def y_pred_multilabel(self, threshold: float = 0.5) -> pd.DataFrame:
"""
Returns:
Indicator matrix representing the predicted labels for each observation
using the given (optional) threshold.
"""
return pred_prob_to_pred_multilabel(self.y_pred_proba, threshold)
def y_pred_multilabel(self) -> pd.DataFrame:
"""
Returns:
Indicator dataframe containing a 0 if each label wasn't predicted and 1 if
it was for each observation.
"""
return pred_prob_to_pred_multilabel(self.y_pred_proba).astype("int")
def _train(self, train_input: gobbli.io.TrainInput,
context: ContainerTaskContext) -> gobbli.io.TrainOutput:
self._write_input(
train_input.X_train,
train_input.y_train_multilabel,
context.host_input_dir / FastText._TRAIN_INPUT_FILE,
)
self._write_input(
train_input.X_valid,
train_input.y_valid_multilabel,
context.host_input_dir / FastText._VALID_INPUT_FILE,
)
container_validation_input_path = (context.container_input_dir /
FastText._VALID_INPUT_FILE)
train_logs, train_loss = self._run_supervised(
train_input.checkpoint,
context.container_input_dir / FastText._TRAIN_INPUT_FILE,
context.container_output_dir / FastText._CHECKPOINT_BASE,
context,
train_input.num_train_epochs,
autotune_validation_file_path=container_validation_input_path,
)
host_checkpoint_path = context.host_output_dir / f"{FastText._CHECKPOINT_BASE}"
labels = train_input.labels()
# Calculate validation accuracy on our own, since the CLI only provides
# precision/recall
predict_logs, pred_prob_df = self._run_predict_prob(
host_checkpoint_path, labels, container_validation_input_path,
context)
if train_input.multilabel:
pred_labels = pred_prob_to_pred_multilabel(pred_prob_df)
gold_labels = multilabel_to_indicator_df(
train_input.y_valid_multilabel, labels)
else:
pred_labels = pred_prob_to_pred_label(pred_prob_df)
gold_labels = train_input.y_valid_multiclass
valid_accuracy = accuracy_score(gold_labels, pred_labels)
# Not ideal, but fastText doesn't provide a way to get validation loss;
# Negate the validation accuracy instead
valid_loss = -valid_accuracy
return gobbli.io.TrainOutput(
train_loss=train_loss,
valid_loss=valid_loss,
valid_accuracy=valid_accuracy,
labels=labels,
multilabel=train_input.multilabel,
checkpoint=host_checkpoint_path,
_console_output="\n".join((train_logs, predict_logs)),
)
def y_pred_multilabel(self, threshold: float = 0.5) -> List[str]:
"""
Args:
threshold: The predicted probability threshold for predictions
Returns:
The predicted labels for this observation (predicted probability greater than
the given threshold)
"""
return pred_prob_to_pred_multilabel(self.y_pred_proba, threshold)