TypeError: data type 'string[pyarrow]' not understood - using Python tool

[nchristensen]

Hi All,

I am trying to run an Entity Matching Model from ING in the python tool on Alteryx. The packages seem to install properly, but when ever I fit the model I recieving the following error.  

(I have attached the code at the bottom of the post)

I am using the example data provided, and it works when I run the jupyter notebook in VScode

# generate example ground-truth names and matching noised names, with typos and missing words.
ground_truth, noised_names = create_example_noised_names(random_seed=42)
train_names, test_names = noised_names[:5000], noised_names[5000:]

Is this an error with the Alteryx Python Environemnt? 

Any help would be greatly appreciated. 

**CODE**

from emm import PandasEntityMatching
from emm.data.create_data import create_example_noised_names

# generate example ground-truth names and matching noised names, with typos and missing words.
ground_truth, noised_names = create_example_noised_names(random_seed=42)
train_names, test_names = noised_names[:5000], noised_names[5000:]

# two example name-pair candidate generators: character-based cosine similarity and sorted neighbouring indexing
indexers = [
{
'type': 'cosine_similarity',
'tokenizer': 'characters', # character-based cosine similarity. alternative: 'words'
'ngram': 2, # 2-character tokens only
'num_candidates': 5, # max 5 candidates per name-to-match
'cos_sim_lower_bound': 0.2, # lower bound on cosine similarity
},
{'type': 'sni', 'window_length': 3} # sorted neighbouring indexing window of size 3.
]
em_params = {
'name_only': True, # only consider name information for matching
'entity_id_col': 'Index', # important to set both index and name columns to pick up
'name_col': 'Name',
'indexers': indexers,
'supervised_on': False, # no supervided model (yet) to select best candidates
'with_legal_entity_forms_match': True, # add feature that indicates match of legal entity forms (e.g. ltd != co)
}
# 1. initialize the entity matcher
p = PandasEntityMatching(em_params)

# 2. fitting: prepare the indexers based on the ground truth names, eg. fit the tfidf matrix of the first indexer.
p.fit(ground_truth) ERROR

# 3. create and fit a supervised model for the PandasEntityMatching object, to pick the best match (this takes a while)
# input is "positive" names column 'Name' that are all supposed to match to the ground truth,
# and an id column 'Index' to check with candidate name-pairs are matching and which not.
# A fraction of these names may be turned into negative names (= no match to the ground truth).
# (internally, candidate name-pairs are automatically generated, these are the input to the classification)
p.fit_classifier(train_names, create_negative_sample_fraction=0.5)

# 4. scoring: generate pandas dataframe of all name-pair candidates.
# The classifier-based probability of match is provided in the column 'nm_score'.
# Note: can also call p.transform() without training the classifier first.
candidates_scored_pd = p.transform(test_names)

# 5. scoring: for each name-to-match, select the best ground-truth candidate.
best_candidates = candidates_scored_pd[candidates_scored_pd.best_match]
best_candidates.head()

[OTrieger]

@nchristensen 

This error is a Python related error not Alteryx. From what I read in Python 3.0 there some changes done, you should check what the new syntax should be to support it, or contact ING.