Added policy executer file for remotely executing
This commit is contained in:
Victor Mylle
@@ -57,6 +57,86 @@ def sample_from_dist(quantiles, output_values):
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# Return the mean of the samples
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return np.mean(new_samples, axis=1)
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def auto_regressive(dataset, model, idx_batch, sequence_length: int = 96):
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device = model.device
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prev_features, targets = dataset.get_batch(idx_batch)
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prev_features = prev_features.to(device)
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targets = targets.to(device)
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if len(list(prev_features.shape)) == 2:
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initial_sequence = prev_features[:, :96]
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else:
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initial_sequence = prev_features[:, :, 0]
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target_full = targets[:, 0].unsqueeze(1) # (batch_size, 1)
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with torch.no_grad():
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new_predictions_full = model(prev_features) # (batch_size, quantiles)
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samples = (
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torch.tensor(sample_from_dist( new_predictions_full))
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.unsqueeze(1)
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.to(device)
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) # (batch_size, 1)
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predictions_samples = samples
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predictions_full = new_predictions_full.unsqueeze(1)
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for i in range(sequence_length - 1):
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if len(list(prev_features.shape)) == 2:
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new_features = torch.cat(
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(prev_features[:, 1:96], samples), dim=1
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) # (batch_size, 96)
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new_features = new_features.float()
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other_features, new_targets = dataset.get_batch_autoregressive(
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np.array(idx_batch) + i + 1
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) # (batch_size, new_features)
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if other_features is not None:
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prev_features = torch.cat(
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(new_features.to(device), other_features.to(device)), dim=1
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) # (batch_size, 96 + new_features)
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else:
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prev_features = new_features
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else:
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other_features, new_targets = dataset.get_batch_autoregressive(
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np.array(idx_batch) + i + 1
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) # (batch_size, 1, new_features)
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# change the other_features nrv based on the samples
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other_features[:, 0, 0] = samples.squeeze(-1)
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# make sure on same device
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other_features = other_features.to(device)
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prev_features = prev_features.to(device)
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prev_features = torch.cat(
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(prev_features[:, 1:, :], other_features), dim=1
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) # (batch_size, 96, new_features)
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target_full = torch.cat(
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(target_full, new_targets.to(device)), dim=1
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) # (batch_size, sequence_length)
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with torch.no_grad():
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new_predictions_full = model(
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prev_features
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) # (batch_size, quantiles)
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predictions_full = torch.cat(
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(predictions_full, new_predictions_full.unsqueeze(1)), dim=1
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) # (batch_size, sequence_length, quantiles)
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samples = (
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torch.tensor(sample_from_dist(new_predictions_full))
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.unsqueeze(-1)
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.to(device)
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) # (batch_size, 1)
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predictions_samples = torch.cat((predictions_samples, samples), dim=1)
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return (
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initial_sequence,
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predictions_full,
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predictions_samples,
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target_full.unsqueeze(-1),
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)
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class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
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def __init__(
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@@ -273,84 +353,7 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
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return fig
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def auto_regressive(self, dataset, idx_batch, sequence_length: int = 96):
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prev_features, targets = dataset.get_batch(idx_batch)
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prev_features = prev_features.to(self.device)
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targets = targets.to(self.device)
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if len(list(prev_features.shape)) == 2:
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initial_sequence = prev_features[:, :96]
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else:
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initial_sequence = prev_features[:, :, 0]
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target_full = targets[:, 0].unsqueeze(1) # (batch_size, 1)
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with torch.no_grad():
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new_predictions_full = self.model(prev_features) # (batch_size, quantiles)
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samples = (
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torch.tensor(sample_from_dist(self.quantiles, new_predictions_full))
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.unsqueeze(1)
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.to(self.device)
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) # (batch_size, 1)
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predictions_samples = samples
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predictions_full = new_predictions_full.unsqueeze(1)
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for i in range(sequence_length - 1):
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if len(list(prev_features.shape)) == 2:
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new_features = torch.cat(
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(prev_features[:, 1:96], samples), dim=1
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) # (batch_size, 96)
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new_features = new_features.float()
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other_features, new_targets = dataset.get_batch_autoregressive(
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np.array(idx_batch) + i + 1
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) # (batch_size, new_features)
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if other_features is not None:
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prev_features = torch.cat(
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(new_features.to(self.device), other_features.to(self.device)), dim=1
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) # (batch_size, 96 + new_features)
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else:
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prev_features = new_features
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else:
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other_features, new_targets = dataset.get_batch_autoregressive(
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np.array(idx_batch) + i + 1
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) # (batch_size, 1, new_features)
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# change the other_features nrv based on the samples
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other_features[:, 0, 0] = samples.squeeze(-1)
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# make sure on same device
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other_features = other_features.to(self.device)
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prev_features = prev_features.to(self.device)
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prev_features = torch.cat(
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(prev_features[:, 1:, :], other_features), dim=1
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) # (batch_size, 96, new_features)
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target_full = torch.cat(
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(target_full, new_targets.to(self.device)), dim=1
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) # (batch_size, sequence_length)
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with torch.no_grad():
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new_predictions_full = self.model(
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prev_features
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) # (batch_size, quantiles)
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predictions_full = torch.cat(
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(predictions_full, new_predictions_full.unsqueeze(1)), dim=1
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) # (batch_size, sequence_length, quantiles)
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samples = (
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torch.tensor(sample_from_dist(self.quantiles, new_predictions_full))
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.unsqueeze(-1)
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.to(self.device)
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) # (batch_size, 1)
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predictions_samples = torch.cat((predictions_samples, samples), dim=1)
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return (
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initial_sequence,
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predictions_full,
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predictions_samples,
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target_full.unsqueeze(-1),
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)
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return auto_regressive(dataset, self.model, idx_batch, sequence_length)
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def plot_quantile_percentages(
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self, task, data_loader, train: bool = True, iteration: int = None, full_day: bool = False
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