Python sklearn.cross_validation() Examples
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code examples of sklearn.cross_validation().
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Example #1
Source File: fixes.py From skutil with BSD 3-Clause "New" or "Revised" License | 6 votes |
def _cv_len(cv, X, y): """This method computes the length of a cross validation object, agnostic of whether sklearn-0.17 or sklearn-0.18 is being used. Parameters ---------- cv : `sklearn.cross_validation._PartitionIterator` or `sklearn.model_selection.BaseCrossValidator` The cv object from which to extract length. If using sklearn-0.17, this can be computed by calling `len` on ``cv``, else it's computed with `cv.get_n_splits(X, y)`. X : pd.DataFrame or np.ndarray, shape(n_samples, n_features) The dataframe or np.ndarray being fit in the grid search. y : np.ndarray, shape(n_samples,) The target being fit in the grid search. Returns ------- int """ return len(cv) if not SK18 else cv.get_n_splits(X, y)
Example #2
Source File: classify_shark.py From ibeis with Apache License 2.0 | 6 votes |
def gen_crossval_idxs(problem, n_folds=2): y = problem.ds.target rng = 43432 if hasattr(problem.ds, 'nids'): # Ensure that an individual does not appear in both the train # and the test dataset from ibeis_cnn.dataset import stratified_kfold_label_split labels = problem.ds.nids _iter = stratified_kfold_label_split(y, labels, n_folds=n_folds, rng=rng) else: xvalkw = dict(n_folds=n_folds, shuffle=True, random_state=rng) import sklearn.cross_validation skf = sklearn.cross_validation.StratifiedKFold(y, **xvalkw) _iter = skf #import sklearn.model_selection #skf = sklearn.model_selection.StratifiedKFold(**xvalkw) #_iter = skf.split(X=np.empty(len(y)), y=y) msg = 'cross-val test on %s' % (problem.ds.name) progiter = ut.ProgIter(_iter, length=n_folds, lbl=msg) for train_idx, test_idx in progiter: yield train_idx, test_idx # @ut.reloadable_class
Example #3
Source File: fixes.py From skutil with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _set_cv(cv, X, y, classifier): """This method returns either a `sklearn.cross_validation._PartitionIterator` or `sklearn.model_selection.BaseCrossValidator` depending on whether sklearn-0.17 or sklearn-0.18 is being used. Parameters ---------- cv : int, `_PartitionIterator` or `BaseCrossValidator` The CV object or int to check. If an int, will be converted into the appropriate class of crossvalidator. X : pd.DataFrame or np.ndarray, shape(n_samples, n_features) The dataframe or np.ndarray being fit in the grid search. y : np.ndarray, shape(n_samples,) The target being fit in the grid search. classifier : bool Whether the estimator being fit is a classifier Returns ------- `_PartitionIterator` or `BaseCrossValidator` """ return check_cv(cv, X, y, classifier) if not SK18 else check_cv(cv, y, classifier)
Example #4
Source File: smk_pipeline.py From ibeis with Apache License 2.0 | 5 votes |
def testdata_smk(*args, **kwargs): """ >>> from ibeis.algo.smk.smk_pipeline import * # NOQA >>> kwargs = {} """ import ibeis import sklearn import sklearn.cross_validation # import sklearn.model_selection ibs, aid_list = ibeis.testdata_aids(defaultdb='PZ_MTEST') nid_list = np.array(ibs.annots(aid_list).nids) rng = ut.ensure_rng(0) xvalkw = dict(n_folds=4, shuffle=False, random_state=rng) skf = sklearn.cross_validation.StratifiedKFold(nid_list, **xvalkw) train_idx, test_idx = six.next(iter(skf)) daids = ut.take(aid_list, train_idx) qaids = ut.take(aid_list, test_idx) config = { 'num_words': 1000, } config.update(**kwargs) qreq_ = SMKRequest(ibs, qaids, daids, config) smk = qreq_.smk #qreq_ = ibs.new_query_request(qaids, daids, cfgdict={'pipeline_root': 'smk', 'proot': 'smk'}) #qreq_ = ibs.new_query_request(qaids, daids, cfgdict={}) return ibs, smk, qreq_
Example #5
Source File: pyglmnet.py From pyglmnet with MIT License | 4 votes |
def _set_cv(cv, estimator=None, X=None, y=None): """Set the default CV depending on whether clf is classifier/regressor.""" # Detect whether classification or regression if estimator in ['classifier', 'regressor']: est_is_classifier = estimator == 'classifier' else: est_is_classifier = is_classifier(estimator) # Setup CV if check_version('sklearn', '0.18'): from sklearn import model_selection as models from sklearn.model_selection import (check_cv, StratifiedKFold, KFold) if isinstance(cv, (int, np.int)): XFold = StratifiedKFold if est_is_classifier else KFold cv = XFold(n_splits=cv) elif isinstance(cv, str): if not hasattr(models, cv): raise ValueError('Unknown cross-validation') cv = getattr(models, cv) cv = cv() cv = check_cv(cv=cv, y=y, classifier=est_is_classifier) else: from sklearn import cross_validation as models from sklearn.cross_validation import (check_cv, StratifiedKFold, KFold) if isinstance(cv, (int, np.int)): if est_is_classifier: cv = StratifiedKFold(y=y, n_folds=cv) else: cv = KFold(n=len(y), n_folds=cv) elif isinstance(cv, str): if not hasattr(models, cv): raise ValueError('Unknown cross-validation') cv = getattr(models, cv) if cv.__name__ not in ['KFold', 'LeaveOneOut']: raise NotImplementedError('CV cannot be defined with str' ' for sklearn < .017.') cv = cv(len(y)) cv = check_cv(cv=cv, X=X, y=y, classifier=est_is_classifier) # Extract train and test set to retrieve them at predict time if hasattr(cv, 'split'): cv_splits = [(train, test) for train, test in cv.split(X=np.zeros_like(y), y=y)] else: # XXX support sklearn.cross_validation cv cv_splits = [(train, test) for train, test in cv] if not np.all([len(train) for train, _ in cv_splits]): raise ValueError('Some folds do not have any train epochs.') return cv, cv_splits