bbox_inches="tight") plt.show() #%% #data = pd.read_csv('../tommi_test_data.csv', sep=";", header=0) data = pd.read_csv('../tommi_test_data_more_diff_steps.csv', sep=";", header=0) data = data.loc[data["Warning_code"] == 0] data = data.reset_index(drop=True) tforce_DF = DataHandler.calculateTotalForce(data) step_t_DF = DataHandler.calculateStepTime(data) #%% Boosting test avg_acc, real_label, pred_label = Ensemble.testBoosting(step_t_DF) pred_label_df = pred_label real_label_df = real_label pred_label_df = pred_label_df.replace("Normal", 0) pred_label_df = pred_label_df.replace("Fall", 1) real_label_df = real_label_df.replace("Normal", 0) real_label_df = real_label_df.replace("Fall", 1) avg_auc = roc_auc_score(real_label_df, pred_label_df) print("AUC score: ", round(avg_auc, 2)) #%% #data = pd.read_csv('../tommi_test_data.csv', sep=";", header=0)
data = data.loc[data["Warning_code"] == 0] data = data.reset_index(drop=True) data = DataHandler.calculateTotalForce(data) data = DataHandler.calculateStepTime(data) data = DataHandler.calculateForceValues(data) data = DataHandler.calculatePhaseForceValues(data) pd.set_option('display.max_columns', None) selected_data = data.loc[:, DataColumns.getSelectedCols3andY()] selected_data #%% Boosting test avg_acc, real_label, pred_label = Ensemble.testBoosting(data) pred_label_df = pred_label real_label_df = real_label pred_label_df = pred_label_df.replace("Normal", 0) pred_label_df = pred_label_df.replace("Fall", 1) real_label_df = real_label_df.replace("Normal", 0) real_label_df = real_label_df.replace("Fall", 1) avg_auc = roc_auc_score(real_label_df, pred_label_df) print("AUC score: ", round(avg_auc, 2)) #%% 2d scatter from sklearn.decomposition import PCA