Évaluer les modèles à notes implicites
Créer des moteurs de recommandation avec PySpark
Jamen Long
Data Scientist at Nike
Pourquoi le RMSE fonctionnait avant
Pourquoi le RMSE ne convient plus
(ROEM) Rank Ordering Error Metric
$$\text{ROEM} = \frac{\sum_{u,i} r^t_{u,i} \text{rank}_{u,i}}{\sum_{u,i} r^t_{u,i}}$$
Mauvaises prédictions avec ROEM
bad_prediction.show()
+-------+------+-----+--------+--------+
|userId |songId|num_plays|badPreds|percRank|
+-------+------+-----+--------+--------+
| 111| 22| 3| 0.0001| 1.000|
| 111| 9| 0| 0.999| 0.000|
| 111| 321| 0| 0.08| 0.500|
| 222| 84| 0|0.000003| 1.000|
| 222| 821| 2| 0.88| 0.000|
| 222| 91| 2| 0.73| 0.500|
| 333| 2112| 0| 0.90| 0.000|
| 333| 42| 2| 0.80| 0.500|
| 333| 6| 0| 0.01| 1.000|
+-------+------+-----+--------+--------+
ROEM : PercRank × lectures
bp = bad_predictions.withColumn("np*rank", col("num_plays")*col("percRank"))
bp.show()
+-------+------+---------+--------+--------+-------+
|userId |songId|num_plays|badPreds|percRank|np*rank|
+-------+------+---------+--------+--------+-------+
| 111| 22| 3| 0.0001| 1.000| 3.00|
| 111| 9| 0| 0.999| 0.000| 0.00|
| 111| 321| 0| 0.08| 0.500| 0.00|
| 222| 84| 0|0.000003| 1.000| 0.00|
| 222| 821| 2| 0.88| 0.000| 0.00|
| 222| 91| 2| 0.73| 0.500| 1.00|
| 333| 2112| 0| 0.90| 0.000| 0.00|
| 333| 42| 2| 0.80| 0.500| 1.00|
| 333| 6| 0| 0.01| 1.000| 0.00|
+-------+------+---------+--------+--------+-------+
ROEM : mauvaises prédictions
+-------+------+---------+--------+--------+-------+
|userId |songId|num_plays|badPreds|percRank|np*rank|
+-------+------+---------+--------+--------+-------+
| 111| 22| 3| 0.0001| 1.000| 3.00|
| 111| 9| 0| 0.999| 0.000| 0.00|
| 111| 321| 0| 0.08| 0.500| 0.00|
| 222| 84| 0|0.000003| 1.000| 0.00|
| 222| 821| 2| 0.88| 0.000| 0.00|
| 222| 91| 2| 0.73| 0.500| 1.00|
| 333| 2112| 0| 0.90| 0.000| 0.00|
| 333| 42| 2| 0.80| 0.500| 1.00|
| 333| 6| 0| 0.01| 1.000| 0.00|
+-------+------+---------+--------+--------+-------+
numerator = bp.groupBy().sum("np*rank").collect()[0][0]
denominator = bp.groupBy().sum("num_plays").collect()[0][0]
print ("ROEM: "), numerator * 1.0/ denominator
ROEM: 5.0 / 9 = 0.556
Bonnes prédictions
gp = good_predictions.withColumn("np*rank", col("num_plays")*col("percRank"))
gp.show()
+-------+------+---------+---------+--------+-------+
|userId |songId|num_plays|goodPreds|percRank|np*rank|
+-------+------+---------+---------+--------+-------+
| 111| 22| 3| 1.1| 0.000| 0.000|
| 111| 77| 0| 0.01| 0.500| 0.000|
| 111| 99| 0| 0.008| 1.000| 0.000|
| 222| 22| 0| 0.0003| 1.000| 0.000|
| 222| 77| 2| 1.5| 0.000| 0.000|
| 222| 99| 2| 1.4| 0.500| 1.000|
| 333| 22| 0| 0.90| 0.500| 0.000|
| 333| 77| 2| 1.6| 0.000| 0.000|
| 333| 99| 0| 0.01| 1.000| 0.000|
+-------+------+---------+---------+--------+-------+
ROEM : bonnes prédictions
+-------+------+---------+---------+--------+-------+
|userId |songId|num_plays|goodPreds|percRank|np*rank|
+-------+------+---------+---------+--------+-------+
| 111| 22| 3| 1.1| 0.000| 0.000|
| 111| 77| 0| 0.01| 0.500| 0.000|
| 111| 99| 0| 0.008| 1.000| 0.000|
| 222| 22| 0| 0.0003| 1.000| 0.000|
| 222| 77| 2| 1.5| 0.000| 0.000|
| 222| 99| 2| 1.4| 0.500| 1.000|
| 333| 22| 0| 0.90| 0.500| 0.000|
| 333| 77| 2| 1.6| 0.000| 0.000|
| 333| 99| 0| 0.01| 1.000| 0.000|
+-------+------+---------+---------+--------+-------+
numerator = gp.groupBy().sum("np*rank").collect()[0][0]
denominator = gp.groupBy().sum("num_plays").collect()[0][0]
print ("ROEM: "), numerator * 1.0/ denominator
ROEM: 1.0 / 9 = 0.1111
ROEM : lien vers la fonction sur GitHub
+-------+------+---------+---------+--------+-------+
|userId |songId|num_plays|goodPreds|percRank|np*rank|
+-------+------+---------+---------+--------+-------+
| 111| 22| 3| 1.1| 0.000| 0.000|
| 111| 77| 0| 0.01| 0.500| 0.000|
| 111| 99| 0| 0.008| 1.000| 0.000|
| 222| 22| 0| 0.0003| 1.000| 0.000|
| 222| 77| 2| 1.5| 0.000| 0.000|
| 222| 99| 2| 1.4| 0.500| 1.000|
| 333| 22| 0| 0.90| 0.500| 0.000|
| 333| 77| 2| 1.6| 0.000| 0.000|
| 333| 99| 0| 0.01| 1.000| 0.000|
+-------+------+---------+---------+--------+-------+
numerator = gp.groupBy().sum("np*rank").collect()[0][0]
denominator = gp.groupBy().sum("num_plays").collect()[0][0]
print ("ROEM: "), numerator * 1.0/ denominator
ROEM: 1.0 / 9 = 0.1111
Construire plusieurs modèles ROEM
(train, test) = implicit_ratings.randomSplit([.8, .2])
# Empty list to be filled with models
model_list = []
# Complete each of the hyperparameter value lists
ranks = [10, 20, 30, 40]
maxIters = [10, 20, 30, 40]
regParams = [.05, .1, .15]
alphas = [20, 40, 60, 80]
# For loop will automatically create and store ALS models
for r in ranks:
for mi in maxIters:
for rp in regParams:
for a in alphas:
model_list.append(ALS(userCol= "userId", itemCol= "songId",
ratingCol= "num_plays", rank = r, maxIter = mi, regParam = rp,
alpha = a, coldStartStrategy="drop",nonnegative = True,
implicitPrefs = True))
Sortie d'erreur
for model in model_list:
# Fits each model to the training data
trained_model = model.fit(train)
# Generates test predictions
predictions = trained_model.transform(test)
# Evaluates each model's performance
ROEM(predictions)
Passons à la pratique !
Créer des moteurs de recommandation avec PySpark
