import decision_tree
# Program start
if len(sys.argv) < 3:
sys.exit('Please, input as arguments:\n' +
'1. the name of the input CSV file.\n' +
'2. the level of verbosity: 0 or 2\n' +
' 0 - output only the decision tree,\n' +
' 1 - also provide some basic information on the ' +
'construction,' +
' 2 - in addition provide the explanations of the ' +
'decision tree construction.\n\n' + 'Example use:\n' +
'python construct_decision_tree.py swim.csv 1')
csv_file_name = sys.argv[1]
verbose = int(sys.argv[2]) # verbosity level, 0 - only decision tree
# Define the equired column to be the last one.
# I.e. a column defining the decision variable.
(heading, complete_data, incomplete_data,
enquired_column) = common.csv_file_to_ordered_data(csv_file_name)
printfv(
1, verbose, "We construct a decision tree given the following " +
str(len(complete_data)) + " data items: \n" + str(complete_data) + "\n\n")
tree = decision_tree.constuct_decision_tree(verbose, heading, complete_data,
enquired_column)
printfv(2, verbose, "\n")
printfv(1, verbose, "***Decision tree graph***\n")
decision_tree.display_tree(tree)
common.dic_key_count(
conditional_counts, (
heading[i], incomplete_item[i],
enquired_group[0]))) / (
common.dic_key_count(enquired_column_classes,
enquired_group[0])))
partial_probs[enquired_group[0]] = probability
probs_sum += probability
for enquired_group in enquired_column_classes.items():
complete_probs[enquired_group[0]
] = partial_probs[enquired_group[0]
] / probs_sum
incomplete_item[enquired_column] = complete_probs
completed_items.append(incomplete_item)
return completed_items
# Program start
if len(sys.argv) < 2:
sys.exit('Please, input as an argument the name of the CSV file.')
(heading, complete_data, incomplete_data,
enquired_column) = common.csv_file_to_ordered_data(sys.argv[1])
# Calculate the Bayesian probability for the incomplete data
# and output it.
completed_data = bayes_probability(
heading, complete_data, incomplete_data, enquired_column)
print completed_data();
