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update examples
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@erdogant
erdogant committed Oct 6, 2024
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130 changes: 109 additions & 21 deletions bnlearn/examples.py
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import matplotlib.pyplot as plt
import bnlearn as bn
# Load example mixed dataset
df = bn.import_example(data='asia')

# Structure learning
model = bn.structure_learning.fit(df)
# Load sprinkler dataset
df = bn.import_example('alarm')

# Learn the DAG in data using hillclimbsearch and BIC
model = bn.structure_learning.fit(df, methodtype='hillclimbsearch', scoretype='bic')
model = bn.structure_learning.fit(df, methodtype='pc', scoretype='bic')
model = bn.structure_learning.fit(df, methodtype='tan', class_node='BP')
model = bn.structure_learning.fit(df, methodtype='tan', class_node='BP')
model = bn.structure_learning.fit(df, methodtype='cl', root_node='BP')
model = bn.structure_learning.fit(df, methodtype='cl')
model = bn.structure_learning.fit(df, methodtype='nb', root_node='BP')


# Compute edge weights using ChiSquare independence test.
model = bn.independence_test(model, df, test='chi_square', prune=False)

bn.plot(model, edge_labels='pvalue', params_static={'figsize': (6, 6), 'font_size': 8, 'arrowsize': 15, 'layout': 'graphviz_layout'}, node_size=700)
dotgraph = bn.plot_graphviz(model)
# Plot the best DAG
bn.plot(model, edge_labels='pvalue', params_static={'maxscale': 4, 'figsize': (15, 15), 'font_size': 14, 'arrowsize': 10})

# Plot using graphiviz
dot = bn.plot_graphviz(model, edge_labels='pvalue')
dot

# %%
import bnlearn as bn
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# We can draw a causal graph by utility funciton.
G = bn.plot(model, pos=G['pos'])
bn.plot(model, edge_labels='pvalue', pos=G['pos'])

bn.plot_graphviz(model)

# %% Continous and mixed
import bnlearn as bn
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df = bn.import_example(data='auto_mpg')

# Structure learning
model = bn.structure_learning.fit(df, methodtype='pc')
model = bn.structure_learning.fit(df, methodtype='hc')

# Compute edge strength
model = bn.independence_test(model, df)
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q1 = bn.inference.fit(model, variables=['acceleration'], evidence={'model_year': 70}, verbose=3)


# %% Get mpg dataset
X = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',
delim_whitespace=True, header=None,
names = ['mpg', 'cylinders', 'displacement',
'horsepower', 'weight', 'acceleration',
'model year', 'origin', 'car name'])
X.dropna(inplace=True)
X.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)
print(X.shape)
X.head()
# %% Get mpg dataset, and manually discritize dataset. Also use distfit for discritizing
import pandas as pd
import datazets as ds

df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',
delim_whitespace=True, header=None,
names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin', 'car name'])

df.dropna(inplace=True)
df.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)
print(df.shape)
df.head()

# mpg cylinders displacement horsepower weight acceleration
# 0 18.0 8.0 307.0 130.0 3504.0 12.0
# 1 15.0 8.0 350.0 165.0 3693.0 11.5
# 2 18.0 8.0 318.0 150.0 3436.0 11.0
# 3 16.0 8.0 304.0 150.0 3433.0 12.0
# 4 17.0 8.0 302.0 140.0 3449.0 10.5


# Define horsepower bins based on domain knowledge
bins = [0, 100, 150, df['horsepower'].max()]

# Discretize horsepower using the defined bins
df['horsepower_category'] = pd.cut(df['horsepower'], bins=bins, labels=['low', 'medium', 'high'], include_lowest=True)

print(df[['horsepower', 'horsepower_category']].head())
# horsepower horsepower_category
# 0 130.0 medium
# 1 165.0 high
# 2 150.0 medium
# 3 150.0 medium
# 4 140.0 medium

del df['horsepower']

# Set the cylinder to integers
df['cylinders'] = df['cylinders'].astype(int).astype(str)


# pip install distfit
# Import library
from distfit import distfit

cols = ['acceleration', 'mpg', 'displacement', 'weight']
for col in cols:
# Initialize and set 95% CII
dist = distfit(alpha=0.05)
dist.fit_transform(df[col])

# Make plot
dist.plot()
plt.show()

bins = [df[col].min(), dist.model['CII_min_alpha'], dist.model['CII_max_alpha'], df[col].max()]
# Discretize acceleration using the defined bins
df[col + '_category'] = pd.cut(df[col], bins=bins, labels=['low', 'medium', 'high'], include_lowest=True)

del df[col]


# Structure learning
model = bn.structure_learning.fit(df, methodtype='ex')

# Compute edge strength
model = bn.independence_test(model, df)

bn.plot(model, edge_labels='pvalue')

dotgraph = bn.plot_graphviz(model, edge_labels='pvalue')
dotgraph



# %% Continous and mixed
# https://www.pywhy.org/dowhy/v0.11.1/example_notebooks/dowhy_causal_discovery_example.html

import bnlearn as bn

# Download dataset
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',
delim_whitespace=True, header=None,
names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin', 'car name'])

# Cleaning
df.dropna(inplace=True)
df.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)


# Load example mixed dataset
df = bn.import_example(data='auto_mpg')
# df = bn.import_example(data='auto_mpg')
# del df['origin']
# df=df.iloc[:,0:5]

# Structure learning
model = bn.structure_learning.fit(df, methodtype='direct-lingam', params_lingam = {'random_state': 2})
# model = bn.structure_learning.fit(df, methodtype='ica-lingam', params_lingam = {'random_state': 2})
bn.plot(model)
bn.plot(model, edge_labels='pvalue');
bn.plot(model, edge_labels='pvalue')

# Compute edge strength with the chi_square test statistic
model = bn.independence_test(model, df, prune=False)
model = bn.independence_test(model, df, prune=True)

# Plot
# bn.plot(model, params_static={'dpi': 100, 'figsize': (15, 10), 'font_size': 8, 'arrowsize': 15, 'arrowsize': 10, 'minscale': 1, 'maxscale': 5}, node_size=1000)
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