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 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 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;
# 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__());
search.setMaxIterations(2048);
search.setParamBounds(ensembleSizes, backrubTemps, boltzmannTemps, steepnessRange, minWeights, maxWeights);
search.setAllSearchToTrue();
optimizer.useAlgorithm(search);
optimizer.optimize();
now = datetime.now();
optimizer.writeFrequenciesToFASTA(optimizer.getBestFrequencies(), "Similarity measure compare " + str(similarityMeasure) + now.strftime('%Y%m%d%H%M%S') + ".fasta");
optimizer.writeBestParamsToText("Similarity measure compare " + str(similarityMeasure) + now.strftime('%Y%m%d%H%M%S'));
