05-evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris_dataset = datasets.load_iris()\n",
    "X = iris_dataset.data\n",
    "y = iris_dataset.target\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 方案1：train and test on the same data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn.fit(X, y)\n",
    "\n",
    "y_pred = knn.predict(X)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print(metrics.accuracy_score(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn.fit(X, y)\n",
    "\n",
    "y_pred = knn.predict(X)\n",
    "\n",
    "print(metrics.accuracy_score(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 方案2：train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STEP 1: split X and y into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((105, 4), (105,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45, 4), (45,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>KNeighborsClassifier()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# STEP 2: train the model on the training set\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "# STEP 3: evaluate the model on the testing set\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# compare actual response values (y_test) with predicted response values (y_pred)\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "y_pred = knn.predict(X_test)\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 方案3：cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
      " 0.93333333 1.         1.         1.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666668\n"
     ]
    }
   ],
   "source": [
    "# use average accuracy as an estimate of out-of-sample accuracy\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 为K近邻模型选择一个最优的K值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.96, 0.9533333333333334, 0.9666666666666666, 0.9666666666666666, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9666666666666668, 0.9666666666666668, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9733333333333334, 0.9800000000000001, 0.9733333333333334, 0.9800000000000001, 0.9666666666666666, 0.9666666666666666, 0.9733333333333334, 0.96, 0.9666666666666666, 0.96, 0.9666666666666666, 0.9533333333333334, 0.9533333333333334, 0.9533333333333334]\n"
     ]
    }
   ],
   "source": [
    "k_range = list(range(1, 31))\n",
    "k_scores = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n",
    "    k_scores.append(scores.mean())\n",
    "print(k_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\n",
    "plt.plot(k_range, k_scores)\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Cross-Validated Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}