| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# Execute this cell to install dependencies\n", |
| "%pip install sf-hamilton[visualization] pandas scikit-learn numpy" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# MPG Simple Advanced Target [](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/hamilton-tutorials/mpg-translation/MPGSimpleAdvancedTarget.ipynb) [](https://github.com/dagworks-inc/hamilton/blob/main/examples/hamilton-tutorials/mpg-translation/MPGSimpleAdvancedTarget.ipynb)\n" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": { |
| "ExecuteTime": { |
| "start_time": "2024-07-20T17:55:33.133151Z" |
| }, |
| "application/vnd.databricks.v1+cell": { |
| "cellMetadata": { |
| "byteLimit": 2048000, |
| "rowLimit": 10000 |
| }, |
| "inputWidgets": {}, |
| "nuid": "f7ca0a2e-99c4-49de-af45-c8c4bddf5685", |
| "showTitle": false, |
| "title": "" |
| }, |
| "jupyter": { |
| "is_executing": true |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "/Users/stefankrawczyk/.pyenv/versions/knowledge_retrieval-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": [ |
| "from hamilton import driver\n", |
| "from IPython.display import HTML, display" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-07-20T17:32:57.962238Z", |
| "start_time": "2024-07-20T17:32:57.947142Z" |
| }, |
| "application/vnd.databricks.v1+cell": { |
| "cellMetadata": { |
| "byteLimit": 2048000, |
| "rowLimit": 10000 |
| }, |
| "inputWidgets": {}, |
| "nuid": "45fcd1cf-5dee-4d3c-b598-823c82654805", |
| "showTitle": false, |
| "title": "" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "%load_ext hamilton.plugins.jupyter_magic" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-07-20T17:37:00.825770Z", |
| "start_time": "2024-07-20T17:37:00.183488Z" |
| }, |
| "application/vnd.databricks.v1+cell": { |
| "cellMetadata": { |
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| "rowLimit": 10000 |
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| "inputWidgets": {}, |
| "nuid": "155ea802-aef6-4d5c-b264-d9ec5b57c733", |
| "showTitle": false, |
| "title": "" |
| } |
| }, |
| "outputs": [ |
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| "source": [ |
| "%%cell_to_module pipeline --display\n", |
| "# when done you can write to file and then load it as a module normally\n", |
| "# add -w to do so\n", |
| "import numpy as np\n", |
| "import pandas as pd\n", |
| "from sklearn.linear_model import LinearRegression\n", |
| "from sklearn.preprocessing import StandardScaler\n", |
| "from sklearn.metrics import mean_absolute_error\n", |
| "\n", |
| "from hamilton.function_modifiers import extract_fields\n", |
| "\n", |
| "\n", |
| "def mpg_df() -> pd.DataFrame:\n", |
| " url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n", |
| " column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n", |
| " 'Acceleration', 'Model Year', 'Origin']\n", |
| "\n", |
| " raw_dataset = pd.read_csv(url, names=column_names,\n", |
| " na_values='?', comment='\\t',\n", |
| " sep=' ', skipinitialspace=True)\n", |
| "\n", |
| " ## some schema manipulation\n", |
| " _mpg_df = raw_dataset.rename(columns={\"Model Year\": \"ModelYear\"})\n", |
| " return _mpg_df\n", |
| "\n", |
| "\n", |
| "@extract_fields({\"train_dataset\": pd.DataFrame, \"test_dataset\": pd.DataFrame})\n", |
| "def data_sets(mpg_df: pd.DataFrame, train_test_split: float = 0.8, seed: int = 123) -> dict:\n", |
| " # Do some feature engineering / data cleaning to create the data sets\n", |
| " # one hot encode -- we know the encoding here.\n", |
| " for value, country in {1: \"USA\", 2: \"Europe\", 3: \"Japan\"}.items():\n", |
| " mpg_df[country] = np.where(mpg_df[\"Origin\"] == value, 1, 0)\n", |
| " raw_dataset = mpg_df.dropna()\n", |
| " # split the pandas dataframe into train and test\n", |
| " train_dataset = raw_dataset.sample(frac=train_test_split, random_state=seed)\n", |
| " test_dataset = raw_dataset.drop(train_dataset.index)\n", |
| "\n", |
| " return {\"train_dataset\": train_dataset, \"test_dataset\": test_dataset}\n", |
| "\n", |
| "\n", |
| "def target_column() -> str:\n", |
| " return \"MPG\"\n", |
| "\n", |
| "\n", |
| "@extract_fields({\"lr_model\": LinearRegression, \"scaler\": StandardScaler})\n", |
| "def linear_model(train_dataset: pd.DataFrame, target_column: str) -> dict:\n", |
| " # Fit the model\n", |
| " # pull out target\n", |
| " train_labels = train_dataset.pop(target_column)\n", |
| " # Convert boolean columns to integers for the model\n", |
| " bool_columns = train_dataset.select_dtypes(include=[bool]).columns\n", |
| " train_dataset[bool_columns] = train_dataset[bool_columns].astype(int)\n", |
| " # Normalize the features for the model\n", |
| " scaler = StandardScaler()\n", |
| " train_dataset_scaled = scaler.fit_transform(train_dataset)\n", |
| "\n", |
| " # Initialize and fit the Linear Regression model\n", |
| " linear_model = LinearRegression()\n", |
| " linear_model.fit(train_dataset_scaled, train_labels)\n", |
| " return {\"lr_model\": linear_model, \"scaler\": scaler}\n", |
| "\n", |
| "\n", |
| "def evaluated_model(lr_model: LinearRegression,\n", |
| " scaler: StandardScaler,\n", |
| " test_dataset: pd.DataFrame, target_column: str) -> dict:\n", |
| " # evaluate the model - pull out target\n", |
| " test_labels = test_dataset.pop(target_column)\n", |
| " # Evaluate the model\n", |
| " # convert boolean columns to integers for the model\n", |
| " bool_columns = test_dataset.select_dtypes(include=[bool]).columns\n", |
| " test_dataset[bool_columns] = test_dataset[bool_columns].astype(int)\n", |
| " test_dataset_scaled = scaler.transform(test_dataset)\n", |
| "\n", |
| " # Predict and evaluate the model\n", |
| " test_pred = lr_model.predict(test_dataset_scaled)\n", |
| " mae = mean_absolute_error(test_labels, test_pred)\n", |
| " test_results = {\n", |
| " \"linear_model\": mae\n", |
| " }\n", |
| " return test_results\n" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-07-20T17:37:04.415885Z", |
| "start_time": "2024-07-20T17:37:04.097149Z" |
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| "text/plain": [ |
| "<hamilton.driver.Driver at 0x1516d7100>" |
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| }, |
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| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "dr = driver.Builder().with_modules(pipeline).build()\n", |
| "dr" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-07-20T17:37:06.029923Z", |
| "start_time": "2024-07-20T17:37:05.934165Z" |
| }, |
| "application/vnd.databricks.v1+cell": { |
| "cellMetadata": { |
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| { |
| "data": { |
| "text/plain": [ |
| "{'evaluated_model': {'linear_model': 2.4926580150007007},\n", |
| " 'linear_model': {'lr_model': LinearRegression(), 'scaler': StandardScaler()}}" |
| ] |
| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "result = dr.execute([\"evaluated_model\", \"linear_model\"])\n", |
| "result" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-07-20T17:37:36.535977Z", |
| "start_time": "2024-07-20T17:37:36.241822Z" |
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| "showTitle": false, |
| "title": "" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "image/svg+xml": [ |
| "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", |
| "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", |
| " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", |
| "<!-- Generated by graphviz version 10.0.1 (20240210.2158)\n", |
| " -->\n", |
| "<!-- Pages: 1 -->\n", |
| "<svg width=\"607pt\" height=\"566pt\"\n", |
| " viewBox=\"0.00 0.00 607.40 565.80\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", |
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| "<g id=\"clust1\" class=\"cluster\">\n", |
| "<title>cluster__legend</title>\n", |
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| "</g>\n", |
| "<!-- target_column -->\n", |
| "<g id=\"node1\" class=\"node\">\n", |
| "<title>target_column</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M119.47,-299.6C119.47,-299.6 28.12,-299.6 28.12,-299.6 22.12,-299.6 16.12,-293.6 16.12,-287.6 16.12,-287.6 16.12,-248 16.12,-248 16.12,-242 22.12,-236 28.12,-236 28.12,-236 119.47,-236 119.47,-236 125.47,-236 131.47,-242 131.47,-248 131.47,-248 131.47,-287.6 131.47,-287.6 131.47,-293.6 125.47,-299.6 119.47,-299.6\"/>\n", |
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| "</g>\n", |
| "<!-- evaluated_model -->\n", |
| "<g id=\"node3\" class=\"node\">\n", |
| "<title>evaluated_model</title>\n", |
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| "</g>\n", |
| "<!-- target_column->evaluated_model -->\n", |
| "<g id=\"edge5\" class=\"edge\">\n", |
| "<title>target_column->evaluated_model</title>\n", |
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| "<g id=\"node8\" class=\"node\">\n", |
| "<title>linear_model</title>\n", |
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| "</g>\n", |
| "<!-- target_column->linear_model -->\n", |
| "<g id=\"edge10\" class=\"edge\">\n", |
| "<title>target_column->linear_model</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"165.57,-253.64 174.71,-248.29 164.23,-246.77 165.57,-253.64\"/>\n", |
| "</g>\n", |
| "<!-- lr_model -->\n", |
| "<g id=\"node2\" class=\"node\">\n", |
| "<title>lr_model</title>\n", |
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| "<!-- lr_model->evaluated_model -->\n", |
| "<g id=\"edge2\" class=\"edge\">\n", |
| "<title>lr_model->evaluated_model</title>\n", |
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| "</g>\n", |
| "<!-- mpg_df -->\n", |
| "<g id=\"node4\" class=\"node\">\n", |
| "<title>mpg_df</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M106.72,-147.6C106.72,-147.6 40.87,-147.6 40.87,-147.6 34.87,-147.6 28.87,-141.6 28.87,-135.6 28.87,-135.6 28.87,-96 28.87,-96 28.87,-90 34.87,-84 40.87,-84 40.87,-84 106.72,-84 106.72,-84 112.72,-84 118.72,-90 118.72,-96 118.72,-96 118.72,-135.6 118.72,-135.6 118.72,-141.6 112.72,-147.6 106.72,-147.6\"/>\n", |
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| "</g>\n", |
| "<!-- data_sets -->\n", |
| "<g id=\"node5\" class=\"node\">\n", |
| "<title>data_sets</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M259.32,-105.6C259.32,-105.6 199.47,-105.6 199.47,-105.6 193.47,-105.6 187.47,-99.6 187.47,-93.6 187.47,-93.6 187.47,-54 187.47,-54 187.47,-48 193.47,-42 199.47,-42 199.47,-42 259.32,-42 259.32,-42 265.32,-42 271.32,-48 271.32,-54 271.32,-54 271.32,-93.6 271.32,-93.6 271.32,-99.6 265.32,-105.6 259.32,-105.6\"/>\n", |
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| "</g>\n", |
| "<!-- mpg_df->data_sets -->\n", |
| "<g id=\"edge6\" class=\"edge\">\n", |
| "<title>mpg_df->data_sets</title>\n", |
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| "<!-- test_dataset -->\n", |
| "<g id=\"node7\" class=\"node\">\n", |
| "<title>test_dataset</title>\n", |
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| "</g>\n", |
| "<!-- data_sets->test_dataset -->\n", |
| "<g id=\"edge9\" class=\"edge\">\n", |
| "<title>data_sets->test_dataset</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"312.85,-77.3 322.85,-73.8 312.85,-70.3 312.85,-77.3\"/>\n", |
| "</g>\n", |
| "<!-- scaler -->\n", |
| "<g id=\"node6\" class=\"node\">\n", |
| "<title>scaler</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M420.18,-187.6C420.18,-187.6 329.57,-187.6 329.57,-187.6 323.57,-187.6 317.57,-181.6 317.57,-175.6 317.57,-175.6 317.57,-136 317.57,-136 317.57,-130 323.57,-124 329.57,-124 329.57,-124 420.18,-124 420.18,-124 426.18,-124 432.18,-130 432.18,-136 432.18,-136 432.18,-175.6 432.18,-175.6 432.18,-181.6 426.18,-187.6 420.18,-187.6\"/>\n", |
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| "</g>\n", |
| "<!-- scaler->evaluated_model -->\n", |
| "<g id=\"edge3\" class=\"edge\">\n", |
| "<title>scaler->evaluated_model</title>\n", |
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| "<!-- test_dataset->evaluated_model -->\n", |
| "<g id=\"edge4\" class=\"edge\">\n", |
| "<title>test_dataset->evaluated_model</title>\n", |
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| "<g id=\"edge1\" class=\"edge\">\n", |
| "<title>linear_model->lr_model</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"299.21,-241.3 309.21,-237.8 299.21,-234.3 299.21,-241.3\"/>\n", |
| "</g>\n", |
| "<!-- linear_model->scaler -->\n", |
| "<g id=\"edge8\" class=\"edge\">\n", |
| "<title>linear_model->scaler</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M282.6,-207.96C290.78,-203.29 299.33,-198.41 307.76,-193.58\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"309.34,-196.72 316.28,-188.72 305.86,-190.64 309.34,-196.72\"/>\n", |
| "</g>\n", |
| "<!-- _data_sets_inputs -->\n", |
| "<g id=\"node9\" class=\"node\">\n", |
| "<title>_data_sets_inputs</title>\n", |
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| "<text text-anchor=\"start\" x=\"43.67\" y=\"-37.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">seed</text>\n", |
| "<text text-anchor=\"start\" x=\"113.17\" y=\"-37.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">int</text>\n", |
| "<text text-anchor=\"start\" x=\"14.8\" y=\"-16.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">train_test_split</text>\n", |
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| "</g>\n", |
| "<!-- _data_sets_inputs->data_sets -->\n", |
| "<g id=\"edge7\" class=\"edge\">\n", |
| "<title>_data_sets_inputs->data_sets</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"175.1,-63.19 185.66,-62.39 176.9,-56.43 175.1,-63.19\"/>\n", |
| "</g>\n", |
| "<!-- input -->\n", |
| "<g id=\"node10\" class=\"node\">\n", |
| "<title>input</title>\n", |
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| "</g>\n", |
| "<!-- function -->\n", |
| "<g id=\"node11\" class=\"node\">\n", |
| "<title>function</title>\n", |
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| "</g>\n", |
| "<!-- output -->\n", |
| "<g id=\"node12\" class=\"node\">\n", |
| "<title>output</title>\n", |
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| "<text text-anchor=\"middle\" x=\"73.8\" y=\"-440\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">output</text>\n", |
| "</g>\n", |
| "<!-- override -->\n", |
| "<g id=\"node13\" class=\"node\">\n", |
| "<title>override</title>\n", |
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| "<polyline fill=\"none\" stroke=\"black\" points=\"96.97,-482.5 108.97,-494.5\"/>\n", |
| "<polyline fill=\"none\" stroke=\"black\" points=\"108.97,-507.1 96.97,-519.1\"/>\n", |
| "<text text-anchor=\"middle\" x=\"73.8\" y=\"-495\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">override</text>\n", |
| "</g>\n", |
| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x151753040>" |
| ] |
| }, |
| "execution_count": 6, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# Visualize Overrides\n", |
| "dr.visualize_execution([\"evaluated_model\"], \n", |
| " overrides={\"linear_model\": result[\"linear_model\"]})" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-07-20T17:37:37.088527Z", |
| "start_time": "2024-07-20T17:37:37.025195Z" |
| }, |
| "application/vnd.databricks.v1+cell": { |
| "cellMetadata": {}, |
| "inputWidgets": {}, |
| "nuid": "b617038c-2ffa-498c-b6c9-bbd6bb79bb1f", |
| "showTitle": false, |
| "title": "" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "{'evaluated_model': {'linear_model': 2.4926580150007007}}" |
| ] |
| }, |
| "execution_count": 7, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# Execute with overrides\n", |
| "dr.execute([\"evaluated_model\"], overrides={\"linear_model\": result[\"linear_model\"]})" |
| ] |
| }, |
| { |
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