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Fixed Adjusted Relative Frequency Table Issue in allocation_tool.py #60

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Merged
YaoTingYao merged 1 commit into from
Feb 11, 2025
Merged

Fixed Adjusted Relative Frequency Table Issue in allocation_tool.py #60

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20 changes: 13 additions & 7 deletions allocation_tool.py
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Original file line number Diff line number Diff line change
Expand Up @@ -439,11 +439,11 @@ def execute_workflow_cnf(self, directory, max_iterations, csv, municipality, def
self.progress_updated.emit(30)

# Check modeling region IDs present in the prediction stage but absent in the fitting stage
id_difference = self.check_modeling_region_ids(csv, out_fn1)
id_difference, pre_model_region_id = self.check_modeling_region_ids(csv, out_fn1)

# If there are missing bins, calculate the relative frequency and create a new csv file
if id_difference.size > 0:
self.calculate_missing_bins_rf(id_difference, csv)
self.calculate_missing_bins_rf(id_difference, csv,pre_model_region_id)

self.progress_updated.emit(40)

Expand Down Expand Up @@ -486,11 +486,11 @@ def execute_workflow_vp(self, directory,max_iterations, csv, municipality, expec
self.progress_updated.emit(30)

# Check modeling region IDs present in the prediction stage but absent in the fitting stage
id_difference = self.check_modeling_region_ids(csv, out_fn1)
id_difference, pre_model_region_id = self.check_modeling_region_ids(csv, out_fn1)

# If there are missing bins, calculate the relative frequency and create a new csv file
if id_difference.size > 0:
self.calculate_missing_bins_rf(id_difference, csv)
self.calculate_missing_bins_rf(id_difference, csv,pre_model_region_id)

self.progress_updated.emit(40)

Expand Down Expand Up @@ -532,14 +532,15 @@ def check_modeling_region_ids(self, csv, out_fn):
pre_model_region_id = np.unique(pre_model_region_arr[pre_model_region_arr != 0])
id_difference = np.setdiff1d(pre_model_region_id, fit_model_region_id)

return id_difference
return id_difference, pre_model_region_id

def calculate_missing_bins_rf (self, id_difference, csv):
def calculate_missing_bins_rf (self, id_difference, csv, pre_model_region_id):
'''
If one or more empty bins are found, compute the jurisdiction-wide weighted average of relative frequencies for
missing bins and create a new csv file
:param csv: csv file of relative frequency in the fitting stage
:param id_difference: A set of modeling region IDs np array that exist only in the prediction stage
:param pre_model_region_id: Prediction modeling region ID np array
:return
'''
# Convert modeling region ids to vulnerability zone id
Expand Down Expand Up @@ -577,5 +578,10 @@ def calculate_missing_bins_rf (self, id_difference, csv):
# Drop column 'v_zone'
df_new=df_new.drop(['v_zone'], axis=1)

# Read CNF Modeling Region ID
mr_cnf_df=pd.DataFrame(pre_model_region_id, columns=['ID'])
df_new_cnf=pd.merge(df_new, mr_cnf_df, on='ID', how='inner')


# Save the new result to csv
df_new.to_csv(csv.split('.')[0] + '_adjusted_for_prediction' + '.csv', index=False)
df_new_cnf.to_csv(csv.split('.')[0] + '_adjusted_for_prediction' + '.csv', index=False)