lime

lime.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMnPEht5MQJF/hLpcLpa95R",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/karthikab5/BizRecProject/blob/dev/lime.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_u28rTWlh3fy",
+        "outputId": "001c88db-9ab4-4cee-ee26-ce62b3b44b96"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "# prompt: code to drive mount\n",
+        "\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Necessary imports\n",
+        "import pandas as pd\n",
+        "from lime.lime_tabular import LimeTabularExplainer\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+        "\n",
+        "# Train a Random Forest Regressor\n",
+        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "# Regression Metrics\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "rmse = mean_squared_error(y_test, y_pred, squared=False)  # Root Mean Squared Error\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f\"Mean Squared Error (MSE): {mse}\")\n",
+        "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
+        "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
+        "print(f\"R-squared (R2): {r2}\")\n",
+        "\n",
+        "# LIME interpretation setup\n",
+        "explainer = LimeTabularExplainer(\n",
+        "    training_data=X_train.values,\n",
+        "    feature_names=feature_columns,\n",
+        "    mode='regression'\n",
+        ")\n",
+        "\n",
+        "# Function to explain a single prediction\n",
+        "def explain_prediction(instance):\n",
+        "    exp = explainer.explain_instance(\n",
+        "        data_row=instance,\n",
+        "        predict_fn=model.predict\n",
+        "    )\n",
+        "    return exp\n",
+        "\n",
+        "# Example usage with a specific instance from X_test\n",
+        "instance_index = 0  # Change this index for different instances\n",
+        "explanation = explain_prediction(X_test.values[instance_index])\n",
+        "\n",
+        "# Show the explanation\n",
+        "print(\"LIME Explanation for instance:\")\n",
+        "explanation.show_in_notebook(show_table=True)  # If you're using a Jupyter Notebook\n",
+        "# Or you can print the explanation details\n",
+        "print(explanation.as_list())\n",
+        "\n",
+        "def predict_success_for_business_ids(business_ids, model, features_df, feature_columns):\n",
+        "    # Filter the dataset for the provided business IDs\n",
+        "    input_data = features_df[features_df[\"business_id\"].isin(business_ids)]\n",
+        "    X_input = input_data[feature_columns]\n",
+        "\n",
+        "    # Predict success probability\n",
+        "    success_probabilities = model.predict(X_input)\n",
+        "\n",
+        "    # Create a DataFrame to display results\n",
+        "    result_df = pd.DataFrame({\n",
+        "        \"business_id\": input_data[\"business_id\"],\n",
+        "        \"success_probability\": success_probabilities * 10000\n",
+        "    })\n",
+        "\n",
+        "    return result_df\n",
+        "\n",
+        "# Example usage\n",
+        "business_ids_list = df['business_id'].tolist()  # Create a list of all business ids\n",
+        "business_ids_to_predict = business_ids_list  # Use this for prediction\n",
+        "success_probabilities = predict_success_for_business_ids(business_ids_to_predict, model, df, feature_columns)\n",
+        "\n",
+        "print(\"Predicted Success Probabilities:\")\n",
+        "print(success_probabilities)\n",
+        "\n",
+        "# Substitute a variable instead of directly giving business ID in SQL\n",
+        "business_df.createOrReplaceTempView(\"business_temp\")\n",
+        "review_df.createOrReplaceTempView(\"review_temp\")\n",
+        "checkin_df.createOrReplaceTempView(\"checkin_temp\")\n",
+        "business_id_to_search = 'SuSEmi52lP8gquHV0XIB9g'  # Replace with your variable\n",
+        "\n",
+        "# Using SQL queries\n",
+        "spark.sql(f\"SELECT * FROM business_temp WHERE business_id = '{business_id_to_search}'\").show()\n",
+        "spark.sql(f\"SELECT * FROM review_temp WHERE business_id = '{business_id_to_search}'\").show()\n",
+        "spark.sql(f\"SELECT * FROM checkin_temp WHERE business_id = '{business_id_to_search}'\").show()\n"
+      ],
+      "metadata": {
+        "id": "1aNFkM6Th-T-"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}