Columns and DataType Not Explicitly Set on line 48 of MLFlowServer.py

Hello!

I found an AI-Specific Code smell in your project.
The smell is called: Columns and DataType Not Explicitly Set

You can find more information about it in this paper: https://dl.acm.org/doi/abs/10.1145/3522664.3528620.

According to the paper, the smell is described as follows:

Problem	If the columns are not selected explicitly, it is not easy for developers to know what to expect in the downstream data schema. If the datatype is not set explicitly, it may silently continue the next step even though the input is unexpected, which may cause errors later. The same applies to other data importing scenarios.
Solution	It is recommended to set the columns and DataType explicitly in data processing.
Impact	Readability

Example:

### Pandas Column Selection
import pandas as pd
df = pd.read_csv('data.csv')
+ df = df[['col1', 'col2', 'col3']]

### Pandas Set DataType
import pandas as pd
- df = pd.read_csv('data.csv')
+ df = pd.read_csv('data.csv', dtype={'col1': 'str', 'col2': 'int', 'col3': 'float'})

You can find the code related to this smell in this link:

seldon-core/servers/mlflowserver/mlflowserver/MLFlowServer.py

Lines 38 to 58 in 546b51f

    
           if not self.ready: 
        
               raise requests.HTTPError("Model not loaded yet") 
        
           if self.xtype == "ndarray": 
        
               result = self._model.predict(X) 
        
           else: 
        
               if feature_names is not None and len(feature_names) > 0: 
        
                   df = pd.DataFrame(data=X, columns=feature_names) 
        
               else: 
        
                   df = pd.DataFrame(data=X) 
        
               result = self._model.predict(df) 
        
           if isinstance(result, pd.DataFrame): 
        
               if self.column_names is None: 
        
                   self.column_names = result.columns.to_list() 
        
               result = result.to_numpy() 
        
           logger.debug(f"Prediction result: {result}") 
        
           return result

.

I also found instances of this smell in other files, such as:

File: https://github.com/SeldonIO/seldon-core/blob/master/examples/kubeflow/pipeline/pipeline_steps/data_downloader/pipeline_step.py#L17-L27 Line: 22
File: https://github.com/SeldonIO/seldon-core/blob/master/examples/ray/batch-split-proxy/training/train.py#L10-L20 Line: 15
File: https://github.com/SeldonIO/seldon-core/blob/master/python/seldon_core/serving_test_gen.py#L77-L87 Line: 82
.

I hope this information is helpful!


	if not self.ready:
	raise requests.HTTPError("Model not loaded yet")

	if self.xtype == "ndarray":
	result = self._model.predict(X)
	else:
	if feature_names is not None and len(feature_names) > 0:
	df = pd.DataFrame(data=X, columns=feature_names)
	else:
	df = pd.DataFrame(data=X)
	result = self._model.predict(df)

	if isinstance(result, pd.DataFrame):
	if self.column_names is None:
	self.column_names = result.columns.to_list()
	result = result.to_numpy()

	logger.debug(f"Prediction result: {result}")
	return result