Skip to content

Models

myogestic.models ships constructor recipes for third-party estimators - thin wrappers that return a fitted-or-fittable object (.fit(X, y) + .predict(X)) with sane defaults. The library never owns the model lifecycle; that stays in your @pipeline.train. Optional dependencies are imported lazily, and each constructor raises a clear ImportError naming the extra to install.

CatBoost

catboost_classifier 

catboost_classifier(**kwargs: Any) -> Any

CatBoostClassifier with quiet defaults.

Source code in myogestic/models/__init__.py 
52
53
54
55
56
57
def catboost_classifier(**kwargs: Any) -> Any:
    """CatBoostClassifier with quiet defaults."""
    cb = _require("catboost", "examples")
    kwargs.setdefault("verbose", 0)
    kwargs.setdefault("allow_writing_files", False)
    return cb.CatBoostClassifier(**kwargs)

catboost_regressor 

catboost_regressor(**kwargs: Any) -> Any

CatBoostRegressor with quiet defaults.

Source code in myogestic/models/__init__.py 
60
61
62
63
64
65
def catboost_regressor(**kwargs: Any) -> Any:
    """CatBoostRegressor with quiet defaults."""
    cb = _require("catboost", "examples")
    kwargs.setdefault("verbose", 0)
    kwargs.setdefault("allow_writing_files", False)
    return cb.CatBoostRegressor(**kwargs)

scikit-learn

sklearn_classifier 

sklearn_classifier(**kwargs: Any) -> Any

RandomForestClassifier (sklearn).

Source code in myogestic/models/__init__.py 
71
72
73
74
def sklearn_classifier(**kwargs: Any) -> Any:
    """RandomForestClassifier (sklearn)."""
    skl = _require("sklearn.ensemble", "dev", pip_name="scikit-learn")
    return skl.RandomForestClassifier(**kwargs)

sklearn_regressor 

sklearn_regressor(**kwargs: Any) -> Any

RandomForestRegressor (sklearn).

Source code in myogestic/models/__init__.py 
77
78
79
80
def sklearn_regressor(**kwargs: Any) -> Any:
    """RandomForestRegressor (sklearn)."""
    skl = _require("sklearn.ensemble", "dev", pip_name="scikit-learn")
    return skl.RandomForestRegressor(**kwargs)

sklearn_extra_trees_classifier 

sklearn_extra_trees_classifier(**kwargs: Any) -> Any

ExtraTreesClassifier (sklearn). Defaults n_estimators=300, n_jobs=-1.

Source code in myogestic/models/__init__.py 
83
84
85
86
87
88
89
def sklearn_extra_trees_classifier(**kwargs: Any) -> Any:
    """ExtraTreesClassifier (sklearn). Defaults n_estimators=300, n_jobs=-1."""
    skl = _require("sklearn.ensemble", "dev", pip_name="scikit-learn")
    kwargs.setdefault("n_estimators", 300)
    kwargs.setdefault("random_state", 0)
    kwargs.setdefault("n_jobs", -1)
    return skl.ExtraTreesClassifier(**kwargs)

sklearn_extra_trees_regressor 

sklearn_extra_trees_regressor(**kwargs: Any) -> Any

ExtraTreesRegressor (sklearn). Same defaults as the classifier.

Source code in myogestic/models/__init__.py 
92
93
94
95
96
97
98
def sklearn_extra_trees_regressor(**kwargs: Any) -> Any:
    """ExtraTreesRegressor (sklearn). Same defaults as the classifier."""
    skl = _require("sklearn.ensemble", "dev", pip_name="scikit-learn")
    kwargs.setdefault("n_estimators", 300)
    kwargs.setdefault("random_state", 0)
    kwargs.setdefault("n_jobs", -1)
    return skl.ExtraTreesRegressor(**kwargs)

sklearn_logistic_classifier 

sklearn_logistic_classifier(**kwargs: Any) -> Any

Multinomial LogisticRegression (sklearn). max_iter=1000 default.

Source code in myogestic/models/__init__.py 
101
102
103
104
105
def sklearn_logistic_classifier(**kwargs: Any) -> Any:
    """Multinomial LogisticRegression (sklearn). max_iter=1000 default."""
    skl = _require("sklearn.linear_model", "dev", pip_name="scikit-learn")
    kwargs.setdefault("max_iter", 1000)
    return skl.LogisticRegression(**kwargs)

Dummy estimators (zero deps)

constant_classifier 

constant_classifier(class_idx: int = 0) -> _ConstantClassifier

Estimator that always predicts class_idx. No deps.

Source code in myogestic/models/__init__.py 
149
150
151
def constant_classifier(class_idx: int = 0) -> _ConstantClassifier:
    """Estimator that always predicts ``class_idx``. No deps."""
    return _ConstantClassifier(class_idx=class_idx)

mean_regressor 

mean_regressor() -> _MeanRegressor

Estimator that predicts the mean of training targets. No deps.

Source code in myogestic/models/__init__.py 
154
155
156
def mean_regressor() -> _MeanRegressor:
    """Estimator that predicts the mean of training targets. No deps."""
    return _MeanRegressor()

Persistence

save_model 

save_model(model: Any, path: str) -> str

Pickle model to path via joblib. Returns the path.

Source code in myogestic/models/__init__.py 
162
163
164
165
def save_model(model: Any, path: str) -> str:
    """Pickle `model` to `path` via joblib. Returns the path."""
    joblib.dump(model, path)
    return path

load_model 

load_model(path: str) -> Any

Load a joblib-saved model from path.

Source code in myogestic/models/__init__.py 
168
169
170
def load_model(path: str) -> Any:
    """Load a joblib-saved model from `path`."""
    return joblib.load(path)