Make Predictions from an "FFMRegressor" Object

Make Predictions from an "FFMRegressor" Object

# S3 method for FFMRegressor
predict(
  model,
  data,
  key,
  features = NULL,
  handle.missing = NULL,
  thread.ratio = NULL
)

Arguments

model

R6Class object
A "FFMRegressor" object for prediction.
data

DataFrame
DataFrame containting data of user-item interaction and global side features for prediction.
key

character
Name of the ID column.
features

list of characters, optional
Name of feature columns in data.
If not provided, it defaults all non-ID columns of data.
handle.missing

c("remove", "replace"), optional
Specifies how to handle missing features of data:

  • "remove" remove missing rows

  • "replace" replace missing rows with 0
thread.ratio

double, optional
Controls the proportion of available threads that can be used by this function.
The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads.
Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored.
Defaults to 0.

Format

S3 methods

Value

DataFrame
Prediction made by the model, structured as follows:

  • ID: id

  • SCORE: Predict value

  • CONFIDENCE: the confidence of a category (NULL for "regression")

Examples

> data$Collect()
  ID USER  MOVIE TIMESTAMP
1  1    A Movie0         3
2  2    A Movie4         1
3  3    B Movie3         2
4  4    B   <NA>         5
5  5    C Movie2         2
6  6    F Movie4         3
7  7    D Movie2         2
8  8    D Movie4         1
9  9    E Movie7         4

Call the function:

> reg.result <- predict.FFMRegressor(model = FFMRgsr,
                                     data = data.pred.df.fit,
                                     thread.ratio = 1)

Output:

  ID               SCORE CONFIDENCE
1  1   2.978197866860172         NA
2  2 0.43883354766746385         NA
3  3   3.765106298778723         NA
4  4  1.8874204073998788         NA
5  5   3.588371752514674         NA

See also

hanaml.FFMRegressor