BUG: Error introduced in dataiku.core.saved_model

UserBird

Looks like an error was introduced in 5.0.1 (it worked in 5.0.0) that prevents dataiku.core.saved_model.Predictor.predict from working properly because it raises an error with Pandas

ValueError: If using all scalar values, you must pass an index

Full Error message

/home/dataiku/dataiku-dss-5.0.1/python/dataiku/core/saved_model.pyc in predict(self, df, with_input_cols, with_prediction, with_probas, with_conditional_outputs, with_proba_percentile)
    591                 column_types[k] = np.object
    592         pred_df = self._get_prediction_dataframe(dates_handled.astype(column_types), with_prediction, with_probas, with_conditional_outputs,
--> 593                                                  with_proba_percentile)
    594         if with_input_cols:
    595             return pd.concat([df, pred_df], axis=1)

/home/dataiku/dataiku-dss-5.0.1/python/dataiku/core/saved_model.pyc in _get_prediction_dataframe(self, input_df, with_prediction, with_probas, with_conditional_outputs, with_proba_percentile)
    456                                   with_conditional_outputs, with_proba_percentile):
    457         if self.params.model_type == "PREDICTION":
--> 458             pred_df = self._prediction_type_dataframe(input_df, with_prediction, with_probas)
    459             self._add_percentiles_and_condoutputs(pred_df, with_proba_percentile, with_conditional_outputs)
    460             return pred_df

/home/dataiku/dataiku-dss-5.0.1/python/dataiku/core/saved_model.pyc in _prediction_type_dataframe(self, input_df, with_prediction, with_probas)
    488         if prediction_type == "REGRESSION":
    489             if with_prediction:
--> 490                 pred_df = pd.DataFrame({"prediction": self._clf.predict(X)[0]})
    491             else:
    492                 raise ValueError("Predicting a regression model with with_prediction=False. Oops.")

/home/dataiku/dss/condaenv/lib/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
    273                                  dtype=dtype, copy=copy)
    274         elif isinstance(data, dict):
--> 275             mgr = self._init_dict(data, index, columns, dtype=dtype)
    276         elif isinstance(data, ma.MaskedArray):
    277             import numpy.ma.mrecords as mrecords

/home/dataiku/dss/condaenv/lib/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
    409             arrays = [data[k] for k in keys]
    410 
--> 411         return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    412 
    413     def _init_ndarray(self, values, index, columns, dtype=None, copy=False):

/home/dataiku/dss/condaenv/lib/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
   5494     # figure out the index, if necessary
   5495     if index is None:
-> 5496         index = extract_index(arrays)
   5497     else:
   5498         index = _ensure_index(index)

/home/dataiku/dss/condaenv/lib/python2.7/site-packages/pandas/core/frame.pyc in extract_index(data)
   5533 
   5534         if not indexes and not raw_lengths:
-> 5535             raise ValueError('If using all scalar values, you must pass'
   5536                              ' an index')
   5537 

ValueError: If using all scalar values, you must pass an index

Samuel_R_

Hello,

Thank you for your feedback !

We could reproduce the issue. It will be fixed it in the forthcoming 5.0.2 release.

Best regards

ricslator

The error message says that if you're passing scalar values, you have to pass an index. Pandas unfortunately always needs an index when created a DatFrame from Dictionary. What this is essentially asking for is a column number for each dictionary to correspond to each dictionary. You can either set it yourself, or use an object with the following structure so pandas can determine the index itself:

df = pd.DataFrame({'A': [a], 'B': [b]})

or use scalar values and pass an index:

pd.DataFrame({'A': a, 'B': b}, index=[0])