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+%global _empty_manifest_terminate_build 0
+Name:		python-bnlearn
+Version:	0.7.14
+Release:	1
+Summary:	Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
+License:	MIT License
+URL:		https://erdogant.github.io/bnlearn
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/15/ec/4bda7df6fb11131364b02e2f1dc3a8f7b13935a28ab2fd3af8b3bf98dd53/bnlearn-0.7.14.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pgmpy
+Requires:	python3-networkx
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-tqdm
+Requires:	python3-ismember
+Requires:	python3-scikit-learn
+Requires:	python3-funcsigs
+Requires:	python3-statsmodels
+Requires:	python3-louvain
+Requires:	python3-packaging
+Requires:	python3-df2onehot
+Requires:	python3-fsspec
+Requires:	python3-pypickle
+Requires:	python3-tabulate
+Requires:	python3-ipywidgets
+Requires:	python3-pyvis
+Requires:	python3-requests
+
+%description
+# bnlearn - Library for Bayesian network learning and inference
+
+[![Python](https://img.shields.io/pypi/pyversions/bnlearn)](https://img.shields.io/pypi/pyversions/bnlearn)
+[![PyPI Version](https://img.shields.io/pypi/v/bnlearn)](https://pypi.org/project/bnlearn/)
+![GitHub Repo stars](https://img.shields.io/github/stars/erdogant/bnlearn)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/bnlearn/blob/master/LICENSE)
+[![Forks](https://img.shields.io/github/forks/erdogant/bnlearn.svg)](https://github.com/erdogant/bnlearn/network)
+[![Open Issues](https://img.shields.io/github/issues/erdogant/bnlearn.svg)](https://github.com/erdogant/bnlearn/issues)
+[![Project Status](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
+[![Downloads](https://pepy.tech/badge/bnlearn/month)](https://pepy.tech/project/bnlearn/)
+[![Downloads](https://pepy.tech/badge/bnlearn)](https://pepy.tech/project/bnlearn)
+[![DOI](https://zenodo.org/badge/231263493.svg)](https://zenodo.org/badge/latestdoi/231263493)
+[![Docs](https://img.shields.io/badge/Sphinx-Docs-Green)](https://erdogant.github.io/bnlearn/)
+[![Medium](https://img.shields.io/badge/Medium-Blog-black)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#medium-blog)
+![GitHub repo size](https://img.shields.io/github/repo-size/erdogant/bnlearn)
+[![Donate](https://img.shields.io/badge/Support%20this%20project-grey.svg?logo=github%20sponsors)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#colab-notebook)
+<!---[![BuyMeCoffee](https://img.shields.io/badge/buymea-coffee-yellow.svg)](https://www.buymeacoffee.com/erdogant)-->
+<!---[![Coffee](https://img.shields.io/badge/coffee-black-grey.svg)](https://erdogant.github.io/donate/?currency=USD&amount=5)-->
+
+
+### 
+
+``bnlearn`` is Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
+Because probabilistic graphical models can be difficult in usage, Bnlearn for python (this package) is build on the <a href="https://github.com/pgmpy/pgmpy">pgmpy</a> package and contains the most-wanted pipelines. Navigate to [API documentations](https://erdogant.github.io/bnlearn/) for more detailed information.
+
+# 
+**⭐️ Star this repo if you like it ⭐️**
+# 
+
+### Blogs
+
+Read the blogs to get a structured overview of bayesian methods and detailed usage of ``bnlearn``.
+
+* [Step-by-step guide for structure learning.](https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5)
+* [Step-by-step guide for knowledge-driven models.](https://towardsdatascience.com/a-step-by-step-guide-in-designing-knowledge-driven-models-using-bayesian-theorem-7433f6fd64be)
+
+# 
+
+
+### [Documentation pages](https://erdogant.github.io/bnlearn/)
+
+On the [documentation pages](https://erdogant.github.io/bnlearn/) you can find detailed information about the working of the ``bnlearn`` with many examples. 
+
+# 
+
+### Installation
+
+##### It is advisable to create a new environment (e.g. with Conda). 
+```bash
+conda create -n env_bnlearn python=3.8
+conda activate env_bnlearn
+```
+
+##### Install bnlearn from PyPI
+```bash
+pip install bnlearn
+```
+
+##### Install bnlearn from github source
+```bash
+pip install git+https://github.com/erdogant/bnlearn
+```
+
+##### The following functions are available after installation:
+
+```python
+# Import library
+import bnlearn as bn
+
+# Structure learning
+bn.structure_learning.fit()
+
+# Compute edge strength with the test statistic
+bn.independence_test(model, df, test='chi_square', prune=True)
+
+# Parameter learning
+bn.parameter_learning.fit()
+
+# Inference
+bn.inference.fit()
+
+# Make predictions
+bn.predict()
+
+# Based on a DAG, you can sample the number of samples you want.
+bn.sampling()
+
+# Load well known examples to play arround with or load your own .bif file.
+bn.import_DAG()
+
+# Load simple dataframe of sprinkler dataset.
+bn.import_example()
+
+# Compare 2 graphs
+bn.compare_networks()
+
+# Plot graph
+bn.plot()
+
+# To make the directed grapyh undirected
+bn.to_undirected()
+
+# Convert to one-hot datamatrix
+bn.df2onehot()
+
+# Derive the topological ordering of the (entire) graph 
+bn.topological_sort()
+
+# See below for the exact working of the functions
+```
+
+##### The following methods are also included:
+* inference
+* sampling
+* comparing two networks
+* loading bif files
+* conversion of directed to undirected graphs
+
+
+# 
+
+### Method overview
+Learning a Bayesian network can be split into the underneath problems which are all implemented in this package:
+
+* **Structure learning**: Given the data: Estimate a DAG that captures the dependencies between the variables.
+   * There are multiple manners to perform structure learning.
+      * Exhaustivesearch
+      * Hillclimbsearch
+      * NaiveBayes
+      * TreeSearch
+          * Chow-liu
+          * Tree-augmented Naive Bayes (TAN)
+
+* **Parameter learning**: Given the data and DAG: Estimate the (conditional) probability distributions of the individual variables.
+* **Inference**: Given the learned model: Determine the exact probability values for your queries.
+
+# 
+
+
+### Examples
+
+A structured overview of all examples are now available on the [documentation pages](https://erdogant.github.io/bnlearn/).
+
+##### Structure learning
+
+* [Example: Learn structure on the Sprinkler dataset based on a simple dataframe](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-1)
+
+* [Example: Comparison method and scoring types types for structure learning](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-2)
+
+* [Example: Learn structure on  more complex dataset (Asia)](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-3)
+
+##### Parameter learning
+
+* [Example: Parameter learning using a DAG and dataframe](https://erdogant.github.io/bnlearn/pages/html/Examples.html#parameter-learning)
+
+
+##### Inferences
+
+* [Example: Make predictions on a dataframe using inference](https://erdogant.github.io/bnlearn/pages/html/Predict.html)
+
+
+##### Sampling
+
+* [Example: Sampling to create datasets](https://erdogant.github.io/bnlearn/pages/html/Sampling%20and%20datasets.html)
+
+
+##### Complete examples
+
+* [Example: Create a Bayesian Network, learn its parameters from data and perform the inference](https://erdogant.github.io/bnlearn/pages/html/Examples.html#create-a-bayesian-network-learn-its-parameters-from-data-and-perform-the-inference)
+
+* [Example: Use case in the medical domain](https://erdogant.github.io/bnlearn/pages/html/UseCases.html)
+
+* [Example: Use case Titanic](https://erdogant.github.io/bnlearn/pages/html/UseCases.html#)
+
+
+
+##### Plotting  
+* [Example: Interactive plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html#)
+
+* [Example: Static plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html#static-plot)
+
+* [Example: Comparison of two networks](https://erdogant.github.io/bnlearn/pages/html/Plot.html#comparison-of-two-networks)
+
+##### Various
+
+* [Example: Saving and loading of bnlearn models](https://erdogant.github.io/bnlearn/pages/html/saving%20and%20loading.html)
+
+* [Example: Data conversions such as creating sparse datamatrix from source-target and weights](https://erdogant.github.io/bnlearn/pages/html/dataframe%20conversions.html?highlight=target#)
+
+* [Example: Load DAG from BIF files](https://erdogant.github.io/bnlearn/pages/html/Examples.html?highlight=comparison#import-from-bif)
+
+
+ #
+
+### Various basic examples
+
+
+```python
+
+    import bnlearn as bn
+    # Example dataframe sprinkler_data.csv can be loaded with: 
+    df = bn.import_example()
+    # df = pd.read_csv('sprinkler_data.csv')
+```
+
+##### df looks like this
+
+```python
+
+Cloudy  Sprinkler  Rain  Wet_Grass
+0         0          1     0          1
+1         1          1     1          1
+2         1          0     1          1
+3         0          0     1          1
+4         1          0     1          1
+..      ...        ...   ...        ...
+995       0          0     0          0
+996       1          0     0          0
+997       0          0     1          0
+998       1          1     0          1
+999       1          0     1          1
+
+```
+
+```python
+
+    model = bn.structure_learning.fit(df)
+    # Compute edge strength with the chi_square test statistic
+    model = bn.independence_test(model, df)
+    G = bn.plot(model)
+```
+
+<p align="center">
+  <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig_sprinkler_sl.png" width="600" />
+</p>
+
+* Choosing various methodtypes and scoringtypes:
+
+```python
+
+    model_hc_bic  = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
+    model_hc_k2   = bn.structure_learning.fit(df, methodtype='hc', scoretype='k2')
+    model_hc_bdeu = bn.structure_learning.fit(df, methodtype='hc', scoretype='bdeu')
+    model_ex_bic  = bn.structure_learning.fit(df, methodtype='ex', scoretype='bic')
+    model_ex_k2   = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
+    model_ex_bdeu = bn.structure_learning.fit(df, methodtype='ex', scoretype='bdeu')
+    model_cl      = bn.structure_learning.fit(df, methodtype='cl', root_node='Wet_Grass')
+    model_tan     = bn.structure_learning.fit(df, methodtype='tan', root_node='Wet_Grass', class_node='Rain')
+```
+
+## Example: Parameter Learning
+```python
+    import bnlearn as bn
+    # Import dataframe
+    df = bn.import_example()
+    # As an example we set the CPD at False which returns an "empty" DAG
+    model = bn.import_DAG('sprinkler', CPD=False)
+    # Now we learn the parameters of the DAG using the df
+    model_update = bn.parameter_learning.fit(model, df)
+    # Make plot
+    G = bn.plot(model_update)
+```
+
+## Example: Inference
+```python
+    import bnlearn as bn
+    model = bn.import_DAG('sprinkler')
+    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
+    print(query)
+    print(query.df)
+    
+    # Lets try another inference
+    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1})
+    print(query)
+    print(query.df)
+
+```
+
+<hr>
+
+### References
+* https://erdogant.github.io/bnlearn/
+* http://pgmpy.org
+* https://programtalk.com/python-examples/pgmpy.factors.discrete.TabularCPD/
+* http://www.bnlearn.com/bnrepository/
+
+
+### Contributors
+Setting up and maintaining bnlearn has been possible thanks to users and contributors. Thanks to:
+
+<p align="left">
+  <a href="https://github.com/erdogant/bnlearn/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=erdogant/bnlearn" />
+  </a>
+</p>
+
+
+### Citation
+Please cite ``bnlearn`` in your publications if this is useful for your research. See column right for citation information.
+
+### Maintainer
+* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
+* Contributions are welcome.
+* If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :)
+
+
+
+
+
+%package -n python3-bnlearn
+Summary:	Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
+Provides:	python-bnlearn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-bnlearn
+# bnlearn - Library for Bayesian network learning and inference
+
+[![Python](https://img.shields.io/pypi/pyversions/bnlearn)](https://img.shields.io/pypi/pyversions/bnlearn)
+[![PyPI Version](https://img.shields.io/pypi/v/bnlearn)](https://pypi.org/project/bnlearn/)
+![GitHub Repo stars](https://img.shields.io/github/stars/erdogant/bnlearn)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/bnlearn/blob/master/LICENSE)
+[![Forks](https://img.shields.io/github/forks/erdogant/bnlearn.svg)](https://github.com/erdogant/bnlearn/network)
+[![Open Issues](https://img.shields.io/github/issues/erdogant/bnlearn.svg)](https://github.com/erdogant/bnlearn/issues)
+[![Project Status](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
+[![Downloads](https://pepy.tech/badge/bnlearn/month)](https://pepy.tech/project/bnlearn/)
+[![Downloads](https://pepy.tech/badge/bnlearn)](https://pepy.tech/project/bnlearn)
+[![DOI](https://zenodo.org/badge/231263493.svg)](https://zenodo.org/badge/latestdoi/231263493)
+[![Docs](https://img.shields.io/badge/Sphinx-Docs-Green)](https://erdogant.github.io/bnlearn/)
+[![Medium](https://img.shields.io/badge/Medium-Blog-black)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#medium-blog)
+![GitHub repo size](https://img.shields.io/github/repo-size/erdogant/bnlearn)
+[![Donate](https://img.shields.io/badge/Support%20this%20project-grey.svg?logo=github%20sponsors)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#colab-notebook)
+<!---[![BuyMeCoffee](https://img.shields.io/badge/buymea-coffee-yellow.svg)](https://www.buymeacoffee.com/erdogant)-->
+<!---[![Coffee](https://img.shields.io/badge/coffee-black-grey.svg)](https://erdogant.github.io/donate/?currency=USD&amount=5)-->
+
+
+### 
+
+``bnlearn`` is Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
+Because probabilistic graphical models can be difficult in usage, Bnlearn for python (this package) is build on the <a href="https://github.com/pgmpy/pgmpy">pgmpy</a> package and contains the most-wanted pipelines. Navigate to [API documentations](https://erdogant.github.io/bnlearn/) for more detailed information.
+
+# 
+**⭐️ Star this repo if you like it ⭐️**
+# 
+
+### Blogs
+
+Read the blogs to get a structured overview of bayesian methods and detailed usage of ``bnlearn``.
+
+* [Step-by-step guide for structure learning.](https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5)
+* [Step-by-step guide for knowledge-driven models.](https://towardsdatascience.com/a-step-by-step-guide-in-designing-knowledge-driven-models-using-bayesian-theorem-7433f6fd64be)
+
+# 
+
+
+### [Documentation pages](https://erdogant.github.io/bnlearn/)
+
+On the [documentation pages](https://erdogant.github.io/bnlearn/) you can find detailed information about the working of the ``bnlearn`` with many examples. 
+
+# 
+
+### Installation
+
+##### It is advisable to create a new environment (e.g. with Conda). 
+```bash
+conda create -n env_bnlearn python=3.8
+conda activate env_bnlearn
+```
+
+##### Install bnlearn from PyPI
+```bash
+pip install bnlearn
+```
+
+##### Install bnlearn from github source
+```bash
+pip install git+https://github.com/erdogant/bnlearn
+```
+
+##### The following functions are available after installation:
+
+```python
+# Import library
+import bnlearn as bn
+
+# Structure learning
+bn.structure_learning.fit()
+
+# Compute edge strength with the test statistic
+bn.independence_test(model, df, test='chi_square', prune=True)
+
+# Parameter learning
+bn.parameter_learning.fit()
+
+# Inference
+bn.inference.fit()
+
+# Make predictions
+bn.predict()
+
+# Based on a DAG, you can sample the number of samples you want.
+bn.sampling()
+
+# Load well known examples to play arround with or load your own .bif file.
+bn.import_DAG()
+
+# Load simple dataframe of sprinkler dataset.
+bn.import_example()
+
+# Compare 2 graphs
+bn.compare_networks()
+
+# Plot graph
+bn.plot()
+
+# To make the directed grapyh undirected
+bn.to_undirected()
+
+# Convert to one-hot datamatrix
+bn.df2onehot()
+
+# Derive the topological ordering of the (entire) graph 
+bn.topological_sort()
+
+# See below for the exact working of the functions
+```
+
+##### The following methods are also included:
+* inference
+* sampling
+* comparing two networks
+* loading bif files
+* conversion of directed to undirected graphs
+
+
+# 
+
+### Method overview
+Learning a Bayesian network can be split into the underneath problems which are all implemented in this package:
+
+* **Structure learning**: Given the data: Estimate a DAG that captures the dependencies between the variables.
+   * There are multiple manners to perform structure learning.
+      * Exhaustivesearch
+      * Hillclimbsearch
+      * NaiveBayes
+      * TreeSearch
+          * Chow-liu
+          * Tree-augmented Naive Bayes (TAN)
+
+* **Parameter learning**: Given the data and DAG: Estimate the (conditional) probability distributions of the individual variables.
+* **Inference**: Given the learned model: Determine the exact probability values for your queries.
+
+# 
+
+
+### Examples
+
+A structured overview of all examples are now available on the [documentation pages](https://erdogant.github.io/bnlearn/).
+
+##### Structure learning
+
+* [Example: Learn structure on the Sprinkler dataset based on a simple dataframe](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-1)
+
+* [Example: Comparison method and scoring types types for structure learning](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-2)
+
+* [Example: Learn structure on  more complex dataset (Asia)](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-3)
+
+##### Parameter learning
+
+* [Example: Parameter learning using a DAG and dataframe](https://erdogant.github.io/bnlearn/pages/html/Examples.html#parameter-learning)
+
+
+##### Inferences
+
+* [Example: Make predictions on a dataframe using inference](https://erdogant.github.io/bnlearn/pages/html/Predict.html)
+
+
+##### Sampling
+
+* [Example: Sampling to create datasets](https://erdogant.github.io/bnlearn/pages/html/Sampling%20and%20datasets.html)
+
+
+##### Complete examples
+
+* [Example: Create a Bayesian Network, learn its parameters from data and perform the inference](https://erdogant.github.io/bnlearn/pages/html/Examples.html#create-a-bayesian-network-learn-its-parameters-from-data-and-perform-the-inference)
+
+* [Example: Use case in the medical domain](https://erdogant.github.io/bnlearn/pages/html/UseCases.html)
+
+* [Example: Use case Titanic](https://erdogant.github.io/bnlearn/pages/html/UseCases.html#)
+
+
+
+##### Plotting  
+* [Example: Interactive plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html#)
+
+* [Example: Static plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html#static-plot)
+
+* [Example: Comparison of two networks](https://erdogant.github.io/bnlearn/pages/html/Plot.html#comparison-of-two-networks)
+
+##### Various
+
+* [Example: Saving and loading of bnlearn models](https://erdogant.github.io/bnlearn/pages/html/saving%20and%20loading.html)
+
+* [Example: Data conversions such as creating sparse datamatrix from source-target and weights](https://erdogant.github.io/bnlearn/pages/html/dataframe%20conversions.html?highlight=target#)
+
+* [Example: Load DAG from BIF files](https://erdogant.github.io/bnlearn/pages/html/Examples.html?highlight=comparison#import-from-bif)
+
+
+ #
+
+### Various basic examples
+
+
+```python
+
+    import bnlearn as bn
+    # Example dataframe sprinkler_data.csv can be loaded with: 
+    df = bn.import_example()
+    # df = pd.read_csv('sprinkler_data.csv')
+```
+
+##### df looks like this
+
+```python
+
+Cloudy  Sprinkler  Rain  Wet_Grass
+0         0          1     0          1
+1         1          1     1          1
+2         1          0     1          1
+3         0          0     1          1
+4         1          0     1          1
+..      ...        ...   ...        ...
+995       0          0     0          0
+996       1          0     0          0
+997       0          0     1          0
+998       1          1     0          1
+999       1          0     1          1
+
+```
+
+```python
+
+    model = bn.structure_learning.fit(df)
+    # Compute edge strength with the chi_square test statistic
+    model = bn.independence_test(model, df)
+    G = bn.plot(model)
+```
+
+<p align="center">
+  <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig_sprinkler_sl.png" width="600" />
+</p>
+
+* Choosing various methodtypes and scoringtypes:
+
+```python
+
+    model_hc_bic  = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
+    model_hc_k2   = bn.structure_learning.fit(df, methodtype='hc', scoretype='k2')
+    model_hc_bdeu = bn.structure_learning.fit(df, methodtype='hc', scoretype='bdeu')
+    model_ex_bic  = bn.structure_learning.fit(df, methodtype='ex', scoretype='bic')
+    model_ex_k2   = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
+    model_ex_bdeu = bn.structure_learning.fit(df, methodtype='ex', scoretype='bdeu')
+    model_cl      = bn.structure_learning.fit(df, methodtype='cl', root_node='Wet_Grass')
+    model_tan     = bn.structure_learning.fit(df, methodtype='tan', root_node='Wet_Grass', class_node='Rain')
+```
+
+## Example: Parameter Learning
+```python
+    import bnlearn as bn
+    # Import dataframe
+    df = bn.import_example()
+    # As an example we set the CPD at False which returns an "empty" DAG
+    model = bn.import_DAG('sprinkler', CPD=False)
+    # Now we learn the parameters of the DAG using the df
+    model_update = bn.parameter_learning.fit(model, df)
+    # Make plot
+    G = bn.plot(model_update)
+```
+
+## Example: Inference
+```python
+    import bnlearn as bn
+    model = bn.import_DAG('sprinkler')
+    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
+    print(query)
+    print(query.df)
+    
+    # Lets try another inference
+    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1})
+    print(query)
+    print(query.df)
+
+```
+
+<hr>
+
+### References
+* https://erdogant.github.io/bnlearn/
+* http://pgmpy.org
+* https://programtalk.com/python-examples/pgmpy.factors.discrete.TabularCPD/
+* http://www.bnlearn.com/bnrepository/
+
+
+### Contributors
+Setting up and maintaining bnlearn has been possible thanks to users and contributors. Thanks to:
+
+<p align="left">
+  <a href="https://github.com/erdogant/bnlearn/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=erdogant/bnlearn" />
+  </a>
+</p>
+
+
+### Citation
+Please cite ``bnlearn`` in your publications if this is useful for your research. See column right for citation information.
+
+### Maintainer
+* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
+* Contributions are welcome.
+* If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :)
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for bnlearn
+Provides:	python3-bnlearn-doc
+%description help
+# bnlearn - Library for Bayesian network learning and inference
+
+[![Python](https://img.shields.io/pypi/pyversions/bnlearn)](https://img.shields.io/pypi/pyversions/bnlearn)
+[![PyPI Version](https://img.shields.io/pypi/v/bnlearn)](https://pypi.org/project/bnlearn/)
+![GitHub Repo stars](https://img.shields.io/github/stars/erdogant/bnlearn)
+[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/bnlearn/blob/master/LICENSE)
+[![Forks](https://img.shields.io/github/forks/erdogant/bnlearn.svg)](https://github.com/erdogant/bnlearn/network)
+[![Open Issues](https://img.shields.io/github/issues/erdogant/bnlearn.svg)](https://github.com/erdogant/bnlearn/issues)
+[![Project Status](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
+[![Downloads](https://pepy.tech/badge/bnlearn/month)](https://pepy.tech/project/bnlearn/)
+[![Downloads](https://pepy.tech/badge/bnlearn)](https://pepy.tech/project/bnlearn)
+[![DOI](https://zenodo.org/badge/231263493.svg)](https://zenodo.org/badge/latestdoi/231263493)
+[![Docs](https://img.shields.io/badge/Sphinx-Docs-Green)](https://erdogant.github.io/bnlearn/)
+[![Medium](https://img.shields.io/badge/Medium-Blog-black)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#medium-blog)
+![GitHub repo size](https://img.shields.io/github/repo-size/erdogant/bnlearn)
+[![Donate](https://img.shields.io/badge/Support%20this%20project-grey.svg?logo=github%20sponsors)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://erdogant.github.io/bnlearn/pages/html/Documentation.html#colab-notebook)
+<!---[![BuyMeCoffee](https://img.shields.io/badge/buymea-coffee-yellow.svg)](https://www.buymeacoffee.com/erdogant)-->
+<!---[![Coffee](https://img.shields.io/badge/coffee-black-grey.svg)](https://erdogant.github.io/donate/?currency=USD&amount=5)-->
+
+
+### 
+
+``bnlearn`` is Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
+Because probabilistic graphical models can be difficult in usage, Bnlearn for python (this package) is build on the <a href="https://github.com/pgmpy/pgmpy">pgmpy</a> package and contains the most-wanted pipelines. Navigate to [API documentations](https://erdogant.github.io/bnlearn/) for more detailed information.
+
+# 
+**⭐️ Star this repo if you like it ⭐️**
+# 
+
+### Blogs
+
+Read the blogs to get a structured overview of bayesian methods and detailed usage of ``bnlearn``.
+
+* [Step-by-step guide for structure learning.](https://towardsdatascience.com/a-step-by-step-guide-in-detecting-causal-relationships-using-bayesian-structure-learning-in-python-c20c6b31cee5)
+* [Step-by-step guide for knowledge-driven models.](https://towardsdatascience.com/a-step-by-step-guide-in-designing-knowledge-driven-models-using-bayesian-theorem-7433f6fd64be)
+
+# 
+
+
+### [Documentation pages](https://erdogant.github.io/bnlearn/)
+
+On the [documentation pages](https://erdogant.github.io/bnlearn/) you can find detailed information about the working of the ``bnlearn`` with many examples. 
+
+# 
+
+### Installation
+
+##### It is advisable to create a new environment (e.g. with Conda). 
+```bash
+conda create -n env_bnlearn python=3.8
+conda activate env_bnlearn
+```
+
+##### Install bnlearn from PyPI
+```bash
+pip install bnlearn
+```
+
+##### Install bnlearn from github source
+```bash
+pip install git+https://github.com/erdogant/bnlearn
+```
+
+##### The following functions are available after installation:
+
+```python
+# Import library
+import bnlearn as bn
+
+# Structure learning
+bn.structure_learning.fit()
+
+# Compute edge strength with the test statistic
+bn.independence_test(model, df, test='chi_square', prune=True)
+
+# Parameter learning
+bn.parameter_learning.fit()
+
+# Inference
+bn.inference.fit()
+
+# Make predictions
+bn.predict()
+
+# Based on a DAG, you can sample the number of samples you want.
+bn.sampling()
+
+# Load well known examples to play arround with or load your own .bif file.
+bn.import_DAG()
+
+# Load simple dataframe of sprinkler dataset.
+bn.import_example()
+
+# Compare 2 graphs
+bn.compare_networks()
+
+# Plot graph
+bn.plot()
+
+# To make the directed grapyh undirected
+bn.to_undirected()
+
+# Convert to one-hot datamatrix
+bn.df2onehot()
+
+# Derive the topological ordering of the (entire) graph 
+bn.topological_sort()
+
+# See below for the exact working of the functions
+```
+
+##### The following methods are also included:
+* inference
+* sampling
+* comparing two networks
+* loading bif files
+* conversion of directed to undirected graphs
+
+
+# 
+
+### Method overview
+Learning a Bayesian network can be split into the underneath problems which are all implemented in this package:
+
+* **Structure learning**: Given the data: Estimate a DAG that captures the dependencies between the variables.
+   * There are multiple manners to perform structure learning.
+      * Exhaustivesearch
+      * Hillclimbsearch
+      * NaiveBayes
+      * TreeSearch
+          * Chow-liu
+          * Tree-augmented Naive Bayes (TAN)
+
+* **Parameter learning**: Given the data and DAG: Estimate the (conditional) probability distributions of the individual variables.
+* **Inference**: Given the learned model: Determine the exact probability values for your queries.
+
+# 
+
+
+### Examples
+
+A structured overview of all examples are now available on the [documentation pages](https://erdogant.github.io/bnlearn/).
+
+##### Structure learning
+
+* [Example: Learn structure on the Sprinkler dataset based on a simple dataframe](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-1)
+
+* [Example: Comparison method and scoring types types for structure learning](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-2)
+
+* [Example: Learn structure on  more complex dataset (Asia)](https://erdogant.github.io/bnlearn/pages/html/Examples.html#example-3)
+
+##### Parameter learning
+
+* [Example: Parameter learning using a DAG and dataframe](https://erdogant.github.io/bnlearn/pages/html/Examples.html#parameter-learning)
+
+
+##### Inferences
+
+* [Example: Make predictions on a dataframe using inference](https://erdogant.github.io/bnlearn/pages/html/Predict.html)
+
+
+##### Sampling
+
+* [Example: Sampling to create datasets](https://erdogant.github.io/bnlearn/pages/html/Sampling%20and%20datasets.html)
+
+
+##### Complete examples
+
+* [Example: Create a Bayesian Network, learn its parameters from data and perform the inference](https://erdogant.github.io/bnlearn/pages/html/Examples.html#create-a-bayesian-network-learn-its-parameters-from-data-and-perform-the-inference)
+
+* [Example: Use case in the medical domain](https://erdogant.github.io/bnlearn/pages/html/UseCases.html)
+
+* [Example: Use case Titanic](https://erdogant.github.io/bnlearn/pages/html/UseCases.html#)
+
+
+
+##### Plotting  
+* [Example: Interactive plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html#)
+
+* [Example: Static plotting](https://erdogant.github.io/bnlearn/pages/html/Plot.html#static-plot)
+
+* [Example: Comparison of two networks](https://erdogant.github.io/bnlearn/pages/html/Plot.html#comparison-of-two-networks)
+
+##### Various
+
+* [Example: Saving and loading of bnlearn models](https://erdogant.github.io/bnlearn/pages/html/saving%20and%20loading.html)
+
+* [Example: Data conversions such as creating sparse datamatrix from source-target and weights](https://erdogant.github.io/bnlearn/pages/html/dataframe%20conversions.html?highlight=target#)
+
+* [Example: Load DAG from BIF files](https://erdogant.github.io/bnlearn/pages/html/Examples.html?highlight=comparison#import-from-bif)
+
+
+ #
+
+### Various basic examples
+
+
+```python
+
+    import bnlearn as bn
+    # Example dataframe sprinkler_data.csv can be loaded with: 
+    df = bn.import_example()
+    # df = pd.read_csv('sprinkler_data.csv')
+```
+
+##### df looks like this
+
+```python
+
+Cloudy  Sprinkler  Rain  Wet_Grass
+0         0          1     0          1
+1         1          1     1          1
+2         1          0     1          1
+3         0          0     1          1
+4         1          0     1          1
+..      ...        ...   ...        ...
+995       0          0     0          0
+996       1          0     0          0
+997       0          0     1          0
+998       1          1     0          1
+999       1          0     1          1
+
+```
+
+```python
+
+    model = bn.structure_learning.fit(df)
+    # Compute edge strength with the chi_square test statistic
+    model = bn.independence_test(model, df)
+    G = bn.plot(model)
+```
+
+<p align="center">
+  <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig_sprinkler_sl.png" width="600" />
+</p>
+
+* Choosing various methodtypes and scoringtypes:
+
+```python
+
+    model_hc_bic  = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
+    model_hc_k2   = bn.structure_learning.fit(df, methodtype='hc', scoretype='k2')
+    model_hc_bdeu = bn.structure_learning.fit(df, methodtype='hc', scoretype='bdeu')
+    model_ex_bic  = bn.structure_learning.fit(df, methodtype='ex', scoretype='bic')
+    model_ex_k2   = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
+    model_ex_bdeu = bn.structure_learning.fit(df, methodtype='ex', scoretype='bdeu')
+    model_cl      = bn.structure_learning.fit(df, methodtype='cl', root_node='Wet_Grass')
+    model_tan     = bn.structure_learning.fit(df, methodtype='tan', root_node='Wet_Grass', class_node='Rain')
+```
+
+## Example: Parameter Learning
+```python
+    import bnlearn as bn
+    # Import dataframe
+    df = bn.import_example()
+    # As an example we set the CPD at False which returns an "empty" DAG
+    model = bn.import_DAG('sprinkler', CPD=False)
+    # Now we learn the parameters of the DAG using the df
+    model_update = bn.parameter_learning.fit(model, df)
+    # Make plot
+    G = bn.plot(model_update)
+```
+
+## Example: Inference
+```python
+    import bnlearn as bn
+    model = bn.import_DAG('sprinkler')
+    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
+    print(query)
+    print(query.df)
+    
+    # Lets try another inference
+    query = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1})
+    print(query)
+    print(query.df)
+
+```
+
+<hr>
+
+### References
+* https://erdogant.github.io/bnlearn/
+* http://pgmpy.org
+* https://programtalk.com/python-examples/pgmpy.factors.discrete.TabularCPD/
+* http://www.bnlearn.com/bnrepository/
+
+
+### Contributors
+Setting up and maintaining bnlearn has been possible thanks to users and contributors. Thanks to:
+
+<p align="left">
+  <a href="https://github.com/erdogant/bnlearn/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=erdogant/bnlearn" />
+  </a>
+</p>
+
+
+### Citation
+Please cite ``bnlearn`` in your publications if this is useful for your research. See column right for citation information.
+
+### Maintainer
+* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
+* Contributions are welcome.
+* If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :)
+
+
+
+
+
+%prep
+%autosetup -n bnlearn-0.7.14
+
+%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-bnlearn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.14-1
+- Package Spec generated