ceptron 0.1.3: temporal_causal/XS-discovery

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README.md

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+---
+tags:
+  - causal-discovery
+  - time-series
+  - temporal-causal
+  - prior-fitted-network
+license: cc-by-nc-4.0
+license_link: LICENSE
+---
+
+# Ceptron
+
+Open foundation models, one family per folder under `models/`, served by the `ceptron` Python package (one
+sub-package per family).
+
+| family | answers |
+|---|---|
+| `temporal_causal` | which variables of a time series cause which, at which lag, in one forward pass (built on TCPFN) |
+
+## Use
+
+Install into a virtual environment (Python 3.10 or newer). Ubuntu, Debian and Homebrew Python refuse a plain
+`pip install` outside one:
+
+```
+python3 -m venv ceptron-env
+source ceptron-env/bin/activate        # Windows: ceptron-env\Scripts\activate
+pip install "git+https://github.com/ProfitOpsAI/ceptron" pandas
+```
+
+On Ubuntu or Debian, if `python3 -m venv` says ensurepip is not available, run `sudo apt install python3-venv` first.
+Or with [uv](https://docs.astral.sh/uv/), which needs no system packages:
+
+```
+uv venv ceptron-env
+source ceptron-env/bin/activate
+uv pip install "git+https://github.com/ProfitOpsAI/ceptron" pandas
+```
+
+Then run `python` in that environment and paste this, or save it as `quickstart.py` and run `python quickstart.py`.
+It builds a small series with two planted causes and asks the model to find them; the first run downloads the model (about 260 MB):
+
+```python
+import numpy as np
+import pandas as pd
+from ceptron.temporal_causal import CausalModel
+
+# A toy series with two planted causes: price drives demand 2 steps later, ads drive visits 3 steps later.
+rng = np.random.default_rng(0)
+T = 400
+x = np.zeros((T, 4))
+noise = rng.normal(size=(T, 4))
+for t in range(3, T):
+    x[t] = 0.4 * x[t - 1] + noise[t]
+    x[t, 1] += 0.9 * x[t - 2, 0]   # price -> demand, lag 2
+    x[t, 3] += 0.8 * x[t - 3, 2]   # ads -> visits, lag 3
+df = pd.DataFrame(x, columns=["price", "demand", "ads", "visits"])
+
+model = CausalModel.from_pretrained("ProfitOpsAI/ceptron", model="XS-discovery")
+result = model.discover(df)
+print(result)
+```
+
+You should see (the last digit can differ on other hardware):
+
+```
+Discovery: 3 edges over 4 variables (graph head logit +22.53)
+  ads -> visits  lag 3  p 0.79
+  price -> demand  lag 2  p 0.76
+  demand -> price  lag 23  p 0.28
+```
+
+Both planted causes come first, with the right lags. The third, much weaker link is a false alarm: XS marks as many
+links as its probabilities add up to, and on very small graphs that count tends to be one too many, so read the
+probabilities, not only the list.
+
+On your own data, one column per variable and one row per time step (drop the timestamp column):
+
+```python
+df = pd.read_csv("my_series.csv").drop(columns=["timestamp"])
+result = model.discover(df, col_types={"orders": "count"})   # declare count, binary, ordinal, bounded columns
+print(result)
+result.to_frame()                                             # the marked links as a DataFrame
+```
+
+For an irregular clock, pass `time_gap`: the gap before each row, in steps of the regular clock.
+
+## Models
+
+Each model folder holds its own card, `config.json` and `model.safetensors`.
+
+| family | model | parameters | weights sha256 |
+|---|---|---:|---|
+| temporal_causal | [XS-discovery](models/temporal_causal/XS-discovery/README.md) | 65.5 M | `3366c5844caa4fb1...` |
+
+## Sizes
+
+Each family comes in sizes. **XS is available now**; **S, M and L are coming soon**. XS trades accuracy for speed:
+it is the fast first look. For higher precision, recall and F1, harder series and more of the causal questions the
+models answer natively, use S, M and L, coming soon; each release publishes its own measured numbers, so you can pick
+the size by the accuracy you need.
+
+| size | status | good for |
+|---|---|---|
+| XS | available: [`XS-discovery`](models/temporal_causal/XS-discovery/README.md) | a fast first causal map of a time series in seconds on a CPU or a fraction of a second on a GPU (100 columns × 400 rows: 0.8 s CPU, 0.03 s GPU): which variables drive which, and after how many steps. Strongest on process/operations and retail/demand series (AUROC 94 % and 88 % on our development set). [What XS is good for](models/temporal_causal/XS-discovery/README.md#what-xs-is-good-for) |
+| S | coming soon | higher precision, recall and F1 than XS; harder series |
+| M | coming soon | higher precision, recall and F1 than XS; harder series |
+| L | coming soon | higher precision, recall and F1 than XS; harder series |
+
+## License
+
+The model weights, configurations and cards in this repository are licensed under
+[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (see `LICENSE`): anyone may use, study, modify and
+share them for non-commercial purposes, including research, teaching and personal projects. Commercial use of the
+models needs a separate license from ProfitOps Inc. The `ceptron` code on GitHub is
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).

config.json

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+{
+ "library": "ceptron",
+ "models": {
+  "temporal_causal": [
+   "XS-discovery"
+  ]
+ }
+}

models/temporal_causal/XS-discovery/README.md

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+# XS-discovery
+
+The discovery model of Ceptron Temporal Causal, size XS, built on TCPFN (temporal causal prior-data fitted networks).
+It answers one question, which variables in a multivariate time series
+cause which, at which lag, from the network's own outputs: a graph-level head says whether there is any edge, and if so
+the top `round(sum of pair probabilities)` pairs are marked. Nothing is fitted on the request and there is no
+statistical fallback.
+
+## What XS is good for
+
+XS is the small, fast entry point: it answers on a CPU in seconds and on a GPU in a fraction of a second, with no
+fitting and no tuning. Time grows with columns × rows; memory stays about 1 GB:
+
+| columns | CPU, 400 rows | CPU, 2,000 rows | GPU, 400 rows | GPU, 2,000 rows |
+|---:|---:|---:|---:|---:|
+| 10 | 0.1 s | 0.7 s | 0.01 s | 0.03 s |
+| 50 | 0.4 s | 4 s | 0.01 s | 0.13 s |
+| 100 | 0.8 s | 8 s | 0.03 s | 0.25 s |
+| 200 | 1.8 s | 18 s | 0.06 s | 0.5 s |
+| 500 | | | 0.2 s | 1.5 s |
+
+(Measured on a desktop CPU with 8 threads and on an NVIDIA RTX 5080; a laptop CPU is usually slower, and the first
+run also downloads the model.) Use it to get a first causal map of a multivariate time series, which variables drive which, and after how
+many steps, before investing in a deeper analysis.
+
+It is strongest on:
+
+* **Process and operations series:** controlled process data on a regular clock (AUROC 94 %, F1 79 % on our
+  development set).
+* **Retail and demand series:** daily sales, prices, promotions and traffic (AUROC 88 %, F1 70 %).
+* **Mixed column types:** continuous, count, binary, ordinal and bounded columns, declared with `col_types`.
+* **Lagged effects:** a cause that acts after a delay of several steps.
+
+Use it with care on business-style tables with many weak links (AUROC 79–84 %, F1 41–47 %), and expect weaker answers
+on coarsely sampled or irregular series, long delays (20 to 60 steps), slow hidden trends and same-step links between
+count or binary columns. Effect sizes, identifiability and root cause come with the larger sizes and later XS
+releases.
+
+**Need higher precision or F1?** XS is the fast first look. For higher precision, recall and F1 and for harder
+series, use the S, M and L sizes, coming soon; each release publishes its own measured numbers, so you can choose the
+size by the accuracy your decision needs.
+
+## Use
+
+Install into a virtual environment (Python 3.10 or newer). Ubuntu, Debian and Homebrew Python refuse a plain
+`pip install` outside one:
+
+```
+python3 -m venv ceptron-env
+source ceptron-env/bin/activate        # Windows: ceptron-env\Scripts\activate
+pip install "git+https://github.com/ProfitOpsAI/ceptron" pandas
+```
+
+On Ubuntu or Debian, if `python3 -m venv` says ensurepip is not available, run `sudo apt install python3-venv` first.
+Or with [uv](https://docs.astral.sh/uv/), which needs no system packages:
+
+```
+uv venv ceptron-env
+source ceptron-env/bin/activate
+uv pip install "git+https://github.com/ProfitOpsAI/ceptron" pandas
+```
+
+Then run `python` in that environment and paste this, or save it as `quickstart.py` and run `python quickstart.py`.
+It builds a small series with two planted causes and asks the model to find them; the first run downloads the model (about 260 MB):
+
+```python
+import numpy as np
+import pandas as pd
+from ceptron.temporal_causal import CausalModel
+
+# A toy series with two planted causes: price drives demand 2 steps later, ads drive visits 3 steps later.
+rng = np.random.default_rng(0)
+T = 400
+x = np.zeros((T, 4))
+noise = rng.normal(size=(T, 4))
+for t in range(3, T):
+    x[t] = 0.4 * x[t - 1] + noise[t]
+    x[t, 1] += 0.9 * x[t - 2, 0]   # price -> demand, lag 2
+    x[t, 3] += 0.8 * x[t - 3, 2]   # ads -> visits, lag 3
+df = pd.DataFrame(x, columns=["price", "demand", "ads", "visits"])
+
+model = CausalModel.from_pretrained("ProfitOpsAI/ceptron", model="XS-discovery")
+result = model.discover(df)
+print(result)
+```
+
+You should see (the last digit can differ on other hardware):
+
+```
+Discovery: 3 edges over 4 variables (graph head logit +22.53)
+  ads -> visits  lag 3  p 0.79
+  price -> demand  lag 2  p 0.76
+  demand -> price  lag 23  p 0.28
+```
+
+Both planted causes come first, with the right lags. The third, much weaker link is a false alarm: XS marks as many
+links as its probabilities add up to, and on very small graphs that count tends to be one too many, so read the
+probabilities, not only the list.
+
+On your own data, one column per variable and one row per time step (drop the timestamp column):
+
+```python
+df = pd.read_csv("my_series.csv").drop(columns=["timestamp"])
+result = model.discover(df, col_types={"orders": "count"})   # declare count, binary, ordinal, bounded columns
+print(result)
+result.to_frame()                                             # the marked links as a DataFrame
+```
+
+For an irregular clock, pass `time_gap`: the gap before each row, in steps of the regular clock.
+
+## What it answers, and what it refuses
+
+* **Answers:** temporal causal discovery on a regular or irregular clock with continuous, count, binary, ordinal and
+  bounded columns: a directed graph with lags, plus the strongest links with their probabilities. Probabilities are
+  the network's readouts, not calibrated probabilities. The effect sign is not reported.
+* **Refuses:** missing cells, text columns and non-numeric data, and series shorter than one patch
+  (8 rows). It does not estimate effect sizes, identifiability or root causes.
+* **Known weak spots:** coarsely sampled and irregularly spaced series; long delays (20 to 60 steps), slow hidden
+  trends, state memory and delays that vary over time; contemporaneous links between count or binary columns. Control
+  loops, sensor read-through effects and industrial process mechanisms were not part of its training.
+
+## Measured on a development benchmark (files inspected during development, not a holdout)
+
+AUROC / F1 in %, every temporal file sent, unanswered scored as 50 / 0:
+
+| set | files | AUROC | F1 |
+|---|---:|---:|---:|
+| controlled process series | 72 | 94.3 | 79.1 |
+| retail daily series | 26 | 88.4 | 69.6 |
+| business-style files answered by every compared checkpoint | 103 | 83.6 | 47.2 |
+| business-style files, all sent | 118 | 79.3 | 41.2 |
+
+## Files
+
+| file | what |
+|---|---|
+| `model.safetensors` | the weights, 65.5 M parameters |
+| `config.json` | the architecture and the serving settings (windows read, decision rule, column types) |
+
+sha256 of `model.safetensors`: `3366c5844caa4fb198e9929c176d092a520cd4bbd91168494ec33d268291b69d` (checked by `CausalModel` on every load).
+
+## License
+
+The weights and this card: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/), non-commercial use only;
+commercial use needs a separate license from ProfitOps Inc. The `ceptron` code that runs them is
+[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).

models/temporal_causal/XS-discovery/config.json

@@ -0,0 +1,42 @@
+{
+ "name": "XS-discovery",
+ "library": "ceptron",
+ "family": "temporal_causal",
+ "model_type": "joint_panel",
+ "model": {
+  "d_model": 512,
+  "n_heads": 8,
+  "n_layers": 20,
+  "ff_factor": 4,
+  "patch_len": 8,
+  "max_lag": 100,
+  "max_patches": 256,
+  "pair_head": "cross_time",
+  "cross_dim": 32,
+  "cross_groups": 8,
+  "cross_shifts": 2,
+  "edge_head": "factorized",
+  "time_gap_input": true,
+  "categorical_input": true,
+  "n_col_types": 4,
+  "graph_head": true
+ },
+ "standardization": "per_column_zscore_clip10",
+ "serving": {
+  "window": 336,
+  "small_window": 672,
+  "small_max_columns": 10,
+  "window_policy": "all_half_overlap_v2_tail",
+  "decision_rule": "graph_head_topk_expected_count_v1",
+  "col_type_codes": {
+   "continuous": 0,
+   "ordinal": 1,
+   "binary": 1,
+   "nominal": 1,
+   "count": 2,
+   "bounded": 3
+  }
+ },
+ "n_parameters": 65503928,
+ "weights_sha256": "3366c5844caa4fb198e9929c176d092a520cd4bbd91168494ec33d268291b69d"
+}

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