metric_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Metric Model"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7218a36262abd069"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Import Dependencies**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "3bd2d9fe06fd2d7a"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:42.877062848Z",
     "start_time": "2023-08-16T16:33:42.616864973Z"
    }
   },
   "id": "f95efe22a4dcff81"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Preprocessing data function**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f55f792e0a67efd7"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def preprocess_data(url, column_names):\n",
    "    data = pd.read_csv(url, names=column_names)\n",
    "    data[\"Volume\"] = (\n",
    "        (4 / 3) * np.pi * (data[\"Length\"] * data[\"Diameter\"] * data[\"Height\"])\n",
    "    )\n",
    "    label_encoder = LabelEncoder()\n",
    "    data[\"Sex\"] = label_encoder.fit_transform(data[\"Sex\"])\n",
    "    X = data.drop([\"Rings\"], axis=1)\n",
    "    y_age = data[\"Rings\"]\n",
    "    y_sex = data[\"Sex\"]\n",
    "    return X, y_age, y_sex"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:42.917393024Z",
     "start_time": "2023-08-16T16:33:42.916679451Z"
    }
   },
   "id": "ee4e67ddf5e64395"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Scaling and splitting data function**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7e5b611f1c51473f"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def split_data(X, y_age, y_sex):\n",
    "    X_scaled = StandardScaler().fit_transform(X)\n",
    "    (\n",
    "        X_train,\n",
    "        X_test,\n",
    "        y_age_train,\n",
    "        y_age_test,\n",
    "        y_sex_train,\n",
    "        y_sex_test,\n",
    "    ) = train_test_split(X_scaled, y_age, y_sex, test_size=0.2, random_state=42)\n",
    "    X_train, X_val, y_age_train, y_age_val, y_sex_train, y_sex_val = train_test_split(\n",
    "        X_train, y_age_train, y_sex_train, test_size=0.2, random_state=42\n",
    "    )\n",
    "    return (\n",
    "        X_train,\n",
    "        X_val,\n",
    "        X_test,\n",
    "        y_age_train,\n",
    "        y_age_val,\n",
    "        y_age_test,\n",
    "        y_sex_train,\n",
    "        y_sex_val,\n",
    "        y_sex_test,\n",
    "    )"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:42.917923193Z",
     "start_time": "2023-08-16T16:33:42.917194464Z"
    }
   },
   "id": "842fe15036208cde"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Model training function**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9fe6ee7b130981f7"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def train_model(X_train, y_train, param_grid):\n",
    "    model = KNeighborsClassifier()\n",
    "    grid_search = GridSearchCV(model, param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    best_params = grid_search.best_params_\n",
    "    best_model = KNeighborsClassifier(**best_params)\n",
    "    best_model.fit(X_train, y_train)\n",
    "    return best_model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:42.918236642Z",
     "start_time": "2023-08-16T16:33:42.917334217Z"
    }
   },
   "id": "99b092dddfce8cd"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Evaluation function**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d78a9f70ee8c3182"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def evaluate_model(model, X_val, y_val):\n",
    "    y_val_pred = model.predict(X_val)\n",
    "    return accuracy_score(y_val, y_val_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:42.919498752Z",
     "start_time": "2023-08-16T16:33:42.917542206Z"
    }
   },
   "id": "9bd2be778c6d6365"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Load and preprocess the data**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6a41b908d1226a69"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n",
    "column_names = [\n",
    "    \"Sex\",\n",
    "    \"Length\",\n",
    "    \"Diameter\",\n",
    "    \"Height\",\n",
    "    \"Whole weight\",\n",
    "    \"Shucked weight\",\n",
    "    \"Viscera weight\",\n",
    "    \"Shell weight\",\n",
    "    \"Rings\",\n",
    "]\n",
    "X, y_age, y_sex = preprocess_data(url, column_names)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:45.966273422Z",
     "start_time": "2023-08-16T16:33:42.917672461Z"
    }
   },
   "id": "99f9ba492c66b3b8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Split the data into train, validation and test sets**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8ee9698161c0bd7c"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "(\n",
    "    X_train,\n",
    "    X_val,\n",
    "    X_test,\n",
    "    y_age_train,\n",
    "    y_age_val,\n",
    "    y_age_test,\n",
    "    y_sex_train,\n",
    "    y_sex_val,\n",
    "    y_sex_test,\n",
    ") = split_data(X, y_age, y_sex)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:45.967921267Z",
     "start_time": "2023-08-16T16:33:45.958046764Z"
    }
   },
   "id": "aab7adf8095a148f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Train and evaluate age classification model**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b75fdc88959a525d"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chtholine/PycharmProjects/medvedenko_ml_tasks/venv/lib64/python3.11/site-packages/sklearn/model_selection/_split.py:725: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Age Classification (Best Model): 0.242152466367713\n",
      "Accuracy for Age Classification (Best Model. Test): 0.24401913875598086\n"
     ]
    }
   ],
   "source": [
    "age_param_grid = {\"n_neighbors\": [3, 5, 7, 9]}\n",
    "age_model = train_model(X_train, y_age_train, age_param_grid)\n",
    "age_accuracy = evaluate_model(age_model, X_val, y_age_val)\n",
    "age_accuracy_test = evaluate_model(age_model, X_test, y_age_test)\n",
    "print(f\"Accuracy for Age Classification (Best Model): {age_accuracy}\")\n",
    "print(f\"Accuracy for Age Classification (Best Model. Test): {age_accuracy_test}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:46.531604222Z",
     "start_time": "2023-08-16T16:33:45.970998494Z"
    }
   },
   "id": "d0a57b49f4a07839"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Train and evaluate sex classification model**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1ba87978664301cc"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Sex Classification (Best Model): 0.9985052316890882\n",
      "Accuracy for Sex Classification (Best Model. Test): 1.0\n"
     ]
    }
   ],
   "source": [
    "sex_param_grid = {\"n_neighbors\": [3, 5, 7, 9]}\n",
    "sex_model = train_model(X_train, y_sex_train, sex_param_grid)\n",
    "sex_accuracy = evaluate_model(sex_model, X_val, y_sex_val)\n",
    "sex_accuracy_test = evaluate_model(sex_model, X_test, y_sex_test)\n",
    "print(f\"Accuracy for Sex Classification (Best Model): {sex_accuracy}\")\n",
    "print(f\"Accuracy for Sex Classification (Best Model. Test): {sex_accuracy_test}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:47.045318354Z",
     "start_time": "2023-08-16T16:33:46.531039552Z"
    }
   },
   "id": "e6c53260aaadf068"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Visualization**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5159cc707d43d6fa"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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17NjxmC7pffr08e0/0mmnnXbMe8THxx8zz8PxPqdnz57HtJ/3c3Jzc5tcuyStWbNGLpdL/fr10759+3zbBw4cqNWrV+uGG27we/8jAzWvnj176quvvvK93rdvn0pLS31zMxzNOynnueeeq0svvVSLFy/Wc889p3PPPVcXXXSRrrrqKssntAcAoDHcbrfefPNNDRkyxC/gGDhwoP785z9rw4YNysnJ8TvneKvynX766X6/DCoqKtLixYu1Zs2aYyazLi0tbbAe77/XvXv39tseHh6u7t27H/Nc0rlz52OGAsbHx2vbtm2+1/v27dOuXbtO+u/68Xifj070y8wjtdTzTkRExDHTGcTHxx8z12VzHP317NChg1JTU/3aetSoUVq6dKnWrl2rUaNGaffu3fryyy81c+bMRn3GlVdeqSuvvFJlZWX6/PPP9dprr+kf//iHbr/9dv3jH/9QREREk79O9913n959911t3bpVCxYsOOlUGUBrQCgFtAPfffedSktLjxtOeEVGRmrlypX6+OOP9f7772vdunVas2aNXnrpJf35z3+W3W4/6ec0ZR6oU+V2uxtVUyA09DnmUZOiB4t3Itajf3votX///pP+Bu9oHo9HycnJmj9//nH3ex8KDcPQokWLtGnTJr333ntat26dpkyZomXLlumll15q9G9VAQCwykcffaS8vDy9+eabevPNN4/Zv3r16mNCqcb49a9/rY0bN2rcuHHq16+foqOj5fF4dMsttwT0maExzz8ej0fp6ekNLkTSuXPnBs/1hmNHT+R+qhqaU6uhHvrBes5rSFpams466yy98cYbGjVqlN544w05HA5dfvnlTXqfmJgYDRs2TMOGDZPD4dDrr7+uzz//XOeee26Tv05bt271BVWB/voAViGUAtqBv//975J00gcsm82moUOHaujQoXrggQe0ZMkS/f73v9fHH3+s7OzsE07Q2RxH9vKR6kKeffv2+U1Q2VCPpNzcXL/gpSm1de3aVRs2bFBZWZlfb6ndu3f79gdC165dtW3bNnk8Hr/fHno/p0uXLk1+T+/y1T/72c/0gx/8wG+fx+PR5MmTtXr1at15552+9//mm2+OeZ+j275Hjx7asGGDBg8e3KhwMTMzU5mZmbr77ru1evVq3XPPPVqzZo1+/OMfN/maAAAIptWrVys5OVnTpk07Zt8777yjd955RzNnzvT79/DofzelugnBvc8MxcXF2rBhg+666y5NmDDB75iT8f57vXv3br9nm5qaGh04cEDZ2dmNvjavHj166Ouvv9bQoUOb/PzWq1cv9erVS//6179UXl5+0l84NfZ5xztVxNG9xk6lh3xzn0337dvnGyIn1fUKy8vL0/nnn+933KhRozRv3jwdPnxY//jHP3TBBRcoPj6+2fX2799fr7/+uvLy8iQ17etUUVGhBx54QGlpacrKytIzzzyjiy666LgT8wOtCXNKAW3chg0b9Ic//EHdunXT1Vdf3eBxRUVFx2zr16+fJPmWpI2KipKkkw5ba6y//e1vfuP033777WMeCLp3767PP//cb1nc9957T99++63fezWltvPPP19ut1srV6702/7cc8/JMIxjHkia6/zzz1deXp7WrFnj21ZbW6sVK1YoOjr6mFCpMby9pG655RZddtllfn+uuOIK3yp8Ut1wyvT0dN+KQV7//e9/j/nt2uWXXy63233c1Xhqa2t97VpcXHzMb3uPvk8AAAhVVVVV+n//7//pggsuOObf0csuu0w33HCDysvL9e677/qdt3btWh06dMj3evPmzfr88899zwwN9ep5/vnnT1pTdna2HA6HVqxY4fdv7CuvvKLS0lLfinpNcfnll+vQoUN6+eWXj9lXVVWlioqKE54/ceJEFRUVaerUqaqtrT1m/wcffKD33ntPUuOfd7p27Sq73X7MHFMvvvhik6/Pq7nPpi+99JLf3Jgvvviiamtrj3kGvPLKK2UYhn73u99p//79J3yW9qqsrNTGjRuPu887b2mvXr0kNe3rNH/+fH377beaN2+e7r//fnXt2lX3338/z19o9egpBbQh//nPf7R792653W7l5+fr448/1ocffqguXbro6aefVkRERIPnPvXUU/r00081fPhwde3aVU6nUy+88II6d+6ss88+W1Ldb3Pi4uK0atUqdejQQdHR0Ro4cGCTh4p5xcfH6/rrr9eYMWPkdDr1/PPPq2fPnvrJT37iO+bHP/6x/vnPf+qWW27R5Zdfrm+++UarV68+ZihiU2q78MILNWTIEP3+97/XwYMHlZGRoQ8//FD/+te/dPPNN59wmGNTjB07Vi+99JLuv/9+ffnll+ratav++c9/6n//+5+mTJnSqGWWj7Z69Wr169fvuPNcSXXXNmvWLH355Zc666yzdPfdd+vOO+/UddddpzFjxqikpEQrV65Uenq6X1B17rnnauzYsfrjH/+orVu3+rqY7927V2+//bYefPBBXXbZZXr99df14osv6qKLLlKPHj1UXl6ul19+WTExMQEL8wAAaCnvvvuuysvLdeGFFx53f2ZmppKSkvTGG2/oiiuu8G3v0aOHrrvuOl133XWqqanR8uXLlZCQoFtuuUVS3RCtH/zgB3rmmWfkcrnUqVMnffjhh42alDspKUm33XabFi9erFtuuUUXXnih9uzZoxdeeEEDBgxoVBBytGuuuUZvvfWWpk+fro8//liDBw+W2+3W7t279fbbb+uZZ57RgAEDGjz/iiuu0LZt27RkyRJ99dVXuvLKK9WlSxcVFRVp3bp12rBhgxYsWCCp8c87sbGxuuyyy/SXv/xFhmGoe/fuev/99084v9XJREZGKi0tTW+99ZZOP/10JSQk6IwzzjhmQZejuVwu/fznP9fll1/ua+uzzz5bP/rRj/yOS0pK0nnnnae3335bcXFxuuCCC05aU2VlpX76058qMzNT5513njp37qzS0lKtXbtWn376qS666CKdeeaZkhr/ddqwYYNeeOEFTZgwQWeddZYkae7cubrxxhu1cOFCTZ48uXkNCIQAQimgDVm0aJGkuhVPEhISlJ6erilTpmjMmDEnDUAuvPBCHTx4UK+++qoKCwuVmJioc889V3fddZdvxTWHw6F58+bp8ccf14wZM1RbW6u5c+c2O5S6/fbbtW3bNv3pT39SeXm5hg4dqunTp/t+6yVJ5513nu6//34tW7ZMc+bMUf/+/bVkyRI98sgjfu/VlNpsNpuefvppLVq0SGvWrNFrr72mrl27avLkyfrlL3/ZrGs5nsjISK1YsULz58/X66+/rrKyMvXq1Utz585t0iqFXl9++aV2796tO++8s8FjRowYoVmzZumNN97QWWedpQsvvFCPP/64nnzySS1YsECnn3665s6dq7/97W/asWOH37kPP/yw+vfvr1WrVun3v/+97Ha7unbtqquvvlqDBw+WVBdeffHFF1qzZo3y8/MVGxurgQMHav78+c2+DwAACJY33nhDERERGjZs2HH322w2XXDBBVq9erUKCwt920eNGiWbzabnn39eTqdTAwcO1EMPPaSOHTv6jlmwYIFmzZqlF154QaZpatiwYVq6dKnOO++8k9Z11113KSkpSX/5y180d+5cxcfH6yc/+Yl+85vf+K0S11g2m01PPfWUnnvuOf3973/XO++8o6ioKHXr1k033nijr6fOidx999364Q9/qBUrVujFF19UcXGx4uLiNGjQIP3hD3/wBThNed7x9rxatWqVwsPDddlll2ny5Ml+K/c11ezZszVr1izNnTtXLpdLEyZMOGkoNW3aNK1evVqLFi2Sy+XSyJEjNXXq1OMOobvmmmv03nvv6fLLL2/Uoi5xcXGaPXu23n//fb322mvKy8uT3W5Xr169NHnyZL/VnxvzdSorK9ODDz6oM888U7fffrvv3HPOOUc33XSTli1bpksuuUSZmZmNbzQghBimVTP1AgAsc8011ygpKUnLli2zuhQAAICQtXbtWv3qV7/SypUrdc4551hdDtDmMKcUALRhLpfrmLkgPv74Y3399dc699xzLaoKAACgdfjrX/+q7t27+6azABBYDN8DgDbs0KFD+sUvfqGrr75aHTt21O7du7Vq1Sqlpqbqpz/9qdXlAQAAhKQ333xT27Zt0/vvv68HH3ww4KtQA6hDKAUAbVh8fLzOOuss/fWvf1VBQYGio6M1fPhw3XPPPUpMTLS6PAAAgJD0m9/8RtHR0fq///s/XX/99VaXA7RZzCkFAAAAAACAoGNOKQAAgFbgk08+0e23366cnBxlZGRo7dq1xxyza9cu3X777Tr77LOVmZmpa6+9Vrm5ub791dXVmjlzpoYMGaKsrCzdddddys/P93uP3NxcjR8/XoMGDdLQoUP1yCOPHDM3HQAAQCAQSgEAALQCFRUVysjI0PTp04+7/5tvvtH111+v3r17a8WKFXrjjTd05513KiIiwnfMnDlz9N5772nhwoVasWKFDh8+rAkTJvj2u91u3XbbbXK5XFq1apXmzZun119/XYsWLWrx6wMAAO0Pw/cAAABamYyMDD311FO66KKLfNvuvvtuhYWF6bHHHjvuOaWlpRo6dKjmz5+vyy67TFJdz6orrrhCL730kjIzM/Xvf/9bt99+u9atW6eUlBRJ0osvvqj58+drw4YNCg8Pb/mLAwAA7QY9pQAAAFo5j8ej999/X6effrrGjRunoUOH6sc//rHfEL8tW7bI5XIpOzvbt61Pnz7q0qWLNm3aJEnatGmT0tPTfYGUJOXk5KisrEw7d+4M2vUAAID2gdX3juB0lurIfmOGISUnxx6zHc1DewYW7RlYtGfg0JaBRXsGVkPt6d3eWjmdTlVUVGjp0qX69a9/rXvuuUfr1q3ThAkTtHz5cp177rnKz8+Xw+FQXFyc37nJycnKy8uTJOXn5/sFUpJ8r73HNFZBAfdsSzMMKSkplrZGyOHeRCjj/gwObzufDKHUEUxTx70pG9qO5qE9A4v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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 1x2 grid for subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Visualize the distribution of ages\n",
    "sns.histplot(y_age, bins=30, kde=True, ax=axes[0])\n",
    "axes[0].set_title(\"Distribution of Ages\")\n",
    "axes[0].set_xlabel(\"Age\")\n",
    "axes[0].set_ylabel(\"Count\")\n",
    "\n",
    "# Visualize the number of abalones by sex\n",
    "data = pd.read_csv(url, names=column_names)\n",
    "sex_counts = data[\"Sex\"].value_counts()\n",
    "axes[1].bar(sex_counts.index, sex_counts.values)\n",
    "axes[1].set_xlabel(\"Sex\")\n",
    "axes[1].set_ylabel(\"Count\")\n",
    "axes[1].set_title(\"Abalone Count by Sex\")\n",
    "axes[1].set_xticks(range(len(sex_counts.index)))\n",
    "axes[1].set_xticklabels(sex_counts.index)\n",
    "\n",
    "# Adjust layout and display the subplots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T16:33:49.800392548Z",
     "start_time": "2023-08-16T16:33:47.048458019Z"
    }
   },
   "id": "ea2a814158b9e153"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}