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db20 - changes #1010

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d1b0d93
Updated glm predict in heart failure and dtrain in greenmanufacturing
Oct 15, 2025
83b9aeb
Cleared Outputs
Oct 15, 2025
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51 changes: 1 addition & 50 deletions UseCases/Credit_Risk_Assessment/Credit_Risk_Assessment_Python.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -30,37 +30,6 @@
"<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Vantage's ClearScape Analytics provides a comprehensive library of in-database (inDb) functions tailored for various stages of the data science lifecycle, including data exploration, preparation, feature engineering, model training, and evaluation. These functions seamlessly execute within the Vantage SQL engine, ensuring high performance and scalability while eliminating the need for data movement.<br>By leveraging the Teradata Python Package, specifically the teradataml OpenSourceML component, users gain the ability to utilize popular open-source machine learning packages like scikit-learn directly within the database environment. This means that data doesn't need to be transferred to the client for analysis, streamlining the workflow and enhancing efficiency.<br>The OpenSourceML package simplifies the integration of open-source machine learning functionalities into Vantage, offering a consistent interface for executing these algorithms. Users can leverage familiar syntax and arguments, making it easier to transition from traditional open-source environments to the Vantage platform.<br>Running these machine learning functions within Vantage enables organizations to scale their models effectively. Horizontal scaling allows for the training of segmented models based on various criteria such as region or user type. Meanwhile, vertical scaling enables the consolidation of data from millions or even billions of interactions, empowering organizations to leverage vast datasets for model training and analysis.<br>Furthermore, the models developed using these functionalities can be seamlessly deployed for operational use, enabling real-time understanding and prediction of actions. This operational deployment capability ensures that organizations can derive actionable insights and make informed decisions in a timely manner, driving value across various business domains.</p> "
]
},
{
"cell_type": "markdown",
"id": "a15f66f0-06dd-4b73-b63a-303f553fec9d",
"metadata": {},
"source": [
"<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
"\n",
"<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>1. Connect to Vantage</b></p>\n",
"<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Let us start by checking the version of the teradataml installed. The Openml functions used in this notebook will require Version 20.0.0.0.</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e88980d-8f59-4c99-82ae-0fbad739a4c4",
"metadata": {},
"outputs": [],
"source": [
"pip show teradataml"
]
},
{
"cell_type": "markdown",
"id": "a92123de-5042-46f3-bc13-a847f5de3d89",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\">\n",
"<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Note: </b><i>If the VM has lower version, please uncomment the below code and execute the cell. After the cell executes, please restart the kernel. The simplest way to restart the Kernel is by typing zero zero: <b> 0 0</b></i></p>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -945,24 +914,6 @@
"remove_context()"
]
},
{
"cell_type": "markdown",
"id": "cba4d575-f1bf-4356-b140-c9823c55bcab",
"metadata": {},
"source": [
"<p style = 'font-size:16px;font-family:Arial;color:#00233C'>If you have updated the teradataml package, reinstall the package by uncommenting and running the below code cell.</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34f7e846-32da-4b3e-8b64-d77b3445f69a",
"metadata": {},
"outputs": [],
"source": [
"#!pip install teradataml==17.20.0.6 --force-reinstall"
]
},
{
"cell_type": "markdown",
"id": "3c14e3ed-305b-4aad-9605-c7e9acfefa96",
Expand Down Expand Up @@ -1014,7 +965,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
"version": "3.11.14"
}
},
"nbformat": 4,
Expand Down
6 changes: 3 additions & 3 deletions UseCases/Green_Manufacturing/Green_Manufacturing_Python.ipynb
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Expand Up @@ -62,7 +62,7 @@
"%%capture\n",
"# # '%%capture' suppresses the display of installation steps of the following packages\n",
"# !pip install xgboost==1.7.3\n",
"# !pip install colorlover\n"
"# !pip install colorlover"
]
},
{
Expand Down Expand Up @@ -469,7 +469,7 @@
" 'objective': 'reg:linear',\n",
" 'silent': 1\n",
"}\n",
"dtrain = xgb.DMatrix(train_X, train_y, feature_names=train_X.columns.values)\n",
"dtrain = xgb.DMatrix(train_X, train_y, feature_names=list(train_X.columns.values))\n",
"model = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=100, feval=xgb_r2_score, maximize=True)\n",
"\n",
"# plot the important features #\n",
Expand Down Expand Up @@ -1071,7 +1071,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
"version": "3.11.14"
}
},
"nbformat": 4,
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5 changes: 3 additions & 2 deletions UseCases/Heart_Failure_Prediction/Heart_Failure_Prediction_Python.ipynb
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Expand Up @@ -729,7 +729,8 @@
" id_column = 'id',\n",
" accumulate = 'death',\n",
" output_prob = True,\n",
" output_responses = ['0', '1']\n",
" output_responses = ['0', '1'],\n",
" family = 'Binomial'\n",
")\n",
"\n",
"out = glm_prediction.result.assign(prediction = glm_prediction.result.prediction.cast(type_ = BYTEINT))\n",
Expand Down Expand Up @@ -1027,7 +1028,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
"version": "3.11.14"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
Expand Down