def getdata(self, phasebins=0, freqbins=0, timebins=0, DMbins=0, intervals=0, subbands=0, bandpass=0, ratings=None):

"""

input: feature=feature_size

possible features:

phasebins: summmed profile, data cube (self.profs) summed(projected) to the phase axis.

freqbins: summed frequency profile, data cube projected to the frequency axis

timebins: summed time profile, data cube projected to the time axis.

DMbins: DM curves.

intervals: the time vs phase image

subbands: the subband vs phase image

ratings: List of possible rating stored in the pfd file, possible values including: period, redchi2, offredchi2, avgvoverc

usage examples:

"""

self.dedisperse(DM=self.bestdm, doppler=1)

profs = self.profs

self.extracted_feature = {}

def getsumprofs(M):

feature = '%s:%s' % ('phasebins', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

self.extracted_feature[feature] = normalize(downsample(profs.sum(0).sum(0),M).ravel())

return self.extracted_feature[feature]

def getfreqprofs(M):

feature = '%s:%s' % ('freqbins', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

self.extracted_feature[feature] = normalize(downsample(profs.sum(1).sum(0),M).ravel())

return self.extracted_feature[feature]

def gettimeprofs(M):

feature = '%s:%s' % ('timebins', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

self.extracted_feature[feature] = normalize(downsample(profs.sum(0).sum(1),M).ravel())

return self.extracted_feature[feature]

def getbandpass(M):

feature = '%s:%s' % ('bandpass', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

self.extracted_feature[feature] = normalize(downsample(profs.sum(0).sum(1),M).ravel())

return self.extracted_feature[feature]

def getDMcurve(M): # return the normalized DM curve downsampled to M points

feature = '%s:%s' % ('DMbins', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

ddm = (self.dms.max() - self.dms.min())/2.

loDM, hiDM = (self.bestdm - ddm , self.bestdm + ddm)

N = 100

interp = False

sumprofs = self.profs.sum(0)

if not interp:

profs = sumprofs

else:

profs = np.zeros(np.shape(sumprofs), dtype='d')

DMs = psr_utils.span(loDM, hiDM, N)

chis = np.zeros(N, dtype='f')

subdelays_bins = self.subdelays_bins.copy()

for ii, DM in enumerate(DMs):

subdelays = psr_utils.delay_from_DM(DM, self.barysubfreqs)

hifreqdelay = subdelays[-1]

subdelays = subdelays - hifreqdelay

delaybins = subdelays*self.binspersec - subdelays_bins

if interp:

interp_factor = 16

for jj in range(self.nsub):

profs[jj] = psr_utils.interp_rotate(sumprofs[jj], delaybins[jj],

zoomfact=interp_factor)

# Note: Since the interpolation process slightly changes the values of the

# profs, we need to re-calculate the average profile value

avgprof = (profs/self.proflen).sum()

else:

new_subdelays_bins = np.floor(delaybins+0.5)

for jj in range(self.nsub):

profs[jj] = psr_utils.rotate(profs[jj], int(new_subdelays_bins[jj]))

subdelays_bins += new_subdelays_bins

avgprof = self.avgprof

sumprof = profs.sum(0)

chis[ii] = self.calc_redchi2(prof=sumprof, avg=avgprof)

DMcurve = normalize(downsample(chis, M))

self.extracted_feature[feature] = DMcurve

return self.extracted_feature[feature]

def greyscale(img):

global_max = np.maximum.reduce(np.maximum.reduce(img))

min_parts = np.minimum.reduce(img, 1)

img = (img-min_parts[:,np.newaxis])/global_max

return img

def getintervals(M):

feature = '%s:%s' % ('intervals', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

img = greyscale(self.profs.sum(1))

#U,S,V = svd(img)

#imshow(img)

#m,n = img.shape

#S = resize(S,[m,1]) * eye(m,n)

#k = 6

#imshow(np.dot(U[:,1:k], dot(S[1:k,1:k],V[1:k,:])))

#show()

#if M <= len(S):

#return S[:M]

#else:

#while len(S) < M:

#np.append(S, 0.)

#return S

self.extracted_feature[feature] = normalize(downsample(img, M).ravel())

return self.extracted_feature[feature]

def getsubbands(M):

feature = '%s:%s' % ('subbands', M)

if M == 0:

return np.array([])

if not feature in self.extracted_feature:

img = greyscale(self.profs.sum(0))

#U,S,V = svd(img)

#if M <= len(S):

#return S[:M]

#else:

#while len(S) < M:

#np.append(S, 0.)

#return S

self.extracted_feature[feature] = normalize(downsample(img, M).ravel())

return self.extracted_feature[feature]

def getratings(L):

feature = '%s:%s' % ('ratings', L)

if L == None:

return np.array([])

if not feature in self.extracted_feature:

result = []

for rating in L:

if rating == 'period':

result.append(self.topo_p1)

elif rating == 'redchi2':

result.append(self.calc_redchi2())

elif rating == 'varprof':

result.append(self.calc_varprof())

elif rating == 'offredchi2':

result.append(self.estimate_offsignal_redchi2())

elif rating == 'avgvoverc':

result.append(self.avgvoverc)

else:

result.append(self.__dict__[rating])

self.extracted_feature[feature] = np.array(result)

return self.extracted_feature[feature]

data = np.hstack((getsumprofs(phasebins), getfreqprofs(freqbins), gettimeprofs(timebins), getbandpass(bandpass), getDMcurve(DMbins), getintervals(intervals), getsubbands(subbands), getratings(ratings)))

@classmethod

def cross_val_score(self, classifier, data, target, cv=5, verbose=False):

#classifier = classifier()

nclasses = len(np.unique(target))

if verbose:cv = 1

scores = np.array([])

for i in range(cv):

L = len(data)

data = np.array(data)

index = range(L)

# keep shuffling until training set has all types

while 1:

shuffle(index)

cut = int(0.6*L)

training_idx = index[:cut]

test_idx = index[cut:]

training_data = data[training_idx]

training_target = target[training_idx]

test_data = data[test_idx]

test_target = target[test_idx]

if len(np.unique(training_target)) == len(np.unique(target)):

break

n_samples = len(training_data)

training_data = training_data.reshape((n_samples, -1))

#classifier = svm.SVC(gamma=0.1, scale_C=False)

classifier.fit(training_data, training_target)

if nclasses == 2:

F1 = singleclass_score(classifier, test_data, test_target, verbose=verbose)

else:

F1 = multiclass_score(classifier, test_data, test_target,

nclasses = nclasses, verbose=verbose)

scores = np.append(scores, F1)

"""

Returns the mean F1 score on the given test data and labels

or, if specified, the F1 score for a particular target 'aclass'

Parameters

----------

classifier : the classifier (must have .predict and .fit routines)

test_data : array-like, shape = [n_samples, n_features]

Training set.

test_target : array-like, shape = [n_samples]

Labels for test_data.

nclasses : number of target classifications

aclass = None : if specified, return the F1 score for this class, not the total

Returns

-------

z : float (= average F1 score)

"""

pred_cls = {}

true_cls = {}

for cls in range(nclasses):

pred_cls[cls] = set([])

true_cls[cls] = set([])

#sort the prediction and true classes

for i, s in enumerate(test_data):

predict = classifier.predict(s)[0]

true_cls[test_target[i]].add(i)

pred_cls[predict].add(i)

# a one vs all F1 test

tot_accuracy = 0.

tot_F1 = 0.

aF1 = 0

for k in range(nclasses):

hit = pred_cls[k] & true_cls[k]

miss = pred_cls[k] - true_cls[k]

falsepos = true_cls[k] - pred_cls[k]

precision = np.divide(float(len(hit)), len(pred_cls[k]))

recall = np.divide(float(len(hit)), len(true_cls[k]))

accuracy = (np.divide(float(len(hit)), len(true_cls[k])) * 100)

tot_accuracy += accuracy

#F1 = 2/((1./precision)+(1./recall))

F1 = 2 * precision * recall / (precision + recall)

if aclass != None:

if k == aclass:

aF1 = F1

tot_F1 += F1

if verbose:

print "\nClass %s:" % k

print 'accuracy: ', '%.0f%%' % (np.divide(float(len(hit)),len(true_cls[k])) * 100)

print 'miss: ', '%.0f%%' % (np.divide(float(len(miss)),len(true_cls[k])) * 100)

print 'false positives: ', '%.0f%%' % (np.divide(float(len(falsepos)),len(pred_cls[k]))* 100)

print 'precision: ', '%.0f%%' % (precision* 100)

print 'recall: ', '%.0f%%' % (recall* 100)

tot_accuracy = tot_accuracy / nclasses

tot_F1 = tot_F1 / nclasses

if aclass == None:

return tot_F1

else:

# print "returngin F1 score for class", aclass

pulsar = set([])

truepulsar = set([])

pred = []

for i,s in enumerate(test_data):

predict = classifier.predict(s)[0]

pred.append(predict)

#print test_target[i], int(predict)

if int(test_target[i]) == 1:

truepulsar.add(i)

if int(predict) == 1:

pulsar.add(i)

hit = pulsar & truepulsar

miss = truepulsar - pulsar

falsepos = pulsar - truepulsar

precision = np.divide(float(len(hit)),len(pulsar))

recall = np.divide(float(len(hit)),len(truepulsar))

#F1 = 2/((1./precision)+(1./recall))

F1 = 2 * precision * recall / (precision + recall)

pred = np.array(pred)

#print np.mean(np.where(pred == test_target, 1, 0))

if verbose:

print 'accuracy: ', '%.0f%%' % (np.divide(float(len(hit)),len(truepulsar)) * 100)

print 'miss: ', '%.0f%%' % (np.divide(float(len(miss)),len(truepulsar)) * 100)

print 'false positives: ', '%.0f%%' % (np.divide(float(len(falsepos)),len(pulsar))* 100)

print 'precision: ', '%.0f%%' % (precision* 100)

print 'recall: ', '%.0f%%' % (recall* 100)

print 'F1: ', F1

Xval=None,

yval=None,

gamma=None,

pct=0.6,

plot=False):

"""

returns the training and cross validation set errors

for a learning curve

(good for exploring effect of number of training samples)

Args:

X : training data

y : training value

Xval : test data

yval : test value

gamma : default None uses the objects value,

otherwise gamma=0.

pct (0<pct<1) : split the data as "pct" training, 1-pct testing

only if Xval = None

default pct = 0.6

plot : False/[True] optionally plot the learning curve

Note: if Xval == None, then we assume (X,y) is the entire set of data,

and we split them up using split_data(data,target)

returns three vectors of length(ntrials):

error_train : training error for the N=length(pct*X)

error_val : error on x-val data, when trainined on "i" samples

ntrials

error = cost_Function(lambda=0)

notes:

* a high error indicates lots of bias,

that you are probably underfitting the problem

(so add more neurons/layers, or lower regularization)

* for lots of trials, a high gap between training_error

and test_error (x-val error) indicates lots of variance

(you are over-fitting, so remove some neurons/layers,

or increase the regularization parameter)

"""

if not Xval:

X, y, Xval, yval = split_data(X, y, pct=pct)

#if gamma == None:

#gamma = classifier.gamma

m = X.shape[0]

#need at least one training item...

stepsize = max(m/25,1)

ntrials = range(1,m,stepsize)

mm = len(ntrials)

t_error = np.zeros(mm)

v_error = np.zeros(mm)

for i, v in enumerate(ntrials):

#fit with regularization

classifier.fit(X[0:v+1], y[0:v+1])

# but compute error without regularization

t_error[i] = 1 - classifier.score(X[0:v+1], y[0:v+1])

# use entire x-val set

v_error[i] = 1 - classifier.score(Xval, yval)

if plot:

plt.plot(ntrials, t_error, 'r+', label='training')

plt.plot(ntrials, v_error, 'bx', label='x-val')

plt.xlabel('training set size')

plt.ylabel('error')

plt.legend()

plt.show()

Xval=None,

yval=None,

gammas=None,

pct=0.6,

plot=False):

"""

use a cross-validation set to evaluate various regularization

parameters (gamma)

specifically:

train the NN, then loop over a range of regularization parameters

and select best 'gamma' (=min(costFunction(cross-val data))

Args:

X : training data

y : training value

Xval : test data

yval : test value

pct (0<pct<1) : if Xval=None, split into 'pct' training

"1-pct" testing

gammas : a *list* of regularization values to sample

default None uses

[0., 0.0001, 0.0005, 0.001, 0.05, 0.1, .5, 1, 1.5, 15]

plot : False/[True] optionally plot the validation cure

Note: if Xval == None, then we assume (X,y) is the entire set of data,

and we split them up using split_data(data,target)

returns:

train_error(gamma), cross_val_error(gamma), gamma, best_gamma

"""

if not Xval:

X, y, Xval, yval = split_data(X, y, pct)

if not gammas:

gammas = [0., 0.0001, 0.0005, 0.001, 0.05, 0.1, .5, 1., 1.5, 15.]

train_error = np.zeros(len(gammas))

xval_error = np.zeros(len(gammas))

for gi, gv in enumerate(gammas):

classifier.fit(X, y, gamma=gv)

train_error[gi] = classifier.costFunctionU(X, y, gamma=gv)

xval_error[gi] = classifier.costFunctionU(Xval, yval, gamma=gv)

if plot:

plt.plot(gammas, train_error, label='Train')

plt.plot(gammas, xval_error, label='Cross Validation')

plt.xlabel('gamma')

plt.ylabel('F1')

plt.legend()

plt.show()

"""

Given some complete set of data and their targets,

split the indices into 'pct' training, '1-pct' cross-vals

Args:

data = input data

target = data classifications

pct = 0 < pct < 1, default 0.6

returns:

training_data, training_target, test_data, test_target

"""

from random import shuffle

if isinstance(data,type([])):

data = np.array(data)

L = len(target)

index = range(L)

cut = int(pct*L)

while 1:

shuffle(index)

training_idx = index[:cut]

training_target = target[training_idx]

training_data = data[training_idx]

test_idx = index[cut:]

test_target = target[test_idx]

test_data = data[test_idx]

# make sure training has samples from all classes

if len(np.unique(training_target)) == len(np.unique(target)):

break

feature,

originaldata,

bounds=None,

Npts=10,

plot=False,

pct=0.4):

"""

returns the training and cross validation set errors

for a range of sizes of a given feature.

(good for diagnosing overfitting or underfitting/

high bias or high variance)

Args:

classifier: classifier

feature : string, name of the given feature

(e.g. phasebins, intervals)

originaldata : the original data loaded from pickled file, have ['pfds']

and ['target']

bounds : the range of feature sizes to explore

plot : whether or not to plot the scores

Npts : plot Npts points

pct (0<pct<1) : split the data as "pct" training, 1-pct testing

only if Xval = None

default pct = 0.6

plot : False/[True] optionally plot the learning curve

Note: if Xval == None, then we assume (X,y) is the entire set of data,

and we split them up using split_data(data,target)

returns three vectors of length(ntrials):

train_score: training error for the N=length(pct*X)

test_score: error on x-val data, when trainined on "i" samples

ntrials

vals = values of feature sizes (e.g. 8 -- 32)

notes:

* a high error indicates lots of bias,

that you are probably underfitting the problem

(so add more neurons/layers, or lower regularization)

* for lots of trials, a high gap between training_error

and test_error (x-val error) indicates lots of variance

(you are over-fitting, so remove some neurons/layers,

or increase the regularization parameter)

"""

pfds = originaldata['pfds']

orig_target = originaldata['target']

classmap = {0:[4,5], 1:[6,7]}

target = orig_target[:]

for k, v in classmap.iteritems():

for val in v:

target[orig_target == val] = k

if bounds == None:

vals = mgrid[8:32:1j*Npts]

else:

vals = mgrid[bounds[0]:bounds[1]:1j*Npts]

#kws = {'phasebins':0}

kws = {}

train_score = np.zeros_like(vals)

test_score = np.zeros_like(vals)

for i, val in enumerate(vals):

kws[feature] = int(val)

data = [pf.getdata(**kws) for pf in pfds]

train_data, train_target, test_data, test_target = split_data(data,target, pct=pct)

classifier.fit(train_data,train_target)

train_score[i] = 1-classifier.score(train_data, train_target)

test_score[i] = 1-classifier.score(test_data, test_target)

if plot:

plt.plot(vals, train_score, 'r+', label='training')

plt.plot(vals, test_score, 'bx', label='x-val')

plt.xlabel(feature)

plt.ylabel('error')

plt.legend()

plt.show()

"""

A class to hold the data and provide methods for testing AIs.

"""

def __init__(self, filename, classmap=None):

"""

initialize from a filename, to create a Datafitter instance

that holds the data and perform fitting using provided classifier

"""

self.trainclassifiers = {}

with open(filename, 'r') as fileobj:

originaldata = cPickle.load(fileobj)

self.pfds = originaldata['pfds']

self.orig_target = originaldata['target']

if classmap == None:

self.classmap = {0:[4,5], 1:[6,7]}

else:

self.classmap = classmap

self.target = self.orig_target[:]

for k, v in self.classmap.iteritems():

for val in v:

self.target[self.orig_target == val] = k

def update_classmap(self,classmap):

"""

update the target mapping

Args:

classmap: dictionary mapping target values to key values

Eg. classmap = {0:[4,5], 1:[6,7]} maps target 4 and 5 to '0'

"""

self.target = self.orig_target[:]

self.classmap = classmap

for k, v in self.classmap.iteritems():

for val in v:

self.target[self.orig_target == val] = k

def prepare_data(self, **kwds):

"""

input: the keywords for pfddata class's getdata method

output: [pf.getdata(**kwds) for pf in self.pfds]

"""

self.kwds = kwds

self.data = [pf.getdata(**kwds) for pf in self.pfds]

self.split()

#return self.data

def split(self, pct=0.6):

train_data, train_target, test_data, test_target = split_data(self.data, self.target, pct=pct)

self.train_data = train_data

self.train_target = train_target

self.test_data = test_data

self.test_target = test_target

# reset which classifiers have been trained

self.trainclassifiers = {}

def cross_val_score(self, classifier, cv=10, verbose=False):

#L = len(self.data[0])

#classifier = clsFunc(L)

scores = cross_validation.cross_val_score(classifier, self.data, self.target, cv=cv)

print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)

return scores

def learning_curve(self, classifier,

Xval=None,

yval=None,

gamma=None,

pct=0.6,

plot=True):

if not 'test_data' in self.__dict__ or not 'test_target' in self.__dict__:

self.train(classifier)

return learning_curve(classifier, self.data, self.target, Xval=Xval, yval=yval, gamma=gamma, pct=pct, plot=plot)

def feature_curve(self, classifier,

feature,

bounds=None,

Npts=10,

plot=True,

pct=0.4):

self.kwds = {}

self.kwds[feature] = bounds[1]

return feature_curve(classifier, feature, {'pfds':self.pfds, 'target':self.target}, bounds=bounds, Npts=Npts, pct=pct, plot=plot)

def train(self, clf):

if not 'test_data' in self.__dict__ or not 'test_target' in self.__dict__:

self.split()

self.trainclassifiers[clf] = True

clf.fit(self.train_data, self.train_target)

def plot_prediction(self, clf, what):

if not 'test_data' in self.__dict__ or not 'test_target' in self.__dict__:

self.train(clf)

elif not clf in self.trainclassifiers:

clf.fit(self.train_data, self.train_target)

pdts = clf.predict(self.test_data)

truepulsar = set([])

pulsar = set([])

for i,p in enumerate(pdts):

if int(self.test_target[i]) == 1:

truepulsar.add(i)

if int(p) == 1:

pulsar.add(i)

hit = pulsar & truepulsar

miss = truepulsar - pulsar

falsepos = pulsar - truepulsar

precision = np.divide(float(len(hit)),len(pulsar))

recall = np.divide(float(len(hit)),len(truepulsar))

F1 = 2 * precision * recall / (precision + recall)

print 'accuracy: ', '%.0f%%' % (np.divide(float(len(hit)),len(truepulsar)) * 100)

print 'miss: ', '%.0f%%' % (np.divide(float(len(miss)),len(truepulsar)) * 100)

print 'false positives: ', '%.0f%%' % (np.divide(float(len(falsepos)),len(pulsar))* 100)

print 'precision: ', '%.0f%%' % (precision* 100)

print 'recall: ', '%.0f%%' % (recall* 100)

print 'F1: ', F1

if what in ['miss', 'falsepos', 'truepulsar', 'pulsar']:

what = list(locals()[what])

else:

what = list(miss)

plt.figure(figsize=(8,8))

i = 0

axisNum = 0

ncol = min(8,int(np.sqrt(len(what))))

nrow = ncol

if nrow*ncol < len(what):

nrow += 1

for row in range(nrow):

for col in range(ncol):

axisNum += 1

ax = plt.subplot(nrow, ncol, axisNum)

try:

feature = [k for k in sorted(self.kwds, key=lambda x:self.kwds.get(x), reverse=True)][0]

if feature in ['intervals', 'subbands']:

N = self.kwds[feature]

ax.imshow(self.test_data[what[i]].reshape(N,N))

else:

ax.plot(self.test_data[what[i]])

except IndexError:pass

i += 1

ax.set_yticklabels([])

ax.set_xticklabels([])

plt.show()

def plot_samples(self, sample_list=[]):

"""

plot the list of 'test_data' samples, given a list of

their index numbers

Args:

sample_list = list of sample indices to plot (maximum 64)

"""

if isinstance(sample_list,type(set([]))):

sample_list = list(sample_list)

plt.figure(figsize=(8,8))

axisNum = 0

ncol = min(8,int(np.sqrt(len(sample_list))))

nrow = ncol

if nrow*ncol < len(sample_list):

nrow += 1

i = 0

for row in range(nrow):

for col in range(ncol):

axisNum += 1

ax = plt.subplot(nrow, ncol, axisNum)

try:

feature = [k for k in sorted(self.kwds, key=lambda x:self.kwds.get(x), reverse=True)][0]

if feature in ['intervals', 'subbands']:

N = self.kwds[feature]

ax.imshow(self.test_data[sample_list[i]].reshape(N,N),

cmap=plt.cmap.gray)

else:

ax.plot(self.test_data[sample_list[i]])

except IndexError:pass

i += 1

ax.set_yticklabels([])

ax.set_xticklabels([])

plt.show()

def classifier_comparison(self, classifiers=[], true_miss=True):

"""

given a list of classifiers, train them if necessary,

calculate their predictions on the self.target_data,

and return the overlap in the classifications prediction

Args:

classifiers : list of classifiers

true_miss : default (T: only return objects that are, indeed, pulsars)

(F: return all objects predicted to be pulsars)

returns (index of):

"intersection of all pulsars", "union of all pulsars"

if the object is, indeed, a pulsar

Note: assume pulsar is classed/targetted as '1'

"""

if not 'test_data' in self.__dict__ or not 'test_target' in self.__dict__:

self.split()

#intersection of all pulsars

ipulsar = set([])

#union of all pulsars

upulsar = set([])

for cli, clf in enumerate(classifiers):

if clf not in self.trainclassifiers:

self.train(clf)

# clf.fit(self.train_data, self.train_target)

p = clf.predict(self.test_data)

if cli == 0:

ipulsar = set(np.where(p == 1)[0])

else:

ipulsar = ipulsar.intersection(np.where(p == 1)[0])

upulsar = upulsar.union(np.where(p == 1)[0])

# only keep the true pulsars

if true_miss:

true_pulsars = set(np.where(self.test_target == 1)[0])

ipulsar = ipulsar.intersection(true_pulsars)

upulsar = upulsar.intersection(true_pulsars)