@author: lhe39759
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 11 10:21:16 2018
@author: lhe39759
"""
import numpy as np
from tempfile import TemporaryFile
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
#----------------------------------------------------------------------
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
#############################################################
#############Data Loading & Conversion######################
X, y = fetch_rrlyrae_combined()
np.savetxt('AstroML_Data.txt', X)
np.savetxt('AstroML_Labels.txt', y)
print("Done")
from astroML.datasets import fetch_rrlyrae_combined
import numpy as np
# get data and save
data, labels = fetch_rrlyrae_combined()
np.savez('rrlyrae', data=data, labels=labels)
# Author: Jake VanderPlas <*****@*****.**>
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
import numpy as np
from matplotlib import pyplot as plt
from sklearn.naive_bayes import GaussianNB
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import split_samples
from astroML.utils import completeness_contamination
#----------------------------------------------------------------------
# get data and split into training & testing sets
X, y = fetch_rrlyrae_combined()
X = X[:, [1, 0, 2, 3]] # rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
random_state=0)
N_tot = len(y)
N_st = np.sum(y == 0)
N_rr = N_tot - N_st
N_train = len(y_train)
N_test = len(y_test)
N_plot = 5000 + N_rr
#----------------------------------------------------------------------
# perform Naive Bayes
classifiers = []
predictions = []
# see http://astroML.github.com
from __future__ import *
import numpy as np
from matplotlib import pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from astroML.datasets import fetch_rrlyrae_combined
from astroML.utils import completeness_contamination
from astroML.utils import split_samples
#----------------------------------------------------------------------
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)
#----------------------------------------------------------------------
# get data and split into training & testing sets
X, y = fetch_rrlyrae_combined() # ug, gr, ri, iz
X = X[:, [1, 0, 2, 3]] #rearrange columns for better 1-color results
(X_train, X_test), (y_train, y_test) = split_samples(X, y, [0.75, 0.25],
random_state=0)
N_tot = len(y)
N_st = np.sum(y == 0) #stars have y = 0
N_rr = N_tot - N_st
N_train = len(y_train)
N_test = len(y_test)
N_plot = 5000 + N_rr
#----------------------------------------------------------------------
# Fit RandomForestClassifier
Ncolors = np.arange(1, X.shape[1] + 1)
