def test_run():
x, y = get_iris(True)
print_summary(x)
x, y = get_boston()
print_summary(x)
x, y = get_titanic(True)
print_summary(x)
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
from sklearn2.utils import model_name
from sklearn2.datasets import get_iris
# here we use the multi output format for multiclass classif
# this make it possible to use roc_auc_score
random.seed(0)
pd.options.display.width = 160
x, y = get_iris(True)
y2 = pd.get_dummies(y).values
x_train, x_test, y_train, y_test, y2_train, y2_test = train_test_split(
x, y, y2, test_size=0.33, random_state=42)
rfc = RandomForestClassifier(n_estimators=100,
oob_score=True,
class_weight='balanced',
random_state=0)
dtc = DecisionTreeClassifier(criterion='entropy',
max_features=0.85,
min_samples_split=2,
random_state=0,
max_depth=14)