import pandas as pd
import numpy as np
import os


from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif

features_dic = {}
results_dic = {}

script_dir = os.path.dirname(__file__)
rel_path = "dane_zawaly.xlsx"
abs_file_path = os.path.join(script_dir, rel_path)

dataExcel = pd.read_excel(abs_file_path, nrows=901)
df = pd.DataFrame(dataExcel)
# print(df)


feature_data = df.iloc[:, :-1]
diagnose_classes = np.array(df['Klasa'])


# Create an SelectKBest object to select features with two best ANOVA F-Values
fvalue_selector = SelectKBest(f_classif)

# Apply the SelectKBest object to the features and target
feature_data_kbest = fvalue_selector.fit(feature_data, diagnose_classes)

# print('Original number of features:', feature_data.shape[1])
# print('Reduced number of features:', feature_data_kbest.shape[1])
# print('Klasy', feature_data_kbest)
ranking = fvalue_selector.scores_

ranking_ulepszony = pd.DataFrame(ranking)
# ranking_ulepszony.insert(1, 'Numer cechy', pd.DataFrame(list(range(1,60))))
# ranking_ulepszony.sort_values(ranking_ulepszony[0])

print(ranking_ulepszony)

# print(ranking)