# 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

    import bnlearn as bn
    # Example dataframe sprinkler_data.csv can be loaded with: 
    df = bn.import_example()
    # df = pd.read_csv('sprinkler_data.csv')

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

    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)

    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')

    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)

    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)