implementation_on_dataset1-checkpoint.ipynb

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      "Index(['Patient number', 'Idiopathic RM', 'Age', 'AMH', 'FSH', 'LH',\n",
      "       'Number of miscarriages', 'Estradiol', 'Diabetes', 'Hypothyroidism',\n",
      "       'Hyperthyroidism', 'Thrombophilic defects', 'APS'],\n",
      "      dtype='object')\n",
      "Q-Table after training:\n",
      "[[10.72738634 23.43103025]\n",
      " [14.35967981 23.25400597]\n",
      " [11.93791149 23.23365525]\n",
      " [10.30320494 23.30810014]\n",
      " [12.11481516 23.34148348]\n",
      " [10.00013463 23.36909258]\n",
      " [ 9.80272839 23.26572526]\n",
      " [13.84306037 23.32331152]\n",
      " [23.24586641 13.99934146]\n",
      " [10.94679775 23.29086855]\n",
      " [10.55951532 23.33149656]\n",
      " [23.36962316  7.78068253]\n",
      " [23.29805386  9.31695246]\n",
      " [23.24242266 12.23875106]\n",
      " [12.60939121 23.24916414]\n",
      " [11.86754605 23.27564057]\n",
      " [23.35951045 11.45854092]\n",
      " [23.49338039 12.51045131]\n",
      " [14.36438972 23.46844204]\n",
      " [23.58380561 14.3189651 ]\n",
      " [11.86669846 23.57662941]\n",
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      " [11.13548347 23.36290992]\n",
      " [23.24886695 14.75664273]\n",
      " [23.32352972 11.40766952]\n",
      " [11.55482243 23.2901316 ]\n",
      " [23.34838247 12.46088275]\n",
      " [23.37145112 11.63241297]\n",
      " [23.1798031  12.4892528 ]\n",
      " [23.33826728 12.14932977]\n",
      " [10.83735492 23.46421805]\n",
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      " [14.67101954 23.385214  ]\n",
      " [11.15228134 23.39617819]\n",
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      " [12.0542132  23.24032206]\n",
      " [10.89306744 23.3171219 ]\n",
      " [12.29231257 23.35375758]\n",
      " [12.04224702 23.40580359]\n",
      " [ 8.46320339 23.39439895]\n",
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      " [10.24002225 23.3947329 ]\n",
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      " [13.20688949 23.42644748]\n",
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      " [23.43939032 12.82638492]\n",
      " [11.62685456 23.30909248]\n",
      " [23.33003761 12.27014252]\n",
      " [23.35810624 10.04722307]\n",
      " [13.04788135 23.40184438]\n",
      " [23.43548817 10.49271731]\n",
      " [11.29322836 23.27959081]\n",
      " [23.27787689 13.26609745]\n",
      " [23.25158399 12.03758046]\n",
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      " [12.52288699 23.24345866]\n",
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      " [23.03406128 14.81765367]\n",
      " [22.9651961  12.73514061]\n",
      " [11.96004056 22.95244584]\n",
      " [11.10224374 22.97738197]\n",
      " [11.86902653 22.97046662]\n",
      " [23.06378603 11.92962669]\n",
      " [12.07885611 23.08174306]\n",
      " [11.76561418 23.07335308]\n",
      " [11.89983951 23.07712195]\n",
      " [10.88362651 22.91710178]\n",
      " [10.93635784 22.95436065]\n",
      " [22.97614716 10.36288008]\n",
      " [22.88291222 11.13900439]\n",
      " [12.66270723 22.81860326]\n",
      " [10.32901211 22.76181696]\n",
      " [22.82036323  9.23130241]\n",
      " [11.60464012 22.89484034]\n",
      " [11.70696349 22.88789576]\n",
      " [13.18273887 22.9288905 ]\n",
      " [12.70125259 22.93610473]\n",
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      " [14.6920242  23.3806991 ]\n",
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      " [23.52323595 12.28428042]\n",
      " [23.47555549 10.49499355]\n",
      " [23.35945918 10.46452986]]\n"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Load and preprocess the dataset\n",
    "file_path = 'dataset_for_mp.xlsx'\n",
    "df = pd.read_excel(file_path)\n",
    "\n",
    "# Display the column names to ensure correctness\n",
    "print(df.columns)\n",
    "\n",
    "# Identify non-numeric columns\n",
    "non_numeric_columns = ['Patient number']\n",
    "\n",
    "# Ensure the columns exist in the DataFrame\n",
    "non_numeric_columns = [col for col in non_numeric_columns if col in df.columns]\n",
    "\n",
    "df_numeric = df.drop(columns=non_numeric_columns)\n",
    "scaler = StandardScaler()\n",
    "df_normalized = scaler.fit_transform(df_numeric)\n",
    "\n",
    "# Define the state space, action space, and Q-table\n",
    "num_states = df_normalized.shape[0]\n",
    "num_actions = 2  # For simplicity, let's assume 2 actions: success (1) or failure (0)\n",
    "Q = np.zeros((num_states, num_actions))\n",
    "\n",
    "# Hyperparameters\n",
    "alpha = 0.1  # Learning rate\n",
    "gamma = 0.99  # Discount factor\n",
    "epsilon = 1.0  # Exploration-exploitation trade-off\n",
    "epsilon_decay = 0.995\n",
    "min_epsilon = 0.01\n",
    "num_episodes = 1000\n",
    "max_steps = 100\n",
    "\n",
    "# Q-Learning Algorithm\n",
    "for episode in range(num_episodes):\n",
    "    state = np.random.randint(0, num_states)\n",
    "    for step in range(max_steps):\n",
    "        if np.random.uniform(0, 1) < epsilon:\n",
    "            action = np.random.randint(0, num_actions)\n",
    "        else:\n",
    "            action = np.argmax(Q[state, :])\n",
    "        \n",
    "        next_state = (state + 1) % num_states  # Simplified state transition\n",
    "        reward = np.random.random()  # Simplified reward\n",
    "\n",
    "        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])\n",
    "        state = next_state\n",
    "        \n",
    "        done = (step == max_steps - 1)  # Simplified termination condition\n",
    "        if done:\n",
    "            break\n",
    "\n",
    "    epsilon = max(min_epsilon, epsilon * epsilon_decay)\n",
    "\n",
    "print(\"Q-Table after training:\")\n",
    "print(Q)"
   ]
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