MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/how-to-guides/subsetting-sampling.ipynb

{
  "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/subsetting-sampling.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Sampling and Subsetting\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Once a Dataflow has been created, it is possible to act on only a subset of the records contained in it. This can help when working with very large datasets or when only a portion of the records is truly relevant."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Head\n",
        "\n",
        "The `head` method will take the number of records specified, run them through the transformations in the Dataflow, and then return the result as a Pandas dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.dataprep as dprep\n",
        "\n",
        "dflow = dprep.read_csv('../data/crime_duplicate_headers.csv')\n",
        "dflow.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Take\n",
        "\n",
        "The `take` method adds a step to the Dataflow that will keep the number of records specified (counting from the beginning) and drop the rest. Unlike `head`, which does not modify the Dataflow, all operations applied on a Dataflow on which `take` has been applied will affect only the records kept."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow_top_five = dflow.take(5)\n",
        "dflow_top_five.to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Skip\n",
        "\n",
        "It is also possible to skip a certain number of records in a Dataflow, such that transformations are only applied after a specific point. Depending on the underlying data source, a Dataflow with a `skip` step might still have to scan through the data in order to skip past the records."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow_skip_top_one = dflow_top_five.skip(1)\n",
        "dflow_skip_top_one.to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Take Sample\n",
        "\n",
        "In addition to taking records from the top of the dataset, it's also possible to take a random sample of the dataset. This is done through the `take_sample(probability, seed=None)` method. This method will scan through all of the records available in the Dataflow and include them based on the probability specified. The `seed` parameter is optional. If a seed is not provided, a stable one is generated, ensuring that the results for a specific Dataflow remain consistent. Different calls to `take_sample` will receive different seeds."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dflow_sampled = dflow.take_sample(0.1)\n",
        "dflow_sampled.to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`skip`, `take`, and `take_sample` can all be combined. With this, we can achieve behaviors like getting a random 10% sample fo the middle N records of a dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "seed = 1\n",
        "dflow_nested_sample = dflow.skip(1).take(5).take_sample(0.5, seed)\n",
        "dflow_nested_sample.to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Take Stratified Sample\n",
        "Besides sampling all by a probability, we also have stratified sampling, provided the strata and strata weights, the probability to sample each stratum with.\n",
        "This is done through the `take_stratified_sample(columns, fractions, seed=None)` method.\n",
        "For all records, we will group each record by the columns specified to stratify, and based on the stratum x weight information in `fractions`, include said record.\n",
        "\n",
        "Seed behavior is same as in `take_sample`.\n",
        "\n",
        "If a stratum is not specified or the record cannot be grouped by said stratum, we default the weight to sample by to 0 (it will not be included).\n",
        "\n",
        "The order of `fractions` must match the order of `columns`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fractions = {}\n",
        "fractions[('ASSAULT',)] = 0.5\n",
        "fractions[('BATTERY',)] = 0.2\n",
        "fractions[('ARSON',)] = 0.5\n",
        "fractions[('THEFT',)] = 1.0\n",
        "\n",
        "columns = ['Primary Type']\n",
        "\n",
        "single_strata_sample = dflow.take_stratified_sample(columns=columns, fractions = fractions, seed = 42)\n",
        "single_strata_sample.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Stratified sampling on multiple columns is also supported."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fractions = {}\n",
        "fractions[('ASSAULT', '560')] = 0.5\n",
        "fractions[('BATTERY', '460')] = 0.2\n",
        "fractions[('ARSON', '1020')] = 0.5\n",
        "fractions[('THEFT', '820')] = 1.0\n",
        "\n",
        "columns = ['Primary Type', 'IUCR']\n",
        "\n",
        "multi_strata_sample = dflow.take_stratified_sample(columns=columns, fractions = fractions, seed = 42)\n",
        "multi_strata_sample.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Caching\n",
        "It is usually a good idea to cache the sampled Dataflow for later uses.\n",
        "\n",
        "See [here](cache.ipynb) for more details about caching."
      ]
    }
  ],
  "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
}