Scikit Learn多类分类（完美结果）

Question

你好，我是scikit学习的新手，我正在尝试做一些文本多类分类，我正在遵循this教程。
我的数据集有4个类'fipdl', 'lna','m5s','pd'，所以我得到了4个文件夹（一个用于类），每个文件夹包含120个文本文件，大约25行文本（facebook状态）。 我把90%用于培训，10%用于测试。
10%的txt文件名以“ts”开头，我正在使用这些文件进行测试。
所以我的代码是：

import sys
import os
import time

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn.preprocessing import MultiLabelBinarizer

def usage():
    print("Usage:")
    print("python %s <data_dir>" % sys.argv[0])

if __name__ == '__main__':

if len(sys.argv) < 2:
    usage()
    sys.exit(1)

data_dir = sys.argv[1]
classes = ['fipdl', 'lna','m5s','pd']

# Read the data
train_data = []
train_labels = []
test_data = []
test_labels = []

for curr_class in classes:
    dirname = os.path.join(data_dir, curr_class)
    for fname in os.listdir(dirname):
        with open(os.path.join(dirname, fname), 'r') as f:
            content = f.read()
            if fname.startswith('ts'):
                test_data.append(content)
                test_labels.append(curr_class)

            else:
                train_data.append(content)
                train_labels.append(curr_class)



# Create feature vectors
vectorizer = TfidfVectorizer(min_df=5,
                             max_df = 0.8,
                             sublinear_tf=True,
                             use_idf=True)
train_vectors = vectorizer.fit_transform(train_data)
test_vectors = vectorizer.transform(test_data)
# Perform classification with SVM, kernel=rbf
classifier_rbf = svm.SVC()
t0 = time.time()
classifier_rbf.fit(train_vectors, train_labels)
t1 = time.time()
prediction_rbf = classifier_rbf.predict(test_vectors)
t2 = time.time()
time_rbf_train = t1-t0
time_rbf_predict = t2-t1

# Perform classification with SVM, kernel=linear
classifier_linear = svm.SVC(kernel='linear')
t0 = time.time()
classifier_linear.fit(train_vectors, train_labels)
t1 = time.time()
prediction_linear = classifier_linear.predict(test_vectors)
t2 = time.time()
time_linear_train = t1-t0
time_linear_predict = t2-t1

# Perform classification with SVM, kernel=linear
classifier_liblinear = svm.LinearSVC()
t0 = time.time()
classifier_liblinear.fit(train_vectors, train_labels)
t1 = time.time()
prediction_liblinear = classifier_liblinear.predict(test_vectors)
t2 = time.time()
time_liblinear_train = t1-t0
time_liblinear_predict = t2-t1

# Print results in a nice table
print("Results for SVC(kernel=rbf)")
print("Training time: %fs; Prediction time: %fs" % (time_rbf_train, time_rbf_predict))
print(classification_report(test_labels, prediction_rbf))
print("Results for SVC(kernel=linear)")
print("Training time: %fs; Prediction time: %fs" % (time_linear_train, time_linear_predict))
print(classification_report(test_labels, prediction_linear))
print("Results for LinearSVC()")
print("Training time: %fs; Prediction time: %fs" % (time_liblinear_train, time_liblinear_predict))
print(classification_report(test_labels, prediction_liblinear))

输出：

^{pr2}$

现在结果似乎太好了，不可能是真的，因为每种方法都给了我1的精确度。
我想最好是尝试预测我传递的字符串而不是测试集，因为要做更多的测试，所以我将原始代码改为：

import sys
import os
import time

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn.preprocessing import MultiLabelBinarizer

def usage():
    print("Usage:")
    print("python %s <data_dir>" % sys.argv[0])

if __name__ == '__main__':

    if len(sys.argv) < 2:
        usage()
        sys.exit(1)

    data_dir = sys.argv[1]
    classes = ['fipdl', 'lna','m5s','pd']

    # Read the data
    train_data = []
    train_labels = []
    test_data = []
    test_labels = []

    for curr_class in classes:
        dirname = os.path.join(data_dir, curr_class)
        for fname in os.listdir(dirname):
            with open(os.path.join(dirname, fname), 'r') as f:
                content = f.read()
                if fname.startswith('ts'):
                    test_data.append(content)
                    test_labels.append(curr_class)

                else:
                    train_data.append(content)
                    train_labels.append(curr_class)



    # Create feature vectors
    vectorizer = TfidfVectorizer(min_df=5,
                                 max_df = 0.8,
                                 sublinear_tf=True,
                                 use_idf=True)
    string = ['string to predict'] #my string
    vector = vectorizer.transform(string) #convert
    train_vectors = vectorizer.fit_transform(train_data)

    test_vectors = vectorizer.transform(test_data)
    # Perform classification with SVM, kernel=rbf
    classifier_rbf = svm.SVC()
    t0 = time.time()
    classifier_rbf.fit(train_vectors, train_labels)
    t1 = time.time()
    prediction_rbf = classifier_rbf.predict(vector) #predict
    t2 = time.time()
    time_rbf_train = t1-t0
    time_rbf_predict = t2-t1

    # Perform classification with SVM, kernel=linear
    classifier_linear = svm.SVC(kernel='linear')
    t0 = time.time()
    classifier_linear.fit(train_vectors, train_labels)
    t1 = time.time()
    prediction_linear = classifier_linear.predict(test_vectors)
    t2 = time.time()
    time_linear_train = t1-t0
    time_linear_predict = t2-t1

    # Perform classification with SVM, kernel=linear
    classifier_liblinear = svm.LinearSVC()
    t0 = time.time()
    classifier_liblinear.fit(train_vectors, train_labels)
    t1 = time.time()
    prediction_liblinear = classifier_liblinear.predict(test_vectors)
    t2 = time.time()
    time_liblinear_train = t1-t0
    time_liblinear_predict = t2-t1

    # Print results in a nice table
    print("Results for SVC(kernel=rbf)")
    print("Training time: %fs; Prediction time: %fs" % (time_rbf_train, time_rbf_predict))
    print(classification_report(test_labels, prediction_rbf))
    print("Results for SVC(kernel=linear)")
    print("Training time: %fs; Prediction time: %fs" % (time_linear_train, time_linear_predict))
    print(classification_report(test_labels, prediction_linear))
    print("Results for LinearSVC()")
    print("Training time: %fs; Prediction time: %fs" % (time_liblinear_train, time_liblinear_predict))
    print(classification_report(test_labels, prediction_liblinear))

但它失败了

ValueError: Found arrays with inconsistent numbers of samples: [18 44]

我遗漏了什么？或者这是一个完全错误的方法？
如有任何帮助，我们将不胜感激，
先谢谢尼科。在

Answer 1

# Create feature vectors
    vectorizer = TfidfVectorizer(min_df=5,
                                 max_df = 0.8,
                                 sublinear_tf=True,
                                 use_idf=True)
    string = ['string to predict'] #my string
    vector = vectorizer.transform(string) #convert
    train_vectors = vectorizer.fit_transform(train_data)

您将创建向量器的新实例，并在拟合它之前，使用transform方法。只需更改最后两行的顺序，如下所示：

^{pr2}$

Answer 2

即使我还没有弄清楚为什么它能给我带来完美的结果，我还是决定用一种不同的方法对我的文本进行分类（使用多项式nb），并用我选择的字符串进行测试。我不确定这是否是最好的方法，但它是有效的，所以我决定张贴作为答案： （请注意，并非所有代码行都是必需的）

# -*- coding: utf-8 -*-
import sys
import os
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix, f1_score
from sklearn.datasets import load_files
from sklearn.svm import SVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB


string = sys.argv[1] #the string i'd like to predict
sets = load_files('scikit') #load my personal dataset




count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(sets.data)



tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)


tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
X_train_tfidf.shape


clf = MultinomialNB().fit(X_train_tfidf, sets.target)
docs_new = [string]
X_new_counts = count_vect.transform(docs_new)
X_new_tfidf = tfidf_transformer.transform(X_new_counts)
predicted = clf.predict(X_new_tfidf)

for doc, category in zip(docs_new, predicted):
     print('%r => %s' % (doc, sets.target_names[category])) #print result of prediction

然后从控制台运行script.py "string to predict"

可以对这段代码做很多改进，比如转储经过训练的模型，但对我的使用来说已经足够了。在