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The wrapper of A rebalance method from KDD called CFAMG #416

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8554139
origin CFAMG from WangLei-CQU
LinGinQiu Sep 29, 2025
dade7a9
rename the directory
LinGinQiu Sep 29, 2025
039fdff
adjust the format of the hyperparameters
LinGinQiu Sep 29, 2025
c3beecd
wrap the cfamg into _cfam.py as a rebalance methods
LinGinQiu Sep 29, 2025
d854aa9
Automatic `pre-commit` fixes
LinGinQiu Sep 29, 2025
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83 changes: 83 additions & 0 deletions tsml_eval/_wip/rt/transformations/collection/imbalance/_cfam.py
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import os
import argparse
import numpy as np
import pandas as pd
import torch
from collections import OrderedDict
from typing import Optional, Union

from sklearn.utils import check_random_state
from tsml_eval._wip.rt.transformations.collection.imbalance.pk_cfamg.cfamg import CFAMG
from tsml_eval._wip.rt.transformations.collection.imbalance.pk_cfamg.main import parse_args
from tsml_eval._wip.rt.transformations.collection.imbalance.pk_cfamg.data_preprocess import set_seed
from aeon.transformations.collection import BaseCollectionTransformer

__all__ = ["CFAM"]


class CFAM(BaseCollectionTransformer):
_tags = {
"capability:multivariate": True,
"capability:unequal_length": False,
"requires_y": True,
}

def __init__(self,
random_state=None,
):
self.random_state = random_state
self.generated_samples = None
set_seed(self.random_state)
super().__init__()

def _fit(self, X, y=None):
args = parse_args()
args.w_lambda, args.w_beta = 1, 1
unique, counts = np.unique(y, return_counts=True)
target_stats = dict(zip(unique, counts))
class_majority = max(target_stats, key=target_stats.get)
class_minority = min(target_stats, key=target_stats.get)
X_majority, X_minority = X[y == class_majority], X[y == class_minority]
y_majority, y_minority = y[y == class_majority], y[y == class_minority]

class_label_project = {class_majority: 0, class_minority: 1}
self.class_label_project = class_label_project
y = np.array([class_label_project[label] for label in y])
y_majority = np.array([class_label_project[label] for label in y_majority])
y_minority = np.array([class_label_project[label] for label in y_minority])

ir = len(y_majority) / len(y_minority)
dataset = {
"train_data": (X, y),
"train_data_pos": (X_minority, y_minority),
"train_data_neg": (X_majority, y_majority),
"ir": ir,
}
args.dataset = dataset
self.CFAMG_model = CFAMG(args)
self.CFAMG_model.train_on_data()
return self

def _transform(self, X, y=None):
X_train, y_train, generated_samples = self.CFAMG_model.generator_sample()
inv_class_label_project = {v: k for k, v in self.class_label_project.items()}
y_train = np.array([inv_class_label_project[label] for label in y_train])
self.generated_samples = generated_samples
return X_train, y_train


if __name__ == "__main__":

n_samples = 100 # Total number of labels
majority_num = 90 # number of majority class
minority_num = n_samples - majority_num # number of minority class
np.random.seed(42)

X = np.random.rand(n_samples, 1, 10)
y = np.array([0] * majority_num + [1] * minority_num)
print(np.unique(y, return_counts=True))
smote = CFAM(random_state=42)

X_resampled, y_resampled = smote.fit_transform(X, y)
print(X_resampled.shape)
print(np.unique(y_resampled, return_counts=True))
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112 changes: 112 additions & 0 deletions tsml_eval/_wip/rt/transformations/collection/imbalance/pk_cfamg/README.md
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# Mitigating Data Imbalance in Time Series Classification Based on Counterfactual Minority Samples Augmentation

In this repository, we provide the original PyTorch implementation of CFAMG framework.

## Dataset

We conducted extensive experiments on 53 real-world benchmark datasets selected from the UCR and UEA time series classification repositories. For details, please refer to the following table：
| Type | Dataset | Dimensions | Pos Class | #Length | | Training | | | Test | |
|:------------:|:------------------------------:|:----------:|:----------------------:|:-------:|:----:|:--------:|:------:|:----:|:----:|:-------:|
| | | | | | #Neg | #Pos | IR | #Neg | #Pos | IR |
| Univariate | ACSF1 | 1 | 1 | 1460 | 90 | 10 | 9.00 | 90 | 10 | 9.00 |
| Multivariate | ArticularyWordRecognition | 9 | 25.0 | 144 | 264 | 11 | 24.00 | 288 | 12 | 24.00 |
| Univariate | Car | 1 | 3 | 577 | 49 | 11 | 4.45 | 41 | 19 | 2.16 |
| Univariate | Computers | 1 | 2 | 720 | 125 | 41 | 3.05 | 125 | 125 | 1.00 |
| Univariate | DodgerLoopDay | 1 | 5, 2 | 288 | 51 | 16 | 3.19 | 56 | 21 | 2.67 |
| Univariate | Earthquakes | 1 | 1 | 512 | 264 | 58 | 4.55 | 104 | 35 | 2.97 |
| Univariate | ECG5000 | 1 | 1 | 140 | 208 | 97 | 2.14 | 1873 | 2627 | 0.71 |
| Univariate | EthanolLevel | 1 | 4 | 1751 | 378 | 126 | 3.00 | 376 | 124 | 3.03 |
| Univariate | FaceAll | 1 | 14 | 131 | 520 | 40 | 13.00 | 1658 | 32 | 51.81 |
| Univariate | FacesUCR | 1 | 11, 14 | 131 | 190 | 10 | 19.00 | 1940 | 110 | 17.64 |
| Univariate | FiftyWords | 1 | 41, 50, 49, 42, 25, 34 | 270 | 438 | 12 | 36.50 | 423 | 32 | 13.22 |
| Univariate | Fish | 1 | 4 | 463 | 154 | 21 | 7.33 | 146 | 29 | 5.03 |
| Univariate | FreezerRegularTrain | 1 | 2 | 301 | 75 | 25 | 3.00 | 1425 | 1425 | 1.00 |
| Univariate | GestureMidAirD2 | 1 | 23, 26 | 360 | 192 | 16 | 12.00 | 120 | 10 | 12.00 |
| Univariate | GestureMidAirD3 | 1 | 3, 13 | 360 | 192 | 16 | 12.00 | 120 | 10 | 12.00 |
| Univariate | GesturePebbleZ1 | 1 | 2 | 455 | 112 | 20 | 5.60 | 147 | 25 | 5.88 |
| Univariate | GesturePebbleZ2 | 1 | 2 | 455 | 123 | 23 | 5.35 | 136 | 22 | 6.18 |
| Univariate | GunPoint | 1 | 1 | 150 | 26 | 10 | 2.60 | 74 | 76 | 0.97 |
| Univariate | GunPointAgeSpan | 1 | 2 | 150 | 68 | 22 | 3.09 | 160 | 156 | 1.03 |
| Univariate | Ham | 1 | 1 | 431 | 57 | 17 | 3.35 | 54 | 51 | 1.06 |
| Multivariate | HandMovementDirection | 10 | backward | 400 | 120 | 40 | 3.00 | 59 | 15 | 3.93 |
| Univariate | Haptics | 1 | 1 | 1092 | 137 | 18 | 7.61 | 248 | 60 | 4.13 |
| Univariate | Herring | 1 | 2 | 512 | 39 | 10 | 3.90 | 38 | 26 | 1.46 |
| Univariate | InlineSkate | 1 | 1, 7 | 1882 | 80 | 20 | 4.00 | 446 | 104 | 4.29 |
| Univariate | ItalyPowerDemand | 1 | 2 | 24 | 34 | 11 | 3.09 | 513 | 516 | 0.99 |
| Univariate | Lightning2 | 1 | -1 | 637 | 40 | 20 | 2.00 | 33 | 28 | 1.18 |
| Univariate | Mallat | 1 | 5, 1, 3 | 1024 | 41 | 14 | 2.93 | 1459 | 886 | 1.65 |
| Univariate | MedicalImages | 1 | 8, 6 | 99 | 368 | 13 | 28.31 | 726 | 34 | 21.35 |
| Univariate | MelbournePedestrian | 1 | 9 | 24 | 1040 | 98 | 10.61 | 2129 | 190 | 11.21 |
| Univariate | MiddlePhalanxOutlineCorrect | 1 | 0 | 80 | 388 | 70 | 5.54 | 166 | 125 | 1.33 |
| Univariate | MixedShapesRegularTrain | 1 | 5 | 1024 | 400 | 100 | 4.00 | 2111 | 314 | 6.72 |
| Univariate | MixedShapesSmallTrain | 1 | 5 | 1024 | 80 | 20 | 4.00 | 2111 | 314 | 6.72 |
| Multivariate | NATOPS | 24 | 2.0 | 51 | 150 | 30 | 5.00 | 150 | 30 | 5.00 |
| Univariate | PickupGestureWiimoteZ | 1 | 10, 9 | 361 | 40 | 10 | 4.00 | 40 | 10 | 4.00 |
| Univariate | PigAirwayPressure | 1 | 52, 51, 50, 49, 48 | 2000 | 94 | 10 | 9.40 | 188 | 20 | 9.40 |
| Univariate | PigArtPressure | 1 | 52, 51, 50, 49, 48 | 2000 | 94 | 10 | 9.40 | 188 | 20 | 9.40 |
| Univariate | ProximalPhalanxOutlineAgeGroup | 1 | 1 | 80 | 328 | 72 | 4.56 | 188 | 17 | 11.06 |
| Univariate | ProximalPhalanxTW | 1 | 3 | 80 | 384 | 16 | 24.00 | 203 | 2 | 101.50 |
| Multivariate | RacketSports | 6 | squash_backhandboast | 30 | 117 | 34 | 3.44 | 118 | 34 | 3.47 |
| Univariate | RefrigerationDevices | 1 | 3 | 720 | 250 | 125 | 2.00 | 250 | 125 | 2.00 |
| Univariate | ScreenType | 1 | 3 | 720 | 250 | 125 | 2.00 | 250 | 125 | 2.00 |
| Multivariate | SelfRegulationSCP2 | 7 | positivity | 1152 | 100 | 33 | 3.03 | 90 | 90 | 1.00 |
| Univariate | SemgHandGenderCh2 | 1 | 2 | 1500 | 150 | 50 | 3.00 | 390 | 210 | 1.86 |
| Univariate | SemgHandMovementCh2 | 1 | 6 | 1500 | 375 | 75 | 5.00 | 375 | 75 | 5.00 |
| Univariate | ShakeGestureWiimoteZ | 1 | 10, 9 | 385 | 40 | 10 | 4.00 | 40 | 10 | 4.00 |
| Univariate | SmallKitchenAppliances | 1 | 3 | 720 | 250 | 125 | 2.00 | 250 | 125 | 2.00 |
| Univariate | SmoothSubspace | 1 | 3 | 15 | 100 | 50 | 2.00 | 100 | 50 | 2.00 |
| Univariate | Strawberry | 1 | 1 | 235 | 394 | 73 | 5.40 | 238 | 132 | 1.80 |
| Univariate | Trace | 1 | 2 | 275 | 79 | 21 | 3.76 | 71 | 29 | 2.45 |
| Univariate | TwoPatterns | 1 | 2 | 128 | 763 | 237 | 3.22 | 2989 | 1011 | 2.96 |
| Univariate | Wine | 1 | 2 | 234 | 30 | 10 | 3.00 | 27 | 27 | 1.00 |
| Univariate | Worms | 1 | 5 | 900 | 164 | 17 | 9.65 | 69 | 8 | 8.62 |
| Univariate | WormsTwoClass | 1 | 1 | 900 | 105 | 25 | 4.20 | 44 | 33 | 1.33 |

## Usage

#### Requirements

The code was trained with `python 3.8`, `pytorch 1.13.1, `cuda 11.7`, and `cudnn 8.5.0`.

```shell
# create virtual environment
conda create --name CFAMG python=3.8

# activate environment
conda activate CFAMG

# Install dependencies
conda env create -f environment.yml

#### Run code

The UCR and UEA datasets can be accessed through the **tslearn** package, which can be installed as follows:

```shell
pip install tslearn

```python
from tslearn.datasets import UCR_UEA_datasets

To train and generate minority samples CFAMG framework on a dataset, run the following command:

```shell
python main.py --log_dir <model_save_path> --save_freq <frequency_to_save_model_checkpoint> --num_epochs <max_iters_training> --latent_size <latent_size> --batch_size <batch_size> --lr <learning rate> --tensorboard --seed <seed>```

We choose classifiers from the **tsai library** as a unified benchmark, and tsai can be installed as follows

```shell
pip install tsai


## Directory Structure

The code directory structure is shown as follows:
```shell

CFAMG
├── main.py # entry for model training
├── cfamg.py # training, generating minority samples of CFAMG
├── data_preprocess.py # dataset loading and preprocessing
├── model_utlis.py # common utility functions
├── network.py # network layer definition
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