lime 模型

Article outline

文章大綱

• Introduction
  介紹
• Data Background
  資料背景
• Aim of the article
  本文的目的
• Exploratory analysis
  探索性分析
• Training a Random Forest Model
  訓(xùn)練隨機(jī)森林模型
• Global Importance
  全球重要性
• Local Importance
  當(dāng)?shù)刂匾?

介紹 (Introduction)

In the supervised machine learning world, there are two types of algorithmic task often performed. One is called regression (predicting continuous values) and the other is called classification (predicting discrete values). Black box algorithms such as SVM, random forest, boosted trees, neural networks provide better prediction accuracy than conventional algorithms. The problem starts when we want to understand the impact (magnitude and direction) of different variables. In this article, I have presented an example of Random Forest binary classification algorithm and its interpretation at the global and local level using Local Interpretable Model-agnostic Explanations (LIME).

在有監(jiān)督的機(jī)器學(xué)習(xí)世界中，經(jīng)常執(zhí)行兩種類型的算法任務(wù)。 一種稱為回歸(預(yù)測連續(xù)值)，另一種稱為分類(預(yù)測離散值)。 與傳統(tǒng)算法相比，諸如SVM，隨機(jī)森林，增強(qiáng)樹，神經(jīng)網(wǎng)絡(luò)之類的黑匣子算法提供了更好的預(yù)測精度。 當(dāng)我們想了解不同變量的影響(大小和方向)時(shí)，問題就開始了。 在本文中，我提供了一個(gè)使用本地可解釋模型不可知解釋(LIME)在全球和本地級(jí)別進(jìn)行隨機(jī)森林二進(jìn)制分類算法及其解釋的示例。

資料背景 (Data Background)

In this example, we are going to use the Pima Indian Diabetes 2 data set obtained from the UCI Repository of machine learning databases (Newman et al. 1998).

在本示例中，我們將使用從機(jī)器學(xué)習(xí)數(shù)據(jù)庫的UCI存儲(chǔ)庫中獲得的Pima Indian Diabetes 2數(shù)據(jù)集( Newman等，1998 )。

This data set is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the data set is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the data set. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

該數(shù)據(jù)集最初來自美國國立糖尿病與消化與腎臟疾病研究所。 數(shù)據(jù)集的目的是根據(jù)數(shù)據(jù)集中包含的某些診斷測量值來診斷性預(yù)測患者是否患有糖尿病。 從較大的數(shù)據(jù)庫中選擇這些實(shí)例受到一些限制。 特別是，這里的所有患者均為皮馬印第安人血統(tǒng)至少21歲的女性。

The Pima Indian Diabetes 2 data set is the refined version (all missing values were assigned as NA) of the Pima Indian diabetes data. The data set contains the following independent and dependent variables.

Pima印度糖尿病2數(shù)據(jù)集是Pima印度糖尿病數(shù)據(jù)的精煉版本(所有缺失值均指定為NA)。 數(shù)據(jù)集包含以下獨(dú)立變量和因變量。

Independent variables (symbol: I)

自變量(符號(hào)：I)

• I1: pregnant: Number of times pregnant

  I1： 懷孕 ：懷孕次數(shù)

• I2: glucose: Plasma glucose concentration (glucose tolerance test)

  I2： 葡萄糖 ：血漿葡萄糖濃度(葡萄糖耐量試驗(yàn))

• I3: pressure: Diastolic blood pressure (mm Hg)

  I3： 壓力 ：舒張壓(毫米汞柱)

• I4: triceps: Triceps skin fold thickness (mm)

  I4： 三頭肌 ：三頭肌的皮膚折疊厚度(毫米)

• I5: insulin: 2-Hour serum insulin (mu U/ml)

  I5： 胰島素 ：2小時(shí)血清胰島素(mu U / ml)

• I6: mass: Body mass index (weight in kg/(height in m)\2)

  I6： 質(zhì)量 ：體重指數(shù)(重量，單位：kg /(身高，單位：m)\2)

• I7: pedigree: Diabetes pedigree function

  I7： 譜系 ：糖尿病譜系功能

• I8: age: Age (years)

  I8： 年齡 ：年齡(年)

Dependent Variable (symbol: D)

因變量(符號(hào)：D)

• D1: diabetes: diabetes case (pos/neg)

  D1： 糖尿病 ：糖尿病病例(正/負(fù))

建模目的 (Aim of the Modelling)

• fitting a random forest ensemble binary classification model that accurately predicts whether or not the patients in the data set have diabetes
  擬合隨機(jī)森林綜合二元分類模型，該模型可準(zhǔn)確預(yù)測數(shù)據(jù)集中的患者是否患有糖尿病
• understanding the global influence of variables on diabetes prediction
  了解變量對(duì)糖尿病預(yù)測的全球影響
• understanding the influence of variables on the local level for the individual patient
  了解變量對(duì)個(gè)體患者局部水平的影響

加載庫 (Loading Libraries)

The very first step will be to load relevant libraries.

第一步將是加載相關(guān)的庫。

import pandas as pd # data mnipulation
import numpy as np # number manipulation/crunching
import matplotlib.pyplot as plt # plotting# Classification report
from sklearn.metrics import classification_report # Train Test split
from sklearn.model_selection import train_test_split# Random forest classifier
from sklearn.ensemble import RandomForestClassifier

Reading dataset

讀取數(shù)據(jù)集

After data loading, the next essential step is to perform an exploratory data analysis which helps in data familiarization. Use the head( ) function to view the top five rows of the data.

數(shù)據(jù)加載后，下一個(gè)基本步驟是執(zhí)行探索性數(shù)據(jù)分析，這有助于數(shù)據(jù)熟悉。 使用head()函數(shù)查看數(shù)據(jù)的前五行。

diabetes = pd.read_csv("diabetes.csv")
diabetes.head()First five observations前五個(gè)觀察

The below table showed that the diabetes data set includes 392 observations and 9 columns/variables. The independent variables include integer 64 and float 64 data types, whereas dependent/response (diabetes) variable is of string (neg/pos) data type also known as an object.

下表顯示糖尿病數(shù)據(jù)集包括392個(gè)觀察值和9列/變量 。 自變量包括整數(shù)64和浮點(diǎn)64數(shù)據(jù)類型，而因變量/響應(yīng)(糖尿病)變量為字符串(neg / pos)數(shù)據(jù)類型，也稱為對(duì)象 。

Let's print the column names

讓我們打印列名

diabetes.columnsColumn names列名

將輸出變量映射到0和1 (Mapping output variable into 0 and 1)

Before proceeding to model fitting, it is often essential to ensure that the data type is consistent with the library/package that you are going to use. In diabetes, data set the dependent variable (diabetes) consists of strings/characters i.e., neg/pos, which need to be converted into integers by mapping neg: 0 and pos: 1 using the .map( ) method.

在進(jìn)行模型擬合之前，通常必須確保數(shù)據(jù)類型與要使用的庫/包一致。 在糖尿病中，數(shù)據(jù)集因變量(糖尿病)由字符串/字符(即neg / pos )組成，需要使用.map()方法通過映射neg：0和pos：1將其轉(zhuǎn)換為整數(shù)。

diabetes["diabetes"] = diabetes["diabetes"].map({"neg":0, "pos":1})diabetes["diabetes"].value_counts()Output class label counts輸出類別標(biāo)簽計(jì)數(shù)

Now you can see that the dependent variable “diabetes” is converted from object to an integer 64 type.

現(xiàn)在您可以看到因變量“ diabetes ”從對(duì)象轉(zhuǎn)換為整數(shù)64類型。

The next step is to gaining knowledge about basic data summary statistics using .describe( ) method, which computes count, mean, standard deviation, minimum, maximum and percentile (25th, 50th and 75th) values. This helps you to detect any anomaly in your dataset. Such as variables with high variance or extremely skewed data.

下一步是使用.describe()方法獲取有關(guān)基本數(shù)據(jù)摘要統(tǒng)計(jì)信息的知識(shí)，該方法計(jì)算計(jì)數(shù)，均值，標(biāo)準(zhǔn)差，最小值，最大值和百分位數(shù)(第25、50和75位)。 這可以幫助您檢測數(shù)據(jù)集中的任何異常。 例如具有高方差的變量或數(shù)據(jù)嚴(yán)重偏斜。

訓(xùn)練射頻模型 (Training RF Model)

The next step is splitting the diabetes data set into train and test split using train_test_split of sklearn.model_selection module and fitting a random forest model using the sklearn package/library.

下一步是分裂糖尿病數(shù)據(jù)集到列車和使用sklearn.model_selection模塊的train_test_split以及使用該sklearn包/庫擬合隨機(jī)森林模型試驗(yàn)分裂。

訓(xùn)練和測試拆分 (Train and Test Split)

The whole data set generally split into 80% train and 20% test data set (general rule of thumb). The 80% train data is being used for model training, while the rest 20% could be used for model generalized and local model interpretation.

整個(gè)數(shù)據(jù)集通常分為80％的訓(xùn)練數(shù)據(jù)集和20％的測試數(shù)據(jù)集(一般經(jīng)驗(yàn)法則)。 80％的訓(xùn)練數(shù)據(jù)用于模型訓(xùn)練，而其余20％的數(shù)據(jù)可用于模型概括和局部模型解釋。

Y = diabetes['diabetes']
X = diabetes[['pregnant', 'glucose', 'pressure', 'triceps', 'insulin', 'mass',
'pedigree', 'age']]X_featurenames = X.columns# Split the data into train and test data:
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)

In order to fit a Random Forest model, first, you need to install sklearn package/library and then you need to import RandomForestClassifier from sklearn.ensemble. Here, have fitted around 10000 trees with a max depth of 20.

為了適應(yīng)隨機(jī)森林模型，首先，您需要安裝sklearn包/庫，那么你需要從sklearn.ensemble進(jìn)口RandomForestClassifier。 這里， 已安裝約10000棵樹，最大深度為20。

# Build the model with the random forest regression algorithm:
model = RandomForestClassifier(max_depth = 20, random_state = 0, n_estimators = 10000)
model.fit(X_train, Y_train)

分類報(bào)告 (Classification Report)

Let’s predict the test data class labels using predict( ) and generate a classification report. The classification report revealed that the micro average of F1 score (used for unbalanced data) is about 0.71, which indicates that the trained model has a classification strength of 71%.

讓我們使用predict()預(yù)測測試數(shù)據(jù)類標(biāo)簽并生成分類報(bào)告。 分類報(bào)告顯示，F1評(píng)分的微觀平均值(用于不平衡數(shù)據(jù))約為0.71，這表明訓(xùn)練后的模型的分類強(qiáng)度為71％。

y_pred = model.predict(X_test)print(classification_report(Y_test, y_pred, target_names=["Diabetes -ve", "Diabetes +ve"]))Classification report分類報(bào)告

特征重要性圖 (Feature Importance Plot)

The advantage of tree-based algorithms is that it provides global variable importance, which means you can rank them based on their contribution to the model. Here, you can observe that the glucose variable has the highest influence in the model, followed by Insulin. The problem with global importance is that it gives an average overview of variable contributing to the model.

基于樹的算法的優(yōu)勢在于它提供了全局變量重要性，這意味著您可以根據(jù)它們對(duì)模型的貢獻(xiàn)對(duì)其進(jìn)行排名。 在這里，您可以觀察到葡萄糖變量在模型中影響最大，其次是胰島素 。 具有全球重要性的問題是，它給出了有助于模型的變量的平均概圖。

feat_importances = pd.Series(model.feature_importances_, index = X_featurenames)feat_importances.nlargest(5).plot(kind = 'barh')Global variable importance全局變量重要性

From the BlackBox model, it is nearly impossible to get a feeling for its inner functioning. This brings us to a question of trust: do you trust that a certain prediction from the model is correct? Or do you even trust that the model is making sound predictions?

從BlackBox模型中，幾乎不可能對(duì)它的內(nèi)部功能有所了解。 這給我們帶來了一個(gè)信任問題：您是否相信模型中的某個(gè)預(yù)測是正確的？ 還是您甚至相信模型可以做出合理的預(yù)測？

創(chuàng)建一個(gè)模型解釋器 (Creating a model explainer)

LIME is short for Local Interpretable Model-Agnostic Explanations. Local refers to local fidelity — i.e., we want the explanation to really reflect the behaviour of the classifier “around” the instance being predicted. This explanation is useless unless it is interpretable — that is, unless a human can make sense of it. Lime is able to explain any model without needing to ‘peak’ into it, so it is model-agnostic.

LIME是本地可解釋模型不可知的解釋的縮寫。 本地是指本地保真度-即，我們希望解釋能真正反映分類器在“預(yù)測”實(shí)例周圍的行為。 除非可以解釋 ，否則這種解釋是無用的，也就是說，除非人類可以理解。 Lime能夠解釋任何模型而無需“說話”，所以它與模型無關(guān) 。

Behind the workings of LIME lies the assumption that every complex model is linear on a local scale and asserting that it is possible to fit a simple model around a single observation that will mimic how the global model behaves at that locality (Pedersen and Benesty, 2016)

LIME的工作原理是，假設(shè)每個(gè)復(fù)雜模型在局部范圍內(nèi)都是線性的，并斷言有可能在單個(gè)觀測值附近擬合一個(gè)簡單模型，以模擬全局模型在該位置的行為(Pedersen和Benesty，2016年) )

LIME explainer fitting steps

LIME解釋器安裝步驟

• import the lime library
  導(dǎo)入石灰?guī)?
• import lime.lime_tabular
  導(dǎo)入lime.lime_tabular

• Fit an explainer using LimeTabularExplainer( ) function

  使用LimeTabularExplainer()函數(shù)擬合解釋器

• Supply the x_train values, feature names and class names as ‘Diabetes -ve’, ‘Diabetes +ve’

  提供x_train值， 特征名稱和類名稱，例如' Diabetes -ve '，' Diabetes + ve '

• Here we used the lasso_path for feature selection

  這里我們使用lasso_path進(jìn)行特征選擇

• binned continuous variable into discrete values (discretize_continuous = True) based on “quartile”

  根據(jù)“ 四分位數(shù) ”將連續(xù)變量分為離散值( discretize_continuous = True )

• Select mode as classification

  選擇模式作為分類

import limeimport lime.lime_tabularexplainer = lime.lime_tabular.LimeTabularExplainer(X_train.values, feature_names = X_featurenames, class_names = ['Diabetes -ve', 'Diabetes +ve'], feature_selection = "lasso_path", discretize_continuous = True, discretizer = "quartile", verbose = True, mode = 'classification')

For local level explanation let’s pick an observation from test data who is diabetes +ve. Let’s select the 3rd observation (index 254). Here are the first 5 observations from X_test dataset including 3rd observation (index number 254)

對(duì)于局部級(jí)別的解釋，讓我們從測試數(shù)據(jù)中選擇一個(gè)觀察者，即糖尿病+ ve。 讓我們選擇第三個(gè)觀察值(索引254)。 這是X_test數(shù)據(jù)集中的前5個(gè)觀察值，包括第3個(gè)觀察值(索引號(hào)254)

X_test.iloc[0:5]

Let’s observe the output variable. You can observe the 3rd observation (index 254) has a value of 1 which indicates it is diabetes +ve.

讓我們觀察一下輸出變量。 您可以觀察到第三個(gè)觀察值(索引254)的值為1，表示糖尿病+ ve。

Y_test.iloc[0:5]

Let’s see whether LIME able to interpret which variables contribute to +ve diabetes and what is the impact magnitude and direction for observation 3 (index number 254)

讓我們看看LIME是否能夠解釋哪些變量導(dǎo)致+ ve糖尿病，以及觀察的影響幅度和方向是什么(索引號(hào)254)3

解釋第一個(gè)觀察 (Explain the first observation)

For model explanation, one needs to supply the observation and the model predicted probabilities.

為了進(jìn)行模型解釋，需要提供觀察值和模型預(yù)測的概率。

The output shows the local level LIME model intercept is 0.245 and LIME model prediction is 0.613 (Prediction_local). The original random forest model prediction 0.589. Now, we can plot the explaining variables to show their contribution. In the plot, the right side green bar shows support for +ve diabetes while left side red bars contradicts the support. The variable glucose > 142 shows the highest support for +ve diabetes for the selected observation. In other words for observation 3 in the test dataset having glucose> 142 primarily contributed to +ve diabetes.

輸出顯示本地級(jí)別的LIME模型截距為0.245，而LIME模型的預(yù)測值為0.613(Prediction_local)。 原始隨機(jī)森林模型預(yù)測值為0.589。 現(xiàn)在，我們可以繪制解釋變量以顯示它們的作用。 在該圖中，右側(cè)的綠色條表示對(duì)+ ve糖尿病的支持，而左側(cè)的紅色條與支持相反。 對(duì)于選定的觀察，可變葡萄糖> 142顯示對(duì)+ ve糖尿病的最高支持。 換句話說，對(duì)于葡萄糖> 142的測試數(shù)據(jù)集中的觀察3，其主要導(dǎo)致+ ve糖尿病。

exp = explainer.explain_instance(X_test.iloc[2], model.predict_proba)exp.as_pyplot_figure()Individual explanation plot個(gè)別說明圖

Similarly, you can plot a detailed explanation using the show_in_notebook( ) function.

同樣，您可以使用show_in_notebook()函數(shù)繪制詳細(xì)說明。

exp = explainer.explain_instance(X_test.iloc[2], model.predict_proba)exp.show_in_notebook(show_table = True, show_all = False)Prediction explanation plot預(yù)測說明圖

In summary, black-box models nowadays not a black box anymore. There are plenty of algorithms that have been proposed by researchers. Some of them are LIME, Sharp values etc. The above explanation mechanism could be used for all major classification and regression algorithms, even for the deep neural networks.

總而言之，黑匣子模型如今已不再是黑匣子了。 研究人員已經(jīng)提出了許多算法。 其中一些是LIME，Sharp值等。以上解釋機(jī)制可用于所有主要的分類和回歸算法，甚至適用于深度神經(jīng)網(wǎng)絡(luò)。

If you learned something new and liked this article, follow on Twitter, LinkedIn, YouTube or Github.

如果您學(xué)到了新知識(shí)并喜歡本文，請(qǐng)?jiān)?strong>Twitter ， LinkedIn ， YouTube 或 Github上關(guān)注 。

Note

注意

This article was first published on onezero.blog, a data science, machine learning and research related blogging platform maintained by me.

本文首次發(fā)表于onezero.blog ，數(shù)據(jù)科學(xué)，機(jī)器學(xué)習(xí)和研究相關(guān)的博客平臺(tái)維護(hù)由我。

More Interesting Readings — I hope you’ve found this article useful! Below are some interesting readings hope you like them too —

更多有趣的讀物 - 希望您對(duì)本文有所幫助！ 以下是一些有趣的讀物，希望您也喜歡 -

翻譯自: https://towardsdatascience.com/diabetes-prediction-model-explanation-using-lime-onezeroblog-583d1f509d89

lime 模型

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