| { |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "Uncomment and run the cell below if you are in a Google Colab environment. It will:\n", |
| "1. Mount google drive. You will be asked to authenticate and give permissions.\n", |
| "2. Change directory to google drive.\n", |
| "3. Make a directory \"hamilton-tutorials\"\n", |
| "4. Change directory to it.\n", |
| "5. Clone this repository to your google drive\n", |
| "6. Move your current directory to the hello_world example\n", |
| "7. Install requirements.\n", |
| "\n", |
| "This means that any modifications will be saved, and you won't lose them if you close your browser." |
| ], |
| "metadata": { |
| "collapsed": false |
| } |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "outputs": [], |
| "source": [ |
| "## 1. Mount google drive\n", |
| "# from google.colab import drive\n", |
| "# drive.mount('/content/drive')\n", |
| "## 2. Change directory to google drive.\n", |
| "# %cd /content/drive/MyDrive\n", |
| "## 3. Make a directory \"hamilton-tutorials\"\n", |
| "# !mkdir hamilton-tutorials\n", |
| "## 4. Change directory to it.\n", |
| "# %cd hamilton-tutorials\n", |
| "## 5. Clone this repository to your google drive\n", |
| "# !git clone https://github.com/DAGWorks-Inc/hamilton/\n", |
| "## 6. Move your current directory to the hello_world example\n", |
| "# %cd hamilton/examples/hello_world\n", |
| "## 7. Install requirements.\n", |
| "# %pip install -r requirements.txt\n", |
| "# clear_output() # optionally clear outputs\n", |
| "# To check your current working directory you can type `!pwd` in a cell and run it." |
| ], |
| "metadata": { |
| "collapsed": false |
| } |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "outputs": [], |
| "source": [ |
| "# Cell 1 - import the things you need\n", |
| "import logging\n", |
| "import sys\n", |
| "\n", |
| "import numpy as np\n", |
| "import pandas as pd\n", |
| "\n", |
| "from hamilton import ad_hoc_utils, driver\n", |
| "\n", |
| "logging.basicConfig(stream=sys.stdout)" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:17.334441Z", |
| "start_time": "2023-11-08T00:06:13.711650Z" |
| } |
| } |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "pycharm": { |
| "name": "#%%\n" |
| }, |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:17.404504Z", |
| "start_time": "2023-11-08T00:06:17.343242Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# Cell 2 - import modules to create part of the DAG from\n", |
| "# We use the autoreload extension that comes with ipython to automatically reload modules when\n", |
| "# the code in them changes.\n", |
| "\n", |
| "# import the jupyter extension\n", |
| "%load_ext autoreload\n", |
| "# set it to only reload the modules imported\n", |
| "%autoreload 1\n", |
| "# import the function modules you want to reload when they change.\n", |
| "# i.e. these should be your modules you write your functions in. As you change them,\n", |
| "# they will be reimported without you having to do anything.\n", |
| "%aimport my_functions" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "pycharm": { |
| "name": "#%%\n" |
| }, |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:17.419982Z", |
| "start_time": "2023-11-08T00:06:17.411216Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# Cell 3 - Define your new Hamilton functions & curate them into a TemporaryFunctionModule object.\n", |
| "# This enables you to add functions to your DAG without creating a proper module.\n", |
| "# This is ONLY INTENDED FOR QUICK DEVELOPMENT. For moving to production move these to an actual module.\n", |
| "\n", |
| "# Look at `my_functions` to see how these functions connect.\n", |
| "def signups() -> pd.Series:\n", |
| " \"\"\"Returns sign up values\"\"\"\n", |
| " return pd.Series([1, 10, 50, 100, 200, 400])\n", |
| "\n", |
| "\n", |
| "def spend() -> pd.Series:\n", |
| " \"\"\"Returns the spend values\"\"\"\n", |
| " return pd.Series([10, 10, 20, 40, 40, 50])\n", |
| "\n", |
| "\n", |
| "def log_spend_per_signup(spend_per_signup: pd.Series) -> pd.Series:\n", |
| " \"\"\"Simple function taking the logarithm of spend over signups.\"\"\"\n", |
| " return np.log(spend_per_signup)\n", |
| "\n", |
| "\n", |
| "# Place the functions into a temporary module -- the idea is that this should house a curated set of functions.\n", |
| "# Don't be afraid to make multiple of them -- however we'd advise you to not use this method for production.\n", |
| "# Also note, that using a temporary function module does not work for scaling onto Ray, Dask, or Pandas on Spark.\n", |
| "temp_module = ad_hoc_utils.create_temporary_module(\n", |
| " spend, signups, log_spend_per_signup, module_name=\"function_example\"\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": { |
| "pycharm": { |
| "name": "#%%\n" |
| }, |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:17.461900Z", |
| "start_time": "2023-11-08T00:06:17.422033Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "WARNING:hamilton.telemetry:Note: Hamilton collects completely anonymous data about usage. This will help us improve Hamilton over time. See https://github.com/dagworks-inc/hamilton#usage-analytics--data-privacy for details.\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# Cell 4 - Instantiate the Hamilton driver and pass it the right things in.\n", |
| "\n", |
| "initial_config = {}\n", |
| "# we need to tell hamilton where to load function definitions from\n", |
| "dr = driver.Driver(initial_config, my_functions, temp_module) # can pass in multiple modules\n", |
| "# we need to specify what we want in the final dataframe.\n", |
| "output_columns = [\n", |
| " \"spend\",\n", |
| " \"signups\",\n", |
| " \"avg_3wk_spend\",\n", |
| " \"spend_per_signup\",\n", |
| " \"spend_zero_mean_unit_variance\",\n", |
| "]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:18.080346Z", |
| "start_time": "2023-11-08T00:06:17.452028Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
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| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# Cell 5 - visualize execution\n", |
| "# To visualize do `pip install \"sf-hamilton[visualization]\"` if you want these to work\n", |
| "\n", |
| "# visualize all possible functions\n", |
| "dr.display_all_functions() # no args needed for jupyter" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "pycharm": { |
| "name": "#%%\n" |
| }, |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:18.639068Z", |
| "start_time": "2023-11-08T00:06:18.070616Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
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| "text/plain": "<graphviz.graphs.Digraph at 0x142ce6460>" |
| }, |
| "execution_count": 6, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# visualize just the execution path\n", |
| "dr.visualize_execution(output_columns) # no other args needed for jupyter" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "outputs": [ |
| { |
| "data": { |
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id=\"edge4\" class=\"edge\">\n<title>spend_std_dev->spend_zero_mean_unit_variance</title>\n<path fill=\"none\" stroke=\"black\" d=\"M233.93,-36.55C283.98,-40.58 357.45,-46.48 420.76,-51.57\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"420.39,-55.13 430.64,-52.44 420.95,-48.15 420.39,-55.13\"/>\n</g>\n<!-- spend_zero_mean->spend_zero_mean_unit_variance -->\n<g id=\"edge3\" class=\"edge\">\n<title>spend_zero_mean->spend_zero_mean_unit_variance</title>\n<path fill=\"none\" stroke=\"red\" d=\"M403.05,-82.1C408.78,-81.3 414.68,-80.47 420.67,-79.64\"/>\n<polygon fill=\"red\" stroke=\"red\" points=\"421.1,-82.97 430.52,-78.12 420.13,-76.04 421.1,-82.97\"/>\n</g>\n<!-- function -->\n<g id=\"node6\" class=\"node\">\n<title>function</title>\n<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M72.85,-178.1C72.85,-178.1 28,-178.1 28,-178.1 22,-178.1 16,-172.1 16,-166.1 16,-166.1 16,-153.5 16,-153.5 16,-147.5 22,-141.5 28,-141.5 28,-141.5 72.85,-141.5 72.85,-141.5 78.85,-141.5 84.85,-147.5 84.85,-153.5 84.85,-153.5 84.85,-166.1 84.85,-166.1 84.85,-172.1 78.85,-178.1 72.85,-178.1\"/>\n<text text-anchor=\"middle\" x=\"50.42\" y=\"-154\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">function</text>\n</g>\n</g>\n</svg>\n", |
| "text/plain": "<graphviz.graphs.Digraph at 0x142ce6ac0>" |
| }, |
| "execution_count": 7, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# visualize the path of execution between two functions\n", |
| "dr.visualize_path_between(\"spend_mean\", \"spend_zero_mean_unit_variance\",\n", |
| " strict_path_visualization=False)" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:19.243065Z", |
| "start_time": "2023-11-08T00:06:18.632623Z" |
| } |
| } |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-11-08T00:06:19.580251Z", |
| "start_time": "2023-11-08T00:06:19.520006Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": " spend signups avg_3wk_spend spend_per_signup \\\n0 10 1 NaN 10.000 \n1 10 10 NaN 1.000 \n2 20 50 13.333333 0.400 \n3 40 100 23.333333 0.400 \n4 40 200 33.333333 0.200 \n5 50 400 43.333333 0.125 \n\n spend_zero_mean_unit_variance \n0 -1.064405 \n1 -1.064405 \n2 -0.483821 \n3 0.677349 \n4 0.677349 \n5 1.257934 ", |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>spend</th>\n <th>signups</th>\n <th>avg_3wk_spend</th>\n <th>spend_per_signup</th>\n <th>spend_zero_mean_unit_variance</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>10</td>\n <td>1</td>\n <td>NaN</td>\n <td>10.000</td>\n <td>-1.064405</td>\n </tr>\n <tr>\n <th>1</th>\n <td>10</td>\n <td>10</td>\n <td>NaN</td>\n <td>1.000</td>\n <td>-1.064405</td>\n </tr>\n <tr>\n <th>2</th>\n <td>20</td>\n <td>50</td>\n <td>13.333333</td>\n <td>0.400</td>\n <td>-0.483821</td>\n </tr>\n <tr>\n <th>3</th>\n <td>40</td>\n <td>100</td>\n <td>23.333333</td>\n <td>0.400</td>\n <td>0.677349</td>\n </tr>\n <tr>\n <th>4</th>\n <td>40</td>\n <td>200</td>\n <td>33.333333</td>\n <td>0.200</td>\n <td>0.677349</td>\n </tr>\n <tr>\n <th>5</th>\n <td>50</td>\n <td>400</td>\n <td>43.333333</td>\n <td>0.125</td>\n <td>1.257934</td>\n </tr>\n </tbody>\n</table>\n</div>" |
| }, |
| "execution_count": 8, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# let's create the dataframe!\n", |
| "dr.execute(output_columns)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-05-22T22:27:18.053784Z", |
| "start_time": "2023-05-22T22:27:18.046215Z" |
| } |
| }, |
| "outputs": [], |
| "source": [] |
| } |
| ], |
| "metadata": { |
| "kernelspec": { |
| "display_name": "Python 3 (ipykernel)", |
| "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": "3.8.13" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 1 |
| } |
