some changes

deep_river/anomaly/__init__.py

@@ -6,7 +6,9 @@
     AnomalyMinMaxScaler,
     AnomalyStandardScaler,
 )
-
+"""
+This module contains the anomaly detection algorithms for the deep_river package.
+"""
 __all__ = [
     "Autoencoder",
     "RollingAutoencoder",

deep_river/classification/__init__.py

@@ -1,7 +1,13 @@
 from deep_river.classification.classifier import Classifier
 from deep_river.classification.rolling_classifier import RollingClassifier
-
+from deep_river.classification.zoo import LogisticRegression, \
+    MultiLayerPerceptron
+"""
+This module contains the classifiers for the deep_river package.
+"""
 __all__ = [
     "Classifier",
     "RollingClassifier",
+    "MultiLayerPerceptron",
+    "LogisticRegression"
 ]

deep_river/classification/zoo.py

@@ -0,0 +1,130 @@
+from typing import Type, Union, Callable
+
+from torch import nn
+
+from deep_river.classification import Classifier
+
+
+
+class LogisticRegression(Classifier):
+    """
+
+    """
+
+    class LRModule(nn.Module):
+        def __init__(self, n_features):
+            super(LogisticRegression.LRModule, self).__init__()
+            self.dense0 = nn.Linear(n_features, 1)
+            self.softmax = nn.Softmax(dim=-1)
+
+        def forward(self, X, **kwargs):
+            X = self.dense0(X)
+            return self.softmax(X)
+    def __init__(
+        self,
+        loss_fn: Union[str, Callable] = "binary_cross_entropy_with_logits",
+        optimizer_fn: Union[str, Callable] = "sgd",
+        lr: float = 1e-3,
+        output_is_logit: bool = True,
+        is_class_incremental: bool = False,
+        device: str = "cpu",
+        seed: int = 42,
+        **kwargs,
+    ):
+        print(kwargs)
+        super().__init__(
+            module=LogisticRegression.LRModule,
+            loss_fn=loss_fn,
+            output_is_logit=output_is_logit,
+            is_class_incremental=is_class_incremental,
+            optimizer_fn=optimizer_fn,
+            device=device,
+            lr=lr,
+            seed=seed,
+            **kwargs,
+        )
+
+    @classmethod
+    def _unit_test_params(cls):
+        """
+        Returns a dictionary of parameters to be used for unit testing the
+        respective class.
+
+        Yields
+        -------
+        dict
+            Dictionary of parameters to be used for unit testing the
+            respective class.
+        """
+
+        yield {
+            "loss_fn": "binary_cross_entropy_with_logits",
+            "optimizer_fn": "sgd",
+        }
+
+class MultiLayerPerceptron(Classifier):
+    """
+
+    """
+
+    class MLPModule(nn.Module):
+        def __init__(self, n_width, n_depth, n_features):
+            super(MultiLayerPerceptron.MLPModule, self).__init__()
+            self.dense0 = nn.Linear(n_features, n_width)
+            self.block = [nn.Linear(n_width, n_width) for _ in range(n_depth)]
+            self.denselast = nn.Linear(n_width, 1)
+            self.softmax = nn.Softmax(dim=-1)
+
+        def forward(self, X, **kwargs):
+            X = self.dense0(X)
+            for layer in self.block:
+                X = layer(X)
+            X = self.denselast(X)
+            return self.softmax(X)
+
+    def __init__(
+        self,
+        n_width: int = 5,
+        n_depth: int = 5,
+        loss_fn: Union[str, Callable] = "binary_cross_entropy_with_logits",
+        optimizer_fn: Union[str, Callable] = "sgd",
+        lr: float = 1e-3,
+        output_is_logit: bool = True,
+        is_class_incremental: bool = False,
+        device: str = "cpu",
+        seed: int = 42,
+        **kwargs,
+    ):
+        self.n_width = n_width
+        self.n_depth = n_depth
+        kwargs['n_width'] = n_width
+        kwargs['n_depth'] = n_depth
+        super().__init__(
+            module=MultiLayerPerceptron.MLPModule,
+            loss_fn=loss_fn,
+            output_is_logit=output_is_logit,
+            is_class_incremental=is_class_incremental,
+            optimizer_fn=optimizer_fn,
+            device=device,
+            lr=lr,
+            seed=seed,
+            **kwargs,
+        )
+
+    @classmethod
+    def _unit_test_params(cls):
+        """
+        Returns a dictionary of parameters to be used for unit testing the
+        respective class.
+
+        Yields
+        -------
+        dict
+            Dictionary of parameters to be used for unit testing the
+            respective class.
+        """
+
+        yield {
+            "loss_fn": "binary_cross_entropy_with_logits",
+            "optimizer_fn": "sgd",
+        }

deep_river/regression/__init__.py

@@ -1,7 +1,11 @@
 from deep_river.regression.regressor import Regressor
 from deep_river.regression.rolling_regressor import RollingRegressor
-
+from deep_river.regression.multioutput import MultiTargetRegressor
+"""
+This module contains the regressors for the deep_river package.
+"""
 __all__ = [
     "Regressor",
     "RollingRegressor",
+    "MultiTargetRegressor"
 ]