python/examples/advanced/Transaction_Examples.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Transactions allow several profiles to be commited to WhyLabs as a group. Let's start with some setup.\n"
],
"metadata": {
"id": "u5FQGlNpNVUX"
}
},
{
"cell_type": "code",
"source": [
"!pip install whylogs"
],
"metadata": {
"id": "rDZLfAYMi7vi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import whylogs as why\n",
"from whylabs_client.api.transactions_api import TransactionsApi\n",
"from whylogs.core.schema import DatasetSchema\n",
"from whylogs.core.segmentation_partition import segment_on_column\n",
"from whylogs.api.writer.whylabs import WhyLabsWriter\n",
"from whylogs.api.writer.whylabs_transaction_writer import WhyLabsTransactionWrirter\n",
"import os\n",
"from uuid import uuid4\n",
"from whylogs.datasets import Ecommerce\n",
"import numpy as np\n",
"import pandas as pd\n",
"from datetime import datetime, timedelta, timezone"
],
"metadata": {
"id": "3eyEw1UUi_nl"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"os.environ[\"WHYLABS_DEFAULT_ORG_ID\"] = \"org-XXX\"\n",
"os.environ[\"WHYLABS_DEFAULT_DATASET_ID\"] = \"model-XXX\"\n",
"os.environ[\"WHYLABS_API_KEY\"] = \"XXXX:org-XXX\""
],
"metadata": {
"id": "h3Fq8l14XmpA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Get example dataset"
],
"metadata": {
"id": "ul-WBntiyS9x"
}
},
{
"cell_type": "code",
"source": [
"dataset = Ecommerce()"
],
"metadata": {
"id": "kbVch_DaySHW"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"daily_batches = dataset.get_inference_data(number_batches=20)\n",
"list_daily_batches = list(daily_batches)"
],
"metadata": {
"id": "G_wcHlmBypVj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"columns = ['product','sales_last_week','market_price','rating','category','output_discount','output_prediction','output_score']\n",
"\n",
"df = list_daily_batches[0].data[columns]"
],
"metadata": {
"id": "ltwx9rVJyzDc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
},
"id": "Wd0Ms0UDynEi",
"outputId": "505fef55-900a-4f85-d86d-a0f48ccb3a69"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" product \\\n",
"date \n",
"2024-02-23 00:00:00+00:00 1-2-3 Noodles - Veg Masala Flavour \n",
"2024-02-23 00:00:00+00:00 Jaggery Powder - Organic, Sulphur Free \n",
"2024-02-23 00:00:00+00:00 Pudding - Assorted \n",
"2024-02-23 00:00:00+00:00 Perfectly Moist Dark Chocolate Fudge Cake Mix ... \n",
"2024-02-23 00:00:00+00:00 Pasta/Spaghetti Spoon - Nylon, Silicon Handle,... \n",
"\n",
" sales_last_week market_price rating \\\n",
"date \n",
"2024-02-23 00:00:00+00:00 2 12.0 4.200000 \n",
"2024-02-23 00:00:00+00:00 1 280.0 3.996552 \n",
"2024-02-23 00:00:00+00:00 3 50.0 4.400000 \n",
"2024-02-23 00:00:00+00:00 1 495.0 4.000000 \n",
"2024-02-23 00:00:00+00:00 1 299.0 3.732046 \n",
"\n",
" category output_discount \\\n",
"date \n",
"2024-02-23 00:00:00+00:00 Snacks and Branded Foods 0 \n",
"2024-02-23 00:00:00+00:00 Gourmet and World Food 0 \n",
"2024-02-23 00:00:00+00:00 Gourmet and World Food 0 \n",
"2024-02-23 00:00:00+00:00 Gourmet and World Food 0 \n",
"2024-02-23 00:00:00+00:00 Kitchen, Garden and Pets 1 \n",
"\n",
" output_prediction output_score \n",
"date \n",
"2024-02-23 00:00:00+00:00 0 1.000000 \n",
"2024-02-23 00:00:00+00:00 0 0.571833 \n",
"2024-02-23 00:00:00+00:00 1 0.600000 \n",
"2024-02-23 00:00:00+00:00 1 0.517833 \n",
"2024-02-23 00:00:00+00:00 1 0.950000 "
],
"text/html": [
"\n",
" <div id=\"df-25055033-040c-46ca-8234-16df4a8af680\" 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>product</th>\n",
" <th>sales_last_week</th>\n",
" <th>market_price</th>\n",
" <th>rating</th>\n",
" <th>category</th>\n",
" <th>output_discount</th>\n",
" <th>output_prediction</th>\n",
" <th>output_score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2024-02-23 00:00:00+00:00</th>\n",
" <td>1-2-3 Noodles - Veg Masala Flavour</td>\n",
" <td>2</td>\n",
" <td>12.0</td>\n",
" <td>4.200000</td>\n",
" <td>Snacks and Branded Foods</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-23 00:00:00+00:00</th>\n",
" <td>Jaggery Powder - Organic, Sulphur Free</td>\n",
" <td>1</td>\n",
" <td>280.0</td>\n",
" <td>3.996552</td>\n",
" <td>Gourmet and World Food</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.571833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-23 00:00:00+00:00</th>\n",
" <td>Pudding - Assorted</td>\n",
" <td>3</td>\n",
" <td>50.0</td>\n",
" <td>4.400000</td>\n",
" <td>Gourmet and World Food</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-23 00:00:00+00:00</th>\n",
" <td>Perfectly Moist Dark Chocolate Fudge Cake Mix ...</td>\n",
" <td>1</td>\n",
" <td>495.0</td>\n",
" <td>4.000000</td>\n",
" <td>Gourmet and World Food</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.517833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-02-23 00:00:00+00:00</th>\n",
" <td>Pasta/Spaghetti Spoon - Nylon, Silicon Handle,...</td>\n",
" <td>1</td>\n",
" <td>299.0</td>\n",
" <td>3.732046</td>\n",
" <td>Kitchen, Garden and Pets</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.950000</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-25055033-040c-46ca-8234-16df4a8af680')\"\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-25055033-040c-46ca-8234-16df4a8af680 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-25055033-040c-46ca-8234-16df4a8af680');\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-ecea6782-a271-42b8-9eb5-48b2838b74ad\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ecea6782-a271-42b8-9eb5-48b2838b74ad')\"\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-ecea6782-a271-42b8-9eb5-48b2838b74ad button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 4133,\n \"fields\": [\n {\n \"column\": \"product\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3074,\n \"samples\": [\n \"Baby Feeding Bottles For Milk & Water With Handle\",\n \"Cucumber Sheet Mask\",\n \"Gomaya Khanda - Desi Cow Dung Cakes For Agnihotra And Pooja Purposes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sales_last_week\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 26,\n \"num_unique_values\": 15,\n \"samples\": [\n 12,\n 11,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"market_price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 654.8267810878871,\n \"min\": 5.0,\n \"max\": 12500.0,\n \"num_unique_values\": 588,\n \"samples\": [\n 81.25,\n 343.0,\n 890.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6451922536814806,\n \"min\": 1.0,\n \"max\": 5.0,\n \"num_unique_values\": 102,\n \"samples\": [\n 3.3,\n 4.029661016949152,\n 4.168953068592058\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"Foodgrains, Oil and Masala\",\n \"Snacks and Branded Foods\",\n \"Cleaning and Household\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"output_discount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"output_prediction\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"output_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.15383585519854434,\n \"min\": 0.5,\n \"max\": 1.0,\n \"num_unique_values\": 1151,\n \"samples\": [\n 0.795,\n 0.510123015873016\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"source": [
"## Writer setup"
],
"metadata": {
"id": "waeIbbTdukNr"
}
},
{
"cell_type": "code",
"source": [
"why.init(force_local=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fsAyYwaVy6a2",
"outputId": "389f8828-fd88-4509-fb5d-e08fa822e5ab"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Initializing session with config /root/.config/whylogs/config.ini\n",
"\n",
"✅ Using session type: LOCAL. Profiles won't be uploaded or written anywhere automatically.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<whylogs.api.whylabs.session.session.LocalSession at 0x7f57fad06ec0>"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"writer = WhyLabsWriter()"
],
"metadata": {
"id": "unQY11ndew5O"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Uploading multiple profiles with different timestamps\n",
"`WhyLabsWriter::start_transaction()` signals the start of a transaction. Profiles sent to WhyLabs with `WhyLabsWriter::write()` during the transaction are uploaded to WhyLabs immediately, but won't be processed until `WhyLabsWriter::commit_transaction()` is called."
],
"metadata": {
"id": "NDNcskT58rRl"
}
},
{
"cell_type": "code",
"source": [
"transaction_id = writer.start_transaction()\n",
"print(f\"Started transaction {transaction_id}\")\n",
"for i in range(5):\n",
" batch_df = list_daily_batches[i].data[columns]\n",
" profile = why.log(batch_df)\n",
" timestamp = datetime.now(tz=timezone.utc) - timedelta(days=i+1)\n",
" profile.set_dataset_timestamp(timestamp)\n",
" status, id = writer.write(profile)\n",
" print(status, id)\n",
"writer.commit_transaction()\n",
"print(\"Commiting transaction\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8MsbsK0y6Xkf",
"outputId": "1cc72c0b-3295-49c2-8f1a-213c0310578f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Started transaction df4ff687-f881-4633-8a44-3d1c24f631d3\n",
"True log-v12vewf7Cu9j3aVV\n",
"True log-PWW3D23edKlU0aFt\n",
"True log-hDYs8dGamli2LHdq\n",
"True log-4JIe3jWBahpMou07\n",
"True log-2bmc0Rl3u4oGBIu8\n",
"Commiting transaction\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Uploading multiple profiles with the same batch timestamp\n",
"The `WhyLabsTransactionWriter` can be used as a context manager to simplify transaction error handling and ensure `commit_transaction()` is called."
],
"metadata": {
"id": "K7hEBIyuzXh0"
}
},
{
"cell_type": "code",
"source": [
"timestamp = datetime.now(tz=timezone.utc) - timedelta(days=2)\n",
"timestamp"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jDaD9rUeujK6",
"outputId": "58a2f305-a317-4793-9c09-1ec323ef02ca"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"datetime.datetime(2024, 2, 20, 0, 14, 58, 753029, tzinfo=datetime.timezone.utc)"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"try:\n",
" with WhyLabsTransactionWriter() as writer:\n",
" print(\"Started transaction\")\n",
" for i in range(5):\n",
" batch_df = list_daily_batches[i].data[columns]\n",
" profile = why.log(df)\n",
" profile.set_dataset_timestamp(timestamp)\n",
" status, id = writer.write(profile)\n",
" print(status, id)\n",
"except Exception:\n",
" print(\"Transaction failed\")\n",
"\n",
"print(\"Committed transaction\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QAjpae7K0CR3",
"outputId": "728cd3b2-509d-4077-8eea-f37d91115ab9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Started transaction\n",
"True log-yaHvpXyNRO53ilWo\n",
"True log-Zsa0lbCCqjzzjLGJ\n",
"True log-pg57yHO6RuvO4Q8J\n",
"True log-FSYoOwtmE8x51xSr\n",
"True log-v3G6VyLUn1x1crVy\n",
"Committed transaction\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"If a `write()` call during the transaction fails (returns a `False` status), the transaction's commit will fail raising an exception."
],
"metadata": {
"id": "PaAQy-RDftXU"
}
},
{
"cell_type": "markdown",
"source": [
"## Segmented profiles\n",
"\n",
"Each segment in a segmneted profile get uploaded to WhyLabs in a separate S3 interaction. Segmented profiles can be sent as a transaction so that all the segments are committed to WhyLabs at once. In this case, the status returned from `WhyLabsWriter::write()` is the logical and of the statuses of each segment, and it returns a list of all the segmented ids.\n"
],
"metadata": {
"id": "PaolLMia9Zrs"
}
},
{
"cell_type": "code",
"source": [
"schema = DatasetSchema(segments=segment_on_column(\"output_discount\"))\n",
"profile = why.log(df, schema=schema)\n",
"with WhyLabsTransactionWriter() as writer:\n",
" status, id = writer.write(profile)\n",
"\n",
"print(f\"{status} {id}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d5kNH7eigJBb",
"outputId": "9dea6a91-b29f-4a0e-ee9b-e11e00e50664"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"True log-Rhlr7KzY6pp7vla5; log-8ihsF7KAbhAfNFM6\n"
]
}
]
}
]
}