def initModel(self, _data, _csv, _attributes, _fileIn=""):
self.model.clear()
N = []
O = []
L = []
csv = CSV()
csv.load(_fileIn)
self.model.inputLayerKeys = csv.header[1:]
self.model.training = _fileIn
if not "{" in _attributes[0].type:
N, O = self.parseNodes(_data, 1)
L = self.parseLayers(self.model.config.hiddenLayers, N, O)
self.model.modelType = Type.REGRESSION
self.model.outputLayerKeys.append(csv.header[0])
else:
classes = self.extractClasses(_attributes)
N, O = self.parseNodes(_data, len(classes))
L = self.parseLayers(self.model.config.hiddenLayers, N, O)
self.model.modelType = Type.CLASSIFICATION
self.model.outputLayerKeys = classes
for i in range(len(L)):
W, T = self.generateWeightMatrix(L[i])
self.model.weights.append(W)
self.model.thresholds.append(T)
W, T = self.generateWeightMatrix(O)
self.model.weights.append(W)
self.model.thresholds.append(T)
self.model.L = L
def initExperiment(_args):
FileHandler().createFolder("results")
resultFolder = "results/" + args.name + "/"
FileHandler().createFolder(resultFolder)
resultFile = resultFolder + "result.csv"
if _args.classification:
e = Experiment(args.classification, args.name)
models = initModels(_args, Type.CLASSIFICATION)
e.classification(models, 10)
if _args.gen_code:
exportCode(_args, resultFolder, _args.classification, models)
if _args.visualize:
files = [e.path("cv_" + str(i) + ".csv") for i in range(len(models))]
xTicks = [model.modelName for model in models]
ResultVisualizer().boxplots(files, _args.visualize, xTicks, ylabel=_args.visualize)
elif _args.correlation:
csv = CSV()
csv.load(args.correlation)
csv.computeCorrelationMatrix(resultFile)
if _args.visualize:
ResultVisualizer().colorMap(resultFile)
elif _args.regression:
e = Experiment(args.regression, args.name)
models = initModels(_args, Type.REGRESSION)
e.regression(models, 10)
if _args.gen_code:
exportCode(_args, resultFolder, _args.regression, models)
if _args.visualize:
files = [e.path("cv_" + str(i) + ".csv") for i in range(len(models))]
xTicks = [model.modelName for model in models]
ResultVisualizer().boxplots(files, _args.visualize, xTicks, ylabel=_args.visualize)
print("[LIMITS]: results written to src/" + resultFolder)
def run(self, _models, _folds, _type):
if self.genDataSets:
csv = CSV()
csv.load(self.training)
csv.randomize(self.seed)
if _type == Type.REGRESSION:
csv.createFolds(_folds, self.resultFolder + "tmp/")
elif _type == Type.CLASSIFICATION:
classes = csv.stratify(_folds, self.resultFolder + "tmp/")
R = ResultMatrix()
for i in range(0, len(_models)):
model = _models[i]
model.modelType = _type
config = Configuration(self.training, model, _folds)
config.resultFolder = self.resultFolder
config.tmpFolder = self.resultFolder + "tmp/"
cv = CrossValidation(config, str(i))
r = cv.run(csv.id, csv.id)
results = np.hstack([r.data.mean(0),
r.data.std(0)]) # TUDO: mean only if size>1 !
R.add(r.header + [x + "_std" for x in r.header], results)
if self.verbose:
if i == 0:
r.printHeader()
r.printAggregated()
FileHandler().saveMatrix(R.header, R.data,
self.resultFolder + "results.csv")
if self.clear:
FileHandler().clearFolder(self.resultFolder + "tmp/")
return R.header, R.data
def run(self, _training, _models, _platforms):
R = ResultMatrix()
M = [];
for model in _models:
# run the cross validation to compute the model performance
M.append(model.toString())
e = Experiment(_training)
header, result = e.regression([model], 10)
R.add(header, result)
# train with the global training data and export code
training_arff = "tmp/recommend.arff"
csv = CSV()
csv.load(_training)
csv.convertToARFF(training_arff, False)
attributes = csv.findAttributes(0)
lAtt = len(attributes)-1
WEKA().train(model, training_arff, "0")
data = "\n".join(FileHandler().read("tmp/raw0.txt"))
codeFile = "recommend.c"
model.exportCode(data, csv, attributes, codeFile)
# complile platform-specific code
for platform in _platforms:
""
#print(model.toString() + " : " + platform.toString())
print(R.header, R.data)
print(M)
from data.CSV import CSV
from plot.ResultVisualizer import ResultVisualizer
# define the training data set
training = "../examples/mnoA.csv"
# compute amd export the correlation matrix
csv = CSV()
csv.load(training)
resultFolder = "results/example_correlation/"
resultFile = resultFolder + "corr.csv"
csv.computeCorrelationMatrix(resultFile)
ResultVisualizer().colorMap(resultFile,
savePNG=resultFolder + 'example_correlation.png')
# all results are written to results/example_correlation/