lr.regParam, [10.**x for x in range(-4, 5)]).build())
# COMMAND ----------
# MAGIC %md
# MAGIC Perform grid search using RMSE as metric to minimise.
# COMMAND ----------
gridSearchResults = [None] * len(paramMaps)
evaluator = RegressionEvaluator(metricName='rmse',
labelCol='rating',
predictionCol='prediction')
for i, model in lr.fitMultiple(featurizedTrainingSet, paramMaps):
predictions = model.transform(featurizedTestSet, paramMaps[i])
gridSearchResults[i] = evaluator.evaluate(predictions)
print('α = {elasticNetParam:.2f}, λ = {regParam:.0e}, '
'RMSE = {rmse:.3f}'.format(**{
k.name: v
for k, v in paramMaps[i].items()
},
rmse=gridSearchResults[i]))
bestModelIndex = np.argmin(gridSearchResults)
bestParams = {k.name: v for k, v in paramMaps[bestModelIndex].items()}
# COMMAND ----------
print('Best hyperparameters found:')
