osprey/plugins/plugin_pylearn2.py
def fit(self, X, y=None):
        """
        Build a trainer and run main_loop.

        Parameters
        ----------
        X : array_like
            Training examples.
        y : array_like, optional
            Labels.
        """
        from pylearn2.config import yaml_parse
        from pylearn2.train import Train

        # build trainer
        params = self.get_params()
        yaml_string = Template(self.yaml_string).substitute(params)
        self.trainer = yaml_parse.load(yaml_string)
        assert isinstance(self.trainer, Train)
        if self.trainer.dataset is not None:
            raise ValueError('Train YAML database must evaluate to None.')
        self.trainer.dataset = self._get_dataset(X, y)

        # update monitoring dataset(s)
        if (hasattr(self.trainer.algorithm, 'monitoring_dataset') and
                self.trainer.algorithm.monitoring_dataset is not None):
            monitoring_dataset = self.trainer.algorithm.monitoring_dataset
            if len(monitoring_dataset) == 1 and '' in monitoring_dataset:
                monitoring_dataset[''] = self.trainer.dataset
            else:
                monitoring_dataset['train'] = self.trainer.dataset
            self.trainer.algorithm._set_monitoring_dataset(monitoring_dataset)
        else:
            self.trainer.algorithm._set_monitoring_dataset(
                self.trainer.dataset)

        # run main loop
        self.trainer.main_loop()
def fit(self, X, y=None):
        """
        Build a trainer and run main_loop.

        Parameters
        ----------
        X : array_like
            Training examples.
        y : array_like, optional
            Labels.
        """
        from pylearn2.config import yaml_parse
        from pylearn2.train import Train

        # build trainer
        params = self.get_params()
        yaml_string = Template(self.yaml_string).substitute(params)
        self.trainer = yaml_parse.load(yaml_string)
        assert isinstance(self.trainer, Train)
        if self.trainer.dataset is not None:
            raise ValueError('Train YAML database must evaluate to None.')
        self.trainer.dataset = self._get_dataset(X, y)

        # update monitoring dataset(s)
        if (hasattr(self.trainer.algorithm, 'monitoring_dataset') and
                self.trainer.algorithm.monitoring_dataset is not None):
            monitoring_dataset = self.trainer.algorithm.monitoring_dataset
            if len(monitoring_dataset) == 1 and '' in monitoring_dataset:
                monitoring_dataset[''] = self.trainer.dataset
            else:
                monitoring_dataset['train'] = self.trainer.dataset
            self.trainer.algorithm._set_monitoring_dataset(monitoring_dataset)
        else:
            self.trainer.algorithm._set_monitoring_dataset(
                self.trainer.dataset)

        # run main loop
        self.trainer.main_loop()
def fit ( self , X , y = None ) : from pylearn2 . config import yaml_parse from pylearn2 . train import Train # build trainer params = self . get_params ( ) yaml_string = Template ( self . yaml_string ) . substitute ( params ) self . trainer = yaml_parse . load ( yaml_string ) assert isinstance ( self . trainer , Train ) if self . trainer . dataset is not None : raise ValueError ( 'Train YAML database must evaluate to None.' ) self . trainer . dataset = self . _get_dataset ( X , y ) # update monitoring dataset(s) if ( hasattr ( self . trainer . algorithm , 'monitoring_dataset' ) and self . trainer . algorithm . monitoring_dataset is not None ) : monitoring_dataset = self . trainer . algorithm . monitoring_dataset if len ( monitoring_dataset ) == 1 and '' in monitoring_dataset : monitoring_dataset [ '' ] = self . trainer . dataset else : monitoring_dataset [ 'train' ] = self . trainer . dataset self . trainer . algorithm . _set_monitoring_dataset ( monitoring_dataset ) else : self . trainer . algorithm . _set_monitoring_dataset ( self . trainer . dataset ) # run main loop self . trainer . main_loop ( )
Build a trainer and run main_loop.

        Parameters
        ----------
        X : array_like
            Training examples.
        y : array_like, optional
            Labels.
Build a trainer and run main_loop .