我的主要问题是如何对数据进行整形以适应模型中的多元时间序列数据。我当前的代码如下所示：
def diff_stats_mod (X_train, X_test, y_train, y_test):
################init########################
score_dict = {}
n=0
################Create a list of models to evaluate################
models, names = list(), list()
models.append(LogisticRegression())
names.append('LR')
models.append(DecisionTreeClassifier())
names.append('DTC')
models.append(SVC())
names.append('SVM')
models.append(RandomForestClassifier())
names.append('RF')
models.append(GradientBoostingClassifier())
names.append('GBM')
################evaluate models################
for i in range(len(models)):
model = models[i]
model.fit(X_train, y_train)
pred = model.predict(X_test)
not_include = 0
###############Ensure that the prediction is not all positive or all neg###############
while len(set(pred)) == 1:
model = models[i]
model.fit(X_train, y_train)
pred = model.predict(X_test)
if n == 10:
not_include = 1
break
n+=1
###############Exclude all models whos predictions are off the same class only###############
if not_include != 1:
confu_mat = confusion_matrix(y_test, pred)
fb_score = fbeta_score(y_test, pred, 0.9) * 100
score_dict['{}'.format(names[i])] = fb_score
score_dict['{} confusion matrix'.format(names[i])] = confu_mat
else:
fb_score = NaN
score_dict['{}'.format(names[i])] = fb_score
################try a range of k values################
for k in range(1, 11):
################Load and evaluate knn models################
not_include = 0
model = KNeighborsClassifier(n_neighbors=k)
model.fit(X_train, y_train)
pred = model.predict(X_test)
###############Ensure that the prediction is not all positive or all neg###############
while len(set(pred)) == 1:
model = KNeighborsClassifier(n_neighbors=k)
model.fit(X_train, y_train)
pred = model.predict(X_test)
if n == 10:
not_include = 1
break
n += 1
###############Exclude all models whos predictions are off the same class only###############
if not_include != 1:
confu_mat = confusion_matrix(y_test, pred)
fb_score = fbeta_score(y_test, pred, 0.9) * 100
score_dict['KNN{}'.format(k)] = fb_score
score_dict['KNN{} confusion matrix'.format(k)] = confu_mat
else:
fb_score = NaN
score_dict['KNN{}'.format(k)] = fb_score
return score_dict
基本上，此函数返回测试集上每个模型的fbeta分数。它将重新训练给出所有相同类别的预测的模型（最多十次），如果十次之后，该特定模型仍将所有预测输出为同一类别，则将其排除。
这是我的数据的一部分：
time_stamp pxID act hr
2015-06-06 17:00:00 7983 8.466666666666667 97.46555633544922
2015-06-06 17:30:00 7983 10.413333333333332 99.16444473266601
2015-06-06 18:00:00 7983 5.400000000000001 94.62666702270508
2015-06-06 18:30:00 7983 14.759999999999998 95.76777776082356
2015-06-06 19:00:00 7983 17.026666666666667 100.43111089070638
2015-08-04 10:30:00 8005 4.774020720186061 18.555715289243377
2015-08-04 11:00:00 8005 7.1056325549244574 20.01443100917877
2015-08-04 11:30:00 8005 9.088101464843694 24.019171214407546
2015-08-04 12:00:00 8005 4.32230745513258 20.9444548661983
2015-08-04 12:30:00 8005 4.464612178539353 18.433279992371574
2015-08-16 19:00:00 8026 1.4452551387583383 9.943809217794078
2015-08-16 19:30:00 8026 2.7265866427381216 13.206866297538518
2015-08-16 20:00:00 8026 2.2795014957992974 9.11883132666883
2015-08-16 20:30:00 8026 1.536946186246722 10.04255596582319
2015-08-16 21:00:00 8026 2.0673098515634667 9.219173212211949
基本上，有许多ID和观测值。当我尝试将这些数据输入模型时，关于数据的尺寸存在错误。我知道诸如Logistic回归之类的模型可以接受多维输入，但是我不确定如何格式化输入，也不确定要在LogisticRegression和其他模型中包含哪些参数才能处理多维数据。对于这个分类问题，我想利用HR和Act数据。
由于我习惯于处理每一行都反映一个观察结果的数据，因此我对如何解决此问题感到困惑。但是，这些数据表明多行反映了一个观察结果。
我的主要问题是：如何格式化数据以用作SKlearn模型的输入？