from yaglm.toy_data import sample_sparse_lin_reg from yaglm.GlmTuned import GlmCV, GlmTrainMetric from yaglm.config.loss import Huber from yaglm.config.penalty import Lasso, GroupLasso from yaglm.config.flavor import Adaptive, NonConvex from yaglm.metrics.info_criteria import InfoCriteria from yaglm.infer.Inferencer import Inferencer from yaglm.infer.lin_reg_noise_var import ViaRidge # sample sparse linear regression data X, y, _ = sample_sparse_lin_reg(n_samples=100, n_features=10) # fit a lasso penalty tuned via cross-validation with the 1se rule GlmCV(loss='lin_reg', penalty=Lasso(), # specify penalty with config object select_rule='1se' ).fit(X, y) # fit an adaptive lasso tuned via cross-validation # initialized with a lasso tuned with cross-validation GlmCV(loss='lin_reg', penalty=Lasso(flavor=Adaptive()), initializer='default' ).fit(X, y) # fit an adaptive lasso and tuned via EBIC # estimate the noise variance via a ridge-regression method GlmTrainMetric(loss='lin_reg', penalty=Lasso(flavor=Adaptive()), inferencer=Inferencer(scale=ViaRidge()), # noise variance estimator scorer=InfoCriteria(crit='ebic') # Info criteria ).fit(X, y) 

--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In [11], line 37 25 GlmCV(loss='lin_reg', 26 penalty=Lasso(flavor=Adaptive()), 27 initializer='default' 28 ).fit(X, y) 30 # fit an adaptive lasso and tuned via EBIC 31 # estimate the noise variance via a ridge-regression method 32 GlmTrainMetric(loss='lin_reg', 33 penalty=Lasso(flavor=Adaptive()), 34 35 inferencer=Inferencer(scale=ViaRidge()), # noise variance estimator 36 scorer=InfoCriteria(crit='ebic') # Info criteria ---> 37 ).fit(X, y) File ~/opt/anaconda3/envs/automated_reliability_tests/lib/python3.10/site-packages/yaglm-_0.3.3_-py3.10.egg/yaglm/GlmTuned.py:341, in GlmTrainMetric.fit(self, X, y, sample_weight, offsets) 334 start_time = time() 336 ############################################## 337 # setup, preprocess, and prefitting routines # 338 ############################################## 339 pro_data, raw_data, pre_pro_out, \ 340 configs, solver, init_data, inferencer = \ --> 341 self.setup_and_prefit(X=X, y=y, 342 sample_weight=sample_weight, 343 offsets=offsets) 345 # store inferencer 346 self.inferencer_ = inferencer File ~/opt/anaconda3/envs/automated_reliability_tests/lib/python3.10/site-packages/yaglm-_0.3.3_-py3.10.egg/yaglm/base.py:429, in BaseGlm.setup_and_prefit(self, X, y, sample_weight, offsets) 425 raw_data = {'X': X, 'y': y, 426 'sample_weight': sample_weight, 'offsets': offsets} 428 # run any prefitting inference --> 429 inferencer = self.run_prefit_inference(**raw_data) 431 # preproceess X, y 432 pro_data, pre_pro_out = self.preprocess(**raw_data, copy=True) File ~/opt/anaconda3/envs/automated_reliability_tests/lib/python3.10/site-packages/yaglm-_0.3.3_-py3.10.egg/yaglm/base.py:718, in BaseGlm.run_prefit_inference(self, X, y, sample_weight, offsets) 715 if self.inferencer is not None: 716 # TODO: do we want to do a copy here? 717 inferencer = deepcopy(self.inferencer) --> 718 inferencer.pre_fit(estimator=self, X=X, y=y, 719 sample_weight=sample_weight, 720 offsets=offsets) 721 return inferencer 723 else: TypeError: Inferencer.pre_fit() got an unexpected keyword argument 'offset 

 inferencer.pre_fit(estimator=self, X=X, y=y, sample_weight=sample_weight, offsets=offsets) 

def pre_fit(self, estimator, X, y, sample_weight=None):