Model crashes for very small data about deep-forest HOT 5 CLOSED

Hi @Alaya-in-Matrix, what is the version of numpy installed.

from deep-forest.

@xuyxu It's numpy 1.19.5

deep-forest==0.1.5
joblib==1.1.0
numpy==1.19.5
scikit-learn==1.0.2
scipy==1.7.3
threadpoolctl==3.0.0

from deep-forest.

The regression demo works fine with your package environment:

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

from deepforest import CascadeForestRegressor

X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
model = CascadeForestRegressor(random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("\nTesting MSE: {:.3f}".format(mse))

In addition, the CascadeForestRegressor also works fine if using xs = np.random.randn(10,1) in your code snippet. The exception possibly is caused by using a toy dataset with too few number of samples.

from deep-forest.

@xuyxu That's exactly what I'm reporting, why would DF work for xs = np.random.randn(10,1) but not work for randn(5,1)? that makes no sense

from deep-forest.

Closed via #14

from deep-forest.