def dt2():
dt = LearningDecisionTree(
training_datafile=training_datafile2,
csv_class_column_index=1,
csv_columns_for_features=[2, 3, 4, 5, 6, 7],
entropy_threshold=0.01,
max_depth_desired=8,
save_training_datafile=training_datafile3,
)
dt.get_training_data()
dt.calculate_first_order_probabilities()
dt.calculate_class_priors()
#dt.show_training_data()
root_node = dt.construct_decision_tree_classifier()
return dt, root_node
#!/usr/bin/env python
## classify_by_asking_questions.py
import LearningDecisionTree
# training_datafile = "training_symbolic.csv"
training_datafile = "animals.csv"
dt = LearningDecisionTree.LearningDecisionTree(
training_datafile=training_datafile,
csv_class_column_index=1,
csv_columns_for_features=[2, 3, 4, 5, 6],
entropy_threshold=0.01,
# max_depth_desired = 8,
)
dt.get_training_data()
dt.calculate_first_order_probabilities()
dt.calculate_class_priors()
# UNCOMMENT THE FOLLOWING LINE if you would like to see the training
# data that was read from the disk file:
#dt.show_training_data()
root_node = dt.construct_decision_tree_classifier()
# UNCOMMENT THE FOLLOWING LINE if you would like to see the decision
# tree displayed in your terminal window:
root_node.display_decision_tree(" ")
classification = dt.classify_by_asking_questions(root_node)
# animals.py
# By Ashley Hale
#
# Experiment with the animals.csv training set
import LearningDecisionTree
from LearningDecisionTree import *
if __name__ == '__main__':
'''
This test code assumes animals.csv exists in the current directory.
'''
dt = LearningDecisionTree(
training_datafile="animals.csv",
csv_class_column_index=2,
csv_columns_for_features=[3, 4, 5, 6, 7, 8],
entropy_threshold=0.01,
max_depth_desired=8,
# csv_cleanup_needed = 1,
# save_training_datafile = "new_animals.csv",
# debug1 = 'debug1',
)
# printBox is a helper function in the code above and is not needed
# for anything in the LearningDecisionTree - it's just useful for printing
printBox("Testing with training data file '%s'" % dt._training_datafile)
# ALL of these calls need to be done to set up the decision tree, except
# show_training_data.
#
# root_node will be of type DTNode:
# https://engineering.purdue.edu/kak/distDT/DecisionTree-3.4.3.html#DTNode
dt.get_training_data() # Required