vvs
129 lines
4.4 KiB
Markdown
129 lines
4.4 KiB
Markdown
# sem_cython12 - sample projects
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Three short, runnable Python projects that demonstrate the `sem_cython12`
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library on small but realistic problems. Each demo is a single file,
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self-contained, and produces a clear printable result.
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The demos use **only** `sem_cython12.wrapper`, `numpy`, and (for the
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Iris and anomaly demos) `scikit-learn`.
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## What each demo shows
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| File | Domain | "Wow" |
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| [`01_iris_boundary.py`](./01_iris_boundary.py) | The 1936 Iris dataset | Rediscovers the famous versicolor/virginica boundary specimens **without training a classifier** and without setting any threshold. |
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| [`02_anomaly_detection.py`](./02_anomaly_detection.py) | Synthetic 5-D anomalies | Detects 10/10 injected anomalies with **a single function call** and matches/beats sklearn's IsolationForest on ROC AUC. |
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| [`03_multicriteria_selection.py`](./03_multicriteria_selection.py) | Multi-criteria candidate ranking | Identifies the **hidden all-rounders** that naive sum-of-scores ranking misses entirely. |
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## Install
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```bash
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# Get the library (private repo)
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git clone https://git.sevana.biz/vvs/sem_cython12.git ../sem_cython12
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export PYTHONPATH="$(pwd)/../sem_cython12:$PYTHONPATH"
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# Demo dependencies
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pip install -r requirements.txt
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```
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The pre-built Linux x86_64 / CPython 3.12 binary ships with the
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library; no compilation step is required.
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## Run
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```bash
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python 01_iris_boundary.py
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python 02_anomaly_detection.py
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python 03_multicriteria_selection.py
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```
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Each demo finishes in well under a second on a laptop.
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## What you'll see
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### 01_iris_boundary.py
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```
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Auto-derived kernel scale lam = 3.4762
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Top 10 most ambiguous specimens (highest cross-species score):
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rank idx species sim->setosa sim->versic sim->virgin cross
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1 138 virginica 0.2330 0.9096 1.0000 0.9096
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2 70 versicolor 0.2396 1.0000 0.9096 0.9096
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3 127 virginica 0.2222 0.8806 1.0000 0.8806
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4 83 versicolor 0.2084 1.0000 0.8689 0.8689
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5 133 virginica 0.2062 0.8689 1.0000 0.8689
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...
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Top 10 distribution by species:
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setosa : 0 of 10
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versicolor : 3 of 10
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virginica : 7 of 10
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*** Confirmed: zero setosa specimens; the top-10 boundary cases ***
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*** all come from the famous versicolor/virginica overlap zone. ***
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```
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### 02_anomaly_detection.py
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```
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SEM (sem_cython12 - one batch_max_similarity call)
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Top-10 retrieved as anomalous: precision = 10/10
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ROC AUC = 1.0000
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Baseline: sklearn IsolationForest (default settings)
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Top-10 retrieved as anomalous: precision = 10/10
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ROC AUC = 1.0000
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SEM matches IsolationForest within noise (+0.0000 AUC),
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with one function call and zero tuning.
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```
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### 03_multicriteria_selection.py
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```
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Best-tradeoff frontier size : 35
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Cross-criterion winners (NRW) : 31
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Hidden all-rounders we injected : 5 (indices 0-4)
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NRW recovered hidden all-rounders : 5/5 [0, 1, 2, 3, 4]
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Naive top-10 found hidden all-rounders: 3/5 [1, 2, 3]
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*** SEM's NRW filter recovered 5/5 hidden all-rounders. ***
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*** Naive sum-of-scores top-10 found only 3/5. ***
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*** SEM surfaces 2 candidates the naive ranking misses ***
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*** because they don't peak on any single criterion. ***
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```
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## What to try next
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- Replace the synthetic data in `02_*` with your own observations and
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see what gets flagged.
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- Replace the synthetic candidate matrix in `03_*` with your
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real-world multi-criteria evaluation (job applicants, vendor
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proposals, product features, drug screens).
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- Extend `01_*` to your own classification problems: any time you
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have multiple classes with overlapping members, the NRW operator
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surfaces the structurally informative boundary cases.
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The library has more capabilities than these three demos exercise.
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See the `sem_cython12.wrapper` API for the full operator set
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(pairwise distances, multi-class similarity matrix, incremental
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aggregation, etc.).
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## Licence
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The demos and the underlying `sem_cython12` library are licensed
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under the terms in the [LICENSE](./LICENSE) file:
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- Research and non-commercial use: free under the conditions
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stated in the licence.
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- Commercial use: requires a separate written commercial licence.
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Contact `sales@sevana.biz`.
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- The Software is provided strictly "AS IS", without warranty of
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any kind.
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Please read the LICENSE file in full before using the demos or the
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underlying library.
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