ium_s451499/ium_05/predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyTorch train model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytanie niezbędnych bibliotek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytanie danych z pliku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/btc_test.csv')\n",
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przygotowanie danych\n",
    "Powinienembył zrobić to w zadaniu 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data['date'] = le.fit_transform(data['date'])\n",
    "data['hour'] = le.fit_transform(data['hour'])\n",
    "data['Volume BTC'] = data['Volume BTC']/10\n",
    "\n",
    "# Przekształć łańcuchy znaków na liczby aby zapobiec 'TypeError: can't convert np.ndarray of type numpy.object_.'\n",
    "for col in data.columns:\n",
    "    data[col] = pd.to_numeric(data[col], errors='coerce')\n",
    "\n",
    "# Zamień brakujące wartości na 0 aby zapobiec 'IndexError: Target -9223372036854775808 is out of bounds.'\n",
    "data = data.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przygotowanie inputs oraz targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Przekształć dane na tensory PyTorch\n",
    "inputs = torch.tensor(data[['date', 'hour', 'Volume BTC']].values, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(inputs.shape[1], 64),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(64, 1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytanie wytrenowanego modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Flatten(start_dim=1, end_dim=-1)\n",
       "  (1): Linear(in_features=3, out_features=64, bias=True)\n",
       "  (2): ReLU()\n",
       "  (3): Linear(in_features=64, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"model.pth\"))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predykcja modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[772837.5000],\n",
      "        [772837.5000],\n",
      "        [772837.5000],\n",
      "        ...,\n",
      "        [772837.5000],\n",
      "        [772837.5000],\n",
      "        [772837.5000]], grad_fn=<MulBackward0>)\n"
     ]
    }
   ],
   "source": [
    "predictions = model(inputs)\n",
    "predicted_data = (predictions.float() * 10)\n",
    "print(predicted_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zapis danych do pliku csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "detach() missing 1 required positional arguments: \"input\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[300], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predicted_data_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mnumpy(predicted_data))\n\u001b[0;32m      2\u001b[0m predicted_data_df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredict_result.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[1;31mTypeError\u001b[0m: detach() missing 1 required positional arguments: \"input\""
     ]
    }
   ],
   "source": [
    "predicted_data_df = pd.DataFrame(torch.detach(predicted_data).numpy())\n",
    "predicted_data_df.to_csv(\"predict_result.csv\", index=False)"
   ]
  }
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