聊天软交互原理

by Déborah Mesquita

由DéborahMesquita

来自不同城市的人们如何在freeCodeCamp聊天室中进行交互 (How people from different cities interact in the freeCodeCamp chatrooms)

推理统计入门以及如何使用spaCy从文本中提取信息 (A primer on Inferential statistics and how to extract information from text using spaCy)

In Data Science we usually talk a lot about Exploratory Data Analysis (Descriptive Statistics), but there is another “statistical world” that can also be very useful: the world of Inferential Statistics.

在数据科学中，我们通常谈论探索性数据分析 (描述性统计)，但是还有另一个“统计世界”也可能非常有用： 推论统计 。

Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. — Statistical Inference

推论统计分析可推断人口的属性，例如，通过检验假设并推导出估计。 描述性统计数据仅与观察到的数据的属性有关，并不基于数据来自更大的总体这一假设。 — 统计推断

In this article we’ll use the gitter-history dataset from freeCodeCamp open data to answer this question: is there a different mention pattern in the chat-rooms from different cities?

在本文中，我们将使用来自freeCodeCamp开放数据的gitter-history数据集来回答此问题： 来自不同城市的聊天室中是否存在不同的提及模式？

We’ll learn about inferential statistics and also learn how to extract information from text using the Matcher class from spaCy. First let’s extract the data, then we’ll get our hands dirty with statistics (hey, it’s fun! you’ll see).

我们将学习推论统计，还将学习如何使用spaCy的Matcher类从文本中提取信息。 首先，让我们提取数据，然后使用统计数据来处理问题(嘿，这很有趣！您会看到的)。

使用spacy.Matcher提取信息 (Extracting information with spacy.Matcher)

The way we use the Matcher is very similar to the way we use regular expressions (in fact we can use regex to create patterns). Each rule can have many patterns, and a pattern consists of a list of dicts, where each dict describes a token.

我们使用Matcher的方式与我们使用正则表达式的方式非常相似(实际上，我们可以使用正则表达式来创建模式)。 每个规则可以具有许多模式，并且一个模式由一系列字典组成，其中每个字典描述一个标记。

// this pattern matches all tokens == 'hello' (lowercase){'LOWER': 'hello'}

Let’s create some examples of things we can extract from the messages.

让我们创建一些我们可以从消息中提取的内容的示例。

问候 (Greetings)

Here we have 4 patterns for the same rule ("GREETINGS"):

在这里，我们为相同的规则提供了4种模式( "GREETINGS" )：

matcher = Matcher(nlp.vocab)

self.matcher.add("GREETINGS", None,                 [{"LOWER": "good"}, {"LOWER": "morning"}],                   [{"LOWER": "good"}, {"LOWER": "evening"}],                     [{"LOWER": "good"}, {"LOWER": "afternoon"}],                     [{"LOWER": "good"}, {"LOWER": "night"}])

matches = matcher(text)

标点符号的邮件 (Messages with punctuation)

We can use all the available token attributes as patterns. Let’s see if a message has a punctuation token.

我们可以将所有可用的令牌属性用作模式。 让我们看看消息中是否包含标点符号。

matcher = Matcher(nlp.vocab)

self.matcher.add("PUNCT", None,                 [{"IS_PUNCT": True}])

matches = matcher(text)

人们的感觉如何？ (What are people feeling?)

Here things get a little more interesting. We’ll match the lemma of the verb be to detect all the conjugations of the verb. The matcher also lets you use quantifiers, specified as the 'OP' key. We’ll match all the adverb tokens after the verb be (with 'OP': '*' we can match any and all of them).

在这里，事情变得更加有趣。 我们将匹配动词be的引理 ，以检测该动词的所有词缀。 匹配器还允许您使用指定为'OP'键的量词。 我们将在动词be之后匹配所有副词标记(使用'OP': '*'我们可以匹配所有它们)。

After that there is a lot of possibility for the two following words, so we’ll use the wildcard token {} to match them.

之后，后面的两个词很有可能，因此我们将使用通配符{}进行匹配。

matcher = Matcher(nlp.vocab)

self.matcher.add("FEELING", None,                 [                 {"LOWER": "i"}, {"LEMMA":"be"},                    {"POS": "ADV", "OP": "*"},                     {"POS": "ADJ"}                 ])

matches = matcher(text)

提及 (Mentions)

There is not a token attribute to @some_token, so let’s create one.

@some_token没有令牌属性，因此让我们创建一个。

mention_flag = lambda text: bool(re.compile(r'\@(\w+)').match(text))

IS_MENTION = nlp.vocab.add_flag(mention_flag)

self.matcher.add("MENTION", None, [{IS_MENTION: True}])

matches = matcher(text)

I built a dataset with mentions for the rest of the article.

在本文的其余部分，我用提及建立了一个数据集。

[menssage, mention, sent_at, city]

You can find all the code here.

您可以在此处找到所有代码。

推论统计入门 (A primer on Inferential statistics)

Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution (Statistical inference).

统计推断是使用数据分析来推断潜在概率分布的属性的过程( 统计推断 )。

We have samples and we want to compare them. With Test Statistic we can measure the probability of they coming from the same distribution or not. Applying this to our scenario, if the probability of the mentions coming from the same distribution is below a threshold (defined by us) then we’ll be able to infer that people from different cities have different mention patterns.

我们有样本，我们想比较它们。 通过测试统计，我们可以衡量它们是否来自相同分布的可能性 。 将其应用于我们的情况， 如果来自同一分布的提及的可能性低于阈值 (由我们定义)，那么我们将能够推断出来自不同城市的人们具有不同的提及模式 。

Let’s define some concepts to clarify things (all the definitions are taken from Wikipedia):

让我们定义一些概念来澄清事物(所有定义均来自Wikipedia)：

• Frequency distribution: a list, table or graph that displays the frequency of various outcomes in a sample

  频率分布 ：列表，表格或图表，显示样本中各种结果的频率

• Null hypothesis: a general statement or default position that there is no relationship between two measured phenomena, or no association among groups

  零假设 ：一般的陈述或默认位置，即两个测得的现象之间没有关系 ，或者组之间没有关联

• p-value: the probability, when the null hypothesis is true, of obtaining a result equal to or more extreme than what was actually observed. The smaller the p-value, the higher the significance because it tells the investigator that the hypothesis under consideration may not adequately explain the observation

  p值 ：当零假设成立时，获得等于或大于实际观察到的结果的可能性。 p值越小，意义越高，因为它告诉研究者所考虑的假设可能不足以解释观察结果。

• Statistical significance: something is statistically significant if it allows us to reject the null hypothesis

  统计显着性 ：如果某种事物允许我们拒绝原假设，那么它在统计上是有意义的

One thing to keep in mind while dealing with statistical hypothesis tests is that it goes like this:

在处理统计假设检验时要记住的一件事是，它像这样：

1. We assume something is true
   我们假设某事是真的
2. Then we try to prove that it’s impossible that it can be true
   然后我们尝试证明不可能是真的
3. Then when we see that indeed, this probably can’t be true for the results we got, we reject the claim
   然后，当我们看到确实如此时，对于我们得到的结果可能就不正确了，我们拒绝了该主张。

“Null hypothesis testing is a reductio ad absurdum argument adapted to statistics. In essence, a claim is assumed valid if its counter-claim is improbable”. — P-value

“ 零假设检验是一种适用于统计的还原性荒谬论据。 从本质上讲，如果反请求是不可能的，则认为该请求有效 。 — P值

In our case we are dealing with categorical variables (a variable that can take on one of a limited, and usually fixed number of possible values). Because of that, we’ll use the Chi-squared distribution.

在我们的案例中，我们正在处理分类变量(可以采用有限且通常为固定数量的可能值之一的变量)。 因此，我们将使用卡方分布。

In probability theory and statistics, the chi-squared distribution (also chi-square or χ2-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. It’s one of the most widely used probability distributions in inferential statistics, notably in hypothesis testing or in construction of confidence intervals. — Chi-squared distribution

在概率论和统计学中，具有k个自由度的卡方分布 (也称为卡方或χ2分布 )是k个独立标准正态随机变量的平方和的分布。 它是推论统计中使用最广泛的概率分布之一，特别是在假设检验或构造置信区间中。 — 卡方分布

“Statisticians have identified several common distributions, known as probability distributions. From these distributions it is possible to calculate the probability of getting particular scores based on the frequencies with which a particular score occurs in a distribution with these common shapes.” — Discovering Statistics Using R

统计人员确定了几种常见的分布，称为概率分布。 从这些分布中，可以根据具有这些共同形状的分布中特定分数出现的频率，计算获得特定分数的概率。” — 使用R查找统计信息

了解卡方检验的同质性 (Understanding the chi-square test for homogeneity)

We want to know if the mention distribution is the same for each city. First we assume that they indeed come from the same population, then we get all the messages from each city and sum them up. This distribution (all the messages together) should be the same for each city if we assume they come from the same population.

我们想知道每个城市的提及分布是否相同。 首先，我们假设它们确实来自同一人口，然后我们从每个城市获取所有消息并进行汇总。 如果我们假设每个城市都来自同一人口，则每个城市的分布(所有消息加在一起)应该相同。

We cannot prove that the distributions are different using statistics, but we can reject that they are the same.

我们不能使用统计数据证明分布是不同的，但是我们可以拒绝它们是相同的 。

“The reason that we need the null hypothesis is because we cannot prove the experimental hypothesis using statistics, but we can reject the null hypothesis. If our data give us confidence to reject the null hypothesis then this provides support for our experimental hypothesis. However, be aware that even if we can reject the null hypothesis, this doesn’t prove the experimental hypothesis — it merely supports it.” — Discovering Statistics Using R

“我们需要原假设的原因是因为我们无法使用统计数据证明实验假设，但是我们可以拒绝原假设。 如果我们的数据使我们有信心拒绝原假设，那么这为我们的实验假设提供了支持。 但是，请注意，即使我们可以拒绝原假设， 也不能证明实验假设-它只是支持该假设。 ” — 使用R发现统计信息

This is very important. We are not proving that the experimental (or alternative) hypothesis is true. We are saying that at a given significance level it’s likely that it’s true.

这个非常重要。 我们没有证明实验(或替代)假设是正确的。 我们说的是， 在给定的重要性水平下，这很可能是真的 。

“So, rather than talking about accepting or rejecting a hypothesis (which some textbooks tell you to do) we should be talking about ‘the chances of obtaining the data we’ve collected assuming that the null hypothesis is true’.” — Discovering Statistics Using R

“因此，与其说接受或拒绝一个假设(有些教科书告诉你这样做)，不如说是'假设零假设是真实的，'获得我们收集的数据的机会'。” — 使用R查找统计信息

In essence, when we collect data to test theories we can only talk in terms of the probability of obtaining a particular set of data (Field, Andy). And to judge that we use the p-values.

本质上，当我们收集数据以检验理论时，我们只能谈论获得特定数据集(Field，Andy) 的可能性 。 并判断我们使用p值。

• High p-values: your data are likely with a true null

  高p值 ：您的数据可能为true

• Low p-values: your data are unlikely with a true null, (How to Correctly Interpret P Values)

  低p值 ：您的数据不太可能带有真正的null，( 如何正确解释P值 )

We’ll set our significance level to 5% (p-value threshold of 0.05).

我们将显着性水平设置为5％( p值阈值为0.05)。

Ok, now back to the test.

好的，现在返回测试。

数据 (The data)

We’ll use the dataset of all chat activity in freeCodeCamp’s Gitter chatrooms. This dataset can be found here.

我们将在freeCodeCamp的Gitter聊天室中使用所有聊天活动的数据集。 该数据集可在此处找到。

Our sample has all messages from the San Francisco, Toronto, Boston, Belgrade, London and Sao Paulo sent between 2015–08–16 and 2016–08–16 (one year of messages).

我们的样本包含2015年8月16日至2016年8月16日之间从旧金山，多伦多，波士顿，贝尔格莱德，伦敦和圣保罗发送的所有邮件(一年的邮件)。

进行卡方检验同质性的条件 (Conditions for conducting the chi-square test for homogeneity)

To use the chi-square test we need to meet some conditions:

要使用卡方检验，我们需要满足一些条件：

1. For each population, the sampling method is simple random sampling
   对于每个人口，抽样方法都是简单的随机抽样
2. All of the expected counts are 5 or greater
   所有预期计数均为5或更大

We’ll assume the first condition is met (1 year of data from each city). Let’s find out if the second condition is met.

我们假设满足第一个条件(每个城市的一年数据)。 让我们找出是否满足第二个条件。

探索数据 (Exploring the data)

Since we are sipping at the waters of statistics let’s use R instead of Python.

由于我们在统计领域statistics之以鼻，因此我们使用R代替Python。

I created a JSON file and we’ll load it into a dataframe using the jsonlite library. To see the contents we’ll use the tally function.

我创建了一个JSON文件，我们将使用jsonlite库将其加载到数据帧中。 要查看内容，我们将使用提示功能。

> library(jsonlite)

> df <- fromJSON("experiment_sample_data.json")

> library(mosaic)

> mentiontable <- tally(~city+mention, data=df, margins=T)> mentiontable              mentioncity            NO YES  Belgrade     184  45  Boston       383 121  London       278  98  SanFrancisco 156  51  SaoPaulo     153 132  Toronto      379  81

Now is the time to introduce the contingency tables.

现在该介绍列联表。

• Contingency tables: in statistics, a contingency table (also known as a cross tabulation or crosstab) is a type of table in a matrix format that displays the (multivariate) frequency distribution of the variables.

  列联表 ：列联表在统计数据中(也称为交叉表或交叉表)是以矩阵格式显示变量的(多变量)频率分布的一种表。

With chisq.test we can perform chi-squared contingency table tests and goodness-of-fit tests. Let’s calculate the expected counts for this sample.

使用chisq.test我们可以执行卡方列联表测试和拟合优度测试。 让我们计算该样本的预期计数。

Expected outcome = (sum of data in that row)×(sum of data in that column) / total data.

预期结果=(该行中数据的总和)×(该列中数据的总和)/总数据。

So the expected number of messages with mentions (mention=YES) for the city of Sao Paulo is:

因此，对于圣保罗市，带有提及(mention = YES)的消息的预期数量为：

285*407/1557 = 74,49903

285 * 407/1557 = 74,49903

The expected value of chisq.test gives the expected counts under the null hypothesis for all the cities:

该expected值chisq.test给出了零假设所有的城市下的预期计数：

> chisq.test(mentiontable)$expected               mentioncity                 NO       YES  Belgrade     170.3333  58.66667  Boston       374.8821 129.11790  London       279.6739  96.32606  SanFrancisco 153.9694  53.03057  SaoPaulo     211.9869  73.01310  Toronto      342.1543 117.84571

The expected counts are all greater than 5, so we can perform the test.

预期计数均大于5，因此我们可以执行测试。

执行卡方检验 (Performing the chi-square test)

We’ll assume the distributions are the same, so the total column is the best estimate of what this distribution should be:

我们假设分布是相同的，因此total列是对该分布应为最佳的估计：

> tally(~mention, data=df)mention  NO  YES 1150  407

The chi-squared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies.

卡方检验用于确定预期频率和观测频率之间是否存在显着差异。

For each cell, the expected frequency is subtracted from the observed frequency, the difference is squared, and the total is divided by the expected frequency. The values are then summed across all cells. This sum is the chi-square test statistic — The chi-square test

对于每个单元， 从观察到的频率中减去期望频率，对差值进行平方，然后将总数除以期望频率 。 然后将所有单元格的值相加。 该和是卡方检验统计量— 卡方检验

With the value from the chi-square test and with the value for the degrees of freedom (number_if_rows -1 × number_of_columns -1) we can calculate the probability of getting the results by chance or not.

使用卡方检验的值和自由度的值(number_if_rows -1×number_of_columns -1)，我们可以计算是否偶然获得结果的概率。

> chisq.test(mentiontable)        Pearson's Chi-squared test

data:  mentiontableX-squared = 84.667, df = 5, p-value < 2.2e-16

The p-value is lower than the alpha value (0.05), so we will reject the null hypothesis. This means that with these results for each city, it is unlikely that all the cities have the same distribution of mentions.

p值低于alpha值(0.05)，因此我们将拒绝原假设 。 这意味着，根据每个城市的这些结果，不太可能所有城市都具有相同的提及分布。

We can also examine the source of differences of the test.

我们还可以检查差异的来源。

检查残差作为差异来源 (Examining residuals for the source of differences)

We have the expected values for each city, so it’s possible to see the residuals: (observed - expected) / sqrt(expected)

我们具有每个城市的期望值，因此可以看到残差：( (observed - expected) / sqrt(expected)

The standardized residual, provides a measure of deviation of the observed from expected which retains the direction of deviation (whether observed was more or less than expected is interesting for interpretations) for each cell in the table. It is scaled much like a standard normal distribution providing a scale for “large” deviations for absolute values that are over 2 or 3. — Intermediate Statistics with R

标准化残差提供了观测值与预期值之间的偏差的量度，该值保留了表中每个单元格的偏差方向(对于解释而言，观测到的值是大于还是小于预期是有意义的)。 它的缩放比例很像标准正态分布，为大于2或3的绝对值提供了“大”偏差的标度。—带R的中间统计量

mosaicplot(mentiontable, shade=T)

For São Paulo and Toronto the number of messages with mention and NO mention appear to be more than 2.4 standard deviations away from the expected values. The São Paulo chat-room has more people mentioning other people than expected, and for Toronto there are less people mentioning other people than expected.

对于圣保罗和多伦多，带有提及和未提及消息的数量似乎比预期值多2.4个标准差。 圣保罗聊天室有更多的人提其他人于预期，多伦多也有少人提及其他人超过预期。

That’s interesting. A next step would be to explore the sources of these differences. Maybe it’s because of the number of people in each chat-room? Or maybe they already know each other, so they have more one on one conversations?

那很有意思。 下一步将是探索这些差异的根源。 也许是因为每个聊天室中的人数？ 也许他们已经彼此认识，所以他们有更多的一对一对话？

结论 (In conclusion)

In Inferential Statistics we deduce properties of an underlying probability distribution. When you have a categorical variable you can use the chi-square test to find the probability of the distribution being the same for two or more populations (or subgroups of a population).

在推论统计中，我们推导了潜在概率分布的属性。 如果具有分类变量，则可以使用卡方检验来发现两个或多个总体(或总体的子组)的分布概率相同。

And the steps to use a statistical hypothesis test are:

使用统计假设检验的步骤是：

1. First assume the null hypothesis is true
   首先假设原假设为真
2. Then try to prove that it’s impossible that it can be true
   然后尝试证明不可能是真的
3. Then if we see that indeed, this probably can’t be true for the results we got, we reject the null hypothesis (or otherwise we fail to reject and accept that the data supports the experimental hypothesis)
   然后，如果我们发现确实如此，那么对于我们得到的结果可能就不正确了，我们将拒绝原假设(否则，我们将无法拒绝并接受数据支持实验假设)

Besides that, we also saw that Spacy.Matcher is a great way to extract information from text. Here we did the experiment with mentions from each message but the code has other extracted patterns we could explore.

除此之外，我们还看到Spacy.Matcher是从文本中提取信息的好方法。 在这里，我们通过对每条消息的提及进行了实验，但是代码中还有我们可以探索的其他提取模式。

And that’s it! Thanks for reading!

就是这样！ 谢谢阅读！

翻译自: https://www.freecodecamp.org/news/how-people-from-different-cities-interact-in-the-freecodecamp-chatrooms-a22378571790/

聊天软交互原理