Explain Prediction error

[rorcores]

Hi,

I'm trying to run the predictions method on a pandas dataframe.

I trained the model and have loaded new data with which to make predictions on.

My model is of type: xgboost.core.Booster

My statement is as follows:

expl = eli5.explain_prediction_xgboost(model, features_pred.iloc[0,:])

The error message I get is below. I have tried loading the pandas df as a numpy array instead, and I have tried reverting back to an earlier version of eli5, but nothing seems to fix this. Tearing my hair about a bit. Any ideas? Thanks kindly

Here is the error:

TypeError: object of type 'numpy.int32' has no len()

`TypeError Traceback (most recent call last)

in 4 # for i in range(len(features_pred)): 5 ----> 6 expl = eli5.explain_prediction_xgboost(model, features_pred.iloc[0,:]) 7 8 #expl = eli5.explain_prediction_xgboost(booster, features_pred.iloc[0]) /databricks/python/lib/python3.5/site-packages/eli5/xgboost.py in explain_prediction_xgboost(xgb, doc, vec, top, top_targets, target_names, targets, feature_names, feature_re, feature_filter, vectorized, is_regression, missing) 194 195 scores_weights = _prediction_feature_weights( --> 196 booster, dmatrix, n_targets, feature_names, xgb_feature_names) 197 198 x = get_X0(add_intercept(X)) /databricks/python/lib/python3.5/site-packages/eli5/xgboost.py in _prediction_feature_weights(booster, dmatrix, n_targets, feature_names, xgb_feature_names) 244 xgb_feature_names = {f: i for i, f in enumerate(xgb_feature_names)} 245 tree_dumps = booster.get_dump(with_stats=True) --> 246 assert len(tree_dumps) == len(leaf_ids) 247 248 target_feature_weights = partial( TypeError: object of type 'numpy.int32' has no len()`