oxits 0.1.0

Time series classification and transformation library for Rust
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"""Generate golden test data for classification module."""

import numpy as np
from pyts.classification import BOSSVS, SAXVSM, KNeighborsClassifier, TimeSeriesForest


def generate():
    fixtures = []
    rng = np.random.RandomState(42)

    X_train = rng.randn(20, 30)
    y_train = np.array(["A"] * 10 + ["B"] * 10)
    X_test = rng.randn(6, 30)
    y_test = np.array(["A"] * 3 + ["B"] * 3)

    # KNN with Euclidean
    knn = KNeighborsClassifier(n_neighbors=3, metric="euclidean")
    knn.fit(X_train, y_train)
    predictions = knn.predict(X_test)
    score = knn.score(X_test, y_test)
    fixtures.append({
        "test_name": "knn_euclidean",
        "params": {"n_neighbors": 3, "metric": "euclidean"},
        "input": {
            "X": X_train.tolist(),
            "y": y_train.tolist(),
            "X_test": X_test.tolist(),
        },
        "expected": {
            "predictions": predictions.tolist(),
            "score": float(score),
        },
        "tolerance": 1e-8,
    })

    # KNN with DTW
    knn = KNeighborsClassifier(n_neighbors=1, metric="dtw")
    knn.fit(X_train, y_train)
    predictions = knn.predict(X_test)
    score = knn.score(X_test, y_test)
    fixtures.append({
        "test_name": "knn_dtw",
        "params": {"n_neighbors": 1, "metric": "dtw"},
        "input": {
            "X": X_train.tolist(),
            "y": y_train.tolist(),
            "X_test": X_test.tolist(),
        },
        "expected": {
            "predictions": predictions.tolist(),
            "score": float(score),
        },
        "tolerance": 1e-8,
    })

    # BOSSVS
    bossvs = BOSSVS(window_size=8, word_size=4, n_bins=4)
    bossvs.fit(X_train, y_train)
    predictions = bossvs.predict(X_test)
    score = bossvs.score(X_test, y_test)
    fixtures.append({
        "test_name": "bossvs_basic",
        "params": {"window_size": 8, "word_size": 4, "n_bins": 4},
        "input": {
            "X": X_train.tolist(),
            "y": y_train.tolist(),
            "X_test": X_test.tolist(),
        },
        "expected": {
            "predictions": predictions.tolist(),
            "score": float(score),
        },
        "tolerance": 1e-6,
    })

    # SAXVSM
    saxvsm = SAXVSM(window_size=8, word_size=4, n_bins=4)
    saxvsm.fit(X_train, y_train)
    predictions = saxvsm.predict(X_test)
    score = saxvsm.score(X_test, y_test)
    fixtures.append({
        "test_name": "saxvsm_basic",
        "params": {"window_size": 8, "word_size": 4, "n_bins": 4},
        "input": {
            "X": X_train.tolist(),
            "y": y_train.tolist(),
            "X_test": X_test.tolist(),
        },
        "expected": {
            "predictions": predictions.tolist(),
            "score": float(score),
        },
        "tolerance": 1e-6,
    })

    return fixtures