Loading deepchem/models/torch_models/__init__.py 0 → 100644 +0 −0 Empty file added. deepchem/models/chemberta.py→deepchem/models/torch_models/chemberta.py +68 −0 Original line number Diff line number Diff line Loading @@ -81,3 +81,71 @@ class ChemBERTaforSequenceClassification(BertPreTrainedModel): outputs = (loss,) + outputs return outputs # (loss), logits, (hidden_states), (attentions) # BELOW code is taken from modles.py methods, for basic idea of structure to follow. def fit(self, dataset, nb_epoch=10, batch_size=32, **kwargs): """ Fits a model on data in a Dataset object. """ # TODO(rbharath/enf): We need a structured way to deal with potential GPU # memory overflows. for epoch in range(nb_epoch): log("Starting epoch %s" % str(epoch + 1), self.verbose) losses = [] for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size): losses.append(self.fit_on_batch(X_batch, y_batch, w_batch)) log("Avg loss for epoch %d: %f" % (epoch + 1, np.array(losses).mean()), self.verbose) def predict(self, dataset, transformers=[], batch_size=None): """ Uses self to make predictions on provided Dataset object. Returns: y_pred: numpy ndarray of shape (n_samples,) """ y_preds = [] n_tasks = self.get_num_tasks() ind = 0 for (X_batch, _, _, ids_batch) in dataset.iterbatches( batch_size, deterministic=True): n_samples = len(X_batch) y_pred_batch = self.predict_on_batch(X_batch) # Discard any padded predictions y_pred_batch = y_pred_batch[:n_samples] y_pred_batch = undo_transforms(y_pred_batch, transformers) y_preds.append(y_pred_batch) y_pred = np.concatenate(y_preds) return y_pred def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False): """ Evaluates the performance of this model on specified dataset. Parameters ---------- dataset: dc.data.Dataset Dataset object. metric: deepchem.metrics.Metric Evaluation metric transformers: list List of deepchem.transformers.Transformer per_task_metrics: bool If True, return per-task scores. Returns ------- dict Maps tasks to scores under metric. """ evaluator = Evaluator(self, dataset, transformers) if not per_task_metrics: scores = evaluator.compute_model_performance(metrics) return scores else: scores, per_task_scores = evaluator.compute_model_performance( metrics, per_task_metrics=per_task_metrics) return scores, per_task_scores Loading
deepchem/models/chemberta.py→deepchem/models/torch_models/chemberta.py +68 −0 Original line number Diff line number Diff line Loading @@ -81,3 +81,71 @@ class ChemBERTaforSequenceClassification(BertPreTrainedModel): outputs = (loss,) + outputs return outputs # (loss), logits, (hidden_states), (attentions) # BELOW code is taken from modles.py methods, for basic idea of structure to follow. def fit(self, dataset, nb_epoch=10, batch_size=32, **kwargs): """ Fits a model on data in a Dataset object. """ # TODO(rbharath/enf): We need a structured way to deal with potential GPU # memory overflows. for epoch in range(nb_epoch): log("Starting epoch %s" % str(epoch + 1), self.verbose) losses = [] for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size): losses.append(self.fit_on_batch(X_batch, y_batch, w_batch)) log("Avg loss for epoch %d: %f" % (epoch + 1, np.array(losses).mean()), self.verbose) def predict(self, dataset, transformers=[], batch_size=None): """ Uses self to make predictions on provided Dataset object. Returns: y_pred: numpy ndarray of shape (n_samples,) """ y_preds = [] n_tasks = self.get_num_tasks() ind = 0 for (X_batch, _, _, ids_batch) in dataset.iterbatches( batch_size, deterministic=True): n_samples = len(X_batch) y_pred_batch = self.predict_on_batch(X_batch) # Discard any padded predictions y_pred_batch = y_pred_batch[:n_samples] y_pred_batch = undo_transforms(y_pred_batch, transformers) y_preds.append(y_pred_batch) y_pred = np.concatenate(y_preds) return y_pred def evaluate(self, dataset, metrics, transformers=[], per_task_metrics=False): """ Evaluates the performance of this model on specified dataset. Parameters ---------- dataset: dc.data.Dataset Dataset object. metric: deepchem.metrics.Metric Evaluation metric transformers: list List of deepchem.transformers.Transformer per_task_metrics: bool If True, return per-task scores. Returns ------- dict Maps tasks to scores under metric. """ evaluator = Evaluator(self, dataset, transformers) if not per_task_metrics: scores = evaluator.compute_model_performance(metrics) return scores else: scores, per_task_scores = evaluator.compute_model_performance( metrics, per_task_metrics=per_task_metrics) return scores, per_task_scores
