Recipes/ClearScape_Functions/VectorDistance.ipynb

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    "<header>\n",
    "   <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",
    "       VectorDistance Function in Vantage\n",
    "  <br>\n",
    "       <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",
    "    </p>\n",
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   "source": [
    "<p style = 'font-size:20px;font-family:Arial'><b>Introduction</b></p>\n",
    "<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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    "<hr style=\"height:2px;border:none;\">\n",
    "<b style = 'font-size:20px;font-family:Arial'>1. Initiate a connection to Vantage</b>"
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   "source": [
    "<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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   "source": [
    "from teradataml import *\n",
    "\n",
    "# Modify the following to match the specific client environment settings\n",
    "display.max_rows = 5"
   ]
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   "cell_type": "markdown",
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    "<hr style=\"height:1px;border:none;\">\n",
    "<p style = 'font-size:18px;font-family:Arial'><b>1.1 Connect to Vantage</b></p>\n",
    "<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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   "source": [
    "%run -i ../../UseCases/startup.ipynb\n",
    "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n",
    "print(eng)"
   ]
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   "source": [
    "%%capture\n",
    "execute_sql('''SET query_band='DEMO=PP_VectorDistance_Python.ipynb;' UPDATE FOR SESSION; ''')"
   ]
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   "source": [
    "<p style = 'font-size:16px;font-family:Arial'>Begin running steps with Shift + Enter keys. </p>"
   ]
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   "source": [
    "<hr style='height:1px;border:none;'>\n",
    "\n",
    "<p style = 'font-size:18px;font-family:Arial'><b>1.2 Getting Data for This Demo</b></p>\n",
    "\n",
    "<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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   "source": [
    "load_example_data(\"vectordistance\", [\"target_mobile_data_dense\", \"ref_mobile_data_dense\"])"
   ]
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   "source": [
    "<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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    "%run -i ../../UseCases/run_procedure.py \"call space_report();\"        # Takes 10 seconds"
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    "<hr style=\"height:2px;border:none;\">\n",
    "<b style = 'font-size:20px;font-family:Arial'>2. Data Exploration</b>\n",
    "<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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    "target_mobile_data_dense=DataFrame(\"target_mobile_data_dense\")\n",
    "ref_mobile_data_dense=DataFrame(\"ref_mobile_data_dense\")"
   ]
  },
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   "id": "3c726cb7-02ba-4874-a04c-65a1c67286b9",
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   "source": [
    "target_mobile_data_dense"
   ]
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    "ref_mobile_data_dense"
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   "source": [
    "<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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   "source": [
    "help(VectorDistance)"
   ]
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    "# Compute the cosine, euclidean, manhattan distance between the target and reference vectors.\n",
    "VectorDistance_out = VectorDistance(target_id_column=\"userid\",\n",
    "                                    target_feature_columns=['CallDuration', 'DataCounter', 'SMS'],\n",
    "                                    ref_id_column=\"userid\",\n",
    "                                    ref_feature_columns=['CallDuration', 'DataCounter', 'SMS'],\n",
    "                                    distance_measure=['Cosine', 'Euclidean', 'Manhattan'],\n",
    "                                    topk=2,\n",
    "                                    target_data=target_mobile_data_dense,\n",
    "                                    reference_data=ref_mobile_data_dense)\n",
    "\n",
    "# Print the result DataFrame.\n",
    "VectorDistance_out.result"
   ]
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   "source": [
    "<hr style=\"height:2px;border:none;\">\n",
    "<b style = 'font-size:20px;font-family:Arial'>3. Cleanup</b>"
   ]
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   "source": [
    "<hr style=\"height:1px;border:none;\">\n",
    "<p style = 'font-size:18px;font-family:Arial'> <b>Databases and Tables </b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial'>The following code will clean up tables and databases created above.</p>"
   ]
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   "source": [
    "db_drop_table(\"target_mobile_data_dense\")"
   ]
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   "id": "01e9eb6e-70d8-40a7-8386-9fc51c5f5cab",
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    "db_drop_table(\"ref_mobile_data_dense\")"
   ]
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   "id": "157fe3d4-4e0e-4d92-b343-9f758f3bf690",
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   "source": [
    "remove_context()"
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   "source": [
    "<hr style=\"height:1px;border:none;\">\n",
    "<p style = 'font-size:16px;font-family:Arial'><b>Links:</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial'>\n",
    "    <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",
    "    <li>VectorDistance function reference: <a href = 'https://docs.teradata.com/search/all?query=VectorDistance&content-lang=en-US'>here</a></li>\n",
    "</ul>"
   ]
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   "source": [
    "<footer style=\"padding-bottom:35px; border-bottom:3px solid #91A0Ab\">\n",
    "    <div style=\"float:left;margin-top:14px\">ClearScape Analytics™</div>\n",
    "    <div style=\"float:right;\">\n",
    "        <div style=\"float:left; margin-top:14px\">\n",
    "            Copyright © Teradata Corporation - 2025. All Rights Reserved\n",
    "        </div>\n",
    "    </div>\n",
    "</footer>"
   ]
  }
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