此问题是this one的扩展，其重点是LSTM而不是CRF。不幸的是，我没有使用CRF的经验，这就是为什么我要问这些问题。
问题：
我想预测多个非独立组的二进制信号序列。我的数据集很小（每组约1000条记录），所以我想在这里尝试CRF模型。
可用数据：
我有一个包含以下变量的数据集：
使用此数据集，我希望预测group_a_activity和group_b_activity均为0或1。
请注意，这些组被认为是互相关的，并且可以从时间戳中提取其他特征-为简单起见，我们可以假定从时间戳中仅提取了1个特征。
我到目前为止所拥有的：
这是您可以在自己的计算机上复制的数据设置。
# libraries
import re
import numpy as np
import pandas as pd
data_length = 18 # how long our data series will be
shift_length = 3 # how long of a sequence do we want
df = (pd.DataFrame # create a sample dataframe
.from_records(np.random.randint(2, size=[data_length, 3]))
.rename(columns={0:'a', 1:'b', 2:'extra'}))
df.head() # check it out
# shift (assuming data is sorted already)
colrange = df.columns
shift_range = [_ for _ in range(-shift_length, shift_length+1) if _ != 0]
for c in colrange:
for s in shift_range:
if not (c == 'extra' and s > 0):
charge = 'next' if s > 0 else 'last' # 'next' variables is what we want to predict
formatted_s = '{0:02d}'.format(abs(s))
new_var = '{var}_{charge}_{n}'.format(var=c, charge=charge, n=formatted_s)
df[new_var] = df[c].shift(s)
# drop unnecessary variables and trim missings generated by the shift operation
df.dropna(axis=0, inplace=True)
df.drop(colrange, axis=1, inplace=True)
df = df.astype(int)
df.head() # check it out
# a_last_03 a_last_02 ... extra_last_02 extra_last_01
# 3 0 1 ... 0 1
# 4 1 0 ... 0 0
# 5 0 1 ... 1 0
# 6 0 0 ... 0 1
# 7 0 0 ... 1 0
[5 rows x 15 columns]
在进入CRF部分之前，我怀疑我不能从多任务学习的角度（通过一个模型预测A和B的模式）来解决这个问题，因此我将不得不分别预测它们。
现在，CRF部分。我找到了一些相关的示例（这里是one），但是它们都倾向于根据先前的顺序来预测单个类的值。
这是我在这里尝试使用CRF的尝试：
import pycrfsuite
crf_features = [] # a container for features
crf_labels = [] # a container for response
# lets focus on group A only for this one
current_response = [c for c in df.columns if c.startswith('a_next')]
# predictors are going to have to be nested otherwise I'll run into problems with dimensions
current_predictors = [c for c in df.columns if not 'next' in c]
current_predictors = set([re.sub('_\d+$','',v) for v in current_predictors])
for index, row in df.iterrows():
# not sure if its an effective way to iterate over a DF...
iter_features = []
for p in current_predictors:
pred_feature = []
# note that 0/1 values have to be converted into booleans
for k in range(shift_length):
iter_pred_feature = p + '_{0:02d}'.format(k+1)
pred_feature.append(p + "=" + str(bool(row[iter_pred_feature])))
iter_features.append(pred_feature)
iter_response = [row[current_response].apply(lambda z: str(bool(z))).tolist()]
crf_labels.extend(iter_response)
crf_features.append(iter_features)
trainer = pycrfsuite.Trainer(verbose=True)
for xseq, yseq in zip(crf_features, crf_labels):
trainer.append(xseq, yseq)
trainer.set_params({
'c1': 0.0, # coefficient for L1 penalty
'c2': 0.0, # coefficient for L2 penalty
'max_iterations': 10, # stop earlier
# include transitions that are possible, but not observed
'feature.possible_transitions': True
})
trainer.train('testcrf.crfsuite')
tagger = pycrfsuite.Tagger()
tagger.open('testcrf.crfsuite')
tagger.tag(xseq)
# ['False', 'True', 'False']
看来我确实设法使其正常工作，但是我不确定是否已正确处理它。我将在“问题”部分中提出问题，但首先，这是使用keras_contrib软件包的另一种方法：
from keras import Sequential
from keras_contrib.layers import CRF
from keras_contrib.losses import crf_loss
# we are gonna have to revisit data prep stage again
# separate predictors and response
response_df_dict = {}
for g in ['a','b']:
response_df_dict[g] = df[[c for c in df.columns if 'next' in c and g in c]]
# reformat for LSTM
# the response for every row is a matrix with depth of 2 (the number of groups) and width = shift_length
# the predictors are of the same dimensions except the depth is not 2 but the number of predictors that we have
response_array_list = []
col_prefix = set([re.sub('_\d+$','',c) for c in df.columns if 'next' not in c])
for c in col_prefix:
current_array = df[[z for z in df.columns if z.startswith(c)]].values
response_array_list.append(current_array)
# reshape into samples (1), time stamps (2) and channels/variables (0)
response_array = np.array([response_df_dict['a'].values,response_df_dict['b'].values])
response_array = np.reshape(response_array, (response_array.shape[1], response_array.shape[2], response_array.shape[0]))
predictor_array = np.array(response_array_list)
predictor_array = np.reshape(predictor_array, (predictor_array.shape[1], predictor_array.shape[2], predictor_array.shape[0]))
model = Sequential()
model.add(CRF(2, input_shape=(predictor_array.shape[1],predictor_array.shape[2])))
model.summary()
model.compile(loss=crf_loss, optimizer='adam', metrics=['accuracy'])
model.fit(predictor_array, response_array, epochs=10, batch_size=1)
model_preds = model.predict(predictor_array) # not gonna worry about train/test split here
问题：
我的主要问题是我是否正确构建了两个CRF模型。让我担心的是：（1）关于CRF模型的文档很少，（2）CRF主要用于预测给定序列的单个标签，（3）输入要素是嵌套的，（4）用于多任务方式，我不确定它是否有效。
我还有一些其他问题：
pycrfuite和一种基于keras_contrib）有何不同？它们的优缺点是什么？非常感谢！