Description

Make predictions based on model trained from AlsTrainBatchOp.

There are two types of predictions: 1) rating prediction: given user and item, predict the rating. 2) recommend prediction: given a list of users, recommend k items for each users.

Parameters

Name Description Type Required？ Default Value
userCol User column name String ✓
itemCol Item column name String ✓
predictionCol Column name of prediction. String ✓

Script Example

Code

  1. data = np.array([
  2.     [1, 1, 0.6],
  3.     [2, 2, 0.8],
  4.     [2, 3, 0.6],
  5.     [4, 1, 0.6],
  6.     [4, 2, 0.3],
  7.     [4, 3, 0.4],
  8. ])
  10. df_data = pd.DataFrame({
  11.     "user": data[:, 0],
  12.     "item": data[:, 1],
  13.     "rating": data[:, 2],
  14. })
  15. df_data["user"] = df_data["user"].astype('int')
  16. df_data["item"] = df_data["item"].astype('int')
  18. data = dataframeToOperator(df_data, schemaStr='user bigint, item bigint, rating double', op_type='batch')
  20. als = AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating") \
  21.     .setNumIter(10).setRank(10).setLambda(0.01)
  22. predictor = AlsPredictBatchOp()\
  23.     .setUserCol("user").setItemCol("item").setPredictionCol("predicted_rating")
  25. model = als.linkFrom(data)
  26. predictor.linkFrom(model, data).print()

Results

  1.    user  item  rating  predicted_rating
  2. 0     1     1     0.6          0.579622
  3. 1     2     2     0.8          0.766851
  4. 2     2     3     0.6          0.581079
  5. 3     4     1     0.6          0.574481
  6. 4     4     2     0.3          0.298500
  7. 5     4     3     0.4          0.382157