To SMOTE, or not to SMOTE?

Last update: Jan 03, 2022

Related tags

Deep Learning to-smote-or-not

Overview

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.

To SMOTE, or not to SMOTE?

Related tags

Overview

To SMOTE, or not to SMOTE?

Installation

Running experiments

Analyze

Citation

Security

License

Owner

Amazon Web Services

The audio-video synchronization of MKV Container Format is exploited to achieve data hiding

Lightweight tool to perform MITM attack on local network

Artificial Intelligence search algorithm base on Pacman

PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning

Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

PyGCL: A PyTorch Library for Graph Contrastive Learning

Semantic Segmentation in Pytorch

A graph adversarial learning toolbox based on PyTorch and DGL.

A task-agnostic vision-language architecture as a step towards General Purpose Vision

The implementation of DeBERTa

This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

PPO Lagrangian in JAX

EfficientNetV2 implementation using PyTorch

This is the official PyTorch implementation of our paper: "Artistic Style Transfer with Internal-external Learning and Contrastive Learning".

mmfewshot is an open source few shot learning toolbox based on PyTorch

Neural Scene Graphs for Dynamic Scene (CVPR 2021)

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

Implementation of FSGNN

Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)

Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).