Hi, I am trying this code ::
model_ids = list(aml.leaderboard['model_id'].as_data_frame().iloc[:,0])
model_ids
The o/p is 👍
['GBM_3_AutoML_2_20220607_145812',
'GBM_4_AutoML_2_20220607_145812',
'GBM_2_AutoML_2_20220607_145812',
'StackedEnsemble_AllModels_1_AutoML_2_20220607_145812',
'GBM_grid_1_AutoML_2_20220607_145812_model_1',
'StackedEnsemble_BestOfFamily_1_AutoML_2_20220607_145812',
'GBM_5_AutoML_2_20220607_145812',
'DRF_1_AutoML_2_20220607_145812',
'XRT_1_AutoML_2_20220607_145812',
'GLM_1_AutoML_2_20220607_145812',
'GBM_1_AutoML_2_20220607_145812',
'DeepLearning_1_AutoML_2_20220607_145812']
se = h2o.get_model([mid for mid in model_ids if "GBM" in mid][2])
metalearner = h2o.get_model(se.metalearner()[0])
se
Upto here its ok & I can view se BUT ::
metalearner = h2o.get_model(aml.leader.metalearner()['GBM'])
OR I try this out::
metalearner = h2o.get_model(se.metalearner()['GBM'])
In both case, I get the same error ::::
AttributeError: type object 'ModelBase' has no attribute 'metalearner'
I also tried with other options like ==> "StackedEnsemble_AllModels" --> still same error msg.
The h2o.init() has this:
Checking whether there is an H2O instance running at http://localhost:54321 . connected.
H2O_cluster_uptime: 22 hours 13 mins
H2O_cluster_timezone: Asia/Kolkata
H2O_data_parsing_timezone: UTC
H2O_cluster_version: 3.36.1.1
H2O_cluster_version_age: 1 month and 24 days
H2O_cluster_name: H2O_from_python_shamik_bhattacharya_b6w07m
H2O_cluster_total_nodes: 1
H2O_cluster_free_memory: 3.764 Gb
H2O_cluster_total_cores: 6
H2O_cluster_allowed_cores: 6
H2O_cluster_status: locked, healthy
H2O_connection_url: http://localhost:54321
H2O_connection_proxy: {"http": null, "https": null}
H2O_internal_security: False
Python_version: 3.7.13 final
Hi, I am trying this code :: model_ids = list(aml.leaderboard['model_id'].as_data_frame().iloc[:,0]) model_ids
The o/p is 👍 ['GBM_3_AutoML_2_20220607_145812', 'GBM_4_AutoML_2_20220607_145812', 'GBM_2_AutoML_2_20220607_145812', 'StackedEnsemble_AllModels_1_AutoML_2_20220607_145812', 'GBM_grid_1_AutoML_2_20220607_145812_model_1', 'StackedEnsemble_BestOfFamily_1_AutoML_2_20220607_145812', 'GBM_5_AutoML_2_20220607_145812', 'DRF_1_AutoML_2_20220607_145812', 'XRT_1_AutoML_2_20220607_145812', 'GLM_1_AutoML_2_20220607_145812', 'GBM_1_AutoML_2_20220607_145812', 'DeepLearning_1_AutoML_2_20220607_145812']
se = h2o.get_model([mid for mid in model_ids if "GBM" in mid][2])
metalearner = h2o.get_model(se.metalearner()[0])
se
Upto here its ok & I can view se BUT :: metalearner = h2o.get_model(aml.leader.metalearner()['GBM']) OR I try this out:: metalearner = h2o.get_model(se.metalearner()['GBM'])
In both case, I get the same error ::::
AttributeError: type object 'ModelBase' has no attribute 'metalearner'
I also tried with other options like ==> "StackedEnsemble_AllModels" --> still same error msg.
The h2o.init() has this:
Checking whether there is an H2O instance running at http://localhost:54321 . connected. H2O_cluster_uptime: 22 hours 13 mins H2O_cluster_timezone: Asia/Kolkata H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.36.1.1 H2O_cluster_version_age: 1 month and 24 days H2O_cluster_name: H2O_from_python_shamik_bhattacharya_b6w07m H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 3.764 Gb H2O_cluster_total_cores: 6 H2O_cluster_allowed_cores: 6 H2O_cluster_status: locked, healthy H2O_connection_url: http://localhost:54321 H2O_connection_proxy: {"http": null, "https": null} H2O_internal_security: False Python_version: 3.7.13 final