class ValueTest(object):
def __init__(self, settings, s_function_width, is_train_df = True, s_function_center = 0.5, threshold = -1):
self.path = settings.backup_folder
self.settings = settings
self.d_results = [settings.class_2, settings.class_1]
self.x_range = np.arange(settings.set_min, settings.set_max, settings.fuzzy_sets_precision)
self.s_function_width = s_function_width
self.fuzzyHelper = FuzzyHelper(settings)
self.loadData(settings, is_train_df)
def highlightClassOne(self, row):
return ['background-color: green' if self.settings.class_1 == x else "" for x in row]
def loadData(self, settings, is_train_df):
self.decision = pickle.load(open(self.path + "decision.p", "rb"))
reductor = pickle.load(open(self.path + "reductor.p", "rb"))
features = pickle.load(open(self.path + "features.p", "rb"))
decision_table_with_reduct = pickle.load(open(self.path + "decision_table_with_reduct.p", "rb"))
self.rules_extractor = RulesExtractor(decision_table_with_reduct, reductor.reduct, settings)
self.rule_antecedents = self.rules_extractor.worker(decision_table_with_reduct, features, self.d_results, self.decision)
if is_train_df:
self.df = pickle.load(open(self.path + "train_features_df.p", "rb"))
self.data_type = "Train"
else:
self.df = pickle.load(open(self.path + "test_features_df.p", "rb"))
self.data_type = "Test"
def sFunction(self, settings, s_function_center, title, show_results = True):
print("-----------------------------------------------------------------------------------")
start = time.time()
_, df = self.fuzzyHelper.sFunctionsValue(s_function_center, self.s_function_width, self.df, settings, self.x_range, self.rules_extractor, self.rule_antecedents, self.d_results, self.decision)
end = time.time()
measured_time = end - start
accuracy, precision, recall, fscore, support = self.fuzzyHelper.getScores(df)
if show_results:
print("-----------------------------------------------------------------------------------")
print("Center Point: {}".format(s_function_center))
print("Time: {}".format(measured_time))
print("-----------------------------------------------------------------------------------")
# df = df.sort_values(by=["Predicted Value"])
# self.decision.view()
# display(df.style.apply(self.highlightClassOne, axis = 1))
pickle.dump(df, open(settings.backup_folder + self.data_type + "_df_results.p", "wb"))
self.fuzzyHelper.saveResults(settings.results_folder + settings.results_file, [settings.test_type, settings.dataset_name, settings.style, settings.gausses, settings.adjustment, self.data_type, title, accuracy, precision[0], precision[1], recall[0], recall[1], fscore[0], fscore[1], support[0], support[1], s_function_center, self.s_function_width, "---", measured_time])
return fscore
def noOptymalizationWorker(self, settings, show_results = True):
return self.sFunction(settings, 0.5, "No Optymalization", show_results)
def sOptymalizationWorker(self, settings, center_point, show_results = True):
self.sFunction(settings, center_point, "Value S-Functions", show_results)
def thresholdWorker(self, settings, s_function_center, threshold, precision = 0.001, show_results = True):
print("-----------------------------------------------------------------------------------")
start = time.time()
_, df = self.fuzzyHelper.sFunctionsValue(s_function_center, self.s_function_width, self.df, settings, self.x_range, self.rules_extractor, self.rule_antecedents, self.d_results, self.decision)
_, df = self.fuzzyHelper.thresholdOptValue(threshold, df)
end = time.time()
measured_time = end - start
accuracy, precision, recall, fscore, support = self.fuzzyHelper.getScores(df)
print("-----------------------------------------------------------------------------------")
print("Center Point: {}".format(s_function_center))
print("Threshold: {}".format(threshold))
print(self.data_type + " Accuracy: {}".format(accuracy))
print("Time: {}".format(measured_time))
print("-----------------------------------------------------------------------------------")
df = df.sort_values(by=["Predicted Value"])
if show_results:
display(df.style.apply(self.highlightClassOne, axis = 1))
self.decision.view()
pickle.dump(df, open(settings.backup_folder + self.data_type + "_df_results.p", "wb"))
self.fuzzyHelper.saveResults(settings.results_folder + settings.results_file, [settings.test_type, settings.dataset_name, settings.style, settings.gausses, settings.adjustment, self.data_type, "Value Threshold", accuracy, precision[0], precision[1], recall[0], recall[1], fscore[0], fscore[1], support[0], support[1], s_function_center, self.s_function_width, threshold, measured_time])
def printAllDataframe(self):
self.fuzzyHelper.printAllDataframe(self.df.sort_values(by=['Predicted Value']))
def plotHistogram(self, bins = 100):
self.fuzzyHelper.plotHistogram(self.df, bins)
class Ki67(object):
def __init__(self,
settings,
s_function_width,
is_train_df=True,
s_function_center=0.5,
threshold=-1):
self.path = settings.backup_folder
self.settings = settings
self.d_results = [settings.class_2, settings.class_1]
self.x_range = np.arange(settings.set_min, settings.set_max,
settings.fuzzy_sets_precision)
self.s_function_width = s_function_width
self.fuzzyHelper = FuzzyHelper(settings)
self.loadData(settings, is_train_df)
def highlightClassOne(self, row):
return [
'background-color: green' if self.settings.class_1 == x else ""
for x in row
]
def loadData(self, settings, is_train_df):
self.decision = pickle.load(open(self.path + "decision.p", "rb"))
reductor = pickle.load(open(self.path + "reductor.p", "rb"))
features = pickle.load(open(self.path + "features.p", "rb"))
decision_table_with_reduct = pickle.load(
open(self.path + "decision_table_with_reduct.p", "rb"))
self.rules_extractor = RulesExtractor(decision_table_with_reduct,
reductor.reduct, settings)
self.rule_antecedents = self.rules_extractor.worker(
decision_table_with_reduct, features, self.d_results,
self.decision)
if is_train_df:
self.df = pickle.load(open(self.path + "train_features_df.p",
"rb"))
self.data_type = "Train"
else:
self.df = pickle.load(open(self.path + "test_features_df.p", "rb"))
self.data_type = "Test"
def sFunction(self, settings, s_function_center, title, show_results=True):
start = time.time()
df = self.fuzzyHelper.sFunctionsValueKi67(
s_function_center, self.s_function_width, self.df, settings,
self.x_range, self.rules_extractor, self.rule_antecedents,
self.d_results, self.decision)
end = time.time()
measured_time = end - start
print(
"-----------------------------------------------------------------------------------"
)
print("Time: {}".format(measured_time))
print(
"-----------------------------------------------------------------------------------"
)
if not os.path.exists(settings.backup_folder + "Images/"):
os.makedirs(settings.backup_folder + "Images/")
pickle.dump(
df,
open(
settings.backup_folder + "Images/" + self.data_type + "_" +
settings.file_name + "_" + settings.class_1 + "_df_results.p",
"wb"))
self.fuzzyHelper.saveResultsKi67(
settings.results_folder + settings.results_file, [
settings.test_type, "Ki67", settings.file_name, settings.style,
settings.gausses, settings.adjustment, settings.class_1,
self.data_type, title, s_function_center,
self.s_function_width, "---", measured_time
])
def noOptymalizationWorker(self, settings, show_results=True):
return self.sFunction(settings, 0.5, "Choose Features group",
show_results)
def sOptymalizationWorker(self, settings, center_point, show_results=True):
self.sFunction(settings, center_point, "Value S-Functions",
show_results)
def printAllDataframe(self):
self.fuzzyHelper.printAllDataframe(
self.df.sort_values(by=['Predicted Value']))
def plotHistogram(self, bins=100):
self.fuzzyHelper.plotHistogram(self.df, bins)