{"cells": [{"metadata": {"collapsed": true, "ExecuteTime": {"end_time": "2025-09-07T09:16:22.766042Z", "start_time": "2025-09-07T09:16:22.764406Z"}}, "cell_type": "markdown", "source": "# Advanced Active Learning for Regression Tasks", "id": "8ce144342232ba84"}, {"metadata": {"ExecuteTime": {"end_time": "2025-09-08T08:18:49.075281Z", "start_time": "2025-09-08T08:18:49.072921Z"}}, "cell_type": "markdown", "source": ["> **_Google Colab Note:_** If the notebook fails to run after installing the needed packages, try to restart the runtime (Ctrl + M) under Runtime -> Restart session.\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-activeml/scikit-activeml.github.io/blob/gh-pages/latest/generated/tutorials_colab//07_pool_advanced_regression.ipynb)"], "id": "aac9375494dc8ed4"}, {"metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "#!pip install scikit-activeml[opt] torch torchvision xgboost", "id": "415f87e49685b455"}, {"metadata": {}, "cell_type": "markdown", "source": ["This tutorial demonstrates how to perform pool-based active learning for regression and compare query strategies using two model families:\n", "- a multilayer perceptron (MLP) trained via [skorch](https://github.com/skorch-dev/skorch) (`torch` under the hood),\n", "- a tree-boosting regressor from [xgboost](https://github.com/dmlc/xgboost),\n", "- a random forest regressor from [sklearn](https://github.com/scikit-learn/scikit-learn)."], "id": "cbdf7ede8b8b713"}, {"metadata": {"ExecuteTime": {"end_time": "2025-11-30T23:47:09.806982Z", "start_time": "2025-11-30T23:47:07.108124Z"}}, "cell_type": "code", "source": ["# Comment in for speed up, if you have cuML installed.\n", "# %load_ext cuml.accel\n", "import matplotlib as mlp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "import torch\n", "import warnings\n", "\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from skorch.toy import MLPModule\n", "from skactiveml.pool import (\n", " RandomSampling,\n", " GreedySamplingTarget,\n", " SubSamplingWrapper,\n", ")\n", "from skactiveml.regressor import SkorchRegressor, SklearnRegressor\n", "from skactiveml.utils import MISSING_LABEL, call_func\n", "from skorch.callbacks import LRScheduler\n", "from torch import nn\n", "from torch.optim.lr_scheduler import CosineAnnealingLR\n", "from tqdm import tqdm\n", "from xgboost import XGBRegressor\n", "\n", "CACHE_PATH = \".cache\"\n", "os.makedirs(CACHE_PATH, exist_ok=True)\n", "mlp.rcParams[\"figure.facecolor\"] = \"white\"\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "warnings.filterwarnings(\"ignore\")"], "id": "7e96e7a80c9c9b29", "outputs": [{"name": "stdout", "output_type": "stream", "text": ["cuML: Accelerator installed.\n"]}], "execution_count": 2}, {"metadata": {}, "cell_type": "markdown", "source": ["## Load Dataset\n", "We use our the `fetch_openml` function from `sklearn.datasets` to load the `superconduct` dataset from [OpenML](https://www.openml.org/). The dataset contains information about superconductors, for which the critical temperature as a continuous target is to predicted."], "id": "d41a7926e55a4104"}, {"metadata": {"ExecuteTime": {"end_time": "2025-11-30T23:47:10.182960Z", "start_time": "2025-11-30T23:47:09.853300Z"}}, "cell_type": "code", "source": ["# Load and cache dataset.\n", "X_full, y_full = fetch_openml(\n", " data_id=43174, return_X_y=True, data_home=CACHE_PATH\n", ")\n", "X_full = X_full.values\n", "y_full = y_full.values\n", "n_features = X_full.shape[1]\n", "\n", "# Create 80-20 train-test split.\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X_full, y_full, test_size=0.2\n", ")\n", "\n", "# Standardize numerical features.\n", "sc = StandardScaler().fit(X_train)\n", "X_train = sc.transform(X_train)\n", "X_test = sc.transform(X_test)"], "id": "1d38ecfdaed939b0", "outputs": [], "execution_count": 3}, {"metadata": {}, "cell_type": "markdown", "source": ["## Create Regression Models\n", "\n", "We create a dictionary of functions that can be used to create the regression models with varying seeds."], "id": "5876f715edf2d227"}, {"metadata": {"ExecuteTime": {"end_time": "2025-11-30T23:47:10.188128Z", "start_time": "2025-11-30T23:47:10.185484Z"}}, "cell_type": "code", "source": ["# Setup dictionary of functions for creating regression models.\n", "regressor_dict = {}\n", "\n", "# Create random forest regressor.\n", "regressor_dict[\"RF\"] = lambda seed: SklearnRegressor(\n", " RandomForestRegressor(random_state=seed), random_state=seed\n", ")\n", "\n", "# Add gradient-boosted decision tree.\n", "regressor_dict[\"XGB\"] = lambda seed: SklearnRegressor(\n", " XGBRegressor(device=DEVICE, random_state=seed), random_state=seed\n", ")\n", "\n", "regressor_dict[\"MLP\"] = lambda seed: SkorchRegressor(\n", " module=MLPModule,\n", " criterion=nn.HuberLoss,\n", " neural_net_param_dict={\n", " # Module-related parameters.\n", " \"module__input_units\": n_features,\n", " \"module__output_units\": 1,\n", " \"module__hidden_units\": 256,\n", " \"module__num_hidden\": 2,\n", " \"module__dropout\": 0.1,\n", " # Optimizer-related parameters.\n", " \"max_epochs\": 100,\n", " \"batch_size\": 128,\n", " \"optimizer\": torch.optim.RAdam,\n", " \"optimizer__lr\": 1e-2,\n", " \"callbacks\": [\n", " (\"lr_scheduler\", LRScheduler(policy=CosineAnnealingLR, T_max=100))\n", " ],\n", " # General parameters.\n", " \"verbose\": 0,\n", " \"device\": DEVICE,\n", " \"train_split\": False,\n", " \"iterator_train__shuffle\": True,\n", " \"torch_load_kwargs\": {\"weights_only\": True},\n", " },\n", " sample_dtype=np.float32,\n", " missing_label=MISSING_LABEL,\n", " random_state=seed,\n", ")"], "id": "afaaf67ec481089c", "outputs": [], "execution_count": 4}, {"metadata": {}, "cell_type": "markdown", "source": ["## Create Query Strategies\n", "For simplicity, we only test two query strategies, of which `GreedySamplingI` is a dedicated strategy for regression tasks. If other strategies are to be included, we refer to our [overview of query strategies](https://scikit-activeml.github.io/latest/generated/strategy_overview.html)."], "id": "317a0dddbcc4ab52"}, {"metadata": {"ExecuteTime": {"end_time": "2025-11-30T23:47:10.232093Z", "start_time": "2025-11-30T23:47:10.230458Z"}}, "cell_type": "code", "source": ["query_strategy_dict = {\n", " \"RandomSampling\": lambda seed: RandomSampling(random_state=seed),\n", " \"GreedySamplingI\": lambda seed: GreedySamplingTarget(\n", " method=\"GSi\", random_state=seed\n", " ),\n", "}"], "id": "5d257707798d420f", "outputs": [], "execution_count": 5}, {"metadata": {}, "cell_type": "markdown", "source": ["## Perform Active Learning Cycle\n", "Each active learning expirment starts with zero labels and covers 30 cycles with an acquisition batch size of 128."], "id": "8ccfb0efea85b497"}, {"metadata": {"ExecuteTime": {"end_time": "2025-11-30T23:57:05.068451Z", "start_time": "2025-11-30T23:47:10.273461Z"}}, "cell_type": "code", "source": ["n_reps = 3\n", "n_cycles = 30\n", "query_batch_size = 128\n", "n_sub_set = 1000\n", "\n", "results = {}\n", "\n", "for reg_name in regressor_dict:\n", " print(reg_name)\n", " for qs_name in query_strategy_dict:\n", " r2_scores = np.full((n_reps, n_cycles), np.nan)\n", " for i_rep in range(n_reps):\n", " y = np.full_like(y_train, fill_value=MISSING_LABEL)\n", "\n", " reg = regressor_dict[reg_name](i_rep)\n", " qs = SubSamplingWrapper(\n", " query_strategy_dict[qs_name](i_rep),\n", " max_candidates=n_sub_set,\n", " exclude_non_subsample=True,\n", " random_state=i_rep,\n", " )\n", " reg.fit(X_train, y)\n", "\n", " for c in tqdm(\n", " range(n_cycles), desc=f\"Repeat {i_rep + 1} with {qs_name}\"\n", " ):\n", " query_idx = call_func(\n", " qs.query,\n", " X=X_train,\n", " y=y,\n", " batch_size=query_batch_size,\n", " reg=reg,\n", " fit_reg=False,\n", " )\n", " y[query_idx] = y_train[query_idx]\n", " reg.fit(X_train, y)\n", " score = reg.score(X_test, y_test)\n", " r2_scores[i_rep, c] = score\n", "\n", " results[f\"{reg_name}-{qs_name}\"] = r2_scores"], "id": "6370648e4db6e55e", "outputs": [{"name": "stdout", "output_type": "stream", "text": ["RF\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Repeat 1 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:08<00:00, 3.41it/s]\n", "Repeat 2 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:08<00:00, 3.34it/s]\n", "Repeat 3 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:09<00:00, 3.32it/s]\n", "Repeat 1 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:24<00:00, 1.22it/s]\n", "Repeat 2 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:25<00:00, 1.18it/s]\n", "Repeat 3 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:24<00:00, 1.21it/s]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["XGB\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Repeat 1 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:04<00:00, 6.00it/s]\n", "Repeat 2 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:04<00:00, 6.08it/s]\n", "Repeat 3 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:04<00:00, 6.03it/s]\n", "Repeat 1 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:21<00:00, 1.40it/s]\n", "Repeat 2 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:20<00:00, 1.48it/s]\n", "Repeat 3 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:21<00:00, 1.42it/s]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["MLP\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Repeat 1 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [01:03<00:00, 2.10s/it]\n", "Repeat 2 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [01:00<00:00, 2.02s/it]\n", "Repeat 3 with RandomSampling: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [01:00<00:00, 2.02s/it]\n", "Repeat 1 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [01:16<00:00, 2.54s/it]\n", "Repeat 2 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [01:16<00:00, 2.54s/it]\n", "Repeat 3 with GreedySamplingI: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [01:17<00:00, 2.58s/it]\n"]}], "execution_count": 6}, {"metadata": {}, "cell_type": "markdown", "source": ["## Visualize Results\n", "In the following, we plot the obtained learning curve of the R2 scores including the area under learning curve (AULC) score per regression model and query strategy."], "id": "b06109bc8e3e4f19"}, {"metadata": {"ExecuteTime": {"end_time": "2025-11-30T23:57:05.234764Z", "start_time": "2025-11-30T23:57:05.114995Z"}}, "cell_type": "code", "source": ["plt.figure(figsize=(16, 9))\n", "for reg_name, ls in zip(regressor_dict, [\"-\", \"--\", \":\"]):\n", " for qs_name, c in zip(query_strategy_dict, [\"g\", \"b\"]):\n", " key = qs_name\n", " result = results[f\"{reg_name}-{qs_name}\"]\n", " reshaped_result = result.reshape((-1, n_cycles))\n", " errorbar_mean = np.mean(reshaped_result, axis=0)\n", " errorbar_std = np.std(reshaped_result, axis=0)\n", " plt.errorbar(\n", " np.arange(1, n_cycles + 1) * query_batch_size,\n", " errorbar_mean,\n", " errorbar_std,\n", " label=f\"{reg_name}-{qs_name}: ALCU={np.mean(errorbar_mean):.3f}\",\n", " alpha=0.5,\n", " color=c,\n", " linestyle=ls,\n", " )\n", "plt.grid()\n", "plt.legend(loc=\"lower right\", fontsize=\"x-large\")\n", "plt.xlabel(\"Number of Labeled Samples\", fontsize=\"x-large\")\n", "plt.ylabel(\"R2 Score\", fontsize=\"x-large\")\n", "plt.show()"], "id": "cb301ff83a41e9fe", "outputs": [{"data": {"text/plain": ["
"], "image/png": 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"}, "metadata": {}, "output_type": "display_data", "jetTransient": {"display_id": null}}], "execution_count": 7}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 2}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "2.7.6"}, "nbsphinx": {"orphan": true}}, "nbformat": 4, "nbformat_minor": 5}