Big_Data/Tutorium 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db3dfd7e-3faf-401d-ac43-f888f445563a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5222c9f-320f-42ac-a20c-46b235e14116",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a82b5fcc-1eed-45bc-9ac3-65242a42acc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9665eb0a-82a0-4b38-8279-e1ce4b85dfa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50007140-4393-4fdd-a774-1ec4b8db9ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load iris dataset\n",
    "data = load_iris()\n",
    "X_full = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "y = pd.Series(data.target)  # Species as numeric labels (0, 1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "667d2530-3d5f-439f-80ed-b1b07902629d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit linear regression model for the full model\n",
    "model = LinearRegression()\n",
    "model.fit(X_full, y)\n",
    "y_pred = model.predict(X_full)\n",
    "residuals = y - y_pred\n",
    "\n",
    "# Residuals vs Fitted\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.scatterplot(x=y_pred, y=residuals)\n",
    "plt.axhline(0, color='red', linestyle='--')\n",
    "plt.title(\"Residuals vs Fitted Values\")\n",
    "plt.xlabel(\"Fitted Values\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Autocorrelation of residuals\n",
    "plt.figure(figsize=(6, 4))\n",
    "plot_acf(residuals, lags=20)\n",
    "plt.title(\"Autocorrelation of Residuals\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Q-Q Plot for residuals\n",
    "plt.figure(figsize=(6, 4))\n",
    "sm.qqplot(residuals, line='s')\n",
    "plt.title(\"Q-Q Plot of Residuals\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b24c560-c4f2-4dc8-b570-e6430df3d93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All non-empty combinations of the features\n",
    "feature_names = X_full.columns.tolist()\n",
    "combinations = []\n",
    "for r in range(1, len(feature_names) + 1):\n",
    "    combinations.extend(itertools.combinations(feature_names, r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "574e9024-fbef-4c75-9bec-9652591651c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3ad39f4-c1da-4f36-ac8c-14190bf3dfe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cross-validation setup\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a819a2c6-033a-4a63-92b1-d97c10ff6967",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BIC calculation helper\n",
    "def compute_bic(n, mse, k):\n",
    "    return n * np.log(mse) + k * np.log(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "effbc2b4-197a-4a51-9643-154a60417dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try all combinations\n",
    "for combo in combinations:\n",
    "    X = X_full[list(combo)]\n",
    "    bic_scores = []\n",
    "    coefs = []\n",
    "    \n",
    "    for train_idx, test_idx in kf.split(X):\n",
    "        X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
    "        y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]\n",
    "        \n",
    "        model = LinearRegression()\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        mse = mean_squared_error(y_test, y_pred)\n",
    "        \n",
    "        n = len(y_test)\n",
    "        k = X.shape[1] + 1  # Number of parameters (coefficients + intercept)\n",
    "        bic = compute_bic(n, mse, k)\n",
    "        bic_scores.append(bic)\n",
    "        coefs.append(model.coef_)\n",
    "    \n",
    "    avg_bic = np.mean(bic_scores)\n",
    "    avg_coef = np.mean(coefs, axis=0)\n",
    "    \n",
    "    results.append({\n",
    "        \"Features\": combo,\n",
    "        \"BIC\": avg_bic,\n",
    "        \"Coefficients\": avg_coef\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22f12490-8a71-4d5f-abc9-e739045e63df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                Features        BIC                                                                           Coefficients\n",
      "                                     petal length (cm), petal width (cm) -79.966119                                              [0.17796320237066632, 0.6310854591966504]\n",
      "                                                        petal width (cm) -79.935376                                                                    [1.028084679087288]\n",
      "                                      sepal width (cm), petal width (cm) -78.272210                                              [-0.16548923827800194, 0.993352500599156]\n",
      "                  sepal length (cm), petal length (cm), petal width (cm) -78.159707                        [-0.13614298020063995, 0.25187698514269174, 0.5870393766101574]\n",
      "                   sepal width (cm), petal length (cm), petal width (cm) -77.208867                         [-0.11236946402530915, 0.14877065541738183, 0.672522147599077]\n",
      "                                     sepal length (cm), petal width (cm) -76.449874                                            [0.0004422302955151648, 1.0276993666680874]\n",
      "                   sepal length (cm), sepal width (cm), petal width (cm) -74.986676                        [0.05489849537791543, -0.18729534023952985, 0.9400712879485124]\n",
      "                                                       petal length (cm) -74.609844                                                                   [0.4404275685811787]\n",
      "sepal length (cm), sepal width (cm), petal length (cm), petal width (cm) -74.050455 [-0.11132652022667129, -0.039417808876468105, 0.22805675504036355, 0.6096716323413338]\n",
      "                                    sepal length (cm), petal length (cm) -73.855114                                             [-0.18379890365847038, 0.5154692946597005]\n",
      "                                     sepal width (cm), petal length (cm) -71.011363                                            [-0.04516426811005535, 0.43549330263873876]\n",
      "                  sepal length (cm), sepal width (cm), petal length (cm) -70.426945                         [-0.2369466563239726, 0.09625805574290999, 0.5472156556436609]\n",
      "                                     sepal length (cm), sepal width (cm) -41.087834                                               [0.7352514155349378, -0.635832492759423]\n",
      "                                                       sepal length (cm) -33.713489                                                                   [0.7745557259258307]\n",
      "                                                        sepal width (cm) -11.311518                                                                  [-0.7970243145470655]\n"
     ]
    }
   ],
   "source": [
    "# Convert results to DataFrame and sort by BIC\n",
    "df_results = pd.DataFrame(results)\n",
    "df_results[\"Features\"] = df_results[\"Features\"].apply(lambda x: \", \".join(x))\n",
    "df_results = df_results.sort_values(by=\"BIC\")\n",
    "\n",
    "# Show results\n",
    "print(df_results.to_string(index=False))# Find and print the best model based on BIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "937e43b6-9d9a-40e3-b48e-2af781e0488c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best Model Based on BIC:\n",
      "Features     : petal length (cm), petal width (cm)\n",
      "BIC          : -79.97\n",
      "Coefficients : [0.1779632  0.63108546]\n"
     ]
    }
   ],
   "source": [
    "# Find and print the best model based on BIC\n",
    "best_model = df_results.iloc[0]\n",
    "\n",
    "print(\"\\nBest Model Based on BIC:\")\n",
    "print(f\"Features     : {best_model['Features']}\")\n",
    "print(f\"BIC          : {best_model['BIC']:.2f}\")\n",
    "print(f\"Coefficients : {best_model['Coefficients']}\")"
   ]
  }
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