{ "cells": [ { "cell_type": "markdown", "id": "f1104795", "metadata": {}, "source": [ "***\n", "# Building and Explaining a Classifier using AutoMLx\n", "

by the Oracle AutoMLx Team

\n", "\n", "***" ] }, { "cell_type": "markdown", "id": "43086674", "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": "d93cdc1d", "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": "35d7f621", "metadata": {}, "source": [ "\n", "## Setup\n", "\n", "Basic setup for the Notebook." ] }, { "cell_type": "code", "execution_count": 1, "id": "adfa3035", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:39.008334Z", "iopub.status.busy": "2025-05-22T12:02:39.008133Z", "iopub.status.idle": "2025-05-22T12:02:39.570041Z", "shell.execute_reply": "2025-05-22T12:02:39.569364Z" } }, "outputs": [], "source": [ "\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "id": "f630157b", "metadata": {}, "source": [ "Load the required modules." ] }, { "cell_type": "code", "execution_count": 2, "id": "2921eeaf", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:39.572469Z", "iopub.status.busy": "2025-05-22T12:02:39.571858Z", "iopub.status.idle": "2025-05-22T12:02:42.135111Z", "shell.execute_reply": "2025-05-22T12:02:42.134411Z" } }, "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": "fdf971f5", "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": "b9c8216c", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:42.137819Z", "iopub.status.busy": "2025-05-22T12:02:42.137008Z", "iopub.status.idle": "2025-05-22T12:02:43.042874Z", "shell.execute_reply": "2025-05-22T12:02:43.042168Z" } }, "outputs": [], "source": [ "dataset = fetch_openml(name='adult',version=1, as_frame=True)\n", "df, y = dataset.data, dataset.target" ] }, { "cell_type": "markdown", "id": "65edfd2f", "metadata": {}, "source": [ "Lets look at a few of the values in the data" ] }, { "cell_type": "code", "execution_count": 4, "id": "f2f8c029", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:43.045153Z", "iopub.status.busy": "2025-05-22T12:02:43.044587Z", "iopub.status.idle": "2025-05-22T12:02:43.220252Z", "shell.execute_reply": "2025-05-22T12:02:43.219513Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age workclass fnlwgt education education-num \\\n", "0 2 State-gov 77516.0 Bachelors 13.0 \n", "1 3 Self-emp-not-inc 83311.0 Bachelors 13.0 \n", "2 2 Private 215646.0 HS-grad 9.0 \n", "3 3 Private 234721.0 11th 7.0 \n", "4 1 Private 338409.0 Bachelors 13.0 \n", "\n", " marital-status occupation relationship race sex \\\n", "0 Never-married Adm-clerical Not-in-family White Male \n", "1 Married-civ-spouse Exec-managerial Husband White Male \n", "2 Divorced Handlers-cleaners Not-in-family White Male \n", "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", "4 Married-civ-spouse Prof-specialty Wife Black Female \n", "\n", " capitalgain capitalloss hoursperweek native-country \n", "0 1 0 2 United-States \n", "1 0 0 0 United-States \n", "2 0 0 2 United-States \n", "3 0 0 2 United-States \n", "4 0 0 2 Cuba " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "b65bdf76", "metadata": {}, "source": [ "The Adult dataset contains a mix of numerical and string data, making it a challenging problem to train machine learning models on." ] }, { "cell_type": "code", "execution_count": 5, "id": "ac2088e7", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:43.222537Z", "iopub.status.busy": "2025-05-22T12:02:43.221914Z", "iopub.status.idle": "2025-05-22T12:02:43.258994Z", "shell.execute_reply": "2025-05-22T12:02:43.258396Z" } }, "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": "0c7b1771", "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": "23df0203", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:44.065035Z", "iopub.status.busy": "2025-05-22T12:02:44.064492Z", "iopub.status.idle": "2025-05-22T12:02:44.160489Z", "shell.execute_reply": "2025-05-22T12:02:44.159858Z" } }, "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": "d420314e", "metadata": {}, "source": [ "\n", "## AutoML" ] }, { "cell_type": "markdown", "id": "00594577", "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": "e1471f54", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:44.162244Z", "iopub.status.busy": "2025-05-22T12:02:44.162036Z", "iopub.status.idle": "2025-05-22T12:02:49.506957Z", "shell.execute_reply": "2025-05-22T12:02:49.506177Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:44,429] [automlx.backend] Overwriting ray session directory to /tmp/eykbdcqr/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": "1b72e4b7", "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": "8a04b3de", "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": "ea20f8c6", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:02:49.509864Z", "iopub.status.busy": "2025-05-22T12:02:49.509514Z", "iopub.status.idle": "2025-05-22T12:06:03.286970Z", "shell.execute_reply": "2025-05-22T12:06:03.286214Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:49,631] [automlx.interface] Dataset shape: (34189,14)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:53,129] [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": [ "[2025-05-22 05:02:53,579] [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": [ "[2025-05-22 05:02:53,588] [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": [ "[2025-05-22 05:02:53,658] [automlx.data_transform] Running preprocessing. Number of features: 15\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:54,300] [automlx.data_transform] Preprocessing completed. Took 0.642 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:54,330] [automlx.process] Running Model Generation\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:54,382] [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": [ "[2025-05-22 05:02:54,383] [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": [ "[2025-05-22 05:02:54,384] [automlx.process] Model Generation completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:02:54,459] [automlx.model_selection] Running Model Selection\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:03:50,030] [automlx.model_selection] Model Selection completed - Took 55.571 sec - Selected models: [['XGBClassifier']]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:03:50,057] [automlx.adaptive_sampling] Running Adaptive Sampling. Dataset shape: (34189,16).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:03:57,500] [automlx.trials] Adaptive Sampling completed - Took 7.4434 sec.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:03:57,626] [automlx.feature_selection] Starting feature ranking for XGBClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:04:10,635] [automlx.feature_selection] Feature Selection completed. Took 13.050 secs.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:04:10,733] [automlx.trials] Running Model Tuning for ['XGBClassifier']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:05:53,001] [automlx.trials] Best parameters for XGBClassifier: {'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 275, 'use_label_encoder': False}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:05:53,001] [automlx.trials] Model Tuning completed. Took: 102.268 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:05:59,090] [automlx.interface] Re-fitting pipeline\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:05:59,105] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_765025b5-8\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:02,033] [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": "466956ff", "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": "88177aa2", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:03.289382Z", "iopub.status.busy": "2025-05-22T12:06:03.289087Z", "iopub.status.idle": "2025-05-22T12:06:04.765692Z", "shell.execute_reply": "2025-05-22T12:06:04.764968Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score on test data : 0.9138093686649291\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": "bd01d69a", "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": "ab6327a1", "metadata": {}, "source": [ "AutoMLx provides a `print_summary` API to output all the different trials performed." ] }, { "cell_type": "code", "execution_count": 12, "id": "8266330a", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:04.767816Z", "iopub.status.busy": "2025-05-22T12:06:04.767431Z", "iopub.status.idle": "2025-05-22T12:06:04.834922Z", "shell.execute_reply": "2025-05-22T12:06:04.834332Z" } }, "outputs": [ { "data": { "text/html": [ "
General Summary
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(34189, 14)
None
KFoldSplit(Shuffle=True, Seed=7, folds=5, stratify by=target)
neg_log_loss
XGBClassifier
{'learning_rate': 0.1, 'min_child_weight': 1, 'max_depth': 3, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 1, 'n_estimators': 275, 'use_label_encoder': False}
24.4.1
3.9.21 (main, Dec 11 2024, 16:24:11) \\n[GCC 11.2.0]
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Trials Summary
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Step# Samples# FeaturesAlgorithmHyperparametersScore (neg_log_loss)Runtime (Seconds)Memory Usage (GB)Finished
Model Selection2735215XGBClassifier{'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}-0.30865.29120.3548Thu May 22 05:03:08 2025
Model Selection2735115CatBoostClassifier{'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}-0.32374.87800.3289Thu May 22 05:03:03 2025
Model Selection2735215RandomForestClassifier{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced'}-0.369816.60200.3785Thu May 22 05:03:08 2025
Model Selection2735215ExtraTreesClassifier{'n_estimators': 100, 'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 0.777777778, 'class_weight': 'balanced', 'criterion': 'gini'}-0.376710.43250.3002Thu May 22 05:03:05 2025
Model Selection2735115LogisticRegressionClassifier{'C': 1.0, 'solver': 'liblinear', 'class_weight': 'balanced'}-0.39741.01570.3178Thu May 22 05:03:07 2025
Model Selection2735115LGBMClassifier{'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'}-0.54874.52490.3552Thu May 22 05:03:04 2025
Model Selection2735115DecisionTreeClassifier{'min_samples_split': 0.00125, 'min_samples_leaf': 0.000625, 'max_features': 1.0, 'class_weight': None}-0.74161.80000.2755Thu May 22 05:03:03 2025
Model Selection2735115TorchMLPClassifier{'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}-0.833218.79190.6396Thu May 22 05:03:49 2025
Model Selection2735215GaussianNB{}-0.94570.53670.3274Thu May 22 05:03:03 2025
Adaptive Sampling2735215AdaptiveSamplingStage_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}-0.30867.99140.5999Thu May 22 05:03:55 2025
...........................
Model Tuning2735115XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 16, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}-0.69063.71000.3506Thu May 22 05:04:33 2025
Model Tuning2735215XGBClassifier{'learning_rate': 0.0001, 'min_child_weight': 2, 'max_depth': 2, 'reg_alpha': 0, 'booster': 'gbtree', 'reg_lambda': 0, 'n_estimators': 50, 'use_label_encoder': False}-0.69063.67750.4014Thu May 22 05:04:32 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.90770.6185Thu May 22 05:04:37 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.70500.6047Thu May 22 05:04:37 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.76810.6185Thu May 22 05:04:36 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.84970.3820Thu May 22 05:04:36 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.71440.6162Thu May 22 05:04:37 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.57670.6125Thu May 22 05:04:37 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.59780.6483Thu May 22 05:04:37 2025
Model Tuning2735215XGBClassifier{'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}-0.69063.75120.6047Thu May 22 05:04:38 2025
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "est1.print_summary()" ] }, { "cell_type": "markdown", "id": "390e0c75", "metadata": {}, "source": [ "We also provide the capability to visualize the results of each stage of the AutoMLx pipeline." ] }, { "cell_type": "markdown", "id": "379da0b0", "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": "0a352eb7", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:04.836986Z", "iopub.status.busy": "2025-05-22T12:06:04.836601Z", "iopub.status.idle": "2025-05-22T12:06:05.117056Z", "shell.execute_reply": "2025-05-22T12:06:05.116393Z" } }, "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": "8446d7bd", "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": "618507a0", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:05.119123Z", "iopub.status.busy": "2025-05-22T12:06:05.118768Z", "iopub.status.idle": "2025-05-22T12:06:05.296801Z", "shell.execute_reply": "2025-05-22T12:06:05.296149Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "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 = trials['# Samples'].tolist()\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": "6a0daf9c", "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": "62b3ca1c", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:05.298876Z", "iopub.status.busy": "2025-05-22T12:06:05.298488Z", "iopub.status.idle": "2025-05-22T12:06:05.498810Z", "shell.execute_reply": "2025-05-22T12:06:05.498121Z" } }, "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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", "text/plain": [ "
" ] }, "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": "468a1783", "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": "b4ab0dbf", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:05.500815Z", "iopub.status.busy": "2025-05-22T12:06:05.500495Z", "iopub.status.idle": "2025-05-22T12:06:05.730892Z", "shell.execute_reply": "2025-05-22T12:06:05.730254Z" } }, "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": "3c27f42a", "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": "3e6d27a4", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:05.733113Z", "iopub.status.busy": "2025-05-22T12:06:05.732778Z", "iopub.status.idle": "2025-05-22T12:06:05.888129Z", "shell.execute_reply": "2025-05-22T12:06:05.887500Z" } }, "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": [ [ 0.9326785714285715, 0.06732142857142857 ], [ 0.37706342311033886, 0.6229365768896612 ] ] } ], "layout": { "annotations": [ { "font": { "color": "#000000" }, "showarrow": false, "text": "93.27", "x": "<=50K", "xref": "x", "y": "<=50K", "yref": "y" }, { "font": { "color": "#FFFFFF" }, "showarrow": false, "text": "6.73", "x": ">50K", "xref": "x", "y": "<=50K", "yref": "y" }, { "font": { "color": "#FFFFFF" }, "showarrow": false, "text": "37.71", "x": "<=50K", "xref": "x", "y": ">50K", "yref": "y" }, { "font": { "color": "#000000" }, "showarrow": false, "text": "62.29", "x": ">50K", "xref": "x", "y": ">50K", "yref": "y" }, { "font": { "color": "black", "size": 14 }, "showarrow": false, "text": "Predicted value", "x": 0.5, "xref": "paper", "y": -0.15, "yref": "paper" }, { "font": { "color": "black", "size": 14 }, "showarrow": false, "text": "Actual", "textangle": -90, "x": -0.15, "xref": "paper", "y": 0.5, "yref": "paper" } ], "margin": { "l": 150, "t": 50 }, "template": { "data": { "bar": [ { "error_x": { 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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": "870b49e7", "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": "9fad2963", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:05.890138Z", "iopub.status.busy": "2025-05-22T12:06:05.889827Z", "iopub.status.idle": "2025-05-22T12:06:05.947785Z", "shell.execute_reply": "2025-05-22T12:06:05.947153Z" } }, "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": "40e1e09a", "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": "e5b093e6", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:05.949840Z", "iopub.status.busy": "2025-05-22T12:06:05.949464Z", "iopub.status.idle": "2025-05-22T12:06:06.007810Z", "shell.execute_reply": "2025-05-22T12:06:06.007167Z" } }, "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": "f7ec6089", "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": "ece837a3", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:06.009950Z", "iopub.status.busy": "2025-05-22T12:06:06.009593Z", "iopub.status.idle": "2025-05-22T12:06:19.226520Z", "shell.execute_reply": "2025-05-22T12:06:19.225662Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,116] [automlx.interface] Dataset shape: (34189,14)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,176] [automlx.interface] Adaptive Sampling disabled.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,223] [automlx.data_transform] Running preprocessing. Number of features: 15\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,836] [automlx.data_transform] Preprocessing completed. Took 0.613 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,858] [automlx.process] Running Model Generation\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,913] [automlx.process] Model Generation completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:06,944] [automlx.model_selection] Running Model Selection\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:08,368] [automlx.model_selection] Model Selection completed - Took 1.424 sec - Selected models: [['LogisticRegressionClassifier', 'GaussianNB']]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:08,451] [automlx.feature_selection] Starting feature ranking for LogisticRegressionClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:11,788] [automlx.feature_selection] Feature Selection completed. Took 3.337 secs.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:11,943] [automlx.trials] Running Model Tuning for ['LogisticRegressionClassifier']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:13,075] [automlx.trials] Best parameters for LogisticRegressionClassifier: {'C': 0.0363696875, 'solver': 'liblinear', 'class_weight': 'balanced'}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:13,077] [automlx.trials] Model Tuning completed. Took: 1.134 secs\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:13,137] [automlx.feature_selection] Starting feature ranking for GaussianNB\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:16,664] [automlx.feature_selection] Feature Selection completed. Took 3.527 secs.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:16,808] [automlx.trials] skipping model tuning for: []\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:17,102] [automlx.interface] Re-fitting pipeline\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:17,122] [automlx.final_fit] Skipping updating parameter seed, already fixed by FinalFit_d2282cd6-a\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2025-05-22 05:06:18,746] [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": "d2a5bc9b", "metadata": {}, "source": [ "\n", "## Machine Learning Explainability" ] }, { "cell_type": "markdown", "id": "b3ffccbf", "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": "79828a5d", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:19.229827Z", "iopub.status.busy": "2025-05-22T12:06:19.229050Z", "iopub.status.idle": "2025-05-22T12:06:19.326598Z", "shell.execute_reply": "2025-05-22T12:06:19.325909Z" } }, "outputs": [], "source": [ "explainer = automlx.MLExplainer(est1,\n", " X_train,\n", " y_train)" ] }, { "cell_type": "markdown", "id": "7592bcb6", "metadata": {}, "source": [ "\n", "### Model Explanations (Global Feature importance)" ] }, { "cell_type": "markdown", "id": "c6eebd00", "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": "b31ab9d2", "metadata": {}, "source": [ "#### Computing the importance" ] }, { "cell_type": "markdown", "id": "0c91a973", "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": "ec40cef7", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:19.328972Z", "iopub.status.busy": "2025-05-22T12:06:19.328399Z", "iopub.status.idle": "2025-05-22T12:06:27.230914Z", "shell.execute_reply": "2025-05-22T12:06:27.230202Z" } }, "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": "abc36fd0", "metadata": {}, "source": [ "#### Visualization" ] }, { "cell_type": "markdown", "id": "317de9ee", "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": "1c20f6ad", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:27.233541Z", "iopub.status.busy": "2025-05-22T12:06:27.232956Z", "iopub.status.idle": "2025-05-22T12:06:27.294738Z", "shell.execute_reply": "2025-05-22T12:06:27.294136Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_model_default.show_in_notebook()" ] }, { "cell_type": "markdown", "id": "6fd4db47", "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": "038bd58b", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:27.407524Z", "iopub.status.busy": "2025-05-22T12:06:27.407026Z", "iopub.status.idle": "2025-05-22T12:06:30.084876Z", "shell.execute_reply": "2025-05-22T12:06:30.084238Z" } }, "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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" ] }, "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": "dc081485", "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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{ "text": "Individual Conditional Expectations (ICE)", "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..." }, "visible": true, "zeroline": true, "zerolinecolor": "DarkGrey", "zerolinewidth": 1 }, "yaxis": { "anchor": "x", "categoryorder": "category ascending", "domain": [ 0.8794871794871795, 1.0 ], 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_explain_feature_dependence_default.show_in_notebook(ice=True)" ] }, { "cell_type": "markdown", "id": "d0c417c2", "metadata": {}, "source": [ "PDPs can be computed for an arbitrary number of variables; however, they can only be visualized with up to 4. We show an example with 3 below." ] }, { "cell_type": "code", "execution_count": 27, "id": "6d5ee556", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:30.290620Z", "iopub.status.busy": "2025-05-22T12:06:30.290049Z", "iopub.status.idle": "2025-05-22T12:06:35.276789Z", "shell.execute_reply": "2025-05-22T12:06:35.276089Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "fill": "toself", "fillcolor": "rgb(5.0, 8.0, 184.0)", "legendgroup": "0", "line": { "color": "rgba(0,0,0,0)" }, "name": "confidence interval", "opacity": 0.3, "showlegend": true, "type": "scatter", "x": [ 5.0, 8.0, 9.0, 10.0, 12.0, 13.0, 14.0, 14.0, 13.0, 12.0, 10.0, 9.0, 8.0, 5.0 ], "xaxis": "x3", "y": [ 0.8971549711071343, 0.8663595187979964, 0.8663595187979964, 0.8663595187979964, 0.8663595187979964, 0.8663595187979964, 0.8663595187979964, 0.8441850673836442, 0.8441850673836442, 0.8441850673836442, 0.8441850673836442, 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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": "b12a2bf2", "metadata": {}, "source": [ "\n", "### Prediction Explanations (Local Feature Importance)" ] }, { "cell_type": "markdown", "id": "8085af4e", "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": "6fde57cb", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:35.278856Z", "iopub.status.busy": "2025-05-22T12:06:35.278566Z", "iopub.status.idle": "2025-05-22T12:06:38.267977Z", "shell.execute_reply": "2025-05-22T12:06:38.267284Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "error_x": { "array": [ 0.005838192875878146, 0.015047544052402839, 0.014130041071795201, 0.00357882423885314 ], "arrayminus": [ 0.005838192875878147, 0.015047544052402837, 0.014130041071795205, 0.00357882423885314 ], "type": "data" }, "hovertemplate": "(%{x:.4g}, %{y})", "legendgroup": "negative", "marker": { "color": "#626567" }, "name": "28988=negative", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4550781567914899bffd5d20fe6339ad", "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": "f7a73897", "metadata": {}, "source": [ "\n", "## Interactive What-If Explainers" ] }, { "cell_type": "markdown", "id": "dc687045", "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": "870a3b17", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:38.480155Z", "iopub.status.busy": "2025-05-22T12:06:38.479857Z", "iopub.status.idle": "2025-05-22T12:06:38.954259Z", "shell.execute_reply": "2025-05-22T12:06:38.953712Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd29f8b5a6ca40d8ae0e0034e49b5c3f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

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"left", "title": { "text": "Prediction probability (1)" }, "visible": true, "zeroline": true, "zerolinecolor": "DarkGrey", "zerolinewidth": 1 } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1b812e036d3f4833bfdcd1eeb2b2e3ee", "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": "4073e7de", "metadata": {}, "source": [ "\n", "## Counterfactual Explanations" ] }, { "cell_type": "markdown", "id": "3cec4560", "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": "ff40ee39", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:38.962367Z", "iopub.status.busy": "2025-05-22T12:06:38.961935Z", "iopub.status.idle": "2025-05-22T12:06:40.464626Z", "shell.execute_reply": "2025-05-22T12:06:40.464054Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f31a8a8e30a84c50866f9c369822a55c", "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": "54e69dc7", "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": "33b68a90", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:40.475934Z", "iopub.status.busy": "2025-05-22T12:06:40.475491Z", "iopub.status.idle": "2025-05-22T12:06:40.752881Z", "shell.execute_reply": "2025-05-22T12:06:40.752271Z" } }, "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": "3efbd6da", "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": "0844666d", "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": "882a9565", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:40.755307Z", "iopub.status.busy": "2025-05-22T12:06:40.754820Z", "iopub.status.idle": "2025-05-22T12:06:40.821597Z", "shell.execute_reply": "2025-05-22T12:06:40.821001Z" } }, "outputs": [], "source": [ "explainer_sklearn.configure_explain_prediction(tabulator_type=\"kernel_shap\",\n", " sampling={'technique': 'random', 'n_samples': 2000}\n", " )" ] }, { "cell_type": "markdown", "id": "7fb8ba77", "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": "71742715", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:40.823920Z", "iopub.status.busy": "2025-05-22T12:06:40.823496Z", "iopub.status.idle": "2025-05-22T12:06:41.113026Z", "shell.execute_reply": "2025-05-22T12:06:41.112334Z" } }, "outputs": [], "source": [ "explainer_sklearn.configure_explain_prediction(\n", " explainer_type='surrogate',\n", " method='lime'\n", " )" ] }, { "cell_type": "code", "execution_count": 35, "id": "2e4b4e95", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:41.115481Z", "iopub.status.busy": "2025-05-22T12:06:41.115160Z", "iopub.status.idle": "2025-05-22T12:06:41.557875Z", "shell.execute_reply": "2025-05-22T12:06:41.557266Z" } }, "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": "60e0e25e", "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": "51e84452", "metadata": {}, "source": [ "\n", "##### Explain the model with observational evaluator_type" ] }, { "cell_type": "code", "execution_count": 36, "id": "7240628c", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:41.561108Z", "iopub.status.busy": "2025-05-22T12:06:41.560778Z", "iopub.status.idle": "2025-05-22T12:06:41.619453Z", "shell.execute_reply": "2025-05-22T12:06:41.618929Z" } }, "outputs": [], "source": [ "explainer_sklearn.configure_explain_model(evaluator_type=\"observational\")" ] }, { "cell_type": "code", "execution_count": 37, "id": "c69dffb2", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:41.621311Z", "iopub.status.busy": "2025-05-22T12:06:41.621077Z", "iopub.status.idle": "2025-05-22T12:06:45.893486Z", 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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": "ef440ebf", "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": "8516bd5f", "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": "a4a6434f", "metadata": { "execution": { "iopub.execute_input": "2025-05-22T12:06:45.895472Z", "iopub.status.busy": "2025-05-22T12:06:45.895212Z", "iopub.status.idle": "2025-05-22T12:06:49.037761Z", "shell.execute_reply": "2025-05-22T12:06:49.037060Z" } }, "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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"#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": "#ECECEC", "linecolor": "LightGrey", "linewidth": 1, "matches": "y", "mirror": true, "nticks": 3, "range": [ 0, 0.34356000000000003 ], "scaleanchor": "y", "showline": true, "showticklabels": true, "side": "left", "tickformat": "p", "visible": true, "zeroline": true, "zerolinecolor": "DarkGrey", "zerolinewidth": 1 }, "yaxis2": { "anchor": "x2", "categoryorder": "category ascending", "domain": [ 0.1, 0.8461538461538461 ], "gridcolor": "LightGrey", "linecolor": "Grey", "linewidth": 1, "matches": "y2", "mirror": true, "range": [ -0.031136550933586363, 0.016406696854730052 ], "scaleanchor": "y2", "showline": true, "showticklabels": true, "side": "left", "title": { "text": "ALE of P(<=50K)" }, "visible": true, "zeroline": true, "zerolinecolor": "DarkGrey", "zerolinewidth": 1 } } }, "text/html": [ "
" ] }, "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": "286a08f8", "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 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.21" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "00fe8e717a454e9fbed5bc9dcd36e6e8": { "buffers": [ { "data": 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", 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Select and Explore Predictions

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Model Predictions

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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapitalgaincapitallosshoursperweeknative-country
Original Sample2Private151856.0HS-grad9.0Married-civ-spouseProtective-servHusbandWhiteMale002United-States
Modified Sample2Private151855.9998765774Masters9.000070813837981Married-civ-spouseProtective-servHusbandWhiteMale002United-States
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 Prediction (True value: 0)
Original Sample0
Modified Sample1
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