Autism_Detection_with_ML.ipynb

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      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "**Importing Libraries**"
      ],
      "metadata": {
        "id": "CPRgOOVfIv19"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn\n",
        "!pip install scikit-learn-intelex\n"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 0
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        "id": "Bg6i4oYxI4XN",
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      "execution_count": 1,
      "outputs": [
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          "name": "stdout",
          "text": [
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from time import time\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearnex import patch_sklearn, unpatch_sklearn\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the data\n",
        "data = pd.read_csv(\"/content/autism_data.csv\")\n",
        "\n",
        "# Display the first 5 rows of the dataset\n",
        "display(data.head(5))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "e_RogCLrI67B",
        "outputId": "2a616d64-9e60-4664-97ec-ac8acdb95e6f"
      },
      "execution_count": 2,
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              "   A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
              "0         1         1         1         1         0         0         1   \n",
              "1         1         1         0         1         0         0         0   \n",
              "2         1         1         0         1         1         0         1   \n",
              "3         1         1         0         1         0         0         1   \n",
              "4         1         0         0         0         0         0         0   \n",
              "\n",
              "   A8_Score  A9_Score  A10_Score  ...  gender       ethnicity jundice austim  \\\n",
              "0         1         0          0  ...       f  White-European      no     no   \n",
              "1         1         0          1  ...       m          Latino      no    yes   \n",
              "2         1         1          1  ...       m          Latino     yes    yes   \n",
              "3         1         0          1  ...       f  White-European      no    yes   \n",
              "4         1         0          0  ...       f               ?      no     no   \n",
              "\n",
              "   contry_of_res used_app_before result     age_desc relation Class/ASD  \n",
              "0  United States              no    6.0  18 and more     Self        NO  \n",
              "1         Brazil              no    5.0  18 and more     Self        NO  \n",
              "2          Spain              no    8.0  18 and more   Parent       YES  \n",
              "3  United States              no    6.0  18 and more     Self        NO  \n",
              "4          Egypt              no    2.0  18 and more        ?        NO  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "    }\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-77ba0565-ffd2-4f84-9729-6cc8175a4cd7 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"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove missing values\n",
        "data.dropna(inplace=True)\n",
        "\n",
        "# Convert categorical data to numerical (One-Hot Encoding)\n",
        "data_raw = data['Class/ASD']\n",
        "features_raw = data[['age', 'gender', 'ethnicity', 'jundice', 'austim', 'contry_of_res', 'result',\n",
        "                      'relation','A1_Score','A2_Score','A3_Score','A4_Score','A5_Score','A6_Score','A7_Score','A8_Score',\n",
        "                      'A9_Score','A10_Score']]\n",
        "\n",
        "# Apply MinMaxScaler to numerical features\n",
        "scaler = MinMaxScaler()\n",
        "num = ['age','result']\n",
        "features_raw[num] = scaler.fit_transform(features_raw[num])\n",
        "\n",
        "# One-hot encoding for categorical variables\n",
        "features_final = pd.get_dummies(features_raw)\n",
        "\n",
        "# Encode labels\n",
        "data_classes = data_raw.apply(lambda x: 1 if x == 'YES' else 0)\n",
        "\n",
        "# Split dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features_final, data_classes, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "DHaYYnj2JDnP",
        "outputId": "aa1a6b4d-00a5-48ee-cfbf-473b397a0673"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-966acf75c983>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  features_raw[num] = scaler.fit_transform(features_raw[num])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unpatch Intel OneAPI for this part\n",
        "unpatch_sklearn()\n",
        "\n",
        "# Train a Random Forest model without Intel OneAPI extensions\n",
        "model = RandomForestClassifier(random_state=42)\n",
        "\n",
        "start_time = time()\n",
        "model.fit(X_train, y_train)\n",
        "end_time = time()\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate accuracy and training time\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "train_time_no_oneapi = end_time - start_time\n",
        "\n",
        "print(f\"Without Intel OneAPI - Accuracy: {accuracy:.4f}, Training time: {train_time_no_oneapi:.4f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jYuRTIbLJTKY",
        "outputId": "2c84dbf9-fefc-4ddd-ea3f-1aec779c4cec"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Without Intel OneAPI - Accuracy: 1.0000, Training time: 0.2986 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Patch Intel OneAPI for this part\n",
        "patch_sklearn()\n",
        "\n",
        "# Train the same Random Forest model with Intel OneAPI extensions\n",
        "model_oneapi = RandomForestClassifier(random_state=42)\n",
        "\n",
        "start_time = time()\n",
        "model_oneapi.fit(X_train, y_train)\n",
        "end_time = time()\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred_oneapi = model_oneapi.predict(X_test)\n",
        "\n",
        "# Calculate accuracy and training time\n",
        "accuracy_oneapi = accuracy_score(y_test, y_pred_oneapi)\n",
        "train_time_oneapi = end_time - start_time\n",
        "\n",
        "print(f\"With Intel OneAPI - Accuracy: {accuracy_oneapi:.4f}, Training time: {train_time_oneapi:.4f} seconds\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gk04VVb2JU4i",
        "outputId": "66ec59ce-d63f-4b40-f6f0-da4f606f29be"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "With Intel OneAPI - Accuracy: 1.0000, Training time: 0.5824 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n--- Comparison ---\")\n",
        "print(f\"Without Intel OneAPI - Training time: {train_time_no_oneapi:.4f} seconds\")\n",
        "print(f\"With Intel OneAPI    - Training time: {train_time_oneapi:.4f} seconds\")\n",
        "\n",
        "speedup = train_time_no_oneapi / train_time_oneapi\n",
        "print(f\"Speedup with Intel OneAPI: {speedup:.2f}x\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7AHuVOCvJXpS",
        "outputId": "4358ac70-af43-4535-ec30-ce13c1930166"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Comparison ---\n",
            "Without Intel OneAPI - Training time: 1.0087 seconds\n",
            "With Intel OneAPI    - Training time: 0.5824 seconds\n",
            "Speedup with Intel OneAPI: 1.73x\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# Data\n",
        "data = {\n",
        "    'Method': ['Without Intel OneAPI', 'With Intel OneAPI'],\n",
        "    'Training Time (seconds)': [1.0087, 0.5824]\n",
        "}\n",
        "\n",
        "# Create DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Plot\n",
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(8, 6))\n",
        "bar_plot = sns.barplot(x='Method', y='Training Time (seconds)', data=df, palette='viridis')\n",
        "\n",
        "# Annotate the bars with the training times\n",
        "for index, value in enumerate(df['Training Time (seconds)']):\n",
        "    bar_plot.text(index, value + 0.02, f'{value:.4f}', color='black', ha=\"center\")\n",
        "\n",
        "# Title and labels\n",
        "plt.title('Training Time Comparison with and without Intel OneAPI', fontsize=16)\n",
        "plt.ylabel('Training Time (seconds)', fontsize=12)\n",
        "plt.xlabel('Method', fontsize=12)\n",
        "\n",
        "# Add the speedup annotation\n",
        "speedup = 1.73\n",
        "plt.text(1, 0.75, f'Speedup with Intel OneAPI: {speedup}x', color='blue', fontsize=14, ha='center')\n",
        "\n",
        "# Show plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "id": "3hczoV5iJas0",
        "outputId": "c8e5af67-1512-4346-b9ee-8c277ae6b99d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-2c1de939c6a9>:17: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  bar_plot = sns.barplot(x='Method', y='Training Time (seconds)', data=df, palette='viridis')\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
            "  data_subset = grouped_data.get_group(pd_key)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6sNCqfAfKEOW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}