在做点击率预估GBDT+LR模型时，使用lightgbm训练时，报错
lgb_train = lgb.Dataset(X_train, label = y_train)
lgb_eval = lgb.Dataset(X_test, label = y_test)
params = {
'task': 'train',
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': {'binary_logloss'},
'num_leaves': 64,
'num_trees': 100,
'learning_rate': 0.01,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
num_leaf = 64
print('Start training...')
gbm = lgb.train(params,
lgb_train,
num_boost_round=100,
valid_sets=lgb_train)
有如下报错
ValueError Traceback (most recent call last)
<ipython-input-25-f7e4a8d2f52c> in <module>()
25 lgb_train,
26 num_boost_round=100,
---> 27 valid_sets=lgb_train)
~/anaconda3/lib/python3.7/site-packages/lightgbm/engine.py in train(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)
269
270 try:
--> 271 booster = Booster(params=params, train_set=train_set)
272 if is_valid_contain_train:
273 booster.set_train_data_name(train_data_name)
~/anaconda3/lib/python3.7/site-packages/lightgbm/basic.py in __init__(self, params, train_set, model_file, model_str, silent)
2603 )
2604
-> 2605 train_set.construct()
2606
2607 params.update(train_set.get_params())
~/anaconda3/lib/python3.7/site-packages/lightgbm/basic.py in construct(self)
1817 init_score=self.init_score, predictor=self._predictor,
1818 silent=self.silent, feature_name=self.feature_name,
-> 1819 categorical_feature=self.categorical_feature, params=self.params)
1820 if self.free_raw_data:
1821 self.data = None
~/anaconda3/lib/python3.7/site-packages/lightgbm/basic.py in _lazy_init(self, data, label, reference, weight, group, init_score, predictor, silent, feature_name, categorical_feature, params)
1475 feature_name,
1476 categorical_feature,
-> 1477 self.pandas_categorical)
1478 label = _label_from_pandas(label)
~/anaconda3/lib/python3.7/site-packages/lightgbm/basic.py in _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical)
564 if isinstance(data, pd_DataFrame):
565 if len(data.shape) != 2 or data.shape[0] < 1:
--> 566 raise ValueError('Input data must be 2 dimensional and non empty.')
567 if feature_name == 'auto' or feature_name is None:
568 data = data.rename(columns=str)
ValueError: Input data must be 2 dimensional and non empty.
报错字面的意思就是“输入数据必须是二维且非空的”，于是检查我的X_train和y_train，发现X_train是二维的，而y_train是 pandas.core.series.Series
ok，fine。。。是我切分数据集的时候的锅
X = df[cols_all[1:]]
lbl = preprocessing.LabelEncoder()
X['site_domain'] = lbl.fit_transform(X['site_domain'].astype(str))
X['site_id'] = lbl.fit_transform(X['site_id'].astype(str))
X['site_category'] = lbl.fit_transform(X['site_category'].astype(str))
X['app_id'] = lbl.fit_transform(X['app_id'].astype(str))
X['app_category'] = lbl.fit_transform(X['app_category'].astype(str))
X_train = X.iloc[:-2000]
X_test = X.iloc[-2000:]
y = df['click']
y_train = y.iloc[:-2000]
y_test = y.iloc[-2000:]
1、不要用这种方式拆分数据集
x = data.iloc[:, 2:]
y = data.loc['Survived']
x_train, x_test, y_train，y_test = train_test_split(x, y, test_size=0.25, ramdon_state=0)
2、如果已经使用，那么再将pandas.core.series.Series转化为dataframe吧
y_train.to_frame()
tip：这种方法不能将index一起转换，如果需要转化index的话，那就
ok,fine
背景在做点击率预估GBDT+LR模型时，使用lightgbm训练时，报错# create dataset for lightgbmlgb_train = lgb.Dataset(X_train, label = y_train)lgb_eval = lgb.Dataset(X_test, label = y_test)params = { 'task': 'train', 'boosting_type': 'gbdt', 'objective': 'binary',
近期请国内外头部出版社可尽快私信博主！——心比天高，仗剑走天涯，保持热爱，奔赴向梦想！低调，谦虚，自律，反思，成长，还算是比较正能量的博主，公益免费传播……内心特别想在AI界做出一些可以推进历史进程影响力的东西(兴趣使然，有点小情怀，也有点使命感呀)…
04-12
@ValueError: The parameter image must be a 2-dimensional arrayimg = skft.local_binary_pattern(image, 8, 2)
这条语句出错，错误提示：ValueError: The parameter image must be a 2-dimensional
各位大神，请教一下怎么解决。
欢迎使用Markdown编辑器
你好！ 这是你第一次使用 Markdown编辑器 所展示的欢迎页。如果你想学习如何使用Markdow
Error when checking input: expected input_1 to have 2 dimensions, but got array with shape (3, 6, 2)
ValueError: Error when checking input:
expected input_1 to have 2 dimensions,
but got array with shape (69, 43, 1)
在跑神经网络时候出现的这个错误，感觉是最开始输入或者某一层输入的问题，发现把inpt = Input(shape=(43,)) 改成inpt = Input(shape=(43,1)) 就OK了
---------------------------------------------------------------------------
LightGBMError Traceback (most recent call last)
<timed exec> in <module>...
这个错误通常是由于输入的数组是一个多维数组，而某个函数只能处理一维数组，导致出现这个错误。例如，有些函数只能处理一维的标签数据，而不能处理多维的矩阵数据。
解决这个错误的方法是检查代码中使用的函数是否能够处理多维数组，如果不能，就需要将多维数组转换为一维数组。常见的方法是使用`numpy`库的`flatten`函数或`ravel`函数将多维数组转换为一维数组。
下面是一个示例，假设我们有一个二维数组`a`，我们需要将其传递给某个函数，但是函数只能处理一维数组，就会出现上述错误：
``` python
import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
# 函数只能处理一维数组，出现ValueError: Input array must be 1 dimensional错误
result = some_function(a)
这个错误的解决方法是使用`flatten`函数或`ravel`函数将多维数组转换为一维数组：
``` python
import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
# 将二维数组a转换为一维数组
a_flat = a.flatten()
# 或者使用ravel函数
a_ravel = np.ravel(a)
# 将一维数组传递给函数
result = some_function(a_flat)
这样就可以解决这个错误。需要注意的是，使用`flatten`函数或`ravel`函数转换数组时，如果对新的数组进行修改，会影响原始数组。因此，在使用这些函数时需要注意这一点。
报错subprocess.CalledProcessError: Command ‘[‘which‘, ‘g++‘]‘ returned non-zero exit status 1.
16993
spark报错：WARN TaskSchedulerImpl: Initial job has not accepted any resources； check your cluster UI...
pytest简介 及 和unittest（pyunit）的区别
报错：Allocation of ******* exceeds 10% of free system memory.
