diff --git a/TaskManager.py b/TaskManager.py index 64b3489..17ea920 100644 --- a/TaskManager.py +++ b/TaskManager.py @@ -14,6 +14,18 @@ } self.sum_count = 0 # 合計の試行回数 self.target_count = count # 各タイプを実行する目標回数 + def reset(self): + self.counters = { + "right": 0, + "left": 0, + "neutral": 0 + } + self.taskCounters = { + "screen": 0, + "sound": 0, + "task": 0 + } + self.sum_count = 0 # 合計の試行回数 def get_next_type(self, mode): if mode == "mind": diff --git a/__pycache__/TaskManager.cpython-310.pyc b/__pycache__/TaskManager.cpython-310.pyc new file mode 100644 index 0000000..e14811c --- /dev/null +++ b/__pycache__/TaskManager.cpython-310.pyc Binary files differ diff --git a/__pycache__/model_training.cpython-39.pyc b/__pycache__/model_training.cpython-39.pyc new file mode 100644 index 0000000..0030637 --- /dev/null +++ b/__pycache__/model_training.cpython-39.pyc Binary files differ diff --git a/__pycache__/nouha_recv.cpython-310.pyc b/__pycache__/nouha_recv.cpython-310.pyc new file mode 100644 index 0000000..b9edad7 --- /dev/null +++ b/__pycache__/nouha_recv.cpython-310.pyc Binary files differ diff --git a/__pycache__/preprocessing.cpython-39.pyc b/__pycache__/preprocessing.cpython-39.pyc new file mode 100644 index 0000000..1ca3d55 --- /dev/null +++ b/__pycache__/preprocessing.cpython-39.pyc Binary files differ diff --git a/csvfiles/nouhadata_Attention2025-01-04,17-57-56-899582.csv b/csvfiles/nouhadata_Attention2025-01-04,17-57-56-899582.csv new file mode 100644 index 0000000..b63036e --- /dev/null +++ b/csvfiles/nouhadata_Attention2025-01-04,17-57-56-899582.csv @@ -0,0 +1,4 @@ +attention,key +, +, +, diff --git a/csvfiles/nouhadata_Attention2025-01-05,16-58-19-609364.csv b/csvfiles/nouhadata_Attention2025-01-05,16-58-19-609364.csv new file mode 100644 index 0000000..012d107 --- /dev/null +++ b/csvfiles/nouhadata_Attention2025-01-05,16-58-19-609364.csv @@ -0,0 +1,32 @@ +attention,key +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, 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index 0000000..027a66b --- /dev/null +++ b/csvfiles/nouhadata_Meditation2025-01-05,16-58-19-609364.csv @@ -0,0 +1,32 @@ +meditation,key +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, +, diff --git a/main-script.py b/main-script.py new file mode 100644 index 0000000..dcca386 --- /dev/null +++ b/main-script.py @@ -0,0 +1,107 @@ +import argparse +import os +import json +from datetime import datetime +import logging +from preprocessing import preprocess_eeg_data +from model_training import EEGClassifier + +def setup_logging(output_dir): + """ロギングの設定""" + log_file = os.path.join(output_dir, f'eeg_classification_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log') + + logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(log_file), + logging.StreamHandler() + ] + ) + +def save_results(results, output_dir): + """結果の保存""" + # 結果からモデルオブジェクトを除去（JSON化できないため） + serializable_results = {} + for model_name, model_results in results.items(): + serializable_results[model_name] = { + k: v for k, v in model_results.items() + if k not in ['model', 'confusion_matrix'] + } + # confusion_matrixをリストに変換 + serializable_results[model_name]['confusion_matrix'] = \ + model_results['confusion_matrix'].tolist() + + # 結果をJSONファイルとして保存 + results_file = os.path.join(output_dir, f'results_{datetime.now().strftime("%Y%m%d_%H%M%S")}.json') + with open(results_file, 'w') as f: + json.dump(serializable_results, f, indent=4) + +def main(): + # コマンドライン引数の設定 + parser = argparse.ArgumentParser(description='EEG信号分類プログラム') + parser.add_argument('input_file', help='入力CSVファイルのパス') + parser.add_argument('--output_dir', default='output', help='出力ディレクトリ') + parser.add_argument('--test_size', type=float, default=0.2, help='テストデータの割合') + parser.add_argument('--random_state', type=int, default=42, help='乱数シード') + parser.add_argument('--cv_folds', type=int, default=5, help='クロスバリデーションの分割数') + parser.add_argument('--skip_plots', action='store_true', help='プロットの生成をスキップ') + + args = parser.parse_args() + + # 出力ディレクトリの作成 + os.makedirs(args.output_dir, exist_ok=True) + + # ロギングの設定 + setup_logging(args.output_dir) + + try: + # 処理開始のログ + logging.info(f"Processing started for file: {args.input_file}") + + # データの前処理 + logging.info("Starting data preprocessing...") + processed_data = preprocess_eeg_data( + args.input_file, + test_size=args.test_size, + random_state=args.random_state, + validate=True + ) + logging.info("Data preprocessing completed") + + # モデルのトレーニングと評価 + logging.info("Starting model training and evaluation...") + classifier = EEGClassifier(random_state=args.random_state) + results = classifier.train_and_evaluate(processed_data, cv_folds=args.cv_folds) + logging.info("Model training and evaluation completed") + + # 結果の保存 + logging.info("Saving results...") + save_results(results, args.output_dir) + + if not args.skip_plots: + logging.info("Generating plots...") + # 特徴量重要度のプロット + for model_name in ['random_forest', 'xgboost']: + classifier.plot_feature_importance( + model_name=model_name, + output_path=os.path.join(args.output_dir, f'feature_importance_{model_name}.png') + ) + + # 混同行列のプロット + for model_name in results.keys(): + classifier.plot_confusion_matrix( + model_name=model_name, + results=results, + output_path=os.path.join(args.output_dir, f'confusion_matrix_{model_name}.png') + ) + logging.info("Plot generation completed") + + logging.info("All processing completed successfully") + + except Exception as e: + logging.error(f"An error occurred: {str(e)}", exc_info=True) + raise + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/model-training.py b/model-training.py new file mode 100644 index 0000000..182f13f --- /dev/null +++ b/model-training.py @@ -0,0 +1,240 @@ +import numpy as np +import pandas as pd +from sklearn.svm import SVC +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score +from sklearn.model_selection import GridSearchCV, StratifiedKFold +import xgboost as xgb +from typing import Dict, Any +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.preprocessing import LabelBinarizer + +class EEGClassifier: + def __init__(self, random_state: int = 42): + """ + EEG信号の分類モデルを管理するクラス + + Parameters: + random_state: int, 乱数シード + """ + self.random_state = random_state + self.models = self._define_models() + self.best_models = {} + self.feature_importance = {} + + def _define_models(self) -> Dict[str, Dict[str, Any]]: + """モデルとハイパーパラメータの定義""" + return { + 'svm': { + 'model': SVC(probability=True, random_state=self.random_state), + 'params': { + 'C': [0.1, 1, 10, 100], + 'kernel': ['rbf', 'poly'], + 'gamma': ['scale', 'auto'], + 'class_weight': ['balanced', None] + } + }, + 'random_forest': { + 'model': RandomForestClassifier(random_state=self.random_state), + 'params': { + 'n_estimators': [100, 200, 300], + 'max_depth': [10, 20, 30, None], + 'min_samples_split': [2, 5, 10], + 'class_weight': ['balanced', 'balanced_subsample', None] + } + }, + 'xgboost': { + 'model': xgb.XGBClassifier(random_state=self.random_state), + 'params': { + 'n_estimators': [100, 200, 300], + 'max_depth': [3, 5, 7], + 'learning_rate': [0.01, 0.1], + 'min_child_weight': [1, 3, 5], + 'subsample': [0.8, 0.9, 1.0], + 'colsample_bytree': [0.8, 0.9, 1.0] + } + } + } + + def train_and_evaluate(self, processed_data: Dict[str, Any], cv_folds: int = 5) -> Dict[str, Dict[str, Any]]: + """ + モデルの学習と評価を実行 + + Parameters: + processed_data: preprocess_eeg_dataからの出力 + cv_folds: クロスバリデーションの分割数 + + Returns: + Dict: 各モデルの評価結果 + """ + X_train = processed_data['X_train'] + X_test = processed_data['X_test'] + y_train = processed_data['y_train'] + y_test = processed_data['y_test'] + feature_names = processed_data['feature_names'] + + results = {} + + for name, model_info in self.models.items(): + print(f"\nTraining {name}...") + + # クロスバリデーション設定 + cv = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=self.random_state) + + # グリッドサーチ + grid_search = GridSearchCV( + model_info['model'], + model_info['params'], + cv=cv, + scoring=['accuracy', 'f1_macro', 'roc_auc_ovr'], + refit='f1_macro', + n_jobs=-1, + verbose=1 + ) + + # モデルの学習 + grid_search.fit(X_train, y_train) + + # 最適モデルの保存 + best_model = grid_search.best_estimator_ + self.best_models[name] = best_model + + # テストデータでの予測 + y_pred = best_model.predict(X_test) + y_pred_proba = best_model.predict_proba(X_test) + + # 特徴量の重要度（可能な場合） + if hasattr(best_model, 'feature_importances_'): + self.feature_importance[name] = pd.DataFrame({ + 'feature': feature_names, + 'importance': best_model.feature_importances_ + }).sort_values('importance', ascending=False) + + # 評価指標の計算 + lb = LabelBinarizer() + lb.fit(y_train) + y_test_bin = lb.transform(y_test) + + results[name] = { + 'model': best_model, + 'best_params': grid_search.best_params_, + 'cv_results': { + metric: grid_search.cv_results_[f'mean_test_{metric}'][grid_search.best_index_] + for metric in ['accuracy', 'f1_macro', 'roc_auc_ovr'] + }, + 'test_accuracy': grid_search.score(X_test, y_test), + 'classification_report': classification_report(y_test, y_pred), + 'confusion_matrix': confusion_matrix(y_test, y_pred), + 'roc_auc': roc_auc_score(y_test_bin, y_pred_proba, multi_class='ovr') + } + + # 結果の表示 + self._print_results(name, results[name]) + + return results + + def _print_results(self, model_name: str, results: Dict[str, Any]) -> None: + """結果の表示""" + print(f"\nResults for {model_name}:") + print("\nBest Parameters:") + print(results['best_params']) + print("\nCross-validation Results:") + for metric, value in results['cv_results'].items(): + print(f"{metric}: {value:.4f}") + print("\nTest Set Results:") + print(f"Accuracy: {results['test_accuracy']:.4f}") + print(f"ROC AUC: {results['roc_auc']:.4f}") + print("\nClassification Report:") + print(results['classification_report']) + + def plot_feature_importance(self, model_name: str, top_n: int = 20) -> None: + """ + 特徴量の重要度をプロット + + Parameters: + model_name: 表示するモデルの名前 + top_n: 表示する特徴量の数 + """ + if model_name not in self.feature_importance: + print(f"Feature importance not available for {model_name}") + return + + importance_df = self.feature_importance[model_name].head(top_n) + + plt.figure(figsize=(12, 6)) + sns.barplot(data=importance_df, x='importance', y='feature') + plt.title(f'Top {top_n} Important Features ({model_name})') + plt.tight_layout() + if output_path: + plt.savefig(output_path, bbox_inches='tight', dpi=300) + plt.close() + else: + if output_path: + plt.savefig(output_path, bbox_inches='tight', dpi=300) + plt.close() + else: + plt.show() + + def plot_confusion_matrix(self, model_name: str, results: Dict[str, Dict[str, Any]], + output_path: str = None) -> None: + """ + 混同行列をプロットする + + Parameters: + model_name: str, モデルの名前 + results: Dict, モデルの評価結果 + output_path: str, 保存先のパス（Noneの場合は表示のみ） + """ + if model_name not in results: + print(f"Results not found for {model_name}") + return + + cm = results[model_name]['confusion_matrix'] + plt.figure(figsize=(8, 6)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') + plt.title(f'Confusion Matrix ({model_name})') + plt.xlabel('Predicted') + plt.ylabel('True') + plt.tight_layout() + + if output_path: + plt.savefig(output_path, bbox_inches='tight', dpi=300) + plt.close() + else: + plt.show() + """ + 混同行列をプロット + + Parameters: + model_name: 表示するモデルの名前 + results: train_and_evaluateの結果 + """ + if model_name not in results: + print(f"Results not found for {model_name}") + return + + cm = results[model_name]['confusion_matrix'] + plt.figure(figsize=(8, 6)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') + plt.title(f'Confusion Matrix ({model_name})') + plt.xlabel('Predicted') + plt.ylabel('True') + plt.tight_layout() + plt.show() + +# 使用例 +if __name__ == "__main__": + # データの前処理（前回のコードで実行） + # processed_data = preprocess_eeg_data("path_to_your_eeg_data.csv") + + # モデルのトレーニングと評価 + # classifier = EEGClassifier() + # results = classifier.train_and_evaluate(processed_data) + + # 特徴量の重要度の表示 + # classifier.plot_feature_importance('random_forest') + + # 混同行列の表示 + # classifier.plot_confusion_matrix('random_forest', results) + pass \ No newline at end of file diff --git a/model_training.py b/model_training.py new file mode 100644 index 0000000..0e063a3 --- /dev/null +++ b/model_training.py @@ -0,0 +1,240 @@ +import numpy as np +import pandas as pd +from sklearn.svm import SVC +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score +from sklearn.model_selection import GridSearchCV, StratifiedKFold +import xgboost as xgb +from typing import Dict, Any +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.preprocessing import LabelBinarizer + +class EEGClassifier: + def __init__(self, random_state: int = 42): + """ + EEG信号の分類モデルを管理するクラス + + Parameters: + random_state: int, 乱数シード + """ + self.random_state = random_state + self.models = self._define_models() + self.best_models = {} + self.feature_importance = {} + + def _define_models(self) -> Dict[str, Dict[str, Any]]: + """モデルとハイパーパラメータの定義""" + return { + 'svm': { + 'model': SVC(probability=True, random_state=self.random_state), + 'params': { + 'C': [0.1, 1, 10, 100], + 'kernel': ['rbf', 'poly'], + 'gamma': ['scale', 'auto'], + 'class_weight': ['balanced', None] + } + }, + 'random_forest': { + 'model': RandomForestClassifier(random_state=self.random_state), + 'params': { + 'n_estimators': [100, 200, 300], + 'max_depth': [10, 20, 30, None], + 'min_samples_split': [2, 5, 10], + 'class_weight': ['balanced', 'balanced_subsample', None] + } + }, + 'xgboost': { + 'model': xgb.XGBClassifier(random_state=self.random_state), + 'params': { + 'n_estimators': [100, 200, 300], + 'max_depth': [3, 5, 7], + 'learning_rate': [0.01, 0.1], + 'min_child_weight': [1, 3, 5], + 'subsample': [0.8, 0.9, 1.0], + 'colsample_bytree': [0.8, 0.9, 1.0] + } + } + } + + def train_and_evaluate(self, processed_data: Dict[str, Any], cv_folds: int = 5) -> Dict[str, Dict[str, Any]]: + """ + モデルの学習と評価を実行 + + Parameters: + processed_data: preprocess_eeg_dataからの出力 + cv_folds: クロスバリデーションの分割数 + + Returns: + Dict: 各モデルの評価結果 + """ + X_train = processed_data['X_train'] + X_test = processed_data['X_test'] + y_train = processed_data['y_train'] + y_test = processed_data['y_test'] + feature_names = processed_data['feature_names'] + + results = {} + + for name, model_info in self.models.items(): + print(f"\nTraining {name}...") + + # クロスバリデーション設定 + cv = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=self.random_state) + + # グリッドサーチ + grid_search = GridSearchCV( + model_info['model'], + model_info['params'], + cv=cv, + scoring=['accuracy', 'f1_macro', 'roc_auc_ovr'], + refit='f1_macro', + n_jobs=-1, + verbose=1 + ) + + # モデルの学習 + grid_search.fit(X_train, y_train) + + # 最適モデルの保存 + best_model = grid_search.best_estimator_ + self.best_models[name] = best_model + + # テストデータでの予測 + y_pred = best_model.predict(X_test) + y_pred_proba = best_model.predict_proba(X_test) + + # 特徴量の重要度（可能な場合） + if hasattr(best_model, 'feature_importances_'): + self.feature_importance[name] = pd.DataFrame({ + 'feature': feature_names, + 'importance': best_model.feature_importances_ + }).sort_values('importance', ascending=False) + + # 評価指標の計算 + lb = LabelBinarizer() + lb.fit(y_train) + y_test_bin = lb.transform(y_test) + + results[name] = { + 'model': best_model, + 'best_params': grid_search.best_params_, + 'cv_results': { + metric: grid_search.cv_results_[f'mean_test_{metric}'][grid_search.best_index_] + for metric in ['accuracy', 'f1_macro', 'roc_auc_ovr'] + }, + 'test_accuracy': grid_search.score(X_test, y_test), + 'classification_report': classification_report(y_test, y_pred), + 'confusion_matrix': confusion_matrix(y_test, y_pred), + 'roc_auc': roc_auc_score(y_test_bin, y_pred_proba, multi_class='ovr') + } + + # 結果の表示 + self._print_results(name, results[name]) + + return results + + def _print_results(self, model_name: str, results: Dict[str, Any]) -> None: + """結果の表示""" + print(f"\nResults for {model_name}:") + print("\nBest Parameters:") + print(results['best_params']) + print("\nCross-validation Results:") + for metric, value in results['cv_results'].items(): + print(f"{metric}: {value:.4f}") + print("\nTest Set Results:") + print(f"Accuracy: {results['test_accuracy']:.4f}") + print(f"ROC AUC: {results['roc_auc']:.4f}") + print("\nClassification Report:") + print(results['classification_report']) + + def plot_feature_importance(self, model_name: str, top_n: int = 20, output_path: str = None) -> None: + """ + 特徴量の重要度をプロット + + Parameters: + model_name: 表示するモデルの名前 + top_n: 表示する特徴量の数 + """ + if model_name not in self.feature_importance: + print(f"Feature importance not available for {model_name}") + return + + importance_df = self.feature_importance[model_name].head(top_n) + + plt.figure(figsize=(12, 6)) + sns.barplot(data=importance_df, x='importance', y='feature') + plt.title(f'Top {top_n} Important Features ({model_name})') + plt.tight_layout() + if output_path: + plt.savefig(output_path, bbox_inches='tight', dpi=300) + plt.close() + else: + if output_path: + plt.savefig(output_path, bbox_inches='tight', dpi=300) + plt.close() + else: + plt.show() + + def plot_confusion_matrix(self, model_name: str, results: Dict[str, Dict[str, Any]], + output_path: str = None) -> None: + """ + 混同行列をプロットする + + Parameters: + model_name: str, モデルの名前 + results: Dict, モデルの評価結果 + output_path: str, 保存先のパス（Noneの場合は表示のみ） + """ + if model_name not in results: + print(f"Results not found for {model_name}") + return + + cm = results[model_name]['confusion_matrix'] + plt.figure(figsize=(8, 6)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') + plt.title(f'Confusion Matrix ({model_name})') + plt.xlabel('Predicted') + plt.ylabel('True') + plt.tight_layout() + + if output_path: + plt.savefig(output_path, bbox_inches='tight', dpi=300) + plt.close() + else: + plt.show() + """ + 混同行列をプロット + + Parameters: + model_name: 表示するモデルの名前 + results: train_and_evaluateの結果 + """ + if model_name not in results: + print(f"Results not found for {model_name}") + return + + cm = results[model_name]['confusion_matrix'] + plt.figure(figsize=(8, 6)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') + plt.title(f'Confusion Matrix ({model_name})') + plt.xlabel('Predicted') + plt.ylabel('True') + plt.tight_layout() + plt.show() + +# 使用例 +if __name__ == "__main__": + # データの前処理（前回のコードで実行） + # processed_data = preprocess_eeg_data("path_to_your_eeg_data.csv") + + # モデルのトレーニングと評価 + # classifier = EEGClassifier() + # results = classifier.train_and_evaluate(processed_data) + + # 特徴量の重要度の表示 + # classifier.plot_feature_importance('random_forest') + + # 混同行列の表示 + # classifier.plot_confusion_matrix('random_forest', results) + pass \ No newline at end of file diff --git a/preprocessing.py b/preprocessing.py new file mode 100644 index 0000000..9c35d14 --- /dev/null +++ b/preprocessing.py @@ -0,0 +1,257 @@ +import numpy as np +import pandas as pd +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split +from scipy import signal +from scipy.fft import fft, fftfreq +from typing import List, Dict, Tuple, Union, Any +import logging + +def validate_data(df: pd.DataFrame) -> Tuple[bool, str, Tuple[List[int], List[str]]]: + """ + データの妥当性をチェック + + Parameters: + df: DataFrame 入力データ + + Returns: + bool: データが有効かどうか + str: エラーメッセージ（エラーがある場合） + tuple: (クリーニングが必要な行のインデックス, クリーニングの理由) + """ + expected_columns = ['alpha', 'beta', 'theta', 'delta', 'gamma', 'key'] + valid_keys = [0, 1, 2] # ニュートラル、右、左 + rows_to_clean = [] + cleaning_reasons = [] + + # カラムの確認 + if not all(col in df.columns for col in expected_columns): + missing_cols = [col for col in expected_columns if col not in df.columns] + return False, f"Missing columns: {missing_cols}", ([], []) + + # 数値データの確認 + numerical_cols = ['alpha', 'beta', 'theta', 'delta', 'gamma'] + for col in numerical_cols: + if not np.issubdtype(df[col].dtype, np.number): + return False, f"Column {col} is not numeric", ([], []) + + # 負の値のチェック + negative_mask = df[col] < 0 + if negative_mask.any(): + negative_rows = df[negative_mask].index.tolist() + rows_to_clean.extend(negative_rows) + cleaning_reasons.extend([f'Negative value in {col}'] * len(negative_rows)) + + # NaNのチェック + nan_mask = df[col].isna() + if nan_mask.any(): + nan_rows = df[nan_mask].index.tolist() + rows_to_clean.extend(nan_rows) + cleaning_reasons.extend([f'NaN in {col}'] * len(nan_rows)) + + # keyの値の確認 + key_nan_mask = df['key'].isna() + if key_nan_mask.any(): + key_nan_rows = df[key_nan_mask].index.tolist() + rows_to_clean.extend(key_nan_rows) + cleaning_reasons.extend(['NaN in key'] * len(key_nan_rows)) + + valid_rows = ~df['key'].isna() + invalid_keys = [k for k in df[valid_rows]['key'].unique() if k not in valid_keys] + if invalid_keys: + invalid_key_mask = df['key'].isin(invalid_keys) + invalid_key_rows = df[invalid_key_mask].index.tolist() + rows_to_clean.extend(invalid_key_rows) + cleaning_reasons.extend(['Invalid key value'] * len(invalid_key_rows)) + + # 重複を除去 + if rows_to_clean: + unique_indices = [] + unique_reasons = [] + seen = set() + for idx, reason in zip(rows_to_clean, cleaning_reasons): + if idx not in seen: + unique_indices.append(idx) + unique_reasons.append(reason) + seen.add(idx) + return True, "Data needs cleaning", (sorted(unique_indices), + [unique_reasons[unique_indices.index(i)] for i in sorted(unique_indices)]) + + return True, "Data is valid", ([], []) + +def load_task_segments(file_path: str) -> List[pd.DataFrame]: + """ + 空行で区切られたタスクデータを読み込む + + Parameters: + file_path: CSVファイルのパス + + Returns: + List[DataFrame]: タスクセグメントのリスト + """ + df = pd.read_csv(file_path) + empty_rows = df.index[df.isnull().all(axis=1)].tolist() + + task_segments = [] + start_idx = 0 + + for end_idx in empty_rows: + if end_idx > start_idx: + segment = df.iloc[start_idx:end_idx].reset_index(drop=True) + if not segment.empty: + task_segments.append(segment) + start_idx = end_idx + 1 + + if start_idx < len(df): + segment = df.iloc[start_idx:].reset_index(drop=True) + if not segment.empty: + task_segments.append(segment) + + return task_segments + +def compute_spectral_features(data: np.ndarray, fs: float = 100.0) -> Dict[str, float]: + """スペクトル特徴量の計算""" + features = {} + + # FFTの計算 + n = len(data) + yf = fft(data) + xf = fftfreq(n, 1/fs)[:n//2] + power_spectrum = 2.0/n * np.abs(yf[0:n//2]) + + # スペクトル特徴量 + features['spectral_mean'] = np.mean(power_spectrum) + features['spectral_std'] = np.std(power_spectrum) + features['spectral_skew'] = pd.Series(power_spectrum).skew() + features['spectral_kurtosis'] = pd.Series(power_spectrum).kurtosis() + + # ピーク周波数 + peak_freq = xf[np.argmax(power_spectrum)] + features['peak_frequency'] = peak_freq + + return features + +def create_features(segment: pd.DataFrame) -> Dict[str, float]: + """1タスク分のデータから特徴量を生成""" + features = {} + basic_features = ['alpha', 'beta', 'theta', 'delta', 'gamma'] + + # 基本統計量 + for feature in basic_features: + # 時系列データの統計量 + features[f'{feature}_mean'] = segment[feature].mean() + features[f'{feature}_std'] = segment[feature].std() + features[f'{feature}_var'] = segment[feature].var() + features[f'{feature}_skew'] = segment[feature].skew() + features[f'{feature}_kurtosis'] = segment[feature].kurtosis() + + # トレンドと変化率 + x = np.arange(len(segment)) + features[f'{feature}_trend'] = np.polyfit(x, segment[feature].values, 1)[0] + features[f'{feature}_max_diff'] = segment[feature].diff().max() + features[f'{feature}_min_diff'] = segment[feature].diff().min() + + # スペクトル特徴量 + spectral_features = compute_spectral_features(segment[feature].values) + for spec_name, spec_value in spectral_features.items(): + features[f'{feature}_{spec_name}'] = spec_value + + # 波形間の比率 + ratios = { + 'alpha_beta_ratio': segment['alpha'] / segment['beta'], + 'theta_beta_ratio': segment['theta'] / segment['beta'], + 'alpha_theta_ratio': segment['alpha'] / segment['theta'] + } + + for ratio_name, ratio_data in ratios.items(): + features[f'{ratio_name}_mean'] = ratio_data.mean() + features[f'{ratio_name}_std'] = ratio_data.std() + + # パワーの合計と相対パワー + total_power = segment[basic_features].sum(axis=1) + for feature in basic_features: + rel_power = segment[feature] / total_power + features[f'{feature}_rel_mean'] = rel_power.mean() + features[f'{feature}_rel_std'] = rel_power.std() + + return features + +def preprocess_eeg_data(file_path: str, test_size: float = 0.2, + random_state: int = 42, validate: bool = True, + clean_data: bool = True) -> Dict[str, Any]: + """ + EEGデータの前処理パイプライン + + Parameters: + file_path: str, 入力CSVファイルのパス + test_size: float, テストデータの割合 + random_state: int, 乱数シード + validate: bool, データの妥当性チェックを行うかどうか + clean_data: bool, 問題のあるデータを自動的に除去するかどうか + + Returns: + Dict: 前処理済みのデータセット + """ + # データの読み込みと妥当性チェック + df = pd.read_csv(file_path) + if validate: + is_valid, message, (rows_to_clean, cleaning_reasons) = validate_data(df) + + if not is_valid: + raise ValueError(f"Invalid data: {message}") + + if rows_to_clean and clean_data: + logging.warning(f"Found {len(rows_to_clean)} rows that need cleaning:") + for idx, (row, reason) in enumerate(zip(rows_to_clean, cleaning_reasons)): + logging.warning(f"Row {row}: {reason}") + + # 問題のある行を削除 + df = df.drop(rows_to_clean).reset_index(drop=True) + logging.info(f"Removed {len(rows_to_clean)} problematic rows. Remaining rows: {len(df)}") + + elif rows_to_clean and not clean_data: + raise ValueError(f"Data contains {len(rows_to_clean)} problematic rows. Set clean_data=True to automatically remove them.") + + # タスクセグメントの読み込み + segments = load_task_segments(file_path) + + X = [] + y = [] + + # 各セグメントの処理 + for segment in segments: + if not segment.empty: + features = create_features(segment) + label = segment['key'].iloc[0] # セグメント内の最初のラベルを使用 + + X.append(features) + y.append(label) + + if X and y: # データが存在する場合のみ処理 + X = pd.DataFrame(X) + y = np.array(y) + + # 特徴量の前処理 + X = X.replace([np.inf, -np.inf], np.nan) + X = X.fillna(X.mean()) + + # データの分割 + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_size, random_state=random_state, stratify=y + ) + + # スケーリング + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + + return { + 'X_train': X_train_scaled, + 'X_test': X_test_scaled, + 'y_train': y_train, + 'y_test': y_test, + 'feature_names': X.columns, + 'scaler': scaler + } + + return None # データが存在しない場合 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..52c97d4 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,16 @@ +# Core data processing and scientific computing +numpy>=1.21.0 +pandas>=1.3.0 +scipy>=1.7.0 + +# Machine Learning +scikit-learn>=1.0.0 +xgboost>=1.5.0 + +# Visualization +matplotlib>=3.4.0 +seaborn>=0.11.0 + +# Optional but recommended for enhanced functionality +joblib>=1.1.0 # For parallel processing +tqdm>=4.62.0 # For progress bars \ No newline at end of file diff --git a/results/confusion_matrix_random_forest.png b/results/confusion_matrix_random_forest.png new file mode 100644 index 0000000..8d09292 --- /dev/null +++ b/results/confusion_matrix_random_forest.png Binary files differ diff --git a/results/confusion_matrix_svm.png b/results/confusion_matrix_svm.png new file mode 100644 index 0000000..62648d7 --- /dev/null +++ b/results/confusion_matrix_svm.png Binary files differ diff --git a/results/confusion_matrix_xgboost.png b/results/confusion_matrix_xgboost.png new file mode 100644 index 0000000..a908091 --- /dev/null +++ b/results/confusion_matrix_xgboost.png Binary files differ diff --git a/results/eeg_classification_20241225_175838.log b/results/eeg_classification_20241225_175838.log new file mode 100644 index 0000000..8fb452f --- /dev/null +++ b/results/eeg_classification_20241225_175838.log @@ -0,0 +1,9 @@ +2024-12-25 17:58:38,597 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 17:58:38,597 - INFO - Starting data preprocessing... +2024-12-25 17:58:38,607 - ERROR - An error occurred: Invalid data: Invalid key values found: [nan] +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 64, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 157, in preprocess_eeg_data + raise ValueError(f"Invalid data: {message}") +ValueError: Invalid data: Invalid key values found: [nan] diff --git a/results/eeg_classification_20241225_181700.log b/results/eeg_classification_20241225_181700.log new file mode 100644 index 0000000..6bd598f --- /dev/null +++ b/results/eeg_classification_20241225_181700.log @@ -0,0 +1,9 @@ +2024-12-25 18:17:00,733 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:17:00,733 - INFO - Starting data preprocessing... +2024-12-25 18:17:00,738 - ERROR - An error occurred: Invalid data: Invalid key values found: [nan] +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 64, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 157, in preprocess_eeg_data + raise ValueError(f"Invalid data: {message}") +ValueError: Invalid data: Invalid key values found: [nan] diff --git a/results/eeg_classification_20241225_181708.log b/results/eeg_classification_20241225_181708.log new file mode 100644 index 0000000..defd156 --- /dev/null +++ b/results/eeg_classification_20241225_181708.log @@ -0,0 +1,9 @@ +2024-12-25 18:17:08,263 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:17:08,263 - INFO - Starting data preprocessing... +2024-12-25 18:17:08,269 - ERROR - An error occurred: Invalid data: Invalid key values found: [nan] +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 64, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 157, in preprocess_eeg_data + raise ValueError(f"Invalid data: {message}") +ValueError: Invalid data: Invalid key values found: [nan] diff --git a/results/eeg_classification_20241225_182037.log b/results/eeg_classification_20241225_182037.log new file mode 100644 index 0000000..fbf252e --- /dev/null +++ b/results/eeg_classification_20241225_182037.log @@ -0,0 +1,7 @@ +2024-12-25 18:20:37,626 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:20:37,626 - INFO - Starting data preprocessing... +2024-12-25 18:20:37,626 - ERROR - An error occurred: preprocess_eeg_data() got an unexpected keyword argument 'clean_data' +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 65, in main + processed_data = preprocess_eeg_data( +TypeError: preprocess_eeg_data() got an unexpected keyword argument 'clean_data' diff --git a/results/eeg_classification_20241225_182210.log b/results/eeg_classification_20241225_182210.log new file mode 100644 index 0000000..9b008c6 --- /dev/null +++ b/results/eeg_classification_20241225_182210.log @@ -0,0 +1,7 @@ +2024-12-25 18:22:10,291 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:22:10,291 - INFO - Starting data preprocessing... +2024-12-25 18:22:10,292 - ERROR - An error occurred: preprocess_eeg_data() got an unexpected keyword argument 'clean_data' +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 65, in main + processed_data = preprocess_eeg_data( +TypeError: preprocess_eeg_data() got an unexpected keyword argument 'clean_data' diff --git a/results/eeg_classification_20241225_183257.log b/results/eeg_classification_20241225_183257.log new file mode 100644 index 0000000..8c55ca3 --- /dev/null +++ b/results/eeg_classification_20241225_183257.log @@ -0,0 +1,7 @@ +2024-12-25 18:32:57,252 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:32:57,253 - INFO - Starting data preprocessing... +2024-12-25 18:32:57,253 - ERROR - An error occurred: preprocess_eeg_data() got an unexpected keyword argument 'clean_data' +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 65, in main + processed_data = preprocess_eeg_data( +TypeError: preprocess_eeg_data() got an unexpected keyword argument 'clean_data' diff --git a/results/eeg_classification_20241225_183412.log b/results/eeg_classification_20241225_183412.log new file mode 100644 index 0000000..78dfe13 --- /dev/null +++ b/results/eeg_classification_20241225_183412.log @@ -0,0 +1,9 @@ +2024-12-25 18:34:12,394 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:34:12,394 - INFO - Starting data preprocessing... +2024-12-25 18:34:12,400 - ERROR - An error occurred: not enough values to unpack (expected 3, got 2) +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 65, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 155, in preprocess_eeg_data + is_valid, message, (rows_to_clean, cleaning_reasons) = validate_data(df) +ValueError: not enough values to unpack (expected 3, got 2) diff --git a/results/eeg_classification_20241225_184211.log b/results/eeg_classification_20241225_184211.log new file mode 100644 index 0000000..30e2d40 --- /dev/null +++ b/results/eeg_classification_20241225_184211.log @@ -0,0 +1,26 @@ +2024-12-25 18:42:11,661 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:42:11,661 - INFO - Starting data preprocessing... +2024-12-25 18:42:11,670 - ERROR - An error occurred: 'task_type' +Traceback (most recent call last): + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3361, in get_loc + return self._engine.get_loc(casted_key) + File "pandas\_libs\index.pyx", line 76, in pandas._libs.index.IndexEngine.get_loc + File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc + File "pandas\_libs\hashtable_class_helper.pxi", line 5198, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas\_libs\hashtable_class_helper.pxi", line 5206, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'task_type' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 65, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 198, in preprocess_eeg_data + is_valid, message, (rows_to_clean, cleaning_reasons) = validate_data(df) + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 34, in validate_data + nan_rows = df[col].isna() + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py", line 3458, in __getitem__ + indexer = self.columns.get_loc(key) + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3363, in get_loc + raise KeyError(key) from err +KeyError: 'task_type' diff --git a/results/eeg_classification_20241225_184506.log b/results/eeg_classification_20241225_184506.log new file mode 100644 index 0000000..a72756a --- /dev/null +++ b/results/eeg_classification_20241225_184506.log @@ -0,0 +1,26 @@ +2024-12-25 18:45:06,481 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:45:06,482 - INFO - Starting data preprocessing... +2024-12-25 18:45:06,488 - ERROR - An error occurred: 'task_type' +Traceback (most recent call last): + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3361, in get_loc + return self._engine.get_loc(casted_key) + File "pandas\_libs\index.pyx", line 76, in pandas._libs.index.IndexEngine.get_loc + File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc + File "pandas\_libs\hashtable_class_helper.pxi", line 5198, in pandas._libs.hashtable.PyObjectHashTable.get_item + File "pandas\_libs\hashtable_class_helper.pxi", line 5206, in pandas._libs.hashtable.PyObjectHashTable.get_item +KeyError: 'task_type' + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 65, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 198, in preprocess_eeg_data + is_valid, message, (rows_to_clean, cleaning_reasons) = validate_data(df) + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 34, in validate_data + nan_rows = df[col].isna() + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py", line 3458, in __getitem__ + indexer = self.columns.get_loc(key) + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3363, in get_loc + raise KeyError(key) from err +KeyError: 'task_type' diff --git a/results/eeg_classification_20241225_184514.log b/results/eeg_classification_20241225_184514.log new file mode 100644 index 0000000..176f789 --- /dev/null +++ b/results/eeg_classification_20241225_184514.log @@ -0,0 +1,58 @@ +2024-12-25 18:45:14,325 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 18:45:14,326 - INFO - Starting data preprocessing... +2024-12-25 18:45:14,333 - WARNING - Found 30 rows that need cleaning: +2024-12-25 18:45:14,334 - WARNING - Row 53: NaN in alpha +2024-12-25 18:45:14,334 - WARNING - Row 130: NaN in alpha +2024-12-25 18:45:14,334 - WARNING - Row 207: NaN in alpha +2024-12-25 18:45:14,334 - WARNING - Row 284: NaN in alpha +2024-12-25 18:45:14,334 - WARNING - Row 360: NaN in alpha +2024-12-25 18:45:14,334 - WARNING - Row 436: NaN in alpha +2024-12-25 18:45:14,334 - WARNING - Row 514: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 591: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 667: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 744: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 930: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 1007: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 1083: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 1161: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 1238: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 1315: NaN in alpha +2024-12-25 18:45:14,335 - WARNING - Row 1392: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 1468: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 1546: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 1623: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 1809: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 1886: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 1962: NaN in alpha +2024-12-25 18:45:14,336 - WARNING - Row 2039: NaN in alpha +2024-12-25 18:45:14,337 - WARNING - Row 2115: NaN in alpha +2024-12-25 18:45:14,337 - WARNING - Row 2193: NaN in alpha +2024-12-25 18:45:14,337 - WARNING - Row 2269: NaN in alpha +2024-12-25 18:45:14,337 - WARNING - Row 2346: NaN in alpha +2024-12-25 18:45:14,337 - WARNING - Row 2423: NaN in alpha +2024-12-25 18:45:14,337 - WARNING - Row 2500: NaN in alpha +2024-12-25 18:45:14,338 - INFO - Removed 30 problematic rows. Remaining rows: 2471 +2024-12-25 18:45:14,602 - INFO - Data preprocessing completed +2024-12-25 18:45:14,602 - INFO - Starting model training and evaluation... +2024-12-25 18:45:42,749 - INFO - Model training and evaluation completed +2024-12-25 18:45:42,749 - INFO - Saving results... +2024-12-25 18:45:42,750 - INFO - Generating plots... +2024-12-25 18:45:42,750 - ERROR - An error occurred: cannot do positional indexing on Int64Index with these indexers [./results\feature_importance_random_forest.png] of type str +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 93, in main + classifier.plot_feature_importance( + File "E:\NextCloud\Document\pythonFile\model_training.py", line 163, in plot_feature_importance + importance_df = self.feature_importance[model_name].head(top_n) + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 5073, in head + return self.iloc[:n] + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py", line 931, in __getitem__ + return self._getitem_axis(maybe_callable, axis=axis) + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py", line 1543, in _getitem_axis + return self._get_slice_axis(key, axis=axis) + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py", line 1578, in _get_slice_axis + labels._validate_positional_slice(slice_obj) + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3684, in _validate_positional_slice + self._validate_indexer("positional", key.stop, "iloc") + File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 5719, in _validate_indexer + raise self._invalid_indexer(form, key) +TypeError: cannot do positional indexing on Int64Index with these indexers [./results\feature_importance_random_forest.png] of type str diff --git a/results/eeg_classification_20241225_190122.log b/results/eeg_classification_20241225_190122.log new file mode 100644 index 0000000..c0aef74 --- /dev/null +++ b/results/eeg_classification_20241225_190122.log @@ -0,0 +1,41 @@ +2024-12-25 19:01:22,332 - INFO - Processing started for file: ./csvfiles/imagine_nouhadata_BandPower2024-12-25,16-32-25-602962.csv +2024-12-25 19:01:22,332 - INFO - Starting data preprocessing... +2024-12-25 19:01:22,339 - WARNING - Found 30 rows that need cleaning: +2024-12-25 19:01:22,340 - WARNING - Row 53: NaN in alpha +2024-12-25 19:01:22,340 - WARNING - Row 130: NaN in alpha +2024-12-25 19:01:22,340 - WARNING - Row 207: NaN in alpha +2024-12-25 19:01:22,340 - WARNING - Row 284: NaN in alpha +2024-12-25 19:01:22,340 - WARNING - Row 360: NaN in alpha +2024-12-25 19:01:22,340 - WARNING - Row 436: NaN in alpha +2024-12-25 19:01:22,340 - WARNING - Row 514: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 591: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 667: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 744: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 930: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 1007: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 1083: NaN in alpha +2024-12-25 19:01:22,341 - WARNING - Row 1161: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1238: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1315: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1392: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1468: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1546: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1623: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1809: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1886: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 1962: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 2039: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 2115: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 2193: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 2269: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 2346: NaN in alpha +2024-12-25 19:01:22,342 - WARNING - Row 2423: NaN in alpha +2024-12-25 19:01:22,343 - WARNING - Row 2500: NaN in alpha +2024-12-25 19:01:22,343 - INFO - Removed 30 problematic rows. Remaining rows: 2471 +2024-12-25 19:01:22,579 - INFO - Data preprocessing completed +2024-12-25 19:01:22,580 - INFO - Starting model training and evaluation... +2024-12-25 19:01:50,454 - INFO - Model training and evaluation completed +2024-12-25 19:01:50,454 - INFO - Saving results... +2024-12-25 19:01:50,455 - INFO - Generating plots... +2024-12-25 21:17:07,365 - INFO - Plot generation completed +2024-12-25 21:17:07,366 - INFO - All processing completed successfully diff --git a/results/eeg_classification_20241225_211945.log b/results/eeg_classification_20241225_211945.log new file mode 100644 index 0000000..b7034f1 --- /dev/null +++ b/results/eeg_classification_20241225_211945.log @@ -0,0 +1,15 @@ +2024-12-25 21:19:45,096 - INFO - Processing started for file: E:\NextCloud\Document\pythonFile\csvfiles\nouhadata_BandPower2024-12-18,11-34-33-451453.csv +2024-12-25 21:19:45,096 - INFO - Starting data preprocessing... +2024-12-25 21:19:45,186 - ERROR - An error occurred: With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters. +Traceback (most recent call last): + File "E:\NextCloud\Document\pythonFile\main-script.py", line 64, in main + processed_data = preprocess_eeg_data( + File "E:\NextCloud\Document\pythonFile\preprocessing.py", line 239, in preprocess_eeg_data + X_train, X_test, y_train, y_test = train_test_split( + File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper + return func(*args, **kwargs) + File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 2785, in train_test_split + n_train, n_test = _validate_shuffle_split( + File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py", line 2415, in _validate_shuffle_split + raise ValueError( +ValueError: With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters. diff --git a/results/eeg_classification_20241225_212029.log b/results/eeg_classification_20241225_212029.log new file mode 100644 index 0000000..3229054 --- /dev/null +++ b/results/eeg_classification_20241225_212029.log @@ -0,0 +1,41 @@ +2024-12-25 21:20:29,086 - INFO - Processing started for file: E:\NextCloud\Document\pythonFile\csvfiles\vision_nouhadata_BandPower2024-12-25,16-42-48-847946.csv +2024-12-25 21:20:29,086 - INFO - Starting data preprocessing... +2024-12-25 21:20:29,097 - WARNING - Found 30 rows that need cleaning: +2024-12-25 21:20:29,098 - WARNING - Row 43: NaN in alpha +2024-12-25 21:20:29,098 - WARNING - Row 119: NaN in alpha +2024-12-25 21:20:29,098 - WARNING - Row 197: NaN in alpha +2024-12-25 21:20:29,098 - WARNING - Row 274: NaN in alpha +2024-12-25 21:20:29,098 - WARNING - Row 350: NaN in alpha +2024-12-25 21:20:29,098 - WARNING - Row 427: NaN in alpha +2024-12-25 21:20:29,098 - WARNING - Row 504: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 580: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 658: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 735: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 922: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 999: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1075: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1153: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1230: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1307: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1384: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1460: NaN in alpha +2024-12-25 21:20:29,099 - WARNING - Row 1538: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 1615: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 1802: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 1879: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 1955: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2033: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2110: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2187: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2264: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2340: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2418: NaN in alpha +2024-12-25 21:20:29,100 - WARNING - Row 2494: NaN in alpha +2024-12-25 21:20:29,101 - INFO - Removed 30 problematic rows. Remaining rows: 2465 +2024-12-25 21:20:29,313 - INFO - Data preprocessing completed +2024-12-25 21:20:29,313 - INFO - Starting model training and evaluation... +2024-12-25 21:20:56,335 - INFO - Model training and evaluation completed +2024-12-25 21:20:56,335 - INFO - Saving results... +2024-12-25 21:20:56,336 - INFO - Generating plots... +2024-12-25 21:21:03,645 - INFO - Plot generation completed +2024-12-25 21:21:03,645 - INFO - All processing completed successfully diff --git a/results/feature_importance_random_forest.png b/results/feature_importance_random_forest.png new file mode 100644 index 0000000..9e06b4e --- /dev/null +++ b/results/feature_importance_random_forest.png Binary files differ diff --git a/results/feature_importance_xgboost.png b/results/feature_importance_xgboost.png new file mode 100644 index 0000000..00fa8c9 --- /dev/null +++ b/results/feature_importance_xgboost.png Binary files differ diff --git a/results/results_20241225_184542.json b/results/results_20241225_184542.json new file mode 100644 index 0000000..5742b53 --- /dev/null +++ b/results/results_20241225_184542.json @@ -0,0 +1,103 @@ +{ + "svm": { + "best_params": { + "C": 1, + "class_weight": "balanced", + "gamma": "scale", + "kernel": "rbf" + }, + "cv_results": { + "accuracy": 0.41, + "f1_macro": 0.2977777777777778, + "roc_auc_ovr": 0.4 + }, + "test_accuracy": 0.3333333333333333, + "classification_report": " precision recall f1-score support\n\n 0.0 0.25 0.50 0.33 2\n 1.0 1.00 0.50 0.67 2\n 2.0 0.00 0.00 0.00 2\n\n accuracy 0.33 6\n macro avg 0.42 0.33 0.33 6\nweighted avg 0.42 0.33 0.33 6\n", + "roc_auc": 0.5, + "confusion_matrix": [ + [ + 1, + 0, + 1 + ], + [ + 1, + 1, + 0 + ], + [ + 2, + 0, + 0 + ] + ] + }, + "random_forest": { + "best_params": { + "class_weight": "balanced", + "max_depth": 10, + "min_samples_split": 10, + "n_estimators": 300 + }, + "cv_results": { + "accuracy": 0.42000000000000004, + "f1_macro": 0.36222222222222217, + "roc_auc_ovr": 0.49444444444444446 + }, + "test_accuracy": 0.27777777777777773, + "classification_report": " precision recall f1-score support\n\n 0.0 0.25 0.50 0.33 2\n 1.0 0.50 0.50 0.50 2\n 2.0 0.00 0.00 0.00 2\n\n accuracy 0.33 6\n macro avg 0.25 0.33 0.28 6\nweighted avg 0.25 0.33 0.28 6\n", + "roc_auc": 0.20833333333333334, + "confusion_matrix": [ + [ + 1, + 1, + 0 + ], + [ + 1, + 1, + 0 + ], + [ + 2, + 0, + 0 + ] + ] + }, + "xgboost": { + "best_params": { + "colsample_bytree": 1.0, + "learning_rate": 0.01, + "max_depth": 3, + "min_child_weight": 1, + "n_estimators": 300, + "subsample": 1.0 + }, + "cv_results": { + "accuracy": 0.42000000000000004, + "f1_macro": 0.3822222222222222, + "roc_auc_ovr": 0.4333333333333334 + }, + "test_accuracy": 0.27777777777777773, + "classification_report": " precision recall f1-score support\n\n 0.0 0.25 0.50 0.33 2\n 1.0 0.50 0.50 0.50 2\n 2.0 0.00 0.00 0.00 2\n\n accuracy 0.33 6\n macro avg 0.25 0.33 0.28 6\nweighted avg 0.25 0.33 0.28 6\n", + "roc_auc": 0.4166666666666667, + "confusion_matrix": [ + [ + 1, + 1, + 0 + ], + [ + 1, + 1, + 0 + ], + [ + 2, + 0, + 0 + ] + ] + } +} \ No newline at end of file diff --git a/results/results_20241225_190150.json b/results/results_20241225_190150.json new file mode 100644 index 0000000..5742b53 --- /dev/null +++ b/results/results_20241225_190150.json @@ -0,0 +1,103 @@ +{ + "svm": { + "best_params": { + "C": 1, + "class_weight": "balanced", + "gamma": "scale", + "kernel": "rbf" + }, + "cv_results": { + "accuracy": 0.41, + "f1_macro": 0.2977777777777778, + "roc_auc_ovr": 0.4 + }, + "test_accuracy": 0.3333333333333333, + "classification_report": " precision recall f1-score support\n\n 0.0 0.25 0.50 0.33 2\n 1.0 1.00 0.50 0.67 2\n 2.0 0.00 0.00 0.00 2\n\n accuracy 0.33 6\n macro avg 0.42 0.33 0.33 6\nweighted avg 0.42 0.33 0.33 6\n", + "roc_auc": 0.5, + "confusion_matrix": [ + [ + 1, + 0, + 1 + ], + [ + 1, + 1, + 0 + ], + [ + 2, + 0, + 0 + ] + ] + }, + "random_forest": { + "best_params": { + "class_weight": "balanced", + "max_depth": 10, + "min_samples_split": 10, + "n_estimators": 300 + }, + "cv_results": { + "accuracy": 0.42000000000000004, + "f1_macro": 0.36222222222222217, + "roc_auc_ovr": 0.49444444444444446 + }, + "test_accuracy": 0.27777777777777773, + "classification_report": " precision recall f1-score support\n\n 0.0 0.25 0.50 0.33 2\n 1.0 0.50 0.50 0.50 2\n 2.0 0.00 0.00 0.00 2\n\n accuracy 0.33 6\n macro avg 0.25 0.33 0.28 6\nweighted avg 0.25 0.33 0.28 6\n", + "roc_auc": 0.20833333333333334, + "confusion_matrix": [ + [ + 1, + 1, + 0 + ], + [ + 1, + 1, + 0 + ], + [ + 2, + 0, + 0 + ] + ] + }, + "xgboost": { + "best_params": { + "colsample_bytree": 1.0, + "learning_rate": 0.01, + "max_depth": 3, + "min_child_weight": 1, + "n_estimators": 300, + "subsample": 1.0 + }, + "cv_results": { + "accuracy": 0.42000000000000004, + "f1_macro": 0.3822222222222222, + "roc_auc_ovr": 0.4333333333333334 + }, + "test_accuracy": 0.27777777777777773, + "classification_report": " precision recall f1-score support\n\n 0.0 0.25 0.50 0.33 2\n 1.0 0.50 0.50 0.50 2\n 2.0 0.00 0.00 0.00 2\n\n accuracy 0.33 6\n macro avg 0.25 0.33 0.28 6\nweighted avg 0.25 0.33 0.28 6\n", + "roc_auc": 0.4166666666666667, + "confusion_matrix": [ + [ + 1, + 1, + 0 + ], + [ + 1, + 1, + 0 + ], + [ + 2, + 0, + 0 + ] + ] + } +} \ No newline at end of file diff --git a/results/results_20241225_212056.json b/results/results_20241225_212056.json new file mode 100644 index 0000000..fc4fe92 --- /dev/null +++ b/results/results_20241225_212056.json @@ -0,0 +1,103 @@ +{ + "svm": { + "best_params": { + "C": 10, + "class_weight": "balanced", + "gamma": "scale", + "kernel": "rbf" + }, + "cv_results": { + "accuracy": 0.54, + "f1_macro": 0.5244444444444445, + "roc_auc_ovr": 0.7277777777777779 + }, + "test_accuracy": 0.13333333333333333, + "classification_report": " precision recall f1-score support\n\n 0.0 0.00 0.00 0.00 2\n 1.0 0.00 0.00 0.00 2\n 2.0 0.33 0.50 0.40 2\n\n accuracy 0.17 6\n macro avg 0.11 0.17 0.13 6\nweighted avg 0.11 0.17 0.13 6\n", + "roc_auc": 0.4583333333333333, + "confusion_matrix": [ + [ + 0, + 2, + 0 + ], + [ + 0, + 0, + 2 + ], + [ + 1, + 0, + 1 + ] + ] + }, + "random_forest": { + "best_params": { + "class_weight": "balanced", + "max_depth": 10, + "min_samples_split": 2, + "n_estimators": 200 + }, + "cv_results": { + "accuracy": 0.6599999999999999, + "f1_macro": 0.5755555555555555, + "roc_auc_ovr": 0.8055555555555556 + }, + "test_accuracy": 0.19047619047619047, + "classification_report": " precision recall f1-score support\n\n 0.0 0.00 0.00 0.00 2\n 1.0 0.00 0.00 0.00 2\n 2.0 0.40 1.00 0.57 2\n\n accuracy 0.33 6\n macro avg 0.13 0.33 0.19 6\nweighted avg 0.13 0.33 0.19 6\n", + "roc_auc": 0.5, + "confusion_matrix": [ + [ + 0, + 1, + 1 + ], + [ + 0, + 0, + 2 + ], + [ + 0, + 0, + 2 + ] + ] + }, + "xgboost": { + "best_params": { + "colsample_bytree": 0.8, + "learning_rate": 0.1, + "max_depth": 3, + "min_child_weight": 1, + "n_estimators": 100, + "subsample": 1.0 + }, + "cv_results": { + "accuracy": 0.63, + "f1_macro": 0.5977777777777777, + "roc_auc_ovr": 0.7722222222222223 + }, + "test_accuracy": 0.13333333333333333, + "classification_report": " precision recall f1-score support\n\n 0.0 0.00 0.00 0.00 2\n 1.0 0.00 0.00 0.00 2\n 2.0 0.33 0.50 0.40 2\n\n accuracy 0.17 6\n macro avg 0.11 0.17 0.13 6\nweighted avg 0.11 0.17 0.13 6\n", + "roc_auc": 0.5416666666666666, + "confusion_matrix": [ + [ + 0, + 1, + 1 + ], + [ + 1, + 0, + 1 + ], + [ + 1, + 0, + 1 + ] + ] + } +} \ No newline at end of file diff --git a/run.py b/run.py index b5a5a4f..2c3ace6 100644 --- a/run.py +++ b/run.py @@ -10,16 +10,16 @@ import TaskManager #”重要” offLineModeをFalseにすると脳波データの通信が行われるため、UDP通信が起動していない場合にFalseにするとバグ発生の可能性あり -offLineMode = True -WIDTH = 3200 #ウィンドウの横サイズ -HEIGHT = 1800 #ウィンドウの縦サイズ +offLineMode = False +WIDTH = 1280 #ウィンドウの横サイズ +HEIGHT = 720 #ウィンドウの縦サイズ TIME = 7000 #タスクの継続時間（慣れ時間含む、ミリ秒） BREAK_TIME = 8000 #タスク間の休憩時間（ミリ秒） LONG_BREAK_TIME = 30000 #長めの休憩時間（ミリ秒） LONG_BREAK_INTERVAL = 10 #何タスクごとに長めの休憩を取るか PREP_TIME = 2000 #各タスクの「慣れ」時間（ミリ秒） TASK_COUNT = 10 #右、左、ニュートラルそれぞれのタスク数（デフォルトの場合それぞれ5回ずつタスクを実施） -TASK = "sound" +TASK = "task" #UDPの準備 HOST = '' PORT = 8001 @@ -35,25 +35,42 @@ position_rect = (0, 0, 0, 0) position_circle = (0, 0) get_tick_time = [0, 0] #[before, now] +_count = 0 +task_count = 0 # 指示内容を定義 +# instructions = { +# "right": [ + +# "右手を想像して箸で食べ物を掴んでください。", +# "右手のボタンを押してください。", +# "右手のレバーを操作してください。", +# "右手を想像してドアノブを回してください。", +# "右手を想像してボールを投げてください。", +# "右手を想像して箸で食べ物を掴んでください。", +# ], +# "left": [ + +# "左手を想像して箸で食べ物を掴んでください。", +# "左手のボタンを押してください。", +# "左手のレバーを操作してください。", +# "左手を想像してドアノブを回してください。", +# "左手を想像してボールを投げてください。", +# "左手を想像して箸で食べ物を掴んでください。", +# ], +# "neutral": [ +# "目を閉じてリラックスしてください。", +# "深呼吸をしてください。", +# "座禅を組んで瞑想してください。" +# ] +# } instructions = { "right": [ - "右手を想像して箸で食べ物を掴んでください。", - "右手のボタンを押してください。", - "右手のレバーを操作してください。", - "右手を想像してドアノブを回してください。", - "右手を想像してボールを投げてください。", - "右手を想像して箸で食べ物を掴んでください。", + "右手で手を開く、閉じる動作をしてください。", ], "left": [ - "左手を想像して箸で食べ物を掴んでください。", - "左手のボタンを押してください。", - "左手のレバーを操作してください。", - "左手を想像してドアノブを回してください。", - "左手を想像してボールを投げてください。", - "左手を想像して箸で食べ物を掴んでください。", + "左手で手を開く、閉じる動作をしてください。", ], "neutral": [ "目を閉じてリラックスしてください。", @@ -86,16 +103,23 @@ #脳波データの受け取りと処理 def send_nouhadata(s, choice, mind, process): + global _count, task_count print_text = text_type[choice] print("タスク経過時間：", str(get_tick_time[1] - get_tick_time[0]), print_text) if offLineMode == False: data, address = s.recvfrom(1024) - process.Receive_BrainWave(nouha=data, key=mind, address=address) + if task_count < 1: + pass + else: + process.Receive_BrainWave(nouha=data, key=mind, address=address) + _count += 1 + print(_count) get_tick_time[1] = pygame.time.get_ticks() def take_a_break(s, task_manager, choice, process, time): #タスク間の休憩 + global task_count process.Receive_BrainWave(nouha=0, key="break", address=0) get_tick_time[0] = pygame.time.get_ticks() print(f"現在の実行回数: {task_manager.get_counts()}") @@ -103,7 +127,7 @@ if check_exit(s, choice, process): break get_tick_time[1] = pygame.time.get_ticks() - + task_count += 1 #アプリの終了時の処理 def check_exit(s, choice, process): for event in pygame.event.get(): @@ -140,9 +164,12 @@ start(screen) while True: try: - + global _count, task_count + _count = 0 get_tick_time[0] = pygame.time.get_ticks() mind = task_manager.get_next_type("mind") + if task_count == 0: + task_manager.reset() if mind is None: print("すべてのタスクが完了しました。ウィンドウを閉じて終了してください。") while True: @@ -186,6 +213,8 @@ pygame.display.update() if get_tick_time[1] - get_tick_time[0] >= PREP_TIME: send_nouhadata(s, choice, mind, process) + count += 1 + print(count) if check_exit(s, choice, process): break get_tick_time[1] = pygame.time.get_ticks() @@ -251,13 +280,13 @@ #画面の更新 pygame.display.update() - if task_manager.get_sum_count() % LONG_BREAK_INTERVAL == 0: - #タスク間の休憩(長め) - take_a_break(s, task_manager=task_manager, choice=choice, process=process, time=LONG_BREAK_TIME) - else: - #タスク間の休憩 - take_a_break(s, task_manager=task_manager, choice=choice, process=process, time=BREAK_TIME) - + # if task_manager.get_sum_count() % LONG_BREAK_INTERVAL == 0: + # #タスク間の休憩(長め) + # take_a_break(s, task_manager=task_manager, choice=choice, process=process, time=LONG_BREAK_TIME) + # else: + # #タスク間の休憩 + # take_a_break(s, task_manager=task_manager, choice=choice, process=process, time=BREAK_TIME) + take_a_break(s, task_manager=task_manager, choice=choice, process=process, time=BREAK_TIME) except SystemExit: pygame.quit() s.close() diff --git "a/sounds/left_\345\267\246\346\211\213\343\201\247\346\211\213\343\202\222\351\226\213\343\201\217\343\200\201\351\226\211\343\201\230\343\202\213\345\213\225\344\275\234\343\202\222\343\201\227\343\201\246\343\201\217\343\201\240\343\201\225\343\201\204\343\200\202.wav" 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