Part2_BERT_input.ipynb

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    {
      "cell_type": "code",
      "source": [
        "pip install transformers torch\n"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.33.1-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.17.1-py3-none-any.whl (294 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.8/294.8 kB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m60.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m59.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.6)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.17.1 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "import torch\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "model.eval()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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          ]
        },
        "id": "A39IP3lwEbBP",
        "outputId": "4a7115f7-68a5-490b-eb53-5084ad2ef8ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92bc9b7f69c846c6a6af7766a5ba8cb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93485b294da54be9bf8014eb712773c6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16b7b1ce72a94cfcb2a957ae5ac0e564"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92771925589d4a809983ba5001b03823"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_sentiment(text):\n",
        "    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "        probs = logits.softmax(dim=1)\n",
        "        sentiment_score = probs[0][1].item()  # Assuming 1 corresponds to positive sentiment\n",
        "        return sentiment_score\n"
      ],
      "metadata": {
        "id": "LN4sEemNE3KW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset_df = df.sample(n=500, random_state=42)"
      ],
      "metadata": {
        "id": "EmSc1gGeFIid"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yr5oHbogCLfN",
        "outputId": "4ef6ed59-1a69-4944-8817-578077bbc7da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "question_title      0\n",
              "question_content    0\n",
              "answer              0\n",
              "grade               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subset_df['Question_Sentiment'] = subset_df['question_content'].apply(predict_sentiment)\n"
      ],
      "metadata": {
        "id": "XIc7jeE8E-X1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset_df['Answer_Sentiment'] = subset_df['answer'].apply(predict_sentiment)"
      ],
      "metadata": {
        "id": "6boLt46zHfb4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset_df.head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7PamVlAgHDGZ",
        "outputId": "ad899c22-d09e-4ae4-d104-1763aa2e94b3"
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      "execution_count": null,
      "outputs": [
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              "      <td>would grateful anybody could take look assignm...</td>\n",
              "      <td>hi review great btw could review mine mind thanks</td>\n",
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              "      <td>sure thank</td>\n",
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              "      <td>please review assignment</td>\n",
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              "      <td>thank much</td>\n",
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              "      <td>ok revisa el mio gracias</td>\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subset_df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "Rwl3pmwmHW2I",
        "outputId": "5d7180c1-0118-4602-9471-6a55107e758f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-f41d0418d4c5>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  subset_df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       grade  Question_Sentiment  Answer_Sentiment\n",
              "grade               1.000000            0.157716         -0.080579\n",
              "Question_Sentiment  0.157716            1.000000         -0.097251\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Extraction using bert"
      ],
      "metadata": {
        "id": "GVYewwP5PRib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertModel\n",
        "import torch\n"
      ],
      "metadata": {
        "id": "fhrUlPlOHzq3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertModel.from_pretrained('bert-base-uncased')\n",
        "model.eval()  #\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CSZwxDwfJfGG",
        "outputId": "e33a9330-7f54-4a81-c3ab-a7cf608230a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertModel(\n",
              "  (embeddings): BertEmbeddings(\n",
              "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "    (position_embeddings): Embedding(512, 768)\n",
              "    (token_type_embeddings): Embedding(2, 768)\n",
              "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (encoder): BertEncoder(\n",
              "    (layer): ModuleList(\n",
              "      (0-11): 12 x BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pooler): BertPooler(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create an empty list to store the BERT embeddings\n",
        "bert_embeddings = []\n",
        "\n",
        "# Iterate through each row in the DataFrame\n",
        "for index, row in subset_df.iterrows():\n",
        "    # Concatenate the 'question_title', 'question_content', and 'answer' columns\n",
        "    text = row['question_title'] + ' ' + row['question_content'] + ' ' + row['answer']\n",
        "\n",
        "    # Tokenize the text and convert it to BERT embeddings\n",
        "    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "        embeddings = outputs.last_hidden_state.mean(dim=1)  # Average the embeddings across all tokens\n",
        "        bert_embeddings.append(embeddings)\n",
        "\n",
        "# Convert the list of BERT embeddings to a DataFrame\n",
        "bert_embeddings_df = pd.DataFrame(torch.cat(bert_embeddings).numpy())\n",
        "\n",
        "# Concatenate the BERT embeddings DataFrame with the original DataFrame\n",
        "result_df = pd.concat([df, bert_embeddings_df], axis=1)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hsj3WNQgJ4s5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLyr-N-3JmXI",
        "outputId": "03b30547-2aee-4b12-f667-270c637e646d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "question_title      0\n",
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              "answer              0\n",
              "grade               0\n",
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          "metadata": {},
          "execution_count": 26
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      "cell_type": "code",
      "source": [
        "result_df.dropna(inplace= True)"
      ],
      "metadata": {
        "id": "PTqe8agwJztp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "Up_B4thfQXbN",
        "outputId": "ae171d7d-bfe9-4cc4-f67b-e470af5160f9"
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              "      <td>0.366793</td>\n",
              "      <td>0.011230</td>\n",
              "      <td>-0.001572</td>\n",
              "      <td>-0.181883</td>\n",
              "      <td>...</td>\n",
              "      <td>0.046385</td>\n",
              "      <td>-0.065006</td>\n",
              "      <td>-0.127343</td>\n",
              "      <td>-0.025654</td>\n",
              "      <td>-0.119414</td>\n",
              "      <td>0.030480</td>\n",
              "      <td>-0.247958</td>\n",
              "      <td>-0.124945</td>\n",
              "      <td>-0.049228</td>\n",
              "      <td>-0.036783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>please review assignment</td>\n",
              "      <td>dear someone please review assignment could al...</td>\n",
              "      <td>thanks lot review assignment review</td>\n",
              "      <td>0.861111</td>\n",
              "      <td>0.221717</td>\n",
              "      <td>0.182200</td>\n",
              "      <td>0.693043</td>\n",
              "      <td>-0.126354</td>\n",
              "      <td>0.177964</td>\n",
              "      <td>-0.379516</td>\n",
              "      <td>...</td>\n",
              "      <td>0.048652</td>\n",
              "      <td>-0.134217</td>\n",
              "      <td>0.092305</td>\n",
              "      <td>0.048658</td>\n",
              "      <td>-0.179255</td>\n",
              "      <td>-0.125334</td>\n",
              "      <td>-0.186899</td>\n",
              "      <td>-0.314749</td>\n",
              "      <td>-0.152709</td>\n",
              "      <td>-0.045591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>please review assignment</td>\n",
              "      <td>dear someone please review assignment could al...</td>\n",
              "      <td>hey glad review could please review mine well ...</td>\n",
              "      <td>0.861111</td>\n",
              "      <td>0.226730</td>\n",
              "      <td>0.382649</td>\n",
              "      <td>0.678196</td>\n",
              "      <td>-0.029209</td>\n",
              "      <td>0.166483</td>\n",
              "      <td>-0.224788</td>\n",
              "      <td>...</td>\n",
              "      <td>0.212152</td>\n",
              "      <td>0.083012</td>\n",
              "      <td>-0.197447</td>\n",
              "      <td>-0.156456</td>\n",
              "      <td>-0.044797</td>\n",
              "      <td>-0.150559</td>\n",
              "      <td>-0.295737</td>\n",
              "      <td>0.074154</td>\n",
              "      <td>-0.175765</td>\n",
              "      <td>-0.059989</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 772 columns</p>\n",
              "</div>\n",
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              "        const element = document.querySelector('#df-5e6945dc-2e68-4302-b0e8-fa8af71d34fb');\n",
              "        const dataTable =\n",
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              "                                                    [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",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "assigning values in variables"
      ],
      "metadata": {
        "id": "qkHXn-q5QgUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_df = result_df"
      ],
      "metadata": {
        "id": "uHIiijz_L7jo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# List of column names to drop\n",
        "columns_to_drop = ['question_title', 'question_content', 'answer','grade']\n",
        "\n",
        "# Drop the specified columns from the DataFrame\n",
        "x_df = x_df.drop(columns=columns_to_drop)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hXbGBMGfMbtH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X= x_df.values"
      ],
      "metadata": {
        "id": "kvRxOkbaNCl_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y= result_df['grade'].values"
      ],
      "metadata": {
        "id": "z7ghUzkmNRQk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the data"
      ],
      "metadata": {
        "id": "4Z_Q3JpwQtD8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "id": "_Nk07aFpNbnY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the data"
      ],
      "metadata": {
        "id": "1zjju4GFRgQ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "\n",
        "X_test= scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "99fOMY2zRjhl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear Regression"
      ],
      "metadata": {
        "id": "Dj1yXGnRQzbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "\n",
        "\n",
        "\n",
        "model = LinearRegression()\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Mean Absolute Error (MAE):\", mae)\n",
        "print(\"R-squared (R2) Score:\", r2)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enl7SFuiLZqC",
        "outputId": "58717c82-f335-4054-8cdc-89fe98797cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error (MAE): 0.0018038769315978007\n",
            "R-squared (R2) Score: -4.977266262687554e+25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree Regressor"
      ],
      "metadata": {
        "id": "e28lKYlWRp1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "impo"
      ],
      "metadata": {
        "id": "9YUu4gSOLZm_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9RAHJCXTL7M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "131978c7-4bec-40ed-aae6-3a7282dc09ff"
      },
      "source": [
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import numpy as np\n",
        "\n",
        "for depth in [1,2,3,4,5,6,7,8,9,10,20]:\n",
        "  dt = DecisionTreeRegressor(max_depth=depth)\n",
        "\n",
        "  dt.fit(X_train, y_train)\n",
        "\n",
        "  dt = DecisionTreeRegressor(max_depth=depth) # a fresh model which is not trained yet\n",
        "  valAccuracy = cross_val_score(dt, X_train, y_train, cv=10) # syntax : cross_val_Score(freshModel,fts, target, cv= 10/5)\n",
        "  print(\"Depth  : \", depth,  \" Cross val score : \" ,np.mean(valAccuracy))\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Depth  :  1  Cross val score :  -16.678378378378376\n",
            "Depth  :  2  Cross val score :  -187.73243243243246\n",
            "Depth  :  3  Cross val score :  -209.23193456614513\n",
            "Depth  :  4  Cross val score :  -206.30170697012804\n",
            "Depth  :  5  Cross val score :  -196.3839971550498\n",
            "Depth  :  6  Cross val score :  -198.09039829302986\n",
            "Depth  :  7  Cross val score :  -202.74935988620197\n",
            "Depth  :  8  Cross val score :  -191.06657183499289\n",
            "Depth  :  9  Cross val score :  -201.59608819345658\n",
            "Depth  :  10  Cross val score :  -204.55035561877668\n",
            "Depth  :  20  Cross val score :  -203.92603129445234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xWWkA0IMOPAF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN Regressor"
      ],
      "metadata": {
        "id": "y2VskXSVUEb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import numpy as np\n",
        "\n",
        "# Define a list of values for k\n",
        "k_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50]\n",
        "\n",
        "for k in k_values:\n",
        "    knn = KNeighborsRegressor(n_neighbors=k)  # Initialize the KNN regressor with the current k value\n",
        "    knn.fit(X_train, y_train)  # Train the KNN model on the training data\n",
        "\n",
        "    # Calculate the R-squared score on the training data\n",
        "    train_score = knn.score(X_train, y_train)\n",
        "\n",
        "    # Calculate the cross-validation score using 10-fold cross-validation\n",
        "    cv_scores = cross_val_score(knn, X_train, y_train, cv=10)\n",
        "    cv_score_mean = np.mean(cv_scores)\n",
        "\n",
        "    print(f\"K value: {k}, Train Score: {train_score:.4f}, CV Score (mean): {cv_score_mean:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuwDSANLWKoP",
        "outputId": "2da197ac-b0bb-4ce0-d1cb-88e2a04e2525"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K value: 1, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 2, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 3, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 4, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 5, Train Score: 0.7500, CV Score (mean): 0.0000\n",
            "K value: 6, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 7, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 8, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 9, Train Score: 1.0000, CV Score (mean): 1.0000\n",
            "K value: 10, Train Score: 0.7500, CV Score (mean): 0.0000\n",
            "K value: 20, Train Score: 0.7500, CV Score (mean): 0.0000\n",
            "K value: 50, Train Score: 0.7500, CV Score (mean): 0.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create a KNN Regressor model with k=5 (you can change k as needed)\n",
        "k = 5\n",
        "model = KNeighborsRegressor(n_neighbors=k)\n",
        "\n",
        "# Train the model on the training data\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate Mean Absolute Error (MAE)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "# Calculate R-squared (R2) score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(\"Mean Absolute Error (MAE):\", mae)\n",
        "print(\"R-squared (R2) Score:\", r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WIFxXwDdLkMs",
        "outputId": "bff5ea3a-b599-4c57-ae1b-0770a1207ee5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error (MAE): 1.1102230246251565e-16\n",
            "R-squared (R2) Score: 0.8888888888888888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "XGBoost"
      ],
      "metadata": {
        "id": "dYpcx881WbE1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import xgboost as xgb\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "\n",
        "# Initialize and train the XGBoost regressor\n",
        "xgb_regressor = xgb.XGBRegressor(\n",
        "    objective='reg:squarederror',  # Use squared error for regression\n",
        "    n_estimators=100,             # Number of boosting rounds\n",
        "    learning_rate=0.1,            # Learning rate\n",
        "\n",
        "    random_state=42               # Random seed for reproducibility\n",
        ")\n",
        "\n",
        "xgb_regressor.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = xgb_regressor.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
        "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
        "print(f\"R-squared (R2) Score: {r2}\")\n",
        "\n",
        "# Optionally, you can save the trained model for future use\n",
        "# xgb_regressor.save_model('xgboost_grade_predictor.model')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4SbmlusL7Jp",
        "outputId": "ac2bc30a-d5e5-4eff-bf67-1d23c290a998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error (MAE): 9.893882659928188e-06\n",
            "Root Mean Squared Error (RMSE): 9.893882659928188e-06\n",
            "R-squared (R2) Score: -8.824102449769596e+20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s7qcU1eBM7ur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8iQ1Jum6NAVl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neural Netrwork Model"
      ],
      "metadata": {
        "id": "LkyU2bydXJOD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "# Assuming you have already defined X_train, X_test, y_train, and y_test as NumPy arrays\n",
        "\n",
        "# Convert data to PyTorch tensors\n",
        "X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n",
        "X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n",
        "y_train_tensor = torch.tensor(y_train, dtype=torch.float32)\n",
        "y_test_tensor = torch.tensor(y_test, dtype=torch.float32)\n",
        "\n",
        "# Define a custom neural network model\n",
        "class GradePredictor(nn.Module):\n",
        "    def __init__(self, input_size):\n",
        "        super(GradePredictor, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, 64)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(64, 32)\n",
        "        self.fc3 = nn.Linear(32, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "# Initialize the model and optimizer\n",
        "model = GradePredictor(input_size=X_train_tensor.shape[1])\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "criterion = nn.MSELoss()  # Mean Squared\n",
        "\n"
      ],
      "metadata": {
        "id": "BeY3wky6NAYV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have defined X_train_tensor, y_train_tensor, model, optimizer, and criterion as mentioned earlier\n",
        "\n",
        "# Set the number of training epochs\n",
        "num_epochs = 10  # You can adjust this as needed\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(num_epochs):\n",
        "    # Set the model to training mode\n",
        "    model.train()\n",
        "\n",
        "    # Forward pass\n",
        "    outputs = model(X_train_tensor)\n",
        "\n",
        "    # Compute the loss\n",
        "    loss = criterion(outputs, y_train_tensor)\n",
        "\n",
        "    # Backpropagation and optimization\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
        "\n",
        "print('Training finished.')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCZ_W6e3Obf6",
        "outputId": "05f4104f-7fc2-4ca1-ead2-926d7962b66e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/10], Loss: 0.8759\n",
            "Epoch [2/10], Loss: 0.6923\n",
            "Epoch [3/10], Loss: 0.5411\n",
            "Epoch [4/10], Loss: 0.4076\n",
            "Epoch [5/10], Loss: 0.2908\n",
            "Epoch [6/10], Loss: 0.1955\n",
            "Epoch [7/10], Loss: 0.1267\n",
            "Epoch [8/10], Loss: 0.0872\n",
            "Epoch [9/10], Loss: 0.0752\n",
            "Epoch [10/10], Loss: 0.0818\n",
            "Training finished.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([371])) that is different to the input size (torch.Size([371, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the model to evaluation mode\n",
        "model.eval()\n",
        "\n",
        "# Disable gradient computation for evaluation\n",
        "with torch.no_grad():\n",
        "    # Forward pass on the test data\n",
        "    test_outputs = model(X_test_tensor)\n",
        "\n",
        "# Convert the predictions back to NumPy arrays\n",
        "y_pred = test_outputs.numpy()\n",
        "\n",
        "# Calculate evaluation metrics\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(f'Mean Absolute Error: {mae:.4f}')\n",
        "print(f'Mean Squared Error: {mse:.4f}')\n",
        "print(f'R-squared (R2) Score: {r2:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9Xth_cMOeA3",
        "outputId": "a68bd49c-88c9-4265-e6c3-ac381acf1dba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.2542\n",
            "Mean Squared Error: 0.1095\n",
            "R-squared (R2) Score: -986647767771897559866333462528.0000\n"
          ]
        }
      ]
    }
  ]
}