plt.rcParams["figure.figsize"] = (14, 12)
plt.ticklabel_format(style='plain', useOffset=False)
#%%
#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)
#%% Bagging test
avg_acc, real_label, pred_label = Ensemble.testBagging(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))
#%% 2d scatter
from sklearn.decomposition import PCA
plt.gcf().subplots_adjust(left=0.13)
plt.rcParams["figure.figsize"] = (14, 12)
plt.ticklabel_format(style='plain', useOffset=False)
#%%
data = pd.read_csv('../tommi+diego_test_data.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)
#%% Bagging test
avg_acc, real_label, pred_label = Ensemble.testBagging(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))
#%% 2d scatter
from sklearn.decomposition import PCA
