1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
|
import argparse
import ast
import collections
import os
import pandas
import numpy as np
np.set_printoptions(linewidth=1000)
import sklearn.dummy
import sklearn.ensemble
import sklearn.model_selection
import sklearn.tree
import canonicalize
import dynamic
import regex
import main
import Orange
from Orange.evaluation import CrossValidation, CA, AUC, LogLoss, Precision, Recall
from learning.rules import RL4T, RL4TFull
datasets = [
("introduction/fahrenheit_to_celsius", [100]),
("introduction/ballistics", [45, 100]),
("hw-fkkt/hw1", [298, 1.4, 0.028964]),
("introduction/pythagorean_theorem", [3, 4]),
("while_and_if/buy_five", [5,4,3,2,1]),
("while_and_if/top_shop", [2,4,1,0]),
("introduction/average", [2,4,5]),
("introduction-fkkt/pythagorean_theorem_fkkt", [3,4]),
("introduction-fkkt/what_is_your_name", ["Ana"]),
("while_and_if/competition", [3, 2, 4, 1]),
#("introduction-fkkt/hello_world", []),
("introduction-fkkt/area_of_a_triangle", [3,4]),
("lists_and_if-fkkt/is_palindrome", ["kisik"]),
("for-fkkt/sum_and_average", [[23,42,87,34,-1]]),
("for-fkkt/star_tree", [3]),
("lists_and_if-fkkt/temp_converter", ['32\nK\n']),
("for-fkkt/sum_to_n", [7]),
("while_and_if/checking_account", [10,-100,1000,-10000]),
("introduction-fkkt/molar_mass", [2]),
("lists_and_if-fkkt/itm", [165,70]),
("functions/greatest_negative", [4,-6,2,-1], ["max_neg"]),
("for-fkkt/star_triangle", [3]),
("lists_and_if-fkkt/square_equation", [1,2,1]),
("while_and_if/consumers_anonymous", [10,5,90,1,1,0]),
("while_and_if/minimax", [2,4,1,0]),
("functions/greatest_absolutist", [-8,6,2,0], ["max_abs"]),
("functions/greatest", [-8,6,2,0], ["max_val"])
]
datasets = [
("functions/greatest_absolutist", [-8,6,2,0], ["max_abs"]),]
#("while_and_if/consumers_anonymous", [10,5,90,1,1,0])]
#("while_and_if/buy_five", [5,4,3,2,1]),]
def create_data(path, names, include_dynamic, inputs):
problem_name = os.path.basename(path)
programs = main.get_programs(path, names, do_canonicalize=True)
attrs = collections.OrderedDict()
attrs.update(regex.get_attributes(programs))
if include_dynamic:
attrs.update(dynamic.get_attributes(programs, "", inputs))
#print('Attributes:', attrs.keys())
orange_attrs = []
for at in attrs:
orange_attrs.append(Orange.data.DiscreteVariable(at, values=('F', 'T')))
cl = Orange.data.DiscreteVariable('correct', values=('F', 'T'))
mcode = Orange.data.StringVariable('code')
orange_domain = Orange.data.Domain(orange_attrs, cl, metas=[mcode])
orange_data = Orange.data.Table.from_domain(orange_domain)
for program in programs:
if not program:
continue
instance = Orange.data.Instance(orange_domain)
for at in attrs:
instance[at] = program in attrs[at]['programs']
instance[cl] = programs[program]['correct']
instance[mcode] = program
for _ in range(len(programs[program]['users'])):
orange_data.append(instance)
return orange_data, attrs
def get_coverages(rules, data):
if not rules:
return 0, 0
corr, inc = np.zeros(len(data), dtype=bool), np.zeros(len(data), dtype=bool)
for r in rules:
if r.target_class == 0: # 0 ... 'F', should be like that or it wont work
inc |= r.covered_examples
else:
corr |= r.covered_examples
corr_perc = (corr & (data.Y == 1)).sum()
corr_perc /= data.Y.sum()
inc_perc = (inc & (data.Y == 0)).sum()
inc_perc /= len(data) - data.Y.sum()
return corr_perc, inc_perc
def learn_and_write(filename, learner_pos, learner_all, data):
rules_pos = learner_pos(data).rule_list
rules_all = learner_all(data).rule_list
pos_cov_corr, pos_cov_inc = get_coverages(rules_pos, data)
all_cov_corr, all_cov_inc = get_coverages(rules_all, data)
with open(os.path.join(output_path, filename), "wt") as f:
print("Only positive values of attributes:", file=f)
for r in rules_pos:
print(r, r.curr_class_dist, r.quality, np.where(r.covered_examples==1)[0], file=f)
print("Coverage incorrect: ", pos_cov_inc, file=f)
print("Coverage correct: ", pos_cov_corr, file=f)
print(file=f)
print("All values (positive and negative) of attributes:", file=f)
for r in rules_all:
print(r, r.curr_class_dist, r.quality, np.where(r.covered_examples==1)[0], file=f)
print("Coverage incorrect: ", all_cov_inc, file=f)
print("Coverage correct: ", all_cov_corr, file=f)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Get patterns from student programs.')
parser.add_argument('path', help='path to data directory')
parser.add_argument('output_path', help='path to output data directory')
args = parser.parse_args()
path = args.path.rstrip('/')
rule_learner_positive = RL4TFull(parent_alpha=0.05, threshold=0.9)
rule_learner_all = RL4TFull(parent_alpha=0.05, threshold=0.9, positive_only=False)
learners = [
rule_learner_positive,
rule_learner_all,
Orange.classification.TreeLearner(),
Orange.classification.RandomForestLearner(n_estimators=100),
Orange.classification.MajorityLearner()]
for d in datasets:
print(d)
# create orange data
names = d[2] if len(d)==3 else None
problem_path = os.path.join(path, d[0])
data, attrs = create_data(problem_path, names, False, [])
data_dyn, attrs_dyn = create_data(problem_path, names, True, [str(v) for v in d[1]])
# save data
output_path = os.path.join(args.output_path, d[0])
os.makedirs(output_path, exist_ok=True)
data.save(os.path.join(output_path, "regex.tab"))
data_dyn.save(os.path.join(output_path, "regex_dynamic.tab"))
with open(os.path.join(output_path, "regex_attributes.txt"), "wt") as fatt:
for at in attrs:
fatt.write("{}: {}\n".format(at, str(attrs[at]["desc"]).replace('\n',' ')))
with open(os.path.join(output_path, "both_attributes.txt"), "wt") as fatt:
for at in attrs_dyn:
fatt.write("{}: {}\n".format(at, str(attrs_dyn[at]["desc"]).replace('\n',' ')))
# learn rules regex
learn_and_write("rules_regex.txt", rule_learner_positive, rule_learner_all, data)
learn_and_write("rules_both.txt", rule_learner_positive, rule_learner_all, data_dyn)
res = CrossValidation(data, learners, k=5, random_state=0)
res_dyn = CrossValidation(data_dyn, learners, k=5, random_state=0)
with open(os.path.join(output_path, "results.txt"), "wt") as f:
print("Methods: rules(positive only), rules(all values), decision tree, random forest, majority", file=f)
print(file=f)
print("Without dynamic attributes: ", file=f)
print("ca", CA(res), file=f)
print("auc", AUC(res), file=f)
print("ll", LogLoss(res), file=f)
print("precision", Precision(res, target=0), file=f)
print("recall", Recall(res, target=0), file=f)
print(file=f)
print("With dynamic attributes: ", file=f)
print("ca", CA(res_dyn), file=f)
print("auc", AUC(res_dyn), file=f)
print("ll", LogLoss(res_dyn), file=f)
print("precision", Precision(res_dyn, target=0), file=f)
print("recall", Recall(res_dyn, target=0), file=f)
|
