# Classification Dl 4 Tsc > Use hfawaz/dl-4-tsc for time-series classification after ts-classification-data-prep, including UCRArchive_2018 TSV files, MTS .npy arrays, fixed 3D tensors shaped (n_samples, n_timestamps, n_variables), TensorFlow/Keras deep classifiers, main.py experiment runs, metrics/logs, plotting artifacts, and leakage-aware evaluation. - Skill: `hibra999/classification-dl-4-tsc` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add hibra999/classification-dl-4-tsc` - Raw SKILL.md: https://api.skillmd.com/api/skills/hibra999/classification-dl-4-tsc/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Hibra999 (https://skillmd.com/u/hibra999) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/hibra999/classification-dl-4-tsc --- # dl-4-tsc Classification Use this skill after `ts-classification-data-prep`. `dl-4-tsc` is best for reproducing or adapting the official deep-learning time-series classification experiments from hfawaz/dl-4-tsc. Do not use this skill for forecasting. Treat it as research code and experiment scripts, not a packaged sklearn-style library. ## Minimum Install Official README workflow: ```bash docker run --name dl4tsc --gpus all -idt hassanfawaz/dl-4-tsc:0.3 docker exec -it dl4tsc bash ``` Manual setup uses `pip-requirements.txt`. The repository notes that the current code uses TensorFlow 2.0, while the paper results were generated with the older TensorFlow 1.14 implementation. ## Data Contract - Require a completed `ts-classification-data-prep` contract: labels, split IDs, class balance, channel order, sampling policy, padding/interpolation policy, and leakage notes. - UCR format: `/dl-4-tsc/archives/UCRArchive_2018//_TRAIN.tsv` and `_TEST.tsv`, with label in the first column. - MTS format: `/dl-4-tsc/archives/mts_archive//x_train.npy`, `y_train.npy`, `x_test.npy`, `y_test.npy`. - Runtime model input is fixed length. Univariate UCR data starts as `(n_samples, n_timestamps)` and `main.py` reshapes it to `(n_samples, n_timestamps, 1)`. - Multivariate arrays must be `(n_samples, n_timestamps, n_variables)`, not `(n_samples, n_variables, n_timestamps)`. - The repo does not document native unequal-length classifier input; pad, truncate, or interpolate before training, and record the policy. Read `references/dl4tsc-data-validation.md` before converting custom data. ## Classifier Selection Official `main.py` classifier keys: - `fcn`: fully convolutional network. - `mlp`: multilayer perceptron baseline. - `resnet`: residual network. - `encoder`: encoder-style convolutional classifier. - `cnn`: Time-CNN. - `mcnn`: multi-scale CNN. - `tlenet`: t-LeNet. - `twiesn`: time warping invariant echo state network. - `mcdcnn`: multi-channel deep CNN. - `inception`: InceptionTime-style classifier included in current code. Read `references/dl4tsc-model-map.md` before claiming probability support, multivariate behavior, or constructor signatures. ## Run Pattern ```bash python -m main UCRArchive_2018 Coffee fcn _itr_0 ``` Argument order is `archive_name dataset_name classifier_name itr`. `run_all` is documented in code for batch experiments, but agents should prefer explicit dataset/classifier runs for reproducibility. For custom prepared data, create the expected archive directory structure first, then run `main.py`. The code uses `/dl-4-tsc/` as `root_dir`; change it deliberately if the repo is not mounted there. ## Evaluation And Outputs - Official logging writes `history.csv`, `df_metrics.csv`, `df_best_model.csv`, saved predictions, model files, and training-loss plots under `results/`. - Official metrics include accuracy, macro precision, macro recall, and duration. - Add external F1-macro, balanced accuracy, confusion matrix, ROC-AUC, or average precision only when predictions/probabilities are exported correctly for the class setup. - The repo does not document a public `predict_proba` API. Keras models use class scores internally, but expose probabilities only through custom model loading/inference code. - Plotting utilities include training-loss plots plus ResNet filter/CAM visualization helpers. ## Anti-Leakage Rules - Split train/validation/test before scaling, interpolation, length selection, label encoding, class weighting, augmentation, or model selection. - Do not use random splits unless the prep contract explicitly says sample order has no temporal, subject, device, or entity dependency. Use stratified or group-aware splits when needed. - Fit all normalization, interpolation, feature construction, and label encoders on train folds only. - Official UCR reading standardizes each sample independently; do not replace this with full-dataset scaling. - Official MTS conversion computes interpolation length from train plus test. For strict custom evaluation, choose length from train data or fixed external metadata. - Official `main.py` fits one-hot labels on train plus test labels. For strict workflows, fit label mapping on train labels and treat unseen held-out labels as a data-contract error. ## Common Errors - Using old README examples with archive name `TSC`; current constants use `UCRArchive_2018`. - Running outside `/dl-4-tsc/` without updating the hard-coded `root_dir`. - Passing multivariate arrays in channel-first order. - Expecting sklearn `fit/predict/predict_proba`, stratified CV, or calibration utilities. - Letting held-out test data choose padding length, interpolation length, class mapping, or model hyperparameters. - Assuming all README table models cover current code; current `utils/constants.py` includes `inception` in addition to the legacy table models. ## References - Read `references/dl4tsc-model-map.md` for supported classifier keys and documented caveats. - Read `references/dl4tsc-data-validation.md` for accepted formats, split handling, tensor axes, and leakage controls. - Read `references/official-sources.md` for official sources consulted. - Use `scripts/validate_dl4tsc_arrays.py` to validate MTS/custom `.npy` train/test tensors before training. ## Ready Checklist - `ts-classification-data-prep` contract is complete and leakage risks are documented. - Data is in official UCR TSV or MTS NPY layout, with fixed-length samples. - Tensor axes are `(samples, timestamps, variables)` after conversion. - Classifier key is one of the official `main.py` keys. - Validation is stratified or group-aware where required, and no transform was fit on held-out data. - Metrics include imbalance-aware scores beyond accuracy when classes are skewed.