class hana_ml.algorithms.pal.recommender.FFMRegressor(ordering=None, normalise=None, include_linear=None, include_constant=None, early_stop=None, random_state=None, factor_num=None, max_iter=None, train_size=None, learning_rate=None, linear_lamb=None, poly2_lamb=None, tol=None, exit_interval=None, handle_missing=None)

Field-Aware Factorization Machine with the task of Regression.

factor_numint, optional

The factorization dimensionality. Default to 4.

random_stateint, optional

Specifies the seed for random number generator.

  • 0: Uses the current time as the seed.

  • Others: Uses the specified value as the seed.

Default to 0.

train_sizefloat, optional

The proportion of data used for training, and the remaining data set for validation.

For example, 0.8 indicates that 80% for training, and the remaining 20% for validation.

Default to 0.8 if number of instances not less than 40, 1.0 otherwise.

max_iterint, optional

Specifies the maximum number of iterations for the ALS algorithm.

Default to 20

orderingListOfStrings, optional(deprecated)

Specifies the categories orders for ranking.

This parameter is meaningless for regression problems and will be removed in future release.

No default value.

normalisebool, optional

Specifies whether to normalize each instance so that its L1 norm is 1.

Default to True.

include_constantbool, optional

Specifies whether to include the constant part.

Default to True.

include_linearbool, optional

Specifies whether to include the linear part of the model.

Default to True.

early_stopbool, optional

Specifies whether to early stop the SGD optimization.

Valid only if the value of train_size is less than 1.

Default to True.

learning_ratefloat, optional

The learning rate for SGD iteration.

Default to 0.2.

linear_lambfloat, optional

The L2 regularization parameter for the linear coefficient vector.

Default to 1e-5.

poly2_lambfloat, optional

The L2 regularization parameter for factorized coefficient matrix of the quadratic term.

Default to 1e-5.

tolfloat, optional

The criterion to determine the convergence of SGD.

Default to 1e-5.

exit_intervalint, optional

The interval of two iterations for comparison to determine the convergence.

Default to 5.

handle_missingstr, optional

Specifies how to handle missing feature:

  • 'skip': remove rows with missing values.

  • 'fill_zero': replace missing values with 0.

Default to 'fill_zero'.


Input dataframe for regression training:

>>> df_train_regression.collect()
   USER                   MOVIE  TIMESTAMP  CTR
0     A                  Movie1        3.0    0
1     A                  Movie2        3.0    5
2     A                  Movie4        1.0    0
3     A                  Movie5        2.0    1
4     A                  Movie6        3.0    2
5     A                  Movie8        2.0    0
6     A          Movie0, Movie3        1.0    5
7     B                  Movie2        3.0    4
8     B                  Movie3        2.0    4
9     B                  Movie4        2.0    0
10    B                    None        4.0    3
11    B                  Movie7        1.0    4
12    B                  Movie8        2.0    0
13    B                  Movie0        3.0    4
14    C                  Movie1        2.0    3
15    C  Movie2, Movie5, Movie7        4.0    2
16    C                  Movie4        3.0    1
17    C                  Movie5        1.0    0
18    C                  Movie6        NaN    5
19    C                  Movie7        3.0    0
20    C                  Movie8        1.0    5
21    C                  Movie0        2.0    3
22    D                  Movie1        3.0    0
23    D                  Movie3        2.0    5
24    D          Movie4, Movie7        2.0    5
25    D                  Movie6        2.0    5
26    D                  Movie7        4.0    0
27    D                  Movie8        3.0    1
28    D                  Movie0        3.0    1
29    E                  Movie1        2.0    1
30    E                  Movie2        2.0    5
31    E                  Movie3        2.0    3
32    E                  Movie4        4.0    2
33    E                  Movie5        3.0    5
34    E                  Movie6        2.0    0
35    E                  Movie7        4.0    2
36    E                  Movie8        3.0    0

Creating FFMRegressor instance:

>>>  ffm = FFMRegressor(factor_num=4, early_stop=True, learning_rate=0.2, max_iter=20, train_size=0.8,
                        linear_lamb=1e-5, poly2_lamb=1e-6, random_state=1)

Performing fit() on given dataframe:

>>> ffm.fit(data=self.df_train_regression, categorical_variable='TIMESTAMP')
>>> ffm.stats_.collect
     STAT_NAME          STAT_VALUE
0         task          regression
1  feature_num                  18
2    field_num                   3
3        k_num                   4
4         iter                  15
5      tr-loss  0.4503367758101421
6      va-loss  1.6896813062750056

Performing predict() on given prediction dataset:

>>> res = ffm.predict(data=self.df_predict, key='ID', thread_ratio=1)
>>> res.collect()
   ID                SCORE CONFIDENCE
0   1    2.978197866860172       None
1   2  0.43883354766746385       None
2   3    3.765106298778723       None
3   4   1.8874204073998788       None
4   5    3.588371752514674       None
5   6   1.3448502862740495       None
6   7    5.268571202934171       None
7   8   0.8713338730015039       None
8   9    2.347070689885986       None

Model metadata content.

The DataFrame inclusive of the following information:
  • Feature name,

  • Field name,

  • The factorization number,

  • The parameter value.


Statistic values.


Cross validation content.


fit(data[, key, features, label, ...])

Fit the FFMRegressor model with the input training data.

predict(data[, key, features, thread_ratio, ...])

Prediction for the input data with the trained FFMRegressor model.

property fit_hdbprocedure

Returns the generated hdbprocedure for fit.

property predict_hdbprocedure

Returns the generated hdbprocedure for predict.

fit(data, key=None, features=None, label=None, categorical_variable=None, delimiter=None)

Fit the FFMRegressor model with the input training data. Model parameters should be given by initializing the model first.


Data to be fit.

keystr, optional

Name of the ID column.

If key is not provided, then:

  • if data is indexed by a single column, then key defaults to that index column;

  • otherwise, it is assumed that data contains no ID column.

featuresstr/ListOfStrings, optional

Name of the feature columns.

delimiterstr, optional

The delimiter to separate string features.

For example, "China, USA" indicates two feature values "China" and "USA".

Default to ','.

labelstr, optional

Specifies the dependent variable.

For regression, the label column must have numerical data type.

Default to last column name.

categorical_variablestr/ListofStrings, optional

Indicates whether or not a column data is actually corresponding to a category variable even the data type of this column is INTEGER.

By default, 'VARCHAR' or 'NVARCHAR' is category variable, and 'INTEGER' or 'DOUBLE' is continuous variable.

Fitted object.
predict(data, key=None, features=None, thread_ratio=None, handle_missing=None)

Prediction for the input data with the trained FFMRegressor model.


Data to be fit.

keystr, optional

Name of the ID column.

Mandatory if data is not indexed, or the index of data contains multiple columns.

Defaults to the single index column of data if not provided.

featuresstr/ListOfStrings, optional

Global side features column name in the training dataframe.

thread_ratiofloat, optional

The ratio of available threads.

  • 0: single thread

  • 0~1: percentage

  • Others: heuristically determined

Default to -1.

handle_missing{'skip', 'fill_zero'}, optional

Specifies how to handle missing feature:

  • 'skip': remove rows with missing values.

  • 'fill_zero': replace missing values with 0.

Default to 'fill_zero'.


Prediction result, structured as follows:

  • 1st column : ID

  • 2nd column : SCORE, i.e. predicted values

  • 3rd column : CONFIDENCE, all NULLs.

Inherited Methods from PALBase

Besides those methods mentioned above, the FFMRegressor class also inherits methods from PALBase class, please refer to PAL Base for more details.