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Adding error messages for invalid inputs
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Related-Issue: #24
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Johnu George committed May 14, 2015
1 parent 69b7821 commit 958ff7c
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Showing 2 changed files with 79 additions and 40 deletions.
1 change: 0 additions & 1 deletion cognitive/app/view/view_operation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,7 +105,6 @@ def set_operation(self, operation, data):
op.save()

elif operation == 'input':
print data
if data["input_file_type"] == "csv":
print data["data_values"], data["input_file"], data["input_file_type"]
filename = "/tmp/" + str(data["experiment"]) + "_" + data["input_file"]
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118 changes: 79 additions & 39 deletions cognitive/app/view/view_result_local.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,8 @@ def run(self):
graph = exp.workflow.graph_data
graph_data = {}
print graph
status = "success"
err_msg = ""
tmp = graph.split(',')

for elem in tmp:
Expand Down Expand Up @@ -96,14 +98,21 @@ def run(self):
model_type = op.function_arg_id
print model_type, train_data_percentage, target_column
target_feature = feature_names[target_column]
actual_target_column = input_data.columns.get_loc(target_feature)
input_feature_columns = range(len(input_data.columns))
input_feature_columns.remove(actual_target_column)
input_features = input_data.columns[input_feature_columns]
classifier = Classifier(
input_data, model_type, train_data_percentage,
input_features, target_feature)
output_data = classifier.learn()
try:
actual_target_column = input_data.columns.get_loc(target_feature)
input_feature_columns = range(len(input_data.columns))
input_feature_columns.remove(actual_target_column)
input_features = input_data.columns[input_feature_columns]
classifier = Classifier(
input_data, model_type, train_data_percentage,
input_features, target_feature)
output_data = classifier.learn()
except ValueError as e:
status = "failure"
err_msg = " Invalid input for the model training"
except KeyError as e:
status = "failure"
err_msg = target_feature + " column is not available for Model Training"

# TODO: [refactor] elif?
if op.function_type == 'Update':
Expand All @@ -118,54 +127,77 @@ def run(self):
column_id = float(op.function_arg_id)
column_name = feature_names[column_id]
if column_name not in input_data:
print "Column name ", column_name, " not present. Skipping"
continue # throw error in module status
if input_data[column_name].dtype == 'object':
print "Column name ", column_name, " is not integer/float. Skipping"
continue # throw error in module status
input_data[column_name] += constant_value
#print "Column name ", column_name, " not present. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = column_name + " column is not available for current operation"
elif input_data[column_name].dtype == 'object':
#print "Column name ", column_name, " is not integer/float. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = " Invalid input in column "+ column_name+ " for the current operation"
else:
input_data[column_name] += constant_value
if op.function_subtype == 'Sub':
constant_value = float(op.function_subtype_arg)
column_id = float(op.function_arg_id)
column_name = feature_names[column_id]
if column_name not in input_data:
print "Column name ", column_name, " not present. Skipping"
continue # throw error in module status
if input_data[column_name].dtype == 'object':
print "Column name ", column_name, " is not integer/float. Skipping"
continue # throw error in module status
input_data[column_name] -= constant_value
#print "Column name ", column_name, " not present. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = column_name + " column is not available for current operation"
elif input_data[column_name].dtype == 'object':
#print "Column name ", column_name, " is not integer/float. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = " Invalid input in column "+ column_name+ " for the current operation"
else:
input_data[column_name] -= constant_value
if op.function_subtype == 'Mult':
constant_value = float(op.function_subtype_arg)
column_id = float(op.function_arg_id)
column_name = feature_names[column_id]
if column_name not in input_data:
print "Column name ", column_name, " not present. Skipping"
continue # throw error in module status
if input_data[column_name].dtype == 'object':
print "Column name ", column_name, " is not integer/float. Skipping"
continue # throw error in module status
input_data[column_name] *= constant_value
#print "Column name ", column_name, " not present. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = column_name + " column is not available for current operation"
elif input_data[column_name].dtype == 'object':
#print "Column name ", column_name, " is not integer/float. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = " Invalid input in column "+ column_name+ " for the current operation"
else:
input_data[column_name] *= constant_value
if op.function_subtype == 'Div':
constant_value = float(op.function_subtype_arg)
column_id = float(op.function_arg_id)
column_name = feature_names[column_id]
if column_name not in input_data:
print "Column name ", column_name, " not present. Skipping"
continue # throw error in module status
if input_data[column_name].dtype == 'object':
print "Column name ", column_name, " is not integer/float. Skipping"
continue # throw error in module status
input_data[column_name] /= constant_value
#print "Column name ", column_name, " not present. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = column_name + " column is not available for current operation"
elif input_data[column_name].dtype == 'object':
#print "Column name ", column_name, " is not integer/float. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = " Invalid input in column "+ column_name+ " for the current operation"
else:
input_data[column_name] /= constant_value
if op.function_subtype == 'Normalize':
column_id = float(op.function_arg_id)
column_name = feature_names[column_id]
sum_array = input_data.sum(axis=0)
if column_name not in sum_array:
print "Column name ", column_name, " not present. Skipping"
continue # throw error in module status
normalization_value = sum_array[column_name]
input_data[column_name] = input_data[column_name] / normalization_value
#print "Column name ", column_name, " not present. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = column_name + " column is not available for current operation"
else:
normalization_value = sum_array[column_name]
input_data[column_name] = input_data[column_name] / normalization_value

# TODO: [refactor] elif?
if op.function_type == 'Filter':
Expand All @@ -185,9 +217,12 @@ def run(self):
for elem in column_id_list:
column_name = feature_names[elem]
if column_name not in input_data:
print "Column name ", column_name, " not present. Skipping"
continue # throw error in module status
column_name_list.append(column_name)
#print "Column name ", column_name, " not present. Skipping"
#continue # throw error in module status
status = "failure"
err_msg = column_name + " column is not available for current operation"
else:
column_name_list.append(column_name)
if column_name_list:
input_data = input_data.drop_duplicates(subset=column_name_list)
if op.function_subtype == 'RemoveMissing':
Expand Down Expand Up @@ -235,6 +270,9 @@ def run(self):
if output_data is not None:
self.result["output"] = output_data

self.result["status"] = status
self.result["message"] = err_msg

self.result["missing_values"] = list(input_data.isnull().sum().values)
mean = input_data.mean().round(2)
median = input_data.median().round(2)
Expand Down Expand Up @@ -288,6 +326,8 @@ def run(self):
CACHE[self.experiment] = input_data

print self.result
print self.result["status"]
print self.result["message"]
break


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