CherkNevis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1733d9de-60ea-46b9-b07e-0036aac64dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_size = 14\n",
    "daily_cases = [1,11,3,4,5,6,7,8,9,10,23,4,5,6,7,899,34,8,44,5,657,57,0,5,65,7,6,7]\n",
    "\n",
    "train_data = daily_cases[:-test_data_size] # take all elements of daily_cases except the last 14\n",
    "test_data = daily_cases[-test_data_size:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c511900c-20f9-450a-87e4-119cf53935df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 0]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listt = [1,2,3,4,5,6,7,8,9,0]\n",
    "listt[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8345fec-19ca-4284-a52a-34289e603427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 11, 3, 4, 5, 6, 7, 8, 9, 10, 23, 4, 5, 6]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_data = daily_cases[:-test_data_size]\n",
    "\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3d7145d-b1d0-45ff-bc08-f5cb444ac99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original daily_cases:\n",
      "2024-06-15    100\n",
      "2024-06-16    150\n",
      "2024-06-17    200\n",
      "2024-06-18    300\n",
      "2024-06-19    400\n",
      "Freq: D, dtype: int64\n",
      "2024-06-15      NaN\n",
      "2024-06-16     50.0\n",
      "2024-06-17     50.0\n",
      "2024-06-18    100.0\n",
      "2024-06-19    100.0\n",
      "Freq: D, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2024-08-02    10\n",
       "2024-08-03    20\n",
       "2024-08-04    30\n",
       "2024-08-05    40\n",
       "2024-08-06    50\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample cumulative cases Series\n",
    "import pandas as pd\n",
    "data = [100, 150, 200, 300, 400]\n",
    "index = pd.date_range(start='2024-06-15', periods=5)\n",
    "daily_cases = pd.Series(data, index=index)\n",
    "\n",
    "# Original daily_cases (cumulative data)\n",
    "print(\"Original daily_cases:\")\n",
    "print(daily_cases)\n",
    "ss = daily_cases.diff()\n",
    "print(ss)\n",
    "my_pandas_series = pd.Series([10,20,30,40,50], index = pd.date_range(start = '2024-08-02', periods = 5))\n",
    "my_pandas_series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0602486-94af-493a-9f5c-f9c0661bcaca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Transformed daily_cases:\n",
      "2024-06-15     NaN\n",
      "2024-06-16   -50.0\n",
      "2024-06-17     0.0\n",
      "2024-06-18    50.0\n",
      "2024-06-19     0.0\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Apply the transformation\n",
    "daily_cases = daily_cases.diff()\n",
    "\n",
    "# Transformed daily_cases (daily data)\n",
    "print(\"\\nTransformed daily_cases:\")\n",
    "print(daily_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abe21bdd-dc98-4ec9-861a-d7358ed56e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Example series\n",
    "daily_cases = pd.Series([10, 20, 15])\n",
    "\n",
    "# Step 1: Calculate differences\n",
    "diff = daily_cases.diff()  # Result: [NaN, 10.0, -5.0]\n",
    "\n",
    "# Step 2: Replace NaN with the first element\n",
    "filled_diff = diff.fillna(daily_cases[0])  # Result: [10.0, 10.0, -5.0]\n",
    "\n",
    "# Step 3: Convert to integers\n",
    "final_result = filled_diff.astype(np.int64)  # Result: [10, 10, -5]\n",
    "\n",
    "# Assign back to daily_cases\n",
    "daily_cases = final_result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43880cb4-0fbd-45f2-af33-14d4b4316736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Train Data: [0.   0.25 0.5  0.75 1.  ]\n",
      "Scaled Test Data: [1.25 1.5  1.75 2.   2.25]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Example data\n",
    "train_data = np.array([10, 20, 30, 40, 50])\n",
    "test_data = np.array([60, 70, 80, 90, 100])\n",
    "\n",
    "# Initialize the scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "\n",
    "# Fit the scaler on the training data\n",
    "scaler = scaler.fit(np.expand_dims(train_data, axis=1))\n",
    "\n",
    "# Transform the training data\n",
    "train_data_scaled = scaler.transform(np.expand_dims(train_data, axis=1))\n",
    "\n",
    "# Transform the test data\n",
    "test_data_scaled = scaler.transform(np.expand_dims(test_data, axis=1))\n",
    "\n",
    "# Print results\n",
    "print(\"Scaled Train Data:\", train_data_scaled.flatten())\n",
    "print(\"Scaled Test Data:\", test_data_scaled.flatten())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b239718-9dd7-4e58-9657-67e00cbd596f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Train Data: [[0.  ]\n",
      " [0.25]\n",
      " [0.5 ]\n",
      " [0.75]\n",
      " [1.  ]]\n",
      "Scaled Test Data: [[1.25]\n",
      " [1.5 ]\n",
      " [1.75]\n",
      " [2.  ]\n",
      " [2.25]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Scaled Train Data:\", train_data_scaled)\n",
    "print(\"Scaled Test Data:\", test_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23476fd8-6145-4f7d-81f7-adbc4855e452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LSTM_model.svg'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchviz import make_dot\n",
    "\n",
    "# Define the LSTM model\n",
    "class LSTMNet(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size, n_layers=1):\n",
    "        super(LSTMNet, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_layers = n_layers\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, n_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        h0 = torch.zeros(self.n_layers, x.size(0), self.hidden_size).to(x.device)\n",
    "        c0 = torch.zeros(self.n_layers, x.size(0), self.hidden_size).to(x.device)\n",
    "        out, _ = self.lstm(x, (h0, c0))\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out\n",
    "\n",
    "# Hyperparameters\n",
    "input_size = 1\n",
    "hidden_size = 50\n",
    "output_size = 1\n",
    "num_layers = 1\n",
    "\n",
    "# Initialize the model\n",
    "model = LSTMNet(input_size, hidden_size, output_size, num_layers)\n",
    "\n",
    "# Create a dummy input\n",
    "dummy_input = torch.randn(1, 10, input_size)  # Batch size = 1, Sequence length = 10, Input size = 1\n",
    "\n",
    "# Generate the computation graph\n",
    "output = model(dummy_input)\n",
    "dot = make_dot(output, params=dict(model.named_parameters()))\n",
    "dot.format = 'svg'\n",
    "dot.render(\"LSTM_model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18b89e50-14a6-4bd1-9841-e876ae2bc983",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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