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Commit 7415b7d6 authored by aastorg2's avatar aastorg2
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adding debugging files

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dtree_synth.py
+ 24
− 3
View file @ 7415b7d6
import itertools
import pickle import pickle
from typing import List, Tuple from typing import List, Tuple
import numpy as np import numpy as np
import z3 import z3
import csv
from dtree_learner import DTreeLearner as Learner from dtree_learner import DTreeLearner as Learner
from dtree_teacher_gem_stanley import DTreeTeacherGEMStanley as Teacher from dtree_teacher_gem_stanley import DTreeTeacherGEMStanley as Teacher
...@@ -27,13 +28,15 @@ def load_positive_examples(file_name: str) -> List[Tuple[float, ...]]: ...@@ -27,13 +28,15 @@ def load_positive_examples(file_name: str) -> List[Tuple[float, ...]]:
def test_synth_dtree(): def test_synth_dtree():
positive_examples = load_positive_examples( positive_examples = load_positive_examples(
"data/collect_images_2021-11-22-17-59-46.cs598.filtered.pickle") "data/collect_images_2021-11-22-17-59-46.cs598.filtered.pickle")
positive_examples = positive_examples[:20:] # Select only first few examples #positive_examples = positive_examples[:20:] # Select only first few examples
ex_dim = len(positive_examples[0]) ex_dim = len(positive_examples[0])
print("#examples: %d" % len(positive_examples)) print("#examples: %d" % len(positive_examples))
print("Dimension of each example: %d" % ex_dim) print("Dimension of each example: %d" % ex_dim)
assert all(len(ex) == ex_dim and not any(np.isnan(ex)) assert all(len(ex) == ex_dim and not any(np.isnan(ex))
for ex in positive_examples) for ex in positive_examples)
...@@ -42,7 +45,7 @@ def test_synth_dtree(): ...@@ -42,7 +45,7 @@ def test_synth_dtree():
# 0.0 <= x <= 30.0 and -1.0 <= y <= 0.9 and 0.2 <= theta <= 0.22 # 0.0 <= x <= 30.0 and -1.0 <= y <= 0.9 and 0.2 <= theta <= 0.22
teacher.set_old_state_bound(lb=[0.0, -1.0, 0.2], ub=[30.0, -0.9, 0.22]) teacher.set_old_state_bound(lb=[0.0, -1.0, 0.2], ub=[30.0, -0.9, 0.22])
synth_dtree(positive_examples, teacher, num_max_iterations=5) synth_dtree(positive_examples, teacher, num_max_iterations=50)
def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10): def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
...@@ -53,12 +56,20 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10): ...@@ -53,12 +56,20 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
[0., 0., -1.]]) [0., 0., -1.]])
b_vec_0 = np.zeros(2) b_vec_0 = np.zeros(2)
learner.set_grammar([(a_mat_0, b_vec_0)]) learner.set_grammar([(a_mat_0, b_vec_0)])
#writing positive examples in csv file
f = open("positive_sample.csv", 'w')
data_out = csv.writer(f)
for ps in positive_examples:
data_out.writerow(itertools.chain(ps,["true"]))
f.close()
learner.add_positive_examples(*positive_examples) learner.add_positive_examples(*positive_examples)
past_candidate_list = [] past_candidate_list = []
for k in range(num_max_iterations): for k in range(num_max_iterations):
print(f"Iteration {k}:", sep='') print(f"Iteration {k}:", sep='')
print("learning ....") print("learning ....")
candidate_dnf = learner.learn() candidate_dnf = learner.learn()
print("done learning") print("done learning")
...@@ -67,8 +78,10 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10): ...@@ -67,8 +78,10 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
return return
print(f"candidate DNF: {candidate_dnf}") print(f"candidate DNF: {candidate_dnf}")
past_candidate_list.append(candidate_dnf) past_candidate_list.append(candidate_dnf)
# QUERYING TEACHER IF THERE ARE NEGATIVE EXAMPLES # QUERYING TEACHER IF THERE ARE NEGATIVE EXAMPLES
print(f"number of paths: {len(candidate_dnf)}")
negative_examples = [] negative_examples = []
for candidate in candidate_dnf: for candidate in candidate_dnf:
result = teacher.check(candidate) result = teacher.check(candidate)
...@@ -84,10 +97,18 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10): ...@@ -84,10 +97,18 @@ def synth_dtree(positive_examples, teacher, num_max_iterations: int = 10):
print("Reason Unknown", teacher.reason_unknown()) print("Reason Unknown", teacher.reason_unknown())
return past_candidate_list return past_candidate_list
fneg = open("negative_sample.csv", 'w')
data_out = csv.writer(fneg)
for pn in negative_examples:
data_out.writerow(itertools.chain(pn,["true"]))
fneg.close()
print(f"negative examples: {negative_examples}") print(f"negative examples: {negative_examples}")
if len(negative_examples) > 0: if len(negative_examples) > 0:
learner.add_negative_examples(*negative_examples) learner.add_negative_examples(*negative_examples)
else: else:
file = open('winnerDnf', 'wb')
pickle.dump(candidate_dnf, file)
print("we are done!") print("we are done!")
return past_candidate_list return past_candidate_list
......
negative_sample.csv 0 → 100644
+ 6
− 0
View file @ 7415b7d6
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0.0,-0.999269129298403,0.2,1.037571685298403,-0.206528906,true
0.0,-0.9935347011340955,0.1999999999999994,1.049718775526631,-0.2082379429999994,true
positive_sample.csv 0 → 100644
+ 187
− 0
View file @ 7415b7d6
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