print("---" * 20)
print("grouping complete ={}".format(len(grouped_df)))
grouped_df = grouped_df.groupby(["i_item_sk"
]).sum(split_every=2).reset_index()
grouped_df.columns = ["i_item_sk", "cnt"]
result_df = grouped_df.map_partitions(
lambda df: df.sort_values(by=["cnt"], ascending=False))
result_df.columns = ["lastviewed_item", "cnt"]
result_df["purchased_item"] = q03_purchased_item_IN
cols_order = ["purchased_item", "lastviewed_item", "cnt"]
result_df = result_df[cols_order]
result_df = result_df.persist()
### todo: remove this later after more testing
wait(result_df)
print(len(result_df))
result_df = result_df.head(q03_limit)
print("result complete")
print("---" * 20)
return result_df
if __name__ == "__main__":
from xbb_tools.cluster_startup import attach_to_cluster
import cudf
import dask_cudf
client = attach_to_cluster(cli_args)
run_dask_cudf_query(cli_args=cli_args, client=client, query_func=main)
(
cust_and_clicks_ddf["clicks_in_category"]
> cust_and_clicks_ddf["clicks_in_category"].mean()
)
.reset_index(drop=True)
.astype(np.int64)
)
# Converting the dataframe to float64 as cuml logistic reg requires this
ml_input_df = cust_and_clicks_ddf.astype("float64")
ml_input_df = ml_input_df.persist()
ml_tasks = [delayed(build_and_predict_model)(df) for df in ml_input_df.to_delayed()]
results_dict = client.compute(*ml_tasks, sync=True)
return results_dict
if __name__ == "__main__":
from xbb_tools.cluster_startup import attach_to_cluster
import cudf
import dask_cudf
import cuml
client = attach_to_cluster(cli_args)
run_dask_cudf_query(
cli_args=cli_args, client=client, query_func=main, write_func=write_result,
)
