chore(nalp): Only missing adversarial models without annotated typing.

nalp/models/discriminators/conv.py

@@ -1,6 +1,8 @@
 """Convolutional discriminator.
 """
 
+from typing import Optional
+
 import tensorflow as tf
 from tensorflow.keras.layers import Conv2D, Dense, Dropout
 
@@ -16,13 +18,18 @@ class ConvDiscriminator(Discriminator):
 
     """
 
-    def __init__(self, n_samplings=3, alpha=0.3, dropout_rate=0.3):
+    def __init__(
+        self,
+        n_samplings: Optional[int] = 3,
+        alpha: Optional[float] = 0.3,
+        dropout_rate: Optional[float] = 0.3,
+    ) -> None:
         """Initialization method.
 
         Args:
-            n_samplings (int): Number of downsamplings to perform.
-            alpha (float): LeakyReLU activation threshold.
-            dropout_rate (float): Dropout activation rate.
+            n_samplings: Number of downsamplings to perform.
+            alpha: LeakyReLU activation threshold.
+            dropout_rate: Dropout activation rate.
 
         """
 
@@ -52,24 +59,24 @@ def __init__(self, n_samplings=3, alpha=0.3, dropout_rate=0.3):
         logger.info("Class overrided.")
 
     @property
-    def alpha(self):
-        """float: LeakyReLU activation threshold."""
+    def alpha(self) -> float:
+        """LeakyReLU activation threshold."""
 
         return self._alpha
 
     @alpha.setter
-    def alpha(self, alpha):
+    def alpha(self, alpha: float) -> None:
         self._alpha = alpha
 
-    def call(self, x, training=True):
+    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:
-            x (tf.tensor): A tensorflow's tensor holding input data.
-            training (bool): Whether architecture is under training or not.
+            x: A tensorflow's tensor holding input data.
+            training: Whether architecture is under training or not.
 
         Returns:
-            The same tensor after passing through each defined layer.
+            (tf.Tensor): The same tensor after passing through each defined layer.
 
         """
 

nalp/models/discriminators/embedded_text.py

@@ -1,6 +1,8 @@
 """Embedded-text discriminator.
 """
 
+from typing import Optional, Tuple
+
 import tensorflow as tf
 from tensorflow.keras.layers import Conv2D, Dense, Dropout, Embedding, MaxPool1D
 
@@ -18,22 +20,22 @@ class EmbeddedTextDiscriminator(Discriminator):
 
     def __init__(
         self,
-        vocab_size=1,
-        max_length=1,
-        embedding_size=32,
-        n_filters=(64),
-        filters_size=(1),
-        dropout_rate=0.25,
-    ):
+        vocab_size: Optional[int] = 1,
+        max_length: Optional[int] = 1,
+        embedding_size: Optional[int] = 32,
+        n_filters: Optional[Tuple[int, ...]] = (64),
+        filters_size: Optional[Tuple[int, ...]] = (1),
+        dropout_rate: Optional[float] = 0.25,
+    ) -> None:
         """Initialization method.
 
         Args:
-            vocab_size (int): The size of the vocabulary.
-            max_length (int): Maximum length of the sequences.
-            embedding_size (int): The size of the embedding layer.
-            n_filters (tuple): Number of filters to be applied.
-            filters_size (tuple): Size of filters to be applied.
-            dropout_rate (float): Dropout activation rate.
+            vocab_size: The size of the vocabulary.
+            max_length: Maximum length of the sequences.
+            embedding_size: The size of the embedding layer.
+            n_filters: Number of filters to be applied.
+            filters_size: Size of filters to be applied.
+            dropout_rate: Dropout activation rate.
 
         """
 
@@ -72,15 +74,15 @@ def __init__(
 
         logger.info("Class overrided.")
 
-    def call(self, x, training=True):
+    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:
-            x (tf.tensor): A tensorflow's tensor holding input data.
-            training (bool): Whether architecture is under training or not.
+            x: A tensorflow's tensor holding input data.
+            training: Whether architecture is under training or not.
 
         Returns:
-            The same tensor after passing through each defined layer.
+            (tf.Tensor): The same tensor after passing through each defined layer.
 
         """
 

nalp/models/discriminators/linear.py

@@ -1,6 +1,8 @@
 """Linear discriminator.
 """
 
+from typing import Optional
+
 import tensorflow as tf
 from tensorflow.keras.layers import Dense
 
@@ -16,12 +18,14 @@ class LinearDiscriminator(Discriminator):
 
     """
 
-    def __init__(self, n_samplings=3, alpha=0.01):
+    def __init__(
+        self, n_samplings: Optional[int] = 3, alpha: Optional[float] = 0.01
+    ) -> None:
         """Initialization method.
 
         Args:
-            n_samplings (int): Number of downsamplings to perform.
-            alpha (float): LeakyReLU activation threshold.
+            n_samplings: Number of downsamplings to perform.
+            alpha: LeakyReLU activation threshold.
 
         """
 
@@ -43,24 +47,24 @@ def __init__(self, n_samplings=3, alpha=0.01):
         logger.info("Class overrided.")
 
     @property
-    def alpha(self):
-        """float: LeakyReLU activation threshold."""
+    def alpha(self) -> float:
+        """LeakyReLU activation threshold."""
 
         return self._alpha
 
     @alpha.setter
-    def alpha(self, alpha):
+    def alpha(self, alpha: float) -> None:
         self._alpha = alpha
 
-    def call(self, x, training=True):
+    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:
-            x (tf.tensor): A tensorflow's tensor holding input data.
-            training (bool): Whether architecture is under training or not.
+            x: A tensorflow's tensor holding input data.
+            training: Whether architecture is under training or not.
 
         Returns:
-            The same tensor after passing through each defined layer.
+            (tf.Tensor): The same tensor after passing through each defined layer.
 
         """
 

nalp/models/discriminators/lstm.py

@@ -1,6 +1,9 @@
 """Long Short-Term Memory discriminator.
 """
 
+from typing import Optional
+
+import tensorflow as tf
 from tensorflow.keras.layers import RNN, Dense, LSTMCell
 
 from nalp.core import Discriminator
@@ -18,12 +21,14 @@ class LSTMDiscriminator(Discriminator):
 
     """
 
-    def __init__(self, embedding_size=32, hidden_size=64):
+    def __init__(
+        self, embedding_size: Optional[int] = 32, hidden_size: Optional[int] = 64
+    ) -> None:
         """Initialization method.
 
         Args:
-            embedding_size (int): The size of the embedding layer.
-            hidden_size (int): The amount of hidden neurons.
+            embedding_size: The size of the embedding layer.
+            hidden_size: The amount of hidden neurons.
 
         """
 
@@ -47,14 +52,14 @@ def __init__(self, embedding_size=32, hidden_size=64):
 
         logger.info("Class overrided.")
 
-    def call(self, x):
+    def call(self, x: tf.Tensor) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:
-            x (tf.tensor): A tensorflow's tensor holding input data.
+            x: A tensorflow's tensor holding input data.
 
         Returns:
-            The same tensor after passing through each defined layer.
+            (tf.Tensor): The same tensor after passing through each defined layer.
 
         """
 

nalp/models/discriminators/text.py

@@ -1,6 +1,8 @@
 """Text-based discriminator.
 """
 
+from typing import Optional, Tuple
+
 import tensorflow as tf
 from tensorflow.keras.layers import Conv2D, Dense, Dropout, MaxPool1D
 
@@ -18,20 +20,20 @@ class TextDiscriminator(Discriminator):
 
     def __init__(
         self,
-        max_length=1,
-        embedding_size=32,
-        n_filters=(64),
-        filters_size=(1),
-        dropout_rate=0.25,
-    ):
+        max_length: Optional[int] = 1,
+        embedding_size: Optional[int] = 32,
+        n_filters: Optional[Tuple[int, ...]] = (64),
+        filters_size: Optional[Tuple[int, ...]] = (1),
+        dropout_rate: Optional[float] = 0.25,
+    ) -> None:
         """Initialization method.
 
         Args:
-            max_length (int): Maximum length of the sequences.
-            embedding_size (int): The size of the embedding layer.
-            n_filters (tuple): Number of filters to be applied.
-            filters_size (tuple): Size of filters to be applied.
-            dropout_rate (float): Dropout activation rate.
+            max_length: Maximum length of the sequences.
+            embedding_size: The size of the embedding layer.
+            n_filters: Number of filters to be applied.
+            filters_size: Size of filters to be applied.
+            dropout_rate: Dropout activation rate.
 
         """
 
@@ -67,15 +69,15 @@ def __init__(
 
         logger.info("Class overrided.")
 
-    def call(self, x, training=True):
+    def call(self, x: tf.Tensor, training: Optional[bool] = True) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:
-            x (tf.tensor): A tensorflow's tensor holding input data.
-            training (bool): Whether architecture is under training or not.
+            x: A tensorflow's tensor holding input data.
+            training: Whether architecture is under training or not.
 
         Returns:
-            The same tensor after passing through each defined layer.
+            (tf.Tensor): The same tensor after passing through each defined layer.
 
         """
 

nalp/models/generators/bi_lstm.py

@@ -1,10 +1,13 @@
 """Bi-directional Long Short-Term Memory generator.
 """
 
+from typing import Optional
+
 import tensorflow as tf
 from tensorflow.keras.layers import RNN, Dense, Embedding, LSTMCell
 
 from nalp.core import Generator
+from nalp.encoders.integer import IntegerEncoder
 from nalp.utils import logging
 
 logger = logging.get_logger(__name__)
@@ -19,14 +22,20 @@ class BiLSTMGenerator(Generator):
 
     """
 
-    def __init__(self, encoder=None, vocab_size=1, embedding_size=32, hidden_size=64):
+    def __init__(
+        self,
+        encoder: Optional[IntegerEncoder] = None,
+        vocab_size: Optional[int] = 1,
+        embedding_size: Optional[int] = 32,
+        hidden_size: Optional[int] = 64,
+    ) -> None:
         """Initialization method.
 
         Args:
-            encoder (IntegerEncoder): An index to vocabulary encoder.
-            vocab_size (int): The size of the vocabulary.
-            embedding_size (int): The size of the embedding layer.
-            hidden_size (int): The amount of hidden neurons.
+            encoder: An index to vocabulary encoder.
+            vocab_size: The size of the vocabulary.
+            embedding_size: The size of the embedding layer.
+            hidden_size: The amount of hidden neurons.
 
         """
 
@@ -66,23 +75,23 @@ def __init__(self, encoder=None, vocab_size=1, embedding_size=32, hidden_size=64
         logger.info("Class overrided.")
 
     @property
-    def encoder(self):
-        """obj: An encoder generic object."""
+    def encoder(self) -> IntegerEncoder:
+        """An encoder generic object."""
 
         return self._encoder
 
     @encoder.setter
-    def encoder(self, encoder):
+    def encoder(self, encoder: IntegerEncoder) -> None:
         self._encoder = encoder
 
-    def call(self, x):
+    def call(self, x: tf.Tensor) -> tf.Tensor:
         """Method that holds vital information whenever this class is called.
 
         Args:
-            x (tf.tensor): A tensorflow's tensor holding input data.
+            x: A tensorflow's tensor holding input data.
 
         Returns:
-            The same tensor after passing through each defined layer.
+            (tf.Tensor): The same tensor after passing through each defined layer.
 
         """