diff --git a/ANN_Data_Generator.py b/ANN_Data_Generator.py
index 76bd497c8ee6add879b6db6be933947401c686e2..f56e026d965a9444408a2425925f3376696a9dc4 100644
--- a/ANN_Data_Generator.py
+++ b/ANN_Data_Generator.py
@@ -142,8 +142,8 @@ class TrainingDataGenerator(object):
# Create normalized input data
norm_input_matrix = self._normalize_data(input_matrix)
- return {'input': input_matrix, 'output': output_matrix,
- 'normalized_input': norm_input_matrix}
+ return {'input_data.raw': input_matrix, 'output_data': output_matrix,
+ 'input_data.normalized': norm_input_matrix}
def _generate_cell_data(self, num_samples, initial_conditions, is_smooth):
"""Generates random training input and output.
@@ -280,5 +280,5 @@ class TrainingDataGenerator(object):
"""Saves data."""
print('Saving training data.')
for key in data.keys():
- name = self._data_dir + '/' + key + '_data.npy'
+ name = self._data_dir + '/' + key + '.npy'
np.save(name, data[key])
diff --git a/ANN_Training.py b/ANN_Training.py
index 27f86b6f3f96ca1921b6ed1df9b5b241b6d93d51..f937c444c8dc030ec413ab127a62a907bd5c8ed3 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -7,8 +7,10 @@ Docstring-Style: D200, D400
TODO: Add README for ANN training
TODO: Fix random seed
-TODO: Write-protect all data and models
-TODO: Put legend outside plot (bbox_to_anchor)
+TODO: Improve file structure and naming (e.g. use '.' instead of '__') -> Done
+TODO: Write-protect all data and models -> Done
+TODO: Put legend outside plot (bbox_to_anchor) -> Done
+TODO: Improve legend layout -> Done
TODO: Put plotting into separate function -> Done
TODO: Reduce number of testing epochs to 50 -> Done
TODO: Rename 'data_directory' to 'data_dir' -> Done
@@ -27,7 +29,7 @@ from sklearn.metrics import accuracy_score, precision_recall_fscore_support, \
roc_auc_score
import ANN_Model
-from Plotting import plot_classification_accuracy, plot_boxplot
+from Plotting import plot_classification_barplot, plot_classification_boxplot
matplotlib.use('Agg')
@@ -247,10 +249,10 @@ class ModelTrainer(object):
os.makedirs(model_dir)
# Save model and loss
- torch.save(self._model.state_dict(), model_dir + '/model__' +
- model_name + '.pt')
- torch.save(self._validation_loss, model_dir + '/loss__' +
- model_name + '.pt')
+ torch.save(self._model.state_dict(), model_dir + '/' +
+ model_name + '.model.pt')
+ torch.save(self._validation_loss, model_dir + '/' +
+ model_name + '.loss.pt')
def read_training_data(directory: str, normalized: bool = True) -> \
@@ -271,8 +273,8 @@ def read_training_data(directory: str, normalized: bool = True) -> \
"""
# Get training dataset from saved file and map to Torch tensor and dataset
- input_file = directory + ('/normalized_input_data.npy'
- if normalized else '/input_data.npy')
+ input_file = directory + ('/input_data.normalized.npy'
+ if normalized else '/input_data.raw.npy')
output_file = directory + '/output_data.npy'
return TensorDataset(*map(torch.tensor, (np.load(input_file),
np.load(output_file))))
@@ -332,8 +334,13 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
print('Finished training models with 5-fold cross validation!')
print(f'Training time: {toc_train - tic_train:0.4f}s\n')
+ # Set paths for plot files if not existing already
+ plot_dir = directory + '/model evaluation'
+ if not os.path.exists(plot_dir):
+ os.makedirs(plot_dir)
+
print('Saving evaluation results in json format.')
- with open(directory + '/' + '_'.join(models.keys()) + '.json', 'w')\
+ with open(plot_dir + '/' + '_'.join(models.keys()) + '.json', 'w')\
as json_file:
json_file.write(json.dumps(classification_stats))
toc = time.perf_counter()
@@ -341,7 +348,7 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
def plot_evaluation_results(evaluation_file: str, directory: str,
- colors: dict = None) -> None:
+ colors: dict = None) -> None:
"""Plots given evaluation results of model classifications.
Plots evaluation results for all measures for which a color is given. If
@@ -373,13 +380,13 @@ def plot_evaluation_results(evaluation_file: str, directory: str,
print('\nPlotting evaluation of trained models...')
print('Plotting data in boxplot.')
models = classification_stats[list(colors.keys())[0]].keys()
- plot_boxplot(classification_stats, colors)
+ plot_classification_boxplot(classification_stats, colors)
print('Plotting averaged data in barplot.')
classification_stats = {measure: {model: np.array(
classification_stats[measure][model]).mean()
for model in models}
for measure in colors}
- plot_classification_accuracy(classification_stats, colors)
+ plot_classification_barplot(classification_stats, colors)
print('Finished plotting evaluation of trained models!\n')
# Set paths for plot files if not existing already
@@ -391,11 +398,7 @@ def plot_evaluation_results(evaluation_file: str, directory: str,
print('Saving plots.')
file_name = evaluation_file.split('/')[-1].rstrip('.json')
for identifier in plt.get_figlabels():
- # Set path for figure directory if not existing already
- if not os.path.exists(plot_dir + '/' + identifier):
- os.makedirs(plot_dir + '/' + identifier)
-
plt.figure(identifier)
- plt.savefig(plot_dir + '/' + identifier + '/' + file_name + '.pdf')
+ plt.savefig(plot_dir + '/' + file_name + '.' + identifier + '.pdf')
toc = time.perf_counter()
print(f'Total runtime: {toc - tic:0.4f}s')
diff --git a/Plotting.py b/Plotting.py
index 7afc200d5fb9fd507fc53550d3693dd21a30a161..0db5713422ac89ee249757f7e0b96186ed61d1f3 100644
--- a/Plotting.py
+++ b/Plotting.py
@@ -261,7 +261,7 @@ def calculate_exact_solution(
return grid, exact
-def plot_classification_accuracy(evaluation_dict: dict, colors: dict) -> None:
+def plot_classification_barplot(evaluation_dict: dict, colors: dict) -> None:
"""Plots classification accuracy.
Plots given evaluation measures in a bar plot for each model.
@@ -278,7 +278,7 @@ def plot_classification_accuracy(evaluation_dict: dict, colors: dict) -> None:
font_size = 16 - (len(max(model_names, key=len))//3)
pos = np.arange(len(model_names))
width = 1/(3*len(model_names))
- fig = plt.figure('classification_accuracy')
+ fig = plt.figure('barplot')
ax = fig.add_axes([0.15, 0.3, 0.6, 0.6])
step_len = 1
adjustment = -(len(model_names)//2)*step_len
@@ -299,7 +299,7 @@ def plot_classification_accuracy(evaluation_dict: dict, colors: dict) -> None:
ncol=1, fancybox=True, fontsize=8)
-def plot_boxplot(evaluation_dict: dict, colors: dict) -> None:
+def plot_classification_boxplot(evaluation_dict: dict, colors: dict) -> None:
"""Plots classification accuracy.
Plots given evaluation measures in a boxplot for each model.
@@ -314,7 +314,7 @@ def plot_boxplot(evaluation_dict: dict, colors: dict) -> None:
"""
model_names = evaluation_dict[list(colors.keys())[0]].keys()
font_size = 16 - (len(max(model_names, key=len))//3)
- fig = plt.figure('boxplot_accuracy')
+ fig = plt.figure('boxplot')
ax = fig.add_axes([0.15, 0.3, 0.6, 0.6])
step_len = 1.5
boxplots = []
diff --git a/config.yaml b/config.yaml
index 979aca1d07583d62e80fbf68abaea61f131135e7..edeea1a3771f534960da20ebdf951fdd67048318 100644
--- a/config.yaml
+++ b/config.yaml
@@ -21,7 +21,7 @@ Approximation:
detector_config:
fold_len: 16
whisker_len: 3
- model_state: 'model__Adam.pt'
+ model_state: 'Adam.model.pt'
init_cond: 'Sine'
init_config:
diff --git a/workflows/ANN_data.smk b/workflows/ANN_data.smk
index ab8890f8639d0da20dbe26b212636c24e9b6dac7..ef0bc3be22b14a50ee998e0c29b2bb4f317c3691 100644
--- a/workflows/ANN_data.smk
+++ b/workflows/ANN_data.smk
@@ -11,8 +11,8 @@ DIR = config['data_dir']
rule generate_data:
output:
- protected(DIR+'/input_data.npy'),
- protected(DIR+'/normalized_input_data.npy'),
+ protected(DIR+'/input_data.raw.npy'),
+ protected(DIR+'/input_data.normalized.npy'),
protected(DIR+'/output_data.npy')
default_target: True
params:
diff --git a/workflows/ANN_training.smk b/workflows/ANN_training.smk
index a80af1f206e98d86996bdb773c9d6e98e9a8c88e..5842c6afbbb8c0a00e049fddd4b2752d1d7b490f 100644
--- a/workflows/ANN_training.smk
+++ b/workflows/ANN_training.smk
@@ -12,17 +12,17 @@ MODELS = config['models']
rule all:
input:
- expand(DIR+'/trained models/model__{model}.pt', model=MODELS),
- DIR+'/model evaluation/classification_accuracy/'
- + '_'.join(MODELS.keys()) + '.pdf'
+ expand(DIR+'/trained models/{model}.model.pt', model=MODELS),
+ DIR+'/model evaluation/'+'_'.join(MODELS.keys())
+ +'.barplot.pdf'
default_target: True
rule plot_test_results:
input:
- json_file=DIR+'/'+ '_'.join(MODELS.keys()) + '.json'
+ json_file=DIR+'/model evaluation/'+ '_'.join(MODELS.keys()) + '.json'
output:
- DIR+'/model evaluation/classification_accuracy/'
- + '_'.join(MODELS.keys())+'.pdf'
+ DIR+'/model evaluation/'+'_'.join(MODELS.keys())
+ +'.barplot.pdf'
params:
colors = config['classification_colors']
log:
@@ -43,11 +43,11 @@ rule plot_test_results:
rule test_model:
input:
- DIR+'/input_data.npy',
- DIR+'/normalized_input_data.npy',
+ DIR+'/input_data.raw.npy',
+ DIR+'/input_data.normalized.npy',
DIR+'/output_data.npy'
output:
- protected(DIR+'/'+'_'.join(MODELS.keys())+'.json')
+ protected(DIR+'/model evaluation/'+'_'.join(MODELS.keys())+'.json')
params:
num_iterations = config['num_iterations'],
compare_normalization = config['compare_normalization']
@@ -69,12 +69,12 @@ rule test_model:
rule train_model:
input:
- DIR+'/input_data.npy',
- DIR+'/normalized_input_data.npy',
+ DIR+'/input_data.raw.npy',
+ DIR+'/input_data.normalized.npy',
DIR+'/output_data.npy'
output:
- protected(DIR+'/trained models/model__{model}.pt'),
- protected(DIR+'/trained models/loss__{model}.pt')
+ protected(DIR+'/trained models/{model}.model.pt'),
+ protected(DIR+'/trained models/{model}.loss.pt')
log:
DIR+'/log/train_model_{model}.log'
run: