svc_task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "620a3476",
   "metadata": {},
   "source": [
    "# SVC Algorythm Hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a95a2f",
   "metadata": {},
   "source": [
    "**Import Dependencies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "600c5f37",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:41.358151896Z",
     "start_time": "2023-08-16T13:43:41.002310603Z"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbcf15f",
   "metadata": {},
   "source": [
    "**Load and Prepare Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2627e92b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:41.374744179Z",
     "start_time": "2023-08-16T13:43:41.356803673Z"
    }
   },
   "outputs": [],
   "source": [
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45cfdab",
   "metadata": {},
   "source": [
    "**Split Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b58a415",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:41.383879887Z",
     "start_time": "2023-08-16T13:43:41.379108584Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_val, y_train_val, test_size=0.25, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af428d88",
   "metadata": {},
   "source": [
    "**Define Hyperparameters and Grid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31e6587d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:41.398897450Z",
     "start_time": "2023-08-16T13:43:41.384678736Z"
    }
   },
   "outputs": [],
   "source": [
    "C_values = [0.1, 1, 10]\n",
    "kernel_values = [\"linear\", \"poly\", \"rbf\"]\n",
    "\n",
    "param_grid = {\"C\": C_values, \"kernel\": kernel_values}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c284658b",
   "metadata": {},
   "source": [
    "**Create Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b11b184d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:41.399431808Z",
     "start_time": "2023-08-16T13:43:41.388225924Z"
    }
   },
   "outputs": [],
   "source": [
    "model = SVC(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb41056",
   "metadata": {},
   "source": [
    "**Grid Search**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e7b497c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:42.121745552Z",
     "start_time": "2023-08-16T13:43:41.393286092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "GridSearchCV(cv=3, estimator=SVC(random_state=42),\n             param_grid={'C': [0.1, 1, 10],\n                         'kernel': ['linear', 'poly', 'rbf']})",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=SVC(random_state=42),\n             param_grid={&#x27;C&#x27;: [0.1, 1, 10],\n                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=SVC(random_state=42),\n             param_grid={&#x27;C&#x27;: [0.1, 1, 10],\n                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(model, param_grid, cv=3)\n",
    "grid_search.fit(X_train_val, y_train_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed599db",
   "metadata": {},
   "source": [
    "**Best Parameters and Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53d7f675",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:42.125886533Z",
     "start_time": "2023-08-16T13:43:42.120364642Z"
    }
   },
   "outputs": [],
   "source": [
    "best_parameters = grid_search.best_params_\n",
    "best_accuracy = grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb48f75",
   "metadata": {},
   "source": [
    "**Print Results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5dd2fecc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:42.168667552Z",
     "start_time": "2023-08-16T13:43:42.126588931Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters | {'C': 10, 'kernel': 'rbf'}\n",
      "Accuracy | 0.9881697981906751\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters |\", best_parameters)\n",
    "print(\"Accuracy |\", best_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d002a1",
   "metadata": {},
   "source": [
    "**Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f1ebefb",
   "metadata": {
    "scrolled": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T13:43:42.340468952Z",
     "start_time": "2023-08-16T13:43:42.167729369Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = grid_search.cv_results_\n",
    "sorted_indices = results[\"rank_test_score\"].argsort()\n",
    "sorted_hyperparams = [\n",
    "    f\"C: {results['param_C'][i]}, kernel: {results['param_kernel'][i]}\"\n",
    "    for i in sorted_indices\n",
    "]\n",
    "sorted_accuracies = results[\"mean_test_score\"][sorted_indices]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(sorted_hyperparams, sorted_accuracies)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Hyperparameters\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Hyperparameters combination efficiency\")\n",
    "plt.ylim(0.9, 1)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}