Error while using get_predictor method

Mohammed

I am facing an error while using the get_predictor.

import dataiku
from dataiku import pandasutils as pdu
import pandas as pd 
 
# Example: load a DSS dataset as a Pandas dataframe
model = dataiku.Model("model_id")
 
model.get_info()
model.get_predictor()

I get this error.

---------------------------------------------------------------------------

TypeError Traceback (most recent call last)<ipython-input-29-7152fc869c78> in <module>----> 1 model.get_predictor()/opt/dataiku-dss-12.5.1/python/dataiku/core/saved_model.py in get_predictor(self, version_id) 272 sm = res["saved_model"] 273 model_folder_context = build_readonly_saved_model_folder_context(model_folder, self.project_key, self.get_id())--> 274 self._predictors[version_id] = build_predictor_for_saved_model(model_folder_context, sm.get("conditionalOutputs", [])) 275 return self._predictors[version_id] 276
/opt/dataiku-dss-12.5.1/python/dataiku/core/saved_model.py in build_predictor_for_saved_model(model_folder_context, conditional_outputs) 457 model_type = core_params.get("taskType") 458 return build_predictor(model_type, model_folder_context, model_folder_context, split_folder_context,--> 459 conditional_outputs, core_params, split_desc) 460 461
/opt/dataiku-dss-12.5.1/python/dataiku/core/saved_model.py in build_predictor(model_type, model_folder_context, preprocessing_folder_context, split_folder_context, conditional_outputs, core_params, split_desc, train_split_desc, train_split_folder_context) 571 return KerasPredictor(model_params, preprocessing, model, batch_size=100) 572 else:--> 573 preprocessing = Preprocessing(preprocessing_handler, modeling_params) 574 feature_names = _get_or_compute_feature_names(model_params.model_perf, collector_data, preprocessing.pipeline) 575 return Predictor(model_params, preprocessing, feature_names, clf)/opt/dataiku-dss-12.5.1/python/dataiku/core/saved_model.py in __init__(self, preprocessing_handler, modeling_params, with_prediction) 654 class Preprocessing: 655 def __init__(self, preprocessing_handler, modeling_params, with_prediction=False):--> 656 self.pipeline = preprocessing_handler.build_preprocessing_pipeline() 657 self.pipeline_with_target = preprocessing_handler.build_preprocessing_pipeline(with_target=True, 658 allow_empty_mf=True,/opt/dataiku-dss-12.5.1/python/dataiku/doctor/preprocessing_handler.py in build_preprocessing_pipeline(self, *args, **kwargs) 224 def build_preprocessing_pipeline(self, *args, **kwargs): 225 pipeline = PreprocessingPipeline(steps=list(self.preprocessing_steps(*args, **kwargs)))--> 226 pipeline.init_resources(self) 227 return pipeline 228
/opt/dataiku-dss-12.5.1/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py in init_resources(self, resource_handler) 2512 def init_resources(self, resource_handler): 2513 for step in self.steps:-> 2514 step.init_resources(resource_handler) 2515 2516 def fit_and_process(self, input_df, *args, **kwargs):/opt/dataiku-dss-12.5.1/python/dataiku/doctor/preprocessing/dataframe_preprocessing.py in init_resources(self, resources_handler) 2178 2179 def init_resources(self, resources_handler):-> 2180 self.res = resources_handler.get_resource("custom_prep", "pkl") 2181 if self.input_col in self.res: 2182 self.processor = self.res[self.input_col]/opt/dataiku-dss-12.5.1/python/dataiku/doctor/preprocessing_handler.py in get_resource(self, resource_name, resource_type) 176 return self.__resources[resource_name] 177 self.__resource_types[resource_name] = resource_type--> 178 resource = read_resource(self._data_folder_context, resource_name, resource_type) 179 if resource is None: 180 resource = {}/opt/dataiku-dss-12.5.1/python/dataiku/doctor/preprocessing_handler.py in read_resource(folder_context, resource_name, resource_type) 122 with folder_context.get_file_path_to_read(zipped_name) as zipped_path: 123 with gzip.open(zipped_path, "rb") as f:--> 124 return dku_pickle.load(f) 125 # Maintain compatibility for non-gzipped pkl files 126 elif folder_context.isfile(legacy_filename):/opt/dataiku-dss-12.5.1/python/dataiku/base/dku_pickle.py in load(pkl_file) 49 """ 50 try:---> 51 return SklearnUnpickler(pkl_file).load() 52 except UnicodeDecodeError: 53 raise PickleLoadException(u"Failed to unpickle {}. You might have been trying to load a resource saved in a python 2 code environment with a python 3 one.".format(pkl_file.name))/opt/dataiku-dss-12.5.1/python/dataiku/base/dku_pickle.py in load(self) 151 152 def load(self):--> 153 return super(SklearnUnpickler, self).load()

TypeError: code() takes at most 15 arguments (16 given)

---

Operating system used: Windows

Alexandru

Hi,
Yes you can control the python version from the Lab under :

You are likely using the built-in which was using Python2 before and now just retraining ont he same Built-in will automatically use Python3.

Thanks

Alexandru

Hi,
There is likely an issue with the model being trained on older python version.

"trying to load a resource saved in a python 2 code environment with a python 3"

Please try to retrain the model with Python 3 , and publish the newly trained model. Retry the get_predictor code again

If the issues persists please open a support ticket with the job diagnostics( with the get_predictor code) and model training diagnostics.

Thanks

Mohammed

@AlexT
Thanks for your response.
How do I retrain the model with Python 3? This model is trained in the LAB. Do I have control over which Python version to use?