Open githubmorgan opened 2 months ago
Hello, thanks for posting. Which version of sklearn are you using? Then I can try to reproduce the issue.
Hi,
I’m using 1.5.0
Cheers Morgan
On Sun, 9 Jun 2024 at 6:59 PM, Max Baak @.***> wrote:
Hello, thanks for posting. Which version of sklearn are you using? Then I can try to reproduce the issue.
— Reply to this email directly, view it on GitHub https://github.com/ing-bank/EntityMatchingModel/issues/16#issuecomment-2156356191, or unsubscribe https://github.com/notifications/unsubscribe-auth/BGNVCKOBFAY3A5IDI5CNPVDZGP4OPAVCNFSM6AAAAABJAT5LM6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNJWGM2TMMJZGE . You are receiving this because you authored the thread.Message ID: @.***>
Apologies for the delay. I can reproduce it - indeed it's due to changes in sklearn v1.5.0. I'll look into how to fix this.
Many thanks.
On Wed, Jul 3, 2024 at 3:05 AM Max Baak @.***> wrote:
Apologies for the delay. I can reproduce it - indeed it's due to changes in sklearn v1.5.0. I'll look into how to fix this.
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I am having the same issue.. Many thanks for looking into it. Can you also let me know for which version of sklearn does it work before?
We're looking at it ... For now using: scikit-learn < 1.5.0 should solve it. (I may just a quick patch release with this constraint for now.)
Hi,
Am trying the example and when i get to
_# instantiate a matching model nm = PandasEntityMatching({ 'name_only': True, 'preprocessor': 'preprocess_merge_abbr', 'indexers': [{ 'type': 'cosine_similarity', 'tokenizer': 'words', 'ngram': 1, 'num_candidates': 5, 'cos_sim_lower_bound': 0.2, }], 'supervised_on': True, 'supervised_model_filename': 'sem_nm.pkl', 'supervised_model_dir': '.', }) nm.fit(gt)
i'm getting the following error...
AttributeError Traceback (most recent call last) Cell In[10], line 19 2 nm = PandasEntityMatching({ 3 'name_only': True, 4 'preprocessor': 'preprocess_merge_abbr', (...) 14 'supervised_model_dir': '.', 15 }) 17 # matching of names is done against the ground-truth dataset (gt). 18 # for this we need to fit our indexers to the ground-truth. ---> 19 nm.fit(gt)
File ~/.local/lib/python3.10/site-packages/emm/pipeline/pandas_entity_matching.py:251, in PandasEntityMatching.fit(self, ground_truth_df, copy_ground_truth) 249 if copy_ground_truth: 250 self.ground_truth_df = ground_truth_df.copy() --> 251 self.model = self.pipeline.fit(ground_truth_df) 252 self.n_ground_truth = len(ground_truth_df) 254 timer.log_param("n", self.n_ground_truth)
File ~/.local/lib/python3.10/site-packages/sklearn/base.py:1473, in _fit_context..decorator..wrapper(estimator, *args, **kwargs)
1466 estimator._validate_params()
1468 with config_context(
1469 skip_parameter_validation=(
1470 prefer_skip_nested_validation or global_skip_validation
...
---> 79 idf_diag = self._tfidf._idf_diag
80 idf_diag = idf_diag - scipy.sparse.diags(np.ones(idf_diag.shape[0]), shape=idf_diag.shape, dtype=self.dtype)
81 self._tfidf._idf_diag = idf_diag
AttributeError: 'TfidfTransformer' object has no attribute '_idf_diag'