misha-chertushkin
293 lines
9.9 KiB
Plaintext
293 lines
9.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Introduction\n",
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"This notebook shows how to use TimesFM with finetuning. \n",
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"\n",
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"In order to perform finetuning, you need to create the Pytorch Dataset in a proper format. The example of the Dataset is provided below.\n",
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"The finetuning code can be found in timesfm.finetuning_torch.py. This notebook just imports the methods from finetuning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Dataset Creation"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from os import path\n",
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"from typing import Optional, Tuple\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import torch\n",
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"import torch.multiprocessing as mp\n",
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"import yfinance as yf\n",
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"from timesfm.finetuning_torch import FinetuningConfig, TimesFMFinetuner\n",
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"from huggingface_hub import snapshot_download\n",
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"from torch.utils.data import Dataset\n",
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"\n",
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"from timesfm import TimesFm, TimesFmCheckpoint, TimesFmHparams\n",
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"from timesfm.pytorch_patched_decoder import PatchedTimeSeriesDecoder\n",
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"import os\n",
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"\n",
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"\n",
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"class TimeSeriesDataset(Dataset):\n",
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" \"\"\"Dataset for time series data compatible with TimesFM.\"\"\"\n",
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"\n",
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" def __init__(self, series: np.ndarray, context_length: int, horizon_length: int):\n",
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" \"\"\"\n",
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" Initialize dataset.\n",
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"\n",
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" Args:\n",
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" series: Time series data\n",
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" context_length: Number of past timesteps to use as input\n",
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" horizon_length: Number of future timesteps to predict\n",
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" \"\"\"\n",
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" self.series = series\n",
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" self.context_length = context_length\n",
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" self.horizon_length = horizon_length\n",
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" self._prepare_samples()\n",
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"\n",
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" def _prepare_samples(self) -> None:\n",
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" \"\"\"Prepare sliding window samples from the time series.\"\"\"\n",
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" self.samples = []\n",
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" total_length = self.context_length + self.horizon_length\n",
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"\n",
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" for start_idx in range(0, len(self.series) - total_length + 1):\n",
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" end_idx = start_idx + self.context_length\n",
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" x_context = self.series[start_idx:end_idx]\n",
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" x_future = self.series[end_idx : end_idx + self.horizon_length]\n",
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" self.samples.append((x_context, x_future))\n",
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"\n",
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" def __len__(self) -> int:\n",
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" return len(self.samples)\n",
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"\n",
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" def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n",
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" x_context, x_future = self.samples[index]\n",
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"\n",
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" x_context = torch.tensor(x_context, dtype=torch.float32)\n",
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" x_future = torch.tensor(x_future, dtype=torch.float32)\n",
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"\n",
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" input_padding = torch.zeros_like(x_context)\n",
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" freq = torch.zeros(1, dtype=torch.long)\n",
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"\n",
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" return x_context, input_padding, freq, x_future\n",
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"\n",
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"\n",
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"def prepare_datasets(\n",
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" series: np.ndarray, context_length: int, horizon_length: int, train_split: float = 0.8\n",
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") -> Tuple[Dataset, Dataset]:\n",
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" \"\"\"\n",
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" Prepare training and validation datasets from time series data.\n",
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"\n",
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" Args:\n",
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" series: Input time series data\n",
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" context_length: Number of past timesteps to use\n",
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" horizon_length: Number of future timesteps to predict\n",
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" train_split: Fraction of data to use for training\n",
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"\n",
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" Returns:\n",
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" Tuple of (train_dataset, val_dataset)\n",
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" \"\"\"\n",
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" train_size = int(len(series) * train_split)\n",
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" train_data = series[:train_size]\n",
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" val_data = series[train_size:]\n",
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"\n",
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" # Create datasets\n",
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" train_dataset = TimeSeriesDataset(train_data, context_length=context_length, horizon_length=horizon_length)\n",
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"\n",
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" val_dataset = TimeSeriesDataset(val_data, context_length=context_length, horizon_length=horizon_length)\n",
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"\n",
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" return train_dataset, val_dataset\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Model Creation"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_model(load_weights: bool = False):\n",
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" device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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" repo_id = \"google/timesfm-2.0-500m-pytorch\"\n",
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" hparams = TimesFmHparams(\n",
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" backend=device,\n",
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" per_core_batch_size=32,\n",
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" horizon_len=128,\n",
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" num_layers=50,\n",
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" use_positional_embedding=False,\n",
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" context_len=192,\n",
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" )\n",
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" tfm = TimesFm(hparams=hparams, checkpoint=TimesFmCheckpoint(huggingface_repo_id=repo_id))\n",
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"\n",
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" model = PatchedTimeSeriesDecoder(tfm._model_config)\n",
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" if load_weights:\n",
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" checkpoint_path = path.join(snapshot_download(repo_id), \"torch_model.ckpt\")\n",
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" loaded_checkpoint = torch.load(checkpoint_path, weights_only=True)\n",
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" model.load_state_dict(loaded_checkpoint)\n",
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" return model, hparams, tfm._model_config\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_predictions(\n",
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" model: TimesFm,\n",
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" val_dataset: Dataset,\n",
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" save_path: Optional[str] = \"predictions.png\",\n",
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") -> None:\n",
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" \"\"\"\n",
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" Plot model predictions against ground truth for a batch of validation data.\n",
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"\n",
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" Args:\n",
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" model: Trained TimesFM model\n",
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" val_dataset: Validation dataset\n",
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" save_path: Path to save the plot\n",
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" \"\"\"\n",
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" import matplotlib.pyplot as plt\n",
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"\n",
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" model.eval()\n",
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"\n",
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" x_context, x_padding, freq, x_future = val_dataset[0]\n",
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" x_context = x_context.unsqueeze(0) # Add batch dimension\n",
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" x_padding = x_padding.unsqueeze(0)\n",
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" freq = freq.unsqueeze(0)\n",
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" x_future = x_future.unsqueeze(0)\n",
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"\n",
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" device = next(model.parameters()).device\n",
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" x_context = x_context.to(device)\n",
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" x_padding = x_padding.to(device)\n",
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" freq = freq.to(device)\n",
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" x_future = x_future.to(device)\n",
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"\n",
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" with torch.no_grad():\n",
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" predictions = model(x_context, x_padding.float(), freq)\n",
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" predictions_mean = predictions[..., 0] # [B, N, horizon_len]\n",
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" last_patch_pred = predictions_mean[:, -1, :] # [B, horizon_len]\n",
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"\n",
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" context_vals = x_context[0].cpu().numpy()\n",
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" future_vals = x_future[0].cpu().numpy()\n",
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" pred_vals = last_patch_pred[0].cpu().numpy()\n",
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"\n",
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" context_len = len(context_vals)\n",
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" horizon_len = len(future_vals)\n",
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"\n",
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" plt.figure(figsize=(12, 6))\n",
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"\n",
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" plt.plot(range(context_len), context_vals, label=\"Historical Data\", color=\"blue\", linewidth=2)\n",
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"\n",
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" plt.plot(\n",
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" range(context_len, context_len + horizon_len),\n",
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" future_vals,\n",
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" label=\"Ground Truth\",\n",
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" color=\"green\",\n",
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" linestyle=\"--\",\n",
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" linewidth=2,\n",
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" )\n",
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"\n",
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" plt.plot(range(context_len, context_len + horizon_len), pred_vals, label=\"Prediction\", color=\"red\", linewidth=2)\n",
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"\n",
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" plt.xlabel(\"Time Step\")\n",
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" plt.ylabel(\"Value\")\n",
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" plt.title(\"TimesFM Predictions vs Ground Truth\")\n",
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" plt.legend()\n",
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" plt.grid(True)\n",
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"\n",
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" if save_path:\n",
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" plt.savefig(save_path)\n",
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" print(f\"Plot saved to {save_path}\")\n",
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"\n",
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" plt.close()\n",
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"\n",
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"\n",
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"def get_data(context_len: int, horizon_len: int) -> Tuple[Dataset, Dataset]:\n",
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" df = yf.download(\"AAPL\", start=\"2010-01-01\", end=\"2019-01-01\")\n",
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" time_series = df[\"Close\"].values\n",
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"\n",
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" train_dataset, val_dataset = prepare_datasets(\n",
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" series=time_series,\n",
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" context_length=context_len,\n",
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" horizon_length=horizon_len,\n",
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" train_split=0.8,\n",
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" )\n",
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"\n",
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" print(f\"Created datasets:\")\n",
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" print(f\"- Training samples: {len(train_dataset)}\")\n",
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" print(f\"- Validation samples: {len(val_dataset)}\")\n",
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" return train_dataset, val_dataset\n",
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"\n",
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"\n",
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"def single_gpu_example():\n",
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" \"\"\"Basic example of finetuning TimesFM on stock data.\"\"\"\n",
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" model, hparams, tfm_config = get_model(load_weights=True)\n",
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" config = FinetuningConfig(batch_size=256, num_epochs=5, learning_rate=1e-4, use_wandb=True)\n",
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"\n",
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" train_dataset, val_dataset = get_data(128, tfm_config.horizon_len)\n",
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" finetuner = TimesFMFinetuner(model, config)\n",
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"\n",
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" print(\"\\nStarting finetuning...\")\n",
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" results = finetuner.finetune(train_dataset=train_dataset, val_dataset=val_dataset)\n",
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"\n",
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" print(\"\\nFinetuning completed!\")\n",
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" print(f\"Training history: {len(results['history']['train_loss'])} epochs\")\n",
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"\n",
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" plot_predictions(\n",
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" model=model,\n",
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" val_dataset=val_dataset,\n",
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" save_path=\"timesfm_predictions.png\",\n",
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" )\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"single_gpu_example()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "timesfm-311",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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