From 29e56677831337315cb35568c734bf03285983d2 Mon Sep 17 00:00:00 2001 From: sherpan Date: Tue, 14 May 2024 13:57:43 -0700 Subject: [PATCH 1/3] xgboost sponsor notebooks --- .../xgboost/notebooks/xg_comet.ipynb | 170 ++++++++++++++++++ .../xgboost/notebooks/xg_data_panel.gif | Bin 0 -> 3618949 bytes 2 files changed, 170 insertions(+) create mode 100644 integrations/model-training/xgboost/notebooks/xg_comet.ipynb create mode 100644 integrations/model-training/xgboost/notebooks/xg_data_panel.gif diff --git a/integrations/model-training/xgboost/notebooks/xg_comet.ipynb b/integrations/model-training/xgboost/notebooks/xg_comet.ipynb new file mode 100644 index 00000000..17f520f8 --- /dev/null +++ b/integrations/model-training/xgboost/notebooks/xg_comet.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "[Comet](https://www.comet.com/?utm_source=xgboost&utm_medium=colab&utm_content=intro_cell) helps Data Scientists track, compare, debug, and visualize their model training runs.\n", + "\n", + "\n", + "**This notebook shows you how to log your XGBoost Training Runs with Comet.** For more information about Comet's integration with XGBoost visit our [Docs](https://www.comet.com/docs/v2/integrations/ml-frameworks/xgboost/?utm_source=xgboost&utm_medium=colab&utm_content=intro_cell) page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Install Required Packages " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install xgboost comet_ml --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialize Comet\n", + "\n", + "Create your free account at [Comet.com](https://www.comet.com/signup?utm_source=xgboost&utm_medium=colab&utm_content=signup_cell) and grab your API key which can be found under account settings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import comet_ml\n", + "\n", + "comet_ml.init(project_name=\"comet-xgboost-tutorial\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train A Classification Model on the Iris Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "from xgboost import XGBClassifier\n", + "# read data\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "experiment = comet_ml.Experiment()\n", + "\n", + "data = load_iris(as_frame=True)\n", + "X_train, X_test, y_train, y_test = train_test_split(data['data'], data['target'], test_size=.2)\n", + "# create model instance\n", + "bst = XGBClassifier(n_estimators=10, max_depth=5, learning_rate=0.1, objective='binary:logistic')\n", + "# fit model\n", + "bst.fit(X_train, y_train, eval_set=[(X_test, y_test)],\n", + " eval_metric=\"merror\",)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# View the Results in Comet \n", + "\n", + "Comet auto-logs the hyper-parameters, model graph, and metrics for a XGBoost training run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "experiment.display(\"parameters\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Log a prediction table to Comet " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make predictions\n", + "y_pred = bst.predict(X_test)\n", + "\n", + "print(type(X_test))\n", + "\n", + "debug_df = X_test.copy()\n", + "\n", + "debug_df['pred'] = y_pred\n", + "debug_df['ground_truth'] = y_test\n", + "\n", + "experiment.log_table('prediction_debug_table.csv', debug_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Debug Model Predictions with Comet's Data Panel\n", + "\n", + "![Gif](xg_data_panel.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.11.0 ('nb': venv)", + "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": "3.11.0" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8c9587381b2341d562742e36a89690be32a732b11830813473890249dd40a07d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/integrations/model-training/xgboost/notebooks/xg_data_panel.gif b/integrations/model-training/xgboost/notebooks/xg_data_panel.gif new file mode 100644 index 0000000000000000000000000000000000000000..827795cedf3b4d04db3c88f63acb709257bbb61f 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a/integrations/model-training/xgboost/notebooks/xg_comet.ipynb b/integrations/model-training/xgboost/notebooks/xg_comet.ipynb index 17f520f8..f73e7b21 100644 --- a/integrations/model-training/xgboost/notebooks/xg_comet.ipynb +++ b/integrations/model-training/xgboost/notebooks/xg_comet.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "0M3sL7lrGHC3" + }, "source": [ " \n", "\n", @@ -14,33 +16,41 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "F52bYB07GHDB" + }, "source": [ - "# Install Required Packages " + "# Install Required Packages" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "zS0IaNKSGHDE" + }, "outputs": [], "source": [ - "%pip install xgboost comet_ml --quiet" + "%pip install xgboost comet_ml" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "WtUkfOzSGHDR" + }, "source": [ "# Initialize Comet\n", "\n", - "Create your free account at [Comet.com](https://www.comet.com/signup?utm_source=xgboost&utm_medium=colab&utm_content=signup_cell) and grab your API key which can be found under account settings." + "Create your free account at [Comet.com](https://www.comet.com/signup?utm_source=xgboost&utm_medium=colab&utm_content=signup_cell)." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "k4J9C0e4GHDT" + }, "outputs": [], "source": [ "import comet_ml\n", @@ -50,40 +60,89 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "bWsjy3XUGHDU" + }, "source": [ - "# Train A Classification Model on the Iris Dataset" + "# Import libraries" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "Ufimm7d4GHDW" + }, "outputs": [], "source": [ - "\n", - "\n", "from xgboost import XGBClassifier\n", - "# read data\n", "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Create a Comet Experiment" + ], + "metadata": { + "id": "3RJx33J7GUiE" + } + }, + { + "cell_type": "code", + "source": [ + "experiment = comet_ml.Experiment()" + ], + "metadata": { + "id": "4IUCQ21vGRZL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Train A Classification Model on the Iris Dataset" + ], + "metadata": { + "id": "sFKxD6L7GZdJ" + } + }, + { + "cell_type": "code", + "source": [ + "data = load_iris(as_frame=True)\n", "\n", - "experiment = comet_ml.Experiment()\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data[\"data\"], data[\"target\"], test_size=0.2\n", + ")\n", "\n", - "data = load_iris(as_frame=True)\n", - "X_train, X_test, y_train, y_test = train_test_split(data['data'], data['target'], test_size=.2)\n", "# create model instance\n", - "bst = XGBClassifier(n_estimators=10, max_depth=5, learning_rate=0.1, objective='binary:logistic')\n", + "bst = XGBClassifier(\n", + " n_estimators=10,\n", + " max_depth=5,\n", + " learning_rate=0.1,\n", + " objective=\"binary:logistic\",\n", + " eval_metric=\"merror\",\n", + ")\n", + "\n", "# fit model\n", - "bst.fit(X_train, y_train, eval_set=[(X_test, y_test)],\n", - " eval_metric=\"merror\",)" - ] + "bst.fit(X_train, y_train, eval_set=[(X_test, y_test)])" + ], + "metadata": { + "id": "Xv51yx3eGY4B" + }, + "execution_count": null, + "outputs": [] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "OcoTd5jZGHDY" + }, "source": [ - "# View the Results in Comet \n", + "# View the Results in Comet\n", "\n", "Comet auto-logs the hyper-parameters, model graph, and metrics for a XGBoost training run" ] @@ -91,25 +150,29 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "MEJAAveDGHDb" + }, "outputs": [], "source": [ - "\n", - "\n", - "experiment.display(\"parameters\")\n" + "experiment.display(\"parameters\")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "sXopQ7iaGHDe" + }, "source": [ - "# Log a prediction table to Comet " + "# Log a prediction table to Comet" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "m4P1zBrMGHDg" + }, "outputs": [], "source": [ "# make predictions\n", @@ -119,24 +182,30 @@ "\n", "debug_df = X_test.copy()\n", "\n", - "debug_df['pred'] = y_pred\n", - "debug_df['ground_truth'] = y_test\n", + "debug_df[\"pred\"] = y_pred\n", + "debug_df[\"ground_truth\"] = y_test\n", + "\n", + "experiment.log_table(\"prediction_debug_table.csv\", debug_df)\n", "\n", - "experiment.log_table('prediction_debug_table.csv', debug_df)" + "experiment.end()" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "tqhjVfL5GHDh" + }, "source": [ "# Debug Model Predictions with Comet's Data Panel\n", "\n", - "![Gif](xg_data_panel.gif)" + "![Gif](https://github.com/comet-ml/comet-examples/blob/xgboost_dp_example/integrations/model-training/xgboost/notebooks/xg_data_panel.gif?raw=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "UDlxdjpeGHDi" + }, "source": [] } ], @@ -163,8 +232,11 @@ "interpreter": { "hash": "8c9587381b2341d562742e36a89690be32a732b11830813473890249dd40a07d" } + }, + "colab": { + "provenance": [] } }, "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat_minor": 0 +} \ No newline at end of file From ee06d07142998a2b011618429b3e6eaf1690bb83 Mon Sep 17 00:00:00 2001 From: sherpan Date: Mon, 20 May 2024 16:22:20 -0700 Subject: [PATCH 3/3] renamed file --- ...w_to_use_comet_with_xgboost_tutorial.ipynb | 170 ++++++++++++++++++ 1 file changed, 170 insertions(+) create mode 100644 integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb diff --git a/integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb b/integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb new file mode 100644 index 00000000..ced9e84a --- /dev/null +++ b/integrations/model-training/xgboost/notebooks/how_to_use_comet_with_xgboost_tutorial.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "[Comet](https://www.comet.com/?utm_source=xgboost&utm_medium=colab&utm_content=intro_cell) helps Data Scientists track, compare, debug, and visualize their model training runs. Many ML teams leverage Comet as their single system of record of model's performance in both training anf production \n", + "This notebook shows you how to log your XGBoost Training Runs with Comet with just a additional lines of code\n", + "\n", + "For more information about Comet's integration with XGBoost visit our [Docs](https://www.comet.com/docs/v2/integrations/ml-frameworks/xgboost/?utm_source=xgboost&utm_medium=colab&utm_content=intro_cell) page." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿšง Install Required Packages " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install xgboost comet_ml --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿงช Initialize Comet\n", + "\n", + "Create your free account at [Comet.com](https://www.comet.com/signup?utm_source=xgboost&utm_medium=colab&utm_content=signup_cell) and grab your API key which can be found under account settings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import comet_ml\n", + "\n", + "comet_ml.init(project_name=\"comet-xgboost-tutorial\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿ‘๏ธ Train A Classification Model on the Iris Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "from xgboost import XGBClassifier\n", + "# read data\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "experiment = comet_ml.Experiment()\n", + "\n", + "data = load_iris(as_frame=True)\n", + "X_train, X_test, y_train, y_test = train_test_split(data['data'], data['target'], test_size=.2)\n", + "# create model instance\n", + "bst = XGBClassifier(n_estimators=10, max_depth=5, learning_rate=0.1, objective='binary:logistic')\n", + "# fit model\n", + "bst.fit(X_train, y_train, eval_set=[(X_test, y_test)],\n", + " eval_metric=\"merror\",)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿ“Š View the Results in Comet \n", + "\n", + "Comet auto-logs the hyper-parameters, model graph, and metrics for a XGBoost training run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "experiment.display(\"charts\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿ“™ Log a prediction table to Comet " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make predictions\n", + "y_pred = bst.predict(X_test)\n", + "\n", + "print(type(X_test))\n", + "\n", + "debug_df = X_test.copy()\n", + "\n", + "debug_df['pred'] = y_pred\n", + "debug_df['ground_truth'] = y_test\n", + "\n", + "experiment.log_table('prediction_debug_table.csv', debug_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿ–ฅ๏ธ Debug Model Predictions with Comet's Data Panel\n", + "\n", + "![Gif](xg_data_panel.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.11.0 ('nb': venv)", + "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": "3.11.0" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8c9587381b2341d562742e36a89690be32a732b11830813473890249dd40a07d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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