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+ + + + + + + + + + \ No newline at end of file diff --git a/api/classifier/index.html b/api/classifier/index.html new file mode 100644 index 0000000..800de39 --- /dev/null +++ b/api/classifier/index.html @@ -0,0 +1,2379 @@ + + + + + + + + + + + + + + + + + + + + + + + + + classifier - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + + + +

classifier

+ + +
+ + + + +

+ pytorch_lattice.classifier.Classifier + + +

+ + +
+ + +

A classifier for tabular data using calibrated models.

+

Note: currently only handles binary classification targets.

+

Example: +

X, y = pyl.datasets.heart()
+clf = pyl.Classifier(X.columns)
+clf.configure("age").num_keypoints(10).monotonicity("increasing")
+clf.fit(X, y)
+

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
features + +
+

A dict mapping feature names to their corresponding FeatureConfig +instances.

+
+
model_config + +
+

The model configuration to use for fitting the classifier.

+
+
self.model + +
+

The fitted model. This will be None until fit is called.

+
+
+ +
+ Source code in pytorch_lattice/classifier.py + 
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class Classifier:
+    """A classifier for tabular data using calibrated models.
+
+    Note: currently only handles binary classification targets.
+
+    Example:
+    ```python
+    X, y = pyl.datasets.heart()
+    clf = pyl.Classifier(X.columns)
+    clf.configure("age").num_keypoints(10).monotonicity("increasing")
+    clf.fit(X, y)
+    ```
+
+    Attributes:
+        features: A dict mapping feature names to their corresponding `FeatureConfig`
+            instances.
+        model_config: The model configuration to use for fitting the classifier.
+        self.model: The fitted model. This will be `None` until `fit` is called.
+    """
+
+    def __init__(
+        self,
+        feature_names: list[str],
+        model_config: Optional[Union[LinearConfig, LatticeConfig]] = None,
+    ):
+        """Initializes an instance of `Classifier`."""
+        self.features = {
+            feature_name: FeatureConfig(name=feature_name)
+            for feature_name in feature_names
+        }
+        self.model_config = model_config if model_config is not None else LinearConfig()
+        self.model: Optional[Union[CalibratedLinear, CalibratedLattice]] = None
+
+    def configure(self, feature_name: str):
+        """Returns a `FeatureConfig` object for the given feature name."""
+        return self.features[feature_name]
+
+    def fit(
+        self,
+        X: pd.DataFrame,
+        y: np.ndarray,
+        epochs: int = 50,
+        batch_size: int = 64,
+        learning_rate: float = 1e-3,
+        shuffle: bool = False,
+    ) -> Classifier:
+        """Returns this classifier after fitting a model to the given data.
+
+        Note that calling this function will overwrite any existing model and train a
+        new model from scratch.
+
+        Args:
+            X: A `pd.DataFrame` containing the features for the training data.
+            y: A `np.ndarray` containing the labels for the training data.
+            epochs: The number of epochs for which to fit the classifier.
+            batch_size: The batch size to use for fitting.
+            learning_rate: The learning rate to use for fitting the model.
+            shuffle: Whether to shuffle the data before fitting.
+        """
+        model = self._create_model(X)
+        optimizer = torch.optim.Adam(model.parameters(recurse=True), lr=learning_rate)
+        loss_fn = torch.nn.BCEWithLogitsLoss()
+
+        dataset = Dataset(X, y, model.features)
+        dataloader = torch.utils.data.DataLoader(
+            dataset, batch_size=batch_size, shuffle=shuffle
+        )
+        for _ in trange(epochs, desc="Training Progress"):
+            for inputs, labels in dataloader:
+                optimizer.zero_grad()
+                outputs = model(inputs)
+                loss = loss_fn(outputs, labels)
+                loss.backward()
+                optimizer.step()
+                model.apply_constraints()
+
+        self.model = model
+        return self
+
+    def predict(self, X: pd.DataFrame, logits: bool = False) -> np.ndarray:
+        """Returns predictions for the given data.
+
+        Args:
+            X: a `pd.DataFrame` containing to data for which to generate predictions.
+            logits: If `True`, returns the logits of the predictions. Otherwise, returns
+                probabilities.
+        """
+        if self.model is None:
+            raise RuntimeError("Cannot predict before fitting the model.")
+
+        self.model.eval()
+        X_copy = X[[feature.feature_name for feature in self.model.features]].copy()
+        prepare_features(X_copy, self.model.features)
+        X_tensor = torch.tensor(X_copy.values).double()
+        with torch.no_grad():
+            preds = self.model(X_tensor).numpy()
+
+        if logits:
+            return preds
+        else:
+            return 1.0 / (1.0 + np.exp(-preds))
+
+    def save(self, filepath: str):
+        """Saves the classifier to the specified path.
+
+        Args:
+            filepath: The directory where the classifier will be saved. If the directory
+                does not exist, this function will attempt to create it. If the
+                directory already exists, this function will overwrite any existing
+                content with conflicting filenames.
+        """
+        if not os.path.exists(filepath):
+            os.makedirs(filepath)
+        with open(os.path.join(filepath, "clf_attrs.pkl"), "wb") as f:
+            attrs = {key: self.__dict__[key] for key in ["features", "model_config"]}
+            pickle.dump(attrs, f)
+        if self.model is not None:
+            model_path = os.path.join(filepath, "model.pt")
+            torch.save(self.model, model_path)
+
+    @classmethod
+    def load(cls, filepath: str) -> Classifier:
+        """Loads a `Classifier` from the specified path.
+
+        Args:
+            filepath: The filepath from which to load the classifier. The filepath
+                should point to the filepath used in the `save` method when saving the
+                classifier.
+
+        Returns:
+            A `Classifier` instance.
+        """
+        with open(os.path.join(filepath, "clf_attrs.pkl"), "rb") as f:
+            attrs = pickle.load(f)
+
+        clf = cls([])
+        clf.__dict__.update(attrs)
+
+        model_path = os.path.join(filepath, "model.pt")
+        if os.path.exists(model_path):
+            clf.model = torch.load(model_path)
+
+        return clf
+
+    ################################################################################
+    ############################## PRIVATE METHODS #################################
+    ################################################################################
+
+    def _create_model(
+        self, X: pd.DataFrame
+    ) -> Union[CalibratedLinear, CalibratedLattice]:
+        """Returns a model based on `self.features` and `self.model_config`."""
+        features: list[Union[CategoricalFeature, NumericalFeature]] = []
+
+        for feature_name, feature in self.features.items():
+            if X[feature_name].dtype.kind in ["S", "O", "b"]:  # string, object, bool
+                if feature._categories is None:
+                    categories = X[feature_name].unique().tolist()
+                    feature.categories(categories)
+                else:
+                    categories = feature._categories
+                if feature._monotonicity is not None and isinstance(
+                    feature._monotonicity, list
+                ):
+                    monotonicity_pairs = feature._monotonicity
+                else:
+                    monotonicity_pairs = None
+                features.append(
+                    CategoricalFeature(
+                        feature_name=feature_name,
+                        categories=categories,
+                        missing_input_value=MISSING_INPUT_VALUE,
+                        monotonicity_pairs=monotonicity_pairs,
+                        lattice_size=feature._lattice_size,
+                    )
+                )
+            else:  # numerical feature
+                if feature._monotonicity is not None and isinstance(
+                    feature._monotonicity, str
+                ):
+                    monotonicity = feature._monotonicity
+                else:
+                    monotonicity = None
+                features.append(
+                    NumericalFeature(
+                        feature_name=feature_name,
+                        data=np.array(X[feature_name].values),
+                        num_keypoints=feature._num_keypoints,
+                        input_keypoints_init=feature._input_keypoints_init,
+                        missing_input_value=MISSING_INPUT_VALUE,
+                        monotonicity=monotonicity,
+                        projection_iterations=feature._projection_iterations,
+                        lattice_size=feature._lattice_size,
+                    )
+                )
+
+        if isinstance(self.model_config, LinearConfig):
+            return CalibratedLinear(
+                features,
+                self.model_config.output_min,
+                self.model_config.output_max,
+                self.model_config.use_bias,
+                self.model_config.output_calibration_num_keypoints,
+            )
+        else:
+            return CalibratedLattice(
+                features,
+                True,
+                self.model_config.output_min,
+                self.model_config.output_max,
+                self.model_config.kernel_init,
+                self.model_config.interpolation,
+                self.model_config.output_calibration_num_keypoints,
+            )
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(feature_names, model_config=None) + +

+ + +
+ +

Initializes an instance of Classifier.

+ +
+ Source code in pytorch_lattice/classifier.py + 
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def __init__(
+    self,
+    feature_names: list[str],
+    model_config: Optional[Union[LinearConfig, LatticeConfig]] = None,
+):
+    """Initializes an instance of `Classifier`."""
+    self.features = {
+        feature_name: FeatureConfig(name=feature_name)
+        for feature_name in feature_names
+    }
+    self.model_config = model_config if model_config is not None else LinearConfig()
+    self.model: Optional[Union[CalibratedLinear, CalibratedLattice]] = None
+
+
+
+ +
+ + +
+ + + + +

+ configure(feature_name) + +

+ + +
+ +

Returns a FeatureConfig object for the given feature name.

+ +
+ Source code in pytorch_lattice/classifier.py + 
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+59
+60
def configure(self, feature_name: str):
+    """Returns a `FeatureConfig` object for the given feature name."""
+    return self.features[feature_name]
+
+
+
+ +
+ + +
+ + + + +

+ fit(X, y, epochs=50, batch_size=64, learning_rate=0.001, shuffle=False) + +

+ + +
+ +

Returns this classifier after fitting a model to the given data.

+

Note that calling this function will overwrite any existing model and train a +new model from scratch.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
X + DataFrame + +
+

A pd.DataFrame containing the features for the training data.

+
+ 		+ required +
y + ndarray + +
+

A np.ndarray containing the labels for the training data.

+
+ 		+ required +
epochs + int + +
+

The number of epochs for which to fit the classifier.

+
+ 		+ 50 +
batch_size + int + +
+

The batch size to use for fitting.

+
+ 		+ 64 +
learning_rate + float + +
+

The learning rate to use for fitting the model.

+
+ 		+ 0.001 +
shuffle + bool + +
+

Whether to shuffle the data before fitting.

+
+ 		+ False +
+ +
+ Source code in pytorch_lattice/classifier.py + 
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def fit(
+    self,
+    X: pd.DataFrame,
+    y: np.ndarray,
+    epochs: int = 50,
+    batch_size: int = 64,
+    learning_rate: float = 1e-3,
+    shuffle: bool = False,
+) -> Classifier:
+    """Returns this classifier after fitting a model to the given data.
+
+    Note that calling this function will overwrite any existing model and train a
+    new model from scratch.
+
+    Args:
+        X: A `pd.DataFrame` containing the features for the training data.
+        y: A `np.ndarray` containing the labels for the training data.
+        epochs: The number of epochs for which to fit the classifier.
+        batch_size: The batch size to use for fitting.
+        learning_rate: The learning rate to use for fitting the model.
+        shuffle: Whether to shuffle the data before fitting.
+    """
+    model = self._create_model(X)
+    optimizer = torch.optim.Adam(model.parameters(recurse=True), lr=learning_rate)
+    loss_fn = torch.nn.BCEWithLogitsLoss()
+
+    dataset = Dataset(X, y, model.features)
+    dataloader = torch.utils.data.DataLoader(
+        dataset, batch_size=batch_size, shuffle=shuffle
+    )
+    for _ in trange(epochs, desc="Training Progress"):
+        for inputs, labels in dataloader:
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            loss = loss_fn(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            model.apply_constraints()
+
+    self.model = model
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ load(filepath) + + + classmethod + + +

+ + +
+ +

Loads a Classifier from the specified path.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
filepath + str + +
+

The filepath from which to load the classifier. The filepath +should point to the filepath used in the save method when saving the +classifier.

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Classifier + +
+

A Classifier instance.

+
+
+ +
+ Source code in pytorch_lattice/classifier.py + 
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@classmethod
+def load(cls, filepath: str) -> Classifier:
+    """Loads a `Classifier` from the specified path.
+
+    Args:
+        filepath: The filepath from which to load the classifier. The filepath
+            should point to the filepath used in the `save` method when saving the
+            classifier.
+
+    Returns:
+        A `Classifier` instance.
+    """
+    with open(os.path.join(filepath, "clf_attrs.pkl"), "rb") as f:
+        attrs = pickle.load(f)
+
+    clf = cls([])
+    clf.__dict__.update(attrs)
+
+    model_path = os.path.join(filepath, "model.pt")
+    if os.path.exists(model_path):
+        clf.model = torch.load(model_path)
+
+    return clf
+
+
+
+ +
+ + +
+ + + + +

+ predict(X, logits=False) + +

+ + +
+ +

Returns predictions for the given data.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
X + DataFrame + +
+

a pd.DataFrame containing to data for which to generate predictions.

+
+ 		+ required +
logits + bool + +
+

If True, returns the logits of the predictions. Otherwise, returns +probabilities.

+
+ 		+ False +
+ +
+ Source code in pytorch_lattice/classifier.py + 
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def predict(self, X: pd.DataFrame, logits: bool = False) -> np.ndarray:
+    """Returns predictions for the given data.
+
+    Args:
+        X: a `pd.DataFrame` containing to data for which to generate predictions.
+        logits: If `True`, returns the logits of the predictions. Otherwise, returns
+            probabilities.
+    """
+    if self.model is None:
+        raise RuntimeError("Cannot predict before fitting the model.")
+
+    self.model.eval()
+    X_copy = X[[feature.feature_name for feature in self.model.features]].copy()
+    prepare_features(X_copy, self.model.features)
+    X_tensor = torch.tensor(X_copy.values).double()
+    with torch.no_grad():
+        preds = self.model(X_tensor).numpy()
+
+    if logits:
+        return preds
+    else:
+        return 1.0 / (1.0 + np.exp(-preds))
+
+
+
+ +
+ + +
+ + + + +

+ save(filepath) + +

+ + +
+ +

Saves the classifier to the specified path.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
filepath + str + +
+

The directory where the classifier will be saved. If the directory +does not exist, this function will attempt to create it. If the +directory already exists, this function will overwrite any existing +content with conflicting filenames.

+
+ 		+ required +
+ +
+ Source code in pytorch_lattice/classifier.py + 
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def save(self, filepath: str):
+    """Saves the classifier to the specified path.
+
+    Args:
+        filepath: The directory where the classifier will be saved. If the directory
+            does not exist, this function will attempt to create it. If the
+            directory already exists, this function will overwrite any existing
+            content with conflicting filenames.
+    """
+    if not os.path.exists(filepath):
+        os.makedirs(filepath)
+    with open(os.path.join(filepath, "clf_attrs.pkl"), "wb") as f:
+        attrs = {key: self.__dict__[key] for key in ["features", "model_config"]}
+        pickle.dump(attrs, f)
+    if self.model is not None:
+        model_path = os.path.join(filepath, "model.pt")
+        torch.save(self.model, model_path)
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/constrained_module/index.html b/api/constrained_module/index.html new file mode 100644 index 0000000..6cd7508 --- /dev/null +++ b/api/constrained_module/index.html @@ -0,0 +1,1340 @@ + + + + + + + + + + + + + + + + + + + + + + + + + constrained_module - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

constrained_module

+ + +
+ + + + +

+ pytorch_lattice.constrained_module.ConstrainedModule + + +

+ + +
+

+ Bases: Module

+ + +

A base class for constrained implementations of a torch.nn.Module.

+ +
+ Source code in pytorch_lattice/constrained_module.py + 
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class ConstrainedModule(torch.nn.Module):
+    """A base class for constrained implementations of a `torch.nn.Module`."""
+
+    @torch.no_grad()
+    @abstractmethod
+    def apply_constraints(self) -> None:
+        """Applies defined constraints to the module."""
+        raise NotImplementedError()
+
+    @torch.no_grad()
+    @abstractmethod
+    def assert_constraints(
+        self, eps: float = 1e-6
+    ) -> Union[list[str], dict[str, list[str]]]:
+        """Asserts that the module satisfied specified constraints."""
+        raise NotImplementedError()
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ apply_constraints() + + + abstractmethod + + +

+ + +
+ +

Applies defined constraints to the module.

+ +
+ Source code in pytorch_lattice/constrained_module.py + 
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@torch.no_grad()
+@abstractmethod
+def apply_constraints(self) -> None:
+    """Applies defined constraints to the module."""
+    raise NotImplementedError()
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + + + abstractmethod + + +

+ + +
+ +

Asserts that the module satisfied specified constraints.

+ +
+ Source code in pytorch_lattice/constrained_module.py + 
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@torch.no_grad()
+@abstractmethod
+def assert_constraints(
+    self, eps: float = 1e-6
+) -> Union[list[str], dict[str, list[str]]]:
+    """Asserts that the module satisfied specified constraints."""
+    raise NotImplementedError()
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/datasets/index.html b/api/datasets/index.html new file mode 100644 index 0000000..b860e9e --- /dev/null +++ b/api/datasets/index.html @@ -0,0 +1,1391 @@ + + + + + + + + + + + + + + + + + + + + + + + + + datasets - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
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+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

datasets

+ + +
+ + + + +

+ pytorch_lattice.datasets + + +

+ +
+ +

Functions for loading datasets to use with the PyTorch Lattice package.

+ + + +
+ + + + + + + + + + +
+ + + + +

+ adult() + +

+ + +
+ +

Loads the UCI Adult Income dataset.

+

The UCI Adult Income dataset is a classification dataset with 48,842 rows and 14 +columns. The target is binary, with 0 indicating an income of less than $50k and 1 +indicating an income of at least $50k. The features are a mix of categorical and +numerical features. For more information, see +https://archive.ics.uci.edu/dataset/2/adult

+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ tuple[DataFrame, ndarray] + +
+

A tuple (X, y) of the features and target.

+
+
+ +
+ Source code in pytorch_lattice/datasets.py + 
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def adult() -> tuple[pd.DataFrame, np.ndarray]:
+    """Loads the UCI Adult Income dataset.
+
+    The UCI Adult Income dataset is a classification dataset with 48,842 rows and 14
+    columns. The target is binary, with 0 indicating an income of less than $50k and 1
+    indicating an income of at least $50k. The features are a mix of categorical and
+    numerical features. For more information, see
+    https://archive.ics.uci.edu/dataset/2/adult
+
+    Returns:
+        A tuple `(X, y)` of the features and target.
+    """
+    X = pd.read_csv(
+        "https://raw.githubusercontent.com/ControlAI/datasets/main/adult.csv"
+    )
+    y = np.array(X.pop("label").values)
+    return X, y
+
+
+
+ +
+ + +
+ + + + +

+ heart() + +

+ + +
+ +

Loads the UCI Statlog (Heart) dataset.

+

The UCI Statlog (Heart) dataset is a classification dataset with 303 rows and 14 +columns. The target is binary, with 0 indicating no heart disease and 1 indicating +heart disease. The features are a mix of categorical and numerical features. For +more information, see https://archive.ics.uci.edu/ml/datasets/heart+Disease.

+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ tuple[DataFrame, ndarray] + +
+

A tuple (X, y) of the features and target.

+
+
+ +
+ Source code in pytorch_lattice/datasets.py + 
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def heart() -> tuple[pd.DataFrame, np.ndarray]:
+    """Loads the UCI Statlog (Heart) dataset.
+
+    The UCI Statlog (Heart) dataset is a classification dataset with 303 rows and 14
+    columns. The target is binary, with 0 indicating no heart disease and 1 indicating
+    heart disease. The features are a mix of categorical and numerical features. For
+    more information, see https://archive.ics.uci.edu/ml/datasets/heart+Disease.
+
+    Returns:
+        A tuple `(X, y)` of the features and target.
+    """
+    X = pd.read_csv(
+        "https://raw.githubusercontent.com/ControlAI/datasets/main/heart.csv"
+    )
+    y = np.array(X.pop("target").values)
+    return X, y
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/enums/index.html b/api/enums/index.html new file mode 100644 index 0000000..0146c88 --- /dev/null +++ b/api/enums/index.html @@ -0,0 +1,1760 @@ + + + + + + + + + + + + + + + + + + + + + + + + + enums - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

enums

+ + +
+ + + + +

+ pytorch_lattice.enums + + +

+ +
+ +

Enum Classes for PyTorch Lattice.

+ + + +
+ + + + + + + + +
+ + + + +

+ CategoricalCalibratorInit + + +

+ + +
+

+ Bases: _Enum

+ + +

Type of kernel initialization to use for CategoricalCalibrator.

+
    +
  • UNIFORM: initialize the kernel with uniformly distributed values. The sample range + will be [output_min, output_max] if both are provided.
    • +
  • CONSTANT: initialize the kernel with a constant value for all categories. This + value will be (output_min + output_max) / 2 if both are provided.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class CategoricalCalibratorInit(_Enum):
+    """Type of kernel initialization to use for CategoricalCalibrator.
+
+    - UNIFORM: initialize the kernel with uniformly distributed values. The sample range
+        will be [`output_min`, `output_max`] if both are provided.
+    - CONSTANT: initialize the kernel with a constant value for all categories. This
+        value will be `(output_min + output_max) / 2` if both are provided.
+    """
+
+    UNIFORM = "uniform"
+    CONSTANT = "constant"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ InputKeypointsInit + + +

+ + +
+

+ Bases: _Enum

+ + +

Type of initialization to use for NumericalCalibrator input keypoints.

+
    +
  • QUANTILES: initialize the input keypoints such that each segment will see the same + number of examples.
    • +
  • UNIFORM: initialize the input keypoints uniformly spaced in the feature range.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class InputKeypointsInit(_Enum):
+    """Type of initialization to use for NumericalCalibrator input keypoints.
+
+    - QUANTILES: initialize the input keypoints such that each segment will see the same
+        number of examples.
+    - UNIFORM: initialize the input keypoints uniformly spaced in the feature range.
+    """
+
+    QUANTILES = "quantiles"
+    UNIFORM = "uniform"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ InputKeypointsType + + +

+ + +
+

+ Bases: _Enum

+ + +

The type of input keypoints to use.

+
    +
  • FIXED: the input keypoints will be fixed during initialization.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class InputKeypointsType(_Enum):
+    """The type of input keypoints to use.
+
+    - FIXED: the input keypoints will be fixed during initialization.
+    """
+
+    FIXED = "fixed"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ Interpolation + + +

+ + +
+

+ Bases: _Enum

+ + +

Enum for interpolation method of lattice.

+
    +
  • HYPERCUBE: n-dimensional hypercube surrounding input point(s).
    • +
  • SIMPLEX: uses only one of the n! simplices in the n-dim hypercube.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class Interpolation(_Enum):
+    """Enum for interpolation method of lattice.
+
+    - HYPERCUBE: n-dimensional hypercube surrounding input point(s).
+    - SIMPLEX: uses only one of the n! simplices in the n-dim hypercube.
+    """
+
+    HYPERCUBE = "hypercube"
+    SIMPLEX = "simplex"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ LatticeInit + + +

+ + +
+

+ Bases: _Enum

+ + +

Type of kernel initialization to use for CategoricalCalibrator.

+
    +
  • LINEAR: initialize the kernel with weights represented by a linear function, + conforming to monotonicity and unimodality constraints.
    • +
  • RANDOM_MONOTONIC: initialize the kernel with a uniformly random sampled + lattice layer weight tensor, conforming to monotonicity and unimodality + constraints.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class LatticeInit(_Enum):
+    """Type of kernel initialization to use for CategoricalCalibrator.
+
+    - LINEAR: initialize the kernel with weights represented by a linear function,
+        conforming to monotonicity and unimodality constraints.
+    - RANDOM_MONOTONIC: initialize the kernel with a uniformly random sampled
+        lattice layer weight tensor, conforming to monotonicity and unimodality
+        constraints.
+    """
+
+    LINEAR = "linear"
+    RANDOM_MONOTONIC = "random_monotonic"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ Monotonicity + + +

+ + +
+

+ Bases: _Enum

+ + +

Type of monotonicity constraint.

+
    +
  • INCREASING: increasing monotonicity i.e. increasing input increases output.
    • +
  • DECREASING: decreasing monotonicity i.e. increasing input decreases output.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class Monotonicity(_Enum):
+    """Type of monotonicity constraint.
+
+    - INCREASING: increasing monotonicity i.e. increasing input increases output.
+    - DECREASING: decreasing monotonicity i.e. increasing input decreases output.
+    """
+
+    INCREASING = "increasing"
+    DECREASING = "decreasing"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ NumericalCalibratorInit + + +

+ + +
+

+ Bases: _Enum

+ + +

Type of kernel initialization to use for NumericalCalibrator.

+
    +
  • EQUAL_HEIGHTS: initialize the kernel such that all segments have the same height.
    • +
  • EQUAL_SLOPES: initialize the kernel such that all segments have the same slope.
    • +
+ +
+ Source code in pytorch_lattice/enums.py + 
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class NumericalCalibratorInit(_Enum):
+    """Type of kernel initialization to use for NumericalCalibrator.
+
+    - EQUAL_HEIGHTS: initialize the kernel such that all segments have the same height.
+    - EQUAL_SLOPES: initialize the kernel such that all segments have the same slope.
+    """
+
+    EQUAL_HEIGHTS = "equal_heights"
+    EQUAL_SLOPES = "equal_slopes"
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/feature_config/index.html b/api/feature_config/index.html new file mode 100644 index 0000000..0824490 --- /dev/null +++ b/api/feature_config/index.html @@ -0,0 +1,1810 @@ + + + + + + + + + + + + + + + + + + + + + + + + + feature_config - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
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+ + + +
+
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+ + + + + + + +
+
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+ + + +
+
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+ + + +
+
+
+ + + +
+
+ + + + + + + +

feature_config

+ + +
+ + + + +

+ pytorch_lattice.feature_config + + +

+ +
+ +

Configuration objects for the PyTorch Lattice library.

+ + + +
+ + + + + + + + +
+ + + + +

+ FeatureConfig + + +

+ + +
+ + +

A configuration object for a feature in a calibrated model.

+

This configuration object handles both numerical and categorical features. If the +categeories attribute is None, then this feature will be handled as numerical. +Otherwise, it will be handled as categorical.

+

Example: +

fc = FeatureConfig(name="feature_name").num_keypoints(10).monotonicity("increasing")
+

+ + + +

Attributes:

+ + + + + + + + + + + + + + + +
NameTypeDescription
name + +
+

The name of the feature.

+
+
+ +
+ Source code in pytorch_lattice/feature_config.py + 
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class FeatureConfig:
+    """A configuration object for a feature in a calibrated model.
+
+    This configuration object handles both numerical and categorical features. If the
+    `categeories` attribute is `None`, then this feature will be handled as numerical.
+    Otherwise, it will be handled as categorical.
+
+    Example:
+    ```python
+    fc = FeatureConfig(name="feature_name").num_keypoints(10).monotonicity("increasing")
+    ```
+
+    Attributes:
+        name: The name of the feature.
+    """
+
+    def __init__(self, name: str):
+        """Initializes an instance of `FeatureConfig` with default values."""
+        self.name = name
+        self._categories: Optional[list[str]] = None
+        self._num_keypoints: int = 5
+        self._input_keypoints_init: InputKeypointsInit = InputKeypointsInit.QUANTILES
+        self._input_keypoints_type: InputKeypointsType = InputKeypointsType.FIXED
+        self._monotonicity: Optional[Union[Monotonicity, list[tuple[str, str]]]] = None
+        self._projection_iterations: int = 8
+        self._lattice_size: int = 2  # only used in lattice models
+
+    def categories(self, categories: list[str]) -> FeatureConfig:
+        """Sets the categories for a categorical feature."""
+        self._categories = categories
+        return self
+
+    def num_keypoints(self, num_keypoints: int) -> FeatureConfig:
+        """Sets the categories for a categorical feature."""
+        self._num_keypoints = num_keypoints
+        return self
+
+    def input_keypoints_init(
+        self, input_keypoints_init: InputKeypointsInit
+    ) -> FeatureConfig:
+        """Sets the input keypoints initialization method for a numerical calibrator."""
+        self._input_keypoints_init = input_keypoints_init
+        return self
+
+    def input_keypoints_type(
+        self, input_keypoints_type: InputKeypointsType
+    ) -> FeatureConfig:
+        """Sets the input keypoints type for a numerical calibrator."""
+        self._input_keypoints_type = input_keypoints_type
+        return self
+
+    def monotonicity(
+        self, monotonicity: Optional[Union[Monotonicity, list[tuple[str, str]]]]
+    ) -> FeatureConfig:
+        """Sets the monotonicity constraint for a feature."""
+        self._monotonicity = monotonicity
+        return self
+
+    def projection_iterations(self, projection_iterations: int) -> FeatureConfig:
+        """Sets the number of projection iterations for a numerical calibrator."""
+        self._projection_iterations = projection_iterations
+        return self
+
+    def lattice_size(self, lattice_size: int) -> FeatureConfig:
+        """Sets the lattice size for a feature."""
+        self._lattice_size = lattice_size
+        return self
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(name) + +

+ + +
+ +

Initializes an instance of FeatureConfig with default values.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def __init__(self, name: str):
+    """Initializes an instance of `FeatureConfig` with default values."""
+    self.name = name
+    self._categories: Optional[list[str]] = None
+    self._num_keypoints: int = 5
+    self._input_keypoints_init: InputKeypointsInit = InputKeypointsInit.QUANTILES
+    self._input_keypoints_type: InputKeypointsType = InputKeypointsType.FIXED
+    self._monotonicity: Optional[Union[Monotonicity, list[tuple[str, str]]]] = None
+    self._projection_iterations: int = 8
+    self._lattice_size: int = 2  # only used in lattice models
+
+
+
+ +
+ + +
+ + + + +

+ categories(categories) + +

+ + +
+ +

Sets the categories for a categorical feature.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def categories(self, categories: list[str]) -> FeatureConfig:
+    """Sets the categories for a categorical feature."""
+    self._categories = categories
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ input_keypoints_init(input_keypoints_init) + +

+ + +
+ +

Sets the input keypoints initialization method for a numerical calibrator.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def input_keypoints_init(
+    self, input_keypoints_init: InputKeypointsInit
+) -> FeatureConfig:
+    """Sets the input keypoints initialization method for a numerical calibrator."""
+    self._input_keypoints_init = input_keypoints_init
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ input_keypoints_type(input_keypoints_type) + +

+ + +
+ +

Sets the input keypoints type for a numerical calibrator.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def input_keypoints_type(
+    self, input_keypoints_type: InputKeypointsType
+) -> FeatureConfig:
+    """Sets the input keypoints type for a numerical calibrator."""
+    self._input_keypoints_type = input_keypoints_type
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ lattice_size(lattice_size) + +

+ + +
+ +

Sets the lattice size for a feature.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def lattice_size(self, lattice_size: int) -> FeatureConfig:
+    """Sets the lattice size for a feature."""
+    self._lattice_size = lattice_size
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ monotonicity(monotonicity) + +

+ + +
+ +

Sets the monotonicity constraint for a feature.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def monotonicity(
+    self, monotonicity: Optional[Union[Monotonicity, list[tuple[str, str]]]]
+) -> FeatureConfig:
+    """Sets the monotonicity constraint for a feature."""
+    self._monotonicity = monotonicity
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ num_keypoints(num_keypoints) + +

+ + +
+ +

Sets the categories for a categorical feature.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def num_keypoints(self, num_keypoints: int) -> FeatureConfig:
+    """Sets the categories for a categorical feature."""
+    self._num_keypoints = num_keypoints
+    return self
+
+
+
+ +
+ + +
+ + + + +

+ projection_iterations(projection_iterations) + +

+ + +
+ +

Sets the number of projection iterations for a numerical calibrator.

+ +
+ Source code in pytorch_lattice/feature_config.py + 
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def projection_iterations(self, projection_iterations: int) -> FeatureConfig:
+    """Sets the number of projection iterations for a numerical calibrator."""
+    self._projection_iterations = projection_iterations
+    return self
+
+
+
+ +
+ + + +
+ +
+ +
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+ +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/layers/index.html b/api/layers/index.html new file mode 100644 index 0000000..2b96853 --- /dev/null +++ b/api/layers/index.html @@ -0,0 +1,7691 @@ + + + + + + + + + + + + + + + + + + + + + + + + + layers - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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layers

+ + +
+ + + + +

+ pytorch_lattice.layers.CategoricalCalibrator + + +

+ + +
+

+ Bases: ConstrainedModule

+ + +

A categorical calibrator.

+

This module takes an input of shape (batch_size, 1) and calibrates it by mapping a +given category to its learned output value. The output will have the same shape as +the input.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
kernel + +
+

torch.nn.Parameter that stores the categorical mapping weights.

+
+
+

Example: +

inputs = torch.tensor(...)  # shape: (batch_size, 1)
+calibrator = CategoricalCalibrator(
+    num_categories=5,
+    missing_input_value=-1,
+    output_min=0.0
+    output_max=1.0,
+    monotonicity_pairs=[(0, 1), (1, 2)],
+    kernel_init=CateegoricalCalibratorInit.UNIFORM,
+)
+outputs = calibrator(inputs)
+

+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
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class CategoricalCalibrator(ConstrainedModule):
+    """A categorical calibrator.
+
+    This module takes an input of shape `(batch_size, 1)` and calibrates it by mapping a
+    given category to its learned output value. The output will have the same shape as
+    the input.
+
+    Attributes:
+        All: `__init__` arguments.
+        kernel: `torch.nn.Parameter` that stores the categorical mapping weights.
+
+    Example:
+    ```python
+    inputs = torch.tensor(...)  # shape: (batch_size, 1)
+    calibrator = CategoricalCalibrator(
+        num_categories=5,
+        missing_input_value=-1,
+        output_min=0.0
+        output_max=1.0,
+        monotonicity_pairs=[(0, 1), (1, 2)],
+        kernel_init=CateegoricalCalibratorInit.UNIFORM,
+    )
+    outputs = calibrator(inputs)
+    ```
+    """
+
+    def __init__(
+        self,
+        num_categories: int,
+        missing_input_value: Optional[float] = None,
+        output_min: Optional[float] = None,
+        output_max: Optional[float] = None,
+        monotonicity_pairs: Optional[list[tuple[int, int]]] = None,
+        kernel_init: CategoricalCalibratorInit = CategoricalCalibratorInit.UNIFORM,
+    ) -> None:
+        """Initializes an instance of `CategoricalCalibrator`.
+
+        Args:
+            num_categories: The number of known categories.
+            missing_input_value: If provided, the calibrator will learn to map all
+                instances of this missing input value to a learned output value just
+                the same as it does for known categories. Note that `num_categories`
+                will be one greater to include this missing category.
+            output_min: Minimum output value. If `None`, the minimum output value will
+                be unbounded.
+            output_max: Maximum output value. If `None`, the maximum output value will
+                be unbounded.
+            monotonicity_pairs: List of pairs of indices `(i,j)` indicating that the
+                calibrator output for index `j` should be greater than or equal to that
+                of index `i`.
+            kernel_init: Initialization scheme to use for the kernel.
+
+        Raises:
+            ValueError: If `monotonicity_pairs` is cyclic.
+            ValueError: If `kernel_init` is invalid.
+        """
+        super().__init__()
+
+        self.num_categories = (
+            num_categories + 1 if missing_input_value is not None else num_categories
+        )
+        self.missing_input_value = missing_input_value
+        self.output_min = output_min
+        self.output_max = output_max
+        self.monotonicity_pairs = monotonicity_pairs
+        if monotonicity_pairs:
+            self._monotonicity_graph = defaultdict(list)
+            self._reverse_monotonicity_graph = defaultdict(list)
+            for i, j in monotonicity_pairs:
+                self._monotonicity_graph[i].append(j)
+                self._reverse_monotonicity_graph[j].append(i)
+            try:
+                self._monotonically_sorted_indices = [
+                    *TopologicalSorter(self._reverse_monotonicity_graph).static_order()
+                ]
+            except CycleError as exc:
+                raise ValueError("monotonicity_pairs is cyclic") from exc
+        self.kernel_init = kernel_init
+
+        self.kernel = torch.nn.Parameter(torch.Tensor(self.num_categories, 1).double())
+        if kernel_init == CategoricalCalibratorInit.CONSTANT:
+            if output_min is not None and output_max is not None:
+                init_value = (output_min + output_max) / 2
+            elif output_min is not None:
+                init_value = output_min
+            elif output_max is not None:
+                init_value = output_max
+            else:
+                init_value = 0.0
+            torch.nn.init.constant_(self.kernel, init_value)
+        elif kernel_init == CategoricalCalibratorInit.UNIFORM:
+            if output_min is not None and output_max is not None:
+                low, high = output_min, output_max
+            elif output_min is None and output_max is not None:
+                low, high = output_max - 0.05, output_max
+            elif output_min is not None and output_max is None:
+                low, high = output_min, output_min + 0.05
+            else:
+                low, high = -0.05, 0.05
+            torch.nn.init.uniform_(self.kernel, low, high)
+        else:
+            raise ValueError(f"Unknown kernel init: {kernel_init}")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Calibrates categorical inputs through a learned mapping.
+
+        Args:
+            x: The input tensor of category indices of shape `(batch_size, 1)`.
+
+        Returns:
+            torch.Tensor of shape `(batch_size, 1)` containing calibrated input values.
+        """
+        if self.missing_input_value is not None:
+            missing_category_tensor = torch.zeros_like(x) + (self.num_categories - 1)
+            x = torch.where(x == self.missing_input_value, missing_category_tensor, x)
+        # TODO: test if using torch.gather is faster than one-hot matmul.
+        one_hot = torch.nn.functional.one_hot(
+            torch.squeeze(x, -1).long(), num_classes=self.num_categories
+        ).double()
+        return torch.mm(one_hot, self.kernel)
+
+    @torch.no_grad()
+    def apply_constraints(self) -> None:
+        """Projects kernel into desired constraints."""
+        projected_kernel_data = self.kernel.data
+        if self.monotonicity_pairs:
+            projected_kernel_data = self._approximately_project_monotonicity_pairs(
+                projected_kernel_data
+            )
+        if self.output_min is not None:
+            projected_kernel_data = torch.maximum(
+                projected_kernel_data, torch.tensor(self.output_min)
+            )
+        if self.output_max is not None:
+            projected_kernel_data = torch.minimum(
+                projected_kernel_data, torch.tensor(self.output_max)
+            )
+        self.kernel.data = projected_kernel_data
+
+    @torch.no_grad()
+    def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+        """Asserts that layer satisfies specified constraints.
+
+        This checks that weights at the indexes of monotonicity pairs are in the correct
+        order and that the output is within bounds.
+
+        Args:
+            eps: the margin of error allowed
+
+        Returns:
+            A list of messages describing violated constraints including violated
+            monotonicity pairs. If no constraints  violated, the list will be empty.
+        """
+        weights = torch.squeeze(self.kernel.data)
+        messages = []
+
+        if self.output_max is not None and torch.max(weights) > self.output_max + eps:
+            messages.append("Max weight greater than output_max.")
+        if self.output_min is not None and torch.min(weights) < self.output_min - eps:
+            messages.append("Min weight less than output_min.")
+
+        if self.monotonicity_pairs:
+            violation_indices = [
+                (i, j)
+                for (i, j) in self.monotonicity_pairs
+                if weights[i] - weights[j] > eps
+            ]
+            if violation_indices:
+                messages.append(f"Monotonicity violated at: {str(violation_indices)}.")
+
+        return messages
+
+    @torch.no_grad()
+    def keypoints_inputs(self) -> torch.Tensor:
+        """Returns a tensor of keypoint inputs (category indices)."""
+        if self.missing_input_value is not None:
+            return torch.cat(
+                (
+                    torch.arange(self.num_categories - 1),
+                    torch.tensor([self.missing_input_value]),
+                ),
+                0,
+            )
+        return torch.arange(self.num_categories)
+
+    @torch.no_grad()
+    def keypoints_outputs(self) -> torch.Tensor:
+        """Returns a tensor of keypoint outputs."""
+        return torch.squeeze(self.kernel.data, -1)
+
+    ################################################################################
+    ############################## PRIVATE METHODS #################################
+    ################################################################################
+
+    def _approximately_project_monotonicity_pairs(self, kernel_data) -> torch.Tensor:
+        """Projects kernel such that the monotonicity pairs are satisfied.
+
+        The kernel will be projected such that `kernel_data[i] <= kernel_data[j]`. This
+        results in calibrated outputs that adhere to the desired constraints.
+
+        Args:
+            kernel_data: The tensor of shape `(self.num_categories, 1)` to be projected
+                into the constraints specified by `self.monotonicity pairs`.
+
+        Returns:
+            Projected kernel data. To prevent the kernel from drifting in one direction,
+            the data returned is the average of the min/max and max/min projections.
+        """
+        projected_kernel_data = torch.unbind(kernel_data, 0)
+
+        def project(data, monotonicity_graph, step, minimum):
+            projected_data = list(data)
+            sorted_indices = self._monotonically_sorted_indices
+            if minimum:
+                sorted_indices = sorted_indices[::-1]
+            for i in sorted_indices:
+                if i in monotonicity_graph:
+                    projection = projected_data[i]
+                    for j in monotonicity_graph[i]:
+                        if minimum:
+                            projection = torch.minimum(projection, projected_data[j])
+                        else:
+                            projection = torch.maximum(projection, projected_data[j])
+                        if step == 1.0:
+                            projected_data[i] = projection
+                        else:
+                            projected_data[i] = (
+                                step * projection + (1 - step) * projected_data[i]
+                            )
+            return projected_data
+
+        projected_kernel_min_max = project(
+            projected_kernel_data, self._monotonicity_graph, 0.5, minimum=True
+        )
+        projected_kernel_min_max = project(
+            projected_kernel_min_max,
+            self._reverse_monotonicity_graph,
+            1.0,
+            minimum=False,
+        )
+        projected_kernel_min_max = torch.stack(projected_kernel_min_max)
+
+        projected_kernel_max_min = project(
+            projected_kernel_data, self._reverse_monotonicity_graph, 0.5, minimum=False
+        )
+        projected_kernel_max_min = project(
+            projected_kernel_max_min, self._monotonicity_graph, 1.0, minimum=True
+        )
+        projected_kernel_max_min = torch.stack(projected_kernel_max_min)
+
+        return (projected_kernel_min_max + projected_kernel_max_min) / 2
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(num_categories, missing_input_value=None, output_min=None, output_max=None, monotonicity_pairs=None, kernel_init=CategoricalCalibratorInit.UNIFORM) + +

+ + +
+ +

Initializes an instance of CategoricalCalibrator.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
num_categories + int + +
+

The number of known categories.

+
+ 		+ required +
missing_input_value + Optional[float] + +
+

If provided, the calibrator will learn to map all +instances of this missing input value to a learned output value just +the same as it does for known categories. Note that num_categories +will be one greater to include this missing category.

+
+ 		+ None +
output_min + Optional[float] + +
+

Minimum output value. If None, the minimum output value will +be unbounded.

+
+ 		+ None +
output_max + Optional[float] + +
+

Maximum output value. If None, the maximum output value will +be unbounded.

+
+ 		+ None +
monotonicity_pairs + Optional[list[tuple[int, int]]] + +
+

List of pairs of indices (i,j) indicating that the +calibrator output for index j should be greater than or equal to that +of index i.

+
+ 		+ None +
kernel_init + CategoricalCalibratorInit + +
+

Initialization scheme to use for the kernel.

+
+ 		+ UNIFORM +
+ + + +

Raises:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If monotonicity_pairs is cyclic.

+
+
+ ValueError + +
+

If kernel_init is invalid.

+
+
+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
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def __init__(
+    self,
+    num_categories: int,
+    missing_input_value: Optional[float] = None,
+    output_min: Optional[float] = None,
+    output_max: Optional[float] = None,
+    monotonicity_pairs: Optional[list[tuple[int, int]]] = None,
+    kernel_init: CategoricalCalibratorInit = CategoricalCalibratorInit.UNIFORM,
+) -> None:
+    """Initializes an instance of `CategoricalCalibrator`.
+
+    Args:
+        num_categories: The number of known categories.
+        missing_input_value: If provided, the calibrator will learn to map all
+            instances of this missing input value to a learned output value just
+            the same as it does for known categories. Note that `num_categories`
+            will be one greater to include this missing category.
+        output_min: Minimum output value. If `None`, the minimum output value will
+            be unbounded.
+        output_max: Maximum output value. If `None`, the maximum output value will
+            be unbounded.
+        monotonicity_pairs: List of pairs of indices `(i,j)` indicating that the
+            calibrator output for index `j` should be greater than or equal to that
+            of index `i`.
+        kernel_init: Initialization scheme to use for the kernel.
+
+    Raises:
+        ValueError: If `monotonicity_pairs` is cyclic.
+        ValueError: If `kernel_init` is invalid.
+    """
+    super().__init__()
+
+    self.num_categories = (
+        num_categories + 1 if missing_input_value is not None else num_categories
+    )
+    self.missing_input_value = missing_input_value
+    self.output_min = output_min
+    self.output_max = output_max
+    self.monotonicity_pairs = monotonicity_pairs
+    if monotonicity_pairs:
+        self._monotonicity_graph = defaultdict(list)
+        self._reverse_monotonicity_graph = defaultdict(list)
+        for i, j in monotonicity_pairs:
+            self._monotonicity_graph[i].append(j)
+            self._reverse_monotonicity_graph[j].append(i)
+        try:
+            self._monotonically_sorted_indices = [
+                *TopologicalSorter(self._reverse_monotonicity_graph).static_order()
+            ]
+        except CycleError as exc:
+            raise ValueError("monotonicity_pairs is cyclic") from exc
+    self.kernel_init = kernel_init
+
+    self.kernel = torch.nn.Parameter(torch.Tensor(self.num_categories, 1).double())
+    if kernel_init == CategoricalCalibratorInit.CONSTANT:
+        if output_min is not None and output_max is not None:
+            init_value = (output_min + output_max) / 2
+        elif output_min is not None:
+            init_value = output_min
+        elif output_max is not None:
+            init_value = output_max
+        else:
+            init_value = 0.0
+        torch.nn.init.constant_(self.kernel, init_value)
+    elif kernel_init == CategoricalCalibratorInit.UNIFORM:
+        if output_min is not None and output_max is not None:
+            low, high = output_min, output_max
+        elif output_min is None and output_max is not None:
+            low, high = output_max - 0.05, output_max
+        elif output_min is not None and output_max is None:
+            low, high = output_min, output_min + 0.05
+        else:
+            low, high = -0.05, 0.05
+        torch.nn.init.uniform_(self.kernel, low, high)
+    else:
+        raise ValueError(f"Unknown kernel init: {kernel_init}")
+
+
+
+ +
+ + +
+ + + + +

+ apply_constraints() + +

+ + +
+ +

Projects kernel into desired constraints.

+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
138
+139
+140
+141
+142
+143
+144
+145
+146
+147
+148
+149
+150
+151
+152
+153
+154
@torch.no_grad()
+def apply_constraints(self) -> None:
+    """Projects kernel into desired constraints."""
+    projected_kernel_data = self.kernel.data
+    if self.monotonicity_pairs:
+        projected_kernel_data = self._approximately_project_monotonicity_pairs(
+            projected_kernel_data
+        )
+    if self.output_min is not None:
+        projected_kernel_data = torch.maximum(
+            projected_kernel_data, torch.tensor(self.output_min)
+        )
+    if self.output_max is not None:
+        projected_kernel_data = torch.minimum(
+            projected_kernel_data, torch.tensor(self.output_max)
+        )
+    self.kernel.data = projected_kernel_data
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + +

+ + +
+ +

Asserts that layer satisfies specified constraints.

+

This checks that weights at the indexes of monotonicity pairs are in the correct +order and that the output is within bounds.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
eps + float + +
+

the margin of error allowed

+
+ 		+ 1e-06 +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

A list of messages describing violated constraints including violated

+
+
+ list[str] + +
+

monotonicity pairs. If no constraints violated, the list will be empty.

+
+
+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
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@torch.no_grad()
+def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+    """Asserts that layer satisfies specified constraints.
+
+    This checks that weights at the indexes of monotonicity pairs are in the correct
+    order and that the output is within bounds.
+
+    Args:
+        eps: the margin of error allowed
+
+    Returns:
+        A list of messages describing violated constraints including violated
+        monotonicity pairs. If no constraints  violated, the list will be empty.
+    """
+    weights = torch.squeeze(self.kernel.data)
+    messages = []
+
+    if self.output_max is not None and torch.max(weights) > self.output_max + eps:
+        messages.append("Max weight greater than output_max.")
+    if self.output_min is not None and torch.min(weights) < self.output_min - eps:
+        messages.append("Min weight less than output_min.")
+
+    if self.monotonicity_pairs:
+        violation_indices = [
+            (i, j)
+            for (i, j) in self.monotonicity_pairs
+            if weights[i] - weights[j] > eps
+        ]
+        if violation_indices:
+            messages.append(f"Monotonicity violated at: {str(violation_indices)}.")
+
+    return messages
+
+
+
+ +
+ + +
+ + + + +

+ forward(x) + +

+ + +
+ +

Calibrates categorical inputs through a learned mapping.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Tensor + +
+

The input tensor of category indices of shape (batch_size, 1).

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

torch.Tensor of shape (batch_size, 1) containing calibrated input values.

+
+
+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
+    """Calibrates categorical inputs through a learned mapping.
+
+    Args:
+        x: The input tensor of category indices of shape `(batch_size, 1)`.
+
+    Returns:
+        torch.Tensor of shape `(batch_size, 1)` containing calibrated input values.
+    """
+    if self.missing_input_value is not None:
+        missing_category_tensor = torch.zeros_like(x) + (self.num_categories - 1)
+        x = torch.where(x == self.missing_input_value, missing_category_tensor, x)
+    # TODO: test if using torch.gather is faster than one-hot matmul.
+    one_hot = torch.nn.functional.one_hot(
+        torch.squeeze(x, -1).long(), num_classes=self.num_categories
+    ).double()
+    return torch.mm(one_hot, self.kernel)
+
+
+
+ +
+ + +
+ + + + +

+ keypoints_inputs() + +

+ + +
+ +

Returns a tensor of keypoint inputs (category indices).

+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
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@torch.no_grad()
+def keypoints_inputs(self) -> torch.Tensor:
+    """Returns a tensor of keypoint inputs (category indices)."""
+    if self.missing_input_value is not None:
+        return torch.cat(
+            (
+                torch.arange(self.num_categories - 1),
+                torch.tensor([self.missing_input_value]),
+            ),
+            0,
+        )
+    return torch.arange(self.num_categories)
+
+
+
+ +
+ + +
+ + + + +

+ keypoints_outputs() + +

+ + +
+ +

Returns a tensor of keypoint outputs.

+ +
+ Source code in pytorch_lattice/layers/categorical_calibrator.py + 
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@torch.no_grad()
+def keypoints_outputs(self) -> torch.Tensor:
+    """Returns a tensor of keypoint outputs."""
+    return torch.squeeze(self.kernel.data, -1)
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.layers.Lattice + + +

+ + +
+

+ Bases: ConstrainedModule

+ + +

A Lattice Module.

+

Layer performs interpolation using one of 'units' d-dimensional lattices with +arbitrary number of keypoints per dimension. Each lattice vertex has a trainable +weight, and input is considered to be a d-dimensional point within the lattice.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
kernel + +
+

torch.nn.Parameter of shape (prod(lattice_sizes), units) which +stores +weights at each vertex of lattice.

+
+
+

Example: +

lattice_sizes = [2, 2, 4, 3]
+inputs=torch.tensor(...) # shape: (batch_size, len(lattice_sizes))
+lattice=Lattice(
+    lattice_sizes,
+    clip_inputs=True,
+    interpolation=Interpolation.HYPERCUBE,
+    units=1,
+)
+outputs = Lattice(inputs)
+

+ +
+ Source code in pytorch_lattice/layers/lattice.py + 
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class Lattice(ConstrainedModule):
+    """A Lattice Module.
+
+    Layer performs interpolation using one of 'units' d-dimensional lattices with
+    arbitrary number of keypoints per dimension. Each lattice vertex has a trainable
+    weight, and input is considered to be a d-dimensional point within the lattice.
+
+    Attributes:
+        All: `__init__` arguments.
+        kernel: `torch.nn.Parameter` of shape `(prod(lattice_sizes), units)` which
+            stores
+            weights at each vertex of lattice.
+
+    Example:
+    ```python
+    lattice_sizes = [2, 2, 4, 3]
+    inputs=torch.tensor(...) # shape: (batch_size, len(lattice_sizes))
+    lattice=Lattice(
+        lattice_sizes,
+        clip_inputs=True,
+        interpolation=Interpolation.HYPERCUBE,
+        units=1,
+    )
+    outputs = Lattice(inputs)
+    ```
+    """
+
+    def __init__(
+        self,
+        lattice_sizes: Union[list[int], tuple[int]],
+        output_min: Optional[float] = None,
+        output_max: Optional[float] = None,
+        kernel_init: LatticeInit = LatticeInit.LINEAR,
+        monotonicities: Optional[list[Optional[Monotonicity]]] = None,
+        clip_inputs: bool = True,
+        interpolation: Interpolation = Interpolation.HYPERCUBE,
+        units: int = 1,
+    ) -> None:
+        """Initializes an instance of 'Lattice'.
+
+        Args:
+            lattice_sizes: List or tuple of size of lattice along each dimension.
+            output_min: Minimum output value for weights at vertices of lattice.
+            output_max: Maximum output value for weights at vertices of lattice.
+            kernel_init: Initialization scheme to use for the kernel.
+            monotonicities: `None` or list of `NONE` or
+                `Monotonicity.INCREASING` of length `len(lattice_sizes)` specifying
+                monotonicity of each feature of lattice. A monotonically decreasing
+                 feature should use `Monotonicity.INCREASING` in the lattice layer but
+                `Monotonicity.DECREASING` in the calibrator.
+            clip_inputs: Whether input points should be clipped to the range of lattice.
+            interpolation: Interpolation scheme for a given input.
+            units: Dimensionality of weights stored at each vertex of lattice.
+
+        Raises:
+            ValueError: if `kernel_init` is invalid.
+            NotImplementedError: Random monotonic initialization not yet implemented.
+        """
+        super().__init__()
+
+        self.lattice_sizes = list(lattice_sizes)
+        self.output_min = output_min
+        self.output_max = output_max
+        self.kernel_init = kernel_init
+        self.clip_inputs = clip_inputs
+        self.interpolation = interpolation
+        self.units = units
+
+        if monotonicities is not None:
+            self.monotonicities = monotonicities
+        else:
+            self.monotonicities = [None] * len(lattice_sizes)
+
+        if output_min is not None and output_max is not None:
+            output_init_min, output_init_max = output_min, output_max
+        elif output_min is not None:
+            output_init_min, output_init_max = output_min, output_min + 4.0
+        elif output_max is not None:
+            output_init_min, output_init_max = output_max - 4.0, output_max
+        else:
+            output_init_min, output_init_max = -2.0, 2.0
+        self._output_init_min, self._output_init_max = output_init_min, output_init_max
+
+        @torch.no_grad()
+        def initialize_kernel() -> torch.Tensor:
+            if self.kernel_init == LatticeInit.LINEAR:
+                return self._linear_initializer()
+            if self.kernel_init == LatticeInit.RANDOM_MONOTONIC:
+                raise NotImplementedError(
+                    "Random monotonic initialization not yet implemented."
+                )
+            raise ValueError(f"Unknown kernel init: {self.kernel_init}")
+
+        self.kernel = torch.nn.Parameter(initialize_kernel())
+
+    def forward(self, x: Union[torch.Tensor, list[torch.Tensor]]) -> torch.Tensor:
+        """Calculates interpolation from input, using method of self.interpolation.
+
+        Args:
+            x: input tensor. If `units == 1`, tensor of shape:
+                `(batch_size, ..., len(lattice_size))` or list of `len(lattice_sizes)`
+                tensors of same shape: `(batch_size, ..., 1)`. If `units > 1`, tensor of
+                shape `(batch_size, ..., units, len(lattice_sizes))` or list of
+                `len(lattice_sizes)` tensors OF same shape `(batch_size, ..., units, 1)`
+
+        Returns:
+            torch.Tensor of shape `(batch_size, ..., units)` containing interpolated
+            values.
+
+        Raises:
+            ValueError: If the type of interpolation is unknown.
+        """
+        x = [xi.double() for xi in x] if isinstance(x, list) else x.double()
+        if self.interpolation == Interpolation.HYPERCUBE:
+            return self._compute_hypercube_interpolation(x)
+        if self.interpolation == Interpolation.SIMPLEX:
+            return self._compute_simplex_interpolation(x)
+        raise ValueError(f"Unknown interpolation type: {self.interpolation}")
+
+    @torch.no_grad()
+    def apply_constraints(self) -> None:
+        """Aggregate function for enforcing constraints of lattice."""
+        weights = self.kernel.clone()
+
+        if self._count_non_zeros(self.monotonicities):
+            lattice_sizes = self.lattice_sizes
+            monotonicities = self.monotonicities
+            if self.units > 1:
+                lattice_sizes = lattice_sizes + [int(self.units)]
+                if self.monotonicities:
+                    monotonicities = monotonicities + [None]
+
+            weights = weights.reshape(*lattice_sizes)
+            weights = self._approximately_project_monotonicity(
+                weights, lattice_sizes, monotonicities
+            )
+
+        if self.output_min is not None:
+            weights = torch.clamp_min(weights, self.output_min)
+        if self.output_max is not None:
+            weights = torch.clamp_max(weights, self.output_max)
+
+        self.kernel.data = weights.view(-1, self.units)
+
+    @torch.no_grad()
+    def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+        """Asserts that layer satisfies specified constraints.
+
+        This checks that weights follow monotonicity and bounds constraints.
+
+        Args:
+            eps: the margin of error allowed
+
+        Returns:
+            A list of dicts describing violated constraints including indices of
+            monotonicity violations. If no constraints violated, the list will be empty.
+        """
+        messages = []
+        lattice_sizes = self.lattice_sizes
+        monotonicities = self.monotonicities
+        weights = self.kernel.data.clone()
+
+        if weights.shape[1] > 1:
+            lattice_sizes = lattice_sizes + [int(weights.shape[1])]
+            if monotonicities:
+                monotonicities = monotonicities + [None]
+
+        # Reshape weights to match lattice sizes
+        weights = weights.reshape(*lattice_sizes)
+
+        for i in range(len(monotonicities or [])):
+            if monotonicities[i] != Monotonicity.INCREASING:
+                continue
+            weights_layers = torch.unbind(weights, dim=i)
+
+            for j in range(1, len(weights_layers)):
+                diff = torch.min(weights_layers[j] - weights_layers[j - 1])
+                if diff.item() < -eps:
+                    messages.append(f"Monotonicity violated at feature index {i}.")
+
+        if self.output_max is not None and torch.max(weights) > self.output_max + eps:
+            messages.append("Max weight greater than output_max.")
+        if self.output_min is not None and torch.min(weights) < self.output_min - eps:
+            messages.append("Min weight less than output_min.")
+
+        return messages
+
+    ################################################################################
+    ############################## PRIVATE METHODS #################################
+    ################################################################################
+
+    def _linear_initializer(self) -> torch.Tensor:
+        """Creates initial weights tensor for linear initialization.
+
+        Args:
+            monotonicities: monotonicity constraints of lattice, enforced in
+                initialization.
+
+        Returns:
+            `torch.Tensor` of shape `(prod(lattice_sizes), units)`
+        """
+        monotonicities = self.monotonicities[:]
+
+        if monotonicities is None:
+            monotonicities = [None] * len(self.lattice_sizes)
+
+        num_constraint_dims = self._count_non_zeros(monotonicities)
+        if num_constraint_dims == 0:
+            monotonicities = [Monotonicity.INCREASING] * len(self.lattice_sizes)
+            num_constraint_dims = len(self.lattice_sizes)
+
+        dim_range = (
+            float(self._output_init_max - self._output_init_min) / num_constraint_dims
+        )
+        one_d_weights = []
+
+        for monotonicity, dim_size in zip(monotonicities, self.lattice_sizes):
+            if monotonicity is not None:
+                one_d = np.linspace(start=0.0, stop=dim_range, num=dim_size)
+            else:
+                one_d = np.array([0.0] * dim_size)
+
+            one_d_weights.append(torch.tensor(one_d, dtype=torch.double).unsqueeze(0))
+
+        weights = self._batch_outer_operation(one_d_weights, operation=torch.add)
+        weights = (weights + self._output_init_min).view(-1, 1)
+        if self.units > 1:
+            weights = weights.repeat(1, self.units)
+
+        return weights
+
+    @staticmethod
+    def _count_non_zeros(*iterables) -> int:
+        """Returns total number of non 0/None enum elements in given iterables.
+
+        Args:
+            *iterables: Any number of the value `None` or iterables of `None` or
+                `Monotonicity` enum values.
+        """
+        result = 0
+        for iterable in iterables:
+            if iterable is not None:
+                for element in iterable:
+                    if element is not None:
+                        result += 1
+        return result
+
+    def _compute_simplex_interpolation(
+        self, inputs: Union[torch.Tensor, list[torch.Tensor]]
+    ) -> torch.Tensor:
+        """Evaluates a lattice using simplex interpolation.
+
+        Each `d`-dimensional unit hypercube of the lattice can be partitioned into `d!`
+        disjoint simplices with `d+1` vertices. `S` is the unique simplex which contains
+        input point `P`, and `S` has vertices `ABCD...`. For any vertex such as `A`, a
+        new simplex `S'` can be created using the vertices `PBCD...`. The weight of `A`
+        within the interpolation is then `vol(S')/vol(S)`. This process is repeated
+        for every vertex in `S`, and the resulting values are summed.
+
+        This interpolation can be computed in `O(D log(D))` time because it is only
+        necessary to compute the volume of the simplex containing input point `P`. For
+        context, the unit hypercube can be partitioned into `d!` simplices by starting
+        at `(0,0,...,0)` and incrementing `0` to `1` dimension-by-dimensionuntil one
+        reaches `(1,1,...,1)`. There are `d!` possible paths from `(0,0,...,0)` to
+        `(1,1,...,1)`, which account for the number of unique, disjoint simplices
+        created by the method. There are `d` steps for each possible path where each
+        step comprises the vertices of one simplex. Thus, one can find the containing
+        simplex for input `P` by argsorting the coordinates of `P` in descending order
+        and pathing along said order. To compute the intepolation weights simply take
+        the deltas from `[1, desc_sort(P_coords), 0]`.
+
+        Args:
+            inputs: input tensor. If `units == 1`, tensor of shape:
+                `(batch_size, ..., len(lattice_size))` or list of `len(lattice_sizes)`
+                tensors of same shape: `(batch_size, ..., 1)`. If `units > 1`, tensor of
+                shape `(batch_size, ..., units, len(lattice_sizes))` or list of
+                `len(lattice_sizes)` tensors of same shape `(batch_size, ..., units, 1)`
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, ..., units)` containing interpolated
+            values.
+        """
+        if isinstance(inputs, list):
+            inputs = torch.cat(inputs, dim=-1)
+
+        if self.clip_inputs:
+            inputs = self._clip_onto_lattice_range(inputs)
+
+        lattice_rank = len(self.lattice_sizes)
+        input_dim = len(inputs.shape)
+        all_size_2 = all(size == 2 for size in self.lattice_sizes)
+
+        # Strides are the index shift (with respect to flattened kernel data) of each
+        # dimension, which can be used in a dot product with multi-dimensional
+        # coordinates to give an index for the flattened lattice weights.
+        # Ex): for lattice_sizes = [4, 3, 2], we get strides = [6, 2, 1]: when looking
+        # at lattice coords (i, j, k) and kernel data flattened into 1-D, incrementing i
+        # corresponds to a shift of 6 in flattened kernel data, j corresponds to a shift
+        # of 2, and k corresponds to a shift of 1. Consequently, we can do
+        # (coords * strides) for any coordinates to obtain the flattened index.
+        strides = torch.tensor(
+            np.cumprod([1] + self.lattice_sizes[::-1][:-1])[::-1].copy()
+        )
+        if not all_size_2:
+            lower_corner_coordinates = inputs.int()
+            lower_corner_coordinates = torch.min(
+                lower_corner_coordinates, torch.tensor(self.lattice_sizes) - 2
+            )
+            inputs = inputs - lower_corner_coordinates.float()
+
+        sorted_indices = torch.argsort(inputs, descending=True)
+        sorted_inputs = torch.sort(inputs, descending=True).values
+
+        # Pad the 1 and 0 onto the ends of sorted coordinates and compute deltas.
+        no_padding_dims = [(0, 0)] * (input_dim - 1)
+        flat_no_padding = [item for sublist in no_padding_dims for item in sublist]
+        sorted_inputs_padded_left = torch.nn.functional.pad(
+            sorted_inputs, [1, 0] + flat_no_padding, value=1.0
+        )
+        sorted_inputs_padded_right = torch.nn.functional.pad(
+            sorted_inputs, [0, 1] + flat_no_padding, value=0.0
+        )
+        weights = sorted_inputs_padded_left - sorted_inputs_padded_right
+
+        # Use strides to find indices of simplex vertices in flattened form.
+        sorted_strides = torch.gather(strides, 0, sorted_indices.view(-1)).view(
+            sorted_indices.shape
+        )
+        if all_size_2:
+            corner_offset_and_sorted_strides = torch.nn.functional.pad(
+                sorted_strides, [1, 0] + flat_no_padding
+            )
+        else:
+            lower_corner_offset = (lower_corner_coordinates * strides).sum(
+                dim=-1, keepdim=True
+            )
+            corner_offset_and_sorted_strides = torch.cat(
+                [lower_corner_offset, sorted_strides], dim=-1
+            )
+        indices = torch.cumsum(corner_offset_and_sorted_strides, dim=-1)
+
+        # Get kernel data from corresponding simplex vertices.
+        if self.units == 1:
+            gathered_params = torch.index_select(
+                self.kernel.view(-1), 0, indices.view(-1)
+            ).view(indices.shape)
+        else:
+            unit_offset = torch.tensor(
+                [[i] * (lattice_rank + 1) for i in range(self.units)]
+            )
+            flat_indices = indices * self.units + unit_offset
+            gathered_params = torch.index_select(
+                self.kernel.view(-1), 0, flat_indices.view(-1)
+            ).view(indices.shape)
+
+        return (gathered_params * weights).sum(dim=-1, keepdim=self.units == 1)
+
+    def _compute_hypercube_interpolation(
+        self,
+        inputs: Union[torch.Tensor, list[torch.Tensor]],
+    ) -> torch.Tensor:
+        """Performs hypercube interpolation using the surrounding unit hypercube.
+
+        Args:
+            inputs: input tensor. If `units == 1`, tensor of shape:
+                `(batch_size, ..., len(lattice_size))` or list of `len(lattice_sizes)`
+                tensors of same shape: `(batch_size, ..., 1)`. If `units > 1`, tensor of
+                shape `(batch_size, ..., units, len(lattice_sizes))` or list of
+                `len(lattice_sizes)` tensors of same shape `(batch_size, ..., units, 1)`
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, ..., units)` containing interpolated
+            value(s).
+        """
+        interpolation_weights = self._compute_hypercube_interpolation_weights(
+            inputs=inputs, clip_inputs=self.clip_inputs
+        )
+        if self.units == 1:
+            return torch.matmul(interpolation_weights, self.kernel)
+
+        return torch.sum(interpolation_weights * self.kernel.t(), dim=-1)
+
+    def _compute_hypercube_interpolation_weights(
+        self, inputs: Union[torch.Tensor, list[torch.Tensor]], clip_inputs: bool = True
+    ) -> torch.Tensor:
+        """Computes weights for hypercube lattice interpolation.
+
+        For each n-dim unit in "inputs," the weights matrix will generate the weights
+        corresponding to the unit's location within its surrounding hypercube. These
+        weights can then be multiplied by the lattice layer's kernel to compute the
+        actual hypercube interpolation. Specifically, the outer product of the set
+        `(1-x_i, x_i)` for all x_i in input unit x calculates the weights for each
+        vertex in the surrounding hypercube, and every other vertex in the lattice is
+        set to zero since it is not used. In addition, for consecutive dimensions of
+        equal size in the lattice, broadcasting is used to speed up calculations.
+
+        Args:
+            inputs: torch.Tensor of shape `(batch_size, ..., len(lattice_sizes)` or list
+                of `len(lattice_sizes)` tensors of same shape `(batch_size, ..., 1)`
+            clip_inputs: Boolean to determine whether input values outside lattice
+                bounds should be clipped to the min or max supported values.
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, ..., prod(lattice_sizes))` containing
+            the weights which can be matrix multiplied with the kernel to perform
+            hypercube interpolation.
+        """
+        if isinstance(inputs, list):
+            input_dtype = inputs[0].dtype
+        else:
+            input_dtype = inputs.dtype
+
+        # Special case: 2^d lattice with input passed in as a single tensor
+        if all(size == 2 for size in self.lattice_sizes) and not isinstance(
+            inputs, list
+        ):
+            w = torch.stack([(1.0 - inputs), inputs], dim=-1)
+            if clip_inputs:
+                w = torch.clamp(w, min=0, max=1)
+            one_d_interpolation_weights = list(torch.unbind(w, dim=-2))
+            return self._batch_outer_operation(one_d_interpolation_weights)
+
+        if clip_inputs:
+            inputs = self._clip_onto_lattice_range(inputs)
+
+        # Set up buckets of consecutive equal dimensions for broadcasting later
+        dim_keypoints = {}
+        for dim_size in set(self.lattice_sizes):
+            dim_keypoints[dim_size] = torch.tensor(
+                list(range(dim_size)), dtype=input_dtype
+            )
+        bucketized_inputs = self._bucketize_consecutive_equal_dims(inputs)
+        one_d_interpolation_weights = []
+
+        for tensor, bucket_size, dim_size in bucketized_inputs:
+            if bucket_size > 1:
+                tensor = torch.unsqueeze(tensor, dim=-1)
+            distance = torch.abs(tensor - dim_keypoints[dim_size])
+            weights = 1.0 - torch.minimum(
+                distance, torch.tensor(1.0, dtype=distance.dtype)
+            )
+            if bucket_size == 1:
+                one_d_interpolation_weights.append(weights)
+            else:
+                one_d_interpolation_weights.extend(torch.unbind(weights, dim=-2))
+
+        return self._batch_outer_operation(one_d_interpolation_weights)
+
+    @staticmethod
+    def _batch_outer_operation(
+        list_of_tensors: list[torch.Tensor],
+        operation: Optional[Callable] = None,
+    ) -> torch.Tensor:
+        """Computes the flattened outer product of a list of tensors.
+
+        Args:
+            list_of_tensors: List of tensors of same shape `(batch_size, ..., k[i])`
+                where everything except `k_i` matches.
+            operation: A torch operation which supports broadcasting to be applied. If
+                `None` is provided, this will apply `torch.mul` for the first several
+                tensors and `torch.matmul` for the remaining tensors.
+
+        Returns:
+            `torch.Tensor` of shape `(batch_size, ..., k_i * k_j * ...)` containing a
+            flattened version of the outer product.
+        """
+        if len(list_of_tensors) == 1:
+            return list_of_tensors[0]
+
+        result = torch.unsqueeze(list_of_tensors[0], dim=-1)
+
+        for i, tensor in enumerate(list_of_tensors[1:]):
+            if not operation:
+                op = torch.mul if i < 6 else torch.matmul
+            else:
+                op = operation
+
+            result = op(result, torch.unsqueeze(tensor, dim=-2))
+            shape = [-1] + [int(size) for size in result.shape[1:]]
+            new_shape = shape[:-2] + [shape[-2] * shape[-1]]
+            if i < len(list_of_tensors) - 2:
+                new_shape.append(1)
+            result = torch.reshape(result, new_shape)
+
+        return result
+
+    @overload
+    def _clip_onto_lattice_range(self, inputs: torch.Tensor) -> torch.Tensor:
+        ...
+
+    @overload
+    def _clip_onto_lattice_range(
+        self, inputs: list[torch.Tensor]
+    ) -> list[torch.Tensor]:
+        ...
+
+    def _clip_onto_lattice_range(
+        self,
+        inputs: Union[torch.Tensor, list[torch.Tensor]],
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        """Clips inputs onto valid input range for given lattice_sizes.
+
+        Args:
+            inputs: `inputs` argument of `_compute_interpolation_weights()`.
+
+        Returns:
+            `torch.Tensor` of shape `inputs` with values within range
+            `[0, dim_size - 1]`.
+        """
+        clipped_inputs: Union[torch.Tensor, list[torch.Tensor]]
+        if not isinstance(inputs, list):
+            upper_bounds = torch.tensor(
+                [dim_size - 1.0 for dim_size in self.lattice_sizes]
+            ).double()
+            clipped_inputs = torch.clamp(
+                inputs, min=torch.zeros_like(upper_bounds), max=upper_bounds
+            )
+        else:
+            dim_upper_bounds = {}
+            for dim_size in set(self.lattice_sizes):
+                dim_upper_bounds[dim_size] = torch.tensor(
+                    dim_size - 1.0, dtype=inputs[0].dtype
+                )
+            dim_lower_bound = torch.zeros(1, dtype=inputs[0].dtype)
+
+            clipped_inputs = [
+                torch.clamp(
+                    one_d_input, min=dim_lower_bound, max=dim_upper_bounds[dim_size]
+                )
+                for one_d_input, dim_size in zip(inputs, self.lattice_sizes)
+            ]
+
+        return clipped_inputs
+
+    def _bucketize_consecutive_equal_dims(
+        self,
+        inputs: Union[torch.Tensor, list[torch.Tensor]],
+    ) -> Iterator[tuple[torch.Tensor, int, int]]:
+        """Creates buckets of equal sized dimensions for broadcasting ops.
+
+        Args:
+            inputs: `inputs` argument of `_compute_interpolation_weights()`.
+
+        Returns:
+            An `Iterable` containing `(torch.Tensor, int, int)` where the tensor
+            contains individual values from "inputs" corresponding to its bucket, the
+            first `int` is bucket size, and the second `int` is size of the dimension of
+            the bucket.
+        """
+        if not isinstance(inputs, list):
+            bucket_sizes = []
+            bucket_dim_sizes = []
+            current_size = 1
+            for i in range(1, len(self.lattice_sizes)):
+                if self.lattice_sizes[i] != self.lattice_sizes[i - 1]:
+                    bucket_sizes.append(current_size)
+                    bucket_dim_sizes.append(self.lattice_sizes[i - 1])
+                    current_size = 1
+                else:
+                    current_size += 1
+            bucket_sizes.append(current_size)
+            bucket_dim_sizes.append(self.lattice_sizes[-1])
+            inputs = torch.split(inputs, split_size_or_sections=bucket_sizes, dim=-1)
+        else:
+            bucket_sizes = [1] * len(self.lattice_sizes)
+            bucket_dim_sizes = self.lattice_sizes
+
+        return zip(inputs, bucket_sizes, bucket_dim_sizes)
+
+    def _approximately_project_monotonicity(
+        self,
+        weights: torch.Tensor,
+        lattice_sizes: list[int],
+        monotonicities: list[Optional[Monotonicity]],
+    ) -> torch.Tensor:
+        """Projects weights of lattice to meet monotonicity constraints.
+
+        Note that this projection is an approximation which guarantees monotonicity
+        constraints but is not an exact projection with respect to the L2 norm.
+
+        Algorithm:
+        1. `max_projection`: For each vertex V in the lattice, the weight is adjusted to
+        be the maximum of all weights of vertices X such that X has all coordinates
+        less than or equal to V in monotonic dimensions.
+
+        2. `half_projection`: We adjust the weights to be the average of the original
+        weights and the `max_projection` weights.
+
+        3. `min_projection`: For each vertex V in the lattice, the weight is adjusted
+        based on the `half_projection` to be the minimum of all weights of vertices X
+        such that V has all coordinates less than or equal to X in monotonic dimensions.
+
+        This algorithm ensures that weights conform to the monotonicity constraints
+        while getting closer to a true projection by adjusting both up/downwards.
+
+        Args:
+            weights: `torch.Tensor` of kernel data reshaped into `(lattice_sizes)` if
+                `units == 1` or `(lattice_sizes, units)` if `units > 1`.
+            lattice_sizes: List of size of each dimension of lattice, but for
+                `units > 1`, `units` is appended to the end for computation purposes.
+            monotonicities: List of `None` or `Monotonicity.INCREASING`
+                of length `len(lattice_sizes)` for `units == 1` or
+                `len(lattice_sizes)+1` if `units > 1` specifying monotonicity of each
+                feature of lattice.
+
+        Returns:
+            `torch.Tensor` of shape `self.kernel` with updated weights which meet
+            monotonicity constraints.
+        """
+        max_projection = weights
+        for dim in range(len(lattice_sizes)):
+            if monotonicities[dim] is None:
+                continue
+            layers = list(torch.unbind(max_projection, dim))
+            for i in range(1, len(layers)):
+                layers[i] = torch.max(layers[i], layers[i - 1])
+            max_projection = torch.stack(layers, dim)
+
+        half_projection = (weights + max_projection) / 2.0
+
+        min_projection = half_projection
+        for dim in range(len(lattice_sizes)):
+            if monotonicities[dim] is None:
+                continue
+            layers = list(torch.unbind(min_projection, dim))
+            for i in range(len(layers) - 2, -1, -1):
+                # Compute cumulative minimum in reverse order
+                layers[i] = torch.min(layers[i], layers[i + 1])
+            min_projection = torch.stack(layers, dim)
+
+        return min_projection
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(lattice_sizes, output_min=None, output_max=None, kernel_init=LatticeInit.LINEAR, monotonicities=None, clip_inputs=True, interpolation=Interpolation.HYPERCUBE, units=1) + +

+ + +
+ +

Initializes an instance of 'Lattice'.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
lattice_sizes + Union[list[int], tuple[int]] + +
+

List or tuple of size of lattice along each dimension.

+
+ 		+ required +
output_min + Optional[float] + +
+

Minimum output value for weights at vertices of lattice.

+
+ 		+ None +
output_max + Optional[float] + +
+

Maximum output value for weights at vertices of lattice.

+
+ 		+ None +
kernel_init + LatticeInit + +
+

Initialization scheme to use for the kernel.

+
+ 		+ LINEAR +
monotonicities + Optional[list[Optional[Monotonicity]]] + +
+

None or list of NONE or +Monotonicity.INCREASING of length len(lattice_sizes) specifying +monotonicity of each feature of lattice. A monotonically decreasing + feature should use Monotonicity.INCREASING in the lattice layer but +Monotonicity.DECREASING in the calibrator.

+
+ 		+ None +
clip_inputs + bool + +
+

Whether input points should be clipped to the range of lattice.

+
+ 		+ True +
interpolation + Interpolation + +
+

Interpolation scheme for a given input.

+
+ 		+ HYPERCUBE +
units + int + +
+

Dimensionality of weights stored at each vertex of lattice.

+
+ 		+ 1 +
+ + + +

Raises:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

if kernel_init is invalid.

+
+
+ NotImplementedError + +
+

Random monotonic initialization not yet implemented.

+
+
+ +
+ Source code in pytorch_lattice/layers/lattice.py + 
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def __init__(
+    self,
+    lattice_sizes: Union[list[int], tuple[int]],
+    output_min: Optional[float] = None,
+    output_max: Optional[float] = None,
+    kernel_init: LatticeInit = LatticeInit.LINEAR,
+    monotonicities: Optional[list[Optional[Monotonicity]]] = None,
+    clip_inputs: bool = True,
+    interpolation: Interpolation = Interpolation.HYPERCUBE,
+    units: int = 1,
+) -> None:
+    """Initializes an instance of 'Lattice'.
+
+    Args:
+        lattice_sizes: List or tuple of size of lattice along each dimension.
+        output_min: Minimum output value for weights at vertices of lattice.
+        output_max: Maximum output value for weights at vertices of lattice.
+        kernel_init: Initialization scheme to use for the kernel.
+        monotonicities: `None` or list of `NONE` or
+            `Monotonicity.INCREASING` of length `len(lattice_sizes)` specifying
+            monotonicity of each feature of lattice. A monotonically decreasing
+             feature should use `Monotonicity.INCREASING` in the lattice layer but
+            `Monotonicity.DECREASING` in the calibrator.
+        clip_inputs: Whether input points should be clipped to the range of lattice.
+        interpolation: Interpolation scheme for a given input.
+        units: Dimensionality of weights stored at each vertex of lattice.
+
+    Raises:
+        ValueError: if `kernel_init` is invalid.
+        NotImplementedError: Random monotonic initialization not yet implemented.
+    """
+    super().__init__()
+
+    self.lattice_sizes = list(lattice_sizes)
+    self.output_min = output_min
+    self.output_max = output_max
+    self.kernel_init = kernel_init
+    self.clip_inputs = clip_inputs
+    self.interpolation = interpolation
+    self.units = units
+
+    if monotonicities is not None:
+        self.monotonicities = monotonicities
+    else:
+        self.monotonicities = [None] * len(lattice_sizes)
+
+    if output_min is not None and output_max is not None:
+        output_init_min, output_init_max = output_min, output_max
+    elif output_min is not None:
+        output_init_min, output_init_max = output_min, output_min + 4.0
+    elif output_max is not None:
+        output_init_min, output_init_max = output_max - 4.0, output_max
+    else:
+        output_init_min, output_init_max = -2.0, 2.0
+    self._output_init_min, self._output_init_max = output_init_min, output_init_max
+
+    @torch.no_grad()
+    def initialize_kernel() -> torch.Tensor:
+        if self.kernel_init == LatticeInit.LINEAR:
+            return self._linear_initializer()
+        if self.kernel_init == LatticeInit.RANDOM_MONOTONIC:
+            raise NotImplementedError(
+                "Random monotonic initialization not yet implemented."
+            )
+        raise ValueError(f"Unknown kernel init: {self.kernel_init}")
+
+    self.kernel = torch.nn.Parameter(initialize_kernel())
+
+
+
+ +
+ + +
+ + + + +

+ apply_constraints() + +

+ + +
+ +

Aggregate function for enforcing constraints of lattice.

+ +
+ Source code in pytorch_lattice/layers/lattice.py + 
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@torch.no_grad()
+def apply_constraints(self) -> None:
+    """Aggregate function for enforcing constraints of lattice."""
+    weights = self.kernel.clone()
+
+    if self._count_non_zeros(self.monotonicities):
+        lattice_sizes = self.lattice_sizes
+        monotonicities = self.monotonicities
+        if self.units > 1:
+            lattice_sizes = lattice_sizes + [int(self.units)]
+            if self.monotonicities:
+                monotonicities = monotonicities + [None]
+
+        weights = weights.reshape(*lattice_sizes)
+        weights = self._approximately_project_monotonicity(
+            weights, lattice_sizes, monotonicities
+        )
+
+    if self.output_min is not None:
+        weights = torch.clamp_min(weights, self.output_min)
+    if self.output_max is not None:
+        weights = torch.clamp_max(weights, self.output_max)
+
+    self.kernel.data = weights.view(-1, self.units)
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + +

+ + +
+ +

Asserts that layer satisfies specified constraints.

+

This checks that weights follow monotonicity and bounds constraints.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
eps + float + +
+

the margin of error allowed

+
+ 		+ 1e-06 +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

A list of dicts describing violated constraints including indices of

+
+
+ list[str] + +
+

monotonicity violations. If no constraints violated, the list will be empty.

+
+
+ +
+ Source code in pytorch_lattice/layers/lattice.py + 
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@torch.no_grad()
+def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+    """Asserts that layer satisfies specified constraints.
+
+    This checks that weights follow monotonicity and bounds constraints.
+
+    Args:
+        eps: the margin of error allowed
+
+    Returns:
+        A list of dicts describing violated constraints including indices of
+        monotonicity violations. If no constraints violated, the list will be empty.
+    """
+    messages = []
+    lattice_sizes = self.lattice_sizes
+    monotonicities = self.monotonicities
+    weights = self.kernel.data.clone()
+
+    if weights.shape[1] > 1:
+        lattice_sizes = lattice_sizes + [int(weights.shape[1])]
+        if monotonicities:
+            monotonicities = monotonicities + [None]
+
+    # Reshape weights to match lattice sizes
+    weights = weights.reshape(*lattice_sizes)
+
+    for i in range(len(monotonicities or [])):
+        if monotonicities[i] != Monotonicity.INCREASING:
+            continue
+        weights_layers = torch.unbind(weights, dim=i)
+
+        for j in range(1, len(weights_layers)):
+            diff = torch.min(weights_layers[j] - weights_layers[j - 1])
+            if diff.item() < -eps:
+                messages.append(f"Monotonicity violated at feature index {i}.")
+
+    if self.output_max is not None and torch.max(weights) > self.output_max + eps:
+        messages.append("Max weight greater than output_max.")
+    if self.output_min is not None and torch.min(weights) < self.output_min - eps:
+        messages.append("Min weight less than output_min.")
+
+    return messages
+
+
+
+ +
+ + +
+ + + + +

+ forward(x) + +

+ + +
+ +

Calculates interpolation from input, using method of self.interpolation.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Union[Tensor, list[Tensor]] + +
+

input tensor. If units == 1, tensor of shape: +(batch_size, ..., len(lattice_size)) or list of len(lattice_sizes) +tensors of same shape: (batch_size, ..., 1). If units > 1, tensor of +shape (batch_size, ..., units, len(lattice_sizes)) or list of +len(lattice_sizes) tensors OF same shape (batch_size, ..., units, 1)

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

torch.Tensor of shape (batch_size, ..., units) containing interpolated

+
+
+ Tensor + +
+

values.

+
+
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If the type of interpolation is unknown.

+
+
+ +
+ Source code in pytorch_lattice/layers/lattice.py + 
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def forward(self, x: Union[torch.Tensor, list[torch.Tensor]]) -> torch.Tensor:
+    """Calculates interpolation from input, using method of self.interpolation.
+
+    Args:
+        x: input tensor. If `units == 1`, tensor of shape:
+            `(batch_size, ..., len(lattice_size))` or list of `len(lattice_sizes)`
+            tensors of same shape: `(batch_size, ..., 1)`. If `units > 1`, tensor of
+            shape `(batch_size, ..., units, len(lattice_sizes))` or list of
+            `len(lattice_sizes)` tensors OF same shape `(batch_size, ..., units, 1)`
+
+    Returns:
+        torch.Tensor of shape `(batch_size, ..., units)` containing interpolated
+        values.
+
+    Raises:
+        ValueError: If the type of interpolation is unknown.
+    """
+    x = [xi.double() for xi in x] if isinstance(x, list) else x.double()
+    if self.interpolation == Interpolation.HYPERCUBE:
+        return self._compute_hypercube_interpolation(x)
+    if self.interpolation == Interpolation.SIMPLEX:
+        return self._compute_simplex_interpolation(x)
+    raise ValueError(f"Unknown interpolation type: {self.interpolation}")
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.layers.Linear + + +

+ + +
+

+ Bases: ConstrainedModule

+ + +

A constrained linear module.

+

This module takes an input of shape (batch_size, input_dim) and applied a linear +transformation. The output will have the same shape as the input.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
kernel + +
+

torch.nn.Parameter that stores the linear combination weighting.

+
+
bias + +
+

torch.nn.Parameter that stores the bias term. Only available is +use_bias is true.

+
+
+

Example: +

input_dim = 3
+inputs = torch.tensor(...)  # shape: (batch_size, input_dim)
+linear = Linear(
+    input_dim,
+    monotonicities=[
+        None,
+        Monotonicity.INCREASING,
+        Monotonicity.DECREASING
+    ],
+    use_bias=False,
+    weighted_average=True,
+)
+outputs = linear(inputs)
+

+ +
+ Source code in pytorch_lattice/layers/linear.py + 
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class Linear(ConstrainedModule):
+    """A constrained linear module.
+
+    This module takes an input of shape `(batch_size, input_dim)` and applied a linear
+    transformation. The output will have the same shape as the input.
+
+    Attributes:
+        All: `__init__` arguments.
+        kernel: `torch.nn.Parameter` that stores the linear combination weighting.
+        bias: `torch.nn.Parameter` that stores the bias term. Only available is
+            `use_bias` is true.
+
+    Example:
+    ```python
+    input_dim = 3
+    inputs = torch.tensor(...)  # shape: (batch_size, input_dim)
+    linear = Linear(
+        input_dim,
+        monotonicities=[
+            None,
+            Monotonicity.INCREASING,
+            Monotonicity.DECREASING
+        ],
+        use_bias=False,
+        weighted_average=True,
+    )
+    outputs = linear(inputs)
+    ```
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        monotonicities: Optional[list[Optional[Monotonicity]]] = None,
+        use_bias: bool = True,
+        weighted_average: bool = False,
+    ) -> None:
+        """Initializes an instance of `Linear`.
+
+        Args:
+            input_dim: The number of inputs that will be combined.
+            monotonicities: If provided, specifies the monotonicity of each input
+                dimension.
+            use_bias: Whether to use a bias term for the linear combination.
+            weighted_average: Whether to make the output a weighted average i.e. all
+                coefficients are positive and add up to a total of 1.0. No bias term
+                will be used, and `use_bias` will be set to false regardless of the
+                original value. `monotonicities` will also be set to increasing for all
+                input dimensions to ensure that all coefficients are positive.
+
+        Raises:
+            ValueError: If monotonicities does not have length input_dim (if provided).
+        """
+        super().__init__()
+
+        self.input_dim = input_dim
+        if monotonicities and len(monotonicities) != input_dim:
+            raise ValueError("Monotonicities, if provided, must have length input_dim.")
+        self.monotonicities = (
+            monotonicities
+            if not weighted_average
+            else [Monotonicity.INCREASING] * input_dim
+        )
+        self.use_bias = use_bias if not weighted_average else False
+        self.weighted_average = weighted_average
+
+        self.kernel = torch.nn.Parameter(torch.Tensor(input_dim, 1).double())
+        torch.nn.init.constant_(self.kernel, 1.0 / input_dim)
+        if use_bias:
+            self.bias = torch.nn.Parameter(torch.Tensor(1).double())
+            torch.nn.init.constant_(self.bias, 0.0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Transforms inputs using a linear combination.
+
+        Args:
+            x: The input tensor of shape `(batch_size, input_dim)`.
+
+        Returns:
+            torch.Tensor of shape `(batch_size, 1)` containing transformed input values.
+        """
+        result = torch.mm(x, self.kernel)
+        if self.use_bias:
+            result += self.bias
+        return result
+
+    @torch.no_grad()
+    def apply_constraints(self) -> None:
+        """Projects kernel into desired constraints."""
+        projected_kernel_data = self.kernel.data
+
+        if self.monotonicities:
+            if Monotonicity.INCREASING in self.monotonicities:
+                increasing_mask = torch.tensor(
+                    [
+                        [0.0] if m == Monotonicity.INCREASING else [1.0]
+                        for m in self.monotonicities
+                    ]
+                )
+                projected_kernel_data = torch.maximum(
+                    projected_kernel_data, projected_kernel_data * increasing_mask
+                )
+            if Monotonicity.DECREASING in self.monotonicities:
+                decreasing_mask = torch.tensor(
+                    [
+                        [0.0] if m == Monotonicity.DECREASING else [1.0]
+                        for m in self.monotonicities
+                    ]
+                )
+                projected_kernel_data = torch.minimum(
+                    projected_kernel_data, projected_kernel_data * decreasing_mask
+                )
+
+        if self.weighted_average:
+            norm = torch.norm(projected_kernel_data, 1)
+            norm = torch.where(norm < 1e-8, 1.0, norm)
+            projected_kernel_data /= norm
+
+        self.kernel.data = projected_kernel_data
+
+    @torch.no_grad()
+    def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+        """Asserts that layer satisfies specified constraints.
+
+        This checks that decreasing monotonicity corresponds to negative weights,
+        increasing monotonicity corresponds to positive weights, and weights sum to 1
+        for weighted_average=True.
+
+        Args:
+            eps: the margin of error allowed
+
+        Returns:
+            A list of messages describing violated constraints. If no constraints
+            violated, the list will be empty.
+        """
+        messages = []
+
+        if self.weighted_average:
+            total_weight = torch.sum(self.kernel.data)
+            if torch.abs(total_weight - 1.0) > eps:
+                messages.append("Weights do not sum to 1.")
+
+        if self.monotonicities:
+            monotonicities_constant = torch.tensor(
+                [
+                    1
+                    if m == Monotonicity.INCREASING
+                    else -1
+                    if m == Monotonicity.DECREASING
+                    else 0
+                    for m in self.monotonicities
+                ],
+                device=self.kernel.device,
+                dtype=self.kernel.dtype,
+            ).view(-1, 1)
+
+            violated_monotonicities = (self.kernel * monotonicities_constant) < -eps
+            violation_indices = torch.where(violated_monotonicities)
+            if violation_indices[0].numel() > 0:
+                messages.append(
+                    f"Monotonicity violated at: {violation_indices[0].tolist()}"
+                )
+
+        return messages
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(input_dim, monotonicities=None, use_bias=True, weighted_average=False) + +

+ + +
+ +

Initializes an instance of Linear.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
input_dim + int + +
+

The number of inputs that will be combined.

+
+ 		+ required +
monotonicities + Optional[list[Optional[Monotonicity]]] + +
+

If provided, specifies the monotonicity of each input +dimension.

+
+ 		+ None +
use_bias + bool + +
+

Whether to use a bias term for the linear combination.

+
+ 		+ True +
weighted_average + bool + +
+

Whether to make the output a weighted average i.e. all +coefficients are positive and add up to a total of 1.0. No bias term +will be used, and use_bias will be set to false regardless of the +original value. monotonicities will also be set to increasing for all +input dimensions to ensure that all coefficients are positive.

+
+ 		+ False +
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If monotonicities does not have length input_dim (if provided).

+
+
+ +
+ Source code in pytorch_lattice/layers/linear.py + 
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def __init__(
+    self,
+    input_dim: int,
+    monotonicities: Optional[list[Optional[Monotonicity]]] = None,
+    use_bias: bool = True,
+    weighted_average: bool = False,
+) -> None:
+    """Initializes an instance of `Linear`.
+
+    Args:
+        input_dim: The number of inputs that will be combined.
+        monotonicities: If provided, specifies the monotonicity of each input
+            dimension.
+        use_bias: Whether to use a bias term for the linear combination.
+        weighted_average: Whether to make the output a weighted average i.e. all
+            coefficients are positive and add up to a total of 1.0. No bias term
+            will be used, and `use_bias` will be set to false regardless of the
+            original value. `monotonicities` will also be set to increasing for all
+            input dimensions to ensure that all coefficients are positive.
+
+    Raises:
+        ValueError: If monotonicities does not have length input_dim (if provided).
+    """
+    super().__init__()
+
+    self.input_dim = input_dim
+    if monotonicities and len(monotonicities) != input_dim:
+        raise ValueError("Monotonicities, if provided, must have length input_dim.")
+    self.monotonicities = (
+        monotonicities
+        if not weighted_average
+        else [Monotonicity.INCREASING] * input_dim
+    )
+    self.use_bias = use_bias if not weighted_average else False
+    self.weighted_average = weighted_average
+
+    self.kernel = torch.nn.Parameter(torch.Tensor(input_dim, 1).double())
+    torch.nn.init.constant_(self.kernel, 1.0 / input_dim)
+    if use_bias:
+        self.bias = torch.nn.Parameter(torch.Tensor(1).double())
+        torch.nn.init.constant_(self.bias, 0.0)
+
+
+
+ +
+ + +
+ + + + +

+ apply_constraints() + +

+ + +
+ +

Projects kernel into desired constraints.

+ +
+ Source code in pytorch_lattice/layers/linear.py + 
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@torch.no_grad()
+def apply_constraints(self) -> None:
+    """Projects kernel into desired constraints."""
+    projected_kernel_data = self.kernel.data
+
+    if self.monotonicities:
+        if Monotonicity.INCREASING in self.monotonicities:
+            increasing_mask = torch.tensor(
+                [
+                    [0.0] if m == Monotonicity.INCREASING else [1.0]
+                    for m in self.monotonicities
+                ]
+            )
+            projected_kernel_data = torch.maximum(
+                projected_kernel_data, projected_kernel_data * increasing_mask
+            )
+        if Monotonicity.DECREASING in self.monotonicities:
+            decreasing_mask = torch.tensor(
+                [
+                    [0.0] if m == Monotonicity.DECREASING else [1.0]
+                    for m in self.monotonicities
+                ]
+            )
+            projected_kernel_data = torch.minimum(
+                projected_kernel_data, projected_kernel_data * decreasing_mask
+            )
+
+    if self.weighted_average:
+        norm = torch.norm(projected_kernel_data, 1)
+        norm = torch.where(norm < 1e-8, 1.0, norm)
+        projected_kernel_data /= norm
+
+    self.kernel.data = projected_kernel_data
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + +

+ + +
+ +

Asserts that layer satisfies specified constraints.

+

This checks that decreasing monotonicity corresponds to negative weights, +increasing monotonicity corresponds to positive weights, and weights sum to 1 +for weighted_average=True.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
eps + float + +
+

the margin of error allowed

+
+ 		+ 1e-06 +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

A list of messages describing violated constraints. If no constraints

+
+
+ list[str] + +
+

violated, the list will be empty.

+
+
+ +
+ Source code in pytorch_lattice/layers/linear.py + 
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@torch.no_grad()
+def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+    """Asserts that layer satisfies specified constraints.
+
+    This checks that decreasing monotonicity corresponds to negative weights,
+    increasing monotonicity corresponds to positive weights, and weights sum to 1
+    for weighted_average=True.
+
+    Args:
+        eps: the margin of error allowed
+
+    Returns:
+        A list of messages describing violated constraints. If no constraints
+        violated, the list will be empty.
+    """
+    messages = []
+
+    if self.weighted_average:
+        total_weight = torch.sum(self.kernel.data)
+        if torch.abs(total_weight - 1.0) > eps:
+            messages.append("Weights do not sum to 1.")
+
+    if self.monotonicities:
+        monotonicities_constant = torch.tensor(
+            [
+                1
+                if m == Monotonicity.INCREASING
+                else -1
+                if m == Monotonicity.DECREASING
+                else 0
+                for m in self.monotonicities
+            ],
+            device=self.kernel.device,
+            dtype=self.kernel.dtype,
+        ).view(-1, 1)
+
+        violated_monotonicities = (self.kernel * monotonicities_constant) < -eps
+        violation_indices = torch.where(violated_monotonicities)
+        if violation_indices[0].numel() > 0:
+            messages.append(
+                f"Monotonicity violated at: {violation_indices[0].tolist()}"
+            )
+
+    return messages
+
+
+
+ +
+ + +
+ + + + +

+ forward(x) + +

+ + +
+ +

Transforms inputs using a linear combination.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Tensor + +
+

The input tensor of shape (batch_size, input_dim).

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

torch.Tensor of shape (batch_size, 1) containing transformed input values.

+
+
+ +
+ Source code in pytorch_lattice/layers/linear.py + 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
+    """Transforms inputs using a linear combination.
+
+    Args:
+        x: The input tensor of shape `(batch_size, input_dim)`.
+
+    Returns:
+        torch.Tensor of shape `(batch_size, 1)` containing transformed input values.
+    """
+    result = torch.mm(x, self.kernel)
+    if self.use_bias:
+        result += self.bias
+    return result
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.layers.NumericalCalibrator + + +

+ + +
+

+ Bases: ConstrainedModule

+ + +

A numerical calibrator.

+

This module takes an input of shape (batch_size, 1) and calibrates it using a +piece-wise linear function that conforms to any provided constraints. The output +will have the same shape as the input.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
kernel + +
+

torch.nn.Parameter that stores the piece-wise linear function weights.

+
+
missing_output + +
+

torch.nn.Parameter that stores the output learned for any +missing inputs. Only available if missing_input_value is provided.

+
+
+

Example: +

inputs = torch.tensor(...)  # shape: (batch_size, 1)
+calibrator = NumericalCalibrator(
+    input_keypoints=np.linspace(1., 5., num=5),
+    output_min=0.0,
+    output_max=1.0,
+    monotonicity=Monotonicity.INCREASING,
+    kernel_init=NumericalCalibratorInit.EQUAL_HEIGHTS,
+)
+outputs = calibrator(inputs)
+

+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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class NumericalCalibrator(ConstrainedModule):
+    """A numerical calibrator.
+
+    This module takes an input of shape `(batch_size, 1)` and calibrates it using a
+    piece-wise linear function that conforms to any provided constraints. The output
+    will have the same shape as the input.
+
+    Attributes:
+        All: `__init__` arguments.
+        kernel: `torch.nn.Parameter` that stores the piece-wise linear function weights.
+        missing_output: `torch.nn.Parameter` that stores the output learned for any
+            missing inputs. Only available if `missing_input_value` is provided.
+
+    Example:
+    ```python
+    inputs = torch.tensor(...)  # shape: (batch_size, 1)
+    calibrator = NumericalCalibrator(
+        input_keypoints=np.linspace(1., 5., num=5),
+        output_min=0.0,
+        output_max=1.0,
+        monotonicity=Monotonicity.INCREASING,
+        kernel_init=NumericalCalibratorInit.EQUAL_HEIGHTS,
+    )
+    outputs = calibrator(inputs)
+    ```
+    """
+
+    def __init__(
+        self,
+        input_keypoints: np.ndarray,
+        missing_input_value: Optional[float] = None,
+        output_min: Optional[float] = None,
+        output_max: Optional[float] = None,
+        monotonicity: Optional[Monotonicity] = None,
+        kernel_init: NumericalCalibratorInit = NumericalCalibratorInit.EQUAL_HEIGHTS,
+        projection_iterations: int = 8,
+    ) -> None:
+        """Initializes an instance of `NumericalCalibrator`.
+
+        Args:
+            input_keypoints: Ordered list of float-valued keypoints for the underlying
+                piece-wise linear function.
+            missing_input_value: If provided, the calibrator will learn to map all
+                instances of this missing input value to a learned output value.
+            output_min: Minimum output value. If `None`, the minimum output value will
+                be unbounded.
+            output_max: Maximum output value. If `None`, the maximum output value will
+                be unbounded.
+            monotonicity: Monotonicity constraint for the underlying piece-wise linear
+                function.
+            kernel_init: Initialization scheme to use for the kernel.
+            projection_iterations: Number of times to run Dykstra's projection
+                algorithm when applying constraints.
+
+        Raises:
+            ValueError: If `kernel_init` is invalid.
+        """
+        super().__init__()
+
+        self.input_keypoints = input_keypoints
+        self.missing_input_value = missing_input_value
+        self.output_min = output_min
+        self.output_max = output_max
+        self.monotonicity = monotonicity
+        self.kernel_init = kernel_init
+        self.projection_iterations = projection_iterations
+
+        # Determine default output initialization values if bounds are not fully set.
+        if output_min is not None and output_max is not None:
+            output_init_min, output_init_max = output_min, output_max
+        elif output_min is not None:
+            output_init_min, output_init_max = output_min, output_min + 4.0
+        elif output_max is not None:
+            output_init_min, output_init_max = output_max - 4.0, output_max
+        else:
+            output_init_min, output_init_max = -2.0, 2.0
+        self._output_init_min, self._output_init_max = output_init_min, output_init_max
+
+        self._interpolation_keypoints = torch.from_numpy(input_keypoints[:-1])
+        self._lengths = torch.from_numpy(input_keypoints[1:] - input_keypoints[:-1])
+
+        # First row of the kernel represents the bias. The remaining rows represent
+        # the y-value delta compared to the previous point i.e. the segment heights.
+        @torch.no_grad()
+        def initialize_kernel() -> torch.Tensor:
+            output_init_range = self._output_init_max - self._output_init_min
+            if kernel_init == NumericalCalibratorInit.EQUAL_HEIGHTS:
+                num_segments = self._interpolation_keypoints.size()[0]
+                segment_height = output_init_range / num_segments
+                heights = torch.tensor([[segment_height]] * num_segments)
+            elif kernel_init == NumericalCalibratorInit.EQUAL_SLOPES:
+                heights = (
+                    self._lengths * output_init_range / torch.sum(self._lengths)
+                )[:, None]
+            else:
+                raise ValueError(f"Unknown kernel init: {self.kernel_init}")
+
+            if monotonicity == Monotonicity.DECREASING:
+                bias = torch.tensor([[self._output_init_max]])
+                heights = -heights
+            else:
+                bias = torch.tensor([[self._output_init_min]])
+            return torch.cat((bias, heights), 0).double()
+
+        self.kernel = torch.nn.Parameter(initialize_kernel())
+
+        if missing_input_value:
+            self.missing_output = torch.nn.Parameter(torch.Tensor(1))
+            torch.nn.init.constant_(
+                self.missing_output,
+                (self._output_init_min + self._output_init_max) / 2.0,
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Calibrates numerical inputs through piece-wise linear interpolation.
+
+        Args:
+            x: The input tensor of shape `(batch_size, 1)`.
+
+        Returns:
+            torch.Tensor of shape `(batch_size, 1)` containing calibrated input values.
+        """
+        interpolation_weights = (x - self._interpolation_keypoints) / self._lengths
+        interpolation_weights = torch.minimum(interpolation_weights, torch.tensor(1.0))
+        interpolation_weights = torch.maximum(interpolation_weights, torch.tensor(0.0))
+        interpolation_weights = torch.cat(
+            (torch.ones_like(x), interpolation_weights), -1
+        )
+        result = torch.mm(interpolation_weights, self.kernel)
+
+        if self.missing_input_value is not None:
+            missing_mask = torch.eq(x, self.missing_input_value).long()
+            result = missing_mask * self.missing_output + (1.0 - missing_mask) * result
+
+        return result
+
+    @torch.no_grad()
+    def apply_constraints(self) -> None:
+        """Jointly projects kernel into desired constraints.
+
+        Uses Dykstra's alternating projection algorithm to jointly project onto all
+        given constraints. This algorithm projects with respect to the L2 norm, but it
+        approached the norm from the "wrong" side. To ensure that all constraints are
+        strictly met, we do final approximate projections that project strictly into the
+        feasible space, but this is not an exact projection with respect to the L2 norm.
+        Enough iterations make the impact of this approximation negligible.
+        """
+        constrain_bounds = self.output_min is not None or self.output_max is not None
+        constrain_monotonicity = self.monotonicity is not None
+        num_constraints = sum([constrain_bounds, constrain_monotonicity])
+
+        # We do nothing to the weights in this case
+        if num_constraints == 0:
+            return
+
+        original_bias, original_heights = self.kernel.data[0:1], self.kernel.data[1:]
+        previous_bias_delta: dict[str, torch.Tensor] = defaultdict(
+            lambda: torch.zeros_like(original_bias)
+        )
+        previous_heights_delta: dict[str, torch.Tensor] = defaultdict(
+            lambda: torch.zeros_like(original_heights)
+        )
+
+        def apply_bound_constraints(bias, heights):
+            previous_bias = bias - previous_bias_delta["BOUNDS"]
+            previous_heights = heights - previous_heights_delta["BOUNDS"]
+            if constrain_monotonicity:
+                bias, heights = self._project_monotonic_bounds(
+                    previous_bias, previous_heights
+                )
+            else:
+                bias, heights = self._approximately_project_bounds_only(
+                    previous_bias, previous_heights
+                )
+            previous_bias_delta["BOUNDS"] = bias - previous_bias
+            previous_heights_delta["BOUNDS"] = heights - previous_heights
+            return bias, heights
+
+        def apply_monotonicity_constraints(heights):
+            previous_heights = heights - previous_bias_delta["MONOTONICITY"]
+            heights = self._project_monotonicity(previous_heights)
+            previous_heights_delta["MONOTONICITY"] = heights - previous_heights
+            return heights
+
+        def apply_dykstras_projection(bias, heights):
+            if constrain_bounds:
+                bias, heights = apply_bound_constraints(bias, heights)
+            if constrain_monotonicity:
+                heights = apply_monotonicity_constraints(heights)
+            return bias, heights
+
+        def finalize_constraints(bias, heights):
+            if constrain_monotonicity:
+                heights = self._project_monotonicity(heights)
+            if constrain_bounds:
+                if constrain_monotonicity:
+                    bias, heights = self._squeeze_by_scaling(bias, heights)
+                else:
+                    bias, heights = self._approximately_project_bounds_only(
+                        bias, heights
+                    )
+            return bias, heights
+
+        projected_bias, projected_heights = apply_dykstras_projection(
+            original_bias, original_heights
+        )
+        if num_constraints > 1:
+            for _ in range(self.projection_iterations - 1):
+                projected_bias, projected_heights = apply_dykstras_projection(
+                    projected_bias, projected_heights
+                )
+            projected_bias, projected_heights = finalize_constraints(
+                projected_bias, projected_heights
+            )
+
+        self.kernel.data = torch.cat((projected_bias, projected_heights), 0)
+
+    @torch.no_grad()
+    def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+        """Asserts that layer satisfies specified constraints.
+
+        This checks that weights follow monotonicity constraints and that the output is
+        within bounds.
+
+        Args:
+            eps: the margin of error allowed
+
+        Returns:
+            A list of messages describing violated constraints including indices of
+            monotonicity violations. If no constraints violated, the list will be empty.
+        """
+        weights = torch.squeeze(self.kernel.data)
+        messages = []
+
+        if (
+            self.output_max is not None
+            and torch.max(self.keypoints_outputs()) > self.output_max + eps
+        ):
+            messages.append("Max weight greater than output_max.")
+        if (
+            self.output_min is not None
+            and torch.min(self.keypoints_outputs()) < self.output_min - eps
+        ):
+            messages.append("Min weight less than output_min.")
+
+        diffs = weights[1:]
+        violation_indices = []
+
+        if self.monotonicity == Monotonicity.INCREASING:
+            violation_indices = (diffs < -eps).nonzero().tolist()
+        elif self.monotonicity == Monotonicity.DECREASING:
+            violation_indices = (diffs > eps).nonzero().tolist()
+
+        violation_indices = [(i[0], i[0] + 1) for i in violation_indices]
+        if violation_indices:
+            messages.append(f"Monotonicity violated at: {str(violation_indices)}.")
+
+        return messages
+
+    @torch.no_grad()
+    def keypoints_inputs(self) -> torch.Tensor:
+        """Returns tensor of keypoint inputs."""
+        return torch.cat(
+            (
+                self._interpolation_keypoints,
+                self._interpolation_keypoints[-1:] + self._lengths[-1:],
+            ),
+            0,
+        )
+
+    @torch.no_grad()
+    def keypoints_outputs(self) -> torch.Tensor:
+        """Returns tensor of keypoint outputs."""
+        return torch.cumsum(self.kernel.data, 0).T[0]
+
+    ################################################################################
+    ############################## PRIVATE METHODS #################################
+    ################################################################################
+
+    def _project_monotonic_bounds(
+        self, bias: torch.Tensor, heights: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Projects bias and heights into bounds considering monotonicity.
+
+        For computation simplification in the case of decreasing monotonicity, we mirror
+        bias and heights and swap-mirror the output bounds. After doing the standard
+        projection with resepct to increasing monotonicity, we then mirror everything
+        back to get the correct projection.
+
+        Args:
+            bias: The bias of the underlying piece-wise linear function.
+            heights: The heights of each segment of the underlying piece-wise linear
+                function.
+
+        Returns:
+            A tuple containing the projected bias and projected heights.
+        """
+        output_min, output_max = self.output_min, self.output_max
+        decreasing = self.monotonicity == Monotonicity.DECREASING
+        if decreasing:
+            bias, heights = -bias, -heights
+            output_min = None if self.output_max is None else -1 * self.output_max
+            output_max = None if self.output_min is None else -1 * self.output_min
+        if output_max is not None:
+            num_heights = heights.size()[0]
+            output_max_diffs = output_max - (bias + torch.sum(heights, 0))
+            bias_delta = output_max_diffs / (num_heights + 1)
+            bias_delta = torch.minimum(bias_delta, torch.tensor(0.0))
+            if output_min is not None:
+                bias = torch.maximum(bias + bias_delta, torch.tensor(output_min))
+                heights_delta = output_max_diffs / num_heights
+            else:
+                bias += bias_delta
+                heights_delta = bias_delta
+            heights += torch.minimum(heights_delta, torch.tensor(0.0))
+        elif output_min is not None:
+            bias = torch.maximum(bias, torch.tensor(output_min))
+        if decreasing:
+            bias, heights = -bias, -heights
+        return bias, heights
+
+    def _approximately_project_bounds_only(
+        self, bias: torch.Tensor, heights: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Projects bias and heights without considering monotonicity.
+
+        It is worth noting that this projection is an approximation and is not an exact
+        projection with respect to the L2 norm; however, it is sufficiently accurate and
+        efficient in practice for non-monotonic functions.
+
+        Args:
+            bias: The bias of the underlying piece-wise linear function.
+            heights: The heights of each segment of the underlying piece-wise linear
+                function.
+
+        Returns:
+            A tuple containing the projected bias and projected heights.
+        """
+        sums = torch.cumsum(torch.cat((bias, heights), 0), 0)
+        if self.output_min is not None:
+            sums = torch.maximum(sums, torch.tensor(self.output_min))
+        if self.output_max is not None:
+            sums = torch.minimum(sums, torch.tensor(self.output_max))
+        bias = sums[0:1]
+        heights = sums[1:] - sums[:-1]
+        return bias, heights
+
+    def _project_monotonicity(self, heights: torch.Tensor) -> torch.Tensor:
+        """Returns bias and heights projected into desired monotonicity constraints."""
+        if self.monotonicity == Monotonicity.INCREASING:
+            return torch.maximum(heights, torch.tensor(0.0))
+        if self.monotonicity == Monotonicity.DECREASING:
+            return torch.minimum(heights, torch.tensor(0.0))
+        return heights
+
+    def _squeeze_by_scaling(
+        self, bias: torch.Tensor, heights: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Squeezes monotonic calibrators by scaling them into bound constraints.
+
+        It is worth noting that this is not an exact projection with respect to the L2
+        norm; however, it maintains convexity, which projection by shift does not.
+
+        Args:
+            bias: The bias of the underlying piece-wise linear function.
+            heights: The heights of each segment of the underlying piece-wise linear
+                function.
+
+        Returns:
+            A tuple containing the projected bias and projected heights.
+        """
+        decreasing = self.monotonicity == Monotonicity.DECREASING
+        output_max = self.output_max
+        if decreasing:
+            if self.output_min is None:
+                return bias, heights
+            bias, heights = -bias, -heights
+            output_max = None if self.output_min is None else -1 * self.output_min
+        if output_max is None:
+            return bias, heights
+        delta = output_max - bias
+        scaling_factor = torch.where(
+            delta > 0.0001, torch.sum(heights, 0) / delta, torch.ones_like(delta)
+        )
+        heights /= torch.maximum(scaling_factor, torch.tensor(1.0))
+        if decreasing:
+            bias, heights = -bias, -heights
+        return bias, heights
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(input_keypoints, missing_input_value=None, output_min=None, output_max=None, monotonicity=None, kernel_init=NumericalCalibratorInit.EQUAL_HEIGHTS, projection_iterations=8) + +

+ + +
+ +

Initializes an instance of NumericalCalibrator.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
input_keypoints + ndarray + +
+

Ordered list of float-valued keypoints for the underlying +piece-wise linear function.

+
+ 		+ required +
missing_input_value + Optional[float] + +
+

If provided, the calibrator will learn to map all +instances of this missing input value to a learned output value.

+
+ 		+ None +
output_min + Optional[float] + +
+

Minimum output value. If None, the minimum output value will +be unbounded.

+
+ 		+ None +
output_max + Optional[float] + +
+

Maximum output value. If None, the maximum output value will +be unbounded.

+
+ 		+ None +
monotonicity + Optional[Monotonicity] + +
+

Monotonicity constraint for the underlying piece-wise linear +function.

+
+ 		+ None +
kernel_init + NumericalCalibratorInit + +
+

Initialization scheme to use for the kernel.

+
+ 		+ EQUAL_HEIGHTS +
projection_iterations + int + +
+

Number of times to run Dykstra's projection +algorithm when applying constraints.

+
+ 		+ 8 +
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If kernel_init is invalid.

+
+
+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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def __init__(
+    self,
+    input_keypoints: np.ndarray,
+    missing_input_value: Optional[float] = None,
+    output_min: Optional[float] = None,
+    output_max: Optional[float] = None,
+    monotonicity: Optional[Monotonicity] = None,
+    kernel_init: NumericalCalibratorInit = NumericalCalibratorInit.EQUAL_HEIGHTS,
+    projection_iterations: int = 8,
+) -> None:
+    """Initializes an instance of `NumericalCalibrator`.
+
+    Args:
+        input_keypoints: Ordered list of float-valued keypoints for the underlying
+            piece-wise linear function.
+        missing_input_value: If provided, the calibrator will learn to map all
+            instances of this missing input value to a learned output value.
+        output_min: Minimum output value. If `None`, the minimum output value will
+            be unbounded.
+        output_max: Maximum output value. If `None`, the maximum output value will
+            be unbounded.
+        monotonicity: Monotonicity constraint for the underlying piece-wise linear
+            function.
+        kernel_init: Initialization scheme to use for the kernel.
+        projection_iterations: Number of times to run Dykstra's projection
+            algorithm when applying constraints.
+
+    Raises:
+        ValueError: If `kernel_init` is invalid.
+    """
+    super().__init__()
+
+    self.input_keypoints = input_keypoints
+    self.missing_input_value = missing_input_value
+    self.output_min = output_min
+    self.output_max = output_max
+    self.monotonicity = monotonicity
+    self.kernel_init = kernel_init
+    self.projection_iterations = projection_iterations
+
+    # Determine default output initialization values if bounds are not fully set.
+    if output_min is not None and output_max is not None:
+        output_init_min, output_init_max = output_min, output_max
+    elif output_min is not None:
+        output_init_min, output_init_max = output_min, output_min + 4.0
+    elif output_max is not None:
+        output_init_min, output_init_max = output_max - 4.0, output_max
+    else:
+        output_init_min, output_init_max = -2.0, 2.0
+    self._output_init_min, self._output_init_max = output_init_min, output_init_max
+
+    self._interpolation_keypoints = torch.from_numpy(input_keypoints[:-1])
+    self._lengths = torch.from_numpy(input_keypoints[1:] - input_keypoints[:-1])
+
+    # First row of the kernel represents the bias. The remaining rows represent
+    # the y-value delta compared to the previous point i.e. the segment heights.
+    @torch.no_grad()
+    def initialize_kernel() -> torch.Tensor:
+        output_init_range = self._output_init_max - self._output_init_min
+        if kernel_init == NumericalCalibratorInit.EQUAL_HEIGHTS:
+            num_segments = self._interpolation_keypoints.size()[0]
+            segment_height = output_init_range / num_segments
+            heights = torch.tensor([[segment_height]] * num_segments)
+        elif kernel_init == NumericalCalibratorInit.EQUAL_SLOPES:
+            heights = (
+                self._lengths * output_init_range / torch.sum(self._lengths)
+            )[:, None]
+        else:
+            raise ValueError(f"Unknown kernel init: {self.kernel_init}")
+
+        if monotonicity == Monotonicity.DECREASING:
+            bias = torch.tensor([[self._output_init_max]])
+            heights = -heights
+        else:
+            bias = torch.tensor([[self._output_init_min]])
+        return torch.cat((bias, heights), 0).double()
+
+    self.kernel = torch.nn.Parameter(initialize_kernel())
+
+    if missing_input_value:
+        self.missing_output = torch.nn.Parameter(torch.Tensor(1))
+        torch.nn.init.constant_(
+            self.missing_output,
+            (self._output_init_min + self._output_init_max) / 2.0,
+        )
+
+
+
+ +
+ + +
+ + + + +

+ apply_constraints() + +

+ + +
+ +

Jointly projects kernel into desired constraints.

+

Uses Dykstra's alternating projection algorithm to jointly project onto all +given constraints. This algorithm projects with respect to the L2 norm, but it +approached the norm from the "wrong" side. To ensure that all constraints are +strictly met, we do final approximate projections that project strictly into the +feasible space, but this is not an exact projection with respect to the L2 norm. +Enough iterations make the impact of this approximation negligible.

+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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@torch.no_grad()
+def apply_constraints(self) -> None:
+    """Jointly projects kernel into desired constraints.
+
+    Uses Dykstra's alternating projection algorithm to jointly project onto all
+    given constraints. This algorithm projects with respect to the L2 norm, but it
+    approached the norm from the "wrong" side. To ensure that all constraints are
+    strictly met, we do final approximate projections that project strictly into the
+    feasible space, but this is not an exact projection with respect to the L2 norm.
+    Enough iterations make the impact of this approximation negligible.
+    """
+    constrain_bounds = self.output_min is not None or self.output_max is not None
+    constrain_monotonicity = self.monotonicity is not None
+    num_constraints = sum([constrain_bounds, constrain_monotonicity])
+
+    # We do nothing to the weights in this case
+    if num_constraints == 0:
+        return
+
+    original_bias, original_heights = self.kernel.data[0:1], self.kernel.data[1:]
+    previous_bias_delta: dict[str, torch.Tensor] = defaultdict(
+        lambda: torch.zeros_like(original_bias)
+    )
+    previous_heights_delta: dict[str, torch.Tensor] = defaultdict(
+        lambda: torch.zeros_like(original_heights)
+    )
+
+    def apply_bound_constraints(bias, heights):
+        previous_bias = bias - previous_bias_delta["BOUNDS"]
+        previous_heights = heights - previous_heights_delta["BOUNDS"]
+        if constrain_monotonicity:
+            bias, heights = self._project_monotonic_bounds(
+                previous_bias, previous_heights
+            )
+        else:
+            bias, heights = self._approximately_project_bounds_only(
+                previous_bias, previous_heights
+            )
+        previous_bias_delta["BOUNDS"] = bias - previous_bias
+        previous_heights_delta["BOUNDS"] = heights - previous_heights
+        return bias, heights
+
+    def apply_monotonicity_constraints(heights):
+        previous_heights = heights - previous_bias_delta["MONOTONICITY"]
+        heights = self._project_monotonicity(previous_heights)
+        previous_heights_delta["MONOTONICITY"] = heights - previous_heights
+        return heights
+
+    def apply_dykstras_projection(bias, heights):
+        if constrain_bounds:
+            bias, heights = apply_bound_constraints(bias, heights)
+        if constrain_monotonicity:
+            heights = apply_monotonicity_constraints(heights)
+        return bias, heights
+
+    def finalize_constraints(bias, heights):
+        if constrain_monotonicity:
+            heights = self._project_monotonicity(heights)
+        if constrain_bounds:
+            if constrain_monotonicity:
+                bias, heights = self._squeeze_by_scaling(bias, heights)
+            else:
+                bias, heights = self._approximately_project_bounds_only(
+                    bias, heights
+                )
+        return bias, heights
+
+    projected_bias, projected_heights = apply_dykstras_projection(
+        original_bias, original_heights
+    )
+    if num_constraints > 1:
+        for _ in range(self.projection_iterations - 1):
+            projected_bias, projected_heights = apply_dykstras_projection(
+                projected_bias, projected_heights
+            )
+        projected_bias, projected_heights = finalize_constraints(
+            projected_bias, projected_heights
+        )
+
+    self.kernel.data = torch.cat((projected_bias, projected_heights), 0)
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + +

+ + +
+ +

Asserts that layer satisfies specified constraints.

+

This checks that weights follow monotonicity constraints and that the output is +within bounds.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
eps + float + +
+

the margin of error allowed

+
+ 		+ 1e-06 +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

A list of messages describing violated constraints including indices of

+
+
+ list[str] + +
+

monotonicity violations. If no constraints violated, the list will be empty.

+
+
+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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@torch.no_grad()
+def assert_constraints(self, eps: float = 1e-6) -> list[str]:
+    """Asserts that layer satisfies specified constraints.
+
+    This checks that weights follow monotonicity constraints and that the output is
+    within bounds.
+
+    Args:
+        eps: the margin of error allowed
+
+    Returns:
+        A list of messages describing violated constraints including indices of
+        monotonicity violations. If no constraints violated, the list will be empty.
+    """
+    weights = torch.squeeze(self.kernel.data)
+    messages = []
+
+    if (
+        self.output_max is not None
+        and torch.max(self.keypoints_outputs()) > self.output_max + eps
+    ):
+        messages.append("Max weight greater than output_max.")
+    if (
+        self.output_min is not None
+        and torch.min(self.keypoints_outputs()) < self.output_min - eps
+    ):
+        messages.append("Min weight less than output_min.")
+
+    diffs = weights[1:]
+    violation_indices = []
+
+    if self.monotonicity == Monotonicity.INCREASING:
+        violation_indices = (diffs < -eps).nonzero().tolist()
+    elif self.monotonicity == Monotonicity.DECREASING:
+        violation_indices = (diffs > eps).nonzero().tolist()
+
+    violation_indices = [(i[0], i[0] + 1) for i in violation_indices]
+    if violation_indices:
+        messages.append(f"Monotonicity violated at: {str(violation_indices)}.")
+
+    return messages
+
+
+
+ +
+ + +
+ + + + +

+ forward(x) + +

+ + +
+ +

Calibrates numerical inputs through piece-wise linear interpolation.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Tensor + +
+

The input tensor of shape (batch_size, 1).

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

torch.Tensor of shape (batch_size, 1) containing calibrated input values.

+
+
+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
+    """Calibrates numerical inputs through piece-wise linear interpolation.
+
+    Args:
+        x: The input tensor of shape `(batch_size, 1)`.
+
+    Returns:
+        torch.Tensor of shape `(batch_size, 1)` containing calibrated input values.
+    """
+    interpolation_weights = (x - self._interpolation_keypoints) / self._lengths
+    interpolation_weights = torch.minimum(interpolation_weights, torch.tensor(1.0))
+    interpolation_weights = torch.maximum(interpolation_weights, torch.tensor(0.0))
+    interpolation_weights = torch.cat(
+        (torch.ones_like(x), interpolation_weights), -1
+    )
+    result = torch.mm(interpolation_weights, self.kernel)
+
+    if self.missing_input_value is not None:
+        missing_mask = torch.eq(x, self.missing_input_value).long()
+        result = missing_mask * self.missing_output + (1.0 - missing_mask) * result
+
+    return result
+
+
+
+ +
+ + +
+ + + + +

+ keypoints_inputs() + +

+ + +
+ +

Returns tensor of keypoint inputs.

+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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@torch.no_grad()
+def keypoints_inputs(self) -> torch.Tensor:
+    """Returns tensor of keypoint inputs."""
+    return torch.cat(
+        (
+            self._interpolation_keypoints,
+            self._interpolation_keypoints[-1:] + self._lengths[-1:],
+        ),
+        0,
+    )
+
+
+
+ +
+ + +
+ + + + +

+ keypoints_outputs() + +

+ + +
+ +

Returns tensor of keypoint outputs.

+ +
+ Source code in pytorch_lattice/layers/numerical_calibrator.py + 
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+288
+289
+290
@torch.no_grad()
+def keypoints_outputs(self) -> torch.Tensor:
+    """Returns tensor of keypoint outputs."""
+    return torch.cumsum(self.kernel.data, 0).T[0]
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/model_configs/index.html b/api/model_configs/index.html new file mode 100644 index 0000000..4e4f8af --- /dev/null +++ b/api/model_configs/index.html @@ -0,0 +1,1449 @@ + + + + + + + + + + + + + + + + + + + + + + + + + model_configs - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

model_configs

+ + +
+ + + + +

+ pytorch_lattice.model_configs + + +

+ +
+ +

Model configurations classes for PyTorch Calibrated Models.

+ + + +
+ + + + + + + + +
+ + + + +

+ LatticeConfig + + + + dataclass + + +

+ + +
+

+ Bases: _BaseModelConfig

+ + +

Configuration for a calibrated lattice model.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

_BaseModelConfig attributes.

+
+
kernel_init + LatticeInit + +
+

The LatticeInit scheme to use to initialize the lattice kernel.

+
+
interpolation + Interpolation + +
+

The Interpolation scheme to use in the lattice. Note that +HYPERCUBE has exponential time complexity while SIMPLEX has +log-linear time complexity.

+
+
+ +
+ Source code in pytorch_lattice/model_configs.py + 
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@dataclass
+class LatticeConfig(_BaseModelConfig):
+    """Configuration for a calibrated lattice model.
+
+    Attributes:
+        All: `_BaseModelConfig` attributes.
+        kernel_init: The `LatticeInit` scheme to use to initialize the lattice kernel.
+        interpolation: The `Interpolation` scheme to use in the lattice. Note that
+            `HYPERCUBE` has exponential time complexity while `SIMPLEX` has
+            log-linear time complexity.
+    """
+
+    kernel_init: LatticeInit = LatticeInit.LINEAR
+    interpolation: Interpolation = Interpolation.SIMPLEX
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + + +

+ LinearConfig + + + + dataclass + + +

+ + +
+

+ Bases: _BaseModelConfig

+ + +

Configuration for a calibrated linear model.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

_BaseModelConfig attributes.

+
+
use_bias + bool + +
+

Whether to use a bias term for the linear combination.

+
+
+ +
+ Source code in pytorch_lattice/model_configs.py + 
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@dataclass
+class LinearConfig(_BaseModelConfig):
+    """Configuration for a calibrated linear model.
+
+    Attributes:
+        All: `_BaseModelConfig` attributes.
+        use_bias: Whether to use a bias term for the linear combination.
+    """
+
+    use_bias: bool = True
+
+
+ + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/models/index.html b/api/models/index.html new file mode 100644 index 0000000..5c89857 --- /dev/null +++ b/api/models/index.html @@ -0,0 +1,4318 @@ + + + + + + + + + + + + + + + + + + + + + + + + + models - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

models

+ + +
+ + + + +

+ pytorch_lattice.models.CalibratedLattice + + +

+ + +
+

+ Bases: ConstrainedModule

+ + +

PyTorch Calibrated Lattice Model.

+

Creates a torch.nn.Module representing a calibrated lattice model, which will be +constructed using the provided model configuration. Note that the model inputs +should match the order in which they are defined in the feature_configs.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
calibrators + +
+

A dictionary that maps feature names to their calibrators.

+
+
lattice + +
+

The Lattice layer of the model.

+
+
output_calibrator + +
+

The output NumericalCalibrator calibration layer. This +will be None if no output calibration is desired.

+
+
+

Example:

+
feature_configs = [...]
+calibrated_model = CalibratedLattice(feature_configs, ...)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.Adam(calibrated_model.parameters(recurse=True), lr=1e-1)
+
+dataset = pyl.utils.data.Dataset(...)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
+for epoch in range(100):
+    for inputs, labels in dataloader:
+        optimizer.zero_grad()
+        outputs = calibrated_model(inputs)
+        loss = loss_fn(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        calibrated_model.apply_constraints()
+
+ +
+ Source code in pytorch_lattice/models/calibrated_lattice.py + 
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class CalibratedLattice(ConstrainedModule):
+    """PyTorch Calibrated Lattice Model.
+
+    Creates a `torch.nn.Module` representing a calibrated lattice model, which will be
+    constructed using the provided model configuration. Note that the model inputs
+    should match the order in which they are defined in the `feature_configs`.
+
+    Attributes:
+        All: `__init__` arguments.
+        calibrators: A dictionary that maps feature names to their calibrators.
+        lattice: The `Lattice` layer of the model.
+        output_calibrator: The output `NumericalCalibrator` calibration layer. This
+            will be `None` if no output calibration is desired.
+
+    Example:
+
+    ```python
+    feature_configs = [...]
+    calibrated_model = CalibratedLattice(feature_configs, ...)
+
+    loss_fn = torch.nn.MSELoss()
+    optimizer = torch.optim.Adam(calibrated_model.parameters(recurse=True), lr=1e-1)
+
+    dataset = pyl.utils.data.Dataset(...)
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
+    for epoch in range(100):
+        for inputs, labels in dataloader:
+            optimizer.zero_grad()
+            outputs = calibrated_model(inputs)
+            loss = loss_fn(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            calibrated_model.apply_constraints()
+    ```
+    """
+
+    def __init__(
+        self,
+        features: list[Union[NumericalFeature, CategoricalFeature]],
+        clip_inputs: bool = True,
+        output_min: Optional[float] = None,
+        output_max: Optional[float] = None,
+        kernel_init: LatticeInit = LatticeInit.LINEAR,
+        interpolation: Interpolation = Interpolation.HYPERCUBE,
+        output_calibration_num_keypoints: Optional[int] = None,
+    ) -> None:
+        """Initializes an instance of `CalibratedLattice`.
+
+        Args:
+            features: A list of numerical and/or categorical feature configs.
+            clip_inputs: Whether to restrict inputs to the bounds of lattice.
+            output_min: The minimum output value for the model. If `None`, the minimum
+                output value will be unbounded.
+            output_max: The maximum output value for the model. If `None`, the maximum
+                output value will be unbounded.
+            kernel_init: the method of initializing kernel weights. If otherwise
+                unspecified, will default to `LatticeInit.LINEAR`.
+            interpolation: the method of interpolation in the lattice's forward pass.
+                If otherwise unspecified, will default to `Interpolation.HYPERCUBE`.
+            output_calibration_num_keypoints: The number of keypoints to use for the
+                output calibrator. If `None`, no output calibration will be used.
+
+        Raises:
+            ValueError: If any feature configs are not `NUMERICAL` or `CATEGORICAL`.
+        """
+        super().__init__()
+
+        self.features = features
+        self.clip_inputs = clip_inputs
+        self.output_min = output_min
+        self.output_max = output_max
+        self.kernel_init = kernel_init
+        self.interpolation = interpolation
+        self.output_calibration_num_keypoints = output_calibration_num_keypoints
+        self.monotonicities = initialize_monotonicities(features)
+        self.calibrators = initialize_feature_calibrators(
+            features=features,
+            output_min=0,
+            output_max=[feature.lattice_size - 1 for feature in features],
+        )
+
+        self.lattice = Lattice(
+            lattice_sizes=[feature.lattice_size for feature in features],
+            monotonicities=self.monotonicities,
+            clip_inputs=self.clip_inputs,
+            output_min=self.output_min,
+            output_max=self.output_max,
+            interpolation=interpolation,
+            kernel_init=kernel_init,
+        )
+
+        self.output_calibrator = initialize_output_calibrator(
+            output_calibration_num_keypoints=output_calibration_num_keypoints,
+            monotonic=not all(m is None for m in self.monotonicities),
+            output_min=output_min,
+            output_max=output_max,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Runs an input through the network to produce a calibrated lattice output.
+
+        Args:
+            x: The input tensor of feature values of shape `(batch_size, num_features)`.
+
+        Returns:
+            torch.Tensor of shape `(batch_size, 1)` containing the model output result.
+        """
+        result = calibrate_and_stack(x, self.calibrators)
+        result = self.lattice(result)
+        if self.output_calibrator is not None:
+            result = self.output_calibrator(result)
+
+        return result
+
+    @torch.no_grad()
+    def apply_constraints(self) -> None:
+        """Constrains the model into desired constraints specified by the config."""
+        for calibrator in self.calibrators.values():
+            calibrator.apply_constraints()
+        self.lattice.apply_constraints()
+        if self.output_calibrator:
+            self.output_calibrator.apply_constraints()
+
+    @torch.no_grad()
+    def assert_constraints(self, eps: float = 1e-6) -> dict[str, list[str]]:
+        """Asserts all layers within model satisfied specified constraints.
+
+        Asserts monotonicity pairs and output bounds for categorical calibrators,
+        monotonicity and output bounds for numerical calibrators, and monotonicity and
+        weights summing to 1 if weighted_average for linear layer.
+
+        Args:
+            eps: the margin of error allowed
+
+        Returns:
+            A dict where key is feature_name for calibrators and 'linear' for the linear
+            layer, and value is the error messages for each layer. Layers with no error
+            messages are not present in the dictionary.
+        """
+        messages = {}
+
+        for name, calibrator in self.calibrators.items():
+            calibrator_messages = calibrator.assert_constraints(eps)
+            if calibrator_messages:
+                messages[f"{name}_calibrator"] = calibrator_messages
+        lattice_messages = self.lattice.assert_constraints(eps)
+        if lattice_messages:
+            messages["lattice"] = lattice_messages
+        if self.output_calibrator:
+            output_calibrator_messages = self.output_calibrator.assert_constraints(eps)
+            if output_calibrator_messages:
+                messages["output_calibrator"] = output_calibrator_messages
+
+        return messages
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(features, clip_inputs=True, output_min=None, output_max=None, kernel_init=LatticeInit.LINEAR, interpolation=Interpolation.HYPERCUBE, output_calibration_num_keypoints=None) + +

+ + +
+ +

Initializes an instance of CalibratedLattice.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
features + list[Union[NumericalFeature, CategoricalFeature]] + +
+

A list of numerical and/or categorical feature configs.

+
+ 		+ required +
clip_inputs + bool + +
+

Whether to restrict inputs to the bounds of lattice.

+
+ 		+ True +
output_min + Optional[float] + +
+

The minimum output value for the model. If None, the minimum +output value will be unbounded.

+
+ 		+ None +
output_max + Optional[float] + +
+

The maximum output value for the model. If None, the maximum +output value will be unbounded.

+
+ 		+ None +
kernel_init + LatticeInit + +
+

the method of initializing kernel weights. If otherwise +unspecified, will default to LatticeInit.LINEAR.

+
+ 		+ LINEAR +
interpolation + Interpolation + +
+

the method of interpolation in the lattice's forward pass. +If otherwise unspecified, will default to Interpolation.HYPERCUBE.

+
+ 		+ HYPERCUBE +
output_calibration_num_keypoints + Optional[int] + +
+

The number of keypoints to use for the +output calibrator. If None, no output calibration will be used.

+
+ 		+ None +
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If any feature configs are not NUMERICAL or CATEGORICAL.

+
+
+ +
+ Source code in pytorch_lattice/models/calibrated_lattice.py + 
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def __init__(
+    self,
+    features: list[Union[NumericalFeature, CategoricalFeature]],
+    clip_inputs: bool = True,
+    output_min: Optional[float] = None,
+    output_max: Optional[float] = None,
+    kernel_init: LatticeInit = LatticeInit.LINEAR,
+    interpolation: Interpolation = Interpolation.HYPERCUBE,
+    output_calibration_num_keypoints: Optional[int] = None,
+) -> None:
+    """Initializes an instance of `CalibratedLattice`.
+
+    Args:
+        features: A list of numerical and/or categorical feature configs.
+        clip_inputs: Whether to restrict inputs to the bounds of lattice.
+        output_min: The minimum output value for the model. If `None`, the minimum
+            output value will be unbounded.
+        output_max: The maximum output value for the model. If `None`, the maximum
+            output value will be unbounded.
+        kernel_init: the method of initializing kernel weights. If otherwise
+            unspecified, will default to `LatticeInit.LINEAR`.
+        interpolation: the method of interpolation in the lattice's forward pass.
+            If otherwise unspecified, will default to `Interpolation.HYPERCUBE`.
+        output_calibration_num_keypoints: The number of keypoints to use for the
+            output calibrator. If `None`, no output calibration will be used.
+
+    Raises:
+        ValueError: If any feature configs are not `NUMERICAL` or `CATEGORICAL`.
+    """
+    super().__init__()
+
+    self.features = features
+    self.clip_inputs = clip_inputs
+    self.output_min = output_min
+    self.output_max = output_max
+    self.kernel_init = kernel_init
+    self.interpolation = interpolation
+    self.output_calibration_num_keypoints = output_calibration_num_keypoints
+    self.monotonicities = initialize_monotonicities(features)
+    self.calibrators = initialize_feature_calibrators(
+        features=features,
+        output_min=0,
+        output_max=[feature.lattice_size - 1 for feature in features],
+    )
+
+    self.lattice = Lattice(
+        lattice_sizes=[feature.lattice_size for feature in features],
+        monotonicities=self.monotonicities,
+        clip_inputs=self.clip_inputs,
+        output_min=self.output_min,
+        output_max=self.output_max,
+        interpolation=interpolation,
+        kernel_init=kernel_init,
+    )
+
+    self.output_calibrator = initialize_output_calibrator(
+        output_calibration_num_keypoints=output_calibration_num_keypoints,
+        monotonic=not all(m is None for m in self.monotonicities),
+        output_min=output_min,
+        output_max=output_max,
+    )
+
+
+
+ +
+ + +
+ + + + +

+ apply_constraints() + +

+ + +
+ +

Constrains the model into desired constraints specified by the config.

+ +
+ Source code in pytorch_lattice/models/calibrated_lattice.py + 
135
+136
+137
+138
+139
+140
+141
+142
@torch.no_grad()
+def apply_constraints(self) -> None:
+    """Constrains the model into desired constraints specified by the config."""
+    for calibrator in self.calibrators.values():
+        calibrator.apply_constraints()
+    self.lattice.apply_constraints()
+    if self.output_calibrator:
+        self.output_calibrator.apply_constraints()
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + +

+ + +
+ +

Asserts all layers within model satisfied specified constraints.

+

Asserts monotonicity pairs and output bounds for categorical calibrators, +monotonicity and output bounds for numerical calibrators, and monotonicity and +weights summing to 1 if weighted_average for linear layer.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
eps + float + +
+

the margin of error allowed

+
+ 		+ 1e-06 +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + + + + + +
TypeDescription
+ dict[str, list[str]] + +
+

A dict where key is feature_name for calibrators and 'linear' for the linear

+
+
+ dict[str, list[str]] + +
+

layer, and value is the error messages for each layer. Layers with no error

+
+
+ dict[str, list[str]] + +
+

messages are not present in the dictionary.

+
+
+ +
+ Source code in pytorch_lattice/models/calibrated_lattice.py + 
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@torch.no_grad()
+def assert_constraints(self, eps: float = 1e-6) -> dict[str, list[str]]:
+    """Asserts all layers within model satisfied specified constraints.
+
+    Asserts monotonicity pairs and output bounds for categorical calibrators,
+    monotonicity and output bounds for numerical calibrators, and monotonicity and
+    weights summing to 1 if weighted_average for linear layer.
+
+    Args:
+        eps: the margin of error allowed
+
+    Returns:
+        A dict where key is feature_name for calibrators and 'linear' for the linear
+        layer, and value is the error messages for each layer. Layers with no error
+        messages are not present in the dictionary.
+    """
+    messages = {}
+
+    for name, calibrator in self.calibrators.items():
+        calibrator_messages = calibrator.assert_constraints(eps)
+        if calibrator_messages:
+            messages[f"{name}_calibrator"] = calibrator_messages
+    lattice_messages = self.lattice.assert_constraints(eps)
+    if lattice_messages:
+        messages["lattice"] = lattice_messages
+    if self.output_calibrator:
+        output_calibrator_messages = self.output_calibrator.assert_constraints(eps)
+        if output_calibrator_messages:
+            messages["output_calibrator"] = output_calibrator_messages
+
+    return messages
+
+
+
+ +
+ + +
+ + + + +

+ forward(x) + +

+ + +
+ +

Runs an input through the network to produce a calibrated lattice output.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Tensor + +
+

The input tensor of feature values of shape (batch_size, num_features).

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

torch.Tensor of shape (batch_size, 1) containing the model output result.

+
+
+ +
+ Source code in pytorch_lattice/models/calibrated_lattice.py + 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
+    """Runs an input through the network to produce a calibrated lattice output.
+
+    Args:
+        x: The input tensor of feature values of shape `(batch_size, num_features)`.
+
+    Returns:
+        torch.Tensor of shape `(batch_size, 1)` containing the model output result.
+    """
+    result = calibrate_and_stack(x, self.calibrators)
+    result = self.lattice(result)
+    if self.output_calibrator is not None:
+        result = self.output_calibrator(result)
+
+    return result
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.models.CalibratedLinear + + +

+ + +
+

+ Bases: ConstrainedModule

+ + +

PyTorch Calibrated Linear Model.

+

Creates a torch.nn.Module representing a calibrated linear model, which will be +constructed using the provided model configuration. Note that the model inputs +should match the order in which they are defined in the feature_configs.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
calibrators + +
+

A dictionary that maps feature names to their calibrators.

+
+
linear + +
+

The Linear layer of the model.

+
+
output_calibrator + +
+

The output NumericalCalibrator calibration layer. This +will be None if no output calibration is desired.

+
+
+

Example:

+
feature_configs = [...]
+calibrated_model = pyl.models.CalibratedLinear(feature_configs, ...)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.Adam(calibrated_model.parameters(recurse=True), lr=1e-1)
+
+dataset = pyl.utils.data.Dataset(...)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
+for epoch in range(100):
+    for inputs, labels in dataloader:
+        optimizer.zero_grad()
+        outputs = calibrated_model(inputs)
+        loss = loss_fn(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        calibrated_model.apply_constraints()
+
+ +
+ Source code in pytorch_lattice/models/calibrated_linear.py + 
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class CalibratedLinear(ConstrainedModule):
+    """PyTorch Calibrated Linear Model.
+
+    Creates a `torch.nn.Module` representing a calibrated linear model, which will be
+    constructed using the provided model configuration. Note that the model inputs
+    should match the order in which they are defined in the `feature_configs`.
+
+    Attributes:
+        All: `__init__` arguments.
+        calibrators: A dictionary that maps feature names to their calibrators.
+        linear: The `Linear` layer of the model.
+        output_calibrator: The output `NumericalCalibrator` calibration layer. This
+            will be `None` if no output calibration is desired.
+
+    Example:
+
+    ```python
+    feature_configs = [...]
+    calibrated_model = pyl.models.CalibratedLinear(feature_configs, ...)
+
+    loss_fn = torch.nn.MSELoss()
+    optimizer = torch.optim.Adam(calibrated_model.parameters(recurse=True), lr=1e-1)
+
+    dataset = pyl.utils.data.Dataset(...)
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
+    for epoch in range(100):
+        for inputs, labels in dataloader:
+            optimizer.zero_grad()
+            outputs = calibrated_model(inputs)
+            loss = loss_fn(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            calibrated_model.apply_constraints()
+    ```
+    """
+
+    def __init__(
+        self,
+        features: list[Union[NumericalFeature, CategoricalFeature]],
+        output_min: Optional[float] = None,
+        output_max: Optional[float] = None,
+        use_bias: bool = True,
+        output_calibration_num_keypoints: Optional[int] = None,
+    ) -> None:
+        """Initializes an instance of `CalibratedLinear`.
+
+        Args:
+            features: A list of numerical and/or categorical feature configs.
+            output_min: The minimum output value for the model. If `None`, the minimum
+                output value will be unbounded.
+            output_max: The maximum output value for the model. If `None`, the maximum
+                output value will be unbounded.
+            use_bias: Whether to use a bias term for the linear combination. If any of
+                `output_min`, `output_max`, or `output_calibration_num_keypoints` are
+                set, a bias term will not be used regardless of the setting here.
+            output_calibration_num_keypoints: The number of keypoints to use for the
+                output calibrator. If `None`, no output calibration will be used.
+
+        Raises:
+            ValueError: If any feature configs are not `NUMERICAL` or `CATEGORICAL`.
+        """
+        super().__init__()
+
+        self.features = features
+        self.output_min = output_min
+        self.output_max = output_max
+        self.use_bias = use_bias
+        self.output_calibration_num_keypoints = output_calibration_num_keypoints
+        self.monotonicities = initialize_monotonicities(features)
+        self.calibrators = initialize_feature_calibrators(
+            features=features, output_min=output_min, output_max=output_max
+        )
+
+        self.linear = Linear(
+            input_dim=len(features),
+            monotonicities=self.monotonicities,
+            use_bias=use_bias,
+            weighted_average=bool(
+                output_min is not None
+                or output_max is not None
+                or output_calibration_num_keypoints
+            ),
+        )
+
+        self.output_calibrator = initialize_output_calibrator(
+            output_calibration_num_keypoints=output_calibration_num_keypoints,
+            monotonic=not all(m is None for m in self.monotonicities),
+            output_min=output_min,
+            output_max=output_max,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Runs an input through the network to produce a calibrated linear output.
+
+        Args:
+            x: The input tensor of feature values of shape `(batch_size, num_features)`.
+
+        Returns:
+            torch.Tensor of shape `(batch_size, 1)` containing the model output result.
+        """
+        result = calibrate_and_stack(x, self.calibrators)
+        result = self.linear(result)
+        if self.output_calibrator is not None:
+            result = self.output_calibrator(result)
+
+        return result
+
+    @torch.no_grad()
+    def apply_constraints(self) -> None:
+        """Constrains the model into desired constraints specified by the config."""
+        for calibrator in self.calibrators.values():
+            calibrator.apply_constraints()
+        self.linear.apply_constraints()
+        if self.output_calibrator:
+            self.output_calibrator.apply_constraints()
+
+    @torch.no_grad()
+    def assert_constraints(
+        self, eps: float = 1e-6
+    ) -> Union[list[str], dict[str, list[str]]]:
+        """Asserts all layers within model satisfied specified constraints.
+
+        Asserts monotonicity pairs and output bounds for categorical calibrators,
+        monotonicity and output bounds for numerical calibrators, and monotonicity and
+        weights summing to 1 if weighted_average for linear layer.
+
+        Args:
+            eps: the margin of error allowed
+
+        Returns:
+            A dict where key is feature_name for calibrators and 'linear' for the linear
+            layer, and value is the error messages for each layer. Layers with no error
+            messages are not present in the dictionary.
+        """
+        messages: dict[str, list[str]] = {}
+
+        for name, calibrator in self.calibrators.items():
+            calibrator_messages = calibrator.assert_constraints(eps)
+            if calibrator_messages:
+                messages[f"{name}_calibrator"] = calibrator_messages
+        linear_messages = self.linear.assert_constraints(eps)
+        if linear_messages:
+            messages["linear"] = linear_messages
+        if self.output_calibrator:
+            output_calibrator_messages = self.output_calibrator.assert_constraints(eps)
+            if output_calibrator_messages:
+                messages["output_calibrator"] = output_calibrator_messages
+
+        return messages
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(features, output_min=None, output_max=None, use_bias=True, output_calibration_num_keypoints=None) + +

+ + +
+ +

Initializes an instance of CalibratedLinear.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
features + list[Union[NumericalFeature, CategoricalFeature]] + +
+

A list of numerical and/or categorical feature configs.

+
+ 		+ required +
output_min + Optional[float] + +
+

The minimum output value for the model. If None, the minimum +output value will be unbounded.

+
+ 		+ None +
output_max + Optional[float] + +
+

The maximum output value for the model. If None, the maximum +output value will be unbounded.

+
+ 		+ None +
use_bias + bool + +
+

Whether to use a bias term for the linear combination. If any of +output_min, output_max, or output_calibration_num_keypoints are +set, a bias term will not be used regardless of the setting here.

+
+ 		+ True +
output_calibration_num_keypoints + Optional[int] + +
+

The number of keypoints to use for the +output calibrator. If None, no output calibration will be used.

+
+ 		+ None +
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If any feature configs are not NUMERICAL or CATEGORICAL.

+
+
+ +
+ Source code in pytorch_lattice/models/calibrated_linear.py + 
 53
+ 54
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+105
+106
def __init__(
+    self,
+    features: list[Union[NumericalFeature, CategoricalFeature]],
+    output_min: Optional[float] = None,
+    output_max: Optional[float] = None,
+    use_bias: bool = True,
+    output_calibration_num_keypoints: Optional[int] = None,
+) -> None:
+    """Initializes an instance of `CalibratedLinear`.
+
+    Args:
+        features: A list of numerical and/or categorical feature configs.
+        output_min: The minimum output value for the model. If `None`, the minimum
+            output value will be unbounded.
+        output_max: The maximum output value for the model. If `None`, the maximum
+            output value will be unbounded.
+        use_bias: Whether to use a bias term for the linear combination. If any of
+            `output_min`, `output_max`, or `output_calibration_num_keypoints` are
+            set, a bias term will not be used regardless of the setting here.
+        output_calibration_num_keypoints: The number of keypoints to use for the
+            output calibrator. If `None`, no output calibration will be used.
+
+    Raises:
+        ValueError: If any feature configs are not `NUMERICAL` or `CATEGORICAL`.
+    """
+    super().__init__()
+
+    self.features = features
+    self.output_min = output_min
+    self.output_max = output_max
+    self.use_bias = use_bias
+    self.output_calibration_num_keypoints = output_calibration_num_keypoints
+    self.monotonicities = initialize_monotonicities(features)
+    self.calibrators = initialize_feature_calibrators(
+        features=features, output_min=output_min, output_max=output_max
+    )
+
+    self.linear = Linear(
+        input_dim=len(features),
+        monotonicities=self.monotonicities,
+        use_bias=use_bias,
+        weighted_average=bool(
+            output_min is not None
+            or output_max is not None
+            or output_calibration_num_keypoints
+        ),
+    )
+
+    self.output_calibrator = initialize_output_calibrator(
+        output_calibration_num_keypoints=output_calibration_num_keypoints,
+        monotonic=not all(m is None for m in self.monotonicities),
+        output_min=output_min,
+        output_max=output_max,
+    )
+
+
+
+ +
+ + +
+ + + + +

+ apply_constraints() + +

+ + +
+ +

Constrains the model into desired constraints specified by the config.

+ +
+ Source code in pytorch_lattice/models/calibrated_linear.py + 
124
+125
+126
+127
+128
+129
+130
+131
@torch.no_grad()
+def apply_constraints(self) -> None:
+    """Constrains the model into desired constraints specified by the config."""
+    for calibrator in self.calibrators.values():
+        calibrator.apply_constraints()
+    self.linear.apply_constraints()
+    if self.output_calibrator:
+        self.output_calibrator.apply_constraints()
+
+
+
+ +
+ + +
+ + + + +

+ assert_constraints(eps=1e-06) + +

+ + +
+ +

Asserts all layers within model satisfied specified constraints.

+

Asserts monotonicity pairs and output bounds for categorical calibrators, +monotonicity and output bounds for numerical calibrators, and monotonicity and +weights summing to 1 if weighted_average for linear layer.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
eps + float + +
+

the margin of error allowed

+
+ 		+ 1e-06 +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + + + + + +
TypeDescription
+ Union[list[str], dict[str, list[str]]] + +
+

A dict where key is feature_name for calibrators and 'linear' for the linear

+
+
+ Union[list[str], dict[str, list[str]]] + +
+

layer, and value is the error messages for each layer. Layers with no error

+
+
+ Union[list[str], dict[str, list[str]]] + +
+

messages are not present in the dictionary.

+
+
+ +
+ Source code in pytorch_lattice/models/calibrated_linear.py + 
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@torch.no_grad()
+def assert_constraints(
+    self, eps: float = 1e-6
+) -> Union[list[str], dict[str, list[str]]]:
+    """Asserts all layers within model satisfied specified constraints.
+
+    Asserts monotonicity pairs and output bounds for categorical calibrators,
+    monotonicity and output bounds for numerical calibrators, and monotonicity and
+    weights summing to 1 if weighted_average for linear layer.
+
+    Args:
+        eps: the margin of error allowed
+
+    Returns:
+        A dict where key is feature_name for calibrators and 'linear' for the linear
+        layer, and value is the error messages for each layer. Layers with no error
+        messages are not present in the dictionary.
+    """
+    messages: dict[str, list[str]] = {}
+
+    for name, calibrator in self.calibrators.items():
+        calibrator_messages = calibrator.assert_constraints(eps)
+        if calibrator_messages:
+            messages[f"{name}_calibrator"] = calibrator_messages
+    linear_messages = self.linear.assert_constraints(eps)
+    if linear_messages:
+        messages["linear"] = linear_messages
+    if self.output_calibrator:
+        output_calibrator_messages = self.output_calibrator.assert_constraints(eps)
+        if output_calibrator_messages:
+            messages["output_calibrator"] = output_calibrator_messages
+
+    return messages
+
+
+
+ +
+ + +
+ + + + +

+ forward(x) + +

+ + +
+ +

Runs an input through the network to produce a calibrated linear output.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Tensor + +
+

The input tensor of feature values of shape (batch_size, num_features).

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

torch.Tensor of shape (batch_size, 1) containing the model output result.

+
+
+ +
+ Source code in pytorch_lattice/models/calibrated_linear.py + 
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def forward(self, x: torch.Tensor) -> torch.Tensor:
+    """Runs an input through the network to produce a calibrated linear output.
+
+    Args:
+        x: The input tensor of feature values of shape `(batch_size, num_features)`.
+
+    Returns:
+        torch.Tensor of shape `(batch_size, 1)` containing the model output result.
+    """
+    result = calibrate_and_stack(x, self.calibrators)
+    result = self.linear(result)
+    if self.output_calibrator is not None:
+        result = self.output_calibrator(result)
+
+    return result
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.models.features.CategoricalFeature + + +

+ + +
+ + +

Feature configuration for categorical features.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
category_indices + +
+

A dictionary mapping string categories to their index.

+
+
monotonicity_index_pairs + +
+

A conversion of monotonicity_pairs from string +categories to category indices. Only available if monotonicity_pairs are +provided.

+
+
+ +
+ Source code in pytorch_lattice/models/features.py + 
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class CategoricalFeature:
+    """Feature configuration for categorical features.
+
+    Attributes:
+        All: `__init__` arguments.
+        category_indices: A dictionary mapping string categories to their index.
+        monotonicity_index_pairs: A conversion of `monotonicity_pairs` from string
+            categories to category indices. Only available if `monotonicity_pairs` are
+            provided.
+    """
+
+    def __init__(
+        self,
+        feature_name: str,
+        categories: Union[list[int], list[str]],
+        missing_input_value: Optional[float] = None,
+        monotonicity_pairs: Optional[list[tuple[str, str]]] = None,
+        lattice_size: int = 2,
+    ) -> None:
+        """Initializes a `CategoricalFeatureConfig` instance.
+
+        Args:
+            feature_name: The name of the feature. This should match the header for the
+                column in the dataset representing this feature.
+            categories: The categories that should be used for this feature. Any
+                categories not contained will be considered missing or unknown. If you
+                expect to have such missing categories, make sure to
+            missing_input_value: If provided, this feature's calibrator will learn to
+                map all instances of this missing input value to a learned output value.
+            monotonicity_pairs: List of pairs of categories `(category_a, category_b)`
+                indicating that the calibrator output for `category_b` should be greater
+                than or equal to that of `category_a`.
+            lattice_size: The default number of keypoints outputted by the calibrator.
+                Only used within `Lattice` models.
+        """
+        self.feature_name = feature_name
+        self.categories = categories
+        self.missing_input_value = missing_input_value
+        self.monotonicity_pairs = monotonicity_pairs
+        self.lattice_size = lattice_size
+
+        self.category_indices = {category: i for i, category in enumerate(categories)}
+        self.monotonicity_index_pairs = [
+            (self.category_indices[a], self.category_indices[b])
+            for a, b in monotonicity_pairs or []
+        ]
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(feature_name, categories, missing_input_value=None, monotonicity_pairs=None, lattice_size=2) + +

+ + +
+ +

Initializes a CategoricalFeatureConfig instance.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
feature_name + str + +
+

The name of the feature. This should match the header for the +column in the dataset representing this feature.

+
+ 		+ required +
categories + Union[list[int], list[str]] + +
+

The categories that should be used for this feature. Any +categories not contained will be considered missing or unknown. If you +expect to have such missing categories, make sure to

+
+ 		+ required +
missing_input_value + Optional[float] + +
+

If provided, this feature's calibrator will learn to +map all instances of this missing input value to a learned output value.

+
+ 		+ None +
monotonicity_pairs + Optional[list[tuple[str, str]]] + +
+

List of pairs of categories (category_a, category_b) +indicating that the calibrator output for category_b should be greater +than or equal to that of category_a.

+
+ 		+ None +
lattice_size + int + +
+

The default number of keypoints outputted by the calibrator. +Only used within Lattice models.

+
+ 		+ 2 +
+ +
+ Source code in pytorch_lattice/models/features.py + 
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def __init__(
+    self,
+    feature_name: str,
+    categories: Union[list[int], list[str]],
+    missing_input_value: Optional[float] = None,
+    monotonicity_pairs: Optional[list[tuple[str, str]]] = None,
+    lattice_size: int = 2,
+) -> None:
+    """Initializes a `CategoricalFeatureConfig` instance.
+
+    Args:
+        feature_name: The name of the feature. This should match the header for the
+            column in the dataset representing this feature.
+        categories: The categories that should be used for this feature. Any
+            categories not contained will be considered missing or unknown. If you
+            expect to have such missing categories, make sure to
+        missing_input_value: If provided, this feature's calibrator will learn to
+            map all instances of this missing input value to a learned output value.
+        monotonicity_pairs: List of pairs of categories `(category_a, category_b)`
+            indicating that the calibrator output for `category_b` should be greater
+            than or equal to that of `category_a`.
+        lattice_size: The default number of keypoints outputted by the calibrator.
+            Only used within `Lattice` models.
+    """
+    self.feature_name = feature_name
+    self.categories = categories
+    self.missing_input_value = missing_input_value
+    self.monotonicity_pairs = monotonicity_pairs
+    self.lattice_size = lattice_size
+
+    self.category_indices = {category: i for i, category in enumerate(categories)}
+    self.monotonicity_index_pairs = [
+        (self.category_indices[a], self.category_indices[b])
+        for a, b in monotonicity_pairs or []
+    ]
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.models.features.NumericalFeature + + +

+ + +
+ + +

Feature configuration for numerical features.

+ + + +

Attributes:

+ + + + + + + + + + + + + + + + + + + + +
NameTypeDescription
All + +
+

__init__ arguments.

+
+
input_keypoints + +
+

The input keypoints used for this feature's calibrator. These +keypoints will be initialized using the given data under the desired +input_keypoints_init scheme.

+
+
+ +
+ Source code in pytorch_lattice/models/features.py + 
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class NumericalFeature:
+    """Feature configuration for numerical features.
+
+    Attributes:
+        All: `__init__` arguments.
+        input_keypoints: The input keypoints used for this feature's calibrator. These
+            keypoints will be initialized using the given `data` under the desired
+            `input_keypoints_init` scheme.
+    """
+
+    def __init__(
+        self,
+        feature_name: str,
+        data: np.ndarray,
+        num_keypoints: int = 5,
+        input_keypoints_init: InputKeypointsInit = InputKeypointsInit.QUANTILES,
+        missing_input_value: Optional[float] = None,
+        monotonicity: Optional[Monotonicity] = None,
+        projection_iterations: int = 8,
+        lattice_size: int = 2,
+    ) -> None:
+        """Initializes a `NumericalFeatureConfig` instance.
+
+        Args:
+            feature_name: The name of the feature. This should match the header for the
+                column in the dataset representing this feature.
+            data: Numpy array of float-valued data used for calculating keypoint inputs
+                and initializing keypoint outputs.
+            num_keypoints: The number of keypoints used by the underlying piece-wise
+                linear function of a NumericalCalibrator. There will be
+                `num_keypoints - 1` total segments.
+            input_keypoints_init: The scheme to use for initializing the input
+                keypoints. See `InputKeypointsInit` for more details.
+            missing_input_value: If provided, this feature's calibrator will learn to
+                map all instances of this missing input value to a learned output value.
+            monotonicity: Monotonicity constraint for this feature, if any.
+            projection_iterations: Number of times to run Dykstra's projection
+                algorithm when applying constraints.
+            lattice_size: The default number of keypoints outputted by the
+                calibrator. Only used within `Lattice` models.
+
+        Raises:
+            ValueError: If `data` contains NaN values.
+            ValueError: If `input_keypoints_init` is invalid.
+        """
+        self.feature_name = feature_name
+
+        if np.isnan(data).any():
+            raise ValueError("Data contains NaN values.")
+
+        self.data = data
+        self.num_keypoints = num_keypoints
+        self.input_keypoints_init = input_keypoints_init
+        self.missing_input_value = missing_input_value
+        self.monotonicity = monotonicity
+        self.projection_iterations = projection_iterations
+        self.lattice_size = lattice_size
+
+        sorted_unique_values = np.unique(data)
+
+        if input_keypoints_init == InputKeypointsInit.QUANTILES:
+            if sorted_unique_values.size < num_keypoints:
+                logging.info(
+                    "Observed fewer unique values for feature %s than %d desired "
+                    "keypoints. Using the observed %d unique values as keypoints.",
+                    feature_name,
+                    num_keypoints,
+                    sorted_unique_values.size,
+                )
+                self.input_keypoints = sorted_unique_values
+            else:
+                quantiles = np.linspace(0.0, 1.0, num=num_keypoints)
+                self.input_keypoints = np.quantile(
+                    sorted_unique_values, quantiles, method="nearest"
+                )
+        elif input_keypoints_init == InputKeypointsInit.UNIFORM:
+            self.input_keypoints = np.linspace(
+                sorted_unique_values[0], sorted_unique_values[-1], num=num_keypoints
+            )
+        else:
+            raise ValueError(f"Unknown input keypoints init: {input_keypoints_init}")
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(feature_name, data, num_keypoints=5, input_keypoints_init=InputKeypointsInit.QUANTILES, missing_input_value=None, monotonicity=None, projection_iterations=8, lattice_size=2) + +

+ + +
+ +

Initializes a NumericalFeatureConfig instance.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
feature_name + str + +
+

The name of the feature. This should match the header for the +column in the dataset representing this feature.

+
+ 		+ required +
data + ndarray + +
+

Numpy array of float-valued data used for calculating keypoint inputs +and initializing keypoint outputs.

+
+ 		+ required +
num_keypoints + int + +
+

The number of keypoints used by the underlying piece-wise +linear function of a NumericalCalibrator. There will be +num_keypoints - 1 total segments.

+
+ 		+ 5 +
input_keypoints_init + InputKeypointsInit + +
+

The scheme to use for initializing the input +keypoints. See InputKeypointsInit for more details.

+
+ 		+ QUANTILES +
missing_input_value + Optional[float] + +
+

If provided, this feature's calibrator will learn to +map all instances of this missing input value to a learned output value.

+
+ 		+ None +
monotonicity + Optional[Monotonicity] + +
+

Monotonicity constraint for this feature, if any.

+
+ 		+ None +
projection_iterations + int + +
+

Number of times to run Dykstra's projection +algorithm when applying constraints.

+
+ 		+ 8 +
lattice_size + int + +
+

The default number of keypoints outputted by the +calibrator. Only used within Lattice models.

+
+ 		+ 2 +
+ + + +

Raises:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If data contains NaN values.

+
+
+ ValueError + +
+

If input_keypoints_init is invalid.

+
+
+ +
+ Source code in pytorch_lattice/models/features.py + 
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def __init__(
+    self,
+    feature_name: str,
+    data: np.ndarray,
+    num_keypoints: int = 5,
+    input_keypoints_init: InputKeypointsInit = InputKeypointsInit.QUANTILES,
+    missing_input_value: Optional[float] = None,
+    monotonicity: Optional[Monotonicity] = None,
+    projection_iterations: int = 8,
+    lattice_size: int = 2,
+) -> None:
+    """Initializes a `NumericalFeatureConfig` instance.
+
+    Args:
+        feature_name: The name of the feature. This should match the header for the
+            column in the dataset representing this feature.
+        data: Numpy array of float-valued data used for calculating keypoint inputs
+            and initializing keypoint outputs.
+        num_keypoints: The number of keypoints used by the underlying piece-wise
+            linear function of a NumericalCalibrator. There will be
+            `num_keypoints - 1` total segments.
+        input_keypoints_init: The scheme to use for initializing the input
+            keypoints. See `InputKeypointsInit` for more details.
+        missing_input_value: If provided, this feature's calibrator will learn to
+            map all instances of this missing input value to a learned output value.
+        monotonicity: Monotonicity constraint for this feature, if any.
+        projection_iterations: Number of times to run Dykstra's projection
+            algorithm when applying constraints.
+        lattice_size: The default number of keypoints outputted by the
+            calibrator. Only used within `Lattice` models.
+
+    Raises:
+        ValueError: If `data` contains NaN values.
+        ValueError: If `input_keypoints_init` is invalid.
+    """
+    self.feature_name = feature_name
+
+    if np.isnan(data).any():
+        raise ValueError("Data contains NaN values.")
+
+    self.data = data
+    self.num_keypoints = num_keypoints
+    self.input_keypoints_init = input_keypoints_init
+    self.missing_input_value = missing_input_value
+    self.monotonicity = monotonicity
+    self.projection_iterations = projection_iterations
+    self.lattice_size = lattice_size
+
+    sorted_unique_values = np.unique(data)
+
+    if input_keypoints_init == InputKeypointsInit.QUANTILES:
+        if sorted_unique_values.size < num_keypoints:
+            logging.info(
+                "Observed fewer unique values for feature %s than %d desired "
+                "keypoints. Using the observed %d unique values as keypoints.",
+                feature_name,
+                num_keypoints,
+                sorted_unique_values.size,
+            )
+            self.input_keypoints = sorted_unique_values
+        else:
+            quantiles = np.linspace(0.0, 1.0, num=num_keypoints)
+            self.input_keypoints = np.quantile(
+                sorted_unique_values, quantiles, method="nearest"
+            )
+    elif input_keypoints_init == InputKeypointsInit.UNIFORM:
+        self.input_keypoints = np.linspace(
+            sorted_unique_values[0], sorted_unique_values[-1], num=num_keypoints
+        )
+    else:
+        raise ValueError(f"Unknown input keypoints init: {input_keypoints_init}")
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/plots/index.html b/api/plots/index.html new file mode 100644 index 0000000..ba554fa --- /dev/null +++ b/api/plots/index.html @@ -0,0 +1,1414 @@ + + + + + + + + + + + + + + + + + + + + + + + plots - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

plots

+ + +
+ + + + +

+ pytorch_lattice.plots + + +

+ +
+ +

Plotting functions for PyTorch Lattice calibrated models using matplotlib.

+ + + +
+ + + + + + + + + + +
+ + + + +

+ calibrator(model, feature_name) + +

+ + +
+ +

Plots the calibrator for the given feature and calibrated model.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
model + Union[CalibratedLinear, CalibratedLattice] + +
+

The calibrated model for which to plot calibrators.

+
+ 		+ required +
feature_name + str + +
+

The name of the feature for which to plot the calibrator.

+
+ 		+ required +
+ +
+ Source code in pytorch_lattice/plots.py + 
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def calibrator(
+    model: Union[CalibratedLinear, CalibratedLattice],
+    feature_name: str,
+) -> None:
+    """Plots the calibrator for the given feature and calibrated model.
+
+    Args:
+        model: The calibrated model for which to plot calibrators.
+        feature_name: The name of the feature for which to plot the calibrator.
+    """
+    if feature_name not in model.calibrators:
+        raise ValueError(f"Feature {feature_name} not found in model.")
+
+    calibrator = model.calibrators[feature_name]
+    input_keypoints = calibrator.keypoints_inputs().numpy()
+    output_keypoints = calibrator.keypoints_outputs().numpy()
+
+    if isinstance(calibrator, CategoricalCalibrator):
+        model_feature = next(
+            (x for x in model.features if x.feature_name == feature_name), None
+        )
+        if isinstance(model_feature, CategoricalFeature):
+            input_keypoints = np.array(
+                [
+                    model_feature.categories[i]
+                    if i < len(input_keypoints) - 1
+                    else "<Missing>"
+                    for i, ik in enumerate(input_keypoints)
+                ]
+            )
+        plt.xticks(rotation=45)
+        plt.bar(input_keypoints, output_keypoints)
+    else:
+        plt.plot(input_keypoints, output_keypoints)
+
+    plt.title(f"Calibrator: {feature_name}")
+    plt.xlabel("Input Keypoints")
+    plt.ylabel("Output Keypoints")
+    plt.show()
+
+
+
+ +
+ + +
+ + + + +

+ linear_coefficients(model) + +

+ + +
+ +

Plots the coefficients for the linear layer of a calibrated linear model.

+ +
+ Source code in pytorch_lattice/plots.py + 
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def linear_coefficients(model: CalibratedLinear) -> None:
+    """Plots the coefficients for the linear layer of a calibrated linear model."""
+    if not isinstance(model, CalibratedLinear):
+        raise ValueError(
+            "Model must be a `CalibratedLinear` model to plot linear coefficients."
+        )
+    linear_coefficients = dict(
+        zip(
+            [feature.feature_name for feature in model.features],
+            model.linear.kernel.detach().numpy().flatten(),
+        )
+    )
+    if model.use_bias:
+        linear_coefficients["bias"] = model.linear.bias.detach().numpy()[0]
+
+    plt.bar(list(linear_coefficients.keys()), list(linear_coefficients.values()))
+    plt.title("Linear Coefficients")
+    plt.xlabel("Feature Name")
+    plt.xticks(rotation=45)
+    plt.ylabel("Coefficient Value")
+    plt.show()
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api/utils/index.html b/api/utils/index.html new file mode 100644 index 0000000..cd3dcce --- /dev/null +++ b/api/utils/index.html @@ -0,0 +1,2286 @@ + + + + + + + + + + + + + + + + + + + + + + + + + utils - PyTorch Lattice + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + +
+ + +
+ +
+ + + + + + + + + +
+
+ + + +
+
+
+ + + + + + + +
+
+
+ + + +
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+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

utils

+ + +
+ + + + +

+ pytorch_lattice.utils.data + + +

+ +
+ +

Utility functions and classes for handling data.

+ + + +
+ + + + + + + + +
+ + + + +

+ Dataset + + +

+ + +
+

+ Bases: Dataset

+ + +

A class for loading a dataset for a calibrated model.

+ +
+ Source code in pytorch_lattice/utils/data.py + 
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class Dataset(torch.utils.data.Dataset):
+    """A class for loading a dataset for a calibrated model."""
+
+    def __init__(
+        self,
+        X: pd.DataFrame,
+        y: np.ndarray,
+        features: list[Union[NumericalFeature, CategoricalFeature]],
+    ):
+        """Initializes an instance of `Dataset`."""
+        self.X = X.copy()
+        self.y = y.copy()
+
+        selected_features = [feature.feature_name for feature in features]
+        unavailable_features = set(selected_features) - set(self.X.columns)
+        if len(unavailable_features) > 0:
+            raise ValueError(f"Features {unavailable_features} not found in dataset.")
+
+        drop_features = list(set(self.X.columns) - set(selected_features))
+        self.X.drop(drop_features, axis=1, inplace=True)
+        prepare_features(self.X, features)
+
+        self.data = torch.from_numpy(self.X.values).double()
+        self.labels = torch.from_numpy(self.y).double()[:, None]
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self, idx):
+        if isinstance(idx, torch.Tensor):
+            idx = idx.tolist()
+
+        return [self.data[idx], self.labels[idx]]
+
+
+ + + +
+ + + + + + + + + + +
+ + + + +

+ __init__(X, y, features) + +

+ + +
+ +

Initializes an instance of Dataset.

+ +
+ Source code in pytorch_lattice/utils/data.py + 
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def __init__(
+    self,
+    X: pd.DataFrame,
+    y: np.ndarray,
+    features: list[Union[NumericalFeature, CategoricalFeature]],
+):
+    """Initializes an instance of `Dataset`."""
+    self.X = X.copy()
+    self.y = y.copy()
+
+    selected_features = [feature.feature_name for feature in features]
+    unavailable_features = set(selected_features) - set(self.X.columns)
+    if len(unavailable_features) > 0:
+        raise ValueError(f"Features {unavailable_features} not found in dataset.")
+
+    drop_features = list(set(self.X.columns) - set(selected_features))
+    self.X.drop(drop_features, axis=1, inplace=True)
+    prepare_features(self.X, features)
+
+    self.data = torch.from_numpy(self.X.values).double()
+    self.labels = torch.from_numpy(self.y).double()[:, None]
+
+
+
+ +
+ + + +
+ +
+ +
+ + + +
+ + + + +

+ prepare_features(X, features) + +

+ + +
+ +

Maps categorical features to their integer indices in place.

+ +
+ Source code in pytorch_lattice/utils/data.py + 
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def prepare_features(
+    X: pd.DataFrame, features: list[Union[NumericalFeature, CategoricalFeature]]
+):
+    """Maps categorical features to their integer indices in place."""
+    for feature in features:
+        feature_data = X[feature.feature_name]
+
+        if isinstance(feature, CategoricalFeature):
+            feature_data = feature_data.map(feature.category_indices)
+
+        if feature.missing_input_value is not None:
+            feature_data = feature_data.fillna(feature.missing_input_value)
+
+        X[feature.feature_name] = feature_data
+
+
+
+ +
+ + + +
+ +
+ +
+ +
+ + + + +

+ pytorch_lattice.utils.models + + +

+ +
+ +

Utility functions for use in model classes.

+ + + +
+ + + + + + + + + + +
+ + + + +

+ calibrate_and_stack(x, calibrators) + +

+ + +
+ +

Helper function to run calibrators along columns of given data.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
x + Tensor + +
+

The input tensor of feature values of shape (batch_size, num_features).

+
+ 		+ required +
calibrators + ModuleDict + +
+

A dictionary of calibrator functions.

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Tensor + +
+

A torch.Tensor resulting from applying the calibrators and stacking the results.

+
+
+ +
+ Source code in pytorch_lattice/utils/models.py + 
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def calibrate_and_stack(
+    x: torch.Tensor,
+    calibrators: torch.nn.ModuleDict,
+) -> torch.Tensor:
+    """Helper function to run calibrators along columns of given data.
+
+    Args:
+        x: The input tensor of feature values of shape `(batch_size, num_features)`.
+        calibrators: A dictionary of calibrator functions.
+
+    Returns:
+        A torch.Tensor resulting from applying the calibrators and stacking the results.
+    """
+    return torch.column_stack(
+        tuple(
+            calibrator(x[:, i, None])
+            for i, calibrator in enumerate(calibrators.values())
+        )
+    )
+
+
+
+ +
+ + +
+ + + + +

+ initialize_feature_calibrators(features, output_min=None, output_max=None) + +

+ + +
+ +

Helper function to initialize calibrators for calibrated model.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
features + list[Union[NumericalFeature, CategoricalFeature]] + +
+

A list of numerical and/or categorical feature configs.

+
+ 		+ required +
output_min + Optional[float] + +
+

The minimum output value for the model. If None, the minimum +output value will be unbounded.

+
+ 		+ None +
output_max + Union[Optional[float], list[Optional[float]]] + +
+

A list of maximum output value for each feature of the model. If +None, the maximum output value will be unbounded. If a singular value, it +will be taken as the maximum of all features.

+
+ 		+ None +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ModuleDict + +
+

A torch.nn.ModuleDict of calibrators accessible by each feature's name.

+
+
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If any feature configs are not NUMERICAL or CATEGORICAL.

+
+
+ +
+ Source code in pytorch_lattice/utils/models.py + 
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def initialize_feature_calibrators(
+    features: list[Union[NumericalFeature, CategoricalFeature]],
+    output_min: Optional[float] = None,
+    output_max: Union[Optional[float], list[Optional[float]]] = None,
+) -> torch.nn.ModuleDict:
+    """Helper function to initialize calibrators for calibrated model.
+
+    Args:
+        features: A list of numerical and/or categorical feature configs.
+        output_min: The minimum output value for the model. If `None`, the minimum
+            output value will be unbounded.
+        output_max: A list of maximum output value for each feature of the model. If
+            `None`, the maximum output value will be unbounded. If a singular value, it
+            will be taken as the maximum of all features.
+
+    Returns:
+        A `torch.nn.ModuleDict` of calibrators accessible by each feature's name.
+
+    Raises:
+        ValueError: If any feature configs are not `NUMERICAL` or `CATEGORICAL`.
+    """
+    calibrators = torch.nn.ModuleDict()
+    if not isinstance(output_max, list):
+        output_max = [output_max] * len(features)
+    for feature, feature_max in zip(features, output_max):
+        if isinstance(feature, NumericalFeature):
+            calibrators[feature.feature_name] = NumericalCalibrator(
+                input_keypoints=feature.input_keypoints,
+                missing_input_value=feature.missing_input_value,
+                output_min=output_min,
+                output_max=feature_max,
+                monotonicity=feature.monotonicity,
+                kernel_init=NumericalCalibratorInit.EQUAL_SLOPES,
+                projection_iterations=feature.projection_iterations,
+            )
+        elif isinstance(feature, CategoricalFeature):
+            calibrators[feature.feature_name] = CategoricalCalibrator(
+                num_categories=len(feature.categories),
+                missing_input_value=feature.missing_input_value,
+                output_min=output_min,
+                output_max=feature_max,
+                monotonicity_pairs=feature.monotonicity_index_pairs,
+                kernel_init=CategoricalCalibratorInit.UNIFORM,
+            )
+        else:
+            raise ValueError(f"Unknown type {type(feature)} for feature {feature}")
+    return calibrators
+
+
+
+ +
+ + +
+ + + + +

+ initialize_monotonicities(features) + +

+ + +
+ +

Helper function to initialize monotonicities for calibrated model.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
features + list[Union[NumericalFeature, CategoricalFeature]] + +
+

A list of numerical and/or categorical feature configs.

+
+ 		+ required +
+ + + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ list[Optional[Monotonicity]] + +
+

A list of None or Monotonicity.INCREASING based on whether

+
+
+ list[Optional[Monotonicity]] + +
+

each feature has a monotonicity or not.

+
+
+ +
+ Source code in pytorch_lattice/utils/models.py + 
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def initialize_monotonicities(
+    features: list[Union[NumericalFeature, CategoricalFeature]]
+) -> list[Optional[Monotonicity]]:
+    """Helper function to initialize monotonicities for calibrated model.
+
+    Args:
+        features: A list of numerical and/or categorical feature configs.
+
+    Returns:
+        A list of `None` or `Monotonicity.INCREASING` based on whether
+        each feature has a monotonicity or not.
+    """
+    monotonicities = [
+        None
+        if (isinstance(feature, CategoricalFeature) and not feature.monotonicity_pairs)
+        or (isinstance(feature, NumericalFeature) and feature.monotonicity is None)
+        else Monotonicity.INCREASING
+        for feature in features
+    ]
+    return monotonicities
+
+
+
+ +
+ + +
+ + + + +

+ initialize_output_calibrator(monotonic, output_calibration_num_keypoints, output_min=None, output_max=None) + +

+ + +
+ +

Helper function to initialize output calibrator for calibrated model.

+ + + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
monotonic + bool + +
+

Whether output calibrator should have monotonicity constraint.

+
+ 		+ required +
output_calibration_num_keypoints + Optional[int] + +
+

The number of keypoints in output +calibrator. If 0 or None, no output calibrator will be returned.

+
+ 		+ required +
output_min + Optional[float] + +
+

The minimum output value for the model. If None, the minimum +output value will be unbounded.

+
+ 		+ None +
output_max + Optional[float] + +
+

The maximum output value for the model. If None, the maximum +output value will be unbounded.

+
+ 		+ None +
+ + + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Optional[NumericalCalibrator] + +
+

A torch.nn.ModuleDict of calibrators accessible by each feature's name.

+
+
+ + + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If any feature configs are not NUMERICAL or CATEGORICAL.

+
+
+ +
+ Source code in pytorch_lattice/utils/models.py + 
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def initialize_output_calibrator(
+    monotonic: bool,
+    output_calibration_num_keypoints: Optional[int],
+    output_min: Optional[float] = None,
+    output_max: Optional[float] = None,
+) -> Optional[NumericalCalibrator]:
+    """Helper function to initialize output calibrator for calibrated model.
+
+    Args:
+        monotonic: Whether output calibrator should have monotonicity constraint.
+        output_calibration_num_keypoints: The number of keypoints in output
+            calibrator. If `0` or `None`, no output calibrator will be returned.
+        output_min: The minimum output value for the model. If `None`, the minimum
+            output value will be unbounded.
+        output_max: The maximum output value for the model. If `None`, the maximum
+            output value will be unbounded.
+
+    Returns:
+        A `torch.nn.ModuleDict` of calibrators accessible by each feature's name.
+
+    Raises:
+        ValueError: If any feature configs are not `NUMERICAL` or `CATEGORICAL`.
+    """
+    if output_calibration_num_keypoints:
+        output_calibrator = NumericalCalibrator(
+            input_keypoints=np.linspace(0.0, 1.0, num=output_calibration_num_keypoints),
+            missing_input_value=None,
+            output_min=output_min,
+            output_max=output_max,
+            monotonicity=Monotonicity.INCREASING if monotonic else None,
+            kernel_init=NumericalCalibratorInit.EQUAL_HEIGHTS,
+        )
+        return output_calibrator
+    return None
+
+
+
+ +
+ + + +
+ +
+ +
+ + + + + + + + +
+
+ + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 0000000..049a254 --- /dev/null +++ b/assets/_mkdocstrings.css @@ -0,0 +1,64 @@ + +/* Avoid breaking parameter names, etc. in table cells. */ +.doc-contents td code { + word-break: normal !important; +} + +/* No line break before first paragraph of descriptions. */ +.doc-md-description, +.doc-md-description>p:first-child { + display: inline; +} + +/* Max width for docstring sections tables. */ +.doc .md-typeset__table, +.doc .md-typeset__table table { + display: table !important; + width: 100%; +} + +.doc .md-typeset__table tr { + display: table-row; +} + +/* Defaults in Spacy table style. */ +.doc-param-default { + float: right; +} + +/* Keep headings consistent. */ +h1.doc-heading, +h2.doc-heading, +h3.doc-heading, +h4.doc-heading, +h5.doc-heading, +h6.doc-heading { + font-weight: 400; + line-height: 1.5; + color: inherit; + text-transform: none; +} + +h1.doc-heading { + font-size: 1.6rem; +} + +h2.doc-heading { + font-size: 1.2rem; +} + +h3.doc-heading { + font-size: 1.15rem; +} + +h4.doc-heading { + font-size: 1.10rem; +} + +h5.doc-heading { + font-size: 1.05rem; +} + +h6.doc-heading { + font-size: 1rem; +} \ No newline at end of file diff --git a/assets/images/favicon.png b/assets/images/favicon.png new file mode 100644 index 0000000..1cf13b9 Binary files /dev/null and b/assets/images/favicon.png differ diff --git a/assets/javascripts/bundle.81fa17fe.min.js b/assets/javascripts/bundle.81fa17fe.min.js new file mode 100644 index 0000000..885b870 --- /dev/null +++ b/assets/javascripts/bundle.81fa17fe.min.js @@ -0,0 +1,29 @@ +"use strict";(()=>{var wi=Object.create;var ur=Object.defineProperty;var Si=Object.getOwnPropertyDescriptor;var Ti=Object.getOwnPropertyNames,kt=Object.getOwnPropertySymbols,Oi=Object.getPrototypeOf,dr=Object.prototype.hasOwnProperty,Zr=Object.prototype.propertyIsEnumerable;var Xr=(e,t,r)=>t in 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#statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var zr,wa=0;function Sa(){return typeof mermaid=="undefined"||mermaid instanceof Element?ht("https://unpkg.com/mermaid@9.4.3/dist/mermaid.min.js"):j(void 0)}function On(e){return e.classList.remove("mermaid"),zr||(zr=Sa().pipe(w(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Tn,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),J(1))),zr.subscribe(()=>{e.classList.add("mermaid");let t=`__mermaid_${wa++}`,r=T("div",{class:"mermaid"}),o=e.textContent;mermaid.mermaidAPI.render(t,o,(n,i)=>{let s=r.attachShadow({mode:"closed"});s.innerHTML=n,e.replaceWith(r),i==null||i(s)})}),zr.pipe(m(()=>({ref:e})))}var Mn=T("table");function Ln(e){return e.replaceWith(Mn),Mn.replaceWith(dn(e)),j({ref:e})}function Ta(e){let t=q(":scope > input",e),r=t.find(o=>o.checked)||t[0];return _(...t.map(o=>h(o,"change").pipe(m(()=>N(`label[for="${o.id}"]`))))).pipe(V(N(`label[for="${r.id}"]`)),m(o=>({active:o})))}function _n(e,{viewport$:t}){let r=Nr("prev");e.append(r);let o=Nr("next");e.append(o);let n=N(".tabbed-labels",e);return H(()=>{let i=new x,s=i.pipe(Z(),re(!0));return B([i,ye(e)]).pipe(Ce(1,Oe),Y(s)).subscribe({next([{active:a},c]){let p=Je(a),{width:l}=he(a);e.style.setProperty("--md-indicator-x",`${p.x}px`),e.style.setProperty("--md-indicator-width",`${l}px`);let f=er(n);(p.xf.x+c.width)&&n.scrollTo({left:Math.max(0,p.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),B([dt(n),ye(n)]).pipe(Y(s)).subscribe(([a,c])=>{let p=bt(n);r.hidden=a.x<16,o.hidden=a.x>p.width-c.width-16}),_(h(r,"click").pipe(m(()=>-1)),h(o,"click").pipe(m(()=>1))).pipe(Y(s)).subscribe(a=>{let{width:c}=he(n);n.scrollBy({left:c*a,behavior:"smooth"})}),te("content.tabs.link")&&i.pipe(je(1),ne(t)).subscribe(([{active:a},{offset:c}])=>{let p=a.innerText.trim();if(a.hasAttribute("data-md-switching"))a.removeAttribute("data-md-switching");else{let l=e.offsetTop-c.y;for(let u of q("[data-tabs]"))for(let d of q(":scope > input",u)){let v=N(`label[for="${d.id}"]`);if(v!==a&&v.innerText.trim()===p){v.setAttribute("data-md-switching",""),d.click();break}}window.scrollTo({top:e.offsetTop-l});let f=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([p,...f])])}}),i.pipe(Y(s)).subscribe(()=>{for(let a of q("audio, video",e))a.pause()}),Ta(e).pipe(w(a=>i.next(a)),A(()=>i.complete()),m(a=>R({ref:e},a)))}).pipe(rt(ae))}function An(e,{viewport$:t,target$:r,print$:o}){return _(...q(".annotate:not(.highlight)",e).map(n=>gn(n,{target$:r,print$:o})),...q("pre:not(.mermaid) > code",e).map(n=>wn(n,{target$:r,print$:o})),...q("pre.mermaid",e).map(n=>On(n)),...q("table:not([class])",e).map(n=>Ln(n)),...q("details",e).map(n=>Sn(n,{target$:r,print$:o})),...q("[data-tabs]",e).map(n=>_n(n,{viewport$:t})))}function Oa(e,{alert$:t}){return t.pipe(E(r=>_(j(!0),j(!1).pipe(ze(2e3))).pipe(m(o=>({message:r,active:o})))))}function Cn(e,t){let r=N(".md-typeset",e);return H(()=>{let o=new x;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),Oa(e,t).pipe(w(n=>o.next(n)),A(()=>o.complete()),m(n=>R({ref:e},n)))})}function Ma({viewport$:e}){if(!te("header.autohide"))return j(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Le(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),X()),o=We("search");return B([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),X(),E(n=>n?r:j(!1)),V(!1))}function kn(e,t){return H(()=>B([ye(e),Ma(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),X((r,o)=>r.height===o.height&&r.hidden===o.hidden),J(1))}function Hn(e,{header$:t,main$:r}){return H(()=>{let o=new x,n=o.pipe(Z(),re(!0));return o.pipe(ee("active"),Ge(t)).subscribe(([{active:i},{hidden:s}])=>{e.classList.toggle("md-header--shadow",i&&!s),e.hidden=s}),r.subscribe(o),t.pipe(Y(n),m(i=>R({ref:e},i)))})}function La(e,{viewport$:t,header$:r}){return ar(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=he(e);return{active:o>=n}}),ee("active"))}function $n(e,t){return H(()=>{let r=new x;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=se(".md-content h1");return typeof o=="undefined"?L:La(o,t).pipe(w(n=>r.next(n)),A(()=>r.complete()),m(n=>R({ref:e},n)))})}function Rn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),X()),n=o.pipe(E(()=>ye(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),ee("bottom"))));return B([o,n,t]).pipe(m(([i,{top:s,bottom:a},{offset:{y:c},size:{height:p}}])=>(p=Math.max(0,p-Math.max(0,s-c,i)-Math.max(0,p+c-a)),{offset:s-i,height:p,active:s-i<=c})),X((i,s)=>i.offset===s.offset&&i.height===s.height&&i.active===s.active))}function _a(e){let t=__md_get("__palette")||{index:e.findIndex(r=>matchMedia(r.getAttribute("data-md-color-media")).matches)};return j(...e).pipe(ce(r=>h(r,"change").pipe(m(()=>r))),V(e[Math.max(0,t.index)]),m(r=>({index:e.indexOf(r),color:{scheme:r.getAttribute("data-md-color-scheme"),primary:r.getAttribute("data-md-color-primary"),accent:r.getAttribute("data-md-color-accent")}})),J(1))}function Pn(e){let t=T("meta",{name:"theme-color"});document.head.appendChild(t);let r=T("meta",{name:"color-scheme"});return document.head.appendChild(r),H(()=>{let o=new x;o.subscribe(i=>{document.body.setAttribute("data-md-color-switching","");for(let[s,a]of Object.entries(i.color))document.body.setAttribute(`data-md-color-${s}`,a);for(let s=0;s{let i=Ee("header"),s=window.getComputedStyle(i);return r.content=s.colorScheme,s.backgroundColor.match(/\d+/g).map(a=>(+a).toString(16).padStart(2,"0")).join("")})).subscribe(i=>t.content=`#${i}`),o.pipe(Se(ae)).subscribe(()=>{document.body.removeAttribute("data-md-color-switching")});let n=q("input",e);return _a(n).pipe(w(i=>o.next(i)),A(()=>o.complete()),m(i=>R({ref:e},i)))})}function In(e,{progress$:t}){return H(()=>{let r=new x;return r.subscribe(({value:o})=>{e.style.setProperty("--md-progress-value",`${o}`)}),t.pipe(w(o=>r.next({value:o})),A(()=>r.complete()),m(o=>({ref:e,value:o})))})}var qr=Ht(Vr());function Aa(e){e.setAttribute("data-md-copying","");let t=e.closest("[data-copy]"),r=t?t.getAttribute("data-copy"):e.innerText;return e.removeAttribute("data-md-copying"),r}function Fn({alert$:e}){qr.default.isSupported()&&new P(t=>{new qr.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Aa(N(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(w(t=>{t.trigger.focus()}),m(()=>be("clipboard.copied"))).subscribe(e)}function Ca(e){if(e.length<2)return[""];let[t,r]=[...e].sort((n,i)=>n.length-i.length).map(n=>n.replace(/[^/]+$/,"")),o=0;if(t===r)o=t.length;else for(;t.charCodeAt(o)===r.charCodeAt(o);)o++;return e.map(n=>n.replace(t.slice(0,o),""))}function cr(e){let t=__md_get("__sitemap",sessionStorage,e);if(t)return j(t);{let r=me();return Zo(new URL("sitemap.xml",e||r.base)).pipe(m(o=>Ca(q("loc",o).map(n=>n.textContent))),de(()=>L),He([]),w(o=>__md_set("__sitemap",o,sessionStorage,e)))}}function jn(e){let t=se("[rel=canonical]",e);typeof t!="undefined"&&(t.href=t.href.replace("//localhost:","//127.0.0.1:"));let r=new Map;for(let o of q(":scope > *",e)){let n=o.outerHTML;for(let i of["href","src"]){let s=o.getAttribute(i);if(s===null)continue;let a=new URL(s,t==null?void 0:t.href),c=o.cloneNode();c.setAttribute(i,`${a}`),n=c.outerHTML;break}r.set(n,o)}return r}function Wn({location$:e,viewport$:t,progress$:r}){let o=me();if(location.protocol==="file:")return L;let n=cr().pipe(m(l=>l.map(f=>`${new URL(f,o.base)}`))),i=h(document.body,"click").pipe(ne(n),E(([l,f])=>{if(!(l.target instanceof Element))return L;let u=l.target.closest("a");if(u===null)return L;if(u.target||l.metaKey||l.ctrlKey)return L;let d=new URL(u.href);return d.search=d.hash="",f.includes(`${d}`)?(l.preventDefault(),j(new URL(u.href))):L}),le());i.pipe(xe(1)).subscribe(()=>{let l=se("link[rel=icon]");typeof l!="undefined"&&(l.href=l.href)}),h(window,"beforeunload").subscribe(()=>{history.scrollRestoration="auto"}),i.pipe(ne(t)).subscribe(([l,{offset:f}])=>{history.scrollRestoration="manual",history.replaceState(f,""),history.pushState(null,"",l)}),i.subscribe(e);let s=e.pipe(V(pe()),ee("pathname"),je(1),E(l=>ir(l,{progress$:r}).pipe(de(()=>(ot(l,!0),L))))),a=new DOMParser,c=s.pipe(E(l=>l.text()),E(l=>{let f=a.parseFromString(l,"text/html");for(let b of["[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...te("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let z=se(b),K=se(b,f);typeof z!="undefined"&&typeof K!="undefined"&&z.replaceWith(K)}let u=jn(document.head),d=jn(f.head);for(let[b,z]of d)z.getAttribute("rel")==="stylesheet"||z.hasAttribute("src")||(u.has(b)?u.delete(b):document.head.appendChild(z));for(let b of u.values())b.getAttribute("rel")==="stylesheet"||b.hasAttribute("src")||b.remove();let v=Ee("container");return Fe(q("script",v)).pipe(E(b=>{let z=f.createElement("script");if(b.src){for(let K of b.getAttributeNames())z.setAttribute(K,b.getAttribute(K));return b.replaceWith(z),new P(K=>{z.onload=()=>K.complete()})}else return z.textContent=b.textContent,b.replaceWith(z),L}),Z(),re(f))}),le());return h(window,"popstate").pipe(m(pe)).subscribe(e),e.pipe(V(pe()),Le(2,1),M(([l,f])=>l.pathname===f.pathname&&l.hash!==f.hash),m(([,l])=>l)).subscribe(l=>{var f,u;history.state!==null||!l.hash?window.scrollTo(0,(u=(f=history.state)==null?void 0:f.y)!=null?u:0):(history.scrollRestoration="auto",nr(l.hash),history.scrollRestoration="manual")}),e.pipe(Cr(i),V(pe()),Le(2,1),M(([l,f])=>l.pathname===f.pathname&&l.hash===f.hash),m(([,l])=>l)).subscribe(l=>{history.scrollRestoration="auto",nr(l.hash),history.scrollRestoration="manual",history.back()}),c.pipe(ne(e)).subscribe(([,l])=>{var f,u;history.state!==null||!l.hash?window.scrollTo(0,(u=(f=history.state)==null?void 0:f.y)!=null?u:0):nr(l.hash)}),t.pipe(ee("offset"),ke(100)).subscribe(({offset:l})=>{history.replaceState(l,"")}),c}var Dn=Ht(Nn());function Vn(e){let t=e.separator.split("|").map(n=>n.replace(/(\(\?[!=<][^)]+\))/g,"").length===0?"\uFFFD":n).join("|"),r=new RegExp(t,"img"),o=(n,i,s)=>`${i}${s}`;return n=>{n=n.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator}|)(${n.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return s=>(0,Dn.default)(s).replace(i,o).replace(/<\/mark>(\s+)]*>/img,"$1")}}function Mt(e){return e.type===1}function pr(e){return e.type===3}function zn(e,t){let r=an(e);return _(j(location.protocol!=="file:"),We("search")).pipe($e(o=>o),E(()=>t)).subscribe(({config:o,docs:n})=>r.next({type:0,data:{config:o,docs:n,options:{suggest:te("search.suggest")}}})),r}function qn({document$:e}){let t=me(),r=Ue(new URL("../versions.json",t.base)).pipe(de(()=>L)),o=r.pipe(m(n=>{let[,i]=t.base.match(/([^/]+)\/?$/);return n.find(({version:s,aliases:a})=>s===i||a.includes(i))||n[0]}));r.pipe(m(n=>new Map(n.map(i=>[`${new URL(`../${i.version}/`,t.base)}`,i]))),E(n=>h(document.body,"click").pipe(M(i=>!i.metaKey&&!i.ctrlKey),ne(o),E(([i,s])=>{if(i.target instanceof Element){let a=i.target.closest("a");if(a&&!a.target&&n.has(a.href)){let c=a.href;return!i.target.closest(".md-version")&&n.get(c)===s?L:(i.preventDefault(),j(c))}}return L}),E(i=>{let{version:s}=n.get(i);return cr(new URL(i)).pipe(m(a=>{let p=pe().href.replace(t.base,"");return a.includes(p.split("#")[0])?new URL(`../${s}/${p}`,t.base):new URL(i)}))})))).subscribe(n=>ot(n,!0)),B([r,o]).subscribe(([n,i])=>{N(".md-header__topic").appendChild(hn(n,i))}),e.pipe(E(()=>o)).subscribe(n=>{var s;let i=__md_get("__outdated",sessionStorage);if(i===null){i=!0;let a=((s=t.version)==null?void 0:s.default)||"latest";Array.isArray(a)||(a=[a]);e:for(let c of a)for(let p of n.aliases)if(new RegExp(c,"i").test(p)){i=!1;break e}__md_set("__outdated",i,sessionStorage)}if(i)for(let a of oe("outdated"))a.hidden=!1})}function Pa(e,{worker$:t}){let{searchParams:r}=pe();r.has("q")&&(Ke("search",!0),e.value=r.get("q"),e.focus(),We("search").pipe($e(i=>!i)).subscribe(()=>{let i=pe();i.searchParams.delete("q"),history.replaceState({},"",`${i}`)}));let o=Zt(e),n=_(t.pipe($e(Mt)),h(e,"keyup"),o).pipe(m(()=>e.value),X());return B([n,o]).pipe(m(([i,s])=>({value:i,focus:s})),J(1))}function Kn(e,{worker$:t}){let r=new x,o=r.pipe(Z(),re(!0));B([t.pipe($e(Mt)),r],(i,s)=>s).pipe(ee("value")).subscribe(({value:i})=>t.next({type:2,data:i})),r.pipe(ee("focus")).subscribe(({focus:i})=>{i&&Ke("search",i)}),h(e.form,"reset").pipe(Y(o)).subscribe(()=>e.focus());let n=N("header [for=__search]");return h(n,"click").subscribe(()=>e.focus()),Pa(e,{worker$:t}).pipe(w(i=>r.next(i)),A(()=>r.complete()),m(i=>R({ref:e},i)),J(1))}function Qn(e,{worker$:t,query$:r}){let o=new x,n=Ko(e.parentElement).pipe(M(Boolean)),i=e.parentElement,s=N(":scope > :first-child",e),a=N(":scope > :last-child",e);We("search").subscribe(l=>a.setAttribute("role",l?"list":"presentation")),o.pipe(ne(r),$r(t.pipe($e(Mt)))).subscribe(([{items:l},{value:f}])=>{switch(l.length){case 0:s.textContent=f.length?be("search.result.none"):be("search.result.placeholder");break;case 1:s.textContent=be("search.result.one");break;default:let u=tr(l.length);s.textContent=be("search.result.other",u)}});let c=o.pipe(w(()=>a.innerHTML=""),E(({items:l})=>_(j(...l.slice(0,10)),j(...l.slice(10)).pipe(Le(4),Ir(n),E(([f])=>f)))),m(fn),le());return c.subscribe(l=>a.appendChild(l)),c.pipe(ce(l=>{let f=se("details",l);return typeof f=="undefined"?L:h(f,"toggle").pipe(Y(o),m(()=>f))})).subscribe(l=>{l.open===!1&&l.offsetTop<=i.scrollTop&&i.scrollTo({top:l.offsetTop})}),t.pipe(M(pr),m(({data:l})=>l)).pipe(w(l=>o.next(l)),A(()=>o.complete()),m(l=>R({ref:e},l)))}function Ia(e,{query$:t}){return t.pipe(m(({value:r})=>{let o=pe();return o.hash="",r=r.replace(/\s+/g,"+").replace(/&/g,"%26").replace(/=/g,"%3D"),o.search=`q=${r}`,{url:o}}))}function Yn(e,t){let r=new x,o=r.pipe(Z(),re(!0));return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),h(e,"click").pipe(Y(o)).subscribe(n=>n.preventDefault()),Ia(e,t).pipe(w(n=>r.next(n)),A(()=>r.complete()),m(n=>R({ref:e},n)))}function Bn(e,{worker$:t,keyboard$:r}){let o=new x,n=Ee("search-query"),i=_(h(n,"keydown"),h(n,"focus")).pipe(Se(ae),m(()=>n.value),X());return o.pipe(Ge(i),m(([{suggest:a},c])=>{let p=c.split(/([\s-]+)/);if(a!=null&&a.length&&p[p.length-1]){let l=a[a.length-1];l.startsWith(p[p.length-1])&&(p[p.length-1]=l)}else p.length=0;return p})).subscribe(a=>e.innerHTML=a.join("").replace(/\s/g," ")),r.pipe(M(({mode:a})=>a==="search")).subscribe(a=>{switch(a.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(M(pr),m(({data:a})=>a)).pipe(w(a=>o.next(a)),A(()=>o.complete()),m(()=>({ref:e})))}function Gn(e,{index$:t,keyboard$:r}){let o=me();try{let n=zn(o.search,t),i=Ee("search-query",e),s=Ee("search-result",e);h(e,"click").pipe(M(({target:c})=>c instanceof Element&&!!c.closest("a"))).subscribe(()=>Ke("search",!1)),r.pipe(M(({mode:c})=>c==="search")).subscribe(c=>{let p=Re();switch(c.type){case"Enter":if(p===i){let l=new Map;for(let f of q(":first-child [href]",s)){let u=f.firstElementChild;l.set(f,parseFloat(u.getAttribute("data-md-score")))}if(l.size){let[[f]]=[...l].sort(([,u],[,d])=>d-u);f.click()}c.claim()}break;case"Escape":case"Tab":Ke("search",!1),i.blur();break;case"ArrowUp":case"ArrowDown":if(typeof p=="undefined")i.focus();else{let l=[i,...q(":not(details) > [href], summary, details[open] [href]",s)],f=Math.max(0,(Math.max(0,l.indexOf(p))+l.length+(c.type==="ArrowUp"?-1:1))%l.length);l[f].focus()}c.claim();break;default:i!==Re()&&i.focus()}}),r.pipe(M(({mode:c})=>c==="global")).subscribe(c=>{switch(c.type){case"f":case"s":case"/":i.focus(),i.select(),c.claim();break}});let a=Kn(i,{worker$:n});return _(a,Qn(s,{worker$:n,query$:a})).pipe(qe(...oe("search-share",e).map(c=>Yn(c,{query$:a})),...oe("search-suggest",e).map(c=>Bn(c,{worker$:n,keyboard$:r}))))}catch(n){return e.hidden=!0,Ve}}function Jn(e,{index$:t,location$:r}){return 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r=`https://api.github.com/users/${e}`;return Ue(r).pipe(m(o=>({repositories:o.public_repos})),He({}))}}function Zn(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ue(r).pipe(de(()=>L),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),He({}))}function ei(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return Xn(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return Zn(r,o)}return L}var ja;function Wa(e){return ja||(ja=H(()=>{let t=__md_get("__source",sessionStorage);if(t)return j(t);if(oe("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return L}return ei(e.href).pipe(w(o=>__md_set("__source",o,sessionStorage)))}).pipe(de(()=>L),M(t=>Object.keys(t).length>0),m(t=>({facts:t})),J(1)))}function ti(e){let t=N(":scope > :last-child",e);return H(()=>{let r=new x;return 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Xe=kn(Ee("header"),{viewport$:Te}),Lt=nt.pipe(m(()=>Ee("main")),E(e=>Rn(e,{viewport$:Te,header$:Xe})),J(1)),qa=_(...oe("consent").map(e=>cn(e,{target$:gt})),...oe("dialog").map(e=>Cn(e,{alert$:Br})),...oe("header").map(e=>Hn(e,{viewport$:Te,header$:Xe,main$:Lt})),...oe("palette").map(e=>Pn(e)),...oe("progress").map(e=>In(e,{progress$:Gr})),...oe("search").map(e=>Gn(e,{index$:mi,keyboard$:Yr})),...oe("source").map(e=>ti(e))),Ka=H(()=>_(...oe("announce").map(e=>sn(e)),...oe("content").map(e=>An(e,{viewport$:Te,target$:gt,print$:li})),...oe("content").map(e=>te("search.highlight")?Jn(e,{index$:mi,location$:_t}):L),...oe("header-title").map(e=>$n(e,{viewport$:Te,header$:Xe})),...oe("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?jr(pi,()=>Kr(e,{viewport$:Te,header$:Xe,main$:Lt})):jr(lr,()=>Kr(e,{viewport$:Te,header$:Xe,main$:Lt}))),...oe("tabs").map(e=>ri(e,{viewport$:Te,header$:Xe})),...oe("toc").map(e=>oi(e,{viewport$:Te,header$:Xe,main$:Lt,target$:gt})),...oe("top").map(e=>ni(e,{viewport$:Te,header$:Xe,main$:Lt,target$:gt})))),fi=nt.pipe(E(()=>Ka),qe(qa),J(1));fi.subscribe();window.document$=nt;window.location$=_t;window.target$=gt;window.keyboard$=Yr;window.viewport$=Te;window.tablet$=lr;window.screen$=pi;window.print$=li;window.alert$=Br;window.progress$=Gr;window.component$=fi;})(); 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scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n __assign = Object.assign || function __assign(t) {\r\n for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n s = arguments[i];\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n }\r\n return t;\r\n }\r\n return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n var t = {};\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n t[p] = s[p];\r\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n t[p[i]] = s[p[i]];\r\n }\r\n return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n return new (P || (P = Promise))(function (resolve, reject) {\r\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n function verb(n) { return function (v) { return step([n, v]); }; }\r\n function step(op) {\r\n if (f) throw new TypeError(\"Generator is already executing.\");\r\n while (_) try {\r\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n if (y = 0, t) op = [op[0] & 2, t.value];\r\n switch (op[0]) {\r\n case 0: case 1: t = op; break;\r\n case 4: _.label++; return { value: op[1], done: false };\r\n case 5: _.label++; y = op[1]; op = [0]; continue;\r\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n default:\r\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n if (t[2]) _.ops.pop();\r\n _.trys.pop(); continue;\r\n }\r\n op = body.call(thisArg, _);\r\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n if (m) return m.call(o);\r\n if (o && typeof o.length === \"number\") return {\r\n next: function () {\r\n if (o && i >= o.length) o = void 0;\r\n return { value: o && o[i++], done: !o };\r\n }\r\n };\r\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n if (!m) return o;\r\n var i = m.call(o), r, ar = [], e;\r\n try {\r\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n }\r\n catch (error) { e = { error: error }; }\r\n finally {\r\n try {\r\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n }\r\n finally { if (e) throw e.error; }\r\n }\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n for (var ar = [], i = 0; i < arguments.length; i++)\r\n ar = ar.concat(__read(arguments[i]));\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n r[k] = a[j];\r\n return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n if (ar || !(i in from)) {\r\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n ar[i] = from[i];\r\n }\r\n }\r\n return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n function fulfill(value) { resume(\"next\", value); }\r\n function reject(value) { resume(\"throw\", value); }\r\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n var i, p;\r\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var m = o[Symbol.asyncIterator], i;\r\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n if (mod && mod.__esModule) return mod;\r\n var result = {};\r\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n __setModuleDefault(result, mod);\r\n return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an