mirror of
https://github.com/wassname/pytorch-transformer-ts.git
synced 2026-06-27 16:46:28 +08:00
Kashif Rasul
1581 lines
265 KiB
Plaintext
1581 lines
265 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "1e85a78b",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install -U gluonts pytorch-lightning torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "855dbf2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8dc0844c",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:root:Pytorch pre-release version 1.13.0a0+git9e80661 - assuming intent to test it\n",
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"/home/kashif/.env/pytorch/lib/python3.8/site-packages/horovod/common/util.py:258: UserWarning: Framework pytorch installed with version 1.12.0a0+git689df63 but found version 1.13.0a0+git9e80661.\n",
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" This can result in unexpected behavior including runtime errors.\n",
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" Reinstall Horovod using `pip install --no-cache-dir` to build with the new version.\n",
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" warnings.warn(get_version_mismatch_message(name, version, installed_version))\n"
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]
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}
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],
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"source": [
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"from typing import List, Optional, Iterable, Dict, Any\n",
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"from itertools import islice\n",
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"import queue\n",
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"\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"import matplotlib.dates as mdates\n",
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"import tqdm.auto as tqdm\n",
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"\n",
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"from datasets import load_dataset\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.utils.data import DataLoader\n",
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"from torch.optim.lr_scheduler import _LRScheduler, MultiplicativeLR\n",
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"from torch.optim import Optimizer\n",
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"\n",
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"from xformers.factory.model_factory import xFormer, xFormerConfig\n",
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"import pytorch_lightning as pl\n",
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"from pytorch_lightning.loggers import CSVLogger\n",
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"\n",
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"from gluonts.core.component import validated\n",
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"from gluonts.dataset.common import Dataset, ListDataset\n",
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"from gluonts.dataset.field_names import FieldName\n",
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"from gluonts.itertools import Cyclic, PseudoShuffled, IterableSlice\n",
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"from gluonts.time_feature import (\n",
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" TimeFeature,\n",
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" time_features_from_frequency_str,\n",
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")\n",
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"from gluonts.torch.modules.loss import DistributionLoss, NegativeLogLikelihood\n",
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"from gluonts.transform import (\n",
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" Transformation,\n",
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" Chain,\n",
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" RemoveFields,\n",
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" SetField,\n",
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" AsNumpyArray,\n",
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" AddObservedValuesIndicator,\n",
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" AddTimeFeatures,\n",
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" AddAgeFeature,\n",
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" VstackFeatures,\n",
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" InstanceSplitter,\n",
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" ValidationSplitSampler,\n",
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" TestSplitSampler,\n",
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" ExpectedNumInstanceSampler,\n",
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" SelectFields,\n",
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")\n",
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"from gluonts.torch.util import (\n",
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" IterableDataset,\n",
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")\n",
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"from gluonts.evaluation import make_evaluation_predictions, Evaluator\n",
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"from gluonts.torch.model.estimator import PyTorchLightningEstimator\n",
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"from gluonts.torch.model.predictor import PyTorchPredictor\n",
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"from gluonts.torch.distributions import (\n",
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" DistributionOutput,\n",
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" StudentTOutput,\n",
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")\n",
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"from gluonts.torch.util import weighted_average\n",
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"from gluonts.torch.modules.scaler import MeanScaler, NOPScaler\n",
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"from gluonts.torch.modules.feature import FeatureEmbedder\n",
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"from gluonts.time_feature import get_lags_for_frequency\n",
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"from gluonts.dataset.repository.datasets import get_dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "b8af6d90",
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"metadata": {},
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"outputs": [],
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"source": [
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"class TransformerModel(nn.Module):\n",
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" @validated()\n",
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" def __init__(\n",
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" self,\n",
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" freq: str,\n",
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" context_length: int,\n",
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" prediction_length: int,\n",
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" num_feat_dynamic_real: int,\n",
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" num_feat_static_real: int,\n",
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" num_feat_static_cat: int,\n",
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" cardinality: List[int],\n",
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" \n",
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" # transformer arguments\n",
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" nhead: int,\n",
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" num_encoder_layers: int,\n",
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" num_decoder_layers: int,\n",
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" attention_args: Dict[str, Any],\n",
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" activation: str = \"gelu\",\n",
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" dropout: float = 0.1,\n",
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" reversible: bool = False,\n",
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" hidden_layer_multiplier: int = 2,\n",
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" use_rotary_embeddings: bool = False,\n",
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"\n",
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" # univariate input\n",
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" input_size: int = 1,\n",
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" embedding_dimension: Optional[List[int]] = None,\n",
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" distr_output: DistributionOutput = StudentTOutput(),\n",
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" lags_seq: Optional[List[int]] = None,\n",
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" scaling: bool = True,\n",
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" num_parallel_samples: int = 1,\n",
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" ) -> None:\n",
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" super().__init__()\n",
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" \n",
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" self.input_size = input_size\n",
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" \n",
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" self.target_shape = distr_output.event_shape\n",
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" self.num_feat_dynamic_real = num_feat_dynamic_real\n",
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" self.num_feat_static_cat = num_feat_static_cat\n",
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" self.num_feat_static_real = num_feat_static_real\n",
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" self.embedding_dimension = (\n",
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" embedding_dimension\n",
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" if embedding_dimension is not None or cardinality is None\n",
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" else [min(50, (cat + 1) // 2) for cat in cardinality]\n",
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" )\n",
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" self.lags_seq = lags_seq or get_lags_for_frequency(freq_str=freq)\n",
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" self.num_parallel_samples = num_parallel_samples\n",
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" self.history_length = context_length + max(self.lags_seq)\n",
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" self.embedder = FeatureEmbedder(\n",
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" cardinalities=cardinality,\n",
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" embedding_dims=self.embedding_dimension,\n",
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" )\n",
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" if scaling:\n",
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" self.scaler = MeanScaler(dim=1, keepdim=True)\n",
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" else:\n",
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" self.scaler = NOPScaler(dim=1, keepdim=True)\n",
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" \n",
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" # total feature size\n",
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" d_model = self.input_size * len(self.lags_seq) + self._number_of_features\n",
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" \n",
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" self.context_length = context_length\n",
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" self.prediction_length = prediction_length\n",
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" self.distr_output = distr_output\n",
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" self.param_proj = distr_output.get_args_proj(d_model)\n",
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" \n",
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" attention_args[\"dropout\"] = dropout\n",
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" attention_args[\"causal\"] = False\n",
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" attention_args[\"seq_len\"] = self.context_length\n",
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" attention_args[\"num_rules\"] = nhead\n",
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" attention_args[\"attention_query_mask\"] = (torch.rand((context_length, 1)) < 0.5)\n",
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" \n",
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" \n",
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" xformer_config = [\n",
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" # A list of the encoder blocks which constitute the Transformer.\n",
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" # Note that a sequence of different encoder blocks can be used\n",
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" {\n",
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" \"reversible\": reversible, # Optionally make these layers reversible, to save memory\n",
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" \"block_type\": \"encoder\",\n",
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" \"num_layers\": num_encoder_layers, # Optional, this means that this config will repeat N times\n",
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" \"dim_model\": d_model,\n",
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" \"layer_norm_style\": \"pre\", # Optional, pre/post\n",
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" \"position_encoding_config\": {\n",
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" \"name\": \"sine\",\n",
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" \"dim_model\": d_model,\n",
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" },\n",
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" \"multi_head_config\": {\n",
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" \"use_rotary_embeddings\": use_rotary_embeddings,\n",
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" \"num_heads\": nhead,\n",
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" \"residual_dropout\": dropout,\n",
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" \"attention\": attention_args,\n",
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" },\n",
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" \"feedforward_config\": {\n",
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" \"name\": \"MLP\",\n",
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" \"dropout\": dropout,\n",
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" \"activation\": activation,\n",
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" \"hidden_layer_multiplier\": hidden_layer_multiplier,\n",
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" \"dim_model\": d_model,\n",
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" },\n",
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" },\n",
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" ]\n",
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" config = xFormerConfig(xformer_config)\n",
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" # xformer encoder\n",
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" self.encoder = xFormer.from_config(config)\n",
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" \n",
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" # causal vanilla transformer decoder\n",
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" decoder_layer = nn.TransformerDecoderLayer(\n",
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" d_model, \n",
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" nhead, \n",
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" dim_feedforward=d_model*hidden_layer_multiplier, \n",
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" dropout=dropout,\n",
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" activation=activation, \n",
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" layer_norm_eps=1e-5, \n",
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" batch_first=True, \n",
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" norm_first=False,\n",
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" )\n",
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" decoder_norm = nn.LayerNorm(d_model, eps=1e-5)\n",
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" self.decoder = nn.TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)\n",
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" \n",
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" # causal decoder tgt mask for training\n",
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" self.register_buffer(\n",
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" \"tgt_mask\",\n",
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" nn.Transformer.generate_square_subsequent_mask(prediction_length),\n",
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" )\n",
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"\n",
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" @property\n",
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" def _number_of_features(self) -> int:\n",
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" return (\n",
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" sum(self.embedding_dimension)\n",
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" + self.num_feat_dynamic_real\n",
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" + self.num_feat_static_real\n",
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" + self.input_size # the log(scale)\n",
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" )\n",
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"\n",
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" @property\n",
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" def _past_length(self) -> int:\n",
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" return self.context_length + max(self.lags_seq)\n",
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" \n",
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" def get_lagged_subsequences(\n",
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" self,\n",
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" sequence: torch.Tensor,\n",
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" subsequences_length: int,\n",
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" shift: int = 0\n",
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" ) -> torch.Tensor:\n",
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" \"\"\"\n",
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" Returns lagged subsequences of a given sequence.\n",
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" Parameters\n",
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" ----------\n",
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" sequence : Tensor\n",
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" the sequence from which lagged subsequences should be extracted.\n",
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" Shape: (N, T, C).\n",
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" subsequences_length : int\n",
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" length of the subsequences to be extracted.\n",
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" shift: int\n",
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" shift the lags by this amount back.\n",
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" Returns\n",
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" --------\n",
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" lagged : Tensor\n",
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" a tensor of shape (N, S, C, I), where S = subsequences_length and\n",
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" I = len(indices), containing lagged subsequences. Specifically,\n",
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" lagged[i, j, :, k] = sequence[i, -indices[k]-S+j, :].\n",
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" \"\"\"\n",
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" sequence_length = sequence.shape[1]\n",
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" indices = [l - shift for l in self.lags_seq]\n",
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"\n",
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" assert max(indices) + subsequences_length <= sequence_length, (\n",
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" f\"lags cannot go further than history length, found lag {max(indices)} \"\n",
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" f\"while history length is only {sequence_length}\"\n",
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" )\n",
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"\n",
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" lagged_values = []\n",
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" for lag_index in indices:\n",
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" begin_index = -lag_index - subsequences_length\n",
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" end_index = -lag_index if lag_index > 0 else None\n",
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" lagged_values.append(sequence[:, begin_index:end_index, ...])\n",
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" return torch.stack(lagged_values, dim=-1)\n",
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" \n",
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" \n",
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" def create_network_inputs(\n",
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" self, \n",
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" feat_static_cat: torch.Tensor, \n",
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" feat_static_real: torch.Tensor,\n",
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" past_time_feat: torch.Tensor,\n",
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" past_target: torch.Tensor,\n",
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" past_observed_values: torch.Tensor,\n",
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" future_time_feat: Optional[torch.Tensor] = None,\n",
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" future_target: Optional[torch.Tensor] = None,\n",
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" ): \n",
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" # time feature\n",
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" time_feat = (\n",
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" past_time_feat[:, self._past_length - self.context_length :, ...]\n",
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" if future_time_feat is None or future_target is None\n",
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" else torch.cat(\n",
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" (\n",
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" past_time_feat[:, self._past_length - self.context_length :, ...],\n",
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" future_time_feat,\n",
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" ),\n",
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" dim=1,\n",
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" )\n",
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" )\n",
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"\n",
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" # target\n",
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" context = past_target[:, -self.context_length :]\n",
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" observed_context = past_observed_values[:, -self.context_length :] \n",
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" # weights = torch.linspace(0.0001, 1, steps=observed_context.size(-1), device=observed_context.device)\n",
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" _, scale = self.scaler(context, observed_context)\n",
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"\n",
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" inputs = (\n",
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" torch.cat((past_target, future_target), dim=1) / scale\n",
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" if future_target is not None\n",
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" else past_target / scale\n",
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" )\n",
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"\n",
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" inputs_length = (\n",
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" self._past_length + self.prediction_length\n",
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" if future_target is not None\n",
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" else self._past_length\n",
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" )\n",
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" assert inputs.shape[1] == inputs_length\n",
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" \n",
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" subsequences_length = (\n",
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" self.context_length\n",
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" if future_time_feat is None or future_target is None\n",
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" else self.context_length + self.prediction_length\n",
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" )\n",
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" \n",
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" # embeddings\n",
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" embedded_cat = self.embedder(feat_static_cat)\n",
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" log_scale = scale.log() if self.input_size == 1 else scale.squeeze(1).log()\n",
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" static_feat = torch.cat(\n",
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" (embedded_cat, feat_static_real, log_scale),\n",
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" dim=1,\n",
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" )\n",
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" expanded_static_feat = static_feat.unsqueeze(1).expand(\n",
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" -1, time_feat.shape[1], -1\n",
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" )\n",
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" \n",
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" features = torch.cat((expanded_static_feat, time_feat), dim=-1)\n",
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" \n",
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" #self._check_shapes(prior_input, inputs, features)\n",
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" #sequence = torch.cat((prior_input, inputs), dim=1)\n",
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"\n",
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" lagged_sequence = self.get_lagged_subsequences(\n",
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" sequence=inputs,\n",
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" subsequences_length=subsequences_length,\n",
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" )\n",
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"\n",
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" lags_shape = lagged_sequence.shape\n",
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" reshaped_lagged_sequence = lagged_sequence.reshape(\n",
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" lags_shape[0], lags_shape[1], -1\n",
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" )\n",
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"\n",
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" if features is None:\n",
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" transformer_inputs = reshaped_lagged_sequence\n",
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" else:\n",
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" transformer_inputs = torch.cat((reshaped_lagged_sequence, features), dim=-1)\n",
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" \n",
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" return transformer_inputs, scale, static_feat\n",
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" \n",
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" def output_params(self, transformer_inputs):\n",
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" enc_input = transformer_inputs[:, :self.context_length, ...]\n",
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" dec_input = transformer_inputs[:, self.context_length:, ...]\n",
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" \n",
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" enc_out = self.encoder(src=enc_input)\n",
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" dec_output = self.decoder(dec_input, enc_out, tgt_mask=self.tgt_mask)\n",
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" \n",
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" return self.param_proj(dec_output)\n",
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"\n",
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" @torch.jit.ignore\n",
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" def output_distribution(\n",
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" self, params, scale=None, trailing_n=None\n",
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" ) -> torch.distributions.Distribution:\n",
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" sliced_params = params\n",
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" if trailing_n is not None:\n",
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" sliced_params = [p[:, -trailing_n:] for p in params]\n",
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" return self.distr_output.distribution(sliced_params, scale=scale)\n",
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" \n",
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" # for prediction\n",
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" def forward(\n",
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" self,\n",
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" feat_static_cat: torch.Tensor,\n",
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" feat_static_real: torch.Tensor,\n",
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" past_time_feat: torch.Tensor,\n",
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" past_target: torch.Tensor,\n",
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" past_observed_values: torch.Tensor,\n",
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" future_time_feat: torch.Tensor,\n",
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" num_parallel_samples: Optional[int] = None,\n",
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" ) -> torch.Tensor:\n",
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" if num_parallel_samples is None:\n",
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" num_parallel_samples = self.num_parallel_samples\n",
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" \n",
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" encoder_inputs, scale, static_feat = self.create_network_inputs(\n",
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" feat_static_cat,\n",
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" feat_static_real,\n",
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" past_time_feat,\n",
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" past_target,\n",
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" past_observed_values,\n",
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" future_time_feat,\n",
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" )\n",
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" \n",
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" enc_out = self.encoder(src=encoder_inputs)\n",
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" \n",
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" params = self.param_proj(enc_out)\n",
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" distr = self.output_distribution(params, trailing_n=1)\n",
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" \n",
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" repeated_scale = scale.repeat_interleave(\n",
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" repeats=self.num_parallel_samples, dim=0\n",
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" )\n",
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" repeated_static_feat = static_feat.repeat_interleave(\n",
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" repeats=self.num_parallel_samples, dim=0\n",
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" ).unsqueeze(dim=1)\n",
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" repeated_past_target = (\n",
|
|
" past_target.repeat_interleave(\n",
|
|
" repeats=self.num_parallel_samples, dim=0\n",
|
|
" )\n",
|
|
" / repeated_scale\n",
|
|
" )\n",
|
|
" repeated_time_feat = future_time_feat.repeat_interleave(\n",
|
|
" repeats=self.num_parallel_samples, dim=0\n",
|
|
" )\n",
|
|
" repeated_enc_out = enc_out.repeat_interleave(\n",
|
|
" repeats=self.num_parallel_samples, dim=0\n",
|
|
" )\n",
|
|
"\n",
|
|
" future_samples = []\n",
|
|
" \n",
|
|
" for k in range(self.prediction_length):\n",
|
|
" next_features = torch.cat(\n",
|
|
" (repeated_static_feat, repeated_time_feat[:, k : k + 1]),\n",
|
|
" dim=-1,\n",
|
|
" )\n",
|
|
" \n",
|
|
" lagged_sequence = self.get_lagged_subsequences(\n",
|
|
" sequence=repeated_past_target,\n",
|
|
" subsequences_length=1,\n",
|
|
" shift=1, \n",
|
|
" )\n",
|
|
"\n",
|
|
" lags_shape = lagged_sequence.shape\n",
|
|
" reshaped_lagged_sequence = lagged_sequence.reshape(\n",
|
|
" lags_shape[0], lags_shape[1], -1\n",
|
|
" )\n",
|
|
" \n",
|
|
" decoder_input = torch.cat((reshaped_lagged_sequence, next_features), dim=-1)\n",
|
|
"\n",
|
|
" output = self.decoder(decoder_input, repeated_enc_out)\n",
|
|
" \n",
|
|
" params = self.param_proj(output)\n",
|
|
" distr = self.output_distribution(params)\n",
|
|
" next_sample = distr.sample()\n",
|
|
" \n",
|
|
" repeated_past_target = torch.cat(\n",
|
|
" (repeated_past_target, next_sample), dim=1\n",
|
|
" )\n",
|
|
" future_samples.append(next_sample)\n",
|
|
"\n",
|
|
" unscaled_future_samples = (\n",
|
|
" torch.cat(future_samples, dim=1) * repeated_scale\n",
|
|
" )\n",
|
|
" return unscaled_future_samples.reshape(\n",
|
|
" (-1, self.num_parallel_samples, self.prediction_length)\n",
|
|
" + self.target_shape,\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "9b1529a8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TransformerLightningModule(pl.LightningModule):\n",
|
|
" def __init__(\n",
|
|
" self,\n",
|
|
" model: TransformerModel,\n",
|
|
" loss: DistributionLoss = NegativeLogLikelihood(),\n",
|
|
" lr: float = 5e-3,\n",
|
|
" weight_decay: float = 1e-6,\n",
|
|
" ) -> None:\n",
|
|
" super().__init__()\n",
|
|
" self.save_hyperparameters()\n",
|
|
" self.model = model\n",
|
|
" self.loss = loss\n",
|
|
" self.lr = lr\n",
|
|
" self.weight_decay = weight_decay\n",
|
|
" \n",
|
|
" def training_step(self, batch, batch_idx: int):\n",
|
|
" \"\"\"Execute training step\"\"\"\n",
|
|
" train_loss = self(batch)\n",
|
|
" self.log(\n",
|
|
" \"train_loss\",\n",
|
|
" train_loss,\n",
|
|
" on_epoch=True,\n",
|
|
" on_step=False,\n",
|
|
" prog_bar=True,\n",
|
|
" )\n",
|
|
" return train_loss\n",
|
|
"\n",
|
|
" def validation_step(self, batch, batch_idx: int):\n",
|
|
" \"\"\"Execute validation step\"\"\"\n",
|
|
" with torch.no_grad():\n",
|
|
" val_loss = self(batch)\n",
|
|
" self.log(\n",
|
|
" \"val_loss\", val_loss, on_epoch=True, on_step=False, prog_bar=True\n",
|
|
" )\n",
|
|
" return val_loss\n",
|
|
"\n",
|
|
" def configure_optimizers(self):\n",
|
|
" \"\"\"Returns the optimizer to use\"\"\"\n",
|
|
" return torch.optim.Adam(\n",
|
|
" self.model.parameters(),\n",
|
|
" lr=self.lr,\n",
|
|
" weight_decay=self.weight_decay,\n",
|
|
" )\n",
|
|
"\n",
|
|
" def forward(self, batch):\n",
|
|
" feat_static_cat = batch[\"feat_static_cat\"]\n",
|
|
" feat_static_real = batch[\"feat_static_real\"]\n",
|
|
" past_time_feat = batch[\"past_time_feat\"]\n",
|
|
" past_target = batch[\"past_target\"]\n",
|
|
" future_time_feat = batch[\"future_time_feat\"]\n",
|
|
" future_target = batch[\"future_target\"]\n",
|
|
" past_observed_values = batch[\"past_observed_values\"]\n",
|
|
" future_observed_values = batch[\"future_observed_values\"]\n",
|
|
" \n",
|
|
" transformer_inputs, scale, _ = self.model.create_network_inputs(\n",
|
|
" feat_static_cat,\n",
|
|
" feat_static_real,\n",
|
|
" past_time_feat,\n",
|
|
" past_target,\n",
|
|
" past_observed_values,\n",
|
|
" future_time_feat,\n",
|
|
" future_target,\n",
|
|
" )\n",
|
|
" params = self.model.output_params(transformer_inputs)\n",
|
|
" distr = self.model.output_distribution(params, scale)\n",
|
|
"\n",
|
|
" loss_values = self.loss(distr, future_target)\n",
|
|
" \n",
|
|
" if len(self.model.target_shape) == 0:\n",
|
|
" loss_weights = future_observed_values\n",
|
|
" else:\n",
|
|
" loss_weights, _ = future_observed_values.min(dim=-1, keepdim=False)\n",
|
|
"\n",
|
|
" return weighted_average(loss_values, weights=loss_weights)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "f7b4c72f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"PREDICTION_INPUT_NAMES = [\n",
|
|
" \"feat_static_cat\",\n",
|
|
" \"feat_static_real\",\n",
|
|
" \"past_time_feat\",\n",
|
|
" \"past_target\",\n",
|
|
" \"past_observed_values\",\n",
|
|
" \"future_time_feat\",\n",
|
|
"]\n",
|
|
"\n",
|
|
"TRAINING_INPUT_NAMES = PREDICTION_INPUT_NAMES + [\n",
|
|
" \"future_target\",\n",
|
|
" \"future_observed_values\",\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "1937891a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TransformerEstimator(PyTorchLightningEstimator):\n",
|
|
" @validated()\n",
|
|
" def __init__(\n",
|
|
" self,\n",
|
|
" freq: str,\n",
|
|
" prediction_length: int,\n",
|
|
" \n",
|
|
" # Transformer arguments\n",
|
|
" nhead: int,\n",
|
|
" num_encoder_layers: int,\n",
|
|
" num_decoder_layers: int,\n",
|
|
" attention_args = {\"name\": \"scaled_dot_product\"},\n",
|
|
" input_size: int = 1,\n",
|
|
" activation: str = \"gelu\",\n",
|
|
" dropout: float = 0.1,\n",
|
|
" use_rotary_embeddings = False,\n",
|
|
" reversible = False,\n",
|
|
"\n",
|
|
" context_length: Optional[int] = None,\n",
|
|
"\n",
|
|
" num_feat_dynamic_real: int = 0,\n",
|
|
" num_feat_static_cat: int = 0,\n",
|
|
" num_feat_static_real: int = 0,\n",
|
|
" cardinality: Optional[List[int]] = None,\n",
|
|
" embedding_dimension: Optional[List[int]] = None,\n",
|
|
" distr_output: DistributionOutput = StudentTOutput(),\n",
|
|
" loss: DistributionLoss = NegativeLogLikelihood(),\n",
|
|
" scaling: bool = True,\n",
|
|
" lags_seq: Optional[List[int]] = None,\n",
|
|
" time_features: Optional[List[TimeFeature]] = None,\n",
|
|
" num_parallel_samples: int = 100,\n",
|
|
" batch_size: int = 32,\n",
|
|
" num_batches_per_epoch: int = 50,\n",
|
|
" trainer_kwargs: Optional[Dict[str, Any]] = dict(),\n",
|
|
" ) -> None:\n",
|
|
" trainer_kwargs = {\n",
|
|
" \"max_epochs\": 100,\n",
|
|
" **trainer_kwargs,\n",
|
|
" }\n",
|
|
" super().__init__(trainer_kwargs=trainer_kwargs)\n",
|
|
" \n",
|
|
" self.freq = freq\n",
|
|
" self.context_length = (\n",
|
|
" context_length if context_length is not None else prediction_length\n",
|
|
" )\n",
|
|
" self.prediction_length = prediction_length\n",
|
|
" self.distr_output = distr_output\n",
|
|
" self.loss = loss\n",
|
|
" \n",
|
|
" self.input_size = input_size\n",
|
|
" self.nhead = nhead\n",
|
|
" self.num_encoder_layers = num_encoder_layers\n",
|
|
" self.num_decoder_layers = num_decoder_layers\n",
|
|
" self.activation = activation\n",
|
|
" self.dropout = dropout\n",
|
|
" self.attention_args = attention_args\n",
|
|
" self.use_rotary_embeddings = use_rotary_embeddings\n",
|
|
" self.reversible = reversible\n",
|
|
" \n",
|
|
" self.num_feat_dynamic_real = num_feat_dynamic_real\n",
|
|
" self.num_feat_static_cat = num_feat_static_cat\n",
|
|
" self.num_feat_static_real = num_feat_static_real\n",
|
|
" self.cardinality = (\n",
|
|
" cardinality if cardinality and num_feat_static_cat > 0 else [1]\n",
|
|
" )\n",
|
|
" self.embedding_dimension = embedding_dimension\n",
|
|
" self.scaling = scaling\n",
|
|
" self.lags_seq = lags_seq\n",
|
|
" self.time_features = (\n",
|
|
" time_features\n",
|
|
" if time_features is not None\n",
|
|
" else time_features_from_frequency_str(self.freq)\n",
|
|
" )\n",
|
|
"\n",
|
|
" self.num_parallel_samples = num_parallel_samples\n",
|
|
" self.batch_size = batch_size\n",
|
|
" self.num_batches_per_epoch = num_batches_per_epoch\n",
|
|
"\n",
|
|
" self.train_sampler = ExpectedNumInstanceSampler(\n",
|
|
" num_instances=1.0, min_future=prediction_length\n",
|
|
" )\n",
|
|
" self.validation_sampler = ValidationSplitSampler(\n",
|
|
" min_future=prediction_length\n",
|
|
" )\n",
|
|
" \n",
|
|
" def create_transformation(self) -> Transformation:\n",
|
|
" remove_field_names = []\n",
|
|
" if self.num_feat_static_real == 0:\n",
|
|
" remove_field_names.append(FieldName.FEAT_STATIC_REAL)\n",
|
|
" if self.num_feat_dynamic_real == 0:\n",
|
|
" remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)\n",
|
|
"\n",
|
|
" return Chain(\n",
|
|
" [RemoveFields(field_names=remove_field_names)]\n",
|
|
" + (\n",
|
|
" [SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0])]\n",
|
|
" if not self.num_feat_static_cat > 0\n",
|
|
" else []\n",
|
|
" )\n",
|
|
" + (\n",
|
|
" [\n",
|
|
" SetField(\n",
|
|
" output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]\n",
|
|
" )\n",
|
|
" ]\n",
|
|
" if not self.num_feat_static_real > 0\n",
|
|
" else []\n",
|
|
" )\n",
|
|
" + [\n",
|
|
" AsNumpyArray(\n",
|
|
" field=FieldName.FEAT_STATIC_CAT,\n",
|
|
" expected_ndim=1,\n",
|
|
" dtype=np.long,\n",
|
|
" ),\n",
|
|
" AsNumpyArray(\n",
|
|
" field=FieldName.FEAT_STATIC_REAL,\n",
|
|
" expected_ndim=1,\n",
|
|
" ),\n",
|
|
" AsNumpyArray(\n",
|
|
" field=FieldName.TARGET,\n",
|
|
" # in the following line, we add 1 for the time dimension\n",
|
|
" expected_ndim=1 + len(self.distr_output.event_shape),\n",
|
|
" ),\n",
|
|
" AddObservedValuesIndicator(\n",
|
|
" target_field=FieldName.TARGET,\n",
|
|
" output_field=FieldName.OBSERVED_VALUES,\n",
|
|
" ),\n",
|
|
" AddTimeFeatures(\n",
|
|
" start_field=FieldName.START,\n",
|
|
" target_field=FieldName.TARGET,\n",
|
|
" output_field=FieldName.FEAT_TIME,\n",
|
|
" time_features=self.time_features,\n",
|
|
" pred_length=self.prediction_length,\n",
|
|
" ),\n",
|
|
" AddAgeFeature(\n",
|
|
" target_field=FieldName.TARGET,\n",
|
|
" output_field=FieldName.FEAT_AGE,\n",
|
|
" pred_length=self.prediction_length,\n",
|
|
" log_scale=True,\n",
|
|
" ),\n",
|
|
" VstackFeatures(\n",
|
|
" output_field=FieldName.FEAT_TIME,\n",
|
|
" input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]\n",
|
|
" + (\n",
|
|
" [FieldName.FEAT_DYNAMIC_REAL]\n",
|
|
" if self.num_feat_dynamic_real > 0\n",
|
|
" else []\n",
|
|
" ),\n",
|
|
" ),\n",
|
|
" ]\n",
|
|
" )\n",
|
|
"\n",
|
|
" def _create_instance_splitter(\n",
|
|
" self, module: TransformerLightningModule, mode: str\n",
|
|
" ):\n",
|
|
" assert mode in [\"training\", \"validation\", \"test\"]\n",
|
|
"\n",
|
|
" instance_sampler = {\n",
|
|
" \"training\": self.train_sampler,\n",
|
|
" \"validation\": self.validation_sampler,\n",
|
|
" \"test\": TestSplitSampler(),\n",
|
|
" }[mode]\n",
|
|
"\n",
|
|
" return InstanceSplitter(\n",
|
|
" target_field=FieldName.TARGET,\n",
|
|
" is_pad_field=FieldName.IS_PAD,\n",
|
|
" start_field=FieldName.START,\n",
|
|
" forecast_start_field=FieldName.FORECAST_START,\n",
|
|
" instance_sampler=instance_sampler,\n",
|
|
" past_length=module.model._past_length,\n",
|
|
" future_length=self.prediction_length,\n",
|
|
" time_series_fields=[\n",
|
|
" FieldName.FEAT_TIME,\n",
|
|
" FieldName.OBSERVED_VALUES,\n",
|
|
" ],\n",
|
|
" dummy_value=self.distr_output.value_in_support,\n",
|
|
" )\n",
|
|
"\n",
|
|
" def create_training_data_loader(\n",
|
|
" self,\n",
|
|
" data: Dataset,\n",
|
|
" module: TransformerLightningModule,\n",
|
|
" shuffle_buffer_length: Optional[int] = None,\n",
|
|
" **kwargs,\n",
|
|
" ) -> Iterable:\n",
|
|
" transformation = self._create_instance_splitter(\n",
|
|
" module, \"training\"\n",
|
|
" ) + SelectFields(TRAINING_INPUT_NAMES)\n",
|
|
"\n",
|
|
" training_instances = transformation.apply(\n",
|
|
" Cyclic(data)\n",
|
|
" if shuffle_buffer_length is None\n",
|
|
" else PseudoShuffled(\n",
|
|
" Cyclic(data), shuffle_buffer_length=shuffle_buffer_length\n",
|
|
" )\n",
|
|
" )\n",
|
|
"\n",
|
|
" return IterableSlice(\n",
|
|
" iter(\n",
|
|
" DataLoader(\n",
|
|
" IterableDataset(training_instances),\n",
|
|
" batch_size=self.batch_size,\n",
|
|
" **kwargs,\n",
|
|
" )\n",
|
|
" ),\n",
|
|
" self.num_batches_per_epoch,\n",
|
|
" )\n",
|
|
"\n",
|
|
" def create_validation_data_loader(\n",
|
|
" self,\n",
|
|
" data: Dataset,\n",
|
|
" module: TransformerLightningModule,\n",
|
|
" **kwargs,\n",
|
|
" ) -> Iterable:\n",
|
|
" transformation = self._create_instance_splitter(\n",
|
|
" module, \"validation\"\n",
|
|
" ) + SelectFields(TRAINING_INPUT_NAMES)\n",
|
|
"\n",
|
|
" validation_instances = transformation.apply(data)\n",
|
|
"\n",
|
|
" return DataLoader(\n",
|
|
" IterableDataset(validation_instances),\n",
|
|
" batch_size=self.batch_size,\n",
|
|
" **kwargs,\n",
|
|
" )\n",
|
|
" \n",
|
|
" def create_predictor(\n",
|
|
" self,\n",
|
|
" transformation: Transformation,\n",
|
|
" module: TransformerLightningModule,\n",
|
|
" ) -> PyTorchPredictor:\n",
|
|
" prediction_splitter = self._create_instance_splitter(module, \"test\")\n",
|
|
" \n",
|
|
" return PyTorchPredictor(\n",
|
|
" input_transform=transformation + prediction_splitter,\n",
|
|
" input_names=PREDICTION_INPUT_NAMES,\n",
|
|
" prediction_net=module.model,\n",
|
|
" batch_size=self.batch_size,\n",
|
|
" prediction_length=self.prediction_length,\n",
|
|
" device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n",
|
|
" )\n",
|
|
"\n",
|
|
" def create_lightning_module(self) -> TransformerLightningModule:\n",
|
|
" model = TransformerModel(\n",
|
|
" freq=self.freq,\n",
|
|
" context_length=self.context_length,\n",
|
|
" prediction_length=self.prediction_length,\n",
|
|
" num_feat_dynamic_real=1 + self.num_feat_dynamic_real + len(self.time_features),\n",
|
|
" num_feat_static_real=max(1, self.num_feat_static_real),\n",
|
|
" num_feat_static_cat=max(1, self.num_feat_static_cat),\n",
|
|
" cardinality=self.cardinality,\n",
|
|
" embedding_dimension=self.embedding_dimension,\n",
|
|
"\n",
|
|
" # transformer arguments\n",
|
|
" nhead=self.nhead,\n",
|
|
" num_encoder_layers=self.num_encoder_layers,\n",
|
|
" num_decoder_layers=self.num_decoder_layers,\n",
|
|
" activation=self.activation,\n",
|
|
" dropout=self.dropout,\n",
|
|
" attention_args=self.attention_args,\n",
|
|
" use_rotary_embeddings=self.use_rotary_embeddings,\n",
|
|
" reversible=self.reversible,\n",
|
|
"\n",
|
|
" # univariate input\n",
|
|
" input_size=self.input_size,\n",
|
|
" distr_output=self.distr_output,\n",
|
|
" lags_seq=self.lags_seq,\n",
|
|
" scaling=self.scaling,\n",
|
|
" num_parallel_samples=self.num_parallel_samples,\n",
|
|
" )\n",
|
|
" \n",
|
|
" return TransformerLightningModule(model=model, loss=self.loss)"
|
|
]
|
|
},
|
|
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"/tmp/ipykernel_19146/3560964647.py:113: DeprecationWarning: `np.long` is a deprecated alias for `np.compat.long`. To silence this warning, use `np.compat.long` by itself. In the likely event your code does not need to work on Python 2 you can use the builtin `int` for which `np.compat.long` is itself an alias. Doing this will not modify any behaviour and is safe. When replacing `np.long`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
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" | Name | Type | Params\n",
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"------------------------------------------------\n",
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"0 | model | TransformerModel | 112 K \n",
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"INFO:pytorch_lightning.utilities.rank_zero:Epoch 1, global step 200: 'val_loss' reached 6.34025 (best 6.34025), saving model to 'logs/transformer/version_93/checkpoints/epoch=1-step=200.ckpt' as top 1\n"
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"INFO:pytorch_lightning.utilities.rank_zero:Epoch 2, global step 300: 'val_loss' reached 5.88945 (best 5.88945), saving model to 'logs/transformer/version_93/checkpoints/epoch=2-step=300.ckpt' as top 1\n"
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"INFO:pytorch_lightning.utilities.rank_zero:Epoch 4, global step 500: 'val_loss' reached 5.54425 (best 5.54425), saving model to 'logs/transformer/version_93/checkpoints/epoch=4-step=500.ckpt' as top 1\n"
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"INFO:pytorch_lightning.utilities.rank_zero:Epoch 5, global step 600: 'val_loss' reached 5.42859 (best 5.42859), saving model to 'logs/transformer/version_93/checkpoints/epoch=5-step=600.ckpt' as top 1\n"
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"text": [
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"INFO:pytorch_lightning.utilities.rank_zero:Epoch 6, global step 700: 'val_loss' reached 5.16974 (best 5.16974), saving model to 'logs/transformer/version_93/checkpoints/epoch=6-step=700.ckpt' as top 1\n"
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"INFO:pytorch_lightning.utilities.rank_zero:Epoch 9, global step 1000: 'val_loss' reached 5.08167 (best 5.08167), saving model to 'logs/transformer/version_93/checkpoints/epoch=9-step=1000.ckpt' as top 1\n",
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"/home/kashif/.env/pytorch/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:261: UserWarning: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n",
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"/home/kashif/.env/pytorch/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:261: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.\n",
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" rank_zero_warn(\n"
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],
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"source": [
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"predictor = estimator.train(\n",
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" training_data=train_ds,\n",
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" validation_data=val_ds,\n",
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"outputs": [],
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"source": [
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"test_ds = ListDataset(dataset[\"test\"], freq=freq)"
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{
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"execution_count": 53,
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"source": [
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"forecast_it, ts_it = make_evaluation_predictions(\n",
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" dataset=test_ds,\n",
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" predictor=predictor,\n",
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"source": [
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"/home/kashif/gluon-ts-PR/src/gluonts/evaluation/_base.py:251: FutureWarning: Could not cast to float64, falling back to object. This behavior is deprecated. In a future version, when a dtype is passed to 'DataFrame', either all columns will be cast to that dtype, or a TypeError will be raised.\n",
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" metrics_per_ts = pd.DataFrame(rows, dtype=np.float64)\n",
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"/home/kashif/.env/pytorch/lib/python3.8/site-packages/pandas/core/construction.py:781: UserWarning: Warning: converting a masked element to nan.\n",
|
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" subarr = np.array(arr, dtype=dtype, copy=copy)\n"
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]
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}
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],
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"source": [
|
|
"agg_metrics, ts_metrics = evaluator(iter(tss), iter(forecasts))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"id": "998dfc78",
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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"text/plain": [
|
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"{'MSE': 5379897.122561459,\n",
|
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" 'abs_error': 13506736.531825066,\n",
|
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" 'abs_target_sum': 128307480.34037971,\n",
|
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" 'abs_target_mean': 2386.671881331468,\n",
|
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" 'seasonal_error': 190.04280429569238,\n",
|
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" 'MASE': 0.9860190993840805,\n",
|
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" 'MAPE': 0.12076296531031802,\n",
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" 'sMAPE': 0.1342430860519276,\n",
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" 'MSIS': 7.365772325780748,\n",
|
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" 'QuantileLoss[0.1]': 5516005.826675046,\n",
|
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" 'Coverage[0.1]': 0.04629836309523808,\n",
|
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" 'QuantileLoss[0.2]': 9103365.535402268,\n",
|
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" 'Coverage[0.2]': 0.11302083333333333,\n",
|
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" 'QuantileLoss[0.3]': 11589326.578895718,\n",
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" 'Coverage[0.3]': 0.18986235119047618,\n",
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" 'QuantileLoss[0.4]': 12951811.902575932,\n",
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" 'Coverage[0.4]': 0.2767671130952381,\n",
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" 'QuantileLoss[0.5]': 13506736.458244145,\n",
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" 'Coverage[0.5]': 0.36928943452380947,\n",
|
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" 'QuantileLoss[0.6]': 13151443.389767516,\n",
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" 'Coverage[0.6]': 0.46084449404761907,\n",
|
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" 'QuantileLoss[0.7]': 12148937.274402771,\n",
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" 'Coverage[0.7]': 0.5702380952380953,\n",
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" 'QuantileLoss[0.8]': 10166412.156378534,\n",
|
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" 'Coverage[0.8]': 0.69296875,\n",
|
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" 'QuantileLoss[0.9]': 6761442.813759254,\n",
|
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" 'Coverage[0.9]': 0.8289434523809524,\n",
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" 'RMSE': 2319.4605240360224,\n",
|
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" 'NRMSE': 0.9718388783053203,\n",
|
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" 'ND': 0.10526850419004256,\n",
|
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" 'wQuantileLoss[0.1]': 0.04299052410694952,\n",
|
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" 'wQuantileLoss[0.2]': 0.07094960879328673,\n",
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" 'wQuantileLoss[0.3]': 0.09032463694362203,\n",
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" 'wQuantileLoss[0.4]': 0.10094354489868243,\n",
|
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" 'wQuantileLoss[0.5]': 0.10526850361656921,\n",
|
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" 'wQuantileLoss[0.6]': 0.1024994283644164,\n",
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" 'wQuantileLoss[0.7]': 0.0946861183944501,\n",
|
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" 'wQuantileLoss[0.8]': 0.079234758015734,\n",
|
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" 'wQuantileLoss[0.9]': 0.052697183327286935,\n",
|
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" 'mean_absolute_QuantileLoss': 10543942.437344575,\n",
|
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" 'mean_wQuantileLoss': 0.08217714516233304,\n",
|
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" 'MAE_Coverage': 0.10575190145502646,\n",
|
|
" 'OWA': nan}"
|
|
]
|
|
},
|
|
"execution_count": 58,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
|
}
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],
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"source": [
|
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"agg_metrics"
|
|
]
|
|
},
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{
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"cell_type": "code",
|
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"execution_count": 59,
|
|
"id": "10ea7d83",
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "float() argument must be a string or a number, not 'Period'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
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"Input \u001b[0;32mIn [59]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, (forecast, ts) \u001b[38;5;129;01min\u001b[39;00m islice(\u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mzip\u001b[39m(forecasts, tss)), \u001b[38;5;241m9\u001b[39m):\n\u001b[1;32m 6\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m, idx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(ts[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m4\u001b[39m \u001b[38;5;241m*\u001b[39m prediction_length:], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m\"\u001b[39m, )\n\u001b[1;32m 9\u001b[0m forecast\u001b[38;5;241m.\u001b[39mplot( color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mxticks(rotation\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m)\n",
|
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/pyplot.py:2769\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2767\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 2768\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2769\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2770\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2771\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/axes/_axes.py:1634\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m-> 1634\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_line\u001b[49m\u001b[43m(\u001b[49m\u001b[43mline\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view(scalex\u001b[38;5;241m=\u001b[39mscalex, scaley\u001b[38;5;241m=\u001b[39mscaley)\n\u001b[1;32m 1636\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lines\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/axes/_base.py:2288\u001b[0m, in \u001b[0;36m_AxesBase.add_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 2285\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m line\u001b[38;5;241m.\u001b[39mget_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2286\u001b[0m line\u001b[38;5;241m.\u001b[39mset_clip_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch)\n\u001b[0;32m-> 2288\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_line_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mline\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line\u001b[38;5;241m.\u001b[39mget_label():\n\u001b[1;32m 2290\u001b[0m line\u001b[38;5;241m.\u001b[39mset_label(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_child\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_children)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/axes/_base.py:2311\u001b[0m, in \u001b[0;36m_AxesBase._update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 2307\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_update_line_limits\u001b[39m(\u001b[38;5;28mself\u001b[39m, line):\n\u001b[1;32m 2308\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2309\u001b[0m \u001b[38;5;124;03m Figures out the data limit of the given line, updating self.dataLim.\u001b[39;00m\n\u001b[1;32m 2310\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2311\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[43mline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path\u001b[38;5;241m.\u001b[39mvertices\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 2313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/lines.py:999\u001b[0m, in \u001b[0;36mLine2D.get_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 997\u001b[0m \u001b[38;5;124;03m\"\"\"Return the `~matplotlib.path.Path` associated with this line.\"\"\"\u001b[39;00m\n\u001b[1;32m 998\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidy \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx:\n\u001b[0;32m--> 999\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecache\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1000\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_path\n",
|
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/lines.py:652\u001b[0m, in \u001b[0;36mLine2D.recache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m 650\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m always \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx:\n\u001b[1;32m 651\u001b[0m xconv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xorig)\n\u001b[0;32m--> 652\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43m_to_unmasked_float_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxconv\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m 653\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 654\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x\n",
|
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"File \u001b[0;32m~/.env/pytorch/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1298\u001b[0m, in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39masarray(x, \u001b[38;5;28mfloat\u001b[39m)\u001b[38;5;241m.\u001b[39mfilled(np\u001b[38;5;241m.\u001b[39mnan)\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Period'"
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|
]
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},
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{
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"data": {
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|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 1440x1080 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure(figsize=(20, 15))\n",
|
|
"date_formater = mdates.DateFormatter('%b, %d')\n",
|
|
"plt.rcParams.update({'font.size': 15})\n",
|
|
"\n",
|
|
"for idx, (forecast, ts) in islice(enumerate(zip(forecasts, tss)), 9):\n",
|
|
" ax = plt.subplot(3, 3, idx+1)\n",
|
|
"\n",
|
|
" plt.plot(ts[-4 * prediction_length:], label=\"target\", )\n",
|
|
" forecast.plot( color='g')\n",
|
|
" plt.xticks(rotation=60)\n",
|
|
" ax.xaxis.set_major_formatter(date_formater)\n",
|
|
"\n",
|
|
"plt.gcf().tight_layout()\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "59b42f21",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def plot_prob_forecasts(ts_entry, forecast_entry):\n",
|
|
" plot_length = 100\n",
|
|
" prediction_intervals = (50.0, 90.0)\n",
|
|
" legend = [\"observations\", \"median prediction\"] + [f\"{k}% prediction interval\" for k in prediction_intervals][::-1]\n",
|
|
"\n",
|
|
" fig, ax = plt.subplots(1, 1, figsize=(10, 7))\n",
|
|
" ts_entry[-plot_length:].plot(ax=ax) # plot the time series\n",
|
|
" forecast_entry.plot(prediction_intervals=prediction_intervals, color='g')\n",
|
|
" plt.grid(which=\"both\")\n",
|
|
" plt.legend(legend, loc=\"upper left\")\n",
|
|
" plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"id": "35eda4db",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 720x504 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"index = 33\n",
|
|
"plot_prob_forecasts(tss[index], forecasts[index])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"id": "a1f978b1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 720x504 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"index = 33\n",
|
|
"plot_prob_forecasts(tss[index], forecasts[index])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "cbc599e9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|