notebooks/classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning (ML) Classification\n",
    "\n",
    "The most common supervised learning tasks are\n",
    "1. *regression* (predicting values) and\n",
    "2. *classification* (predicting classes).\n",
    "\n",
    "This notebook focuses on building classification models that operate on the [MNIST database](https://en.wikipedia.org/wiki/MNIST_database). The MNIST database consists of handwritten Arabic numerals (i.e. the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9). To learn the basics, we will initially build a *binary* classifier that decides whether or not an instance represents a 5. Once we gain some initial understanding, we will train a classifier to decide which digit an instance most likely represents.\n",
    "\n",
    "As with most data science notebooks, we begin with a preamble of the most essential modules as well as our default plot settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 12)\n",
    "import sklearn\n",
    "\n",
    "assert sklearn.__version__ >= \"1.5.1\"\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.rc(\"figure\", figsize=(7, 5))\n",
    "plt.rc(\"font\", family=\"serif\")\n",
    "plt.rc(\"mathtext\", fontset=\"dejavuserif\")\n",
    "plt.rc(\"axes\", titlesize=24)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating images tells the story of our exploration, so saving them is essential. To avoid repetition, we initially write a workhorse function to save our figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_DIR = Path.cwd().parents[0] / \"img\"\n",
    "IMG_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMG_DIR / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [The MNIST Database](https://en.wikipedia.org/wiki/MNIST_database)\n",
    "The MNIST (Modified National Institute of Standards and Technology) database (sometimes referred to as the MNIST dataset) is a set of 70k small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the \"hello world\" of machine learning. Whenever a new classification algorithm is engineered, applying it to the MNIST dataset is generally one of the first measures of its effectiveness.\n",
    "\n",
    "Scikit-Learn provides many helper functions to download popular datasets. MNIST is one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 1m runtime\n",
    "from sklearn.datasets import fetch_openml\n",
    "import pickle  # Using a local dataset saves considerable time over many runs.\n",
    "\n",
    "use_cached = True\n",
    "if use_cached:\n",
    "    filename = \"mnist-dataset.pkl\"\n",
    "    inpath = Path.cwd() / filename\n",
    "    with inpath.open(\"rb\") as infile:\n",
    "        mnist = pickle.load(infile)\n",
    "if not use_cached:\n",
    "    mnist = fetch_openml(\"mnist_784\", version=1)\n",
    "    cache_dataset = True\n",
    "    if cache_dataset:\n",
    "        filename = \"mnist-dataset.pkl\"\n",
    "        outpath = Path.cwd() / filename\n",
    "        with outpath.open(\"wb\") as outfile:\n",
    "            pickle.dump(mnist, outfile, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "mnist.keys()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets loaded by Scikit-Learn generally have a similar dictionary structure, including a\n",
    "- `DESCR` key describing the dataset,\n",
    "- `data` key containing an array with one row per instance and one column per feature, and a\n",
    "- `target` key containing an array with the labels.\n",
    "Let's look at these arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:(70000, 784)\n",
      "y shape:(70000,)\n"
     ]
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "print(f\"X shape:{X.shape}\\ny shape:{y.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows and columns in a dataset are referred to as *instances* and *features* in machine learning. In the MNIST dataset, there are 70k instances representing images, and each image *instance* has 784 *features* because each image is 28x28 pixels (note that 28^2=784).\n",
    "\n",
    "For each image, each feature represents a single pixel's intensity from 0 (white) to 255 (black). Translating a single instance of this numerical data into actual images is the first thing we're going to do. We'll accomplish this by selecting an instance's feature vector (i.e. selecting a row of data), reshaping it to a 28x28 Numpy array, and then displaying it using Matplotlib's `pyplot.imshow()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(\n",
    "    image_data: pd.Series, plot_name: str = \"some-digit\", save: bool = False\n",
    ") -> None:\n",
    "    # Reshape into a 28x28 array (think of it as a matrix from linear algebra).\n",
    "    image = image_data.values.reshape(28, 28)\n",
    "    plt.imshow(image, cmap=\"binary\")\n",
    "    plt.axis(\"off\")\n",
    "    if save:\n",
    "        save_fig(plot_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab the first row of data in the `DataFrame` (at index 0). This returns as a pandas\n",
    "# `Series`.\n",
    "SOME_DIGIT = X.iloc[0]\n",
    "plt.title(\"An Example Digit\", y=0.95, fontsize=24)\n",
    "plot_digit(SOME_DIGIT, plot_name=\"an-example-digit\", save=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like a 5, and indeed that's what the label tells us. We will use `SOME_DIGIT` throughout this notebook, so it's important to remember that it represents a 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the label is a *string*. Most ML algorithms expect numbers, so let's cast `y` as an integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.uint8(5)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.astype(np.uint8)\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a feel for the complexity of the classification task before us, let's generate a 10x10 grid of images from the MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "for i, image_data in X.iterrows():\n",
    "    if i == 100:\n",
    "        break\n",
    "    plt.subplot(10, 10, i + 1)\n",
    "    # Iterate through the 10x10 grid, plotting a digit at each index.\n",
    "    plot_digit(image_data)\n",
    "plt.suptitle(\"100 Digits from the MNIST Dataset\", fontsize=24, y=0.95)\n",
    "save_fig(\"100-digits\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative Distribution of Digits\n",
    "Before we begin, let's see the relative distribution of digits. Intuitively, we may suspect that these digits were sourced from pages dealing with phone numbers or addresses. It's reasonably to conclude suspect that each digit should have around the same frequency of occurrence in the dataset as the others, meaning that each one should show up about 10% of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 3.5))\n",
    "ax = y.hist(ec=\"black\")\n",
    "ax.set_xticks(list(range(0, 10)))\n",
    "ax.set_title(\"Digit Frequency in the MNIST Dataset\")\n",
    "save_fig(\"digit-frequency\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Train/Test Split\n",
    "**In ML, we should *always* create a test set and set it aside before inspecting the data closely.** The MNIST dataset is actually already split into a training set (the first 60k images) and a test set (the last 10k images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_idx = 60000\n",
    "X_train, X_test, y_train, y_test = X[:cut_idx], X[cut_idx:], y[:cut_idx], y[cut_idx:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set is already appropriately shuffled for us. If it wasn't, we'd have to use [*stratified sampling*](https://en.wikipedia.org/wiki/Stratified_sampling) during our shuffling to ensure all cross-validation folds contain a representative sample of each digit. Moreover, some learning algorithms are sensitive to the order of the training instances, and they perform poorly if they get many similar instances in a row. Appropriately shuffling the dataset before the train/test split decreases the risk of poor predictive performance*.\n",
    "\n",
    "*Time series data is a notable exception to this rule."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Binary Classifier\n",
    "We'll begin by building a classifier that attempts to detect whether a digit represents 5 or $\\neg$ 5 ($\\neg$ is the logical negation operator). Since the classifier only has two options for its predictions, it's known as a *binary classifier*. Let's create the *target vectors* for this classification task. For binary classifiers, target vectors are boolean arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = y_train == 5\n",
    "y_test_5 = y_test == 5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must now pick a classification model and train it. A good place to start is with a *Stochastic Gradient Descent* (SGD) classifer, and we can employ Scikit-Learn's `SGDClassifier` class. This classifier operates efficiently for *very large datasets*. Part of this efficiency resides in the fact that SGD trains instances independently, one at a time (which also makes SGD well-suited for [*online learning*](https://en.wikipedia.org/wiki/Online_machine_learning)). Let's create an `SGDClassifier` and train it on the training set (*not* the entire dataset!). **It's vitally important leave the test set untouched until model training is complete.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=42)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SGDClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SGDClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 15s runtime\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train.values, y_train_5.values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're now able to employ our trained classifier to detect images of the of the number 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.predict([SOME_DIGIT])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classifier guesses that this images represents a 5. Since we checked `SOME_DIGIT` manually, we know this to be a correct prediction. Our next step is to evaluate our model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Measures\n",
    "Based on how most people interpret what error in a model means, evaluating error in a classifier is often significantly trickier to explain than evaluating error in a regressor. Just like regressors, there are many performance measures available.\n",
    "\n",
    "## Measuring Accuracy Using Cross-Validation\n",
    "A reliable way to evaluate a model is to use [*cross-validation*](https://en.wikipedia.org/wiki/Cross-validation_(statistics)). This applies to both regressors and classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing Cross-Validation\n",
    "Occasionally, we will need more control over the cross-validation process than what Scikit-Learn provides off the shelf. In these cases, we can implement the cross-validation ourselves. The following code does roughly the same thing as Scikit-Learn's `cross_val_score()` function, and it prints nearly identical results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9669 0.91625 0.96785 "
     ]
    }
   ],
   "source": [
    "# ~ 25s runtime\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    X_train_fold = X_train.iloc[train_index]\n",
    "    y_train_fold = y_train_5.iloc[train_index]\n",
    "    X_test_fold = X_train.iloc[test_index]\n",
    "    y_test_fold = y_train_5.iloc[test_index]\n",
    "\n",
    "    clone_clf.fit(X_train_fold, y_train_fold)\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "    print(n_correct / len(y_pred), end=\" \")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `StratifiedKFold` class performs stratified sampling to produce folds that contain a representative ratio of each class (about 10% each in this case). At each iteration, the code creates a clone of the classifier, trains that clone on the *training* folds, and then makes predictions on the *test* fold. Lastly, it counts the number of correct predictions and outputs the ratio of correct predictions.\n",
    "\n",
    "Let's use the `cross_val_score()` function to evaluate our `SGDClassifier` model and employ [*K-fold cross-validation*](https://machinelearningmastery.com/k-fold-cross-validation/) with three folds. Remember that K-fold cross-validation means splitting the training set into K folds (in this case, three), then making predictions and evaluating them on each fold using a model trained on the K - 1 other folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95035, 0.96035, 0.9604 ])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 20s runtime\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Achieving above a 93% accuracy for the ratio of correct predictions on all cross-validated folds seems compelling, but this performance metric is actually a red herring. This can be demonstrated by examining how a poorly-implemented classifier performs under `cross_val_score()`. Specifically, let's look at one that classifies every single image as $\\neg$ 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91125, 0.90855, 0.90915])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool)\n",
    "\n",
    "\n",
    "never_5_clf = Never5Classifier()\n",
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why do we have over 90% accuracy with this admittedly horribly-designed model? Recall that only about 10% of the images represent a 5, so if we always guess than an image is $\\neg$ 5, we'll be right about 90% of the time. This concept demonstrates why **accuracy is generally *not* the preferred performance measure for classifiers**, especially when dealing with *skewed datasets*. A skewed dataset is one where some classes are much more frequent than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Confusion Matrix\n",
    "A much better way to evaluate the performance of a classifier is to look at its confusion matrix. For a binary classifier, the general idea is to count the number of times instances of class A are classified as class B. If we were to create a classifier that accounted for all digit types, to know the number of times the classifier confused images representing a 5 with 3, we would look in the fifth row and third column of the confusion matrix.\n",
    "\n",
    "To compute the confusion matrix, we need a set of predictions to compare to the target vectors. We *could* make predictions on the test set, but doing so is bad practice. Touching the test set introduces bias. To reiterate, we *only* want to use the test set at the *very end* of our project, once our classifier is trained and potentially ready to launch. Rather than using the test set, we can employ Scikit-Learn's `cross_val_predict()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ~20s runtime\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the `cross_val_score()` function, `cross_val_predict()` performs K-fold cross-validation, but instead of returning the evaluation scores, **it returns the predictions made on each test fold**. This means we get a *clean* prediction for each instance in the training set. Here, clean means the prediction is made by a model that never saw the data during training.\n",
    "\n",
    "Armed with cross-validated predictions, we can now compute the confusion matrix for our classifier using the `confusion_matrix()` function by passing it the target classes (`y_train_5`) and the predicted classes (`y_train_pred`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53892,   687],\n",
       "       [ 1891,  3530]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How should we interpret this? Each row in a confusion matrix represents represents an *actual* class, while each column represents a *predicted* class.\n",
    "\n",
    "|         | $\\neg5$ |   $5$   |\n",
    "|---------|---------|---------|\n",
    "| $\\neg5$ |    $\\mathrm{TN}$   |   $\\mathrm{FP}$    |\n",
    "|   $5$   |    $\\mathrm{FN}$   |   $\\mathrm{TP}$    |\n",
    "\n",
    "**Row 1**\n",
    "\n",
    "The first row of this matrix considers $\\neg$ 5 images (the *negative* class). In the above instance, roughly 53k of the images were correctly classified as $\\neg$ 5, so we call these *true negatives*. The remaining 687 were wrongly classified as 5 (*false positives*).\n",
    "\n",
    "**Row 2**\n",
    "\n",
    "In the second row considers the images representing 5 (the *positive* class). 1891 were wrongly classified as $\\neg$ 5 (*false negatives*) while the remaining 3530 were correctly classified as 5 (*true positives*).\n",
    "\n",
    "A perfect classifier would have only true positives and true negatives, so its confusion matrix would have nonzero values only on its main diagonal (top left to bottom right)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54579,     0],\n",
       "       [    0,  5421]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_perfect_predictions = y_train_5\n",
    "confusion_matrix(y_train_5, y_train_perfect_predictions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision and Recall\n",
    "\n",
    "The confusion matrix gives us a lot of information, but sometimes we may prefer a concise metric. One option is to the look at the accuracy of positive predictions, or the number of correct positive classifications among all positive classifications. This is called the *precision* of a classifier.\n",
    "\n",
    "$$\\mathrm{precision}=\\mathrm{\\frac{TP}{TP+FP}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TP is the number of true positives and FP is the number of false positives.\n",
    "\n",
    "A trivial way to attain perfect precision is to make one single positive prediction and ensure it is correct. To balance out this possibility, precision is usually employed alongside another metric called *recall*, also known as the *sensitivity* or the *true positive rate* (TPR). Recall is the ratio of positive instances that are correctly detected by the classifier and the actual number of positive instances.\n",
    "\n",
    "$$\\mathrm{recall=\\frac{TP}{TP+FN}=sensitivity=TPR}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note the subtle difference here.** Precision is weighted by the number of positives predicted by the classifier, and FP can vary over a considerable range. Recall is weighted by the number of actual positive instances in the data, which is *fixed*.\n",
    "\n",
    "Scikit-Learn provides several functions to compute classifier metrics, including precision and recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision: 0.84\n",
      "recall: 0.65\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "precision = precision_score(y_train_5, y_train_pred)\n",
    "recall = recall_score(y_train_5, y_train_pred)\n",
    "print(f\"precision: {precision:.2f}\\nrecall: {recall:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before, we had an *accuracy* above 90%, but suddenly our 5-detector doesn't look as shiny. From these metrics, we can say\n",
    "1. When the classifier claims an image represents a 5, it is correct only 84% of the time.\n",
    "2. The classifier only detects 65% of the digits representing 5.\n",
    "\n",
    "**It is often convenient to combine precision and recall into a single metric called the $\\mathrm{F_1}$ score**. The $\\mathrm{F_1}$ score is particularly useful as a simple tool to compare two classifiers. The $\\mathrm{F_1}$ score is the *harmonic mean* of precision and recall.\n",
    "\n",
    "$$\\mathrm{F_1=\\frac{2}{\\frac{1}{precision}+\\frac{1}{recall}}=2\\cdot\\frac{precision\\cdot recall}{precision+recall}=\\frac{TP}{TP+\\frac{FN+FP}{2}}}$$\n",
    "\n",
    "Unlike the arithmetic mean which weights all values equally, the harmonic mean gives much more weight to small values. With this type of weighting, a classifier will only achieve a relatively high $\\mathrm{F_1}$ score if both recall and precision are high.\n",
    "\n",
    "In Scikit-Learn, we can compute the $\\mathrm{F_1}$ score of a classifier with the `f1_score()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score: 0.73\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1score = f1_score(y_train_5, y_train_pred)\n",
    "print(f\"f1 score: {f1score:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The $\\mathrm{F_1}$ score favors classifiers that have *similar* precision and recall, but this is not always desirable. In some contexts, we may want to emphasize precision while in others we may more heavily favor recall.\n",
    "\n",
    "#### Favoring Precision: Videos for Kids\n",
    "As an example, if we are training a classifier to detect videos that are safe for kids, we would probably prefer a classifier that rejects many good videos (low recall) but keeps only safe ones (high precision). If this type of classifier had much higher recall but lets a few extremely inappropriate videos show up in a product geared for kids, just a few mistakes would render the product a failure. Given the severity of consequences for failures, even adding a human reviewer to the pipeline to check the classifier's video selection may be appropriate.\n",
    "\n",
    "#### Favoring Recall: Shoplifting\n",
    "In a different instance, let's consider training a classifier to detect shoplifters in surveillance images. For this case, it's probably fine if the classifier has only 30% precision as long as it has a 99% recall. Even though security reviewers will receive false alerts, nearly *all* shoplifters will be caught.\n",
    "\n",
    "In the end though, we can't have our cake and eat it too. Increasing precision reduces recall, and vice versa. This is called the *precision/recall trade-off*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Precision/Recall (PR) Trade-Off\n",
    "To understand this trade-off, let's look at how the `SGDClassifier` makes its classification decisions. For each instance, it computes a score based on a *decision function*. If that score is greater than a *threshold*, it assigns the instance to the positive class, otherwise it assigns it to the negative class. Adjusting this threshold changes the values of TP, FP, TN, and FN, thus adjusting precision and recall.\n",
    "\n",
    "Consider a situation where a larger *decision score* (calculated by the decision function) is further to the right, and we have several digits to be classified. If a digit is further to the right, it is predicted more strongly to be a 5.\n",
    "\n",
    "$8\\qquad7\\qquad3\\qquad9\\qquad5\\qquad2\\qquad5\\qquad5\\qquad6\\qquad5\\qquad5\\qquad5$\n",
    "\n",
    "Let's see how precision and recall adjust as we change where the threshold is located.\n",
    "\n",
    "Below, $\\frac{6}{8}$ digits past the threshold are indeed 5 (75% precision) and every 5 in the dataset was caught (100% recall).\n",
    "\n",
    "$8\\qquad7\\qquad3\\qquad9\\quad|\\quad5\\qquad2\\qquad5\\qquad5\\qquad6\\qquad5\\qquad5\\qquad5$\n",
    "\n",
    "Now we see $\\frac{4}{5}$ digits past the threshold are 5 (80% precision), but only $\\frac{4}{6}$ digits representing 5 were caught (67% recall).\n",
    "\n",
    "$8\\qquad7\\qquad3\\qquad9\\qquad5\\qquad2\\qquad5\\quad|\\quad5\\qquad6\\qquad5\\qquad5\\qquad5$\n",
    "\n",
    "In this instance, $\\frac{3}{3}$ digits past the threshold are 5 (100% precision), but only $\\frac{3}{6}$ were caught (50% recall).\n",
    "\n",
    "$8\\qquad7\\qquad3\\qquad9\\qquad5\\qquad2\\qquad5\\qquad5\\qquad6\\quad|\\quad5\\qquad5\\qquad5$\n",
    "\n",
    "From this example, we can conclude that **the higher the threshold, the lower the recall**. In practical implementation, a higher threshold also indicates higher precision (in the vast majority of cases).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn does not let us set the threshold of decision functions directly, but it *does* give us access to the *decision scores* used to make predictions. Instead of calling the classifier's `predict()` method, we can call its `decision_function()` method to return a score for each instance, and then we can use any threshold we want to enable making predictions based on these scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2164.22030239])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = sgd_clf.decision_function([SOME_DIGIT])\n",
    "y_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold of    0: [ True]\n",
      "threshold of 8000: [False]\n"
     ]
    }
   ],
   "source": [
    "thresholds = [0, 8000]\n",
    "for threshold in thresholds:\n",
    "    y_some_digit_pred = y_scores > threshold\n",
    "    print(f\"threshold of {threshold:>4}: {y_some_digit_pred}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example demonstrates how raising the threshold decreases the recall. We know that `SOME_DIGIT` actually represents a 5. The classifier detects the 5 correctly when the threshold is 0, but completely misses it when the threshold increases to 8000.\n",
    "\n",
    "How should we decide which threshold to use? First, let's use the `cross_val_predict()` function to get the scores of all instances in the training set, but this time let's specify that we want to return *decision scores* rather than predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1200.93051237, -26883.79202424, -33072.03475406, ...,\n",
       "        13272.12718981,  -7258.47203373, -16877.50840447])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~20s runtime\n",
    "y_scores = cross_val_predict(\n",
    "    sgd_clf, X_train, y_train_5, cv=3, method=\"decision_function\"\n",
    ")\n",
    "y_scores\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equipped with these scores, we can use the `precision_recall_curve()` function to compute precision and recall for all possible thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\n",
    "\n",
    "\n",
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds, save=False):\n",
    "    # [:-1] because there are n - 1 thresholds for n precisions/recalls.\n",
    "    plt.plot(thresholds, precisions[:-1], label=\"Precision\")\n",
    "    plt.plot(thresholds, recalls[:-1], label=\"Recall\")\n",
    "    plt.xlabel(\"Threshold\")\n",
    "    plt.xticks(np.linspace(thresholds[0], thresholds[-1], 5))\n",
    "    plt.ylabel(\"Strength\")\n",
    "    plt.title(\"Precision and Recall vs Threshold\")\n",
    "    plt.legend()\n",
    "    if save:\n",
    "        save_fig(\"precision-and-recall-vs-threshold\")\n",
    "\n",
    "\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds, save=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is the precision curve less smooth than the recall curve? Whereas recall *only* goes down when the decision score threshold is increased, precision still retains the possibility to go up and down. Let's again consider the example sequence of numbers we used previously.\n",
    "\n",
    "precision=80%, recall=67%\n",
    "\n",
    "$8\\qquad7\\qquad3\\qquad9\\qquad5\\qquad2\\qquad5\\quad|\\quad5\\qquad6\\qquad5\\qquad5\\qquad5$\n",
    "\n",
    "precision=75%, recall=50%\n",
    "\n",
    "$8\\qquad7\\qquad3\\qquad9\\qquad5\\qquad2\\qquad5\\qquad5\\quad|\\quad6\\qquad5\\qquad5\\qquad5$\n",
    "\n",
    "Notice that precision actually decreases! Another way to evaluate precision versus recall is to plot the two directly against each other, so let's do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pgPXsJrK1atVSysyYMSPNa/7xxx9KWQ8PjxQteUmeT2T379+f6jmbNWumlNu1a1ea18+IRYsWKeerWLFimmX79OkjAIiNGzem2Pf2228r59m8eXOa56lSpYpdE1mTySTu378vNm3apPwyIUmS6Nu3r4iKikr1vM78HomIiBCTJ08W+/btS/McBoNBeHh4CACiUaNGaZZ1ZCK7detW5XxBQUGZOjY7EyKMGzcuzXPb6rWVGQUKFFBiqFmzZo6cMzX2SGRr1KiRark//vhDrFixQsTExAghzM9v8gkd1q5dm+qxFy5cEEDqv15+8cUXFl8y0pKQkCAKFiyolLf2C9Pzkn/pfeGFF9Itn5sxkc3Fkn/YtmnTRgwcOFD5N2DAANGhQwfh5+enlNFoNCl+Yn5e8j8+JUqUyHAsa9euVY7z8fFJ92fHU6dOWcT1/If0W2+9pexv2LBhutd/+vSpKFKkiGjRokWqZdJLLL/66itlf+PGjYXJZEr1XBERESI0NDTNx5ne9Y4ePWpR5uLFi6meSwjzz1TJE6G5c+daLZc8kXVzcxNGozHVcw4ZMkQp+9VXX6V5/YyIjo4Wnp6eyjlTa0l8+PChcHV1Ff7+/lZbJUeNGqWc4+WXX07zmmFhYen+4cuM5MlGev+KFSsm5syZIy5fvpzuedXwHsmIEiVKCMDcip/Wa8uRiey3336boUTHmucT2R49elh8tvbv31+0bt1a+VUGgHB1dRXz5s1L99y2em1lRvLZwtL7MpJd9khkJ02alKmYJk2apBzbqlWrVMslfQZ9/vnnVvdXqFBBOc/s2bPTvW7Xrl2V8unNACeEEJs3b1bKFylSJN3yuRlv9sojrN1NrNVq4evri7p166Jp06YYMmQIypQpk+FzVqpUKcNl9+3bpyy/8MIL0OnSfumVLl1aWZZlGceOHUOrVq2UbXv37lWWk4/RmBoPD49M3+X9vOQ3PRw8eBDNmjXD5MmT0axZsxR3thYqVMhqx//MSP4Yk+4MT4skSahZs6YyiPnevXsxatSoNI8pU6YMtFptqvuTD0n0+PHjjISdJm9vb/Tu3RtLly4FACxZsgQNGjRIUW7lypVISEjAgAEDoNfrU+xPfsyqVasQGxur3OH/vICAgGzHnZoyZcooN34A5lEUQkNDcfz4cQghcPv2baxYsQKdO3dO91xqeY9cvXoVBw4cwIULFxAVFYWEhASL/UlDSyUmJuLRo0fZfh/YQvKbhlxcXLJ1rg0bNqTYptfrkT9/flSvXh0tW7bE4MGDERgYmKnz5uRrKzN8fX2VG1Lj4uJy9NyOkJm/UwDw2muvYfr06ZBlGXv37sWNGzdQsmRJizIGgwE//PADXF1d0b9//xTnCA8PtxgtJSMznSV/P//555/plk/+uk3rJri8gIlsHvHbb79ZTHiQEzIz7uLZs2eV5Rs3bqQ7xqB4biiSa9euKcsGg8FiqKnUhjzJaS+88AKGDBmCxYsXAwAOHDiAFi1aoHjx4ujatSs6deqEJk2aZPsPY5J///1XWc7oYwwKCrJ6fGry5cuX5v7kw/kkH+YnOwYPHqwksj/99BM+//xzeHl5WZRZsmQJAOD111+3eo5u3bqhdevWyp3nmzdvxubNm1GuXDl07doVHTt2RP369dNM0nNCo0aNUgwJBgCnT59Gly5dcP36dZw5cwatWrXCX3/9BT8/v1TP5ezvkYsXL+Kdd97Br7/+muFjnj59musT2dDQUJt8BuXkayszChUqpCSyT548yZFzOlJmxwcuWbIkWrZsiV9++QWyLGPp0qWYMmWKRZlt27bh/v376NOnj9XROZK/lwHg888/x7Jly9K8bvKJDZK/l1Pj6uqqLDORJcoiay1lqUk+AHhoaChCQ0Mzda3kY5lGRkZa7Hs+CbKlhQsXIjg4GLNmzcKjR48AALdu3cKXX36JL7/8Ej4+PujZsydGjhyJKlWqZOtayZ+zjD7G5OUyMjh6enWYmdl8Mqp+/fqoWLEizp07h5iYGKxevRpDhgxR9h87dgynT59Gw4YNUb58+VTPs2XLFkyePBlfffUVYmNjAZiTrdmzZ2P27Nnw9/dH3759MXLkSLt92UlStWpVbNy4EXXq1IHRaMStW7cwYcIEfPvtt6ke48zvkePHj6Nly5aIjo4GALz00kt45513UK1aNXh7e1uULVWqFG7cuAEgZbLtLJK3dsuy7MBIMi8rr63MKFOmDC5cuAAAuHv3LoQQNvkcsJfM/J1K8vrrrytfkpctW4bJkydbDGn43XffKeWseX7Ci02bNmXq+hkZuztpDGQA6f56k9txQgSyu379+kGY+2dn+N+YMWMcHTYAQKPRYPTo0bh16xZWrlyJTp06WXwzjo6OxtKlS1G9enWMHTvWaf+QO1ryPwBJfxSeXx88eHCa53Bzc8Ps2bNx69YtLFy4EM2bN7dogb1//z4+//xzlC9fHl988UUORp8xNWrUsJj2dOnSpUqClx5neo8YjUb0799fSWLfeOMNrF27Fo0aNUqRxKqFp6enspxTvzTYU3ZeW+lp0aKFshwTE2N18oHcrmvXrsovCbdu3bLomnf79m3s3r0bQUFBFs9VWi5fvpyp93JGunQkf90mfz3nRUxkyS6SDwCe3Z+rnv8pJyYmJlvnywpPT0/069cPW7duxf379/H999+jefPmyn5ZljFr1izMnTs3y9dI/pxl9DEmL+eMP+kmGTBggPKT7rFjx5Sf4p4+fYo1a9bA29s7wzMKFShQAEOHDsXevXsRFhaGBQsWoE6dOsr+hIQEvPvuu/jpp59y/oGkY/z48Ur3DKPRmObrwVnfIwcOHLDopjBhwoQsn8tZ+Pj4KMvx8fEOjCTrMvPayoy2bdtarGekv6Y92PMLh4uLC1555RVlPamrE2BuoZVlGYMGDUq1pfr5CS9s0UUj+etWrV8ocwoTWbKLypUrK8uZ/cn0eXq9HuXKlcux82WXj48PBgwYgL179+L48eMWnfbnzp2b5VbZ5M9ZRltFkvetSn68sylUqBC6dOmirCe1wv7000948uQJ+vbtm6VWBj8/P7z11ls4evQodu/ebZHMz549O/uBZ1JgYKBF6/PSpUtx//59q2Wd9T1y+vRpZblQoULKjH9qlryrSUa64DijzLy2MqNChQpo3769sr5u3bosnWfZsmWoVasWatWqhYULF1otk/xn//QS1Zy42TQzkv8itHXrVkREREAIgaVLl0Kj0WDQoEGpHvv8Z68t/kZFREQoy8nvjciLmMiSXbRs2VJZvnDhgvIzZVqOHTuGypUro0qVKinupk5+vqTpYtMSERGBwYMHY/DgwRZ/mDPjxIkTWLhwYZp3dteqVcviBo0HDx6kOQVjWpI/xujoaItWMWuEEDh58qSynryF2Bkl/0ORNEpBen3Pkvz2229YuHBhmq+j1q1bY968ecp6Rm5+s4UxY8Yorc9xcXGYP3++1XLO+h5J3vKTkf6GjviFJLOSjwCSlKCoUUZfW5k1ffp0pbVxx44duHLlSqaOl2UZn3zyCf766y+cPHkSzZo1s1ouect40j0H1ty6dUvpB28vFStWVEaqMRgM+P7777Fnzx5cv34dbdq0QbFixVI9NiAgwGK0hIxOj/3qq6+icuXKGD9+fLplkyeyISEhGTp/bsVEluyiS5cuyhvfYDBk6Fv+0qVL8e+//0Kj0aSY8/qtt95SPmiPHDmS7rBB69atw5IlS7BixYostyht374db731Fg4cOJBmueeHgMrqjTa1atWy+Il8/fr1aZY/dOgQwsLCAJi7PqTVYuAMWrZsqQxr8/DhQ3z88cc4fPgwqlSpYvG4rfn+++/x1ltvpZucJq8Le94UmFzx4sWVudwBYMGCBVaTVGd9jyT/g33//v00E+xbt26luNHFGQUGBqJw4cIAzD/L37x508ERZU1GX1uZVaNGDUybNg2A+bX47rvvZirZ//bbb3Hu3DkA5uGskv86kFxwcLCyfPXq1VTPZ234SHtI/mV7yZIl6Y6mktzw4cOV5Z9++glGozHN8rdu3cKPP/6If//91+owgs9L/utb9erV0y2fmzGRJbvQ6/UWP+1+9NFHKe6sTu7EiRPKEE3jxo1Lsb9SpUrKh4wsy2l+g338+LHSMvfqq69mejiW56WXYJw5c0ZZrl69erYSqLlz5yo3MM2bNy/V1l2TyWTxHIwfPz5FPy1no9Fo8NprrynrSX84M/JHIklm6qJx48aZjDDnjB07Vrmz+PHjx1iwYEGKMs76HmnZsqXSEmsymVLcnJfcJ598kuo+Z5O8xdpRrfU5ISOvrawYP348+vXrBwDYuXMn3nnnnQwls7/88gvee+89AOaWwjlz5qRa9sUXX7Q4zhqDwWDxy4o99e7dW+l/euHCBaxbtw5+fn4ZGrt30KBBysg1169fT/MxCCEwcuRIGI1GVKpUyaLbVWqSv2aTjx+dJ+X4FAvkNJBsFpj0Zk3JqMzMxmPNyJEjLWbUOXv2bIoyW7duFYUKFRIARN++fVM9V1xcnKhfv75yvlGjRqWYa/zy5cvKfNrFixcXDx8+TPV8yZ8vayZPnqzsHzNmjHj69GmKMqdOnRKVK1cWgHnqyB07dmT5ekk+++wzpVz16tVTzPD1+PFj0bdvX6VM9+7dU51iWAjLmb2aNGmS5rWTP+bJkyenWTYrbt26JTQajcUMSKlNrZtc0gxIGo1GfPbZZ1Zn//r9999F0aJFlVmm/v777xyJOb1pRDNyXOHChUVcXJzVcs74HnnvvfeUc7i5uYnVq1db7DcajWLu3LkW07EinRmYHDmzlxBCrFq1Sjnn+PHjM3xcRqeozQpbv7Yyy2QyiQ8++EA5d/369cXBgwetfr7cuXNHjBo1SpkZrGzZsuLKlStpnj8xMVEEBQUp51+6dKnF/idPnohevXqJNm3aKDPGZeQ5z8yMYelJPrshMjjrVpKLFy+KwoULK59Vs2bNSvFZFRERIV5++WUBmKcWP3nyZLrnffr0qXB1dRUARHBwcKYfU24jCaHSzkGUwoMHDyxmcvr++++V5TZt2ljMcGRtoO2MnPfKlSs4dOgQgJQzz2T0nJ9++ikmTZqE2NhYSJKEF154AcHBwTAajfj7779x7do1SJKEoUOH4ssvv0xzjLy4uDgMHToUK1euhBACvr6+aNiwITw9PXHt2jX8888/MBgMqFixIrZu3Zpi5rJRo0YpN3skf76Sfq4rVKiQ0sq0fv16DB06VGkl8/b2Rp06dRAQEIAnT57g+vXrSt9Cb29vLFiwIMWsL5m5XnIrV67EiBEj8OjRI2g0GtSrVw8lS5ZEZGQkDh48iNjYWOh0OowcORKzZs2yOhFA0gD74eHhyk91hQsXVu5S/vDDD1G+fHmL+j516hT++ecfAEC1atWUn7A++eSTHBsVoX379vj5558BmFtA1qxZk+4xX375JcaNG6f0xyxYsCBq166NggULIioqCpcvX1b6FBcuXBgrVqzIVqvFrFmzlLE1//jjD+Vn0OffA2k9L5cuXUKFChWUcUtbt26tzPY0YsQIvPDCC0pZZ3qPAOaf3wcOHIhVq1Yp20JCQlCjRg3IsowjR47g9u3b6NWrFw4cOKD8ctCjRw94eXmhfPny+PDDD3HhwgXMmjULQOqfJV27dkXXrl2VSS6ef84bNmyo/CSd9JrNioSEBBQvXhwRERF44YUX0uxHnDzumJgYi9m8kh4jkPr7Ny32fm1lxa5duzB27FicOnUKgLlrRqVKleDv74+EhARcuXIFZ8+ehclkgk6nw5AhQ/Dxxx+nO+EKYH7M7dq1U97LVapUQeXKlRETE4MDBw6gUqVK2LJlC2rVqqUMMZb0nCd/vpO/Xnbt2qW8BpP/7WvUqFG6w/o979ixYxYzc50/fz5Tr7nr16+jT58+Sj/ZAgUKoH79+vD19cXdu3fx559/Ij4+HiVLlsTq1astZpBMzc6dO9GhQwcA5l/t0pvBMddzbB5NOen5loK0/tnivJlx9+5dMWXKFFG/fn3h5+cndDqd8PHxEdWqVRPDhw/P0LfS5E6cOCHefvttUalSJZEvXz6h0+mEv7+/aN26tfjuu+9EQkKC1eOSz99t7V/JkiUtysfHx4tt27aJt99+WzRo0ED4+/sLFxcX4eLiIgoXLiyaNWsmPv74YxEeHp4j10suMjJSzJ49WzRp0kQULlxY6PV6UaBAAVG9enXxwQcfpGipfV569ZfUcpGR+s7JVqgNGzYo5/3ll18yfNyTJ0/E2rVrxeDBg0WtWrVEwYIFhV6vF66urqJo0aKiTZs24osvvhCPHz/OdozJW3iy87wkbzlP/m/Tpk0pyjrLeyS5HTt2iO7du4siRYoIvV4vPD09RXBwsBgwYIDYt2+fEML6azyp5T/5rwGp/Utq+U/+a0B6r9msSn6NtJ7PjMSd3vs3NY54bWWFLMtiz5494r333hM1a9YUgYGBymugZMmSokOHDmLu3Lni1q1bmT73lStXxJAhQ0RQUJBwdXUV+fPnF/Xq1RMLFixQXpfWXlfJn++MvF6y8iuiEEJUqVJFABANGjTI0vFCCLF582bRr18/Ubp0aeHh4SFcXFxEkSJFRLt27cTChQut/sKXmu7duwsAwsfHR0RGRmY5ptyCLbJERJQnxcbGolKlSrh+/Tq6deuGjRs3OjokojSdPXsW1atXh8lkwueff44RI0Y4OiSH481eRESUJ3l4eGDp0qXQ6XTYtGmTRZcBImdjMBgwdOhQmEwmNGnSxGJkhLyMiSwREeVZzZo1U0ZiGDhwoNPMZEWUnCzLGDJkCA4fPowKFSpg06ZNVu+FyIuYyBIRUZ42cOBAZSi35s2bO2zcUqLUdO/eHd9//z2aNWuGAwcOZHsYydyEiSwREeV5PXv2xIkTJ9C0aVMcOXLE0eEQWfj7778xa9Ys/PLLLzk2YkxuwZu9iIiIkomKioKvr6+jwyBS8DWZOiayRERERKRK7FpARERERKrERJaIiIiIVCn1eQ3zkEePHsFoNNrlWn5+foiIiLDLtSjnsf7UjfWnbqw/dWP9qZs960+n02V4ZAYmsjDPI24wGGx+HUmSlOuxa7L6sP7UjfWnbqw/dWP9qZsz1x+7FhARERGRKjGRJSIiIiJVYiJLRERERKrERJaIiIiIVImJLBERERGpEhNZIiIiIlIlJrJEREREpEpMZImIiIhIlZjIEhEREZEqMZElIiIiIlVyqilqjUYj1q5di23btuGLL76Av79/muUvXLiAFStWQKfTwWg0on///qhQoYKdoiUiIiIiR3KaFtn79+9j8uTJePToEWRZTrd8REQEPv74Y/Tv3x9TpkxBv379MGvWLERERNghWiIiIiJyNKdJZOPj4/HOO++gWbNmGSq/c+dOFCtWTGmBrVixIooUKYKff/7ZlmESERERkZNwmq4FJUqUAAA8fPgwQ+XPnj2bohtBmTJlcObMmRyPLSeIJ9EQV88hPqwIhF8RQOs0Tz0RERGRKqk2m7p37x7q1atnsc3X1xf3799P9RiDwQCDwaCsS5IEd3d3Zdmmwm/B9PVMRACQqtaGdsQk216PclzSa8TmrxWyCdafurH+1I31p27OXH+qTWQTEhKg1+sttun1eiQkJKR6zKZNm7B+/XplPSgoCLNnz4afn5/N4kwS/zAMSu/dy/8iMDDQ5tck2wgICHB0CJQNrD91Y/2pG+tP3Zyx/lSbyLq6ulq0rgLmFldXV9dUj+nWrRs6duyorCd9s4iIiIDRaLRNoP8R0D5blgXCwsJsej3KeZIkISAgAOHh4RBCODocyiTWn7qx/tSN9adu9q4/nU6X4UZG1SayhQsXxuPHjy22RUVFpTlkl16vT9GKm8TmFVPADyhSArh7E4DgG1nFhGD9qRnrT91Yf+rG+lM3Z6w/pxm1ILMqV66Ma9euWWy7du0aqlSp4qCIMsAJ+5YQERERqZVqEtnPP/8cX375pbLevn173Lp1CxcuXAAAnD9/Hnfu3EG7du0cFWLGOdm3GSIiIiI1cpquBUajEdOnT8fTp08BmBPXggUL4v333wdg7v+a/G45Pz8/fPjhh8rMXgaDAR9++KFdbtzKOrbIEhEREeUUp0lkdTodPvroo1T3jxo1KsW2ChUqYMaMGTaMylbYIktERESUXarpWpArsEGWiIiIKMcwkc2jREICxN2bELLs6FCIiIiIssRpuhbkKQ7qWSCePIY4fRzi7yPA+VNAYiKkpu0h9XvTenkhgMgIwMUNkrePXWMlIiIiSg8TWXtywPBbwmgA/jkO+dAe4OxJQFi2wIr9OyFqNQTKVoYkSRDRjyDOnwbOnzL/HxkBuLpDM3wcEFgcyJcfuHcHuHMTKFMOkm9Buz8mJXaDAXh4H/DJB8nDy2FxEBERkWMwkXUEGw2/JQwGiOMHAY0EqUJ1iH07IA7sAmKi0zxO/mS8eaFkMHDjSsoCCXGQ501M9XjNsHEQiQnA3VuQXqgHqWTws5iEAISApNFAmEwAAEmrhTAkArr/Jqd4HGn+3yc/8OAecPcGxJ2bwN2bEHdvArevAx6egLevOYnW6QDfgsDDCMvEPKQiNP2HAV7egIsbEBEO3A+DeBAOqWhJICEeKF8Vkqd3ek8lERERqQATWbuyTYuskGWI4wchNnwPPHpg3matYIFCkOo0gVS9LuRdG4BTRy33W0tiM0BeMPNZLDt/gtR/GJAQD3H1AnD1wrNEVe8CGBItD3ZxBRIT0r9I7FPzPwAwGs0J7/Mun4M8+W2rh1s8H1Vqmf8/cwJS3SaQug8A8hcyn1f37C0hcQILIiIip8ZE1iFyrkVWRIRDXv4FcOms9QJaHaQX6kNq1NLcGqnRAgA0g96FPG9SyuS1WBCkKjUhVagGBBaD/MkEcytoEjd3ID4u7ZhWLrC+4/kkFshYEmuNuweg1aXb2mzVmRPKojj6O8TR31MtKtVuDGnw/yBpeF8kERGRs2Eia0852MAnhIA4uBti7RLryaAkQWrZGVLLLpAKFEq528ML2gnzIGKiIfZuAzy9IFWrC8kvwKKcdvo3/9309QCIjQGKlDB3DZBNEKu+hfh9F1C+KnD5X+C/rgOZ4u4BxMWal/2LAEVLmLsBFCkBqUhJwNsbCL8LFPQ3l71/FyhQCPD2VVpMxZ2bkD96G/D0Bp4+ATy8zMf7BUCE3zYn0LEx5seQSeL4QXN3jeAKwJ2buF+2IsQbo80tyURERORQkhCcLzUiIgIGg8Hm1zFNGwncvAZoddAu3Jjl8wiDAWL1txAHf3m2saA/NP3ehIh8AMTHQWrYApKX/UYaEAYD5O8+AcJum/vIlikPKbg8ULAwEH4b8AsANFrg8SPzcuwTQJaBfAXMN5nJJqW12GYx3g+DuH4ZUv5CQHwc5G/nAAlpty6nRvPtZrbSqowkSQgMDERYWBj4sac+rD91Y/2pm73rT6/XZ3imVrbIqoyIj4P81XTg4hllm9SkLaSegyC5uTtszgVJr4f2rbHWdwaVfbbs4Wn+3ye/5fE2TmIBQPIPhOQfqKxrv1qboozyBo2Lhfxu31TPJQ/t+mylZDA0Y+dC0tr+MRAREdEzTGTtKntppoiLhfzFFODKefMGvQukAW9DU69p9kMjAMlu8PLwhGbhJvPNah4egI8v5P8NtH7QjSuQ3+xmPr5JW3NXhEKFIbXuBk3dJnaKnIiIKO9hIusQmWuWFycPQ9y5CfHvSXNiBZgTrXc/glS6nA3iI8A8TBjKVlLWtV+vg2l4rzSPEb/vMi/cvAbx3acwffcppA4vARH3IM6cgNRrkPnGvMDiQHQUxMWzQOR9IPYppHY9IXXoDcmV/W+JiIgygomsPWVhOCdx4g9zX87kPL2heW8qpJJlcigwygjJ1Q3Fd5zA3bt3IZ48BnR6yCP6pHuc2PHTs+Ufvkq93M/rIX5e/2yDjy8QHQWpx0DAxxeSqztQsTokd4/sPAwiIqJcg4msI2Swo7SIiU6ZxOp00AwbxyTWgSRJArzzAQC0i7dCGI0Q+3cCLi7mG9qunIc4tCf7F4qOAgDz+MCwbMeXmraD1Os1SC6uz2Y4c3Oz681zREREjsZE1p4y2SCblMBYnOKV4ZCS/dxNjifpdJBadn62oVEr4NURkA/+Yh7yq3AgxO5NkIoHmfdrNECZCpAKFTa37MbGQPy4MFPXFPt/htj/s/nmubjY1L8c1WwAzcAR5gT7abR5qDajAYi4B+j1QPlq5mHTTOZJJkTYLSAxAVKhwoCbB1AqmBNDEBGR02Ii6wgZaJAVt0JTtOpJdZtA06CFjYKinKZp3PrZSr1mVssoKWLT9hBGIxB2yzx1r2yC+OcYxMUzwJ0b5mHLrEma7Sw1fx2G/NfhTMdu8RINrgCpzosQp09AqlEXUunykIqVyvQ5iYiIchoTWbvKeMuWvGH5s1a2GvXMkxVwdIJcTdLpgKRWW8A8MUR7y5vLxJXzkGePebbBwxPw8gHuh9kusCvnIf4bKUOc/etZkuvqBiTEW5Yt4AfNrO/My7FPAZMRko+v7WIjIqI8jYmsExI3rgL//m1eKegPzZAPIOn1jg2KnIIUXMHcL9dgABLjIXl6pygjjEaIv49ALErWv7qAHxAZARQtCalEGYg/91keVK6KeWa0k4fNXR9kOf1gnk9iASAyAvIbXSxnbEsef9f+kFp3SzV2IiKizGAi6xBp9y0Qu5/N+iW17cEkllKQ9HpzH1dr+3Q6SLUbAbUbpX6C10amew1x8xrkjd+bp/q9cRUIvZTxAK0ksQAgNq+E2Lwy9eOq1YGm9+AUUyUTERFZw0TWnjJw04yIfgTx1yHzinc+SA2a2zgoIuukEqWhHTklxXYhBBAVCXh5AwaD+eaxp08gTxpuLqDVmluAI8Izf9F/jkH+59izdf9A690mKtWApv8w801pRESUZzGRdYQ0ht8SR35XftaVGrWC5MLB8cm5SJIE5C9oXtG7mP/38TV3eZBN5jL/Df0l4mPNoyEc2A3x287MXyy1vr///g157BDr8dVsCKltd4g7NyAFlQUCikHSaDJ/bSIicnpMZO0pnRZZIQTE4b3PinOEAlKZ58euldw8gGJBkF5+E3j5zRTlhRDA3VsQxw5A7Pwpxf6sEH8dUn7VeP4r462kuNr1hNi1AdKLbQCjAeL4QWg+/cEcb7LYOPQYEZFzYyLrTO7eMg+1BABlykMKKOrYeIhsTJIkoGgJSN36A936A4C5VTfsDuCbH/DwUiZ4AAA8jIDYthriz9+ydd2kGdSUKYUByO+kPUub1LG3uXXXwxPixlVI9ZtDKuiXrTiIiCh7mMg6EfHPUWVZqt3YgZEQOY6k0QJFS6TcBgB+AZBeew947T1lnxDCPM7uzauQv5wGFAsC7t8BEhNzNC6xfa35/6T1LT9aL+jpDXj5QPPKMCCwGODty5ZdIiIbYSLrINZ+thSnjyvLUtXa9g6JSJUkSQJ8CwC+BaBdvDXFfiGbAFmGJMtwWf0tEgKKQ8Q+BeKeQipfBfI3s3I2oKdPzDe/fTLecrtOD1SpCU3X/pCKlLB+LBERZQoTWXtKo1VGPHkMXLtoXilSgsMPEeUQSaMFNFpIkoRCY2chLCzM3Ir7H2vJL/BfS2/kA8hfTwduhZo3VqkFnDmRtUCMBuDvI5D/PvJsW5nykGo1hORfBChRGvDygaTjcHtERBnFRNZZXDqrjGYgVanp4GCISJIkoKAftJM+t7o/6VeVpK4N4swJQJIgVi8CEhMydpGrFyCuXrA6srRm6GigXBVI3vmy/iCIiHI5JrKOIoRFC6249K+yLJWr4oiIiCgTkroGJXVtkBq3Nu9o1MqinAi/A3nF10B8HHDzaobPL3/7bGY2zQczgYL+kAr6Zz9wIqJchImsXaXRteDyf4msJAFlKtgpHiKyNSmgKLQfzLTYJhLiIZZ9DhERBty8lu455Lnjnq2EVITm7QmQPLxyOlQiItVhIuswz35MFLFPgdvXzSvFgyB5eDomJCKyC8nVDdKbY1JsF0JAfDcP4up54OF96wdfPgf53ZfN52na3jwdcXBFTvpARHkSE1l7Sq1B9ta1Z/1jS5e3XzxE5FQkSYI05H8A/ktqVy+COPo7EBtjtbzYvxNi/38zprm4QOrYB1L5quaZ1xISAL/CKSapICLKTZjIOkqyuztE8p8WS5S2fyxE5HQkSYL08lDg5aHmG8rCbkH+7tNnIyg8LzERYuMPVm8ck5p3hNSuByTfgjaNmYjI3pjI2lNqw28lS2QlJrJE9BxJkoAiJZQRFMT9uxA/b4D449cMHS/2bYfYt91yo0YDyLL5/G98AA0nYSEiFWIi6wTErf8SWa0WKFLSscEQkdOT/ItAGvgOMPAdCFmG+ONXiL//BM6eBEqFAGG3gIT4tE/yXxILAGLRXJgWzTWv6F0AQyKktj0gdekHScc/E0TkvPgJ5Sj/9YkVBoP5jw4ABBaHpOdg6ESUcZJGA+nFNsCLbVLsEzevQv5pKXDxjOUOrRYwmayf0GCe2lfs2gCxa4OyWfPuZKDSC5xul4icChNZu7LyByAi7NnPe5y2kohykFSiDLSjZljdJ2QZ4vhBiO8+zdC55M+nPDtv79fN0/4GlQMKBwJe+ThqAhE5BBNZe3p4T1mUJw2D5tURwNNkdyMXLuqAoIgoL5I0Gkh1mwB1myjbhNEI3L0Bedp7aR4r1i4x///8Obu9Ak37XjkdKhFRqpjI2lPkg2fLEeHmQc4DkiWvhYvYPyYiov9IOh1Qogy0i7cCMA8Bhgunza2xJmO6x4tNK2DatAJSz0GQWndlNwQisjkmso4WfkdZlALYIktEzkOSJKBCNWgXbjQntedOQVw8A3HzKhAVCdy5YfU4sX4ZxPpl5pXqdaHp+gpQpDgTWyLKcUxknYk/W2SJyDlJkgRUqgGpUo0U+0RcLOQRfawfeOoo5FNHzcsurkBignnZ0xuaYeMgla1ko4iJKC9gIussvPNBcvdwdBRERJkmuXtAu3grxJ2bkD96O/WCSUksADx9AnnuWIvdmkVb2GpLRJnCRNZZFPR3dARERNkiFS1hTmhNJiAyAmLnumeTNkiSMuxgauQ3ujxbCa4IqVSweSxbN3cbRk1EasZE1lkUKOToCIiIcoSk1QJ+AcqkDQAgZBMkjRbi8SOIvdsgfl6f9kmunIO4cg5ij/nGM2h1CAsoAuOdm9CMnGK1iwMR5T1MZJ2ElJ+JLBHlXpJGa/4/X35I3QcA3QdAyCbgYQTkL6ZY3PhqlckI452bAAB5/mTzthKlIbXoDMk/AChdnmPZEuVBTGSdBVtkiSiPkTTmllvttG8AwNwl4dEDiFNHIdZ+l/4Jbl6DWDbfcjzbqrWheeMDSK5uNomZiJwLE1lnkd/P0REQETmUpNUChQpDatkZaNkZACCeRAMPwiH5FoDnqT8RvWpx2ic5fRzy2y89Wy9aEpqu/YEqtcznJ6JchYmsk5DYIktElILk7QN4+0CSJOTrNxSxzTtDTkyAPP19SCXKQBz5Le0T3LkB+ev/puktVyXVKXuJSJ2YyDqLAmyRJSLKCEnvAu2Ur8wrr5un0xU3rkCe/n7aB148A9OQzkCZ8sD9MGj6vQlUqA7Jw9PGERORrTCRdRY+vo6OgIhItaSSwcrUuoB5kgaxcx3Erg0pC1+9AACQF85+tq1YKUily0Pq1h+Sl4+twyWiHMJE1kmw7xYRUc6R3D0g9RgI9BgIkZAA+e1eaR9w+zrE7esQB3Y921bQH5p+b0GqUtO2wRJRljGRdQa8u5aIyGYkV1fzRA3xcUD0I8g/fG3ecfFM2gc+vG8eGgyA1PcNSC+2gaTT2zhaIsoMJrJOQPPmGEeHQESU60lu7oCbu8UNXyI2BrhxFfLP64Hz/6R6rFi9CGL1IvN5ajeGNPh9ZWxcInIcJrLOwNvX0REQEeVJkocXUKEatBWqQQgBxDwB7t2GOHoAYv9Oq8eI4wchjh+E1PcNaJp3tHPERJQcp0Gxp4Bi1rf7FrBvHERElIIkSZC8fSAFV4Sm35vQfPUTpC4vp1perF4E05DOEKGX7BglESXHFll78vJOsUlq0AJSvvwOCIaIiNIiubpB6tgH6NgHACAiIyCPeT1FOXnmKPOCiyuQmGA+9vX3oKnXzG6xEuVVbJG1J6MxxSbNoHcdEAgREWWWVMAP2sVboRkzy3qB/5JYABBLPoNpSGfIm1ZC3L9rpwiJ8h4msvZkMjk6AiIiyiYpuKI5oX13crplxc6fII9/09wF4VaoHaIjylvYtcCeTAZHR0BERDlEqlzTPKyXIRGIigRcXCGO7odYt8xqeXnqf7/AlasCqXpdSPWbAToXSK6udoyaKHdhImtPJtnRERARUQ6T9C6AX4B5uXU3iPotIDYsg7h2CQi7lfKAi2cgLp6BWPvds3N06gOpTQ8mtUSZ5HSJ7LFjx7Bp0ybo9XpIkoTBgwejePHiVssaDAb8+OOP+Pfff+Hh4QGDwYCuXbuiTp06do46g2R2LSAiyu0kbx9Irz67/0H+fRfEygVpHiO2rYHYtsZ8fOeXoenUx6YxEuUWTpXIXrlyBV9//TVmzZqFwMBA/P7775gxYwY+++wzuLu7pyi/YcMGHD9+HHPnzoWHhwdCQ0Mxfvx4zJw5E6VKlbL/A0iPzBZZIqK8RtOkLdCkLUR8LMShfRCbVwDxcamWF1tXwbR1FaRBI6Fp0NyOkRKpj1Pd7LV582bUqFEDgYGBAIDGjRvDZDJh//79Vstfv34dZcqUgYeHBwAgKCgIHh4eOHv2rL1CzpznW2QDijomDiIisjvJzQOaFh2h/XIttIu3mm8Ym/YNUKCQ1fJi2XzzTWIR4XaOlEg9nCqRPXPmDMqUKaOsazQalC5dGmfOWJ8Pu169erhw4QIePHgAADh16hSio6Ph6+trj3Az77lRCzQj0r/jlYiIci8poCi0s5dCM2cZpMatrZaRx71hTmgNvGGY6HlO07XgyZMniIuLQ758+Sy2+/r64urVq1aPadq0KRISEjBq1Cjkz58fd+/eRb169VC/fn2r5Q0GAwzJPggkSVK6LEiSlEOPJA3JuhZI9ZpC4x9o+2tSjkl6jdjltUI5jvWnbrm9/qQChYCB7wAD34H8y2bIPy1JUUYe1gNSxerQvPkhJA9PB0SZdbm9/nI7Z64/p0lkExLMA0nr9XqL7Xq9Xtn3vL1792Lz5s2YNWsWAgICcP36dZw5cybVJ3rTpk1Yv369sh4UFITZs2fDz88vhx5F2m4LAfHfsruXNwoGMpFVo4CAAEeHQNnA+lO3PFF/A9+C6DcEkV/OQOyebRa7xLlTMI0w3wjm3qAZCo6dDUnjVD+upilP1F8u5oz15zSJrOt/Q44YnvvpxGAwKPuSE0Jg5cqV6NSpk/LElipVCj/88AMSExPRo0ePFMd069YNHTt2VNaTEt6IiAgYrcy6ldNEsmvExccjLCzM5teknCNJEgICAhAeHg4hRPoHkFNh/albnqy/Pm9A+9JgyCsXQBzYnWJ33OHfcLtTHWjHzIYUUtEBAWZcnqy/XMTe9afT6TLcyOg0iay3tzc8PDzw+PFji+1RUVHw9/dPUT46OhpPnz5N8UD9/f1x9OhRq4msXq9P0eKbxC5vrOQ3e2m0fDOrlBCCdadirD91y3P1J0nQvDIcosNLkMe8brWIafYYc9H2vSB1eAmSi/OORZvn6i+Xccb6c5pEFgAqV66Ma9euKetCCISGhqJ79+4pynp7e0Ov1+PRo0cW2x89emS1BdcpJB9+S6uen4KIiMixpAJ+5lnEhABuhUKeNjJFGbFzHcTOdQAA7eKtdo6QyDGcKpvq2rUrTp48ifBw81AjBw8ehEajQZMmTQAAEydOxOrVqwFA2b5v3z7ExMQAAK5du4bTp0+nerOXwz3XIktERJQZkiRBKlEa2sVbIXVMfdIE05DOENGPUt1PlFs4VYtscHAwhg8fjvnz58PFxQWSJGH8+PHKyAKJiYkWfWgHDhyIdevWYdq0aXBxcUF8fDz69euHdu3aOeohpC15c7yKOucTEeUWCQkJiIyMxMOHD3Hv3j3cuXMH9+7dQ7FixdC3b19Hh5cpmi4vQ3TuCzx5DPl/A1Lsl/83EFLDFtAkm2WMKLdxqkQWAOrUqZPqFLOzZ8+2WHd1dUX//v3tEVbOY4ssEZFNGI1G3L9/H3fu3MHVq1dx48YNREVFITExEUajEbIsIyEhwWJEnIIFCzow4qyTJAnw8VW6HcjDewGGRGW/OLQXpkN7gVIh0Iz7xCmHTyLKDqdLZPOMVG46IyKirFmwYAGuXbsGo9EIk8mEp0+fwvTcRDTWuLu7o0GDBnaI0LYkSYJ2wXrIaxZD7LUctgvXL0N+owv7zlKuw9+37UiZycvFFVLLzo4Nhogol2nTpg0kSUJ0dDSio6MzlMQC5kQ2ODjYxtHZj6bPEGjmLrO6T0RF2jkaIttiImtHmqq1UPjrNdB+vBiSp7ejwyEiylXKlCmDGTNmoH79+vDy8srwce7u7pCTjyqTC0i+BaFdvBWabzZYbJc/eBXiyjkHRUWU85jI2plLqWBI+fI7OgwiolzJxcUFQ4YMwbBhw1CoUKEM9QmNiIjAmDFjMG3aNOzduxfR0dF2iNQ+JJ0emqGjLbbJsz+EvGKBgyIiylmScLaRbR0gIiIixYxitiBJEgIDAxEWFuZ0AwpT+lh/6sb6U7es1F9cXBwWL16MCxcu4OnTpxk6RqvVwtvbG15eXqhZsyaaN28OX1/fbETuHExDrHdn08xcBMnP9tOO8v2nbvauP71er76ZvYiIiHKSu7s7RowYgaNHj2LVqlWIiopKUaZQoUIoWrQobt++jbi4OMTGxiIqKgpRUVG4ffs2fv/9d+TPnx+tWrVC7dq14eLiYv8HkgO0i7dCPrwXYtnnFtvlcW9A87/pkMpXdVBkRNnDRJaIiHK1unXronTp0pg3bx7u3btncRNYpUqV8Nprr0GWZVy9ehUHDhzAhQsXEB8fj+joaCWpXbZsGdavX4/SpUujffv2KF26tOqGstI0aAHhXwTyf1PaJpE/nWDeP30hpMJFHBEaUZYxkSUiolzPz88PU6dOxXfffYczZ87g6dOn8PDwUIbd0mg0CAkJQUhICAAgLCwMe/bswalTp/D06VPExcUhMjISkZGROH/+PDw9PVGnTh20aNECBQoUcORDyxQpuIK5dXbpZxB//maxT57wJgCYx5sNKuuI8IgyjX1kwT6ylDGsP3Vj/albTtbfwYMHsW7dOkiShHnz5kGrTX2CGlmWcf78eezcuRN37txJMayXr68v8ufPj5YtW6JOnTqq6nogzp2C/Nkk6zuLlIDmzQ8hBRbLkWvx/aduztxHloksmMhSxrD+1I31p245XX9hYWH466+/0LFjxwwfExcXh8OHD+O3335DdHQ0Hj9+rOzT6/Xw8vJC9erV0b17d/j4+GQ7RnuR92yBWLsk1f2asXMhlS6XrWvw/aduTGSdHBNZygjWn7qx/tTN2erv/v372L17N06dOoXY2FjExsYq+3x9fVG2bFm89NJLGf5j7Azk/TshflxodZ/mo68gFS2R5XM7W/1R5jCRdXJMZCkjWH/qxvpTN2etP1mWceHCBezYsQO3b9+2GBkhX758KF68OPr06YPixYs7LshMEHGxECu/gTj2u9X9mkVbsnSTm7PWH2UME1knx0SWMoL1p26sP3VTQ/1FRkZi/fr1OHfuHKKiopQ4vb29UbhwYfTu3Rtly6rnJip500qInT+l2K55fxqkCtUydS411B+lzpkTWY5aQERElAMKFCiAN954AzExMdiyZQtOnDiBx48f48mTJ3jy5Anmz5+PAgUKoEePHqhevbrTD9+l6dYfsskAsXuTxXZ53kTz/slfQCpWygGRET3DFlmwRZYyhvWnbqw/dVNj/SUkJODnn3/GgQMHEB0drfyd8fDwgI+PD7p164a6des6fUIrZBny0K6p7tcu3pruOdRYf/SMM7fIamwcCxERUZ7k6uqKrl27Ys6cOejTpw/8/f3h5uaG2NhYhIeHY/ny5Zg8eTJu3rzp6FDTJGk00C7eCs2Hc6zuN82bCGGHxiAia9giC7bIUsaw/tSN9aduuaH+ZFnGsWPHsGXLFjx69AhxcXEAAB8fH5QrVw4DBw6Et7e3g6NMn3z8IMSiuSl3FPSHdtZ3Vo/JDfWXl7FFloiIKI/TaDSoV68eZs6ciddeew1+fn7QarWIjo7G8ePHMXHiRGzduhVGo9HRoaZJU7sxNDMXpdzx8D5MQzpDnDpi/6Aoz2IiS0REZEeSJKFOnTr4+OOP0a5dO/j6+gIAHj16hK1bt2Ls2LE4deqUQ2NMj+QXAM1nK63uk7+eaU5o42Kt7ifKSUxkiYiIHECv16NXr16YMmUKqlWrBm9vbxgMBty/fx/ffvstpk+fjvDwcEeHmSrJy8fcd/brdVb3yyP6sBsB2RwTWSIiIgfy9fXF+++/j5EjR6JYsWLKDWGXL1/GzJkzsWTJEqU/rTOSXFzNCe24T1Psk9/oAiGbHBAV5RVMZImIiJxAcHAwpk2bhr59+6JgwYLQaDR4/Pgx/vjjD4wfPx5nzpxxdIhpkoJCzENxaS2HqJeHdoMwclQDsg0msnaWmJgIg8Fg8XOLyWSCwWCAyWT5rdVgMKQoK8syDAZDipsBMlPWaDTCYDBAluU8UVYIoTw/yVl73rNaNr36zExZwHp95sTrxFnryN5176yvE8B2dc/XSeplnaHuk8qaTCY0adIEM2fORNOmTZX+s5GRkVi4cCEWL16s/B1x1s8I0+erYJQs04uEt3ri6a/bQJTTOLOXnX388ccAgP79+8Pd3R0AcPr0aZw4cQLlypXDiy++qJRduXIljEYj+vTpowzJ8u+//+LIkSMoU6YMmjdvrpRds2YN4uPj0aNHDxQoUAAAcOnSJRw8eBAlS5ZE69atlbLr1q1DTEwMunbtqgxvce3aNfz2228oWrQo2rdvr5TdtGkToqKi0KFDBxQpUgQAcPPmTfz6668oXLgwOnfurJTdtm0bHjx4gDZt2qBEiRIAgLt37+Lnn39WZrNJsmvXLoSFhaFFixYoXbo0AOD+/fvYtm0bfHx80Lt3b6Xsnj17cOvWLTRp0kSZ3vHRo0fYuHEjPDw80K9fP6Xs/v37ERoaigYNGqBSpUoAgOjoaPz0009wcXHBwIEDlbIHDx7E5cuXUadOHVSrZp5uMTY2FqtWrYIkSRg8eLBS9siRIzh37hyaNGmCcuXKATB/Kfnhhx8AAK+//royqPmJEydw+vRpVK1aFXXr1gVg/iO1fPlyAMCAAQPg6uoKADh16hROnjyJihUromHDhsr1vv/+ewgh8PLLL8PT0xMAcPbsWRw7dgwhISFo2rSpUnbVqlVITEzESy+9hHz58gEAzp8/j8OHDyMoKAgtW7ZUyq5duxaxsbHo3r07ChYsCAC4cuUKfv/9dxQvXhxt27ZVym7YsAHR0dHo1KkTAgICAADXr1/H3r17ERgYiI4dOyplt2zZgsjISLRr1w7FihUDANy+fRu7d+9GoUKF0K1bN6Xszp07ce/ePbRq1QqlSpUCAISHh2PHjh3w9fVFr169lLK//PIL7ty5g2bNmiE4OBgA8PDhQ2zevBleXl7o27evUnbfvn24ceMGGjdujPLlyyuvkw0bNsDNzQ0DBgxQyh44cABXr15FvXr1UKVKFQBATEwM1qxZA51Oh0GDBillDx06hIsXL6JWrVqoUaMGACA+Ph4rV5pvdBkyZIhS9tixYzh79iyqV6+O2rVrAzD/sU+q+1dffRV6vR4AcPLkSZw6dQqVK1dG/fr1lXMkleVnRMrPiNu3b2PZsmVO/RnxwgsvoGbNmgCy9xnh5uaGgQMHIigoCBcvXsS9e/dw+fJl/Pnnn7hy5QpCQkKc+zOi82C02/JsZIPtFRuh6/wpkGo1hGboGBDlFLbIEhEROamkJLVkyZLw8fGByWRCeHi40gqavDXU2WgXbwW8LMfFFScOQV7xtYMiotyIEyLAvhMiFCxYEOHh4dBqtcq3c5PJBFmWodFooNVqlfJJMel0OqWsLMswmUyQJAk6nS5LZY1GI4QQ0Gq10Gg0ub6sEEL5SSypNSy15z2tskIIFC1aFPfv34cQwqJs8uc9vfOmVza1+syJ14mz1pE96t7FxUUZ0DvpZ1ZbvE6cte7V/jqRJAmFCxfG7du3lceRUzE4Q91npOzdu3fx9ddfIyIiArIsw8XFBYULF8Y777wDPz8/p/2MEOF3kDhpOHQiWdJdpRa0IyaB1MGZJ0RgIgvO7EUZw/pTN9afurH+zAwGA1asWIG//voLMTExAMyjHnTu3BnNmzdXElVnYjKZcHL/Xsh/7EGNOxehTVZ/mm83Q9Lwx2Fn58yJLF89REREKqHX6/Haa69h2LBhKFSoEAAgKioK69atw5w5c5Tk1pnIsoxT127gdJEQyM/dBCYP7Qr5yG8OioxyAyayREREKlOpUiVMmzYN1atXh4eHB+Li4nDu3DlMmjQJFy9edHR4FjQaDSpXroy6detCP+u7FPvFks8gnjx2QGSUGzCRJSIiUiEPDw+89957eOWVV5A/f34A5lE9vvzyS+zbt8/B0T2j1WrRoEEDtG3bFroChaBZtAUoYPmzsfz+Kw6KjtSOiSwRUS5y6dIljBs3Dp07d8Zbb72FVatWKfvWrFmDt956C507d8a4ceNw6dIlbNmyBTNmzMjUNaZOnYq+fftanJscp0GDBvjoo49QqlQp6HQ6PHnyBOvWrcOyZcucclQDSZKgnb0E0kuvW2w3DekMef9OB0VFasVElogoFylbtixmzpwJAOjZsydefvllZV+fPn3Qs2dPAMDMmTNRtmxZ+Pr6KmOAZtSkSZMQFBSUc0FTtvn6+mLixImoUaMG3N3dERsbi8OHD2PWrFlOO72t1LJzim3ix4UQjx85IBpSKyayRER5WJMmTfD666+nX5Ccnk6nw/Dhw9G+fXt4eXkhMTERly5dwkcffYSIiAiHxWUwGLBo0SJMmTLFYoQgSZKg+WxlivLyqIEQT53vpjVyTpzZi4goj/r999+xceNGhIaGYuvWrcr2o0ePYvny5fD09ET+/PkRFBSEDRs2oFy5cpg4caIy41hCQgIWLFiA69evIy4uDiNHjkSZMmWU81y6dAlLly5Vft5+4YUX0KtXL2i1WqxcuRL79++Hv78/ateujVOnTuHcuXPo378/unTpYt8nIheRJAmdO3dGiRIlsGzZMkRFRSE8PBwzZszA0KFDUaFCBUeHaEHy8oF28VbI+3dC/LhQ2S6PfBlS79chtejslEOKkfNgiywRUR7VpEkTi2lWAfNU0bNnz8Yrr7yCTz75BO+88w7279+P/PnzY+bMmUoSC5in7+3bty/mzJmDGjVq4Lvvnt2R/vjxY0yePBm9evXCnDlzMHXqVBw5cgQbN24EYJ6Ct3nz5rh69SqKFSuGKVOmYOjQoRaTDFDWVa9eHWPGjIG/vz8A85S9CxYswK+//mr3WHQ6HV555RWMGjXKYkKF5KQm7QDvfBbbxNolkN/glxpKG1tkiYhyqfXr12Pv3r0W2x49Srv/4a5du5A/f340aNAAAODj44MXX3wR+/fvT1G2atWqyt3ylSpVwi+//KLs27FjBwoWLIiaNWsCANzc3NCkSRNs27YNvXr1Usp5e3ujdu3aAICWLVtm/kFSqooUKYKPPvoIn3zyCW7duoXo6Ghs3LgR169fx+uvv67MxmVrkiTB3d0dnp6eiI6OtjqgviRJ0M5bAdP8ycC/f1vsMw3pDM2iLWyZJauYyBIR5VI9e/ZEixYtLLbt3bsXn3/+earH3L59O8XNX6nNsFOgQAFl2cPDA7Gxscr6jRs38OjRI4wbN07ZFh8fD61WC6PRqLTMJQ3qT7bh6emJCRMmYNGiRfjnn38QGxuLo0eP4t69exg9ejRcXFwcHaIF7cgpEBfPQP5kvMV2+Y0u0ExfCKlwEQdFRs6KiSwREaUptZaw9Fr0SpQooYygkBp7tQrmZVqtFm+99RZ27tyJnTt34smTJ7h69SpmzpyJsWPHwtXV1abXN5lMOH36NLy9vVG6dOl061wqVwWaRVtSdCuQJ7wJ7eKtqRxFeRU/QYiISFGsWDGEh4dbbMvKHe8lS5ZEWFiYxTimUVFRWLhwYRpHkS21b98eQ4cORb58+SDLMm7cuIEZM2bYfHguWZZx4sQJ/Pbbbxke11aSJGi+3ZRiu2lEX4hkIx8QMZElIiJF27Zt8ejRIxw+fBgAEB0djT///DPT5+nQoQMSEhKUfrNCCKxduxb58uVL50iypSpVquDtt99WktmbN29i5syZNk1mNRoNypcvjxo1amSqBV7SaM0tsF4+zzbGPYU8rAfHmiWFJKz1us5jIiIiLMa2sxVJkhAYGIiwsDCrnd3JubH+1C2v1N+lS5ewfPlynD17FkWLFkXjxo2VSRHWrFmD33//HXfu3EHlypVRtWpV/PnnnwgNDUXlypXx9ttvo0iRIhbDbxUuXBilSpXCnj178O233wIA5s6di5MnT8LT0xMdO3ZEcHAwFi9erJxnzJgxyJcvHy5fvowlS5YgPj4ebm5uqFixIvr16wetVov169dj165dePr0KYKCgpRrpyav1J+9XL16FV988QWioqIAmFvix40bB09PT5tcLzv1J548tjqFrebz1ZA8bBMvWbL3+0+v16faN/95TGTBRJYyhvWnbqy/jBFC4MmTJ/DxedYK9tNPP+HMmTOYNm2aw+Ji/eW80NBQzJ8/X0lmixYtinHjxsHLyyvHr5UT9WeaNRq4esFim2b+KkieOR8vWXLmRJZdC4iISBEfH4+xY8ciISEBABATE4Pff/8dTZs2dWxglOOCgoLw/vvvK0Oo3blzBzNmzMCTJ08cHJl12g/nQKrX1GKbPPJl64Upz+CoBUREpNDr9ShXrhw+/PBDuLu7IzExEa1bt0bz5s0dHRrZQMmSJTFq1Ch8+umniIyMxN27dzF9+nSMHz/eolU+OwwGA1auXAlJktC/f/9UJ0XICM3r78P0NAY4c0LZJh7eh1TQPydCJRViIktERAqdTocRI0Y4Ogyyo2LFimHUqFH45JNPEBkZqUxpO3bsWPj6+ubINYxGY46cBwC0IybBNKSzsi5/OJjDcuVh7FpARESUxxUtWhSjR49GwYIFAUBJZh8/fpztc+t0OvTt2xfvvvtutlpjk9MMG2exLi9PfZIPyt2YyBIR5SJnzpyxmJZ26tSp6Nu3L1atWmX3WL788ksMGDAA8+fPz/I5tm7dirfeeguvv/56qmW2b9+Ot956C4MHD87ydQgIDAzEmDFjlNnW7t+/jzlz5ij9pbNKkiR4e3vD19c3x6aZlWrUs1gXh/bCNKQzRDSH5cprmMgSEeUizyeykyZNQlBQkENieeedd/DCCy9k6xydO3dGz5490yzTsWPHdMtQxhQuXBhjx45VWmbv3r2LTz/9NMMTGdiT5s0xKbbJ/xsI8e/fDoiGHIWJLBERESkKFSqEd999V5k04dq1a1i8eHGWzyfLMs6cOYMjR47kaEIs1WwIzfSUM8XJ8ydDxNt2tjJyHrzZi4gol9i8eTP27duHp0+fYtw4cx/CyZMnAwASEhKwYMECXL9+HXFxcRg5ciTKlCkDwNz94Pz582jdujXi4uJw8+ZNnDt3DvPnz0fp0qWxb98+bN26FW5ubpBlGd26dUP9+vUBmGf+WrBgASIjI6HT6eDm5obevXujXLlySlxCCCxfvhwXL15EVFQUhgwZYtFSe/fuXSxatAiPHz+G0WhEpUqV8Nprr8HFxSXVx5p80gZ/f3/lsVDOKFmyJF5//XUsXrwYT548wd9//41NmzahW7dumT6XyWRSZocbNGhQjvWTBQCpcBFovt0MecKbQMSzqZXld3rzBrA8gi2yRES5RNeuXdG8eXMEBQVh5syZmDlzJlxdXQEAhw4dQt++fTFnzhzUqFED3333nXJcUveDgwcPolevXpg1axZatGgBjUaDkydPYsmSJZgwYQJmzZqF9957D5999hkuXDAPTP/jjz/C1dUVc+bMwcyZM1GmTBn89ddfFnEdP34cLVu2xMcff4wOHTrg66+/VvYZDAZMnjwZFStWxGeffYZ58+bh5s2babYA3r9/H7Nnz8Yrr7yCTz75BG+++Sb27duXk08lAahWrRq6dOkCT09PxMXFYc+ePcrUxZkhSRKCg4NRpUqVHOsja3F+jQbamYsgdepjsV3eYv9+4WR/TGSJiPKAqlWrKgPfV6pUCaGhoSnKVKtWTZlN591330WpUqWwbt06NG7cWLkBKDAwEFWqVMHOnTsBAA8fPsSjR4+QmJgIAOjUqVOKyRNKly6NYsWKKdeOiIhATEwMAOD3339HZGQkunTpAsA8jm3nzp3x66+/4tEj6zfu7Nq1C/nz50eDBg0AAD4+PkoLMeWsVq1aoX79+nB1dUVMTAxWr16NS5cuZeocOp0OzZs3R/fu3XO0NfZ5ms6WkyOI7Wtg+nqmza5HzoGJLBFRHlCgQAFl2cPDA7GxsSnKJN3gk9zNmzdx8uRJjBs3Tvl37949JXHt2bMnQkND8frrr+Pbb79FREQEihQpkua1ASjXv3HjBvLnz6+0HAPmZFmWZdy6dcvqY7l9+zYCAgIstmV0OkvKvP79+6NChQrQ6XRKV5J79+45OiyrNLOXWm44dcRizFnKfdhHlogoD9Bo0m+3SK1M06ZN8fLL1qcCLV++PL777jv8+eef2LNnD9577z288cYb6NixY5rnzen52m3xkzWZSZKEd955B9OmTcP169fx6NEjfPLJJ5g8eTK8vLwcHZ4FqUAhaMZ9AnnmKIvt4nYopGKOGb2DbIstskREuUjypDExMTHbMyqVKFECd+7csdh2+vRppWvBn3/+CZ1Oh6ZNm2L69Ono1q0bdu/eneHzlyxZEo8ePbIYqzQsLAwajQbFixe3ekyxYsUQHh5usS0iIiLD16TM0+l0GD16NAoXLgzgWT/lpJb5tBgMBvzwww+YO3cuDAaDrUOFFFQWmq9+stgmT3kX4vplm1+b7I+JLBFRLuLj46P0P12yZAn+/jt7Y2q+9NJLOHr0qNKnNj4+HitWrFD6vG7btg2nTp1SyhuNxhRdC9LSpEkTFChQANu2bVOO37ZtG1q1aqX06X1e27Zt8ejRI+XGo+joaBw4cCArD48ywdPTE6NHj1a6ity+fRtffPFFhlrX4+PjrXZnsRXJ1Q3Sq+9abJNn/A/CDok02Zckcvr3HRWKiIiwz7dESUJgYCDCwsJy/Gc1sj3Wn7rllfqLiorCtGnToNPp4OnpCZ1OhzNnzsDT0xMdO3ZEcHAwFi9ejNDQUFSuXBljxozBokWLcPLkSXh6eqJEiRKYNGmSxTl/++03bNy4Ee7u7pAkCe3atVNu6Nq/fz927NgBvV4Po9GI/PnzY+jQoShQoAAWL16MgwcPAgCaNWuGVq1aYf78+bh06RLKli2Lt99+G6VKlcLdu3fx7bffIjo6Whl+a9CgQXB1dcXWrVvx888/4/79+6hSpQrGjRsHFxcXi+G3ChQogKCgIKxfvx7lypXDxIkT4e7ubu+nPs+4du0aPvvsM0RHR8PV1RVt27ZF9+7dUy0vhEBUVBT8/Pyy/QtBZslbVkFsX2OxjcNyZZ69Pz/1en2G+70zkQUTWcoY1p+6sf7UjfXnXI4ePYoffvgBMTEx8Pb2xrBhw1CxYsVUyzuy/uSN30P8vOFZLG26QdNzkF1jUDtnTmTZtYCIiIgypW7duqhduzb0ej2ePHmCxYsXIyoqytFhWSV1G2CxLnZvgrz4EwdFQznN6UYtOHbsGDZt2gS9Xg9JkjB48OBUO/wDwL1797BixQo8ffoUjx8/hru7O1577TXO8kJERGRDAwYMwI0bN3Dt2jVERkZi3rx5mDRpUoqxYmVZxqVLl3D37l0EBATYfYQJSZKg+Xgx5LFDlG3i2AGI/sMguXvYNRbKeU7VInvlyhV8/fXXGDFiBKZOnYrmzZtjxowZiIuzPmdydHQ0pk6divbt22Py5MmYO3cuXFxcUtzNSkRERDlLo9HgvffeU8Yfvnv3LpYvX56inMlkwsGDB7F9+3aYTCY7R2kmFSoMzYxvLbaJlQscEgvlLKdKZDdv3owaNWogMDAQANC4cWOYTCbs378/1fJly5ZV+uVotVoMHToUFSpUsFfIREREeZaPjw/efPNNeHt7Q6PR4MGDBynKSJKEkiVLoly5cg4d71fyD4TUuLWyLo4dgLgf5rB4KGc4VSJ75swZiy4BGo0GpUuXxpkzZ6yWP3bsWIqkNSAgwGIWGSIiIrKdsmXLolOnTmjXrh3GjBmTYr9Op0ObNm3Qp08fm05RmxFS3zcs1uXxQx0UCeUUm72i5s2bh/fffz/D5Z88eYK4uDjky5fPYruvry+uXr2aonx8fDzu378PWZbxxRdfICIiAm5ubmjfvj1q1Khh9RoGg8FidAJJkpQhWuzxLTHpGpyBRp1Yf+rG+lM31p9za9u2bZr7naX+JBdX4NURkJd/oWwzjewH3eerHBiV83OW+rMmW4lseHg4zp07h6ioKMiybLHv4sWLmTpX0qwuer3eYrter7eY8SVJ0sDKa9euxeTJk1GqVCmcOXMGM2bMwLhx41C1atUUx2zatAnr169X1oOCgjB79my7z9H9/BzhpC6sP3Vj/akb60/dnKL+eg3ArWSJLJ4+gWlYTxTbdMhxMamEU9Tfc7KcyO7btw+LFi3KsfHEXF1dASDFeK4Gg0HZl1zSNIw1a9ZEqVKlAABVqlRB5cqVsXPnTquJbLdu3Szm/076ZhEREWGXQZolSUJAQADCw8M5DqIKsf7UjfWnbqw/9TIajfjpp5+g1WrRo0cPh3cvAADthHkwTX/2q7FITMCtVztB9/EiB0blvOz9/tPpdBluZMzyq2nTpk0YOHAgGjZsCG9v7xTNzaNHj87U+by9veHh4YHHjx9bbI+KioK/v3+K8j4+PtDr9Sn6wxYqVAiXLl2yeg29Xp+ixTeJPT8YhRD8IFYx1p+6sf7UjfWnPrIsK9MmO039lQyG5tPvIf9v4LNtEWEwfvMxtG9+6Li4nJzT1F8yWb7Zy8XFBe3atYOPj4/VPhODBw/O9DkrV66Ma9euKetCCISGhlptXdVoNChXrlyKAZgfP36MQoUKZfraRERElPO0Wi26du2KwYMHQ6vVOjocheSTH5r5z/WN/euw0yVqlLYsJ7IBAQGIj49Pdf/Dhw8zfc6uXbvi5MmTyjiwBw8ehEajQZMmTQAAEydOxOrVq5XyXbp0wfHjx5XhPm7fvo3Tp0+jTZs2mb42ERER5TyNRgN/f38ULVpU6RboLCRPL2i+3WSxTX6jC5NZFcly14IOHTrgq6++wosvvogiRYrAzc3NYv/69etRv379TJ0zODgYw4cPx/z58+Hi4gJJkjB+/HhlZIHExESLPrTVqlXDoEGDMGfOHLi5ucFkMmH48OGoWbNmVh8WERER5SGSRguUrQxcOqtsk6eOhHby5w6MijJKEln82tG7d+90y6xduzYrp7a7iIiIFDeZ2YIkSQgMDERYWBi/7akQ60/dWH/qxvpTL1mWce3aNfj6+qJgwYJOOYSTkE2Qh3az2Kb5ep15uC6y+/tPr9fb/mavggUL4qWXXkp1/7p167J6aiIiIsolTCYTfvvtNwDAoEGDnGLUgudJGi00i7ZAfqOLsk0e3gvaxVsdGBVlRJZfTeXLl0fTpk1T3W9tEgMiIiLKWyRJQtGiReHq6uqUrbFJJEmC1KYbxO5nfWbl/T9D07SdA6Oi9GS5a0Fuwq4FlBGsP3Vj/akb60/d1FJ/ybsYhMUlIjQ2AWW++hFFihRxcGSOlSu7FgDmfi9//PEHTp06hSdPnsDb2xs1atRAw4YNne7ORGfx9OlTxMbGws3NTflmmpiYCKPRCK1WazH5Q9LsZW5ubsrzmTTNrkajsbjBLjNl4+LiIISAq6urMhSK0WhEYmKixbS9WS3r4uKi/HRkMpmQkJCQrbLx8fGQZdliHODMlJVlWRlhw8PDQymbkJAAk8kEnU4HFxeXdMvKsozExERlmxACcXFxAAB3d/cU9Zn8vOmVzUjd58TrxFp95ubXSfL69PT0zHDZ9F4nyeszM2UzU/dZfZ3k9s+I5M9PWvXpiM+IzNZ9XvuMePr0KUwmkxKDs31GeHh4mLsYTPkK37/aGxP/vQkZgKZOHcyZOxd9+/YFOZ8sZ5sJCQmYMmUKvv76axw6dAinT5/GoUOH8NVXX2Hq1KkWf/DpGS8vLwQHByMyMlLZ9s033yAkJAQTJkywKFu1alWEhITgzp07yrbly5cjJCQEo0aNsihbt25dhISE4PLly8q2n376CSEhIRg2bJhF2aZNmyIkJARnzpxRtm3duhUhISEYNGiQRdn27dsjJCQER48eVbbt2bMHISEh6NOnj0XZHj16ICQkBPv371e2HTp0CCEhIejcubNF2VdeeQUhISH4+eeflW0nT55ESEgIWrZsaVF2yJAhCAkJwcaNG5Vt58+fR0hICBo1amRRdsSIEQgJCcGPP/6obLt+/TpCQkJSjGYxZswYhISEYMmSJcq2e/fuISQkBBUqVLAoO2XKFAQHB2PmzJnKtujoaISEhCAkJMRiZrjZs2cjJCQEs2fPVrYZjUalbHR0tLL9yy+/REhICKZMmWJxvQoVKiAkJAT37t1Tti1ZsgQhISEYM2aMRdmaNWsiJCQE169fV7b9+OOPCAkJwYgRIyzKNmrUCCEhITh//ryybePGjQgJCcGQIUMsyrZs2RIhISE4efKksu3nn39GSEgIXnnlFYuynTt3RkhICA4dejbF4/79+xESEoIePXpYlO3Tpw9CQkKwZ88eZdvRo0cREhKC9u3bW5QdNGgQQkJCsHXrs35qZ86cQUhISIquTcOGDUNISAh++uknZdvly5cREhKCunXrWpQdNWoUQkJCsHz5cmXbnTt3EBISkmLc6gkTJiAkJATffPONsi0yMlKpz+RmzJiBkJAQzJs3T9kWFxenlE1KVABg3rx5CAkJwYwZMyzOkVSWnxEpPyOOHDmC4OBgp/2MCAkJwZdffqls42fEM506dYKXlxf++OMPZZuzfkaEQYfx/yWxACALgTGjR+Pu3bsg55PlFtm1a9ciJiYG//vf/1C2bFl4eXkhJiYGly5dwrp167B27doUL2QiIiLKW4xGI9q0aYOmTZtCluX0D3Cw0NDQFNtMsozQ0NA838XAGWW5j+w777yDGTNmwMfHJ8W+x48fY8KECRbfTJ2ZPfvI+vj4IDw8nF0LVNq1oHjx4nj48KEyTR9/Nsx6WUd0LUjq4xUXF8euBSqre0mS4O/vr7QqOuNnBLsWWK97g8Gg/PoxYMAAJTZn+4xIqs+7d++ibt26Fkm3FsDhN3qj2ORnv7bkJc7cRzbLiex7772Hzz77LMv7nQlv9qKMYP2pG+tP3Vh/6iXLMu7du4eCBQtCr9c79cgFSX788Ud8+OGHkGUZWgAfVymJPsUL5dnhuJw5kc1yH9mkF6Y19+7dU8XPB0RERGRbGo0GRYoUQalSpVRzI3i/fv1w9OhR/LRmDQ43q4I+xQsBAOQfvnJwZPS8LPeRbdy4MaZMmYLOnTsjODhY6SN7+fJlbN++Hc2aNcvJOImIiIjspkiRIihSpAhMP7go28TBX4ABbzswKnpelhPZbt264ebNm1i2bFmKfXXr1kX37t2zFRgRERGpnyzLuHXrFh4/fgxvb29VdC0wGo3KqAktPvkemlEDHRwRpSbLiaxWq8X777+Pf//9F//8848yjmz16tVRsWLFnIyRiIiIVMpkMuGXX34B4LxT1D4vMTERr7/+OgDzEF2uyfaJm1chlSjjmMAohWy/mipVqoRKlSql2P7gwQMUKlQou6cnIiIiFZMkCYULF4aLi4sqWmMBc8y1atVSlpOTp70HzeerIHl4OSI0eo7Nel3PnTvXVqcmIiIildDpdOjSpQtee+01VbTGAuYh0LZs2YItW7bA3d0dmvemWuyXPx7toMjoeZl6Rf3444/Ily8fOnbsmGKWkeeFh4dnKzAiIiIiZyBVrA74BQAR/+U24bch7odB8g90aFyUyRbZI0eO4K+//gIAXL161SYBERERETkbzfSFFuvy+KEc09gJZKpF9tNPP1XGgAsICMDkyZNTLTt6NJvdiYiI8jqj0Yjt27dDr9ejbdu2yixgziwuLg49evQAAGzYsME825pGA+nVdyGWf66Uk8cOgXbWd44Kk5DJRDZpKj0AGDNmTJpl09tPREREuZ8QAhEREcqyGggh8M8//yjLSTQNW8D0+89A6CXzhof3IWQZkkomesiNstzrumDBgim2hYWF4datWwgJCbG6n4iIiPIWrVaLtm3bIn/+/KpojQXMDXfff/+9spyc5sM5kId2VdbloV2hmbkIkl+APUOk/2Q5kf3tt9+wYcMGNGvWDD169MCpU6cwZ84cmEwmuLu7Y8KECQgODs7JWImIiEhlNBoNSpQogcDAQISFhamiVVan06Fly5ZW90kaDVCpBvDv38o2edwb0CzaoprhxXKTLLeF//HHH2jXrh26dOkCAFi9ejUCAwMxa9Ys9OjRA2vXrs2xIImIiIichebdj1JsE2sW2z8Qynoi++TJE3To0AE6nQ7h4eG4fv06evXqhaCgIHTq1AmPHj3KyTiJiIhIhWRZxu3bt3H16lXIsuzocDLEZDLhwIEDOHDgAEwmU4r9kiRBu3grEFhc2Sb2bYd4GGHPMAnZSGQ1yTo2Hz16FB4eHsosGABU0w+GiIiIbMdkMmHnzp1YuXKl1aTQGSUkJKBv377o27cvEhISUi2nmTDPYl3+8HUIg8HW4VEyWU5k9Xo9zp8/j0ePHmH37t2oW7euMmNHZGSkal6sREREZDuSJKFgwYIoXLiwavqQSpKEihUromLFimnGLLm4QurzhsU2sWReKqXJFrJ8s1ePHj0wffp0GI1GuLm5KX1l9+3bh82bN6N69eo5FSMRERGplE6nQ48ePVR1s5e7uzt+/fXXDJXVtOgI099/AhfPAADEX4cgjEZIKpmOV+2y/CxXr14dn332Ga5du2Yx3Ja/vz+6d++OSpUq5ViQRERERM5K8/5UyEO7Keti4/eQXnrdgRHlHdn6uuDv7w9/f3+LbZUrVwZg7jfr5+eXndMTEREROT1JowUCigLhdwAA4tctkAv6Q9Oik4Mjy/1sNhXFxo0bbXVqIiIiUgmj0Yht27Zh+fLlMBqNjg4nQ+Li4tCzZ0/07NkTcXFxGTpG8+Fci3WxZjFEQrwtwqNkMtUiO27cOPj7+2PkyJHo3bu3rWIiIiKiXEIIgbCwMGVZDYQQ+PPPP5XljJA8vaCZ8Bnk6e8p2+QJb0E7d5lNYiSzTCWyfn5+KFCgAAAgX758aNWqldVyQgjs3bs3+9ERERGRqmm1WrRs2RK+vr6qGZrTxcUFCxcuVJYzSipZBlLX/hCbV5o3RD20RXiUTKYS2ffee/Yto2TJkujVq1eqZS9fvpz1qIiIiChX0Gg0KF26tKpGLdDpdOjUKWv9W6X2vZ4lsgDErVBIxYNyKjR6Tpb7yI4fPz5b+4mIiIhyG0mSAC9vZV3+8RsHRpP7ZTmRjYiIwM6dO/HLL79YbN++fTtu376d7cCIiIhI/WRZRnh4OG7evKmqKWqPHz+O48ePZ2mCJ6nv0GcrVy/kYGT0vCwnsrt27cK6desQEWE5r3B8fDymTp2KixcvZjs4IiIiUjeTyYStW7di2bJlqpn1MyEhAV27dkXXrl3TnKI2NVLNhhbrIioyp0Kj52Q5kT19+jSmTp2Kfv36WWzv2bMnxowZgzVr1mQ7OCIiIlI3SZLg4+ODAgUKqGqK2lKlSqFUqVJZill67qY2+YNXcygyel6WJ0SQJAnFixe3uq9MmTKIjY3NclBERESUO+h0OvTp00dVN3u5u7vj0KFD2TqH1Pt1iLVLcigiSk2WW2SfPn2a6otRlmU8efIky0ERERERqZmmZReLdXnLjw6KJHfLciJbpkwZfPPNN4iJibHYHhMTg4ULFyIkJCTbwRERERGplqu7sii2r4VIzHx/W0pblrsWvPzyyxg/fjwOHToEf39/eHl5ISYmBvfv34e7uztmzJiRk3ESERGRChmNRuzZswdubm5o3LixKiZFiI+Px5AhQwAAixcvhpubW5bOo5m1GPJ7/ZV1eXgvaBdvzZEYySzLLbIBAQGYPXs2GjRogNjYWFy5cgWxsbFo2LAhPv74YxQuXDgn4yQiIiIVEkLg1q1buHz5sir6xwLmLpL79u3Dvn37sjVkmOTlA6lxa4ttankO1CLLLbIAUKhQIQwfPjynYiEiIqJcRqvVokmTJqqaolav12PevHnKcnZoBrwN08FnY+7LM/4H7YR52TonPZOtRDbJgwcP8OTJEwQFBUGWZWg0WW7oJSIiolxEo9GgXLlyqhq1QK/Xo3fv3jl2PqlmQ4i//hsF4cYVCKMRki5HUrA8L1sZ5+HDhzFixAgMHz4cs2bNAgB8+eWXWLlyZTpHEhEREeUN0uD3Ldblt7o7KJLcJ8uJ7JEjR/D555/Dz88P7dq1UzpC9+7dGzdu3MC2bdtyLEgiIiJSJ1mW8eDBA4SHh6tqitqzZ8/i7NmzOTIbmaTTQ2rY0mKbuHk12+elbCSymzdvxgcffICJEyfi1VdfVRLZgIAAjBw5MtsDCRMREZH6mUwmbNy4Ed9++62qpqht06YN2rRpk6Upaq3RvDrCYl2e9l6OnDevy3IiGx8fj1q1alnd5+npqZpvXURERGQ7kiTBw8MD3t7eqpqiNiAgAAEBATkas2aU5dCkwpCYY+fOq7Lc09hgMCAxMREuLi4p9sXHx6eYKIGIiIjyHp1Oh/79+6vqZi93d3f89ddfOX5eqVwVyw0XzgBVaub4dfKSLLfIVqhQAR9//DGuXbtmsf3Bgwf4/PPPUaVKlVSOJCIiIsqjKlRTFuUvpjgwkNwhyy2y/fr1w8SJEzF27Fi4uLhAlmW89tprePr0KQICAvDmm2/mZJxEREREqqdp1xPy+X+Udfn4QWhqN3ZgROqW5UQ2f/78mDNnDrZv347Tp0/jyZMn8Pb2RrVq1dChQwd4eHjkZJxERESkQkajEfv374e7uzvq1aunikkR4uPjMWKE+easL774IstT1FojJWuRBQCxaC7ARDbLspzI3rhxAwDQvXt3vPTSSzkWEBEREeUeQgiEhoYCAOrWrevgaDJGlmXs2LEDADB//vwcP79m7jLIHwxS1kXUQ0i+BXP8OnlBlhPZ0aNHo0iRIpg0aRLy58+fkzERERFRLqHVatGwYUP4+PioojUWMM/sNWPGDGU5pz2ftIoj+yG17ZHj18kLspzIFihQAHPnzoWOU6wRERFRKjQaDSpVqqSqUQv0ej1effVVm15D6vIyxJZVAACxbwfARDZLsjxqgb+/f5pJ7L59+7J6aiIiIqJcTaqcbNitRw8gZHVMFuFsspzItmvXDqtXr4bRaLS6f/fu3VkOioiIiHIHIQQeP36Mhw8fqqI1FjD3kb127RquXbtmswmepFIhFuvi+B82uU5ul+V+Abt378adO3fwyy+/ICAgIMUdfeHh4dkOjoiIiNTNaDRi7dq1AIBBgwapoktifHw8Gjc2jyRw+fJl243EVL4qcOE0AEB89ylEnRdVM/uZs8hyi+zVq1dRtGhRlCpVKkeHpSAiIqLcxcXFBa6uro4OI1N8fHzg4+Nj02toOvaxWJff6GLT6+VGWf5aFBAQgMmTJ6e6f/To0Vk9NREREeUSSTdOqelmLw8PD5w/f972FwqpmGKTEIKtspmQ6RbZqKgobNy4Efny5cO2bdvw9OlTq+UmTZqU7eCIiIiIcitJo4F28VbLjffuOCYYlcpUi2xERATGjRuH6OhoAMDp06exe/duzJ49G56enhZlvby8Mh3MsWPHsGnTJuj1ekiShMGDB6N48eLpHrdr1y4sXboUkydPRqVKlTJ9XSIiIiKHKRYE3DZPGiFPHJYyuaVUZapFdu3atXB3d8fgwYMxduxYDBw4EAaDAZs3b852IFeuXMHXX3+NESNGYOrUqWjevDlmzJiBuLi4NI+LjIzE1q2scCIiImdkMpmwf/9+bN68GSaTOoaYSkhIwMiRIzFy5EgkJCTY/HpSK8u+scJosPk1c4tMJbIXL17ExIkT0apVK1SvXh3t27fHBx98gHPnzmU7kM2bN6NGjRoIDAwEADRu3Fh58adl6dKl6NatW7avT0RERDlPlmVcunQJ//zzj82GssppJpMJ69atw7p16+ySfGsaNLdYl6eOtPk1c4tMdS3QarXw8/Oz2BYcHIzY2NhsB3LmzBn07NlTWddoNChdujTOnDmDdu3aWT3mxIkT0Ol0qFatWravT0RERDlPo9Ggbt268Pb2hkaT5cGS7Eqn02HChAnKsj1IDVtAHNprXgm7ZZdr5gaZqp3Uhs6wNg/xmDFjMHv27Ayd98mTJ4iLi0O+fPkstvv6+uLq1atWj4mPj8eaNWswfvx4GAwZa4I3GAwWZSVJgru7u7Jsa0nX4N2I6sT6UzfWn7qx/tRLp9OhevXqCAgIQHh4uCpGLXB1dcWwYcPsek3Nq+/ClJTIAoDRAEnvYtcYUuPM779MJbJGoxEPHjxIsd1kMqXYntqMX9Yk9T95PiHW6/Wp9k1Zu3YtWrVqhfz58+P+/fsZus6mTZuwfv16ZT0oKAizZ89O0cpsawEBAXa9HuUs1p+6sf7UjfWnbqy/tCVvh813+Qw8W3R0WCzWOGP9ZSqRvX37NoYPH251X2rbMyKppff5llWDwWC1FfjatWu4cuUKXnnllUxdp1u3bujY8dmLIumbRURERKYS76ySJElV30jJEutP3Vh/6sb6Uy8hBOLi4uDn55fqkJ3ORpZl3Lt3DwBQuHBhu3WJkCq/AHH2JADg0YE9iK5Y0y7XTY+93386nS7DjYyZSmTz5cuHVq1apVtOCIG9e/emWy6Jt7c3PDw88PjxY4vtUVFR8Pf3T1H+5MmTSExMxNSpUwE8S4CXL18OT09PvPnmm1a/Nej1eqvdIJJithchBD+IVYz1p26sP3Vj/amPwWDAypUrAahnitq4uDjUrGlOIm06Re3zajUC/ktkxd9HnO617ozvv0y9mnx9fdGrV68MlT1x4kSmAqlcuTKuXbumrAshEBoaiu7du6co27NnT4sbw+7fv4+3334br776KseRJSIicjKSJDll/8q0OCLhlspXg5Immoyc5SsDMtVWPn36dJuUBYCuXbvi5MmTCA8PBwAcPHgQGo0GTZo0AQBMnDgRq1evztQ5iYiIyLH0ej2GDBmCiRMnpvqrqLPx8PDAjRs3cOPGDfu1xgKQCj73c/qZzDUK5kWZ+rrh4pLxu+cyUxYwD+M1fPhwzJ8/Hy4uLpAkCePHj1dGFUhMTLQ6OsHy5ctx+fJlZblo0aIYOXJkpq5NRERE5BSCKwJXzOPzy19O4yxf6XCqjip16tRBnTp1rO5LbSivV1991YYREREREdmPpt+bkKeMUNblfduhae5coxc4E3WMTExERESqZDKZ8Mcff2DHjh2qmqJ23LhxGDdunF2mqE1OKlbKYl2sXmTX66sNE1kiIiKyGVmWce7cOZw4cUJVU9R+//33+P777x2SfGumfm2xLm5anxyKnKxrAREREeUuGo0GL7zwguqmqH3//feVZXuTAotbrMvT3mNf2VQwkSUiIiKb0Wq1qFWrFgIDAxEWFuZ045Ba4+Ligv/9738OjUHz1oeQv5nl0BjUQB1fjYiIiIjykur1LFZF6CUHBeLcmMgSERGRzQghkJCQgPj4eFW0xgLmmB8/fozHjx87LGZJowFcXJV1eeYoh8Th7JjIEhERkc0YjUZ8//33mD17NoxGo6PDyZC4uDhUrFgRFStWRFxcnMPi0Lw9wWJdhN1yUCTOi4ksERERkROSKlSzWJcnDXdQJM6LN3sRERGRzeh0OgwePBgBAQG4d++eo8PJEHd3d1y/fh2AY0YtSE4z7hOLbgUi7DakwGIOjMi5sEWWiIiIbEaSJGg0Gmi1WkiS5OhwMkSSJOj1euj1eofHLAWVtViXJw1zUCTOiYksERERkROTOEVtqpjIEhERkc2YTCYcOXIEv/zyi2qmqE1MTMS0adMwbdo0JCYmOjocSH2GWKyLqEgHReJ8mMgSERGRzciyjNOnT+PPP/9UzRS1RqMRCxcuxMKFC51ipIXnuzeIf445KBLnw5u9iIiIyGY0Gg2qVq0KT09PVU1R++abbyrLzkBq2ALi0F4AgFi5AGjS1sEROQfnqB0iIiLKlbRaLerVq6e6KWonTpzo6DAsSC+2VRJZABBPn0Dy9HZgRM5BHV+NiIiIiPIwqXQ5i3X58ykOisS5MJElIiIimxFCQJZlmEwmVbTGAuaYDQYDDAaDU8Ustev5bCX0kuMCcSJMZImIiMhmjEYjvvvuO0yfPt0pbpzKiLi4OJQqVQqlSpVy6BS1z5M69bVYFwaDgyJxHkxkiYiIiFRA0uuBYqWUdXlYD8cF4yR4sxcRERHZjE6nw8CBAxEQEIDISHWMf+ru7o5z584py07lzk2LVfnwPmgaNHdQMI7HFlkiIiKyGUmS4OrqCjc3N4dP95pRkiQhX758yJcvn9PFrPl2k8W6WDYfQiXj89oCE1kiIiIilZAkCZrJn1tsE79sSqV07sdEloiIiGzGZDLhxIkT2L9/v6qmqP3000/x6aefOsUUtc+TigVZrIsN3zsoEsdjIktEREQ2I8syTp48id9//11VU9TOmzcP8+bNc9qRFjTzVlqsi6iHDorEsXizFxEREdmMRqNBxYoV4eHhoZoparVaLQYOHKgsOyPJ28diXf5gELSLtzooGsdhIktEREQ2o9Vq0ahRI1VNUevq6oqZM2c6Oox0ST0HQaxf5ugwHEodX42IiIiIyIKmTTeLdREX66BIHIeJLBEREZFaubgoi2LLjw4MxDGYyBIREZHNGAwGLF68GNOmTYNBJVOqxsbGomTJkihZsiRiY527lVNq10tZFnu3qaLrRk5iIktEREQ2JYRQzYgFSYxGo9OOWJCc1Ka7xbo4ut8xgTgIb/YiIiIim9HpdOjXrx/8/f0RExPj6HAyxM3NDSdOnFCWnZmk1wMVqwPnTgEAxK6NQL1mDo3JntgiS0RERDYjSRI8PT3h4+PjdNO9pkaj0SAwMBCBgYGqGDJM0/5Z9wLcueG4QBzA+WuHiIiIiFIXUtFiVdy96aBA7I+JLBEREdmMyWTCP//8g0OHDqlqitpvvvkG33zzjVNOUfs8SaMFPLyUdXHiDwdGY19MZImIiMhmZFnG0aNHsWfPHtXc8GU0GjF9+nRMnz5dFTd8AYDUsfezlSePHReInfFmLyIiIrIZjUaDsmXLwt3dXRX9TQHzbGS9evVSltVAKlYKSQNvifOnHRqLPTGRJSIiIpvRarVo2rSp6qaonT9/vqPDyJxChZ8t37vjuDjsTB1fjYiIiIgodckTWQAiPs5BgdgXE1kiIiIilXt+aDP5o3ccFIl9MZElIiIimzEYDFi+fDlmzZqlqilqK1SogAoVKjj9FLXJSS06PVt5eB9CJc93djCRJSIiIptKTExEQkKCo8PIlOjoaERHRzs6jEyReg+23HDprGMCsSPe7EVEREQ2o9Pp0Lt3b/j5+akmmXVzc8PBgweVZbWQJAlSw5YQh/YAAOT5k6FdvNXBUdkWW2SJiIjIZiRJQr58+VCwYEFVTVFbunRplC5dWjVDhiWRqtd1dAh2pa7aISIiIqLUVa1tsSpuhzooEPtgIktEREQ2I8sy/v33Xxw7dkw1M3sl3aC2fPly1dyglkR6rgVZXjrfMYHYCRNZIiIishmTyYRDhw7h559/hslkcnQ4GWIwGDB+/HiMHz9edYksAEiDRj5buRUKER3lqFBsjoksERER2YwkSQgKCkLFihVV1Ue2Q4cO6NChg+r6yAKAVKexxbr8vwEOisT2OGoBERER2YxOp0OrVq1UNUWtm5sbFi1a5OgwskzS6SF16guxbbWyTdy+DqlYKccFZSPq+5pBRERERGnSdO5rsS6vW+qgSGyLiSwRERFRLiS9OuLZyrlTDovDlpjIEhERkc0YjUasXLkS8+bNg9FodHQ4GRIXF4eaNWuiZs2aiIuLc3Q4WSbVamSxLlQyakRmsI8sERER2YwQArGxscqyGgghEB4eriyrleT63KxkV84DZSs5JhgbYSJLRERENqPVatG9e3f4+fmpZvgtV1dX7N69W1lWM6l2Y4jj5ul25bljc92UtexaQERERDaj0WhQqFAhBAQEqGYoK61Wi8qVK6Ny5crQarWODidbpLY9LNZF+B0HRWIb6nhFEREREVGmSSVKW6zLE9/KVX1lmcgSERGRzciyjIsXL+LUqVOqmqJ27dq1WLt2rSpn9nqe9Mowyw2PHzkmEBtgIktEREQ2YzKZ8Pvvv2PLli2q6SNrMBjw/vvv4/33388ViazmxbaAd75nG26HOi6YHMabvYiIiMhmJElC8eLF4ebmpqopaps3b64s5wZSnRch9m4DAIgzf0GqUsvBEeUMp0tkjx07hk2bNkGv10OSJAwePBjFixe3Wvbw4cPYt28fZFlGXFwc/Pz80L9/f/j7+9s5aiIiIrJGp9OhXbt2qpuidsWKFY4OI2cVepYbid92AC8PdWAwOcepvmZcuXIFX3/9NUaMGIGpU6eiefPmmDFjRqqDEX/55Zfo2LEjJk2ahBkzZsDFxQUzZ87MFT8DEBEREeUUqUFLi3U1fKHICKdKZDdv3owaNWogMDAQANC4cWOYTCbs37/favnatWujevXqAMxN/+3atcPdu3cRGpp7+n4QERERZZfk4Wm54e5NxwSSw5wqkT1z5gzKlCmjrGs0GpQuXRpnzpyxWv7999+3WNfr9QDAFlkiIiInYTQasWbNGnz55ZeqmqK2YcOGaNiwoaqnqE0h+Q1f9+46Lo4c5DSJ7JMnTxAXF4d8+fJZbPf19cX9+/czdI5Lly4hf/78KFeunC1CJCIiokwSQiA6OhqRkZGq+TlbCIHr16/j+vXrqok5I6SWnZVlce2CAyPJOU5zs1dCQgKAZ62qSfR6vbIvLQaDAdu2bcPrr78Onc76wzIYDBattZIkwd3dXVm2taRrqOWuTbLE+lM31p+6sf7US6fToUuXLihQoAB0Op0q6tDNzQ2bN29WltUQc0ZILq5ISsvF7k2Qer2WseOc+P3nNIls0lzGz3cLMBgMGZrneNGiRahfvz7q1KmTaplNmzZh/fr1ynpQUBBmz54NPz+/LEadNQEBAXa9HuUs1p+6sf7UjfWnTkWLFnV0CJlWrFgxR4eQ44xtuyBs7XfKekBAQKaSU2d8/zlNIuvt7Q0PDw88fvzYYntUVFS6w2n9+OOPcHV1RZ8+fdIs161bN3Ts2FFZT6q8iIgIu/TbkSQJAQEBCA8Pz1U/VeQVrD91Y/2pG+tP3Vh/zunuim+hadUl3XL2rj+dTpfhRkanSWQBoHLlyrh27ZqyLoRAaGgounfvnuoxmzdvxsOHD/H2228DgHJ86dKlU5TV6/Upui4kv5a9CCH4RlYx1p+6sf7UjfWnPrIs48aNG3j48CF8fX2d8ufp5xmNRvz8888AgHbt2qXaZVGVgisCV84BAOQ/frXoN5seZ3z/Oc3NXgDQtWtXnDx5EuHh4QCAgwcPQqPRoEmTJgCAiRMnYvXq1Ur5X375BQcPHkTbtm0RGhqKq1ev4sSJE7h5M3cMKUFERKR2JpMJe/bswfr161UzRW1iYiLefPNNvPnmm0hMTHR0ODlK89aHz1bu3ICQ1VEnqXGqrxjBwcEYPnw45s+fDxcXF0iShPHjxys3ZCUmJip9aOPi4rBkyRIIITBhwgSL8wwbNszusRMREVFKkiQhMDBQ+buuBpIkoX79+spybiL5+AKSBhAyAED88SukF9s6NqhskISztRE7QEREhF3Gnk16M6tlij6yxPpTN9afurH+1I3151xME94C7t0xr5QpD+2Hc9Isb+/60+v1Ge4j61RdC4iIiIjItjQD3n624ubuuEByABNZIiIiorykWMlny//+7bg4coBT9ZElIiKi3MVoNGLr1q3Q6XTo0KEDtFqto0NKV1xcHDp3Nt/Nv3XrVuVenVzDzcPREeQYJrJERERkM0IIPHz4UFlWAyEEzp07pyznNpLG8gd5EX4bUoA6J4BgIktEREQ2o9Vq0b59exQoUEAVrbGAebbRpOE+MzK7qCp5eAGxMQAAeeMP0A4b5+CAsoZ9ZImIiMhmNBoNihUrhjJlykCjUUfaodVq8eKLL+LFF19UTfKdWVKfIc9W/j7iuECySR2vKCIiIiLKMVLNBhbrQpYdFEn2MJElIiIim5FlGTdv3sSlS5cgqyRZMhqN2LNnD/bs2QOj0ejocGxCcrHsMiG2r3VQJNnDRJaIiIhsxmQyYdeuXVi9erWqpqgdOHAgBg4cmOumqLUQVFZZFNtWOzCQrGMiS0RERDYjSRL8/PxQpEgR1Uz3KkkSqlWrhmrVqqkm5qzQDB3j6BCyjaMWEBERkc3odDp069ZNVVPUuru7Y+fOnY4Ow+akgpbTwIqHESm2OTu2yBIRERHlVe7JJke4d9txcWQRE1kiIiKiPEpq30tZlretcWAkWcOuBURERGQzRqMRO3fuhIuLC1q1aqWKcVnj4uLQp08fAMCaNWty3xS1yXl4Plu+ct5xcWQRW2SJiIjIZoQQuHfvHm7duqWK/rGAOeYTJ07gxIkTqok5q6T6LSzWxb9/OyiSrGGLLBEREdmMVqtF69atkT9/flW0xgKAi4sLlixZoiznZpJeDxQuCty7AwAQxw9CqlTDwVFlHFtkiYiIyGY0Gg1KlSqF8uXLq2aKWp1Oh7Zt26Jt27bQ6XJ/m5+m56vKsji0R1WzfKnjFUVEREREtlGhmsWqOH7QQYFkHhNZIiIishlZlnH37l1cv35dNVPUmkwmHD58GIcPH1bNbGTZIbm6AcVKPdtw65rDYsksJrJERERkMyaTCdu3b8f333+vmqQwISEBvXr1Qq9evZCQkODocOxC06mvsiz+PeW4QDIp93f8ICIiIoeRJAn58+eHTqdTzXSvkiShbNmyynKeEFj82fLtUMfFkUlMZImIiMhmdDodevXqpbopan/77TdHh2FfhYtYrApZhqSCm/OcP0IiIiIisqkUSevVC44JJJOYyBIRERERUKa8siiO7ndcHJnARJaIiIhsxmg0YseOHVixYgWMRqOjw8mQpClq+/Tpg7i4OEeHYzdS4zbKsvh9lwMjyTgmskRERGQzQgjcuXMH165dU0X/WMAc88GDB3Hw4EHVxJwTpDovWqyrYWIE3uxFRERENqPVatGsWTP4+vqqaoraL7/8UlnOKyS9HtDpAaMBACCPHQzt7KUOjiptTGSJiIjIZjQaDUJCQlQ1aoFOp0P37t0dHYZDSK27QuxcZ16JfGAevcCJv4CwawERERERAQA03V6xWJfffyWVks6BiSwRERHZjCzLuH//Pu7cuaOqKWpPnTqFU6dOqWY2spwkNe/4bOXpE8h/H3FcMOlg14J0GI1GxMbG5tj54uLikJiYmGPnI/ti/dmPh4cHdDp+RBGpnclkwubNmwEAgwYNUsX7OiEhAR06dAAAXL58GR4eHg6OyL6knoMg9m1X1uWvZ0Bu3taBEaXO+V9NDmQ0GvH06VN4e3tDk0OzW+j1ehgMhhw5F9kf688+ZFnGkydP4OnpqYo/ekSUOkmS4OXlBa1Wq5rpXiVJQrFixZTlvEbS66GZMA/y9PeVbXd6NIZ20RbAyZ4PSaih17WNRUREWE1OoqOj4eXllWNJLMBESO1Yf/YjyzJiYmLg4+OTI+eTJElVN5uQJdafurH+1EleuwRizxZlXarzIjRDRtn8unq9Hn5+fhkqyz6y6cjJJJaIMo7vPSIix9L0fh2oUktZl+o0cWA01vE3OyIiIiKySjtiEsS/J+EVcRex1es4XYs6E1kiIiKyGaPRiH379sHNzQ0NGzZUxaQI8fHxGDZsGABgwYIFcHNzc3BEjqWpXBP5AjsiNizM0aGkwN/ucpm///4bPXv2RNGiRfHiiy+iZ8+e6NixI1q0aIGVK1faJYadO3eiTZs26Rf8z2uvvYZFixbZJBZZllG1alVcuXIFPXv2RIUKFVC9enX07NkT3bt3R5MmTfDaa6/hzp07Nrm+NVu2bEGrVq1QtGhRZVv//v1RoUIFfPrpp3aLg4jIHoQQuHHjBi5evOh0rXmpkWUZu3fvxu7du1UzZFhexRbZXKZGjRpYv349ihYtiuHDh6N3794AgOPHj6NHjx7w9vZGly5dbBqDr68vSpcuneHyxYsXh7+/v01iOXXqFLy9vREcHIz169ejZ8+eKFasGObPnw/APMRKv379MGzYMGzZsiXtk+WQLl26wM/PD7169VK2rVy5Ej179rTL9YmI7Emr1aJx48bIly+fKlpjAfPNRnPmzFGWyXkxkc0jateujXLlymHnzp02T2QbNGiABg0aZLj8lClTbBbLb7/9hmbNmqW639XVFe3bt8fEiRMRExMDLy8vm8VCRJQXaTQaVKhQQVWjFuj1evTr18/RYVAGsGtBHmI0GqHX6xEaGqp0P1i9ejWGDBmCFi1aoEKFCkq5GTNmoFWrVujRowf69u2LCxcuWJxr/fr1aNWqFbp27Yo2bdrgo48+QnR0NP744w907NgRRYsWxa1btwCY+xqNGjUKHTt2RK9evfDSSy9h3759AIDp06ejbt26KVojk87fsWNHtG7dGtu2bVP2jRo1CtWrV8eIESMwY8YM9OjRA40bN8b+/ftTPOZ9+/almcgCUIbTSj5W4NWrV9GvXz907NgRXbp0waRJkxAXF6fsf/r0KUaPHo0WLVqgR48e6Ny5MzZs2KDs//TTT9GhQwf07NkT7du3x6pVq9KMIaP+/vtvdO/eHR07dkSHDh3w1ltv4fLlyzh79myK5/3jjz9G9erVMXLkSCXmnj17onTp0vjmm2/wzjvvoEOHDihatCgGDBiAokWLomXLltizZw8AYOzYsahQoQJeecU8PWFGXhdERET2xBbZTDJNfx94/Cjrx0sAMvtlNF9+aCfMy/I1AXO/zMuXL2PKlCkICgpSuh9s374dS5cuhV6vR+fOnQEAc+fOxcmTJ7F9+3a4urpi8+bN6NWrF/788094eXnh999/x/jx4/Hzzz+jdOnSePjwIVq1aoWePXuiUaNG+Oabb1CvXj3l2kuXLkVoaCi2bzfPErJmzRps3boVzZs3x4QJE+Du7o4///xTKf/7779j3Lhx2LlzJ4KDg3H+/Hl07NgRAQEBqF27Nj755BOMHDkSv/76K7Zs2YLx48djyZIlGD16NI4dO6ac5+HDh7h48WKarcORkZFYt24dunXrBk9PTwCW3Q0GDBgAo9GI1157DZMnT8Znn30GABg9ejSePHmC3bt3Q6fTYePGjfjmm2/Qo0cPAMCGDRuwadMmFC5cGA8ePEDr1q1RunRpi+clsx4+fIiXX34Zs2bNQpcuXWA0GvHqq69i//79GDJkSIrnfezYsbh3756y7unpifXr16Nu3brYtGkT1q1bh3z58qF379746quv0L17d7Rs2RItW7YEAEyaNAmnT5/GihUrAKT/uiAiskYIgUePHkGr1aqiNRb4f3v3HhVVvT5+/D3DDOAFBBUFr6DkDU2pSCwzK81rB1BUtJYp2dG8+z3e8oaaeryVpZAnjxTlSUVBKck8aaZp2bGSTnhFBNNEhAy56AAzsH9/8GMfR0AZhYGp57WWa7n3fGbvZ+aZzTzz2Z/92SVjZM+fPw/AQw89JNMB1mKSGUtlZ8GN6/f/L+s+nnOfhXNERIR6sdeOHTvYsmULvXr1MmsTEBCAg4MDWq2W+Ph4DAYDmzdvZuzYsTg4OAAQGBhIQUGB2iu6fv16tTADaNSoEbNnz8bV1bXcONLT08nOziYnJ0fd5/jx4yuMe/369fTr1w9vb28AOnbsyNNPP82GDRvM2vn4+KhtevTowZUrV7hx44b6+FdffYW/vz916tQxe96hQ4cIDg6mb9+++Pn5MWLECN555x318bi4OLKysnjppZcA0Ol0jBgxgujoaAoKCvjll1+Ii4vj1VdfVe86FRgYyMCBA9Vt7Nixg6ZNmwLQuHFj/P39+eqrryp8zZXxwQcfUL9+fXVoiE6nY9q0abRr187ibfXv358GDRoAEB0djbOzM0OHDmX37t3qF80XX3zB888/D1Cpz4UQQpTHZDIRExPDxo0bMZlMNR1OpeTn5/Pss8/y7LPPkp+fX9PhiLuQHllLNSi/WKu0++yRvR+3X+xVEQ8PD7Plixcvkp+fT3h4OFFRUep6Nzc3srOzATh79ixPPvmk2fNCQkIq3MeYMWM4cOAAjz/+OIMGDWLo0KF37SUtb/uenp5qj26p0kIRUHsE8/LycHFxAUqGFTz77LNltt+7d2/efvttiouLmTdvHps2bWLUqFFqj+zZs2cpKipi+PDh6nPy8/Nxd3fn2rVrnDt3DgAvLy/1ca1Wy4wZM9TlM2fOMHv2bG7duoVOpyM5OfmeQxzu5dy5c7Ru3dpsnZ+f331t6868AwQFBbFixQq+++47evToQWxsLCtWrAAq97kQQoiKODo62lyvZsOGDWs6BFEJUsha6EFP8de2W5xWdAXpwoULyxST96tNmzZ8/fXXHDhwgJ07dzJ8+HDGjx/PwoULH2i7t8deOr61tDexuLiYw4cPM3v27Aqfr9VqmTdvHnFxcfzrX/8y6yVu2LAhMTExZZ6j1+tJTEy8a1wnTpwgNDSUjRs3MnjwYACmT59e7afUyrsfeFFRUbk5Lu8Lxd3dnZ49exIbG4u3tzcFBQXqvcZLVeXnQgjx56DX6xk9erRNXexVt27de/6tF7WDbf08EtXO09MTR0dHLly4YLb+gw8+4LvvvgOgQ4cO/PLLL2aPx8fHk5SUVO42jxw5Ql5eHv379ycyMpJly5ap4y7L06FDB1JTU83W/fLLL3To0KHSr+PHH3/E1dUVT0/Pu7ZzdnZm5MiRREZGqqe8OnToQEZGBnl5eWo7o9HItGnTMJlMtG/fXo3p9sfDw8MBOH78OBqNhkGDBpk9/qDat2/PpUuXzNb997//5csvvwRQe5Rvjzs9Pd2ifQwdOpTPPvuMbdu2ERQUpK6vzOdCCCGEsDYpZIWZOnXqMG7cOKKiotTxpikpKWzevFkdizl16lS++OILLl68CEBaWhorVqygcePG5W4zNjbWbI5Wo9F413lmS7efkpIClJxSP3ToEFOmTKn06/jqq6/KHVZQnldeeYX09HR16EJgYCAeHh5mY3I3b96MVqtFp9PRunVrAgMD2bx5M0VFRQBs27ZNvYK/Xbt2FBUVqRew/f7771VS7I0dO5bc3Fx1TGphYSFLly5V5zh0dXWlefPm/PDDDwAkJydz+vRpi/YxcOBATCYT7733ntqbDJX7XAghhBDWplFsoY+/mmVmZpbbY5aTk4Ozs3OV7qu6hxYkJCSwfPlyjh07Rtu2bWnVqlWZO3plZGQwceJEjh07RqdOnejTpw9z5sxRHzeZTKxZs4bPP/8cNzc39Ho9r7/+Ol27dlXb7Ny5k02bNlGvXj3s7OyYNWsW/v7+HD16lJUrV5KQkICvry+LFi3i5s2bREREqHdHcXR0ZOnSpXh7e7Ns2TL27NlDTk4Ofn5+fPTRR2bbd3BwoLCwkMmTJ6uzKixatIhPP/0UgODgYEJCQpg+fbq6zzVr1jBjxgzmzp1L7969gZKib9SoUZw6dQoHBwe8vb358MMP1V7MCRMm8N133+Hr68sHH3xASkoKixYt4urVq7i4uNC2bVsWL15MgwYNMBqN3Lx5k8WLF3PixAlcXFxwd3dnxYoV6gVUb775Jtu3b8fLy4umTZty7do1kpKSCAoKolu3boSHh3P69Gl69OjBW2+9xbx58/jxxx9xdnZm2LBhzJw5s8L8Ll26FKPRiEajYdiwYYwePVp9/ODBgyxevBg3Nze6detGRkYGR44coU+fPqxdu5bg4GBOnDhBixYtePzxx1m7dm2ZfUyZMoXi4mIiIiLM1lfmc1HVqvIY1Gg0NnVqU5iT/Nkuk8nEkSNHqFOnDn5+fjZxU4TSaSMB1q5d+6e/Ra21jz+9Xo+bm1ul2kohyx+rkBUlhXrPnj1JTExUr7CvKpI/65JCVpSS/Nkuo9GoXiQ6duxYdbaX2uzWrVs89NBDAJw/f566devWcEQ1qzYXsrX/0ySEhXJycliyZEmVF7FCCCEsZ2dnR48ePXB2draJ3lgoKaQWL16s/l/UXlLIij8cb29vdX5ZIYQQNUur1dKlSxeb6lHX6/W8+uqrNR2GqAS52EsIIYQQQtgk6ZEVQgghRLVRFIW8vDzq1KljE72xUDIX+ZUrVwBo3ry5zd3M4c9EClkhhBBCVBuTycS2bdsA27nYKz8/H39/f0Au9qrtav+nSQghhBA2TafTlXv3wdqsTp06NR2CqAQpZIUQQghRbfR6PaGhoTZ1sVfdunVJTk6u6TBEJcigDyGEEEIIYZOkkBVCCCGEEDZJCtk/mISEBIKDg2nevDm9evUiODiYwYMH89xzz5W5Ve3d7N27l379+lm8/3/+85+cPHnS4ufdad++fezbt++e7aKjo/n222/V5dTUVPX1377eGm7evElwcDBt2rQhOjr6vrczY8YMunXrxvTp0ytsM3PmzHu2qYjRaOTNN98kKCiIgIAAnnzySd599927PicnJ4cpU6YwaNAg+vXrx1tvvVXm9GBSUhLBwcEMGTKEAQMGsHfv3jLb+fzzzxk4cCBBQUEMHTqUc+fOWRy/EMK2FBUV8fXXX/Ppp59SVFRU0+FUSkFBAbNmzWLWrFkUFBTUdDjiLqSQ/YPx9fUlJiYGgEmTJhETE0N8fDwrV65k3rx5fPLJJ5XajouLC23atLF4/5s3b+bUqVMWP+9OlS1kd+7cybFjx9RlLy8v9fVbW7169YiJian0bfUqsm7dOnr37n3XNmvXrr1nm4rMmzePzMxMdu3axSeffMKUKVP45ptv7vqcqVOn4ujoyGeffUZcXBzx8fFs2rRJfTwvL4+RI0cyatQodu3aRXh4ONOnTychIUFtk5CQwPTp0wkPD2f37t2EhIQwatQo8vLy7ut1CCFsQ3FxMWfPniUhIYHi4uKaDqdSioqK2Lp1K1u3brWZ4vvPqtZd7HX8+HF2796NXq9Ho9Ewbtw4WrZsWWXta0paWhqpqam0a9fugQud++Hn50f79u3Zu3cvAQEB92z/xBNP8MQTT1ghMmFNZ86cYdeuXfz000/qFcRBQUG0b9++wuecPn2a/fv3c/jwYaDkSt7Ro0fz1ltvMW7cOOzs7IiOjkZRFIKCggBo27YtzzzzDO+++y7//Oc/AYiIiOC5555TfyANHTqU5cuXs2PHDkJDQ6vzZQshapBWq+Wxxx7DycnJZuZj1el0zJ49W/2/qL1qVXaSk5OJiIhg5cqVeHh4cPjwYZYvX866devKnQbD0vY1Zdu2bcyePZvi4mK0Wi2rV69m5MiRVo/DZDKp94zOzMxk4cKFXLhwAUVR6NChA2+88Qaurq4cPXqUlStXkpCQwHfffUfLli2ZOXMmBw4coFevXjRt2pQTJ06QkZHBG2+8ofYMjhw5kszMTCIiIti5cyf+/v7MnDmzTBw3btxgyZIlJCUl4ejoSHFxMfPmzcPPzw+AZcuWcejQIQCCg4NxcnLigw8+KLOdqVOncurUKS5fvsyxY8do06YNq1evVh9PTU0lKiqK1NRUmjRpQnh4OK6urqSmpjJr1iyOHTvG2rVrOXjwICkpKaSlpXHmzBlMJhOrVq3i0KFDODs7Y29vT1hYGB06dAAgMTGRxYsXo9FoKCwsxNvbm7lz59KkSRN139evX2fKlCmkpKSg1WqJiIigVatW6uNfffUVa9euRaPRkJ+fz4svvsjYsWPvmr9169axbds2WrZsycMPP0xRUZHF9y3ft28fHTt2xMnJSV3n4OCAr69vhc85evQo9erVM7vtb7du3bh+/Tpnzpyhc+fOHD16lC5duphNr9OtWzfWr19vtp3bh0KU3rbyyJEjUsgK8QdmZ2fHI488YlOzFtjb2zNt2rSaDkNUQq36aRQXF4evry8eHh4APPXUUxQVFalFzYO2rwlpaWlqEQslp1jmzJlDWlqaVeP45JNPOH/+PMOHDwdg3LhxODs7s3//fvbv3w+UDEUA6NmzJxs3bjR7fump7C+//JIRI0YQGxvLmDFj1F+sUFKwu7m5qUMayitiAdLT07lw4QKffPIJsbGxzJ07l7Fjx5KdnQ3AggUL6N27N7179yYmJqbcIhZg/fr1+Pj4MHz4cGJiYsyKWID9+/fz7rvv8u9//5vs7GwiIyMB8+EH8fHxhIeHs3//ftq2bQvAmjVrOHHiBPHx8cTGxjJixAiGDRumngKfMmUKw4YNIyYmht27d3P58uUy07R89tlnrFy5ks8++wx3d3fefPNN9bGkpCRCQ0MJCwsjPj6ejz76iHXr1hEXF1fu64SS/L333nvs3LmT2NhYXnjhBf79739X2L4iZ8+epUmTJkRERDB06FACAgJYs2bNXceAXbp0qcxZhNLlS5cu3bVNTk4OWVlZ/P777+Tm5pZp06RJEy5fvmzx6xBCCCGglhWyiYmJajEBJT02bdq0ITExsUra14TU1NQyY4KKioq4ePFite87IiJCvdhrx44dbNmyhV69evHNN9/www8/MHHiRAA0Gg0TJkzg8OHD/Pe//73rNn18fNSeuR49enDlyhVu3LhhUVxeXl5ERkaqp2u6d++OXq83G09ZFQYPHoxOp1NPa5U3djcgIAAHBwe0Wi3x8fEYDAY2b97M2LFjcXBwACAwMJCCggL27NkDlBTiv/76K1DS07Bq1So6duxott0+ffpQr149APz9/c32HRERQbdu3Xj88ccBaNasGYGBgWa9l3eKjIykX79+tG7dGoBHHnkEHx8fi9+T7OxsDh48yK1bt4iJiSEqKooDBw7wt7/9rcLnGAwG7O3tzdaVvjcGg6HCNqXLBoNBbVdem9LHhBB/TIqiYDAYuHnzpk30xkJJzNevX+f69es2E/OfVa0ZWpCbm4vBYKBBgwZm611cXLhw4cIDt68pXl5eaLVas2LWzs4OT0/Pat/3pEmTGDFiRJn1586dw87OzuxUt5eXF1DSY9e1a9cKt9m0aVP1//Xr1wdKLvRxcXGpdFx6vZ5du3apPYparZbs7GwyMjIqvY3KuD1WJycncnNzy7Qp7c0vdfHiRfLz8wkPDycqKkpd7+bmpvYYz507lyVLlhAfH09AQAAhISG4urqabcfd3V39f/369c0uaDp37lyZwtfLy4sPP/wQo9GoDv+4XXJycpmLu5o3b17BKy+xaNEiTp8+DUCnTp1YunQpdnZ2aDQapk6dikajwdXVlQkTJjB58mSWLFlCo0aNymynTp06FBYWmq0r7cEtHcJTXpvS5dvvr15em9o0DEgIUfVMJhNbtmwBbOcWtQaDgYcffhiQW9TWdrXm01T6xXjnl7hery/3tKel7aFk2iGj0aguazQa9Uu0um6d16xZM1avXs2cOXPUMY2rVq2iWbNm1bK/6nb7mMzS98zSX6vvvfce69evZ+/evWoB3b179yr/1VuZ8aMVtVm4cCFPPvlkuY+NGTOGQYMGERsby7Zt29i4cSPbt2/nkUceUdvceUFDdfyiv9dndunSpWXWeXh44OrqqvaoArRo0QKAy5cvl1vItmrViszMTLN1pculP4YqauPs7KwW+c7OzmXaZGRkmP2gKk9VHZul27G122SKEpI/23V7zjQajU3k0BZjrk61+firNYVs6Rfr7YVm6fLtX7r32x5g9+7dZlMzeXl5sWrVqgpnETAYDOX2jllq9OjR9OnTh9TUVLy8vKxWxOp0unLj9/HxoaioiLS0NLWQLD1V3rlzZ/R6vfqL+fZtlBZnpcsVtSldzsvLU3ttb3f8+HG6du1Ku3bt1HVGo9FsOzqdjuLiYvR6Pbdu3cLBwaHcotPOzg6tVqvur169euqBdmdcGo2mzPtx53v00EMP4ejoyMWLF816QCMjI+nUqRM9evRg3759vPDCC0yZMoXXXnuNF154gbi4OLp37w6UHOh3vpbb992xY0d++eUXs/1evnwZb29v9Vf/ne/1Qw89xOXLl82ek5aWRuvWrS36jD755JPExsaabfv3338HqHBbvXv3ZsmSJVy6dEkdynPy5EkaN25M165d0Wq1PP3002zYsMHsfuqJiYn06tVL3WbPnj05deqUuqwoCidPnmTGjBkVvgZ7e/syveYP6vbecmF7JH+2KSwsrKZDsJgMKSirNh5/taaQdXJyom7duurp21I3btwwuxr8fttDyTRDgwcPVpdLv3AzMzMxmUxl2hcWFpYplO+Xm5sbbm5u6PX6KtvmvZhMpnL31b17dx577DE2bNjAqlWrUBSF8PBwnn76aXx8fDAajer7cfs2SodHlC6X16ZRo0Zcv34dg8HAM888U+5NCby9vdm+fTvp6ek0atSI77//nmvXrpltx9XVleTkZIxGI6GhoSxYsKDcKaJcXV3JysrCaDTSv39/9uzZoxbPd8auKEqZ9+PO90in0zFu3DgiIyMZNGgQLi4upKSk8N5776ljZGfMmIGvry9NmzbFZDJhMpnw9PRUt6Moitl2TSaT2b5fe+01+vXrx7fffoufnx9Xr14lNjaWJUuWVPheh4aGMmfOHJKTk2ndujU//fQTJ06coGXLlhZ9ngYMGMCaNWuIjIzklVdeobCwkPfff5+BAwfi6uqK0WgkJSWFp556ipMnT+Lq6kq7du3o27cv4eHhrF69GoPBQFRUFBMnTqSoqIiioiKCg4PZsGEDMTExBAYGkpKSwpdffsn27dvV+CZOnMjIkSNJSkrCy8uL2NhYtFotQ4YMqfA1FBYWcvXq1Uq/vrvRaDS4u7uTnp4uX1A2SPJn2yR/ts3a+dPpdJWeqrTWFLJQ0huYkpKiLiuKQmpqKkOGDKmS9nq9vsKenz/KgZWQkMDy5cuBkouK9uzZU+4dvTZv3syCBQvo27evOv1WeHg4gDr9FpQUXYsWLSI+Pl6dDWLZsmWEhISoUym99tprrFmzho4dOzJhwgRWrVrFp59+yquvvlpujFOnTuXXX39lwIABdOzYEU9PT/VKejs7O4KDgxkxYgTjx48nKCiIli1bVjjPaWhoKDNnziQgIIDnn3+eW7duMWbMGKCkByAsLIyzZ8+yY8cOcnJymDx5MosWLVIvdAsLC6NPnz7MmTNH3easWbMA+Mtf/qL++Hj33Xdp2LAhUNLD/vLLL1O/fn1u3bpF9+7defnll4GS6cJKpyCzt7dXp97KzMxk+PDh7Nixg3bt2hEZGUlYWBharRaDwcD06dMJDAwESgrl0vd69uzZrF69moCAAFJTUxk2bBgtW7bE29ubAQMGcOjQIWbOnMnatWvv+rko5ejoyMcff8z8+fPZtWsXGo0GX19fs9efnJxMy5Ytzcb9vvPOOyxYsIDBgwdTWFjIwIED+etf/6o+Xr9+fbZu3cq8efP48MMPyc/PZ926dWbTevn6+rJu3TomTpyIo6MjWq2WrVu3lttrf7uqPjYVRfnDHO9/RpI/2yb5s221MX8apRZFlJyczBtvvMGqVatwd3fn66+/ZuvWreq8sAsXLqRTp07qHKz3al9ZmZmZ5fYI5eTk4OzsXGWvD7Bqj6yoen/0/BUUFDBs2DBGjRpFSEhITYdTpcegRqOxqXkshTnJn22T/Nk2a+dPr9fbZo+st7c3kyZN4u2338be3h6NRsP8+fPVovTOU/33ai+EsExWVhbBwcG1oogVQggh7qVW9cjWFOmRFZUl+bMu6ZEVpSR/tk3yZ9tqc49srbohghBCCCGEEJUlhawQQgghhLBJUsgKIYQQQgibJIXsPdx+a1khhPXIsSeEEOJepJC9i7p165KbmytfqEJYWXFxMbm5uXJ/cyGEEHdVq6bfqm10Oh316tUjLy+vyrZpb29PYWFhlW1PWJfkz3rq1aun3gZZCCGEKI98S9yDTqeT6X8EIPkTQgghahsZWiCEEEIIIWySFLJCCCGEEMImSSErhBBCCCFskhSyQgghhBDCJsnFXmD1K6PlSmzbJvmzbZI/2yb5s22SP9tmrfxZsh+NIpdfCyGEEEIIGyRDC6zIYDAwZ84cDAZDTYci7oPkz7ZJ/myb5M+2Sf5sW23OnxSyVqQoCqmpqTIHqY2S/Nk2yZ9tk/zZNsmfbavN+ZNCVgghhBBC2CQpZIUQQgghhE2SQtaK9Ho9wcHB6PX6mg5F3AfJn22T/Nk2yZ9tk/zZttqcP5m1QAghhBBC2CTpkRVCCCGEEDZJClkhhBBCCGGT5BYbVez48ePs3r0bvV6PRqNh3LhxtGzZssrai+plST6+/fZbDh48SHFxMQaDATc3N1566SWaNGli5ahFqfs9nvbt28f7779PWFgYPj4+VohUlMfS/F27do0tW7Zw8+ZNsrOzqVOnDqGhobRt29aKUYtSluTPaDTy8ccfc+rUKerWrYvRaCQwMJDHH3/cylGLUiaTiejoaPbs2cP69evv+V129uxZtmzZgk6nw2Qy8dJLL9GxY0crRXsbRVSZ8+fPK6NHj1bS0tIURVGUQ4cOKePHj1du3bpVJe1F9bI0HyEhIUpCQoKiKIpSVFSkbNiwQZk2bZpSWFhorZDFbe73eLp+/bry2muvKcOGDVNOnjxpjVBFOSzNX3Z2tjJx4kTl1KlTiqIoislkUhYvXqwcPXrUajGL/7E0f9u2bVMmTpyo3Lx5U1EURUlJSVFGjhyppKamWitkcZtr164p8+bNUzZs2KAMGzZMuXbt2l3bZ2RkKKNHj1ZOnz6tKIqinDp1Shk9erSSkZFhjXDNyNCCKhQXF4evry8eHh4APPXUUxQVFXHo0KEqaS+ql6X58PPzo1u3bgBotVoGDBhAWloaqampVopY3O5+j6f333+foKAgK0Qo7uZ+/n62a9eOTp06AWBnZ8f48eNrpkdIWJy/ixcv0rZtW+rWrQuAl5cXdevW5eTJk9YKWdwmPz+fKVOm8Mwzz1Sq/d69e2nRooV6vHXq1IlmzZrx+eefV2eY5ZJCtgolJiaandLSarW0adOGxMTEKmkvqpel+fi///s/s+XSaUmMRmP1BSkqdD/H0w8//IBOp6Nr167WCFHchaX5O378eJmi1d3dnYYNG1ZrnKJ8lubP39+fs2fP8ttvvwHw008/kZOTg4uLizXCFXdo1aoV7u7ulW5/8uTJMkN42rZtWyP1i4yRrSK5ubkYDAYaNGhgtt7FxYULFy48cHtRvaoiH0lJSbi6utK+ffvqCFHcxf3kLz8/n+3btzN//nz58VHDLM1ffn4+GRkZFBcXs379ejIzM3F0dGTgwIH4+vpaK2zx/93P8de7d28KCgqYOXMmrq6upKWl4e/vT48ePawRsnhA165dw9/f32ydi4sLGRkZVo9FCtkqUlBQAFBmsmC9Xq8+9iDtRfV60HwYjUb27NnDK6+8gk4nh5W13U/+oqOj6du3L66urjXyx1f8j6X5u3XrFlCSw7CwMDw9PUlMTGT58uXMmzePhx9+uPqDFqr7Of6+/PJL4uLiWLlyJe7u7ly8eJHExEQ0Gk21xyseXEFBQa2pX2RoQRVxcHAAyp5WNhqN6mMP0l5UrwfNx6ZNm+jRo4dccVtDLM1fSkoKycnJ9O3b1yrxibuzNH9abclX16OPPoqnpycAXbp0oXPnzuzdu7d6gxVlWJo/RVH417/+Rd++fdXT2Z6eniQkJLB79+7qD1g8MAcHh1pTv0jXURVxcnKibt26ZGdnm62/ceNGuVNYWNpeVK8HycfHH3+Mg4MDISEh1RmiuAtL83fixAkKCwtZunQp8L8v4KioKOrVq8eECRMsGi8mHoyl+XN2dkav15cZD9u4cWOSkpKqNVZRlqX5y8nJ4ebNm7i5uZmtb9KkCf/5z38YOnRotcYrHlzTpk1rTf0ihWwV6ty5MykpKeqyoiikpqYyZMiQKmkvqtf95CMuLo7r168zefJkAPX5bdq0qd5gRRmW5C84OJjg4GB1OSMjg8mTJzNmzBiZR7aGWJI/rVZL+/btuXHjhtn67OxsGjduXN2hinJYkj8nJyf0ej1ZWVlm67OysuSMpI3o3Lkz58+fN1uXkpJCly5drB6LDC2oQoGBgZw4cYL09HQAjhw5glar5emnnwZg4cKFbNu2rdLthXVZmr8vvviCI0eO0L9/f1JTU7lw4QI//PADly5dqpH4/+wszZ+oXSzNX0BAAN9//7161fuvv/7Kzz//TL9+/awfvLAof6XrDx48SF5eHlBSBP38889ysVct9c4777BhwwZ1eeDAgVy+fJmzZ88CcObMGa5cucKAAQOsHpv0yFYhb29vJk2axNtvv429vT0ajYb58+dTp04dAAoLC83GlNyrvbAuS/JnMBiIjIxEURQWLFhgtp2JEydaPXZh+fFXKioqSu1ZiIqKonnz5kyfPt2aoQssz1/Xrl0ZO3Ysq1evxtHRkaKiIiZNmsSjjz5aUy/hT83S/L388svs3LmTN954A3t7e/Lz83nxxRdrpBASJXf1WrZsGTdv3gRKCtdGjRqp00wajUazC/Hc3NyYO3euemcvo9HI3LlzywwXsQaNoiiK1fcqhBBCCCHEA5KhBUIIIYQQwiZJISuEEEIIIWySFLJCCCGEEMImSSErhBBCCCFskhSyQgghhBDCJkkhK4QQQgghbJIUskIIIYQQwiZJISuEEEIIIWyS3NlLCCEEAIsWLeLq1atkZ2ezY8cOoORuZz/++CPXrl0jLCwMHx+fGo5SCCH+RwpZIYSwgsLCQubPn8+NGzfIzs6mefPm6HQ6iouLuXXrFg0bNiQoKIjHHnusxmJcunQpO3bsICYmRl03ZswY/Pz8WLJkSY3FJYQQFZFCVgghrMDe3p41a9aoheLrr79OkyZNgJIi9x//+Adr1qxh4cKFdO7cuYajFUII2yBjZIUQoobZ29vzwgsvoCgKX3/9dU2HI4QQNkN6ZIUQohYoKioCIDc3V11nMpmIiYnhm2++UYchPPXUUwwZMgSt9n/9EPn5+Wzfvp3vv/8eR0dHADw9Penbty8dOnQA4D//+Q/79+8nNzeX4uJidDodgwYNomfPnlZ8lUIIUbWkkBVCiBqWl5dHbGwsgNnFVG+//TZJSUksXboUd3d3rl69SlhYGFlZWbz66qtASbG7bNkyCgoK+Pvf/46zszN5eXmsWLGCTz/9VC1kDxw4wKOPPsqAAQMAuHTpEosXL8bBwQE/Pz8rv2IhhKgaUsgKIUQN+Pvf/45OpyM3N5esrCzq16/PX/7yF/r37w/AyZMnOX78OC+99BLu7u4AeHh48Pzzz7Nz504CAwNxc3Pj6NGjJCUlMXv2bJydnQGoX78+Q4YM4ejRo+r+QkNDadq0qbrcqlUrHn74YQ4cOCCFrBDCZkkhK4QQNaD0Yq/SC70AgoOD0elK/iz//PPPAGqPaqlWrVqhKAqnTp2id+/e/PTTTwB4e3ubtXvsscfMZkBwcHAgKiqKc+fOUVRUhEaj4bffflOLXyGEsEVSyAohRA2yt7cnNDSUSZMm8dFHH/HXv/4VgJycHAA2btyoFrdQMpSgQYMGGAwG4H9jauvXr1/hPvLz81myZAlOTk68/vrruLi4ABAREcHp06er42UJIYRVSCErhBA1rH79+vTp04e9e/cSGBhIkyZN1J7SadOm4enpWeFznZycgJJxtqUF6p2SkpK4evUqI0eOrLCNEELYIpl+SwghaoGBAwcCsHv3bgC6du0KwMWLF83aFRcXs379eq5cuQJAt27dAEhOTjZrd+LECd555x0AjEYjABqNxqzNjRs3qix+IYSoCVLICiFELdCoUSOeeOIJDh8+zG+//YaPjw/+/v7ExsaSnp4OlEzRFR0dzdWrV/Hw8ACgZ8+etGvXjujoaHU4QnZ2Nlu3bqVLly4AtG/fHicnJ/bt20d+fj5QcjFZYmJiDbxSIYSoOhpFUZSaDkIIIf7oyrtFbYsWLfjb3/6mtrl48SKzZ8+mUaNGNGvWjNdff51du3Zx5MgRdDodOp2O9u3bExISYjYm1mAwEB0drc4jq9PpeP7553nuuefUNufPn2fLli2kp6fj4eGBh4cHWVlZ/Pzzz7Ro0YJp06axadMmrl69SnZ2Nq1bt2bo0KGcPXuWH3/8kWvXrtG0aVP8/f158cUXrfreCSFERaSQFUIIIYQQNkmGFgghhBBCCJskhawQQgghhLBJUsgKIYQQQgibJIWsEEIIIYSwSVLICiGEEEIImySFrBBCCCGEsElSyAohhBBCCJskhawQQgghhLBJUsgKIYQQQgibJIWsEEIIIYSwSVLICiGEEEIImySFrBBCCCGEsElSyAohhBBCCJv0/wCkIfeLo2itawAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches\n",
    "\n",
    "plt.plot(recalls, precisions, linewidth=2, label=\"Precision/Recall curve\")\n",
    "\n",
    "threshold = -6000\n",
    "idx = (thresholds >= threshold).argmax()\n",
    "# How does `argmax` work? It returns the maximum value of a numpy array, but if the\n",
    "# array has multiple occurrences of a maximum value, the index corresponding to the\n",
    "# FIRST occurrence is returned. `(threshold >= threshold)` produces a boolean array,\n",
    "# so the first instance of True is returned, or the first instance where a value\n",
    "# exceeds the threshold.\n",
    "plt.plot([recalls[idx], recalls[idx]], [0.0, precisions[idx]], \"k:\")\n",
    "plt.plot([0.0, recalls[idx]], [precisions[idx], precisions[idx]], \"k:\")\n",
    "plt.plot(\n",
    "    [recalls[idx]],\n",
    "    [precisions[idx]],\n",
    "    \"ko\",\n",
    "    ms=3,\n",
    "    label=f\"Point at threshold {round(thresholds[idx]):,d}\",\n",
    ")\n",
    "plt.gca().add_patch(\n",
    "    patches.FancyArrowPatch(\n",
    "        (0.79, 0.60),\n",
    "        (0.61, 0.78),\n",
    "        connectionstyle=\"arc3,rad=.2\",\n",
    "        arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
    "        color=\"#444444\",\n",
    "    )\n",
    ")\n",
    "plt.text(0.50, 0.62, \"Higher\\nthreshold\", color=\"#333333\")\n",
    "recall_dropoff = 0.8\n",
    "precision_dropoff = precisions[(recalls < recall_dropoff).argmax()]\n",
    "dropoff_kwargs = dict(color=\"grey\", linestyles=\":\")\n",
    "plt.vlines(x=recall_dropoff, ymin=0, ymax=precision_dropoff, **dropoff_kwargs)\n",
    "plt.hlines(y=precision_dropoff, xmin=0, xmax=recall_dropoff, **dropoff_kwargs)\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.title(\"Precision vs Recall (PR Curve)\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "save_fig(\"precision-vs-recall\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grey dotted line shows that precision starts to fall off sharply around 80% recall. In most cases, we'll probably want to select a PR trade-off at or before this significant drop, but our specific choice will depend on other characteristics of the project (recall the applications for kids versus shoplifting we discussed previously).\n",
    "\n",
    "If we'd like to aim for a specific precision level (say, 90%), we'll need to find the lowest threshold that gives us at least 90% precision. How do we accomplish this? We could write a function `idx_closest_to_value()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def idx_closest_to_value(array, value):\n",
    "    array = np.array(array)\n",
    "    return np.abs(array - value).argmin()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function reads well, but the same functionality is easily accessed through Numpy's `argmin` or `argmax`. Consider a snippet from the code we used to generate the ROC curve previously.\n",
    "```python\n",
    "threshold = -6000\n",
    "idx = (thresholds >= threshold).argmax()\n",
    "```\n",
    "\n",
    "How does `argmax` work here? `argmax()` returns the maximum value of a Numpy array, but if the array has multiple occurrences of a maximum value, the index corresponding to the *first* occurrence is returned. `(threshold >= threshold)` produces a *boolean* array (e.g. `[False, False, False, ..., True, True, ...]`), so the *first* instance of `True` is returned, which ends up being the first instance where a threshold value exceeds -6000.\n",
    "\n",
    "Instead of `idx_closest_to_value()`, we can just use `argmax` (or `argmin` in a similar manner)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(3370.0194991439557)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_90_precision = thresholds[(precisions >= 0.90).argmax()]\n",
    "threshold_90_precision\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make predictions (on the training set for now), instead of calling the classifier's `predict()` method, we can look at the `y_scores` above the 90% precision threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ...,  True, False, False])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_90 = y_scores >= threshold_90_precision\n",
    "y_train_pred_90\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then check these predictions' precision and recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision: 0.90\n",
      "recall: 0.48\n"
     ]
    }
   ],
   "source": [
    "print(f\"precision: {precision_score(y_train_5, y_train_pred_90):.2f}\")\n",
    "print(f\"recall: {recall_score(y_train_5, y_train_pred_90):.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now constructed a 90% precision classifier. It only catches about 50% of the digits representing $5$, but when it *does* catch a $5$, it classifies it correctly 90% of the time. By adjusting the threshold, we are able to create a classifier with virtually *any* precision we desire, but it's important to keep in mind that a high-precision classifier isn't useful if its recall is too low. If a business stakeholder says\n",
    "\n",
    ">\"I'd like to see this classifier attain 99% precision.\"\n",
    "\n",
    "an appropriate rhetorical response is\n",
    "\n",
    ">\"At what recall?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The ROC Curve\n",
    "The *receiver operating characteristic*, or *ROC curve*, is another common tool used with *binary* classifiers. This curve is similar to the PR curve, but instead it plots the TPR (remember that this is a synonym for recall or sensitivity) against the *false positive rate* (also known as the *fall-out* or FPR). The FPR is the ratio of negative instances incorrectly classified as positives\n",
    "\n",
    "$$\\mathrm{FPR=\\frac{FN}{TN+FP}=fall\\text{-}out}$$\n",
    "\n",
    "Another metric to increase our understanding here is the *true negative rate* (also known as the *specificity* or $TNR$) which is the ratio of negative instances correctly classified as negatives.\n",
    "\n",
    "$$\\mathrm{TNR=\\frac{TN}{TN+FP}=specificity}$$\n",
    "\n",
    "Note that $\\mathrm{FPR}=1-\\mathrm{TNR}$, so the ROC curve plots *sensitivity* (i.e. recall) versus (1 - specificity). To actually plot this curve, we'll employ the `roc_curve()` function to compute the TPR and FPR for various threshold values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)\n",
    "idx_90 = (thresholds <= threshold_90_precision).argmax()\n",
    "tpr_90, fpr_90 = tpr[idx_90], fpr[idx_90]\n",
    "\n",
    "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve\")\n",
    "plt.plot([0, 1], [0, 1], \"k:\", label=\"Random classifier's ROC curve\")\n",
    "plt.plot([fpr_90], [tpr_90], \"ko\", label=\"Threshold for 90% precision\")\n",
    "\n",
    "plt.gca().add_patch(\n",
    "    patches.FancyArrowPatch(\n",
    "        (0.20, 0.89),\n",
    "        (0.07, 0.70),\n",
    "        connectionstyle=\"arc3,rad=.4\",\n",
    "        arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
    "        color=\"#444444\",\n",
    "    )\n",
    ")\n",
    "plt.text(0.12, 0.71, \"Higher\\nthreshold\", color=\"#333333\")\n",
    "plt.title(\"Recall vs Fall-Out (ROC Curve)\")\n",
    "plt.xlabel(\"False Positive Rate (Fall-Out)\")\n",
    "plt.ylabel(\"True Positive Rate (Recall)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "save_fig(\"roc-curve\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we are faced with a trade-off. The higher the recall, the more false positives the classifier produces. The dotted line represents the ROC curve of a purely random classifier. A *good* classifier stays as far away from the diagonal dotted line as possible, reaching for the top-left corner $(0,1)$.\n",
    "\n",
    "One way to compare classifiers is to measure the ROC curve's *area under the curve*, AUC. A perfect classifier will have a ROC AUC equal to 1, whereas a purely random classifier will have a ROC AUC equal to 0.5. Scikit-Learn provides a function to compute the ROC curve's AUC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9604938554008616)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the ROC curve is so similar to the PR curve, it can be ambiguous to outsiders as to why they are significant in evaluating classifier effectiveness in their own ways. As a general rule of thumb, we should prefer the PR curve for evaluating classifier effectively whenever\n",
    "- The *positive class is rare* or\n",
    "- We are *more concerned with false positives* rather than false negatives,\n",
    "otherwise the ROC curve is more appropriate.\n",
    "\n",
    "As an example, looking to the previous ROC curve and ROC AUC score, we may naively think the classifier performs excellently. This high performing ROC curve is a direct consequence of *few* positive 5 classifications compared to the amount of $\\neg$ 5 negative classifications. Instead, the PR curve clearly indicates the classifier has room for improvement in that it can reach further toward the upper *right* corner."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Random Forest (RF) Classifier\n",
    "Let's now train a `RandomForestClassifer` and compare its ROC curve and ROC AUC score to those of the `SGDClassifier`. First, we'll need to get decision function scores for each instance in the training set, but due to the nature of RF classifiers, decision functions are not appropriate (we can learn about these details later). Instead, Scikit-Learn's `RandomForestClassifer` has a `predict_proba()` method. In general, Scikit-Learn classifiers have a `decision_function()` method, a `predict_proba()` method, or both. The `predict_proba()` method returns an array containing a row per instance and a column per class, each containing the *probability* that the given instance belongs to the given class (e.g. this instance has a 70% chance of representing a 5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ~45s runtime\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(\n",
    "    forest_clf, X_train, y_train_5, cv=3, method=\"predict_proba\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `roc_curve()` function expects labels and scores, but instead of scores, we can give it class probabilities. In the context of our binary 5-predictor, we can use the positive class' probability as the score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1]  # Grab all rows from the positive class.\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we are equipped with the RF classifier's fall-out and recall, we can plot its ROC curve against the SGD classifier's ROC curve to see how they compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_90 = (thresholds <= threshold_90_precision).argmax()\n",
    "tpr_90, fpr_90 = tpr[idx_90], fpr[idx_90]\n",
    "\n",
    "precisions_forest, recalls_forest, thresholds_pr_forest = precision_recall_curve(\n",
    "    y_train_5, y_scores_forest\n",
    ")\n",
    "threshold_90_precision_forest = thresholds_pr_forest[\n",
    "    (precisions_forest >= 0.90).argmax()\n",
    "]\n",
    "\n",
    "idx_90_forest = (thresholds_forest <= threshold_90_precision_forest).argmax()\n",
    "\n",
    "tpr_90_forest, fpr_90_forest = tpr_forest[idx_90_forest], fpr_forest[idx_90_forest]\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"SGD\")\n",
    "plt.plot(\n",
    "    [fpr_90], [tpr_90], \"o\", ms=3, color=\"red\", label=\"SGD Threshold for 90% Precision\"\n",
    ")\n",
    "plt.plot(fpr_forest, tpr_forest, label=\"Random Forest\")\n",
    "plt.plot(\n",
    "    [fpr_90_forest],\n",
    "    [tpr_90_forest],\n",
    "    \"o\",\n",
    "    ms=3,\n",
    "    color=\"blue\",\n",
    "    label=\"RF Threshold for 90% Precision\",\n",
    ")\n",
    "plt.plot([0, 1], [0, 1], \"k:\", label=\"Random\")\n",
    "plt.title(\"Recall vs Fall-Out (ROC Curve)\\nfor Several Binary 5-Classifiers\")\n",
    "plt.ylabel(\"True Positive Rate (Recall)\")\n",
    "plt.xlabel(\"False Positive Rate (Fall-Out)\")\n",
    "plt.legend()\n",
    "save_fig(\"recall-vs-fallout-comparison\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows that the Random Forest classifier is superior to the SGD classifier because its ROC curve is much closer to the top-left corner, and it has a greater AUC. Let's now calculate all the associated metrics for the Random Forest classifier.\n",
    "\n",
    "Note that we could use `cross_val_predict(forest_clf, X_train, y_train_5, cv=3)` to compute `y_train_pred_forest`, but since we *already have* the estimated probabilities (`y_probas_forest`), we can get the same predictions of which instances represent 5 or $\\neg$ 5 by using a probability threshold of 50% (i.e. if an instance's probability of being a 5 is greater than or equal to 50%, classify it as such)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "               Precision Score: 0.9897\n",
      "                  Recall Score: 0.8725\n",
      "                      F1 Score: 0.9275\n",
      "                 ROC AUC Score: 0.9983\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_train_pred_forest = y_probas_forest[:, 1] >= 0.5\n",
    "precision_score_forest = precision_score(y_train_5, y_train_pred_forest)\n",
    "recall_score_forest = recall_score(y_train_5, y_train_pred_forest)\n",
    "f1_score_forest = f1_score(y_train_5, y_train_pred_forest)\n",
    "roc_auc_score_forest = roc_auc_score(y_train_5, y_scores_forest)\n",
    "\n",
    "precision_report_str = \"Precision Score\"\n",
    "recall_report_str = \"Recall Score\"\n",
    "f1_report_str = \"F1 Score\"\n",
    "roc_auc_report_str = \"ROC AUC Score\"\n",
    "\n",
    "report_str_forest = f\"\"\"\n",
    "{precision_report_str:>30}: {precision_score_forest:.4f}\n",
    "{recall_report_str:>30}: {recall_score_forest:.4f}\n",
    "{f1_report_str:>30}: {f1_score_forest:.4f}\n",
    "{roc_auc_report_str:>30}: {roc_auc_score_forest:.4f}\n",
    "\"\"\"\n",
    "print(report_str_forest)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "## Multiclass Classification\n",
    "Whereas binary classifiers distinguish between two classes, *multiclass classifiers* (also known as *multinomial classifiers*) can distinguish between more than two classes.\n",
    "\n",
    "Some algorithms (such as Logistic Regression classifiers, Random Forest classifiers, and naive Bayes classifiers) are capable of handling multiple classes natively. Others (such as SGD classifiers or support vector machine (SVM) classifiers) are strictly binary classifiers. There are various strategies we can use to perform multiclass classification with multiple binary classifiers.\n",
    "\n",
    "One way to create a system that can classify the digit images in the MNIST dataset into 10 classes is to train 10 separate binary classifiers. Apply the same approach for constructing our 5-detector, we'd train a 0-detector, a 1-detector, a 2-detector, and so on. To classify a given image, we'd obtain the decision score from each classifier for our instance and then select the class corresponding to the classifier with the highest overall score. This strategy is called the *one-versus-the-rest* (OvR) strategy or the *one-versus-all* (OvA) strategy.\n",
    "\n",
    "Another multiclass strategy is to train a binary classifier for every pair of digits. This means we'd train a classifier to distinguish 0 from 1, 0 from 2, ..., 0 from 9, 1 from 2, 1 from 3, ..., 1 from 9, and so on. Mathematically, we'd be training 45 classifiers because ${10\\choose2}=\\frac{10!}{2!(10-2)!}=45$. This is called the *one-versus-one* (OvO) strategy. To classify a single instance with this strategy, we will need to run an image through all 45 classifiers to see which one wins the most \"duels\". The main advantage of OvO is that each classifier only needs to be trained on *part* of the training set for the two classes it must distinguish between.\n",
    "\n",
    "Some algorithms such as SVM classifiers scale poorly with the size of the training set. For these algorithms, OvO is preferred because it is faster to train many classifiers on small training sets than to train few classifiers on large training sets. For most binary classification algorithms though, OvR is preferred."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-Learn detects when we try to use a binary classification algorithm for a multiclass classification task and automatically runs OvR or OvO depending on the algorithm. We can observe this functionality with the `sklearn.svm.SVC` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~3m runtime\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC()\n",
    "# Note we are using `y_train` and not `y_train_5`.\n",
    "svm_clf.fit(X_train.values, y_train.values)\n",
    "svm_clf.predict([SOME_DIGIT])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fit()` method trains the support vector classifier (SVC) on the training set using the origin target classes including all digits from 0 to 9 (`y_train`) rather than the 5-versus-the-rest target classes we constructed and defined previously as `y_train_5`. The `predict()` method then correctly classifies the `SOME_DIGIT` instance as a 5. Under the hood, Scikit-Learn actually used the OvO strategy, meaning it trained 45 separate binary classifiers, got their associated decision scores for the image, and then selected the class that won the most duels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we call the `decision_function()` method, we see that ten scores are returned per instance rather than just one. This is because there is one decision score per class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.72501977,  2.72809088,  7.2510018 ,  8.3076379 , -0.31087254,\n",
       "         9.3132482 ,  1.70975103,  2.76765202,  6.23049537,  4.84771048]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([SOME_DIGIT])\n",
    "some_digit_scores\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the highest score corresponds to the 5 class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(5)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.uint8(5)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a classifier is trained, it stores the list of target classes in its `classes_` attribute, ordered by value. In this case, the index of each class in the `classes_` array conveniently matches the class itself, but in general this isn't the case.\n",
    "\n",
    "If we want to force Scikit-Learn to use OvO or OvR, we can directly employ the `OneVsOneClassifier` or `OverVsRestClassifier` classes by creating an instance and passing a classifier to its constructor (and it does not even have to be a binary classifier). As an example, the following code creates a multiclass classifier using the OvR strategy based on an SVC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=uint8)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 20m runtime\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_clf = OneVsRestClassifier(SVC())\n",
    "ovr_clf.fit(X_train.values, y_train.values)\n",
    "ovr_clf.predict([SOME_DIGIT])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training an `SGDClassifier` is just as easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3], dtype=uint8)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 2m runtime\n",
    "sgd_clf.fit(X_train.values, y_train.values)\n",
    "sgd_clf.predict([SOME_DIGIT])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, Scikit-Learn used the OvR strategy under the hood, so since there are ten classes, it trained ten binary classifiers, and again, the `decision_function()` method returns one value per class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n",
       "          1823.73154031, -22320.14822878,  -1385.80478895,\n",
       "        -26188.91070951, -16147.51323997,  -4604.35491274,\n",
       "        -12050.767298  ]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([SOME_DIGIT])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that the SGD classifier is fairly confident in its prediction that `SOME_DIGIT` represents a 3, but 5 is the second-closest value. Since we know a classification of 3 is incorrect, let's evaluate this classifier to learn more. As usual, we'll employ cross-validation with the `cross_val_score()` function to evaluate this SGD classifier's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87365, 0.85835, 0.8689 ])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 3m runtime\n",
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This classifier gets over 85% on all test folds. To reiterate, if we used a random classifier, we'd get a 10% accuracy, so this is not such a bad score, but we can still do much better. [*Scaling*](https://towardsdatascience.com/all-about-feature-scaling-bcc0ad75cb35) the inputs can increase our accuracy. Remember that scaling is essential for machine learning algorithms that calculate *distances between data*. If data is unscaled, features with higher value ranges start dominating when calculating distances. Algorithms that are sensitive to the *relative scales of features* are usually those that employ numerical feature values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8983, 0.891 , 0.9018])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 10m runtime\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.values.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simply scaling the training data increased our SGD classifier's accuracy to above 89% on all folds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error Analysis\n",
    "If this were a real project we'd want to *shortlist promising models* and *fine-tune our system* with the following general checklists.\n",
    "\n",
    "#### Shortlisting Promising Models\n",
    "1. Is the dataset huge? If so, try sampling smaller training sets to train many different models in more reasonable time frames. Keep in mind that this approaches penalizes more complex models like neural networks or random forests.\n",
    "2. Train many quick-and-dirty models from different categories (e.g. linear, naive Bayes, SVM, random forest, or neural net) using standard parameters.\n",
    "3. Measure and compare the performance of each model using K-fold cross-validation to compute the mean and standard deviation of your chosen performance measure on each of the K folds.\n",
    "4. Analyze the types of errors the models make and ask yourself how a human would have adjusted the data to avoid these errors.\n",
    "5. Perform a quick round of feature selection and feature engineering, keeping track of the initial domain of features, and those chosen for the current round.\n",
    "6. Perform one or two more quick iterations of the five previous steps.\n",
    "7. Shortlist the top three-to-five most promising models, preferring models that can be measured by several different error metrics (e.g. mean absolute error, median absolute error, mean squared error, root mean squared error, explained variance score, coefficient of determination (R), and sample standard deviation to name a few).\n",
    "\n",
    "#### Fine-Tuning the System\n",
    "1. Use as much data as possible for these steps.\n",
    "2. Automate as many of these steps as possible.\n",
    "3. Fine-tune the hyperparameters of the shortlisted models with cross-validation.\n",
    "4. Treat the chosen data transformation choices as hyperparameters (e.g. replacing `NaN` with 0).\n",
    "5. Unless there are few hyperparameters to explore, prefer random search over grid search.\n",
    "6. If training takes a considerable amount of time, applying Bayesian optimization search (with Gaussian process priors) is appropriate.\n",
    "7. Try ensemble models. Combining the best-performing models often produces better performance than running them individually.\n",
    "8. Once a final model is chosen, measure its performance on the test set to estimate the generalization error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is just a tutorial, we'll assume that we've found a promising model and now we want to find ways to improve it. One way is to analyze the types of errors it makes.\n",
    "\n",
    "First, let's look at the confusion matrix. We'll need to make predictions using the `cross_val_predict()` function, and then call the `confusion_matrix()` function like we did earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5577,    0,   22,    5,    8,   43,   36,    6,  225,    1],\n",
       "       [   0, 6400,   37,   24,    4,   44,    4,    7,  212,   10],\n",
       "       [  27,   27, 5220,   92,   73,   27,   67,   36,  378,   11],\n",
       "       [  22,   17,  117, 5227,    2,  203,   27,   40,  403,   73],\n",
       "       [  12,   14,   41,    9, 5182,   12,   34,   27,  347,  164],\n",
       "       [  27,   15,   30,  168,   53, 4444,   75,   14,  535,   60],\n",
       "       [  30,   15,   42,    3,   44,   97, 5552,    3,  131,    1],\n",
       "       [  21,   10,   51,   30,   49,   12,    3, 5684,  195,  210],\n",
       "       [  17,   63,   48,   86,    3,  126,   25,   10, 5429,   44],\n",
       "       [  25,   18,   30,   64,  118,   36,    1,  179,  371, 5107]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 10m runtime\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix for multiclass classification can be difficult to decrypt in numerical form. Instead, it's often more convenient to look at an image representation of the confusion matrix using Matplotlib's `matshow()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(conf_mx, cmap=\"plasma\")\n",
    "plt.yticks(list(range(0,10)))\n",
    "plt.xticks(list(range(0,10)))\n",
    "plt.grid()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Absolute Number of Errors\", fontsize=12, color=\"black\", labelpad=10)\n",
    "plt.ylabel(\"Actual Classes\", fontsize=12, color=\"black\")\n",
    "plt.title(\"Predicted Classes\", fontsize=12, color=\"black\")\n",
    "plt.suptitle(\"Confusion Matrix (Absolute Number of Errors)\", fontsize=16, y=1.0)\n",
    "save_fig(\"confusion-matrix-absolute\", tight_layout=False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confusion matrix indicates the model performs reasonably well because most images are on the main diagonal, meaning they were classified correctly. The 5 square looks slightly darker than the other digits', which *could* mean that there are fewer images of 5 in the dataset *or* that the classifier does not perform as well on 5 as on other digits. In fact, we can verify the validity of these possibilities.\n",
    "\n",
    "We can refocus the plot on the errors. First, we'll need to divide each value in the confusion matrix by the number of images in the corresponding class so that we can compare *error rates* instead of absolute *numbers of errors*. Looking only as the absolute numbers of errors makes abundant classes unfairly look as though they perform poorly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll want to fill the diagonals with zeros to keep only the errors, and then we'll be ready to plot the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fill_diagonal(norm_conf_mx, 0)\n",
    "plt.matshow(norm_conf_mx, cmap=\"plasma\")\n",
    "plt.yticks(list(range(0,10)))\n",
    "plt.xticks(list(range(0,10)))\n",
    "plt.grid()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label(\"Misclassification Rate\", fontsize=12, color=\"black\", labelpad=10)\n",
    "plt.ylabel(\"Actual Classes\", fontsize=12, color=\"black\")\n",
    "plt.title(\"Predicted Classes\", fontsize=12, color=\"black\")\n",
    "plt.suptitle(\"Confusion Matrix (Error Rates)\", fontsize=16, y=1.0)\n",
    "save_fig(\"confusion-matrix-rates\", tight_layout=False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see the kinds of errors our classifier tends to make. Remember that rows in a confusion matrix represent *actual* classes while columns represent *predicted* classes. The column for class 8 is quite bright. This tells us that many images get misclassified as 8. Note though, the *row* for class 8 is not that bad. This tells us that images that actually represent 8 are generally correctly classified as an 8. Note also that the confusion matrix is not necessarily symmetrical. In the case of 5 and 3, we see they are often misclassified with each other (in both directions).\n",
    "\n",
    "Analyzing a classifier's confusion matrix often gives us insights into ways to improve our classifier. The above plot for our SGD classifier indicates that our efforts should be primarily spent on reducing the number of false 8 classifications. We could perhaps remedy this by gathering more training data for digits that *look like 8 but are not* so that our classifier can learn to distinguish them from a real 8. We could also engineer new features to help the classifier. An example of this would be writing an algorithm to count the number of closed loops within a digit (e.g. 8 has two loops while 6 has 1 and 5 has none). We could also preprocess the images in our dataset with [Scikit-Image](https://scikit-image.org/), [Pillow](https://pypi.org/project/pillow/), or [OpenCV](https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html) to make certain patterns (like closed loops) stand out more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing *individual* errors can also be a good way to gain insights on what our classifier is doing and why it is failing, but often this exercise is difficult and time-consuming. As an example, let's further investigate the relationship between 5 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3, 5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "size = 6\n",
    "pad = 0.2\n",
    "plt.figure(figsize=(size, size))\n",
    "for images, (label_col, label_row) in [\n",
    "    (X_ba, (0, 0)),\n",
    "    (X_bb, (1, 0)),\n",
    "    (X_aa, (0, 1)),\n",
    "    (X_ab, (1, 1)),\n",
    "]:\n",
    "    for idx, image_data in enumerate(images[: size * size].values):\n",
    "        x = idx % size + label_col * (size + pad)\n",
    "        y = idx // size + label_row * (size + pad)\n",
    "        plt.imshow(\n",
    "            image_data.reshape(28, 28), cmap=\"binary\", extent=(x, x + 1, y, y + 1)\n",
    "        )\n",
    "plt.xticks([size / 2, size + pad + size / 2], [str(cl_a), str(cl_b)])\n",
    "plt.yticks([size / 2, size + pad + size / 2], [str(cl_b), str(cl_a)])\n",
    "plt.plot([size + pad / 2, size + pad / 2], [0, 2 * size + pad], \"k:\")\n",
    "plt.plot([0, 2 * size + pad], [size + pad / 2, size + pad / 2], \"k:\")\n",
    "plt.axis([0, 2 * size + pad, 0, 2 * size + pad])\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.grid()\n",
    "plt.suptitle(\"Investigating $5$ vs $3$ Classifications\", fontsize=24)\n",
    "save_fig(\"error-analysis-5-vs-3\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two 5x5 blocks on the left of the above plot show digits classified as 3 while the two blocks on the right show images classified as 5. Some of the digits the classifier gets are (i.e. the bottom-left and top-right blocks) are so badly-written that even a human would have trouble classifying them (e.g. the digit in the tenth row and second column looks like a 1). Despite this, most misclassified images seem like obvious errors, and it's hard to understand why the classifier made the mistakes it. Our brains are fantastic pattern recognition systems. The visual systems of the eye do a lot of complex preprocessing before any information reaches the brain, so the fact that observing these errors *feels* simple doesn't mean that they *are* simple.\n",
    "\n",
    "The reason for the poor performance here is the use of the simple `SGDClassifier`, a linear model. All it does is assign a weight per class to each pixel, and when it sees a new image, it just sums up the weighted pixel intensities to get a score for each class. Since 3 and 5 differ by only a few pixels, an SGD classifier will easily confuse them.\n",
    "\n",
    "The main difference between 3 and 5 is the position of the small line that joins the top line of the digit to the bottom arc. A drawn 3 with this upper piece shifted to the left looks like a 5 and vice versa. In other words, the SGD classifier is sensitive to image shifting and rotation. One way to reduce the confusion between 3 and 5 would be to preprocess the MNIST digit images to ensure they are well-centered and not too rotated. This preprocessing would most likely help to reduce other errors as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilabel Classification\n",
    "Until now, each instance has been assigned to just one class. In some cases, we may want a classifier to output multiple classes for each instance.\n",
    "\n",
    "Consider a face-recognition classifier. What should it do if it recognizes several people in the same picture? It should attach one tag per person it recognizes. Say the classifier has been trained to recognize three face: Alice, Bob, and Charlie. When the classifier is shown a picture of Alice and Charlie, it should output `[1, 0, 1]`. Such a classification system that outputs multiple binary tags is called a *multilabel classification* system.\n",
    "\n",
    "Building a face-recognition classifier is beyond the scope of this tutorial, but we can look at a simpler example for illustration purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = y_train >= 7\n",
    "y_train_odd = y_train % 2 == 1\n",
    "# `np.c_` stacks two row vectors into columns and then joins them into a matrix.\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train.values, y_multilabel)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above creates a `y_multilabel` array containing two target labels for each digit image. The first indicates whether the digit is large (meaning it represents a 7, 8, or 9) while the second indicates whether the digit is odd. The next lines instantiate a `KNeighborsClassifier` (which supports multilabel classification -- not all classifiers do), and then we train it using the *multiple* targets array. Now when we make a prediction, the output contains two labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.predict([SOME_DIGIT])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classifications are correct. 5 is not large and it is odd.\n",
    "\n",
    "There are many ways to evaluate a multilabel classifier, and selecting the right metric again depends on the project. One approach is to measure the $\\mathrm{F_1}$ score for each individual label (or any other binary classifier metric we previously discussed) and then compute the average score. Let's implement this approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9764102655606048)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ~ 1m runtime\n",
    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
    "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method of error measurement assumes all labels are equally important, but this might not always be the case. In particular, if we have many more pictures of Alice than of Bob or Charlie, we may want to give more weight to the classifier's score on pictures of Alice. One option is to give each label a weight equal to the number of instances with its associated target label (known as the label's *support*). To accomplish this, we would set `average='weighted'` in the previous code block. [Scikit-Learn offers a few other averaging options and multilabel classifier metrics](https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics), but they are outside of the scope of this intro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multioutput Classification\n",
    "The last type of classification task we are going to discuss is *multioutput-multiclass* classification (sometimes more simply referred to as *multioutput* classification). This style is a generalization of multilabel classification where each label can be a multiclass, meaning it can have more than just two possible values.\n",
    "\n",
    "To illustrate this, let's build a system that removes noise from images. The classifier will take a noisy image representing a digit and will output a clean representation of the digit if it performs well (represented as an array of pixel intensities, just like the MNIST images). Notice here that the classifier's output is multilabel (one label per pixel) and each label can have multiple values (pixel intensity ranges from 0 to 255).\n",
    "\n",
    "It's valid to wonder how this type of machine learning differs from regression since the predictions are numerical values. In truth, the line between classification and regression is blurry. It's absolutely valid to argue that predicting pixel intensity is more akin to regression than classification. Taking this a step further, multioutput systems are not limited to classification tasks and can even be designed to output multiple labels per instance including both class labels and value labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notwithstanding this regression-classification blurriness, let's start by creating the training and test sets for our image cleaner by taking MNIST images and adding noise to their pixel intensities. The target images will be the original images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "RNG = np.random.default_rng(seed=42)\n",
    "noise = RNG.integers(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise\n",
    "noise = RNG.integers(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check an example of how adding noise warped our image data. Against everything we know, *just this once*, ***for example purposes***, let's look at an image from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_explore = 1000\n",
    "plt.subplot(121)\n",
    "plot_digit(y_test_mod.iloc[idx_explore])\n",
    "plt.subplot(122)\n",
    "plot_digit(X_test_mod.iloc[idx_explore])\n",
    "plt.suptitle(\"Adding Noise to a Digit\", y=0.87, fontsize=20)\n",
    "save_fig(\"adding-noise-to-a-digit\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now train a classifier to clean this image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_clf.fit(X_train_mod.values, y_train_mod)\n",
    "clean_digit = pd.DataFrame(knn_clf.predict([X_test_mod.iloc[idx_explore]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx_explore = 1000\n",
    "plt.subplot(131)\n",
    "plot_digit(y_test_mod.iloc[idx_explore])\n",
    "plt.subplot(132)\n",
    "plot_digit(X_test_mod.iloc[idx_explore])\n",
    "plt.subplot(133)\n",
    "plot_digit(clean_digit)\n",
    "plt.suptitle(\"Adding Noise to a Digit and then Removing It\", y=0.75, fontsize=20)\n",
    "save_fig(\"adding-noise-to-a-digit-and-then-removing\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cleaned image definitely looks similar to the target, but it's also evident that there's some erroneous data loss, but again, it would take some nontrivial investigation to understand exactly why.\n",
    "\n",
    "## Next Steps?\n",
    "This concludes our general tour of machine learning classification. We should now know how to select appropriate metrics for classification tasks, adjust the precision/recall trade-off according to specific project goals, compare classifiers, and build reasonably well-performing classification systems for a variety of tasks."
   ]
  }
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