| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "from hamilton import driver, dataflows" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "# 1. Install from PyPI\n", |
| "Install the package from PyPI with `!pip install sf-hamilton-contrib`.\n", |
| "Then see run.py for details. " |
| ], |
| "metadata": { |
| "collapsed": false |
| } |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 2. Dynamic installation\n", |
| "Install the package dynamically with without installing/upgrading sf-hamilton-contrib." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "xgboost_optuna = dataflows.import_module(\"xgboost_optuna\", \"zilto\")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": {}, |
| "outputs": [ |
| { |
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| "source": [ |
| "dr = (\n", |
| " driver.Builder()\n", |
| " .with_modules(xgboost_optuna)\n", |
| " .with_config(dict(task=\"classification\"))\n", |
| " .build()\n", |
| ")\n", |
| "\n", |
| "dr.display_all_functions()" |
| ] |
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| { |
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| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
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| "<title>%3</title>\n", |
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| "<!-- y_test_pred -->\n", |
| "<g id=\"node1\" class=\"node\">\n", |
| "<title>y_test_pred</title>\n", |
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| "<title>test_score</title>\n", |
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| "<!-- y_test_pred->test_score -->\n", |
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| "<title>y_test_pred->test_score</title>\n", |
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| "<g id=\"node2\" class=\"node\">\n", |
| "<title>base_model</title>\n", |
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| "<title>best_model</title>\n", |
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| "<g id=\"edge6\" class=\"edge\">\n", |
| "<title>base_model->best_model</title>\n", |
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| "<title>hyperparameter_search</title>\n", |
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| "<title>base_model->hyperparameter_search</title>\n", |
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| "<g id=\"node3\" class=\"node\">\n", |
| "<title>cross_validation_folds</title>\n", |
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| "<!-- cross_validation_folds->hyperparameter_search -->\n", |
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| "<title>cross_validation_folds->hyperparameter_search</title>\n", |
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| "<!-- optuna_distributions -->\n", |
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| "<title>optuna_distributions</title>\n", |
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| "<title>optuna_distributions->hyperparameter_search</title>\n", |
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| "<title>higher_is_better</title>\n", |
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| "<!-- study -->\n", |
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| "<title>study</title>\n", |
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| "<title>higher_is_better->study</title>\n", |
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| "<title>study->hyperparameter_search</title>\n", |
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| "<title>scorer</title>\n", |
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| "<title>model_config</title>\n", |
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| "<title>_cross_validation_folds_inputs->hyperparameter_search</title>\n", |
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| "</g>\n", |
| "<!-- _test_score_inputs -->\n", |
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| "<title>_test_score_inputs</title>\n", |
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| "<text text-anchor=\"start\" x=\"490\" y=\"-120.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">numpy.ndarray | pandas.core.frame.DataFrame</text>\n", |
| "</g>\n", |
| "<!-- _test_score_inputs->test_score -->\n", |
| "<g id=\"edge21\" class=\"edge\">\n", |
| "<title>_test_score_inputs->test_score</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"415,-49.94 404.41,-50.16 412.83,-56.59 415,-49.94\"/>\n", |
| "</g>\n", |
| "<!-- input -->\n", |
| "<g id=\"node17\" class=\"node\">\n", |
| "<title>input</title>\n", |
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| "<text text-anchor=\"middle\" x=\"382\" y=\"-681.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">input</text>\n", |
| "</g>\n", |
| "<!-- function -->\n", |
| "<g id=\"node18\" class=\"node\">\n", |
| "<title>function</title>\n", |
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| "</g>\n", |
| "<!-- output -->\n", |
| "<g id=\"node19\" class=\"node\">\n", |
| "<title>output</title>\n", |
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| "<text text-anchor=\"middle\" x=\"202\" y=\"-681.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">output</text>\n", |
| "</g>\n", |
| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x7f6a2839ee30>" |
| ] |
| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "from sklearn.datasets import load_breast_cancer\n", |
| "from sklearn.model_selection import train_test_split\n", |
| "\n", |
| "# Load the Boston Housing dataset (regression example)\n", |
| "data = load_breast_cancer()\n", |
| "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)\n", |
| "\n", |
| "inputs = dict(\n", |
| " X_train=X_train,\n", |
| " y_train=y_train,\n", |
| " X_test=X_test,\n", |
| " y_test=y_test,\n", |
| ")\n", |
| "\n", |
| "final_vars = [\"best_model\", \"best_hyperparameters\", \"test_score\"]\n", |
| "\n", |
| "dr.visualize_execution(\n", |
| " final_vars=final_vars,\n", |
| " inputs=inputs,\n", |
| " output_file_path=None,\n", |
| " orient=\"TB\",\n", |
| " deduplicate_inputs=True,\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "[I 2023-11-15 18:40:48,705] A new study created in memory with name: no-name-7bf4dece-e402-4b38-9818-9c564d2d68b2\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/plain": [ |
| "{'best_model': XGBClassifier(base_score=None, booster='gbtree', callbacks=None,\n", |
| " colsample_bylevel=None, colsample_bynode=None,\n", |
| " colsample_bytree=0.5256259560017136, device='cpu',\n", |
| " early_stopping_rounds=None, enable_categorical=True,\n", |
| " eval_metric=None, feature_types=None, gamma=0.11302140972445468,\n", |
| " grow_policy=None, importance_type=None,\n", |
| " interaction_constraints=None, learning_rate=0.032344180499316345,\n", |
| " max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n", |
| " max_delta_step=9, max_depth=4, max_leaves=None,\n", |
| " min_child_weight=3, missing=nan, monotone_constraints=None,\n", |
| " multi_strategy=None, n_estimators=550, n_jobs=None,\n", |
| " num_parallel_tree=None, random_state=None, ...),\n", |
| " 'best_hyperparameters': {'n_estimators': 550,\n", |
| " 'learning_rate': 0.032344180499316345,\n", |
| " 'max_depth': 4,\n", |
| " 'gamma': 0.11302140972445468,\n", |
| " 'colsample_bytree': 0.5256259560017136,\n", |
| " 'min_child_weight': 3,\n", |
| " 'max_delta_step': 9},\n", |
| " 'test_score': 0.9736842105263158}" |
| ] |
| }, |
| "execution_count": 6, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "dr.execute(\n", |
| " final_vars=final_vars,\n", |
| " inputs=inputs,\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 3. Local copy" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# do 2. Dynamic installation\n", |
| "xgboost_optuna = dataflows.import_module(\"xgboost_optuna\", \"zilto\")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# then create local copy\n", |
| "dataflows.copy(xgboost_optuna, destination_path=\"./my_copy\")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# import your local copy\n", |
| "from my_copy import xgboost_optuna" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
| "image/svg+xml": [ |
| "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", |
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| "<!-- Generated by graphviz version 2.43.0 (0)\n", |
| " -->\n", |
| "<!-- Title: %3 Pages: 1 -->\n", |
| "<svg width=\"2759pt\" height=\"639pt\"\n", |
| " viewBox=\"0.00 0.00 2759.00 639.41\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", |
| "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 635.41)\">\n", |
| "<title>%3</title>\n", |
| "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-635.41 2755,-635.41 2755,4 -4,4\"/>\n", |
| "<g id=\"clust1\" class=\"cluster\">\n", |
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| "<text text-anchor=\"middle\" x=\"56\" y=\"-569.21\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Legend</text>\n", |
| "</g>\n", |
| "<!-- y_test_pred -->\n", |
| "<g id=\"node1\" class=\"node\">\n", |
| "<title>y_test_pred</title>\n", |
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| "</g>\n", |
| "<!-- test_score -->\n", |
| "<g id=\"node16\" class=\"node\">\n", |
| "<title>test_score</title>\n", |
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| "</g>\n", |
| "<!-- y_test_pred->test_score -->\n", |
| "<g id=\"edge24\" class=\"edge\">\n", |
| "<title>y_test_pred->test_score</title>\n", |
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| "<g id=\"node2\" class=\"node\">\n", |
| "<title>base_model</title>\n", |
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| "<text text-anchor=\"start\" x=\"781\" y=\"-582.21\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">base_model</text>\n", |
| "<text text-anchor=\"start\" x=\"800\" y=\"-554.21\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Callable</text>\n", |
| "</g>\n", |
| "<!-- best_model -->\n", |
| "<g id=\"node12\" class=\"node\">\n", |
| "<title>best_model</title>\n", |
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| "</g>\n", |
| "<!-- base_model->best_model -->\n", |
| "<g id=\"edge11\" class=\"edge\">\n", |
| "<title>base_model->best_model</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M885.16,-571.21C957.34,-571.01 1085.94,-570.79 1196,-571.41\"/>\n", |
| "</g>\n", |
| "<!-- hyperparameter_search -->\n", |
| "<g id=\"node14\" class=\"node\">\n", |
| "<title>hyperparameter_search</title>\n", |
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| "</g>\n", |
| "<!-- base_model->hyperparameter_search -->\n", |
| "<g id=\"edge17\" class=\"edge\">\n", |
| "<title>base_model->hyperparameter_search</title>\n", |
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| "<g id=\"node3\" class=\"node\">\n", |
| "<title>study</title>\n", |
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| "</g>\n", |
| "<!-- study->hyperparameter_search -->\n", |
| "<g id=\"edge21\" class=\"edge\">\n", |
| "<title>study->hyperparameter_search</title>\n", |
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| "<title>task</title>\n", |
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| "</g>\n", |
| "<!-- scorer -->\n", |
| "<g id=\"node5\" class=\"node\">\n", |
| "<title>scorer</title>\n", |
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| "<!-- scorer->higher_is_better -->\n", |
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| "<title>scorer->higher_is_better</title>\n", |
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| "<title>scoring_func</title>\n", |
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| "<title>scorer->scoring_func</title>\n", |
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| }, |
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| "metadata": {}, |
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| } |
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| "source": [ |
| "dr = (\n", |
| " driver.Builder()\n", |
| " .with_modules(xgboost_optuna)\n", |
| " .with_config(dict(task=\"classification\"))\n", |
| " .build()\n", |
| ")\n", |
| "\n", |
| "dr.display_all_functions()" |
| ] |
| } |
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