Feature counting (how much features in each tree): +cls.featCount() # or cls.sampleClassFeatCount(row, row[target_col]) +( + (3.5, 0.5, 3, 4), # (mean, std, min, max) + [3, 4, 3, 4, 3, 4] # List of counting of features in each tree. +) + +# Feature importance: +cls.featImportance() # or cls.sampleClassFeatImportance(row, row[target_col]) +{ + 'feat 1': 0.900000, + 'feat 2': 0.804688, + 'feat 3': 0.398438, + ... +} + +# Permutation feature importance: +cls.featPairImportance() # or cls.sampleClassFeatPairImportance(row, row[target_col]) +{ + ('feat 1', 'feat 2'): 0.12, + ('feat 1', 'feat 3'): 0.13, + ('feat 2', 'feat 3'): 0.23, + ... +} + +# Permutation feature importance in matrix (dataframe): +cls.featCorrDataFrame() # or cls.sampleClassFeatCorrDataFrame(row, row[target_col]) + feat 1 feat 2 feat 3 +feat 1 0.900000 0.120000 0.130000 +feat 2 0.120000 0.804688 0.230000 +feat 3 0.130000 0.230000 0.398438 + +# For merge different models (forests): +... +cls.fit() +cls2.fit() + +# Simply add all trees from cls2 in cls. +cls.mergeForest(cls2) + +# Merge all trees from both models and keep the trees with scores within the top N survived scores. +cls.mergeForest(cls2, N, 'score') + +# Merge all trees from both models and keep N random trees. +cls.mergeForest(cls2, N, 'random') + +``` + +### Notes: + +- Classes values must be converted to `str` before make predicts. +- `fit` always add new trees (keep the trees generated before). + +### LoadDicts: + +LoadDicts works loading all `JSON` files inside a given path, creating an object helper to use this files as dictionaries. + +For example: +```python +>>> from random_forest_mc.utils import LoadDicts +>>> # JSONs: path/data.json, path/metdada.json +>>> dicts = LoadDicts("path/") +>>> # you have: dicts.data and dicts.metdada as dictionaries +>>> # And a list of dictionaries loaded in: +>>> dicts.List +["data", "metdada"] +``` + +## Fundamentals: + +- Based on Random Forest method principles: ensemble of models (decision trees). + +- In bootstrap process: + + - the data sampled ensure the balance between classes, for training and validation; + + - the list of features used are randomly sampled (with random number of features and order). + +- For each tree: + + - fallowing the sequence of a given list of features, the data is splited half/half based on meadian value; + + - the splitting process ends when the samples have one only class; + + - validation process based on dynamic threshold can discard the tree. + +- For use the forest: + + - all trees predictions are combined as a vote; + + - it is possible to use soft or hard-voting. + +- Positive side-effects: + + - possible more generalization caused by the combination of overfitted trees, each tree is highly specialized in a smallest and different set of feature; + + - robustness for unbalanced and missing data, in case of missing data, the feature could be skipped without degrade the optimization process; + + - in prediction process, a missing value could be dealt with a tree replication considering the two possible paths; + + - the survived trees have a potential information about feature importance. + + - Robust for mssing values in categorical features during prediction process. + +### References + +[2] [Laboratory of Decision Tree and Random Forest (`github/ysraell/random-forest-lab`)](https://github.com/ysraell/random-forest-lab). GitHub repository. + +[3] Credit Card Fraud Detection. Anonymized credit card transactions labeled as fraudulent or genuine. Kaggle. Access: <https://www.kaggle.com/mlg-ulb/creditcardfraud>. + +### Development Framework (optional) + +- [My data science Docker image](https://github.com/ysraell/my-ds). + +With this image you can run all notebooks and scripts Python inside this repository. + +### TO-DO list. + +For TO-DO list see `TODO.md`. + + +%package -n python3-random-forest-mc +Summary: This project is about use Random Forest approach using a dynamic tree selection Monte Carlo based. +Provides: python-random-forest-mc +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-random-forest-mc +# Random Forest with Tree Selection Monte Carlo Based (RF-TSMC) + + +<a href="https://pypi.org/project/random-forest-mc"><img src="https://img.shields.io/pypi/pyversions/random-forest-mc" alt="Python versions"></a> +<a href="https://pypi.org/project/random-forest-mc"><img src="https://img.shields.io/pypi/v/random-forest-mc?color=blue" alt="PyPI version"></a> + + + +[](https://lgtm.com/projects/g/ysraell/random-forest-mc/alerts/) +[](https://lgtm.com/projects/g/ysraell/random-forest-mc/context:python) +[](https://github.com/psf/black) + +This project is about use Random Forest approach for *multiclass classification* using a dynamic tree selection Monte Carlo based. The first implementation is found in [2] (using Common Lisp). + +## Install: + +Install using `pip`: + +```bash +$ pip3 install random-forest-mc +``` + +Install from this repo: + +```bash +$ git clone https://github.com/ysraell/random-forest-mc.git +$ cd random-forest-mc +$ pip3 install . +``` + +## Usage: + +Example of a full cycle using `titanic.csv`: + +```python +import numpy as np +import pandas as pd + +from random_forest_mc.model import RandomForestMC +from random_forest_mc.utils import LoadDicts, load_file_json, dump_file_json + +dicts = LoadDicts("tests/") +dataset_dict = dicts.datasets_metadata +ds_name = "titanic" +params = dataset_dict[ds_name] +target_col = params["target_col"] +dataset = ( + pd.read_csv(params["csv_path"])[params["ds_cols"] + [params["target_col"]]] + .dropna() + .reset_index(drop=True) +) +dataset["Age"] = dataset["Age"].astype(np.uint8) +dataset["SibSp"] = dataset["SibSp"].astype(np.uint8) +dataset["Pclass"] = dataset["Pclass"].astype(str) +dataset["Fare"] = dataset["Fare"].astype(np.uint32) +cls = RandomForestMC( + n_trees=8, target_col=target_col, max_discard_trees=4 +) +cls.process_dataset(dataset) +cls.fit() # or with cls.fitParallel(max_workers=8) +y_test = dataset[params["target_col"]].to_list() +cls.setWeightedTrees(True) # predictions weighted by survive scores +y_pred = cls.testForest(dataset) +accuracy_hard = sum([v == p for v, p in zip(y_test, y_pred)]) / len(y_pred) +cls.setSoftVoting(True) # for predicitons using soft voting strategy +y_pred = cls.testForest(dataset) +accuracy_soft = sum([v == p for v, p in zip(y_test, y_pred)]) / len(y_pred) + +# Simply predictions: + +# One row +row = dataset.loc[0] +cls.predict(row) +{'0': 0.75, '1': 0.25} + +# Multiple rows (dataset) +cls.predict(dataset.sample(n=10)) +['0', '1', ...] + +# Get the probabilities: +cls.predict_proba(dataset.sample(n=10)) +[ + {'0': 0.75, '1': 0.25}, + {'0': 1.0, '1': 0.0}, + ... + {'0': 0.625, '1': 0.375} +] + +# Works with missing values: + +cols = list(dataset.columns) +cols.pop(cols.index('Class')) +ds = dataset[cols[:10]+['Class']] + +row = ds.loc[0] +cls.predict(row) +{'0': 0.75, '1': 0.25} + +cls.predict(dataset.sample(n=10)) +['0', '1', ...] + +# Saving model: +ModelDict = cls.model2dict() +dump_file_json(path_dict, ModelDict) +del ModelDict + +# Loading model +ModelDict = load_file_json(path_dict) +cls = RandomForestMC() +cls.dict2model(ModelDict) +# Before run fit again, load dataset. Check if the features are the same! +cls.process_dataset(dataset) + +row = dataset.loc[0] +# Feature counting (how much features in each tree): +cls.featCount() # or cls.sampleClassFeatCount(row, row[target_col]) +( + (3.5, 0.5, 3, 4), # (mean, std, min, max) + [3, 4, 3, 4, 3, 4] # List of counting of features in each tree. +) + +# Feature importance: +cls.featImportance() # or cls.sampleClassFeatImportance(row, row[target_col]) +{ + 'feat 1': 0.900000, + 'feat 2': 0.804688, + 'feat 3': 0.398438, + ... +} + +# Permutation feature importance: +cls.featPairImportance() # or cls.sampleClassFeatPairImportance(row, row[target_col]) +{ + ('feat 1', 'feat 2'): 0.12, + ('feat 1', 'feat 3'): 0.13, + ('feat 2', 'feat 3'): 0.23, + ... +} + +# Permutation feature importance in matrix (dataframe): +cls.featCorrDataFrame() # or cls.sampleClassFeatCorrDataFrame(row, row[target_col]) + feat 1 feat 2 feat 3 +feat 1 0.900000 0.120000 0.130000 +feat 2 0.120000 0.804688 0.230000 +feat 3 0.130000 0.230000 0.398438 + +# For merge different models (forests): +... +cls.fit() +cls2.fit() + +# Simply add all trees from cls2 in cls. +cls.mergeForest(cls2) + +# Merge all trees from both models and keep the trees with scores within the top N survived scores. +cls.mergeForest(cls2, N, 'score') + +# Merge all trees from both models and keep N random trees. +cls.mergeForest(cls2, N, 'random') + +``` + +### Notes: + +- Classes values must be converted to `str` before make predicts. +- `fit` always add new trees (keep the trees generated before). + +### LoadDicts: + +LoadDicts works loading all `JSON` files inside a given path, creating an object helper to use this files as dictionaries. + +For example: +```python +>>> from random_forest_mc.utils import LoadDicts +>>> # JSONs: path/data.json, path/metdada.json +>>> dicts = LoadDicts("path/") +>>> # you have: dicts.data and dicts.metdada as dictionaries +>>> # And a list of dictionaries loaded in: +>>> dicts.List +["data", "metdada"] +``` + +## Fundamentals: + +- Based on Random Forest method principles: ensemble of models (decision trees). + +- In bootstrap process: + + - the data sampled ensure the balance between classes, for training and validation; + + - the list of features used are randomly sampled (with random number of features and order). + +- For each tree: + + - fallowing the sequence of a given list of features, the data is splited half/half based on meadian value; + + - the splitting process ends when the samples have one only class; + + - validation process based on dynamic threshold can discard the tree. + +- For use the forest: + + - all trees predictions are combined as a vote; + + - it is possible to use soft or hard-voting. + +- Positive side-effects: + + - possible more generalization caused by the combination of overfitted trees, each tree is highly specialized in a smallest and different set of feature; + + - robustness for unbalanced and missing data, in case of missing data, the feature could be skipped without degrade the optimization process; + + - in prediction process, a missing value could be dealt with a tree replication considering the two possible paths; + + - the survived trees have a potential information about feature importance. + + - Robust for mssing values in categorical features during prediction process. + +### References + +[2] [Laboratory of Decision Tree and Random Forest (`github/ysraell/random-forest-lab`)](https://github.com/ysraell/random-forest-lab). GitHub repository. + +[3] Credit Card Fraud Detection. Anonymized credit card transactions labeled as fraudulent or genuine. Kaggle. Access: <https://www.kaggle.com/mlg-ulb/creditcardfraud>. + +### Development Framework (optional) + +- [My data science Docker image](https://github.com/ysraell/my-ds). + +With this image you can run all notebooks and scripts Python inside this repository. + +### TO-DO list. + +For TO-DO list see `TODO.md`. + + +%package help +Summary: Development documents and examples for random-forest-mc +Provides: python3-random-forest-mc-doc +%description help +# Random Forest with Tree Selection Monte Carlo Based (RF-TSMC) + + +<a href="https://pypi.org/project/random-forest-mc"><img src="https://img.shields.io/pypi/pyversions/random-forest-mc" alt="Python versions"></a> +<a href="https://pypi.org/project/random-forest-mc"><img src="https://img.shields.io/pypi/v/random-forest-mc?color=blue" alt="PyPI version"></a> + + + +[](https://lgtm.com/projects/g/ysraell/random-forest-mc/alerts/) +[](https://lgtm.com/projects/g/ysraell/random-forest-mc/context:python) +[](https://github.com/psf/black) + +This project is about use Random Forest approach for *multiclass classification* using a dynamic tree selection Monte Carlo based. The first implementation is found in [2] (using Common Lisp). + +## Install: + +Install using `pip`: + +```bash +$ pip3 install random-forest-mc +``` + +Install from this repo: + +```bash +$ git clone https://github.com/ysraell/random-forest-mc.git +$ cd random-forest-mc +$ pip3 install . +``` + +## Usage: + +Example of a full cycle using `titanic.csv`: + +```python +import numpy as np +import pandas as pd + +from random_forest_mc.model import RandomForestMC +from random_forest_mc.utils import LoadDicts, load_file_json, dump_file_json + +dicts = LoadDicts("tests/") +dataset_dict = dicts.datasets_metadata +ds_name = "titanic" +params = dataset_dict[ds_name] +target_col = params["target_col"] +dataset = ( + pd.read_csv(params["csv_path"])[params["ds_cols"] + [params["target_col"]]] + .dropna() + .reset_index(drop=True) +) +dataset["Age"] = dataset["Age"].astype(np.uint8) +dataset["SibSp"] = dataset["SibSp"].astype(np.uint8) +dataset["Pclass"] = dataset["Pclass"].astype(str) +dataset["Fare"] = dataset["Fare"].astype(np.uint32) +cls = RandomForestMC( + n_trees=8, target_col=target_col, max_discard_trees=4 +) +cls.process_dataset(dataset) +cls.fit() # or with cls.fitParallel(max_workers=8) +y_test = dataset[params["target_col"]].to_list() +cls.setWeightedTrees(True) # predictions weighted by survive scores +y_pred = cls.testForest(dataset) +accuracy_hard = sum([v == p for v, p in zip(y_test, y_pred)]) / len(y_pred) +cls.setSoftVoting(True) # for predicitons using soft voting strategy +y_pred = cls.testForest(dataset) +accuracy_soft = sum([v == p for v, p in zip(y_test, y_pred)]) / len(y_pred) + +# Simply predictions: + +# One row +row = dataset.loc[0] +cls.predict(row) +{'0': 0.75, '1': 0.25} + +# Multiple rows (dataset) +cls.predict(dataset.sample(n=10)) +['0', '1', ...] + +# Get the probabilities: +cls.predict_proba(dataset.sample(n=10)) +[ + {'0': 0.75, '1': 0.25}, + {'0': 1.0, '1': 0.0}, + ... + {'0': 0.625, '1': 0.375} +] + +# Works with missing values: + +cols = list(dataset.columns) +cols.pop(cols.index('Class')) +ds = dataset[cols[:10]+['Class']] + +row = ds.loc[0] +cls.predict(row) +{'0': 0.75, '1': 0.25} + +cls.predict(dataset.sample(n=10)) +['0', '1', ...] + +# Saving model: +ModelDict = cls.model2dict() +dump_file_json(path_dict, ModelDict) +del ModelDict + +# Loading model +ModelDict = load_file_json(path_dict) +cls = RandomForestMC() +cls.dict2model(ModelDict) +# Before run fit again, load dataset. Check if the features are the same! +cls.process_dataset(dataset) + +row = dataset.loc[0] +# Feature counting (how much features in each tree): +cls.featCount() # or cls.sampleClassFeatCount(row, row[target_col]) +( + (3.5, 0.5, 3, 4), # (mean, std, min, max) + [3, 4, 3, 4, 3, 4] # List of counting of features in each tree. +) + +# Feature importance: +cls.featImportance() # or cls.sampleClassFeatImportance(row, row[target_col]) +{ + 'feat 1': 0.900000, + 'feat 2': 0.804688, + 'feat 3': 0.398438, + ... +} + +# Permutation feature importance: +cls.featPairImportance() # or cls.sampleClassFeatPairImportance(row, row[target_col]) +{ + ('feat 1', 'feat 2'): 0.12, + ('feat 1', 'feat 3'): 0.13, + ('feat 2', 'feat 3'): 0.23, + ... +} + +# Permutation feature importance in matrix (dataframe): +cls.featCorrDataFrame() # or cls.sampleClassFeatCorrDataFrame(row, row[target_col]) + feat 1 feat 2 feat 3 +feat 1 0.900000 0.120000 0.130000 +feat 2 0.120000 0.804688 0.230000 +feat 3 0.130000 0.230000 0.398438 + +# For merge different models (forests): +... +cls.fit() +cls2.fit() + +# Simply add all trees from cls2 in cls. +cls.mergeForest(cls2) + +# Merge all trees from both models and keep the trees with scores within the top N survived scores. +cls.mergeForest(cls2, N, 'score') + +# Merge all trees from both models and keep N random trees. +cls.mergeForest(cls2, N, 'random') + +``` + +### Notes: + +- Classes values must be converted to `str` before make predicts. +- `fit` always add new trees (keep the trees generated before). + +### LoadDicts: + +LoadDicts works loading all `JSON` files inside a given path, creating an object helper to use this files as dictionaries. + +For example: +```python +>>> from random_forest_mc.utils import LoadDicts +>>> # JSONs: path/data.json, path/metdada.json +>>> dicts = LoadDicts("path/") +>>> # you have: dicts.data and dicts.metdada as dictionaries +>>> # And a list of dictionaries loaded in: +>>> dicts.List +["data", "metdada"] +``` + +## Fundamentals: + +- Based on Random Forest method principles: ensemble of models (decision trees). + +- In bootstrap process: + + - the data sampled ensure the balance between classes, for training and validation; + + - the list of features used are randomly sampled (with random number of features and order). + +- For each tree: + + - fallowing the sequence of a given list of features, the data is splited half/half based on meadian value; + + - the splitting process ends when the samples have one only class; + + - validation process based on dynamic threshold can discard the tree. + +- For use the forest: + + - all trees predictions are combined as a vote; + + - it is possible to use soft or hard-voting. + +- Positive side-effects: + + - possible more generalization caused by the combination of overfitted trees, each tree is highly specialized in a smallest and different set of feature; + + - robustness for unbalanced and missing data, in case of missing data, the feature could be skipped without degrade the optimization process; + + - in prediction process, a missing value could be dealt with a tree replication considering the two possible paths; + + - the survived trees have a potential information about feature importance. + + - Robust for mssing values in categorical features during prediction process. + +### References + +[2] [Laboratory of Decision Tree and Random Forest (`github/ysraell/random-forest-lab`)](https://github.com/ysraell/random-forest-lab). GitHub repository. + +[3] Credit Card Fraud Detection. Anonymized credit card transactions labeled as fraudulent or genuine. Kaggle. Access: <https://www.kaggle.com/mlg-ulb/creditcardfraud>. + +### Development Framework (optional) + +- [My data science Docker image](https://github.com/ysraell/my-ds). + +With this image you can run all notebooks and scripts Python inside this repository. + +### TO-DO list. + +For TO-DO list see `TODO.md`. + + +%prep +%autosetup -n random_forest_mc-1.0.3 + +%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-random-forest-mc -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.3-1 +- Package Spec generated @@ -0,0 +1 @@ +8480a9e02b8b9ab1e51465a126a589df random_forest_mc-1.0.3.tar.gz |