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Description of Software and Data

The code and data in the zip file referenced above are a snapshot of the software and data that were used in the research reported in the paper "How to Purchase Labels? A Cost-effective Approach Using Active Learning Markets" by Xiwen Huang, Pierre Pinson. This repository is also available via GitHub.

The goal of this repository is to replicate the numerical experiments in the paper.

Computer and Software Environment

The following describes the computer hardware conditions and software environment on which the authors produce the results reported in the paper.

All results in this paper were produced on the following hardware and software environment:

Hardware
Device: MacBook Pro (14-inch, Nov 2023)
Chip: Apple M3
Memory: 8 GB unified memory
Internal storage: 1 TB SSD

Operating System
macOS Tahoe 26.1

Software Environment
Python 3.11.7
Dedicated Conda environment:

conda create -n project_env python=3.11.7
conda activate project_env

All required Python packages are listed in requirements.txt and installed using:

pip install -r requirements.txt

This setup was used to generate every figure, table, and experiment reported in the paper.

Dependencies

The code in this repository was tested with the following software versions:

• Python 3.11.7
• NumPy 1.26.4
• pandas 2.1.4
• scikit-learn 1.2.2
• Matplotlib 3.8.0
• ucimlrepo 0.0.7
• SciPy 1.11.4 (used for the Wilcoxon tests)
• openpyxl 3.1.2 (for reading/writing Excel files, if needed)

Installation

1. Clone or download the repository
   

   
   git clone https://github.com/xiwenhuang123/Active_learning_market_IJDS.git
   cd Active_learning_market_IJDS
   
   

2. Create and activate a Python environment (recommended)

   Using Conda:
   

   
   conda create -n project_env python=3.11.7
   conda activate project_env
   
   

   Using Venv:
   

   
   python -m venv project_env 
   source project_env/bin/activate
   #Windows:project_env\Scripts\activate
   
   

3. Install the required Python packages

   If a requirements.txt file is present:

   
   pip install -r requirements.txt
   
   

   Otherwise, install the core dependencies directly:

   
   pip install numpy pandas scikit-learn matplotlib scipy openpyxl
   

4. Data and folder structure

After installation, the repository is organised as follows (main folders):

Active_learning_market_IJDS/
├── IJDS_Variance.py                 # Real-estate, variance-based 
      ├── IJDS_MSE.py                     # Predictive-ability (MSE) scenario
├── Monte_carlo_variance_scenario.py # Monte Carlo variance robustness
├── Monte_carlo_MSE_scenario.py      # Monte Carlo MSE robustness

├── Plots/
│   ├── VarianceScenario/            # Plots for variance-based 
│   ├── MSEScenario/                 # Plots for MSE-based scenario
│   └── Sensitivity_Results/         # CSV files for sensitivity tables
├── data/
│   ├── Real_estate_valuation/       # Real estate valuation data
│   └── Hog_buildings/               # Building energy (Hog) datasets
└── README.md
└── requirements.txt

Make sure the subfolders in data/ contain the corresponding .csv files, e.g.:

• data/Real_estate_valuation/*.csv
• data/Hog_buildings/Hog_education_Madge.csv
• data/Hog_buildings/Hog_education_Rachael.csv

The plotting and results folders (Plots/VarianceScenario, Plots/MSEScenario, Plots/Sensitivity_Results) will be created automatically by the scripts if they do not already exist.

Reproducibility Workflow

Figures 3–5, Figure 8a-8f, Table 2 in the paper, and Tables 4–5 in the Appendix

• Data File:Real estate valuation data set.xlsx
• Code File: IJDS_Variance.py
• Output: PDF plots for Figures 3–6 and Figures 9a–9f, txt for Table 2 (saved in Plots/VarianceScenario/); Sensitivity Tables 4–5 saved as CSV files in Plots/Sensitivity_Results/.
• Run Time at the Above-Specified Computer Conditions: 5.5 minutes

Figure 6-7 in Section 4.1.5

• Data File: Real estate valuation data set.xlsx
• Code File: Monte_carlo_variance_scenario.py
• Output: PDF plots for Figures 7–8, saved in Plots/VarianceScenario/.
• Run Time at the Above-Specified Computer Conditions: 4.2 minutes

Figure 9-10, Figures 13a-13f in the paper, Table 3 in the paper, and Table 5 in the Appendix.

• Data Files: Hog_industrial_Rachael.csv, Hog_industrial_Madge.csv
• Code File: IJDS_MSE.py
• Output: PDF plots for Figures 10-11 and Figures 14a–14f, (saved in Plots/MSEScenario/); Sensitivity Tables 6-7 saved as CSV files in Plots/Sensitivity_Results/.
• Run Time at the Above-Specified Computer Conditions: 35.88 minutes

Figure 11-12 in Section 4.2.5

• Data Files: Hog_industrial_Rachael.csv,Hog_industrial_Madge.csv
• Code File: Monte_carlo_MSE_scenario.py
• Output: PDF plots for Figures 12-13, saved in Plots/MSEScenario/.
• Run Time at the Above-Specified Computer Conditions: 34.59 minutes

Ongoing Development:

This code is being developed on an ongoing basis at the author-maintained (please specify) package. In particular, the source code in this repository corresponds to (which version of the package function).

Cite

To cite the contents of this repository, please cite both the paper and this repository using their respective DOIs.

Article: https://doi.org/10.1287/ijds.2025.0093
Software and Data Repository: https://doi.org/10.1287/ijds.2025.0093.cd

License

Copyright (c) (Huang and Pinson)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.