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[REVIEW] CPU Dask-SQL q05 #254

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May 31, 2022
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[REVIEW] CPU Dask-SQL q05 #254

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40 changes: 30 additions & 10 deletions gpu_bdb/bdb_tools/q05_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,15 +15,22 @@
#

import cupy as cp
import numpy as np

import cuml
from cuml.metrics import confusion_matrix
import sklearn

from cuml.metrics import confusion_matrix as cuml_confusion_matrix

from bdb_tools.cupy_metrics import cupy_precision_score

from bdb_tools.readers import build_reader

from sklearn.metrics import roc_auc_score
from sklearn.metrics import (
roc_auc_score,
precision_score,
confusion_matrix
)

# Logistic Regression params
# solver = "LBFGS" Used by passing `penalty=None` or "l2"
Expand All @@ -43,6 +50,7 @@ def read_tables(config, c=None):
data_format=config["file_format"],
basepath=config["data_dir"],
split_row_groups=config["split_row_groups"],
backend=config["backend"],
)

item_ddf = table_reader.read("item", relevant_cols=items_columns, index=False)
Expand All @@ -69,23 +77,29 @@ def build_and_predict_model(ml_input_df):
Create a standardized feature matrix X and target array y.
Returns the model and accuracy statistics
"""

import cudf

feature_names = ["college_education", "male"] + [
"clicks_in_%d" % i for i in range(1, 8)
]
X = ml_input_df[feature_names]
# Standardize input matrix
X = (X - X.mean()) / X.std()
y = ml_input_df["clicks_in_category"]

if isinstance(ml_input_df, cudf.DataFrame):
model_backend = cuml.LogisticRegression
else:
model_backend = sklearn.linear_model.LogisticRegression
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Can we update on this #248 why we chose sklearn instead of dask-ml . CC: @ChrisJar

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Thanks for testing this @ChrisJar


model = cuml.LogisticRegression(
model = model_backend(
tol=convergence_tol,
penalty="none",
solver="qn",
fit_intercept=True,
max_iter=iterations,
C=C,
)

model.fit(X, y)
#
# Predict and evaluate accuracy
Expand All @@ -94,11 +108,17 @@ def build_and_predict_model(ml_input_df):
results_dict = {}
y_pred = model.predict(X)

results_dict["auc"] = roc_auc_score(y.values_host, y_pred.values_host)
results_dict["precision"] = cupy_precision_score(cp.asarray(y), cp.asarray(y_pred))
results_dict["confusion_matrix"] = confusion_matrix(
cp.asarray(y, dtype="int32"), cp.asarray(y_pred, dtype="int32")
)
if isinstance(ml_input_df, cudf.DataFrame):
results_dict["auc"] = roc_auc_score(y.values_host, y_pred.values_host)
results_dict["precision"] = cupy_precision_score(cp.asarray(y), cp.asarray(y_pred))
results_dict["confusion_matrix"] = cuml_confusion_matrix(
cp.asarray(y, dtype="int32"), cp.asarray(y_pred, dtype="int32")
)
else:
results_dict["auc"] = roc_auc_score(y.to_numpy(), y_pred)
results_dict["precision"] = precision_score(y.to_numpy(), y_pred)
results_dict["confusion_matrix"] = confusion_matrix(y.to_numpy(dtype="int32"), y_pred)

results_dict["output_type"] = "supervised"
return results_dict