{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework\n",
    "\n",
    "- Use this assignment to practice the chapter's concepts and coding skills in runnable Python code.\n",
    "- Sign in from the account menu before you start working on the assignment.\n",
    "- Write and run your code directly in the coding cells.\n",
    "- Multiple submissions are allowed. The highest score is kept.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**1. Testing on Training Data**\n",
    "\n",
    "A 1-nearest-neighbor classifier is tested on the same customers it was trained on. Its accuracy there is an honest estimate of how well it will classify new customers.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 1 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q1\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q1\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# Each training customer is its own nearest neighbor, so a 1-NN classifier is always right on its training data. Only a separate test set gives an honest estimate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**2. Scale Matters**\n",
    "\n",
    "A classifier uses annual income (in dollars) and number of late payments. Before computing distances, both attributes should be converted to standard units.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 2 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q2\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q2\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# Otherwise income, measured in thousands of dollars, swamps late payments in the distance, and the classifier effectively uses one attribute.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**3. Scaling With the Test Set**\n",
    "\n",
    "When converting attributes to standard units, it is fine to compute the mean and SD from the whole data set, including the test set.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 3 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q3\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q3\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# Compute the mean and SD from the training set only and apply them to the test set. Otherwise the test data influence the classifier, and the test is no longer a fair stand-in for new cases.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**4. One Neighbor**\n",
    "\n",
    "With k = 1, a nearest-neighbor classifier predicts the class of the single training example closest to the new case.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 4 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q4\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q4\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# k is the number of neighbors that vote; with k = 1, the closest one decides.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**5. High Accuracy**\n",
    "\n",
    "Only 2% of a store's orders are fraudulent. A fraud classifier that is 98% accurate is therefore useful.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 5 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q5\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch14-hw-q5\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# Labeling every order \"not fraud\" is also 98% accurate and catches no fraud at all. Compare accuracy with that baseline, and check how many fraud cases are actually caught.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 6. Distance Between Two Customers\n",
    "# Each row of customers describes one customer in standard units.\n",
    "#   1. Convert the rows for \"Ana\" and \"Ben\" to arrays with np.array(...loc[...]).\n",
    "#   2. Compute the distance between them, rounded to 3 decimals.\n",
    "# Print exactly:\n",
    "# Distance: 1.414\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"visits_su\": [0.5, -0.5, 1.2],\n",
    "    \"spend_su\": [1.0, 0.0, -0.3],\n",
    "}, index=[\"Ana\", \"Ben\", \"Cy\"])\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance: 1.414\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"visits_su\": [0.5, -0.5, 1.2],\n",
    "    \"spend_su\": [1.0, 0.0, -0.3],\n",
    "}, index=[\"Ana\", \"Ben\", \"Cy\"])\n",
    "\n",
    "ana = np.array(customers.loc['Ana'])\n",
    "ben = np.array(customers.loc['Ben'])\n",
    "print(\"Distance:\", round(np.sqrt(np.sum((ana - ben) ** 2)), 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 7. Will They Renew?\n",
    "#   1. Find the 3 subscribers nearest to new_subscriber.\n",
    "#   2. Print their labels (nearest first) and the majority prediction.\n",
    "# Print exactly:\n",
    "# Nearest labels: ['renew', 'cancel', 'renew']\n",
    "# Prediction: renew\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "subscribers = pd.DataFrame({\n",
    "    \"usage_su\": [1.0, -0.8, 0.4, -1.2, 0.1, 0.9],\n",
    "    \"tenure_su\": [0.6, -0.5, -0.2, 0.8, 0.3, -1.0],\n",
    "    \"label\": [\"renew\", \"cancel\", \"cancel\", \"cancel\", \"renew\", \"renew\"],\n",
    "})\n",
    "new_subscriber = np.array([0.3, 0.1])\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest labels: ['renew', 'cancel', 'renew']\n",
      "Prediction: renew\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "subscribers = pd.DataFrame({\n",
    "    \"usage_su\": [1.0, -0.8, 0.4, -1.2, 0.1, 0.9],\n",
    "    \"tenure_su\": [0.6, -0.5, -0.2, 0.8, 0.3, -1.0],\n",
    "    \"label\": [\"renew\", \"cancel\", \"cancel\", \"cancel\", \"renew\", \"renew\"],\n",
    "})\n",
    "new_subscriber = np.array([0.3, 0.1])\n",
    "\n",
    "attributes = subscribers[['usage_su', 'tenure_su']]\n",
    "subscribers['distance'] = np.sqrt(((attributes - new_subscriber) ** 2).sum(axis=1))\n",
    "nearest = subscribers.sort_values('distance').head(3)\n",
    "\n",
    "print(\"Nearest labels:\", nearest['label'].tolist())\n",
    "print(\"Prediction:\", nearest['label'].value_counts().idxmax())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 8. Scaling the Test Set\n",
    "#   1. Convert the test customers' monthly_spend to standard units using the\n",
    "#      TRAINING mean and SD (np.std). Round each value to 2 decimals.\n",
    "# Print exactly:\n",
    "# Scaled test spend: [-1.06, 0.35, 1.41]\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "train_spend = pd.Series([40, 60, 50, 70, 30])\n",
    "test_spend = pd.Series([35, 55, 70])\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled test spend: [-1.06, 0.35, 1.41]\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "train_spend = pd.Series([40, 60, 50, 70, 30])\n",
    "test_spend = pd.Series([35, 55, 70])\n",
    "\n",
    "mean = np.mean(train_spend)\n",
    "sd = np.std(train_spend)\n",
    "scaled = ((test_spend - mean) / sd).round(2)\n",
    "print(\"Scaled test spend:\", scaled.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 9. Accuracy and the Baseline\n",
    "# A fraud classifier was checked on 20 test orders.\n",
    "#   1. Compute the classifier's accuracy.\n",
    "#   2. Compute the accuracy of always predicting \"ok\".\n",
    "#   3. Count the fraud orders the classifier caught (actual and predicted are both \"fraud\").\n",
    "# Print exactly:\n",
    "# Accuracy: 0.9\n",
    "# Always-ok accuracy: 0.9\n",
    "# Fraud caught: 1 of 2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "orders = pd.DataFrame({\n",
    "    \"actual\": [\"ok\"] * 18 + [\"fraud\"] * 2,\n",
    "    \"predicted\": [\"ok\"] * 17 + [\"fraud\"] + [\"ok\", \"fraud\"],\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9\n",
      "Always-ok accuracy: 0.9\n",
      "Fraud caught: 1 of 2\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "orders = pd.DataFrame({\n",
    "    \"actual\": [\"ok\"] * 18 + [\"fraud\"] * 2,\n",
    "    \"predicted\": [\"ok\"] * 17 + [\"fraud\"] + [\"ok\", \"fraud\"],\n",
    "})\n",
    "\n",
    "fraud = orders['actual'] == 'fraud'\n",
    "caught = (fraud & (orders['predicted'] == 'fraud')).sum()\n",
    "\n",
    "print(\"Accuracy:\", (orders['actual'] == orders['predicted']).mean())\n",
    "print(\"Always-ok accuracy:\", (orders['actual'] == 'ok').mean())\n",
    "print(\"Fraud caught:\", caught, \"of\", fraud.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 10. Choosing k\n",
    "# The same test set was classified with k = 1, 3, and 5.\n",
    "#   1. Compute the test accuracy for each k.\n",
    "#   2. Print the k with the highest accuracy.\n",
    "# Print exactly:\n",
    "# k1: 0.625\n",
    "# k3: 0.875\n",
    "# k5: 0.625\n",
    "# Best: k3\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "results = pd.DataFrame({\n",
    "    \"actual\": [\"A\", \"B\", \"A\", \"A\", \"B\", \"B\", \"A\", \"B\"],\n",
    "    \"k1\": [\"A\", \"A\", \"A\", \"B\", \"B\", \"A\", \"A\", \"B\"],\n",
    "    \"k3\": [\"A\", \"B\", \"A\", \"A\", \"B\", \"A\", \"A\", \"B\"],\n",
    "    \"k5\": [\"A\", \"B\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\"],\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k1: 0.625\n",
      "k3: 0.875\n",
      "k5: 0.625\n",
      "Best: k3\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "results = pd.DataFrame({\n",
    "    \"actual\": [\"A\", \"B\", \"A\", \"A\", \"B\", \"B\", \"A\", \"B\"],\n",
    "    \"k1\": [\"A\", \"A\", \"A\", \"B\", \"B\", \"A\", \"A\", \"B\"],\n",
    "    \"k3\": [\"A\", \"B\", \"A\", \"A\", \"B\", \"A\", \"A\", \"B\"],\n",
    "    \"k5\": [\"A\", \"B\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\"],\n",
    "})\n",
    "\n",
    "accuracy = {}\n",
    "for k in ['k1', 'k3', 'k5']:\n",
    "    accuracy[k] = (results['actual'] == results[k]).mean()\n",
    "    print(k + \":\", accuracy[k])\n",
    "print(\"Best:\", max(accuracy, key=accuracy.get))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    ".ch14-hw-grader { max-width: 900px; margin-top: 1rem; }\n",
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    ".ch14-hw-actions button:disabled { cursor: not-allowed; opacity: 0.65; }\n",
    ".ch14-hw-result { font-weight: 700; }\n",
    "</style>\n",
    "\n",
    "<div class='ch14-hw-grader' id='ch14-hw-grader'>\n",
    "  <div class='ch14-hw-feedback-list' aria-live='polite' hidden>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q1'><strong>Question 1</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q2'><strong>Question 2</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q3'><strong>Question 3</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q4'><strong>Question 4</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q5'><strong>Question 5</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q6'><strong>Question 6</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q7'><strong>Question 7</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q8'><strong>Question 8</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q9'><strong>Question 9</strong><span></span></div>\n",
    "    <div class='ch14-hw-feedback-row' data-question='q10'><strong>Question 10</strong><span></span></div>\n",
    "  </div>\n",
    "  <div class='ch14-hw-actions'>\n",
    "    <button type='button' id='ch14-hw-submit' disabled>Submit</button>\n",
    "    <button type='button' class='secondary' id='ch14-hw-reset'>Clear Results</button>\n",
    "    <span class='ch14-hw-result' id='ch14-hw-result' aria-live='polite'></span>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "<script>\n",
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    "  var homeworkId = 'ch14-homework';\n",
    "\n",
    "  function getParams() {\n",
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    "    return fetch('/api/v1/session.php', { credentials: 'same-origin' })\n",
    "      .then(function (response) { return response.ok ? response.json() : null; })\n",
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    "\n",
    "  function collectCodeCells() {\n",
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    "    var answers = {};\n",
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    "      answers[question] = checked ? checked.value : '';\n",
    "    });\n",
    "    return answers;\n",
    "  }\n",
    "\n",
    "  function applyFeedback(grader, feedback) {\n",
    "    var list = grader.querySelector('.ch14-hw-feedback-list');\n",
    "    list.hidden = false;\n",
    "    ['q1', 'q2', 'q3', 'q4', 'q5', 'q6', 'q7', 'q8', 'q9', 'q10'].forEach(function (question) {\n",
    "      var row = grader.querySelector('[data-question=' + question + ']');\n",
    "      var message = row.querySelector('span');\n",
    "      var item = feedback[question];\n",
    "      row.className = 'ch14-hw-feedback-row';\n",
    "      if (!item) return;\n",
    "      message.textContent = item.message + ' (' + item.score + ' / ' + item.max_score + ')';\n",
    "      row.classList.add(item.correct ? 'correct' : 'incorrect');\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function clearFeedback(grader) {\n",
    "    var list = grader.querySelector('.ch14-hw-feedback-list');\n",
    "    list.hidden = true;\n",
    "    grader.querySelectorAll('.ch14-hw-feedback-row').forEach(function (row) {\n",
    "      row.className = 'ch14-hw-feedback-row';\n",
    "      var message = row.querySelector('span');\n",
    "      if (message) message.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function saveAttempt(payload) {\n",
    "    return fetch('/api/v1/homework-attempts.php', {\n",
    "      method: 'POST',\n",
    "      headers: { 'Content-Type': 'application/json' },\n",
    "      credentials: 'same-origin',\n",
    "      body: JSON.stringify(payload)\n",
    "    }).then(function (response) {\n",
    "      return response.text().then(function (text) {\n",
    "        var payload = text ? JSON.parse(text) : null;\n",
    "        if (!payload || !response.ok || !payload.ok) {\n",
    "          throw new Error((payload && (payload.message || payload.error)) || 'Submission API is unavailable.');\n",
    "        }\n",
    "        return payload;\n",
    "      });\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function initHomeworkGrader() {\n",
    "    var grader = document.getElementById('ch14-hw-grader');\n",
    "    if (!grader || grader.dataset.ready === 'true') return;\n",
    "    grader.dataset.ready = 'true';\n",
    "\n",
    "    var identity = getParams();\n",
    "    var actions = grader.querySelector('.ch14-hw-actions');\n",
    "    var result = document.getElementById('ch14-hw-result');\n",
    "    var submit = document.getElementById('ch14-hw-submit');\n",
    "    var reset = document.getElementById('ch14-hw-reset');\n",
    "\n",
    "    loadSessionIdentity(identity, actions, submit, result);\n",
    "\n",
    "    submit.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      submit.disabled = true;\n",
    "      result.textContent = 'Submitting homework.';\n",
    "\n",
    "      saveAttempt({\n",
    "        homework_id: homeworkId,\n",
    "        answers: collectAnswers(),\n",
    "        code: collectCodeCells(),\n",
    "        canvas_course_id: identity.canvas_course_id,\n",
    "        canvas_assignment_id: identity.canvas_assignment_id,\n",
    "        canvas_user_id: identity.canvas_user_id\n",
    "      })\n",
    "        .then(function (payload) {\n",
    "          var best = (payload.best_score === null || typeof payload.best_score === 'undefined') ? payload.score : payload.best_score;\n",
    "          var attempt = (payload.attempt_count === null || typeof payload.attempt_count === 'undefined') ? '' : ' Attempt ' + payload.attempt_count + '.';\n",
    "          applyFeedback(grader, payload.feedback || {});\n",
    "          result.textContent = 'Submission saved.' + attempt + ' Score: ' + payload.score + ' / ' + payload.max_score + '. Best score: ' + best + ' / ' + payload.max_score + '. Canvas sync: ' + payload.canvas_sync_status + '.';\n",
    "        })\n",
    "        .catch(function (error) {\n",
    "          result.textContent = 'Could not save this attempt: ' + error.message;\n",
    "        })\n",
    "        .finally(function () {\n",
    "          submit.disabled = false;\n",
    "        });\n",
    "    });\n",
    "\n",
    "    reset.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      result.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  if (document.readyState === 'loading') {\n",
    "    document.addEventListener('DOMContentLoaded', initHomeworkGrader);\n",
    "  } else {\n",
    "    initHomeworkGrader();\n",
    "  }\n",
    "})();\n",
    "</script>\n"
   ]
  }
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