informer_model/notebooks/evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb\n",
    "import os\n",
    "import pandas as pd\n",
    "import plotly.graph_objs as go\n",
    "from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer\n",
    "from pytorch_forecasting.data.timeseries import TimeSeriesDataSet\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from ml.model import load_model_from_wandb\n",
    "from ml.data import get_dataset_from_wandb, get_train_validation_split, build_time_series_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model predicting price (with Quantiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 310.03MB. 10 files... \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   10 of 10 files downloaded.  \n",
      "Done. 0:0:0.4\n",
      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: 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",
      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n"
     ]
    }
   ],
   "source": [
    "RUN_ID = \"filipstefaniuk/wne-masters-thesis-testing/x352wmas\"\n",
    "\n",
    "run = wandb.Api().run(RUN_ID)\n",
    "in_sample, out_of_sample = get_dataset_from_wandb(run)\n",
    "test_data = out_of_sample\n",
    "train_data, valid_data = get_train_validation_split(run.config, in_sample)\n",
    "train = build_time_series_dataset(run.config, train_data)\n",
    "valid = build_time_series_dataset(run.config, valid_data)\n",
    "test = build_time_series_dataset(run.config, test_data)\n",
    "model = load_model_from_wandb(run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: PossibleUserWarning:\n",
      "\n",
      "The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(\n",
    "        valid.to_dataloader(train=False, batch_size=64),\n",
    "        # test.to_dataloader(train=False, batch_size=64),\n",
    "        mode=\"raw\",\n",
    "        return_index=True,\n",
    "        trainer_kwargs={\n",
    "            'logger': False\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline returns 0.22730028053392815\n",
      "Strategy returns 0.15714438080714976\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    }
   ],
   "source": [
    "from strategy.strategy import BuyAndHoldStrategy, PriceQuantilePredictionStrategy\n",
    "from strategy.evaluation import evaluate_strategy\n",
    "\n",
    "model_predictions = pd.DataFrame(predictions.index)\n",
    "model_predictions['prediction'] = list(torch.squeeze(predictions.output.prediction).cpu().numpy())\n",
    "\n",
    "result_baseline = evaluate_strategy(valid_data, BuyAndHoldStrategy())\n",
    "result_strategy = evaluate_strategy(valid_data, PriceQuantilePredictionStrategy(model_predictions), exchange_fee=0.001)\n",
    "\n",
    "print('Baseline returns', result_baseline['total_return'])\n",
    "print('Strategy returns', result_strategy['total_return'])\n",
    "\n",
    "go.Figure([\n",
    "    go.Scatter(y=result_baseline['portfolio_value']),\n",
    "    go.Scatter(y=result_strategy['portfolio_value'])]).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model predicting returns (with GMADL loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 310.03MB. 10 files... \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   10 of 10 files downloaded.  \n",
      "Done. 0:0:0.4\n",
      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: UserWarning:\n",
      "\n",
      "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",
      "\n",
      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:208: UserWarning:\n",
      "\n",
      "Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "RUN_ID = \"filipstefaniuk/wne-masters-thesis-testing/m6zetdhf\"\n",
    "\n",
    "run = wandb.Api().run(RUN_ID)\n",
    "in_sample, out_of_sample = get_dataset_from_wandb(run)\n",
    "train_data, valid_data = get_train_validation_split(run.config, in_sample)\n",
    "train = build_time_series_dataset(run.config, train_data)\n",
    "valid = build_time_series_dataset(run.config, valid_data)\n",
    "test = build_time_series_dataset(run.config, out_of_sample)\n",
    "model = load_model_from_wandb(run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "/home/filip/anaconda3/envs/wne-msc-thesis/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: PossibleUserWarning:\n",
      "\n",
      "The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(\n",
    "        valid.to_dataloader(train=False, batch_size=64),\n",
    "        # test.to_dataloader(train=False, batch_size=64),\n",
    "        mode=\"raw\",\n",
    "        return_index=True,\n",
    "        trainer_kwargs={\n",
    "            'logger': False\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline returns 0.2240889082270856\n",
      "Strategy returns -0.3902088328046026\n"
     ]
    },
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     "metadata": {},
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   ],
   "source": [
    "from strategy.evaluation import evaluate_strategy\n",
    "from strategy.strategy import BuyAndHoldStrategy, ReturnsPredictionStrategy\n",
    "\n",
    "model_predictions = pd.DataFrame(predictions.index)\n",
    "model_predictions['prediction'] = predictions.output.prediction.reshape(-1).cpu().numpy()\n",
    "\n",
    "result_baseline = evaluate_strategy(valid_data, BuyAndHoldStrategy())\n",
    "result_strategy = evaluate_strategy(valid_data, ReturnsPredictionStrategy(model_predictions, threshold=0.001))\n",
    "\n",
    "print('Baseline returns', result_baseline['total_return'])\n",
    "print('Strategy returns', result_strategy['total_return'])\n",
    "\n",
    "go.Figure([\n",
    "    go.Scatter(y=result_baseline['portfolio_value']),\n",
    "    go.Scatter(y=result_strategy['portfolio_value'])]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Plot predictions\n",
    "go.Figure([go.Scatter(y=model_predictions['prediction'])]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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