Humans-as-a-Sensor for Buildings: Intensive Longitudinal Indoor Comfort Models

This repository covers the technical implementation details of the Humans-as-a-Sensor for Buildings: Intensive Longitudinal Indoor Comfort Models paper.

This methodology heavily uses the Cozie Fitbit Clockface app that is outlined in Is your clock-face cozie? A smartwatch methodology for the in-situ collection of occupant comfort data

For Interactive Code Click on the Following Binder Link

Note: The notebooks might take some time to load, if they don't try refreshing the page.

Requirements

To install requirements:

pip install -r requirements.txt 

Data pre-processing

All datasets can be found in data/, and the raw dataset file is 2019-11-15_cozie_full_masked.csv

Execute the following notebooks on this folder data/ to generate the respective .csv files:

• datasets_generation_room_preference.ipynb generates the .csv with the differente features set (or tiers) explained on the paper (Figure 3) and saves them in data/data-processed-preferences/
• train_test_generation.ipynb generates the train and test .csv for each participant and saves them in data/data-processed-preferences/

The format for each .csv is as follows:

<latest_dataset>_<feature_set>_<train/test>.csv

where feature_set is either fs1, fs2, fs3, fs4, fs5, or fs6 (Figure 7). The train-test split is done participant-wise such that a participant's datapoints are only on the train set or in the test set, but not both.

In each.csv file for each occupants, the ID of the subject is appended at the end of the file name:

<date_of_dataset_<feature_set>_<train/val>_<user_id>.csv

Example 2019-11-15_fs6_val_cresh25.csv

This file correspond to the dataset extracted on 2019-11-15 (which is the latest one and the one used in the paper), with features from feature set fs6, val stands for test set, for user cresh25.

Training and Evaluation

To train the model(s) in the paper, run the following ipynb files in notebooks/

• group_modeling.ipynb creates the model using all participants available in the train set and calculates the micro and macro F1 score on the test set. These values are then saved in:

/data/data-processed-preferences/<date>_grouped_<micro/macro>.pickle

<date> refers to the date the dataset was processed. As stated in the section above, for this paper this value is set to 2019-11-15.

<micro/macro> refers to either a micro or macro F1 score.

Example 2019-11-15_grouped_micro.pickle

This file contains a dictionary with the micro F1 score for the data processed on 2019-11-15 for all feature sets (f1 ... f6) and all three subjective comfort (thermal, light, and aural). Inside this file the dictionary key fs1_thermal refers to the micro F1 score on the feature set fs1 for thermal comfort prediction, whereas fs1_light will be similar except it refers to the visual comfort prediction.

• personal_modeling.ipynb creates one model for each participant using only that participant's train set and calculates the micro and macro F1 score on each participant's test set. Similar to the notebook mentioned above, it saves the values in:

data/data-processed-preferences/<date>_personal_<micro/macro>

The main difference from the grouped pickle files is that on this file, the dictionary contains a list of all participant's micro or macro F1 score.

Example Inside 2019-11-15_personal_micro.pickle, the dictionary key fs1_thermal will contain a list where its elements are the micro F1 score on the feature set fs1 for thermal comfort prediction.

• The models used were not saved. However, the hyperparameters were fixed: Random Forest was used with the default parameters and n_estimators = 1000. Throughout all notebooks, a seed of value 13 was used.

• modeling_functions.py contains the defined functions used for training and evaluation purposes.

Results

All the figures in the paper can be reproduced with notebooks inside publications-plots/:

• comfort-tiles.ipynb reproduces Figure 4
• PublicationPlots_v1.ipynb reproduces Figure 5
• PublicationPlots_v2.ipynb reproduces Figure 7
• plots.ipynb reproduces Figure 8
• PublicationPlots_v3.ipynb reproduces Figure 9

Contributing

MIT License