print("-------------------------")
print("Validation set case")
df = pd.read_csv("dataset/golf.txt")
validation_df = pd.read_csv("dataset/golf.txt")
config = {'algorithm': 'ID3', 'enableParallelism': enableParallelism}
model = cb.fit(df, config, validation_df=validation_df)
print("-------------------------")
print("Feature importance")
#decision_rules = model["trees"][0].__dict__["__name__"]+".py"
decision_rules = model["trees"][0].__dict__["__spec__"].origin
print(cb.feature_importance(decision_rules))
print("-------------------------")
print("ID3 for nominal features and nominal target:")
df = pd.read_csv("dataset/golf.txt")
config = {'algorithm': 'ID3', 'enableParallelism': enableParallelism}
model = cb.fit(df, config)
validation_df = pd.read_csv("dataset/golf.txt")
print("External validation")
cb.evaluate(model, validation_df)
cb.save_model(model)
#!pip install chefboost
from chefboost import Chefboost as cb
import pandas as pd
import matplotlib.pyplot as plt
if __name__ == '__main__':
df = pd.read_csv("golf.txt")
config = config = {'algorithm': 'C4.5', 'enableParallelism': True}
model = cb.fit(df, config)
fi = cb.feature_importance()
fi.plot.bar()
plt.show()
