| ---
|
| license: cc-by-4.0
|
| task_categories:
|
| - tabular-regression
|
| tags:
|
| - continual-learning
|
| - catastrophic-forgetting
|
| - synthetic
|
| - tabular
|
| - regression
|
| pretty_name: Ten-part regression forgetting benchmark
|
| size_categories:
|
| - 10K<n<100K
|
| configs:
|
| - config_name: seed_0
|
| data_files:
|
| - split: train
|
| path: data/seed_0/train.csv
|
| - split: test
|
| path: data/seed_0/test.csv
|
| - config_name: seed_1
|
| data_files:
|
| - split: train
|
| path: data/seed_1/train.csv
|
| - split: test
|
| path: data/seed_1/test.csv
|
| - config_name: seed_2
|
| data_files:
|
| - split: train
|
| path: data/seed_2/train.csv
|
| - split: test
|
| path: data/seed_2/test.csv
|
| - config_name: seed_3
|
| data_files:
|
| - split: train
|
| path: data/seed_3/train.csv
|
| - split: test
|
| path: data/seed_3/test.csv
|
| - config_name: seed_4
|
| data_files:
|
| - split: train
|
| path: data/seed_4/train.csv
|
| - split: test
|
| path: data/seed_4/test.csv
|
| ---
|
|
|
| # Ten-part regression forgetting benchmark
|
|
|
| A synthetic multi-variable regression stream for measuring **catastrophic
|
| forgetting**. One shared nonlinear target `f: R^6 -> R`; ten input
|
| distributions with disjoint support; one scalar output. A model is trained on
|
| part 0, then part 1, …, evaluated on every part after every stage. Task identity
|
| is never given, so this is a *domain-incremental* regression benchmark.
|
|
|
| It is the data behind the forgetting experiments in
|
| `neural_memory/FORGETTING.md`: a plain MLP fits each part to R² ≈ 0.85–0.93 and
|
| then loses it all (mean backward transfer −2.1), replay of 32 points per part
|
| removes the forgetting (−0.03), and EWC/SI barely help.
|
|
|
| ## Structure
|
|
|
| | config | splits | rows |
|
| |---|---|---|
|
| | `seed_0` … `seed_4` | `train` (5,120), `test` (2,560) | 7,680 per config |
|
|
|
| Each config is an independent draw of the whole stream (same generator, different
|
| seed). Rows are grouped by `task_id` in training order and keep the generator's
|
| row order inside a part. Files are CSV; the Hub converts them to Parquet for its
|
| viewer, and numeric columns load as `float64` holding the original float32
|
| values.
|
|
|
| | column | type | meaning |
|
| |---|---|---|
|
| | `task_id` | int | part index, 0–9 (training order) |
|
| | `x0` … `x5` | float | input variables |
|
| | `y` | float | target; noisy on `train`, clean on `test` |
|
| | `y_clean` | float | the noiseless target `f(x)` |
|
|
|
| Training targets carry noise with standard deviation `0.1 · std(f)` on that
|
| part; test targets are noiseless, so a score measures the learned function
|
| rather than a noise draw.
|
|
|
| ## Loading
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| train = load_dataset("ccde/forgetting-regression", "seed_0", split="train")
|
| part3 = train.filter(lambda row: row["task_id"] == 3)
|
|
|
| # or the whole stream, part by part, in benchmark order
|
| stream = [train.filter(lambda row, k=k: row["task_id"] == k) for k in range(10)]
|
| ```
|
|
|
| ```python
|
| import pandas as pd
|
|
|
| frame = pd.read_csv("hf://datasets/ccde/forgetting-regression/data/seed_0/train.csv")
|
| ```
|
|
|
| ## Generation
|
|
|
| Part `t` draws `x = c_t + s · z_t · L_t^T` and `y = f(x) + ε`, with
|
|
|
| - `c_t`: part mean on the sphere of radius 3 in R^6, placed by greedy
|
| farthest-point sampling (minimum pairwise distance 3.80 for seed 0);
|
| - `L_t`: random rotation × log-spaced axes spanning a factor of 2 (geometric
|
| mean 1), so each part is a differently oriented ellipsoid;
|
| - `z_t`: unit-covariance sample whose marginal cycles Gaussian → uniform →
|
| Laplace from part to part;
|
| - `s = 0.35`: within-part scale; parts are disjoint (a nearest-centre classifier
|
| recovers the part with accuracy ≥ 0.99);
|
| - `ε`: training-only noise, `0.1 · std(f)`.
|
|
|
| The shared target is
|
|
|
| ```
|
| f(x) = sin(1.5 x1) cos(1.5 x2) + 0.6 tanh(x3 x4) + 0.3 ((mean_i x_i^2) - 9)/4
|
| ```
|
|
|
| ## Evaluation protocol
|
|
|
| Score each part by `R² = 1 − MSE / Var(y_clean)`, giving a 10 × 10 matrix
|
| `a[t][j]`: R² on part `j` after training part `t`. Report `ACC` (mean of the
|
| final row), `BWT` (mean over `j < 9` of `a[9][j] − a[j][j]`; negative means
|
| forgetting), `LA` (mean diagonal) and `FWT`. Per-part normalisation matters:
|
| part target standard deviations range from 0.35 to 0.60.
|
|
|
| ## Limitations
|
|
|
| - Synthetic and deliberately disjoint: it stresses retention, not
|
| generalisation within a part.
|
| - One target family, six inputs, ten parts. Vary `f` for a harder stream; the
|
| generator supports per-part rotations of `f`.
|
| - No task identifiers, no validation split, no real-world data.
|
| - The five configs are independent streams, not five views of one stream.
|
|
|
| ## Provenance
|
|
|
| Generated by `modules/forgetting_data.py` (seed = config suffix) from the
|
| `neural_memory` repository; `benchmarks/export_forgetting.py` rebuilds these
|
| files. Version 1.0.
|
|
|
