def _setup(self):
# setup drag an drop
self.setDragDropMode(self.InternalMove)
self.setAcceptDrops(True)
self.setDropIndicatorShown(True)
self.setDragDropOverwriteMode(True)
self._tree_model = TreeModel(self._api, self._root, parent=self)
self._tree_proxy = makeQSortFilterProxyModel(self)
self._tree_proxy.setSourceModel(self._tree_model)
self.setSortingEnabled(True)
self.sortByColumn(0, QtCore.Qt.AscendingOrder)
self._tree_proxy.setDynamicSortFilter(True)
self._tree_proxy.setFilterCaseSensitivity(QtCore.Qt.CaseInsensitive)
self._tree_proxy.setSortRole(TreeModel.sortRole)
self._tree_proxy.setFilterRole(TreeModel.filterRole)
self.setModel(self._tree_proxy)
self.setColumnWidth(0, 175)
header = self.header()
header.setStretchLastSection(False)
setHeaderViewResizeMode(header, QtWidgets.QHeaderView.Stretch, 0)
self.setAnimated(True)
def test_TreeModel():
# typical model test
exponent = 8
mantissa = 24
file = "tests/my_expression.txt"
f = open(file, "r")
file_name = (ntpath.basename(file).split(".")[0]).lower() # (file.split(".")[0]).lower()
text = f.read()
text = text[:-1]
f.close()
myYacc = FPRyacc(text, False)
start_time = time()
T = TreeModel(myYacc, mantissa, exponent, [40, 10], 100, 250000, error_model="high_precision")
end_time = time()
print("Exe time --- %s seconds ---" % (end_time - start_time))
plt.close("all")
matplotlib.rcParams.update({'font.size': 11})
fig, a = plt.subplots(2, 2)
dist = T.tree.root_value[0].distribution
t0 = dist.range_()[0]
tf = dist.range_()[1]
x = np.linspace(t0, tf, 100)
a[0][0].plot(x, dist.pdf(x))
a[0][0].set_title("Unquantized operations")
dist = T.tree.root_value[1].distribution
t0 = dist.range_()[0]
tf = dist.range_()[1]
x = np.linspace(t0, tf, 100)
a[0][1].plot(x, dist.pdf(x))
a[0][1].set_title("Last error")
dist = T.tree.root_value[2].distribution
t0 = dist.range_()[0]
tf = dist.range_()[1]
x = np.linspace(t0, tf, 100)
a[1][0].plot(x, dist.pdf(x))
a[1][0].set_title("Quantized operations")
plt.show()
"Next coin rel y",
"Crates around next coin",
"Bomb possible",
"Sum all crates",
"Agent pos rel to wall",
"Free space down",
"Free space right",
"Free space up",
"Free space left",
"Survivable spaces down",
"Survivable spaces right",
"Survivable spaces up",
"Survivable spaces left",
"Survivable spaces wait"
]
labels = ['UP', 'DOWN', 'LEFT', 'RIGHT', 'BOMB', 'WAIT']
model = TreeModel(1000, (21, ), (6, ), 1)
model.load('agent_code/minimal_tree_1/actor.p')
X = np.load('agent_code/minimal_tree_1/actor_X.npy')
y = np.load('agent_code/minimal_tree_1/actor_y.npy')[:, 0]
plt.figure(figsize=(25, 20))
tree.plot_tree(model.model,
feature_names=cols,
class_names=y,
max_depth=10,
fontsize=4)
plt.show()
#print(tree.export_text(model.model)[:10000])
def process_file(file, mantissa, exp, range_my_dict, abs_my_dict):
try:
print("\n\n\n\n" + file + "\n\n\n\n\n")
f = open(file, "r")
file_name = (ntpath.basename(file).split(".")[0]
).lower() # (file.split(".")[0]).lower()
text = f.read()
text = text[:-1]
f.close()
myYacc = FPRyacc(text, False)
start_time = time.time()
T = TreeModel(
myYacc, mantissa, exp, [40, 10], 50,
250000) #, error_model="typical", dependent_mode="p-box")
end_time = time.time()
print("Exe time --- %s seconds ---" % (end_time - start_time))
finalTime = end_time - start_time
if os.path.exists(output_path + file_name):
shutil.rmtree(output_path + file_name)
os.makedirs(output_path + file_name)
#loadedSamples, values_samples, abs_err_samples, rel_err_samples = T.generate_error_samples(5, file_name)
loadedGolden, values_golden, abs_err_golden, rel_err_golden, err_golden = T.generate_error_samples(
golden_model_time, file_name, golden=True)
with open(
output_path + file_name + "/" + file_name +
"_constraints_log.out", "w+") as log:
print(T.logging_constraints, file=log)
f = open(
output_path + file_name + "/" + file_name + "_CDF_summary.out",
"w+")
f.write("Execution Time:" + str(finalTime) + "s \n\n")
plot_range_analysis_CDF(T, loadedGolden, values_golden, f, file_name,
range_my_dict.get(file_name))
plot_abs_error_analysis_CDF(T, loadedGolden, abs_err_golden, f,
file_name, abs_my_dict.get(file_name),
rel_my_dict.get(file_name))
#plot_error_analysis_CDF(T, loadedGolden, err_golden, f, file_name, abs_my_dict.get(file_name), rel_my_dict.get(file_name))
#plot_abs_error_analysis_CDF(T.relative_err_distr, loadedGolden, rel_err_samples, rel_err_golden, f, file_name, abs_my_dict.get(file_name), rel_my_dict.get(file_name))
f.flush()
f.close()
f = open(
output_path + file_name + "/" + file_name + "_PDF_summary.out",
"w+")
f.write("Execution Time:" + str(finalTime) + "s \n\n")
#plot_range_analysis_PDF(T.final_quantized_distr, loadedGolden, values_golden, f, file_name, range_my_dict.get(file_name))
#plot_error_analysis_PDF(T.abs_err_distr, loadedGolden, abs_err_samples, abs_err_golden, f, file_name, abs_my_dict.get(file_name), rel_my_dict.get(file_name))
f.flush()
f.close()
except Exception as e:
logging.error(traceback.format_exc())
finally:
del values_golden, abs_err_golden, rel_err_golden
gc.collect()
matplotlib.pyplot.close("all")
