def long(generations:int):
data = "/netapp/home/tianjiao.zhang/data/microstates.dat";
targetFreqs = "/netapp/home/tianjiao.zhang/data/ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
ensembleSizes = numpy.array([16, 24, 32, 64, 128]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([-1, 5]);
steepnessRange = numpy.array([1, 7]);
minWeights = numpy.array([0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 1, 1, 1]);
optimizer = Optimizer(MACROSTATES, True);
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readFormattedMicrostateData(data);
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), True, 128, 1.25, 0.25);
search.setMaxIterations(generations);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setAllSearchToTrue();
search.suppressOutputs = True;
optimizer.useAlgorithm(search);
optimizer.optimize();
now = datetime.now();
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), "var ensembles " + now.strftime('%Y%m%d%H%M') + ".fasta");
optimizer.writeBestParamsToText("var ensembles " + now.strftime('%Y%m%d%H%M'));
def smalltestPrevOptimalVals():
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
# only looking at MACROSTATE.TS
# only optimizing backrub temperature and steepness
ensembleSizes = numpy.array([50]);
backrubTemps = numpy.array([1.8]);
boltzmannTemps = numpy.array([0.0]);
steepnessRange = numpy.array([3.0]);
minWeights = numpy.array([0.80, 0.55, 0, 0.90, 0.35, 1.00]);
maxWeights = numpy.array([0.80, 0.55, 0, 0.90, 0.35, 1.00]);
print("Initializing objects\n");
targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqsAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat6.tsv";
dataAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat5.tsv";
optimizer = Optimizer(MACROSTATES);
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
except:
optimizer.readTargetFrequencies(targetFreqsAlt);
optimizer.readData(dataAlt);
print("Files read in");
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 1, 1, 0.25);
search.setMaxIterations(1);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(False, False, False, False, numpy.array([False, False, False, False, False, False]));
optimizer.useAlgorithm(search);
#print("Cos similiarity");
#optimizer.optimize();
#optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), "testOutCos.fasta");
#print(optimizer.getBestParameters()['match']);
print("\nJS Dist");
#search.setSimilarityMeasure(JensenShannonDistance(optimizer.targetFrequencies));
optimizer.optimize();
now = datetime.now();
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), "prev opt vals " + now.strftime('%Y%m%d%H%M') + ".fasta");
optimizer.writeBestParamsToText("prev opt vals " + now.strftime('%Y%m%d%H%M'))
print(optimizer.getBestParameters()['match']);
return None;
def testChi2(iterations = 64):
print("Hello!\n");
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
# only looking at MACROSTATE.TS
# only optimizing backrub temperature and steepness
ensembleSizes = numpy.array([20, 50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 0, 1, 1, 1]);
print("Initializing objects\n");
data = "/netapp/home/tianjiao.zhang/data/DHFR_MSD_M20loop_repeat1.tsv";
#data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat" + str(i + 1) + ".tsv";
#targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqs = "/netapp/home/tianjiao.zhang/data/ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
optimizer = Optimizer(MACROSTATES);
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
except:
optimizer.readTargetFrequencies(targetFreqsAlt);
optimizer.readData(dataAlt);
print("Files read in");
search = CuckooSearch(optimizer.models, Chi2Kernel(optimizer.targetFrequencies), False, 64, 1, 0.25);
search.setMaxIterations(iterations);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(False, True, True, True, numpy.array([True, True, False, True, True, True]));
optimizer.useAlgorithm(search);
print("\nChi2 kernel");
#search.setSimilarityMeasure(JensenShannonDistance(optimizer.targetFrequencies));
optimizer.optimize();
now = datetime.now();
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), "Chi2 test " + now.strftime('%Y%m%d%H%M%S') + ".fasta");
optimizer.writeBestParamsToText("Chi2 test " + now.strftime('%Y%m%d%H%M%S'));
print(optimizer.getBestParameters()['match']);
return None;
def smallTestBoltz():
print("Hello!\n");
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
ensembleSizes = numpy.array([128]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([-1, 5]);
steepnessRange = numpy.array([1, 7]);
minWeights = numpy.array([0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 1, 1, 1]);
print("Initializing objects\n");
targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqsAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
dataMicro = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\microstates.dat";
dataMicroAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\microstates.dat";
optimizer = Optimizer(MACROSTATES, True);
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readFormattedMicrostateData(dataMicro);
except FileNotFoundError:
optimizer.readtargetfrequencies(targetfreqsalt);
optimizer.readFormattedMicrostatedata(datamicroalt);
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), True, 64, 1, 0.25);
search.setMaxIterations(2048);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(False, True, True, True, numpy.array([True, True, True, True, True, True]));
optimizer.useAlgorithm(search);
optimizer.optimize();
now = datetime.now();
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), "var ensembles " + now.strftime('%Y%m%d%H%M') + ".fasta");
optimizer.writeBestParamsToText("var ensembles " + now.strftime('%Y%m%d%H%M'));
#for i in range(8):
# thread = optimizerThread();
# thread.copyOptimizer(optimizer);
# thread.run();
return None;
def onlyMacro():
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
i = 0;
# only looking at MACROSTATE.TS
# only optimizing backrub temperature and steepness
ensembleSizes = numpy.array([20, 50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 0, 1, 1, 1]);
data = "/netapp/home/tianjiao.zhang/data/DHFR_MSD_M20loop_repeat" + str(i + 1) + ".tsv";
#data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat" + str(i + 1) + ".tsv";
#targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqs = "/netapp/home/tianjiao.zhang/data/ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
optimizer = Optimizer(MACROSTATES);
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = " JSD";
search.setMaxIterations(2048);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(True, True, True, True, numpy.array([True, True, False, True, True, True]));
optimizer.useAlgorithm(search);
optimizer.optimize();
name = "Macrostates " + str(i + 1) + measure + datetime.now().strftime('%Y%m%d%H%M');
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), name + ".fasta", 3);
optimizer.writeBestParamsToText(name + ".txt");
return None;
data = "/netapp/home/tianjiao.zhang/data/DHFR_MSD_M20loop_repeat1.tsv"
targetFreqs = "/netapp/home/tianjiao.zhang/data/ecDHFR_openseq_bacterial_representative_final_align_trim.fasta"
optimizer = Optimizer(MACROSTATES)
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs)
optimizer.readData(data)
except:
optimizer.readTargetFrequencies(targetFreqsAlt)
optimizer.readData(dataAlt)
search = CuckooSearch(optimizer.models,
JensenShannonDistance(optimizer.targetFrequencies),
False, 128, 1, 0.25)
search.setMaxIterations(16)
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps,
steepnessRange, minWeights, maxWeights)
search.setSearchParameters(False, True, True, True,
numpy.array([True, True, False, True, True, True]))
optimizer.useAlgorithm(search)
optimizer.optimize()
print("16 iterations | runtime {:s} | match {:.6f}".format(
str(search.elapsedTime),
optimizer.getBestParameters()['match']))
search = CuckooSearch(optimizer.models,
JensenShannonDistance(optimizer.targetFrequencies),
False, 128, 1, 0.25)
def DHFRcomparemeasures(similarity:int):
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
ensembleSizes = numpy.array([20, 50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 0, 1, 1, 1]);
data = "/netapp/home/tianjiao.zhang/data/DHFR_MSD_M20loop_repeat1.tsv";
targetFreqs = "/netapp/home/tianjiao.zhang/data/ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
optimizer = Optimizer(MACROSTATES);
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
measure = "";
if similarity == 0:
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = " JSD";
elif similarity == 1:
search = CuckooSearch(optimizer.models, CosineSimilarity(optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = " Cos";
elif similarity == 2:
search = CuckooSearch(optimizer.models, KLDivergence(optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = " KLD";
elif similarity == 3:
search = CuckooSearch(optimizer.models, EntropyWeightsMixedSimilarity(CosineSimilarity(), JensenShannonDistance(), optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = " Mix"
elif similarity == 4:
search = CuckooSearch(optimizer.models, EntropyWeightedSimilarity(JensenShannonDistance(), optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = "Weighted JSD";
else:
search = CuckooSearch(optimizer.models, Chi2Kernel(optimizer.targetFrequencies), False, 64, 1, 0.25);
measure = "Chi2 kernel";
search.setMaxIterations(2048);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(True, True, True, True, numpy.array([True, True, False, True, True, True]));
optimizer.useAlgorithm(search);
optimizer.optimize();
name = "DHFR compare measures " + measure + " " + datetime.now().strftime('%Y%m%d%H%M');
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), name + ".fasta", 3);
optimizer.writeBestParamsToText(name + ".txt");
def simpleRepeatTest():
print("Hello!\n");
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
ensembleSizes = numpy.array([50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 1]);
maxWeights = numpy.array([0, 0, 0, 0, 0, 1]);
print("Initializing objects\n");
targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqsAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataMicro = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\20160120_M20_enumeration_scores.tsv";
dataMicroAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\20160120_M20_enumeration_scores.tsv";
optimizer = Optimizer(MACROSTATES);
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
except:
optimizer.readTargetFrequencies(targetFreqsAlt);
optimizer.readData(dataAlt);
print("Files read in");
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 1, 1, 0.25);
search.setMaxIterations(1);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
#search.setSearchParameters(False, True, True, True, numpy.array([True, True, False, True, True, True]));
search.setAllSearchToFalse();
search.suppressOutputs = True;
optimizer.useAlgorithm(search);
print("\nJS Dist");
for i in range(64):
optimizer.optimize();
params = optimizer.getBestParameters();
m = search.population[0];
#print(search.similarityMeasure.getSimilarityMeasure(m.getFrequencies()));
# TODO: getModelByParams doesn't always return the same object.
m1 = Model.constructFromExisting(optimizer.getModelByParams(m.backrubTemp, m.ensembleSize, m.boltzmannTemp), m.ensembleSize, m.backrubTemp, m.boltzmannTemp, m.getWeights(), m.steepness);
#print(search.similarityMeasure.getSimilarityMeasure(m1.getFrequencies()));
if not m.equalTo(m1):
print("\t{:s}".format(Optimizer.calcParamsID(m.backrubTemp, m.ensembleSize, m.boltzmannTemp)));
print("\t{:s}".format(Optimizer.calcParamsID(m1.backrubTemp, m1.ensembleSize, m1.boltzmannTemp)));
#m2 = Model.constructFromExisting(m, m.ensembleSize, m.backrubTemp, m.boltzmannTemp, m.getWeights(), m.steepness);
#print(search.similarityMeasure.getSimilarityMeasure(m2.getFrequencies()));
#print(m.equalTo(m2));
#print(m2.backrubTemp);
#print(m2.boltzmannTemp);
#print(m2.ensembleSize);
#print(m2.steepness);
#print(m2.weights);
#print(search.similarityMeasure.getSimilarityMeasure(m2.getFrequencies()));
#print(numpy.sum(numpy.sum(numpy.abs( m.getFrequencies() - m2.getFrequencies()))));
return None;
def repeatTest():
print("Hello!\n");
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
ensembleSizes = numpy.array([50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 0, 1, 1, 1]);
print("Initializing objects\n");
targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqsAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataMicro = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\20160120_M20_enumeration_scores.tsv";
dataMicroAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\20160120_M20_enumeration_scores\\20160120_M20_enumeration_scores.tsv";
optimizer = Optimizer(MACROSTATES);
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
except:
optimizer.readTargetFrequencies(targetFreqsAlt);
optimizer.readData(dataAlt);
print("Files read in");
for i in range(32):
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 8, 1, 0.25);
search.setMaxIterations(16);
search.suppressOutputs = True;
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(False, True, True, True, numpy.array([True, True, False, True, True, True]));
optimizer.useAlgorithm(search);
print("\nJS Dist");
#search.setSimilarityMeasure(JensenShannonDistance(optimizer.targetFrequencies));
optimizer.optimize();
params = optimizer.getBestParameters();
print(params['match']);
print(optimizer.verifyFoundParams(params['ensembleSize'], params['backrubTemp'], params['boltzmannTemp'], params['steepness'], params['weights']));
search = CuckooSearch(optimizer.models, CosineSimilarity(optimizer.targetFrequencies), False, 8, 1, 0.25);
search.setMaxIterations(16);
search.suppressOutputs = True;
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(False, True, True, True, numpy.array([True, True, False, True, True, True]));
optimizer.useAlgorithm(search);
print("\nCosine")
optimizer.optimize();
params = optimizer.getBestParameters();
print(params['match']);
print(optimizer.verifyFoundParams(params['ensembleSize'], params['backrubTemp'], params['boltzmannTemp'], params['steepness'], params['weights']));
return None;
def testRandUniformInput():
MACROSTATES = enum("E-DHF-NADPH", "E-NADPH", "E-OPEN", "E-THF", "E-THF-NADPX", "TS");
RESIDUES = enum('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y');
# only looking at MACROSTATE.TS
# only optimizing backrub temperature and steepness
ensembleSizes = numpy.array([50]);
backrubTemps = numpy.array([0.3, 0.6, 0.9, 1.2, 1.5, 1.8]);
boltzmannTemps = numpy.array([0, -1, 1, 5.0]);
steepnessRange = numpy.array([0.5, 5]);
minWeights = numpy.array([0, 0, 0, 0, 0, 0]);
maxWeights = numpy.array([1, 1, 0, 1, 1, 1]);
print("Initializing objects\n");
targetFreqs = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
targetFreqsAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta";
data = "C:\\Users\\Candy\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
dataAlt = "C:\\Users\\Candy_000\\SkyDrive\\Documents\\rotation 2\\DHFR_MSD_M20loop\\DHFR_MSD_M20loop_repeat1.tsv";
optimizer = Optimizer(MACROSTATES);
# slightly different paths on my two computers
try:
optimizer.readTargetFrequencies(targetFreqs);
optimizer.readData(data);
except:
optimizer.readTargetFrequencies(targetFreqsAlt);
optimizer.readData(dataAlt);
# make energies uniform
for model in optimizer.models:
optimizer.models[model].macrostateResidueEnergies = numpy.ones_like(optimizer.models[model].macrostateResidueEnergies);
search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), False, 1, 1, 0.25);
search.setMaxIterations(1);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setSearchParameters(False, False, False, False, numpy.array([False, False, False, False, False, False]));
optimizer.useAlgorithm(search);
outfile = open("uniform energy similarities.txt", 'w');
optimizer.optimize();
outfile.write("JSD: {:.4f}\n".format(optimizer.getBestParameters()['match']));
search.setSimilarityMeasure(CosineSimilarity(optimizer.targetFrequencies));
optimizer.optimize();
outfile.write("Cosine similarity: {:.4f}\n".format(optimizer.getBestParameters()['match']));
search.setSimilarityMeasure(KLDivergence(optimizer.targetFrequencies));
optimizer.optimize();
outfile.write("K-L divergence: {:.4f}\n".format(optimizer.getBestParameters()['match']));
search.setSimilarityMeasure(EntropyWeightsMixedSimilarity(CosineSimilarity(), JensenShannonDistance(), optimizer.targetFrequencies));
optimizer.optimize();
outfile.write("Weighted mixed similarity: {:.4f}\n".format(optimizer.getBestParameters()['match']));
outfile.close();
return None;
data = "/netapp/home/tianjiao.zhang/data/microstates.dat"; targetFreqs = "/netapp/home/tianjiao.zhang/data/ecDHFR_openseq_bacterial_representative_final_align_trim.fasta"; dataAlt = "C:\\users\\candy\\skydrive\\documents\\rotation 2\\DHFR microstates\\microstates.dat"; targetFreqsAlt = "C:\\users\\candy\\skydrive\\documents\\rotation 2\\ecDHFR_openseq_bacterial_representative_final_align_trim.fasta"; optimizer = Optimizer(MACROSTATES, True); # slightly different paths on my two computers optimizer.readTargetFrequencies(targetFreqs); optimizer.readFormattedMicrostateData(data); if similarityMeasure == 0: search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), True, 32, 1, 0.25); elif similarityMeasure == 1: search = CuckooSearch(optimizer.models, CosineSimilarity(optimizer.targetFrequencies), True, 32, 1, 0.25); elif similarityMeasure == 2: search = CuckooSearch(optimizer.models, KLDivergence(optimizer.targetFrequencies), True, 32, 1, 0.25); elif similarityMeasure == 3: search = CuckooSearch(optimizer.models, Chi2Kernel(optimizer.targetFrequencies), True, 32, 1, 0.25); elif similarityMeasure == 4: search = CuckooSearch(optimizer.models, EntropyWeightedSimilarity(JensenShannonDistance(), optimizer.targetFrequencies), True, 32, 1, 0.25); elif similarityMeasure == 5: search = CuckooSearch(optimizer.models, EntropyWeightsMixedSimilarity(JensenShannonDistance(), CosineSimilarity(), optimizer.targetFrequencies), True, 32, 1, 0.25); else: search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), True, 32, 1, 0.25); print(search.similarityMeasure.__str__());
def optimize():
if microstate_optimize == True:
# init search algorithm
#search = CuckooSearch(optimizer.models, JensenShannonDistance(optimizer.targetFrequencies), True, 64, 1, 0.25)
if simMeas_id == 'JS':
search = CuckooSearch(
optimizer.models,
JensenShannonDistance(optimizer.targetFrequencies), True, 64,
1, 0.25)
elif simMeas_id == 'CS':
search = CuckooSearch(
optimizer.models,
CosineSimilarity(optimizer.targetFrequencies), True, 64, 1,
0.25)
# elif simMeas_id == 'EW':
# search = CuckooSearch(optimizer.models, EntropyWeightedSimilarity(CosineSimilarity, optimizer.targetFrequencies), True, 64, 1, 0.25)
# elif simMeas_id == 'EWM':
# search = CuckooSearch(optimizer.models, EntropyWeightsMixedSimilarity(optimizer.targetFrequencies), True, 64, 1, 0.25)
elif simMeas_id == 'KL':
search = CuckooSearch(optimizer.models,
KLDivergence(optimizer.targetFrequencies),
True, 64, 1, 0.25)
elif simMeas_id == 'C2':
search = CuckooSearch(optimizer.models,
Chi2Kernel(optimizer.targetFrequencies),
True, 64, 1, 0.25)
search.setMaxIterations(iterations)
# set parameters
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps,
steepnessRange, minWeights, maxWeights)
search.setSearchParameters(True, False, True, True, usedstates)
# load search algorithm
optimizer.useAlgorithm(search)
# optimize
optimizer.optimize()
#now = datetime.now()
optimizer.writeFrequenciesToFASTA(
optimizer.getBestFrequencies(),
os.path.join(output_path, "var_ensembles_" + job_tag + ".fasta"))
optimizer.writeBestParamsToText(
os.path.join(output_path, "var_ensembles_" + job_tag))
else:
# init search algorithm
if simMeas_id == 'JS':
search = CuckooSearch(
optimizer.models,
JensenShannonDistance(optimizer.targetFrequencies), False, 64,
1, 0.25)
elif simMeas_id == 'CS':
search = CuckooSearch(
optimizer.models,
CosineSimilarity(optimizer.targetFrequencies), False, 64, 1,
0.25)
# elif simMeas_id == 'EW':
# search = CuckooSearch(optimizer.models, EntropyWeightedSimilarity(optimizer.targetFrequencies), False, 64, 1, 0.25)
# elif simMeas_id == 'EWM':
# search = CuckooSearch(optimizer.models, EntropyWeightsMixedSimilarity(optimizer.targetFrequencies), False, 64, 1, 0.25)
elif simMeas_id == 'KL':
search = CuckooSearch(optimizer.models,
KLDivergence(optimizer.targetFrequencies),
False, 64, 1, 0.25)
elif simMeas_id == 'C2':
search = CuckooSearch(optimizer.models,
Chi2Kernel(optimizer.targetFrequencies),
False, 64, 1, 0.25)
search.setMaxIterations(iterations)
# set parameters
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps,
steepnessRange, minWeights, maxWeights)
search.setSearchParameters(False, False, False, True, usedstates)
# load search algorithm
optimizer.useAlgorithm(search)
# optimize
optimizer.optimize()
#now = datetime.now()
optimizer.writeFrequenciesToFASTA(
optimizer.getBestFrequencies(),
os.path.join(output_path, "fixed_ensembles_" + job_tag + ".fasta"))
optimizer.writeBestParamsToText(
os.path.join(output_path, "fixed_ensembles_" + job_tag))