diff --git a/ANN_Training.py b/ANN_Training.py
index 158c6f52972f08e8cdacadea34fdbc8cb784db1f..27f86b6f3f96ca1921b6ed1df9b5b241b6d93d51 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -9,8 +9,9 @@ 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: Put plotting into separate function
+TODO: Put plotting into separate function -> Done
TODO: Reduce number of testing epochs to 50 -> Done
+TODO: Rename 'data_directory' to 'data_dir' -> Done
"""
import numpy as np
@@ -105,8 +106,8 @@ class ModelTrainer(object):
self._optimizer = getattr(torch.optim, optimizer)(
self._model.parameters(), **optimizer_config)
self._validation_loss = torch.zeros(self._num_epochs//10)
- print(type(self._model), type(self._loss_function),
- type(self._optimizer), type(self._validation_loss))
+ # print(type(self._model), type(self._loss_function),
+ # type(self._optimizer), type(self._validation_loss))
def epoch_training(self, dataset: torch.utils.data.dataset.TensorDataset,
num_epochs: int = None, verbose: bool = True) -> None:
@@ -126,7 +127,7 @@ class ModelTrainer(object):
Flag whether commentary in console is wanted. Default: False.
"""
- print(type(dataset))
+ # print(type(dataset))
tic = time.perf_counter()
if num_epochs is None:
num_epochs = self._num_epochs
@@ -278,10 +279,11 @@ def read_training_data(directory: str, normalized: bool = True) -> \
def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
- colors: dict = None,
compare_normalization: bool = False) -> None:
"""Evaluates the classification of a given set of models.
+ Evaluates the classification and saves the results in a json file.
+
Parameters
----------
models : dict
@@ -290,34 +292,22 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
Path to directory for saving resulting plots.
num_iterations : int, optional
Number of iterations for evaluation. Default: 100.
- colors : dict, optional
- Dictionary containing plotting colors. If None, set to default colors.
- Default: None.
compare_normalization : bool, optional
Flag whether both normalized and raw data should be evaluated.
Default: False.
"""
tic = time.perf_counter()
- if colors is None:
- colors = {'Accuracy': 'magenta', 'Precision_Smooth': 'red',
- 'Precision_Troubled': '#8B0000', 'Recall_Smooth': 'blue',
- 'Recall_Troubled': '#00008B', 'F-Score_Smooth': 'green',
- 'F-Score_Troubled': '#006400', 'AUROC': 'yellow'}
-
print('Read normalized training data.')
datasets = {'normalized': read_training_data(directory)}
if compare_normalization:
print('Read raw, non-normalized training data.')
datasets['raw'] = read_training_data(directory, False)
- classification_stats = {measure: {model + ' (' + dataset + ')': []
- for model in models
- for dataset in datasets}
- for measure in colors}
print('\nTraining models with 5-fold cross validation...')
print('Number of iterations:', num_iterations)
tic_train = time.perf_counter()
+ classification_stats = {}
for iteration in range(num_iterations):
for train_index, test_index in KFold(
n_splits=5, shuffle=True).split(datasets['normalized']):
@@ -327,7 +317,12 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
for model in models:
result = models[model].test_model(training_set, test_set)
- for measure in colors:
+ for measure in result.keys():
+ if measure not in classification_stats.keys():
+ classification_stats[measure] = \
+ {model + ' (' + dataset + ')': []
+ for model in models
+ for dataset in datasets}
classification_stats[measure][model + ' (' + dataset +
')'].append(
result[measure])
@@ -337,21 +332,55 @@ 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')
+ print('Saving evaluation results in json format.')
with open(directory + '/' + '_'.join(models.keys()) + '.json', 'w')\
as json_file:
json_file.write(json.dumps(classification_stats))
- with open(directory + '/' + '_'.join(models.keys()) + '.json')\
- as json_file:
+ toc = time.perf_counter()
+ print(f'Total runtime: {toc - tic:0.4f}s')
+
+
+def plot_evaluation_results(evaluation_file: str, directory: str,
+ colors: dict = None) -> None:
+ """Plots given evaluation results of model classifications.
+
+ Plots evaluation results for all measures for which a color is given. If
+ colors is set to None, all measures are plotted with a default color
+ scheme.
+
+ Parameters
+ ----------
+ evaluation_file: str
+ Path to file containing evaluation results.
+ directory : str
+ Path to directory for saving resulting plots.
+ colors : dict, optional
+ Dictionary containing plotting colors. If None, set to default colors.
+ Default: None.
+
+ """
+ tic = time.perf_counter()
+ if colors is None:
+ colors = {'Accuracy': 'magenta', 'Precision_Smooth': 'red',
+ 'Precision_Troubled': '#8B0000', 'Recall_Smooth': 'blue',
+ 'Recall_Troubled': '#00008B', 'F-Score_Smooth': 'green',
+ 'F-Score_Troubled': '#006400', 'AUROC': 'yellow'}
+
+ print('Reading evaluation results.')
+ with open(evaluation_file) as json_file:
classification_stats = json.load(json_file)
- print('Plotting evaluation of trained models.')
+ print('\nPlotting evaluation of trained models...')
+ print('Plotting data in boxplot.')
+ models = classification_stats[list(colors.keys())[0]].keys()
plot_boxplot(classification_stats, colors)
- classification_stats = {measure: {model + ' (' + dataset + ')': np.array(
- classification_stats[measure][model + ' (' + dataset + ')']).mean()
- for model in models
- for dataset in datasets}
+ 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)
+ print('Finished plotting evaluation of trained models!\n')
# Set paths for plot files if not existing already
plot_dir = directory + '/model evaluation'
@@ -360,13 +389,13 @@ def evaluate_models(models: dict, directory: str, num_iterations: int = 100,
# Save plots
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 + '/' +
- '_'.join(models.keys()) + '.pdf')
+ plt.savefig(plot_dir + '/' + identifier + '/' + file_name + '.pdf')
toc = time.perf_counter()
print(f'Total runtime: {toc - tic:0.4f}s')
diff --git a/workflows/ANN_training.smk b/workflows/ANN_training.smk
index 23004ae9945e3b989f3245fcf3e4321190b2cb29..66de3521b5276f94bb58cf87a527eeefab1c18c7 100644
--- a/workflows/ANN_training.smk
+++ b/workflows/ANN_training.smk
@@ -17,17 +17,39 @@ rule all:
+ '_'.join(MODELS.keys()) + '.pdf'
default_target: True
+rule plot_test_results:
+ input:
+ json_file=DIR+'/'+ '_'.join(MODELS.keys()) + '.json'
+ output:
+ DIR+'/model evaluation/classification_accuracy/'
+ + '_'.join(MODELS.keys())+'.pdf'
+ params:
+ colors = config['classification_colors']
+ log:
+ DIR+'/log/plot_test_results.log'
+ run:
+ models = {}
+ with open(str(log), 'w') as logfile:
+ sys.stdout = logfile
+ sys.stderr = logfile
+ for model in MODELS:
+ trainer= ANN_Training.ModelTrainer(
+ {'model_name': model, 'dir': DIR, 'model_dir': DIR,
+ **MODELS[model]})
+ models[model] = trainer
+ plot_evaluation_results(evaluation_file=input.json_file,
+ directory=DIR, colors=params.colors)
+
+
rule test_model:
input:
DIR+'/input_data.npy',
DIR+'/normalized_input_data.npy',
DIR+'/output_data.npy'
output:
- DIR+'/model evaluation/classification_accuracy/'
- + '_'.join(MODELS.keys()) + '.pdf'
+ DIR+'/'+'_'.join(MODELS.keys())+'.json'
params:
num_iterations = config['num_iterations'],
- colors = config['classification_colors'],
compare_normalization = config['compare_normalization']
log:
DIR+'/log/test_model.log'
@@ -42,7 +64,7 @@ rule test_model:
**MODELS[model]})
models[model] = trainer
evaluate_models(models=models, directory=DIR,
- num_iterations=params.num_iterations, colors=params.colors,
+ num_iterations=params.num_iterations,
compare_normalization=params.compare_normalization)
rule train_model: