Mercurial > repos > bgruening > sklearn_model_validation

comparison model_validation.xml @ 28:e3505cd4ebbf draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Fri, 30 Apr 2021 23:43:48 +0000
parents 376c88f35e0e
children fe320a259dbf
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27:376c88f35e0e 28:e3505cd4ebbf
236 else: 236 else:
237 rval = pd.DataFrame(predicted) 237 rval = pd.DataFrame(predicted)
238 elif selected_function == 'learning_curve': 238 elif selected_function == 'learning_curve':
239 try: 239 try:
240 train_sizes = safe_eval(options['train_sizes']) 240 train_sizes = safe_eval(options['train_sizes'])
241 except: 241 except Exception:
242 sys.exit("Unsupported train_sizes input! Supports int/float in tuple and array-like structure.") 242 sys.exit("Unsupported train_sizes input! Supports int/float in tuple and array-like structure.")
243 if type(train_sizes) is tuple: 243 if type(train_sizes) is tuple:
244 train_sizes = np.linspace(*train_sizes) 244 train_sizes = np.linspace(*train_sizes)
245 options['train_sizes'] = train_sizes 245 options['train_sizes'] = train_sizes
246 train_sizes_abs, train_scores, test_scores = validator(estimator, X, y, **options) 246 train_sizes_abs, train_scores, test_scores = validator(estimator, X, y, **options)