35_evaluating_classification.ipynb

{
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     "slide_type": "slide"
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   "source": [
    "# ICS 434: DATA SCIENCE FUNDAMENTALS\n",
    "\n",
    "## Performance Evaluation: Classification \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data Set\n",
    "\n",
    "* This Session is based on Chapter 3 of [\"Hands-On Machine Learning with Scikit-Learn and TensorFlow\"](http://shop.oreilly.com/product/0636920052289.do)\n",
    "\n",
    "* We will use the MNIST dataset \n",
    "\n",
    "* Image of (28*28) pixels representing digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pixel_0</th>\n",
       "      <th>Pixel_1</th>\n",
       "      <th>Pixel_2</th>\n",
       "      <th>Pixel_3</th>\n",
       "      <th>Pixel_4</th>\n",
       "      <th>Pixel_5</th>\n",
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       "      <th>Pixel_8</th>\n",
       "      <th>Pixel_9</th>\n",
       "      <th>...</th>\n",
       "      <th>Pixel_775</th>\n",
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       "      <th>Pixel_777</th>\n",
       "      <th>Pixel_778</th>\n",
       "      <th>Pixel_779</th>\n",
       "      <th>Pixel_780</th>\n",
       "      <th>Pixel_781</th>\n",
       "      <th>Pixel_782</th>\n",
       "      <th>Pixel_783</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
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       "   Pixel_0  Pixel_1  Pixel_2  Pixel_3  Pixel_4  Pixel_5  Pixel_6  Pixel_7   \n",
       "0        0        0        0        0        0        0        0        0  \\\n",
       "1        0        0        0        0        0        0        0        0   \n",
       "2        0        0        0        0        0        0        0        0   \n",
       "3        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   Pixel_8  Pixel_9  ...  Pixel_775  Pixel_776  Pixel_777  Pixel_778   \n",
       "0        0        0  ...          0          0          0          0  \\\n",
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       "\n",
       "   Pixel_779  Pixel_780  Pixel_781  Pixel_782  Pixel_783  target  \n",
       "0          0          0          0          0          0       0  \n",
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       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/mnist.tsv\", index_col=[0], sep=\"\\t\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 784)\n",
      "(70000,)\n"
     ]
    }
   ],
   "source": [
    "X, y = data.iloc[:, 0:-1], data['target']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Splitting the Data Into Training and Testing\n",
    "\n",
    "* The data we loaded contains both training and testing chunks\n",
    "\n",
    " * The training is contained in the first 60k instances\n",
    "\n",
    " * The test set is contained in the remaining 10k instances\n",
    " \n",
    "* The data is oreder by class (all 0s first, then all 1s, etc...)\n",
    "  * we need to shuflle it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pixel_0     0\n",
       "Pixel_1     0\n",
       "Pixel_2     0\n",
       "Pixel_3     0\n",
       "Pixel_4     0\n",
       "Pixel_5     0\n",
       "Pixel_6     0\n",
       "Pixel_7     0\n",
       "Pixel_8     0\n",
       "Pixel_9     0\n",
       "Pixel_10    0\n",
       "Pixel_11    0\n",
       "Pixel_12    0\n",
       "Pixel_13    0\n",
       "Pixel_14    0\n",
       "Pixel_15    0\n",
       "Pixel_16    0\n",
       "Pixel_17    0\n",
       "Pixel_18    0\n",
       "Pixel_19    0\n",
       "Name: 36000, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[36000].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit = X.iloc[36000]\n",
    "y[36000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 4, 2, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "shuffle_index = np.random.permutation(60000)\n",
    "\n",
    "X_train, y_train = X_train.iloc[shuffle_index], y_train.iloc[shuffle_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Binary Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30409    4\n",
       "19641    3\n",
       "6637     1\n",
       "44331    7\n",
       "44895    7\n",
       "        ..\n",
       "42140    7\n",
       "20101    3\n",
       "14118    2\n",
       "11581    1\n",
       "21516    3\n",
       "Name: target, Length: 60000, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Classifying the Digits Data\n",
    "\n",
    "* We are going to train a simple Stochastic Gradient Descent Classifier\n",
    "\n",
    "  * We will only focus on the digit 5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=25)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_cls = KNeighborsClassifier(n_neighbors=25)\n",
    "\n",
    "knn_cls.fit(X_train.to_numpy(), y_train_5.to_numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Testing on a Single Value\n",
    "\n",
    "* We test it on the digit (some_digit), which we know represents 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31986    5\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.iloc[[31986]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pixel_0      0\n",
       "Pixel_1      0\n",
       "Pixel_2      0\n",
       "Pixel_3      0\n",
       "Pixel_4      0\n",
       "            ..\n",
       "Pixel_779    0\n",
       "Pixel_780    0\n",
       "Pixel_781    0\n",
       "Pixel_782    0\n",
       "Pixel_783    0\n",
       "Name: 31986, Length: 784, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[31986]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ True]),\n",
       " 31986    5\n",
       " Name: target, dtype: int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cls.predict(X.iloc[[31986]].to_numpy()), "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Predicting the Accuracy\n",
    "\n",
    "\n",
    "* We use cross-validation to predict the accuracy\n",
    "\n",
    "\n",
    "* Number of correct predictions of the digit 5\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = knn_cls.predict(X_train.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30409    False\n",
       "19641    False\n",
       "6637     False\n",
       "44331    False\n",
       "44895    False\n",
       "         ...  \n",
       "42140    False\n",
       "20101    False\n",
       "14118    False\n",
       "11581    False\n",
       "21516    False\n",
       "Name: target, Length: 60000, dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9930666666666667"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_train_pred == y_train_5)/len(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Are the Results Good?\n",
    "\n",
    "\n",
    "*  The score are pretty good, but that does not mean that our classifier does well\n",
    "\n",
    "\n",
    "*  There are only 5421 instance that have 5 as a label\n",
    "\n",
    "\n",
    "*  If we predict all values to be non-5, our accuracy will be  $ (60000 - 5421.0) / 60000 = 0.90965$\n",
    "\n",
    "\n",
    "* This is not a good indicator of accuracy with this data set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Confusion Matrix\n",
    "\n",
    "* A much better way to evaluate the data is using a confusion matrix\n",
    " * Number of times class A  is classified as class B\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5421"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54452,   127],\n",
       "       [  289,  5132]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"https://www.dropbox.com/scl/fi/uelzg9027uhpjsjc6u8gb/confusion_matrix.png?rlkey=ftpzdtx1itd2xgvldg5iik6fl&dl=1\" alt=\"drawing\" style=\"width:600px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Precision\n",
    "\n",
    "* Note that for a perfect predictor, the off-diagonal values will be 0\n",
    "\n",
    "* An important metric that can be extracted from the confusion matrix is the Precision \n",
    "$$\n",
    "\\text{Precision} = \\frac{TP}{TP+FP}\n",
    "$$\n",
    "\n",
    "* The `%` of correct predictions of the positive class\n",
    "  * Among all the instances we predicted as 5, how many were actually 5s?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9758509222285605"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Recall \n",
    "\n",
    "\n",
    "* Another important metric that can be extracted from the confusion matrix is the Recall \n",
    "$$\n",
    "\\text{Recall} = \\frac{TP}{TP+FN}\n",
    "$$\n",
    "\n",
    "\n",
    "* The recall and precision are typically used together\n",
    "\n",
    "* The % of instances of the positive class we are able able to identify\n",
    "\n",
    "  * Among all the instances of the class 5 in our data, how many did were correctly identified?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9466888028039108"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The F1 Score\n",
    "\n",
    "- The F$_1$ score is a convenient metric that combines both Precision and Recall\n",
    "$$ \n",
    "F_1 = 2\\times\\frac{Precision\\times Recall}{Precision + Recall}\n",
    "$$\n",
    "- It's a harmonic mean of the Precision and Recall\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9610486891385767"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(y_train_5, y_train_pred)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Precision/Recall Tradeoff\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "* Scikit has a useful function `precision_recall_curve` to compute how the precisions and recall change with the threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_probs = knn_cls.predict_proba(X_train.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_train_probs[:,1])\n",
    "\n",
    "# precision and recall don't have the same dim as threshold. \n",
    "# 1 and 0 are added for precision and recall respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(8, 6))\n",
    "pyplot.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "pyplot.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "pyplot.xlabel(\"Threshold\", fontsize=16)\n",
    "pyplot.legend(loc=\"upper left\", fontsize=16)\n",
    "pyplot.ylim([0.6, 1])\n",
    "pyplot.xlim([0, 1])\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.9.16"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}