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This repository was archived by the owner on Feb 10, 2022. It is now read-only.
This repository was archived by the owner on Feb 10, 2022. It is now read-only.

Wrong parameter passed to scikit in  #29

@guneemwelloeux

Description

@guneemwelloeux

When using PyHeartex (master, bbfbea4), running a very basic example (similar to what is in examples/quickstart.py)

pipeline = make_pipeline(TfidfVectorizer(), SVC())
# Start serving this model
serve(pipeline)

I get the following error when Label-studio is trying to update the model:

16:41:09 ValueError: Pipeline.fit does not accept the login parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.
Traceback (most recent call last):
  File "/Users/wollivier/.pyenv/versions/3.7.6/envs/arcana/lib/python3.7/site-packages/rq/worker.py", line 812, in perform_job
    rv = job.perform()
  File "/Users/wollivier/.pyenv/versions/3.7.6/envs/arcana/lib/python3.7/site-packages/rq/job.py", line 588, in perform
    self._result = self._execute()
  File "/Users/wollivier/.pyenv/versions/3.7.6/envs/arcana/lib/python3.7/site-packages/rq/job.py", line 594, in _execute
    return self.func(*self.args, **self.kwargs)
  File "/Users/wollivier/dev/ARCANA/pyheartex/htx/model_manager.py", line 187, in train_script_wrapper
    resources = train_script(data_stream, workdir, **train_kwargs)
  File "/Users/wollivier/dev/ARCANA/pyheartex/htx/adapters/sklearn.py", line 47, in fit_sklearn_classifier
    model.fit(texts, choices_idx, **kwargs)
  File "/Users/wollivier/.pyenv/versions/3.7.6/envs/arcana/lib/python3.7/site-packages/sklearn/pipeline.py", line 352, in fit
    Xt, fit_params = self._fit(X, y, **fit_params)
  File "/Users/wollivier/.pyenv/versions/3.7.6/envs/arcana/lib/python3.7/site-packages/sklearn/pipeline.py", line 281, in _fit
    "=sample_weight)`.".format(pname))
ValueError: Pipeline.fit does not accept the login parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.

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