def test(algorithm, datasetfolder):
"""algorithm is a function that takes a motif length and a set of sequences
and returns a list of sequences,positions of the best motif
datasetfolder is the name of the dataset to test on
returns PerformanceResults of the run"""
seq = readSequences(os.path.join(datasetfolder, "sequences.fa"))
motifLength = readMotifLength(os.path.join(datasetfolder,
"motiflength.txt"))
actualLocations = readSites(os.path.join(datasetfolder, "sites.txt"))
assert len(seq) == len(actualLocations), "there should be a site location"
"for every sequence"
startTime = time.clock()
result = algorithm(motifLength, seq)
endTime = time.clock()
elapsedTime = endTime - startTime
runScore = M.informationInSelection(motifLength, result)
actualScore = M.informationInSelection(motifLength,
zip(seq, actualLocations))
pwfilename = os.path.join(datasetfolder, "predictedmotif.txt")
writePositionWeightMatrix(pwfilename, motifLength, result)
sitesfilename = os.path.join(datasetfolder, "predictedsites.txt")
writeSites(sitesfilename, [site for (_, site) in result])
return PerformanceResults(time=elapsedTime,
algorithmScore=runScore,
motifScore=actualScore)
def test(algorithm, datasetfolder):
"""algorithm is a function that takes a motif length and a set of sequences
and returns a list of sequences,positions of the best motif
datasetfolder is the name of the dataset to test on
returns PerformanceResults of the run"""
seq = readSequences(os.path.join(datasetfolder, "sequences.fa"))
motifLength = readMotifLength(
os.path.join(datasetfolder, "motiflength.txt"))
actualLocations = readSites(os.path.join(datasetfolder, "sites.txt"))
assert len(seq) == len(actualLocations), "there should be a site location"
"for every sequence"
startTime = time.clock()
result = algorithm(motifLength, seq)
endTime = time.clock()
elapsedTime = endTime - startTime
runScore = M.informationInSelection(motifLength, result)
actualScore = M.informationInSelection(motifLength,
zip(seq, actualLocations))
pwfilename = os.path.join(datasetfolder, "predictedmotif.txt")
writePositionWeightMatrix(pwfilename, motifLength, result)
sitesfilename = os.path.join(datasetfolder, "predictedsites.txt")
writeSites(sitesfilename, [site for (_, site) in result])
return PerformanceResults(time=elapsedTime,
algorithmScore=runScore,
motifScore=actualScore)
def uniformRandomSampling(iterations, motifLength, sequences):
"""Finds motifs by selecting a random placement, and checking the
fitness"""
# chose an arbitrary best to start
bestSelection = [(s, 0) for s in sequences]
for _ in range(iterations):
selections =\
[(s, random.randrange(len(s) - motifLength + 1))
for s in sequences]
if M.informationInSelection(motifLength, selections) >\
M.informationInSelection(motifLength, bestSelection):
bestSelection = selections
return bestSelection
def uniformRandomSampling(iterations, motifLength, sequences):
"""Finds motifs by selecting a random placement, and checking the
fitness"""
# chose an arbitrary best to start
bestSelection = [(s, 0) for s in sequences]
for _ in range(iterations):
selections =\
[(s, random.randrange(len(s) - motifLength + 1))
for s in sequences]
if M.informationInSelection(motifLength, selections) >\
M.informationInSelection(motifLength, bestSelection):
bestSelection = selections
return bestSelection
def update_stock(stock):
try:
stock_data = Motif.get_stock_data(stock)
stock_data_list.append(stock_data)
print(stock + ' refreshed')
except Exception as e:
print(stock + ' not refreshed:\n' + str(e))
def writePositionWeightMatrix(filename, motifLength, selections):
subseq = M.extractSelections(motifLength, selections)
counts = [{base: 0 for base in Gen.bases} for i in range(motifLength)]
for seq in subseq:
for i, char in enumerate(seq):
counts[i][char] += 1
with open(filename, 'w') as f:
f.write(">PMOTIF\t" + str(motifLength) + "\n")
for pos in counts:
line = "\t".join(str(pos[c]) for c in Gen.bases)
f.write(line + "\n")
f.write("<")
spec = 0.0
print "%5.4f" % spec,
print "PPV:",
try:
ppv = float(self.getProp('num.Feature.Hits.TP')) / self.getProp(
'num.Feature.Hits')
except ZeroDivisionError:
ppv = 0.0
print "%5.4f" % ppv
# -----------------------------------------------------------------------
# main()
# Generate Motif object from patterns
motiflist = map(lambda x: Motif.Motif(x), patternlist)
globalAnalysis = SummaryAnalysis('TOTAL')
globalAnalysis.displayHeadings()
# Load Sitefile
siteFile = SiteFile(sitefilename)
# Open PDB dbh
pdbdbh = proteindatabankdbh.dbopen()
# For each PDB
for pdbid in pdblist:
print >> sys.stderr, pdbid
if motiflist:
def evalWithSeq(seq):
return lambda pos: M.infoInPositions(motifLength, seq, pos)
def evalPositions(pos):
return M.infoInPositions(motifLength, sequences, pos)
def evalWithSeq(seq):
return lambda pos: M.infoInPositions(motifLength, seq, pos)
def evalPositions(pos):
return M.infoInPositions(motifLength, sequences, pos)
# TRAIN SVM
print("TRAIN SVM")
Cobj = SVM(xtrain, y_train)
Cobj.train(C=1.)
Cobj.svm_save("results/svm_trained.pkl")
# COMPUTE gPOIM
print("COMPUTE gPOIM")
small_k = 2
Pobj = gPOIM()
Pobj.set_up(Cobj, samplesize=100, small_k=small_k)
Pobj.save("results/gPOIM.pkl")
# PLOT gPOIM
view.plot_gPOIM(Pobj.gPOIM, "results/gPOIM.pdf")
# EXTRACT Motif
print("EXTRACT MOTIF")
motif_start, motif_len = get_motif_pos_len_similar_diffPOIM(Pobj.gPOIM)
motif_len = [20]
Mobj = Motif()
motif = Mobj.find(2,
motif_len,
Pobj.gPOIM,
motif_start,
base=['A', 'C', 'G', 'T'])
np.savetxt("results/pwm.pkl", motif[0])
seqLogo("results/pwm.pkl")
'https://www.motifinvesting.com/motifs#catalog=our&checked=featured&checked=hot&limit=90',
'https://www.motifinvesting.com/motifs#catalog=community&checked=featured&checked=hot&checked=purchased&checked=exclude-etfs&limit=90&offset=0'
]
stock_data_list = []
def update_stock(stock):
try:
stock_data = Motif.get_stock_data(stock)
stock_data_list.append(stock_data)
print(stock + ' refreshed')
except Exception as e:
print(stock + ' not refreshed:\n' + str(e))
if __name__ == '__main__':
motifs = Motif.search_professional_motifs()
stocks = set(['LNKD', 'TWTR', 'MSFT', 'GOOG', 'AMBA', 'FB', 'AAPL', 'NFLX', 'PYPL', ])
for motif in motifs:
print('motif:', motif)
_stocks = Motif.get_stocks_from_motif(motif)
for stock in _stocks:
#print('stock:', stock)
stocks.add(stock)
print(len(stocks))
#style0 = xlwt.easyxf('font: name Times New Roman, color-index red, bold on', num_format_str='#,##0.00')
#style1 = xlwt.easyxf(num_format_str='D-MMM-YY')
#wb = xlwt.Workbook()
#ws = wb.add_sheet('US Stock')
