diff --git a/.gitignore b/.gitignore index e0ae686..787010c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,176 +1,17 @@ -__pycache__/ -database/ -models/*.model -labels_and_data/ -IPqM-Fall.zip +/__pycache__ +/.venv +/venv -output/MLP/chest/*.png -output/MLP/right/*.png -output/MLP/left/*.png +/database +/labels_and_data -output/CNN1D/chest/*.png -output/CNN1D/right/*.png -output/CNN1D/left/*.png +builders/__pycache__ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class +/output_old/ +/analise_global_old/ -# C extensions -*.so +Miniconda3-latest-Linux-x86_64.sh +output/**/trial_*/* -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST +*.npz -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control -.pdm.toml -.pdm-python -.pdm-build/ - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ \ No newline at end of file diff --git a/NeuralArchitectures.py b/NeuralArchitectures.py deleted file mode 100644 index 15843b2..0000000 --- a/NeuralArchitectures.py +++ /dev/null @@ -1,151 +0,0 @@ -import torch -import torch.nn as nn - - -class CNN1D(nn.Module): - def __init__(self, input_shape, n_conv_layers, first_conv_layer_size, num_dense_layers, first_dense_layer_size, num_labels=2): - super(CNN1D, self).__init__() - - # filter_size = 50 - # kernel_size = 5 - # num_layers = 3 - # num_dense_layers = 2 - # dense_neurons = 100 - - # learning_rate = 0.0001 - # decision_threshold = 0.5 - - self.conv_layer = nn.ModuleList() - - # Hiperparametros definidos de forma fixa - self.kernel_size = 3 - self.dropout_rate = 0.3 - - self.max_pool = 2 - last_layer_channels = 0 - dense_neurons = first_dense_layer_size - dense_layer_droprate = 4 - - # Para cada seq de (Conv1d + ReLU + MaxPool1d + Dropout) - for i in range(n_conv_layers): - - # PARA CONV1D: Se pading = 0 e stride = 1 |-> [batch, j, k] -> [batch, j*2, k - kernel + 1] - if i == 0: - self.conv_layer.append( - nn.Conv1d(input_shape[1], first_conv_layer_size, self.kernel_size)) - last_layer_channels = first_conv_layer_size - else: - # past_layer_out = self.get_feature_size(i-1, n_channels_init) - self.conv_layer.append( - nn.Conv1d(last_layer_channels, last_layer_channels*2, self.kernel_size)) - last_layer_channels *= 2 - # Relu não altera as dimensoes do tensor - Função de Ativação - self.conv_layer.append(nn.ReLU()) - - # PARA MAXPOOL: Divide a metade |-> [batch, j, k] -> [batch, j, k/2] - self.conv_layer.append(nn.MaxPool1d(self.max_pool)) - # Dropout não altera as dimensoes do tensor - self.conv_layer.append(nn.Dropout(self.dropout_rate)) - - # Camada Flatten - self.flatten = nn.Flatten() - - # Simula n sequencias de (Conv1d(kenrnel_size) + MaxPool1D(max_pool)), baseado num numero inicial de passos e retorna o numero de features após essas operações - last_layer_features = self.get_feature_size( - n_conv_layers, input_shape[0]) - - # Calcular com quantos neuronios a 1ª camada densa deve ter -> nº de canais * nº de features da última camada - self.first_dense_input = last_layer_channels * last_layer_features - - self.fc_layers = nn.ModuleList() - for i in range(num_dense_layers): - if i == 0: - self.fc_layers.append( - nn.Linear(self.first_dense_input, dense_neurons)) - else: - self.fc_layers.append( - nn.Linear(dense_neurons, dense_neurons//dense_layer_droprate)) - dense_neurons //= dense_layer_droprate - self.fc_layers.append(nn.ReLU()) - - # Output Layer - self.output_layer = nn.Linear(dense_neurons, 1) - - def get_feature_size(self, k, init_val): - def feature_sequence(i, a0): - if i == 0: - return a0 - else: - return (feature_sequence(i-1, a0) - self.kernel_size + 1) // self.max_pool - return feature_sequence(k, init_val) - - def forward(self, x): - # print("Input:", x.shape) - # print() - for layer in self.conv_layer: - x = layer(x) - # if layer._get_name() in ("Conv1d", "MaxPool1d"): - # print(layer._get_name(), x.shape) - # if layer._get_name() in ("MaxPool1d"): print() - - x = self.flatten(x) # x = x.view(x.size(0), -1) - # print("Flatten:", x.shape) - # print() - - for fc_layer in self.fc_layers: - x = fc_layer(x) - # if fc_layer._get_name() in ("Linear"): - # print(fc_layer._get_name(), x.shape) - - # print() - x = self.output_layer(x) - # print("Output:", x.shape) - # x = torch.softmax(x, dim=1) - # x = torch.argmax(x, dim=1) - # print("Argmax:", x.shape) - return x - - -class CustomMLP(nn.Module): - def __init__(self, input_shape, num_asc_layers=1, num_desc_layers=3, num_labels=1): - super(CustomMLP, self).__init__() - - if num_labels > 1: - raise NotImplemented("Essa feature ainda não foi desenvolvida de forma estável") - - # Taxa de crescimento entre camadas - self.asc_factor = 2 - # Taxa de decrescimento entre camadas - self.desc_factor = 3 - - self.layers = nn.ModuleList() - self.dropout_factor = 0.6 - # Recebe como entrada input_shape tuple(N_observ, N_vari) - # Caso Multivariado (N_vari > 1) - Add um flatten - if input_shape[1] > 1: self.layers.append(nn.Flatten()) - - input_shape = input_shape[0] * input_shape[1] - - for i in range(num_asc_layers): - self.layers.append(nn.Linear(in_features=input_shape, out_features=input_shape * self.asc_factor)) - input_shape *= self.asc_factor - self.layers.append(nn.ReLU()) - self.layers.append(nn.Dropout(self.dropout_factor)) - - for i in range(num_desc_layers): - self.layers.append(nn.Linear(in_features=input_shape, out_features=input_shape // self.desc_factor)) - input_shape //= self.desc_factor - self.layers.append(nn.ReLU()) - self.layers.append(nn.Dropout(self.dropout_factor)) - - self.layers.pop(-1) - # Classificação binária -> Saida em um único neurônio - self.output_layer = nn.Linear(in_features=input_shape, out_features=num_labels) - - def forward(self, x): - - for i, layer in enumerate(self.layers): - x = layer(x) - x = self.output_layer(x) - - return x diff --git a/README.md b/README.md index 2b99aab..22abbbe 100644 --- a/README.md +++ b/README.md @@ -1,83 +1,219 @@ -# Fall-Detect +# Fall Detect PyTorch -### Antes de tudo -Com o objetivo de isolar o projeto, é recomendado a criação de um ambiente virtual do python: +Adaptação para o PyTorch do trabalho original : https://AILAB-CEFET-RJ/falldetection -``` -python3 -m venv nome-do-enviroment -``` +Baseado no artigo (não publicado) - A Machine Learning Approach to Automatic Fall Detection of Soldiers: https://arxiv.org/abs/2501.15655v2 -## Importação dos dados +Além da adaptação, K-Fold Cross Validation e o modelo LSTM foi implementado e testado junto aos demais. -Para execução desse repositório, será necessário utilizar os requisitos descritos no arquivo requirements.txt. É possivel instalar todos os pacotes necessários com o unico comando abaixo: +Os sensores no trabalho original não são combinados (left, chest, right), por motivos de "escolher o melhor". -``` -cd fall-detect/ +Suporta 3 arquiteturas de redes neurais, **CNN1D**, **MLP** e **LSTM**, com **otimização bayesiana de hiperparâmetros via Optuna**. + +Também foi implementado o Early Stopping e o Median Pruning do Optuna. + +## Funcionalidades + +- **3 Arquiteturas de Redes Neurais**: CNN1D, MLP, LSTM +- **Otimização Bayesiana**: Via Optuna com Early Stopping +- **Análise de Importância**: Permutation Feature Importance +- **Explicabilidade**: Análise SHAP para interpretabilidade +- **Curvas de Aprendizado**: Análise de performance vs dados +- **Validação Cruzada**: K-Fold Cross Validation +- **Pipeline Automatizado**: Script `all.sh` para execução completa +- **Análise Global**: Comparações entre modelos e cenários + +## Instalação e Configuração + +### 1. Instale as dependências + +```bash pip install -r requirements.txt ``` -Após instalação dos pacotes necessários, importaremos a base de dados a ser utilizada. Essa a base de dados encontra-se diposnível publicamente [aqui](https://zenodo.org/records/12760391). +### 2. Baixe e Descompacte os Dados Originais +Disponível em: https://zenodo.org/records/12760391 -## Geração dos datasets +### 3. Gere os Datasets e Labels +```bash +python generate_datasets.py chest +python generate_datasets.py left +python generate_datasets.py right +``` -Uma vez com os pacotes necessários instalados e a base de dados baixada e **descompactada**. Será realizado a criação do dataset através dos comandos: +### 4. Valide os Datasets +```bash +python validate_datasets.py ``` -python3 training_data_generator.py chest -python3 training_data_generator.py right -python3 training_data_generator.py left + +## Configurações + +O arquivo `config.py` centraliza todas as configurações do projeto: + +- **Dispositivo**: Configuração automática de GPU/CPU +- **Seeds**: Reprodutibilidade dos experimentos +- **Diretórios**: Caminhos para dados e saídas +- **Cenários**: Configurações dos diferentes cenários de dados +- **Hiperparâmetros**: Ranges para otimização +- **Treinamento**: Configurações de treinamento + +## Modos de Execução + +### Pipeline Automatizado Completo + +```bash +# Executar pipeline completo para múltiplas configurações +bash all.sh ``` -Uma vez com o dataset de cada modalidade (chest, right e left) criado é possivel seguir para a etapa de treinamento da Rede Neural. +O script `all.sh` executa automaticamente: +1. Validação dos datasets +2. Busca de hiperparâmetros (se necessário) +3. Treinamento final (se necessário) +4. Permutation Feature Importance +5. Curvas de aprendizado +6. Análise SHAP +7. Análise global + +## Cenários Disponíveis -## Treinamento e Plotagem -Para o treinamento, execute o script `training.py` com os parâmetros que deseja como **cenários**, **sensor**, **tipo de classificação**, etc. Em caso de dúvidas, verifique seção de **--help** do script. +- **Sc1_acc_T**: Acelerômetro magnitude, domínio temporal +- **Sc1_gyr_T**: Giroscópio magnitude, domínio temporal +- **Sc1_acc_F**: Acelerômetro magnitude, domínio frequência +- **Sc1_gyr_F**: Giroscópio magnitude, domínio frequência +- **Sc_2_acc_T**: Acelerômetro 3 eixos, domínio temporal +- **Sc_2_gyr_T**: Giroscópio 3 eixos, domínio temporal +- **Sc_2_acc_F**: Acelerômetro 3 eixos, domínio frequência +- **Sc_2_gyr_F**: Giroscópio 3 eixos, domínio frequência +- **Sc_3_T**: Acelerômetro + Giroscópio magnitude, domínio temporal +- **Sc_3_F**: Acelerômetro + Giroscópio magnitude, domínio frequência +- **Sc_4_T**: Acelerômetro + Giroscópio 3 eixos, domínio temporal (Recomendado) +- **Sc_4_F**: Acelerômetro + Giroscópio 3 eixos, domínio frequência +> **Nota**: Sc_4_T é o cenário mais informativo e recomendado para análise. + +O mais indicado seria um cenário 5, com: +- **Sc_5_T**: Acelerômetro + Giroscópio 3 eixos, + Magnitudes, domínio temporal + +## Posições + +- **chest**: Dados do peito (1020 samples) (Recomendado) +- **left**: Dados do lado esquerdo (450 samples) +- **right**: Dados do lado direito (450 samples) + +## Tipos de Labels + +- **binary_one**: Classificação binária (2 classes) +- **binary_two**: Classificação binária alternativa (2 classes) (Recomendado) + +## Modelos de Rede Neural + +- **CNN1D**: Rede neural convolucional 1D +- **MLP**: Multi-layer perceptron +- **LSTM**: Long short-term memory + +## Estrutura do Projeto + +``` +fall-detect/ +├── config.py # Configurações centralizadas +├── hyperparameter_search.py # Script para busca de hiperparâmetros +├── post_trials.py # Processamento pós-trials do Optuna +├── final_training.py # Script para treinamento final + análise +├── utils.py # Funções utilitárias organizadas +├── neural_networks.py # Arquiteturas das redes neurais +├── requirements.txt # Dependências +├── all.sh # Pipeline automatizado completo +├── analysis.py # Análise global dos resultados +├── learning_curve.py # Geração de curva de aprendizado +├── permutation_importance.py # Permutation Feature Importance +├── shap_importance.py # Análise SHAP para explicabilidade +├── validate_datasets.py # Validação dos datasets gerados +├── generate_datasets.py # Geração de datasets +├── builders/ # Builders para dados +├── labels_and_data/ # Dados e labels +├── database/ # Base de dados +├── analise_global/ # Resultados agregados e gráficos +└── README.md # Este arquivo ``` ------------------------------------------------------------------------------------------- -Exemplos: - -python3 training.py -s Sc1_acc_T -p chest -l binary_one -nn CNN1D -c 2 -d 3 -python3 training.py -s Sc_2_acc_F -p chest -nn CNN1D --n_conv 2 --n_dense 3 --epochs 10 ------------------------------------------------------------------------------------------- - - -> python3 training.py -s Sc_2_gyr_T -p chest -nn MLP --epochs 50 - ------------------------------------------------------------------------------------------- -Datasets | Labels ------------------------------------------------------------------------------------------- -Treinamento: torch.Size([3622, 3, 1020]) | torch.Size([3622]) | 3182(87%)-440(12%) -Validação: torch.Size([967, 3, 1020]) | torch.Size([967]) | 849(87%)-118(12%) -Teste: torch.Size([1449, 3, 1020]) | torch.Size([1449]) | 1273(87%)-176(12%) ------------------------------------------------------------------------------------------- -Arquitetura da Rede Neural: -... -... -... ------------------------------------------------------------------------------------------- -[ 1/50] train_loss: 1.01968 valid_loss: 0.44025 -[ 2/50] train_loss: 0.45207 valid_loss: 0.42807 -[ 3/50] train_loss: 0.44221 valid_loss: 0.42522 -... -[49/50] train_loss: 0.42600 valid_loss: 0.42295 -[50/50] train_loss: 0.43025 valid_loss: 0.42292 -Gráfico de Perda gerado com sucesso. (Verifique o diretório ...) -... - -Relatório de classificação no dataset de treino: - precision recall f1-score support - - 0 0.81 0.91 0.86 176 - 1 0.99 0.97 0.98 1273 - - accuracy 0.96 1449 - macro avg 0.90 0.94 0.92 1449 -weighted avg 0.97 0.96 0.96 1449 +## Saídas Geradas + +### Busca de Hiperparâmetros +- `best_hyperparameters.json`: Melhores hiperparâmetros encontrados +- `test_data.npz`: Dados de treino/validação e teste salvos +- `optuna_trials.csv`: Resultados de todos os trials +- `param_importance.png`/`.html`: Importância dos hiperparâmetros +- Diretórios `trial_X/`: Resultados de cada trial do Optuna + +### Treinamento Final + Análise +- Diretórios `model_X/`: Resultados de cada modelo treinado + - `model_X.pt`: Modelo salvo + - `metrics_model_X.csv`/`.txt`: Métricas do modelo + - `loss_curve_model_X.png`: Curva de loss + - `confusion_matrix_model_X.png`: Matriz de confusão + - `roc_curve_model_X.png`: Curva ROC + - `classification_report_model_X.txt`: Relatório de classificação +- **Análise automática:** + - `all_metrics.csv`: Métricas de todos os modelos + - `summary_metrics.csv`: Estatísticas resumidas + - `metrics_boxplot.png`: Boxplot das métricas + - `best_models/`: Diretório com cópias dos melhores modelos + +### Permutation Importance +- `permutation_importance.csv`: Importância das features via permutação +- `permutation_importance.png`: Gráfico de importância das features + +### Curva de Aprendizado +- `learning_curve.csv`: Dados da curva de aprendizado +- `learning_curve_metrics.csv`: Métricas por fração de dados +- `learning_curve.png`: Gráfico da curva de aprendizado + +### Análise SHAP +- `shap_values_*.npy`: Valores SHAP salvos +- `shap_importance_*.csv`: Importância das features via SHAP +- `shap_importance_*.png`: Gráficos de importância SHAP +- `shap_importance_class*_*.csv/png`: Análise por classe + +### Análise Global (`analise_global/`) +- **Boxplots**: Comparações entre modelos e métricas +- **Curvas ROC**: Comparações de performance +- **Matrizes de Confusão**: Agregadas por modelo +- **Curvas de Aprendizado**: Comparações de learning curves +- **Importância de Features**: Permutation e SHAP +- **Análise Optuna**: Convergência e importância de parâmetros +- **Relatórios de Classificação**: Métricas detalhadas + +Aqui, focamos em gerar o melhor modelo, são dispositivos diferentes com frequências diferentes, além de que precisariamos das 3 entradas para funcionar, então foi usado para análise o dataset do peito. + +## Exemplos de Uso + +### Execução Rápida (Recomendado) +```bash +# Pipeline completo para o cenário mais informativo +bash all.sh ``` -Após o treinamento será gerado um grafico, no diretório indicado, para análise do desempenho da rede neural ao longo do treinamento e será aplicado o `classification_report` com o dataset de teste. ---- +### Execução Personalizada +```bash +# 1. Validar datasets +python validate_datasets.py + +# 2. Buscar hiperparâmetros para LSTM +python hyperparameter_search.py -scenario Sc_4_T -position chest -label_type binary_two --nn LSTM + +# 3. Treinar modelos finais +python final_training.py -scenario Sc_4_T -position chest -label_type binary_two --nn LSTM --num_models 20 -#### Observações -Alguns arquivos presentes no repositório servem apenas como comparação com o projeto original (`run_of_the_neural_network_model.py` ou `model_builders/`) ou auxilio (`commands.txt`). \ No newline at end of file +# 4. Análise de importância +python permutation_importance.py -scenario Sc_4_T -position chest -label_type binary_two --nn LSTM + +# 5. Curva de aprendizado +python learning_curve.py -scenario Sc_4_T -position chest -label_type binary_two --nn LSTM + +# 6. Análise SHAP +python shap_importance.py -scenario Sc_4_T -position chest -label_type binary_two --nn LSTM + +# 7. Análise global +python analysis.py +``` diff --git a/agg_results.json b/agg_results.json deleted file mode 100644 index ddd32bf..0000000 --- a/agg_results.json +++ /dev/null @@ -1,2018 +0,0 @@ -{ - "Sc1_acc_F_chest": { - "CNN1D": { - "0": { - "precision": 0.8913043478260869, - "recall": 0.6988636363636364, - "f1-score": 0.7834394904458599, - "support": 176.0 - }, - "1": { - "precision": 0.9595728451563692, - "recall": 0.9882168106834249, - "f1-score": 0.9736842105263158, - "support": 1273.0 - }, - "accuracy": 0.9530710835058661, - "macro avg": { - "precision": 0.9254385964912281, - "recall": 0.8435402235235306, - "f1-score": 0.8785618504860879, - "support": 1449.0 - }, - "weighted avg": { - "precision": 0.9512807433412348, - "recall": 0.9530710835058661, - "f1-score": 0.9505765012549837, - "support": 1449.0 - } - }, - "MLP": { - "0": { - "precision": 0.0, - "recall": 0.0, - "f1-score": 0.0, - "support": 176.0 - }, - "1": { - "precision": 0.8785369220151829, - "recall": 1.0, - 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define_key_subkey(filename): - key = "" - subkey = "" - - aux = filename.split("_") - - if filename.startswith("Sc1") or filename.startswith("Sc_3") or filename.startswith("Sc_4"): - def define_key(x): return "_".join(x[:3] + x[4:]) - - elif filename.startswith("Sc_2"): - def define_key(x): return "_".join(x[:4] + x[5:]) - - key = define_key(aux) - subkey = "CNN1D" if filename.find( - "CNN1D") > -1 else "MLP" if filename.find("MLP") > -1 else "" - - return key, subkey - -# Para cada resultado '.json' registrado no diretório results, carregue o arquivo e agrupe-o em um novo json file -for i, filename in enumerate(sorted(os.listdir("results"))): - if filename.endswith(".json"): - with open(f"results/{filename}", "r") as file: - - key, subkey = define_key_subkey(filename[:-5]) - # Temos que aplicar essa lógica para não sobrescrever agg_results[key][subkey] - if agg_results.get(key) is None: - agg_results[key] = {} - - agg_results[key].update({subkey: json.load(file)}) - -with open(agg_filename, "w") as f: - json.dump(agg_results, f, indent=4) - -print(f"{i+1} elementos agrupados em {agg_filename}") diff --git a/analise_global/boxplots/acc/boxplot_CNN1D_chest_Sc_4_T_binary_two_Accuracy.png b/analise_global/boxplots/acc/boxplot_CNN1D_chest_Sc_4_T_binary_two_Accuracy.png new file mode 100644 index 0000000..0305985 Binary files /dev/null and b/analise_global/boxplots/acc/boxplot_CNN1D_chest_Sc_4_T_binary_two_Accuracy.png differ diff --git a/analise_global/boxplots/acc/boxplot_LSTM_chest_Sc_4_T_binary_two_Accuracy.png b/analise_global/boxplots/acc/boxplot_LSTM_chest_Sc_4_T_binary_two_Accuracy.png new file mode 100644 index 0000000..3d37c41 Binary files /dev/null and b/analise_global/boxplots/acc/boxplot_LSTM_chest_Sc_4_T_binary_two_Accuracy.png differ diff --git a/analise_global/boxplots/acc/boxplot_MLP_chest_Sc_4_T_binary_two_Accuracy.png b/analise_global/boxplots/acc/boxplot_MLP_chest_Sc_4_T_binary_two_Accuracy.png new file mode 100644 index 0000000..8bfaaee 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b/analise_global/shap/shap_importance_LSTM_chest_Sc_4_T_binary_two_20250717_0433.png new file mode 100644 index 0000000..ccd8b54 Binary files /dev/null and b/analise_global/shap/shap_importance_LSTM_chest_Sc_4_T_binary_two_20250717_0433.png differ diff --git a/analise_global/shap/shap_importance_MLP_chest_Sc_4_T_binary_two_20250717_0429.csv b/analise_global/shap/shap_importance_MLP_chest_Sc_4_T_binary_two_20250717_0429.csv new file mode 100644 index 0000000..f539151 --- /dev/null +++ b/analise_global/shap/shap_importance_MLP_chest_Sc_4_T_binary_two_20250717_0429.csv @@ -0,0 +1,7 @@ +feature,mean_abs_shap +acc_x,0.07034639511140994 +acc_y,0.06677483510778984 +acc_z,0.9785646159388125 +gyr_x,0.7005032920651137 +gyr_y,0.3061375998368021 +gyr_z,0.28895507934037595 diff --git a/analise_global/shap/shap_importance_MLP_chest_Sc_4_T_binary_two_20250717_0429.png b/analise_global/shap/shap_importance_MLP_chest_Sc_4_T_binary_two_20250717_0429.png new file mode 100644 index 0000000..257c8ba Binary 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files /dev/null and b/analise_global/shap/shap_values_MLP_chest_Sc_4_T_binary_two_20250717_0429.npy differ diff --git a/analise_global/summary_final_models.csv b/analise_global/summary_final_models.csv new file mode 100644 index 0000000..df3f804 --- /dev/null +++ b/analise_global/summary_final_models.csv @@ -0,0 +1,4 @@ +model_type,position,scenario,label_type,boxplot_path,MCC_mean,MCC_std,Accuracy_mean,Accuracy_std,Precision_mean,Precision_std,Sensitivity_mean,Sensitivity_std,Specificity_mean,Specificity_std,F1_mean,F1_std +CNN1D,chest,Sc_4_T,binary_two,analise_global\boxplots\all\boxplot_CNN1D_chest_Sc_4_T_binary_two.png,0.9402322837471871,0.012666522473700275,0.9880380794701169,0.0026105361607224524,0.9977163148645485,0.002219962445917942,0.9888785912881382,0.0029766638314991383,0.9810077519372242,0.01856295477139281,0.9932732439672778,0.0014742024927045423 +LSTM,chest,Sc_4_T,binary_two,analise_global\boxplots\all\boxplot_LSTM_chest_Sc_4_T_binary_two.png,0.8061682796087583,0.05671639117086379,0.958692052980053,0.010408586882862447,0.9892447049243749,0.00951293726277538,0.9643188137163143,0.006069886344500585,0.9116279069760376,0.07879007499919247,0.9765945581878211,0.0058283854642307694 +MLP,chest,Sc_4_T,binary_two,analise_global\boxplots\all\boxplot_MLP_chest_Sc_4_T_binary_two.png,0.9478257290313776,0.019341744419195197,0.9898592715230968,0.004185647683431149,0.9946865167052851,0.003169660656423498,0.9939759036143656,0.00566141345124343,0.9554263565884067,0.026838774540497976,0.9943161601446555,0.002371922022814161 diff --git a/builders/model_builders.py b/builders/model_builders.py deleted file mode 100644 index 08a3280..0000000 --- a/builders/model_builders.py +++ /dev/null @@ -1,388 +0,0 @@ -# Escrito por Leandro Soares - https://github.com/SoaresLMB -import os -import numpy as np -from matplotlib import pyplot as plt -from sklearn.model_selection import train_test_split -from sklearn.metrics import matthews_corrcoef, confusion_matrix, roc_auc_score, roc_curve, classification_report, confusion_matrix -import keras -from keras.utils import to_categorical -from keras.optimizers import SGD - - -from keras.models import Sequential -from keras.layers import Dense, Dropout, Conv1D, Flatten, MaxPooling1D -import optuna -import csv -import itertools -from sklearn.metrics import confusion_matrix - -"Plot the training and validation accuracy graphs." - - -def plot_training_and_validation_accuracy_graphs(historic, output_dir, i, neural_network_type): - - acc = 'accuracy' if neural_network_type == "CNN1D" else "acc" - val_acc = 'val_accuracy' if neural_network_type == "CNN1D" else 'val_acc' - - training_accuracy = historic.history[acc] - validation_accuracy = historic.history[val_acc] - - epochs = range(1, len(training_accuracy) + 1) - - plt.plot(epochs, training_accuracy, "-g", label="Training Data Accuracy") - plt.plot(epochs, validation_accuracy, "-b",label="Validation Data Accuracy") - plt.legend() - plt.xlabel('Epochs') - plt.ylabel('Accuracy') - plt.savefig(os.path.join(output_dir, f"accuracy_plot_model_{i}.png")) - plt.close() - - -'''Use the predict function to predict the classes corresponding to each array of data representing an activity.''' - - -def return_ypredicted_and_ytrue(model, X_test, y_test, decision_threshold): - y_predicted_probabilities = model.predict(X_test) - y_predicted = ( - y_predicted_probabilities[:, 1] >= decision_threshold).astype(int) - y_true = np.argmax(y_test, axis=1) - - return y_predicted, y_true, y_predicted_probabilities - - -'''Create the structure of the confusion matrix.''' - - -def create_confusion_matrix(y_true, y_predicted): - cm = confusion_matrix(y_true, y_predicted) - - tn, fp, fn, tp = cm.ravel() - - return cm, tp, tn, fp, fn - - -'''Use the confusion matrix created by create_confusion_matrix and plot it as a graph.''' - - -def plot_confusion_matrix(cm, number_of_labels, output_dir, i): - - plt.imshow(cm, cmap=plt.cm.Blues) - plt.title('Confusion Matrix') - plt.colorbar() - tick_marks = np.arange(0, number_of_labels) - plt.xticks(tick_marks, rotation=90) - plt.yticks(tick_marks) - - thresh = cm.max() / 2. - for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): - plt.text(j, i, format(cm[i, j], 'd'), - horizontalalignment="center", - color="white" if cm[i, j] > thresh else "black") - - plt.tight_layout() - plt.ylabel('Real Label') - plt.xlabel('Predicted Label') - plt.savefig(os.path.join(output_dir, f"confusion_matrix_model_{i}.png")) - plt.close() - - -'''Create the the classification report as text file and save in the directory''' - - -def save_classification_report(y_predicted, y_true, number_of_labels, output_dir, i): - target_names = np.arange(0, number_of_labels).astype(str) - report = classification_report( - y_true, y_predicted, target_names=target_names) - with open(os.path.join(output_dir, f"classification_report_model_{i}.txt"), "w") as report_file: - report_file.write(report) - - -'''Plot the ROC curve and save it in the output directory''' - - -def plot_roc_curve(y_predicted, y_true, output_dir, i): - roc_auc = roc_auc_score(y_true, y_predicted) - fpr, tpr, thresholds = roc_curve(y_true, y_predicted) - plt.figure(figsize=(8, 6)) - plt.plot(fpr, tpr, color='blue', lw=2, - label='Curva ROC (área = %0.2f)' % roc_auc) - plt.plot([0, 1], [0, 1], color='gray', linestyle='--', lw=2) # Diagonal - plt.xlim([0.0, 1.0]) - plt.ylim([0.0, 1.05]) - plt.xlabel('Taxa de Falso Positivo') - plt.ylabel('Taxa de Verdadeiro Positivo') - plt.title('ROC') - plt.legend(loc="lower right") - plt.grid(True) - plt.savefig(os.path.join(output_dir, f"roc_curve_model_{i}.png")) - plt.close() - - -'''Calculate the metrics that will be used to measure the model's effectiveness.''' - - -def calculate_metrics(tp, tn, fp, fn, y_test, y_predicted): - - mcc = matthews_corrcoef(y_test, y_predicted) - sensitivity = tp / (tp + fn) - specificity = tn / (tn + fp) - precision = tp / (tp + fp) - accuracy = (tp + tn) / (tp + tn + fp + fn) - - metrics = { - "MCC": mcc, - 'Sensitivity': sensitivity, - 'Specificity': specificity, - 'Precision': precision, - 'Accuracy': accuracy - } - return metrics - - -'''Save the calculated metrics in a CSV file and store it in the output directory.''' - - -def record_the_metrics_in_the_table(metrics, tp, tn, fp, fn, i, output_dir): - metrics["tp"] = tp - metrics["tn"] = tn - metrics["fp"] = fp - metrics["fn"] = fn - - file_path = os.path.join(output_dir, f'metrics_model_{i}.csv') - with open(file_path, "a", newline="") as csvfile: - writer = csv.DictWriter(csvfile, - fieldnames=["Model", "MCC", "Sensitivity", "Specificity", "Precision", "Accuracy", - "tp", "tn", "fp", "fn"]) - if csvfile.tell() == 0: # Verifica se o arquivo está vazio para escrever o cabeçalho - writer.writeheader() - writer.writerow({"Model": i, - "MCC": metrics["MCC"], - "Sensitivity": metrics["Sensitivity"], - "Specificity": metrics["Specificity"], - "Precision": metrics["Precision"], - "Accuracy": metrics["Accuracy"], - "tp": metrics["tp"], - "tn": metrics["tn"], - "fp": metrics["fp"], - "fn": metrics["fn"]}) - - -'''Save a text file with the best trial among the executed trials in the output directory.''' - - -def save_best_trial_to_csv(best_trial, best_params, file_path): - with open(file_path, mode='w', newline='') as file: - writer = csv.writer(file) - writer.writerow(["Trial Number", best_trial.number]) - writer.writerow(["Value", best_trial.value]) - writer.writerow(["Parameters"]) - for key, value in best_params.items(): - writer.writerow([key, value]) - - -'''Run all files related to metrics and graphs.''' - - -def save_results(model, historic, X_test, y_test, number_of_labels, i, decision_threshold, output_dir, neural_network_type): - - model.save(os.path.join(output_dir, f"model_{i}.keras")) - plot_training_and_validation_accuracy_graphs( - historic, output_dir, i, neural_network_type) - y_predicted, y_true, y_predicted_probabilities = return_ypredicted_and_ytrue( - model, X_test, y_test, decision_threshold) - cm, tp, tn, fp, fn = create_confusion_matrix(y_true, y_predicted) - plot_confusion_matrix(cm, number_of_labels, output_dir, i) - save_classification_report( - y_predicted, y_true, number_of_labels, output_dir, i) - plot_roc_curve(y_predicted, y_true, output_dir, i) - metrics = calculate_metrics(tp, tn, fp, fn, y_true, y_predicted) - record_the_metrics_in_the_table(metrics, tp, tn, fp, fn, i, output_dir) - - -'''Create the structure of the CNN 1D network to be optimized.''' - - -def cnn1d_architecture(input_shape, X_train, y_train, X_val, y_val, - filter_size, kernel_size, num_layers, num_dense_layers, dense_neurons, - dropout, learning_rate, number_of_labels): - - - print("Input:", input_shape) - print("X_train:", X_train.shape) - max_pool = 2 - model = Sequential() - for i in range(num_layers): - - if i == 0: - model.add(Conv1D(filters=filter_size, kernel_size=kernel_size, - activation="relu", input_shape=input_shape)) - else: - if filter_size < kernel_size: - filter_size = kernel_size - filter_size *= 2 - model.add(Conv1D(filters=filter_size, kernel_size=kernel_size, activation="relu")) - - model.add(MaxPooling1D(pool_size=max_pool)) - model.add(Dropout(dropout)) - - model.add(Flatten()) - - for i in range(num_dense_layers): - model.add(Dense(dense_neurons, activation='relu')) - - model.add(Dense(number_of_labels, activation='softmax')) - - optimizer = keras.optimizers.Adam(learning_rate=learning_rate) - model.compile(optimizer=optimizer, - loss='categorical_crossentropy', metrics=['accuracy']) - - model.summary() - - historic = model.fit(X_train, y_train, batch_size=32, - epochs=25, validation_data=(X_val, y_val), verbose=0) - return model, historic - - -'''Create the structure of the MLP network to be optimized.''' - - -def mlp_architecture(input_dim, X_train, y_train, X_val, y_val, num_layers, dense_neurons, dropout, learning_rate, number_of_labels): - model = Sequential() - - batch = int(len(y_train) / 30) - - for i in range(num_layers): - if i == 0: - model.add(Dense(dense_neurons, input_dim=input_dim, kernel_initializer='normal', activation='relu')) - model.add(Dropout(dropout)) - else: - model.add(Dense(dense_neurons, kernel_initializer='normal', activation='relu')) - model.add(Dropout(dropout)) - - model.add(Dense(number_of_labels, kernel_initializer='normal', activation='softmax')) - - optimizer = SGD(learning_rate=learning_rate) - model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["acc"]) - historic = model.fit(X_train, y_train, epochs=300, - batch_size=batch, validation_data=(X_val, y_val), verbose=1) - - return model, historic - - -'''Split the dataset into training, validation, and test sets.''' - - -def generate_training_testing_and_validation_sets(data=None, label=None): - - X = np.load(data) - y = np.load(label) - # 40% para treinamento - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42) - # 30% + 30% para validação e teste - X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=42) - # to_categorical???? - semelhante a get_dummy? - y_train = to_categorical(y_train) - y_test = to_categorical(y_test) - y_val = to_categorical(y_val) - - return X_train, X_test, y_train, y_test, X_val, y_val - - -'''Define the search space and the parameters to be optimized.''' - - -def objective(trial, input_shape, X_train, y_train, X_val, y_val, neural_network_type, output_dir, number_of_labels): - - mcc = None - - if neural_network_type == "CNN1D": - - # Definindo o espaço de busca dos hiperparâmetros - filter_size = trial.suggest_int('filter_size', 8, 600, log=True) - kernel_size = trial.suggest_int('kernel_size', 2, 6) - num_layers = trial.suggest_int('num_layers', 2, 4) - num_dense_layers = trial.suggest_int('num_dense_layers', 1, 3) - dense_neurons = trial.suggest_int('dense_neurons', 60, 320, log=True) - dropout = trial.suggest_float('dropout', 0.1, 0.5, step=0.1) - learning_rate = trial.suggest_categorical('learning_rate', [0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01]) - decision_threshold = trial.suggest_float('decision_threshold', 0.5, 0.9, step=0.1) - - model, historic = cnn1d_architecture(input_shape, X_train, y_train, X_val, y_val, filter_size, kernel_size, - num_layers, num_dense_layers, dense_neurons, dropout, learning_rate, number_of_labels) - - y_pred_prob = model.predict(X_val) - y_pred = (y_pred_prob[:, 1] >= decision_threshold).astype(int) - - mcc = matthews_corrcoef(y_val.argmax(axis=1), y_pred) - - optimized_params = { - "filter_size": filter_size, - "kernel_size": kernel_size, - "num_layers": num_layers, - "num_dense_layers": num_dense_layers, - "dense_neurons": dense_neurons, - "dropout": dropout, - "learning_rate": learning_rate, - "decision_threshold": decision_threshold - } - - elif neural_network_type == "MLP": - - num_layers = trial.suggest_int('num_layers', 1, 5) - dense_neurons = trial.suggest_int('dense_neurons', 20, 4000, log=True) - dropout = trial.suggest_float('dropout', 0.1, 0.5, step=0.1) - learning_rate = trial.suggest_categorical( - 'learning_rate', [0.001, 0.003, 0.005, 0.007, 0.01, 0.03, 0.05, 0.07]) - decision_threshold = trial.suggest_float( - 'decision_threshold', 0.5, 0.9, step=0.1) - - model, historic = mlp_architecture(input_shape, X_train, y_train, X_val, - y_val, num_layers, dense_neurons, dropout, learning_rate, number_of_labels) - - y_pred_prob = model.predict(X_val) - y_pred = (y_pred_prob[:, 1] >= decision_threshold).astype(int) - - mcc = matthews_corrcoef(X_val.argmax(axis=1), y_pred) - - optimized_params = { - "num_layers": num_layers, - "dense_neurons": dense_neurons, - "dropout": dropout, - "learning_rate": learning_rate, - "decision_threshold": decision_threshold - } - - file_path = os.path.join(output_dir, 'optimization_results.csv') - file_exists = os.path.isfile(file_path) - - with open(file_path, "a", newline='') as csvfile: - fieldnames = ["Trial", "MCC"] + list(optimized_params.keys()) - writer = csv.DictWriter(csvfile, fieldnames=fieldnames) - - if not file_exists: - writer.writeheader() - - row = {"Trial": trial.number, "MCC": mcc} - row.update(optimized_params) - writer.writerow(row) - - return mcc - - -'''Creates an Optuna study object that defines the maximization direction to optimize the objective function.''' - - -def create_study_object(objective, input_shape, X_train, y_train, X_val, y_val, neural_network_type, neural_network_results_dir, number_of_labels): - - study = optuna.create_study(direction="maximize") - - study.optimize(lambda trial: objective(trial, input_shape, X_train, y_train, X_val, y_val, neural_network_type, neural_network_results_dir, number_of_labels), n_trials=5) - - best_trial = study.best_trial - best_params = best_trial.params - - return best_trial, best_params - - -if __name__ == "__main__": - pass diff --git a/extract_reports.sh b/extract_reports.sh deleted file mode 100755 index c3dfd9f..0000000 --- a/extract_reports.sh +++ /dev/null @@ -1,22 +0,0 @@ -#! /bin/bash - -# Definindo as variáveis para cada loop -cenarios=("Sc1_acc_T" "Sc1_gyr_T" "Sc1_acc_F" "Sc1_gyr_F" "Sc_2_acc_T" "Sc_2_gyr_T" "Sc_2_acc_F" "Sc_2_gyr_F" "Sc_3_T" "Sc_3_F" "Sc_4_T" "Sc_4_F") -redenerural=("MLP" "CNN1D") -sensores=("chest" "right" "left") - -# Loop aninhado -for cenario in "${cenarios[@]}"; do - for nn in "${redenerural[@]}"; do - for sensor in "${sensores[@]}"; do - if [ ! -e "./results/${cenario}_${nn}_${sensor}.json" ]; then - echo "Criando ${cenario}_${nn}_${sensor}.json..." - python3 training.py -s "$cenario" -p "$sensor" -nn "$nn" --export - fi - done - done -done - - -# Agrupando resultados -python3 agg_results.py diff --git a/models/1730337919_MLP_bin_0.01_chest_Sc1_acc_T.model b/models/1730337919_MLP_bin_0.01_chest_Sc1_acc_T.model deleted file mode 100644 index c00a887..0000000 Binary files a/models/1730337919_MLP_bin_0.01_chest_Sc1_acc_T.model and /dev/null differ diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/all_metrics.csv b/output/CNN1D/chest/Sc_4_T/binary_two/all_metrics.csv new file mode 100644 index 0000000..22ae972 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/all_metrics.csv @@ -0,0 +1,21 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +1.0,0.9375223867856816,0.9888785912881382,0.9767441860457544,0.9971962616821496,0.9875827814568718,1067,126,3,12 +2.0,0.9322667862085712,0.9898053753474524,0.9612403100767744,0.995340167753868,0.9867549668873355,1068,124,5,11 +3.0,0.9361959627666212,0.9851714550508818,0.999999999999225,0.999999999999906,0.9867549668873355,1063,129,0,16 +4.0,0.953800495678198,0.9916589434660804,0.9844961240302448,0.9981343283581158,0.9908940397350172,1070,127,2,9 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b/output/CNN1D/chest/Sc_4_T/binary_two/best_hyperparameters.json new file mode 100644 index 0000000..78c031f --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/best_hyperparameters.json @@ -0,0 +1,40 @@ +{ + "scenario": "Sc_4_T", + "position": "chest", + "label_type": "binary_two", + "model_type": "CNN1D", + "best_value": 0.9650388976214959, + "best_params": { + "dropout": 0.1, + "learning_rate": 0.0005895289443882064, + "decision_threshold": 0.5, + "filter_size": 59, + "kernel_size": 6, + "num_layers": 4, + "num_dense_layers": 1, + "dense_neurons": 300 + }, + "n_trials": 20, + "optimization_history": [ + 0.8683556845241359, + 0.8678020161958327, + 0.9650388976214959, + 0.36988443293254036, + 0.9214781196623403, + 0.8925026823583482, + 0.9111925972681382, + 0.9478650857071962, + 0.9481151233720893, + 0.7135135487135519, + 0.9496359794426157, + 0.9505911273925157, + 0.9521575193985103, + 0.9474981236964355, + 0.9460382934454683, + 0.9211544266034309, + 0.9369545959374896, + 0.9207957225287086, + 0.9620707422030137, + 0.9236889483052512 + ] +} \ No newline at end of file diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/correlation_heatmap.png b/output/CNN1D/chest/Sc_4_T/binary_two/correlation_heatmap.png new file mode 100644 index 0000000..2fa726c Binary files /dev/null and b/output/CNN1D/chest/Sc_4_T/binary_two/correlation_heatmap.png differ diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/learning_curve.png b/output/CNN1D/chest/Sc_4_T/binary_two/learning_curve.png new file mode 100644 index 0000000..bfa8612 Binary files /dev/null and b/output/CNN1D/chest/Sc_4_T/binary_two/learning_curve.png differ diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/learning_curve_metrics.csv b/output/CNN1D/chest/Sc_4_T/binary_two/learning_curve_metrics.csv new file mode 100644 index 0000000..c533346 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/learning_curve_metrics.csv @@ -0,0 +1,13 @@ +Fraction,MCC,F1,Accuracy,Train_Loss,Val_Loss 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a/output/CNN1D/chest/Sc_4_T/binary_two/model_1/loss_curve_model_1.png b/output/CNN1D/chest/Sc_4_T/binary_two/model_1/loss_curve_model_1.png new file mode 100644 index 0000000..51e205e Binary files /dev/null and b/output/CNN1D/chest/Sc_4_T/binary_two/model_1/loss_curve_model_1.png differ diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/model_1/losses_model_1.csv b/output/CNN1D/chest/Sc_4_T/binary_two/model_1/losses_model_1.csv new file mode 100644 index 0000000..8976255 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/model_1/losses_model_1.csv @@ -0,0 +1,26 @@ +epoch,train_loss,val_loss +1,0.12147128340692245,0.05375164007906982 +2,0.05791929659259521,0.07716376523838393 +3,0.056294833935801064,0.03976004071206086 +4,0.043946675582469466,0.03230673132126075 +5,0.05308563073820491,0.06697143210969385 +6,0.03342201109998772,0.08608411136670076 +7,0.02439044612007767,0.11461247333400501 +8,0.07460692036878874,0.0609316425239564 +9,0.02091535418718901,0.0398057588458721 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a/output/CNN1D/chest/Sc_4_T/binary_two/trial_25/classification_report_model_25fold3.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_25/classification_report_model_25fold3.txt new file mode 100644 index 0000000..19e8e0d --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_25/classification_report_model_25fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.93 0.97 0.95 95 + 1 1.00 0.99 0.99 871 + + accuracy 0.99 966 + macro avg 0.96 0.98 0.97 966 +weighted avg 0.99 0.99 0.99 966 diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/trial_25/classification_report_model_25fold4.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_25/classification_report_model_25fold4.txt new file mode 100644 index 0000000..bd54f0d --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_25/classification_report_model_25fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.96 0.97 0.96 95 + 1 1.00 1.00 1.00 871 + + accuracy 0.99 966 + macro avg 0.98 0.98 0.98 966 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a/output/CNN1D/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold3.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold3.txt new file mode 100644 index 0000000..777127e --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.97 0.96 0.96 95 + 1 1.00 1.00 1.00 871 + + accuracy 0.99 966 + macro avg 0.98 0.98 0.98 966 +weighted avg 0.99 0.99 0.99 966 diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold4.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold4.txt new file mode 100644 index 0000000..282f73a --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.97 0.97 0.97 95 + 1 1.00 1.00 1.00 871 + + accuracy 0.99 966 + macro avg 0.98 0.98 0.98 966 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a/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold3.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold3.txt new file mode 100644 index 0000000..a6137f8 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.98 0.98 0.98 95 + 1 1.00 1.00 1.00 871 + + accuracy 1.00 966 + macro avg 0.99 0.99 0.99 966 +weighted avg 1.00 1.00 1.00 966 diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold4.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold4.txt new file mode 100644 index 0000000..cd82045 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.92 0.98 0.95 95 + 1 1.00 0.99 0.99 871 + + accuracy 0.99 966 + macro avg 0.96 0.98 0.97 966 +weighted avg 0.99 0.99 0.99 966 diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold5.txt b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold5.txt new file mode 100644 index 0000000..0fa92a1 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.98 0.98 0.98 96 + 1 1.00 1.00 1.00 870 + + accuracy 1.00 966 + macro avg 0.99 0.99 0.99 966 +weighted avg 1.00 1.00 1.00 966 diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold1.csv b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold1.csv new file mode 100644 index 0000000..14345e1 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn 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b/output/CNN1D/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold1.txt new file mode 100644 index 0000000..126a7b6 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.93 0.99 0.96 95 + 1 1.00 0.99 1.00 871 + + accuracy 0.99 966 + macro avg 0.96 0.99 0.98 966 +weighted avg 0.99 0.99 0.99 966 diff --git a/output/CNN1D/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold1.csv b/output/CNN1D/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold1.csv new file mode 100644 index 0000000..a930e49 --- /dev/null +++ b/output/CNN1D/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +28fold1,0.9551345210653538,0.9919632606198631,0.9894736842094848,0.9988439306357226,0.9917184265009324,864,94,1,7 diff --git 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0.97 966 + macro avg 0.90 0.94 0.92 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_21/classification_report_model_21fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_21/classification_report_model_21fold5.txt new file mode 100644 index 0000000..41702aa --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_21/classification_report_model_21fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.85 0.89 0.87 96 + 1 0.99 0.98 0.99 870 + + accuracy 0.97 966 + macro avg 0.92 0.93 0.93 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_21/metrics_model_21fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_21/metrics_model_21fold1.csv new file mode 100644 index 0000000..d33ed8f --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_21/metrics_model_21fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn 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100644 index 0000000..9db6eb6 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.95 0.86 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.89 0.96 0.92 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold2.txt new file mode 100644 index 0000000..1ad148b --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.80 0.96 0.87 95 + 1 1.00 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.90 0.97 0.93 966 +weighted avg 0.98 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold3.txt 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a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold5.txt new file mode 100644 index 0000000..118f4a0 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/classification_report_model_22fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.84 0.97 0.90 96 + 1 1.00 0.98 0.99 870 + + accuracy 0.98 966 + macro avg 0.92 0.97 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold1.csv new file mode 100644 index 0000000..42ce183 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +22fold1,0.844723778444373,0.9712973593569493,0.9473684210516343,0.9941245593418337,0.9689440993787817,846,90,5,25 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a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold4.csv new file mode 100644 index 0000000..12049b2 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +22fold4,0.8729847317233137,0.9770378874855364,0.9578947368410969,0.9953216374267841,0.9751552795030046,851,91,4,20 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold5.csv new file mode 100644 index 0000000..cf57489 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/metrics_model_22fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +22fold5,0.8893786043774772,0.9793103448274736,0.9687499999989909,0.9964912280700589,0.978260869565116,852,93,3,18 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_22/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/trial_summary.json new file mode 100644 index 0000000..9027439 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_22/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 22, + "model_type": "LSTM", + "params": { + "dropout": 0.4, + "learning_rate": 0.0013038862806190213, + "decision_threshold": 0.6, + "hidden_dim": 98, + "num_layers": 1 + }, + "mean_mcc": 0.8613265262276453, + "mcc_scores": [ + 0.844723778444373, + 0.8597576817949277, + 0.8397878347981343, + 0.8729847317233137, + 0.8893786043774772 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold1.txt new file mode 100644 index 0000000..c55267e --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.73 0.97 0.83 95 + 1 1.00 0.96 0.98 871 + + accuracy 0.96 966 + macro avg 0.86 0.96 0.91 966 +weighted avg 0.97 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold2.txt new file mode 100644 index 0000000..26cc388 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 0.85 0.82 95 + 1 0.98 0.97 0.98 871 + + accuracy 0.96 966 + macro avg 0.89 0.91 0.90 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold3.txt new file mode 100644 index 0000000..57425dd --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.82 0.86 0.84 95 + 1 0.98 0.98 0.98 871 + + accuracy 0.97 966 + macro avg 0.90 0.92 0.91 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold4.txt new file mode 100644 index 0000000..4dd51a7 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.85 0.81 0.83 95 + 1 0.98 0.98 0.98 871 + + accuracy 0.97 966 + macro avg 0.91 0.90 0.90 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold5.txt new file mode 100644 index 0000000..de7c1e8 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/classification_report_model_23fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.93 0.85 96 + 1 0.99 0.97 0.98 870 + + accuracy 0.97 966 + macro avg 0.89 0.95 0.91 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold1.csv new file mode 100644 index 0000000..20c7c2d --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +23fold1,0.8217540231127463,0.9609644087254924,0.9684210526305596,0.9964285714284528,0.9616977225671882,837,92,3,34 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold2.csv new file mode 100644 index 0000000..002f57e --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +23fold2,0.7982673162670575,0.9747416762341016,0.8526315789464709,0.9837775202779856,0.9627329192545587,849,81,14,22 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold3.csv new file mode 100644 index 0000000..226a9b7 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +23fold3,0.8235267610718858,0.9793340987369713,0.8631578947359335,0.9849884526557754,0.9679089026914112,853,82,13,18 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold4.csv new file mode 100644 index 0000000..e1cd760 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +23fold4,0.8098680718795904,0.983926521239841,0.8105263157886204,0.9794285714284594,0.9668737060040407,857,77,18,14 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold5.csv new file mode 100644 index 0000000..61dfa48 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/metrics_model_23fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +23fold5,0.8330431173499183,0.9712643678159802,0.9270833333323676,0.991784037558569,0.9668737060040407,845,89,7,25 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_23/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/trial_summary.json new file mode 100644 index 0000000..e9bdaf9 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_23/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 23, + "model_type": "LSTM", + "params": { + "dropout": 0.30000000000000004, + "learning_rate": 0.0005385620974134254, + "decision_threshold": 0.5, + "hidden_dim": 77, + "num_layers": 1 + }, + "mean_mcc": 0.8172918579362397, + "mcc_scores": [ + 0.8217540231127463, + 0.7982673162670575, + 0.8235267610718858, + 0.8098680718795904, + 0.8330431173499183 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold1.txt new file mode 100644 index 0000000..44c851e --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.95 0.85 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.88 0.96 0.92 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold2.txt new file mode 100644 index 0000000..6ff8756 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.77 0.78 95 + 1 0.97 0.98 0.98 871 + + accuracy 0.96 966 + macro avg 0.88 0.87 0.88 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold3.txt new file mode 100644 index 0000000..7a12729 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.87 0.83 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.96 966 + macro avg 0.88 0.92 0.90 966 +weighted avg 0.97 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold4.txt new file mode 100644 index 0000000..798aceb --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.82 0.97 0.89 95 + 1 1.00 0.98 0.99 871 + + accuracy 0.98 966 + macro avg 0.91 0.97 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold5.txt new file mode 100644 index 0000000..cd39271 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_24/classification_report_model_24fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.75 1.00 0.86 96 + 1 1.00 0.96 0.98 870 + 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a/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold3.csv new file mode 100644 index 0000000..5d27448 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +25fold3,0.8257131519156534,0.9873708381169933,0.8105263157886204,0.9794988610477244,0.9699792960661522,860,77,18,11 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold4.csv new file mode 100644 index 0000000..4998b31 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +25fold4,0.7843297737003548,0.9655568312283621,0.8842105263148587,0.9870892018778183,0.9575569358177062,841,84,11,30 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold5.csv new file mode 100644 index 0000000..7fb0618 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/metrics_model_25fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +25fold5,0.8537979547861821,0.9643678160918431,0.9999999999989583,0.9999999999998808,0.9679089026914112,839,96,0,31 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_25/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/trial_summary.json new file mode 100644 index 0000000..ebf732d --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_25/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 25, + "model_type": "LSTM", + "params": { + "dropout": 0.5, + "learning_rate": 0.0030193156440540742, + "decision_threshold": 0.5, + "hidden_dim": 98, + "num_layers": 2 + }, + "mean_mcc": 0.8289763306743912, + "mcc_scores": [ + 0.8320585066169199, + 0.8489822663528458, + 0.8257131519156534, + 0.7843297737003548, + 0.8537979547861821 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold1.txt new file mode 100644 index 0000000..cb76f81 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.66 0.96 0.78 95 + 1 1.00 0.95 0.97 871 + + accuracy 0.95 966 + macro avg 0.83 0.95 0.88 966 +weighted avg 0.96 0.95 0.95 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold2.txt new file mode 100644 index 0000000..da75512 --- /dev/null +++ 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index 0000000..95a51b3 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.76 0.85 0.81 95 + 1 0.98 0.97 0.98 871 + + accuracy 0.96 966 + macro avg 0.87 0.91 0.89 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold5.txt new file mode 100644 index 0000000..42e875c --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/classification_report_model_26fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.82 0.97 0.89 96 + 1 1.00 0.98 0.99 870 + + accuracy 0.98 966 + macro avg 0.91 0.97 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold1.csv new file mode 100644 index 0000000..cfbb525 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +26fold1,0.772540400932249,0.9471871412168832,0.9578947368410969,0.9951749095294335,0.9482401656313718,825,91,4,46 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold2.csv new file mode 100644 index 0000000..48321dd --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +26fold2,0.770737060190893,0.9575200918483401,0.9052631578937839,0.9893238434162527,0.9523809523808537,834,86,9,37 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold3.csv new file mode 100644 index 0000000..9830cb5 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +26fold3,0.783019160742372,0.9793340987369713,0.7999999999991578,0.9782110091741997,0.9616977225671882,853,76,19,18 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold4.csv new file mode 100644 index 0000000..4bd910c --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +26fold4,0.7849797951710024,0.9712973593569493,0.8526315789464709,0.9837209302324437,0.9596273291924472,846,81,14,25 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold5.csv new file mode 100644 index 0000000..64f7bb6 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/metrics_model_26fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +26fold5,0.8759443382674191,0.9758620689654051,0.9687499999989909,0.9964788732393196,0.9751552795030046,849,93,3,21 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_26/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/trial_summary.json new file mode 100644 index 0000000..66ca4ae --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_26/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 26, + "model_type": "LSTM", + "params": { + "dropout": 0.4, + "learning_rate": 0.0001925228921052992, + "decision_threshold": 0.6, + "hidden_dim": 75, + "num_layers": 1 + }, + "mean_mcc": 0.797444151060787, + "mcc_scores": [ + 0.772540400932249, + 0.770737060190893, + 0.783019160742372, + 0.7849797951710024, + 0.8759443382674191 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold1.txt new file mode 100644 index 0000000..7b075c8 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.72 0.91 0.80 95 + 1 0.99 0.96 0.97 871 + + accuracy 0.96 966 + macro avg 0.85 0.93 0.89 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold2.txt new file mode 100644 index 0000000..90eae99 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.68 0.95 0.79 95 + 1 0.99 0.95 0.97 871 + + accuracy 0.95 966 + macro avg 0.84 0.95 0.88 966 +weighted avg 0.96 0.95 0.95 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold3.txt new file mode 100644 index 0000000..b0c5ec6 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.80 0.83 0.81 95 + 1 0.98 0.98 0.98 871 + + accuracy 0.96 966 + macro avg 0.89 0.90 0.90 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold4.txt new file mode 100644 index 0000000..d6be70a --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.80 0.84 0.82 95 + 1 0.98 0.98 0.98 871 + + accuracy 0.96 966 + macro avg 0.89 0.91 0.90 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold5.txt new file mode 100644 index 0000000..023014f --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/classification_report_model_27fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.85 0.96 0.90 96 + 1 1.00 0.98 0.99 870 + + accuracy 0.98 966 + macro avg 0.92 0.97 0.95 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold1.csv new file mode 100644 index 0000000..f46b6bb --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +27fold1,0.782036594965758,0.9609644087254924,0.9052631578937839,0.9893617021275426,0.9554865424429653,837,86,9,34 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold2.csv new file mode 100644 index 0000000..1ffd535 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +27fold2,0.7795286195610701,0.9517795637197529,0.9473684210516343,0.9940047961629503,0.9513457556934832,829,90,5,42 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold3.csv new file mode 100644 index 0000000..1f72753 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_27/metrics_model_27fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn 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file mode 100644 index 0000000..3191f85 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.83 0.37 0.51 95 + 1 0.94 0.99 0.96 871 + + accuracy 0.93 966 + macro avg 0.88 0.68 0.74 966 +weighted avg 0.93 0.93 0.92 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold2.txt new file mode 100644 index 0000000..f2646ee --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.75 0.95 0.84 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.96 966 + macro avg 0.87 0.96 0.91 966 +weighted avg 0.97 0.96 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold3.txt new file mode 100644 index 0000000..99f9b0d --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.91 0.84 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.89 0.94 0.91 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold4.txt new file mode 100644 index 0000000..cf5c1a2 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.75 0.97 0.84 95 + 1 1.00 0.96 0.98 871 + + accuracy 0.96 966 + macro avg 0.87 0.97 0.91 966 +weighted avg 0.97 0.96 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold5.txt new file mode 100644 index 0000000..abb921a --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/classification_report_model_28fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 0.97 0.87 96 + 1 1.00 0.97 0.98 870 + + accuracy 0.97 966 + macro avg 0.89 0.97 0.93 966 +weighted avg 0.98 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold1.csv new file mode 100644 index 0000000..80307f0 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +28fold1,0.526230635995658,0.9919632606198631,0.3684210526311911,0.9350649350648338,0.9306418219460734,864,35,60,7 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold2.csv new file mode 100644 index 0000000..29421ef --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +28fold2,0.8241956077866142,0.9655568312283621,0.9473684210516343,0.9940898345152489,0.9637681159419291,841,90,5,30 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold3.csv new file mode 100644 index 0000000..fb31458 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +28fold3,0.8227881348424613,0.9724454649826667,0.9052631578937839,0.9894859813082956,0.9658385093166701,847,86,9,24 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold4.csv new file mode 100644 index 0000000..1ed58bf --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +28fold4,0.8333106619175311,0.9644087256026447,0.9684210526305596,0.9964412811386718,0.9648033126292996,840,92,3,31 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold5.csv new file mode 100644 index 0000000..15a4d8b --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/metrics_model_28fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +28fold5,0.8587975657200749,0.9712643678159802,0.9687499999989909,0.9964622641508258,0.9710144927535226,845,93,3,25 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_28/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/trial_summary.json new file mode 100644 index 0000000..1784e84 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_28/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 28, + "model_type": "LSTM", + "params": { + "dropout": 0.4, + "learning_rate": 0.006160739882437236, + "decision_threshold": 0.6, + "hidden_dim": 178, + "num_layers": 1 + }, + "mean_mcc": 0.7730645212524678, + "mcc_scores": [ + 0.526230635995658, + 0.8241956077866142, + 0.8227881348424613, + 0.8333106619175311, + 0.8587975657200749 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold1.txt new file mode 100644 index 0000000..0c9c876 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.65 0.95 0.77 95 + 1 0.99 0.94 0.97 871 + + accuracy 0.95 966 + macro avg 0.82 0.95 0.87 966 +weighted avg 0.96 0.95 0.95 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold2.txt new file mode 100644 index 0000000..917a9cf --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.65 0.97 0.78 95 + 1 1.00 0.94 0.97 871 + + accuracy 0.95 966 + macro avg 0.82 0.96 0.87 966 +weighted avg 0.96 0.95 0.95 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold3.txt new file mode 100644 index 0000000..5e538a5 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.82 0.98 0.89 95 + 1 1.00 0.98 0.99 871 + + accuracy 0.98 966 + macro avg 0.91 0.98 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold4.txt new file mode 100644 index 0000000..c7a82e6 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.89 0.89 0.89 95 + 1 0.99 0.99 0.99 871 + + accuracy 0.98 966 + macro avg 0.94 0.94 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold5.txt new file mode 100644 index 0000000..47d7e60 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/classification_report_model_29fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.74 0.99 0.85 96 + 1 1.00 0.96 0.98 870 + + accuracy 0.96 966 + macro avg 0.87 0.98 0.91 966 +weighted avg 0.97 0.96 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold1.csv new file mode 100644 index 0000000..dcc64af --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +29fold1,0.759289294566885,0.9448909299654483,0.9473684210516343,0.9939613526568848,0.9451345755692603,823,90,5,48 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold2.csv new file mode 100644 index 0000000..a2f502f --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +29fold2,0.7661062362198037,0.9425947187140135,0.9684210526305596,0.9963592233008499,0.9451345755692603,821,92,3,50 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold3.csv new file mode 100644 index 0000000..b897e4d --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +29fold3,0.8857442405353443,0.9770378874855364,0.9789473684200222,0.9976553341147716,0.9772256728777455,851,93,2,20 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/metrics_model_29fold4.csv new file mode 100644 index 0000000..4c375af --- 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b/output/LSTM/chest/Sc_4_T/binary_two/trial_29/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 29, + "model_type": "LSTM", + "params": { + "dropout": 0.5, + "learning_rate": 0.0010426148164504132, + "decision_threshold": 0.8, + "hidden_dim": 117, + "num_layers": 2 + }, + "mean_mcc": 0.8268277723140492, + "mcc_scores": [ + 0.759289294566885, + 0.7661062362198037, + 0.8857442405353443, + 0.8832557858480875, + 0.8397433044001261 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_3/classification_report_model_3fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_3/classification_report_model_3fold1.txt new file mode 100644 index 0000000..ed166bd --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_3/classification_report_model_3fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.69 0.95 0.80 95 + 1 0.99 0.95 0.97 871 + + accuracy 0.95 966 + macro avg 0.84 0.95 0.89 966 +weighted avg 0.96 0.95 0.96 966 diff --git 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a/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold1.txt new file mode 100644 index 0000000..9d81eb0 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.61 0.98 0.75 95 + 1 1.00 0.93 0.96 871 + + accuracy 0.94 966 + macro avg 0.80 0.96 0.86 966 +weighted avg 0.96 0.94 0.94 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold2.txt new file mode 100644 index 0000000..321178e --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.74 0.82 0.78 95 + 1 0.98 0.97 0.97 871 + + accuracy 0.95 966 + macro avg 0.86 0.90 0.88 966 +weighted avg 0.96 0.95 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold3.txt new file mode 100644 index 0000000..6ea1617 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 0.85 0.82 95 + 1 0.98 0.98 0.98 871 + + accuracy 0.96 966 + macro avg 0.89 0.91 0.90 966 +weighted avg 0.97 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold4.txt new file mode 100644 index 0000000..8aaf732 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.78 0.98 0.87 95 + 1 1.00 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.89 0.97 0.93 966 +weighted avg 0.98 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold5.txt new file mode 100644 index 0000000..abb921a --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/classification_report_model_31fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 0.97 0.87 96 + 1 1.00 0.97 0.98 870 + + accuracy 0.97 966 + macro avg 0.89 0.97 0.93 966 +weighted avg 0.98 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_31/metrics_model_31fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/metrics_model_31fold1.csv new file mode 100644 index 0000000..7b5881b --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_31/metrics_model_31fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn 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index 0000000..9bbbc9c --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.67 0.98 0.79 95 + 1 1.00 0.95 0.97 871 + + accuracy 0.95 966 + macro avg 0.83 0.96 0.88 966 +weighted avg 0.97 0.95 0.95 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold2.txt new file mode 100644 index 0000000..c7b20f0 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.77 0.93 0.84 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.88 0.95 0.91 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold3.txt new file mode 100644 index 0000000..7bf0e0b --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 0.97 0.87 95 + 1 1.00 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.89 0.97 0.93 966 +weighted avg 0.98 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold4.txt new file mode 100644 index 0000000..785f21f --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.74 0.98 0.85 95 + 1 1.00 0.96 0.98 871 + + accuracy 0.96 966 + macro avg 0.87 0.97 0.91 966 +weighted avg 0.97 0.96 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold5.txt new file mode 100644 index 0000000..bf034da --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/classification_report_model_32fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 1.00 0.88 96 + 1 1.00 0.97 0.98 870 + + accuracy 0.97 966 + macro avg 0.89 0.99 0.93 966 +weighted avg 0.98 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold1.csv new file mode 100644 index 0000000..ea3fc32 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +32fold1,0.7857507286712432,0.9471871412168832,0.9789473684200222,0.9975816203142687,0.9503105590061127,825,93,2,46 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold2.csv new file mode 100644 index 0000000..b67d9f0 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +32fold2,0.8274314354686821,0.9701492537312318,0.9263157894727091,0.991784037558569,0.9658385093166701,845,88,7,26 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold3.csv new file mode 100644 index 0000000..37408f1 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +32fold3,0.8619070565501613,0.9724454649826667,0.9684210526305596,0.9964705882351769,0.9720496894408931,847,92,3,24 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold4.csv new file mode 100644 index 0000000..6abc7f4 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +32fold4,0.8359198876069646,0.9632606199769272,0.9789473684200222,0.9976218787156959,0.9648033126292996,839,93,2,32 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold5.csv new file mode 100644 index 0000000..def02bd --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/metrics_model_32fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +32fold5,0.8737099834067246,0.9701149425286241,0.9999999999989583,0.9999999999998814,0.9730848861282636,844,96,0,26 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_32/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/trial_summary.json new file mode 100644 index 0000000..5ab8c41 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_32/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 32, + "model_type": "LSTM", + "params": { + "dropout": 0.4, + "learning_rate": 0.0008319852534948313, + "decision_threshold": 0.7, + "hidden_dim": 94, + "num_layers": 1 + }, + "mean_mcc": 0.8369438183407552, + "mcc_scores": [ + 0.7857507286712432, + 0.8274314354686821, + 0.8619070565501613, + 0.8359198876069646, + 0.8737099834067246 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold1.txt new file mode 100644 index 0000000..a48c431 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.74 0.91 0.81 95 + 1 0.99 0.96 0.98 871 + + accuracy 0.96 966 + macro avg 0.86 0.93 0.89 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold2.txt new file mode 100644 index 0000000..4d02551 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.79 0.92 0.85 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.89 0.94 0.92 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold3.txt new file mode 100644 index 0000000..ddc3032 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.77 0.97 0.86 95 + 1 1.00 0.97 0.98 871 + + accuracy 0.97 966 + macro avg 0.88 0.97 0.92 966 +weighted avg 0.97 0.97 0.97 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold4.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold4.txt new file mode 100644 index 0000000..167febe --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold4.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.83 0.97 0.89 95 + 1 1.00 0.98 0.99 871 + + accuracy 0.98 966 + macro avg 0.91 0.97 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold5.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold5.txt new file mode 100644 index 0000000..307b0e2 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/classification_report_model_33fold5.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.82 0.81 0.82 96 + 1 0.98 0.98 0.98 870 + + accuracy 0.96 966 + macro avg 0.90 0.90 0.90 966 +weighted avg 0.96 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold1.csv new file mode 100644 index 0000000..018f7d6 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +33fold1,0.7937423154031699,0.9644087256026447,0.9052631578937839,0.9893992932861025,0.9585921325050767,840,86,9,31 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold2.csv new file mode 100644 index 0000000..5f79db6 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +33fold2,0.8337320576560242,0.9735935706083841,0.9157894736832465,0.9906542056073608,0.9679089026914112,848,87,8,23 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold3.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold3.csv new file mode 100644 index 0000000..0ed4131 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold3.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +33fold3,0.849358287827986,0.9690011481055144,0.9684210526305596,0.9964580873670605,0.9689440993787817,844,92,3,27 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold4.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold4.csv new file mode 100644 index 0000000..ad29b65 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold4.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +33fold4,0.8838856152543529,0.9781859931112539,0.9684210526305596,0.9964912280700589,0.9772256728777455,852,92,3,19 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold5.csv new file mode 100644 index 0000000..404e02c --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/metrics_model_33fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +33fold5,0.7966645954694175,0.9804597701148298,0.8124999999991537,0.9793340987369713,0.9637681159419291,853,78,18,17 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_33/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/trial_summary.json new file mode 100644 index 0000000..3d09261 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_33/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 33, + "model_type": "LSTM", + "params": { + "dropout": 0.4, + "learning_rate": 0.0013095977980279525, + "decision_threshold": 0.6, + "hidden_dim": 84, + "num_layers": 1 + }, + "mean_mcc": 0.8314765743221901, + "mcc_scores": [ + 0.7937423154031699, + 0.8337320576560242, + 0.849358287827986, + 0.8838856152543529, + 0.7966645954694175 + ] +} \ No newline at end of file diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_34/classification_report_model_34fold1.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_34/classification_report_model_34fold1.txt new file mode 100644 index 0000000..9c30abe --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_34/classification_report_model_34fold1.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.72 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--git a/output/LSTM/chest/Sc_4_T/binary_two/trial_35/classification_report_model_35fold2.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/classification_report_model_35fold2.txt new file mode 100644 index 0000000..d35a599 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/classification_report_model_35fold2.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.77 0.89 0.83 95 + 1 0.99 0.97 0.98 871 + + accuracy 0.96 966 + macro avg 0.88 0.93 0.90 966 +weighted avg 0.97 0.96 0.96 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_35/classification_report_model_35fold3.txt b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/classification_report_model_35fold3.txt new file mode 100644 index 0000000..dee354c --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/classification_report_model_35fold3.txt @@ -0,0 +1,8 @@ + precision recall f1-score support + + 0 0.76 0.75 0.75 95 + 1 0.97 0.97 0.97 871 + + accuracy 0.95 966 + macro avg 0.86 0.86 0.86 966 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0.98 966 + macro avg 0.91 0.97 0.94 966 +weighted avg 0.98 0.98 0.98 966 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_35/metrics_model_35fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/metrics_model_35fold1.csv new file mode 100644 index 0000000..e4f354c --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/metrics_model_35fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +35fold1,0.5803596092577108,0.9655568312283621,0.5894736842099058,0.9556818181817095,0.9285714285713325,841,56,39,30 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_35/metrics_model_35fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/metrics_model_35fold2.csv new file mode 100644 index 0000000..8bede54 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_35/metrics_model_35fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn 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a/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold1.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold1.csv new file mode 100644 index 0000000..c27e69b --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold1.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +36fold1,0.8028131844644486,0.9529276693454704,0.9789473684200222,0.9975961538460338,0.9554865424429653,830,93,2,41 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold2.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold2.csv new file mode 100644 index 0000000..3ab0767 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold2.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +36fold2,0.8316610239470263,0.9712973593569493,0.9263157894727091,0.9917936694019939,0.9668737060040407,846,88,7,25 diff --git 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a/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold5.csv b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold5.csv new file mode 100644 index 0000000..2dcb8ad --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/metrics_model_36fold5.csv @@ -0,0 +1,2 @@ +Model,MCC,Sensitivity,Specificity,Precision,Accuracy,tp,tn,fp,fn +36fold5,0.879931139748494,0.9735632183906926,0.9895833333323025,0.9988207547168633,0.9751552795030046,847,95,1,23 diff --git a/output/LSTM/chest/Sc_4_T/binary_two/trial_36/trial_summary.json b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/trial_summary.json new file mode 100644 index 0000000..5fe0d23 --- /dev/null +++ b/output/LSTM/chest/Sc_4_T/binary_two/trial_36/trial_summary.json @@ -0,0 +1,19 @@ +{ + "trial_number": 36, + "model_type": "LSTM", + "params": { + "dropout": 0.30000000000000004, + "learning_rate": 0.0007775863252818595, + "decision_threshold": 0.6, + "hidden_dim": 144, + "num_layers": 1 + }, + "mean_mcc": 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