Loading deepchem/models/__init__.py +14 −2 Original line number Diff line number Diff line Loading @@ -26,11 +26,13 @@ class Model(object): Abstract base class for different ML models. """ non_sklearn_models = ["SingleTaskDNN", "MultiTaskDNN", "DockingDNN"] def __init__(self, task_types, model_params, model_instance=None, initialize_raw_model=True, verbosity=None, **kwargs): def __init__(self, task_types, model_params, fit_transformers=None, model_instance=None, initialize_raw_model=True, verbosity=None, **kwargs): self.model_class = model_instance.__class__ self.task_types = task_types self.model_params = model_params self.fit_transformers = fit_transformers self.raw_model = None assert verbosity in [None, "low", "high"] self.verbosity = verbosity Loading Loading @@ -92,8 +94,18 @@ class Model(object): for epoch in range(self.model_params["nb_epoch"]): log("Starting epoch %s" % str(epoch+1), self.verbosity) for (X_batch, y_batch, w_batch, _) in dataset.iterbatches(batch_size): if self.fit_transformers: X_batch, y_batch, w_batch = self.transform_on_batch(X_batch, y_batch, w_batch) self.fit_on_batch(X_batch, y_batch, w_batch) def transform_on_batch(self, X, y, w): """ Transforms data in a Dataset object with Transformer objects. """ # Create dataset for transformer in self.fit_transformers: transformer.transform(batch_dataset) # TODO(rbharath): The structure of the produced df might be # complicated. Better way to model? def predict(self, dataset, transformers): Loading Loading
deepchem/models/__init__.py +14 −2 Original line number Diff line number Diff line Loading @@ -26,11 +26,13 @@ class Model(object): Abstract base class for different ML models. """ non_sklearn_models = ["SingleTaskDNN", "MultiTaskDNN", "DockingDNN"] def __init__(self, task_types, model_params, model_instance=None, initialize_raw_model=True, verbosity=None, **kwargs): def __init__(self, task_types, model_params, fit_transformers=None, model_instance=None, initialize_raw_model=True, verbosity=None, **kwargs): self.model_class = model_instance.__class__ self.task_types = task_types self.model_params = model_params self.fit_transformers = fit_transformers self.raw_model = None assert verbosity in [None, "low", "high"] self.verbosity = verbosity Loading Loading @@ -92,8 +94,18 @@ class Model(object): for epoch in range(self.model_params["nb_epoch"]): log("Starting epoch %s" % str(epoch+1), self.verbosity) for (X_batch, y_batch, w_batch, _) in dataset.iterbatches(batch_size): if self.fit_transformers: X_batch, y_batch, w_batch = self.transform_on_batch(X_batch, y_batch, w_batch) self.fit_on_batch(X_batch, y_batch, w_batch) def transform_on_batch(self, X, y, w): """ Transforms data in a Dataset object with Transformer objects. """ # Create dataset for transformer in self.fit_transformers: transformer.transform(batch_dataset) # TODO(rbharath): The structure of the produced df might be # complicated. Better way to model? def predict(self, dataset, transformers): Loading
