def main(col_names=None):
if len(sys.argv) < 2:
print("Please specify input csv file name")
return
csv_file_name = sys.argv[1]
data = []
with open(csv_file_name) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
for row in readCSV:
data.append(list(row))
train = resample(data[1:], replace=True, n_samples=int(len(data)))
test = []
for i in data[1:]:
if i not in train:
test.append(i)
tree = dtree_build.buildtree(train, min_gain=0.01, min_samples=5)
dtree_build.printtree(tree, '', col_names)
result2 = naive_bayes.build(train)
# print(result2)
# max_tree_depth = dtree_build.max_depth(tree)
# print("max number of questions=" + str(max_tree_depth))
# print(test)
out_put = [['instance', 'actual', 'predicted', 'probability']]
total = 0
correct = 0
correct2 = 0
for i in test:
total += 1
result = dtree_build.classify(i, tree)
out = naive_bayes.classifier(result2, i)
sum_probability = 0
max_number = 0
choice = ''
for n, m in result.items():
sum_probability += m
if m >= max_number:
max_number = m
choice = n
if choice == i[-1]:
correct += 1
if out == int(i[-1]):
correct2 += 1
sublist = [total, i[-1], choice, max_number / sum_probability]
out_put.append(sublist)
# print(result)
with open("predicted.csv", "w") as output:
writer = csv.writer(output)
writer.writerows(out_put)
print("Accuracy for decision tree is", correct / len(test))
print("Accuracy for naive bayes is", correct2 / len(test))
def main(train_f, image_f, test_f, output_f):
rows = tsv.get_list(train_f)
for i in range(len(rows)):
# Convert to numeric, then pop the Pokemon name
rows[i] = move_tree.convert_numeric(rows[i])
rows[i].pop(0)
tree = dtree_build.buildtree(rows)
dtree_draw.drawtree(tree, labels, jpeg=image_f)
classify_pokemon(tree, test_f, output_f)
def main(train_f, image_f, test_f, output_f):
data = open(train_f)
moves = []
rows = []
# Create a 2D array to pass into the function which creates the tree
for line in data:
arr = line.rstrip().split('\t')
moves.append(arr.pop(0))
entry = convert_numeric(arr) # Convert arr into integers where appropriate
rows.append(entry)
data.close()
tree = dtree_build.buildtree(rows)
dtree_draw.drawtree(tree, labels, jpeg=image_f)
classify_moves(tree, test_f, output_f)
def main(col_names=None):
# parse command-line arguments to read the name of the input csv file
# and optional 'draw tree' parameter
if len(sys.argv) < 2: # input file name should be specified
print("Please specify input csv file name")
return
csv_file_name = sys.argv[1]
data = []
with open(csv_file_name) as csvfile:
readCSV = csv.reader(csvfile, delimiter=',')
for row in readCSV:
list = []
for attribute in row:
try:
list += [float(attribute)]
except:
list += [attribute]
data.append(list)
print("Total number of records = ", len(data))
tree = dtree_build.buildtree(data, min_gain=0.01, min_samples=5)
dtree_build.printtree(tree, '', col_names)
max_tree_depth = dtree_build.max_depth(tree)
print("max number of questions=" + str(max_tree_depth))
if len(sys.argv) > 2: # draw option specified
import dtree_draw
dtree_draw.drawtree(tree, jpeg=csv_file_name + '.jpg')
if len(sys.argv) > 3: # create json file for d3.js visualization
import json
import dtree_to_json
json_tree = dtree_to_json.dtree_to_jsontree(tree, col_names)
print(json_tree)
# create json data for d3.js interactive visualization
with open(csv_file_name + ".json", "w") as write_file:
json.dump(json_tree, write_file)
import dtree_build
import sys
if __name__ == "__main__":
# fruits with their size and color
fruits = [[4, 'red', 'apple'], [4, 'green', 'apple'], [1, 'red', 'cherry'],
[1, 'green', 'grape'], [5, 'red', 'apple']]
tree = dtree_build.buildtree(fruits)
dtree_build.printtree(tree, '', ["size", "color"])
print("fruit [2, 'red'] is: ", dtree_build.classify([2, 'red'], tree))
print("fruit [4.5, 'red'] is: ", dtree_build.classify([4.5, 'red'], tree))
print("fruit [1.4, 'green'] is: ",
dtree_build.classify([1.4, 'green'], tree))
max_tree_depth = dtree_build.max_depth(tree)
print("max number of questions=" + str(max_tree_depth))
if len(sys.argv) > 1: # draw option specified
import dtree_draw
dtree_draw.drawtree(tree, jpeg='fruits_dt.jpg')