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vishalmehta1991/mg_rf_dask.py

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  1. vishalmehta1991 renamed this gist Oct 31, 2019. 1 changed file with 0 additions and 0 deletions.
    0 mg_rf_dask.txt → mg_rf_dask.py
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  2. vishalmehta1991 renamed this gist Oct 31, 2019. 1 changed file with 0 additions and 0 deletions.
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  3. vishalmehta1991 created this gist Oct 31, 2019.
    26 changes: 26 additions & 0 deletions mg_rf_dask.py
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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
    cluster = LocalCUDACluster(threads_per_worker=1, n_workers=n_workers)
    c = Client(cluster)
    workers = c.has_what().keys()

    # Shard the data across all workers
    X_train_df, y_train_df = dask_utils.persist_across_workers(c,[X_train_df,y_train_df],workers=workers)

    # Build and train the model
    cu_rf_mg = cuRFC_mg(**cu_rf_params)
    cu_rf_mg.fit(X_train_df, y_train_df)

    # Check the accuracy on a test set
    cu_rf_mg_predict = cu_rf_mg.predict(X_test)
    acc_score = accuracy_score(cu_rf_mg_predict, y_test, normalize=True)
    c.close()
    cluster.close()