| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# Execute this cell to install dependencies\n", |
| "%pip install sf-hamilton[visualization]" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Resusing Functions Example [](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/reusing_functions/reusing_functions.ipynb) [](https://github.com/dagworks-inc/hamilton/blob/main/examples/reusing_functions/reusing_functions.ipynb)\n", |
| "\n", |
| "\n", |
| "This notebook demonstrates the use of the subdag operator.\n", |
| "\n", |
| "The subdag operator allows you to effectively run a driver within a node.\n", |
| "In this case, we are calculating unique website visitors from the following set of parameters:\n", |
| "\n", |
| "1. Region = CA (canada) or US (United States)\n", |
| "2. Granularity of data = (day, week, month)" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Pre-requisites" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "You need to have the following installed and set up:\n", |
| "- Hamilton\n", |
| "- Pandas\n", |
| "\n", |
| "Execute the code commented below to install the above if you don't have them already installed." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# %pip install sf-hamilton pandas" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "### Importing all the things you need\n" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "from typing import Any, Dict\n", |
| "import pandas as pd\n", |
| "from hamilton.base import ResultMixin, SimplePythonGraphAdapter\n", |
| "from hamilton import driver\n", |
| "%load_ext hamilton.plugins.jupyter_magic" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "`unique_users` module" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
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| "<title>filtered_interactions</title>\n", |
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| "source": [ |
| "%%cell_to_module unique_users --display\n", |
| "\n", |
| "import pandas as pd\n", |
| "\n", |
| "_grain_mapping = {\"day\": \"D\", \"week\": \"W\", \"month\": \"M\"}\n", |
| "\n", |
| "\n", |
| "def _validate_grain(grain: str):\n", |
| " assert grain in [\"day\", \"week\", \"month\"]\n", |
| "\n", |
| "\n", |
| "def filtered_interactions(website_interactions: pd.DataFrame, region: str) -> pd.DataFrame:\n", |
| " return website_interactions[website_interactions.region == region]\n", |
| "\n", |
| "\n", |
| "def unique_users(filtered_interactions: pd.DataFrame, grain: str) -> pd.Series:\n", |
| " \"\"\"Gives the number of shares traded by the frequency\"\"\"\n", |
| " _validate_grain(grain)\n", |
| " return filtered_interactions.resample(_grain_mapping[grain])[\"user_id\"].nunique()" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "`reusable_subdags` module" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": {}, |
| "outputs": [ |
| { |
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| "<title>function</title>\n", |
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| "output_type": "display_data" |
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| ], |
| "source": [ |
| "%%cell_to_module reusable_subdags --display\n", |
| "import pandas as pd\n", |
| "import unique_users\n", |
| "\n", |
| "from hamilton.function_modifiers import subdag, value\n", |
| "\n", |
| "\n", |
| "def website_interactions() -> pd.DataFrame:\n", |
| " \"\"\"Gives event-driven data with a series\n", |
| "\n", |
| " :return: Some mock event data.\n", |
| " \"\"\"\n", |
| " data = [\n", |
| " (\"20220901-14:00:00\", 1, \"US\"),\n", |
| " (\"20220901-18:30:00\", 2, \"US\"),\n", |
| " (\"20220901-19:00:00\", 1, \"US\"),\n", |
| " (\"20220902-08:00:00\", 3, \"US\"),\n", |
| " (\"20220903-16:00:00\", 1, \"US\"),\n", |
| " (\"20220907-13:00:00\", 4, \"US\"),\n", |
| " (\"20220910-14:00:00\", 1, \"US\"),\n", |
| " (\"20220911-12:00:00\", 3, \"US\"),\n", |
| " (\"20220914-11:00:00\", 1, \"US\"),\n", |
| " (\"20220915-07:30:00\", 2, \"US\"),\n", |
| " (\"20220916-06:00:00\", 1, \"US\"),\n", |
| " (\"20220917-16:00:00\", 2, \"US\"),\n", |
| " (\"20220920-17:00:00\", 5, \"US\"),\n", |
| " (\"20220922-09:30:00\", 2, \"US\"),\n", |
| " (\"20220922-10:00:00\", 1, \"US\"),\n", |
| " (\"20220924-07:00:00\", 6, \"US\"),\n", |
| " (\"20220924-08:00:00\", 1, \"US\"),\n", |
| " (\"20220925-21:00:00\", 1, \"US\"),\n", |
| " (\"20220926-15:30:00\", 2, \"US\"),\n", |
| " (\"20220901-14:00:00\", 7, \"CA\"),\n", |
| " (\"20220901-18:30:00\", 8, \"CA\"),\n", |
| " (\"20220901-19:00:00\", 9, \"CA\"),\n", |
| " (\"20220902-08:00:00\", 7, \"CA\"),\n", |
| " (\"20220903-16:00:00\", 10, \"CA\"),\n", |
| " (\"20220907-13:00:00\", 9, \"CA\"),\n", |
| " (\"20220910-14:00:00\", 8, \"CA\"),\n", |
| " (\"20220911-12:00:00\", 11, \"CA\"),\n", |
| " (\"20220914-11:00:00\", 12, \"CA\"),\n", |
| " (\"20220915-07:30:00\", 7, \"CA\"),\n", |
| " (\"20220916-06:00:00\", 9, \"CA\"),\n", |
| " (\"20220917-16:00:00\", 10, \"CA\"),\n", |
| " (\"20220920-17:00:00\", 7, \"CA\"),\n", |
| " (\"20220922-09:30:00\", 11, \"CA\"),\n", |
| " (\"20220922-10:00:00\", 8, \"CA\"),\n", |
| " (\"20220924-07:00:00\", 9, \"CA\"),\n", |
| " (\"20220924-08:00:00\", 10, \"CA\"),\n", |
| " (\"20220925-21:00:00\", 13, \"CA\"),\n", |
| " (\"20220926-15:30:00\", 14, \"CA\"),\n", |
| " ]\n", |
| " df = (\n", |
| " pd.DataFrame(data, columns=[\"timestamp\", \"user_id\", \"region\"])\n", |
| " .set_index(\"timestamp\")\n", |
| " .sort_index()\n", |
| " )\n", |
| " df.index = pd.DatetimeIndex(df.index)\n", |
| " return df\n", |
| "\n", |
| "\n", |
| "@subdag(\n", |
| " unique_users,\n", |
| " inputs={\"grain\": value(\"day\")},\n", |
| " config={\"region\": \"US\"},\n", |
| ")\n", |
| "def daily_unique_users_US(unique_users: pd.Series) -> pd.Series:\n", |
| " return unique_users\n", |
| "\n", |
| "\n", |
| "@subdag(\n", |
| " unique_users,\n", |
| " inputs={\"grain\": value(\"week\")},\n", |
| " config={\"region\": \"US\"},\n", |
| ")\n", |
| "def weekly_unique_users_US(unique_users: pd.Series) -> pd.Series:\n", |
| " return unique_users\n", |
| "\n", |
| "\n", |
| "@subdag(\n", |
| " unique_users,\n", |
| " inputs={\"grain\": value(\"month\")},\n", |
| " config={\"region\": \"US\"},\n", |
| ")\n", |
| "def monthly_unique_users_US(unique_users: pd.Series) -> pd.Series:\n", |
| " return unique_users\n", |
| "\n", |
| "\n", |
| "@subdag(\n", |
| " unique_users,\n", |
| " inputs={\"grain\": value(\"day\")},\n", |
| " config={\"region\": \"CA\"},\n", |
| ")\n", |
| "def daily_unique_users_CA(unique_users: pd.Series) -> pd.Series:\n", |
| " return unique_users\n", |
| "\n", |
| "\n", |
| "@subdag(\n", |
| " unique_users,\n", |
| " inputs={\"grain\": value(\"week\")},\n", |
| " config={\"region\": \"CA\"},\n", |
| ")\n", |
| "def weekly_unique_users_CA(unique_users: pd.Series) -> pd.Series:\n", |
| " return unique_users\n", |
| "\n", |
| "\n", |
| "@subdag(\n", |
| " unique_users,\n", |
| " inputs={\"grain\": value(\"month\")},\n", |
| " config={\"region\": \"CA\"},\n", |
| ")\n", |
| "def monthly_unique_users_CA(unique_users: pd.Series) -> pd.Series:\n", |
| " return unique_users" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "C:\\Users\\obeng\\AppData\\Local\\Temp\\tmpvmsp6sci.py:18: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", |
| " return filtered_interactions.resample(_grain_mapping[grain])[\"user_id\"].nunique()\n" |
| ] |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " daily_unique_users_US daily_unique_users_CA \\\n", |
| "timestamp \n", |
| "2022-09-01 2.0 3.0 \n", |
| "2022-09-02 1.0 1.0 \n", |
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| "2022-09-14 1.0 1.0 \n", |
| "2022-09-15 1.0 1.0 \n", |
| "2022-09-16 1.0 1.0 \n", |
| "2022-09-17 1.0 1.0 \n", |
| "2022-09-18 0.0 0.0 \n", |
| "2022-09-19 0.0 0.0 \n", |
| "2022-09-20 1.0 1.0 \n", |
| "2022-09-21 0.0 0.0 \n", |
| "2022-09-22 2.0 2.0 \n", |
| "2022-09-23 0.0 0.0 \n", |
| "2022-09-24 2.0 2.0 \n", |
| "2022-09-25 1.0 1.0 \n", |
| "2022-09-26 1.0 1.0 \n", |
| "2022-09-27 NaN NaN \n", |
| "2022-09-28 NaN NaN \n", |
| "2022-09-29 NaN NaN \n", |
| "2022-09-30 NaN NaN \n", |
| "2022-10-01 NaN NaN \n", |
| "2022-10-02 NaN NaN \n", |
| "\n", |
| " weekly_unique_users_US weekly_unique_users_CA \\\n", |
| "timestamp \n", |
| "2022-09-01 3.0 4.0 \n", |
| "2022-09-02 3.0 4.0 \n", |
| "2022-09-03 3.0 4.0 \n", |
| "2022-09-04 3.0 4.0 \n", |
| "2022-09-05 3.0 3.0 \n", |
| "2022-09-06 3.0 3.0 \n", |
| "2022-09-07 3.0 3.0 \n", |
| "2022-09-08 3.0 3.0 \n", |
| "2022-09-09 3.0 3.0 \n", |
| "2022-09-10 3.0 3.0 \n", |
| "2022-09-11 3.0 3.0 \n", |
| "2022-09-12 2.0 4.0 \n", |
| "2022-09-13 2.0 4.0 \n", |
| "2022-09-14 2.0 4.0 \n", |
| "2022-09-15 2.0 4.0 \n", |
| "2022-09-16 2.0 4.0 \n", |
| "2022-09-17 2.0 4.0 \n", |
| "2022-09-18 2.0 4.0 \n", |
| "2022-09-19 4.0 6.0 \n", |
| "2022-09-20 4.0 6.0 \n", |
| "2022-09-21 4.0 6.0 \n", |
| "2022-09-22 4.0 6.0 \n", |
| "2022-09-23 4.0 6.0 \n", |
| "2022-09-24 4.0 6.0 \n", |
| "2022-09-25 4.0 6.0 \n", |
| "2022-09-26 1.0 1.0 \n", |
| "2022-09-27 1.0 1.0 \n", |
| "2022-09-28 1.0 1.0 \n", |
| "2022-09-29 1.0 1.0 \n", |
| "2022-09-30 1.0 1.0 \n", |
| "2022-10-01 1.0 1.0 \n", |
| "2022-10-02 1.0 1.0 \n", |
| "\n", |
| " monthly_unique_users_US monthly_unique_users_CA \n", |
| "timestamp \n", |
| "2022-09-01 6.0 8.0 \n", |
| "2022-09-02 6.0 8.0 \n", |
| "2022-09-03 6.0 8.0 \n", |
| "2022-09-04 6.0 8.0 \n", |
| "2022-09-05 6.0 8.0 \n", |
| "2022-09-06 6.0 8.0 \n", |
| "2022-09-07 6.0 8.0 \n", |
| "2022-09-08 6.0 8.0 \n", |
| "2022-09-09 6.0 8.0 \n", |
| "2022-09-10 6.0 8.0 \n", |
| "2022-09-11 6.0 8.0 \n", |
| "2022-09-12 6.0 8.0 \n", |
| "2022-09-13 6.0 8.0 \n", |
| "2022-09-14 6.0 8.0 \n", |
| "2022-09-15 6.0 8.0 \n", |
| "2022-09-16 6.0 8.0 \n", |
| "2022-09-17 6.0 8.0 \n", |
| "2022-09-18 6.0 8.0 \n", |
| "2022-09-19 6.0 8.0 \n", |
| "2022-09-20 6.0 8.0 \n", |
| "2022-09-21 6.0 8.0 \n", |
| "2022-09-22 6.0 8.0 \n", |
| "2022-09-23 6.0 8.0 \n", |
| "2022-09-24 6.0 8.0 \n", |
| "2022-09-25 6.0 8.0 \n", |
| "2022-09-26 6.0 8.0 \n", |
| "2022-09-27 6.0 8.0 \n", |
| "2022-09-28 6.0 8.0 \n", |
| "2022-09-29 6.0 8.0 \n", |
| "2022-09-30 6.0 8.0 \n", |
| "2022-10-01 NaN NaN \n", |
| "2022-10-02 NaN NaN \n" |
| ] |
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| "<title>daily_unique_users_US</title>\n", |
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| "source": [ |
| "\"\"\"A pretty simple pipeline to demonstrate function reuse.\n", |
| "Note that this *also* demonstrates building a custom results builder!\n", |
| "\n", |
| "Why not use the standard one? Well, because time-series joining is weird.\n", |
| "In this case, we're running the subdag for different granularities (daily, weekly, and monthly),\n", |
| "and we want the functions that provide outputs for these granularities to yield one\n", |
| "row per datapoint. This means that the weekly series will have 7x less much data as the daily\n", |
| "series. Monthly series will have less than the weekly series, etc...\n", |
| "\n", |
| "This results builder handles it by upsampling them all to a specified granularity.\n", |
| "\n", |
| "Specifically, it:\n", |
| "1. Up/down-samples all time-series to the granularity specified in the constructor\n", |
| "2. Joins them as normal (a full outer join)\n", |
| "\n", |
| "Note that pandas also has capabilities for as-of joins, but those tend to be messy and tricky to work\n", |
| "with. Furthermore, the as-of joins don't usually work with multiple. Upsampling/downsampling does the\n", |
| "trick quite well and adds more control to the user.\n", |
| "\n", |
| "Note that it might be nice to pass in an argument to say which data is the \"spine\" column,\n", |
| "allowing us to have a basis of data to join with. For now, however, this should be a useful piece\n", |
| "of code to help with time-series joining!\n", |
| "\n", |
| "Furthermore, you could actually include upsampling *in* the DAG -- this has the added feature of\n", |
| "encoding an index/spine column, and could be run as the final step for each subdag. Doing so is left\n", |
| "as an exercise to the reader.\n", |
| "\n", |
| "As an example, consider the following outputs:\n", |
| "1. monthly_unique_users_US\n", |
| "\n", |
| "timestamp\n", |
| "2022-09-30 6\n", |
| "Freq: M, Name: user_id, dtype: int64\n", |
| "\n", |
| "2. weekly_unique_users_US\n", |
| "\n", |
| "2022-09-04 3\n", |
| "2022-09-11 3\n", |
| "2022-09-18 2\n", |
| "2022-09-25 4\n", |
| "2022-10-02 1\n", |
| "Freq: W-SUN, Name: user_id, dtype: int64\n", |
| "\n", |
| "3. daily_unique_users_US\n", |
| "timestamp\n", |
| "2022-09-01 2\n", |
| "2022-09-02 1\n", |
| "2022-09-03 1\n", |
| "2022-09-04 0\n", |
| "...\n", |
| "2022-09-22 2\n", |
| "2022-09-23 0\n", |
| "2022-09-24 2\n", |
| "2022-09-25 1\n", |
| "2022-09-26 1\n", |
| "Freq: D, Name: user_id, dtype: int64\n", |
| "\n", |
| "Joining these with upsample granularity of \"D\" would produce:\n", |
| "\n", |
| " daily_unique_users_US weekly_unique_users_US monthly_unique_users_US\n", |
| "timestamp\n", |
| "2022-09-01 2.0 3.0 6.0\n", |
| "2022-09-02 1.0 3.0 6.0\n", |
| "2022-09-03 1.0 3.0 6.0\n", |
| "2022-09-04 0.0 3.0 6.0\n", |
| "2022-09-05 0.0 3.0 6.0\n", |
| "2022-09-06 0.0 3.0 6.0\n", |
| "...\n", |
| "2022-09-28 NaN 1.0 6.0\n", |
| "2022-09-29 NaN 1.0 6.0\n", |
| "2022-09-30 NaN 1.0 6.0\n", |
| "2022-10-01 NaN 1.0 NaN\n", |
| "2022-10-02 NaN 1.0 NaN\n", |
| "\"\"\"\n", |
| "\n", |
| "\n", |
| "class TimeSeriesJoinResultsBuilder(ResultMixin):\n", |
| " def __init__(self, upsample_frequency: str):\n", |
| " \"\"\"Initializes a results builder that does a time-series join\n", |
| " :param upsample_frequency: Argument to pandas sample() function.\n", |
| " \"\"\"\n", |
| " self.sampling_methodology = upsample_frequency\n", |
| "\n", |
| " def resample(self, time_series: pd.Series):\n", |
| " return time_series.resample(\n", |
| " self.sampling_methodology\n", |
| " ).bfill() # TODO -- think through the right fill -- ffill()/bfill()/whatnot\n", |
| "\n", |
| " @staticmethod\n", |
| " def is_time_series(series: Any):\n", |
| " if not isinstance(series, pd.Series):\n", |
| " return False\n", |
| " if not series.index.inferred_type == \"datetime64\":\n", |
| " return False\n", |
| " return True\n", |
| "\n", |
| " def build_result(self, **outputs: Dict[str, Any]) -> Any:\n", |
| " non_ts_output = [\n", |
| " key\n", |
| " for key, value in outputs.items()\n", |
| " if not TimeSeriesJoinResultsBuilder.is_time_series(value)\n", |
| " ]\n", |
| " if len(non_ts_output) > 0:\n", |
| " raise ValueError(\n", |
| " f\"All outputs from DAG must be time-series -- the following are not: {non_ts_output}\"\n", |
| " )\n", |
| " resampled_results = {key: self.resample(value) for key, value in outputs.items()}\n", |
| " return pd.DataFrame(resampled_results).bfill()\n", |
| "\n", |
| "\n", |
| "\n", |
| "dr = (driver.Builder()\n", |
| " .with_config({})\n", |
| " .with_modules(reusable_subdags)\n", |
| " .with_adapters(SimplePythonGraphAdapter(\n", |
| " result_builder=TimeSeriesJoinResultsBuilder(upsample_frequency=\"D\")\n", |
| " ),\n", |
| ")\n", |
| "\n", |
| "result = dr.execute(\n", |
| " [\n", |
| " \"daily_unique_users_US\",\n", |
| " \"daily_unique_users_CA\",\n", |
| " \"weekly_unique_users_US\",\n", |
| " \"weekly_unique_users_CA\",\n", |
| " \"monthly_unique_users_US\",\n", |
| " \"monthly_unique_users_CA\",\n", |
| " ]\n", |
| ")\n", |
| "execution_graph = dr.visualize_execution([\n", |
| " \"daily_unique_users_US\",\n", |
| " \"daily_unique_users_CA\",\n", |
| " \"weekly_unique_users_US\",\n", |
| " \"weekly_unique_users_CA\",\n", |
| " \"monthly_unique_users_US\",\n", |
| " \"monthly_unique_users_CA\",\n", |
| " ], \"./reusable_subdags\", {\"format\": \"png\"})\n", |
| "\n", |
| "print(result)\n", |
| "display(execution_graph)" |
| ] |
| } |
| ], |
| "metadata": { |
| "kernelspec": { |
| "display_name": "venv", |
| "language": "python", |
| "name": "python3" |
| }, |
| "language_info": { |
| "codemirror_mode": { |
| "name": "ipython", |
| "version": 3 |
| }, |
| "file_extension": ".py", |
| "mimetype": "text/x-python", |
| "name": "python", |
| "nbconvert_exporter": "python", |
| "pygments_lexer": "ipython3", |
| "version": "3.11.5" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 2 |
| } |
