Metadata-Version: 2.4
Name: soakpy
Version: 0.0.4
Summary: SOAK splitting utility
Author-email: Tung Nguyen <nguyenlamtung10@gmail.com>
License-Expression: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: scipy
Dynamic: license-file

# SOAK: Same/Other/All K-fold Cross-Validation
SOAK is designed to estimate the **similarity of patterns** found across different subsets of a dataset. It extends traditional K-fold cross-validation with "Same," "Other," and "All" splitting strategies to provide a robust measure of pattern similarity.

# Usage
## Low-level: SOAK split only
```python
import numpy as np
import soakpy

# --- synthetic data ---
X = np.arange(10).reshape(-1, 1)
X = np.append(X, [10, 12, 14])
y = X.ravel()
subset_vec = np.array(['even' if x % 2 == 0 else 'odd' for x in X.ravel()])

# --- Initialize soak object ---
for subset_value, category, fold_id, random_seed, train_idx_final, test_same_idx in soakpy.split(subset_vec, n_splits=2, n_random_seeds=2):
    print(f"test subset: {subset_value:6s} --- category: {category:6s} --- test fold: {fold_id}")
    print(f"y_test : {y[test_same_idx]}")
    print(f"y_train: {y[train_idx_final]}")
    print("-"*50)
```

```
test subset: even   --- category: same   --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [ 6  8 12 14]
--------------------------------------------------
test subset: even   --- category: other  --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [1 9]
--------------------------------------------------
test subset: even   --- category: same-ds --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [ 6 14]
--------------------------------------------------
test subset: even   --- category: same-ds --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [ 8 14]
--------------------------------------------------
test subset: even   --- category: all    --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [ 1  6  8  9 12 14]
--------------------------------------------------
test subset: even   --- category: all-ds --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [ 1 14]
--------------------------------------------------
test subset: even   --- category: all-ds --- test fold: 1
y_test : [ 0  2  4 10]
y_train: [12 14]
--------------------------------------------------
test subset: odd    --- category: same   --- test fold: 1
y_test : [3 5 7]
y_train: [1 9]
--------------------------------------------------
test subset: odd    --- category: other  --- test fold: 1
y_test : [3 5 7]
y_train: [ 6  8 12 14]
--------------------------------------------------
test subset: odd    --- category: other-ds --- test fold: 1
y_test : [3 5 7]
y_train: [12 14]
--------------------------------------------------
test subset: odd    --- category: other-ds --- test fold: 1
y_test : [3 5 7]
y_train: [ 8 14]
--------------------------------------------------
test subset: odd    --- category: all    --- test fold: 1
y_test : [3 5 7]
y_train: [ 1  6  8  9 12 14]
--------------------------------------------------
test subset: odd    --- category: all-ds --- test fold: 1
y_test : [3 5 7]
y_train: [8 9]
--------------------------------------------------
test subset: odd    --- category: all-ds --- test fold: 1
y_test : [3 5 7]
y_train: [ 8 14]
--------------------------------------------------
test subset: even   --- category: same   --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [ 0  2  4 10]
--------------------------------------------------
test subset: even   --- category: other  --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [3 5 7]
--------------------------------------------------
test subset: even   --- category: same-ds --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [0 2]
--------------------------------------------------
test subset: even   --- category: same-ds --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [ 4 10]
--------------------------------------------------
test subset: even   --- category: all    --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [ 0  2  3  4  5  7 10]
--------------------------------------------------
test subset: even   --- category: all-ds --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [0 5]
--------------------------------------------------
test subset: even   --- category: all-ds --- test fold: 2
y_test : [ 6  8 12 14]
y_train: [ 2 10]
--------------------------------------------------
test subset: odd    --- category: same   --- test fold: 2
y_test : [1 9]
y_train: [3 5 7]
--------------------------------------------------
test subset: odd    --- category: other  --- test fold: 2
y_test : [1 9]
y_train: [ 0  2  4 10]
--------------------------------------------------
test subset: odd    --- category: other-ds --- test fold: 2
y_test : [1 9]
y_train: [0 4]
--------------------------------------------------
test subset: odd    --- category: other-ds --- test fold: 2
y_test : [1 9]
y_train: [ 2 10]
--------------------------------------------------
test subset: odd    --- category: all    --- test fold: 2
y_test : [1 9]
y_train: [ 0  2  3  4  5  7 10]
--------------------------------------------------
test subset: odd    --- category: all-ds --- test fold: 2
y_test : [1 9]
y_train: [2 7]
--------------------------------------------------
test subset: odd    --- category: all-ds --- test fold: 2
y_test : [1 9]
y_train: [2 5]
--------------------------------------------------
```

## High-level: Analyze dataset and Visualize
```python
import soakpy
import pandas as pd

df = pd.read_csv("https://github.com/lamtung16/soak_regression/raw/refs/heads/main/data/WorkersCompensation.csv.xz")
soak_obj = soakpy.SOAK(df=df, subset_col="Gender", target_col="UltimateIncurredClaimCost")
soak_obj.analyze(model_list=["featureless", "tree"], n_splits=5, n_random_seeds=5, log_target=True)
soak_obj.visualize(subset_value='M', model="tree", metric="rmse", figsize=(12, 2.5))
```
![Image](data:image/png;base64,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)
