from pyspark.ml.evaluation import RegressionEvaluator
# Create an RMSE evaluator using the label and predicted columns
reg_eval = RegressionEvaluator(predictionCol="prediction", labelCol="rating", metricName="rmse")
tolerance = 0.03
ranks = [4, 8, 12]
errors = [0, 0, 0]
models = [0, 0, 0]
err = 0
min_error = float('inf')
best_rank = -1
for rank in ranks:
# Set the rank here:
als.<FILL_IN>
# Create the model with these parameters.
model = als.fit(training_df)
# Run the model to create a prediction. Predict against the validation_df.
predict_df = model.<FILL_IN>
# Remove NaN values from prediction (due to SPARK-14489)
predicted_ratings_df = predict_df.filter(predict_df.prediction != float('nan'))
# Run the previously created RMSE evaluator, reg_eval, on the predicted_ratings_df DataFrame
error = reg_eval.<FILL_IN>
errors[err] = error
models[err] = model
print 'For rank %s the RMSE is %s' % (rank, error)
if error < min_error:
min_error = error
best_rank = err
from pyspark.ml.evaluation import RegressionEvaluator
# Create an RMSE evaluator using the label and predicted columns
reg_eval = RegressionEvaluator(predictionCol="prediction", labelCol="rating", metricName="rmse")
tolerance = 0.03
ranks = [4, 8, 12]
errors = [0, 0, 0]
models = [0, 0, 0]
err = 0
min_error = float('inf')
best_rank = -1
for rank in ranks:
# Set the rank here:
als.<FILL_IN>
# Create the model with these parameters.
model = als.fit(training_df)
# Run the model to create a prediction. Predict against the validation_df.
predict_df = model.<FILL_IN>
# Remove NaN values from prediction (due to SPARK-14489)
predicted_ratings_df = predict_df.filter(predict_df.prediction != float('nan'))
# Run the previously created RMSE evaluator, reg_eval, on the predicted_ratings_df DataFrame
error = reg_eval.<FILL_IN>
errors[err] = error
models[err] = model
print 'For rank %s the RMSE is %s' % (rank, error)
if error < min_error:
min_error = error
best_rank = err
