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
