嘗試運(yùn)行此命令時(shí)，我收到以下錯(cuò)誤。我真的不知道如何解決這個(gè)問(wèn)題。由于我對(duì)此很陌生，所以我非常感謝任何幫助。在這里，我使用不同的模型來(lái)檢查哪一個(gè)在這里最好。檢查此導(dǎo)入代碼：import numpy as np import matplotlib.pyplot as plt import matplotlib.colors as mcolorsimport pandas as pd import plotly.express as pxfrom datetime import date, timedeltaimport randomimport mathimport timeimport operator import foliumimport plotly.offline as pyfrom sklearn.linear_model import LinearRegression, BayesianRidgefrom sklearn.model_selection import RandomizedSearchCV, train_test_splitfrom sklearn.preprocessing import PolynomialFeaturesfrom fbprophet import Prophetfrom fbprophet.plot import plot_plotly, add_changepoints_to_plotfrom sklearn import preprocessing, cross_validationfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.neural_network import MLPRegressorfrom sklearn.svm import SVRfrom sklearn.metrics import mean_squared_error, mean_absolute_errorfrom statsmodels.tsa.arima_model import ARIMAfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacfimport statsmodels.api as smfrom keras.models import Sequentialfrom keras.layers import LSTM,Densefrom keras.layers import Dropoutfrom sklearn.preprocessing import MinMaxScalerfrom tensorflow.keras.preprocessing.sequence import TimeseriesGeneratorplt.style.use('fivethirtyeight')%matplotlib inline 多項(xiàng)式回歸的模型開(kāi)發(fā)data1 = confirmed_df.melt(value_vars=dates1, var_name='Date', value_name='Confirmed')data1 = data1.groupby('Date')['Confirmed'].sum().reset_index()data1.head()X = pd.DataFrame(data=data1, columns=data1.Date)y = data1.Confirmedy -= y.mean()#cross_validation.train_test_split(x, y, test_size=0.1,random_state=0)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
2 回答
茅侃侃
TA貢獻(xiàn)1842條經(jīng)驗(yàn) 獲得超22個(gè)贊
我想你想跟蹤你的腳本的性能，但是你需要指定要從中調(diào)用方法time（）的模塊，所以要解決這個(gè)問(wèn)題，只需調(diào)用，請(qǐng)參閱下面證明的示例tic=time()tic = time.time()
#!/usr/bin/python
import time; # This is required to include time module.
ticks = time.time()
print "Number of ticks since 12:00am, January 1, 1970:", ticks
慕尼黑8549860
TA貢獻(xiàn)1818條經(jīng)驗(yàn) 獲得超11個(gè)贊
錯(cuò)誤告訴您這是一個(gè)模塊，而不是一個(gè)函數(shù)。你在劇本的頂部有這樣的臺(tái)詞嗎？time
import time
如果是這樣，那么要獲取要在模塊中調(diào)用函數(shù)的當(dāng)前時(shí)間：timetime
tic = time.time()
如果使用，則可以在運(yùn)行代碼之前捕獲此類錯(cuò)誤。在調(diào)用模塊的代碼上運(yùn)行，就好像它是一個(gè)函數(shù)一樣，會(huì)產(chǎn)生 .使用宏可以顯示差異：mypymypytimeerror: Module not callablereveal_type
import time
reveal_type(time) # note: Revealed type is '_importlib_modulespec.ModuleType'
reveal_type(time.time) # note: Revealed type is 'def () -> builtins.float'
如果您只想從模塊中獲取函數(shù)，則可以專門導(dǎo)入該函數(shù)，以便在代碼范圍內(nèi)引用該函數(shù)而不是整個(gè)模塊：timetimetime
from time import time
reveal_type(time) # note: Revealed type is 'def () -> builtins.float'
reveal_type(time.time) # error: "Callable[[], float]" has no attribute "time"
因此，現(xiàn)在我們已經(jīng)將函數(shù)直接導(dǎo)入到我們的作用域中，是正確的（因?yàn)?是一個(gè)函數(shù)），而不是（因?yàn)樵摵瘮?shù)沒(méi)有名為 的成員）。timetime()timetime.time()timetime
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