{ "cells": [ { "cell_type": "markdown", "id": "20ec0f8d", "metadata": {}, "source": [ "***\n", "# Building and Explaining a Classifier using AutoMLx\n", "

by the Oracle AutoMLx Team

\n", "\n", "***" ] }, { "cell_type": "markdown", "id": "9b985fc1", "metadata": {}, "source": [ "Classification Demo Notebook.\n", "\n", "Copyright © 2025, Oracle and/or its affiliates.\n", "\n", "Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/" ] }, { "cell_type": "markdown", "id": "57d4d21b", "metadata": {}, "source": [ "## Overview of this Notebook\n", "\n", "In this notebook we will build a classifier using the Oracle AutoMLx tool for the public Census Income dataset. The dataset is a binary classification dataset, and more details about the dataset can be found at https://archive.ics.uci.edu/ml/datasets/Adult.\n", "We explore the various options provided by the Oracle AutoMLx tool, allowing the user to exercise control over the AutoMLx training process. We then evaluate the different models trained by AutoMLx. Finally we provide an overview of the possibilities that Oracle AutoMLx offers for explaining the predictions of the tuned model.\n", "\n", "---\n", "## Prerequisites\n", "\n", " - Experience level: Novice (Python and Machine Learning)\n", " - Professional experience: Some industry experience\n", "---\n", "\n", "## Business Use\n", "\n", "Data analytics and modeling problems using Machine Learning (ML) are becoming popular and often rely on data science expertise to build accurate ML models. Such modeling tasks primarily involve the following steps:\n", "- Preprocess dataset (clean, impute, engineer features, normalize).\n", "- Pick an appropriate model for the given dataset and prediction task at hand.\n", "- Tune the chosen model’s hyperparameters for the given dataset.\n", "\n", "All of these steps are significantly time consuming and heavily rely on data scientist expertise. Unfortunately, to make this problem harder, the best feature subset, model, and hyperparameter choice widely varies with the dataset and the prediction task. Hence, there is no one-size-fits-all solution to achieve reasonably good model performance. Using a simple Python API, AutoML can quickly jump-start the datascience process with an accurately-tuned model and appropriate features for a given prediction task.\n", "\n", "## Table of Contents\n", "\n", "- Setup\n", "- Load the Census Income dataset\n", "- AutoML\n", " - Setting the execution engine\n", " - Create an Instance of Oracle AutoMLx\n", " - Train a Model using AutoMLx\n", " - Analyze the AutoMLx optimization process \n", " - Algorithm Selection\n", " - Adaptive Sampling\n", " - Feature Selection\n", " - Hyperparameter Tuning\n", " - Confusion Matrix\n", " - Advanced AutoMLx Configuration \n", "- Machine Learning Explainability (MLX)\n", " - Initialize an MLExplainer\n", " - Model Explanations (Global Feature Importance)\n", " - Feature Dependence Explanations (PDP + ICE)\n", " - Prediction Explanations (Local Feature Importance)\n", " - Aggregate Local Feature Importance\n", " - Interactive What-If Explainers\n", " - Counterfactual Explanations\n", " - Advanced Feature Importance Options\n", " - Configure prediction explanation\n", " - Explain the model or explain the world\n", " - Advanced Feature Dependence Options (ALE)\n", "- References" ] }, { "cell_type": "markdown", "id": "99388a84", "metadata": {}, "source": [ "\n", "## Setup\n", "\n", "Basic setup for the Notebook." ] }, { "cell_type": "code", "execution_count": 1, "id": "2cb7a457", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:04:59.198961Z", "iopub.status.busy": "2025-04-25T10:04:59.198523Z", "iopub.status.idle": "2025-04-25T10:05:00.002181Z", "shell.execute_reply": "2025-04-25T10:05:00.001648Z" } }, "outputs": [], "source": [ "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "id": "ad83cf51", "metadata": {}, "source": [ "Load the required modules." ] }, { "cell_type": "code", "execution_count": 2, "id": "6fcc1e56", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:05:00.004524Z", "iopub.status.busy": "2025-04-25T10:05:00.003985Z", "iopub.status.idle": "2025-04-25T10:05:03.344639Z", "shell.execute_reply": "2025-04-25T10:05:03.344044Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "import plotly.figure_factory as ff\n", "from sklearn.metrics import roc_auc_score, confusion_matrix\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.compose import make_column_selector as selector\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.compose import make_column_selector as selector\n", "import time\n", "import datetime\n", "# Settings for plots\n", "plt.rcParams['figure.figsize'] = [10, 7]\n", "plt.rcParams['font.size'] = 15\n", "\n", "import automlx\n", "from automlx import init" ] }, { "cell_type": "markdown", "id": "7796b3e4", "metadata": {}, "source": [ "\n", "## Load the Census Income dataset\n", "We start by reading in the dataset from OpenML." ] }, { "cell_type": "code", "execution_count": 3, "id": "9a06420f", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:05:03.347224Z", "iopub.status.busy": "2025-04-25T10:05:03.346530Z", "iopub.status.idle": "2025-04-25T10:05:04.284214Z", "shell.execute_reply": "2025-04-25T10:05:04.283606Z" } }, "outputs": [], "source": [ "dataset = fetch_openml(name='adult',version=1, as_frame=True)\n", "df, y = dataset.data, dataset.target" ] }, { "cell_type": "markdown", "id": "abceeeac", "metadata": {}, "source": [ "Lets look at a few of the values in the data" ] }, { "cell_type": "code", "execution_count": 4, "id": "41d71a78", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:05:04.286478Z", "iopub.status.busy": "2025-04-25T10:05:04.285972Z", "iopub.status.idle": "2025-04-25T10:05:04.328608Z", "shell.execute_reply": "2025-04-25T10:05:04.328101Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_df = pd.DataFrame(y)\n", "y_df.columns = ['income']\n", "\n", "fig = px.histogram(y_df, x=\"income\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "422fcd88", "metadata": {}, "source": [ "We now separate the predictions (`y`) from the training data (`X`) for both the training (70%) and test (30%) datasets. The training set will be used to create a Machine Learning model using AutoMLx, and the test set will be used to evaluate the model's performance on unseen data." ] }, { "cell_type": "code", "execution_count": 8, "id": "23545948", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:05:06.366123Z", "iopub.status.busy": "2025-04-25T10:05:06.365499Z", "iopub.status.idle": "2025-04-25T10:05:06.405558Z", "shell.execute_reply": "2025-04-25T10:05:06.405090Z" } }, "outputs": [ { "data": { "text/plain": [ "((34189, 14), (14653, 14))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Several of the columns are incorrectly labeled as category type in the original dataset\n", "numeric_columns = ['age', 'capitalgain', 'capitalloss', 'hoursperweek']\n", "for col in df.columns:\n", " if col in numeric_columns:\n", " df[col] = df[col].astype(int)\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(df,\n", " y.map({'>50K': 1, '<=50K': 0}).astype(int),\n", " train_size=0.7,\n", " random_state=0)\n", "\n", "X_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "id": "76f8fa50", "metadata": {}, "source": [ "\n", "## AutoML" ] }, { "cell_type": "markdown", "id": "fd63fc6f", "metadata": {}, "source": [ "\n", "### Setting the execution engine\n", "The AutoMLx package offers the function `init`, which allows to initialize the parallelization engine." ] }, { "cell_type": "code", "execution_count": 9, "id": "7db30224", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:05:06.407580Z", "iopub.status.busy": "2025-04-25T10:05:06.407034Z", "iopub.status.idle": "2025-04-25T10:05:11.949363Z", "shell.execute_reply": "2025-04-25T10:05:11.948598Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:06,718] [automlx.backend] Overwriting ray session directory to /tmp/n4cg234n/ray, which will be deleted at engine shutdown. If you wish to retain ray logs, provide _temp_dir in ray_setup dict of engine_opts when initializing the AutoMLx engine.\n" ] } ], "source": [ "init(engine='ray')" ] }, { "cell_type": "markdown", "id": "38f44a48", "metadata": {}, "source": [ "\n", "### Create an instance of Oracle AutoMLx\n", "\n", "The Oracle AutoMLx solution provides a pipeline that automatically finds a tuned model given a prediction task and a training dataset. In particular it allows to find a tuned model for any supervised prediction task, for example, classification or regression where the target can be binary, categorical or real-valued.\n", "\n", "AutoML consists of five main steps:\n", "- **Preprocessing** : Clean, impute, engineer, and normalize features.\n", "- **Algorithm Selection** : Identify the right classification algorithm -in this notebook- for a given dataset, choosing from amongst:\n", " - AdaBoostClassifier\n", " - CatBoostClassifier\n", " - DecisionTreeClassifier\n", " - ExtraTreesClassifier\n", " - TorchMLPClassifier\n", " - KNeighborsClassifier\n", " - LGBMClassifier\n", " - LinearSVC\n", " - LogisticRegression\n", " - RandomForestClassifier\n", " - SVC\n", " - XGBClassifier\n", " - GaussianNB\n", " - TabNetClassifier\n", "- **Adaptive Sampling** : Select a subset of the data samples for the model to be trained on.\n", "- **Feature Selection** : Select a subset of the data features, based on the previously selected model.\n", "- **Hyperparameter Tuning** : Find the right model parameters that maximize score for the given dataset.\n", "\n", "All these pieces are readily combined into a simple AutoMLx pipeline which automates the entire Machine Learning process with minimal user input/interaction." ] }, { "cell_type": "markdown", "id": "2ef797fc", "metadata": {}, "source": [ "\n", "### Train a model using AutoMLx\n", "\n", "The AutoMLx API is quite simple to work with. We create an instance of the pipeline. Next, the training data is passed to the `fit()` function which executes the previously mentioned steps." ] }, { "cell_type": "code", "execution_count": 10, "id": "d46a13cd", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:05:11.952277Z", "iopub.status.busy": "2025-04-25T10:05:11.951871Z", "iopub.status.idle": "2025-04-25T10:06:58.126309Z", "shell.execute_reply": "2025-04-25T10:06:58.125606Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:12,061] [automlx.interface] Dataset shape: (34189,14)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:19,855] [sanerec.autotuning.parameter] Hyperparameter epsilon autotune range is set to its validation range. This could lead to long training times\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,017] [sanerec.autotuning.parameter] Hyperparameter repeat_quality_threshold autotune range is set to its validation range. This could lead to long training times\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,041] [sanerec.autotuning.parameter] Hyperparameter scope autotune range is set to its validation range. This could lead to long training times\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,155] [automlx.data_transform] Running preprocessing. Number of features: 15\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,812] [automlx.data_transform] Preprocessing completed. Took 0.657 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,854] [automlx.process] Running Model Generation\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,909] [automlx.process] KNeighborsClassifier is disabled. The KNeighborsClassifier model is only recommended for datasets with less than 10000 samples and 1000 features.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,909] [automlx.process] SVC is disabled. The SVC model is only recommended for datasets with less than 10000 samples and 1000 features.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,911] [automlx.process] Model Generation completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:21,985] [automlx.model_selection] Running Model Selection\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:53,108] [automlx.model_selection] Model Selection completed - Took 31.123 sec - Selected models: [['XGBClassifier']]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:53,134] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (34189,16).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:55,173] [automlx.trials] Adaptive Sampling completed - Took 2.0383 sec.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:05:55,267] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:03,981] [automlx.feature_selection] Feature Selection completed. Took 8.730 secs.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:04,028] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:47,769] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.10242113515453982, 'min_child_weight': 2, 'max_depth': 4, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 141, 'use_label_encoder': False}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:47,770] [automlx.trials] Model Tuning completed. Took: 43.742 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:54,215] [automlx.interface] Re-fitting pipeline\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:54,230] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_806b0073-0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:06:56,578] [automlx.interface] AutoMLx completed.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est1 = automlx.Pipeline()\n", "est1.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "id": "a6d0d90c", "metadata": {}, "source": [ "A model is then generated (`est1`) and can be used for prediction tasks. We use the `roc_auc_score` scoring metric to evaluate the performance of this model on unseen data (`X_test`)." ] }, { "cell_type": "code", "execution_count": 11, "id": "a0e9c5d9", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:06:58.128575Z", "iopub.status.busy": "2025-04-25T10:06:58.128284Z", "iopub.status.idle": "2025-04-25T10:07:00.026193Z", "shell.execute_reply": "2025-04-25T10:07:00.025538Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score on test data : 0.9140858492615117\n" ] } ], "source": [ "y_proba = est1.predict_proba(X_test)\n", "score_default = roc_auc_score(y_test, y_proba[:, 1])\n", "\n", "print(f'Score on test data : {score_default}')" ] }, { "cell_type": "markdown", "id": "845fead6", "metadata": {}, "source": [ "\n", "### Analyze the AutoMLx optimization process\n", "\n", "During the AutoMLx process, a summary of the optimization process is logged. It consists of:\n", "- Information about the training data .\n", "- Information about the AutoMLx Pipeline, such as:\n", " - Selected features that AutoMLx found to be most predictive in the training data;\n", " - Selected algorithm that was the best choice for this data;\n", " - Selected hyperparameters for the selected algorithm." ] }, { "cell_type": "markdown", "id": "0d49e4b4", "metadata": {}, "source": [ "AutoMLx provides a `print_summary` API to output all the different trials performed." ] }, { "cell_type": "code", "execution_count": 12, "id": "561d66f4", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:00.028168Z", "iopub.status.busy": "2025-04-25T10:07:00.027826Z", "iopub.status.idle": "2025-04-25T10:07:00.097517Z", "shell.execute_reply": "2025-04-25T10:07:00.096936Z" } }, "outputs": [ { "data": { "text/html": [ "
General Summary
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Trials Summary
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Step# Samples# FeaturesAlgorithmHyperparametersScore (neg_log_loss)All MetricsRuntime (Seconds)Memory Usage (GB)Finished
Model Selection{1: 4000, 2: 4000, 3: 4000, 5: 4000, 4: 4000}15XGBClassifier{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 100, 'use_label_encoder': False}-0.3813{'neg_log_loss': -0.38129734602336923}1.11770.3214Fri Apr 25 03:05:43 2025
Model Selection{2: 4000, 5: 4000, 1: 4000, 3: 4000, 4: 4000}15CatBoostClassifier{'iterations': 235, 'learning_rate': 0.787168, 'leaf_estimation_method': 'Newton', 'colsample_bylevel': 0.096865, 'depth': 3, 'l2_leaf_reg': 2.567326, 'feature_border_type': 'UniformAndQuantiles', 'model_size_reg': 3.85132, 'leaf_estimation_iterations': 1, 'boosting_type': 'Plain', 'bootstrap_type': 'MVS', 'auto_class_weights': 'SqrtBalanced', 'allow_writing_files': False, 'allow_const_label': True}-0.3881{'neg_log_loss': -0.38808832474759763}10.83420.3193Fri Apr 25 03:05:42 2025
Model Selection{3: 4000, 1: 4000, 5: 4000, 4: 4000, 2: 4000}15LGBMClassifier{'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': 'balanced'}-0.3898{'neg_log_loss': -0.38980519262954033}12.88140.3083Fri Apr 25 03:05:43 2025
Model Selection{1: 4000, 5: 4000, 3: 4000, 4: 4000, 2: 4000}15LogisticRegressionClassifier{'C': 1.0, 'solver': 'liblinear', 'class_weight': 'balanced'}-0.3908{'neg_log_loss': -0.3907935186356912}0.68740.3279Fri Apr 25 03:05:43 2025
Model Selection{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000}15RandomForestClassifier{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced'}-0.4093{'neg_log_loss': -0.40930045927988684}2.77730.2950Fri Apr 25 03:05:43 2025
Model Selection{2: 4000, 1: 4000, 3: 4000, 4: 4000, 5: 4000}15ExtraTreesClassifier{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced', 'criterion': 'gini'}-0.4196{'neg_log_loss': -0.4196315514455088}3.13100.2772Fri Apr 25 03:05:41 2025
Model Selection{3: 4000, 5: 4000, 4: 4000, 2: 4000, 1: 4000}15TorchMLPClassifier{'optimizer_class': 'Adam', 'shuffle_dataset_each_epoch': True, 'optimizer_params': {}, 'criterion_class': None, 'criterion_params': {}, 'scheduler_class': None, 'scheduler_params': {}, 'batch_size': 128, 'lr': 0.001, 'epochs': 18, 'input_transform': 'auto', 'tensorboard_dir': None, 'use_tqdm': None, 'prediction_batch_size': 128, 'prediction_input_transform': 'auto', 'shuffling_buffer_size': None, 'depth': 4, 'num_logits': 1000, 'div_factor': 2, 'activation': 'ReLU', 'dropout': 0.1}-0.8294{'neg_log_loss': -0.8293741212177753}49.11000.6041Fri Apr 25 03:05:52 2025
Model Selection{1: 4000, 2: 4000, 3: 4000, 4: 4000, 5: 4000}15GaussianNB{}-1.0622{'neg_log_loss': -1.0622002049203993}0.39640.2871Fri Apr 25 03:05:41 2025
Model Selection{4: 4000, 3: 4000, 1: 4000, 2: 4000, 5: 4000}15DecisionTreeClassifier{'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}-4.6323{'neg_log_loss': -4.632288461805368}1.35650.2737Fri Apr 25 03:05:41 2025
Adaptive Sampling{1: 15493, 2: 15493, 3: 15493, 4: 15493, 5: 15494}15AdaptiveSamplingStage_XGBClassifier{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 100, 'use_label_encoder': False}-0.349{'neg_log_loss': -0.3490238812123456}3.36920.6036Fri Apr 25 03:05:54 2025
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Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.0007113117640155693, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911964137047958}1.21470.6153Fri Apr 25 03:06:19 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.0007513117640155693, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911964137047958}1.23640.6092Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01778279410038923, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911964480070908}1.18560.6153Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0.01878279410038923, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911964560751593}1.18090.6098Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.2249365300761397, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911968446197632}1.22410.6153Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0.2249765300761397, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911968446197632}1.16580.6098Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911999162246485}1.24650.6134Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1.001, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6911999174202924}1.21020.6153Fri Apr 25 03:06:20 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 5.623413251903491, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.6912154271501312}1.11380.6098Fri Apr 25 03:06:21 2025
Model Tuning{1: 12222, 2: 12222, 3: 12222, 4: 12223, 5: 12223}15XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 0, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 5.6244132519034915, 'n_estimators': 50, 'use_label_encoder': False}-0.6912{'neg_log_loss': -0.691215428004673}1.21200.6153Fri Apr 25 03:06:21 2025
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "est1.print_summary()" ] }, { "cell_type": "markdown", "id": "8c69a9b2", "metadata": {}, "source": [ "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline." ] }, { "cell_type": "markdown", "id": "ea101c26", "metadata": {}, "source": [ "\n", "#### Algorithm Selection\n", "\n", "The plot below shows the scores predicted by Algorithm Selection for each algorithm. The horizontal line shows the average score across all algorithms. Algorithms below the line are colored turquoise, whereas those with a score higher than the mean are colored teal." ] }, { "cell_type": "code", "execution_count": 13, "id": "1080e2dd", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:00.099566Z", "iopub.status.busy": "2025-04-25T10:07:00.099068Z", "iopub.status.idle": "2025-04-25T10:07:00.406866Z", "shell.execute_reply": "2025-04-25T10:07:00.406253Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Each trial is a row in a dataframe that contains\n", "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n", "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Model Selection')]\n", "name_of_score_column = f\"Score ({est1._inferred_score_metric[0].name})\"\n", "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n", "trials.dropna(subset=[name_of_score_column], inplace = True)\n", "scores = trials[name_of_score_column].tolist()\n", "models = trials['Algorithm'].tolist()\n", "colors = []\n", "\n", "y_margin = 0.10 * (max(scores) - min(scores))\n", "s = pd.Series(scores, index=models).sort_values(ascending=False)\n", "s = s.dropna()\n", "for f in s.keys():\n", " if f.strip() == est1.selected_model_.strip():\n", " colors.append('orange')\n", " elif s[f] >= s.mean():\n", " colors.append('teal')\n", " else:\n", " colors.append('turquoise')\n", "\n", "fig, ax = plt.subplots(1)\n", "ax.set_title(\"Algorithm Selection Trials\")\n", "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n", "ax.set_ylabel(est1._inferred_score_metric[0].name)\n", "s.plot.bar(ax=ax, color=colors, edgecolor='black')\n", "ax.axhline(y=s.mean(), color='black', linewidth=0.5)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c8856dfb", "metadata": {}, "source": [ "\n", "#### Adaptive Sampling\n", "\n", "Following Algorithm Selection, Adaptive Sampling aims to find the smallest dataset sample that can be created without compromising validation set score for the chosen model. This is used to speed up feature selection and tuning; however, the full dataset is still used to train the final model." ] }, { "cell_type": "code", "execution_count": 14, "id": "fd72b252", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:00.408708Z", "iopub.status.busy": "2025-04-25T10:07:00.408363Z", "iopub.status.idle": "2025-04-25T10:07:00.596906Z", "shell.execute_reply": "2025-04-25T10:07:00.596275Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Each trial is a row in a dataframe that contains\n", "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n", "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Adaptive Sampling')]\n", "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n", "trials.dropna(subset=[name_of_score_column], inplace = True)\n", "scores = trials[name_of_score_column].tolist()\n", "n_samples = [int(sum(d.values()) / len(d)) if isinstance(d, dict) else d for d in trials['# Samples']]\n", "\n", "\n", "y_margin = 0.10 * (max(scores) - min(scores))\n", "fig, ax = plt.subplots(1)\n", "ax.set_title(\"Adaptive Sampling ({})\".format(est1.selected_model_))\n", "ax.set_xlabel('Dataset sample size')\n", "ax.set_ylabel(est1._inferred_score_metric[0].name)\n", "ax.grid(color='g', linestyle='-', linewidth=0.1)\n", "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n", "ax.plot(n_samples, scores, 'k:', marker=\"s\", color='teal', markersize=3)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c4382074", "metadata": {}, "source": [ "\n", "#### Feature Selection\n", "\n", "After finding a sample subset, the next step is to find a relevant feature subset to maximize score for the chosen algorithm. AutoMLx Feature Selection follows an intelligent search strategy, looking at various possible feature rankings and subsets, and identifying that smallest feature subset that does not compromise on score for the chosen algorithm. The orange line shows the optimal number of features chosen by Feature Selection." ] }, { "cell_type": "code", "execution_count": 15, "id": "0cbce094", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:00.598809Z", "iopub.status.busy": "2025-04-25T10:07:00.598424Z", "iopub.status.idle": "2025-04-25T10:07:00.785104Z", "shell.execute_reply": "2025-04-25T10:07:00.784524Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Features selected: ['age', 'capitalgain', 'capitalloss', 'education', 'education-num', 'fnlwgt', 'hoursperweek', 'marital-status', 'native-country', 'occupation', 'race', 'relationship', 'sex_1', 'sex_2', 'workclass']\n", "Features dropped: []\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(f\"Features selected: {est1.selected_features_names_}\")\n", "dropped_features = df.drop(est1.selected_features_names_raw_, axis=1).columns\n", "print(f\"Features dropped: {dropped_features.to_list()}\")\n", "\n", "# Each trial is a row in a dataframe that contains\n", "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n", "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Feature Selection')]\n", "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n", "trials.dropna(subset=[name_of_score_column], inplace = True)\n", "trials.sort_values(by=['# Features'],ascending=True, inplace = True)\n", "scores = trials[name_of_score_column].tolist()\n", "n_features = trials['# Features'].tolist()\n", "\n", "y_margin = 0.10 * (max(scores) - min(scores))\n", "fig, ax = plt.subplots(1)\n", "ax.set_title(\"Feature Selection Trials\")\n", "ax.set_xlabel(\"Number of Features\")\n", "ax.set_ylabel(est1._inferred_score_metric[0].name)\n", "ax.grid(color='g', linestyle='-', linewidth=0.1)\n", "ax.set_ylim(min(scores) - y_margin, max(scores) + y_margin)\n", "ax.plot(n_features, scores, 'k:', marker=\"s\", color='teal', markersize=3)\n", "ax.axvline(x=len(est1.selected_features_names_), color='orange', linewidth=2.0)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "bafd5362", "metadata": {}, "source": [ "\n", "#### Hyperparameter Tuning\n", "\n", "Hyperparameter Tuning is the last stage of the AutoMLx pipeline, and focuses on improving the chosen algorithm's score on the reduced dataset (after Adaptive Sampling and Feature Selection). We use a novel algorithm to search across many hyperparameters dimensions, and converge automatically when optimal hyperparameters are identified. Each trial represents a particular hyperparameters configuration for the selected model." ] }, { "cell_type": "code", "execution_count": 16, "id": "d24a6513", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:00.787034Z", "iopub.status.busy": "2025-04-25T10:07:00.786644Z", "iopub.status.idle": "2025-04-25T10:07:01.029328Z", "shell.execute_reply": "2025-04-25T10:07:01.028725Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Each trial is a row in a dataframe that contains\n", "# Algorithm, Number of Samples, Number of Features, Hyperparameters, Score, Runtime, Memory Usage, Step as features\n", "trials = est1.completed_trials_summary_[est1.completed_trials_summary_[\"Step\"].str.contains('Model Tuning')]\n", "trials.replace([np.inf, -np.inf], np.nan, inplace=True)\n", "trials.dropna(subset=[name_of_score_column], inplace = True)\n", "trials.drop(trials[trials['Finished'] == -1].index, inplace = True)\n", "trials['Finished']= trials['Finished'].apply(lambda x: time.mktime(datetime.datetime.strptime(x,\n", " \"%a %b %d %H:%M:%S %Y\").timetuple()))\n", "trials.sort_values(by=['Finished'],ascending=True, inplace = True)\n", "scores = trials[name_of_score_column].tolist()\n", "score = []\n", "score.append(scores[0])\n", "for i in range(1,len(scores)):\n", " if scores[i]>= score[i-1]:\n", " score.append(scores[i])\n", " else:\n", " score.append(score[i-1])\n", "y_margin = 0.10 * (max(score) - min(score))\n", "\n", "fig, ax = plt.subplots(1)\n", "ax.set_title(\"Hyperparameter Tuning Trials\")\n", "ax.set_xlabel(\"Iteration $n$\")\n", "ax.set_ylabel(est1._inferred_score_metric[0].name)\n", "ax.grid(color='g', linestyle='-', linewidth=0.1)\n", "ax.set_ylim(min(score) - y_margin, max(score) + y_margin)\n", "ax.plot(range(1, len(trials) + 1), score, 'k:', marker=\"s\", color='teal', markersize=3)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "af9781b2", "metadata": {}, "source": [ "\n", "#### Confusion Matrix\n", "\n", "We can use a Confusion Matrix to help us visualize the model's behavior. Note that the displayed confusion matrix represents percentages." ] }, { "cell_type": "code", "execution_count": 17, "id": "ee9da09a", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:01.031355Z", "iopub.status.busy": "2025-04-25T10:07:01.030867Z", "iopub.status.idle": "2025-04-25T10:07:01.229887Z", "shell.execute_reply": "2025-04-25T10:07:01.229277Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorscale": [ [ 0.0, "#440154" ], [ 0.1111111111111111, "#482878" ], [ 0.2222222222222222, "#3e4989" ], [ 0.3333333333333333, "#31688e" ], [ 0.4444444444444444, "#26828e" ], [ 0.5555555555555556, "#1f9e89" ], [ 0.6666666666666666, "#35b779" ], [ 0.7777777777777778, "#6ece58" ], [ 0.8888888888888888, "#b5de2b" ], [ 1.0, "#fde725" ] ], "reversescale": false, "showscale": false, "type": "heatmap", "x": [ "<=50K", ">50K" ], "y": [ "<=50K", ">50K" ], "z": [ [ 0.9329464285714286, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred = est1.predict(X_test)\n", "cm = confusion_matrix(y_test.astype(int), y_pred, labels=[False, True])\n", "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "\n", "text = [[f\"{y*100:.2f}\" for y in x] for x in cm]\n", "fig = ff.create_annotated_heatmap(cm, x=['<=50K', '>50K'], y=['<=50K', '>50K'], annotation_text=text, colorscale='Viridis')\n", "fig.add_annotation(dict(font=dict(color=\"black\",size=14),\n", " x=0.5,\n", " y=-0.15,\n", " showarrow=False,\n", " text=\"Predicted value\",\n", " xref=\"paper\",\n", " yref=\"paper\"))\n", "\n", "fig.add_annotation(dict(font=dict(color=\"black\",size=14),\n", " x=-0.15,\n", " y=0.5,\n", " showarrow=False,\n", " text=\"Actual\",\n", " textangle=-90,\n", " xref=\"paper\",\n", " yref=\"paper\"))\n", "fig.update_layout(margin=dict(t=50, l=150))\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "c3596896", "metadata": {}, "source": [ "\n", "### Advanced AutoMLx Configuration\n", "\n", "You can also configure the pipeline with suitable parameters according to your needs." ] }, { "cell_type": "code", "execution_count": 18, "id": "cfcca7f7", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:01.231676Z", "iopub.status.busy": "2025-04-25T10:07:01.231423Z", "iopub.status.idle": "2025-04-25T10:07:01.288638Z", "shell.execute_reply": "2025-04-25T10:07:01.288056Z" } }, "outputs": [], "source": [ "custom_pipeline = automlx.Pipeline(\n", " task='classification',\n", " model_list=[ # Specify the models you want the AutoMLx to consider\n", " 'LogisticRegression',\n", " 'LGBMClassifier',\n", " 'GaussianNB'\n", " ],\n", " n_algos_tuned=2, # Choose how many models to tune\n", " min_features=[ # Specify minimum features to force the model to use. It can take 3 possible types of values:\n", " 'native-country', # If int, 0 < min_features <= n_features,\n", " 'marital-status', # If float, 0 < min_features <= 1.0, 1.0 means disabling feature selection\n", " 'education-num' # If list, names of features to keep, for example ['a', 'b'] means keep features 'a' and 'b'\n", " ],\n", " adaptive_sampling=False, # Disable or enable Adaptive Sampling step. Default to `True`\n", " preprocessing=True, # Disable or enable Preprocessing step. Default to `True`\n", " search_space={ # You can specify the hyper-parameters and ranges we search\n", " 'LGBMClassifier': {\n", " 'learning_rate': {'range': [0.01, 10], 'type': 'continuous'},\n", " 'boosting_type': {'range': ['gbdt', 'dart'], 'type': 'categorical'},\n", " },\n", " },\n", " max_tuning_trials=2, # The maximum number of tuning trials. Can be integer or Dict (max number for each model)\n", " score_metric='f1_macro', # Any scikit-learn metric or a custom function\n", ")" ] }, { "cell_type": "markdown", "id": "51d63967", "metadata": {}, "source": [ "\n", "### Use a custom validation set\n", "You can specify a custom validation set that you want AutoMLx to use to evaluate the quality of models and configurations.\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "fff902f2", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:01.290633Z", "iopub.status.busy": "2025-04-25T10:07:01.290261Z", "iopub.status.idle": "2025-04-25T10:07:01.348892Z", "shell.execute_reply": "2025-04-25T10:07:01.348307Z" } }, "outputs": [], "source": [ "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, train_size=0.7, random_state=0)" ] }, { "cell_type": "markdown", "id": "889f0970", "metadata": {}, "source": [ "A few of the advanced settings can be passed directly to the pipeline's fit method, instead of its constructor." ] }, { "cell_type": "code", "execution_count": 20, "id": "72e12a1c", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:01.350639Z", "iopub.status.busy": "2025-04-25T10:07:01.350324Z", "iopub.status.idle": "2025-04-25T10:07:19.276060Z", "shell.execute_reply": "2025-04-25T10:07:19.275368Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:01,457] [automlx.interface] Dataset shape: (34189,14)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:01,518] [automlx.interface] Adaptive Sampling disabled.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:01,563] [automlx.data_transform] Running preprocessing. Number of features: 15\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:02,094] [automlx.data_transform] Preprocessing completed. Took 0.531 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:02,127] [automlx.process] Running Model Generation\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:02,185] [automlx.process] Model Generation completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:02,217] [automlx.model_selection] Running Model Selection\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:03,661] [automlx.model_selection] Model Selection completed - Took 1.444 sec - Selected models: [['LGBMClassifier', 'LogisticRegressionClassifier']]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:03,746] [automlx.feature_selection] Starting feature ranking for LGBMClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:07,088] [automlx.feature_selection] Feature Selection completed. Took 3.342 secs.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:07,215] [automlx.trials] Running Model Tuning for ['LGBMClassifier']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:08,436] [automlx.trials] Best parameters for LGBMClassifier: {'num_leaves': 31, 'boosting_type': 'gbdt', 'learning_rate': 0.01, 'min_child_weight': 0.001, 'max_depth': -1, 'reg_alpha': 0, 'reg_lambda': 1, 'n_estimators': 100, 'class_weight': None}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:08,437] [automlx.trials] Model Tuning completed. Took: 1.222 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:08,521] [automlx.feature_selection] Starting feature ranking for LogisticRegressionClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:15,245] [automlx.feature_selection] Feature Selection completed. Took 6.724 secs.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:15,272] [automlx.trials] Running Model Tuning for ['LogisticRegressionClassifier']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:16,400] [automlx.trials] Best parameters for LogisticRegressionClassifier: {'C': 0.0363696875, 'solver': 'liblinear', 'class_weight': 'balanced'}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:16,401] [automlx.trials] Model Tuning completed. Took: 1.130 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:16,754] [automlx.interface] Re-fitting pipeline\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:16,780] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_33c32b58-6\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-04-25 03:07:18,836] [automlx.interface] AutoMLx completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ROC AUC Score on test data : 0.901457699826238\n" ] } ], "source": [ "custom_pipeline.fit(\n", " X_train,\n", " y_train,\n", " X_val,\n", " y_val,\n", " time_budget= 20, # Specify time budget in seconds\n", " cv='auto' # Automatically pick a good cross-validation (cv) strategy for the user's dataset.\n", " # Ignored if X_valid and y_valid are provided.\n", " # Can also be:\n", " # - An integer (For example, to use 5-fold cross validation)\n", " # - A list of data indices to use as splits (for advanced, such as time-based splitting)\n", ")\n", "y_proba = custom_pipeline.predict_proba(X_test)\n", "score_modellist = roc_auc_score(y_test, y_proba[:, 1])\n", "\n", "print(f'ROC AUC Score on test data : {score_modellist}')" ] }, { "cell_type": "markdown", "id": "1e61e464", "metadata": {}, "source": [ "\n", "## Machine Learning Explainability" ] }, { "cell_type": "markdown", "id": "9c58d5b6", "metadata": {}, "source": [ "For a variety of decision-making tasks, getting only a prediction as model output is not sufficient. A user may wish to know why the model outputs that prediction, or which data features are relevant for that prediction. For that purpose the Oracle AutoMLx solution defines the `MLExplainer` object, which allows to compute a variety of model explanations\n", "\n", "\n", "### Initializing an MLExplainer\n", "\n", "The `MLExplainer` object takes as argument the trained model, the training data and labels, as well as the task." ] }, { "cell_type": "code", "execution_count": 21, "id": "84d374f7", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:19.278475Z", "iopub.status.busy": "2025-04-25T10:07:19.278079Z", "iopub.status.idle": "2025-04-25T10:07:19.372035Z", "shell.execute_reply": "2025-04-25T10:07:19.371420Z" } }, "outputs": [], "source": [ "explainer = automlx.MLExplainer(est1,\n", " X_train,\n", " y_train)" ] }, { "cell_type": "markdown", "id": "48148767", "metadata": {}, "source": [ "\n", "### Model Explanations (Global Feature importance)" ] }, { "cell_type": "markdown", "id": "4d3979d7", "metadata": {}, "source": [ "The notion of Global Feature Importance intuitively measures how much the model's performance (relative to the provided train labels) would change if a given feature were dropped from the dataset, without retraining the model. This notion of feature importance considers each feature independently from all other features." ] }, { "cell_type": "markdown", "id": "e447554c", "metadata": {}, "source": [ "#### Computing the importance" ] }, { "cell_type": "markdown", "id": "1677784a", "metadata": {}, "source": [ "By default we use a permutation method to successively measure the importance of each feature. Such a method therefore runs in linear time with respect to the\n", "number of features in the dataset.\n", "\n", "The method `explain_model()` allows to compute such feature importances. It also provides 95% confidence intervals for each feature importance attribution." ] }, { "cell_type": "code", "execution_count": 22, "id": "91136fe4", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:19.374180Z", "iopub.status.busy": "2025-04-25T10:07:19.373988Z", "iopub.status.idle": "2025-04-25T10:07:27.741076Z", "shell.execute_reply": "2025-04-25T10:07:27.740477Z" } }, "outputs": [], "source": [ "result_explain_model_default = explainer.explain_model(\n", " n_iter=5, # Can also be 'auto' to pick a good value for the explainer and task\n", "\n", " scoring_metric='balanced_accuracy', # Global feature importance measures how much each feature improved the\n", " # model's score. Users can chose the scoring metric used here.\n", ")" ] }, { "cell_type": "markdown", "id": "25083d11", "metadata": {}, "source": [ "#### Visualization" ] }, { "cell_type": "markdown", "id": "074bc7e3", "metadata": {}, "source": [ "There are two options to show the explanation's results:\n", "- `to_dataframe()` will return a dataframe of the results.\n", "- `show_in_notebook()` will show the results as a bar plot.\n", "\n", "The features are returned in decreasing order of importance. We see that `marital-status` and `education-num` are considered to be the most important features." ] }, { "cell_type": "code", "execution_count": 23, "id": "675936c8", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:27.743567Z", "iopub.status.busy": "2025-04-25T10:07:27.743113Z", "iopub.status.idle": "2025-04-25T10:07:27.823293Z", "shell.execute_reply": "2025-04-25T10:07:27.822782Z" } }, "outputs": [ { "data": { "text/html": [ "
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FeatureAttributionLower BoundUpper Bound
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_model_default.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "be7b7623", "metadata": {}, "source": [ "\n", "### Feature Dependence Explanations (PDP + ICE)\n", "\n", "Another way to measure dependency on a feature is through a partial dependence plot (PDP) or an individual conditional expectation (ICE) plot. For accumulated local effects (ALE) explanations, see Advanced Feature Dependence Options (ALE)\n", "\n", "Given a dataset, a PDP displays the average output of the model as a function of the value of the selected set of features (Up to 4 features).\n", "\n", "It can be computed for a single feature, as in the cell below. The X-axis is the value of the `education-num` feature and the y-axis is the corresponding outputted price. Since we are considering the whole dataset, the average over outputs is given by the red line, while the shaded interval corresponds to a 95% confidence interval for the average.\n", "\n", "The histogram on top shows the distribution of the value of the `education-num` feature in the dataset." ] }, { "cell_type": "code", "execution_count": 25, "id": "104cc68a", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:28.015762Z", "iopub.status.busy": "2025-04-25T10:07:28.015329Z", "iopub.status.idle": "2025-04-25T10:07:30.743922Z", "shell.execute_reply": "2025-04-25T10:07:30.743279Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "fill": "toself", "fillcolor": "rgb(248,0,0)", "legendgroup": "None", "line": { "color": "rgba(0,0,0,0)" }, "name": "confidence interval", "opacity": 0.3, "showlegend": false, "type": "scatter", "x": [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 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{ "text": "Partial Dependence Plot (PDP)", "x": 0.5 }, "width": 850, "xaxis": { "anchor": "y", "categoryorder": "category ascending", "domain": [ 0.07058823529411765, 0.98 ], "gridcolor": "#ECECEC", "linecolor": "LightGrey", "linewidth": 1, "matches": "x2", "mirror": true, "scaleanchor": "x2", "showline": true, "showticklabels": false, "side": "bottom", "visible": true, "zeroline": true, "zerolinecolor": "DarkGrey", "zerolinewidth": 1 }, "xaxis2": { "anchor": "y2", "categoryorder": "category ascending", "domain": [ 0.07058823529411765, 0.98 ], "gridcolor": "LightGrey", "linecolor": "Grey", "linewidth": 1, "matches": "x2", "mirror": true, "range": [ 0.25, 16.75 ], "scaleanchor": "x2", "showline": true, "showticklabels": true, "side": "bottom", "title": { "text": "education-num" }, "visible": true, "zeroline": true, "zerolinecolor": "DarkGrey", "zerolinewidth": 1 }, "yaxis": { "anchor": "x", "categoryorder": "category ascending", "domain": [ 0.8794871794871795, 1.0 ], "gridcolor": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_feature_dependence_default = explainer.explain_feature_dependence('education-num')\n", "result_explain_feature_dependence_default.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "669bfbd4", "metadata": {}, "source": [ "The ICE plot is automatically computed at the same time as any one-feature PDP. It can be accessed by passing `ice=True` to `show_in_notebook`.\n", "\n", "Similar to PDPs, ICE plots show the median prediction as a model red line. However, the variance in the model's predictions are shown by plotting the predictions of a sample of individual data instances as light grey lines. 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_feature_dependence_default.show_in_notebook(ice=True)" ] }, { "cell_type": "markdown", "id": "cecc0689", "metadata": {}, "source": [ "PDPs can be computed for an arbitrary number of variables; however, they can only be visualized with up to 4. 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_feature_dependence_default = explainer.explain_feature_dependence(['education-num', 'hoursperweek', 'sex'])\n", "result_explain_feature_dependence_default.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "9dfbfa80", "metadata": {}, "source": [ "\n", "### Prediction Explanations (Local Feature Importance)" ] }, { "cell_type": "markdown", "id": "692e51fe", "metadata": {}, "source": [ "Given a data sample, one can also obtain the local importance, which is the importance of the features for the model's prediction on that sample.\n", "In the following cell, we consider sample $10$. The function `explain_prediction()` computes the local importance for a given sample.\n", "\n", "`education-num=9.0` means that the value of feature `education-num` for that sample is `9.0`. Removing that feature would change the model's prediction by the magnitude of the bar. That is, in this case, the model's prediction for the probability that the person makes less than 50K is approximately 0.05-0.10 larger because the model knows the value of `education-num` is `9.0`." ] }, { "cell_type": "code", "execution_count": 28, "id": "ef7478d8", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:35.870296Z", "iopub.status.busy": "2025-04-25T10:07:35.869699Z", "iopub.status.idle": "2025-04-25T10:07:38.862708Z", "shell.execute_reply": "2025-04-25T10:07:38.862069Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "error_x": { "array": [ 0.0005147749839100527, 0.0027937385806290627 ], "arrayminus": [ 0.0005147749839100527, 0.0027937385806290627 ], "type": "data" }, "hovertemplate": "(%{x:.4g}, %{y})", "legendgroup": "negative", "marker": { "color": "#626567" }, "name": "95592=negative", "orientation": "h", "showlegend": false, "text": "", "textposition": "outside", 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{ "text": "10 Sample Predictions to Explain", "x": 0.5 }, "width": 1080, "xaxis": { "anchor": "y", "domain": [ 0.0, 0.45 ] }, "xaxis2": { "anchor": "y2", "domain": [ 0.55, 1.0 ] }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0.0, 1.0 ] } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f7da2ea6c555430e99e0514ed98f9f86", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='

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'), Output()), layout=Layout(align_items…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alfi = explainer.aggregate(explanations=result_explain_prediction_default)\n", "alfi.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "04983f7f", "metadata": {}, "source": [ "\n", "## Interactive What-If Explainers" ] }, { "cell_type": "markdown", "id": "28359a4d", "metadata": {}, "source": [ "The Oracle AutoMLx solution offers also What-IF tool to explain a trained ML model's predictions through a simple interactive interface.\n", "\n", "You can use What-IF explainer to explore and visualize immediately how changing a sample value will affect the model's prediction. Furthermore, What-IF can be used to visualize how model's predictions are related to any feature of the dataset." ] }, { "cell_type": "code", "execution_count": 30, "id": "7dc9c4c8", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:39.130512Z", "iopub.status.busy": "2025-04-25T10:07:39.130036Z", "iopub.status.idle": "2025-04-25T10:07:39.627887Z", "shell.execute_reply": "2025-04-25T10:07:39.627265Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d0c214ad439345b4886ccc84523e0836", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9e0dbc692430451981c02f1ca50381ce", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(VBox(children=(HTML(value='\\n

Select and Explore Samp…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer.explore_whatif(X_test, y_test)" ] }, { "cell_type": "markdown", "id": "28cd75be", "metadata": {}, "source": [ "\n", "## Counterfactual Explanations" ] }, { "cell_type": "markdown", "id": "a2dfd497", "metadata": {}, "source": [ "Counterfactual explainers are another set of advanced features that Oracle AutoMLx supports, which help to explain a trained ML model's predictions by identifying the minimal set of changes necessary to flip the model's decision, resulting in a different outcome. To achieve this, the solution frames the explanation process as an optimization problem, similar to adversarial discoveries, while ensuring that the counterfactual perturbations used are feasible and diverse.\n", "\n", "With the Oracle AutoMLx solution, users are guaranteed a close to zero-failure rate in generating a set of counterfactual explanations; the explainers might only fail if the reference training set doesn't contain any example with the desired class.\n", "AutoMLx also provides support for simple constraints on features, using `features_to_fix` and `permitted_range`, to ensure the feasibility of the generated counterfactual examples. Additionally, users can use tunable parameters to control the proximity and diversity of the explanations to the original input.\n", "\n", "The Oracle AutoMLx solution supports the following strategy for creating counterfactual examples.\n", "\n", "- `ace`: The AutoMLx counterfactual explainer introduced by Oracle Labs that uses KDTree structures to find a set of nearest but diverse counterfactuals per sample.\n", "\n", "The final results can be returned either through the interactive interface of `What-IF` tools to show the model's prediction sensitivity or static tables and figures." ] }, { "cell_type": "code", "execution_count": 31, "id": "4df9a903", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:39.635594Z", "iopub.status.busy": "2025-04-25T10:07:39.634883Z", "iopub.status.idle": "2025-04-25T10:07:41.537461Z", "shell.execute_reply": "2025-04-25T10:07:41.536833Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d29d851fb1f04401b88f798de032d1aa", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(VBox(children=(HTML(value='\\n

Select and Explore Samp…" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "explainer.configure_explain_counterfactual(strategy='ace')\n", "explanations = explainer.explain_counterfactual(X_test[0:1],\n", " n_counterfactuals=3,\n", " desired_pred='auto',\n", " features_to_fix=['age'])\n", "explanations[0].show_in_notebook()" ] }, { "cell_type": "markdown", "id": "dbc3fcc0", "metadata": {}, "source": [ "\n", "### Advanced Feature Importance Options\n", "\n", "We now show how to use an alternative method for computing feature dependence. Here, we will explain a custom `scikit-learn` model. Note that the MLExplainer object is capable to explain any classification model, as long as the model follows a scikit-learn-style interface with the `predict` and `predict_proba` functions.\n", "\n", "We then create the explainer object." ] }, { "cell_type": "code", "execution_count": 32, "id": "f984c418", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:41.554664Z", "iopub.status.busy": "2025-04-25T10:07:41.554045Z", "iopub.status.idle": "2025-04-25T10:07:41.834030Z", "shell.execute_reply": "2025-04-25T10:07:41.833332Z" } }, "outputs": [], "source": [ "numeric_transformer = Pipeline(\n", " steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n", ")\n", "\n", "categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n", "\n", "preprocessor = ColumnTransformer(\n", " transformers=[\n", " (\"num\", numeric_transformer, selector(dtype_exclude=[object, 'category'])),\n", " (\"cat\", categorical_transformer, selector(dtype_include=[object, 'category'])),\n", " ]\n", ")\n", "scikit_model = Pipeline(\n", " steps=[(\"preprocessor\", preprocessor), (\"classifier\", LogisticRegression())]\n", ")\n", "\n", "\n", "scikit_model.fit(X_train, y_train)\n", "\n", "explainer_sklearn = automlx.MLExplainer(\n", " scikit_model,\n", " X_train,\n", " y_train,\n", " target_names=[ # Used for plot labels/legends.\n", " \"<=50K\",\n", " \">50K\"\n", " ],\n", " selected_features='auto', # These features are used by the model; automatically inferred for AutoML Pipelines,\n", " task=\"classification\",\n", " col_types=None # Specify type of features\n", " )" ] }, { "cell_type": "markdown", "id": "5fd1d7f2", "metadata": {}, "source": [ "\n", "### Configure prediction explanation\n", "\n", "#### Include the effects of feature interactions (with Shapley feature importance)\n", "\n", "The Oracle AutoMLx solution allows one to change the effect of feature interactions. This can be done through the `tabulator_type` argument of both global and local importance methods.\n", "\n", "`tabulator_type` can be set to one of the following options:\n", "\n", "- `permutation`: This value is the default method in the MLExplainer object, with the behaviour described above\n", "\n", "- `shapley`: Feature importance is computed using the popular game-theoretic Shapley value method. Technically, this measures the importance of each feature while including the effect of all feature interactions. As a result, it runs in exponential time with respect to the number of features in the dataset. This method also includes the interaction effects of the other features, which means that if two features contain duplicate information, they will be less important. Note that the interpretation of this method's result is a bit different from the permutation method's result. An interested reader may find this a good source for learning more about it.\n", "\n", "- `kernel_shap`: Feature importance attributions will be calculated using an approximation of the Shapley value method. It typically provides relatively high-quality approximations; however, it currently does not provide confidence intervals.\n", "\n", "- `shap_pi`: Feature importance attributions will be computed using an approximation of the Shapley value method. It runs in linear time, but may miss the effect of interactions between some features, which may therefore produce lower-quality results. Most likely, you will notice that this method yields larger confidence intervals than the other two.\n", "\n", "**Summary: `permutation` can miss important features for AD. Exact SHAP (`shapley`) doesn't, but it is exponential. `kernel_shap` is an approximation of exact SHAP method that does not provide confidence intervals. `shap_pi` is linear, thus faster than exact SHAP and kernel_shap but unstable and very random leads to lower quality approximations.**" ] }, { "cell_type": "markdown", "id": "16ee6d35", "metadata": {}, "source": [ "\n", "##### Local feature importance with kernel_shap\n", "\n", "In this example, we also enable sampling within the explainer to speed up the running time, because kernel SHAP is slower than permutation feature importance." ] }, { "cell_type": "code", "execution_count": 33, "id": "79e15d04", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:41.836672Z", "iopub.status.busy": "2025-04-25T10:07:41.836108Z", "iopub.status.idle": "2025-04-25T10:07:41.899902Z", "shell.execute_reply": "2025-04-25T10:07:41.899309Z" } }, "outputs": [], "source": [ "explainer_sklearn.configure_explain_prediction(tabulator_type=\"kernel_shap\",\n", " sampling={'technique': 'random', 'n_samples': 2000}\n", " )" ] }, { "cell_type": "markdown", "id": "8e4ccd7e", "metadata": {}, "source": [ "#### Local feature importance using surrogate models (LIME+)\n", "\n", "The Oracle AutoMLx solution allows one to change the type of local explainer effect of feature interactions. This can be done through the `explainer_type` argument of local importance methods.\n", "\n", "`explainer_type` can be set to one of the following options:\n", "\n", "- `perturbation`: This value is the default explainer type in local feature importance. As we showed above, the explanation(s) will be computed by perturbing the features of the indicated data instance(s) and measuring the impact on the model predictions.\n", "\n", "- `surrogate`: The LIME-style explanation(s) will be computed by fitting a surrogate model to the predictions of the original model in a small region around the indicated data instance(s) and measuring the importance of the features to the interpretable surrogate model. The method of surrogate explainer can be set to one of the following options:\n", " - `systematic`: An Oracle-labs-improved version of LIME that uses a systematic sampling and custom sample weighting. (Default)\n", " - `lime`: Local interpretable model-agnostic explanations (LIME) algorithm (https://arxiv.org/pdf/1602.04938)." ] }, { "cell_type": "code", "execution_count": 34, "id": "56e5e436", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:41.902157Z", "iopub.status.busy": "2025-04-25T10:07:41.901622Z", "iopub.status.idle": "2025-04-25T10:07:42.193932Z", "shell.execute_reply": "2025-04-25T10:07:42.193131Z" } }, "outputs": [], "source": [ "explainer_sklearn.configure_explain_prediction(\n", " explainer_type='surrogate',\n", " method='lime'\n", " )" ] }, { "cell_type": "code", "execution_count": 35, "id": "a5f0d104", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:42.196738Z", "iopub.status.busy": "2025-04-25T10:07:42.196176Z", "iopub.status.idle": "2025-04-25T10:07:42.634814Z", "shell.execute_reply": "2025-04-25T10:07:42.634193Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "index = 0\n", "result_explain_prediction_kernel_shap = explainer_sklearn.explain_prediction(X_train.iloc[index:index+1,:])\n", "result_explain_prediction_kernel_shap[0].show_in_notebook()" ] }, { "cell_type": "markdown", "id": "4bfb5319", "metadata": {}, "source": [ "\n", "#### Explain the model or Explain the world\n", "\n", "Oracle AutoMLx solution also provides the `evaluator_type` attribute, which allows one to choose whether to get feature importance attributions that explain exactly which features the model has learned to use (`interventional`), or which features the model or a retrained model could have learned to use (`observational`).\n", "\n", "- `interventional` : The computed feature importances are as faithful to the\n", " model as possible. That is, features that are ignored by\n", " the model will not be considered important. This setting\n", " should be preferred if the primary goal is to learn about\n", " the machine learning model itself. Technically, this\n", " setting is called 'interventional', because the method will\n", " intervene on the data distribution when assessing the\n", " importance of features. The intuition of feature importance attributions computed with this method is that the features are dropped from the dataset and the model is not allowed to retrain.\n", "\n", "- `observational` : The computed feature importances are more faithful to\n", " the relationships that exist in the real world (i.e., relationships\n", " observed in the dataset), even if your specific model did not learn\n", " to use them. For example, when using a permutation tabulator, a feature\n", " that is used by the model will not show a large impact on the model's\n", " performance if there is a second feature that contains near-duplicate\n", " information, because a re-trained model could have learned to use the\n", " other feature instead. (Similarly, for Shapley-based tabulators, a\n", " feature that is ignored by the model may have a non-zero feature\n", " importance if it could have been used by the model to\n", " predict the target.) This setting should be preferred if the\n", " model is merely a means to learn more about the\n", " relationships that exist within the data. Technically, this\n", " setting is called 'observational', because it observes the\n", " relationships in the data without breaking the existing\n", " data distribution." ] }, { "cell_type": "markdown", "id": "397e7495", "metadata": {}, "source": [ "\n", "##### Explain the model with observational evaluator_type" ] }, { "cell_type": "code", "execution_count": 36, "id": "14b5f91c", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:42.637620Z", "iopub.status.busy": "2025-04-25T10:07:42.636960Z", "iopub.status.idle": "2025-04-25T10:07:42.696747Z", "shell.execute_reply": "2025-04-25T10:07:42.696137Z" } }, "outputs": [], "source": [ "explainer_sklearn.configure_explain_model(evaluator_type=\"observational\")" ] }, { "cell_type": "code", "execution_count": 37, "id": "a6455c7f", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:42.698924Z", "iopub.status.busy": "2025-04-25T10:07:42.698435Z", "iopub.status.idle": "2025-04-25T10:07:47.214667Z", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_model_observational = explainer_sklearn.explain_model()\n", "result_explain_model_observational.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "d70585b8", "metadata": {}, "source": [ "\n", "### Advanced Feature Dependence Options (ALE)\n", "\n", "We now show how to use an alternative method for computing feature dependence: accumulated local effects (ALE). ALE explanations are sometimes considered a better alternative to PDPs when features are correlated, because it does not evaluate the model outside of its training distribution in these cases. For more information, see https://christophm.github.io/interpretable-ml-book/ale.html.\n", "\n", "Given a dataset, an ALE displays the average change in the output of the model, accumulated of multiple small changes in one or two features, when all other features are held fixed. By default, the ALE explanations are centered around 0, and thus, unlike PDPs, ALEs show the change in the prediction measured by changing a given feature, rather than the average model's prediction for a particular feature value." ] }, { "cell_type": "markdown", "id": "70b297d7", "metadata": {}, "source": [ "The plot below is the ALE plot for the `education-num` and `sex` features. The X-axis shows the values of `education-num` however, there are now multiple lines, one for each value of the feature `sex`.\n", "\n", "The histogram displays the joint distribution of the two features." ] }, { "cell_type": "code", "execution_count": 38, "id": "3b1c672d", "metadata": { "execution": { "iopub.execute_input": "2025-04-25T10:07:47.216829Z", "iopub.status.busy": "2025-04-25T10:07:47.216382Z", "iopub.status.idle": "2025-04-25T10:07:50.311759Z", "shell.execute_reply": "2025-04-25T10:07:50.311203Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "fill": "toself", "fillcolor": "#4C78A8", "legendgroup": "Female", "line": { "color": "rgba(0,0,0,0)" }, "name": "confidence interval", "opacity": 0.3, "showlegend": true, "type": "scatter", "x": [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 16.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer_sklearn.configure_explain_feature_dependence(explanation_type='ale')\n", "result_explain_feature_dependence_default = explainer_sklearn.explain_feature_dependence(['education-num', 'sex'])\n", "result_explain_feature_dependence_default.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "7a611bb9", "metadata": {}, "source": [ "\n", "## References\n", "* Oracle AutoML http://www.vldb.org/pvldb/vol13/p3166-yakovlev.pdf\n", "* scikit-learn https://scikit-learn.org/stable/\n", "* Interpretable Machine Learning https://christophm.github.io/interpretable-ml-book/\n", "* LIME https://arxiv.org/pdf/1602.04938\n", "* UCI https://archive.ics.uci.edu/ml/datasets/Adult" ] } ], "metadata": { "jupytext": { "formats": "ipynb,md,py:percent" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, 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", 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Model Predictions

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Model Prediction Probabilities

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