{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Experiment Tracking: Coloring Multiple Runs\n\nThis example demostrates how to use opt-sugar in combination with mlflow\nfor single objective optimization experiment tracking\n\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# sphinx_gallery_thumbnail_path = '_static/coloring_experiment.png'\nimport datetime\nfrom urllib.parse import urlparse\nfrom itertools import product\nimport logging\nimport random\n\nimport gurobipy as gp\nimport mlflow\nfrom mlflow.exceptions import MlflowException\n\n# import sys; sys.path.append('/Users/Juan.ChaconLeon/opt/opt-sugar/src') # when running locally\nfrom opt_sugar import low_sugar\n\n# The generate_graph_data generates random graphs given a graph size and an edge probability.\nfrom utils.coloring import generate_graph_data\n\n# TODO: reformat this example similar to\n# https://github.com/scikit-learn/scikit-learn/blob/main/examples/calibration/plot_calibration_multiclass.py\n\n# ..NOTE:: check the coloring example first to see how the model was formulated.\n\nfrom utils.coloring import ColoringModelBuilder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tracking an Optimization Experiment\n\nAdd description here.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "logging.getLogger(\"mlflow\").setLevel(logging.CRITICAL) # Can be set DEBUG\n\n\n\ndef get_build(mip_focus):\n def build(data):\n # Create a new model\n m = gp.Model(\"coloring\")\n model_builder = ColoringModelBuilder(data)\n model_builder.build_variables(m)\n model_builder.build_constraints(m)\n model_builder.build_objective(m)\n # setting parameters\n m.setParam('MIPFocus', mip_focus)\n m.setParam('Method', 4) # for deterministic concurrent runs\n return m\n return build\n\n\ndef callback(m):\n objective = m.getObjective()\n color_count = objective.getValue()\n callback_result = {\"color_count\": color_count, \"MIPFocus\": m.getParamInfo('MIPFocus')[2], \"RunTime\": m.RunTime}\n return callback_result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MIPFocus effect Over Runtime Case 1: Same data multiple runs\n\nAdd description here.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Let's first set the random seed for reproducible results\nrandom.seed(42)\n\ntry:\n experiment_name = f\"coloring_experiment_1_{datetime.datetime.now().strftime('%Y_%m_%d')}\"\n experiment_id = mlflow.create_experiment(name=experiment_name)\nexcept MlflowException:\n experiment_id = mlflow.get_experiment_by_name(name=experiment_name).experiment_id\n\n# Generating a graph instance\ndata = generate_graph_data(node_count=15, edge_probability=0.5)\n\n# Building\nfor mip_focus, _ in product([0, 1, 2, 3], range(25)):\n with mlflow.start_run(experiment_id=experiment_id):\n build = get_build(mip_focus)\n opt_model = low_sugar.Model(build)\n result = opt_model.optimize(data=data, callback=callback)\n\n # Note: Above is replacement for opt_model.fit(data, fit_callback) and opt_model.predict(data)\n mlflow.log_param(\"MIPFocus\", result[\"callback_result\"][\"MIPFocus\"])\n mlflow.log_metric(\"RunTime\", result[\"callback_result\"][\"RunTime\"])\n mlflow.log_metric(\"color_count\", result[\"callback_result\"][\"color_count\"])\n\n tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n print(f\"tracking_url_type_store: {tracking_url_type_store}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MIPFocus effect Over Runtime Case 1: Different input data\n\nAdd description here.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Again Let's first set the random seed for reproducible results\nrandom.seed(21)\n\ntry:\n experiment_name = f\"coloring_experiment_2_{datetime.datetime.now().strftime('%Y_%m_%d')}\"\n experiment_id = mlflow.create_experiment(name=experiment_name)\nexcept MlflowException:\n experiment_id = mlflow.get_experiment_by_name(name=experiment_name).experiment_id\n\n# Building\nfor mip_focus, data_i in product([0, 1, 2, 3], range(20)):\n\n data = generate_graph_data(node_count=15, edge_probability=0.5)\n\n for try_i in range(3):\n\n with mlflow.start_run(experiment_id=experiment_id):\n build = get_build(mip_focus)\n opt_model = low_sugar.Model(build)\n result = opt_model.optimize(data=data, callback=callback)\n\n # Note: Above is replacement for opt_model.fit(data, fit_callback) and opt_model.predict(data)\n mlflow.log_param(\"MIPFocus\", result[\"callback_result\"][\"MIPFocus\"])\n mlflow.log_metric(\"RunTime\", result[\"callback_result\"][\"RunTime\"])\n mlflow.log_metric(\"color_count\", result[\"callback_result\"][\"color_count\"])\n\n tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n print(f\"tracking_url_type_store: {tracking_url_type_store}\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 0 }