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apache / hamilton / refs/heads/fix/sdk-code-display / . / examples / materialization / notebook.ipynb
blob: 14c7387c3b2ba720bb14a8a9e4da678bb90cd1eb [file] [log] [blame]
{
"cells": [
{
"cell_type": "code",
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"id": "7bf6a40d",
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"ExecuteTime": {
"end_time": "2023-09-04T17:30:02.674284Z",
"start_time": "2023-09-04T17:29:59.371085Z"
}
},
"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": [
"import json\n",
"import os\n",
"\n",
"import data_loaders\n",
"import model_training\n",
"\n",
"from hamilton import base, driver\n",
"from hamilton.io.materialization import to\n",
"import pandas as pd\n",
"\n",
"import custom_materializers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7a449245",
"metadata": {
"ExecuteTime": {
"end_time": "2023-09-04T17:30:02.682841Z",
"start_time": "2023-09-04T17:30:02.679637Z"
}
},
"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": [
"dag_config = {\n",
" \"test_size_fraction\": 0.5,\n",
" \"shuffle_train_test_split\": True,\n",
" \"data_loader\" : \"iris\",\n",
" \"clf\" : \"logistic\",\n",
" \"penalty\" : \"l2\"\n",
"}\n",
"dr = (\n",
" driver.Builder()\n",
" .with_adapter(base.DefaultAdapter())\n",
" .with_config(dag_config)\n",
" .with_modules(data_loaders, model_training)\n",
" .build()\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "397b09bc",
"metadata": {
"ExecuteTime": {
"end_time": "2023-09-04T17:30:03.208108Z",
"start_time": "2023-09-04T17:30:02.696138Z"
}
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"outputs": [
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},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"materializers = [\n",
" to.json(\n",
" dependencies=[\"model_parameters\"],\n",
" id=\"model_params_to_json\",\n",
" path=\"./data/params.json\"\n",
" ),\n",
" # classification report to .txt file\n",
" to.file(\n",
" dependencies=[\"classification_report\"],\n",
" id=\"classification_report_to_txt\",\n",
" path=\"./data/classification_report.txt\",\n",
" ),\n",
" # materialize the model to a pickle file\n",
" to.pickle(\n",
" dependencies=[\"fit_clf\"], id=\"clf_to_pickle\", path=\"./data/clf.pkl\"\n",
" ),\n",
" # materialize the predictions we made to a csv file\n",
" to.csv(\n",
" dependencies=[\"predicted_output_with_labels\"],\n",
" id=\"predicted_output_with_labels_to_csv\",\n",
" path=\"./data/predicted_output_with_labels.csv\",\n",
" ),\n",
" ]\n",
"\n",
"dr.visualize_materialization(\n",
" *materializers,\n",
" additional_vars=[\"classification_report\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5727b54",
"metadata": {},
"outputs": [],
"source": [
"materialization_results, additional_vars = dr.materialize(\n",
" # materialize model parameters to json\n",
" *materializers,\n",
" additional_vars=[\"classification_report\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8bdfde70",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 94\n",
" 1 0.91 0.93 0.92 85\n",
" 2 0.97 0.99 0.98 96\n",
" 3 0.99 0.97 0.98 93\n",
" 4 0.99 0.92 0.95 88\n",
" 5 0.95 0.95 0.95 85\n",
" 6 0.99 0.97 0.98 97\n",
" 7 0.97 0.97 0.97 89\n",
" 8 0.88 0.88 0.88 82\n",
" 9 0.91 0.97 0.94 90\n",
"\n",
" accuracy 0.96 899\n",
" macro avg 0.95 0.95 0.95 899\n",
"weighted avg 0.96 0.96 0.96 899\n",
"\n"
]
}
],
"source": [
"print(additional_vars['classification_report'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a6f5fe83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 94\n",
" 1 0.91 0.93 0.92 85\n",
" 2 0.97 0.99 0.98 96\n",
" 3 0.99 0.97 0.98 93\n",
" 4 0.99 0.92 0.95 88\n",
" 5 0.95 0.95 0.95 85\n",
" 6 0.99 0.97 0.98 97\n",
" 7 0.97 0.97 0.97 89\n",
" 8 0.88 0.88 0.88 82\n",
" 9 0.91 0.97 0.94 90\n",
"\n",
" accuracy 0.96 899\n",
" macro avg 0.95 0.95 0.95 899\n",
"weighted avg 0.96 0.96 0.96 899\n",
"\n"
]
}
],
"source": [
"print(open((materialization_results['classification_report_to_txt']['path'])).read())"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
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