这期内容当中小编将会给大家带来有关Python中怎么实现数据挖掘，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。
第一步：加载数据，浏览一下
让我们跳过真正的第一步（完善资料，了解我们要做的是什么，这在实践过程中是非常重要的），直接到 https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection 下载 demo 里需要用的 zip 文件，解压到 data 子目录下。你能看到一个大概 0.5MB 大小，名为 SMSSpamCollection 的文件：
Python
$ <span class="kw">ls</span> -l data <span class="kw">total</span> 1352 <span class="kw">-rw-r--r--@</span> 1 kofola staff 477907 Mar 15 2011 SMSSpamCollection <span class="kw">-rw-r--r--@</span> 1 kofola staff 5868 Apr 18 2011 readme <span class="kw">-rw-r-----@</span> 1 kofola staff 203415 Dec 1 15:30 smsspamcollection.zip |
电动chache
这份文件包含了 5000 多份 SMS 手机信息（查看 readme 文件以获得更多信息）：
In [2]:
messages = [line.rstrip() for line in open('./data/SMSSpamCollection')] print len(messages) |
5574
文本集有时候也称为“语料库”，我们来打印 SMS 语料库中的前 10 条信息：
In [3]:
Python
for message_no, message in enumerate(messages[:10]): print message_no, message |
Python
0 ham Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat... 1 ham Ok lar... Joking wif u oni... 2 spam Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's 3 ham U dun say so early hor... U c already then say... 4 ham Nah I don't think he goes to usf, he lives around here though 5 spam FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, ?1.50 to rcv 6 ham Even my brother is not like to speak with me. They treat me like aids patent. 7 ham As per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune 8 spam WINNER!! As a valued network customer you have been selected to receivea ?900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only. 9 spam Had your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030 |
我们看到一个 TSV 文件（用制表符 tab 分隔），它的第一列是标记正常信息（ham）或“垃圾文件”（spam）的标签，第二列是信息本身。
这个语料库将作为带标签的训练集。通过使用这些标记了 ham/spam 例子，我们将训练一个自动分辨 ham/spam 的机器学习模型。然后，我们可以用训练好的模型将任意未标记的信息标记为 ham 或 spam。
我们可以使用 Python 的 Pandas 库替我们处理 TSV 文件（或 CSV 文件，或 Excel 文件）：
In [4]:
Python
messages = pandas.read_csv('./data/SMSSpamCollection', sep='t', quoting=csv.QUOTE_NONE, names=["label", "message"]) print messages |
Python
label message 0 ham Go until jurong point, crazy.. Available only ... 1 ham Ok lar... Joking wif u oni... 2 spam Free entry in 2 a wkly comp to win FA Cup fina... 3 ham U dun say so early hor... U c already then say... 4 ham Nah I don't think he goes to usf, he lives aro... 5 spam FreeMsg Hey there darling it's been 3 week's n... 6 ham Even my brother is not like to speak with me. ... 7 ham As per your request 'Melle Melle (Oru Minnamin... 8 spam WINNER!! As a valued network customer you have... 9 spam Had your mobile 11 months or more? U R entitle... 10 ham I'm gonna be home soon and i don't want to tal... 11 spam SIX chances to win CASH! From 100 to 20,000 po... 12 spam URGENT! You have won a 1 week FREE membership ... 13 ham I've been searching for the right words to tha... 14 ham I HAVE A DATE ON SUNDAY WITH WILL!! 15 spam XXXMobileMovieClub: To use your credit, click ... 16 ham Oh k...i'm watching here:) 17 ham Eh u remember how 2 spell his name... Yes i di... 18 ham Fine if that?s the way u feel. That?s the way ... 19 spam England v Macedonia - dont miss the goals/team... 20 ham Is that seriously how you spell his name? 21 ham I‘m going to try for 2 months ha ha only joking 22 ham So ü pay first lar... Then when is da stock co... 23 ham Aft i finish my lunch then i go str down lor. ... 24 ham Ffffffffff. Alright no way I can meet up with ... 25 ham Just forced myself to eat a slice. I'm really ... 26 ham Lol your always so convincing. 27 ham Did you catch the bus ? Are you frying an egg ... 28 ham I'm back &amp; we're packing the car now, I'll... 29 ham Ahhh. Work. I vaguely remember that! What does... ... ... ... 5544 ham Armand says get your ass over to epsilon 5545 ham U still havent got urself a jacket ah? 5546 ham I'm taking derek &amp; taylor to walmart, if I... 5547 ham Hi its in durban are you still on this number 5548 ham Ic. There are a lotta childporn cars then. 5549 spam Had your contract mobile 11 Mnths? Latest Moto... 5550 ham No, I was trying it all weekend ;V 5551 ham You know, wot people wear. T shirts, jumpers, ... 5552 ham Cool, what time you think you can get here? 5553 ham Wen did you get so spiritual and deep. That's ... 5554 ham Have a safe trip to Nigeria. Wish you happines... 5555 ham Hahaha..use your brain dear 5556 ham Well keep in mind I've only got enough gas for... 5557 ham Yeh. Indians was nice. Tho it did kane me off ... 5558 ham Yes i have. So that's why u texted. Pshew...mi... 5559 ham No. I meant the calculation is the same. That ... 5560 ham Sorry, I'll call later 5561 ham if you aren't here in the next &lt;#&gt; hou... 5562 ham Anything lor. Juz both of us lor. 5563 ham Get me out of this dump heap. My mom decided t... 5564 ham Ok lor... Sony ericsson salesman... I ask shuh... 5565 ham Ard 6 like dat lor. 5566 ham Why don't you wait 'til at least wednesday to ... 5567 ham Huh y lei... 5568 spam REMINDER FROM O2: To get 2.50 pounds free call... 5569 spam This is the 2nd time we have tried 2 contact u... 5570 ham Will ü b going to esplanade fr home? 5571 ham Pity, * was in mood for that. So...any other s... 5572 ham The guy did some bitching but I acted like i'd... 5573 ham Rofl. Its true to its name
[5574 rows x 2 columns] |
我们也可以使用 pandas 轻松查看统计信息：
In [5]:
messages.groupby('label').describe() |
out[5]:
message | ||
| label | ||
| ham | count | 4827 |
| unique | 4518 | |
| top | Sorry, I’ll call later | |
| freq | 30 | |
| spam | count | 747 |
| unique | 653 | |
| top | Please call our customer service representativ… | |
| freq | 4 |
这些信息的长度是多少：
In [6]:
Python
messages['length'] = messages['message'].map(lambda text: len(text)) print messages.head() |
Python
label message length 0 ham Go until jurong point, crazy.. Available only ... 111 1 ham Ok lar... Joking wif u oni... 29 2 spam Free entry in 2 a wkly comp to win FA Cup fina... 155 3 ham U dun say so early hor... U c already then say... 49 4 ham Nah I don't think he goes to usf, he lives aro... 61 |
In [7]:
Python
messages.length.plot(bins=20, kind='hist') |
Out[7]:
Python
<matplotlib.axes._subplots.AxesSubplot at 0x10dd7a990> |
cdn2.b0.upaiyun.com/2015/02/7b930a617449365ee096983ea22bc78a.png">
In [8]:
Python
messages.length.describe() |
Out[8]:
Python
count 5574.000000 mean 80.604593 std 59.919970 min 2.000000 25% 36.000000 50% 62.000000 75% 122.000000 max 910.000000 Name: length, dtype: float64 |
哪些是超长信息？
In [9]:
print list(messages.message[messages.length > 900]) |
["For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later.."] |
spam 信息与 ham 信息在长度上有区别吗？
In [10]:
Python
messages.hist(column='length', by='label', bins=50) |
Out[10]:
Python
array([<matplotlib.axes._subplots.AxesSubplot object at 0x11270da50>, <matplotlib.axes._subplots.AxesSubplot object at 0x1126c7750>], dtype=object) |
太棒了，但是我们怎么能让电脑自己识别文字信息？它可以理解这些胡言乱语吗？
这一节我们将原始信息（字符序列）转换为向量（数字序列）；
这里的映射并非一对一的，我们要用词袋模型（bag-of-words）把每个不重复的词用一个数字来表示。
与第一步的方法一样，让我们写一个将信息分割成单词的函数：
In [11]:
Python
def split_into_tokens(message): message = unicode(message, 'utf8') # convert bytes into proper unicode return TextBlob(message).words |
这还是原始文本的一部分:
In [12]:
Python
messages.message.head() |
Out[12]:
Python
0 Go until jurong point, crazy.. Available only ... 1 Ok lar... Joking wif u oni... 2 Free entry in 2 a wkly comp to win FA Cup fina... 3 U dun say so early hor... U c already then say... 4 Nah I don't think he goes to usf, he lives aro... Name: message, dtype: object |
这是原始文本处理后的样子：
In [13]:
Python
messages.message.head().apply(split_into_tokens) |
Out[13]:
Python
0 [Go, until, jurong, point, crazy, Available, o... 1 [Ok, lar, Joking, wif, u, oni] 2 [Free, entry, in, 2, a, wkly, comp, to, win, F... 3 [U, dun, say, so, early, hor, U, c, already, t... 4 [Nah, I, do, n't, think, he, goes, to, usf, he... Name: message, dtype: object |
自然语言处理（NLP）的问题：
大写字母是否携带信息？
单词的不同形式（“goes”和“go”）是否携带信息？
叹词和限定词是否携带信息？
换句话说，我们想对文本进行更好的标准化。
我们使用 textblob 获取 part-of-speech (POS) 标签：
In [14]:
Python
TextBlob("Hello world, how is it going?").tags # list of (word, POS) pairs |
Out[14]:
Python
[(u'Hello', u'UH'), (u'world', u'NN'), (u'how', u'WRB'), (u'is', u'VBZ'), (u'it', u'PRP'), (u'going', u'VBG')] |
并将单词标准化为基本形式 (lemmas)：
In [15]:
Python
def split_into_lemmas(message): message = unicode(message, 'utf8').lower() words = TextBlob(message).words # for each word, take its "base form" = lemma return [word.lemma for word in words]
messages.message.head().apply(split_into_lemmas) |
Out[15]:
0 [go, until, jurong, point, crazy, available, o... 1 [ok, lar, joking, wif, u, oni] 2 [free, entry, in, 2, a, wkly, comp, to, win, f... 3 [u, dun, say, so, early, hor, u, c, already, t... 4 [nah, i, do, n't, think, he, go, to, usf, he, ... Name: message, dtype: object |
这样就好多了。你也许还会想到更多的方法来改进预处理：解码 HTML 实体（我们上面看到的 & 和 <）；过滤掉停用词 (代词等)；添加更多特征，比如所有字母大写标识等等。
现在，我们将每条消息（词干列表）转换成机器学习模型可以理解的向量。
用词袋模型完成这项工作需要三个步骤：
1. 对每个词在每条信息中出现的次数进行计数（词频）；
2. 对计数进行加权，这样经常出现的单词将会获得较低的权重（逆向文件频率）；
3. 将向量由原始文本长度归一化到单位长度（L2 范式）。
每个向量的维度等于 SMS 语料库中包含的独立词的数量。
In [16]:
Python
bow_transformer = CountVectorizer(analyzer=split_into_lemmas).fit(messages['message']) print len(bow_transformer.vocabulary_) |
Python
8874 |
这里我们使用强大的 python 机器学习训练库 scikit-learn (sklearn)，它包含大量的方法和选项。
我们取一个信息并使用新的 bow_tramsformer 获取向量形式的词袋模型计数:
In [17]:
Python
message4 = messages['message'][3] print message4 |
Python
U dun say so early hor... U c already then say... |
In [18]:
Python
bow4 = bow_transformer.transform([message4]) print bow4 print bow4.shape |
Python
(0, 1158) 1 (0, 1899) 1 (0, 2897) 1 (0, 2927) 1 (0, 4021) 1 (0, 6736) 2 (0, 7111) 1 (0, 7698) 1 (0, 8013) 2 (1, 8874) |
message 4 中有 9 个独立词，它们中的两个出现了两次，其余的只出现了一次。可用性检测，哪些词出现了两次？
In [19]:
Python
print bow_transformer.get_feature_names()[6736] print bow_transformer.get_feature_names()[8013] |
Python
say u |
整个 SMS 语料库的词袋计数是一个庞大的稀疏矩阵：
In [20]:
Python
messages_bow = bow_transformer.transform(messages['message']) print 'sparse matrix shape:', messages_bow.shape print 'number of non-zeros:', messages_bow.nnz print 'sparsity: %.2f%%' % (100.0 * messages_bow.nnz / (messages_bow.shape[0] * messages_bow.shape[1])) |
Python
sparse matrix shape: (5574, 8874) number of non-zeros: 80272 sparsity: 0.16% |
最终，计数后，使用 scikit-learn 的 TFidfTransformer 实现的 TF-IDF 完成词语加权和归一化。
In [21]:
Python
tfidf_transformer = TfidfTransformer().fit(messages_bow) tfidf4 = tfidf_transformer.transform(bow4) print tfidf4 |
Python
(0, 8013) 0.305114653686 (0, 7698) 0.225299911221 (0, 7111) 0.191390347987 (0, 6736) 0.523371210191 (0, 4021) 0.456354991921 (0, 2927) 0.32967579251 (0, 2897) 0.303693312742 (0, 1899) 0.24664322833 (0, 1158) 0.274934159477 |
单词 “u” 的 IDF（逆向文件频率）是什么？单词“university”的 IDF 又是什么？
In [22]:
Python
print tfidf_transformer.idf_[bow_transformer.vocabulary_['u']] print tfidf_transformer.idf_[bow_transformer.vocabulary_['university']] |
Python
2.85068150539 8.23975323521 |
将整个 bag-of-words 语料库转化为 TF-IDF 语料库。
In [23]:
Python
messages_tfidf = tfidf_transformer.transform(messages_bow) print messages_tfidf.shape |
Python
(5574, 8874) |
有许多方法可以对数据进行预处理和向量化。这两个步骤也可以称为“特征工程”，它们通常是预测过程中最耗时间和最无趣的部分，但是它们非常重要并且需要经验。诀窍在于反复评估：分析模型误差，改进数据清洗和预处理方法，进行头脑风暴讨论新功能，评估等等。
我们使用向量形式的信息来训练 spam/ham 分类器。这部分很简单，有很多实现训练算法的库文件。
这里我们使用 scikit-learn，首先选择 Naive Bayes 分类器：
In [24]:
Python
%time spam_detector = MultinomialNB().fit(messages_tfidf, messages['label']) |
Python
CPU times: user 4.51 ms, sys: 987 ?s, total: 5.49 ms Wall time: 4.77 ms |
我们来试着分类一个随机信息：
In [25]:
Python
print 'predicted:', spam_detector.predict(tfidf4)[0] print 'expected:', messages.label[3] |
Python
predicted: ham expected: ham |
太棒了！你也可以用自己的文本试试。
有一个很自然的问题是：我们可以正确分辨多少信息？
In [26]:
Python
all_predictions = spam_detector.predict(messages_tfidf) print all_predictions |
Python
['ham' 'ham' 'spam' ..., 'ham' 'ham' 'ham'] |
In [27]:
Python
print 'accuracy', accuracy_score(messages['label'], all_predictions) print 'confusion matrixn', confusion_matrix(messages['label'], all_predictions) print '(row=expected, col=predicted)' |
Python
accuracy 0.969501255831 confusion matrix [[4827 0] [ 170 577]] (row=expected, col=predicted) |
In [28]:
Python
plt.matshow(confusion_matrix(messages['label'], all_predictions), cmap=plt.cm.binary, interpolation='nearest') plt.title('confusion matrix') plt.colorbar() plt.ylabel('expected label') plt.xlabel('predicted label') |
Out[28]:
Python
<matplotlib.text.Text at 0x11643f6d0> |
我们可以通过这个混淆矩阵计算精度（precision）和召回率（recall），或者它们的组合（调和平均值）F1：
In [29]:
Python
print classification_report(messages['label'], all_predictions) |
Python
precision recall f1-score support
ham 0.97 1.00 0.98 4827 spam 1.00 0.77 0.87 747
avg / total 0.97 0.97 0.97 5574 |
有相当多的指标都可以用来评估模型性能，至于哪个最合适是由任务决定的。比如，将“spam”错误预测为“ham”的成本远低于将“ham”错误预测为“spam”的成本。
在上述“评价”中，我们犯了个大忌。为了简单的演示，我们使用训练数据进行了准确性评估。永远不要评估你的训练数据。这是错误的。
这样的评估方法不能告诉我们模型的实际预测能力，如果我们记住训练期间的每个例子，训练的准确率将非常接近 100%，但是我们不能用它来分类任何新信息。
一个正确的做法是将数据分为训练集和测试集，在模型拟合和调参时只能使用训练数据，不能以任何方式使用测试数据，通过这个方法确保模型没有“作弊”，最终使用测试数据评价模型可以代表模型真正的预测性能。
In [30]:
Python
msg_train, msg_test, label_train, label_test = train_test_split(messages['message'], messages['label'], test_size=0.2)
print len(msg_train), len(msg_test), len(msg_train) + len(msg_test) |
Python
4459 1115 5574 |
按照要求，测试数据占整个数据集的 20%（总共 5574 条记录中的 1115 条），其余的是训练数据（5574 条中的 4459 条）。
让我们回顾整个流程，将所有步骤放入 scikit-learn 的 Pipeline 中:
In [31]:
Python
def split_into_lemmas(message): message = unicode(message, 'utf8').lower() words = TextBlob(message).words # for each word, take its "base form" = lemma return [word.lemma for word in words]
pipeline = Pipeline([ ('bow', CountVectorizer(analyzer=split_into_lemmas)), # strings to token integer counts ('tfidf', TfidfTransformer()), # integer counts to weighted TF-IDF scores ('classifier', MultinomialNB()), # train on TF-IDF vectors w/ Naive Bayes classifier ]) |
实际当中一个常见的做法是将训练集再次分割成更小的集合，例如，5 个大小相等的子集。然后我们用 4 个子集训练数据，用最后 1 个子集计算精度（称之为“验证集”）。重复5次（每次使用不同的子集进行验证），这样可以得到模型的“稳定性“。如果模型使用不同子集的得分差异非常大，那么很可能哪里出错了（坏数据或者不良的模型方差）。返回，分析错误，重新检查输入数据有效性，重新检查数据清洗。
在这个例子里，一切进展顺利：
In [32]:
Python
scores = cross_val_score(pipeline, # steps to convert raw messages into models msg_train, # training data label_train, # training labels cv=10, # split data randomly into 10 parts: 9 for training, 1 for scoring scoring='accuracy', # which scoring metric? n_jobs=-1, # -1 = use all cores = faster ) print scores |
Python
[ 0.93736018 0.96420582 0.94854586 0.94183445 0.96412556 0.94382022 0.94606742 0.96404494 0.94831461 0.94606742] |
得分确实比训练全部数据时差一点点（ 5574 个训练例子中，准确性 0.97），但是它们相当稳定：
In [33]:
Python
print scores.mean(), scores.std() |
Python
0.9504386476 0.00947200821389 |
我们自然会问，如何改进这个模型？这个得分已经很高了，但是我们通常如何改进模型呢？
Naive Bayes 是一个高偏差-低方差的分类器（简单且稳定，不易过度拟合）。与其相反的例子是低偏差-高方差（容易过度拟合）的 k 最临近（kNN）分类器和决策树。Bagging（随机森林）是一种通过训练许多（高方差）模型和求均值来降低方差的方法。
换句话说：
高偏差 = 分类器比较固执。它有自己的想法，数据能够改变的空间有限。另一方面，也没有多少过度拟合的空间（左图）。
低偏差 = 分类器更听话，但也更神经质。大家都知道，让它做什么就做什么可能造成麻烦（右图）。
In [34]:
Python
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)): """ Generate a simple plot of the test and traning learning curve.
Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation.
title : string Title for the chart.
X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression; None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional Defines minimum and maximum yvalues plotted.
cv : integer, cross-validation generator, optional If an integer is passed, it is the number of folds (defaults to 3). Specific cross-validation objects can be passed, see sklearn.cross_validation module for the list of possible objects
n_jobs : integer, optional Number of jobs to run in parallel (default 1). """ plt.figure() plt.title(title) if ylim is not None: plt.ylim(*ylim) plt.xlabel("Training examples") plt.ylabel("Score") train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score") plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
plt.legend(loc="best") return plt |
In [35]:
Python
%time plot_learning_curve(pipeline, "accuracy vs. training set size", msg_train, label_train, cv=5) |
Python
CPU times: user 382 ms, sys: 83.1 ms, total: 465 ms Wall time: 28.5 s |
Out[35]:
Python
<module 'matplotlib.pyplot' from '/Volumes/work/workspace/vew/sklearn_intro/lib/python2.7/site-packages/matplotlib/pyplot.pyc'> |
（我们对数据的 64% 进行了有效训练：保留 20% 的数据作为测试集，保留剩余的 20% 做 5 折交叉验证 = > 0.8*0.8*5574 = 3567个训练数据。）
随着性能的提升，训练和交叉验证都表现良好，我们发现由于数据量较少，这个模型难以足够复杂/灵活地捕获所有的细微差别。在这种特殊案例中，不管怎样做精度都很高，这个问题看起来不是很明显。
关于这一点，我们有两个选择：
使用更多的训练数据，增加模型的复杂性；
使用更复杂（更低偏差）的模型，从现有数据中获取更多信息。
在过去的几年里，随着收集大规模训练数据越来越容易，机器越来越快。方法 1 变得越来越流行（更简单的算法，更多的数据）。简单的算法（如 Naive Bayes）也有更容易解释的额外优势（相对一些更复杂的黑箱模型，如神经网络）。
了解了如何正确地评估模型，我们现在可以开始研究参数对性能有哪些影响。
到目前为止，我们看到的只是冰山一角，还有许多其它参数需要调整。比如使用什么算法进行训练。
上面我们已经使用了 Navie Bayes，但是 scikit-learn 支持许多分类器：支持向量机、最邻近算法、决策树、Ensamble 方法等…
我们会问：IDF 加权对准确性有什么影响？消耗额外成本进行词形还原（与只用纯文字相比）真的会有效果吗？
让我们来看看：
In [37]:
Python
params = { 'tfidf__use_idf': (True, False), 'bow__analyzer': (split_into_lemmas, split_into_tokens), }
grid = GridSearchCV( pipeline, # pipeline from above params, # parameters to tune via cross validation refit=True, # fit using all available data at the end, on the best found param combination n_jobs=-1, # number of cores to use for parallelization; -1 for "all cores" scoring='accuracy', # what score are we optimizing? cv=StratifiedKFold(label_train, n_folds=5), # what type of cross validation to use ) |
In [38]:
Python
%time nb_detector = grid.fit(msg_train, label_train)
print nb_detector.grid_scores_ |
Python
CPU times: user 4.09 s, sys: 291 ms, total: 4.38 s Wall time: 20.2 s [mean: 0.94752, std: 0.00357, params: {'tfidf__use_idf': True, 'bow__analyzer': <function split_into_lemmas at 0x1131e8668>}, mean: 0.92958, std: 0.00390, params: {'tfidf__use_idf': False, 'bow__analyzer': <function split_into_lemmas at 0x1131e8668>}, mean: 0.94528, std: 0.00259, params: {'tfidf__use_idf': True, 'bow__analyzer': <function split_into_tokens at 0x11270b7d0>}, mean: 0.92868, std: 0.00240, params: {'tfidf__use_idf': False, 'bow__analyzer': <function split_into_tokens at 0x11270b7d0>}] |
（首先显示最佳参数组合：在这个案例中是使用 idf=True 和 analyzer=split_into_lemmas 的参数组合）
快速合理性检查
In [39]:
Python
print nb_detector.predict_proba(["Hi mom, how are you?"])[0] print nb_detector.predict_proba(["WINNER! Credit for free!"])[0] |
Python
[ 0.99383955 0.00616045] [ 0.29663109 0.70336891] |
predict_proba 返回每类（ham，spam）的预测概率。在第一个例子中，消息被预测为 ham 的概率 >99%，被预测为 spam 的概率 <1%。如果进行选择模型会认为信息是 ”ham“：
In [40]:
Python
print nb_detector.predict(["Hi mom, how are you?"])[0] print nb_detector.predict(["WINNER! Credit for free!"])[0] |
Python
ham spam |
在训练期间没有用到的测试集的整体得分：
In [41]:
Python
predictions = nb_detector.predict(msg_test) print confusion_matrix(label_test, predictions) print classification_report(label_test, predictions) |
Python
[[973 0] [ 46 96]] precision recall f1-score support
ham 0.95 1.00 0.98 973 spam 1.00 0.68 0.81 142
avg / total 0.96 0.96 0.96 1115 |
这是我们使用词形还原、TF-IDF 和 Navie Bayes 分类器的 ham 检测 pipeline 获得的实际预测性能。
让我们尝试另一个分类器：支持向量机（SVM）。SVM 可以非常迅速的得到结果，它所需要的参数调整也很少（虽然比 Navie Bayes 稍多一点），在处理文本数据方面它是个好的起点。
In [42]:
Python
pipeline_svm = Pipeline([ ('bow', CountVectorizer(analyzer=split_into_lemmas)), ('tfidf', TfidfTransformer()), ('classifier', SVC()), # <== change here ])
# pipeline parameters to automatically explore and tune param_svm = [ {'classifier__C': [1, 10, 100, 1000], 'classifier__kernel': ['linear']}, {'classifier__C': [1, 10, 100, 1000], 'classifier__gamma': [0.001, 0.0001], 'classifier__kernel': ['rbf']}, ]
grid_svm = GridSearchCV( pipeline_svm, # pipeline from above param_grid=param_svm, # parameters to tune via cross validation refit=True, # fit using all data, on the best detected classifier n_jobs=-1, # number of cores to use for parallelization; -1 for "all cores" scoring='accuracy', # what score are we optimizing? cv=StratifiedKFold(label_train, n_folds=5), # what type of cross validation to use ) |
In [43]:
Python
%time svm_detector = grid_svm.fit(msg_train, label_train) # find the best combination from param_svm
print svm_detector.grid_scores_ |
Python
CPU times: user 5.24 s, sys: 170 ms, total: 5.41 s Wall time: 1min 8s [mean: 0.98677, std: 0.00259, params: {'classifier__kernel': 'linear', 'classifier__C': 1}, mean: 0.98654, std: 0.00100, params: {'classifier__kernel': 'linear', 'classifier__C': 10}, mean: 0.98654, std: 0.00100, params: {'classifier__kernel': 'linear', 'classifier__C': 100}, mean: 0.98654, std: 0.00100, params: {'classifier__kernel': 'linear', 'classifier__C': 1000}, mean: 0.86432, std: 0.00006, params: {'classifier__gamma': 0.001, 'classifier__kernel': 'rbf', 'classifier__C': 1}, mean: 0.86432, std: 0.00006, params: {'classifier__gamma': 0.0001, 'classifier__kernel': 'rbf', 'classifier__C': 1}, mean: 0.86432, std: 0.00006, params: {'classifier__gamma': 0.001, 'classifier__kernel': 'rbf', 'classifier__C': 10}, mean: 0.86432, std: 0.00006, params: {'classifier__gamma': 0.0001, 'classifier__kernel': 'rbf', 'classifier__C': 10}, mean: 0.97040, std: 0.00587, params: {'classifier__gamma': 0.001, 'classifier__kernel': 'rbf', 'classifier__C': 100}, mean: 0.86432, std: 0.00006, params: {'classifier__gamma': 0.0001, 'classifier__kernel': 'rbf', 'classifier__C': 100}, mean: 0.98722, std: 0.00280, params: {'classifier__gamma': 0.001, 'classifier__kernel': 'rbf', 'classifier__C': 1000}, mean: 0.97040, std: 0.00587, params: {'classifier__gamma': 0.0001, 'classifier__kernel': 'rbf', 'classifier__C': 1000}] |
因此，很明显的，具有 C=1 的线性核函数是最好的参数组合。
再一次合理性检查：
In [44]:
Python
print svm_detector.predict(["Hi mom, how are you?"])[0] print svm_detector.predict(["WINNER! Credit for free!"])[0] |
Python
ham spam |
In [45]:
Python
print confusion_matrix(label_test, svm_detector.predict(msg_test)) print classification_report(label_test, svm_detector.predict(msg_test)) |
Python
[[965 8] [ 13 129]] precision recall f1-score support
ham 0.99 0.99 0.99 973 spam 0.94 0.91 0.92 142
avg / total 0.98 0.98 0.98 1115 |
这是我们使用 SVM 时可以从 spam 邮件检测流程中获得的实际预测性能。
经过基本分析和调优，真正的工作（工程）开始了。
生成预测器的最后一步是再次对整个数据集合进行训练，以充分利用所有可用数据。当然，我们将使用上面交叉验证找到的最好的参数。这与我们开始做的非常相似，但这次深入了解它的行为和稳定性。在不同的训练/测试子集进行评价。
最终的预测器可以序列化到磁盘，以便我们下次想使用它时，可以跳过所有训练直接使用训练好的模型：
In [46]:
Python
# store the spam detector to disk after training with open('sms_spam_detector.pkl', 'wb') as fout: cPickle.dump(svm_detector, fout)
# ...and load it back, whenever needed, possibly on a different machine svm_detector_reloaded = cPickle.load(open('sms_spam_detector.pkl')) |
加载的结果是一个与原始对象表现相同的对象：
In [47]:
Python
print 'before:', svm_detector.predict([message4])[0] print 'after:', svm_detector_reloaded.predict([message4])[0] |
Python
before: ham after: ham |
生产执行的另一个重要部分是性能。经过快速、迭代模型调整和参数搜索之后，性能良好的模型可以被翻译成不同的语言并优化。可以牺牲几个点的准确性换取一个更小、更快的模型吗？是否值得优化内存使用情况，或者使用 mmap 跨进程共享内存？
请注意，优化并不总是必要的，要从实际情况出发。
还有一些需要考虑的问题，比如，生产流水线还需要考虑鲁棒性（服务故障转移、冗余、负载平衡）、监测（包括异常自动报警）、HR 可替代性（避免关于工作如何完成的“知识孤岛”、晦涩/锁定的技术、调整结果的黑艺术）。现在，开源世界都可以为所有这些领域提供可行的解决方法，由于 OSI 批准的开源许可证，今天展示的所有工具都可以免费用于商业用途。
上述就是小编为大家分享的Python中怎么实现数据挖掘了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注亿速云行业资讯频道。
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