Statmodels.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3BOBIGhJwpQ",
        "outputId": "5dadd4b8-77ea-488d-93a0-7f17f893d9a0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n",
            "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To run the code, you need the following Python packages:\n",
        "pandas\n",
        "seaborn\n",
        "matplotlib\n",
        "scipy\n",
        "statsmodels\n",
        "pymc3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "S4aYgoaLJyk9"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Capstone Project/worksheet_graphs - Sheet1 (1).csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ObIJaUmiJ2zz"
      },
      "outputs": [],
      "source": [
        "#print(data.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "dataset is loaded and cleaned by dropping any rows with missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IgscJ-IlKATZ"
      },
      "outputs": [],
      "source": [
        "cleaned_data = data.dropna()\n",
        "\n",
        "cleaned_data.reset_index(drop=True, inplace=True)\n",
        "\n",
        "data = cleaned_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxwO5mfR2zI7"
      },
      "source": [
        "# Chi-Square Test for Independence\n",
        "Used to determine if there is a significant association between two categorical variables for eg. categorical variables like 'Car Mode' and 'Vehicle Damage'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "71pizHOK2e1a"
      },
      "outputs": [],
      "source": [
        "import scipy.stats as stats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Specify the columns to be tested against 'Vehicle Damage' and perform the chi-square test to evaluate the association between these categorical variables and vehicle damage. The results are saved in a CSV file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7PU74sPR2L4I"
      },
      "outputs": [],
      "source": [
        "# Specify the columns you're interested in testing against 'Vehicle Damage'\n",
        "columns_to_test = [\n",
        "    'Vehicle 1 was Moving', 'Number of Vehicles involved in Accident (w V1)',\n",
        "    'Vehicle 2 was Moving', 'Number of Vehicles involved in Accident (w V2)',\n",
        "    'Car Mode', 'Weather Vehicle 1', 'Weather Vehicle 2',\n",
        "    'Lighting Vehicle 1', 'Lighting Vehicle 2', 'Roadway Surface Vehicle 1',\n",
        "    'Roadway Surface Vehicle 2', 'Roadway Conditions Vehicle 1',\n",
        "    'Roadway Conditions Vehicle 2', 'Movement Preceding Collision Vehicle 1',\n",
        "    'Movement Preceding Collision Vehicle 2', 'Type of Collison Vehicle 1',\n",
        "    'Type of Collison Vehicle 2'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "p6uKyvyB2Vxt"
      },
      "outputs": [],
      "source": [
        "# Iterate over each column and perform Chi-Square Test\n",
        "results = []\n",
        "for column in columns_to_test:\n",
        "    contingency_table = pd.crosstab(cleaned_data[column], cleaned_data['Vehicle Damage'])\n",
        "    chi2, p_value, dof, expected = stats.chi2_contingency(contingency_table)\n",
        "    results.append((column, chi2, p_value, dof))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiRwTcAn2bKx",
        "outputId": "868af474-4c7b-4249-8285-e8ff7bea5e11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                            Column  Chi-Square Statistic  \\\n",
            "0                             Vehicle 1 was Moving              0.908145   \n",
            "1   Number of Vehicles involved in Accident (w V1)              3.974395   \n",
            "2                             Vehicle 2 was Moving              2.025679   \n",
            "3   Number of Vehicles involved in Accident (w V2)              2.598746   \n",
            "4                                         Car Mode              0.902937   \n",
            "5                                Weather Vehicle 1             10.859083   \n",
            "6                                Weather Vehicle 2             10.143427   \n",
            "7                               Lighting Vehicle 1              6.354899   \n",
            "8                               Lighting Vehicle 2              5.752518   \n",
            "9                        Roadway Surface Vehicle 1              4.909849   \n",
            "10                       Roadway Surface Vehicle 2              2.297175   \n",
            "11                    Roadway Conditions Vehicle 1              8.900118   \n",
            "12                    Roadway Conditions Vehicle 2             11.488584   \n",
            "13          Movement Preceding Collision Vehicle 1             15.513618   \n",
            "14          Movement Preceding Collision Vehicle 2             11.815424   \n",
            "15                      Type of Collison Vehicle 1             16.245501   \n",
            "16                      Type of Collison Vehicle 2              8.534318   \n",
            "\n",
            "     P-value  Degrees of Freedom  \n",
            "0   0.635037                   2  \n",
            "1   0.409482                   4  \n",
            "2   0.363186                   2  \n",
            "3   0.627045                   4  \n",
            "4   0.636693                   2  \n",
            "5   0.028194                   4  \n",
            "6   0.118741                   6  \n",
            "7   0.041692                   2  \n",
            "8   0.218408                   4  \n",
            "9   0.296673                   4  \n",
            "10  0.681283                   4  \n",
            "11  0.179274                   6  \n",
            "12  0.003201                   2  \n",
            "13  0.746303                  20  \n",
            "14  0.922282                  20  \n",
            "15  0.180249                  12  \n",
            "16  0.576793                  10  \n"
          ]
        }
      ],
      "source": [
        "# Convert results to a DataFrame for better visualization\n",
        "result_df = pd.DataFrame(results, columns=['Column', 'Chi-Square Statistic', 'P-value', 'Degrees of Freedom'])\n",
        "\n",
        "# Display the results\n",
        "print(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "cw_N7RtCS-fB"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Fvl_8hqzTEBY",
        "outputId": "7ace38d1-48eb-4445-9160-7c22f26939b2"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_a490b18f-cf84-47a3-8952-7a3d10a4e122\", \"bayesian.csv\", 1199)",
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      ],
      "source": [
        "# Save DataFrame to CSV\n",
        "df.to_csv('chisquare.csv', index=False)\n",
        "\n",
        "# Download the file\n",
        "files.download('chisquare.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WdU6qIS2_no"
      },
      "source": [
        "#StatsModels\n",
        "\n",
        "Needs numeric data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Combine related columns to create new features representing car conditions and external conditions. Fit a Bayesian model to analyze the impact of these conditions on vehicle damage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "pzxDlAZc3Ql3"
      },
      "outputs": [],
      "source": [
        "#pip install statsmodels\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "n2LRl7oV3R7W"
      },
      "outputs": [],
      "source": [
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "IKSvAsOO3XJ5"
      },
      "outputs": [],
      "source": [
        "data['Car Conditions'] = data[['Vehicle 1 was Moving', 'Number of Vehicles involved in Accident (w V1)', 'Movement Preceding Collision Vehicle 1', 'Car Mode']].apply(lambda x: '_'.join(x.astype(str)), axis=1)\n",
        "data['External Conditions'] = data[['Weather Vehicle 1', 'Lighting Vehicle 1', 'Roadway Surface Vehicle 1', 'Roadway Conditions Vehicle 1']].apply(lambda x: '_'.join(x.astype(str)), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "HAYYCeYJ3iEW"
      },
      "outputs": [],
      "source": [
        "data['Vehicle Damage'] = data['Vehicle Damage'].astype('category')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tI_PA7aV4bok",
        "outputId": "f85e5b46-f992-44da-e300-511352184851"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "endog has evaluated to an array with multiple columns that has shape (324, 4). This occurs when the variable converted to endog is non-numeric (e.g., bool or str).",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-8fbba57383d1>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Fit the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmixedlm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformula\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Car Mode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfamilies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBinomial\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[0m\u001b[1;32m      8\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\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      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/regression/mixed_linear_model.py\u001b[0m in \u001b[0;36mfrom_formula\u001b[0;34m(cls, formula, data, re_formula, vc_formula, subset, use_sparse, missing, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1044\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"exog_vc\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexog_vc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"groups\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m         \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_formula\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformula\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\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   1047\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m         \u001b[0;31m# expand re names to account for pairs of RE\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/statsmodels/base/model.py\u001b[0m in \u001b[0;36mfrom_formula\u001b[0;34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[0m\n\u001b[1;32m    207\u001b[0m         if (max_endog is not None and\n\u001b[1;32m    208\u001b[0m                 endog.ndim > 1 and endog.shape[1] > max_endog):\n\u001b[0;32m--> 209\u001b[0;31m             raise ValueError('endog has evaluated to an array with multiple '\n\u001b[0m\u001b[1;32m    210\u001b[0m                              \u001b[0;34m'columns that has shape {}. This occurs when '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m                              \u001b[0;34m'the variable converted to endog is non-numeric'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: endog has evaluated to an array with multiple columns that has shape (324, 4). This occurs when the variable converted to endog is non-numeric (e.g., bool or str)."
          ]
        }
      ],
      "source": [
        "formula = 'Q(\"Vehicle Damage\") ~ Q(\"Car Conditions\") + Q(\"External Conditions\")'\n",
        "\n",
        "# Assuming Vehicle Damage is categorical and using Car Mode for random effects for simplicity\n",
        "data['Vehicle Damage'] = data['Vehicle Damage'].astype('category')\n",
        "\n",
        "# Fit the model\n",
        "model = smf.mixedlm(formula, data, groups=data['Car Mode'], family=sm.families.Binomial())\n",
        "result = model.fit()\n",
        "\n",
        "# Print the summary of the model\n",
        "print(result.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9OGvShFp_zMc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMsNwfMC_z5d"
      },
      "source": [
        "#Bayesian Modeling with PyMC3\n",
        "\n",
        "needs to be used on conda or virtual env"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "taqw_eHQArPp"
      },
      "outputs": [],
      "source": [
        "# !pip install theano\n",
        "# !pip install arviz\n",
        "# !pip install pymc3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "2KDbh9yMCr9t"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['MKL_THREADING_LAYER'] = 'GNU'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPzsOlxXFEce",
        "outputId": "e8a034bc-4c11-4a41-f650-92b6f39e088f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting numpy==1.20.3\n",
            "  Using cached numpy-1.20.3.zip (7.8 MB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: numpy\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for numpy \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Building wheel for numpy (pyproject.toml) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[31m  ERROR: Failed building wheel for numpy\u001b[0m\u001b[31m\n",
            "\u001b[0mFailed to build numpy\n",
            "\u001b[31mERROR: Could not build wheels for numpy, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade numpy==1.20.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "AHwzYF4SDylO",
        "outputId": "6e836271-d40f-4238-9377-9e228e9c60f8"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNoSectionError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36mfetch_val_for_key\u001b[0;34m(key, delete_key)\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[0mtheano_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moption\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[0mConfigParser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterpolationError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/configparser.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, section, option, raw, vars, fallback)\u001b[0m\n\u001b[1;32m    782\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--> 783\u001b[0;31m             \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unify_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\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    784\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mNoSectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/configparser.py\u001b[0m in \u001b[0;36m_unify_values\u001b[0;34m(self, section, vars)\u001b[0m\n\u001b[1;32m   1153\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msection\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_section\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mNoSectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1155\u001b[0m         \u001b[0;31m# Update with the entry specific variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNoSectionError\u001b[0m: No section: 'blas'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, cls, type_, delete_key)\u001b[0m\n\u001b[1;32m    326\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--> 327\u001b[0;31m                 val_str = fetch_val_for_key(self.fullname,\n\u001b[0m\u001b[1;32m    328\u001b[0m                                             delete_key=delete_key)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36mfetch_val_for_key\u001b[0;34m(key, delete_key)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mConfigParser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoOptionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mConfigParser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoSectionError\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--> 172\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'blas.ldflags'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-2f7adec075c1>\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[0mtheano\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;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtt\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[1;32m      4\u001b[0m \u001b[0;31m# Simple Theano test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\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/theano/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m from theano.scan_module import (scan, map, reduce, foldl, foldr, clone,\n\u001b[0m\u001b[1;32m    125\u001b[0m                                 scan_checkpoints)\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/scan_module/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0m__contact__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Razvan Pascanu <r.pascanu@gmail>\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_module\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscan_opt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_checkpoints\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscan_checkpoints\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/theano/scan_module/scan_opt.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_scalar_constant_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAlloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAllocEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgof\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/theano/tensor/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopt_uncanonicalize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_scipy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_c\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/theano/tensor/blas.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbool_t\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbasic\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas_headers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_header_text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas_headers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_header_version\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0min2out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dimshuffle_lift\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/theano/tensor/blas_headers.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mldflags\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    988\u001b[0m     \u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Using NumPy C-API based implementation for BLAS functions.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, cls, type_, delete_key)\u001b[0m\n\u001b[1;32m    330\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\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--> 332\u001b[0;31m                     \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\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    333\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[1;32m    334\u001b[0m                     \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\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/theano/configdefaults.py\u001b[0m in \u001b[0;36mdefault_blas_ldflags\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1282\u001b[0m             \u001b[0;31m# If the old private interface is available use it as it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1283\u001b[0m             \u001b[0;31m# don't print information to the user.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1284\u001b[0;31m             \u001b[0mblas_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__config__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas_opt_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1285\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[1;32m   1286\u001b[0m             \u001b[0;31m# We do this import only here, as in some setup, if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'"
          ]
        }
      ],
      "source": [
        "import theano\n",
        "from theano import tensor as tt\n",
        "\n",
        "# Simple Theano test\n",
        "x = tt.scalar('x')\n",
        "y = tt.scalar('y')\n",
        "z = x + y\n",
        "f = theano.function([x, y], z)\n",
        "print(\"Testing Theano computation:\", f(2, 3))  # Should output 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2XDbsZrZCzw6",
        "outputId": "c1601a81-0a83-4005-da75-9aead8bcae17"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNoSectionError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36mfetch_val_for_key\u001b[0;34m(key, delete_key)\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[0mtheano_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moption\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[0mConfigParser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterpolationError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/configparser.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, section, option, raw, vars, fallback)\u001b[0m\n\u001b[1;32m    782\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--> 783\u001b[0;31m             \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unify_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\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    784\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mNoSectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/configparser.py\u001b[0m in \u001b[0;36m_unify_values\u001b[0;34m(self, section, vars)\u001b[0m\n\u001b[1;32m   1153\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msection\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_section\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mNoSectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1155\u001b[0m         \u001b[0;31m# Update with the entry specific variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNoSectionError\u001b[0m: No section: 'blas'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, cls, type_, delete_key)\u001b[0m\n\u001b[1;32m    326\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--> 327\u001b[0;31m                 val_str = fetch_val_for_key(self.fullname,\n\u001b[0m\u001b[1;32m    328\u001b[0m                                             delete_key=delete_key)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36mfetch_val_for_key\u001b[0;34m(key, delete_key)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mConfigParser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoOptionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mConfigParser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoSectionError\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--> 172\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'blas.ldflags'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-b7b91f794800>\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[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_openmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\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/theano/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprinting\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m from theano.scan_module import (scan, map, reduce, foldl, foldr, clone,\n\u001b[0m\u001b[1;32m    125\u001b[0m                                 scan_checkpoints)\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/scan_module/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0m__contact__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Razvan Pascanu <r.pascanu@gmail>\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_module\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscan_opt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_checkpoints\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscan_checkpoints\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/theano/scan_module/scan_opt.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_scalar_constant_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAlloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAllocEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgof\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/theano/tensor/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mopt_uncanonicalize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_scipy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_c\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/theano/tensor/blas.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbool_t\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbasic\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas_headers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_header_text\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas_headers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mblas_header_version\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0min2out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_dimshuffle_lift\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/theano/tensor/blas_headers.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mldflags\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    988\u001b[0m     \u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Using NumPy C-API based implementation for BLAS functions.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, cls, type_, delete_key)\u001b[0m\n\u001b[1;32m    330\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\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--> 332\u001b[0;31m                     \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\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    333\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[1;32m    334\u001b[0m                     \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\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/theano/configdefaults.py\u001b[0m in \u001b[0;36mdefault_blas_ldflags\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1282\u001b[0m             \u001b[0;31m# If the old private interface is available use it as it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1283\u001b[0m             \u001b[0;31m# don't print information to the user.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1284\u001b[0;31m             \u001b[0mblas_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__config__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas_opt_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1285\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[1;32m   1286\u001b[0m             \u001b[0;31m# We do this import only here, as in some setup, if we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'"
          ]
        }
      ],
      "source": [
        "import theano\n",
        "theano.config.blas.check_openmp = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JGBvDhNAAPn8"
      },
      "outputs": [],
      "source": [
        "import pymc3 as pm\n",
        "import theano.tensor as tt\n",
        "print(\"PyMC3 version:\", pm.__version__)\n",
        "print(\"Theano version:\", tt.config.version)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DpcDEoCU_9_U"
      },
      "outputs": [],
      "source": [
        "data['Vehicle Damage'] = pd.Categorical(data['Vehicle Damage'], categories=[\"None\", \"Minor\", \"Moderate\", \"Major\"], ordered=True)\n",
        "data['Car Conditions'] = pd.Categorical(data['Car Conditions'])\n",
        "data['External Conditions'] = pd.Categorical(data['External Conditions'])\n",
        "\n",
        "# Convert categories to codes\n",
        "damage_codes = data['Vehicle Damage'].cat.codes\n",
        "car_conditions_codes = pd.get_dummies(data['Car Conditions'], drop_first=True)\n",
        "external_conditions_codes = pd.get_dummies(data['External Conditions'], drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IHhOjqOWAOZC"
      },
      "outputs": [],
      "source": [
        "with pm.Model() as model:\n",
        "    # Priors for ordinal thresholds (cutpoints)\n",
        "    cutpoints = pm.Normal(\"cutpoints\", mu=0, sigma=1.5, shape=3, transform=pm.distributions.transforms.ordered)\n",
        "\n",
        "    # Priors for coefficients\n",
        "    beta_car = pm.Normal(\"beta_car\", mu=0, sigma=1, shape=car_conditions_codes.shape[1])\n",
        "    beta_ext = pm.Normal(\"beta_ext\", mu=0, sigma=1, shape=external_conditions_codes.shape[1])\n",
        "\n",
        "    # Linear model\n",
        "    mu = (tt.dot(car_conditions_codes.values, beta_car) +\n",
        "          tt.dot(external_conditions_codes.values, beta_ext))\n",
        "\n",
        "    # Model likelihood\n",
        "    Y_obs = pm.OrderedLogistic(\"Y_obs\", mu=mu, cutpoints=cutpoints, observed=damage_codes)\n",
        "\n",
        "    # Sample from the posterior\n",
        "    trace = pm.sample(1000, return_inferencedata=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "XDvL72LSL6VD"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "JPqCg5kJMjQK"
      },
      "outputs": [],
      "source": [
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wDlvXc7RL7ZB",
        "outputId": "47564448-4e0b-46ba-fcba-6fbbe34e8cef"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_5fe6baf4-8c30-4623-84e0-20106db5dabc\", \"bayesian.csv\", 495255)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Save DataFrame to CSV\n",
        "df.to_csv('bayesian.csv', index=False)\n",
        "\n",
        "# Download the file\n",
        "files.download('bayesian.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S-A5B3VsPOVS"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rri33RG-PPzj"
      },
      "source": [
        "#New"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Convert categorical variables to numeric using one-hot encoding, then fit a generalized linear model (GLM) to predict vehicle damage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "SRtUj3l1PUgX"
      },
      "outputs": [],
      "source": [
        "import statsmodels.api as sm\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0_hh_qCXkuq",
        "outputId": "31427ad3-8370-493a-9ee5-8ed24ad2e50f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['File ID', 'Vehicle 1 Manufacturer', 'Vehicle 1 Business Name', 'Date',\n",
            "       'Time', 'Vehicle 1 Year of Manufacturing', 'Vehicle 1 Make',\n",
            "       'Vehicle 1 Model', 'Location of Accident', 'City of Accident',\n",
            "       'County of Accident', 'State of Accident', 'ZIPCODE of Accident',\n",
            "       'Vehicle 1 was Moving', 'Vehicle 1 was Stopped in Traffic',\n",
            "       'Pedestrian Involved in Accident (w V1)',\n",
            "       'Bicyclist involved in Accident (w V1)',\n",
            "       'Any other involved entity (w V1)',\n",
            "       'Number of Vehicles involved in Accident (w V1)', 'Vehicle Damage',\n",
            "       'RB', 'LR1', 'LR2', 'LR3', 'RR1', 'RR2', 'RR3', 'LRP1', 'LRP2', 'LRP3',\n",
            "       'LRP4', 'RRP1', 'RRP2', 'RRP3', 'RRP4', 'FDS1', 'FDS2', 'FDS3', 'FDS4',\n",
            "       'FPS1', 'FPS2', 'FPS3', 'FPS4', 'LFC1', 'LFC2', 'LFC3', 'RFC1', 'RFC2',\n",
            "       'RFC3', 'FB', 'Vehicle 2 Model', 'Vehicle 2 Year of Manufacturing',\n",
            "       'Vehicle 2 was Moving', 'Vehicle 2 was Stopped in Traffic',\n",
            "       'Pedestrian Involved in Accident (w V2)',\n",
            "       'Bicyclist involved in Accident (w V2)',\n",
            "       'Any other involved entity (w V2)', 'Property Damage',\n",
            "       'Property Owner's Details',\n",
            "       'Number of Vehicles involved in Accident (w V2)', 'Car Mode',\n",
            "       'Description', 'Weather Vehicle 1', 'Weather Vehicle 2',\n",
            "       'Lighting Vehicle 1', 'Lighting Vehicle 2', 'Roadway Surface Vehicle 1',\n",
            "       'Roadway Surface Vehicle 2', 'Roadway Conditions Vehicle 1',\n",
            "       'Roadway Conditions Vehicle 2',\n",
            "       'Movement Preceding Collision Vehicle 1',\n",
            "       'Movement Preceding Collision Vehicle 2', 'Type of Collison Vehicle 1',\n",
            "       'Type of Collison Vehicle 2', 'Other Factors', 'Lat1', 'Lng1', 'Lat2',\n",
            "       'Lng2', 'Lat3', 'Lng3'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(data.columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "UTfh-mucWCN5",
        "outputId": "b56f66a7-ccd6-4a73-cc7d-6b398ebfc3e3"
      },
      "outputs": [
        {
          "ename": "KeyError",
          "evalue": "\"['Vehicle Damage'] not found in axis\"",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-c6af0620b5bb>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Ensuring all explanatory variables are numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Vehicle Damage'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Replace 'Vehicle_Damage' with the correct name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Vehicle Damage'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Modify this line based on your actual target variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5256\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5257\u001b[0m         \"\"\"\n\u001b[0;32m-> 5258\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5259\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5260\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4547\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4548\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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-> 4549\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4589\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4590\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-> 4591\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4592\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6697\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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   6698\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6699\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\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   6700\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6701\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Vehicle Damage'] not found in axis\""
          ]
        }
      ],
      "source": [
        "data = pd.get_dummies(data, drop_first=True)\n",
        "\n",
        "# Ensuring all explanatory variables are numeric\n",
        "X = data.drop('Vehicle Damage', axis=1).apply(pd.to_numeric, errors='coerce')  # Replace 'Vehicle_Damage' with the correct name\n",
        "y = data['Vehicle Damage'].apply(pd.to_numeric, errors='coerce')  # Modify this line based on your actual target variable\n",
        "\n",
        "\n",
        "# Drop any columns that still have non-numeric types\n",
        "X = X.select_dtypes(include=[np.number])\n",
        "y = y.dropna()  # Ensuring the target variable has no NaNs\n",
        "\n",
        "# Checking if X and y have the same length\n",
        "X = X.loc[y.index]\n",
        "\n",
        "# Fit a GLM\n",
        "model = sm.GLM(y, sm.add_constant(X), family=sm.families.Binomial())\n",
        "result = model.fit()\n",
        "\n",
        "print(result.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "BmuIX-ldYm2F"
      },
      "outputs": [],
      "source": [
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8JL_vGdlZFI9"
      },
      "outputs": [],
      "source": [
        "data['Car Conditions'] = data[['Vehicle 1 was Moving', 'Number of Vehicles involved in Accident (w V1)', 'Movement Preceding Collision Vehicle 1', 'Car Mode']].apply(lambda x: '_'.join(x.astype(str)), axis=1)\n",
        "data['External Conditions'] = data[['Weather Vehicle 1', 'Lighting Vehicle 1', 'Roadway Surface Vehicle 1', 'Roadway Conditions Vehicle 1']].apply(lambda x: '_'.join(x.astype(str)), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "z35amuRUYjnW",
        "outputId": "9616df7e-5be0-4af3-c264-0260e136917e"
      },
      "outputs": [
        {
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<unknown>, line 1)",
          "output_type": "error",
          "traceback": [
            "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\"\u001b[0m, line \u001b[1;32m3553\u001b[0m, in \u001b[1;35mrun_code\u001b[0m\n    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \u001b[1;32m\"<ipython-input-19-ccfd0e5b2540>\"\u001b[0m, line \u001b[1;32m1\u001b[0m, in \u001b[1;35m<cell line: 1>\u001b[0m\n    model = smf.mixedlm(\"Vehicle Damage ~ Weather + Lighting + Road Conditions\", data,\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/statsmodels/regression/mixed_linear_model.py\"\u001b[0m, line \u001b[1;32m1046\u001b[0m, in \u001b[1;35mfrom_formula\u001b[0m\n    mod = super().from_formula(formula, data, *args, **kwargs)\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py\"\u001b[0m, line \u001b[1;32m203\u001b[0m, in \u001b[1;35mfrom_formula\u001b[0m\n    tmp = handle_formula_data(data, None, formula, depth=eval_env,\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/statsmodels/formula/formulatools.py\"\u001b[0m, line \u001b[1;32m63\u001b[0m, in \u001b[1;35mhandle_formula_data\u001b[0m\n    result = dmatrices(formula, Y, depth, return_type='dataframe',\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/highlevel.py\"\u001b[0m, line \u001b[1;32m309\u001b[0m, in \u001b[1;35mdmatrices\u001b[0m\n    (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/highlevel.py\"\u001b[0m, line \u001b[1;32m164\u001b[0m, in \u001b[1;35m_do_highlevel_design\u001b[0m\n    design_infos = _try_incr_builders(formula_like, data_iter_maker, eval_env,\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/highlevel.py\"\u001b[0m, line \u001b[1;32m66\u001b[0m, in \u001b[1;35m_try_incr_builders\u001b[0m\n    return design_matrix_builders([formula_like.lhs_termlist,\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/build.py\"\u001b[0m, line \u001b[1;32m689\u001b[0m, in \u001b[1;35mdesign_matrix_builders\u001b[0m\n    factor_states = _factors_memorize(all_factors, data_iter_maker, eval_env)\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/build.py\"\u001b[0m, line \u001b[1;32m354\u001b[0m, in \u001b[1;35m_factors_memorize\u001b[0m\n    which_pass = factor.memorize_passes_needed(state, eval_env)\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/eval.py\"\u001b[0m, line \u001b[1;32m478\u001b[0m, in \u001b[1;35mmemorize_passes_needed\u001b[0m\n    subset_names = [name for name in ast_names(self.code)\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/eval.py\"\u001b[0m, line \u001b[1;32m478\u001b[0m, in \u001b[1;35m<listcomp>\u001b[0m\n    subset_names = [name for name in ast_names(self.code)\n",
            "  File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/patsy/eval.py\"\u001b[0m, line \u001b[1;32m109\u001b[0m, in \u001b[1;35mast_names\u001b[0m\n    for node in ast.walk(ast.parse(code)):\n",
            "\u001b[0;36m  File \u001b[0;32m\"/usr/lib/python3.10/ast.py\"\u001b[0;36m, line \u001b[0;32m50\u001b[0;36m, in \u001b[0;35mparse\u001b[0;36m\u001b[0m\n\u001b[0;31m    return compile(source, filename, mode, flags,\u001b[0m\n",
            "\u001b[0;36m  File \u001b[0;32m\"<unknown>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Road Conditions\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ],
      "source": [
        "model = smf.mixedlm(\"Vehicle Damage ~ Weather + Lighting + Road_Conditions\", data,\n",
        "                    groups=data[\"Vehicle 1 Manufacturer\"])\n",
        "result = model.fit()\n",
        "print(result.summary())"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}