torchdtcc 0.0.3

Deep Temporal Contrastive Clustering for time series

pytorch implementation of Deep Temporal Contrastive Clustering

Implementation of the Deep Temporal Contrastive Clustering. Inspired by 07zy's bit older Tensorflow based approach (unfortunately using deprecated Tensorflow version). Therefore I decided to implement it myself in pytorch.

Config

Easiest is to configure it using a YAML file. Example:

# torchdtcc config.yaml

model:
  input_dim:  1
  num_layers:  3
  num_clusters: 3
  hidden_dims:  [100, 50, 50]
  dilation_rates: [1, 4, 16]
  tau_I: 1.0
  tau_C: 1.0
  stable_svd: false

trainer:
  save_path: "dtcc_model.pth"
  learning_rate: 0.005
  weight_decay: 0
  lambda_cd: 0.001
  num_epochs: 200
  update_interval: 5

data:
  dataset_class: "torchdtcc.datasets.meat.MeatArffDataset"
  dataset_args:   # Arguments to initialize your dataset class
    files_path: "./data/meat/"
    # add more args as needed

  batch_size: 64


output:
  soft_clusters: "soft_clusters.npy"
  hard_clusters_argmax: "hard_clusters_argmax.npy"
  hard_clusters_kmeans: "hard_clusters_kmeans.npy"

device: "cuda"  # or "cpu"

Training

import yaml
from torchdtcc.dtcc.trainer import DTCCTrainer
from torchdtcc.dtcc.clustering import Clusterer
from torch.utils.data import DataLoader
from torchdtcc.datasets.meat.arff_meat import MeatArffDataset

import logging

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s %(levelname)s %(name)s %(message)s",
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

with open("config.yaml", "r") as f:
    config = yaml.safe_load(f)

# Prepare dataset
data_cfg = config.get("data", {})
dataset = MeatArffDataset(path=data_cfg['dataset_args']['files_path'])

model_cfg = config.get("model", {})
logging.info(f"STABLE SVD: {model_cfg['stable_svd']}")
 
trainer = DTCCTrainer.from_config(config, dataset)
save_path = config.get("trainer", {}).get("save_path", "")
model = trainer.run(save_path=save_path)

Usage

After training

...
# Assuming the training script above

dataloader = DataLoader(dataset, batch_size=data_cfg.get("batch_size", 64), shuffle=False)
clusterer = Clusterer(config["device"])
clusterer.set_model(model)
labels = clusterer.cluster(dataloader, method="kmeans")  # or "soft", "argmax"
print(f"resulting predictions:\n{labels}")

Load model

import yaml
from torchdtcc.dtcc.trainer import DTCCTrainer
from torchdtcc.dtcc.clustering import Clusterer
from torch.utils.data import DataLoader
from torchdtcc.datasets.meat.arff_meat import MeatArffDataset

import logging

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s %(levelname)s %(name)s %(message)s",
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

with open("config.yaml", "r") as f:
    config = yaml.safe_load(f)

# Prepare dataset and dataloader
data_cfg = config.get("data", {})
dataset = MeatArffDataset(path=data_cfg['dataset_args']['files_path'])
dataloader = DataLoader(dataset, batch_size=data_cfg.get("batch_size", 64), shuffle=False)

model_path = config.get("trainer", {}).get("save_path", "")
model_cfg = config.get("model", {})

clusterer = Clusterer()
clusterer.load_model(
    model_path=model_path,
    model_kwargs=model_cfg,
    device=config.get("device", "cuda")
)
labels = clusterer.cluster(dataloader, method="kmeans")  # or "soft", "argmax"
print(f"resulting predictions:\n{labels}")

Run evaluation

To run the clusterer and print the accuracy, NMI, ARI and RI scores.

# assuming you have run some clustering example above
clusterer.evaluate(dataloader, method="kmeans")

Use your own dataset

This is the definition of the base class for an augmented dataset. DTCC always expects an AugmentedDataset and you need to implement the augmentation.

import torch
from torch.utils.data import Dataset
from abc import abstractmethod

class AugmentedDataset(Dataset):
    def __init__(self, dataframe, feature_cols, target_col):
        self.X = dataframe[feature_cols].values.astype('float32')
        self.y = dataframe[target_col].astype('int64').values

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        x = torch.tensor(self.X[idx])
        if x.ndim == 1:
            x = x.unsqueeze(-1)  # add feature dimension if only batch and seq_len provided
        y = torch.tensor(self.y[idx])
        return x, y
    
    @abstractmethod
    def augmentation(self, batch_x):
        pass

You can use the augmentations submodule for implementing the augmentations. See the meat class as reference:

from scipy.io import arff
import pandas as pd
from torchdtcc.augmentations.basic import jitter
from torchdtcc.augmentations.helper import torch_augmentation_wrapper
from torchdtcc.datasets.augmented_dataset import AugmentedDataset

class MeatArffDataset(AugmentedDataset):
    def __init__(self, path):
        data_train, _ = arff.loadarff(path + 'Meat_TRAIN.arff')
        data_test, _ = arff.loadarff(path + 'Meat_TEST.arff')
        df_train = pd.DataFrame(data_train)
        df_test = pd.DataFrame(data_test)
        df = pd.concat([df_train, df_test], ignore_index=True)
        for col in df.select_dtypes([object]):
            df[col] = df[col].apply(lambda x: x.decode('utf-8') if isinstance(x, bytes) else x)
        feature_cols = [col for col in df.columns if col.startswith('att')]
        target_col = 'target'
        # Ensure target is int
        df[target_col] = df[target_col].astype(int)
        super().__init__(df, feature_cols, target_col)

    def augmentation(self, batch_x):        
        # Ensure [batch, seq_len, features]
        assert batch_x.ndim == 3, f"Input must be 3D, got {batch_x.shape}"
        
        x_aug = torch_augmentation_wrapper(jitter, batch_x)
        # x_augx = torch_augmentation_wrapper(scaling, x_aug) # Import scaling first

        return x_aug