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Commit 19512d82 authored by Guolin Ke's avatar Guolin Ke
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remove set_group_id. fixed bug in set num_pred_iterations.

parent 83007b1c
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Showing with 45 additions and 55 deletions
python-package/lightgbm/basic.py
View file @ 19512d82
......@@ -123,12 +123,17 @@ def c_array(ctype, values):
"""Convert a python array to c array."""
return (ctype * len(values))(*values)
def dict_to_str(data):
def param_dict_to_str(data):
if data is None or len(data) == 0:
return ""
pairs = []
for key in data:
pairs.append(str(key)+'='+str(data[key]))
for key, val in data.items():
if isinstance(val, list):
pairs.append(str(key)+'='+','.join(val))
elif isinstance(val, (int, float, str, bool)):
pairs.append(str(key)+'='+str(val))
else:
raise TypeError('unknow type of parameter:%s , got:%s' %(key, type(val).__name__))
return ' '.join(pairs)
"""marco definition of data type in c_api of LightGBM"""
C_API_DTYPE_FLOAT32 =0
......@@ -145,7 +150,6 @@ C_API_PREDICT_LEAF_INDEX =2
FIELD_TYPE_MAPPER = {"label":C_API_DTYPE_FLOAT32,
"wegiht":C_API_DTYPE_FLOAT32,
"init_score":C_API_DTYPE_FLOAT32,
"group_id":C_API_DTYPE_INT32,
"group":C_API_DTYPE_INT32,
}
......@@ -208,7 +212,7 @@ class Predictor(object):
self.handle,
ctypes.byref(out_num_class)))
self.num_class = out_num_class.value
self.__num_total_model = out_num_iterations.value * self.num_class
self.__num_total_iteration = out_num_iterations.value
elif booster_handle is not None:
self.__is_manage_handle = is_manage_handle
self.handle = booster_handle
......@@ -221,7 +225,7 @@ class Predictor(object):
_safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
self.handle,
ctypes.byref(out_num_iterations)))
self.__num_total_model = out_num_iterations.value * self.num_class
self.__num_total_iteration = out_num_iterations.value
else:
raise TypeError('Need Model file to create a booster')
......@@ -261,9 +265,9 @@ class Predictor(object):
predict_type = C_API_PREDICT_RAW_SCORE
if pred_leaf:
predict_type = C_API_PREDICT_LEAF_INDEX
int_data_has_header = 0
if data_has_header:
int_data_has_header = 1
int_data_has_header = 1 if data_has_header else 0
if num_iteration > self.__num_total_iteration:
num_iteration = self.__num_total_iteration
if is_str(data):
tmp_pred_fname = tempfile.NamedTemporaryFile(prefix="lightgbm_tmp_pred_").name
_safe_call(_LIB.LGBM_BoosterPredictForFile(
......@@ -303,6 +307,15 @@ class Predictor(object):
raise ValueError('len of predict result(%d) cannot be divide nrow(%d)' %(preds.size, nrow) )
return preds
def __get_num_preds(self, num_iteration, nrow, predict_type):
n_preds = self.num_class * nrow
if predict_type == C_API_PREDICT_LEAF_INDEX:
if num_iteration > 0:
n_preds *= min(num_iteration, self.__num_total_iteration)
else:
n_preds *= self.__num_total_iteration
return n_preds
def __pred_for_np2d(self, mat, num_iteration, predict_type):
"""
Predict for a 2-D numpy matrix.
......@@ -316,13 +329,7 @@ class Predictor(object):
"""change non-float data to float data, need to copy"""
data = np.array(mat.reshape(mat.size), dtype=np.float32)
ptr_data, type_ptr_data = c_float_array(data)
n_preds = self.num_class * mat.shape[0]
if predict_type == C_API_PREDICT_LEAF_INDEX:
if num_iteration > 0:
n_preds *= num_iteration
else:
used_iteration = self.__num_total_model / self.num_class
n_preds *= used_iteration
n_preds = self.__get_num_preds(num_iteration, mat.shape[0], predict_type)
preds = np.zeros(n_preds, dtype=np.float32)
out_num_preds = ctypes.c_int64(0)
_safe_call(_LIB.LGBM_BoosterPredictForMat(
......@@ -346,13 +353,7 @@ class Predictor(object):
Predict for a csr data
"""
nrow = len(csr.indptr) - 1
n_preds = self.num_class * nrow
if predict_type == C_API_PREDICT_LEAF_INDEX:
if num_iteration > 0:
n_preds *= num_iteration
else:
used_iteration = self.__num_total_model / self.num_class
n_preds *= used_iteration
n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
preds = np.zeros(n_preds, dtype=np.float32)
out_num_preds = ctypes.c_int64(0)
......@@ -386,7 +387,7 @@ class Dataset(object):
"""
def __init__(self, data, label=None, max_bin=255, reference=None,
weight=None, group_id=None, predictor=None,
weight=None, group=None, predictor=None,
silent=False, params=None):
"""
Dataset used in LightGBM.
......@@ -404,8 +405,8 @@ class Dataset(object):
If this dataset validation, need to use training data as reference
weight : list or numpy 1-D array , optional
Weight for each instance.
group_id : list or numpy 1-D array , optional
group/query id for each instance. Note: if having group/query id, data should group by this id
group : list or numpy 1-D array , optional
group/query size for dataset
silent : boolean, optional
Whether print messages during construction
params: dict, optional
......@@ -420,8 +421,7 @@ class Dataset(object):
return
self.data_has_header = False
"""process for args"""
if params is None:
params = {}
params = {} if params is None else params
self.max_bin = max_bin
self.predictor = predictor
params["max_bin"] = max_bin
......@@ -429,7 +429,7 @@ class Dataset(object):
params["verbose"] = 0
elif "verbose" not in params:
params["verbose"] = 1
params_str = dict_to_str(params)
params_str = param_dict_to_str(params)
"""process for reference dataset"""
ref_dataset = None
if isinstance(reference, Dataset):
......@@ -464,8 +464,8 @@ class Dataset(object):
raise ValueError("label should not be None")
if weight is not None:
self.set_weight(weight)
if group_id is not None:
self.set_group_id(group_id)
if group is not None:
self.set_group(group)
# load init score
if self.predictor is not None and isinstance(self.predictor, Predictor):
init_score = self.predictor.predict(data,
......@@ -482,7 +482,7 @@ class Dataset(object):
init_score = new_init_score
self.set_init_score(init_score)
def create_valid(self, data, label=None, weight=None, group_id=None,
def create_valid(self, data, label=None, weight=None, group=None,
silent=False, params=None):
"""
Create validation data align with current dataset
......@@ -496,15 +496,15 @@ class Dataset(object):
Label of the training data.
weight : list or numpy 1-D array , optional
Weight for each instance.
group_id : list or numpy 1-D array , optional
group/query id for each instance. Note: if having group/query id, data should group by this id
group : list or numpy 1-D array , optional
group/query size for dataset
silent : boolean, optional
Whether print messages during construction
params: dict, optional
other parameters
"""
return Dataset(data, label=label, max_bin=self.max_bin, reference=self,
weight=weight, group_id=group_id, predictor=self.predictor,
weight=weight, group=group, predictor=self.predictor,
silent=silent, params=params)
def subset(self, used_indices, params=None):
......@@ -514,7 +514,7 @@ class Dataset(object):
used_indices = list_to_1d_numpy(used_indices, np.int32)
ret = Dataset(None)
ret.handle = ctypes.c_void_p()
params_str = dict_to_str(params)
params_str = param_dict_to_str(params)
_safe_call(_LIB.LGBM_DatasetGetSubset(
ctypes.byref(self.handle),
used_indices.data_as(ctypes.POINTER(ctypes.c_int32)),
......@@ -624,6 +624,7 @@ class Dataset(object):
The array ofdata to be set
"""
if data is None:
"""set to None"""
_safe_call(_LIB.LGBM_DatasetSetField(
self.handle,
c_str(field_name),
......@@ -713,18 +714,6 @@ class Dataset(object):
self.__group = group
self.set_field('group', group)
def set_group_id(self, group_id):
"""Set group_id of Dataset (used for ranking).
Parameters
----------
group : array like
group_id of Dataset (used for ranking).
"""
if group_id is not None:
group_id = list_to_1d_numpy(group_id, np.int32)
self.set_field('group_id', group_id)
def get_label(self):
"""Get the label of the Dataset.
......@@ -817,8 +806,7 @@ class Booster(object):
self.handle = ctypes.c_void_p()
self.__need_reload_eval_info = True
self.__is_manage_handle = True
if params is None:
params = {}
params = {} if params is None else params
if silent:
params["verbose"] = 0
elif "verbose" not in params:
......@@ -827,7 +815,7 @@ class Booster(object):
"""Training task"""
if not isinstance(train_set, Dataset):
raise TypeError('training data should be Dataset instance, met{}'.format(type(train_set).__name__))
params_str = dict_to_str(params)
params_str = param_dict_to_str(params)
"""construct booster object"""
_safe_call(_LIB.LGBM_BoosterCreate(
train_set.handle,
......@@ -907,7 +895,7 @@ class Booster(object):
params["verbose"] = 0
elif "verbose" not in params:
params["verbose"] = 1
params_str = dict_to_str(params)
params_str = param_dict_to_str(params)
_safe_call(_LIB.LGBM_BoosterResetParameter(
self.handle,
c_str(params_str)))
......@@ -1162,11 +1150,11 @@ class Booster(object):
raise ValueError("data_idx should be smaller than number of dataset")
if self.__inner_predict_buffer[data_idx] is None:
if data_idx == 0:
num_data = self.train_set.num_data() * self.__num_class
n_preds = self.train_set.num_data() * self.__num_class
else:
num_data = self.valid_sets[data_idx - 1].num_data() * self.__num_class
n_preds = self.valid_sets[data_idx - 1].num_data() * self.__num_class
self.__inner_predict_buffer[data_idx] = \
np.array([0.0 for _ in range(num_data)], dtype=np.float32, copy=False)
np.array([0.0 for _ in range(n_preds)], dtype=np.float32, copy=False)
"""avoid to predict many time in one iteration"""
if not self.__is_predicted_cur_iter[data_idx]:
tmp_out_len = ctypes.c_int64(0)
......
src/boosting/gbdt.h
View file @ 19512d82
......@@ -181,6 +181,8 @@ public:
} else {
num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_class_;
}
num_iteration_for_pred_ = std::min(num_iteration_for_pred_,
static_cast<int>(models_.size()) / num_class_);
}
/*!
......
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