for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from hyperactive import Hyperactive
from hyperactive import MetaLearn
data = load_iris()
X = data.data
y = data.target
def model(para, X_train, y_train):
model = DecisionTreeClassifier(
criterion=para["criterion"],
max_depth=para["max_depth"],
min_samples_split=para["min_samples_split"],
min_samples_leaf=para["min_samples_leaf"],
)
scores = cross_val_score(model, X_train, y_train, cv=3)
return scores.mean(), model
search_config = {
model: {
"criterion": ["gini", "entropy"],
"max_depth": range(1, 21),
"min_samples_split": range(2, 21),
"min_samples_leaf": range(1, 21),
}
def test_metalearn():
ml = MetaLearn(search_config)
ml.collect(X, y)
# ml.train()
# ml.search(X, y)
def test_metalearn1():
opt = Hyperactive(search_config, meta_learn=True)
opt.fit(X, y)
test_metalearn()
SimonBlanke /
Hyperactive
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