| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "id": "7bf6a40d", |
| "metadata": {}, |
| "outputs": [], |
| "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": 9, |
| "id": "7a449245", |
| "metadata": {}, |
| "outputs": [], |
| "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": 10, |
| "id": "397b09bc", |
| "metadata": {}, |
| "outputs": [ |
| { |
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| "<title>data</title>\n", |
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| "<title>target_names</title>\n", |
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| "<title>data->target_names</title>\n", |
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| "<title>target</title>\n", |
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| "<path fill=\"none\" stroke=\"black\" d=\"M302.82,-287.7C302.5,-280.24 302.12,-271.32 301.76,-262.97\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"305.22,-262.95 301.3,-253.1 298.23,-263.25 305.22,-262.95\"/>\n", |
| "</g>\n", |
| "<!-- y_test_with_labels->classification_report -->\n", |
| "<g id=\"edge20\" class=\"edge\">\n", |
| "<title>y_test_with_labels->classification_report</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M165.76,-287.85C159.73,-250.99 145.49,-163.92 138.14,-118.96\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"141.45,-118.54 136.38,-109.23 134.54,-119.67 141.45,-118.54\"/>\n", |
| "</g>\n", |
| "<!-- test_size_fraction -->\n", |
| "<g id=\"node20\" class=\"node\">\n", |
| "<title>test_size_fraction</title>\n", |
| "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"187.56\" cy=\"-522\" rx=\"100.48\" ry=\"18\"/>\n", |
| "<text text-anchor=\"middle\" x=\"187.56\" y=\"-516.95\" font-family=\"Times,serif\" font-size=\"14.00\">Input: test_size_fraction</text>\n", |
| "</g>\n", |
| "<!-- test_size_fraction->train_test_split_func -->\n", |
| "<g id=\"edge5\" class=\"edge\">\n", |
| "<title>test_size_fraction->train_test_split_func</title>\n", |
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| "</g>\n", |
| "<!-- model_parameters->model_params_to_json -->\n", |
| "<g id=\"edge16\" class=\"edge\">\n", |
| "<title>model_parameters->model_params_to_json</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"487.3,-191.68 486.68,-181.1 480.56,-189.76 487.3,-191.68\"/>\n", |
| "</g>\n", |
| "<!-- y_train->fit_clf -->\n", |
| "<g id=\"edge9\" class=\"edge\">\n", |
| "<title>y_train->fit_clf</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M466.06,-359.7C466.27,-352.24 466.53,-343.32 466.76,-334.97\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"470.29,-335.2 467.08,-325.1 463.29,-335 470.29,-335.2\"/>\n", |
| "</g>\n", |
| "<!-- target->train_test_split_func -->\n", |
| "<g id=\"edge4\" class=\"edge\">\n", |
| "<title>target->train_test_split_func</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M466.29,-508.16C450.06,-498.37 427.32,-484.65 408.19,-473.1\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"410.22,-469.63 399.85,-467.46 406.6,-475.63 410.22,-469.63\"/>\n", |
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| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x16a2c7730>" |
| ] |
| }, |
| "execution_count": 10, |
| "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", |
| " output_file_path=None,\n", |
| " render_kwargs={},\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "id": "f5727b54", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "/Users/elijahbenizzy/.pyenv/versions/3.9.10/envs/hamilton/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", |
| "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", |
| "\n", |
| "Increase the number of iterations (max_iter) or scale the data as shown in:\n", |
| " https://scikit-learn.org/stable/modules/preprocessing.html\n", |
| "Please also refer to the documentation for alternative solver options:\n", |
| " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", |
| " n_iter_i = _check_optimize_result(\n" |
| ] |
| } |
| ], |
| "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())" |
| ] |
| } |
| ], |
| "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": "3.9.10" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 5 |
| } |
