def test_create_models():
coeffs_data = open('all_features.txt','r').read().encode('utf-8')
pca_data = open('pca_features.txt','r').read().encode('utf-8')
(rf, pca) = models.create_models(coeffs_data, pca_data)
assert type(rf) == type(RandomForestClassifier())
assert type(pca) == type(decomposition.pca.PCA())
def classify_lightcurves():
dataFileNamesR = []
dataFileNamesG = []
fileDirectoryR = lightcurve_path + 'Rfilter/'
fileDirectoryG = lightcurve_path + 'Gfilter/'
fileDirectoryClassifiedR = lightcurve_path + 'classified/Rfilter/'
fileDirectoryClassifiedG = lightcurve_path + 'classified/Gfilter/'
for f in os.listdir(fileDirectoryR):
if f.endswith('.dat'):
dataFileNamesR.append(f)
for f in os.listdir(fileDirectoryG):
if f.endswith('.dat'):
dataFileNamesG.append(f)
dataFileNamesR = natsorted(dataFileNamesR)
dataFileNamesG = natsorted(dataFileNamesG)
rf, pca = models.create_models(features_path + 'all_features.txt',
features_path + 'pca_features.txt')
class_results = []
for filename in dataFileNamesR:
data = np.loadtxt(fileDirectoryR + filename, usecols=np.arange(0, 3))
mjd = [float(i) for i in data[:, 0]]
mag = [float(i) for i in data[:, 1]]
magerr = [float(i) for i in data[:, 2]]
sosort = np.array([mjd, mag, magerr]).T
sosort = sosort[sosort[:, 0].argsort(), ]
mjd = sosort[:, 0]
mag = sosort[:, 1]
magerr = sosort[:, 2] + 0.0001
prediction, ml_pred = microlensing_classifier.predict(
mag, magerr, rf, pca)[0:2]
#print('filename: ', filename, 'prediction: ', prediction, 'ml_pred = ', ml_pred)
result = [filename, prediction, ml_pred]
class_results.append(result)
ml_pred = str(ml_pred)
ml_pred = ml_pred.replace("[", "")
ml_pred = ml_pred.replace("]", "")
location = fileDirectoryClassifiedR + ml_pred + str(filename)
np.savetxt(location, data, fmt='%s')
for filename in dataFileNamesG:
data = np.loadtxt(fileDirectoryG + filename, usecols=np.arange(0, 3))
mjd = [float(i) for i in data[:, 0]]
mag = [float(i) for i in data[:, 1]]
magerr = [float(i) for i in data[:, 2]]
sosort = np.array([mjd, mag, magerr]).T
sosort = sosort[sosort[:, 0].argsort(), ]
mjd = sosort[:, 0]
mag = sosort[:, 1]
magerr = sosort[:, 2]
try:
prediction, ml_pred = microlensing_classifier.predict(
mag, magerr, rf, pca)[0:2]
except (ValueError):
prediction, ml_pred = "NA", "NA"
#print('filename: ', filename, 'prediction: ', prediction, 'ml_pred = ', ml_pred)
result = [filename, prediction, ml_pred]
class_results.append(result)
ml_pred = str(ml_pred)
ml_pred = ml_pred.replace("[", "")
ml_pred = ml_pred.replace("]", "")
location = fileDirectoryClassifiedG + ml_pred + str(filename)
np.savetxt(location, data, fmt='%s')
targetname = str(filename).replace("R.dat", "")
targetname = targetname.replace("G.dat", "")
print(targetname)
#target = Target.objects.get(name=targetname)
#target.save(extras={'Microlensing probability': {'probability': ml_pred, 'timestamp': datetime.datetime.now()}})
return class_results
Created on Fri Jan 13 14:46:19 2017
@author: danielgodinez
"""
import numpy as np
import unittest
import sys
sys.path.append('../../')
from LIA import microlensing_classifier
from LIA import models
data = np.loadtxt('ml_event.txt')
mag = data[:, 1]
magerr = data[:, 2]
rf, pca = models.create_models('all_features.txt', 'pca_features.txt')
class Test(unittest.TestCase):
"""
Unittest to ensure the classifier is working correctly.
"""
def test_predict(value):
value.assertEqual(
microlensing_classifier.predict(mag, magerr, rf, pca)[0], 'ML',
"Classifier failed, predicted class is not correct.")
def test_probability_prediction(value):
pred = microlensing_classifier.predict(mag, magerr, rf, pca)[1]
value.assertTrue(
pred >= 0.4 and pred <= 0.6,
mask = (time>1) & (exptime[dico_time[:,1]]>200) #& (mag>0) & (pscale>0) & (np.median(mag)<19) & (Illumination<1.0) #& (fwhm[dico_time[:,1]]<6) #& (distance>40) #& (pscale>0.8) & (pscale<1.2)
except:
#Moon,Distance,Illumination = get_Moon(time,ra=267.83589537,dec=-30.060817819)
mask = (time>0) #& (mag>0) & (pscale>0) & (fwhm[dico_time[:,1]]<6)
if len(time[mask])>5:
times.append(np.sort(time[mask]))
### Comment this when done once
#n_class = 1000
#training_set.create(times, min_mag=10, max_mag=20, noise=None, cv_n1=2,n_class=n_class,test=False)
model = models.create_models('all_features.txt', model='rf')
### ploting TSNE if needed
#pcapca = np.loadtxt('pca_features.txt',dtype=str)
#vis_data = tsne(n_components=2).fit_transform(pcapca[:,2:].astype(float))
#vis_x = vis_data[:,0]
#vis_y = vis_data[:,1]
#colors = ['red', 'green', 'blue', 'yellow', 'orange', 'purple', 'black']
#fig, ax = plt.subplots()
#for i,object_class in enumerate(['Variable', 'Constant', 'CV', 'ML']):
# x=vis_x[i*n_class:(i+1)*n_class]
# y=vis_y[i*n_class:(i+1)*n_class]
# ax.scatter(x, y, c=colors[i], label=object_class)
# daniels code
from __future__ import division
from main_script import *
#from tsne_python import tsne as TSNE
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE as tsne
from LIA import extract_features, models
LIA_directory = '/home/jclark/examples/'
pca_model = models.create_models(
str(LIA_directory) + 'all_features.txt',
str(LIA_directory) + 'pca_features.txt')[1]
db = '/home/jclark/examples/ROME-FIELD-16_phot.db'
import pdb
pdb.set_trace()
data = np.loadtxt('pca_features.txt', usecols=np.arange(1, 45))
#data = np.loadtxt('all_features.txt',usecols=np.arange(2,49))
x_data = np.asarray(data).astype('float64')
y_data = [1] * 500 + [2] * 500 + [3] * 500 + [4] * 500 + [5] * 10 + [6] * 5 + [
7
] * 2 #V,C,ML,CV
for VAR in [107961]:
#for xxx,VAR in enumerate([74609, 74609]):
mag, magerr = [[], []]
try:
for tel_choice in [1, 2]:
mag, magerr = np.append([mag, magerr],
extract_lightcurve(
VAR,