Geolet - Interpretable GPS Trajectory Classifier
Researchers, businesses, and governments use mobility data to make decisions that affect people's lives in many ways, employing accurate but opaque deep learning models that are difficult to interpret from a human standpoint. To address these limitations, we propose Geolet, a human-interpretable machine-learning model for trajectory classification. We use discriminative sub-trajectories extracted from mobility data to turn trajectories into a simplified representation that can be used as input by any machine learning classifier.
Setup
Using PyPI
pip install geoletManual Setup
git clone https://github.com/cri98li/Geolet
cd Geolet
pip install -e .Dependencies are listed in requirements.txt.
Running the code
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from Geolet.classifier.geoletclassifier import GeoletClassifier, prepare_y
from Geolet import distancers
df = pd.read_csv("animals_prepared.zip").sort_values(by=["tid", "t"])
df = df[["tid", "class", "t", "c1", "c2"]]
tid_train, tid_test, _, _ = train_test_split(df.groupby(by=["tid"]).max().reset_index()["tid"],
df.groupby(by=["tid"]).max().reset_index()["class"],
test_size=.3,
stratify=df.groupby(by=["tid"]).max().reset_index()["class"],
random_state=3)
transform = GeoletClassifier(
precision=3, # Geohash precision for the partitioning phase
geolet_per_class=10, # Number of candidate geolets to subsample randomly before the selecting phase
selector='MutualInformation', # Name of the selector to use. Possible values are ["Random", "MutualInformation"]
top_k=5, # Top k geolets, according to the selector score, to use for transforming the entire dataset.
trajectory_for_stats=100, # Number of trajectory to subsample for selector scoring
bestFittingMeasure=distancers.InterpolatedRouteDistance.interpolatedRootDistanceBestFitting, # best fitting measure to use
distancer='IRD', #Distance Measure to use for the final transformation. Possible values are ["E", "IRD"]
verbose=True,
n_jobs=4
)
X_train = df[df.tid.isin(tid_train)].drop(columns="class").values
y_train = df[df.tid.isin(tid_train)].values[:, 1]
X = df.drop(columns="class").values
y = prepare_y(classes=df.values[:, 1], tids=df.values[:, 0])
X_index, X_dist = transform.fit(X_train, y_train).transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_dist, y, test_size=.3, stratify=y, random_state=3)
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)Jupyter notebooks with examples on real datasets can be found in the examples/ directory.
Docs and reference
You can find the software documentation in the /docs/ folder and
a powerpoint presentation on Geolet can be found here.
You can cite this work with
@inproceedings{DBLP:conf/ida/LandiSGMN23,
author = {Cristiano Landi and
Francesco Spinnato and
Riccardo Guidotti and
Anna Monreale and
Mirco Nanni},
title = {Geolet: An Interpretable Model for Trajectory Classification},
booktitle = {{IDA}},
series = {Lecture Notes in Computer Science},
volume = {13876},
pages = {236--248},
publisher = {Springer},
year = {2023}
}
Extending the algorithm
The original Geolet code, i.e., the code used for the experiments in the paper, is available in the /original_code branch.
The code in the main branch is a reimplementation that speeds up the execution time by about 7%.
