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      "source": [
        "#we want generated student data, [X1,...,X7] -> fundamentals, abstract, vision, intuition, difficulty]\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "# Creating a mock dataset\n",
        "# Let's assume we have the following features: 'time_spent', 'performance_score', 'activity_count', 'content_difficulty'\n",
        "# The target variable is 'content_appropriate' which is 1 if the content difficulty is right, and 0 if it's too difficult.\n",
        "\n",
        "# Generate a mock dataset\n",
        "np.random.seed(42)  # For reproducibility\n",
        "def student_mappings(each_add):\n",
        "    fundamentals = np.random.normal(.8,.1, size=each_add).tolist()\n",
        "    fundamentals.extend(np.random.normal(.6,.15, size = each_add).tolist())\n",
        "    fundamentals.extend(np.random.normal(.45,.12,size=each_add).tolist())\n",
        "    print(len(fundamentals))\n",
        "\n",
        "    abstract = np.random.normal(.45,.2, size=each_add).tolist()\n",
        "    abstract.extend(np.random.normal(.3,.1,size=each_add).tolist())\n",
        "    abstract.extend(np.random.normal(.1,.05,size=each_add).tolist())\n",
        "    print(len(abstract))\n",
        "\n",
        "    applied = np.random.normal(.45,.2, size=each_add).tolist()\n",
        "    applied.extend(np.random.normal(.3,.1,size=each_add).tolist())\n",
        "    applied.extend(np.random.normal(.1,.05,size=each_add).tolist())\n",
        "    print(len(applied))\n",
        "\n",
        "    vision = np.random.normal(.5,.15, size=each_add).tolist()\n",
        "    vision.extend(np.random.normal(.3,.1,size=each_add).tolist())\n",
        "    vision.extend(np.random.normal(.2,.05,size=each_add).tolist())\n",
        "    print(len(vision))\n",
        "\n",
        "    intuition = np.random.normal(.8,.1, size=each_add).tolist()\n",
        "    intuition.extend(np.random.normal(.6,.15, size = each_add).tolist())\n",
        "    intuition.extend(np.random.normal(.45,.12,size=each_add).tolist())\n",
        "    print(len(intuition))\n",
        "\n",
        "    user_difficulty = np.random.uniform(.3, .17, size = each_add).tolist()\n",
        "    user_difficulty.extend(np.random.uniform(.45,.2, size = each_add).tolist())\n",
        "    user_difficulty.extend(np.random.uniform(.7,.2,size=each_add).tolist())\n",
        "    print(len(user_difficulty))\n",
        "    # Combine into a DataFrame\n",
        "    students = pd.DataFrame({\n",
        "        'student_id': [i+1 for i in range(9999)],\n",
        "        'fundamentals': fundamentals,\n",
        "        'abstract': abstract,\n",
        "        'applied': applied,\n",
        "        'vision': vision,\n",
        "        'user_difficulty': user_difficulty\n",
        "    })\n",
        "\n",
        "    return students\n",
        "\n",
        "\n",
        "def question_to_student_mapping(each_add):\n",
        "    fundamentals = np.random.normal(.8, .1, size=each_add).tolist()\n",
        "    fundamentals.extend(np.random.normal(.6, .15, size=each_add).tolist())\n",
        "    fundamentals.extend(np.random.normal(.45, .12, size=each_add).tolist())\n",
        "\n",
        "    abstract = np.random.normal(.45, .2, size=each_add).tolist()\n",
        "    abstract.extend(np.random.normal(.3, .1, size=each_add).tolist())\n",
        "    abstract.extend(np.random.normal(.1, .05, size=each_add).tolist())\n",
        "\n",
        "    applied = np.random.normal(.45, .2, size=each_add).tolist()\n",
        "    applied.extend(np.random.normal(.3, .1, size=each_add).tolist())\n",
        "    applied.extend(np.random.normal(.1, .05, size=each_add).tolist())\n",
        "\n",
        "    vision = np.random.normal(.5, .15, size=each_add).tolist()\n",
        "    vision.extend(np.random.normal(.3, .1, size=each_add).tolist())\n",
        "    vision.extend(np.random.normal(.2, .05, size=each_add).tolist())\n",
        "    # user_difficulty =\n",
        "\n",
        "    intuition = np.random.normal(.8, .1, size=each_add).tolist()\n",
        "    intuition.extend(np.random.normal(.6, .15, size=each_add).tolist())\n",
        "    intuition.extend(np.random.normal(.45, .12, size=each_add).tolist())\n",
        "\n",
        "    teacher_difficulty = np.random.uniform(.4, .12, size=each_add).tolist()\n",
        "    teacher_difficulty.extend(np.random.uniform(.45, .2, size=each_add).tolist())\n",
        "    teacher_difficulty.extend(np.random.uniform(.7, .2, size=each_add).tolist())\n",
        "\n",
        "    questions = pd.DataFrame({\n",
        "        'fundamentals': fundamentals,\n",
        "        'abstract': abstract,\n",
        "        'applied': applied,\n",
        "        'vision': vision,\n",
        "        'teacher_difficulty': teacher_difficulty\n",
        "    })\n",
        "\n",
        "    return questions\n",
        "\n",
        "def responses_by_students(students, questions):\n",
        "    question_response = pd.DataFrame()\n",
        "    question_response['student_id'] = students['student_id']\n",
        "    for i in range(len(questions)):\n",
        "      prob_correct = []\n",
        "      for j in range(len(students)):\n",
        "            prob_correct.append(abs(students['fundamentals'][i] * students['abstract'][i] * students['applied'][i] * students['vision'][i] /students['user_difficulty'][i]))\n",
        "            prob_correct[j] /= abs(questions['fundamentals'][i] * questions['abstract'][i] * questions['applied'][i] * questions['vision'][i] /questions['teacher_difficulty'][i])\n",
        "      students_by_question = [prob_correct[i] for i in range(len(students))]\n",
        "\n",
        "      col = f'question_{i}'\n",
        "      question_response[col] = np.array(students_by_question)\n",
        "\n",
        "    return question_response"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students = student_mappings(3333)\n",
        "students.head()"
      ],
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        },
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          "text": [
            "9999\n",
            "9999\n",
            "9999\n",
            "9999\n",
            "9999\n",
            "9999\n"
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            "text/plain": [
              "   student_id  fundamentals  abstract   applied    vision  user_difficulty\n",
              "0           1      0.849671  0.578878  0.792706  0.424795         0.174025\n",
              "1           2      0.786174  0.314301  0.126016  0.216146         0.238039\n",
              "2           3      0.864769  0.388900  0.519657  0.465667         0.283593\n",
              "3           4      0.952303  0.330524  0.506665  0.202914         0.296247\n",
              "4           5      0.776585  0.472084  0.262696  0.341752         0.199579"
            ],
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              "      <th></th>\n",
              "      <th>student_id</th>\n",
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              "      <td>0.519657</td>\n",
              "      <td>0.465667</td>\n",
              "      <td>0.283593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.952303</td>\n",
              "      <td>0.330524</td>\n",
              "      <td>0.506665</td>\n",
              "      <td>0.202914</td>\n",
              "      <td>0.296247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.776585</td>\n",
              "      <td>0.472084</td>\n",
              "      <td>0.262696</td>\n",
              "      <td>0.341752</td>\n",
              "      <td>0.199579</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdf471f2-a341-4496-8812-e85eefe560b4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fdf471f2-a341-4496-8812-e85eefe560b4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fdf471f2-a341-4496-8812-e85eefe560b4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-888c20db-f55b-4bec-bcf2-36049dc5fe82 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "students",
              "summary": "{\n  \"name\": \"students\",\n  \"rows\": 9999,\n  \"fields\": [\n    {\n      \"column\": \"student_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2886,\n        \"min\": 1,\n        \"max\": 9999,\n        \"samples\": [\n          5345,\n          7445,\n          1732\n        ],\n        \"num_unique_values\": 9999,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fundamentals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19150660107498055,\n        \"min\": -0.020688030194201068,\n        \"max\": 1.1926237706436327,\n        \"samples\": [\n          0.5505766049673021,\n          0.23632491925110133,\n          0.7650683203881803\n        ],\n        \"num_unique_values\": 9999,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abstract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19441027757448545,\n        \"min\": -0.22511582003552005,\n        \"max\": 1.1357820956016427,\n        \"samples\": [\n          0.4511322799203203,\n          0.0024952419990620506,\n          0.24398278901911402\n        ],\n        \"num_unique_values\": 9999,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"applied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1942112651888739,\n        \"min\": -0.28101730302082123,\n        \"max\": 1.172270012563134,\n        \"samples\": [\n          0.1763106812429286,\n          0.13557074846023193,\n          0.16741648011702553\n        ],\n        \"num_unique_values\": 9999,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16638355223973486,\n        \"min\": -0.16984057952933163,\n        \"max\": 1.059175001714478,\n        \"samples\": [\n          0.27763249056220163,\n          0.2421720429540819,\n          0.5411425573558764\n        ],\n        \"num_unique_values\": 9999,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_difficulty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12898507302406675,\n        \"min\": 0.17004954363871033,\n        \"max\": 0.6999143723160124,\n        \"samples\": [\n          0.2049976800924693,\n          0.2886877794188766,\n          0.2887668902146241\n        ],\n        \"num_unique_values\": 9999,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "questions = question_to_student_mapping(100)\n",
        "questions.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Xc3rY8fPjGMi",
        "outputId": "f04d64eb-cf4f-4cef-cb1f-4ffa275178a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fundamentals  abstract   applied    vision  teacher_difficulty\n",
              "0      0.805680  0.112217  0.796915  0.553857            0.225711\n",
              "1      0.832482  0.205185  0.334322  0.531217            0.211800\n",
              "2      0.823210  0.801932  0.425123  0.417900            0.279533\n",
              "3      0.957877  0.406751  0.214153  0.437529            0.181420\n",
              "4      0.925603  0.623825  0.180293  0.736008            0.201529"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ece9e633-64bb-406c-b5d6-fab686014cf0\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fundamentals</th>\n",
              "      <th>abstract</th>\n",
              "      <th>applied</th>\n",
              "      <th>vision</th>\n",
              "      <th>teacher_difficulty</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.805680</td>\n",
              "      <td>0.112217</td>\n",
              "      <td>0.796915</td>\n",
              "      <td>0.553857</td>\n",
              "      <td>0.225711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.832482</td>\n",
              "      <td>0.205185</td>\n",
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              "      <td>0.531217</td>\n",
              "      <td>0.211800</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.437529</td>\n",
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              "      <td>0.623825</td>\n",
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              "      <td>0.736008</td>\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ece9e633-64bb-406c-b5d6-fab686014cf0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d9adeb30-5452-4a3c-9a4d-d6422e46870f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "questions",
              "summary": "{\n  \"name\": \"questions\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"fundamentals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19356186793043031,\n        \"min\": 0.09661016804277461,\n        \"max\": 1.030869903741608,\n        \"samples\": [\n          0.41224088561248845,\n          0.4261928577318055,\n          0.9039076372031685\n        ],\n        \"num_unique_values\": 300,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abstract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1997644356989606,\n        \"min\": -0.07400707055190603,\n        \"max\": 1.0903575676743302,\n        \"samples\": [\n          0.08036833196139274,\n          0.1255127792686798,\n          0.4311702218905764\n        ],\n        \"num_unique_values\": 300,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"applied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20633953179464917,\n        \"min\": -0.256375758546539,\n        \"max\": 0.9433917638847557,\n        \"samples\": [\n          0.09258894933102896,\n          0.14215954219205673,\n          0.08758265665962356\n        ],\n        \"num_unique_values\": 300,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.160276697718764,\n        \"min\": 0.06072594171857981,\n        \"max\": 0.9412682692632235,\n        \"samples\": [\n          0.21209937988025332,\n          0.2589350726680629,\n          0.24374136060237928\n        ],\n        \"num_unique_values\": 300,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"teacher_difficulty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13209381090628503,\n        \"min\": 0.17090665316543546,\n        \"max\": 0.6989681488024255,\n        \"samples\": [\n          0.6888083443163674,\n          0.3853613558444105,\n          0.432370481140574\n        ],\n        \"num_unique_values\": 300,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "student_responses = responses_by_students(students, questions)\n",
        "student_responses.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k0qqwavbjzqM",
        "outputId": "bacbb71c-fd5a-4df1-c777-6241aeef6ceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n",
            "<ipython-input-49-323ef38c832c>:102: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
            "  question_response[col] = np.array(students_by_question)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   student_id  question_0  question_1  question_2  question_3  question_4  \\\n",
              "0           1    5.383191    0.197405    0.683966    0.542843    0.433758   \n",
              "1           2    5.383191    0.197405    0.683966    0.542843    0.433758   \n",
              "2           3    5.383191    0.197405    0.683966    0.542843    0.433758   \n",
              "3           4    5.383191    0.197405    0.683966    0.542843    0.433758   \n",
              "4           5    5.383191    0.197405    0.683966    0.542843    0.433758   \n",
              "\n",
              "   question_5  question_6  question_7  question_8  ...  question_290  \\\n",
              "0    2.615997    0.284718    0.498847    0.229292  ...    120.539216   \n",
              "1    2.615997    0.284718    0.498847    0.229292  ...    120.539216   \n",
              "2    2.615997    0.284718    0.498847    0.229292  ...    120.539216   \n",
              "3    2.615997    0.284718    0.498847    0.229292  ...    120.539216   \n",
              "4    2.615997    0.284718    0.498847    0.229292  ...    120.539216   \n",
              "\n",
              "   question_291  question_292  question_293  question_294  question_295  \\\n",
              "0    106.919081     35.539795     81.142339     91.155617    162.112611   \n",
              "1    106.919081     35.539795     81.142339     91.155617    162.112611   \n",
              "2    106.919081     35.539795     81.142339     91.155617    162.112611   \n",
              "3    106.919081     35.539795     81.142339     91.155617    162.112611   \n",
              "4    106.919081     35.539795     81.142339     91.155617    162.112611   \n",
              "\n",
              "   question_296  question_297  question_298  question_299  \n",
              "0    345.415288      49.70248    758.588847    291.280543  \n",
              "1    345.415288      49.70248    758.588847    291.280543  \n",
              "2    345.415288      49.70248    758.588847    291.280543  \n",
              "3    345.415288      49.70248    758.588847    291.280543  \n",
              "4    345.415288      49.70248    758.588847    291.280543  \n",
              "\n",
              "[5 rows x 301 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc2985e5-5476-47c9-87c7-e88cb806d977\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>student_id</th>\n",
              "      <th>question_0</th>\n",
              "      <th>question_1</th>\n",
              "      <th>question_2</th>\n",
              "      <th>question_3</th>\n",
              "      <th>question_4</th>\n",
              "      <th>question_5</th>\n",
              "      <th>question_6</th>\n",
              "      <th>question_7</th>\n",
              "      <th>question_8</th>\n",
              "      <th>...</th>\n",
              "      <th>question_290</th>\n",
              "      <th>question_291</th>\n",
              "      <th>question_292</th>\n",
              "      <th>question_293</th>\n",
              "      <th>question_294</th>\n",
              "      <th>question_295</th>\n",
              "      <th>question_296</th>\n",
              "      <th>question_297</th>\n",
              "      <th>question_298</th>\n",
              "      <th>question_299</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.383191</td>\n",
              "      <td>0.197405</td>\n",
              "      <td>0.683966</td>\n",
              "      <td>0.542843</td>\n",
              "      <td>0.433758</td>\n",
              "      <td>2.615997</td>\n",
              "      <td>0.284718</td>\n",
              "      <td>0.498847</td>\n",
              "      <td>0.229292</td>\n",
              "      <td>...</td>\n",
              "      <td>120.539216</td>\n",
              "      <td>106.919081</td>\n",
              "      <td>35.539795</td>\n",
              "      <td>81.142339</td>\n",
              "      <td>91.155617</td>\n",
              "      <td>162.112611</td>\n",
              "      <td>345.415288</td>\n",
              "      <td>49.70248</td>\n",
              "      <td>758.588847</td>\n",
              "      <td>291.280543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5.383191</td>\n",
              "      <td>0.197405</td>\n",
              "      <td>0.683966</td>\n",
              "      <td>0.542843</td>\n",
              "      <td>0.433758</td>\n",
              "      <td>2.615997</td>\n",
              "      <td>0.284718</td>\n",
              "      <td>0.498847</td>\n",
              "      <td>0.229292</td>\n",
              "      <td>...</td>\n",
              "      <td>120.539216</td>\n",
              "      <td>106.919081</td>\n",
              "      <td>35.539795</td>\n",
              "      <td>81.142339</td>\n",
              "      <td>91.155617</td>\n",
              "      <td>162.112611</td>\n",
              "      <td>345.415288</td>\n",
              "      <td>49.70248</td>\n",
              "      <td>758.588847</td>\n",
              "      <td>291.280543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>5.383191</td>\n",
              "      <td>0.197405</td>\n",
              "      <td>0.683966</td>\n",
              "      <td>0.542843</td>\n",
              "      <td>0.433758</td>\n",
              "      <td>2.615997</td>\n",
              "      <td>0.284718</td>\n",
              "      <td>0.498847</td>\n",
              "      <td>0.229292</td>\n",
              "      <td>...</td>\n",
              "      <td>120.539216</td>\n",
              "      <td>106.919081</td>\n",
              "      <td>35.539795</td>\n",
              "      <td>81.142339</td>\n",
              "      <td>91.155617</td>\n",
              "      <td>162.112611</td>\n",
              "      <td>345.415288</td>\n",
              "      <td>49.70248</td>\n",
              "      <td>758.588847</td>\n",
              "      <td>291.280543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>5.383191</td>\n",
              "      <td>0.197405</td>\n",
              "      <td>0.683966</td>\n",
              "      <td>0.542843</td>\n",
              "      <td>0.433758</td>\n",
              "      <td>2.615997</td>\n",
              "      <td>0.284718</td>\n",
              "      <td>0.498847</td>\n",
              "      <td>0.229292</td>\n",
              "      <td>...</td>\n",
              "      <td>120.539216</td>\n",
              "      <td>106.919081</td>\n",
              "      <td>35.539795</td>\n",
              "      <td>81.142339</td>\n",
              "      <td>91.155617</td>\n",
              "      <td>162.112611</td>\n",
              "      <td>345.415288</td>\n",
              "      <td>49.70248</td>\n",
              "      <td>758.588847</td>\n",
              "      <td>291.280543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.383191</td>\n",
              "      <td>0.197405</td>\n",
              "      <td>0.683966</td>\n",
              "      <td>0.542843</td>\n",
              "      <td>0.433758</td>\n",
              "      <td>2.615997</td>\n",
              "      <td>0.284718</td>\n",
              "      <td>0.498847</td>\n",
              "      <td>0.229292</td>\n",
              "      <td>...</td>\n",
              "      <td>120.539216</td>\n",
              "      <td>106.919081</td>\n",
              "      <td>35.539795</td>\n",
              "      <td>81.142339</td>\n",
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              "      <td>345.415288</td>\n",
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              "      <td>758.588847</td>\n",
              "      <td>291.280543</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 301 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7553b9b6-fdb0-4e34-94ab-c3a3fb87e8ea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "student_responses"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(student_responses))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKLYY7SD8LPQ",
        "outputId": "2fe88ec8-458d-4b3a-c8b1-4aed494d229c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "students.to_csv('students.csv')\n",
        "questions.to_csv('questions.csv')\n",
        "student_responses.to_csv('prob_responses.csv')"
      ],
      "metadata": {
        "id": "AufSe_-sHa9T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "IDLbdcRdIN3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(students.info())\n",
        "print(questions.info())\n",
        "print(student_responses.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2yUFhFbd2FL",
        "outputId": "aed44cba-dbc1-458b-c4e6-34cfd25be9f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9999 entries, 0 to 9998\n",
            "Data columns (total 6 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   student_id       9999 non-null   int64  \n",
            " 1   fundamentals     9999 non-null   float64\n",
            " 2   abstract         9999 non-null   float64\n",
            " 3   applied          9999 non-null   float64\n",
            " 4   vision           9999 non-null   float64\n",
            " 5   user_difficulty  9999 non-null   float64\n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 468.8 KB\n",
            "None\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 300 entries, 0 to 299\n",
            "Data columns (total 5 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   fundamentals        300 non-null    float64\n",
            " 1   abstract            300 non-null    float64\n",
            " 2   applied             300 non-null    float64\n",
            " 3   vision              300 non-null    float64\n",
            " 4   teacher_difficulty  300 non-null    float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 11.8 KB\n",
            "None\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9999 entries, 0 to 9998\n",
            "Columns: 301 entries, student_id to question_299\n",
            "dtypes: int64(301)\n",
            "memory usage: 23.0 MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import merlin.models.tf as ml\n",
        "\n",
        "inputs = ml.InputBlock(schema)\n",
        "prediction_tasks = ml.PredictionTasks(schema)\n",
        "block = ml.MLPBlock([64])\n",
        "mmoe = ml.MMOEBlock(prediction_tasks, expert_block=ml.MLPBlock([64]), num_experts=4)\n",
        "model = ml.Model(inputs, block, mmoe, prediction_tasks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "q3Lz7DbGeBk2",
        "outputId": "bf5e99f7-0201-4ace-e091-70c05babe576"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'merlin'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-47-d5206e44d028>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmerlin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInputBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprediction_tasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPredictionTasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMLPBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'merlin'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install merlin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igu2tg5IqKaS",
        "outputId": "0e4835fb-fc76-4931-b31e-01c7d29ed6de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting merlin\n",
            "  Downloading merlin-1.12.0-py3-none-any.whl (328 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m328.6/328.6 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cached-property (from merlin)\n",
            "  Downloading cached_property-1.5.2-py2.py3-none-any.whl (7.6 kB)\n",
            "Collecting celery[redis,sqlalchemy]>=5.0.3 (from merlin)\n",
            "  Downloading celery-5.3.6-py3-none-any.whl (422 kB)\n",
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            "\u001b[?25hCollecting coloredlogs (from merlin)\n",
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            "Collecting maestrowf>=1.1.9dev1 (from merlin)\n",
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            "Collecting parse (from merlin)\n",
            "  Downloading parse-1.20.1-py2.py3-none-any.whl (20 kB)\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.3/250.3 kB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting billiard<5.0,>=4.2.0 (from celery[redis,sqlalchemy]>=5.0.3->merlin)\n",
            "  Downloading billiard-4.2.0-py3-none-any.whl (86 kB)\n",
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            "\u001b[?25hCollecting click-didyoumean>=0.3.0 (from celery[redis,sqlalchemy]>=5.0.3->merlin)\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->maestrowf>=1.1.9dev1->merlin) (3.0.0)\n",
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->maestrowf>=1.1.9dev1->merlin) (0.1.2)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit>=3.0.36->click-repl>=0.2.0->celery[redis,sqlalchemy]>=5.0.3->merlin) (0.2.13)\n",
            "Installing collected packages: parse, cached-property, vine, tzdata, redis, humanfriendly, dill, click-didyoumean, billiard, coloredlogs, click-repl, amqp, kombu, maestrowf, celery, merlin\n",
            "Successfully installed amqp-5.2.0 billiard-4.2.0 cached-property-1.5.2 celery-5.3.6 click-didyoumean-0.3.0 click-repl-0.3.0 coloredlogs-15.0.1 dill-0.3.8 humanfriendly-10.0 kombu-5.3.5 maestrowf-1.1.10 merlin-1.12.0 parse-1.20.1 redis-5.0.1 tzdata-2024.1 vine-5.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "#import tensorflow_datasets as tfds\n",
        "\n",
        "#ratings = tfds.load(\"movielens/100k-ratings\", split=\"train\")\n",
        "student_features = students.map(lambda x:{\n",
        "    \"s_fundamentals\": students[\"fundamentals\"],\n",
        "    's_abstract' : students['abstract'],\n",
        "    's_applied': students['applied'],\n",
        "    's_vision': students['vision'],\n",
        "    'user_difficulty' : students['user_difficulty'],\n",
        "    \"q_fundamentals\": questions[\"fundamentals\"],\n",
        "    'q_abstract' : questions['abstract'],\n",
        "    'q_applied': questions['applied'],\n",
        "    'q_vision': questions['vision'],\n",
        "    'teacher_difficulty' : questions['teacher_difficulty']\n",
        "})\n",
        "labels = questions.map(lambda x: {'Q1':student_responses['question_1'],\n",
        "                                  'Q2':student_responses['question_101'],\n",
        "                                  'Q3':student_responses['question_201']})\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(64, activation='relu', input_shape=(5,)),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dense(1)  # Output layer for similarity score\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mse')  # Using mean squared error as the loss function\n",
        "\n",
        "# Train the model\n",
        "model.fit(student_features, labels, epochs=10, batch_size=32)\n",
        "\n",
        "# Make predictions (compute similarity scores)\n",
        "similarity_scores = model.predict(question_features)\n",
        "\n",
        "# Recommend questions for each student\n",
        "recommendations = []\n",
        "for i, student_similarity in enumerate(similarity_scores):\n",
        "    top_question_indices = np.argsort(student_similarity.flatten())[::-1][:3]  # Get indices of top 3 similar questions\n",
        "    student_recommendations = [labels[top_question_index] for top_question_index in top_question_indices]\n",
        "    recommendations.append(student_recommendations)\n",
        "\n",
        "# Print recommendations\n",
        "for i, student_recommendations in enumerate(recommendations):\n",
        "    print(f\"Recommendations for student {i+1}:\")\n",
        "    for j, recommendation in enumerate(student_recommendations):\n",
        "        print(f\"Question {j+1}: {recommendation}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652,
          "referenced_widgets": [
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        "id": "vuJQwCD_xtI5",
        "outputId": "f786a2dc-dd57-4cfd-d315-59e19744c96c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 4.70 MiB (download: 4.70 MiB, generated: 32.41 MiB, total: 37.10 MiB) to /root/tensorflow_datasets/movielens/100k-ratings/0.1.1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "712e5ea3b52543879b53486f499bda88"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e5dc3bb7c0447ad9a95b5c755f53ddb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1f5a3c055cb4464be04bdd02ec4cd8a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bde7bb809cd74cf3ab62591a83782c6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97801efba6f944e5a2c1a8997435fad2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-55-9e6d5c37b02f>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow_datasets\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtfds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mratings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"movielens/100k-ratings\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m ratings = students.map(lambda x:{\n\u001b[1;32m      6\u001b[0m     \u001b[0;34m\"s_fundamentals\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstudents\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"fundamentals\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/logging/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, function, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m       \u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmark_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(name, split, data_dir, batch_size, shuffle_files, download, as_supervised, decoders, read_config, with_info, builder_kwargs, download_and_prepare_kwargs, as_dataset_kwargs, try_gcs)\u001b[0m\n\u001b[1;32m    647\u001b[0m       \u001b[0mtry_gcs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m   )\n\u001b[0;32m--> 649\u001b[0;31m   \u001b[0m_download_and_prepare_builder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdbuilder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload_and_prepare_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    651\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mas_dataset_kwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/load.py\u001b[0m in \u001b[0;36m_download_and_prepare_builder\u001b[0;34m(dbuilder, download, download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m    506\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m     \u001b[0mdownload_and_prepare_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdownload_and_prepare_kwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m     \u001b[0mdbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_and_prepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdownload_and_prepare_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/logging/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, function, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m       \u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmark_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/dataset_builder.py\u001b[0m in \u001b[0;36mdownload_and_prepare\u001b[0;34m(self, download_dir, download_config, file_format)\u001b[0m\n\u001b[1;32m    689\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_from_directory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 691\u001b[0;31m           self._download_and_prepare(\n\u001b[0m\u001b[1;32m    692\u001b[0m               \u001b[0mdl_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m               \u001b[0mdownload_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/dataset_builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, download_config)\u001b[0m\n\u001b[1;32m   1582\u001b[0m             \u001b[0mfiletype_suffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_suffix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1583\u001b[0m         )\n\u001b[0;32m-> 1584\u001b[0;31m         future = split_builder.submit_split_generation(\n\u001b[0m\u001b[1;32m   1585\u001b[0m             \u001b[0msplit_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msplit_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1586\u001b[0m             \u001b[0mgenerator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/split_builder.py\u001b[0m in \u001b[0;36msubmit_split_generation\u001b[0;34m(self, split_name, generator, filename_template, disable_shuffling)\u001b[0m\n\u001b[1;32m    339\u001b[0m     \u001b[0;31m# `_build_from_xyz` method.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_from_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mbuild_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Otherwise, beam required\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m       unknown_generator_type = TypeError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/split_builder.py\u001b[0m in \u001b[0;36m_build_from_generator\u001b[0;34m(self, split_name, generator, filename_template, disable_shuffling)\u001b[0m\n\u001b[1;32m    416\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf'Failed to encode example:\\n{example}\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m       \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m     \u001b[0mshard_lengths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/writer.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, key, example)\u001b[0m\n\u001b[1;32m    236\u001b[0m       \u001b[0mexample\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mExample\u001b[0m \u001b[0mto\u001b[0m \u001b[0mwrite\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtfrecord\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \"\"\"\n\u001b[0;32m--> 238\u001b[0;31m     \u001b[0mserialized_example\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_serializer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserialize_example\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shuffler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mserialized_example\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_examples\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36mserialize_example\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m     96\u001b[0m       \u001b[0mserialize_proto\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mserialized\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExample\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     \"\"\"\n\u001b[0;32m---> 98\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tf_example\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSerializeToString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36mget_tf_example\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m     81\u001b[0m       \u001b[0mThe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExample\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m     return _dict_to_tf_example(\n\u001b[0m\u001b[1;32m     84\u001b[0m         \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten_nest_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flat_example_specs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36m_dict_to_tf_example\u001b[0;34m(example_dict, tensor_info_dict)\u001b[0m\n\u001b[1;32m    139\u001b[0m       }\n\u001b[1;32m    140\u001b[0m   )\n\u001b[0;32m--> 141\u001b[0;31m   features = {\n\u001b[0m\u001b[1;32m    142\u001b[0m       \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrun_with_reraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_item_to_tf_feature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_info\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    140\u001b[0m   )\n\u001b[1;32m    141\u001b[0m   features = {\n\u001b[0;32m--> 142\u001b[0;31m       \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrun_with_reraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_item_to_tf_feature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_info\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m   }\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36mrun_with_reraise\u001b[0;34m(fn, k, example_data, tensor_info)\u001b[0m\n\u001b[1;32m    115\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrun_with_reraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m       utils.reraise(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36m_item_to_tf_feature\u001b[0;34m(item, tensor_info)\u001b[0m\n\u001b[1;32m    176\u001b[0m ) -> tf.train.Feature:\n\u001b[1;32m    177\u001b[0m   \u001b[0;34m\"\"\"Single item to a tf.train.Feature.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m   v = _item_to_np_array(\n\u001b[0m\u001b[1;32m    179\u001b[0m       \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m       \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtensor_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36m_item_to_np_array\u001b[0;34m(item, dtype, shape)\u001b[0m\n\u001b[1;32m    165\u001b[0m   \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m   \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_shape_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0mdtype_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_is_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m     raise ValueError(\n\u001b[1;32m    169\u001b[0m         \u001b[0;34mf\"Unsupported value: {result}\\nCould not convert to bytes list.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow_datasets/core/example_serializer.py\u001b[0m in \u001b[0;36m_is_string\u001b[0;34m(item)\u001b[0m\n\u001b[1;32m    152\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m   \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_is_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    155\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m   elif isinstance(item, np.ndarray) and (  # binary or unicode\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "# Assuming 'students', 'questions', and 'student_responses' are your dataframes or arrays\n",
        "\n",
        "# Define student and question features\n",
        "student_features = np.array([\n",
        "    students['fundamentals'],\n",
        "    students['abstract'],\n",
        "    students['applied'],\n",
        "    students['vision'],\n",
        "    students['user_difficulty']\n",
        "]).T\n",
        "\n",
        "question_features = np.array([\n",
        "    questions['fundamentals'],\n",
        "    questions['abstract'],\n",
        "    questions['applied'],\n",
        "    questions['vision'],\n",
        "    questions['teacher_difficulty']\n",
        "]).T\n",
        "\n",
        "# Define labels (responses)\n",
        "labels = np.array([\n",
        "    student_responses['question_1'],\n",
        "    student_responses['question_101'],\n",
        "    student_responses['question_201'],\n",
        "    student_responses['question_210'],\n",
        "    student_responses['question_150'],\n",
        "    student_responses['question_9'],\n",
        "])\n",
        "\n",
        "# Define model architecture\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(64, activation='relu', input_shape=(5,)),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dense(1)  # Output layer for similarity score\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mse')  # Using mean squared error as the loss function\n",
        "\n",
        "# Train the model\n",
        "for i in range(len(labels)):\n",
        "  model.fit(student_features, labels[i], epochs=10, batch_size=32)\n",
        "\n",
        "# Make predictions (compute similarity scores)\n",
        "similarity_scores = model.predict(question_features)\n",
        "\n",
        "# Recommend questions for each student\n",
        "'''\n",
        "recommendations = []\n",
        "for i, student_similarity in enumerate(similarity_scores):\n",
        "    top_question_indices = np.argsort(student_similarity.flatten())[::-1][:3]  # Get indices of top 3 similar questions\n",
        "    student_recommendations = [labels[top_question_index] for top_question_index in top_question_indices]\n",
        "    recommendations.append(student_recommendations)'''\n",
        "question_recommendations = []\n",
        "for i, student_similarity in enumerate(similarity_scores):\n",
        "    top_question_indices = np.argsort(student_similarity.flatten())[::-1][:3]\n",
        "    question_recommendations.extend(top_question_indices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuQb4hhNWxWS",
        "outputId": "6cc215fd-f2a9-4530-c9cc-38bd7a2f96ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.0014\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.1750e-05\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 5.6971e-06\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 3.9807e-06\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 2.8897e-06\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.1857e-06\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.9384e-06\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.4276e-06\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.2649e-06\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.1710e-06\n",
            "Epoch 1/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2909.3918\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 130.2051\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 83.8915\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 58.4256\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 36.4522\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 19.3413\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 8.2053\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.6224\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.6110\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1098\n",
            "Epoch 1/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 399.9824\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 15.0744\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 8.0236\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 3.7570\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.4866\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4866\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1285\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0262\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0041\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 4.6471e-04\n",
            "Epoch 1/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 3572.4060\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 67.7119\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 11.0944\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 4.0960\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.2666\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.5135\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3340\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2654\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2129\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1673\n",
            "Epoch 1/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 696.1462\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.0085\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.5008\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.1555\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.9010\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.7151\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 0s 2ms/step - loss: 0.5790\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4761\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3984\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3355\n",
            "Epoch 1/10\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 239.3969\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3213\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1863\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1200\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0812\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0588\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0458\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0380\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0328\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0288\n",
            "10/10 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(question_recommendations)):\n",
        "  question_recommendations[i] += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ln2c9SdraufQ",
        "outputId": "1ceddc79-882e-420e-f67a-de3e135c9003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflowjs"
      ],
      "metadata": {
        "id": "1U7AXbeKbgYU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc449009-60fa-4c38-f285-3c15a8081885"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflowjs\n",
            "  Downloading tensorflowjs-4.17.0-py3-none-any.whl (89 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/89.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.1/89.1 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: flax>=0.7.2 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.8.1)\n",
            "Requirement already satisfied: importlib_resources>=5.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (6.1.1)\n",
            "Requirement already satisfied: jax>=0.4.13 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.4.23)\n",
            "Requirement already satisfied: jaxlib>=0.4.13 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.4.23+cuda12.cudnn89)\n",
            "Requirement already satisfied: tensorflow<3,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (2.15.0)\n",
            "Collecting tensorflow-decision-forests>=1.5.0 (from tensorflowjs)\n",
            "  Downloading tensorflow_decision_forests-1.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.3/15.3 MB\u001b[0m \u001b[31m58.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six<2,>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (1.16.0)\n",
            "Requirement already satisfied: tensorflow-hub>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (0.16.1)\n",
            "Requirement already satisfied: packaging~=23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflowjs) (23.2)\n",
            "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (1.25.2)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (1.0.7)\n",
            "Requirement already satisfied: optax in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (0.1.9)\n",
            "Requirement already satisfied: orbax-checkpoint in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (0.4.4)\n",
            "Requirement already satisfied: tensorstore in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (0.1.45)\n",
            "Requirement already satisfied: rich>=11.1 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (13.7.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (4.9.0)\n",
            "Requirement already satisfied: PyYAML>=5.4.1 in /usr/local/lib/python3.10/dist-packages (from flax>=0.7.2->tensorflowjs) (6.0.1)\n",
            "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.13->tensorflowjs) (0.2.0)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.13->tensorflowjs) (3.3.0)\n",
            "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.13->tensorflowjs) (1.11.4)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (16.0.6)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (67.7.2)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.4.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (0.36.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (1.60.1)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.15.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.15.0)\n",
            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<3,>=2.13.0->tensorflowjs) (2.15.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from tensorflow-decision-forests>=1.5.0->tensorflowjs) (1.5.3)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from tensorflow-decision-forests>=1.5.0->tensorflowjs) (0.42.0)\n",
            "Collecting wurlitzer (from tensorflow-decision-forests>=1.5.0->tensorflowjs)\n",
            "  Downloading wurlitzer-3.0.3-py3-none-any.whl (7.3 kB)\n",
            "Requirement already satisfied: tf-keras>=2.14.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow-hub>=0.14.0->tensorflowjs) (2.15.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1->flax>=0.7.2->tensorflowjs) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1->flax>=0.7.2->tensorflowjs) (2.16.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (3.5.2)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (3.0.1)\n",
            "Requirement already satisfied: chex>=0.1.7 in /usr/local/lib/python3.10/dist-packages (from optax->flax>=0.7.2->tensorflowjs) (0.1.85)\n",
            "Requirement already satisfied: etils[epath,epy] in /usr/local/lib/python3.10/dist-packages (from orbax-checkpoint->flax>=0.7.2->tensorflowjs) (1.6.0)\n",
            "Requirement already satisfied: nest_asyncio in /usr/local/lib/python3.10/dist-packages (from orbax-checkpoint->flax>=0.7.2->tensorflowjs) (1.6.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow-decision-forests>=1.5.0->tensorflowjs) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow-decision-forests>=1.5.0->tensorflowjs) (2023.4)\n",
            "Requirement already satisfied: toolz>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from chex>=0.1.7->optax->flax>=0.7.2->tensorflowjs) (0.12.1)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (1.3.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1->flax>=0.7.2->tensorflowjs) (0.1.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (2.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from etils[epath,epy]->orbax-checkpoint->flax>=0.7.2->tensorflowjs) (2023.6.0)\n",
            "Requirement already satisfied: zipp in /usr/local/lib/python3.10/dist-packages (from etils[epath,epy]->orbax-checkpoint->flax>=0.7.2->tensorflowjs) (3.17.0)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow<3,>=2.13.0->tensorflowjs) (3.2.2)\n",
            "Installing collected packages: wurlitzer, tensorflow-decision-forests, tensorflowjs\n",
            "Successfully installed tensorflow-decision-forests-1.8.1 tensorflowjs-4.17.0 wurlitzer-3.0.3\n"
          ]
        }
      ]
    },
    {
      "source": [
        "import json\n",
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights(\"model.h5\")"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "ej0SRFGdu7Uw"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "OPhLK7p0tAeo",
        "outputId": "175e08c2-4120-4b96-c6e2-ef8368138da8"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-74-2a68c72aacc6>, line 1)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-74-2a68c72aacc6>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    $ tensorflowjs_converter --input_format=keras /tmp/my_model.keras /tmp/tfjs_model\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    }
  ]
}