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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,6 @@ | ||
| [project] | ||
| name = "skpro" | ||
| version = "2.1.1" | ||
| version = "2.1.2" | ||
| description = "A unified framework for probability distributions and probabilistic supervised regression" | ||
| authors = [ | ||
| {name = "skpro developers", email = "[email protected]"}, | ||
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@@ -44,7 +44,7 @@ dependencies = [ | |
| "numpy>=1.21.0,<1.27", | ||
| "pandas>=1.1.0,<2.2.0", | ||
| "packaging", | ||
| "scikit-base>=0.6.1,<0.7.0", | ||
| "scikit-base>=0.6.1,<0.8.0", | ||
| "scikit-learn>=0.24.0,<1.4.0", | ||
| "scipy<2.0.0,>=1.2.0", | ||
| ] | ||
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@@ -56,7 +56,7 @@ all_extras = [ | |
| "mapie", | ||
| "matplotlib>=3.3.2", | ||
| "ngboost", | ||
| "polars<0.20.0; python_version < '3.12'", | ||
| "polars<0.21.0", | ||
| "pyarrow<14.0.0; python_version < '3.12'", | ||
| "statsmodels>=0.12.1", | ||
| "tabulate", | ||
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||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,6 @@ | ||
| """skpro.""" | ||
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| __version__ = "2.1.1" | ||
| __version__ = "2.1.2" | ||
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| __all__ = ["show_versions"] | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| """Adapters for probabilistic regressors.""" | ||
| # copyright: skpro developers, BSD-3-Clause License (see LICENSE file) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,6 @@ | ||
| """Adapters for probabilistic regressors, towards sklearn.""" | ||
| # copyright: skpro developers, BSD-3-Clause License (see LICENSE file) | ||
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| from skpro.regression.adapters.sklearn._sklearn_proba import SklearnProbaReg | ||
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| __all__ = ["SklearnProbaReg"] |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,138 @@ | ||
| # copyright: skpro developers, BSD-3-Clause License (see LICENSE file) | ||
| """Adapter to sklearn probabilistic regressors.""" | ||
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| __author__ = ["fkiraly"] | ||
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| import pandas as pd | ||
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| from skpro.regression.base import BaseProbaRegressor | ||
| from skpro.utils.sklearn import prep_skl_df | ||
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| class SklearnProbaReg(BaseProbaRegressor): | ||
| """Adapter to sklearn regressors with variance prediction interface. | ||
| Wraps an sklearn regressor that can be queried for variance prediction, | ||
| and constructs an skpro regressor from it. | ||
| The wrapped resgressor must have a ``predict`` with | ||
| a ``return_std`` argument, and return a tuple of ``(y_pred, y_std)``, | ||
| both ndarray of shape (n_samples,) or (n_samples, n_targets). | ||
| Parameters | ||
| ---------- | ||
| estimator : sklearn regressor | ||
| Estimator to wrap, must have ``predict`` with ``return_std`` argument. | ||
| """ | ||
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| _tags = { | ||
| "capability:multioutput": False, | ||
| "capability:missing": True, | ||
| } | ||
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| def __init__(self, estimator): | ||
| self.estimator = estimator | ||
| super().__init__() | ||
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| # todo: implement this, mandatory | ||
| def _fit(self, X, y): | ||
| """Fit regressor to training data. | ||
| Writes to self: | ||
| Sets fitted model attributes ending in "_". | ||
| Parameters | ||
| ---------- | ||
| X : pandas DataFrame | ||
| feature instances to fit regressor to | ||
| y : pandas DataFrame, must be same length as X | ||
| labels to fit regressor to | ||
| Returns | ||
| ------- | ||
| self : reference to self | ||
| """ | ||
| from sklearn import clone | ||
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| self.estimator_ = clone(self.estimator) | ||
| X_inner = prep_skl_df(X) | ||
| y_inner = prep_skl_df(y) | ||
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| if len(y_inner.columns) == 1: | ||
| y_inner = y_inner.iloc[:, 0] | ||
| self.estimator_.fit(X_inner, y_inner) | ||
| return self | ||
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| def _predict(self, X): | ||
| """Predict labels for data from features. | ||
| State required: | ||
| Requires state to be "fitted" = self.is_fitted=True | ||
| Accesses in self: | ||
| Fitted model attributes ending in "_" | ||
| Parameters | ||
| ---------- | ||
| X : pandas DataFrame, must have same columns as X in `fit` | ||
| data to predict labels for | ||
| Returns | ||
| ------- | ||
| y : pandas DataFrame, same length as `X`, same columns as `y` in `fit` | ||
| labels predicted for `X` | ||
| """ | ||
| X_inner = prep_skl_df(X) | ||
| y_pred = self.estimator_.predict(X_inner) | ||
| return y_pred | ||
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| def _predict_var(self, X): | ||
| """Compute/return variance predictions. | ||
| private _predict_var containing the core logic, called from predict_var | ||
| Parameters | ||
| ---------- | ||
| X : pandas DataFrame, must have same columns as X in `fit` | ||
| data to predict labels for | ||
| Returns | ||
| ------- | ||
| pred_var : pd.DataFrame | ||
| Column names are exactly those of ``y`` passed in ``fit``. | ||
| Row index is equal to row index of ``X``. | ||
| Entries are variance prediction, for var in col index. | ||
| A variance prediction for given variable and fh index is a predicted | ||
| variance for that variable and index, given observed data. | ||
| """ | ||
| X_inner = prep_skl_df(X) | ||
| _, y_std = self.estimator_.predict(X_inner, return_std=True) | ||
| y_std = pd.DataFrame(y_std, index=X.index, columns=X.columns) | ||
| y_var = y_std**2 | ||
| return y_var | ||
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| @classmethod | ||
| def get_test_params(cls, parameter_set="default"): | ||
| """Return testing parameter settings for the estimator. | ||
| Parameters | ||
| ---------- | ||
| parameter_set : str, default="default" | ||
| Name of the set of test parameters to return, for use in tests. If no | ||
| special parameters are defined for a value, will return `"default"` set. | ||
| Returns | ||
| ------- | ||
| params : dict or list of dict, default = {} | ||
| Parameters to create testing instances of the class | ||
| Each dict are parameters to construct an "interesting" test instance, i.e., | ||
| `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. | ||
| `create_test_instance` uses the first (or only) dictionary in `params` | ||
| """ | ||
| from sklearn.gaussian_process import GaussianProcessRegressor | ||
| from sklearn.linear_model import BayesianRidge | ||
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| param1 = {"estimator": BayesianRidge()} | ||
| param2 = {"estimator": GaussianProcessRegressor()} | ||
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| return [param1, param2] |
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