def write_predictions(filename, ind_vars, tree):
"""
Given data from data/filename.csv, the predicted target value
for each passenger ID is written to predictions/filename.csv.
"""
# Get the data set for prediction.
f = file('data/%s.csv' % filename, 'r')
data = get_data(f, ind_vars)
var_dict = simplify_var_dict(ind_vars, None)
f.close()
# Output target variable predictions to csv.
f = file('predictions/%s.csv' % filename, 'w')
f.write('PassengerId,%s\n' % target_var[0])
for datum in data:
distribution = tree.predict(datum, var_dict)
write_prediction(
f,
distribution,
target_var,
int(datum[var_dict['PassengerId']]))
f.close()
# Build tree to predict age:
f = file("data/%s_age.csv" % filename, "r")
max_depth = 6
root = construct(f, ind_vars, target_var, max_depth)
f.close()
f = file("trees/age.tree", "w")
write_tree(root, f)
f.close()
# Trim tree: TODO
# Use tree to predict age:
# Compile list of independent variables used to predict target variable
tree = read_tree("trees/age.tree")
f = file("data/%s_no_age.csv" % filename, "r")
ind_vars[("PassengerId", "continuous")] = None
data = get_data(f, ind_vars)
var_dict = simplify_var_dict(ind_vars, None)
# Output target variable predictions to csv.
f = file("predictions/ages.csv", "w")
f.write("PassengerId,%s\n" % target_var[0])
for datum in data:
distribution = tree.predict(datum, var_dict)
write_prediction(f, distribution, target_var, int(datum[var_dict["PassengerId"]]))
f.close()
# Write a new data file with age values.
write_age_file(filename)
