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add interface and available regularlization (issue:#30) #42

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24 changes: 19 additions & 5 deletions pyfm/pylibfm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,12 @@ class FM:
Whether or not to shuffle training dataset before learning
seed : int
The seed of the pseudo random number generator
reg_0 : float
The regularization parameter of w0
reg_w : float
The regularization parameter of w
reg_v : float
The regularization parameter of each element in v
"""
def __init__(self,
num_factors=10,
Expand All @@ -63,7 +69,11 @@ def __init__(self,
task='classification',
verbose=True,
shuffle_training=True,
seed = 28):
seed = 28,
reg_0 = 0.0,
reg_w = 0.0,
reg_v = 0.0,
):

self.num_factors = num_factors
self.num_iter = num_iter
Expand All @@ -86,9 +96,9 @@ def __init__(self,
self.t0 = t0

# Regularization Parameters (start with no regularization)
self.reg_0 = 0.0
self.reg_w = 0.0
self.reg_v = np.repeat(0.0, num_factors)
self.reg_0 = float(reg_0)
self.reg_w = float(reg_w)
self.reg_v = np.repeat(float(reg_v), num_factors)

# local parameters in the lambda_update step
self.lambda_w_grad = 0.0
Expand Down Expand Up @@ -205,7 +215,11 @@ def fit(self, X, y):
shuffle_training,
task,
self.seed,
verbose)
verbose,
self.reg_0,
self.reg_w,
self.reg_v,
)

return self.fm_fast.fit(X_train_dataset, validation_dataset)

Expand Down
15 changes: 11 additions & 4 deletions pyfm_fast.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,9 @@ cdef class FM_fast(object):
task : int
seed : int
verbose : int
reg_0 : double
reg_w : double
reg_v : np.ndarray[DOUBLE, ndim=1, mode='c']
"""

cdef public double w0
Expand Down Expand Up @@ -106,7 +109,11 @@ cdef class FM_fast(object):
int shuffle_training,
int task,
int seed,
int verbose):
int verbose,
double reg_0,
double reg_w,
np.ndarray[DOUBLE, ndim=1, mode='c'] reg_v,
):

self.w0 = w0
self.w = w
Expand All @@ -130,9 +137,9 @@ cdef class FM_fast(object):
self.seed = seed
self.verbose = verbose

self.reg_0 = 0.0
self.reg_w = 0.0
self.reg_v = np.zeros(self.num_factors)
self.reg_0 = reg_0
self.reg_w = reg_w
self.reg_v = reg_v

self.sumloss = 0.0
self.count = 0
Expand Down