def get_data():
files = os.listdir('./MealNoMealData')
meal_data_files = []
no_meal_data_files = []
for file in files:
if 'Nomeal' in file:
no_meal_data_files.append(os.path.join('./MealNoMealData', file))
else:
meal_data_files.append(os.path.join('./MealNoMealData', file))
data = []
labels = []
for meal_data_file, no_meal_data_file in zip(meal_data_files,
no_meal_data_files):
preprocess_obj = Preprocess(meal_data_file)
meal_df = preprocess_obj.get_dataframe()
meal_features = Features(meal_df)
meal_features.compute_features()
# temp_meal_features = meal_features.pca_decomposition().tolist()
temp_meal_features = meal_features.get_features()
labels += [1] * len(temp_meal_features)
preprocess_obj_ = Preprocess(no_meal_data_file)
no_meal_df = preprocess_obj_.get_dataframe()
no_meal_features = Features(no_meal_df)
no_meal_features.compute_features()
no_meal_features_ = no_meal_features.get_features()
# no_meal_final_features = meal_features.pca.transform(no_meal_features_).tolist()
no_meal_final_features = no_meal_features_
labels += [0] * len(no_meal_features_)
for no_meal_feature in no_meal_final_features:
temp_meal_features.append(no_meal_feature)
for meal_no_meal_feature in temp_meal_features:
data.append(meal_no_meal_feature)
return data, labels
import pickle
from preprocessing import Preprocess
from features import Features
import numpy as np
import pandas as pd
test_file_name = input("Please enter the test file name: ")
preprocess_obj = Preprocess(test_file_name)
test_file_dataframe = preprocess_obj.get_dataframe()
test_file_features_obj = Features(test_file_dataframe)
test_file_features_obj.compute_features()
test_file_features = test_file_features_obj.get_features()
# print(len(test_file_features))
# Random Forest
random_forest_clf = pickle.load(open('RForest_model.pkl', 'rb'))
y_pred = random_forest_clf.predict(test_file_features)
print('Saving the output of RandomForest classifier prediction')
rforest_dataframe = pd.DataFrame(y_pred, columns=['Meal/NoMeal'])
rforest_dataframe.to_csv('RForest_output.csv')
# AdaBoost
adaboost_clf = pickle.load(open('Adaboost_model.pkl', 'rb'))
y_pred = adaboost_clf.predict(test_file_features)
print('Saving the output of AdaBoost classifier prediction')
adaboost_dataframe = pd.DataFrame(y_pred, columns=['Meal/NoMeal'])
adaboost_dataframe.to_csv('Adaboost_output.csv')
# XGBoost
XGBoost_clf = pickle.load(open('XGBoost_model.pkl', 'rb'))
from preprocessing import Preprocess
from features import Features
cgm_meal_pat_1_preprocess = Preprocess('./MealNoMealData/mealData1.csv')
cgm_meal_pat_1 = cgm_meal_pat_1_preprocess.get_dataframe()
print(cgm_meal_pat_1)
cgm_meal_pat_1_feature_obj = Features(cgm_meal_pat_1)
cgm_meal_pat1_features = cgm_meal_pat_1_feature_obj.compute_features()
cgm_meal_pat1_final_features = cgm_meal_pat_1_feature_obj.pca_decomposition()
