#%%
"""File 00factorAnalysis.py

Preliminary analysis of the indicators using factor analysis.

:author: Michel Bierlaire, EPFL
:date: Mon Sep  9 16:04:57 2019

"""

import sys
import pandas as pd
import numpy as np

# The following package can be installed using
# pip install factor_analyzer
# See https://github.com/EducationalTestingService/factor_analyzer
try:
    from factor_analyzer import FactorAnalyzer
except ModuleNotFoundError:
    print('Use "pip install factor_analyzer" to install a requested package')
    sys.exit()

# We first extract the columns containing the indicators
variables = ['Envir01',
             'Envir02',
             'Envir03',
             'Envir04',
             'Envir05',
             'Envir06',
             'Mobil01',
             'Mobil02',
             'Mobil03',
             'Mobil04',
             'Mobil05',
             'Mobil06',
             'Mobil07',
             'Mobil08',
             'Mobil09',
             'Mobil10',
             'Mobil11',
             'Mobil12',
             'Mobil13',
             'Mobil14',
             'Mobil15',
             'Mobil16',
             'Mobil17',
             'Mobil18',
             'Mobil19',
             'Mobil20',
             'Mobil21',
             'Mobil22',
             'Mobil23',
             'Mobil24',
             'Mobil25',
             'Mobil26',
             'Mobil27',
             'ResidCh01',
             'ResidCh02',
             'ResidCh03',
             'ResidCh04',
             'ResidCh05',
             'ResidCh06',
             'ResidCh07',
             'LifSty01',
             'LifSty02',
             'LifSty03',
             'LifSty04',
             'LifSty05',
             'LifSty06',
             'LifSty07',
             'LifSty08',
             'LifSty09',
             'LifSty10',
             'LifSty11',
             'LifSty12',
             'LifSty13',
             'LifSty14']

indicators = pd.read_csv('optima.dat', sep='\t', usecols=variables)

# Negative values are missing values.
indicators[indicators <= 0] = np.nan
indicators = indicators.dropna(axis=0, how='any')

# We perform the factor analysis
fa = FactorAnalyzer(rotation='varimax')
fa.fit(indicators)

# We obtain the factor loadings and label them
labeledResults = pd.DataFrame(fa.loadings_)
labeledResults.index = variables

# We keep only loadings that are 0.4 or higher, in absolute value.
labeledResults[(labeledResults <= 0.4) & (labeledResults >= -0.4)] = ''
print(labeledResults)
