投稿時間:2022-03-15 20:40:06 RSSフィード2022-03-15 20:00 分まとめ(48件)

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TECH	Engadget Japanese	最大4回の測定データを表示可能。2インチ液晶ディスプレイ搭載デジタルメジャー「MILESEEY DT20」	https://japanese.engadget.com/mileseey-dt20-104002053.html		2022-03-15 10:40:02
IT	ITmedia 総合記事一覧	[ITmedia PC USER] AppleがmacOS Monterey 12.3の提供を開始　ユニバーサルコントロールがようやく利用可能に　ただしβ版	https://www.itmedia.co.jp/pcuser/articles/2203/15/news168.html	apple	2022-03-15 19:30:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] バーミヤンの「飲茶食べ放題」が終了　全店舗でのサービス展開は未定	https://www.itmedia.co.jp/business/articles/2203/15/news169.html	itmedia	2022-03-15 19:15:00
IT	ITmedia 総合記事一覧	[ITmedia News] ドコモとソフトバンク、パスワード平文保持か　「パスワードを忘れた」からユーザーへ開示	https://www.itmedia.co.jp/news/articles/2203/15/news172.html	itmedia	2022-03-15 19:15:00
TECH	Techable（テッカブル）	専門知識なしでリユース可能な太陽光パネルを選別。アイテス、自動計測器の受注開始	https://techable.jp/archives/175339	太陽光パネル	2022-03-15 10:00:51
AWS	lambdaタグが付けられた新着投稿 - Qiita	AWS Chalice Tips	https://qiita.com/yabako_kobayashi/items/28ef89caf34db1d3034d	├ーchalice│└ーconfigjsonchalice設定ファイル├ーapppy└ーrequirementstxt下記のautogenpolicyをfalseに変更する。	2022-03-15 19:06:36
python	Pythonタグが付けられた新着投稿 - Qiita	pysparkでデータ加工するための自分用チートシート	https://qiita.com/sho_oboegaki/items/58d8e5c3c0a4632d2ba9	pysparkでデータ加工するための自分用チートシート案件でビッグデータを加工するジョブをそれなりに作成してきました。	2022-03-15 19:44:13
python	Pythonタグが付けられた新着投稿 - Qiita	AWS Chalice Tips	https://qiita.com/yabako_kobayashi/items/28ef89caf34db1d3034d	├ーchalice│└ーconfigjsonchalice設定ファイル├ーapppy└ーrequirementstxt下記のautogenpolicyをfalseに変更する。	2022-03-15 19:06:36
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【初心者用】Rubyでじゃんけんゲームを作ってみた	https://qiita.com/takao_yamasaki/items/1f80a8b1033ab3e4d275	【初心者用】Rubyでじゃんけんゲームを作ってみたやりたいことRubyでじゃんけんゲームを作りたい。	2022-03-15 19:42:29
AWS	AWSタグが付けられた新着投稿 - Qiita	【無料利用枠】AWSでWordPressブログをつくろう Part.4 WordPress設定編	https://qiita.com/pike3/items/1bab8dea37be47bcdf0c	バックアップ用のプラグインはいくつか利用してみたのですが、個人的に「UpdraftPlusWordPressBackupPlugin」が使いやすかったので、こちらをインストールしていきます。	2022-03-15 19:41:27
AWS	AWSタグが付けられた新着投稿 - Qiita	【無料利用枠】AWSでWordPressブログをつくろう Part.3 S3画像保存～SESメール転送編	https://qiita.com/pike3/items/eeaffd9163bce56a4a31	Sへの画像保存設定SESを利用したメール転送Sへの画像保存設定WordPressブログで記事を投稿する際に、よく画像を利用すると思いますが、この画像の保存先がSとなるように設定していきます。	2022-03-15 19:28:32
Docker	dockerタグが付けられた新着投稿 - Qiita	Djangoでモデルを追加しよう！	https://qiita.com/muzudho1/items/2463cc006da69f5ed7b2	はじめに前提知識KeyValueスーパーユーザーを作っておくことDjangoでスーパーユーザーを追加しようこの記事のアーキテクチャKeyValueOSWindowsContainerDockerEditorVisualStudioCode以下VSCodeと表記参考にした元記事はDjangoでCRUDだ。	2022-03-15 19:10:09
技術ブログ	Developers.IO	CloudFormationテンプレートでQuickSightダッシュボードと分析を配布する	https://dev.classmethod.jp/articles/cloudformation-quicksight-dashboard-analysis/	quicksig	2022-03-15 10:26:21
海外TECH	MakeUseOf	A Beginner's Guide to Sling TV: Everything You Need to Know	https://www.makeuseof.com/everything-about-sling-tv/	A Beginner x s Guide to Sling TV Everything You Need to KnowSling TV is one of the most affordable and customizable live TV services in the United States Learn how it stands out from the competition	2022-03-15 10:45:13
海外TECH	MakeUseOf	9 Ways Your Apple Watch Can Make Your Life Simpler	https://www.makeuseof.com/ways-apple-watch-can-make-your-life-simpler/	apple	2022-03-15 10:30:13
海外TECH	DEV Community	Python Modules	https://dev.to/baransel/python-modules-2b5b	Python Modules What is a Module We have always avoided code duplication throughout the Python tutorials series We used functions and classes for this So can we use these functions or classes in another project In this lesson we will cover this subject a little more deeply Let me define the modules immediately Modules are Python files that contain functions classes and properties You can call and use these files in any project you want You are confused yes you did not hear wrong each file you create in Python is actually a module There are two kinds of modules in Python Ready modulesModules we created ourselves In this lesson we will create our own module In the next lessons we will cover ready made modules So do we have to use these modules If we use modules what advantages and disadvantages do we see Why Modules Projects that are large and have a lot of developers are generally divided into modules In this way it provides us with such advantages in the project that is divided into parts Modules avoid code duplication Modules make our project more readable It is simple to change update and add new modules to the project in modules It is easy to maintain in the project in modular structure Modules allow more than one person to work on the same project Creating a Python Module and Including it in the ProjectWe already know that the Python page we created is a module so let s create a new file right away This is a Python file called moduls py We add the function we created as follows def printScreen print Hello world Now we create another Python file We call this file main py We will use the modules we have created here Now we call the moduls py file we created that is the module import modulsNow let s take a look at what s inside this module We use the dir function which we used many times in our previous tutorials Our main py module is as follows import modulsprint dir moduls Output builtins cached doc file loader name package spec printScreen In our previous tutorials we said that functions starting with are special functions Notice that the last element of the list is the printScreen function that we created So can we use this function Let s see now import modulsmoduls printScreen Hello world As you can see we used the functions we created successfully So let s see how we re going to use classes If you are unfamiliar with Python OOP concepts please refer to this tutorial first class Student def init self name surname number self name name self surname surname self number number def information self print Name nSurname nNumber format self name self surname self number stu Student Baransel Arslan stu information Now let s run the class we created on the main py file The point we need to pay attention to here is that we are creating an object from the class we created otherwise we cannot use this class because the Student class we created is an abstract concept Now let s run the main py module Name BaranselSurname ArslanNumber You will notice here is that we created and called a student class object on the moduls py page before so when we called the moduls py module the Student class was automatically run Well what should we do if we want to call and use the module whenever we want in the project we add For this we create our object on the main py page not on the moduls py page import modulsstu Student Baransel Arslan stu information Another calling method is as follows import modulsStudent Baransel Arslan information Output Name BaranselSurname ArslanNumber You can use it any way you want But I strongly recommend that you thoroughly understand Python OOP logic first Now let s come to another point to pay attention to We just used a syntax structure like this when we included a module in the project import module nameThis usage is a standard usage There are other uses as well Let s see now For example if the module name you are calling is too long and difficult to use you can add a project as follows import module name as aliasSo we gave our module an alias Let s use it now import moduls as modmod Student Baransel Arslan information Let s do another method for example we don t want to call all the functions and classes in the module but if we want to call the functions and classes we need we do it like this from module name import function classLet s use it now from moduls import printScreenSince we are calling the printScreen function in moduls py here we can only use this function We cannot use the Student class If we want to use it we also add the Student class from moduls import printScreen StudentWell if there are maybe functions or classes in the module are we going to add them all one by one Of course not we already did that import modulsorfrom moduls import Thus we can now create our own modules and include them in a project	2022-03-15 10:48:32
海外TECH	DEV Community	How to add diagrams,flowcharts & visualizations in github readme with code	https://dev.to/dumboprogrammer/how-to-add-diagramsflowcharts-visualizations-in-github-readme-with-code-2gpk	How to add diagrams flowcharts amp visualizations in github readme with codeCool ass dynamic charts generated by code inside readme is trully awesome ain t it In Gtihub there is a built in way to make flow charts with code Original project named mermaid as an open source project Later adopted by github inside readme to work natively It is extremely easy to do Just follow what I did In the readme md or anything md file usethree back ticks and inside that write the way I did in this blog InstallationCDN lt version gt dist To select a version Replace with the desired version number Latest Version Deploying MermaidTo Deploy Mermaid You will need to install node v which would have npmDownload yarn using npmEnter the following command yarn add mermaidYou can then add mermaid as a dev dependency using this command yarn add dev mermaidMermaid API To deploy mermaid without a bundler one can insert a script tag with an absolute address and a mermaidAPI call into the HTML like so lt script src gt lt script gt lt script gt mermaid initialize startOnLoad true lt script gt Doing so will command the mermaid parser to look for the tags with class mermaid From these tags mermaid will try to read the diagram chart definitions and render them into svg charts Diagram TypesFlowchartgraph TD A gt B A gt C B gt D C gt D Sequence diagramsequenceDiagram participant Alice participant Bob Alice gt gt John Hello John how are you loop Healthcheck John gt gt John Fight against hypochondria end Note right of John Rational thoughts lt br gt prevail John gt gt Alice Great John gt gt Bob How about you Bob gt gt John Jolly good Gantt diagramganttdateFormat YYYY MM DDtitle Adding GANTT diagram to mermaidexcludes weekdays section A sectionCompleted task done des Active task active des dFuture task des after des dFuture task des after des dClass DiagramclassDiagramClass lt AveryLongClass CoolClass ClassClass o ClassClass ClassClass gt C Where am i Class CClass gt ClassClass equals Class Object elementDataClass size Class int chimpClass int gorillaClass lt gt C Cool label EXPERIMENTAL TYPESGit graph experimentalgitGraph options nodeSpacing nodeRadius endcommitbranch newbranchcheckout newbranchcommitcommitcheckout mastercommitcommitmerge newbranchEntity Relationship Diagram experimentalerDiagram CUSTOMER o ORDER places ORDER LINE ITEM contains CUSTOMER DELIVERY ADDRESS usesUser Journey Diagram title My working day section Go to work Make tea Me Go upstairs Me Do work Me Cat section Go home Go downstairs Me Sit down Me	2022-03-15 10:45:14
海外TECH	DEV Community	Typescript Basics: How Generic Types Work	https://dev.to/smpnjn/typescript-basics-how-generic-types-work-527c	Typescript Basics How Generic Types WorkTypescript generics are a way to take a function that that has an argument which we can define when we call that argument the same way we can change an argument of a function when we call it If you re new to the concept of Typescript generics then read on to learn how they work How Typescript Generics WorkImagine a function like this the one below it has an argument which is of type string and it outputs a string on top of that let myFunction function arg string string return Hello the value of your argument is arg This will return Hello the value of your argument is World myFunction World Interestingly myFunction works both with a variety of types such as a string or number When we make something work over a multitude of different circumstances it s called a generic and that s exactly what a Typescript generic is To adjust our Typescript function to become a generic we add a definable argument in lt gt s straight after the function keyword let myFunction function lt NewType gt arg NewType string return Hello the value of your argument is arg This will return Hello the value of your argument is World myFunction lt number gt Think of generic parameters in Typescript the same way as arguments in vanilla Javascript In the example above we ve made a new argument called NewType but you can call it anything Our argument arg is of type NewType Then when we call myFunction we can define the type of our argument in the case above a number Generics with multiple typesImagine another example where we aren t using string literals in the example below we simply add our two arguments together let myFunction function x string y string string return x y Returns HelloWorldmyFunction Hello World Again this works with multiple types most notably strings and numbers Let s try adding a generic type again let myFunction function lt NewType gt x NewType y NewType number return x length y length Except this time we ll get an error Property length does not exist on type NewType The reason this throws an error is because we haven t defined what NewType can and can t do To resolve this example we simply have to mention that x and y will be an Array of NewTypes by using brackets let myFunction function lt NewType gt x NewType y NewType number return x length y length This will return myFunction lt number gt Extending Generic TypesSometimes however we have a different set of circumstances We may have a situation where we want to extend NewType for example if we are accessing a property that NewType should have but the compiler does not know about We can extend generic types using the extends keyword That means we can constrain any types passed to our function so they have a minimum set of properties In the below example all elements of type NewType should have at least the property name type ExtendedNewType name string type User name string age number NewType must contain at least the attribute name so lets add it as an extension of our ExtendedNewType type let myFunction function lt NewType extends ExtendedNewType gt x NewType y NewType string return x name y name This will return Hello World let runFunction myFunction lt User gt name Hello name World Custom TypesWe are defining custom types above If you are new to custom types try reading our guide on creating custom types in Typescript Generic Custom TypesAs well as applying generic types to our functions we can also apply them to our own custom types In the below example we have a user where an ID may be a string or a number We can define a generic type at the top level of our new type and then define it whenever we use the User type Type User can have an ID which is either a number or a stringtype User lt CustomType extends number string gt id CustomType name string age number In this case we define CustomType as a stringlet myUser User lt string gt id name John Doe age In this case we define CustomType as a numberlet myOtherUser User lt number gt id name Jane Seymore age	2022-03-15 10:39:45
海外TECH	DEV Community	Steps Involved in Selecting a Model (Model Selection)	https://dev.to/ganiyuolalekan/steps-involved-in-selecting-a-model-model-selection-1d9n	Steps Involved in Selecting a Model Model Selection Model selection is a key ingredient in the long and essential series of steps involved in creating a machine learning ML model that would be deployed into production This article aims to act as a guide to machine learning engineers new to the process of model selection in machine learning ML We ll start by understanding what model selection is What is Model SelectionModel selection is the task or process of selecting a statistical model from a set of candidate models given data Wikipedia What this implies is that model selection is the activity of undergoing a series of events tasks processes This series of activities help us to determine if a statistical model among others is best suited to make predictions for a task In selecting a model we start by inspecting our dataset because everything we do afterward only matters when we know the kind of data we re working with Is the dataset clean So to begin with we start by looking into the dataset for issues like missing data incorrectly formatted values etc This process is called data cleaning It is the process of fixing or removing incorrect corrupted incorrectly formatted duplicate or incomplete data within a dataset tableau Trust me Data Cleaning is a very lengthy and tiring process It is a whole subject of its own which is necessary and thus valuable materials to assist those new to it is available in the further reading section below What is the size of the dataset The next thing we look into will be the size of the data How big is the data Is the data big enough to be split into sets Train Validation and Test set or is it so small we can t even extract a good enough test set example the iris dataset Let s start by identifying how we can address the small dataset How do we define a small dataset A dataset of sets and lower can be considered small A set higher than can still be considered small based on the problem you re trying to solve if you try to process a small data set naively it will still work If you try to process a large data set naively it will take orders of magnitude longer than acceptable and possibly exhaust your computing resources as well Carlos BargeI consider the metrics by Carlos Barge to be more appropriate for distinguishing a small from a large dataset What constitutes a large dataset isn t just the size of the rows but also the size of the columns After defining a dataset as small various steps should be taken to select a model for that dataset Note When performing a model evaluation consider the rule of thumb for training a model Your model should train on at least an order of magnitude more examples than trainable parameters developers google comThese steps include Transform categorical columns to numeric If any Perform a k fold cross validationElect candidate modelsPerform Model EvaluationModel selectionTo explain this better I would be making use of the iris dataset to examine the measures listed above The complete notebook on the model selection process for the iris dataset set can be on my Kaggle page Transform categorical columns to numericMachine learning models are unable to interpret non numeric values so before proceeding all numeric columns need to be transformed to numeric values In most cases columns that would need to be transformed to numeric values would be categorical columns like low medium high or Yes No or Male Female Scikit learn is a toolbox that was built to handle these conversions they include the LabelEncoder OrdinalEncoder OneHotEncoder etc All this is available in sklearn preprocessing Resources to articles that provide clarification on these tools can be found in the further reading section of this article Perform a k fold cross validationThe k fold cross validation is a procedure used to estimate the skill of the model on new data machine learning mastery K fold cross validating works by splitting the dataset to a specified number of folds say and then shifting the position of the test set to a single fold at each iteration as described above After performing the K fold cross validation we then end up with the N number of the same dataset with N different training and testing sets where N is the number of splits applied on the dataset There are two ways to use k fold cross validation Using k fold cross validation for evaluating a model s performanceUsing k fold cross validation for hyper parameter tuningThere s a lovely article by Rukshan Pramoditha titled k fold cross validation explained in plain English which explains both We would however use k fold for evaluating model performance in this test case Creating a K cross validation fold with sklearn using the iris dataset from sklearn datasets import load irisfrom sklearn model selection import KFold Loads iris datasetdata target load iris return X y True Splits dataset into foldsiris kf KFold n splits shuffle True random state List to store dataset across the the various foldskf data list data train index data test index target train index target test index for train index test index in iris kf split data target The purpose of performing a k fold cross validation is to expand the dataset What do I mean by this The iris dataset for instance has a total of data which is so small that extracting a test and cross validation set will leave us with very little to train with By splitting the dataset into a training and test set across different instances here we try to maximize the use of the available data for training and then test the model Elect candidate modelsNow that we ve successfully split our dataset in K Fold we can proceed to elect the candidate models This is the instance where we look at the kind of task we are solving and the models that can solve address it The Iris dataset is a classification task It has four feature columns which are sepal length cm sepal width cm petal length cm and petal width cm All are continuous feature columns By visualizing the dataset we can tell that the petal width cm and petal length cm feature column is linearly separable from the other feature columns Well this and probably more relationships Question What models best decide these relationships I ll go straight to listing out models that can determine these relationships For more on the reasons we picked the models check out the further reading section We ll be electing the LogisticRegression SVC KNN and RandomForestClassifier Perform Model EvaluationNow that we ve decided on the machine learning ML models we can proceed to evaluate the models with our dataset using cross validation We would make use of the sklearn model selection cross val score to cross validate the dataset and get the scores on the model performance across each fold Model performance on the iris datasetTrying to evaluate best performing models using cross validation from sklearn datasets import load irisfrom sklearn svm import SVCfrom sklearn neighbors import KNeighborsClassifierfrom sklearn ensemble import RandomForestClassifierfrom sklearn linear model import LogisticRegressionfrom sklearn model selection import cross val scoredef model performance data target models Takes a record of the model performance during cross validation returns the record of the model performance along with the model performance rating of the stating which model performed best and which performed worst record Logistic Regression K Nearest Neighbor Random Forest Classifier Support Vector Classifier avg model performance for model name in zip models record keys scores cross val score model data target cv scoring accuracy record name scores scores record name mean score scores mean avg model performance append round float scores mean name record Model Performance Rating sorted avg model performance reverse True return recorddata target load iris return X y True record model performance data target LogisticRegression max iter KNeighborsClassifier RandomForestClassifier SVC for model in list record keys print model record model print n nModel Performance Rating n record Model Performance Rating Model SelectionAfter cross validating the dataset we can now conclude that the best performing models are the Logistic Regression and the K Nearest Neighbor models which both have an accuracy of This implies that either of them would be efficient for deployment Now based on the needs of the problem we can now decide on either of the models If you have needs for a model based learning algorithm you can choose the KNN or the Logistic Regression for instance based learning After cross validating the dataset we can now conclude that the best performing models are the Logistic Regression and the K Nearest Neighbor models which both had an accuracy of Performing cross validation experiments like this on a large dataset would be very expensive computational wise Now that we ve figured out how to address the smaller datasets how do we address larger ones How do we define a large dataset What do I mean by a large dataset A dataset of about rows upwards is large while datasets within the range of say to are reasonably medium Of course this metric isn t the best If you try processing a large dataset naively it will take longer processing time and exhaust computing power This is a more precise metric After determining your dataset is large what are the steps for selecting a model for the dataset then Well unlike with smaller datasets we can t process this dataset naively Thus we have to split it This is where reducing the dataset to three set for training and evaluation comes to play Before we proceed though let s list the steps required to select a model for larger datasets Transform Categorical Columns to Numeric If any Scale Continuous Columns if necessary Split the DatasetElect Candidate ModelPerform Model EvaluationModel SelectionYou can proceed with these steps if you have a cleaned dataset The House Prices ーAdvanced Regression Techniques dataset would be utilized for tutorial purposes as we analyze the steps involved in selecting models for larger datasets The House Prices dataset isn t so large a dataset itself but should explain the concept behind our steps nicely The notebook compiling the codes for the dataset and the work we did can be found on my Kaggle page I would jump right into splitting the dataset Below is the code for cleaning the dataset and transforming the columns ーin case you desire to follow with the House Prices dataset Cleaning and transforming the housing price datasetHouse Prices Advanced Regression Techniques import pandas as pdfrom sklearn pipeline import Pipelinefrom sklearn impute import SimpleImputerfrom sklearn compose import ColumnTransformerfrom sklearn preprocessing import StandardScaler OrdinalEncoder Loading both train and test set into a dataframetrain dataset pd read csv house prices train csv index col Id test dataset pd read csv house prices test csv index col Id Merging both train and test set into one data framedataset pd concat train dataset test dataset Extracing out target in which we hope to predicttarget dataset SalePrice to numpy Dropping some dataset columnsdataset drop Alley FireplaceQu PoolQC Fence MiscFeature SalePrice axis inplace True Specifying the continuous columnscontinuous col list dataset describe columns Specifying the categorical columnscategorical col col for col in dataset columns if col not in continuous col Creating the continuous columns data pipelinecontinuous data pipeline Pipeline imputer SimpleImputer strategy median num scaler StandardScaler Creating the categorical columns data pipelinecategorical data pipeline Pipeline freq imputer SimpleImputer strategy most frequent cat encoder OrdinalEncoder Creating a data pipeline for the whole datasethousing price pipeline ColumnTransformer continous continuous data pipeline continuous col categorical categorical data pipeline categorical col Transformed instance of the dataset Remember target variable contains it s target valuestransformed dataset housing price pipeline fit transform dataset Split the DatasetThe reason we perform an evaluation on machine learning ML models is to ensure they don t under fit or over fit We were able to evaluate the iris data set a small data set using cross validation but given our data set isn t as small validating naively would be computationally expensive Therefore we have to split the dataset into a train and test set Given the entire dataset has a shape of and after cleaning and transformation we can perform cross evaluation on the train set and evaluate our model performance on the test set Splitting the merged dataset of the housing price datasetMerger House Prices Advanced Regression Techniques from sklearn model selection import train test split Splitting the datasetX train X test y train y test train test split transformed dataset target test size shuffle True random state Elect Candidate ModelNow that we ve perfectly split the dataset into both train and test sets we then proceed to elect models that can solve this task We have to understand the dataset I talked about it in my notebook House Prices Prediction Beginner where I gave an overview of the dataset So we re dealing with a regression task consisting of lots of categorical features having models with linear and decision making abilities would be useful like the Decision Tree Regressor or Random Forest Regressor But let s go for the Random Forest Regressor since it s more of an ensemble of Decision Trees We should also pick models like Support Vector Regressor Linear Regression and K Neighbors Regressor since we re performing evaluations The XGBoost will prove to be a very vital tool in your ML journey and I suggest examining its usage in the notebook XGBoost by Kaggle grandmaster Dans Becker More resources on XGBoost in the further reading section Perform Model EvaluationNow that we ve successfully split our dataset and elected the models we want to use It s time to see how the individual models perform on the training dataset Beyond doubt the Random Forest Regressor performed best outperforming the Linear Regression model approximately x Although since our focus is on model selection I avoided cross validating and fine tuning the models In most cases I would fine tune and cross validate the model using grid search while searching out the best accuracy each model can produce before making a decision But the model s default parameters are also decent enough for this task So let s leave it simple Model SelectionAfter splitting the dataset electing the candidate model and performing model evaluation we can come to the conclusion that the Random Forest Regressor will be best suited for deployment having a mean absolute error MAE of Although we didn t quite fine tune the model We can get a much better MAE by fine tuning the Random Forest Regressor but the point has been established You could try out the XGBoost and compare it to see if it performs better What if you fine tune the XGBoost model as well ConclusionWe ve proven that model selection is a key ingredient in the lengthy series of steps involved in creating a machine learning ML model that would be deployed into production We showed the metrics for proving if a dataset is either small or large and the reason for cross validating smaller sets and splitting the larger ones We also talked about why we evaluate models and how we elect candidate models before model evaluation I hope this guide proves to be effective even as you deploy them into your machine learning tasks This article was originally published on Medium by me Further ReadingData CleaningThe Ultimate Guide to Data CleaningData Cleaning with Python and PandasEncoding Categorical ColumnsEncoding Categorical data in Machine LearningGuide to Encoding Categorical Features Using Scikit Learn For Machine LearningScikit Learn ModelsSupport Vector MachineRandom ForestK Nearest NeighborLinear RegressionLogistic RegressionFurther Reading On Model SelectionA “short introduction to model selectionA Gentle Introduction to Model Selection for Machine LearningAssociated NotebooksSteps Involved in Selecting a Model For a Small Data setSteps Involved in Selecting a Model For a larger Data setBookHands On Machine Learning with Scikit Learn Keras and TensorFlow	2022-03-15 10:29:59
海外TECH	DEV Community	Variance: from zero to the root	https://dev.to/ninjin/variance-from-zero-to-the-root-57ek	Variance from zero to the rootHello my name is Dmitriy Karlovskiy and I want to tell you about a fundamental feature of type systems which is often either not understood at all or is not understood correctly through the prism of the implementation of a particular language which due to evolutionary development has many atavisms Therefore even if you think you know what variance is try to look at the issue with fresh eyes We will start from the very basics so that even a beginner will understand everything And let s continue without water so that even the pros would be useful for structuring their knowledge Code examples will be in a pseudo language similar to TypeScript Then the approaches of several real languages ​​will be analyzed And if you are developing your own language then this article will help you not to step on someone else s rake Arguments and ParametersParameter is what we accept Describing the type of a parameter we set a restriction on the set of types that can be passed to us A few examples function parameterfunction log id string number constructor parameterclass logger constructor readonly id Natural template parameterclass Node lt Id extends Number gt id ID Argument is what we are passing When passed an argument is always of some particular type However with static analysis the specific type may not be known which is why the compiler again operates with type restrictions A few examples log concrete typenew Logger promptStringOrNumber Enter id concrete type is only known at runtimenew Node id clearly invalid type compilation error SubtypesTypes can form a hierarchy Subtype is a special case of supertype A subtype can be formed by narrowing the set of possible values ​​of the supertype For example the Natural type is a subtype of Integer and Positive And all three are simultaneously subtypes of Real At the same time the Positive and Integer types are overlapping but neither of them is a judgment of the other Another way to form a subtype is to extend it by combining it with another type orthogonal to it For example there is a color figure having the color property and there is a square having the height property Combining these types we get a colored square And by adding a circle with its radius we can get a colored cylinder HierarchiesFor further narration we need a small hierarchy of animals and a similar hierarchy of cages abstract class Animal abstract class Pet extends Animal class Cat extends Pet class Dog extends Pet class Fox extends Animal class AnimalCage content Animal class PetCage extends AnimalCage content Pet class CatCage extends PetCage content Cat class DogCage extends PetCage content Dog class FoxCage extends AnimalCage content Fox Everything below in the figure is a narrowing of the type above A pet cage can only contain pets not wild ones A cage with a dog can only contain dogs CovarianceThe simplest and most understandable is supertype constraint or covariance In the following example a function parameter is covariant to its specified type That is a function can accept both this type itself and any of its subtypes but cannot accept supertypes or other types function touchPet cage PetCage void log touch cage content touchPet new AnimalCage forbidtouchPet new PetCage allowtouchPet new CatCage allowtouchPet new DogCage allowtouchPet new FoxCage forbidSince we do not change anything in the cage we can easily pass the function to the cage with the cat since it is nothing more than a special case of a cage with a pet ContravarianceIt s a little harder to understand the subtype constraint or contravariance In the following example a function parameter is contravariant to the type specified for it That is a function can accept both this type itself and any of its supertypes but cannot accept subtypes or other types function pushPet cage PetCage void const Pet random gt Cat Dog cage content new Pet pushPet new AnimalCage allowpushPet new PetCage allowpushPet new CatCage forbidpushPet new DogCage forbidpushPet new FoxCage forbidWe can t pass in a cage with a cat because the function can put a dog in there which is not allowed But a cage with any animal can be safely transferred since both a cat and a dog can be placed there InvarianceSubtype and supertype constraint can be both This case is called invariance In the following example a function parameter is invariant to the type specified for it That is a function can only take the specified type and no more function replacePet cage PetCage void touchPet cage pushPet cage replacePet new AnimalCage forbidreplacePet new PetCage allowreplacePet new CatCage forbidreplacePet new DogCage forbidreplacePet new FoxCage forbid The replacePet function inherits the restrictions from the functions that it uses internally from touchPet it took the restriction on the supertype and from pushPet the restriction on the subtype If we pass it a cage with any animal then it will not be able to pass it to the touchPet function which does not know how to work with foxes a wild animal will just bite off its finger And if we pass a cage with a cat then it will not work to call pushPet BivarianceIt is impossible not to mention the exotic absence of restrictions bivariance In the following example a function can accept any type that is a supertype or subtype function ensurePet cage PetCage void if cage content instanceof Pet return pushPet cage ensurePet new AnimalCage allowensurePet new PetCage allowensurePet new CatCage allowensurePet new DogCage allowensurePet new FoxCage forbid You can transfer a cage with an animal into it Then it will check that there is a pet in the cage otherwise it will put a random pet inside And you can also transfer for example a cage with a cat then it simply will not do anything GenericsSome people think that variance has something to do with generalizations Often because variance is often explained using generic containers as an example However in the whole story we still have not had a single generalization entirely specific classes class AnimalCage content Animal class PetCage extends AnimalCage content Pet class CatCage extends PetCage content Cat class DogCage extends PetCage content Dog class FoxCage extends AnimalCage content Fox This was done to show that variance problems have nothing to do with generalizations Generalizations are only needed to reduce copy paste For example the code above can be rewritten with a simple generalization class Cage lt Animal gt content Animal And now you can create instances of any cages const animalCage new Cage lt Animal gt const petCage new Cage lt Pet gt const catCage new Cage lt Cat gt const dogCage new Cage lt Dog gt const foxCage new Cage lt Fox gt Declaration of RestrictionsPlease note that the signatures of all four previously given functions are exactly the same cage PetCage gt voidThat is such a description of the received parameters of the function does not have completeness it cannot be said from it that it can be passed to the function Well except that it is clearly visible that it is definitely not worth transferring a cage with a fox into it Therefore in modern languages ​​there are means for explicitly specifying what type restrictions a parameter has For example the modifiers in ​​and out ​​in C interface ICageIn lt in T gt T content set contravariant generic parameterinterface ICageOut lt out T gt T content get covariant generic parameterinterface ICageInOut lt T gt T content get set invariant generic parameterUnfortunately in C for each version of modifiers you need to enter a separate interface In addition as far as I understand bivariance in C is generally inexpressible Output ParametersFunctions can not only accept but also return values In general the return value can be more than one As an example let s take a function that takes a cage with a pet and returns two pets function getPets input PetCage Pet Pet return input content new Cat Such a function is equivalent to a function that takes in addition to one input parameter also two output parameters function getPets input PetCage amp output PetCage amp output PetCage void output input content output new Cat The outer code allocates extra memory on the stack so that the function puts whatever it wants to return into it And upon completion the calling code will already be able to use these containers for their own purposes From the equivalence of these two functions it follows that the return values ​​of the function unlike the parameters are always contravariant to the specified output type For the function can write to them but cannot read from them Object MethodsObject methods are functions that take an additional object pointer as an implicit parameter That is the following two functions are equivalent class PetCage pushPet void const Pet random gt Cat Dog this content new Pet function pushPet this PetCage void const Pet random gt Cat Dog this content new Pet However it is important to note that a method unlike a regular function is also a member of a class which is an extension of a type This leads to the fact that there is an additional supertype constraint for functions that call this method function fillPetCage cage PetCage void cage pushPet We cannot pass a supertype to it where the pushPet method has not yet been defined This is similar to the case of invariance in that there is a limit both from below and from above However the location of the pushPet method definition may be higher in the hierarchy And that s where the supertype constraint will be Barbara Liskov Substitution Principle LSP Many people think that the supertype subtype relationship is defined not by the methods of narrowing and extending the type mentioned earlier but by the ability to substitute a subtype in any place where the supertype is used Apparently the reason for this misconception is precisely in the LSP However let s read the definition of this principle carefully paying attention to what is primary and what is secondary Functions that use a base type should be able to use subtypes of the base type without knowing it and without violating the correctness of the program For immutable including those that do not refer to mutable objects this principle is automatically followed since there is no way to get a subtype constraint Things are more complicated with mutables since the following two situations are mutually exclusive for the LSP principle The class A has a subclass B where the field B foo is a subtype of A foo Class A has a method that can change the field A foo Accordingly there are only three ways Forbid objects to narrow the types of their fields when inheriting But then you can put an elephant in a cage for a cat Be guided not by LSP but by the variance of each parameter of each function separately But then you have to think a lot and explain to the compiler where what restrictions on types are To spit on everything and go to monastery functional programming where all objects are immutable which means that their accepting parameters are covariant to the declared type TypeScriptIn typescript the logic is simple all function parameters are considered covariant which is not true and return values ​​are contravariant which is true It was shown earlier that function parameters can have absolutely any variance depending on what this function does with these parameters So there are cases like this abstract class Animal is cat dog fox abstract class Pet extends Animal is cat dog class Cat extends Pet is cat class Dog extends Pet is dog class Fox extends Animal is fox class Cage lt Animal gt content animal function pushPet cage Cage lt Pet gt void const Pet Math random gt Cat Dog cage content new Pet pushPet new Cage lt Animal gt forbid to push Pet to Animal Cage pushPet new Cage lt Cat gt allow to push Dog to Cat Cage To solve this problem you have to help the compiler with a rather non trivial code function pushPet lt PetCage extends Cage lt Animal gt gt cage Cage lt Pet gt extends PetCage PetCage never void const Pet Math random gt Cat Dog cage content new Pet pushPet new Cage lt Animal gt allow pushPet new Cage lt Pet gt allow pushPet new Cage lt Cat gt forbid pushPet new Cage lt Dog gt forbid pushPet new Cage lt Fox gt forbid Try online FlowJSFlowJS has a more advanced type system In particular in the type description you can specify its variance for generic parameters and for object fields In our example with cages it looks something like this class Animal class Pet extends Animal class Cat extends Pet class Dog extends Pet class Fox extends Animal class Cage lt Animal gt content Animal function touchPet cage content Pet void console log touch typeof cage content function pushPet cage content Pet void const Pet Number any gt Cat Dog cage content new Pet function replacePet cage conten Pet void touchPet cage pushPet cage touchPet new Cage lt Animal gt forbid touchPet new Cage lt Pet gt allow touchPet new Cage lt Cat gt allow touchPet new Cage lt Dog gt allow touchPet new Cage lt Fox gt forbid pushPet new Cage lt Animal gt allow pushPet new Cage lt Pet gt allow pushPet new Cage lt Cat gt forbid pushPet new Cage lt Dog gt forbid pushPet new Cage lt Fox gt forbid replacePet new Cage lt Animal gt forbid replacePet new Cage lt Pet gt allow replacePet new Cage lt Cat gt forbid replacePet new Cage lt Dog gt forbid replacePet new Cage lt Fox gt forbid Try onlineThe bivariance here is inexpressible Unfortunately I could not find a way to more conveniently set the variance without explicitly describing the types of all fields For example something like this function pushPet cage Contra lt Cage lt Pet gt content gt void const Pet Number any gt Cat Dog cage content new Pet C SharpC was originally designed without any understanding of variance However in and out parameter modifiers were subsequently added to it which allowed the compiler to correctly check the types of arguments passed Unfortunately using these modifiers is again not very convenient using System abstract class Animal abstract class Pet Animal class Cat Pet class Dog Pet class Fox Animal interface ICageIn lt in T gt T content set interface ICageOut lt out T gt T content get interface ICageInOut lt T gt T content get set class Cage lt T gt ICageIn lt T gt ICageOut lt T gt ICageInOut lt T gt public T content get set public class Program static void touchPet ICageOut lt Pet gt cage Console WriteLine cage content static void pushPet ICageIn lt Pet gt cage cage content new Dog static void replacePet ICageInOut lt Pet gt cage touchPet cage as ICageOut lt Pet gt pushPet cage as ICageIn lt Pet gt public static void Main var animalCage new Cage lt Animal gt var petCage new Cage lt Pet gt var catCage new Cage lt Cat gt var dogCage new Cage lt Dog gt var foxCage new Cage lt Fox gt touchPet animalCage forbid touchPet petCage allow touchPet catCage allow touchPet dogCage allow touchPet foxCage forbid pushPet animalCage allow pushPet petCage allow pushPet catCage forbid pushPet dogCage forbid pushPet foxCage forbid replacePet animalCage forbid replacePet petCage allow replacePet catCage forbid replacePet dogCage forbid replacePet foxCage forbid Try online JavaThe ability to switch variance was added to Java rather late and only for generic parameters that themselves appeared relatively recently If the parameter is not generalized then trouble abstract class Animal abstract class Pet extends Animal class Cat extends Pet class Dog extends Pet class Fox extends Animal class Cage lt T gt public T content public class Main static void touchPet Cage lt extends Pet gt cage System out println cage content static void pushPet Cage lt super Pet gt cage cage content new Dog static void replacePet Cage lt Pet gt cage touchPet cage pushPet cage public static void main String args Cage lt Animal gt animalCage new Cage lt Animal gt Cage lt Pet gt petCage new Cage lt Pet gt Cage lt Cat gt catCage new Cage lt Cat gt Cage lt Dog gt dogCage new Cage lt Dog gt Cage lt Fox gt foxCage new Cage lt Fox gt touchPet animalCage forbid touchPet petCage allow touchPet catCage allow touchPet dogCage allow touchPet foxCage forbid pushPet animalCage allow pushPet petCage allow pushPet catCage forbid pushPet dogCage forbid pushPet foxCage forbid replacePet animalCage forbid replacePet petCage allow replacePet catCage forbid replacePet dogCage forbid replacePet foxCage forbid Try Online C C thanks to its powerful template system can express various variations but of course there is a lot of code include lt iostream gt include lt typeinfo gt include lt type traits gt class Animal class Pet public Animal class Cat public Pet class Dog public Pet class Fox public Animal template lt class T gt class Cage public T content template lt class T class std enable if t lt std is base of lt Pet T gt value gt gt void touchPet const Cage lt T gt amp cage std cout lt lt typeid T name template lt class T class std enable if t lt std is base of lt T Pet gt value gt gt void pushPet Cage lt T gt amp cage cage content new Dog void replacePet Cage lt Pet gt amp cage touchPet cage pushPet cage int main void Cage lt Animal gt animalCage new Fox Cage lt Pet gt petCage new Cat Cage lt Cat gt catCage new Cat Cage lt Dog gt dogCage new Dog Cage lt Fox gt foxCage new Fox touchPet animalCage forbid touchPet petCage allow touchPet catCage allow touchPet dogCage allow touchPet foxCage forbid pushPet animalCage allow pushPet petCage allow pushPet catCage forbid pushPet dogCage forbid pushPet foxCage forbid replacePet animalCage forbid replacePet petCage allow replacePet catCage forbid replacePet dogCage forbid replacePet foxCage forbid return Try Online DD does not have any sane means of explicitly indicating variance but it can itself infer types based on their use import std stdio std random abstract class Animal abstract class Pet Animal string name class Cat Pet class Dog Pet class Fox Animal class Cage T T content void touchPet PetCage PetCage cage writeln cage content name void pushPet PetCage PetCage cage cage content uniform gt new Dog new Cat void replacePet PetCage PetCage cage touchPet cage pushPet cage void main Cage Animal animalCage Cage Pet petCage Cage Cat catCage Cage Dog dogCage Cage Fox foxCage animalCage touchPet forbid petCage touchPet allow catCage touchPet allow dogCage touchPet allow foxCage touchPet forbid animalCage pushPet allow petCage pushPet allow catCage pushPet forbid dogCage pushPet forbid foxCage pushPet forbid animalCage replacePet forbid petCage replacePet allow catCage replacePet forbid dogCage replacePet forbid foxCage replacePet forbid Try online EpilogueThat s all for now I hope this material has helped you better understand type constraints and how they are implemented in different languages Somewhere better somewhere worse somewhere not but in general so so Perhaps it is you who will develop a language in which all this will be implemented both conveniently and type safely It would be interesting for me to read your thoughts on this topic	2022-03-15 10:10:44
海外TECH	DEV Community	Launching an app for your small business? Try Flutter	https://dev.to/rachael_ray018/launching-an-app-for-your-small-business-try-flutter-1215	Launching an app for your small business Try FlutterGone are the days when only businesses with substantial budget to spare would choose to go for mobile app development In the current market conditions it has become almost essential for businesses of all sizes to have mobile apps However it is easier said than done Getting a mobile app developed can be financially straining for small businesses and startups This is where cross platform app development comes into the picture Businesses around the world are choosing this option to save considerable time and money In fact of developers use React Native a cross platform app development framework as a core technology for their mobile apps If the developers are using cross platform technologies then certainly there is a demand for it Now that you know a bit about the benefits of cross platform development let s come to the focus point of the article Flutter It would not be entirely wrong to say that Flutter has managed to capture the attention of developers community and business world alike since its launch Before we get into details of how Flutter is a boon for startups it is necessary to understand the two development methods ie native and cross platform app development from the perspective of startups Only then you can better understand what Flutter app development will bring to the table for startups A Common Dilemma for Startups Cross platform vs Native App Development Today mobile apps are surely a great way to attract and retain customers But money is a major concern in mobile app development especially for startups Not to forget that the market is highly competitive with million startups coming up every year In that case startups need a quick and robust solution Enter cross platform app development While startups may be inclined towards native apps given their performance building two separate apps for Android and iOS is tiresome and expensive As a result fan following of the cross platform approach is on a rise Still not convinced The following reasons to opt for cross platform development will change your mind Lesser development effortLess time in testing Quicker development Easily accessible plugins Lesser development cost Reusable code Shorter time to market Flutter is Trending Why Startups Need to Take a NoteFlutter s tagline build beautiful native apps in record time conveys everything there is to know If this is not enough Flutter is the second leading language to develop cross platform apps as per Google Trends Moreover as per a Stack overflow survey it is the third most loved frameworks tools and libraries If these are not enough check out some other reasons why Flutter is the best choice for startups Flutter Offers Powerful Design ExperienceSmall businesses or startups usually struggle to find investors for their app The first thing that catches the eye in an app is its design and user experience Flutter enables developers to create brilliant and intuitive designs It s possible because of the customized widgets for Android and iOS and other UI features As compared to other cross platform tools Flutter offers a wider variety of widgets Also Flutter has Material Design for Android and iOS widgets or Cupertino for iOS applications that helps developers make responsive apps When developers leverage the full potential of Flutter they can create spectacular applications which in turn offer an unparalleled user experience Flutter Enables Faster DevelopmentAs Flutter is a cross platform development tool an app created with it can be uploaded on multiple stores The app gets more exposure and reaches a wider audience The end result is an increase in the number of downloads Usually in developing cross platform apps UI UX is compromised because JavaScript is not a feasible option Some of its features are browser dependent and some need to be implemented differently across browsers But with Flutter the developers can take the code once approach and create a native experience All of this means faster development and time to market too Flutter Increases Developer s ProductivityOne of the best features of Flutter is Hot Reload If there is any error during development then the developers can fix it immediately with the help of this feature There is no need to recompile and deploy the code again Basically they can continue from where they left off Furthermore developers can see the changes in code in real time So the team can quickly add new features experiment and fix bugs Hot reload also increases productivity in the sense that it improves collaboration between designers and developers Be it the look or feel of the app any change can be done and effects are visible immediately within a few seconds Flutter is Integrated with FirebaseFirebase is backed by Google and provides a collection of services like real time databases hosting cloud storage cloud functions and much more The benefit for startups here is that they don t need to invest in a backend They can use Firebase as it will not only make the infrastructure server less but also scalable Firebase also comes with a collection of common tools which further makes the development process easier To achieve consistent delivery in the project developers have the option to combine Firebase with tools to automate the app development process Lastly Flutter Development is Pocket friendlyEvery startup will look for budget friendly options for their app development project Native apps are expensive because developers need to write separate codes for different operating systems This also means separate testing for different apps Not to forget developers with the required skill sets for the specific platform need to be employed too On the other hand with Flutter all it takes is a single codebase for apps to operate on multiple platforms It reduces the testing requirements app maintenance cost and a single team of developers to do the job The end result is saving a lot of money that every startup will appreciate ConclusionThe idea of startups using Flutter for app development is definitely a beneficial one Flutter continues to evolve and the community is growing as well But just like everything else there is a flipside to Flutter too For instance some third party integrations and internationally agreed architectural approaches are missing However these drawbacks are understandable as the framework is relatively new The tech world is quite optimistic that Flutter will continue to make a mark in the world of mobile apps given its advantages Therefore it is time startups give a chance to Flutter and leverage it to realize the full potential of the business	2022-03-15 10:06:41
海外TECH	Engadget	Tesla raises prices across its entire EV lineup	https://www.engadget.com/tesla-raises-prices-across-entire-ev-lineup-101002343.html?src=rss	Tesla raises prices across its entire EV lineupTesla has raised the prices of its electric vehicles for the second time within the month After adding to some long range models last week the automaker has now implemented a much larger price increase across its lineup As Electrek reports its prices now start at for the base Model higher than before The Model Dual Motor All Wheel Drive is now more expensive at and the Performance version now costs more at TeslaMeanwhile Model Y s prices now start at or higher than before for the Long Range version Tesla has increased the Performance version s pricing by as well which means it ll now set you back For both Model S options Tesla has added on top of their previous prices so you ll have to spend at least for one None of the other EVs got a price increase as big as the Model X though which now costs more at Although Tesla has quietly raised prices overnight the move didn t come out of left field On Twitter company chief Elon Musk hinted at the possibility of a price hike He said both Tesla and SpaceX are seeing quot significant recent inflation pressure in raw materials and logistics quot He didn t elaborate but he linked to an article about commodity prices soaring due to fears over the shortage of raw materials that Russia exports nbsp One of the materials affected by the Russian invasion of Ukraine is nickel with its prices soaring and more than doubling since the war started Russia is a key supplier of the metal which is a critical component of lithium ion batteries used by Tesla and other EV manufacturers In addition Electrek says Tesla is experiencing a massive surge in new orders due to heightened interest in electric vehicles caused by the rise in gas prices Tesla amp SpaceX are seeing significant recent inflation pressure in raw materials amp logisticsーElon Musk elonmusk March	2022-03-15 10:10:02
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金融	ニュース - 保険市場TIMES	保険市場、2022年3月版の月間資料請求ランキングを発表	https://www.hokende.com/news/blog/entry/2022/03/15/200000	保険市場、年月版の月間資料請求ランキングを発表月版も東京海上日動あんしん生命が最多ランクインアドバンスクリエイトは月日、同社が運営する国内最大級の保険選びサイト「保険市場」にて、年月版の「月間資料請求ランキング」を発表した。	2022-03-15 20:00:00
海外ニュース	Japan Times latest articles	Japan may allow 10,000 people per day to enter country from April	https://www.japantimes.co.jp/news/2022/03/15/national/entry-arrivals-raised/	border	2022-03-15 19:16:00
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ビジネス	ダイヤモンド・オンライン - 新着記事	【日本株】日経平均株価が3月中に“セリングクライマ ックス”で急落するリスクは減少も、下落基調は継続！ 今は「防衛関連」や｢アフターコロナ関連｣銘柄に注目 - 成り上がり投資術	https://diamond.jp/articles/-/299238	【日本株】日経平均株価が月中に“セリングクライマックスで急落するリスクは減少も、下落基調は継続今は「防衛関連」や「アフターコロナ関連」銘柄に注目成り上がり投資術現在の日経平均株価や株式市場の状況と今後の見通しについて、アナリストの藤井英敏さんが鋭く分析ロシアのウクライナ侵攻に伴う供給懸念から、今後も物価上昇は加速、もしくは高止まりする見込みです。	2022-03-15 19:30:00
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