CIMtools/model_selection/transformation_out.py
def split(self, X, y=None, groups=None):
        """Generate indices to split data into training and test set.
        Parameters
        ----------
        X : array-like, of length n_samples
            Training data, includes reaction's containers
        y : array-like, of length n_samples
            The target variable for supervised learning problems.
        groups : array-like, with shape (n_samples,)
            Group labels for the samples used while splitting the dataset into
            train/test set.
        Yields
        ------
        train : ndarray
            The training set indices for that split.
        test : ndarray
            The testing set indices for that split.
        """
        X, y, groups = indexable(X, y, groups)
        cgrs = [~r for r in X]

        condition_structure = defaultdict(set)

        for structure, condition in zip(cgrs, groups):
            condition_structure[condition].add(structure)

        train_data = defaultdict(list)
        test_data = []

        for n, (structure, condition) in enumerate(zip(cgrs, groups)):
            train_data[structure].append(n)
            if len(condition_structure[condition]) > 1:
                test_data.append(n)

        if self.n_splits > len(train_data):
            raise ValueError("Cannot have number of splits n_splits=%d greater"
                             " than the number of transformations: %d."
                             % (self.n_splits, len(train_data)))

        structures_weight = sorted(((x, len(y)) for x, y in train_data.items()), key=lambda x: x[1], reverse=True)
        fold_mean_size = len(cgrs) // self.n_splits

        if structures_weight[0][1] > fold_mean_size:
            warning('You have transformation that greater fold size')

        for idx in range(self.n_repeats):
            train_folds = [[] for _ in range(self.n_splits)]
            for structure, structure_length in structures_weight:
                if self.shuffle:
                    check_random_state(self.random_state).shuffle(train_folds)
                for fold in train_folds[:-1]:
                    if len(fold) + structure_length <= fold_mean_size:
                        fold.extend(train_data[structure])
                        break
                    else:
                        roulette_param = (structure_length - fold_mean_size + len(fold)) / structure_length
                        if random() > roulette_param:
                            fold.extend(train_data[structure])
                            break
                else:
                    train_folds[-1].extend(train_data[structure])

            test_folds = [[] for _ in range(self.n_splits)]
            for test, train in zip(test_folds, train_folds):
                for index in train:
                    if index in test_data:
                        test.append(index)

            for i in range(self.n_splits):
                train_index = []
                for fold in train_folds[:i]:
                    train_index.extend(fold)
                for fold in train_folds[i+1:]:
                    train_index.extend(fold)
                test_index = test_folds[i]
                yield array(train_index), array(test_index)
def split(self, X, y=None, groups=None):
        """Generate indices to split data into training and test set.
        Parameters
        ----------
        X : array-like, of length n_samples
            Training data, includes reaction's containers
        y : array-like, of length n_samples
            The target variable for supervised learning problems.
        groups : array-like, with shape (n_samples,)
            Group labels for the samples used while splitting the dataset into
            train/test set.
        Yields
        ------
        train : ndarray
            The training set indices for that split.
        test : ndarray
            The testing set indices for that split.
        """
        X, y, groups = indexable(X, y, groups)
        cgrs = [~r for r in X]

        condition_structure = defaultdict(set)

        for structure, condition in zip(cgrs, groups):
            condition_structure[condition].add(structure)

        train_data = defaultdict(list)
        test_data = []

        for n, (structure, condition) in enumerate(zip(cgrs, groups)):
            train_data[structure].append(n)
            if len(condition_structure[condition]) > 1:
                test_data.append(n)

        if self.n_splits > len(train_data):
            raise ValueError("Cannot have number of splits n_splits=%d greater"
                             " than the number of transformations: %d."
                             % (self.n_splits, len(train_data)))

        structures_weight = sorted(((x, len(y)) for x, y in train_data.items()), key=lambda x: x[1], reverse=True)
        fold_mean_size = len(cgrs) // self.n_splits

        if structures_weight[0][1] > fold_mean_size:
            warning('You have transformation that greater fold size')

        for idx in range(self.n_repeats):
            train_folds = [[] for _ in range(self.n_splits)]
            for structure, structure_length in structures_weight:
                if self.shuffle:
                    check_random_state(self.random_state).shuffle(train_folds)
                for fold in train_folds[:-1]:
                    if len(fold) + structure_length <= fold_mean_size:
                        fold.extend(train_data[structure])
                        break
                    else:
                        roulette_param = (structure_length - fold_mean_size + len(fold)) / structure_length
                        if random() > roulette_param:
                            fold.extend(train_data[structure])
                            break
                else:
                    train_folds[-1].extend(train_data[structure])

            test_folds = [[] for _ in range(self.n_splits)]
            for test, train in zip(test_folds, train_folds):
                for index in train:
                    if index in test_data:
                        test.append(index)

            for i in range(self.n_splits):
                train_index = []
                for fold in train_folds[:i]:
                    train_index.extend(fold)
                for fold in train_folds[i+1:]:
                    train_index.extend(fold)
                test_index = test_folds[i]
                yield array(train_index), array(test_index)
def split ( self , X , y = None , groups = None ) : X , y , groups = indexable ( X , y , groups ) cgrs = [ ~ r for r in X ] condition_structure = defaultdict ( set ) for structure , condition in zip ( cgrs , groups ) : condition_structure [ condition ] . add ( structure ) train_data = defaultdict ( list ) test_data = [ ] for n , ( structure , condition ) in enumerate ( zip ( cgrs , groups ) ) : train_data [ structure ] . append ( n ) if len ( condition_structure [ condition ] ) > 1 : test_data . append ( n ) if self . n_splits > len ( train_data ) : raise ValueError ( "Cannot have number of splits n_splits=%d greater" " than the number of transformations: %d." % ( self . n_splits , len ( train_data ) ) ) structures_weight = sorted ( ( ( x , len ( y ) ) for x , y in train_data . items ( ) ) , key = lambda x : x [ 1 ] , reverse = True ) fold_mean_size = len ( cgrs ) // self . n_splits if structures_weight [ 0 ] [ 1 ] > fold_mean_size : warning ( 'You have transformation that greater fold size' ) for idx in range ( self . n_repeats ) : train_folds = [ [ ] for _ in range ( self . n_splits ) ] for structure , structure_length in structures_weight : if self . shuffle : check_random_state ( self . random_state ) . shuffle ( train_folds ) for fold in train_folds [ : - 1 ] : if len ( fold ) + structure_length <= fold_mean_size : fold . extend ( train_data [ structure ] ) break else : roulette_param = ( structure_length - fold_mean_size + len ( fold ) ) / structure_length if random ( ) > roulette_param : fold . extend ( train_data [ structure ] ) break else : train_folds [ - 1 ] . extend ( train_data [ structure ] ) test_folds = [ [ ] for _ in range ( self . n_splits ) ] for test , train in zip ( test_folds , train_folds ) : for index in train : if index in test_data : test . append ( index ) for i in range ( self . n_splits ) : train_index = [ ] for fold in train_folds [ : i ] : train_index . extend ( fold ) for fold in train_folds [ i + 1 : ] : train_index . extend ( fold ) test_index = test_folds [ i ] yield array ( train_index ) , array ( test_index )
Generate indices to split data into training and test set.
        Parameters
        ----------
        X : array-like, of length n_samples
            Training data, includes reaction's containers
        y : array-like, of length n_samples
            The target variable for supervised learning problems.
        groups : array-like, with shape (n_samples,)
            Group labels for the samples used while splitting the dataset into
            train/test set.
        Yields
        ------
        train : ndarray
            The training set indices for that split.
        test : ndarray
            The testing set indices for that split.
Generate indices to split data into training and test set . Parameters ---------- X : array - like of length n_samples Training data includes reaction s containers y : array - like of length n_samples The target variable for supervised learning problems . groups : array - like with shape ( n_samples ) Group labels for the samples used while splitting the dataset into train / test set . Yields ------ train : ndarray The training set indices for that split . test : ndarray The testing set indices for that split .