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)
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/