This is a memo for myself as I read Introduction to Natural Language Processing Applications in 15 Steps. This time, in Chapter 2, Step 06, I will write down my own points.

Preparation

--Personal MacPC: MacOS Mojave version 10.14.6 --docker version: Version 19.03.2 for both Client and Server

Chapter overview

The classifier can predict the class ID from the feature vector by learning with the feature vector and the class ID as inputs.

SVM(Support Vector Machine) --Decision tree ensemble --k-nearest neighbor method

06.1 To master the classifier

You can easily switch the classifier by assigning the classifier to the classifier variable.

# SVC
from sklearn.svm import SVC
classifier = SVC()

# RandomForest
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()

06.2 SVM(Support Vector Machine) --Relatively high performance can be obtained stably --Easy to apply to tasks with relatively little learning data --The kernel method can handle complex problems

The outline of the basic SVM is omitted. A problem that cannot be identified by dividing the feature space on a straight line or plane identification surface (linear identification surface) is called a ** linear separable ** problem. There are the following methods to deal with this.

--Soft margin --Kernel

Hard margin SVM, soft margin SVM

There are two types, ** soft margin SVM ** and simple ** hard margin SVM **, which try to set the boundary surface so as to "allow the boundary to protrude and reduce the protrusion as much as possible". The degree of allowance for protrusion can be specified at the time of instance creation (default 1.0).

kernel

In cases where the boundary cannot be separated to the extent that the boundary is allowed to protrude, the kernel method is used in which the feature vector is copied to a feature space higher in dimension than the original feature space and the discrimination surface is set there. The kernel type can be specified at instance creation.

--RBF kernel (Gauss kernel): Default specification, most orthodox --Polynomial kernel: popular in natural language processing --Other --Kernel unused (linear SVM)

Classes provided by Scikit-learn

sklearn.svm.SVC --Both linear and kernel are available
sklearn.svm.LinearSVC --Implementation specialized for linear SVM
sklearn.linear_model.SGDClassifier --Since sklearn.svm puts all the training data in the memory and calculates it at once, a large amount of memory capacity is required depending on the amount of training data. --Since it is a Stochastic Gradient Descent (stochastic gradient descent method), the training data is read little by little and the learning proceeds, but there are some errors.

06.3 Ensemble

A method of combining a plurality of classifiers to form one classifier is called an ensemble. Although the explanation of the decision tree is omitted, the properties of the decision tree ensemble are as follows.

--No need for pre-processing of features --The number of parameters set by the designer is small

Typical ensemble methods are bagging and boosting (reference).

--Bagging --Learning multiple times separately using part of the data, and finally matching the results - Random Forest --Boostering --Learning using a part of the data, and repeating the learning many times using the previous result --Gradient Boosted Decision Trees (GBDT)

item	Random Forest	GBDT
Ensemble method	Bagging	Boosting
Method	sklearn.ensemble.RandomForestClassifier()	skleran.ensemble.GradientBoostingClassifier()
Creation of decision tree	A few deep trees	Many shallow trees
Run	-	Faster and more memory efficient than Random Forest

06.4 k-nearest neighbor method

It is a classifier that selects k feature vectors close to the input feature vector from the training data and takes a majority vote by their class ID. Depending on the numerical value of k, the identified class ID may change, or if the number of class IDs in the training data is biased, the desired result may not be obtained.

Distance to measure closeness

type	Contents
Euclidean distance	Vector length in space
Manhattan distance	Sum of axial lengths in space
Minkowski distance	A generalized distance of Euclidean distance and Manhattan distance
Levenshtein distance	Insert one character at a time to represent the distance between strings/Delete/Number of times to replace and make the same string

Parametric and nonparametric

item	parametric	ノンparametric
parameter settings	It is necessary to pay attention to the parameters so that the identification surface can be set properly.	You don't have to think about parameters related to the identification surface
Computational cost	Calculation cost of identification surface is required at the time of learning Calculation cost at the time of identification is almost constant	Calculation cost at the time of learning is basically zero The calculation cost at the time of identification increases reflecting the amount of training data.
Number of training data required	Relatively few	Relatively many
Discriminator example	SVM	k-nearest neighbor method

06.5 Apply to Dialogue Agent

Additions / changes from the previous chapter

Identifyer: SVM → Random Forest
TF-IDF ngram_range: 1 ~ 3 → 1 ~ 2

~~
pipeline = Pipeline([
#    ('vectorizer', TfidfVectorizer(tokenizer=self._tokenize, ngram_range=(1, 3))),
    ('vectorizer', TfidfVectorizer(tokenizer=self._tokenize, ngram_range=(1, 2))),
#    ('classifier', SVC()),
    ('classifier', RandomForestClassifier(n_estimators=30)),
])
~~

`Execution result`


# evaluate_dialogue_agent.Modify py loading module name as needed
from dialogue_agent import DialogueAgent

$ docker run -it -v $(pwd):/usr/src/app/ 15step:latest python evaluate_dialogue_agent.py
0.61702127

--Normal implementation (Step01): 37.2% --Addition of preprocessing (Step02): 43.6% --Pre-processing + feature extraction change (Step04): 58.5% --Pretreatment + feature extraction change + classifier change (Step06): 61.7%

[PYTHON] Try the book "Introduction to Natural Language Processing Application Development in 15 Steps" --Chapter 2 Step 06 Memo "Identifier"

Contents