To SMOTE, or not to SMOTE?
This package includes the code required to repeat the experiments in the paper and to analyze the results.
To SMOTE, or not to SMOTE?
Yotam Elor and Hadar Averbuch-Elor
Installation
# Create a new conda environment and activate it
conda create --name to-SMOTE-or-not -y python=3.7
conda activate to-SMOTE-or-not
# Install dependencies
pip install -r requirements.txt
Running experiments
The data is not included with this package. See an example of running a single experiment with a dataset from imblanaced-learn
# Load the data
import pandas as pd
import numpy as np
from imblearn.datasets import fetch_datasets
data = fetch_datasets()["mammography"]
x = pd.DataFrame(data["data"])
y = np.array(data["target"]).reshape((-1, 1))
# Run the experiment
from experiment import experiment
from classifiers import CLASSIFIER_HPS
from oversamplers import OVERSAMPLER_HPS
results = experiment(
x=x,
y=y,
oversampler={
"type": "smote",
"ratio": 0.4,
"params": OVERSAMPLER_HPS["smote"][0],
},
classifier={
"type": "cat", # Catboost
"params": CLASSIFIER_HPS["cat"][0]
},
seed=0,
normalize=False,
clean_early_stopping=False,
consistent=True,
repeats=1
)
# Print the results nicely
import json
print(json.dumps(results, indent=4))
To run all the experiments in our study, wrap the above in loops, for example
for dataset in datasets:
x, y = load_dataset(dataset) # this functionality is not provided
for seed in range(7):
for classifier, classifier_hp_configs in CLASSIFIER_HPS.items():
for classifier_hp in classifier_hp_configs:
for oversampler, oversampler_hp_configs in OVERSAMPLER_HPS.items():
for oversampler_hp in oversampler_hp_configs:
for ratio in [0.1, 0.2, 0.3, 0.4, 0.5]:
results = experiment(
x=x,
y=y,
oversampler={
"type": oversampler,
"ratio": ratio,
"params": oversampler_hp,
},
classifier={
"type": classifier,
"params": classifier_hp
},
seed=seed,
normalize=...,
clean_early_stopping=...,
consistent=...,
repeats=...
)
Analyze
Read the results from the compressed csv file. As the results file is large, it is tracked using git-lfs. You might need to download it manually or install git-lfs.
import os
import pandas as pd
data_path = os.path.join(os.path.dirname(__file__), "../data/results.gz")
df = pd.read_csv(data_path)
Drop nans and filter experiments with consistent classifiers, no normalization and a single validation fold
df = df.dropna()
df = df[
(df["consistent"] == True)
& (df["normalize"] == False)
& (df["clean_early_stopping"] == False)
& (df["repeats"] == 1)
]
Select the best HP configurations according to AUC validation scores. opt_metric is the key used to select the best configuration. For example, for a-priori HPs use opt_metric="test.roc_auc" and for validation-HPs use opt_metric="validation.roc_auc". Additionaly calculate average score and rank
from analyze import filter_optimal_hps
df = filter_optimal_hps(
df, opt_metric="validation.roc_auc", output_metrics=["test.roc_auc"]
)
print(df)
Plot the results
from analyze import avg_plots
avg_plots(df, "test.roc_auc")
Citation
@misc{elor2022smote,
title={To SMOTE, or not to SMOTE?},
author={Yotam Elor and Hadar Averbuch-Elor},
year={2022},
eprint={2201.08528},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Security
See CONTRIBUTING for more information.
License
This library is licensed under the MIT-0 License. See the LICENSE file.
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