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 = regression_tree.buildtree(data, min_gain = 0.005, min_samples = 5)
regression_tree.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 regression_draw
regression_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)
print("course ['teaching', 'not minority', 'female', 'english', 50, 30, 'lower', 7, 4] is: ", dtree_build.classify(['teaching', 'not minority', 'female', 'english', 50, 30, 'lower', 7, 4], tree))
print("course ['teaching', 'not minority', 'male', 'english', 40, 30, 'lower', 6, 4] is: ", dtree_build.classify(['teaching', 'not minority', 'male', 'english', 40, 30, 'lower', 6, 4], tree))
print("course ['teaching', 'not minority', 'male', 'english', 70, 30, 'lower', 4, 4] is: ", dtree_build.classify(['teaching', 'not minority', 'male', 'english', 70, 30, 'lower', 4, 4], tree))
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:
data.append(list(row))
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
pass
# 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')