From 71fff3c41195b7db47afcdbcb3282975faf8bb47 Mon Sep 17 00:00:00 2001 From: "Dr. Kashif Rasul" Date: Sat, 4 Jan 2020 12:23:08 +0100 Subject: [PATCH] typos --- pts/model/deepvar/__init__.py | 1 + pts/model/deepvar/deepvar_estimator.py | 6 +++--- pts/model/deepvar/deepvar_network.py | 21 +++++---------------- 3 files changed, 9 insertions(+), 19 deletions(-) diff --git a/pts/model/deepvar/__init__.py b/pts/model/deepvar/__init__.py index e69de29..de2e680 100644 --- a/pts/model/deepvar/__init__.py +++ b/pts/model/deepvar/__init__.py @@ -0,0 +1 @@ +from .deepvar_estimator import DeepVAREstimator \ No newline at end of file diff --git a/pts/model/deepvar/deepvar_estimator.py b/pts/model/deepvar/deepvar_estimator.py index f3d3653..d5fa798 100644 --- a/pts/model/deepvar/deepvar_estimator.py +++ b/pts/model/deepvar/deepvar_estimator.py @@ -1,6 +1,5 @@ from typing import List, Optional - import numpy as np import pandas as pd import torch @@ -27,6 +26,7 @@ from pts.transform import ( TargetDimIndicator, ) from pts.feature import ( + TimeFeature, fourier_time_features_from_frequency_str, get_fourier_lags_for_frequency, ) @@ -85,7 +85,7 @@ class DeepVAREstimator(PTSEstimator): self.cardinality = cardinality self.embedding_dimension = embedding_dimension self.conditioning_length = conditioning_length - self.use_marginal_t + self.use_marginal_transformation = use_marginal_transformation self.lags_seq = ( lags_seq @@ -204,7 +204,7 @@ class DeepVAREstimator(PTSEstimator): transformation: Transformation, trained_network: DeepVARTrainingNetwork, device: torch.device, - ) -> Predictor: + ) -> PTSPredictor: prediction_network = DeepVARPredictionNetwork( input_size=self.input_size, target_dim=self.target_dim, diff --git a/pts/model/deepvar/deepvar_network.py b/pts/model/deepvar/deepvar_network.py index 971cd9a..125bd35 100644 --- a/pts/model/deepvar/deepvar_network.py +++ b/pts/model/deepvar/deepvar_network.py @@ -64,7 +64,7 @@ class DeepVARTrainingNetwork(nn.Module): batch_first=True, ) - self.proj_dist_args = distr_output.get_args_proj() + self.proj_dist_args = distr_output.get_args_proj(num_cells) self.embed_dim = 1 self.embed = nn.Embedding( @@ -72,9 +72,9 @@ class DeepVARTrainingNetwork(nn.Module): ) if scaling: - self.scaler = MeanScaler(keepdims=True) + self.scaler = MeanScaler(keepdim=True) else: - self.scaler = NOPScaler(keepdims=True) + self.scaler = NOPScaler(keepdim=True) @staticmethod def get_lagged_subsequences( @@ -118,7 +118,7 @@ class DeepVARTrainingNetwork(nn.Module): begin_index = -lag_index - subsequences_length end_index = -lag_index if lag_index > 0 else None lagged_values.append(sequence[:, begin_index:end_index, ...].unsqueeze(1)) - return torch.cat(*lagged_values, dim=1).permute(0, 2, 3, 1) + return torch.cat(lagged_values, dim=1).permute(0, 2, 3, 1) def unroll( self, @@ -156,23 +156,12 @@ class DeepVARTrainingNetwork(nn.Module): .expand(-1, unroll_length, -1) .reshape((-1, unroll_length, self.target_dim * self.embed_dim)) ) - # repeated_index_embeddings = ( - # index_embeddings.expand_dims(axis=1) - # .repeat(axis=1, repeats=unroll_length) - # .reshape((-1, unroll_length, self.target_dim * self.embed_dim)) - # ) # (batch_size, sub_seq_len, input_dim) inputs = torch.cat((input_lags, repeated_index_embeddings, time_feat), dim=-1) # unroll encoder outputs, state = self.rnn(inputs, begin_state) - # inputs=inputs, - # length=unroll_length, - # layout="NTC", - # merge_outputs=True, - # begin_state=begin_state, - # ) # assert_shape(outputs, (-1, unroll_length, self.num_cells)) # for s in state: @@ -412,7 +401,7 @@ class DeepVARTrainingNetwork(nn.Module): # mask the loss at one time step if one or more observations is missing # in the target dimensions (batch_size, subseq_length, 1) - loss_weights = observed_values.min(dim=-1, keepdims=True) + loss_weights = observed_values.min(dim=-1, keepdim=True) # assert_shape(loss_weights, (-1, seq_len, 1))