def create_dates_vector(self,start_date, end_date, coocurring_precision):
if coocurring_precision==MatrixFeatureTransformer.hour_precision:
start_date = DateTimeUtils.round_to_the_hour_before(start_date)
end_date = DateTimeUtils.round_to_the_hour_after(end_date)
time_vector = DateTimeUtils.hours_between(start_date , end_date)
elif coocurring_precision == MatrixFeatureTransformer.quarter_hour_precision:
start_date = DateTimeUtils.round_to_the_hour_before(start_date)
end_date = DateTimeUtils.round_to_the_hour_after(end_date)
time_vector = DateTimeUtils.quarterhours_between(start_date , end_date)[0:-1]
#print time_vector
else:
raise Exception("NOT IMPLEMENTED EXCEPTION : Branch coocurring_precision "+coocurring_precision+" not yet implemented ")
print "start_date "+str(start_date)
print "end_date "+str(end_date)
print coocurring_precision
print "number of date values "+str(len(time_vector))
return time_vector
def create_dates_vector(self, start_date, end_date, coocurring_precision):
if coocurring_precision == MatrixFeatureTransformer.hour_precision:
start_date = DateTimeUtils.round_to_the_hour_before(start_date)
end_date = DateTimeUtils.round_to_the_hour_after(end_date)
time_vector = DateTimeUtils.hours_between(start_date, end_date)
return time_vector
elif coocurring_precision == MatrixFeatureTransformer.quarter_hour_precision:
start_date = DateTimeUtils.round_to_the_hour_before(start_date)
end_date = DateTimeUtils.round_to_the_hour_after(end_date)
time_vector = DateTimeUtils.quarterhours_between(
start_date, end_date)[0:-1]
return time_vector
else:
raise Exception(
"NOT IMPLEMENTED EXCEPTION : Branch coocurring_precision " +
coocurring_precision + " not yet implemented ")
def transform(self):
data_copy = copy.deepcopy(self.data)
if self.coocurring_precision == MatrixFeatureTransformer.hour_precision:
time_id = 0
for target_date in self.time_vector:
#mark all the realizations that correspond to target_date
for date_interval in data_copy.keys():
realization = data_copy[date_interval]
#get all the hours representing the interval
start_realization_time = DateTimeUtils.round_to_the_hour_before(
DataExtractor.start_date_of_realization(date_interval))
end_realization_time = DateTimeUtils.round_to_the_hour_before(
DataExtractor.end_date_of_realization(date_interval))
hours = DateTimeUtils.hours_between(
start_realization_time, end_realization_time)
do_break = False
for hour in hours:
if hour == target_date:
#integrate the vector to the column time_id of the matrix
vector = self.transform_realization_to_vector(
realization)
self.matrix_data[
time_id, :] = self.vector_transformer_operator(
self.matrix_data[time_id, :], vector)
if hour > target_date:
#we need to advance the target date
do_break = True
break
if do_break:
break
del data_copy[date_interval]
time_id += 1
if not self.let_importance_scores_to_1:
self.realization_importance_score = self.ir_importance_score(
self.matrix_data)
print "the number of non zeros for " + self.feature_name + " values is : " + str(
np.count_nonzero(self.matrix_data)) + "/" + str(
np.size(self.matrix_data))
elif self.coocurring_precision == MatrixFeatureTransformer.quarter_hour_precision:
time_id = 0
for target_date in self.time_vector:
#mark all the realizations that correspond to target_date
for date_interval in data_copy.keys():
realization = data_copy[date_interval]
#get all the hours representing the interval
start_realization_time = DateTimeUtils.round_to_the_quarter_before(
DataExtractor.start_date_of_realization(date_interval))
end_realization_time = DateTimeUtils.round_to_the_quarter_before(
DataExtractor.end_date_of_realization(date_interval))
quarters = DateTimeUtils.quarterhours_between(
start_realization_time, end_realization_time)
do_break = False
#print "date interval is "+date_interval
#print "hours are "+str(quarters)
#print "target date is "+str(target_date)
#print
#print
for quarter in quarters:
if quarter == target_date:
#integrate the vector to the column time_id of the matrix
vector = self.transform_realization_to_vector(
realization)
self.matrix_data[
time_id, :] = self.vector_transformer_operator(
self.matrix_data[time_id, :], vector)
if quarter > target_date:
#we need to advance the target date
do_break = True
break
#if quarter<target_date:
#raise Exception("UNEXPECTED BRANCH EXCEPTION ")
if do_break:
break
del data_copy[date_interval]
time_id += 1
if not self.let_importance_scores_to_1:
self.realization_importance_score = self.ir_importance_score(
self.matrix_data)
'''
for testing
'''
'''self.matrix_data = np.zeros((4,8))
self.matrix_data[0,0]=1
self.matrix_data[2,0]=1
self.matrix_data[0,1]=1
self.matrix_data[3,2]=1
#----
self.matrix_data[1,5]=1
self.matrix_data[3,5]=1
self.matrix_data[1,6]=1
self.matrix_data[2,6]=1
self.matrix_data[2,7]=1'''
[
self.matrix_data, self.time_vector
] = self._do_aggregated_special_transformation_with_replative_importance(
self.matrix_data, 4, self.feature_coef, self.time_vector,
self.do_normalize)
print np.shape(self.matrix_data)
print len(self.time_vector)
print "the number of non zeros for " + self.feature_name + " values is : " + str(
np.count_nonzero(self.matrix_data)) + "/" + str(
np.size(self.matrix_data))
else:
raise Exception(
"NOT IMPLEMENTED EXCEPTION : Branch coocurring_precision " +
coocurring_precision + " not yet implemented ")
def transform(self):
data_copy = copy.deepcopy(self.data)
if self.coocurring_precision==MatrixFeatureTransformer.hour_precision:
time_id = 0
for target_date in self.time_vector:
#mark all the realizations that correspond to target_date
for date_interval in data_copy.keys():
realization = data_copy[date_interval]
#get all the hours representing the interval
start_realization_time = DateTimeUtils.round_to_the_hour_before(DataExtractor.start_date_of_realization(date_interval))
end_realization_time = DateTimeUtils.round_to_the_hour_before(DataExtractor.end_date_of_realization(date_interval))
hours = DateTimeUtils.hours_between(start_realization_time , end_realization_time)
do_break = False
for hour in hours:
if hour == target_date:
#integrate the vector to the column time_id of the matrix
vector = self.transform_realization_to_vector(realization)
self.matrix_data[time_id, : ] = self.vector_transformer_operator(self.matrix_data[time_id, : ],vector)
if hour>target_date:
#we need to advance the target date
do_break = True
break
if do_break:
break
del data_copy[date_interval]
time_id +=1
if not self.let_importance_scores_to_1:
self.realization_importance_score = self.ir_importance_score(self.matrix_data)
print "the number of non zeros for "+self.feature_name+" values is : "+str(np.count_nonzero(self.matrix_data))+"/"+str(np.size(self.matrix_data))
elif self.coocurring_precision == MatrixFeatureTransformer.quarter_hour_precision:
time_id = 0
for target_date in self.time_vector:
#mark all the realizations that correspond to target_date
for date_interval in data_copy.keys():
realization = data_copy[date_interval]
#get all the hours representing the interval
start_realization_time = DateTimeUtils.round_to_the_quarter_before(DataExtractor.start_date_of_realization(date_interval))
end_realization_time = DateTimeUtils.round_to_the_quarter_before(DataExtractor.end_date_of_realization(date_interval))
quarters = DateTimeUtils.quarterhours_between(start_realization_time , end_realization_time)
do_break = False
#print "date interval is "+date_interval
#print "hours are "+str(quarters)
#print "target date is "+str(target_date)
#print
#print
for quarter in quarters:
if quarter == target_date:
#integrate the vector to the column time_id of the matrix
vector = self.transform_realization_to_vector(realization)
self.matrix_data[time_id, : ] = self.vector_transformer_operator(self.matrix_data[time_id, : ],vector)
if quarter>target_date:
#we need to advance the target date
do_break = True
break
#if quarter<target_date:
#raise Exception("UNEXPECTED BRANCH EXCEPTION ")
if do_break:
break
del data_copy[date_interval]
time_id +=1
if not self.let_importance_scores_to_1:
self.realization_importance_score = self.ir_importance_score(self.matrix_data)
'''
for testing
'''
'''self.matrix_data = np.zeros((4,8))
self.matrix_data[0,0]=1
self.matrix_data[2,0]=1
self.matrix_data[0,1]=1
self.matrix_data[3,2]=1
#----
self.matrix_data[1,5]=1
self.matrix_data[3,5]=1
self.matrix_data[1,6]=1
self.matrix_data[2,6]=1
self.matrix_data[2,7]=1'''
[self.matrix_data, self.time_vector] = self._do_aggregated_special_transformation_with_replative_importance(self.matrix_data, 4, self.feature_coef, self.time_vector,self.do_normalize)
print np.shape(self.matrix_data)
print len(self.time_vector)
print "the number of non zeros for "+self.feature_name+" values is : "+str(np.count_nonzero(self.matrix_data))+"/"+str(np.size(self.matrix_data))
else:
raise Exception("NOT IMPLEMENTED EXCEPTION : Branch coocurring_precision "+coocurring_precision+" not yet implemented ")