STL stands for Standard Template Library. If you've used C++ even in small projects, you've likely already used STL - which is a great thing! Using STL in C++ makes your code more expressive, simple, and easy to understand. This post will give you an overview of how STL works, some examples, and the basic knowledge you need to get started!
\nLoosely speaking, the first components you'd typically understand and use of STL are Containers & Algorithms. Then there's also Iterators & Functors, but you should try to take them on one by one, in that order perhaps. This blog will expand a bit on STL Containers.
\nSuppose you are at your favorite cinema hall and it's the launch of a big movie. Since it's the launch, there are likely many people waiting in line to buy their tickets. Naturally, you join the queue at the back and wait for your turn. In the computing world, we have a queue too! This is a popular data structure. If you've had a Data Structures class before, you are most likely familiar with some other data structures as well. These are often used, so the STL provides a great implementation of all these data structures, otherwise known as containers.
\nTake arrays, for example. Arrays are elements with the same type, stored in contiguous blocks of memory. In C++, you can use arrays as you would in C, like this:
\nstd::string students[10];\nstudents[0] = "Adam";But wait, STL provides a container for arrays too. It's available in the header <array>. Example usage of it would look like this:
#include <iostream>\n#include <string>\n\nint main() {\n std::array<std::string, 10> students;\n students[0] = "Adam's Friend";\n \n cout << students[0] << endl;\n}So, what's the difference? Why should you use the STL Container class? The performance difference is negligible, but what makes the container classes better is that it is a class. A wrapper class around normal arrays, and offers some advantages like passing by values, bounds checking, etc. As a side note, you'd normally want to use an std::vector instead - which is a dynamically resizable array.
Other containers in C++ include:
\nSequence containers: (accessibility in a sequential manner)
\nstd::arraystd::vector: Dynamically resizable arrays.\nArrays have a set size, while std::vector doesn't. You can keep adding elements in contigious blocks of memory. For example:
std::vector<std::string> students;\nstudents.push_back("Paul");\nstudents.push_back("Jack");Paul & Jack are stored in contiguous memory blocks, even though we didn't provide a specific limit at the start - as we do with arrays.
\nstd::forward_list: Singly linked-list.
A -> B -> C -> DThis is a simple example of linked-lists. Once you're at a point, let's say \"C\" - you can only go forwards.
\nstd::list: Doubly linked-list.
A <-> B <-> C <-> DAbove is a simple example of a doubly linked-list. Unlike Singly linked-lists, if you're at \"C\", you can go either way. To \"B\" or \"D\".
\nstd::deque: Double-ended queues - insertion and removal possible at both ends.Container Adaptors: (different interface of accessibility from sequence containers)
\nstd::queue: A standard queue, where removals are done from the front, and insertions are done at the end. The queue is a FIFO structure (First in, First out).std::priority_queue: A queue in which elements can have a varying level of importance. The ones with the highest importance are at the front, and thus processed first.\nFor example, let's say you're at a barbershop. A person arrived after you. If that person would've made a prior appointment to the shop, he would automatically be placed ahead of you with more \"importance\". Priority queues work similarly.std::stack:\nSuppose you have a pile of 10 books. If you need a book from middle - of course, like most people, you will pull it right from the middle in one go. Let's think about how computers would take this problem. A computer needs a step-by-step instruction, so the first thing it would do is remove the book from the top. And continue to do that until it reaches the book. To keep books as they were again, it would place books one by one at the top of the pile.\nThus, insertions and removals in a stack are only done at the \"top\". Associative containers: (sorted in a specific order, these containers boast search speeds of O(log N))
\nstd::set: Each element inserted into a set is it's own identifier, meaning that unique elements are entered. Each element acts as it's own \"key\" - which uniquely identifies it. For example, suppose you are a volunteer for entering student information for new students entering into the semester. They are uniquely identified by their roll numbers. Suppose you enter the same student's Roll No (the ID) twice into a set, it will be inserted just once - because a \"set\" can only have unique values.std::multiset: Like a set, but here the same multiple elements are allowed. Entering the roll no from before twice into an std::multiset will result in it being added twice.std::map: Take a literal map, for example. When you point to a location on the map, it tells you the details. The \"point\" is the key, and the details of that location are the \"values\". The keys are unique, and values can be anything. An std::map works the same way. Perhaps an example will help understanding more about it:// This maps a student name to his marks. But is this correct?\nstd::map<std::string, int> mapOfStudentNameToMarks;\n// The answer is no. A student's name isn't necessarily unique, multiple students with the same name will have a clash this way.\n// Thus, the correct way would be:\n\nstd::map<int, int> mapOfStudentIDToMarks; // This is correct, because StudentIDs are unique!\n\nmapOfStudentIDToMarks[1] = 100; // StudentID 1 -> 100 Marks.std::multimap: Can you guess what this might do, based on std::multiset?\nMultimap allows multiple elements to have the same key. So, for example, 10 -> 100 & 10 -> 150 are both valid for a multimap.Unordered Associative containers: (like associative containers, but implemented as hash-tables. They can be accessed at O(1), i.e., constant time.
\nstd::unordered_setstd::unordered_multisetstd::unordered_mapstd::unordered_multimap\n-- Containers aren't the only part of STL. It is huge! You are encouraged to read up more on STL, as it makes your life easier in every manner! You don't need to reinvent the wheel. Next, you could try looking up the various algorithms contained in the STL. They're present in the header file <algorithm>. Good luck on your journey!
Before we get into details of finding out optimal clusters, let's first see what the KMeans clustering algorithm is and some basics about it.
\nClustering is an unsupervised ML technique wherein we cluster the data to get insights from it. Clustering the data is quite essential for some business models and problems. It gives us conclusions on what is a cluster, i.e. data which is similar and in the form of cluster or groups.
\n\n\nClustering is the process of dividing the entire data into groups (also known as clusters) based on the patterns in the data.
\n
It is an algorithm for clustering. We will be discussing this method with code in the further sections.
\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\n%matplotlib inlineNow let's discuss the method behind finding out the right number of clusters on a K-Means clustering algorithm.\nSo we will learn how to decide what number of clusters to input into your K-Means algorithm.\nHere we've got a data science problem.\nWe've got only two variables, x and y coordinates.
\nNow, if we run the K means clustering algorithm on this dataset with three clusters or with K pre-determine the clusters to be three, then the result will look something like this.
\nSo you can see here that it jumps from 8000 down to 3000, that's a massive change of 5000. Let's just call them units, 5000 units and then from 3000 as we increase the number of the close from 2 to 3, they jump from 3000 to 1000.
\nAgain quite a large drop and then from three to four what's going to happen is going to jump from 1000 to maybe eight hundred and from 800 to 600, 600 to 500 and so on so as you can see the first two improvements or first two changes from one cluster to two from two to three created some huge jumps or considerable drops in the WTS going forward The WCR says drops not substantially. And this is our hint at selecting the optimum optimal number of clusters; and the method we're going to use is the elbow method, and it is very visual. All you have to do is look at your chart and look for that change, or that's kind of like it does look like an ELBOW.
\nLook for that elbow in your chart where the drop goes from being quite substantial to being not as significant not as proven is not as great, and therefore, that point in your chart will be the optimal number of clusters.
\nIn this case, it is indeed three clusters.
\nThat is the optimal number. And as you can imagine, this method is entirely arbitrary.\nSometimes, the situations are not as pronounced as the elbow might not be as evident as in this case, and therefore, somebody might pick one number of clusters. Someone else might come along and select a different number.
\nfrom sklearn.cluster import KMeans\nwcss = []\n\nfor i in range(1, 11):\n\n kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n\n kmeans.fit(X)\n \n wcss.append(kmeans.inertia_)\n\nplt.plot(range(1, 11), wcss)\n\nplt.title('The Elbow Method')\n\nplt.xlabel('Number of clusters')\n\nplt.ylabel('WCSS')\n\nplt.show()When building APIs, the need to upload files is expected, which can be images, text documents, scripts, pdfs, among others. In the development of this functionality, some problems can be found, such as the number of files, valid file types, sizes of these files, and several others. And to save us from these problems we have the Multer library. Multer is a node.js middleware for handling multipart/form-data that is used to send files in forms.
The first step is to create a NodeJS project on your computer.
\nIn your terminal, type the following command:
\nyarn add express* You can also use NPM for installation
\nCreate a file named app.js inside the src/ folder. The next step is to start our Express server in our app.js
const express = require("express")\nconst app = express()\n\napp.listen(3000 || process.env.PORT, () => {\n console.log("Server on...")\n})With the project created, configured and with Express installed, we will add the multer to our project.
\nyarn add multerThe next step is to import the multer into our app.js file.
const multer = require("multer")We are almost there. Now create a folder called uploads where we will store the uploaded files.
Now we are at a very important stage which is the configuration of diskStorage. DiskStorage is a method made available by multer where we configure the destination of the file, the name of the file and we can also add validations regarding the type of the file. These settings are according to the needs of your project. Below I will leave an elementary example of the configuration.
const storage = multer.diskStorage({\n destination: (req, file, cb) => {\n cb(null, "uploads/")\n },\n filename: (req, file, cb) => {\n cb(null, Date.now() + "-" + file.originalname)\n },\n})In the configuration above, we mentioned the destination for the uploaded files and also change the name of the file .
\nconst uploadStorage = multer({ storage: storage })\n\n// Single file\napp.post("/upload/single", uploadStorage.single("file"), (req, res) => {\n console.log(req.file)\n return res.send("Single file")\n})\n\n//Multiple files\napp.post("/upload/multiple", uploadStorage.array("file", 10), (req, res) => {\n console.log(req.files)\n return res.send("Multiple files")\n})In the code snippet above, we created 2 POST routes for sending files. The first /upload/single route receives only a single file, note that the uploadStorage variable receives our diskStorage settings. As a middleware in the route, it calls the single method for uploading a single file. The /upload/multiple route receives several files, but with a maximum limit of 10 files, note that the uploadStorage variable now calls the ʻarray` method for uploading multiple files.
With all the settings done, our little API is already able to store the files sent.
\nconst express = require("express")\nconst multer = require("multer")\n\nconst app = express()\n\nconst storage = multer.diskStorage({\n destination: (req, file, cb) => {\n cb(null, "uploads/")\n },\n filename: (req, file, cb) => {\n cb(null, Date.now() + "-" + file.originalname)\n },\n})\n\nconst uploadStorage = multer({ storage: storage })\n\n// Single file\napp.post("/upload/single", uploadStorage.single("file"), (req, res) => {\n console.log(req.file)\n return res.send("Single file")\n})\n//Multiple files\napp.post("/upload/multiple", uploadStorage.array("file", 10), (req, res) => {\n console.log(req.files)\n return res.send("Multiple files")\n})\n\napp.listen(3000 || process.env.PORT, () => {\n console.log("Server on...")\n})Now it's up to you!
\n","frontmatter":{"date":"October 12, 2020","updated_date":null,"description":"Learn how to upload files in a NodeJS application using Multer, Multer is a middleware for handling multipart/form-data that is used to send files in forms.","title":"Upload files using NodeJS + Multer","tags":["NodeJs","Express","Multer"],"pinned":null,"coverImage":{"childImageSharp":{"fluid":{"aspectRatio":1.5037593984962405,"src":"/static/49a3115a8c11e7fd9aca612e846c5936/ee604/node-multer-upload.png","srcSet":"/static/49a3115a8c11e7fd9aca612e846c5936/69585/node-multer-upload.png 200w,\n/static/49a3115a8c11e7fd9aca612e846c5936/497c6/node-multer-upload.png 400w,\n/static/49a3115a8c11e7fd9aca612e846c5936/ee604/node-multer-upload.png 800w,\n/static/49a3115a8c11e7fd9aca612e846c5936/f3583/node-multer-upload.png 1200w","sizes":"(max-width: 800px) 100vw, 800px"}}},"author":{"id":"Gabriel Rabelo","github":"gabrielrab","avatar":null}}}},{"node":{"excerpt":"Introduction Learning Deep Features for Discriminative Localization: Machine learning and Deep learning are gaining traction in today’s…","fields":{"slug":"/engineering/class-activation-mapping/"},"html":"Machine learning and Deep learning are gaining traction in today’s world and are making significant and unimaginable progress in almost every industry. However, with the increase in complexity and accuracy of these algorithms, the interpretability of these is at stake- especially the deep learning models which take in more than a million parameters for complex, convoluted models. Class Activation Mapping (CAM) is one such technique which helps us in enhancing the interpretability of such complex models.
\nFor a particular class (or category), Class activation mapping basically indicates the discriminative region of the image, which influenced the deep learning model to make the decision. The architecture is very similar to a convolutional neural network. It comprises several convolution layers, with the layer just before the final output performing Global Average Pooling. The features that are obtained are fed into the fully connected neural network layer governed by the softmax activation function and thus, output us the required probabilities. The importance of the weights with respect to a category can be found out by projecting back the weights onto the last convolution layer’s feature map.
\nThe Global Average Pooling (GAP) is preferred over Global Max Pooling (GMP) because GAP helps us in identifying the full extent of the object as compared to the GMP layer, which identifies one discriminative part. In Global Average Pooling, an average is taken across all the activation maps which help us to find all the possible discriminative regions present in them. Contrary to this, the Global Max Pooling method just considers the most discriminative region. Hence, Global Average Pooling showed better results than Global Max Pooling.
\nLet be the activation map of unit
in the last convolutional layer at spatial location
.
The result of GAP is represented as:-
\nFor a class c, an input to the softmax will be:-
\nOutput of Softmax layer:-
\nThus, the final equation for an activation map of class c would be:-
\n
The localization ability of the CAM method was put to the test when they were trained on the ILSVRC 2014 benchmark dataset. The CAM technique was used on popular CNN models like AlexNet, VGGNet and GoogLeNet by tweaking their models and fitting a GAP layer (similar to the CAM architecture) towards the end. This modified model was giving astounding results with the GAP layer as compared to their traditional architecture in terms of discriminative localization.
\nAfter applying a CAM architecture to fine-grained recognition and pattern discovery (like discovering informative objects in the scenes, concept localization in weakly labelled images, weakly supervised text detector and interpreting visual question answering), we can infer that feature capturing and localization was far more accurate in the CAM based GAP layer architecture, as the complete extent of the features were captured.\nVisualizing Class-specific Units:-\nWhen we use the GAP layer and the ranked softmax weight, we can directly visualize the units, which are the most discriminative for a particular class. Thus, CNN actually learns a bag of words, where each word is a discriminative class-specific unit. A combination of these class-specific units helps to guide CNNs in classifying each image.
\nCAMs are a great technique to interpret the information from the CNN models. However, the disadvantage of CAMs is that they can be noisy, and there might be some loss of spatial information. Hence, the Grad-CAM architecture and the Score-CAM architecture were built upon the CAM architecture to improve the accuracy, feature capturing and retain precise spatial information.
\n","frontmatter":{"date":"October 10, 2020","updated_date":null,"description":"Learn about the importance of the explainability of deep learning models and Class Activation Map Technique","title":"Class Activation Mapping in Deep Learning","tags":["Explainable AI","Deep Learning","CNN","Machine Learning"],"pinned":null,"coverImage":{"childImageSharp":{"fluid":{"aspectRatio":1.5037593984962405,"src":"/static/899d533ed44a50ce8e5dbf8103a0d717/ee604/Cover.png","srcSet":"/static/899d533ed44a50ce8e5dbf8103a0d717/69585/Cover.png 200w,\n/static/899d533ed44a50ce8e5dbf8103a0d717/497c6/Cover.png 400w,\n/static/899d533ed44a50ce8e5dbf8103a0d717/ee604/Cover.png 800w,\n/static/899d533ed44a50ce8e5dbf8103a0d717/f3583/Cover.png 1200w","sizes":"(max-width: 800px) 100vw, 800px"}}},"author":{"id":"Ankit Choraria","github":"Ankit810","avatar":null}}}},{"node":{"excerpt":"What is data enrichment? and its importance Data enrichment is the process of combining first-party data from internal sources with…","fields":{"slug":"/engineering/full-data-science-pipeline-implementation/"},"html":"Data enrichment is the process of combining first-party data from internal sources with disparate data from other internal systems or third-party data from external sources.
\nUsually, the data available from clients or stakeholders are not enough to solve the given problem statement, like if a client comes with a problem statement to build a recommendation engine for his mutual fund industry, the usual data they have is old purchase data but that's not enough as client behaviour changes with time and is impacted by the present market condition, oil prices, etc. which needs to be incorporated in the model to make it efficient.
\nCodes for this tutorial is at Link
\nThe whole process id divided into four steps:
\nI have implemented a full pipeline of data science from scrapping data from web to implementing ml and NLP classification.
\nHere I have scraped data from IMDB website (imdb.py)
\nI have tried to implement simple ML regression on the data (ml_imdb.py)
\nI have prepared the data for NLP classification (multilabel_prep.py)
\nI have implemented multilabel NLP classifier using various techniques like chain classifier etc. (multilabelnlpclassifier.ipynb)
\nWeb scraping is the process of extracting and parsing raw data from the web. Web scraping is a technique which helps data scientist to make their data-rich and is an efficient technique of data collection.
\nThis world is full of data, but unfortunately, most of them are not in the form to be used. Data is like crude oil, or we say it is in unstructured form. For a data scientist or engineer, our first challenge is to make the data model consumption ready, which takes the majority of the time, and this whole process is collectively known as data preprocessing.
\nHTML is a form of primary markup language and the base framework of mostly all websites. For performing web scraping its necessary to know it
\nHere we will start with requesting the web page using python package requests.
\n from requests import get\n url = 'http://www.imdb.com/search/title?release_date=2017&sort=num_votes,desc&page=1'\n response = get(url)\n print(len(response.text))The whole web page is now stored in the variable object response.\nThen we parse the web page using beautifulsoup package.
\n from bs4 import BeautifulSoup\n html_soup = BeautifulSoup(response.text, 'html.parser')\n type(html_soup)Then I will store all the div with the class named lister-item mode-advanced in variable movie_containers.
\nmovie_containers = html_soup.find_all('div', class_ = 'lister-item mode-advanced')Then I iterate through this object and store the information in lists to make my final DataFrame, using simple for loops.
\n# Lists to store the scraped data in\nnames = []\nyears = []\nimdb_ratings = []\nmetascores = []\nvotes = []\n#gross=[] #many movies have no record\nmovie_description=[]\nmovie_duration=[]\nmovie_genre=[]\n# Extract data from individual movie container\nfor container in movie_containers:\n# If the movie has Metascore, then extract:\n if container.find('div', class_ = 'ratings-metascore') is not None:\n# The name\n name = container.h3.a.text\n names.append(name)\n# The year\n year = container.h3.find('span', class_ = 'lister-item-year').text\n years.append(year)\n# The IMDB rating\n imdb = float(container.strong.text)\n imdb_ratings.append(imdb)\n# The Metascore\n m_score = container.find('span', class_ = 'metascore').text\n metascores.append(int(m_score))\n# The number of votes\n vote = container.find('span', attrs = {'name':'nv'})['data-value']\n votes.append(int(vote))\n# Gross income of movie\n #gross_inc =container.find_all('span', attrs = {'name':'nv'})[1]['data-value']\n #gross.append(gross_inc)\n\n# movie description\n movie_desc=container.find_all('p', class_ = 'text-muted')[1].text\n movie_description.append(movie_desc)\n movie_det=container.find_all('p', class_ = 'text-muted')[0]\n\n\n# Movie duration\n movie_dur=movie_det.find('span',class_='runtime').text\n movie_duration.append(movie_dur)\n\n# Movie genre\n movie_gen=movie_det.find('span',class_='genre').text\n movie_genre.append(movie_gen)\n\nimport pandas as pd\none_df = pd.DataFrame({'movie': names,\n'year': years,\n'imdb': imdb_ratings,\n'metascore': metascores,\n'votes': votes,\n#'gross':gross,\n'movie decription':movie_description,\n'movie duration':movie_duration,\n'movie genre':movie_genre\n})\nprint(one_df.info())\none_df.to_csv('50_movie_details.csv')But this was only for one page which has data for 50 movies only which is not enough to build a model.
\nPlease refer my code to understand how I use simple for loops to iterate through all the movies and downloading data for 20 years(approx).
\nWhats is linear regression??
\nIt is one of the most popular and used statistical techniques\n• Used to understand the relationship between variables
\n• Can also be used to predict a value of interest for new observations
\n• The aim is to predict the value of a continuous numeric variable of interest (known as the response or dependent or target variable)
\n• The values of one or more predictor (or independent) variables are used to make the prediction
\n• One predictor = simple regression
\n• More predictors = multiple regression
\nHere I just tried to use metascore of movies firstly to predict IMDB ratings and secondly I wanted to enhance it by using metascore and votes to predict IMDB rating.
\n## ML model\n\nX = data.loc[:, 'metascore'].values\ny = data.loc[:, 'imdb'].values\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 0)\n\nfrom sklearn.linear_model import LinearRegression\n\nregressor = LinearRegression()#making object for reg package\nregressor.fit(X_train.reshape(-1,1), y_train.reshape(-1,1))#to fit the regressor to our training data\n\n#predict the test results\ny_pred =regressor.predict(X_test.reshape(-1,1))\n\nfrom sklearn.metrics import mean_squared_error\nmean_squared_error(y_test, y_pred)\n\n# 0.18041462828221905## Let try with imdb and votes\nX1 = data.loc[:, ['metascore','votes']].values\ny1 = data.loc[:, 'imdb'].values\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size = 0.33, random_state = 0)\n\nfrom sklearn.linear_model import LinearRegression\n\nregressor = LinearRegression()#making object for reg package\nregressor.fit(X_train, y_train)#to fit the regressor to our training data\n\n#predict the test results\ny_pred =regressor.predict(X_test)\n\n\nfrom sklearn.metrics import mean_squared_error\nmean_squared_error(y_test, y_pred)\n# 0.15729132122310804 good scoreI tried to scrape data from the IMDB site and then applied ML regression techniques on it. Later I found that the movies listed are multi-class like Logan belongs to Action, Drama, Sci-Fi, which led me to think about how to implement the classifier model in the multilabel data. Usually, the data we get in real-world is mostly multi labelled like chatbot data; the intent is many and like these movies which are multi-class.
\nHere we will first see how we prep our data for multilabel classification.
\nHere we have all tags in one single column which is not usable while we do classification, so we have to make separate columns for all labels, and if the row doesn't belong to that category, it will be filled by 0 else 1.
\nimport os\nos.chdir('Desktop/web_scraping/imdb scrapper_ml/')\n\nimport pandas as pd\n\ndata=pd.read_csv('multilabel_nlp_classification.csv')\n\nmovie_list=[x for x in data['movie genre']]\nmovie_list1=''\nfor x in data['movie genre']:\n movie_list1+=','+x\n\nli_m=movie_list1.split(',')\nli=[x.strip() for x in li_m]\nlist_s=list(set(li))\n\nfor x in list_s:\n data[x]=0\n\ndata['movie_genre']=[x.strip().split(',') for x in data['movie genre']]\n\nde=data.copy()\n#data.loc[0,'Action']=1\nde['id']=range(0,6116)\n#print(de.loc[de['id']==0,'Action'])\nfor i in range(0,6116):\n for x in de.loc[de['id']==i,'movie_genre']:\n for y in x:\n y=y.strip()\n de.loc[de['id']==i,y]=1\n\nde.to_csv('multilabel_nlp_classification.csv')Now, as our data is ready, we can start with NLP implementation.
\nFor multilabel classification, I used techniques like classifier chain, label powerset, etc.
\nHere the problem statement is that using the movie description our model has to guess which genre the movie belongs to. It is a popular use case. Take an example of ecommerce product description data; now instead of manually assigning the labels to it, we can use a model which will find relevant labels or genre for it and make the content relevant to the type it belongs.
\nI start with Exploratory data analysis and then data cleaning, which is the most crucial step as if all the description has some very 30-50 common words it will simply make the data-heavy and model slow and inefficient.
\nThen we go on to make the data model ready as ML models don't understand text data we have to feed numbers in it. For that purpose, we use TfidfVectorizer.
\nTfidfVectorizer - Transforms text to feature vectors that can be used as input to the estimator.
\nThen simply diving the data in train and test split.
\nx_train = vectorizer.transform(train_text)\ny_train = train.drop(labels = ['id','movie decription'], axis=1)\n\nx_test = vectorizer.transform(test_text)\ny_test = test.drop(labels = ['id','movie decription'], axis=1)I tried first with applying logistic regression and one vs rest classifier.
\nOneVsRestClassifier strategy splits a multi-class classification into one binary classification problem per class.\nOneVsRestClassifier is when we want to do multi-class or multilabel classification, and its strategy consists of fitting one classifier per class. For each classifier, the class is fitted against all the other classes.
\n# Using pipeline for applying logistic regression and one vs rest classifier\nLogReg_pipeline = Pipeline([\n ('clf', OneVsRestClassifier(LogisticRegression(solver='sag'), n_jobs=-1)),\n ])\n\nfor category in categories:\n printmd('**Processing {} comments...**'.format(category))\n \n # Training logistic regression model on train data\n LogReg_pipeline.fit(x_train, train[category])\n \n # calculating test accuracy\n prediction = LogReg_pipeline.predict(x_test)\n print('Test accuracy is {}'.format(accuracy_score(test[category], prediction)))\n print("\\n")Next, I tried with BinaryRelevance
\nIt is a simple technique which treats each label as a separate single class classification problem.
\n# using binary relevance\nfrom skmultilearn.problem_transform import BinaryRelevance\nfrom sklearn.naive_bayes import GaussianNB\n\n# initialize binary relevance multi-label classifier\n# with a gaussian naive bayes base classifier\nclassifier = BinaryRelevance(GaussianNB())\n\n# train\nclassifier.fit(x_train, y_train)\n\n# predict\npredictions = classifier.predict(x_test)Next, I tried using ClassifierChain.
\nIt is almost similar to BinaryRelevance, here the first classifier is trained just on the input data, and then each next classifier is trained on the input space and all the previous classifiers in the chain.
\nfrom skmultilearn.problem_transform import ClassifierChain\nfrom sklearn.linear_model import LogisticRegression\n\n\n# initialize classifier chains multi-label classifier\nclassifier = ClassifierChain(LogisticRegression())\n\n# Training logistic regression model on train data\nclassifier.fit(x_train, y_train)\n\n# predict\npredictions = classifier.predict(x_test)Next, I tried using Label Powerset.
\nHere we transform the problem into a multi-class problem with one multi-class classifier is trained on all unique label combinations found in the training data.
\nfrom skmultilearn.problem_transform import LabelPowerset\n# initialize label powerset multi-label classifier\nclassifier = LabelPowerset(LogisticRegression())\n\n# train\nclassifier.fit(x_train, y_train)\n\n# predict\npredictions = classifier.predict(x_test)Please refer my notebook multilabelnlpclassifier.ipynb from my repo for more details.
\nThe two types of email you can send and receive are plain text emails (any email that contains just plain old text with no formatting) and HTML emails, these are formatted and styled using HTML and inline CSS.\nHTML email is the use of HTML to provide formatting and semantic markup capabilities in an email that are not available in plain text.
\nAn HTML email is designed just like a website with the help of graphics, table columns, colors and links. A non-programmer can also create it since email marketing services provide flexible campaign builders. Email client vendors have not been as developing as web browser vendors in adopting new standards.
\nDefinition
\nEmails which are formatted using Hypertext Markup Language(HTML), as opposed to plain text email.
\nHow to Create an HTML Email
\nMany tools that create and send an email will offer pre-formatted, already built HTML templates that allow you to design emails without knowing or accessing any code of back-end.
\nThe best way to understand any process is to do it yourself, from level zero. We make any changes in the email editor; those changes will be automatically coded into the final result. This email building tool is the best option if you don't have an email designer, but you still want to send any professional marketing emails.
\nIf you want more control over the code of your emails and you are comfortable with HTML(that is just basic and easy), most email tools will allow you to import HTML files directly for using it as custom email templates. They have a wide variety of free HTML email templates available on the internet, and if you are familiar with HTML, it is a straightforward process to use that template in the email building tool of your own choice.
\nTo create an HTML email from scratch, you will need to have advanced knowledge of HTML. Because creating an HTML email from scratch can be quite tricky, we recommend you to work with a developer for this process, or you may go with a template for an easy process.
\nIf you choose to code your HTML email by hand, these are the necessary steps you need to use while creating HTML email:
\nHow to send HTML emails through Outlook?
\nYou can check HTML Email Template here:
\n\n","frontmatter":{"date":"October 09, 2020","updated_date":null,"description":"If you choose to code your HTML email by hand, there are many different things you need to use while creating HTML email.","title":"HTML Email Concept","tags":["Html","Email"],"pinned":null,"coverImage":{"childImageSharp":{"fluid":{"aspectRatio":1.7699115044247788,"src":"/static/68cacf6be60ee3e735f59ee70958946b/ee604/email_picture.png","srcSet":"/static/68cacf6be60ee3e735f59ee70958946b/69585/email_picture.png 200w,\n/static/68cacf6be60ee3e735f59ee70958946b/497c6/email_picture.png 400w,\n/static/68cacf6be60ee3e735f59ee70958946b/ee604/email_picture.png 800w,\n/static/68cacf6be60ee3e735f59ee70958946b/05d05/email_picture.png 1080w","sizes":"(max-width: 800px) 100vw, 800px"}}},"author":{"id":"Nivedita Singh","github":"Nivedita967","avatar":null}}}}]},"markdownRemark":{"excerpt":"Identity is evolving, and developers are at the forefront of this transformation. Every day brings a new learning—adapting to new standards…","fields":{"slug":"/identity/developer-first-identity-provider-loginradius/"},"html":"Identity is evolving, and developers are at the forefront of this transformation. Every day brings a new learning—adapting to new standards and refining approaches to building secure, seamless experiences.
\nWe’re here to support developers on that journey. We know how important simplicity, efficiency, and well-structured documentation are when working with identity and access management solutions. That’s why we’ve redesigned the LoginRadius website—to be faster, more intuitive, and developer-first in every way.
\nThe goal? Having them spend less time searching and more time building.
\nLoginRadius’ vision is to give developers a product that simplifies identity management so they can focus on building, deploying, and scaling their applications. To enhance this experience, we’ve spent the last few months redesigning our interface— making navigation more intuitive and reassuring that essential resources are easily accessible.
\nHere’s a closer look at what’s new and why it’s important:
\n![\"This](\"/f46881583c7518a93bb24e94c32320de/a-developer-friendly-dark-theme.webp\")
Developers spend long hours working in dark-themed IDEs and terminals, so we’ve designed the LoginRadius experience to be developer-friendly and align with that preference.
\nThe new dark mode reduces eye strain, enhances readability, and provides a seamless transition between a coding environment and our platform. Our new design features a clean, modern aesthetic with a consistent color scheme and Barlow typography, ensuring better readability. High-quality graphics and icons are thoughtfully placed to enhance the content without adding visual clutter.
\nSo, whether you’re navigating our API docs or configuring authentication into your system, our improved interface will make those extended development hours more comfortable and efficient.
\n![\"This](\"/e5358b82be414940f3fb146013845933/capabilities.webp\")
We’ve restructured our website to provide a straightforward breakdown of our customer identity and access management platform capabilities, helping you quickly find what you need:
\nThis structured layout ensures you can quickly understand each capability and how it integrates into your identity ecosystem.
\n![\"This](\"/a8c155c2b6faf3d5f4b4de4e2b14d763/developers-menu.webp\")
We’ve been analyzing developer workflows to identify how you access key resources. That’s why we redesigned our navigation with one goal in mind: to reduce clicks and make essential resources readily available.
\nThe new LoginRadius structure puts APIs, SDKs, and integration guides right at the menu bar under the Developers dropdown so you can get started faster. Our Products, Solutions, and Customer Services are also clearly categorized, helping development teams quickly find the right tools and make informed decisions.
\n![\"This](\"/b2f9a964a2da0ea83e2f8596b833bba7/we-support-your-tech-stack.webp\")
Developers now have a clear view of the tech stack available with LoginRadius, designed to support diverse business needs.
\nOur platform offers pre-built SDKs for Node.js, Python, Java, and more, making CIAM integration seamless across popular programming languages and frameworks.
\nCheck out our revamped LoginRadius website and see how the improved experience makes it easier to build, scale, and secure your applications.
\nDo not forget to explore the improved navigation and API documentation, and get started with our free trial today. We’re excited to see what you’ll build with LoginRadius!
\n","frontmatter":{"date":"February 21, 2025","updated_date":null,"description":"LoginRadius’ vision is to give developers a product that simplifies identity management so they can focus on building, deploying, and scaling their applications. To enhance this experience, we’ve redesigned our website interface, making navigation more intuitive and reassuring that essential resources are easily accessible.","title":"Revamped & Ready: Introducing the New Developer-First LoginRadius Website","tags":["Developer tools","API","Identity Management","User Authentication"],"pinned":true,"coverImage":{"childImageSharp":{"fluid":{"aspectRatio":1.7857142857142858,"src":"/static/80b4e4fbe176a10a327d273504607f32/58556/hero-section.webp","srcSet":"/static/80b4e4fbe176a10a327d273504607f32/61e93/hero-section.webp 200w,\n/static/80b4e4fbe176a10a327d273504607f32/1f5c5/hero-section.webp 400w,\n/static/80b4e4fbe176a10a327d273504607f32/58556/hero-section.webp 800w,\n/static/80b4e4fbe176a10a327d273504607f32/99238/hero-section.webp 1200w,\n/static/80b4e4fbe176a10a327d273504607f32/7c22d/hero-section.webp 1600w,\n/static/80b4e4fbe176a10a327d273504607f32/1258b/hero-section.webp 2732w","sizes":"(max-width: 800px) 100vw, 800px"}}},"author":{"id":"Rakesh Soni","github":"oyesoni","avatar":"rakesh-soni.jpg"}}}},"pageContext":{"limit":6,"skip":690,"currentPage":116,"type":"///","numPages":161,"pinned":"ee8a4479-3471-53b1-bf62-d0d8dc3faaeb"}},"staticQueryHashes":["1171199041","1384082988","2100481360","23180105","528864852"]}