Sklearn Compatibility

psipose estimators follow sklearn conventions strictly.

Rules

1. __init__ only stores parameters (no validation, no object construction)

2. Use None as default for mutable objects (encoders, ansatze)

3. Resolve defaults in fit(), not __init__()

4. Fitted attributes end with _ (e.g., weights_, classes_)

5. fit() returns self

6. Use sklearn.utils.validation.check_X_y for input validation

Example

def __init__(self, encoder=None, ansatz=None):
    self.encoder = encoder  # Store None, resolve in fit()
    self.ansatz = ansatz

def fit(self, X, y):
    encoder = self.encoder if self.encoder is not None else AngleFeatureMap()
    # ...
    return self