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IFAS.py
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#!/usr/bin/env python
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxx-----------SPECTRAL PARAMETERIZATION & CLASSIFICATION USING ANN------------xxxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# ------------------------------------------------------------------------------------------------------------------- #
# Import Required Libraries
# ------------------------------------------------------------------------------------------------------------------- #
import os
import re
import glob
import itertools
import numpy as np
import pandas as pd
import seaborn as sn
from astropy.io import ascii
from astropy.convolution import convolve, Gaussian1DKernel
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import FixedLocator, MultipleLocator
from matplotlib.colors import LinearSegmentedColormap
from scipy.stats import norm
from scipy.optimize import curve_fit
from scipy.interpolate import CubicSpline
from sklearn import svm
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Global Variables
# ------------------------------------------------------------------------------------------------------------------- #
fwhm_jacob = 4.50
fwhm_cflib = 0.88
fwhm_miles = 2.50
lower_lim = 3600
upper_lim = 7400
wave_norm = 5550
sample_size = 5
sample_size2 = 3
min_val = 500
max_val = 7950
list_wave = np.arange(lower_lim, upper_lim + 1, sample_size)
list_wave2 = np.arange(lower_lim, upper_lim + 1, sample_size2)
list_type = ['O', 'B', 'A', 'F', 'G', 'K', 'M']
dict_type = {'O': 1000, 'B': 2000, 'A': 3000, 'F': 4000, 'G': 5000, 'K': 6000, 'M': 7000}
dict_class = {'I': 1, 'II': 2, 'III': 3, 'IV': 4, 'V': 5}
list_labels = ['Class O', 'Class A', 'Class B', 'Class F', 'Class G', 'Class K', 'Class M']
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# PATH Of Important Directories
# ------------------------------------------------------------------------------------------------------------------- #
DIR_CURNT = os.getcwd()
DIR_JACOB = "/home/avinash/Dropbox/PyCharm/IFAS/Jacobi/"
DIR_CFLIB = "/home/avinash/Dropbox/PyCharm/IFAS/CFLIB/"
DIR_CFLIB2 = "/home/avinash/Dropbox/PyCharm/IFAS/CFLIB2/"
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Functions For File Handling
# ------------------------------------------------------------------------------------------------------------------- #
def group_similar_files(text_list, common_text, exceptions=''):
"""
Groups similar files based on the string "common_text". Writes the similar files
onto the list 'text_list' (only if this string is not empty) and appends the similar
files to a list 'python_list'.
Args:
text_list : Name of the output text file with names grouped based on the 'common_text'
common_text : String containing partial name of the files to be grouped
exceptions : String containing the partial name of the files that need to be excluded
Returns:
list_files : Python list containing the names of the grouped files
"""
list_files = glob.glob(common_text)
if exceptions != '':
list_exception = exceptions.split(',')
for file_name in glob.glob(common_text):
for text in list_exception:
test = re.search(str(text), file_name)
if test:
try:
list_files.remove(file_name)
except ValueError:
pass
list_files.sort()
if len(text_list) != 0:
with open(str(text_list), "w") as f:
for index in range(0, len(list_files)):
f.write(str(list_files[index]) + "\n")
return list_files
def display_text(text_to_display):
"""
Displays text mentioned in the string 'text_to_display'
Args:
text_to_display : Text to be displayed
Returns:
None
"""
print ("\n" + "# " + "-" * (12 + len(text_to_display)) + " #")
print ("# " + "-" * 5 + " " + str(text_to_display) + " " + "-" * 5 + " #")
print ("# " + "-" * (12 + len(text_to_display)) + " #" + "\n")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Make A List of Files From The Two Catalogs
# ------------------------------------------------------------------------------------------------------------------- #
list_jacob = group_similar_files("", os.path.join(DIR_JACOB, "*.ascii"), exceptions='NewData,TempData')
list_cflib = group_similar_files("", os.path.join(DIR_CFLIB, "*.txt"), exceptions='NewData,TempData')
list_cflib2 = group_similar_files("", os.path.join(DIR_CFLIB2, "*.txt"), exceptions='NewData,TempData')
list_cflib2 = [DIR_CFLIB + file_name.split('/')[-1] for file_name in list_cflib2]
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Jacobi - Resample At 5 A, Normalise At 5550 A And Trim From 3600-7400 A
# ------------------------------------------------------------------------------------------------------------------- #
for file_name in list_jacob:
input_df = pd.read_csv(file_name, names=['Wave', 'Flux'], header=None, sep='\s+', comment='#', engine='python')
spline = CubicSpline(input_df['Wave'].tolist(), input_df['Flux'].tolist())
output_df = pd.DataFrame(spline(list_wave), index=list_wave, columns=['Flux'])
output_df.index.name = 'Wave'
output_df = output_df.reset_index(drop=False)
norm_factor = output_df[output_df['Wave'] == wave_norm]['Flux']
output_df['FluxNorm'] = output_df['Flux'].apply(lambda x: x / norm_factor)
output_df.to_csv(DIR_JACOB + 'NewData_' + file_name.split('/')[-1], sep=' ', index=None, header=True)
display_text("Data From JACOBI Catalog Has Been Pre-Processed")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# CFLIB - Convolve The Spectra With A Gaussian, Resample At 5 A, Normalise At 5550 A And Trim From 3600-7400 A
# ------------------------------------------------------------------------------------------------------------------- #
fwhm_convolve = (fwhm_jacob ** 2 - fwhm_cflib ** 2) ** 0.5
gauss = Gaussian1DKernel(fwhm_convolve)
for file_name in list_cflib:
input_df = pd.read_csv(file_name, names=['Wave', 'Flux'], header=None, sep='\s+', comment='#', engine='python')
input_df['FluxCon'] = convolve(input_df['Flux'].tolist(), gauss)
# input_df.to_csv(DIR_CFLIB + 'TempData_' + file_name.split('/')[-1], sep=' ', index=None, header=True)
spline = CubicSpline(input_df['Wave'].tolist(), input_df['FluxCon'].tolist())
output_df = pd.DataFrame(spline(list_wave), index=list_wave, columns=['Flux'])
output_df.index.name = 'Wave'
output_df = output_df.reset_index(drop=False)
norm_factor = output_df[output_df['Wave'] == wave_norm]['Flux']
output_df['FluxNorm'] = output_df['Flux'].apply(lambda x: x / norm_factor)
output_df.to_csv(DIR_CFLIB + 'NewData_' + file_name.split('/')[-1], sep=' ', index=None, header=True)
display_text("Data From CFLIB Catalog Has Been Pre-Processed")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# CFLIB2 - Convolve The Spectra With A Gaussian, Resample At 3 A, Normalise At 5550 A And Trim From 3600-7400 A
# ------------------------------------------------------------------------------------------------------------------- #
fwhm_convolve2 = (fwhm_miles ** 2 - fwhm_cflib ** 2) ** 0.5
gauss2 = Gaussian1DKernel(fwhm_convolve2)
for file_name in list_cflib2:
input_df = pd.read_csv(file_name, names=['Wave', 'Flux'], header=None, sep='\s+', comment='#', engine='python')
input_df['FluxCon'] = convolve(input_df['Flux'].tolist(), gauss2)
# input_df.to_csv(DIR_CFLIB2 + 'TempData2_' + file_name.split('/')[-1], sep=' ', index=None, header=True)
spline = CubicSpline(input_df['Wave'].tolist(), input_df['FluxCon'].tolist())
output_df = pd.DataFrame(spline(list_wave2), index=list_wave2, columns=['Flux'])
output_df.index.name = 'Wave'
output_df = output_df.reset_index(drop=False)
norm_factor = output_df[output_df['Wave'] == wave_norm]['Flux']
output_df['FluxNorm'] = output_df['Flux'].apply(lambda x: x / norm_factor)
output_df.to_csv(DIR_CFLIB2 + 'NewData2_' + file_name.split('/')[-1], sep=' ', index=None, header=True)
display_text("Data From CFLIB2 Catalog Has Been Pre-Processed")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Log And Encode The Spectral Types
# ------------------------------------------------------------------------------------------------------------------- #
def encode_spectype(spectype):
if re.findall(r'\d+.\d+', spectype):
subclass = re.findall(r'\d+.\d+', spectype)[0]
else:
subclass = re.findall(r'\d+', spectype)[0]
return dict_type[spectype[0]] + float(subclass) * 100 + 2 * (dict_class[spectype[len(subclass) + 1:]] - 1)
dict_spectype = {}
for file_name in list_cflib:
with open(file_name, 'r') as fin:
for line in itertools.islice(fin, 25):
if re.search('PICKTYPE', line):
dict_spectype[file_name.split('/')[-1]] = line.rstrip().split(' ')[-1]
data_cflib = pd.DataFrame(dict_spectype.values(), index=dict_spectype.keys(), columns=['TYPE'])
data_cflib.index.name = 'NAME'
data_cflib = data_cflib.reset_index(drop=False)
data_cflib = data_cflib.sort_values(by='NAME')
data_cflib['ENC'] = data_cflib['TYPE'].apply(lambda x: encode_spectype(x))
data_cflib.to_csv('Log_CFLIB.dat', sep=' ', index=False, header=False)
data_cflib['ENC'].to_csv('Type_CFLIB.dat', sep=' ', index=False, header=False)
data_jacob = pd.read_csv('Jacobi_raw.dat', sep='\s+', engine='python')
data_jacob['NAME'] = data_jacob['NAME'].apply(lambda x: x + '.ascii')
data_jacob = data_jacob.sort_values(by='NAME')
data_jacob['ENC'] = data_jacob['TYPE'].apply(lambda x: encode_spectype(x))
data_jacob.to_csv('Log_Jacobi.dat', sep=' ', index=False, header=False)
data_jacob['ENC'].to_csv('Type_Jacobi.dat', sep=' ', index=False, header=False)
data_cflib2 = pd.read_csv('CFLIB2_Params.dat', sep='|', comment='#', engine='python')
data_cflib2['Name'] = data_cflib2['Name'].apply(lambda x: x if x[0:2] != 'HD' else x[2:])
data_cflib2['Name'] = data_cflib2['Name'].apply(lambda x: x.lstrip('0').rstrip())
data_cflib2['Name'] = data_cflib2['Name'].apply(lambda x: DIR_CFLIB + x + '.txt')
data_cflib2 = data_cflib2.sort_values(by='Name')
data_cflib2 = data_cflib2[data_cflib2['Name'].isin(list_cflib2)]
data_cflib2 = data_cflib2[['Teff', 'log(g)', '[Fe/H]']].astype('float64')
data_cflib2['log(g)'] = data_cflib2['log(g)'] * 100
data_cflib2['[Fe/H]'] = data_cflib2['[Fe/H]'] * 100
data_cflib2.to_csv('Param_CFLIB2.dat', sep=' ', index=False, header=False)
data_miles = pd.read_csv('Miles_Params.dat', sep='\s+', header=None, engine='python')
data_miles = data_miles[[2, 3, 4]]
data_miles[3] = data_miles[3] * 100
data_miles[4] = data_miles[4] * 100
data_miles.to_csv('Param_Miles2.dat', sep=' ', index=False, header=False)
display_text("Spectral Types Of Stars In Jacobi, CFLIB & CFLIB2 Have Been Cataloged")
display_text("Spectral Types Have Been Encoded For Stars In Jacobi And CFLIB Catalogs")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Outputs Flux Information From Jacobi & CFLIB Catalog Onto Individual Data Files
# ------------------------------------------------------------------------------------------------------------------- #
list_newjacob = group_similar_files("", os.path.join(DIR_JACOB, "NewData_*.ascii"))
list_newcflib = group_similar_files("", os.path.join(DIR_CFLIB, "NewData_*.txt"))
list_newcflib2 = group_similar_files("", os.path.join(DIR_CFLIB2, "NewData2_*.txt"))
master_jacob = pd.DataFrame()
for file_name in list_newjacob:
temp_data = pd.read_csv(file_name, sep='\s+', engine='python')
master_jacob = pd.concat([master_jacob, temp_data['FluxNorm']], axis=1)
master_jacob.T.to_csv('Flux_Jacobi.dat', sep=' ', index=False, header=False)
master_cflib = pd.DataFrame()
for file_name in list_newcflib:
temp_data = pd.read_csv(file_name, sep='\s+', engine='python')
master_cflib = pd.concat([master_cflib, temp_data['FluxNorm']], axis=1)
master_cflib.T.to_csv('Flux_CFLIB.dat', sep=' ', index=False, header=False)
master_cflib2 = pd.DataFrame()
for file_name in list_newcflib2:
temp_data = pd.read_csv(file_name, sep='\s+', engine='python')
master_cflib2 = pd.concat([master_cflib2, temp_data['FluxNorm']], axis=1)
master_cflib2.T.to_csv('Flux_CFLIB2.dat', sep=' ', index=False, header=False)
display_text("Complete Flux Data From Jacobi, CFLIB & CFLIB2 Cataloged Into Individual Master Output Files")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Read Training And Testing Data Respectively From Flux*.dat and Type*.dat Files
# ------------------------------------------------------------------------------------------------------------------- #
flux_train = pd.read_csv('Flux_Jacobi.dat', header=None, sep='\s+', dtype='float64', engine='python').as_matrix()
flux_test = pd.read_csv('Flux_CFLIBx.dat', header=None, sep='\s+', dtype='float64', engine='python').as_matrix()
type_train = pd.read_csv('Type_Jacobi.dat', header=None, sep='\s+', dtype='float64', engine='python').T.as_matrix()[0]
type_test = pd.read_csv('Type_CFLIBx.dat', header=None, sep='\s+', dtype='float64', engine='python').T.as_matrix()[0]
type_test = (type_test - 1.5).astype('int')
flux_train2 = pd.read_csv('Flux_Miles2.dat', header=None, sep='\s+', dtype='float64', engine='python').as_matrix()
flux_test2 = pd.read_csv('Flux_CFLIB2.dat', header=None, sep='\s+', dtype='float64', engine='python').as_matrix()
param_train2 = pd.read_csv('Param_Miles2.dat', header=None, sep='\s+', dtype='float64', engine='python').as_matrix()
param_test2 = pd.read_csv('Param_CFLIB2.dat', header=None, sep='\s+', dtype='float64', engine='python').as_matrix()
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot Histograms Of The Training & Testing Datasets
# ------------------------------------------------------------------------------------------------------------------- #
def plot_hist(file_type, title, plot_name):
type_df = pd.read_csv(file_type, sep='\s+', names=['NAME', 'TYPE', 'ENC'], header=None, engine='python')
numbers, bins = np.histogram(type_df['ENC'], bins=[1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000])
width = 0.9 * (bins[1] - bins[0])
centers = (bins[:-1] + bins[1:]) / 2
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.bar(centers, numbers, align='center', width=width)
ax.set_xlim(1000, 8000)
ax.set_ylabel('Number of Stars', fontsize=14)
ax.set_xlabel('Spectral Types', fontsize=14)
ax.set_title(title, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.set_xticks(centers)
ax.set_xticklabels(list_labels)
ax.tick_params(which='both', direction='in', width=0.8, labelsize=14)
fig.savefig(plot_name, format='eps', dpi=4000)
plt.show()
plt.close(fig)
def plot_hist2(info, title, plot_name):
numbers, bins = np.histogram(info, bins=np.arange(3000, 43001, 4000))
width = 0.9 * (bins[1] - bins[0])
centers = (bins[:-1] + bins[1:]) / 2
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.bar(centers, numbers, align='center', width=width)
ax.set_ylabel('Number of Stars', fontsize=14)
ax.set_xlabel('Temperature (In Kelvin)', fontsize=14)
ax.set_title(title, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.set_xticks(centers)
ax.tick_params(which='both', direction='in', width=0.8, labelsize=12)
fig.savefig(plot_name, format='eps', dpi=4000)
plt.show()
plt.close(fig)
def plot_hist3(info, title, plot_name):
numbers, bins = np.histogram(info, bins=np.arange(10, 100, 10))
width = 0.9 * (bins[1] - bins[0])
centers = (bins[:-1] + bins[1:]) / 2
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.bar(centers, numbers, align='center', width=width)
ax.set_ylabel('Number of Stars', fontsize=14)
ax.set_xlabel('Log(g)', fontsize=14)
ax.set_title(title, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.set_xticks(centers)
ax.set_xticklabels(centers / 100.)
ax.tick_params(which='both', direction='in', width=0.8, labelsize=12)
fig.savefig(plot_name, format='eps', dpi=4000)
plt.show()
plt.close(fig)
def plot_hist4(info, title, plot_name):
numbers, bins = np.histogram(info, bins=np.arange(-300, 100, 40))
width = 0.9 * (bins[1] - bins[0])
centers = (bins[:-1] + bins[1:]) / 2
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.bar(centers, numbers, align='center', width=width)
ax.set_ylabel('Number of Stars', fontsize=14)
ax.set_xlabel('[Fe/H]', fontsize=14)
ax.set_title(title, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.set_xticks(centers)
ax.set_xticklabels(centers / 100.)
ax.tick_params(which='both', direction='in', width=0.8, labelsize=12)
fig.savefig(plot_name, format='eps', dpi=4000)
plt.show()
plt.close(fig)
plot_hist('Log_Jacobi.dat', title='Training Dataset', plot_name='Plot_TrainingData.eps')
plot_hist('Log_CFLIB.dat', title='Testing Dataset', plot_name='Plot_TestingData.eps')
plot_hist2(param_train2.T[0], title='Training Dataset', plot_name='Plot_Training2Data.eps')
plot_hist2(param_test2.T[0], title='Testing Dataset', plot_name='Plot_Testing2Data.eps')
plot_hist3(param_train2.T[1], title='Training Dataset', plot_name='Plot_Training3Data.eps')
plot_hist3(param_test2.T[1], title='Testing Dataset', plot_name='Plot_Testing3Data.eps')
plot_hist4(param_train2.T[2], title='Training Dataset', plot_name='Plot_Training4Data.eps')
plot_hist4(param_test2.T[2], title='Testing Dataset', plot_name='Plot_Testing4Data.eps')
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot The Training & Testing Data In Parameter Space
# ------------------------------------------------------------------------------------------------------------------- #
fig = plt.figure(figsize=(16, 8))
cm = plt.cm.get_cmap('RdYlBu')
ax = fig.add_subplot(121)
ax.scatter(param_train2.T[0], param_train2.T[2] / 100, c=param_train2.T[1], cmap=cm)
ax.set_xlabel('Teff (K)', fontsize=16)
ax.set_ylabel('[Fe/H]', fontsize=16)
ax.set_title('Training Data (MILES)', fontsize=16)
ax.tick_params(which='both', direction='in', labelsize=14)
ax.set_xlim(43000, 1000)
ax2 = fig.add_subplot(122, sharey=ax)
ax2.scatter(param_test2.T[0], param_test2.T[2] / 100, c=param_test2.T[1], cmap=cm)
ax2.set_xlabel('Teff (K)', fontsize=16)
ax2.set_ylabel('[Fe/H]', fontsize=16)
ax2.set_title('Testing Data (CFLIB)', fontsize=16)
ax2.tick_params(which='both', direction='in', labelsize=14)
ax2.set_xlim(43000, 1000)
fig.savefig('Plot_ParameterSpace.eps', format='eps', dpi=4000)
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot Spectra Of Stars From The Testing Dataset
# ------------------------------------------------------------------------------------------------------------------- #
def plot_cflib(file_plot, path=DIR_CFLIB):
data_df = pd.read_csv(path + 'TempData_' + file_plot, sep='\s+', engine='python')
newdata_df = pd.read_csv(path + 'NewData_' + file_plot, sep='\s+', engine='python')
spline = CubicSpline(data_df['Wave'].tolist(), data_df['FluxCon'].tolist())
ax.plot(data_df['Wave'], data_df['Flux'], label='Original Spectra')
ax.plot(data_df['Wave'], data_df['FluxCon'], label='Convolved Spectra')
ax.plot(list_wave, spline(list_wave), label='Resampled Spectra')
ax.plot(newdata_df['Wave'], newdata_df['FluxNorm'], label='Normalised Spectra')
ax.legend(fontsize=24)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.set_xlabel('Wavelength (In Angstroms)', fontsize=24)
ax.set_ylabel('Normalised Flux', fontsize=24)
ax.set_title(file_plot.split('.')[0], fontsize=24)
ax.tick_params(which='both', direction='in', width=0.3, labelsize=24)
fig = plt.figure(figsize=(15, 12))
ax = fig.add_subplot(111)
plot_cflib('149757.txt')
ax.set_ylim(1.7, 2.2)
ax.set_xlim(4645, 4700)
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_minor_locator(MultipleLocator(5))
fig.savefig("Plot_PreProcess1.eps", format='eps', dpi=4000)
plt.show()
plt.close(fig)
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111)
plot_cflib('100889.txt')
ax.set_ylim(0.1, 2.7)
ax.set_xlim(3500, 7500)
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(100))
ax.yaxis.set_major_locator(MultipleLocator(0.5))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))
fig.savefig("Plot_PreProcess2.eps", format='eps', dpi=4000)
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot Spectra Of Stars From The Training Dataset
# ------------------------------------------------------------------------------------------------------------------- #
def plot_jacob(file_plot, offset=0, path=DIR_JACOB):
data_df = pd.read_csv(path + 'NewData_' + file_plot, sep='\s+', comment='#', engine='python')
type_df = pd.read_csv('Log_Jacobi.dat', sep='\s+', names=['NAME', 'TYPE', 'ENC'], header=None, engine='python')
label = type_df.loc[type_df['NAME'] == file_plot, 'TYPE'].item()
ax.plot(data_df['Wave'], data_df['FluxNorm'] + offset, label=label[0] + ' Type')
fig = plt.figure(figsize=(15, 12))
ax = fig.add_subplot(111)
plot_jacob('JC_1.ascii', offset=0.75)
plot_jacob('JC_17.ascii', offset=0)
plot_jacob('JC_31.ascii', offset=-0.75)
plot_jacob('JC_53.ascii', offset=-1.75)
ax.legend(fontsize=24)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(100))
ax.set_xlabel('Wavelength (In Angstroms)', fontsize=24)
ax.set_ylabel('Normalised Flux', fontsize=24)
ax.tick_params(which='both', direction='in', width=0.3, labelsize=24)
fig.savefig("Plot_SpecType.eps", format='eps', dpi=4000)
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Training & Testing The Predictions Using The MLP Classifier
# ------------------------------------------------------------------------------------------------------------------- #
mlp = MLPClassifier(hidden_layer_sizes=(66, 66), solver='lbfgs', max_iter=2000, random_state=1,
alpha=1e-6, tol=1e-10)
mlp.fit(X=flux_train, y=type_train)
mlp_pred = mlp.predict(X=flux_test)
mlp_dev = type_test - mlp_pred
print(mlp.score(flux_test, type_test))
# print(classification_report(type_test, mlp_pred))
display_text("Training & Testing Done Using MLP Classifier")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Training & Testing The Predictions Using The KNeighbors Classifier
# ------------------------------------------------------------------------------------------------------------------- #
knc = KNeighborsClassifier(n_neighbors=5, leaf_size=30, weights='distance', algorithm='kd_tree')
knc.fit(X=flux_train, y=type_train)
knc_pred = knc.predict(X=flux_test)
knc_dev = type_test - knc_pred
print(knc.score(flux_test, type_test))
# print(classification_report(type_test, knc_pred))
display_text("Training & Testing Done Using KNeighbors Classifier")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Training & Testing The Predictions Using The SVM-NuSVC Classifier
# ------------------------------------------------------------------------------------------------------------------- #
clf = svm.NuSVC(nu=0.42, kernel='sigmoid', coef0=0.019, tol=1e-5)
clf.fit(X=flux_train, y=type_train)
clf_pred = clf.predict(X=flux_test)
clf_dev = type_test - clf_pred
print(knc.score(flux_test, type_test))
# # print(classification_report(type_test, clf_pred))
display_text("Training & Testing Done Using SVM-NuSVC Classifier")
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Comparing Different Classifiers For Classification
# ------------------------------------------------------------------------------------------------------------------- #
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 7), sharey=True)
knc = KNeighborsClassifier(n_neighbors=6, leaf_size=30, weights='distance')
knc.fit(X=flux_train, y=type_train)
ax1.scatter(type_test, knc.predict(flux_test))
ax1.plot(np.arange(min_val, max_val, 5), np.arange(min_val, max_val, 5), linestyle='--', color='k')
ax1.set_xlim(min_val, max_val)
ax1.set_ylim(min_val, max_val)
ax1.set_ylabel('ANN', fontsize=14)
ax1.set_xlabel('CFLIB Data', fontsize=14)
ax1.set_title("Nearest Neighbors Classifier", y=1.05, fontsize=14)
ax1.yaxis.set_ticks_position('both')
ax1.xaxis.set_ticks_position('both')
ax1.xaxis.set_major_locator(FixedLocator(np.arange(1000, max_val, 1000)))
ax1.xaxis.set_minor_locator(MultipleLocator(250))
ax1.yaxis.set_major_locator(FixedLocator(np.arange(1000, max_val, 1000)))
ax1.yaxis.set_minor_locator(MultipleLocator(250))
ax1.tick_params(which='both', direction='in', width=0.8, labelsize=14)
ax1_opp = ax1.twiny()
ax1_opp.set_xlim(ax1.get_xlim())
ax1_opp.set_xticks(ax1.get_xticks())
ax1_opp.set_xticklabels(list_type)
ax1_opp.tick_params(which='both', direction='in', width=0.8, labelsize=14)
mlp = MLPClassifier(hidden_layer_sizes=(64, 64), solver='lbfgs', max_iter=2000, random_state=1, alpha=1e-5)
mlp.fit(X=flux_train, y=type_train)
ax2.scatter(type_test, mlp.predict(flux_test))
ax2.plot(np.arange(min_val, max_val, 5), np.arange(min_val, max_val, 5), linestyle='--', color='k')
ax2.set_xlim(min_val, max_val)
ax2.set_ylim(min_val, max_val)
ax2.set_xlabel('CFLIB Data', fontsize=14)
ax2.set_title("MLP Classifier", y=1.05, fontsize=14)
ax2.yaxis.set_ticks_position('both')
ax2.xaxis.set_major_locator(FixedLocator(np.arange(1000, max_val, 1000)))
ax2.xaxis.set_minor_locator(MultipleLocator(250))
ax2.yaxis.set_major_locator(FixedLocator(np.arange(1000, max_val, 1000)))
ax2.yaxis.set_minor_locator(MultipleLocator(250))
ax2.tick_params(which='both', direction='in', width=0.8, labelsize=14)
ax2_opp = ax2.twiny()
ax2_opp.set_xlim(ax2.get_xlim())
ax2_opp.set_xticks(ax2.get_xticks())
ax2_opp.set_xticklabels(list_type)
ax2_opp.tick_params(which='both', direction='in', width=0.8, labelsize=14)
plt.subplots_adjust(wspace=0.01)
fig.savefig("Plot_CompClassifiers.eps", format='eps', dpi=2000)
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Testing Combination Of Different Parameters For MLP Classifiers
# ------------------------------------------------------------------------------------------------------------------- #
mlp_params = [{'solver': 'lbfgs', 'learning_rate': 'constant', 'momentum': 0, 'alpha': 1e-5},
{'solver': 'lbfgs', 'learning_rate': 'constant', 'momentum': .9,
'nesterovs_momentum': False, 'alpha': 1e-5},
{'solver': 'lbfgs', 'learning_rate': 'invscaling', 'momentum': .9,
'nesterovs_momentum': True, 'alpha': 1e-5},
{'solver': 'lbfgs', 'learning_rate': 'invscaling', 'momentum': 0,
'learning_rate_init': 0.2, 'alpha': 1e-5},
{'solver': 'lbfgs', 'learning_rate': 'constant', 'momentum': .9,
'nesterovs_momentum': True, 'alpha': 1e-5},
{'solver': 'lbfgs', 'learning_rate': 'invscaling', 'momentum': .9,
'nesterovs_momentum': False, 'alpha': 1e-5},
{'solver': 'adam', 'learning_rate_init': 0.01, 'alpha': 1e-5}]
mlp_labels = ["LBFGS, Constant, No Momentum", "LBFGS, Constant, With Momentum",
"LBFGS, Constant, Nesterov's Momentum", "LBFGS, Inv-scaling, No Momentum",
"LBFGS, Inv-scaling, With Momentum", "LBFGS, Inv-scaling, Nesterov's Momentum", "Adam"]
mlp_plotargs = [{'c': 'red', 'linestyle': '-'},
{'c': 'green', 'linestyle': '-'},
{'c': 'blue', 'linestyle': '-'},
{'c': 'red', 'linestyle': '--'},
{'c': 'green', 'linestyle': '--'},
{'c': 'blue', 'linestyle': '--'},
{'c': 'black', 'linestyle': '-'}]
list_mlppred = []
for label, param in zip(mlp_labels, mlp_params):
display_text("Training: {0}".format(label))
mlp = MLPClassifier(hidden_layer_sizes=(64, 64), random_state=1, max_iter=2000, **param)
mlp.fit(flux_train, type_train)
list_mlppred.append(mlp.predict(X=flux_test))
print("Training Set Score: {0}".format(mlp.score(flux_test, type_test)))
print("Training Set Loss: {0}".format(mlp.loss_))
fig = plt.figure(figsize=(8, 6))
for type_pred, label, args in zip(list_mlppred, mlp_labels, mlp_plotargs):
ax = fig.add_subplot(111)
ax.scatter(type_pred, type_test, label=label, **args)
ax.plot(np.arange(min_val, max_val, 5), np.arange(min_val, max_val, 5), linestyle='--', color='k')
ax.legend(fontsize=9)
ax.set_xlim(min_val, max_val)
ax.set_ylim(min_val, max_val)
ax.set_ylabel('ANN', fontsize=12)
ax.set_xlabel('CFLIB Data', fontsize=12)
ax.set_title("MLP Classifier", y=1.05, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.xaxis.set_major_locator(MultipleLocator(1000))
ax.xaxis.set_minor_locator(MultipleLocator(250))
ax.yaxis.set_major_locator(MultipleLocator(1000))
ax.yaxis.set_minor_locator(MultipleLocator(250))
ax.tick_params(which='both', direction='in', width=0.8, labelsize=14)
ax_opp = ax.twiny()
ax_opp.set_xlim(ax.get_xlim())
ax_opp.set_xticks(ax.get_xticks())
ax_opp.set_xticklabels(list_type)
ax_opp.tick_params(which='both', direction='in', width=0.8, labelsize=14)
fig.savefig("Plot_MLPCompare.eps", format='eps', dpi=2000)
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Testing Combination Of Different Parameters For KNeighbors Classifier
# ------------------------------------------------------------------------------------------------------------------- #
knc_params = [{'n_neighbors': 5, 'weights': 'uniform', 'algorithm': 'brute', 'leaf_size': 30, 'p': 2},
{'n_neighbors': 5, 'weights': 'uniform', 'algorithm': 'ball_tree', 'leaf_size': 100, 'p': 1},
{'n_neighbors': 5, 'weights': 'distance', 'algorithm': 'kd_tree', 'leaf_size': 100, 'p': 1},
{'n_neighbors': 5, 'weights': 'distance', 'algorithm': 'brute', 'leaf_size': 30, 'p': 2},
{'n_neighbors': 5, 'weights': 'distance', 'algorithm': 'kd_tree', 'leaf_size': 50, 'p': 2},
{'n_neighbors': 5, 'weights': 'distance', 'algorithm': 'kd_tree', 'leaf_size': 50, 'p': 2}]
knc_labels = ["Neighbors 6, Uniform, Brute, 30", "Neighbors 6, Uniform, Ball Tree, 30",
"Neighbors 6, Distance, KD Tree, 30", "Neighbors 6, Distance, Brute, 30",
"Neighbors 6, Distance, Ball Tree, 50", "Neighbors 6, Distance, KD Tree, 50"]
knc_plotargs = [{'c': 'red', 'linestyle': '-'}, {'c': 'green', 'linestyle': '-'},
{'c': 'blue', 'linestyle': '-'}, {'c': 'green', 'linestyle': '--'},
{'c': 'yellow', 'linestyle': '--'}, {'c': 'black', 'linestyle': '-'}]
list_kncpred = []
for label, param in zip(knc_labels, knc_params):
display_text("Training: {0}".format(label))
knc = KNeighborsClassifier(**param)
knc.fit(flux_train, type_train)
list_kncpred.append(knc.predict(X=flux_test))
print("Training Set Score: {0}".format(knc.score(flux_test, type_test)))
fig = plt.figure(figsize=(8, 6))
for type_pred, label, args in zip(list_kncpred, knc_labels, knc_plotargs):
ax = fig.add_subplot(111)
ax.scatter(type_pred, type_test, label=label, **args)
ax.plot(np.arange(min_val, max_val, 5), np.arange(min_val, max_val, 5), linestyle='--', color='k')
ax.legend(fontsize=9)
ax.set_xlim(min_val, max_val)
ax.set_ylim(min_val, max_val)
ax.set_ylabel('ANN', fontsize=12)
ax.set_xlabel('CFLIB Data', fontsize=12)
ax.set_title("Nearest Neighbors Classifier", y=1.05, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.xaxis.set_major_locator(MultipleLocator(1000))
ax.xaxis.set_minor_locator(MultipleLocator(250))
ax.yaxis.set_major_locator(MultipleLocator(1000))
ax.yaxis.set_minor_locator(MultipleLocator(250))
ax.tick_params(which='both', direction='in', width=0.8, labelsize=14)
ax_opp = ax.twiny()
ax_opp.set_xlim(ax.get_xlim())
ax_opp.set_xticks(ax.get_xticks())
ax_opp.set_xticklabels(list_type)
ax_opp.tick_params(which='both', direction='in', width=0.8, labelsize=14)
fig.savefig("Plot_KNCompare.eps", format='eps', dpi=2000)
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Creating The Confusion Matrix For The KNeighbors & MLP Classifier
# ------------------------------------------------------------------------------------------------------------------- #
def create_conf(type_pred):
conf_df = pd.DataFrame(type_test, columns=['Original'])
conf_df['Predicted'] = type_pred
conf_df = conf_df.sort_values(by='Original')
conf_matrix = np.zeros([7, 7])
for i in range(0, 7):
for j in range(0, 7):
temp_df = conf_df[(conf_df['Original'] > (i + 1) * 1000) & (conf_df['Original'] < (i + 2) * 1000) &
(conf_df['Predicted'] > (j + 1) * 1000) & (conf_df['Predicted'] < (j + 2) * 1000)]
conf_matrix[j, i] = temp_df.shape[0]
conffine_matrix = np.zeros([70, 70])
for i in range(0, 70):
for j in range(0, 70):
temp_df = conf_df[(conf_df['Original'] > 1000 + i * 100) & (conf_df['Original'] < 1000 + (i + 1) * 100) &
(conf_df['Predicted'] > 1000 + j * 100) & (conf_df['Predicted'] < 1000 + (j + 1) * 100)]
conffine_matrix[j, i] = temp_df.shape[0]
return conf_matrix, conffine_matrix
mlp_conf, mlp_conffine = create_conf(type_pred=mlp_pred)
knc_conf, knc_conffine = create_conf(type_pred=knc_pred)
clf_conf, clf_conffine = create_conf(type_pred=clf_pred)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot The Fit To The Testing Dataset For Different Classifiers
# ------------------------------------------------------------------------------------------------------------------- #
def plot_fit(type_pred, title, plot_name):
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.scatter(type_test, type_pred)
ax.plot(np.arange(min_val, max_val, 5), np.arange(min_val, max_val, 5), linestyle='--', color='k')
ax.legend(fontsize=9)
ax.set_xlim(min_val, max_val)
ax.set_ylim(min_val, max_val)
ax.set_ylabel('ANN', fontsize=12)
ax.set_xlabel('CFLIB Data', fontsize=12)
ax.set_title(title, y=1.05, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.xaxis.set_major_locator(FixedLocator(np.arange(1000, max_val, 1000)))
ax.xaxis.set_minor_locator(MultipleLocator(250))
ax.yaxis.set_major_locator(FixedLocator(np.arange(1000, max_val, 1000)))
ax.yaxis.set_minor_locator(MultipleLocator(250))
ax.tick_params(which='both', direction='in', width=0.8, labelsize=14)
ax_opp = ax.twiny()
ax_opp.set_xlim(ax.get_xlim())
ax_opp.set_xticks(ax.get_xticks())
ax_opp.set_xticklabels(list_type)
ax_opp.tick_params(which='both', direction='in', width=0.8, labelsize=14)
fig.savefig(plot_name, format='eps', dpi=2000)
plt.show()
plt.close(fig)
plot_fit(mlp_pred, title='MLP Classifier', plot_name='Plot_MLPFit.eps')
plot_fit(knc_pred, title='KNeighbors Classifier', plot_name='Plot_KNCFit.eps')
plot_fit(clf_pred, title='SVM-NuSVC Classifier', plot_name='Plot_SVMFit.eps')
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot The Deviation Histogram For Different Classifiers
# ------------------------------------------------------------------------------------------------------------------- #
def plot_dev(type_dev, title, plot_name, bins='sqrt'):
def gaussian_func(x, mean, sigma, const):
return const * np.exp(-(x - mean) ** 2 / (sigma ** 2))
mean, sigma = norm.fit(type_dev)
numbers, bins = np.histogram(type_dev, bins=bins)
width = 0.9 * (bins[1] - bins[0])
centers = (bins[:-1] + bins[1:]) / 2
smooth_centers = np.linspace(min(centers), max(centers), 1000)
popt, pcov = curve_fit(gaussian_func, centers, numbers, p0=[mean, sigma, max(numbers)])
print("Number Of Bins = {0}".format(len(centers)))
print("Mean = {0}".format(mean))
print("Sigma = {0}".format(sigma))
print("\nParameters Of The Gaussian Are:\nMean = {0}\nSigma = {1}\nAmplitude = {2}".format(*popt))
print("Maximum Bar Size = {0}".format(max(numbers)))
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
ax.set_xlim(-1600, 1600)
ax.bar(centers, numbers, align='center', width=width)
ax.plot(smooth_centers, gaussian_func(smooth_centers, *popt), color='r')
ax.text(x=0.5 * ax.get_xlim()[-1], y=0.8 * ax.get_ylim()[-1],
s=" Mean = {0:.1f}\nSigma = {1:.1f}".format(mean, sigma), fontsize=14)
ax.set_ylabel('Number of Stars', fontsize=14)
ax.set_xlabel('Deviation (ANN)', fontsize=14)
ax.set_title(title, fontsize=14)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.xaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_minor_locator(MultipleLocator(125))
ax.yaxis.set_major_locator(MultipleLocator(100))
ax.yaxis.set_minor_locator(MultipleLocator(50))
ax.tick_params(which='both', direction='in', width=0.8, labelsize=14)
fig.savefig(plot_name, format='eps', dpi=2000)
plt.show()
plt.close(fig)
plot_dev(mlp_dev, title='MLP Classifier', plot_name='Plot_MLPDeviation.eps')
plot_dev(knc_dev, title='KNeighbors Classifier', plot_name='Plot_KNCDeviation.eps', bins=50)
plot_dev(clf_dev, title='SVM NuSVC Classifier', plot_name='Plot_SVMDeviation.eps', bins=50)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot The Heatmap Of The Confusion Matrix For Different Classifiers
# ------------------------------------------------------------------------------------------------------------------- #
def plot_confheat(confarr, title, plot_name):
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
xdim, ydim = confarr.shape
xarr = range(xdim)
yarr = range(ydim)
df_cm = pd.DataFrame(confarr, index=xarr, columns=yarr)
sn.set(font_scale=1.4)
cbar = sn.heatmap(df_cm, annot=True, annot_kws={"size": 12})
cbar.set_title('Number of Stars')
ax.set_title(title, fontsize=14)
ax.set_xlabel('Target Class', y=1.10, fontsize=14)
ax.set_ylabel('ANN Class', fontsize=14)
ax.set_xticklabels(list_labels)
ax.set_yticklabels(list_labels[::-1], rotation=0)
fig.savefig(plot_name, format='eps')
plt.show()
plt.close(fig)
plot_confheat(mlp_conf, title='MLP Classifier', plot_name='Plot_MLPConfMatrix.eps')
plot_confheat(knc_conf, title='KNeighbors Classifier', plot_name='Plot_KNCConfMatrix.eps')
plot_confheat(clf_conf, title='SVM NuSVC Classifier', plot_name='Plot_SVMConfMatrix.eps')
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plotting The 3D Confusion Matrix For Different Classifiers
# ------------------------------------------------------------------------------------------------------------------- #
def plot_3dconf(confarr, title, plot_name):
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')
xdim, ydim = confarr.shape
xarr = range(xdim)
yarr = range(ydim)
xmesh, ymesh = np.meshgrid(xarr, yarr)
ax.plot_surface(xmesh, ymesh, confarr, color='r')
ax.xaxis.labelpad = 20
ax.yaxis.labelpad = 20
ax.zaxis.labelpad = 10
ax.set_xlabel('Target Class', fontsize=14)
ax.set_ylabel('ANN Class', fontsize=14, rotation=90)
ax.set_zlabel('Number of Stars', fontsize=14)
ax.set_title(title, fontsize=14)
ax.set_xticklabels(list_labels, rotation='90')
ax.set_yticklabels(list_labels)
ax.view_init(elev=40, azim=40)
fig.savefig(plot_name, format='eps', dpi=2000)
plt.show()
plt.close(fig)
plot_3dconf(mlp_conf, title='MLP Classifier', plot_name='Plot_MLP3DConfMatrix.eps')
plot_3dconf(knc_conf, title='KNeighbors Classifier', plot_name='Plot_KNC3DConfMatrix.eps')
plot_3dconf(clf_conf, title='SVM NuSVC Classifier', plot_name='Plot_SVM3DConfMatrix.eps')
plot_3dconf(mlp_conffine, title='MLP Classifier', plot_name='Plot_MLP3DConfFineMatrix.eps')
plot_3dconf(knc_conffine, title='KNeighbors Classifier', plot_name='Plot_KNC3DConfFineMatrix.eps')
plot_3dconf(clf_conffine, title='SVM NuSVC Classifier', plot_name='Plot_SVM3DConfFineMatrix.eps')
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plotting The Contour Of The Confusion Matrix For Different Classifiers
# ------------------------------------------------------------------------------------------------------------------- #
def plot_confcont(confarr, title, plot_name):
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
xdim, ydim = confarr.shape
xarr = range(xdim)
yarr = range(ydim)
xmesh, ymesh = np.meshgrid(xarr, yarr)
ax.contourf(xmesh, ymesh, confarr)
ax.xaxis.labelpad = 15
ax.yaxis.labelpad = 15
ax.set_xlabel('Target Class', fontsize=16)
ax.set_ylabel('ANN Class', fontsize=16)
ax.set_title(title, fontsize=16)
ax.set_xticklabels(list_labels, rotation=90)
ax.set_yticklabels(list_labels)
fig.savefig(plot_name, format='eps', dpi=2000)
plt.show()
plt.close(fig)
plot_confcont(mlp_conf, title='MLP Classifier', plot_name='Plot_MLPConfContour.eps')
plot_confcont(knc_conf, title='KNeighbors Classifier', plot_name='Plot_KNCConfContour.eps')
plot_confcont(clf_conf, title='SVM NuSVC Classifier', plot_name='Plot_SVMConfContour.eps')
plot_confcont(mlp_conffine, title='MLP Classifier', plot_name='Plot_MLP3DConfFineContour.eps')
plot_confcont(knc_conffine, title='KNeighbors Classifier', plot_name='Plot_KNC3DConfFineContour.eps')
plot_confcont(clf_conffine, title='SVM NuSVC Classifier', plot_name='Plot_SVM3DConfFineContour.eps')
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Parameterization Of Teff Using MLP Classifier
# ------------------------------------------------------------------------------------------------------------------- #
mlp = MLPClassifier(hidden_layer_sizes=(75, 75), activation='relu', solver='lbfgs', max_iter=10000, random_state=1,
alpha=1e-6, tol=1e-30)
mlp.fit(X=flux_train2, y=param_train2.T[0])
temp_pred = mlp.predict(X=flux_test2)
temp_dev = param_test2.T[0] - temp_pred
print("Ratioed Differential Mean = {0}". format(100 * np.mean((np.absolute(param_test2.T[0] - temp_pred)) /
param_test2.T[0])))