# STEP 5: Import model
pzmm.ScoreCode.score_code = ''
lreg = pzmm.ImportModel.import_model(model_files=model_path, model_prefix=model_prefix, project=project,
input_data=X, predict_method=[model.predict_proba, [float, float]],
score_metrics=score_metrics, overwrite_model=True,
target_values=['0', '1'], model_file_name=model_prefix + ".pickle")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[12], line 3
1 # STEP 5: Import model
2 pzmm.ScoreCode.score_code = ''
----> 3 lreg = pzmm.ImportModel.import_model(model_files=model_path, model_prefix=model_prefix, project=project,
4 input_data=X, predict_method=[model.predict_proba, [float, float]],
5 score_metrics=score_metrics, overwrite_model=True,
6 target_values=['0', '1'], model_file_name=model_prefix + ".pickle")
File ~\AppData\Local\miniconda3\envs\ml\Lib\site-packages\sasctl\pzmm\import_model.py:351, in ImportModel.import_model(cls, model_files, model_prefix, project, input_data, predict_method, score_metrics, pickle_type, project_version, missing_values, overwrite_model, score_cas, mlflow_details, predict_threshold, target_values, overwrite_project_properties, target_index, **kwargs)
348 # For SAS Viya 4, the score code can be written beforehand and imported with
349 # all the model files
350 elif current_session().version_info() == 4:
--> 351 score_code_dict = sc.write_score_code(
352 model_prefix,
353 input_data,
354 predict_method,
355 score_metrics=score_metrics,
356 pickle_type=pickle_type,
357 predict_threshold=predict_threshold,
358 score_code_path=None if isinstance(model_files, dict) else model_files,
359 target_values=target_values,
360 missing_values=missing_values,
361 score_cas=score_cas,
362 target_index=target_index,
363 **kwargs,
364 )
365 if score_code_dict:
366 model_files.update(score_code_dict)
File ~\AppData\Local\miniconda3\envs\ml\Lib\site-packages\sasctl\pzmm\write_score_code.py:280, in ScoreCode.write_score_code(cls, model_prefix, input_data, predict_method, target_variable, target_values, score_metrics, predict_threshold, model, pickle_type, missing_values, score_cas, score_code_path, target_index, **kwargs)
266 cls.score_code += (
267 f"\n{'':4}# Check for numpy values and convert to a CAS readable "
268 f"representation\n"
269 f"{'':4}if isinstance(prediction, np.ndarray):\n"
270 f"{'':8}prediction = prediction.tolist()\n\n"
271 )
272 """
273
274 # Check for numpy values and conver to a CAS readable representation
(...)
278
279 """
--> 280 cls._predictions_to_metrics(
281 score_metrics,
282 predict_method[1],
283 target_values=target_values,
284 predict_threshold=predict_threshold,
285 target_index=target_index,
286 )
288 if missing_values:
289 cls._impute_missing_values(input_data, missing_values)
File ~\AppData\Local\miniconda3\envs\ml\Lib\site-packages\sasctl\pzmm\write_score_code.py:1138, in ScoreCode._predictions_to_metrics(cls, metrics, predict_returns, target_values, predict_threshold, h2o_model, target_index)
1136 # Binary classification model
1137 elif len(target_values) == 2:
-> 1138 cls._binary_target(
1139 metrics,
1140 target_values,
1141 predict_returns,
1142 predict_threshold,
1143 target_index,
1144 h2o_model,
1145 )
1146 # Multiclass classification model
1147 elif len(target_values) > 2:
File ~\AppData\Local\miniconda3\envs\ml\Lib\site-packages\sasctl\pzmm\write_score_code.py:1507, in ScoreCode._binary_target(cls, metrics, target_values, returns, threshold, target_index, h2o_model)
1498 elif sum(returns) == 0 and len(returns) == 2:
1499 warn(
1500 "Due to the ambiguity of the provided metrics and prediction return"
1501 " types, the score code assumes that a classification and the "
1502 "target event probability should be returned."
1503 )
1504 cls.score_code += (
1505 f"{'':4}if input_array.shape[0] == 1:\n"
1506 f"{'':8}if prediction[0][{target_index}] > {threshold}:\n"
-> 1507 f"{'':12}{metrics[0]} = \"{target_values[target_index]}\"\n"
1508 f"{'':8}else:\n"
1509 f"{'':12}{metrics[0]} = \"{target_values[abs(target_index-1)]}\"\n"
1510 f"{'':8}return {metrics[0]}, prediction[0][{target_index}]\n"
1511 f"{'':4}else:\n"
1512 f"{'':8}df = pd.DataFrame(prediction)\n"
1513 f"{'':8}proba = df[{target_index}]\n"
1514 f"{'':8}classifications = np.where(df[{target_index}] > {threshold}, '{target_values[target_index]}', '{target_values[abs(target_index-1)]}')\n"
1515 f"{'':8}return pd.DataFrame({{'{metrics[0]}': classifications, '{metrics[1]}': proba}})"
1516 )
1517 """
1518 if input_array.shape[0] == 1:
1519 if prediction[0][1] > .5:
(...)
1528 return pd.DataFrame({'Classification': classifications, 'Probability': proba})
1529 """
1530 # TODO: Potentially add threshold
1531 # Return classification and probability value
TypeError: list indices must be integers or slices, not NoneType
Describe the issue
After updating sasctl to 1.10.1, I experienced TypeError when calling pzmm.ImportModel.import_model with the same code that used to work in 1.10.0.
To Reproduce
Steps or example code to reproduce the issue.
The rest of my sample code can be found here:
https://github.com/pulungw/sascode/blob/main/python/model_manager_register_sample_sklearn.ipynb
Which was based from this article:
https://blogs.sas.com/content/subconsciousmusings/2023/08/11/mlops-for-pirates-and-snakes/
Expected behavior
pzmm.ImportModel.import_model finished execution succesfully like in 1.10.0.
Stack Trace
If you're experiencing an exception, include the full stack trace and error message.
Version
1.10.1