def train_model(df, target_col, parameters, path):
'''
Train a model for a specific target column
Parameter
--------
df: Spark DataFrame
DataFrame to use for training
target_col: str
String Representation of the target_col to use
parameters: dict
Parameters to use for the ALS (=maxIter, regParam, rank)
path: str
Path on the HDFS to save the model
Return
------
None
But Saves model to path
'''
maxIter=parameters["maxIter"]
regParam=parameters["regParam"]
rank=parameters["rank"]
model = ALS(maxIter=maxIter, regParam=regParam, rank=rank,
userCol="user", itemCol="tweet", ratingCol=target_col,
coldStartStrategy="nan", implicitPrefs=True).fit(df)
model.save(path + target_col + "_als_model")
def main(spark, rank, regParam, path, fraction):
train = spark.read.parquet("{}/data/processed/train_{}.parquet".format(path, fraction))
als = ALS(rank=rank, maxIter=5, seed=42, regParam=regParam,
userCol='user_id', itemCol='book_id', ratingCol='rating',
coldStartStrategy="drop")
print("Training ALS model with rank {} and regularization {} with {} of data...".format(rank, regParam, fraction))
model = als.fit(train)
temp_path = "/ALS_{}_{}_{}".format(rank, regParam, fraction)
als_path = temp_path + "/als"
print("Saving model...")
als.save(path + "/models" + als_path)
model_path = temp_path + "/als_model"
model.save(path + "/models" + model_path)
# Run model on test data
test_predictions = best_model.transform(test)
RMSE = evaluator.evaluate(test_predictions)
print("RMSE value is", "%.15f" % RMSE)
test_predictions.show()
n_recommendations = best_model.recommendForAllUsers(10)
n_recommendations.limit(10).show()
n_recommendations = n_recommendations \
.withColumn("rec_exp", explode("recommendations")) \
.select('userId', col("rec_exp.packageId"), col("rec_exp.rating"))
n_recommendations.limit(10).show()
n_recommendations_550031373 = n_recommendations.join(
packages, on='packageId').filter('userId = 550031373')
print("category of recommended items for n_recommendations_550031373")
n_recommendations_550031373.join(ratings_orig, on='packageId').filter(
ratings_orig.userId == '550031373').limit(100).show()
print("category of original score items before recommendation for 550031373")
ratings_orig.join(packages, on='packageId').filter(
ratings_orig.userId == '550031373').limit(100).show()
# Save the model into cluster as files
als.save('../models/FlexiGYMImplicitRecommender_ALS')
model.save('../models/FlexiGYMImplicitRecommender_ALSModel')
(training, test) = ratings.randomSplit([0.8, 0.2])
als = ALS(maxIter=5,
regParam=0.01,
userCol="uid",
itemCol="urlid",
ratingCol="label")
model = als.fit(training)
# Evaluate the model by computing the RMSE on the test data
predictions = model.transform(test)
evaluator = RegressionEvaluator(metricName="rmse",
labelCol="label",
predictionCol="prediction")
rmse = evaluator.evaluate(predictions)
print("Root-mean-square error = " + str(rmse))
predictions.head(5)
predictions.write.csv(
"gs://dataproc-0e3e0110-db09-4037-98cc-dc355651aba0-asia-southeast1/tensorflow/data/picfeed/predictions_test"
)
print "finish predictions"
# Save and load model
als_path = "gs://dataproc-0e3e0110-db09-4037-98cc-dc355651aba0-asia-southeast1/tensorflow/data/picfeed/als_model_test"
als.save(als_path)
als2 = ALS.load(als_path)
# $example off$
print "finish load"
