| ######cuML###### | ######Sklearn###### |
| | |
| from cuml import | from sklearn.ensemble import |
| RandomForestClassifier as cuRF | RandomForestClassifier as sklRF |
| | import multiprocessing as mp |
| | |
| # cuml Random Forest params | #sklearn Random Forest params |
| cu_rf_params = { | skl_rf_params = { |
| ‘n_estimators’: 25, | ‘n_estimators’: 25, |
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Instance | Red | Green | Blue | Size (cm) | Fruit (Label) | |
---|---|---|---|---|---|---|
0 | 1.0 | 0.0 | 0.0 | 7.0 | Apple | |
1 | 0.0 | 1.0 | 0.0 | 20 | Water Melon | |
2 | 1.0 | 0.0 | 0.0 | 1.0 | Cherry | |
3 | 0.0 | 1.0 | 0.0 | 7.5 | Apple | |
4 | 1.0 | 0.0 | 0.0 | 1.0 | Strawberry | |
5 | 1.0 | 0.0 | 0.0 | 0.8 | Cherry |
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Instance | Red | Green | Blue | Size (cm) | Fruit (Label) | |
---|---|---|---|---|---|---|
5 | 1.0 | 0.0 | 0.0 | 0.8 | Cherry | |
0 | 1.0 | 0.0 | 0.0 | 7.0 | Apple | |
0 | 1.0 | 0.0 | 0.0 | 7.0 | Apple | |
4 | 1.0 | 0.0 | 0.0 | 1.0 | Strawberry |
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Instance | Red | Green | Blue | Size (cm) | Fruit (Label) | |
---|---|---|---|---|---|---|
4 | 1.0 | 0.0 | 0.0 | 1.0 | Strawberry | |
4 | 1.0 | 0.0 | 0.0 | 1.0 | Strawberry | |
1 | 0.0 | 1.0 | 0.0 | 20 | Water Melon | |
3 | 0.0 | 1.0 | 0.0 | 7.5 | Apple |
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Instance | Red | Green | Blue | Size (cm) | Fruit (Label) | |
---|---|---|---|---|---|---|
1 | 0.0 | 1.0 | 0.0 | 20 | Water Melon | |
0 | 1.0 | 0.0 | 0.0 | 7.0 | Apple | |
5 | 1.0 | 0.0 | 0.0 | 0.8 | Cherry | |
2 | 1.0 | 0.0 | 0.0 | 1.0 | Cherry |
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(A) Initialize a bit mask indicating which samples are contained in each node | |
(B) Initialize a “node map” indicating which nodes are present at each level | |
(C) ForEach(tree_level) | |
1. Find the node id of all data samples, using the bit mask | |
2. Compute the possible splits for all bins, all columns and all nodes | |
3. Find the best split for each node | |
4. Update the bit mask and sparse node map to feed the next level |
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from cuml.dask.ensemble import RandomForestClassifier as cuRF_mg | |
# cuml Random Forest params | |
cu_rf_params = { | |
‘n_estimators’: 25, | |
‘max_depth’: 13, | |
‘n_bins’: 15, | |
‘n_streams’: 8 | |
} | |
# Start by setting up the CUDA cluster on the local host |