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do some stuff here and there
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@sibre28
sibre28 committed Aug 16, 2024
1 parent 0a00974 commit c811399
Showing 1 changed file with 57 additions and 49 deletions.
106 changes: 57 additions & 49 deletions src/safeds/ml/nn/_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -315,10 +315,8 @@ def fit_by_exhaustive_search(
if not self._contains_choices():
raise FittingWithoutChoiceError

#if isinstance(train_data, TimeSeriesDataset):
# raise LearningError("RNN-Hyperparameter optimization is currently not supported.") # pragma: no cover
if isinstance(train_data, ImageDataset):
raise LearningError("CNN-Hyperparameter optimization is currently not supported.") # pragma: no cover
raise LearningError("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("batch_size", batch_size, lower_bound=_ClosedBound(1))
Expand All @@ -328,12 +326,11 @@ def fit_by_exhaustive_search(

if isinstance(train_data, TabularDataset):
(train_set, test_set) = self._data_split_table(train_data)

elif isinstance(train_data, TimeSeriesDataset):
#elif isinstance(train_data, TimeSeriesDataset):
else: # train_data is TimeSeriesDataset
(train_set, test_set) = self._data_split_time_series(train_data)
else: # train_data is ImageDataset
(train_set, test_set) = self._data_split_image(train_data)
pass
#else: # train_data is ImageDataset
# (train_set, test_set) = self._data_split_image(train_data)

with ProcessPoolExecutor(max_workers=len(list_of_models), mp_context=mp.get_context("spawn")) as executor:
futures = []
Expand All @@ -346,13 +343,10 @@ def fit_by_exhaustive_search(

if isinstance(train_data, TabularDataset):
return self._get_best_fnn_model(list_of_fitted_models, test_set, optimization_metric)
elif isinstance(train_data, TimeSeriesDataset):
#elif isinstance(train_data, TimeSeriesDataset):
else: #train_data is TimeSeriesDataset
return self._get_best_rnn_model(list_of_fitted_models, train_set, test_set, optimization_metric)
else:
# Image Cross Validation
test_data = train_data
pass

#else: # Image Data Splitting necessary

def _data_split_table(self, data: TabularDataset) -> (TabularDataset, TabularDataset):
[train_split, test_split] = data.to_table().split_rows(0.75)
Expand Down Expand Up @@ -387,39 +381,47 @@ def _get_best_fnn_model(
match optimization_metric:
case "mean_squared_error":
best_metric_value = RegressionMetrics.mean_squared_error(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
case "mean_absolute_error":
best_metric_value = RegressionMetrics.mean_absolute_error(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
case "median_absolute_deviation":
best_metric_value = RegressionMetrics.median_absolute_deviation(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
case "coefficient_of_determination":
best_metric_value = RegressionMetrics.coefficient_of_determination(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
else:
match optimization_metric:
case "mean_squared_error":
error_of_fitted_model = RegressionMetrics.mean_squared_error(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
if error_of_fitted_model < best_metric_value:
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
case "mean_absolute_error":
error_of_fitted_model = RegressionMetrics.mean_absolute_error(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
if error_of_fitted_model < best_metric_value:
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
case "median_absolute_deviation":
error_of_fitted_model = RegressionMetrics.median_absolute_deviation(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
if error_of_fitted_model < best_metric_value:
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
case "coefficient_of_determination":
error_of_fitted_model = RegressionMetrics.coefficient_of_determination(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
if error_of_fitted_model > best_metric_value:
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
Expand Down Expand Up @@ -458,7 +460,8 @@ def _get_best_rnn_model(
expected_values = []
for i in range(size - (train_data.forecast_horizon + train_data.window_size)):
if train_data.continuous:
label = test_target[i + train_data.window_size: i + train_data.window_size + train_data.forecast_horizon]
label = test_target[
i + train_data.window_size: i + train_data.window_size + train_data.forecast_horizon]
else:
label = test_target[i + train_data.window_size + train_data.forecast_horizon]
expected_values.append(label)
Expand Down Expand Up @@ -518,8 +521,8 @@ def _get_best_rnn_model(
best_model._is_fitted = True
return best_model

def _data_split_image(self, train_data: ImageDataset) -> (ImageDataset, ImageDataset):
return train_data.split(0.75)
#def _data_split_image(self, train_data: ImageDataset) -> (ImageDataset, ImageDataset):
# return train_data.split(0.75)

def _get_models_for_all_choices(self) -> list[Self]:
all_possible_layer_combinations: list[list] = [[]]
Expand Down Expand Up @@ -798,7 +801,8 @@ def fit(
raise FittingWithChoiceError

if isinstance(train_data, TimeSeriesDataset) and train_data.continuous:
raise NotImplementedError("Continuous Predictions are currently not supported for Time Series Classification.")
raise NotImplementedError(
"Continuous Predictions are currently not supported for Time Series Classification.")

if not self._input_conversion._is_fit_data_valid(train_data):
raise FeatureDataMismatchError
Expand Down Expand Up @@ -895,7 +899,7 @@ def fit_by_exhaustive_search(
FittingWithoutChoiceError
When calling this method on a model without hyperparameter choices.
LearningError
If the training data contains invalid values or if the training failed. Currently raised, when calling this on RNNs or CNNs as well.
If the training data contains invalid values or if the training failed.
"""
_init_default_device()

Expand All @@ -904,8 +908,8 @@ def fit_by_exhaustive_search(

if isinstance(train_data, TimeSeriesDataset) and train_data.continuous:
raise NotImplementedError("Continuous Predictions are currently not supported for Time Series Classification.")
#if isinstance(train_data, ImageDataset):
# raise LearningError("CNN-Hyperparameter optimization is currently not supported.") # pragma: no cover
if isinstance(train_data, ImageDataset) and isinstance(self._input_conversion, InputConversionImageToImage):
raise NotImplementedError("Hyperparameter Optimization for Images as Output is currently not supported.")

_check_bounds("epoch_size", epoch_size, lower_bound=_ClosedBound(1))
_check_bounds("batch_size", batch_size, lower_bound=_ClosedBound(1))
Expand Down Expand Up @@ -933,15 +937,15 @@ def fit_by_exhaustive_search(
if isinstance(train_data, TabularDataset):
return self._get_best_fnn_model(list_of_fitted_models, test_set, optimization_metric, positive_class)
elif isinstance(train_data, TimeSeriesDataset):
return self._get_best_rnn_model(list_of_fitted_models, train_set, test_set, optimization_metric, positive_class)
return self._get_best_rnn_model(list_of_fitted_models, train_set, test_set, optimization_metric,
positive_class)
else:
if isinstance(self._input_conversion, InputConversionImageToColumn):
return self._get_best_cnn_model_column(list_of_fitted_models, train_set, optimization_metric, positive_class)
elif isinstance(self._input_conversion, InputConversionImageToTable):
return self._get_best_cnn_model_table(list_of_fitted_models, train_set, optimization_metric, positive_class)
else: # ImageToImage
raise NotImplementedError("Hyperparameter Optimization for Image to Image is currently not supported.")

return self._get_best_cnn_model_column(list_of_fitted_models, train_set, optimization_metric,
positive_class)
else: # ImageToTable
return self._get_best_cnn_model_table(list_of_fitted_models, train_set, optimization_metric,
positive_class)
def _data_split_table(self, data: TabularDataset) -> (TabularDataset, TabularDataset):
[train_split, test_split] = data.to_table().split_rows(0.75)
train_data = train_split.to_tabular_dataset(
Expand All @@ -952,7 +956,6 @@ def _data_split_table(self, data: TabularDataset) -> (TabularDataset, TabularDat
target_name=train_data.target.name,
extra_names=train_data.extras.column_names,
)
#target_col = train_data.target
return (train_data, test_data)

def _data_split_time_series(self, data: TimeSeriesDataset) -> (TimeSeriesDataset, Table):
Expand Down Expand Up @@ -985,25 +988,29 @@ def _get_best_fnn_model(
best_model = fitted_model
match optimization_metric:
case "accuracy":
best_metric_value = ClassificationMetrics.accuracy(predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
best_metric_value = ClassificationMetrics.accuracy(
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
case "precision":
best_metric_value = ClassificationMetrics.precision(predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
best_metric_value = ClassificationMetrics.precision(
predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
case "recall":
best_metric_value = ClassificationMetrics.recall(predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
case "f1_score":
best_metric_value = ClassificationMetrics.f1_score(predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
best_metric_value = ClassificationMetrics.f1_score(
predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
else:
match optimization_metric:
case "accuracy":
error_of_fitted_model = ClassificationMetrics.accuracy(
predicted=fitted_model.predict(test_features), expected=test_target) # type: ignore[arg-type]
predicted=fitted_model.predict(test_features),
expected=test_target) # type: ignore[arg-type]
if error_of_fitted_model > best_metric_value:
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
Expand All @@ -1015,9 +1022,10 @@ def _get_best_fnn_model(
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
case "recall":
error_of_fitted_model = ClassificationMetrics.recall(predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
error_of_fitted_model = ClassificationMetrics.recall(
predicted=fitted_model.predict(test_features),
expected=test_target,
positive_class=positive_class) # type: ignore[arg-type]
if error_of_fitted_model > best_metric_value:
best_model = fitted_model # pragma: no cover
best_metric_value = error_of_fitted_model # pragma: no cover
Expand Down

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