| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "id": "d573c906", |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# Execute this cell to install dependencies\n", |
| "%pip install sf-hamilton[visualization]" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "id": "a492fd8c", |
| "metadata": {}, |
| "source": [ |
| "# Running the DAG in your notebook [](https://colab.research.google.com/github/dagworks-inc/hamilton/blob/main/examples/hamilton_ui/notebook.ipynb) [](https://github.com/dagworks-inc/hamilton/blob/main/examples/hamilton_ui/notebook.ipynb)\n", |
| "\n", |
| "\n", |
| "This notebook uses the existing module definitions to build the DAG and run it in this notebook." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "id": "a6545e23", |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-05-27T18:02:20.935173Z", |
| "start_time": "2024-05-27T18:02:12.447366Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "from components import feature_transforms, iris_loader, models\n", |
| "from hamilton_sdk import adapters\n", |
| "\n", |
| "from hamilton import driver as h_driver\n", |
| "from hamilton.io.materialization import to\n", |
| "from hamilton.lifecycle import PrintLnHook" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "id": "488727fa", |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-05-27T18:04:30.021612Z", |
| "start_time": "2024-05-27T18:04:29.739958Z" |
| } |
| }, |
| "outputs": [ |
| { |
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| "<!-- sepal_width_cm->sepal_width_cm_std -->\n", |
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| "<title>sepal_width_cm->sepal_width_cm_std</title>\n", |
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| "<g id=\"node7\" class=\"node\">\n", |
| "<title>sepal_length_cm_std</title>\n", |
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| "<!-- sepal_length_cm_std->sepal_length_cm_normalized -->\n", |
| "<g id=\"edge78\" class=\"edge\">\n", |
| "<title>sepal_length_cm_std->sepal_length_cm_normalized</title>\n", |
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| "<g id=\"node8\" class=\"node\">\n", |
| "<title>best_model</title>\n", |
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| "<!-- lr_model.y_train -->\n", |
| "<g id=\"node9\" class=\"node\">\n", |
| "<title>lr_model.y_train</title>\n", |
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| "<!-- lr_model.training_accuracy -->\n", |
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| "<title>lr_model.training_accuracy</title>\n", |
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| "<!-- lr_model.y_train->lr_model.training_accuracy -->\n", |
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| "<title>lr_model.y_train->lr_model.training_accuracy</title>\n", |
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| "<!-- lr_model.fit_clf -->\n", |
| "<g id=\"node46\" class=\"node\">\n", |
| "<title>lr_model.fit_clf</title>\n", |
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| "<!-- lr_model.y_train->lr_model.fit_clf -->\n", |
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| "<title>lr_model.y_train->lr_model.fit_clf</title>\n", |
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| "<title>lr_model</title>\n", |
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| "<title>lr_model->best_model</title>\n", |
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| "<title>svm_model.data_set</title>\n", |
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| "<title>svm_model.train_test_split_func</title>\n", |
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| "<title>lr_model.training_accuracy->lr_model</title>\n", |
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| "<title>petal_length_cm_log</title>\n", |
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| "<title>petal_length_cm_log->data_set_v2</title>\n", |
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| "<title>petal_length_cm</title>\n", |
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| "<title>petal_length_cm_mean</title>\n", |
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| "<title>petal_length_cm_std</title>\n", |
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| "<!-- petal_length_cm->petal_length_cm_std -->\n", |
| "<g id=\"edge52\" class=\"edge\">\n", |
| "<title>petal_length_cm->petal_length_cm_std</title>\n", |
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| "<!-- petal_length_cm_normalized -->\n", |
| "<g id=\"node53\" class=\"node\">\n", |
| "<title>petal_length_cm_normalized</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M1238.35,-266.17C1238.35,-266.17 1054,-266.17 1054,-266.17 1048,-266.17 1042,-260.17 1042,-254.17 1042,-254.17 1042,-214.57 1042,-214.57 1042,-208.57 1048,-202.57 1054,-202.57 1054,-202.57 1238.35,-202.57 1238.35,-202.57 1244.35,-202.57 1250.35,-208.57 1250.35,-214.57 1250.35,-214.57 1250.35,-254.17 1250.35,-254.17 1250.35,-260.17 1244.35,-266.17 1238.35,-266.17\"/>\n", |
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| "<text text-anchor=\"start\" x=\"1127.05\" y=\"-215.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n", |
| "</g>\n", |
| "<!-- petal_length_cm->petal_length_cm_normalized -->\n", |
| "<g id=\"edge79\" class=\"edge\">\n", |
| "<title>petal_length_cm->petal_length_cm_normalized</title>\n", |
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| "<!-- petal_width_cm_normalized -->\n", |
| "<g id=\"node16\" class=\"node\">\n", |
| "<title>petal_width_cm_normalized</title>\n", |
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| "<text text-anchor=\"start\" x=\"1127.05\" y=\"-610.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n", |
| "</g>\n", |
| "<!-- petal_width_cm_normalized->data_set_v2 -->\n", |
| "<g id=\"edge63\" class=\"edge\">\n", |
| "<title>petal_width_cm_normalized->data_set_v2</title>\n", |
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| "<!-- petal_width_cm_normalized->data_set_v1 -->\n", |
| "<g id=\"edge72\" class=\"edge\">\n", |
| "<title>petal_width_cm_normalized->data_set_v1</title>\n", |
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| "</g>\n", |
| "<!-- svm_model.clf -->\n", |
| "<g id=\"node17\" class=\"node\">\n", |
| "<title>svm_model.clf</title>\n", |
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| "<text text-anchor=\"start\" x=\"62.18\" y=\"-548.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">svm_model.clf</text>\n", |
| "<text text-anchor=\"start\" x=\"102.3\" y=\"-520.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">str</text>\n", |
| "</g>\n", |
| "<!-- petal_width_cm_std -->\n", |
| "<g id=\"node18\" class=\"node\">\n", |
| "<title>petal_width_cm_std</title>\n", |
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| "<text text-anchor=\"start\" x=\"858.57\" y=\"-720.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">petal_width_cm_std</text>\n", |
| "<text text-anchor=\"start\" x=\"911.07\" y=\"-692.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">float</text>\n", |
| "</g>\n", |
| "<!-- petal_width_cm_std->petal_width_cm_normalized -->\n", |
| "<g id=\"edge25\" class=\"edge\">\n", |
| "<title>petal_width_cm_std->petal_width_cm_normalized</title>\n", |
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| "</g>\n", |
| "<!-- petal_width_cm_mean -->\n", |
| "<g id=\"node19\" class=\"node\">\n", |
| "<title>petal_width_cm_mean</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M995.75,-661.17C995.75,-661.17 851.9,-661.17 851.9,-661.17 845.9,-661.17 839.9,-655.17 839.9,-649.17 839.9,-649.17 839.9,-609.57 839.9,-609.57 839.9,-603.57 845.9,-597.57 851.9,-597.57 851.9,-597.57 995.75,-597.57 995.75,-597.57 1001.75,-597.57 1007.75,-603.57 1007.75,-609.57 1007.75,-609.57 1007.75,-649.17 1007.75,-649.17 1007.75,-655.17 1001.75,-661.17 995.75,-661.17\"/>\n", |
| "<text text-anchor=\"start\" x=\"850.7\" y=\"-638.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">petal_width_cm_mean</text>\n", |
| "<text text-anchor=\"start\" x=\"911.07\" y=\"-610.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">float</text>\n", |
| "</g>\n", |
| "<!-- petal_width_cm_mean->petal_width_cm_normalized -->\n", |
| "<g id=\"edge24\" class=\"edge\">\n", |
| "<title>petal_width_cm_mean->petal_width_cm_normalized</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1008.13,-629.37C1016.17,-629.37 1024.42,-629.37 1032.69,-629.37\"/>\n", |
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| "</g>\n", |
| "<!-- iris_df -->\n", |
| "<g id=\"node20\" class=\"node\">\n", |
| "<title>iris_df</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M596.05,-489.17C596.05,-489.17 519.7,-489.17 519.7,-489.17 513.7,-489.17 507.7,-483.17 507.7,-477.17 507.7,-477.17 507.7,-437.57 507.7,-437.57 507.7,-431.57 513.7,-425.57 519.7,-425.57 519.7,-425.57 596.05,-425.57 596.05,-425.57 602.05,-425.57 608.05,-431.57 608.05,-437.57 608.05,-437.57 608.05,-477.17 608.05,-477.17 608.05,-483.17 602.05,-489.17 596.05,-489.17\"/>\n", |
| "<text text-anchor=\"start\" x=\"518.5\" y=\"-466.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">iris_df: case</text>\n", |
| "<text text-anchor=\"start\" x=\"523.75\" y=\"-438.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">DataFrame</text>\n", |
| "</g>\n", |
| "<!-- iris_df->sepal_width_cm -->\n", |
| "<g id=\"edge6\" class=\"edge\">\n", |
| "<title>iris_df->sepal_width_cm</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M565.77,-489.24C577.92,-543.82 604.92,-658.37 637.05,-752.37 655.88,-807.47 682.79,-868.94 701.05,-908.78\"/>\n", |
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| "<!-- iris_df->petal_length_cm -->\n", |
| "<g id=\"edge22\" class=\"edge\">\n", |
| "<title>iris_df->petal_length_cm</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M579.42,-425.2C608.68,-379.4 662.35,-295.36 694.58,-244.9\"/>\n", |
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| "<!-- target_class -->\n", |
| "<g id=\"node27\" class=\"node\">\n", |
| "<title>target_class</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M1203.47,-184.17C1203.47,-184.17 1088.87,-184.17 1088.87,-184.17 1082.87,-184.17 1076.87,-178.17 1076.87,-172.17 1076.87,-172.17 1076.87,-132.57 1076.87,-132.57 1076.87,-126.57 1082.87,-120.57 1088.87,-120.57 1088.87,-120.57 1203.47,-120.57 1203.47,-120.57 1209.47,-120.57 1215.47,-126.57 1215.47,-132.57 1215.47,-132.57 1215.47,-172.17 1215.47,-172.17 1215.47,-178.17 1209.47,-184.17 1203.47,-184.17\"/>\n", |
| "<text text-anchor=\"start\" x=\"1087.67\" y=\"-161.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">target_class: case</text>\n", |
| "<text text-anchor=\"start\" x=\"1127.05\" y=\"-133.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n", |
| "</g>\n", |
| "<!-- iris_df->target_class -->\n", |
| "<g id=\"edge34\" class=\"edge\">\n", |
| "<title>iris_df->target_class</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M560.36,-425.17C564.46,-367.56 579.78,-245.16 637.05,-162.37 698.88,-72.99 732.63,-53.51 836.15,-20.37 910.37,3.38 939.41,9.22 1011.5,-20.37 1055.99,-38.64 1093.57,-79.65 1117.45,-111.15\"/>\n", |
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| "<!-- sepal_length_cm -->\n", |
| "<g id=\"node38\" class=\"node\">\n", |
| "<title>sepal_length_cm</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M795.15,-489.17C795.15,-489.17 649.05,-489.17 649.05,-489.17 643.05,-489.17 637.05,-483.17 637.05,-477.17 637.05,-477.17 637.05,-437.57 637.05,-437.57 637.05,-431.57 643.05,-425.57 649.05,-425.57 649.05,-425.57 795.15,-425.57 795.15,-425.57 801.15,-425.57 807.15,-431.57 807.15,-437.57 807.15,-437.57 807.15,-477.17 807.15,-477.17 807.15,-483.17 801.15,-489.17 795.15,-489.17\"/>\n", |
| "<text text-anchor=\"start\" x=\"647.85\" y=\"-466.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">sepal_length_cm: case</text>\n", |
| "<text text-anchor=\"start\" x=\"702.97\" y=\"-438.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n", |
| "</g>\n", |
| "<!-- iris_df->sepal_length_cm -->\n", |
| "<g id=\"edge50\" class=\"edge\">\n", |
| "<title>iris_df->sepal_length_cm</title>\n", |
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| "<!-- petal_width_cm -->\n", |
| "<g id=\"node47\" class=\"node\">\n", |
| "<title>petal_width_cm</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M791.4,-743.17C791.4,-743.17 652.8,-743.17 652.8,-743.17 646.8,-743.17 640.8,-737.17 640.8,-731.17 640.8,-731.17 640.8,-691.57 640.8,-691.57 640.8,-685.57 646.8,-679.57 652.8,-679.57 652.8,-679.57 791.4,-679.57 791.4,-679.57 797.4,-679.57 803.4,-685.57 803.4,-691.57 803.4,-691.57 803.4,-731.17 803.4,-731.17 803.4,-737.17 797.4,-743.17 791.4,-743.17\"/>\n", |
| "<text text-anchor=\"start\" x=\"651.6\" y=\"-720.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">petal_width_cm: case</text>\n", |
| "<text text-anchor=\"start\" x=\"702.97\" y=\"-692.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n", |
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| "<!-- iris_df->petal_width_cm -->\n", |
| "<g id=\"edge59\" class=\"edge\">\n", |
| "<title>iris_df->petal_width_cm</title>\n", |
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| "<g id=\"node21\" class=\"node\">\n", |
| "<title>lr_model.shuffle_train_test_split</title>\n", |
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| "<text text-anchor=\"start\" x=\"1428.87\" y=\"-578.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">lr_model.shuffle_train_test_split</text>\n", |
| "<text text-anchor=\"start\" x=\"1520.37\" y=\"-550.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">bool</text>\n", |
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| "<!-- lr_model.shuffle_train_test_split->lr_model.train_test_split_func -->\n", |
| "<g id=\"edge47\" class=\"edge\">\n", |
| "<title>lr_model.shuffle_train_test_split->lr_model.train_test_split_func</title>\n", |
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| "<g id=\"node22\" class=\"node\">\n", |
| "<title>lr_model.penalty</title>\n", |
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| "<text text-anchor=\"start\" x=\"1749.1\" y=\"-455.07\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">lr_model.penalty</text>\n", |
| "<text text-anchor=\"start\" x=\"1795.6\" y=\"-427.07\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">str</text>\n", |
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| "<!-- lr_model.prefit_clf -->\n", |
| "<g id=\"node34\" class=\"node\">\n", |
| "<title>lr_model.prefit_clf</title>\n", |
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| "<!-- lr_model.penalty->lr_model.prefit_clf -->\n", |
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| "<title>lr_model.penalty->lr_model.prefit_clf</title>\n", |
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| "<title>petal_width_cm_log</title>\n", |
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| "<title>petal_width_cm_log->data_set_v2</title>\n", |
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| "<title>sepal_length_cm->sepal_length_cm_mean</title>\n", |
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| "<title>sepal_length_cm->sepal_length_cm_log</title>\n", |
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| "<title>sepal_length_cm->sepal_length_cm_std</title>\n", |
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| "<title>sepal_length_cm->sepal_length_cm_normalized</title>\n", |
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| "<title>petal_length_cm_mean->petal_length_cm_normalized</title>\n", |
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| "<title>iris_df.load_data.iris_data_raw</title>\n", |
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| "<title>petal_length_cm_std->petal_length_cm_normalized</title>\n", |
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| "<title>lr_model.data_set</title>\n", |
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| "<!-- svm_model.y_train->svm_model.training_accuracy -->\n", |
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| "<title>sepal_length_cm_normalized->data_set_v2</title>\n", |
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| "<title>petal_length_cm_normalized->data_set_v2</title>\n", |
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| "<title>petal_length_cm_normalized->data_set_v1</title>\n", |
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| "<title>iris_df.select_data.iris_data_raw->iris_df</title>\n", |
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| "<title>sepal_width_cm_std->sepal_width_cm_normalized</title>\n", |
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| "<title>svm_model->best_model</title>\n", |
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| "<title>input</title>\n", |
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| "<g id=\"node61\" class=\"node\">\n", |
| "<title>function</title>\n", |
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| ], |
| "text/plain": [ |
| "<hamilton.driver.Driver at 0x151f658b0>" |
| ] |
| }, |
| "execution_count": 2, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# set up config and inputs\n", |
| "config = {\"case\": \"parquet\"}\n", |
| "dag_name = \"machine_learning_dag\"\n", |
| "email = \"elijah@dagworks.io\" # your email\n", |
| "project_id = 38 # your project id\n", |
| "\n", |
| "# create tracker object\n", |
| "tracker = adapters.HamiltonTracker(\n", |
| " username=email,\n", |
| " project_id=project_id,\n", |
| " dag_name=dag_name,\n", |
| " tags={\n", |
| " \"template\": \"machine_learning\",\n", |
| " \"loading_data_from\": \"parquet\",\n", |
| " \"TODO\": \"add_more_tags_to_find_your_run_later\",\n", |
| " },\n", |
| ")\n", |
| "# create the driver object\n", |
| "dr = (\n", |
| " h_driver.Builder()\n", |
| " .with_config(config) # this shapes the DAG\n", |
| " .with_modules(iris_loader, feature_transforms, models)\n", |
| " .with_adapters(tracker, PrintLnHook(verbosity=1))\n", |
| " .build()\n", |
| ")\n", |
| "dr" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "id": "ec464dc9", |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-05-27T18:05:42.223326Z", |
| "start_time": "2024-05-27T18:05:42.218761Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# set up what inputs and outputs we want\n", |
| "inputs = {}\n", |
| "outputs_to_materialize = [\n", |
| " to.parquet(\n", |
| " id=\"data_set_v1_saver\",\n", |
| " path=\"data_set_v1.parquet\",\n", |
| " dependencies=[\"data_set_v1\"],\n", |
| " ),\n", |
| " to.pickle(\n", |
| " id=\"svm_model_saver\",\n", |
| " path=\"svm_model.pkl\",\n", |
| " dependencies=[\"svm_model\"],\n", |
| " ),\n", |
| " to.pickle(\n", |
| " id=\"lr_model_saver\",\n", |
| " path=\"lr_model.pkl\",\n", |
| " dependencies=[\"lr_model\"],\n", |
| " )\n", |
| "]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 8, |
| "id": "2120dd32", |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-05-27T18:05:43.496155Z", |
| "start_time": "2024-05-27T18:05:42.642157Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "\n", |
| "Capturing execution run. Results can be found at http://localhost:8242/dashboard/project/38/runs/85\n", |
| "\n" |
| ] |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Executing node: svm_model.prefit_clf.\n", |
| "Finished debugging node: svm_model.prefit_clf in 398μs. Status: Success.\n", |
| "Executing node: iris_df.load_data.iris_data_raw.\n", |
| "Finished debugging node: iris_df.load_data.iris_data_raw in 5.8ms. Status: Success.\n", |
| "Executing node: iris_df.select_data.iris_data_raw.\n", |
| "Finished debugging node: iris_df.select_data.iris_data_raw in 63.4ms. Status: Success.\n", |
| "Executing node: iris_df.\n", |
| "Finished debugging node: iris_df in 71ms. Status: Success.\n", |
| "Executing node: sepal_length_cm.\n", |
| "Finished debugging node: sepal_length_cm in 16.5ms. Status: Success.\n", |
| "Executing node: sepal_length_cm_mean.\n", |
| "Finished debugging node: sepal_length_cm_mean in 253μs. Status: Success.\n", |
| "Executing node: sepal_length_cm_std.\n", |
| "Finished debugging node: sepal_length_cm_std in 258μs. Status: Success.\n", |
| "Executing node: sepal_length_cm_normalized.\n", |
| "Finished debugging node: sepal_length_cm_normalized in 17.7ms. Status: Success.\n", |
| "Executing node: sepal_width_cm.\n", |
| "Finished debugging node: sepal_width_cm in 18.2ms. Status: Success.\n", |
| "Executing node: sepal_width_cm_mean.\n", |
| "Finished debugging node: sepal_width_cm_mean in 209μs. Status: Success.\n", |
| "Executing node: sepal_width_cm_std.\n", |
| "Finished debugging node: sepal_width_cm_std in 258μs. Status: Success.\n", |
| "Executing node: sepal_width_cm_normalized.\n", |
| "Finished debugging node: sepal_width_cm_normalized in 20.5ms. Status: Success.\n", |
| "Executing node: petal_length_cm.\n", |
| "Finished debugging node: petal_length_cm in 18.9ms. Status: Success.\n", |
| "Executing node: petal_length_cm_mean.\n", |
| "Finished debugging node: petal_length_cm_mean in 271μs. Status: Success.\n", |
| "Executing node: petal_length_cm_std.\n", |
| "Finished debugging node: petal_length_cm_std in 244μs. Status: Success.\n", |
| "Executing node: petal_length_cm_normalized.\n", |
| "Finished debugging node: petal_length_cm_normalized in 10.3ms. Status: Success.\n", |
| "Executing node: petal_width_cm.\n", |
| "Finished debugging node: petal_width_cm in 17.5ms. Status: Success.\n", |
| "Executing node: petal_width_cm_mean.\n", |
| "Finished debugging node: petal_width_cm_mean in 193μs. Status: Success.\n", |
| "Executing node: petal_width_cm_std.\n", |
| "Finished debugging node: petal_width_cm_std in 232μs. Status: Success.\n", |
| "Executing node: petal_width_cm_normalized.\n", |
| "Finished debugging node: petal_width_cm_normalized in 15.9ms. Status: Success.\n", |
| "Executing node: target_class.\n", |
| "Finished debugging node: target_class in 9.69ms. Status: Success.\n", |
| "Executing node: data_set_v1.\n", |
| "Finished debugging node: data_set_v1 in 58.9ms. Status: Success.\n", |
| "Executing node: svm_model.data_set.\n", |
| "Finished debugging node: svm_model.data_set in 29.7ms. Status: Success.\n", |
| "Executing node: svm_model.test_size_fraction.\n", |
| "Finished debugging node: svm_model.test_size_fraction in 71μs. Status: Success.\n", |
| "Executing node: svm_model.shuffle_train_test_split.\n", |
| "Finished debugging node: svm_model.shuffle_train_test_split in 56μs. Status: Success.\n", |
| "Executing node: svm_model.train_test_split_func.\n", |
| "Finished debugging node: svm_model.train_test_split_func in 67ms. Status: Success.\n", |
| "Executing node: svm_model.X_train.\n", |
| "Finished debugging node: svm_model.X_train in 27.9ms. Status: Success.\n", |
| "Executing node: svm_model.y_train.\n", |
| "Finished debugging node: svm_model.y_train in 1.74ms. Status: Success.\n", |
| "Executing node: svm_model.fit_clf.\n", |
| "Finished debugging node: svm_model.fit_clf in 3.1ms. Status: Success.\n", |
| "Executing node: svm_model.training_accuracy.\n", |
| "Finished debugging node: svm_model.training_accuracy in 3.3ms. Status: Success.\n", |
| "Executing node: svm_model.X_test.\n", |
| "Finished debugging node: svm_model.X_test in 26.1ms. Status: Success.\n", |
| "Executing node: svm_model.y_test.\n", |
| "Finished debugging node: svm_model.y_test in 1.52ms. Status: Success.\n", |
| "Executing node: svm_model.testing_accuracy.\n", |
| "Finished debugging node: svm_model.testing_accuracy in 2.05ms. Status: Success.\n", |
| "Executing node: svm_model.\n", |
| "Finished debugging node: svm_model in 437μs. Status: Success.\n", |
| "Executing node: lr_model.penalty.\n", |
| "Finished debugging node: lr_model.penalty in 64.1μs. Status: Success.\n", |
| "Executing node: lr_model.prefit_clf.\n", |
| "Finished debugging node: lr_model.prefit_clf in 148μs. Status: Success.\n", |
| "Executing node: lr_model.data_set.\n", |
| "Finished debugging node: lr_model.data_set in 29.7ms. Status: Success.\n", |
| "Executing node: lr_model.test_size_fraction.\n", |
| "Finished debugging node: lr_model.test_size_fraction in 63.2μs. Status: Success.\n", |
| "Executing node: lr_model.shuffle_train_test_split.\n", |
| "Finished debugging node: lr_model.shuffle_train_test_split in 52μs. Status: Success.\n", |
| "Executing node: lr_model.train_test_split_func.\n", |
| "Finished debugging node: lr_model.train_test_split_func in 54.7ms. Status: Success.\n", |
| "Executing node: lr_model.X_train.\n", |
| "Finished debugging node: lr_model.X_train in 25.2ms. Status: Success.\n", |
| "Executing node: lr_model.y_train.\n", |
| "Finished debugging node: lr_model.y_train in 1.5ms. Status: Success.\n", |
| "Executing node: lr_model.fit_clf.\n", |
| "Finished debugging node: lr_model.fit_clf in 6.59ms. Status: Success.\n", |
| "Executing node: lr_model.training_accuracy.\n", |
| "Finished debugging node: lr_model.training_accuracy in 1.75ms. Status: Success.\n", |
| "Executing node: lr_model.X_test.\n" |
| ] |
| }, |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "\n", |
| "Captured execution run. Results can be found at http://localhost:8242/dashboard/project/38/runs/85\n", |
| "\n" |
| ] |
| }, |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Finished debugging node: lr_model.X_test in 25.7ms. Status: Success.\n", |
| "Executing node: lr_model.y_test.\n", |
| "Finished debugging node: lr_model.y_test in 1.61ms. Status: Success.\n", |
| "Executing node: lr_model.testing_accuracy.\n", |
| "Finished debugging node: lr_model.testing_accuracy in 1.64ms. Status: Success.\n", |
| "Executing node: lr_model.\n", |
| "Finished debugging node: lr_model in 340μs. Status: Success.\n", |
| "Executing node: best_model.\n", |
| "Finished debugging node: best_model in 290μs. Status: Success.\n", |
| "Executing node: data_set_v1_saver.\n", |
| "Finished debugging node: data_set_v1_saver in 5.22ms. Status: Success.\n", |
| "Executing node: svm_model_saver.\n", |
| "Finished debugging node: svm_model_saver in 589μs. Status: Success.\n", |
| "Executing node: lr_model_saver.\n", |
| "Finished debugging node: lr_model_saver in 387μs. Status: Success.\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# execute the DAG and materialize a few things from it\n", |
| "metadata, result = dr.materialize(\n", |
| " # This approach helps centralize & standardize how objects are read/written and also how metadata\n", |
| " # about them is captured. This is useful for tracking lineage and provenance.\n", |
| " *outputs_to_materialize,\n", |
| " additional_vars=[\"best_model\"],\n", |
| " inputs=inputs,\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "id": "5bb3cdd1", |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-05-27T18:05:45.811527Z", |
| "start_time": "2024-05-27T18:05:45.803062Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "{'data_set_v1_saver': {'file_metadata': {'size': 6386,\n", |
| " 'path': 'data_set_v1.parquet',\n", |
| " 'last_modified': 1716833143.4420505,\n", |
| " 'timestamp': 1716858343.44226,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''},\n", |
| " 'dataframe_metadata': {'rows': 150,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['sepal_length_cm_normalized',\n", |
| " 'sepal_width_cm_normalized',\n", |
| " 'petal_length_cm_normalized',\n", |
| " 'petal_width_cm_normalized',\n", |
| " 'target_class'],\n", |
| " 'datatypes': ['float64', 'float64', 'float64', 'float64', 'object']}},\n", |
| " 'svm_model_saver': {'file_metadata': {'size': 7697,\n", |
| " 'path': 'svm_model.pkl',\n", |
| " 'last_modified': 1716833143.4430761,\n", |
| " 'timestamp': 1716858343.443196,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''}},\n", |
| " 'lr_model_saver': {'file_metadata': {'size': 1109,\n", |
| " 'path': 'lr_model.pkl',\n", |
| " 'last_modified': 1716833143.4436038,\n", |
| " 'timestamp': 1716858343.443672,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''}}}" |
| ] |
| }, |
| "execution_count": 9, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "metadata" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "id": "07513852", |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2024-05-27T18:05:48.332870Z", |
| "start_time": "2024-05-27T18:05:48.327916Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "{'best_model': {'logistic': LogisticRegression(),\n", |
| " 'training_accuracy': 0.95,\n", |
| " 'testing_accuracy': 0.9666666666666667}}" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "result" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "id": "6a26a515", |
| "metadata": {}, |
| "outputs": [], |
| "source": [] |
| } |
| ], |
| "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": "3.11.9" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 5 |
| } |
