| { |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| } |
| }, |
| "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." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "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." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "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" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "/Users/stefankrawczyk/.pyenv/versions/3.9.13/envs/temp-py39/lib/python3.9/site-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n", |
| " warnings.warn(\n" |
| ] |
| } |
| ], |
| "source": [ |
| "import polars as pl\n", |
| "\n", |
| "from hamilton import (\n", |
| " base,\n", |
| " driver,\n", |
| ")\n", |
| "from hamilton.function_modifiers import extract_columns\n", |
| "from hamilton.plugins import h_polars" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Writing spend_calculations.py\n" |
| ] |
| } |
| ], |
| "source": [ |
| "%%writefile spend_calculations.py\n", |
| "\n", |
| "import polars as pl\n", |
| "from hamilton.function_modifiers import extract_columns\n", |
| "\n", |
| "# We'll place the spend calculations into a new module\n", |
| "\n", |
| "@extract_columns(\"signups\", \"spend\")\n", |
| "def base_df(base_df_location: str) -> pl.DataFrame:\n", |
| " \"\"\"Loads base dataframe of data.\n", |
| "\n", |
| " :param base_df_location: just showing that we could load this from a file...\n", |
| " :return:\n", |
| " \"\"\"\n", |
| " return pl.DataFrame(\n", |
| " {\n", |
| " \"signups\": pl.Series([1, 10, 50, 100, 200, 400]),\n", |
| " \"spend\": pl.Series([10, 10, 20, 40, 40, 50]),\n", |
| " }\n", |
| " )\n", |
| "\n", |
| "def avg_3wk_spend(spend: pl.Series) -> pl.Series:\n", |
| " \"\"\"Rolling 3 week average spend.\"\"\"\n", |
| " return spend.rolling_mean(3)\n", |
| "\n", |
| "def spend_per_signup(spend: pl.Series, signups: pl.Series) -> pl.Series:\n", |
| " \"\"\"The cost per signup in relation to spend.\"\"\"\n", |
| " return spend / signups\n", |
| "\n", |
| "def spend_mean(spend: pl.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", |
| "def spend_zero_mean(spend: pl.Series, spend_mean: float) -> pl.Series:\n", |
| " \"\"\"Shows function that takes a scalar. In this case to zero mean spend.\"\"\"\n", |
| " return spend - spend_mean\n", |
| "\n", |
| "def spend_std_dev(spend: pl.Series) -> float:\n", |
| " \"\"\"Function that computes the standard deviation of the spend column.\"\"\"\n", |
| " return spend.std()\n", |
| "\n", |
| "def spend_zero_mean_unit_variance(spend_zero_mean: pl.Series, spend_std_dev: float) -> pl.Series:\n", |
| " \"\"\"Function showing one way to make spend have zero mean and unit variance.\"\"\"\n", |
| " return spend_zero_mean / spend_std_dev" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "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": [ |
| "%aimport spend_calculations\n", |
| "\n", |
| "# Set up the driver, input and output columns\n", |
| "\n", |
| "config = {\n", |
| " \"base_df_location\": \"dummy_value\",\n", |
| "}\n", |
| "adapter = base.SimplePythonGraphAdapter(result_builder=h_polars.PolarsDataFrameResult())\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", |
| "]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "collapsed": false, |
| "jupyter": { |
| "outputs_hidden": false |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "shape: (6, 5)\n", |
| "┌───────┬─────────┬───────────────┬──────────────────┬───────────────────────────────┐\n", |
| "│ spend ┆ signups ┆ avg_3wk_spend ┆ spend_per_signup ┆ spend_zero_mean_unit_variance │\n", |
| "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", |
| "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 │\n", |
| "╞═══════╪═════════╪═══════════════╪══════════════════╪═══════════════════════════════╡\n", |
| "│ 10 ┆ 1 ┆ null ┆ 10.0 ┆ -1.064405 │\n", |
| "│ 10 ┆ 10 ┆ null ┆ 1.0 ┆ -1.064405 │\n", |
| "│ 20 ┆ 50 ┆ 13.333333 ┆ 0.4 ┆ -0.483821 │\n", |
| "│ 40 ┆ 100 ┆ 23.333333 ┆ 0.4 ┆ 0.677349 │\n", |
| "│ 40 ┆ 200 ┆ 33.333333 ┆ 0.2 ┆ 0.677349 │\n", |
| "│ 50 ┆ 400 ┆ 43.333333 ┆ 0.125 ┆ 1.257934 │\n", |
| "└───────┴─────────┴───────────────┴──────────────────┴───────────────────────────────┘\n" |
| ] |
| }, |
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| "<g id=\"edge6\" class=\"edge\">\n", |
| "<title>spend->spend_std_dev</title>\n", |
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| "<g id=\"node6\" class=\"node\">\n", |
| "<title>spend_zero_mean</title>\n", |
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| "<title>spend_mean</title>\n", |
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| "<text text-anchor=\"middle\" x=\"254.88\" y=\"-156.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_mean</text>\n", |
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| "<!-- spend->spend_mean -->\n", |
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| "<title>spend_per_signup</title>\n", |
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| "<!-- spend_zero_mean_unit_variance -->\n", |
| "<g id=\"node2\" class=\"node\">\n", |
| "<title>spend_zero_mean_unit_variance</title>\n", |
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| "</g>\n", |
| "<!-- signups -->\n", |
| "<g id=\"node4\" class=\"node\">\n", |
| "<title>signups</title>\n", |
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| "</g>\n", |
| "<!-- signups->spend_per_signup -->\n", |
| "<g id=\"edge12\" class=\"edge\">\n", |
| "<title>signups->spend_per_signup</title>\n", |
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| "</g>\n", |
| "<!-- spend_std_dev->spend_zero_mean_unit_variance -->\n", |
| "<g id=\"edge3\" class=\"edge\">\n", |
| "<title>spend_std_dev->spend_zero_mean_unit_variance</title>\n", |
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| "<!-- spend_zero_mean->spend_zero_mean_unit_variance -->\n", |
| "<g id=\"edge2\" class=\"edge\">\n", |
| "<title>spend_zero_mean->spend_zero_mean_unit_variance</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"204.92,-40.34 195.06,-36.45 200.34,-45.63 204.92,-40.34\"/>\n", |
| "</g>\n", |
| "<!-- base_df -->\n", |
| "<g id=\"node7\" class=\"node\">\n", |
| "<title>base_df</title>\n", |
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| "<text text-anchor=\"middle\" x=\"248.88\" y=\"-300.95\" font-family=\"Times,serif\" font-size=\"14.00\">base_df</text>\n", |
| "</g>\n", |
| "<!-- base_df->spend -->\n", |
| "<g id=\"edge1\" class=\"edge\">\n", |
| "<title>base_df->spend</title>\n", |
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| "<!-- base_df->signups -->\n", |
| "<g id=\"edge5\" class=\"edge\">\n", |
| "<title>base_df->signups</title>\n", |
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| "</g>\n", |
| "<!-- base_df_location -->\n", |
| "<g id=\"node8\" class=\"node\">\n", |
| "<title>base_df_location</title>\n", |
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| "<g id=\"edge9\" class=\"edge\">\n", |
| "<title>base_df_location->base_df</title>\n", |
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| "<title>spend_mean->spend_zero_mean</title>\n", |
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| }, |
| "execution_count": 7, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# Execute the driver.\n", |
| "\n", |
| "df = dr.execute(output_columns)\n", |
| "print(df)\n", |
| "\n", |
| "# To visualize do `pip install \"sf-hamilton[visualization]\"` if you want these to work\n", |
| "dr.visualize_execution(output_columns)" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "metadata": {}, |
| "outputs": [ |
| { |
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| "<!-- spend -->\n", |
| "<g id=\"node1\" class=\"node\">\n", |
| "<title>spend</title>\n", |
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| "<text text-anchor=\"middle\" x=\"249.8\" y=\"-228.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend</text>\n", |
| "</g>\n", |
| "<!-- avg_3wk_spend -->\n", |
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| "<title>avg_3wk_spend</title>\n", |
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| "<text text-anchor=\"middle\" x=\"70.8\" y=\"-156.95\" font-family=\"Times,serif\" font-size=\"14.00\">avg_3wk_spend</text>\n", |
| "</g>\n", |
| "<!-- spend->avg_3wk_spend -->\n", |
| "<g id=\"edge4\" class=\"edge\">\n", |
| "<title>spend->avg_3wk_spend</title>\n", |
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| "<!-- spend_std_dev -->\n", |
| "<g id=\"node5\" class=\"node\">\n", |
| "<title>spend_std_dev</title>\n", |
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| "<text text-anchor=\"middle\" x=\"130.8\" 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=\"edge6\" class=\"edge\">\n", |
| "<title>spend->spend_std_dev</title>\n", |
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| "<!-- spend_zero_mean -->\n", |
| "<g id=\"node6\" class=\"node\">\n", |
| "<title>spend_zero_mean</title>\n", |
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| "<text text-anchor=\"middle\" x=\"292.8\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_zero_mean</text>\n", |
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| "<!-- spend->spend_zero_mean -->\n", |
| "<g id=\"edge7\" class=\"edge\">\n", |
| "<title>spend->spend_zero_mean</title>\n", |
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| "<!-- spend_mean -->\n", |
| "<g id=\"node8\" class=\"node\">\n", |
| "<title>spend_mean</title>\n", |
| "<ellipse fill=\"none\" stroke=\"black\" cx=\"292.8\" cy=\"-162\" rx=\"57.49\" ry=\"18\"/>\n", |
| "<text text-anchor=\"middle\" x=\"292.8\" y=\"-156.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_mean</text>\n", |
| "</g>\n", |
| "<!-- spend->spend_mean -->\n", |
| "<g id=\"edge10\" class=\"edge\">\n", |
| "<title>spend->spend_mean</title>\n", |
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| "<!-- spend_per_signup -->\n", |
| "<g id=\"node10\" class=\"node\">\n", |
| "<title>spend_per_signup</title>\n", |
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| "<text text-anchor=\"middle\" x=\"445.8\" y=\"-156.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_per_signup</text>\n", |
| "</g>\n", |
| "<!-- spend->spend_per_signup -->\n", |
| "<g id=\"edge11\" class=\"edge\">\n", |
| "<title>spend->spend_per_signup</title>\n", |
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| "<!-- spend_zero_mean_unit_variance -->\n", |
| "<g id=\"node2\" class=\"node\">\n", |
| "<title>spend_zero_mean_unit_variance</title>\n", |
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| "<text text-anchor=\"middle\" x=\"211.8\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">spend_zero_mean_unit_variance</text>\n", |
| "</g>\n", |
| "<!-- signups -->\n", |
| "<g id=\"node4\" class=\"node\">\n", |
| "<title>signups</title>\n", |
| "<ellipse fill=\"none\" stroke=\"black\" cx=\"391.8\" cy=\"-234\" rx=\"39.07\" ry=\"18\"/>\n", |
| "<text text-anchor=\"middle\" x=\"391.8\" y=\"-228.95\" font-family=\"Times,serif\" font-size=\"14.00\">signups</text>\n", |
| "</g>\n", |
| "<!-- signups->spend_per_signup -->\n", |
| "<g id=\"edge12\" class=\"edge\">\n", |
| "<title>signups->spend_per_signup</title>\n", |
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| "<!-- spend_std_dev->spend_zero_mean_unit_variance -->\n", |
| "<g id=\"edge3\" class=\"edge\">\n", |
| "<title>spend_std_dev->spend_zero_mean_unit_variance</title>\n", |
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| "<!-- spend_zero_mean->spend_zero_mean_unit_variance -->\n", |
| "<g id=\"edge2\" class=\"edge\">\n", |
| "<title>spend_zero_mean->spend_zero_mean_unit_variance</title>\n", |
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| "</g>\n", |
| "<!-- base_df -->\n", |
| "<g id=\"node7\" class=\"node\">\n", |
| "<title>base_df</title>\n", |
| "<ellipse fill=\"none\" stroke=\"black\" cx=\"293.8\" cy=\"-306\" rx=\"39.58\" ry=\"18\"/>\n", |
| "<text text-anchor=\"middle\" x=\"293.8\" y=\"-300.95\" font-family=\"Times,serif\" font-size=\"14.00\">base_df</text>\n", |
| "</g>\n", |
| "<!-- base_df->spend -->\n", |
| "<g id=\"edge1\" class=\"edge\">\n", |
| "<title>base_df->spend</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M283.37,-288.41C278.17,-280.13 271.75,-269.92 265.93,-260.66\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"268.44,-259.07 260.15,-252.47 262.51,-262.8 268.44,-259.07\"/>\n", |
| "</g>\n", |
| "<!-- base_df->signups -->\n", |
| "<g id=\"edge5\" class=\"edge\">\n", |
| "<title>base_df->signups</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M314.11,-290.5C328.07,-280.52 346.84,-267.11 362.43,-255.98\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"364.08,-258.39 370.18,-249.73 360.01,-252.69 364.08,-258.39\"/>\n", |
| "</g>\n", |
| "<!-- spend_mean->spend_zero_mean -->\n", |
| "<g id=\"edge8\" class=\"edge\">\n", |
| "<title>spend_mean->spend_zero_mean</title>\n", |
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| "</g>\n", |
| "<!-- base_df_location -->\n", |
| "<g id=\"node9\" class=\"node\">\n", |
| "<title>base_df_location</title>\n", |
| "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"293.8\" cy=\"-378\" rx=\"98.44\" ry=\"18\"/>\n", |
| "<text text-anchor=\"middle\" x=\"293.8\" y=\"-372.95\" font-family=\"Times,serif\" font-size=\"14.00\">Input: base_df_location</text>\n", |
| "</g>\n", |
| "<!-- base_df_location->base_df -->\n", |
| "<g id=\"edge9\" class=\"edge\">\n", |
| "<title>base_df_location->base_df</title>\n", |
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| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x13a85a280>" |
| ] |
| }, |
| "execution_count": 8, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "dr.display_all_functions()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": {}, |
| "outputs": [], |
| "source": [] |
| } |
| ], |
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| "display_name": "Python 3 (ipykernel)", |
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