I'm trying to tune my model using the Grid search model in @kaggle notebook. In order to benefit from the GPU, I used this package hummingbird-ml. Thanks in advance
However, I get the following issue:
AttributeError: 'GridSearchCV' object has no attribute 'best_estimator_'
Here is my code:
from hummingbird.ml import convert
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVR
from sklearn.metrics import make_scorer, mean_squared_error
from pprint import pprint
# Hyper-tunning for SVM regressor
import numpy as np
base_svr = SVR()
scorer = make_scorer(mean_squared_error, greater_is_better=False)
param_grid_svr = {'C': [0.01, 0.1,1, 10, 100],
'gamma': [1,0.1, 0.01, 0.001, 0.0001],
'kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],
'epsilon': [0.01, 0.1, 0.2 , 0.3, 1]}
pprint(param_grid_svr)
# Create a GridSearchCV object and fit it to the training data
svr_gs = GridSearchCV(base_svr,param_grid_svr, n_jobs = -1 , scoring=scorer, cv=3 ,refit=True,verbose=2)
# Converting scikit-learn model to PyTorch on CPU
svr_gs_pytorch = convert(svr_gs, 'torch')
# Switching PyTorch from CPU to GPU
%%capture
svr_gs_pytorch.to('cuda')
# Train the model in GPU
svr_gs_pytorch.fit(X_train,y_train)
# print best parameter after tuning
svr_gs_pytorch.best_params_
