def create_ffm():
"""
Create a field-aware factorization machine.
"""
model_type = 'ffm'
handle = XLearnHandle()
_check_call(_LIB.XLearnCreate(c_str(model_type), ctypes.byref(handle)))
return XLearn(handle)
def create_linear():
"""
Create a linear model.
"""
model_type = 'linear'
handle = XLearnHandle()
_check_call(_LIB.XLearnCreate(c_str(model_type), ctypes.byref(handle)))
return XLearn(handle)
def setTrain(self, train_path): """Set file path of training data. Parameters ---------- train_path : str the path of training data """ _check_call(_LIB.XLearnSetTrain(ctypes.byref(self.handle), c_str(train_path)))
def setTest(self, test_path): """Set file path of test data. Parameters ---------- test_path : str the path of test data. """ _check_call(_LIB.XLearnSetTest(ctypes.byref(self.handle), c_str(test_path)))
def setValidate(self, val_path): """Set file path of validation data. Parameters ---------- val_path : str the path of validation data. """ _check_call(_LIB.XLearnSetValidate(ctypes.byref(self.handle), c_str(val_path)))
def cv(self, param): """ Do cross-validation Parameters ---------- param : dict hyper-parameter used by xlearn """ self._set_Param(param) _check_call(_LIB.XLearnCV(ctypes.byref(self.handle)))
def predict(self, model_path, out_path):
"""Predict output
Parameters
----------
model_path : str
path of model checkpoint.
out_path : str
path of output result.
"""
_check_call(_LIB.XLearnPredict(ctypes.byref(self.handle),
c_str(model_path), c_str(out_path)))
def fit(self, param, model_path): """Check hyper-parameters, train model, and dump model. Parameters ---------- param : dict hyper-parameter used by xlearn. model_path : str path of model checkpoint. """ self._set_Param(param) _check_call(_LIB.XLearnFit(ctypes.byref(self.handle), c_str(model_path)))
def setQuiet(self): """Set xlearn to quiet model""" key = 'quiet' _check_call(_LIB.XLearnSetBool(self.handle, c_str(key), ctypes.c_bool(True)))
def setOnDisk(self):
"""Set xlearn to use on-disk training"""
key = 'on_disk'
_check_call(_LIB.XLearnSetBool(ctypes.byref(self.handle),
c_str(key), ctypes.c_bool(True)))
def disableNorm(self): """Disable instance-wise normalization""" key = 'norm' _check_call(_LIB.XLearnSetBool(ctypes.byref(self.handle), c_str(key), ctypes.c_bool(False)))
def disableLockFree(self): """Disable lock free training""" key = 'lock_free' _check_call(_LIB.XLearnSetBool(ctypes.byref(self.handle), c_str(key), ctypes.c_bool(False)))
def show(self): """Show model information """ _check_call(_LIB.XLearnShow(ctypes.byref(self.handle)))
def _set_Param(self, param):
"""Set hyper-parameter for xlearn handle
Parameters
----------
param : dict
xlearn hyper-parameters
"""
for (key, value) in param.items():
if key == 'task':
_check_call(_LIB.XLearnSetStr(ctypes.byref(self.handle),
c_str(key), c_str(value)))
elif key == 'metric':
_check_call(_LIB.XLearnSetStr(ctypes.byref(self.handle),
c_str(key), c_str(value)))
elif key == 'log':
_check_call(_LIB.XLearnSetStr(ctypes.byref(self.handle),
c_str(key), c_str(value)))
elif key == 'lr':
_check_call(_LIB.XLearnSetFloat(ctypes.byref(self.handle),
c_str(key), ctypes.c_float(value)))
elif key == 'k':
_check_call(_LIB.XLearnSetInt(ctypes.byref(self.handle),
c_str(key), ctypes.c_uint(value)))
elif key == 'lambda':
_check_call(_LIB.XLearnSetFloat(ctypes.byref(self.handle),
c_str(key), ctypes.c_float(value)))
elif key == 'init':
_check_call(_LIB.XLearnSetFloat(ctypes.byref(self.handle),
c_str(key), ctypes.c_float(value)))
elif key == 'epoch':
_check_call(_LIB.XLearnSetInt(ctypes.byref(self.handle),
c_str(key), ctypes.c_uint(value)))
elif key == 'fold':
_check_call(_LIB.XLearnSetInt(ctypes.byref(self.handle),
c_str(key), ctypes.c_uint(value)))
else:
raise Exception("Invalid key!", key)
def hello(): """ Say hello to user """ _check_call(_LIB.XLearnHello())
def __del__(self): _check_call(_LIB.XLearnHandleFree(ctypes.byref(self.handle)))
def disableEarlyStop(self): """Disable early-stopping""" key = 'early_stop' _check_call(_LIB.XLearnSetBool(ctypes.byref(self.handle), c_str(key), ctypes.c_bool(False)))
def setSigmoid(self): """Convert output by using sigmoid""" key = 'sigmoid' _check_call(_LIB.XLearnSetBool(ctypes.byref(self.handle), c_str(key), ctypes.c_bool(True)))
def setSign(self): """Convert output to 0 and 1""" key = 'sign' _check_call(_LIB.XLearnSetBool(ctypes.byref(self.handle), c_str(key), ctypes.c_bool(True)))
