用多项式NB（scikit-learn）预测高棉

Question

我正在尝试在 python 上使用 scikit-learn 制作分类器，以预测病毒的核苷酸序列是否对人类具有潜在的致病性。我用 0 而不是 1 确定了一些序列是致病的，序列从序列到类别的区别如下：
ATCGATCGAATCGGATC 1
ATCGGGGGATATATAAATTACATATATTGTTGTATG 1
ATCGTAT 0
ATAAATATTGTATTGCG 0
...
我基本上从 Krish Naik https://github.com/krishnaik06/DNA-Sequencing-Classifier/blob/master/DNA%20Sequencing%20and%20applying%20Classifier.ipynb 那里得到了我的工作，他试图预测蛋白质类别。我只是进行了修改以符合我的目标，但问题是我没有找到任何解决方案来预测新序列的致病性。
你可以在我的 gitlab https://gitlab.com/MasterBioinformaticBiostatistics/bioinformatic/virusid.git 上找到我使用的数据。 这是我在数据中使用的 Krish Naik 的代码（这部分似乎已经建立了模型）：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#Code modified from Krish Naik https://github.com/krishnaik06/DNA-Sequencing-Classifier/blob/master/DNA%20Sequencing%20and%20applying%20Classifier.ipynb
human_data = pd.read_table('final_petit.txt')
human_data.head()

from IPython.display import Image
Image("Capture1.PNG")

# function to convert sequence strings into k-mer words, default size = 6 (hexamer words)
def getKmers(sequence, size=6):
    return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]

human_data['words'] = human_data.apply(lambda x: getKmers(x['sequence']), axis=1)
human_data = human_data.drop('sequence', axis=1)

human_data.head()

human_texts = list(human_data['words'])
for item in range(len(human_texts)):
    human_texts[item] = ' '.join(human_texts[item])
y_data = human_data.iloc[:, 0].values

print(human_texts[2])

y_data

# Creating the Bag of Words model using CountVectorizer()
# This is equivalent to k-mer counting
# The n-gram size of 4 was previously determined by testing
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(ngram_range=(4,4))
X = cv.fit_transform(human_texts)


print(X.shape)


human_data['class'].value_counts().sort_index().plot.bar()

# Splitting the human dataset into the training set and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, 
                                                    y_data, 
                                                    test_size = 0.20, 
                                                    random_state=42)

print(X_train.shape)
print(X_test.shape)


### Multinomial Naive Bayes Classifier ###
# The alpha parameter was determined by grid search previously
from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB(alpha=0.1)
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
print("Confusion matrix\n")
print(pd.crosstab(pd.Series(y_test, name='Actual'), pd.Series(y_pred, name='Predicted')))
def get_metrics(y_test, y_predicted):
    accuracy = accuracy_score(y_test, y_predicted)
    precision = precision_score(y_test, y_predicted, average='weighted')
    recall = recall_score(y_test, y_predicted, average='weighted')
    f1 = f1_score(y_test, y_predicted, average='weighted')
    return accuracy, precision, recall, f1
accuracy, precision, recall, f1 = get_metrics(y_test, y_pred)
print("accuracy = %.3f \nprecision = %.3f \nrecall = %.3f \nf1 = %.3f" % (accuracy, precision, recall, f1))

为了预测新序列，我决定采用相同的方法，获取并计算新序列的高棉词：

#NEW SEQUENCE
new_seq = pd.read_table('sequence.txt')
new_seq.head()

# function to convert sequence strings into k-mer words, default size = 6 (hexamer words)
def getKmers(sequence, size=6):
    return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]

new_seq['words'] = new_seq.apply(lambda x: getKmers(x['sequence']), axis=1)
new_seq = new_seq.drop('sequence', axis=1)

new_seq.head()

new_seqtext = list(new_seq['words'])
for item in range(len(new_seqtext)):
    new_seqtext[item] = ' '.join(new_seqtext[item])
y_data = new_seq.iloc[:, 0].values


y_data

# Creating the Bag of Words model using CountVectorizer()
# This is equivalent to k-mer counting
# The n-gram size of 4 was previously determined by testing
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(ngram_range=(4,4))
U = cv.fit_transform(new_seqtext)

u_pred = classifier.predict(U)

但使用 classifier.predict 的预测似乎无法按预期工作，即使序列已被剪切为高棉计数。

Traceback (most recent call last):
  File "Original.py", line 89, in <module>
    new_seq['words'] = new_seq.apply(lambda x: getKmers(x['sequence']), axis=1)
  File "/home/name/.local/lib/python3.8/site-packages/pandas/core/frame.py", line 6878, in apply
    return op.get_result()
  File "/home/name/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 186, in get_result
    return self.apply_standard()
  File "/home/name/.local/lib/python3.8/site-packages/pandas/core/apply.py", line 295, in apply_standard
    result = libreduction.compute_reduction(
  File "pandas/_libs/reduction.pyx", line 618, in pandas._libs.reduction.compute_reduction
  File "pandas/_libs/reduction.pyx", line 128, in pandas._libs.reduction.Reducer.get_result
  File "Original.py", line 89, in <lambda>
    new_seq['words'] = new_seq.apply(lambda x: getKmers(x['sequence']), axis=1)
  File "/home/name/.local/lib/python3.8/site-packages/pandas/core/series.py", line 871, in __getitem__
    result = self.index.get_value(self, key)
  File "/home/name/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4419, in get_value
    raise e1
  File "/home/name/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4405, in get_value
    return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
  File "pandas/_libs/index.pyx", line 80, in pandas._libs.index.IndexEngine.get_value
  File "pandas/_libs/index.pyx", line 90, in pandas._libs.index.IndexEngine.get_value
  File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 1618, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 1626, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'sequence'

程序在这一步似乎崩溃了：

new_seq['words'] = new_seq.apply(lambda x: getKmers(x['sequence']), axis=1)

我的方法是否太天真而无法获得新序列的高棉语？

Answer 1

已找到队友的解决方案： 而不是使用

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(ngram_range=(4,4))
U = cv.fit_transform(new_seqtext)

你可以使用这个：

U = cv.transform(new_seqtext)

fit_transform 用于构建模型，据我所知，无需再次导入，并且一直声明 cv...