feat: rename epoch_size to epoch_count (#962)

### Summary of Changes

In `fit` methods of `NeuralNetworkRegressor` and
`NeuralNetworkClassifier`, rename the parameter `epoch_size` to
`epoch_count.

The previous name did not match, since an epoch has no size.

---------

Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>

Author: lars-reimann

docs/tutorials/convolutional_neural_network_for_image_classification.ipynb

@@ -197,11 +197,11 @@
    "id": "3d8efa74951725cb"
   },
   {
-   "cell_type": "code",
-   "source": "cnn_fitted = cnn.fit(dataset, epoch_size=8, batch_size=16)",
    "metadata": {
     "collapsed": false
    },
+   "cell_type": "code",
+   "source": "cnn_fitted = cnn.fit(dataset, epoch_count=8, batch_size=16)",
    "id": "381627a94d500675",
    "outputs": [],
    "execution_count": null

docs/tutorials/time_series_forecasting.ipynb

@@ -205,7 +205,7 @@
     "    forecast_horizon=1,\n",
     "    continuous=False,\n",
     "    extra_names= [\"date\"]\n",
-    "), epoch_size=25)"
+    "), epoch_count=25)"
    ],
    "metadata": {
     "collapsed": false,

src/safeds/ml/nn/_model.py

@@ -166,7 +166,7 @@ def from_pretrained_model(huggingface_repo: str) -> NeuralNetworkRegressor:  # p
     def fit(
         self,
         train_data: IFT,
-        epoch_size: int = 25,
+        epoch_count: int = 25,
         batch_size: int = 1,
         learning_rate: float = 0.001,
         callback_on_batch_completion: Callable[[int, float], None] | None = None,
@@ -181,7 +181,7 @@ def fit(
         ----------
         train_data:
             The data the network should be trained on.
-        epoch_size:
+        epoch_count:
             The number of times the training cycle should be done.
         batch_size:
             The size of data batches that should be loaded at one time.
@@ -202,7 +202,7 @@ def fit(
         Raises
         ------
         OutOfBoundsError
-            If epoch_size < 1
+            If epoch_count < 1
             If batch_size < 1
         """
         import torch
@@ -218,7 +218,7 @@ def fit(
         if not self._input_conversion._is_fit_data_valid(train_data):
             raise FeatureDataMismatchError
 
-        _check_bounds("epoch_size", epoch_size, lower_bound=_ClosedBound(1))
+        _check_bounds("epoch_count", epoch_count, lower_bound=_ClosedBound(1))
         _check_bounds("batch_size", batch_size, lower_bound=_ClosedBound(1))
 
         copied_model = copy.deepcopy(self)
@@ -236,7 +236,7 @@ def fit(
         loss_fn = nn.MSELoss()
 
         optimizer = torch.optim.SGD(copied_model._model.parameters(), lr=learning_rate)
-        for _ in range(epoch_size):
+        for _ in range(epoch_count):
             loss_sum = 0.0
             amount_of_loss_values_calculated = 0
             for x, y in iter(dataloader):
@@ -273,7 +273,7 @@ def fit(
     #         "median_absolute_deviation",
     #         "coefficient_of_determination",
     #     ],
-    #     epoch_size: int = 25,
+    #     epoch_count: int = 25,
     #     batch_size: int = 1,
     #     learning_rate: float = 0.001,
     # ) -> Self:
@@ -288,7 +288,7 @@ def fit(
     #         The data the network should be trained on.
     #     optimization_metric:
     #         The metric that should be used for determining the performance of a model.
-    #     epoch_size:
+    #     epoch_count:
     #         The number of times the training cycle should be done.
     #     batch_size:
     #         The size of data batches that should be loaded at one time.
@@ -317,7 +317,7 @@ def fit(
     #             "Hyperparameter optimization is currently not supported for CNN Regression Tasks.",
     #         )  # pragma: no cover
     #
-    #     _check_bounds("epoch_size", epoch_size, lower_bound=_ClosedBound(1))
+    #     _check_bounds("epoch_count", epoch_count, lower_bound=_ClosedBound(1))
     #     _check_bounds("batch_size", batch_size, lower_bound=_ClosedBound(1))
     #
     #     list_of_models = self._get_models_for_all_choices()
@@ -334,7 +334,7 @@ def fit(
     #                 executor.submit(
     #                     model.fit,
     #                     train_set,  # type: ignore[arg-type]
-    #                     epoch_size,
+    #                     epoch_count,
     #                     batch_size,
     #                     learning_rate,
     #                 ),
@@ -774,7 +774,7 @@ def from_pretrained_model(huggingface_repo: str) -> NeuralNetworkClassifier:  #
     def fit(
         self,
         train_data: IFT,
-        epoch_size: int = 25,
+        epoch_count: int = 25,
         batch_size: int = 1,
         learning_rate: float = 0.001,
         callback_on_batch_completion: Callable[[int, float], None] | None = None,
@@ -789,7 +789,7 @@ def fit(
         ----------
         train_data:
             The data the network should be trained on.
-        epoch_size:
+        epoch_count:
             The number of times the training cycle should be done.
         batch_size:
             The size of data batches that should be loaded at one time.
@@ -810,7 +810,7 @@ def fit(
         Raises
         ------
         ValueError
-            If epoch_size < 1
+            If epoch_count < 1
             If batch_size < 1
         """
         import torch
@@ -831,7 +831,7 @@ def fit(
         if not self._input_conversion._is_fit_data_valid(train_data):
             raise FeatureDataMismatchError
 
-        _check_bounds("epoch_size", epoch_size, lower_bound=_ClosedBound(1))
+        _check_bounds("epoch_count", epoch_count, lower_bound=_ClosedBound(1))
         _check_bounds("batch_size", batch_size, lower_bound=_ClosedBound(1))
 
         copied_model = copy.deepcopy(self)
@@ -856,7 +856,7 @@ def fit(
             loss_fn = nn.BCELoss()
 
         optimizer = torch.optim.SGD(copied_model._model.parameters(), lr=learning_rate)
-        for _ in range(epoch_size):
+        for _ in range(epoch_count):
             loss_sum = 0.0
             amount_of_loss_values_calculated = 0
             for x, y in iter(dataloader):
@@ -890,7 +890,7 @@ def fit(
     #     train_data: IFT,
     #     optimization_metric: Literal["accuracy", "precision", "recall", "f1_score"],
     #     positive_class: Any = None,
-    #     epoch_size: int = 25,
+    #     epoch_count: int = 25,
     #     batch_size: int = 1,
     #     learning_rate: float = 0.001,
     # ) -> Self:
@@ -907,7 +907,7 @@ def fit(
     #         The metric that should be used for determining the performance of a model.
     #     positive_class:
     #         The class to be considered positive. Only needs to be provided when choosing precision, recall or f1_score as the optimization metric.
-    #     epoch_size:
+    #     epoch_count:
     #         The number of times the training cycle should be done.
     #     batch_size:
     #         The size of data batches that should be loaded at one time.
@@ -936,7 +936,7 @@ def fit(
     #             "Continuous Predictions are currently not supported for Time Series Classification.",
     #         )
     #
-    #     _check_bounds("epoch_size", epoch_size, lower_bound=_ClosedBound(1))
+    #     _check_bounds("epoch_count", epoch_count, lower_bound=_ClosedBound(1))
     #     _check_bounds("batch_size", batch_size, lower_bound=_ClosedBound(1))
     #
     #     list_of_models = self._get_models_for_all_choices()
@@ -956,7 +956,7 @@ def fit(
     #                 executor.submit(
     #                     model.fit,
     #                     train_set,  # type: ignore[arg-type]
-    #                     epoch_size,
+    #                     epoch_count,
     #                     batch_size,
     #                     learning_rate,
     #                 ),

tests/safeds/ml/nn/test_cnn_workflow.py

@@ -3,6 +3,8 @@
 
 import pytest
 import torch
+from torch.types import Device
+
 from safeds._config import _get_device
 from safeds.data.image.containers import ImageList
 from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
@@ -27,8 +29,6 @@
     MaxPooling2DLayer,
 )
 from safeds.ml.nn.typing import VariableImageSize
-from torch.types import Device
-
 from tests.helpers import configure_test_with_device, device_cpu, device_cuda, images_all, resolve_resource_path
 
 if TYPE_CHECKING:
@@ -88,7 +88,7 @@ def test_should_train_and_predict_model(
             InputConversionImageToTable(image_dataset.input_size),
             layers,
         )
-        nn = nn_original.fit(image_dataset, epoch_size=2)
+        nn = nn_original.fit(image_dataset, epoch_count=2)
         assert nn_original._model is not nn._model
         prediction: ImageDataset = nn.predict(image_dataset.get_input())
         assert one_hot_encoder.inverse_transform(prediction.get_output()) == Table({"class": prediction_label})
@@ -147,7 +147,7 @@ def test_should_train_and_predict_model(
             InputConversionImageToColumn(image_dataset.input_size),
             layers,
         )
-        nn = nn_original.fit(image_dataset, epoch_size=2)
+        nn = nn_original.fit(image_dataset, epoch_count=2)
         assert nn_original._model is not nn._model
         prediction: ImageDataset = nn.predict(image_dataset.get_input())
         assert prediction.get_output() == Column("class", prediction_label)
@@ -188,7 +188,7 @@ def test_should_train_and_predict_model(
             InputConversionImageToImage(image_dataset.input_size),
             layers,
         )
-        nn = nn_original.fit(image_dataset, epoch_size=20)
+        nn = nn_original.fit(image_dataset, epoch_count=20)
         assert nn_original._model is not nn._model
         prediction = nn.predict(image_dataset.get_input())
         assert isinstance(prediction.get_output(), ImageList)
@@ -229,7 +229,7 @@ def test_should_train_and_predict_model_variable_image_size(
             InputConversionImageToImage(VariableImageSize.from_image_size(image_dataset.input_size)),
             layers,
         )
-        nn = nn_original.fit(image_dataset, epoch_size=20)
+        nn = nn_original.fit(image_dataset, epoch_count=20)
         assert nn_original._model is not nn._model
         prediction = nn.predict(
             image_dataset.get_input().resize(

tests/safeds/ml/nn/test_dropout_workflow.py

@@ -1,4 +1,6 @@
 import pytest
+from torch.types import Device
+
 from safeds._config import _get_device
 from safeds.data.tabular.containers import Table
 from safeds.ml.nn import (
@@ -11,8 +13,6 @@
     DropoutLayer,
     ForwardLayer,
 )
-from torch.types import Device
-
 from tests.helpers import configure_test_with_device, get_devices, get_devices_ids
 
 
@@ -32,6 +32,6 @@ def test_forward_model(device: Device) -> None:
         [ForwardLayer(neuron_count=1), DropoutLayer(probability=0.5)],
     )
 
-    fitted_model = model.fit(train_table.to_tabular_dataset("value"), epoch_size=1, learning_rate=0.01)
+    fitted_model = model.fit(train_table.to_tabular_dataset("value"), epoch_count=1, learning_rate=0.01)
     assert fitted_model._model is not None
     assert fitted_model._model.state_dict()["_pytorch_layers.0._layer.weight"].device == _get_device()

tests/safeds/ml/nn/test_forward_workflow.py

@@ -1,4 +1,6 @@
 import pytest
+from torch.types import Device
+
 from safeds._config import _get_device
 from safeds.data.tabular.containers import Table
 from safeds.data.tabular.transformation import StandardScaler
@@ -11,8 +13,6 @@
 from safeds.ml.nn.layers import (
     ForwardLayer,
 )
-from torch.types import Device
-
 from tests.helpers import configure_test_with_device, get_devices, get_devices_ids, resolve_resource_path
 
 
@@ -38,7 +38,7 @@ def test_forward_model(device: Device) -> None:
         [ForwardLayer(neuron_count=1)],
     )
 
-    fitted_model = model.fit(train_table.to_tabular_dataset("target"), epoch_size=1, learning_rate=0.01)
+    fitted_model = model.fit(train_table.to_tabular_dataset("target"), epoch_count=1, learning_rate=0.01)
     fitted_model.predict(test_table.remove_columns_except(["value"]))
     assert fitted_model._model is not None
     assert fitted_model._model.state_dict()["_pytorch_layers.0._layer.weight"].device == _get_device()

tests/safeds/ml/nn/test_lstm_workflow.py

@@ -1,4 +1,6 @@
 import pytest
+from torch.types import Device
+
 from safeds._config import _get_device
 from safeds.data.tabular.containers import Table
 from safeds.data.tabular.transformation import RangeScaler
@@ -13,8 +15,6 @@
     GRULayer,
     LSTMLayer,
 )
-from torch.types import Device
-
 from tests.helpers import configure_test_with_device, get_devices, get_devices_ids, resolve_resource_path
 
 
@@ -45,7 +45,7 @@ def test_lstm_model(device: Device) -> None:
             continuous=True,
             extra_names=["date"],
         ),
-        epoch_size=1,
+        epoch_count=1,
     )
 
     trained_model.predict(test_table)
@@ -57,7 +57,7 @@ def test_lstm_model(device: Device) -> None:
             continuous=False,
             extra_names=["date"],
         ),
-        epoch_size=1,
+        epoch_count=1,
     )
 
     trained_model_2.predict(test_table)