def scores(conf):
parameters = {
'svm__C': np.logspace(-2, 5, 8),
'svm__gamma': np.logspace(-9, 2, 12),
'svm__kernel': ['linear', 'rbf']
}
training_data, training_targets = learning_utils.getData(conf['datasets'], type=conf['type'], split=True,
balanced=conf['balanced'], shuffle=True)
clf = svm.SVC()
pipeline = learning_utils.getPipeline(training_data, clf, 'svm')
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
learning_utils.gs(grid_search, training_data, training_targets)
def scores(conf):
parameters = {
"svm__C": np.logspace(-2, 5, 8),
"svm__gamma": np.logspace(-9, 2, 12),
"svm__kernel": ["linear", "rbf"],
}
training_data, training_targets = learning_utils.getData(
conf["datasets"], type=conf["type"], split=True, balanced=conf["balanced"], shuffle=True
)
clf = svm.SVC()
pipeline = learning_utils.getPipeline(training_data, clf, "svm")
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
learning_utils.gs(grid_search, training_data, training_targets)
def scores(conf):
if conf['tree'] == 'tree':
parameters = {
'tree__criterion': ['gini', 'entropy'],
'tree__splitter': ['best', 'random'],
'tree__max_features': [.1, .2, .5, 'sqrt', 'log2', None],
'tree__max_depth': range(1, 20),
'tree__presort': [True, False]
}
clf = tree.DecisionTreeClassifier()
elif conf['tree'] == 'random':
parameters = {
'tree__criterion': ['gini', 'entropy'],
'tree__n_estimators': range(5, 50),
'tree__max_features': [.1, .2, .5, 'sqrt', 'log2', None],
'tree__max_depth': range(1, 20),
'tree__bootstrap': [True, False],
}
clf = ensemble.RandomForestClassifier()
elif conf['tree'] == 'extra':
parameters = {
'tree__criterion': ['gini', 'entropy'],
'tree__n_estimators': range(5, 50),
'tree__max_features': [.1, .2, .5, 'sqrt', 'log2', None],
'tree__max_depth': range(1, 20),
'tree__bootstrap': [True, False],
}
clf = ensemble.ExtraTreesClassifier()
training_data, training_targets = learning_utils.getData(conf['datasets'], type=conf['type'], split=True,
balanced=conf['balanced'], shuffle=True)
pipeline = learning_utils.getPipeline(training_data, clf, 'tree')
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
learning_utils.gs(grid_search, training_data, training_targets)