def execute_post(dataset, x):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
result = wrapperv2.core(locals(), "shapiro")
result.shapiro()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, formula, na__action, subset, ntree, mtry, replace, classwt, cutoff, strata, sampsize, nodesize, maxnodes, importance, localImp, nPerm, proximity, oob__prox, norm__votes, keep__forest, keep__inbag):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
result = wrapperv2.core(locals(), "rf")
result.rf()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, formula, na__action):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/air.csv'
result = wrapperv2.core(locals(), "svm")
result.svm()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, formula, num_trees, x, max_depth):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
result = wrapperv2.core(locals(), "rf_spark")
result.rf_spark()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, x, y, use, method):
locals()['dataset'] = '/root/TFG/openccml/dataset/mtcars.csv'
result = wrapperv2.core(locals(), "cor")
result.cor()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, model):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/models/arima.rds'
result = wrapperv2.core(locals(), "predict")
result.predict()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, x, s__window, t__window, t__jump, robust):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/nottem.csv'
result = wrapperv2.core(locals(), "stl")
result.stl()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, x, lag, var_type, fitdf):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
result = wrapperv2.core(locals(), "box")
result.box()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, x, y, size, maxit, initFuncParams, learnFuncParams, linOut):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
result = wrapperv2.core(locals(), "rbf")
result.rbf()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, formula):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
result = wrapperv2.core(locals(), "predictSVM")
result.predictSVM()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, x, order):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/air.csv'
result = wrapperv2.core(locals(), "predictarima")
result.predictarima()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, x, formula, layers):
locals()['dataset'] = '/root/TFG/openccml/datasets/iris.csv'
result = wrapperv2.core(locals(), "mlp_spark")
result.mlp_spark()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(dataset, x, order, seasonal, xreg, include__mean, transform__pars,
fixed, init, method, optim__method, optim__control, kappa):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
print(locals())
result = wrapperv2.core(locals(), "arima")
result.arima()
file = result.parameter.getOutput()
def execute_post(dataset, x, lag__max, var_type, plot, na__action, demean):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
result = wrapperv2.core(locals(), "acf")
result.acf()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, centers, iter__max):
print(locals())
result = wrapperv2.core(locals(), "naiveBayes")
result.naiveBayes()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(dataset, formula, na__action):
print(locals())
result = wrapperv2.core(locals(), "naiveBayes")
result.naiveBayes()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(dataset, formula, na__action):
print(locals())
result = wrapperv2.core(locals(), "svm")
result.superVectorMachine()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, formula, data, err__fct, linear__output, likelihood):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/creditset.csv'
result = wrapperv2.core(locals(), "neuralnet")
result.neuralnet()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(dataset, formula, data, subset, na__action, size, rang, decay,
maxit):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
print(locals())
result = wrapperv2.core(locals(), "nnet")
result.nnet()
file = result.parameter.getOutput()
def execute(dataset, x, y, size, maxit, initFuncParams, learnFuncParams,
linOut):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
print(locals())
result = wrapperv2.core(locals(), "mlp")
result.mlp()
file = result.parameter.getOutput()
def execute_post(dataset,x,alternative,k):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
result = wrapperv2.core(locals(), "adftest")
result.adftest()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute_post(dataset, formula, x, max_iter):
locals()['dataset'] = '/root/TFG/openccml/datasets/titanic.csv'
result = wrapperv2.core(locals(), "svc_spark")
result.svc_spark()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(x, method):
print(locals())
result = wrapperv2.core(locals(), "hclust")
result.hierarchicalClustering()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(x, minPts, eps):
print(locals())
result = wrapperv2.core(locals(), "optics")
result.optics()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(x, centers, iter__max):
print(locals())
result = wrapperv2.core(locals(), "kmeans")
result.kmeans()
file = result.parameter.getOutput()
print(file)
with open(file) as pmml:
return pmml.read()
def execute(dataset1, dataset2, x, y, lag__max, var_type, plot, na__action,
demean):
locals()['dataset1'] = '/root/TFG/openccml/wrapperR/ts1.csv'
locals()['dataset2'] = '/root/TFG/openccml/wrapperR/ts2.csv'
print(locals())
result = wrapperv2.core(locals(), "ccf")
result.ccf()
file = result.parameter.getOutput()
def execute_post(dataset, formula, data, subset, na__action, size, rang, decay,
maxit):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/iris.csv'
result = wrapperv2.core(locals(), "nnet")
result.nnet()
file = result.parameter.getOutput()
with open(file) as pmml:
return pmml.read()
def execute(x, y, method, use):
print(locals())
result = wrapperv2.core(locals(), "cor")
result.cor()
file = result.parameter.getOutput()
print(file)
with open(file, 'rb') as pmml:
return pmml
def execute(dataset, y, model, damped, alpha, beta, gamma, phi, additive__only,
var_lambda, biasadj, lower, upper, opt__crit, nmse, bounds, ic,
restrict, allow__multiplicative__trend, use__initial__values):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
print(locals())
result = wrapperv2.core(locals(), "ets")
result.ets()
file = result.parameter.getOutput()
def execute(dataset, y, d, D, max__p, max__q, max__P, max__Q, max__order,
max__d, max__D, start__p, start__q, start__P, start__Q, stationary,
seasonal, ic, stepwise, trace, approximation, truncate, xreg, test,
seasonal__test, allowdrift, allowmean, var_lambda, biasadj,
parallel, num__cores):
locals()['dataset'] = '/root/TFG/openccml/wrapperR/ts2.csv'
print(locals())
result = wrapperv2.core(locals(), "autoarima")
result.autoarima()
file = result.parameter.getOutput()
