| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "id": "initial_id", |
| "metadata": { |
| "collapsed": true, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:04.483891Z", |
| "start_time": "2023-09-18T23:28:04.467608Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "The autoreload extension is already loaded. To reload it, use:\n", |
| " %reload_ext autoreload\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# 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" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "outputs": [], |
| "source": [ |
| "import ray\n", |
| "import pandas as pd\n", |
| "\n", |
| "from hamilton import base, driver\n", |
| "from hamilton.plugins import h_ray" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:04.484177Z", |
| "start_time": "2023-09-18T23:28:04.474636Z" |
| } |
| }, |
| "id": "6eb83d1204ad264c" |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Overwriting spend_calculations.py\n" |
| ] |
| } |
| ], |
| "source": [ |
| "%%writefile spend_calculations.py\n", |
| "\n", |
| "import pandas as pd\n", |
| "\n", |
| "def spend(spend_location: str) -> pd.Series:\n", |
| " \"\"\"Dummy function showing how to wire through loading data.\n", |
| "\n", |
| " :param spend_location:\n", |
| " :return:\n", |
| " \"\"\"\n", |
| " return pd.Series([10, 10, 20, 40, 40, 50])\n", |
| "\n", |
| "\n", |
| "def signups(signups_location: str) -> pd.Series:\n", |
| " \"\"\"Dummy function showing how to wire through loading data.\n", |
| "\n", |
| " :param signups_location:\n", |
| " :return:\n", |
| " \"\"\"\n", |
| " return pd.Series([1, 10, 50, 100, 200, 400])\n", |
| "\n", |
| "\n", |
| "def avg_3wk_spend(spend: pd.Series) -> pd.Series:\n", |
| " \"\"\"Rolling 3 week average spend.\"\"\"\n", |
| " return spend.rolling(3).mean()\n", |
| "\n", |
| "\n", |
| "def spend_per_signup(spend: pd.Series, signups: pd.Series) -> pd.Series:\n", |
| " \"\"\"The cost per signup in relation to spend.\"\"\"\n", |
| " return spend / signups\n", |
| "\n", |
| "\n", |
| "def spend_mean(spend: pd.Series) -> float:\n", |
| " \"\"\"Shows function creating a scalar. In this case it computes the mean of the entire column.\"\"\"\n", |
| " return spend.mean()\n", |
| "\n", |
| "\n", |
| "def spend_zero_mean(spend: pd.Series, spend_mean: float) -> pd.Series:\n", |
| " \"\"\"Shows function that takes a scalar. In this case to zero mean spend.\"\"\"\n", |
| " return spend - spend_mean\n", |
| "\n", |
| "\n", |
| "def spend_std_dev(spend: pd.Series) -> float:\n", |
| " \"\"\"Function that computes the standard deviation of the spend column.\"\"\"\n", |
| " return spend.std()\n", |
| "\n", |
| "\n", |
| "def spend_zero_mean_unit_variance(spend_zero_mean: pd.Series, spend_std_dev: float) -> pd.Series:\n", |
| " \"\"\"Function showing one way to make spend have zero mean and unit variance.\"\"\"\n", |
| " return spend_zero_mean / spend_std_dev" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:04.505831Z", |
| "start_time": "2023-09-18T23:28:04.477897Z" |
| } |
| }, |
| "id": "47131a442758876e" |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "outputs": [], |
| "source": [ |
| "%aimport spend_calculations\n", |
| "\n", |
| "# Set up the driver, input and output columns\n", |
| "\n", |
| "config = { # could load data here via some other means, or delegate to a module as we have done.\n", |
| " # 'signups': pd.Series([1, 10, 50, 100, 200, 400]),\n", |
| " \"signups_location\": \"some_path\",\n", |
| " # 'spend': pd.Series([10, 10, 20, 40, 40, 50]),\n", |
| " \"spend_location\": \"some_other_path\",\n", |
| " }\n", |
| "adapter = h_ray.RayGraphAdapter(result_builder=base.PandasDataFrameResult())\n", |
| "dr = driver.Driver(config, spend_calculations, adapter=adapter)\n", |
| "output_columns = [\n", |
| " \"spend\",\n", |
| " \"signups\",\n", |
| " \"avg_3wk_spend\",\n", |
| " \"spend_per_signup\",\n", |
| " \"spend_zero_mean_unit_variance\",\n", |
| "]" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:04.507804Z", |
| "start_time": "2023-09-18T23:28:04.489070Z" |
| } |
| }, |
| "id": "916c7f1ce61814d0" |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "2023-09-18 19:28:07,329\tINFO worker.py:1621 -- Started a local Ray instance.\n" |
| ] |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "# Execute the driver.\n", |
| "df = dr.execute(output_columns)\n", |
| "print(df)" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:09.929554Z", |
| "start_time": "2023-09-18T23:28:04.518243Z" |
| } |
| }, |
| "id": "120e8dc787f813f4" |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "outputs": [ |
| { |
| "data": { |
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class=\"node\">\n<title>spend_zero_mean</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"351.93\" cy=\"-90\" rx=\"77.97\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"351.93\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_zero_mean</text>\n</g>\n<!-- spend_mean->spend_zero_mean -->\n<g id=\"edge11\" class=\"edge\">\n<title>spend_mean->spend_zero_mean</title>\n<path fill=\"none\" stroke=\"black\" d=\"M351.93,-143.7C351.93,-136.24 351.93,-127.32 351.93,-118.97\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"355.43,-119.1 351.93,-109.1 348.43,-119.1 355.43,-119.1\"/>\n</g>\n<!-- spend->spend_per_signup -->\n<g id=\"edge3\" class=\"edge\">\n<title>spend->spend_per_signup</title>\n<path fill=\"none\" stroke=\"black\" d=\"M324.74,-225.51C287.15,-215.15 218.03,-196.1 165.93,-181.74\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"166.99,-178.13 156.42,-178.85 165.13,-184.88 166.99,-178.13\"/>\n</g>\n<!-- spend->avg_3wk_spend -->\n<g id=\"edge5\" class=\"edge\">\n<title>spend->avg_3wk_spend</title>\n<path fill=\"none\" stroke=\"black\" d=\"M324.68,-217.98C307.6,-208.57 285.25,-196.25 266.04,-185.66\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"268.05,-182.22 257.61,-180.46 264.67,-188.35 268.05,-182.22\"/>\n</g>\n<!-- spend->spend_mean -->\n<g id=\"edge6\" class=\"edge\">\n<title>spend->spend_mean</title>\n<path fill=\"none\" stroke=\"black\" d=\"M351.93,-215.7C351.93,-208.24 351.93,-199.32 351.93,-190.97\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"355.43,-191.1 351.93,-181.1 348.43,-191.1 355.43,-191.1\"/>\n</g>\n<!-- spend_std_dev -->\n<g id=\"node8\" class=\"node\">\n<title>spend_std_dev</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"513.93\" cy=\"-90\" rx=\"65.68\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"513.93\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_std_dev</text>\n</g>\n<!-- spend->spend_std_dev -->\n<g id=\"edge9\" class=\"edge\">\n<title>spend->spend_std_dev</title>\n<path fill=\"none\" stroke=\"black\" d=\"M379.4,-218.05C396.23,-208.28 417.76,-194.65 434.93,-180 457.34,-160.87 479.31,-135.42 494.36,-116.64\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"497.72,-119.03 501.17,-109.01 492.23,-114.69 497.72,-119.03\"/>\n</g>\n<!-- spend->spend_zero_mean -->\n<g id=\"edge10\" class=\"edge\">\n<title>spend->spend_zero_mean</title>\n<path fill=\"none\" stroke=\"black\" d=\"M379.17,-219.01C393.55,-210 409.86,-196.83 417.93,-180 424.84,-165.57 424.84,-158.43 417.93,-144 412.07,-131.78 401.87,-121.49 391.22,-113.33\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"393.43,-109.9 383.25,-106.97 389.38,-115.61 393.43,-109.9\"/>\n</g>\n<!-- signups -->\n<g id=\"node7\" class=\"node\">\n<title>signups</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"126.55,-252 69.3,-252 69.3,-216 126.55,-216 126.55,-252\"/>\n<text text-anchor=\"middle\" x=\"97.93\" y=\"-228.95\" font-family=\"Times,serif\" font-size=\"14.00\">signups</text>\n</g>\n<!-- signups->spend_per_signup -->\n<g id=\"edge4\" class=\"edge\">\n<title>signups->spend_per_signup</title>\n<path fill=\"none\" stroke=\"black\" d=\"M97.93,-215.7C97.93,-208.24 97.93,-199.32 97.93,-190.97\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"101.43,-191.1 97.93,-181.1 94.43,-191.1 101.43,-191.1\"/>\n</g>\n<!-- spend_std_dev->spend_zero_mean_unit_variance -->\n<g id=\"edge2\" class=\"edge\">\n<title>spend_std_dev->spend_zero_mean_unit_variance</title>\n<path fill=\"none\" stroke=\"black\" d=\"M494.73,-72.41C484.74,-63.78 472.33,-53.05 461.26,-43.48\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"463.97,-40.34 454.11,-36.45 459.39,-45.63 463.97,-40.34\"/>\n</g>\n<!-- spend_zero_mean->spend_zero_mean_unit_variance -->\n<g id=\"edge1\" class=\"edge\">\n<title>spend_zero_mean->spend_zero_mean_unit_variance</title>\n<path fill=\"none\" stroke=\"black\" d=\"M371.12,-72.41C381.11,-63.78 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| "text/plain": "<graphviz.graphs.Digraph at 0x13d65f790>" |
| }, |
| "execution_count": 11, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# To visualize do `pip install \"sf-hamilton[visualization]\"` if you want these to work\n", |
| "dr.visualize_execution(output_columns)" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:10.134103Z", |
| "start_time": "2023-09-18T23:28:09.930093Z" |
| } |
| }, |
| "id": "3b6d0ec73941957f" |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "outputs": [], |
| "source": [ |
| "ray.shutdown()" |
| ], |
| "metadata": { |
| "collapsed": false, |
| "ExecuteTime": { |
| "end_time": "2023-09-18T23:28:11.606616Z", |
| "start_time": "2023-09-18T23:28:10.130104Z" |
| } |
| }, |
| "id": "ca9d441a99e8a833" |
| } |
| ], |
| "metadata": { |
| "kernelspec": { |
| "display_name": "Python 3", |
| "language": "python", |
| "name": "python3" |
| }, |
| "language_info": { |
| "codemirror_mode": { |
| "name": "ipython", |
| "version": 2 |
| }, |
| "file_extension": ".py", |
| "mimetype": "text/x-python", |
| "name": "python", |
| "nbconvert_exporter": "python", |
| "pygments_lexer": "ipython2", |
| "version": "2.7.6" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 5 |
| } |
