{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/wnemsc/lib/python3.9/site-packages/pytorch_forecasting/models/base_model.py:30: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset_window(run, window=None):\n",
    "    artifact_name = f\"{run.project}/{run.config['data']['dataset']}\"\n",
    "    artifact = wandb.Api().artifact(artifact_name)\n",
    "    base_path = artifact.download()\n",
    "\n",
    "    name = artifact.metadata['name']\n",
    "    in_sample_name = f\"in-sample-{window or run.config['data']['sliding_window']}\"\n",
    "    in_sample_data = pd.read_csv(os.path.join(\n",
    "        base_path, name + '-' + in_sample_name + '.csv'))\n",
    "    out_of_sample_name = f\"out-of-sample-{window or run.config['data']['sliding_window']}\"\n",
    "    out_of_sample_data = pd.read_csv(os.path.join(\n",
    "        base_path, name + '-' + out_of_sample_name + '.csv'))\n",
    "\n",
    "    return in_sample_data, out_of_sample_data\n",
    "\n",
    "def get_train_validation_split(config, in_sample_data):\n",
    "    validation_part = config['data']['validation']\n",
    "    train_data = in_sample_data.iloc[:int(len(in_sample_data) * (1 - validation_part))]\n",
    "    val_data = in_sample_data.iloc[len(train_data) - config['past_window']:]\n",
    "\n",
    "    return train_data, val_data\n",
    "\n",
    "def build_time_series_dataset(config, data):\n",
    "    data = data.copy()\n",
    "    data['weekday'] = data['weekday'].astype('str')\n",
    "    data['hour'] = data['hour'].astype('str')\n",
    "\n",
    "    time_series_dataset = TimeSeriesDataSet(\n",
    "        data,\n",
    "        time_idx=config['data']['fields']['time_index'],\n",
    "        target=config['data']['fields']['target'],\n",
    "        group_ids=config['data']['fields']['group_ids'],\n",
    "        min_encoder_length=config['past_window'],\n",
    "        max_encoder_length=config['past_window'],\n",
    "        min_prediction_length=config['future_window'],\n",
    "        max_prediction_length=config['future_window'],\n",
    "        static_reals=config['data']['fields']['static_real'],\n",
    "        static_categoricals=config['data']['fields']['static_cat'],\n",
    "        time_varying_known_reals=config['data']['fields']['dynamic_known_real'],\n",
    "        time_varying_known_categoricals=config['data']['fields']['dynamic_known_cat'],\n",
    "        time_varying_unknown_reals=config['data']['fields']['dynamic_unknown_real'],\n",
    "        time_varying_unknown_categoricals=config['data']['fields']['dynamic_unknown_cat'],\n",
    "        randomize_length=False,\n",
    "    )\n",
    "\n",
    "    return time_series_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Maybe save all on cpu\n",
    "def get_model(run):\n",
    "    model_name = run.config['model']['name']\n",
    "    model_path = f\"{run.project}/model-{run.id}:best\"\n",
    "    model_artifact = wandb.Api().artifact(model_path)\n",
    "\n",
    "    if model_name == 'TemporalFusionTransformer':\n",
    "        return TemporalFusionTransformer.load_from_checkpoint(model_artifact.file())\n",
    "\n",
    "    raise ValueError(\"Invalid model name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "RUN_ID = \"filipstefaniuk/wne-masters-thesis-testing/r1gtdlsf\"\n",
    "PROJECT = \"wne-masters-thesis-testing\"\n",
    "run = wandb.Api().run(RUN_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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. 0:0:0.5\n",
      "/usr/local/anaconda3/envs/wnemsc/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",
      "/usr/local/anaconda3/envs/wnemsc/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": [
    "in_sample, _ = get_dataset_window(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",
    "model = get_model(run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/envs/wnemsc/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: 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=15` in the `DataLoader` to improve performance.\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(\n",
    "        valid.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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline returns 0.23008643692049624\n",
      "Strategy returns 0.09137899364187074\n"
     ]
    },
    {
     "data": {
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   ],
   "source": [
    "from strategy.evaluation import evaluate_strategy\n",
    "from strategy.strategy import BuyAndHoldStrategy, ModelReturnsPredictionStrategy\n",
    "\n",
    "model_predictions = pd.DataFrame(predictions.index)\n",
    "model_predictions['prediction'] = predictions.output.prediction.reshape(-1).numpy()\n",
    "\n",
    "result_baseline = evaluate_strategy(valid_data, BuyAndHoldStrategy())\n",
    "result_strategy = evaluate_strategy(valid_data, ModelReturnsPredictionStrategy(model_predictions))\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": 9,
   "metadata": {},
   "outputs": [
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          "font": {
           "color": "#2a3f5f"
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          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
          "mapbox": {
           "style": "light"
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          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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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