Question

以下代码的所有需要的模块都已导入

date = []
usage = []

date = genfromtxt(‘date.csv’)
usage = genfromtxt(‘usage.csv’)
test = genfromtxt(‘test.csv’)

print (len(date))

print (len(usage))

dataframe = pd.DataFrame({
‘Date’: (date),
‘Usage’: (usage)
})

删除NaN数据

dataframe = dataframe.dropna()
print (dataframe)

df = dataframe.drop(dataframe.index[[-1,-4]])

array = df.values

X = array[:,0:1]
Y = array[:,1]

validation_size = 0.20
seed = 7

X_train, X_validation, Y_train, Y_validation =           
model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)

seed = 7
scoring = ‘accuracy’

抽查算法

models = []
models.append((‘LR’, LogisticRegression()))
models.append((‘LDA’, LinearDiscriminantAnalysis()))
models.append((‘KNN’, KNeighborsClassifier()))
models.append((‘CART’, DecisionTreeClassifier()))
models.append((‘NB’, GaussianNB()))
models.append((‘SVM’, SVC()))

依次评估每个模型

results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train,           cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = “%s: %f (%f)” % (name, cv_results.mean(), cv_results.std())
print(msg)

比较算法

fig = plt.figure()
fig.suptitle(‘Algorithm Comparison’)
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()

错误讯息：

1.UserWarning：先验不等于1.重新规范用户警告回溯（最近一次调用最后一次）：

File “data_0.py”, line 111, in

2.出现错误的行：

cv_results = model_selection.cross_val_score(model, X_train, Y_train,cv=kfold, scoring=scoring)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/model_selection/_validation.py”, line 140, 
        in cross_val_score for train, test in cv_iter)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/externals/joblib/parallel.py”, line 758, 
        in __call__ while self.dispatch_one_batch(iterator):

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/externals/joblib/parallel.py”, line 608, 
        in dispatch_one_batch self._dispatch(tasks)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/externals/joblib/parallel.py”, line 571, 
        in _dispatch job = self._backend.apply_async(batch, callback=cb)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/externals/joblib/_parallel_backends.py”, line 109, 
        in apply_async result = ImmediateResult(func)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/externals/joblib/_parallel_backends.py”, line 326, 
        in __init__ self.results = batch()

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/externals/joblib/parallel.py”, line 131, 
        in __call__ return [func(*args, **kwargs) for func, args, kwargs in self.items]

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/model_selection/_validation.py”, line 238, 
        in _fit_and_score estimator.fit(X_train, y_train, **fit_params)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/discriminant_analysis.py”, line 468, 
        in fit self._solve_svd(X, y)

File “/Users/nelsondsouza/anaconda/lib/python2.7/sitepackages/sklearn/discriminant_analysis.py”, line 378, 
        in solve_svd fac = 1. / (n_samples – n_classes)

3.ZeroDivisionError：浮点除零

UserWarning：先验不等于1.重整化[python]

0 个答案: