我正在尝试训练数据集，以预测输入的文本是否来自科幻小说。我对Python是相对较新的，所以我不知道我在做什么错。
代码：
#class17.py
"""
Created on Fri Nov 17 14:07:36 2017
@author: twaters
Read three science fiction novels
Predict a sentence or paragraph
see whether sentence/phrase/book is from a science fiction novel or not
"""
import nltk
import pandas as pd
import csv
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn import model_selection
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from nltk.corpus import stopwords
#nltk.download()
irobot = "C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/irobot.txt"
enders_game = "C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/endersgame.txt"
space_odyssey ="C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/spaceodyssey.txt"
to_kill_a_mockingbird = "C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/tokillamockingbird.txt"
sr = set(stopwords.words('english'))
freq = {}
def main():
#read_novels()
model_novels()
def read_novel(b, is_scifi):
read_file = open(b)
text = read_file.read()
words = text.split()
clean_tokens = words[:]
filtered_list = []
for word in clean_tokens:
word = word.lower()
if word not in sr:
filtered_list.append(word)
freq = nltk.FreqDist(clean_tokens)
#print(filtered_list)
for word in clean_tokens:
count = freq.get(word,0)
freq[word] = count + 1
frequency_list = freq.keys()
with open('C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/novels_data.txt', 'w', encoding='utf-8') as csvfile:
fieldnames = ['word','frequency','is_scifi']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, lineterminator = 'n')
writer.writeheader()
for words in frequency_list:
writer.writerow({'word': words,'frequency': freq[words],'is_scifi':is_scifi})
print("List compiled.")
def read_novels():
read_novel(enders_game, 0)
read_novel(space_odyssey, 0)
read_novel(irobot, 0)
read_novel(to_kill_a_mockingbird, 1)
def model_novels():
df = pd.read_csv('C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/novels_data.txt', 'rb', delimiter='t', encoding='utf-8')
print(df)
#for index in range(2, df.shape[0], 100):
df_subset = df.loc[1:]
#print(df_subset)
X = df_subset.loc[:, 'frequency':'is_scifi']
Y = df_subset.loc[:, 'frequency':'is_scifi']
testing_size = 0.2
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=testing_size, random_state=seed)
selectedModel = LogisticRegression()
selectedModel.fit(X_train, Y_train)
predictions = selectedModel.predict(X_validation)
#%%
#print("Accuracy Score:n", accuracy_score(Y_validation, predictions))
#print("Confusion Matrix:n",confusion_matrix(predictions, Y_validation))
#print("Class report:n", classification_report(Y_validation, predictions))
#df_test = pd.read_csv('C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/novels_data.txt', delimiter='t')
#predictions_test = selectedModel.predict(df_test)
#test_frame = pd.DataFrame(predictions_test)
#test_frame.to_csv('C:/Users/twaters/Desktop/Assignments/SQL/Python/DA Project/novels_data_result.txt', sep='t')
错误：Trackback（最近的最新通话）：
文件"，第1行，在 main（）
file" c：/user/user/user/desktop/sistments/sql/python/da project/class17.py"，第36行，在main中 model_novels（）
file" c：/user/user/user/desktop/sistments/sql/python/da project/class17.py"，第95行，在model_novels中 selectionmodel.fit（x_train，y_train）
文件" d： program文件（x86） anaconda lib lib site-packages sklearn linear_model logister.py"，第1216行， 订单=" C"）
file" d： program文件（x86） anaconda lib lib site-packages sklearn sklearn utils utils vility.py"，第573行，在check_x_y中 suse_min_features，warn_on_dtype，估算器）
file" d： program文件（x86） anaconda lib lib site-packages sklearn sklearn utils utils vility.py"，第453行，在check_array中 _ASSERT_ALL_FINITE（数组）
文件" d： program文件（x86） anaconda lib lib site-packages sklearn sklearn utils utils vility.py"，第44行，在_assert_all_finite中 "或一个太大的价值，对于％r。"％x.dtype）
valueerror：输入包含nan，无穷大或值太大的dtype（'float64'）。
如果您需要访问我正在阅读的文件，我可以链接它们。
谢谢您的帮助！
这是堆栈中的点，您应该注意：
file" c：/user/user/user/desktop/sistments/sql/python/da project/class17.py"，第95行，在model_novels selectionmodel.fit（x_train，y_train）
file" d： program文件（x86） anaconda lib lib site-packages sklearn sklearn utils utils utils veration.py"，第44行，in _assert_all_finite中的第44行或一个太大的值。％x.dtype）
告诉我们格式化X的问题有问题，因此逻辑回归将接受它。
您应该检查X_Train和X以查看它们是否包含错误的值。
这个答案将为您提供一些有关如何做到这一点的指示。
python pandas：检查dataframe中的任何值