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ML Prediction with XGBoost and PySpark

Once a XGBoost model is trained, we would like to use PySpark for batch predictions.

The method we use here is through Pandas UDF.

Why pandas_udf Instead of udf

For a udf function, PySpark evaluates it one record at a time, which is the slowest possible way to execute the prediction. While for a pandas_udf function, it takes a bunch of pandas Series and returns a Series, which is vectorised.

Create a pandas_udf Prediction Function

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@pandas_udf(FloatType())
def get_prediction_result(features):
# convert to 2D-numpy array
numpy_array = np.array(
features
.values
.tolist()
)
# convert to xgboost dmatrix
dmatrix = xgb.DMatrix(numpy_array)

# predict
y = model.predict(dmatrix)

# return with pandas series
series = pd.Series(y)
return series

Let’s say we have a dataframe df with following records:

features
[149.0, 84.0. 38.5, 79.7]
[2.5, 34.0. 35.5, 97.6]
[0.0, 65.3. 58.7, 45.7]

features is the feature column with ArrayType(FloatType()) and each row of features contains 4 feature values.

When we call get_prediction_result function, it receives a Series object. In this case, it would be

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0    [149.0, 84.0, 38.5, 79.7]
1 [2.5, 34.0, 35.5, 97.6]
2 [0.0, 65.3, 58.7, 45.7]
dtype: object

We would need to convert it to a DMatrix object, which is the input for a XGBoost model. We first call its values method, which yields:

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array([list([149.0, 84.0, 38.5, 79.7]), list([2.5, 34.0, 35.5, 97.6]),
list([0.0, 65.3, 58.7, 45.7])], dtype=object)

Then we call tolist to convert it into a list of list, yields:

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[[149.0, 84.0, 38.5, 79.7], [2.5, 34.0, 35.5, 97.6], [0.0, 65.3, 58.7, 45.7]]

After that, we convert it into a 2D numpy array:

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array([[149. ,  84. ,  38.5,  79.7],
[ 2.5, 34. , 35.5, 97.6],
[ 0. , 65.3, 58.7, 45.7]])

Finally, we initialise a DMatrix object with the 2D numpy array we just created. We then run the prediction. As a pandas_udf requires a Series object to return, we convert the prediction scores into a Series at the line 16.

Using a pandas_udf Function

Using a pandas_udf function is just like how we use a normal udf function:

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df.withColumn('prediction', get_prediction_result('features'))

That’s how we integrate the XGBoost batch prediction into our PySpark pipeline.