def btnNaiveBayesClicked(self,priors,FormSetNaiveBayesParam):
if priors.upper() == "NONE":
priors=None
self.algorithm=GaussianNB(priors=priors)
AlgorithmOperation.ApplyAlgorithm(self.algorithm)
self.messageBox(FormSetNaiveBayesParam)
def btnSVCClicked(self, kernel, gamma, tol, verbose, random_state,
FormSetParamSVC):
if random_state.upper() == "NONE":
random_state = None
self.algorithm = SVC(kernel=kernel,
gamma=gamma,
tol=float(tol),
verbose=bool(verbose),
random_state=random_state)
AlgorithmOperation.ApplyAlgorithm(self.algorithm)
self.messageBox(FormSetParamSVC)
def btnKNNClicked(self, k, weights, leaf_size, p, metric, n_jobs,
FormSetKNNParam):
self.algorithm = KNeighborsClassifier(int(k),
weights,
leaf_size=int(leaf_size),
p=int(p),
metric=metric,
metric_params=None,
n_jobs=int(n_jobs))
AlgorithmOperation.ApplyAlgorithm(self.algorithm)
self.messageBox(FormSetKNNParam)
def btnRandomForestClicked(self,n_estimators,criterion,max_depth,min_samples_split,min_samples_leaf,
min_weight_fraction_leaf,min_impurity_decrease,n_jobs,random_state,FormSetRandomForestParam):
if random_state.upper()=="NONE":
random_state=None
if max_depth.upper()=="NONE":
max_depth=None
self.algorithm= RandomForestClassifier(int(n_estimators),criterion,max_depth,
int(min_samples_split),int(min_samples_leaf),
float(min_weight_fraction_leaf),
min_impurity_decrease=float(min_impurity_decrease),
n_jobs=int(n_jobs),random_state=random_state)
AlgorithmOperation.ApplyAlgorithm(self.algorithm)
self.messageBox(FormSetRandomForestParam)
def btnSetAndRunClicked(self,criterion,max_depth,min_samples_split,
min_samples_leaf,min_weight_fraction_leaf,
random_state,max_leaf_nodes,FormSetParamDecisionTree):
if max_leaf_nodes.upper() == "NONE":
max_leaf_nodes = None
if max_depth.upper() == "NONE":
max_depth=None
if random_state.upper() == "NONE":
random_state=None
self.algorithm = tree.DecisionTreeClassifier(criterion, max_depth=max_depth
, min_samples_split=int(min_samples_split)
, min_samples_leaf=int(min_samples_leaf)
, min_weight_fraction_leaf=float(min_weight_fraction_leaf)
, random_state=random_state, max_leaf_nodes=max_leaf_nodes)
AlgorithmOperation.ApplyAlgorithm(self.algorithm)
self.messageBox(FormSetParamDecisionTree)