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Shané Winner
2019-07-28 00:24:43 -07:00
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how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/one-hot-encoder.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# One Hot Encoder\n",
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML Data Prep has the ability to perform one hot encoding on a selected column using `one_hot_encode`. The result Dataflow will have a new binary column for each categorical label encountered in the selected column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"dflow = dprep.read_csv(path='../data/crime-spring.csv')\n",
"dflow.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use `one_hot_encode` from a Dataflow, simply specify the source column. `one_hot_encode` will figure out all the distinct values or categorical labels in the source column using the current data, and it will return a new Dataflow with a new binary column for each categorical label. Note that the categorical labels are remembered in the Dataflow step."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dflow_result = dflow.one_hot_encode(source_column='Location Description')\n",
"dflow_result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, all the new columns will use the `source_column` name as a prefix. However, if you would like to specify your own prefix, simply pass a `prefix` string as a second parameter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dflow_result = dflow.one_hot_encode(source_column='Location Description', prefix='LOCATION_')\n",
"dflow_result.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To have more control over the categorical labels, create a builder using `dataflow.builders.one_hot_encode`. The builder allows to preview and modify the categorical labels before generating a new Dataflow with the results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"builder = dflow.builders.one_hot_encode(source_column='Location Description', prefix='LOCATION_')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To generate the categorical labels, call the `learn` method on the builder object:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"builder.learn()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To preview the categorical labels, simply access them through the property `categorical_labels` on the builder object:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"builder.categorical_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To modify the generated `categorical_labels`, assign a new value to `categorical_labels` or modify the existing one. The following example adds a missing label not found on the sample data to `categorical_labels`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"builder.categorical_labels.append('TOWNHOUSE')\n",
"builder.categorical_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the desired results are achieved, call `builder.to_dataflow` to get the new Dataflow with the encoded labels."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dflow_result = builder.to_dataflow()\n",
"dflow_result.head(5)"
]
}
],
"metadata": {
"authors": [
{
"name": "sihhu"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}