From 5d2215c06c8a477c61194f16426b34dee213e558 Mon Sep 17 00:00:00 2001
From: Orrm23 <burhanusuf7@gmail.com>
Date: Tue, 25 Feb 2025 23:06:19 -0800
Subject: [PATCH] apriori
---
Untitled13.ipynb | 730 +++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 730 insertions(+)
create mode 100644 Untitled13.ipynb
diff --git a/Untitled13.ipynb b/Untitled13.ipynb
new file mode 100644
index 0000000..e8defb0
--- /dev/null
+++ b/Untitled13.ipynb
@@ -0,0 +1,730 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "authorship_tag": "ABX9TyMJAMTJ2UxcPpBAc/ntokzp",
+ "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/Orrm23/DeepSeek-Coder/blob/main/Untitled13.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": {
+ "id": "iU_l3SQ9A65p"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "source": [
+ "#23_MarketBasketAnalysisusingAPIRIORI\n",
+ "### Importing the basic libraries\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "### Load Dataset from Local Directory\n",
+ "from google.colab import files\n",
+ "uploaded = files.upload()\n",
+ "### Importing the dataset\n",
+ "dataset = pd.read_csv('dataset.csv')\n",
+ "print(dataset.shape)\n",
+ "print(dataset.head(5))\n",
+ "### Data Pre-Processing using vectorized operations\n",
+ "# Extract the relevant columns (assuming first 20)\n",
+ "transactions_df = dataset.iloc[:, :20]\n",
+ "# Convert all values to strings\n",
+ "transactions_df = transactions_df.astype(str)\n",
+ "# Convert DataFrame to list of lists\n",
+ "transactions = transactions_df.values.tolist()\n",
+ "### Training APRIORI\n",
+ "!pip install apyori\n",
+ "from apyori import apriori\n",
+ "rules = apriori(transactions = transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2, max_length = 2)\n",
+ "### Result\n",
+ "results = list(rules)\n",
+ "results\n",
+ "### Results in Dataframe\n",
+ "lhs = [tuple(result[2][0][0])[0] for result in results]\n",
+ "rhs = [tuple(result[2][0][1])[0] for result in results]\n",
+ "supports = [result[1] for result in results]\n",
+ "confidences = [result[2][0][2] for result in results]\n",
+ "lifts = [result[2][0][3] for result in results]\n",
+ "resultsinDataFrame = pd.DataFrame(zip(lhs, rhs, supports, confidences, lifts), columns = ['Left Hand Side', 'Right Hand Side', 'Support', 'Confidence', 'Lift'])\n",
+ "resultsinDataFrame"
+ ],
+ "cell_type": "code",
+ "metadata": {
+ "id": "lFqrYPxIAk31",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1291
+ },
+ "outputId": "dfb99183-e856-45ba-8a3d-9d4e58d67a90"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <input type=\"file\" id=\"files-cd4b8c1e-81ca-47e9-afff-0523698ccd96\" name=\"files[]\" multiple disabled\n",
+ " style=\"border:none\" />\n",
+ " <output id=\"result-cd4b8c1e-81ca-47e9-afff-0523698ccd96\">\n",
+ " Upload widget is only available when the cell has been executed in the\n",
+ " current browser session. Please rerun this cell to enable.\n",
+ " </output>\n",
+ " <script>// Copyright 2017 Google LLC\n",
+ "//\n",
+ "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+ "// you may not use this file except in compliance with the License.\n",
+ "// You may obtain a copy of the License at\n",
+ "//\n",
+ "// http://www.apache.org/licenses/LICENSE-2.0\n",
+ "//\n",
+ "// Unless required by applicable law or agreed to in writing, software\n",
+ "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+ "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+ "// See the License for the specific language governing permissions and\n",
+ "// limitations under the License.\n",
+ "\n",
+ "/**\n",
+ " * @fileoverview Helpers for google.colab Python module.\n",
+ " */\n",
+ "(function(scope) {\n",
+ "function span(text, styleAttributes = {}) {\n",
+ " const element = document.createElement('span');\n",
+ " element.textContent = text;\n",
+ " for (const key of Object.keys(styleAttributes)) {\n",
+ " element.style[key] = styleAttributes[key];\n",
+ " }\n",
+ " return element;\n",
+ "}\n",
+ "\n",
+ "// Max number of bytes which will be uploaded at a time.\n",
+ "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+ "\n",
+ "function _uploadFiles(inputId, outputId) {\n",
+ " const steps = uploadFilesStep(inputId, outputId);\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " // Cache steps on the outputElement to make it available for the next call\n",
+ " // to uploadFilesContinue from Python.\n",
+ " outputElement.steps = steps;\n",
+ "\n",
+ " return _uploadFilesContinue(outputId);\n",
+ "}\n",
+ "\n",
+ "// This is roughly an async generator (not supported in the browser yet),\n",
+ "// where there are multiple asynchronous steps and the Python side is going\n",
+ "// to poll for completion of each step.\n",
+ "// This uses a Promise to block the python side on completion of each step,\n",
+ "// then passes the result of the previous step as the input to the next step.\n",
+ "function _uploadFilesContinue(outputId) {\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " const steps = outputElement.steps;\n",
+ "\n",
+ " const next = steps.next(outputElement.lastPromiseValue);\n",
+ " return Promise.resolve(next.value.promise).then((value) => {\n",
+ " // Cache the last promise value to make it available to the next\n",
+ " // step of the generator.\n",
+ " outputElement.lastPromiseValue = value;\n",
+ " return next.value.response;\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "/**\n",
+ " * Generator function which is called between each async step of the upload\n",
+ " * process.\n",
+ " * @param {string} inputId Element ID of the input file picker element.\n",
+ " * @param {string} outputId Element ID of the output display.\n",
+ " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+ " */\n",
+ "function* uploadFilesStep(inputId, outputId) {\n",
+ " const inputElement = document.getElementById(inputId);\n",
+ " inputElement.disabled = false;\n",
+ "\n",
+ " const outputElement = document.getElementById(outputId);\n",
+ " outputElement.innerHTML = '';\n",
+ "\n",
+ " const pickedPromise = new Promise((resolve) => {\n",
+ " inputElement.addEventListener('change', (e) => {\n",
+ " resolve(e.target.files);\n",
+ " });\n",
+ " });\n",
+ "\n",
+ " const cancel = document.createElement('button');\n",
+ " inputElement.parentElement.appendChild(cancel);\n",
+ " cancel.textContent = 'Cancel upload';\n",
+ " const cancelPromise = new Promise((resolve) => {\n",
+ " cancel.onclick = () => {\n",
+ " resolve(null);\n",
+ " };\n",
+ " });\n",
+ "\n",
+ " // Wait for the user to pick the files.\n",
+ " const files = yield {\n",
+ " promise: Promise.race([pickedPromise, cancelPromise]),\n",
+ " response: {\n",
+ " action: 'starting',\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " cancel.remove();\n",
+ "\n",
+ " // Disable the input element since further picks are not allowed.\n",
+ " inputElement.disabled = true;\n",
+ "\n",
+ " if (!files) {\n",
+ " return {\n",
+ " response: {\n",
+ " action: 'complete',\n",
+ " }\n",
+ " };\n",
+ " }\n",
+ "\n",
+ " for (const file of files) {\n",
+ " const li = document.createElement('li');\n",
+ " li.append(span(file.name, {fontWeight: 'bold'}));\n",
+ " li.append(span(\n",
+ " `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+ " `last modified: ${\n",
+ " file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+ " 'n/a'} - `));\n",
+ " const percent = span('0% done');\n",
+ " li.appendChild(percent);\n",
+ "\n",
+ " outputElement.appendChild(li);\n",
+ "\n",
+ " const fileDataPromise = new Promise((resolve) => {\n",
+ " const reader = new FileReader();\n",
+ " reader.onload = (e) => {\n",
+ " resolve(e.target.result);\n",
+ " };\n",
+ " reader.readAsArrayBuffer(file);\n",
+ " });\n",
+ " // Wait for the data to be ready.\n",
+ " let fileData = yield {\n",
+ " promise: fileDataPromise,\n",
+ " response: {\n",
+ " action: 'continue',\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+ " let position = 0;\n",
+ " do {\n",
+ " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+ " const chunk = new Uint8Array(fileData, position, length);\n",
+ " position += length;\n",
+ "\n",
+ " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+ " yield {\n",
+ " response: {\n",
+ " action: 'append',\n",
+ " file: file.name,\n",
+ " data: base64,\n",
+ " },\n",
+ " };\n",
+ "\n",
+ " let percentDone = fileData.byteLength === 0 ?\n",
+ " 100 :\n",
+ " Math.round((position / fileData.byteLength) * 100);\n",
+ " percent.textContent = `${percentDone}% done`;\n",
+ "\n",
+ " } while (position < fileData.byteLength);\n",
+ " }\n",
+ "\n",
+ " // All done.\n",
+ " yield {\n",
+ " response: {\n",
+ " action: 'complete',\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "scope.google = scope.google || {};\n",
+ "scope.google.colab = scope.google.colab || {};\n",
+ "scope.google.colab._files = {\n",
+ " _uploadFiles,\n",
+ " _uploadFilesContinue,\n",
+ "};\n",
+ "})(self);\n",
+ "</script> "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Saving dataset.csv to dataset.csv\n",
+ "(7500, 20)\n",
+ " shrimp almonds avocado vegetables mix green grapes \\\n",
+ "0 burgers meatballs eggs NaN NaN \n",
+ "1 chutney NaN NaN NaN NaN \n",
+ "2 turkey avocado NaN NaN NaN \n",
+ "3 mineral water milk energy bar whole wheat rice green tea \n",
+ "4 low fat yogurt NaN NaN NaN NaN \n",
+ "\n",
+ " whole weat flour yams cottage cheese energy drink tomato juice \\\n",
+ "0 NaN NaN NaN NaN NaN \n",
+ "1 NaN NaN NaN NaN NaN \n",
+ "2 NaN NaN NaN NaN NaN \n",
+ "3 NaN NaN NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN NaN \n",
+ "\n",
+ " low fat yogurt green tea honey salad mineral water salmon antioxydant juice \\\n",
+ "0 NaN NaN NaN NaN NaN NaN NaN \n",
+ "1 NaN NaN NaN NaN NaN NaN NaN \n",
+ "2 NaN NaN NaN NaN NaN NaN NaN \n",
+ "3 NaN NaN NaN NaN NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN NaN NaN NaN \n",
+ "\n",
+ " frozen smoothie spinach olive oil \n",
+ "0 NaN NaN NaN \n",
+ "1 NaN NaN NaN \n",
+ "2 NaN NaN NaN \n",
+ "3 NaN NaN NaN \n",
+ "4 NaN NaN NaN \n",
+ "Collecting apyori\n",
+ " Downloading apyori-1.1.2.tar.gz (8.6 kB)\n",
+ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: apyori\n",
+ " Building wheel for apyori (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for apyori: filename=apyori-1.1.2-py3-none-any.whl size=5954 sha256=6f460bf2d21945572fd7d1a90d9524bf5c416704c59847856c42f2fb0d60d484\n",
+ " Stored in directory: /root/.cache/pip/wheels/77/3d/a6/d317a6fb32be58a602b1e8c6b5d6f31f79322da554cad2a5ea\n",
+ "Successfully built apyori\n",
+ "Installing collected packages: apyori\n",
+ "Successfully installed apyori-1.1.2\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Left Hand Side Right Hand Side Support Confidence Lift\n",
+ "0 light cream chicken 0.004533 0.290598 4.843305\n",
+ "1 mushroom cream sauce escalope 0.005733 0.300699 3.790327\n",
+ "2 pasta escalope 0.005867 0.372881 4.700185\n",
+ "3 fromage blanc honey 0.003333 0.245098 5.178128\n",
+ "4 herb & pepper ground beef 0.016000 0.323450 3.291555\n",
+ "5 tomato sauce ground beef 0.005333 0.377358 3.840147\n",
+ "6 light cream olive oil 0.003200 0.205128 3.120612\n",
+ "7 whole wheat pasta olive oil 0.008000 0.271493 4.130221\n",
+ "8 pasta shrimp 0.005067 0.322034 4.514494"
+ ],
+ "text/html": [
+ "\n",
+ " <div id=\"df-008c783b-279a-40b9-a9e4-1b17fd994e2c\" class=\"colab-df-container\">\n",
+ " <div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Left Hand Side</th>\n",
+ " <th>Right Hand Side</th>\n",
+ " <th>Support</th>\n",
+ " <th>Confidence</th>\n",
+ " <th>Lift</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>light cream</td>\n",
+ " <td>chicken</td>\n",
+ " <td>0.004533</td>\n",
+ " <td>0.290598</td>\n",
+ " <td>4.843305</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>mushroom cream sauce</td>\n",
+ " <td>escalope</td>\n",
+ " <td>0.005733</td>\n",
+ " <td>0.300699</td>\n",
+ " <td>3.790327</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>pasta</td>\n",
+ " <td>escalope</td>\n",
+ " <td>0.005867</td>\n",
+ " <td>0.372881</td>\n",
+ " <td>4.700185</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>fromage blanc</td>\n",
+ " <td>honey</td>\n",
+ " <td>0.003333</td>\n",
+ " <td>0.245098</td>\n",
+ " <td>5.178128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>herb & pepper</td>\n",
+ " <td>ground beef</td>\n",
+ " <td>0.016000</td>\n",
+ " <td>0.323450</td>\n",
+ " <td>3.291555</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>tomato sauce</td>\n",
+ " <td>ground beef</td>\n",
+ " <td>0.005333</td>\n",
+ " <td>0.377358</td>\n",
+ " <td>3.840147</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>light cream</td>\n",
+ " <td>olive oil</td>\n",
+ " <td>0.003200</td>\n",
+ " <td>0.205128</td>\n",
+ " <td>3.120612</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>whole wheat pasta</td>\n",
+ " <td>olive oil</td>\n",
+ " <td>0.008000</td>\n",
+ " <td>0.271493</td>\n",
+ " <td>4.130221</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>pasta</td>\n",
+ " <td>shrimp</td>\n",
+ " <td>0.005067</td>\n",
+ " <td>0.322034</td>\n",
+ " <td>4.514494</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>\n",
+ " <div class=\"colab-df-buttons\">\n",
+ "\n",
+ " <div class=\"colab-df-container\">\n",
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-008c783b-279a-40b9-a9e4-1b17fd994e2c')\"\n",
+ " title=\"Convert this dataframe to an interactive table.\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+ " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+ " </svg>\n",
+ " </button>\n",
+ "\n",
+ " <style>\n",
+ " .colab-df-container {\n",
+ " display:flex;\n",
+ " gap: 12px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-convert:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " .colab-df-buttons div {\n",
+ " margin-bottom: 4px;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-convert:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ "\n",
+ " <script>\n",
+ " const buttonEl =\n",
+ " document.querySelector('#df-008c783b-279a-40b9-a9e4-1b17fd994e2c button.colab-df-convert');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " async function convertToInteractive(key) {\n",
+ " const element = document.querySelector('#df-008c783b-279a-40b9-a9e4-1b17fd994e2c');\n",
+ " const dataTable =\n",
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+ " [key], {});\n",
+ " if (!dataTable) return;\n",
+ "\n",
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+ " + ' to learn more about interactive tables.';\n",
+ " element.innerHTML = '';\n",
+ " dataTable['output_type'] = 'display_data';\n",
+ " await google.colab.output.renderOutput(dataTable, element);\n",
+ " const docLink = document.createElement('div');\n",
+ " docLink.innerHTML = docLinkHtml;\n",
+ " element.appendChild(docLink);\n",
+ " }\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ "\n",
+ "<div id=\"df-99413864-1e27-4eff-b893-b72424d2b6a1\">\n",
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-99413864-1e27-4eff-b893-b72424d2b6a1')\"\n",
+ " title=\"Suggest charts\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <g>\n",
+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+ " </g>\n",
+ "</svg>\n",
+ " </button>\n",
+ "\n",
+ "<style>\n",
+ " .colab-df-quickchart {\n",
+ " --bg-color: #E8F0FE;\n",
+ " --fill-color: #1967D2;\n",
+ " --hover-bg-color: #E2EBFA;\n",
+ " --hover-fill-color: #174EA6;\n",
+ " --disabled-fill-color: #AAA;\n",
+ " --disabled-bg-color: #DDD;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-quickchart {\n",
+ " --bg-color: #3B4455;\n",
+ " --fill-color: #D2E3FC;\n",
+ " --hover-bg-color: #434B5C;\n",
+ " --hover-fill-color: #FFFFFF;\n",
+ " --disabled-bg-color: #3B4455;\n",
+ " --disabled-fill-color: #666;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart {\n",
+ " background-color: var(--bg-color);\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: var(--fill-color);\n",
+ " height: 32px;\n",
+ " padding: 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart:hover {\n",
+ " background-color: var(--hover-bg-color);\n",
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: var(--button-hover-fill-color);\n",
+ " }\n",
+ "\n",
+ " .colab-df-quickchart-complete:disabled,\n",
+ " .colab-df-quickchart-complete:disabled:hover {\n",
+ " background-color: var(--disabled-bg-color);\n",
+ " fill: var(--disabled-fill-color);\n",
+ " box-shadow: none;\n",
+ " }\n",
+ "\n",
+ " .colab-df-spinner {\n",
+ " border: 2px solid var(--fill-color);\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " animation:\n",
+ " spin 1s steps(1) infinite;\n",
+ " }\n",
+ "\n",
+ " @keyframes spin {\n",
+ " 0% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " border-left-color: var(--fill-color);\n",
+ " }\n",
+ " 20% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 30% {\n",
+ " border-color: transparent;\n",
+ " border-left-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 40% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-top-color: var(--fill-color);\n",
+ " }\n",
+ " 60% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " }\n",
+ " 80% {\n",
+ " border-color: transparent;\n",
+ " border-right-color: var(--fill-color);\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " 90% {\n",
+ " border-color: transparent;\n",
+ " border-bottom-color: var(--fill-color);\n",
+ " }\n",
+ " }\n",
+ "</style>\n",
+ "\n",
+ " <script>\n",
+ " async function quickchart(key) {\n",
+ " const quickchartButtonEl =\n",
+ " document.querySelector('#' + key + ' button');\n",
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
+ " try {\n",
+ " const charts = await google.colab.kernel.invokeFunction(\n",
+ " 'suggestCharts', [key], {});\n",
+ " } catch (error) {\n",
+ " console.error('Error during call to suggestCharts:', error);\n",
+ " }\n",
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+ " }\n",
+ " (() => {\n",
+ " let quickchartButtonEl =\n",
+ " document.querySelector('#df-99413864-1e27-4eff-b893-b72424d2b6a1 button');\n",
+ " quickchartButtonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ " })();\n",
+ " </script>\n",
+ "</div>\n",
+ "\n",
+ " <div id=\"id_608141c4-567b-4f50-88d6-7182c8a13969\">\n",
+ " <style>\n",
+ " .colab-df-generate {\n",
+ " background-color: #E8F0FE;\n",
+ " border: none;\n",
+ " border-radius: 50%;\n",
+ " cursor: pointer;\n",
+ " display: none;\n",
+ " fill: #1967D2;\n",
+ " height: 32px;\n",
+ " padding: 0 0 0 0;\n",
+ " width: 32px;\n",
+ " }\n",
+ "\n",
+ " .colab-df-generate:hover {\n",
+ " background-color: #E2EBFA;\n",
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+ " fill: #174EA6;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-generate {\n",
+ " background-color: #3B4455;\n",
+ " fill: #D2E3FC;\n",
+ " }\n",
+ "\n",
+ " [theme=dark] .colab-df-generate:hover {\n",
+ " background-color: #434B5C;\n",
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+ " fill: #FFFFFF;\n",
+ " }\n",
+ " </style>\n",
+ " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('resultsinDataFrame')\"\n",
+ " title=\"Generate code using this dataframe.\"\n",
+ " style=\"display:none;\">\n",
+ "\n",
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+ " width=\"24px\">\n",
+ " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+ " </svg>\n",
+ " </button>\n",
+ " <script>\n",
+ " (() => {\n",
+ " const buttonEl =\n",
+ " document.querySelector('#id_608141c4-567b-4f50-88d6-7182c8a13969 button.colab-df-generate');\n",
+ " buttonEl.style.display =\n",
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+ "\n",
+ " buttonEl.onclick = () => {\n",
+ " google.colab.notebook.generateWithVariable('resultsinDataFrame');\n",
+ " }\n",
+ " })();\n",
+ " </script>\n",
+ " </div>\n",
+ "\n",
+ " </div>\n",
+ " </div>\n"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
+ "variable_name": "resultsinDataFrame",
+ "summary": "{\n \"name\": \"resultsinDataFrame\",\n \"rows\": 9,\n \"fields\": [\n {\n \"column\": \"Left Hand Side\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"light cream\",\n \"mushroom cream sauce\",\n \"tomato sauce\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Right Hand Side\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"chicken\",\n \"escalope\",\n \"shrimp\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Support\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.003896500487537852,\n \"min\": 0.0032,\n \"max\": 0.016,\n \"num_unique_values\": 9,\n \"samples\": [\n 0.008,\n 0.005733333333333333,\n 0.005333333333333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Confidence\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.056168653549259547,\n \"min\": 0.20512820512820515,\n \"max\": 0.37735849056603776,\n \"num_unique_values\": 9,\n \"samples\": [\n 0.2714932126696833,\n 0.30069930069930073,\n 0.37735849056603776\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Lift\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7066844698506499,\n \"min\": 3.120611639881417,\n \"max\": 5.178127589063795,\n \"num_unique_values\": 9,\n \"samples\": [\n 4.130221288078346,\n 3.7903273197390845,\n 3.840147461662528\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {},
+ "execution_count": 1
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "rj5duez_ERkd"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file