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Nupur Lal
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new recipe notebook for vectordistance function
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‎Recipes/ClearScape_Functions/VectorDistance.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bc549e6c-0cc4-4188-94a3-a9bdd3ae3dfa",
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"metadata": {},
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"source": [
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"<header>\n",
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" <p style='font-size:36px;font-family:Arial; color:#F0F0F0; background-color: #00233c; padding-left: 20pt; padding-top: 20pt;padding-bottom: 10pt; padding-right: 20pt;'>\n",
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" VectorDistance function in Vantage\n",
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" <br>\n",
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" <img id=\"teradata-logo\" src=\"https://storage.googleapis.com/clearscape_analytics_demo_data/DEMO_Logo/teradata.svg\" alt=\"Teradata\" style=\"width: 125px; height: auto; margin-top: 20pt;\">\n",
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" </p>\n",
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"</header>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ae7611a-0795-4168-b716-01fee6880cbd",
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"metadata": {},
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"source": [
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"<p style = 'font-size:20px;font-family:Arial'><b>Introduction</b></p>\n",
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"<p style = 'font-size:16px;font-family:Arial'>VectorDistance computes similarity or dissimilarity between two vectors in multi-dimensional space. VectorDistance also supports distance/similarity computation for embeddings which are supported by the Vector data type. The distance between vectors is usually calculated using a distance metric, such as Euclidean, Manhattan, DotProduct, Minkowski, or Cosine. It takes a table of target vectors, and a table of reference vectors and returns a table that contains the distance between target-reference pairs. The function computes the distance between the target pair and the reference pair from the same table if you provide only one table as the input.<br> In this notebook we will see how we can use the VectorDistance function available in Vantage.</p>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6b3a00b4-6661-4c91-9b2d-cb7b0b403140",
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"metadata": {},
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"source": [
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"<hr style=\"height:2px;border:none;\">\n",
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"<b style = 'font-size:20px;font-family:Arial'>1. Initiate a connection to Vantage</b>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2346857f-e0d3-488a-8a3f-ac6dff752c2b",
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"metadata": {},
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"source": [
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"<p style = 'font-size:16px;font-family:Arial'>In the section, we import the required libraries and set environment variables and environment paths (if required)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c5af5af3-29d5-4f6a-8334-9df6924e7787",
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"metadata": {},
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"outputs": [],
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"source": [
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"from teradataml import *\n",
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"\n",
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"# Modify the following to match the specific client environment settings\n",
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"display.max_rows = 5"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ad3dd7b4-831c-4fb3-ab71-719c8c99a71c",
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"metadata": {},
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"source": [
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"<hr style=\"height:1px;border:none;\">\n",
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"<p style = 'font-size:18px;font-family:Arial'><b>1.1 Connect to Vantage</b></p>\n",
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"<p style = 'font-size:16px;font-family:Arial'>You will be prompted to provide the password. Enter your password, press the Enter key, and then use the down arrow to go to the next cell.</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2742444c-4349-4b0f-b4e5-b068a8785cd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"%run -i ../../UseCases/startup.ipynb\n",
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"eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n",
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"print(eng)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e14915b0-7932-4e03-94ba-20f0599c3707",
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"execute_sql('''SET query_band='DEMO=PP_VectorDistance_Python.ipynb;' UPDATE FOR SESSION; ''')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "efe2fd2d-63ff-4278-9157-8b9110d682e8",
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"metadata": {},
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"source": [
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"<p style = 'font-size:16px;font-family:Arial'>Begin running steps with Shift + Enter keys. </p>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f003f332-7489-4bdd-a740-4af2a0a22280",
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"metadata": {},
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"source": [
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"<hr style='height:1px;border:none;'>\n",
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"\n",
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"<p style = 'font-size:18px;font-family:Arial'><b>1.2 Getting Data for This Demo</b></p>\n",
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"\n",
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"<p style = 'font-size:16px;font-family:Arial'>Here, we will get the data which is available in the teradataml library and use the same to show the usage of the function.</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "45c86176-734c-4b1c-ace0-d0c88657b4f8",
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"metadata": {},
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"outputs": [],
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"source": [
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"load_example_data(\"vectordistance\", [\"target_mobile_data_dense\", \"ref_mobile_data_dense\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2401d6d3-4fcd-46fc-8a94-7cafcd1258b0",
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"metadata": {},
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"source": [
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"<p style = 'font-size:16px;font-family:Arial'>Next is an optional step – if you want to see the status of databases/tables created and space used.</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "87429200-db02-450d-9472-4d1e2030124d",
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"metadata": {},
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"outputs": [],
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"source": [
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"%run -i ../../UseCases/run_procedure.py \"call space_report();\" # Takes 10 seconds"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a3762ac-ba27-4fa3-adba-d577262a4290",
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"metadata": {},
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"source": [
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"<hr style=\"height:2px;border:none;\">\n",
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"<b style = 'font-size:20px;font-family:Arial'>2. Data Exploration</b>\n",
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"<p style = 'font-size:16px;font-family:Arial'>Create a \"Virtual DataFrame\" that points to the data set in Vantage. Check the shape of the dataframe as check the datatype of all the columns of the dataframe.</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3d936fab-7ca7-4e94-ba64-95c1da08b74f",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_mobile_data_dense=DataFrame(\"target_mobile_data_dense\")\n",
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"ref_mobile_data_dense=DataFrame(\"ref_mobile_data_dense\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c726cb7-02ba-4874-a04c-65a1c67286b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_mobile_data_dense"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b85f3dd0-9c41-4b56-b989-866a195e8c07",
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"metadata": {},
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"outputs": [],
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"source": [
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"ref_mobile_data_dense"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d0adaf2-461e-48ff-87ce-b6038db8254a",
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"metadata": {},
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"source": [
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"<p style = 'font-size:16px;font-family:Arial'>Let us find the vectordistance between the target and reference datasets.<br>Detailed help can be found by passing function name to built-in help function.</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d413a344-a12d-46ae-a27b-6702733387c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"help(VectorDistance)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8db7efa9-25d9-4da9-a142-ceeb29a9273e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compute the cosine, euclidean, manhattan distance between the target and reference vectors.\n",
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"VectorDistance_out = VectorDistance(target_id_column=\"userid\",\n",
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" target_feature_columns=['CallDuration', 'DataCounter', 'SMS'],\n",
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" ref_id_column=\"userid\",\n",
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" ref_feature_columns=['CallDuration', 'DataCounter', 'SMS'],\n",
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" distance_measure=['Cosine', 'Euclidean', 'Manhattan'],\n",
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" topk=2,\n",
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" target_data=target_mobile_data_dense,\n",
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" reference_data=ref_mobile_data_dense)\n",
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"\n",
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"# Print the result DataFrame.\n",
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"VectorDistance_out.result"
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]
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},
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{
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"cell_type": "markdown",
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"id": "151d5db4-29a9-49d9-8a61-d53f9627a294",
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"metadata": {},
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"source": [
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"<hr style=\"height:2px;border:none;\">\n",
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"<b style = 'font-size:20px;font-family:Arial'>3. Cleanup</b>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a562f058-fb24-4966-a25d-f2960e6ddfb8",
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"metadata": {},
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"source": [
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"<hr style=\"height:1px;border:none;\">\n",
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"<p style = 'font-size:18px;font-family:Arial'> <b>Databases and Tables </b></p>\n",
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"<p style = 'font-size:16px;font-family:Arial'>The following code will clean up tables and databases created above.</p>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e6b3935b-47c2-4a96-bec2-68106d172116",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_drop_table(\"target_mobile_data_dense\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01e9eb6e-70d8-40a7-8386-9fc51c5f5cab",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_drop_table(\"ref_mobile_data_dense\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "157fe3d4-4e0e-4d92-b343-9f758f3bf690",
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"metadata": {},
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"outputs": [],
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"source": [
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"remove_context()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4317a6cf-1479-4aa8-b30a-ee0a3b5231a8",
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"metadata": {},
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"source": [
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"<hr style=\"height:1px;border:none;\">\n",
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"<p style = 'font-size:16px;font-family:Arial'><b>Links:</b></p>\n",
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"<ul style = 'font-size:16px;font-family:Arial'>\n",
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" <li>Teradataml Python reference: <a href = 'https://docs.teradata.com/search/all?query=Python+Package+User+Guide&content-lang=en-US'>here</a></li>\n",
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" <li>VectorDistance function reference: <a href = 'https://docs.teradata.com/search/all?query=VectorDistance&content-lang=en-US'>here</a></li>\n",
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"</ul>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2dcca28-5de5-44d7-88cb-45a12153b3f8",
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"metadata": {},
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"source": [
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"<footer style=\"padding-bottom:35px; border-bottom:3px solid #91A0Ab\">\n",
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" <div style=\"float:left;margin-top:14px\">ClearScape Analytics™</div>\n",
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" <div style=\"float:right;\">\n",
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" <div style=\"float:left; margin-top:14px\">\n",
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" Copyright © Teradata Corporation - 2025. All Rights Reserved\n",
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" </div>\n",
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" </div>\n",
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"</footer>"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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