One place for hosting & domains

      Build

      9 Steps to Build an Online Store and Become Your Own Boss in 2019


      While traditional careers have their benefits, there’s something very appealing about being your own boss. You can work whenever, however, and wherever you want to while still pursuing your passion. The tricky part is knowing how to get started.

      With accessible and easy-to-use tools such as WordPress and WooCommerce, setting up shop online is relatively simple. By launching an e-commerce store, you can take your product ideas to the web and access the vast pool of customers available there.

      This article will walk you through the steps to build your online store with WordPress and WooCommerce and become your own boss in no time. Let’s go!

      Your Store Deserves WooCommerce Hosting

      Sell anything, anywhere, anytime on the world’s biggest eCommerce platform.

      9 Steps to Build an Online Store and Become Your Own Boss

      The very first thing you’ll need to start an online store is a product customers will want to buy. We can’t help you with that, unfortunately — your idea has to be all your own! You’ll also need a way to manufacture your product, either by doing it yourself, hiring a company to do it, or some combination of the two.

      Once you’re done, you’ll be ready to set up your online store and start selling your merchandise, which is where the steps below will come in handy.

      Step 1: Secure Your Web Hosting and Domain Name

      The first two things you need to start any kind of website are a hosting provider and a domain name. Your hosting provider will store your website’s files, while your domain name provides an address where customers can find your store.

      If you’re building a WordPress site (which we recommend), you might also want to consider WordPress hosting. These types of plans are explicitly geared towards the platform, and the servers they run on will be optimized.

      Our shared WordPress hosting plans, for example, are ideal for new WordPress sites. You’ll have access to our 24/7 tech support team, and plans are cost-effective, starting at just $2.59 per month for a single site.

      DreamHost’s Shared WordPress Hosting page.

      What’s more, we can also help you register your domain name. You can quickly check the availability of your desired web address, then register it once you’ve found the perfect fit.

      DreamHost’s domain name search.

      Simply fill in some information to complete the process. Domains usually start at $11.99, but if you’re also hosting your site with a shared WordPress plan, you’ll get yours for free.

      Step 2: Set Up WordPress and WooCommerce

      Regardless of your current host, a WordPress hosting plan likely comes with the platform pre-installed or with a one-click installation option. In some cases, you may need to install WordPress manually.

      Next, you’ll need to set up WooCommerce — a premiere e-commerce solution for WordPress (we’ve compared it to other competitors and think it’s the best ecommerce platform available).

      The first step is to install and activate the WooCommerce plugin.

      The WooCommerce plugin.

      Once this is complete, you’ll be prompted to configure your store using the onboarding wizard — fill in the fields as best you can now, or come back to this step later.

      Step 3: Identify Your ‘Value Proposition’

      Before you begin creating content for your e-commerce business, consider identifying and writing out your value proposition. This is simply a statement explaining the mission and value of your business and products.

      Two of the most important questions your value proposition should answer are:

      1. What problem does my product solve for customers?
      2. What makes my approach to this problem unique compared to other similar businesses?

      Establishing your value proposition now should help you create content later. Also, any copy, product, or long-form content (such as a blog post) should reflect the values you identified in your proposition.

      We’d also suggest sharing your value proposition with customers on your website. Most companies do this on an About page or as a ‘Mission Statement.’ Here’s ours as an example:

      The DreamHost About page.

      Sharing your values with customers can help demonstrate why your product is relevant to them. Plus, you might win over customers who might have otherwise purchased from your competition.

      Step 4: Create Your Product Pages

      Now you’re ready to go back to setting up your online store. Navigate to Products > Add New within WordPress to start adding your first item. There are a lot of settings to consider here, but your priority should be your product photos and description.

      Taking Quality Product Photos

      Showcasing your products in their (literal) best light is crucial. Unprofessional, low-quality photos make your site seem untrustworthy, which will discourage customers from opening their wallet.

      As such, make sure your product photos are well-lit and taken in front of a clean background. If you can, take pictures from a variety of angles, and include some close-ups of unique details to help catch customers’ eyes.

      A product photo of a throw pillow from Wayfair.

      Once you have your product photos, make sure to optimize them with a plugin such as ShortPixel or Optimole before uploading them to your site. This will help prevent large media files from slowing your site down.

      Writing Captivating Product Descriptions

      You’ll also want to craft your product descriptions carefully, to help convince site visitors to become paying customers. Keep your value proposition in mind when you’re writing, and make sure to point out information about how the product will benefit customers.

      A product description for a throw pillow from Wayfair.

      It’s vital to make your description easy to scan, as ‘skimming’ content has become more popular over the years. Keeping paragraphs short, while using formatting techniques such as bullet points and subheadings, can convey more information than a brutal wall of text.

      Specifying Product Data

      Finally, for this section, you’ll want to configure the settings in the Product Data section of the product editor. Here you’ll set your product’s price, add a SKU number and shipping information, specify if it comes in any variations (e.g., other colors or sizes), and more.

      The product data section of the WooCommerce Product Editor.

      Take your time with these, as they’re an essential aspect of your store and business. Once you have the basics down, you may want to consider setting up Linked Products to help cross-sell other store items and enable reviews to add some social proof to your site.

      Step 5: Configure Your Tax Settings

      In the U.S., each state has laws regarding sales tax for internet-based retailers. It’s not a bad idea to talk with a tax attorney before your business gets up and running, but at the very least, you should familiarize yourself with the laws in your area.

      To set up sales tax for your products in WooCommerce, navigate to WooCommerce > Settings > General within WordPress. Make sure the Enable taxes setting is checked, then save your changes.

      The Enable taxes setting in WooCommerce.

      If there wasn’t one before, you should now see a Tax setting tab at the top of your WooCommerce Settings page. Click on it, then configure the settings on the page.

      You can determine whether your prices will automatically include tax at checkout and what information WooCommerce should use to calculate tax for each product. It’s also possible to add Standard, Reduced, and Zero tax rates if needed.

      Step 6: Specify Your Shipping Methods

      Shipping is a make-or-break aspect of running a store. As such, in the Shipping settings tab, you can add practically as many options as you want to implement a delivery strategy.

      If you’re going to make your products available in a wide range of locations, you might want to create ‘shipping zones.’

      They essentially let you offer different rates to customers depending on where they’re located. If you also want to charge extra for international shipping, you can do so here.

      Step 7: Decide Which Payment Gateway to Offer

      In the Payments settings tab, you can specify how customers can pay for their products. By default, WooCommerce will set up Stripe and PayPal vendors for you.

      The Payment Methods settings in WooCommerce.

      However, you can add additional gateways — including popular solutions such as Square and Amazon Pay — with WooCommerce extensions. In addition, you can enable your customers to pay with a check, cash, or by bank transfer.

      The gateways you decide to offer are ultimately up to you, based on familiarity, ease of use, and transaction fees. However, it’s also important to consider your customers, as these criteria are also their primary concerns. As such, gateways such as PayPal are usually a given.

      Step 8: Run Through Your WooCommerce Search Engine Optimization (SEO) Checklist

      You’re almost ready to welcome customers to your store, but first, they need to be able to find it. SEO is the answer. By optimizing your content for search engines, you’ll make it more likely customers can find you while searching for products online.

      As with many site aspects, WordPress plugins can help. Yoast SEO is a highly rated and effective plugin that can help manage on-page SEO factors such as keyword usage, permalinks, and readability.

      The Yoast SEO plugin from the WordPress Plugin Directory.

      If you want something a little more specialized, you can also look into the Yoast WooCommerce SEO plugin.

      The Yoast WooCommerce SEO plugin.

      It’s better suited to WooCommerce than the free version, and can also help promote your products on social media. At $49 per year, it’s cost-effective and may be a solid investment, especially if it helps to bring in a few more organic customers via search engine.

      Step 9: Publish and Promote Your E-Commerce Website

      While you can keep refining your site, you’ll want to publish at this point — think of it as laying down a ‘marker.’ You’ll also want to make sure customers know who you are and what you do. Promoting your site on social media and through email marketing campaigns can help get you started.

      Fortunately, there are a variety of WooCommerce extensions available to help. You can choose popular services such as Drip, MailChimp, and even Instagram to promote your products to followers and subscribers.

      The WooCommerce Instagram extension.

      Marketing will be an ongoing responsibility, so investing in some tools to help you streamline your efforts will be worth it in the long run. The extensions mentioned above range from free to $79 per year. You can also search the WordPress Plugin Directory for more free solutions, although you may find functionality lacks depending on the plugin.

      Building an Online Store

      No one said becoming your own boss was easy, and there’s a lot of work that goes into starting a brand new business. However, WordPress and WooCommerce can simplify many of the tasks required to get your e-commerce site up and running.

      Ready to set up an online shop? Our WooCommerce hosting packages make it easy to sell anything, anywhere, anytime on the world’s biggest eCommerce platform.



      Source link

      How To Build a Deep Learning Model to Predict Employee Retention Using Keras and TensorFlow


      The author selected Girls Who Code to receive a donation as part of the Write for DOnations program.

      Introduction

      Keras is a neural network API that is written in Python. It runs on top of TensorFlow, CNTK, or Theano. It is a high-level abstraction of these deep learning frameworks and therefore makes experimentation faster and easier. Keras is modular, which means implementation is seamless as developers can quickly extend models by adding modules.

      TensorFlow is an open-source software library for machine learning. It works efficiently with computation involving arrays; so it’s a great choice for the model you’ll build in this tutorial. Furthermore, TensorFlow allows for the execution of code on either CPU or GPU, which is a useful feature especially when you’re working with a massive dataset.

      In this tutorial, you’ll build a deep learning model that will predict the probability of an employee leaving a company. Retaining the best employees is an important factor for most organizations. To build your model, you’ll use this dataset available at Kaggle, which has features that measure employee satisfaction in a company. To create this model, you’ll use the Keras sequential layer to build the different layers for the model.

      Prerequisites

      Before you begin this tutorial you’ll need the following:

      Step 1 — Data Pre-processing

      Data Pre-processing is necessary to prepare your data in a manner that a deep learning model can accept. If there are categorical variables in your data, you have to convert them to numbers because the algorithm only accepts numerical figures. A categorical variable represents quantitive data represented by names. In this step, you’ll load in your dataset using pandas, which is a data manipulation Python library.

      Before you begin data pre-processing, you’ll activate your environment and ensure you have all the necessary packages installed to your machine. It’s advantageous to use conda to install keras and tensorflow since it will handle the installation of any necessary dependencies for these packages, and ensure they are compatible with keras and tensorflow. In this way, using the Anaconda Python distribution is a good choice for data science related projects.

      Move into the environment you created in the prerequisite tutorial:

      Run the following command to install keras and tensorflow:

      • conda install tensorflow keras

      Now, open Jupyter Notebook to get started. Jupyter Notebook is opened by typing the following command on your terminal:

      Note: If you're working from a remote server, you'll need to use SSH tunneling to access your notebook. Please revisit step 2 of the prerequisite tutorial for detailed on instructions on setting up SSH tunneling. You can use the following command from your local machine to initiate your SSH tunnel:

      • ssh -L 8888:localhost:8888 your_username@your_server_ip

      After accessing Jupyter Notebook, click on the anaconda3 file, and then click New at the top of the screen, and select Python 3 to load a new notebook.

      Now, you'll import the required modules for the project and then load the dataset in a notebook cell. You'll load in the pandas module for manipulating your data and numpy for converting the data into numpy arrays. You'll also convert all the columns that are in string format to numerical values for your computer to process.

      Insert the following code into a notebook cell and then click Run:

      import pandas as pd
      import numpy as np
      df = pd.read_csv("https://raw.githubusercontent.com/mwitiderrick/kerasDO/master/HR_comma_sep.csv")
      

      You've imported numpy and pandas. You then used pandas to load in the dataset for the model.

      You can get a glimpse at the dataset you're working with by using head(). This is a useful function from pandas that allows you to view the first five records of your dataframe. Add the following code to a notebook cell and then run it:

      df.head()
      

      Alt Checking the head for the dataset

      You'll now proceed to convert the categorical columns to numbers. You do this by converting them to dummy variables. Dummy variables are usually ones and zeros that indicate the presence or absence of a categorical feature. In this kind of situation, you also avoid the dummy variable trap by dropping the first dummy.

      Note: The dummy variable trap is a situation whereby two or more variables are highly correlated. This leads to your model performing poorly. You, therefore, drop one dummy variable to always remain with N-1 dummy variables. Any of the dummy variables can be dropped because there is no preference as long as you remain with N-1 dummy variables. An example of this is if you were to have an on/off switch. When you create the dummy variable you shall get two columns: an on column and an off column. You can drop one of the columns because if the switch isn't on, then it is off.

      Insert this code in the next notebook cell and execute it:

      feats = ['department','salary']
      df_final = pd.get_dummies(df,columns=feats,drop_first=True)
      

      feats = ['department','salary'] defines the two columns for which you want to create dummy variables. pd.get_dummies(df,columns=feats,drop_first=True) will generate the numerical variables that your employee retention model requires. It does this by converting the feats that you define from categorical to numerical variables.

      Step 1

      You've loaded in the dataset and converted the salary and department columns into a format the keras deep learning model can accept. In the next step, you will split the dataset into a training and testing set.

      Step 2 — Separating Your Training and Testing Datasets

      You'll use scikit-learn to split your dataset into a training and a testing set. This is necessary so you can use part of the employee data to train the model and a part of it to test its performance. Splitting a dataset in this way is a common practice when building deep learning models.

      It is important to implement this split in the dataset so the model you build doesn't have access to the testing data during the training process. This ensures that the model learns only from the training data, and you can then test its performance with the testing data. If you exposed your model to testing data during the training process then it would memorize the expected outcomes. Consequently, it would fail to give accurate predictions on data that it hasn't seen.

      You'll start by importing the train_test_split module from the scikit-learn package. This is the module that will provide the splitting functionality. Insert this code in the next notebook cell and run:

      from sklearn.model_selection import train_test_split
      

      With the train_test_split module imported, you'll use the left column in your dataset to predict if an employee will leave the company. Therefore, it is essential that your deep learning model doesn't come into contact with this column. Insert the following into a cell to drop the left column:

      X = df_final.drop(['left'],axis=1).values
      y = df_final['left'].values
      

      Your deep learning model expects to get the data as arrays. Therefore you use numpy to convert the data to numpy arrays with the .values attribute.

      You're now ready to convert the dataset into a testing and training set. You'll use 70% of the data for training and 30% for testing. The training ratio is more than the testing ratio because you'll need to use most of the data for the training process. If desired, you can also experiment with a ratio of 80% for the training set and 20% for the testing set.

      Now add this code to the next cell and run to split your training and testing data to the specified ratio:

      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
      

      Step 2

      You have now converted the data into the type that Keras expects it to be in (numpy arrays), and your data is split into a training and testing set. You'll pass this data to the keras model later in the tutorial. Beforehand you need to transform the data, which you'll complete in the next step.

      Step 3 — Transforming the Data

      When building deep learning models it is usually good practice to scale your dataset in order to make the computations more efficient. In this step, you'll scale the data using the StandardScaler; this will ensure that your dataset values have a mean of zero and a unit variable. This transforms the dataset to be normally distributed. You'll use the scikit-learn StandardScaler to scale the features to be within the same range. This will transform the values to have a mean of 0 and a standard deviation of 1. This step is important because you're comparing features that have different measurements; so it is typically required in machine learning.

      To scale the training set and the test set, add this code to the notebook cell and run it:

      from sklearn.preprocessing import StandardScaler
      sc = StandardScaler()
      X_train = sc.fit_transform(X_train)
      X_test = sc.transform(X_test)
      

      Here, you start by importing the StandardScaler and calling an instance of it. You then use its fit_transform method to scale the training and testing set.

      You have scaled all your dataset features to be within the same range. You can start building the artificial neural network in the next step.

      Step 4 — Building the Artificial Neural Network

      Now you will use keras to build the deep learning model. To do this, you'll import keras, which will use tensorflow as the backend by default. From keras, you'll then import the Sequential module to initialize the artificial neural network. An artificial neural network is a computational model that is built using inspiration from the workings of the human brain. You'll import the Dense module as well, which will add layers to your deep learning model.

      When building a deep learning model you usually specify three layer types:

      • The input layer is the layer to which you'll pass the features of your dataset. There is no computation that occurs in this layer. It serves to pass features to the hidden layers.
      • The hidden layers are usually the layers between the input layer and the output layer—and there can be more than one. These layers perform the computations and pass the information to the output layer.
      • The output layer represents the layer of your neural network that will give you the results after training your model. It is responsible for producing the output variables.

      To import the Keras, Sequential, and Dense modules, run the following code in your notebook cell:

      import keras
      from keras.models import Sequential
      from keras.layers import Dense
      

      You'll use Sequential to initialize a linear stack of layers. Since this is a classification problem, you'll create a classifier variable. A classification problem is a task where you have labeled data and would like to make some predictions based on the labeled data. Add this code to your notebook to create a classifier variable:

      classifier = Sequential()
      

      You've used Sequential to initialize the classifier.

      You can now start adding layers to your network. Run this code in your next cell:

      classifier.add(Dense(9, kernel_initializer = "uniform",activation = "relu", input_dim=18))
      

      You add layers using the .add() function on your classifier and specify some parameters:

      • The first parameter is the number of nodes that your network should have. The connection between different nodes is what forms the neural network. One of the strategies to determine the number of nodes is to take the average of the nodes in the input layer and the output layer.

      • The second parameter is the kernel_initializer. When you fit your deep learning model the weights will be initialized to numbers close to zero, but not zero. To achieve this you use the uniform distribution initializer. kernel_initializer is the function that initializes the weights.

      • The third parameter is the activation function. Your deep learning model will learn through this function. There are usually linear and non-linear activation functions. You use the relu activation function because it generalizes well on your data. Linear functions are not good for problems like these because they form a straight line.

      • The last parameter is input_dim, which represents the number of features in your dataset.

      Now you'll add the output layer that will give you the predictions:

      classifier.add(Dense(1, kernel_initializer = "uniform",activation = "sigmoid"))
      

      The output layer takes the following parameters:

      • The number of output nodes. You expect to get one output: if an employee leaves the company. Therefore you specify one output node.

      • For kernel_initializer you use the sigmoid activation function so that you can get the probability that an employee will leave. In the event that you were dealing with more than two categories, you would use the softmax activation function, which is a variant of the sigmoid activation function.

      Next, you'll apply a gradient descent to the neural network. This is an optimization strategy that works to reduce errors during the training process. Gradient descent is how randomly assigned weights in a neural network are adjusted by reducing the cost function, which is a measure of how well a neural network performs based on the output expected from it.

      The aim of a gradient descent is to get the point where the error is at its least. This is done by finding where the cost function is at its minimum, which is referred to as a local minimum. In gradient descent, you differentiate to find the slope at a specific point and find out if the slope is negative or positive—you're descending into the minimum of the cost function. There are several types of optimization strategies, but you'll use a popular one known as adam in this tutorial.

      Add this code to your notebook cell and run it:

      classifier.compile(optimizer= "adam",loss = "binary_crossentropy",metrics = ["accuracy"])
      

      Applying gradient descent is done via the compile function that takes the following parameters:

      • optimizer is the gradient descent.
      • loss is a function that you'll use in the gradient descent. Since this is a binary classification problem you use the binary_crossentropy loss function.
      • The last parameter is the metric that you'll use to evaluate your model. In this case, you'd like to evaluate it based on its accuracy when making predictions.

      You're ready to fit your classifier to your dataset. Keras makes this possible via the .fit() method. To do this, insert the following code into your notebook and run it in order to fit the model to your dataset:

      classifier.fit(X_train, y_train, batch_size = 10, epochs = 1)
      

      Fitting the dataset

      The .fit() method takes a couple of parameters:

      • The first parameter is the training set with the features.

      • The second parameter is the column that you're making the predictions on.

      • The batch_size represents the number of samples that will go through the neural network at each training round.

      • epochs represents the number of times that the dataset will be passed via the neural network. The more epochs the longer it will take to run your model, which also gives you better results.

      Step 4

      You've created your deep learning model, compiled it, and fitted it to your dataset. You're ready to make some predictions using the deep learning model. In the next step, you'll start making predictions with the dataset that the model hasn't yet seen.

      Step 5 — Running Predictions on the Test Set

      To start making predictions, you'll use the testing dataset in the model that you've created. Keras enables you to make predictions by using the .predict() function.

      Insert the following code in the next notebook cell to begin making predictions:

      y_pred = classifier.predict(X_test)
      

      Since you've already trained the classifier with the training set, this code will use the learning from the training process to make predictions on the test set. This will give you the probabilities of an employee leaving. You'll work with a probability of 50% and above to indicate a high chance of the employee leaving the company.

      Enter the following line of code in your notebook cell in order to set this threshold:

      y_pred = (y_pred > 0.5)
      

      You've created predictions using the predict method and set the threshold for determining if an employee is likely to leave. To evaluate how well the model performed on the predictions, you will next use a confusion matrix.

      Step 6 — Checking the Confusion Matrix

      In this step, you will use a confusion matrix to check the number of correct and incorrect predictions. A confusion matrix, also known as an error matrix, is a square matrix that reports the number of true positives(tp), false positives(fp), true negatives(tn), and false negatives(fn) of a classifier.

      • A true positive is an outcome where the model correctly predicts the positive class (also known as sensitivity or recall).
      • A true negative is an outcome where the model correctly predicts the negative class.
      • A false positive is an outcome where the model incorrectly predicts the positive class.
      • A false negative is an outcome where the model incorrectly predicts the negative class.

      To achieve this you'll use a confusion matrix that scikit-learn provides.

      Insert this code in the next notebook cell to import the scikit-learn confusion matrix:

      from sklearn.metrics import confusion_matrix
      cm = confusion_matrix(y_test, y_pred)
      cm
      

      The confusion matrix output means that your deep learning model made 3305 + 375 correct predictions and 106 + 714 wrong predictions. You can calculate the accuracy with: (3305 + 375) / 4500. The total number of observations in your dataset is 4500. This gives you an accuracy of 81.7%. This is a very good accuracy rate since you can achieve at least 81% correct predictions from your model.

      Output

      array([[3305, 106], [ 714, 375]])

      You've evaluated your model using the confusion matrix. Next, you'll work on making a single prediction using the model that you have developed.

      Step 7 — Making a Single Prediction

      In this step you'll make a single prediction given the details of one employee with your model. You will achieve this by predicting the probability of a single employee leaving the company. You'll pass this employee's features to the predict method. As you did earlier, you'll scale the features as well and convert them to a numpy array.

      To pass the employee's features, run the following code in a cell:

      new_pred = classifier.predict(sc.transform(np.array([[0.26,0.7 ,3., 238., 6., 0.,0.,0.,0., 0.,0.,0.,0.,0.,1.,0., 0.,1.]])))
      

      These features represent the features of a single employee. As shown in the dataset in step 1, these features represent: satisfaction level, last evaluation, number of projects, and so on. As you did in step 3, you have to transform the features in a manner that the deep learning model can accept.

      Add a threshold of 50% with the following code:

      new_pred = (new_pred > 0.5)
      new_pred
      

      This threshold indicates that where the probability is above 50% an employee will leave the company.

      You can see in your output that the employee won't leave the company:

      Output

      array([[False]])

      You might decide to set a lower or higher threshold for your model. For example, you can set the threshold to be 60%:

      new_pred = (new_pred > 0.6)
      new_pred
      

      This new threshold still shows that the employee won't leave the company:

      Output

      array([[False]])

      In this step, you have seen how to make a single prediction given the features of a single employee. In the next step, you will work on improving the accuracy of your model.

      Step 8 — Improving the Model Accuracy

      If you train your model many times you'll keep getting different results. The accuracies for each training have a high variance. In order to solve this problem, you'll use K-fold cross-validation. Usually, K is set to 10. In this technique, the model is trained on the first 9 folds and tested on the last fold. This iteration continues until all folds have been used. Each of the iterations gives its own accuracy. The accuracy of the model becomes the average of all these accuracies.

      keras enables you to implement K-fold cross-validation via the KerasClassifier wrapper. This wrapper is from scikit-learn cross-validation. You'll start by importing the cross_val_score cross-validation function and the KerasClassifier. To do this, insert and run the following code in your notebook cell:

      from keras.wrappers.scikit_learn import KerasClassifier
      from sklearn.model_selection import cross_val_score
      

      To create the function that you will pass to the KerasClassifier, add this code to the next cell:

      def make_classifier():
          classifier = Sequential()
          classifier.add(Dense(9, kernel_initializer = "uniform", activation = "relu", input_dim=18))
          classifier.add(Dense(1, kernel_initializer = "uniform", activation = "sigmoid"))
          classifier.compile(optimizer= "adam",loss = "binary_crossentropy",metrics = ["accuracy"])
          return classifier
      

      Here, you create a function that you'll pass to the KerasClassifier—the function is one of the arguments that the classifier expects. The function is a wrapper of the neural network design that you used earlier. The passed parameters are also similar to the ones used earlier in the tutorial. In the function, you first initialize the classifier using Sequential(), you then use Dense to add the input and output layer. Finally, you compile the classifier and return it.

      To pass the function you've built to the KerasClassifier, add this line of code to your notebook:

      classifier = KerasClassifier(build_fn = make_classifier, batch_size=10, nb_epoch=1)
      

      The KerasClassifier takes three arguments:

      • build_fn: the function with the neural network design
      • batch_size: the number of samples to be passed via the network in each iteration
      • nb_epoch: the number of epochs the network will run

      Next, you apply the cross-validation using Scikit-learn's cross_val_score. Add the following code to your notebook cell and run it:

      accuracies = cross_val_score(estimator = classifier,X = X_train,y = y_train,cv = 10,n_jobs = -1)
      

      This function will give you ten accuracies since you have specified the number of folds as 10. Therefore, you assign it to the accuracies variable and later use it to compute the mean accuracy. It takes the following arguments:

      • estimator: the classifier that you've just defined
      • X: the training set features
      • y: the value to be predicted in the training set
      • cv: the number of folds
      • n_jobs: the number of CPUs to use (specifying it as -1 will make use of all the available CPUs)

      Now you have applied the cross-validation, you can compute the mean and variance of the accuracies. To achieve this, insert the following code into your notebook:

      mean = accuracies.mean()
      mean
      

      In your output you'll see that the mean is 83%:

      Output

      0.8343617910685696

      To compute the variance of the accuracies, add this code to the next notebook cell:

      variance = accuracies.var()
      variance
      

      You see that the variance is 0.00109. Since the variance is very low, it means that your model is performing very well.

      Output

      0.0010935021002275425

      You've improved your model's accuracy by using K-Fold cross-validation. In the next step, you'll work on the overfitting problem.

      Step 9 — Adding Dropout Regularization to Fight Over-Fitting

      Predictive models are prone to a problem known as overfitting. This is a scenario whereby the model memorizes the results in the training set and isn't able to generalize on data that it hasn't seen. Typically you observe overfitting when you have a very high variance on accuracies. To help fight over-fitting in your model, you will add a layer to your model.

      In neural networks, dropout regularization is the technique that fights overfitting by adding a Dropout layer in your neural network. It has a rate parameter that indicates the number of neurons that will deactivate at each iteration. The process of deactivating nerurons is usually random. In this case, you specify 0.1 as the rate meaning that 1% of the neurons will deactivate during the training process. The network design remains the same.

      To add your Dropout layer, add the following code to the next cell:

      from keras.layers import Dropout
      
      classifier = Sequential()
      classifier.add(Dense(9, kernel_initializer = "uniform", activation = "relu", input_dim=18))
      classifier.add(Dropout(rate = 0.1))
      classifier.add(Dense(1, kernel_initializer = "uniform", activation = "sigmoid"))
      classifier.compile(optimizer= "adam",loss = "binary_crossentropy",metrics = ["accuracy"])
      

      You have added a Dropout layer between the input and output layer. Having set a dropout rate of 0.1 means that during the training process 15 of the neurons will deactivate so that the classifier doesn't overfit on the training set. After adding the Dropout and output layers you then compiled the classifier as you have done previously.

      You worked to fight over-fitting in this step with a Dropout layer. Next, you'll work on further improving the model by tuning the parameters you used while creating the model.

      Step 10 — Hyperparameter Tuning

      Grid search is a technique that you can use to experiment with different model parameters in order to obtain the ones that give you the best accuracy. The technique does this by trying different parameters and returning those that give the best results. You'll use grid search to search for the best parameters for your deep learning model. This will help in improving model accuracy. scikit-learn provides the GridSearchCV function to enable this functionality. You will now proceed to modify the make_classifier function to try out different parameters.

      Add this code to your notebook to modify the make_classifier function so you can test out different optimizer functions:

      from sklearn.model_selection import GridSearchCV
      def make_classifier(optimizer):
          classifier = Sequential()
          classifier.add(Dense(9, kernel_initializer = "uniform", activation = "relu", input_dim=18))
          classifier.add(Dense(1, kernel_initializer = "uniform", activation = "sigmoid"))
          classifier.compile(optimizer= optimizer,loss = "binary_crossentropy",metrics = ["accuracy"])
          return classifier
      

      You have started by importing GridSearchCV. You have then made changes to the make_classifier function so that you can try different optimizers. You've initialized the classifier, added the input and output layer, and then compiled the classifier. Finally, you have returned the classifier so you can use it.

      Like in step 4, insert this line of code to define the classifier:

      classifier = KerasClassifier(build_fn = make_classifier)
      

      You've defined the classifier using the KerasClassifier, which expects a function through the build_fn parameter. You have called the KerasClassifier and passed the make_classifier function that you created earlier.

      You will now proceed to set a couple of parameters that you wish to experiment with. Enter this code into a cell and run:

      params = {
          'batch_size':[20,35],
          'epochs':[2,3],
          'optimizer':['adam','rmsprop']
      }
      

      Here you have added different batch sizes, number of epochs, and different types of optimizer functions.

      For a small dataset like yours, a batch size of between 20–35 is good. For large datasets its important to experiment with larger batch sizes. Using low numbers for the number of epochs ensures that you get results within a short period. However, you can experiment with bigger numbers that will take a while to complete depending on the processing speed of your server. The adam and rmsprop optimizers from keras are a good choice for this type of neural network.

      Now you're going to use the different parameters you have defined to search for the best parameters using the GridSearchCV function. Enter this into the next cell and run it:

      grid_search = GridSearchCV(estimator=classifier,
                                 param_grid=params,
                                 scoring="accuracy",
                                 cv=2)
      

      The grid search function expects the following parameters:

      • estimator: the classifier that you're using.
      • param_grid: the set of parameters that you're going to test.
      • scoring: the metric you're using.
      • cv: the number of folds you'll test on.

      Next, you fit this grid_search to your training dataset:

      grid_search = grid_search.fit(X_train,y_train)
      

      Your output will be similar to the following, wait a moment for it to complete:

      Output

      Epoch 1/2 5249/5249 [==============================] - 1s 228us/step - loss: 0.5958 - acc: 0.7645 Epoch 2/2 5249/5249 [==============================] - 0s 82us/step - loss: 0.3962 - acc: 0.8510 Epoch 1/2 5250/5250 [==============================] - 1s 222us/step - loss: 0.5935 - acc: 0.7596 Epoch 2/2 5250/5250 [==============================] - 0s 85us/step - loss: 0.4080 - acc: 0.8029 Epoch 1/2 5249/5249 [==============================] - 1s 214us/step - loss: 0.5929 - acc: 0.7676 Epoch 2/2 5249/5249 [==============================] - 0s 82us/step - loss: 0.4261 - acc: 0.7864

      Add the following code to a notebook cell to obtain the best parameters from this search using the best_params_ attribute:

      best_param = grid_search.best_params_
      best_accuracy = grid_search.best_score_
      

      You can now check the best parameters for your model with the following code:

      best_param
      

      Your output shows that the best batch size is 20, the best number of epochs is 2, and the adam optimizer is the best for your model:

      Output

      {'batch_size': 20, 'epochs': 2, 'optimizer': 'adam'}

      You can check the best accuracy for your model. The best_accuracy number represents the highest accuracy you obtain from the best parameters after running the grid search:

      best_accuracy
      

      Your output will be similar to the following:

      Output

      0.8533193637489285

      You've used GridSearch to figure out the best parameters for your classifier. You have seen that the best batch_size is 20, the best optimizer is the adam optimizer and the best number of epochs is 2. You have also obtained the best accuracy for your classifier as being 85%. You've built an employee retention model that is able to predict if an employee stays or leaves with an accuracy of up to 85%.

      Conclusion

      In this tutorial, you've used Keras to build an artificial neural network that predicts the probability that an employee will leave a company. You combined your previous knowledge in machine learning using scikit-learn to achieve this. To further improve your model, you can try different activation functions or optimizer functions from keras. You could also experiment with a different number of folds, or, even build a model with a different dataset.

      For other tutorials in the machine learning field or using TensorFlow, you can try building a neural network to recognize handwritten digits or other DigitalOcean machine learning tutorials.



      Source link

      How To Build a Search Bar with RxJS


      The author selected Mozilla Foundation to receive a donation as part of the Write for DOnations program.

      Introduction

      Reactive Programming is a paradigm concerned with asynchronous data streams, in which the programming model considers everything to be a stream of data spread over time. This includes keystrokes, HTTP requests, files to be printed, and even elements of an array, which can be considered to be timed over very small intervals. This makes it a perfect fit for JavaScript as asynchronous data is common in the language.

      RxJS is a popular library for reactive programming in JavaScript. ReactiveX, the umbrella under which RxJS lies, has its extensions in many other languages like Java, Python, C++, Swift, and Dart. RxJS is also widely used by libraries like Angular and React.

      RxJS’s implementation is based on chained functions that are aware and capable of handling data over a range of time. This means that one could implement virtually every aspect of RxJS with nothing more than functions that receive a list of arguments and callbacks, and then execute them when signaled to do so. The community around RxJS has done this heavy lifting, and the result is an API that you can directly use in any application to write clean and maintainable code.

      In this tutorial, you will use RxJS to build a feature-rich search bar that returns real-time results to users. You will also use HTML and CSS to format the search bar. The end result will look this this:

      Demonstration of Search Bar

      Something as common and seemingly simple as a search bar needs to have various checks in place. This tutorial will show you how RxJS can turn a fairly complex set of requirements into code that is manageable and easy to understand.

      Prerequisites

      Before you begin this tutorial you’ll need the following:

      The full code for the tutorial is available on Github.

      In this step, you will create and style the search bar with HTML and CSS. The code will use a few common elements from Bootstrap to speed up the process of structuring and styling the page so you can focus on adding custom elements. Bootstrap is a CSS framework that contains templates for common elements like typography, forms, buttons, navigation, grids, and other interface components. Your application will also use Animate.css to add animation to the search bar.

      You will start start by creating a file named search-bar.html with nano or your favorite text editor:

      Next, create the basic structure for your application. Add the following HTML to the new file:

      search-bar.html

      <!DOCTYPE html>
      <html>
      
        <head>
          <title>RxJS Tutorial</title>
          <!-- Load CSS -->
      
          <!-- Load Rubik font -->
      
          <!-- Add Custom inline CSS -->
      
        </head>
      
        <body>
            <!-- Content -->
      
            <!-- Page Header and Search Bar -->
      
            <!-- Results -->
      
            <!-- Load External RxJS -->
      
            <!-- Add custom inline JavaScript -->
            <script>
      
            </script>
        </body>
      
      </html>
      

      As you need CSS from the entire Bootstrap library, go ahead and load the CSS for Bootstrap and Animate.css.

      Add the following code under the Load CSS comment:

      search-bar.html

      ...
      <!-- Load CSS -->
          <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
          <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/3.7.0/animate.min.css" />
      ...
      

      This tutorial will use a custom font called Rubik from the Google Fonts library to style the search bar. Load the font by adding the highlighted code under the Load Rubik font comment:

      search-bar.html

      ...
      <!-- Load Rubik font -->
          <link href="https://fonts.googleapis.com/css?family=Rubik" rel="stylesheet">
      ...
      

      Next, add the custom CSS to the page under the Add Custom inline CSS comment. This will make sure that the headings, search bar, and the results on the page are easy to read and use.

      search-bar.html

      ...
      <!-- Add Custom inline CSS -->
          <style>
            body {
              background-color: #f5f5f5;
              font-family: "Rubik", sans-serif;
            }
      
            .search-container {
              margin-top: 50px;
            }
            .search-container .search-heading {
              display: block;
              margin-bottom: 50px;
            }
            .search-container input,
            .search-container input:focus {
              padding: 16px 16px 16px;
              border: none;
              background: rgb(255, 255, 255);
              box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.2), 0 25px 50px 0 rgba(0, 0, 0, 0.1) !important;
            }
      
            .results-container {
              margin-top: 50px;
            }
            .results-container .list-group .list-group-item {
              background-color: transparent;
              border-top: none !important;
              border-bottom: 1px solid rgba(236, 229, 229, 0.64);
            }
      
            .float-bottom-right {
              position: fixed;
              bottom: 20px;
              left: 20px;
              font-size: 20px;
              font-weight: 700;
              z-index: 1000;
            }
            .float-bottom-right .info-container .card {
              display: none;
            }
            .float-bottom-right .info-container:hover .card,
            .float-bottom-right .info-container .card:hover {
              display: block;
            }
          </style>
      ...
      

      Now that you have all of the styles in place, add the HTML that will define the header and the input bar under the Page Header and Search Bar comment:

      search-bar.html

      ...
      <!-- Content -->
      <!-- Page Header and Search Bar -->
            <div class="container search-container">
              <div class="row justify-content-center">
                <div class="col-md-auto">
                  <div class="search-heading">
                    <h2>Search for Materials Published by Author Name</h2>
                    <p class="text-right">powered by <a href="https://www.crossref.org/">Crossref</a></p>
                  </div>
                </div>
              </div>
              <div class="row justify-content-center">
                <div class="col-sm-8">
                  <div class="input-group input-group-md">
                    <input id="search-input" type="text" class="form-control" placeholder="eg. Richard" aria-label="eg. Richard" autofocus>
                  </div>
                </div>
              </div>
            </div>
      ...
      

      This uses the grid system from Bootstrap to structure the page header and the search bar. You have assigned a search-input identifier to the search bar, which you will use to bind to a listener later in the tutorial.

      Next, you will create a location to display the results of the search. Under the Results comment, create a div with the response-list identifier to add the results later in the tutorial:

      search-bar.html

      ...
      <!-- Results -->
            <div class="container results-container">
              <div class="row justify-content-center">
                <div class="col-sm-8">
                  <ul id="response-list" class="list-group list-group-flush"></ul>
                </div>
              </div>
            </div>
      ...
      

      At this point, the search-bar.html file will look like this:

      search-bar.html

      <!DOCTYPE html>
      <html>
      
        <head>
          <title>RxJS Tutorial</title>
          <!-- Load CSS -->
          <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
          <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/3.7.0/animate.min.css" />
      
          <!-- Load Rubik font -->
          <link href="https://fonts.googleapis.com/css?family=Rubik" rel="stylesheet">
      
          <!-- Add Custom inline CSS -->
          <style>
            body {
              background-color: #f5f5f5;
              font-family: "Rubik", sans-serif;
            }
      
            .search-container {
              margin-top: 50px;
            }
            .search-container .search-heading {
              display: block;
              margin-bottom: 50px;
            }
            .search-container input,
            .search-container input:focus {
              padding: 16px 16px 16px;
              border: none;
              background: rgb(255, 255, 255);
              box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.2), 0 25px 50px 0 rgba(0, 0, 0, 0.1) !important;
            }
      
            .results-container {
              margin-top: 50px;
            }
            .results-container .list-group .list-group-item {
              background-color: transparent;
              border-top: none !important;
              border-bottom: 1px solid rgba(236, 229, 229, 0.64);
            }
      
            .float-bottom-right {
              position: fixed;
              bottom: 20px;
              left: 20px;
              font-size: 20px;
              font-weight: 700;
              z-index: 1000;
            }
            .float-bottom-right .info-container .card {
              display: none;
            }
            .float-bottom-right .info-container:hover .card,
            .float-bottom-right .info-container .card:hover {
              display: block;
            }
          </style>
        </head>
      
        <body>
            <!-- Content -->
            <!-- Page Header and Search Bar -->
            <div class="container search-container">
              <div class="row justify-content-center">
                <div class="col-md-auto">
                  <div class="search-heading">
                    <h2>Search for Materials Published by Author Name</h2>
                    <p class="text-right">powered by <a href="https://www.crossref.org/">Crossref</a></p>
                  </div>
                </div>
              </div>
              <div class="row justify-content-center">
                <div class="col-sm-8">
                  <div class="input-group input-group-md">
                    <input id="search-input" type="text" class="form-control" placeholder="eg. Richard" aria-label="eg. Richard" autofocus>
                  </div>
                </div>
              </div>
            </div>
      
            <!-- Results -->
            <div class="container results-container">
              <div class="row justify-content-center">
                <div class="col-sm-8">
                  <ul id="response-list" class="list-group list-group-flush"></ul>
                </div>
              </div>
            </div>
      
            <!-- Load RxJS -->
      
            <!-- Add custom inline JavaScript -->
            <script>
      
            </script>
        </body>
      
      </html>
      

      In this step, you've laid out the basic structure for your search bar with HTML and CSS. In the next step, you will write a JavaScript function that will accept search terms and return results.

      Step 2 — Writing the JavaScript

      Now that you have the search bar formatted, you are ready to write the JavaScript code that will act as a foundation for the RxJS code that you'll write later in this tutorial. This code will work with RxJS to accept search terms and return results.

      Since you won't need the functionalities that Bootstrap and JavaScript provide in this tutorial, you aren't going to load them. However, you will be using RxJS. Load the RxJS library by adding the following under the Load RxJS comment:

      search-bar.html

      ...
      <!-- Load RxJS -->
          <script src="https://unpkg.com/@reactivex/[email protected]/dist/global/Rx.js"></script>
      ...
      

      Now you will store references of the div from the HTML to which the results will be added. Add the highlighted JavaScript code in the <script> tag under the Add custom inline JavaScript comment:

      search-bar.html

      ...
      <!-- Add custom inline JavaScript -->
      <script>
              const output = document.getElementById("response-list");
      
      </script>
      ...
      

      Next, add the code to convert the JSON response from the API into the HTML elements to display on the page. This code will first clear the contents of the search bar and then set a delay for the search result animation.

      Add the highlighted function between the <script> tags:

      search-bar.html

      ...
      <!-- Add custom inline JavaScript -->
      <script>
          const output = document.getElementById("response-list");
      
              function showResults(resp) {
              var items = resp['message']['items']
              output.innerHTML = "";
              animationDelay = 0;
              if (items.length == 0) {
                output.innerHTML = "Could not find any :(";
              } else {
                items.forEach(item => {
                  resultItem = `
                  <div class="list-group-item animated fadeInUp" style="animation-delay: ${animationDelay}s;">
                    <div class="d-flex w-100 justify-content-between">
      <^>                <h5 class="mb-1">${(item['title'] && item['title'][0]) || "&lt;Title not available&gt;"}</h5>
                    </div>
                    <p class="mb-1">${(item['container-title'] && item['container-title'][0]) || ""}</p>
                    <small class="text-muted"><a href="${item['URL']}" target="_blank">${item['URL']}</a></small>
                    <div> 
                      <p class="badge badge-primary badge-pill">${item['publisher'] || ''}</p>
                      <p class="badge badge-primary badge-pill">${item['type'] || ''}</p> 
                   </div>
                  </div>
                  `;
                  output.insertAdjacentHTML("beforeend", resultItem);
                  animationDelay += 0.1; 
      
                });
              }
            }
      
      </script>
      ...
      

      The code block starting with if is a conditional loop that checks for search results, and displays a message if no results were found. If results are found, then the forEach loop will provide the results with an animation to the user.

      In this step, you laid out the base for the RxJS by writing out a function that can accept results and return it on the page. In the next step, you will make the search bar functional.

      Step 3 — Setting Up a Listener

      RxJS is concerned with data streams, which in this project is a series of characters that the user enters in to the input element, or search bar. In this step, you will add a listener on the input element to listen for updates.

      First, take note of the search-input identifier that you added earlier in the tutorial:

      search-bar.html

      ...
      <input id="search-input" type="text" class="form-control" placeholder="eg. Richard" aria-label="eg. Richard" autofocus>
      ...
      

      Next, create a variable that will hold references for the search-input element. This will become the Observable that the code will use to listen for input events. Observables are a collection of future values or events that an Observer listens to, and are also known as callback functions.

      Add the highlighted line in the <script> tag under the JavaScript from the previous step:

      search-bar.html

      ...
            output.insertAdjacentHTML("beforeend", resultItem);
            animationDelay += 0.1; 
      
          });
        }
      }
      
      
            let searchInput = document.getElementById("search-input");
      ...
      

      Now that you've added a variable to reference input, you will use the fromEvent operator to listen for events. This will add a listener on a DOM, or Document Object Model, element for a certain kind of event. A DOM element could be a html, body, div, or img element on a page. In this case, your DOM element is the search bar.

      Add the following highlighted line under your searchInput variable to pass your parameters to fromEvent. Your searchInput DOM element is the first parameter. This is followed by the input event as the second parameter, which is the event type the code will listen for.

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
      ...
      

      Now that your listener is set up, your code will receive a notification whenever any updates take place on the input element. In the next step you will use operators to take action on such events.

      Step 4 — Adding Operators

      Operators are pure functions with one task—to perform an operation on data. In this step, you will use operators to perform various tasks such as buffering the input parameter, making HTTP requests, and filtering results.

      You will first make sure that the results update in real-time as the user enters queries. To achieve this, you will use the DOM input event from the previous step. The DOM input event contains various details, but for this tutorial you are interested in values typed into the target element. Add the following code to use the pluck operator to take an object and return the value at the specified key:

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
      ...
      

      Now that the events are in the necessary format, you will set the search-term minimum to three characters. In many cases, anything less than three characters will not yield relevant results, or the user may still be in the process of typing.

      You will use the filter operator to set the minimum. It will pass the data further down the stream if it satisfies the specified condition. Set the length condition to greater than 2 to require at least three characters.

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
      ...
      

      You will also make sure that requests are only sent in at 500ms intervals to ease up the load on the API server. To do this, you will use the debounceTime operator to maintain a minimum specified interval between each event that it passes through the stream. Add the highlighted code under the filter operator:

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
              .debounceTime(500)
      ...
      

      The application should also ignore the search term if there have been no changes since the last API call. This will optimize the application by further reducing the number of sent API calls.

      As an example, a user may type super cars, delete the last character (making the term super car), and then add the deleted character back to revert the term back to super cars. As a result, the term did not change, and therefore the search results should not change. In such cases it makes sense to not perform any operations.

      You will use the distinctUntilChanged operator to configure this. This operator remembers the previous data that was passed through the stream and passes another only if it is different.

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
              .debounceTime(500)
              .distinctUntilChanged()
      ...
      

      Now that you have regulated the inputs from the user, you will add the code that will query the API with the search term. To do this, you will use the RxJS implementation of AJAX. AJAX makes API calls asynchronously in the background on a loaded page. AJAX will allow you to avoid reloading the page with results for new search terms and also update the results on the page by fetching the data from the server.

      Next, add the code to use switchMap to chain AJAX to your application. You will also use map to map the input to an output. This code will apply the function passed to it to every item emitted by an Observable.

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
              .debounceTime(500)
              .distinctUntilChanged()
              .switchMap(searchKey => Rx.Observable.ajax(`https://api.crossref.org/works?rows=50&query.author=${searchKey}`)
                .map(resp => ({
                    "status" : resp["status"] == 200,
                    "details" : resp["status"] == 200 ? resp["response"] : [],
                    "result_hash": Date.now()
                  })
                )
              )
      ...
      

      This code breaks the API response into three parts:

      • status: The HTTP status code returned by the API server. This code will only accept 200, or successful, responses.
      • details: The actual response data received. This will contain the results for the queried search term.
      • result_hash: A hash value of the responses returned by the API server, which for the purpose of this tutorial is a UNIX time-stamp. This is a hash of results that changes when the results change. The unique hash value will allow the application to determine if the results have changed and should be updated.

      Systems fail and your code should be prepared to handle errors. To handle errors that may happen in the API call, use the filter operator to only accept successful responses:

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
              .debounceTime(500)
              .distinctUntilChanged()
              .switchMap(searchKey => Rx.Observable.ajax(`https://api.crossref.org/works?rows=50&query.author=${searchKey}`)
                .map(resp => ({
                    "status" : resp["status"] == 200,
                    "details" : resp["status"] == 200 ? resp["response"] : [],
                    "result_hash": Date.now()
                  })
                )
              )
              .filter(resp => resp.status !== false)
      ...
      

      Next, you will add code to only update the DOM if changes are detected in the response. DOM updates can be a resource-heavy operation, so reducing the number of updates will have a positive impact on the application. Since the result_hash will only change when a response changes, you will use it to implement this functionality.

      To do this, use the distinctUntilChanged operator like before. The code will use it to only accept user input when the key has changed.

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
              .debounceTime(500)
              .distinctUntilChanged()
              .switchMap(searchKey => Rx.Observable.ajax(`https://api.crossref.org/works?rows=50&query.author=${searchKey}`)
                .map(resp => ({
                    "status" : resp["status"] == 200,
                    "details" : resp["status"] == 200 ? resp["response"] : [],
                    "result_hash": Date.now()
                  })
                )
              )
              .filter(resp => resp.status !== false)
              .distinctUntilChanged((a, b) => a.result_hash === b.result_hash)
      ...
      

      You previously used the distinctUntilChanged operator to see if the entirety of the data had changed, but in this instance, you check for an updated key in the response. Comparing the entire response would be resource-costly when compared to identifying changes in a single key. Since the key hash is representative of the whole response, it can confidently be used to identify response changes.

      The function accepts two objects, the previous value that it had seen and the new value. We check the hash from these two objects and return True when these two values match, in which case the data is filtered out and not passed further in the pipeline.

      In this step, you created a pipeline that receives a search term entered by the user and then performs various checks on it. After the checks are complete, it makes an API call and returns the response in a format that displays results back to the user. You optimized the resource usage on both the client and server side by limiting API calls when necessary. In the next step, you will configure the application to start listening on the input element, and pass the results to the function that will render it on the page.

      Step 5 — Activating Everything with a Subscription

      subscribe is the final operator of the link that enables the observer to see data events emitted by the Observable. It implements the following three methods:

      • onNext: This specifies what to do when an event is received.
      • onError: This is responsible for handling errors. Calls to onNext and onCompleted will not be made once this method is called.
      • onCompleted: This method is called when onNext has been called for the final time. There would be no more data that will be passed in the pipeline.

      This signature of a subscriber is what enables one to achieve lazy execution, which is the ability to define an Observable pipeline and set it in motion only when you subscribe to it. You won't use this example in your code, but the following shows you how an Observable can be subscribed to:

      Next, subscribe to the Observable and route the data to the method that is responsible for rendering it in the UI.

      search-bar.html

      ...
            let searchInput = document.getElementById("search-input");
            Rx.Observable.fromEvent(searchInput, 'input')
              .pluck('target', 'value')
              .filter(searchTerm => searchTerm.length > 2)
              .debounceTime(500)
              .distinctUntilChanged()
              .switchMap(searchKey => Rx.Observable.ajax(`https://api.crossref.org/works?rows=50&query.author=${searchKey}`)
                .map(resp => ({
                    "status" : resp["status"] == 200,
                    "details" : resp["status"] == 200 ? resp["response"] : [],
                    "result_hash": Date.now()
                  })
                )
              )
              .filter(resp => resp.status !== false)
              .distinctUntilChanged((a, b) => a.result_hash === b.result_hash)
              .subscribe(resp => showResults(resp.details));
      ...
      

      Save and close the file after making these changes.

      Now that you've completed writing the code, you are ready to view and test your search bar. Double-click the search-bar.html file to open it in your web browser. If the code was entered in correctly, you will see your search bar.

      The completed search bar

      Type content in your search bar to test it out.

      A gif of content being entered into the search bar, showing that two characters won't return any results.

      In this step, you subscribed to the Observable to activate your code. You now have a stylized and functioning search bar application.

      Conclusion

      In this tutorial, you created a feature-rich search bar with RxJS, CSS, and HTML that provides real-time results to users. The search bar requires a minimum of three characters, updates automatically, and is optimized for both the client and the API server.

      What could be considered a complex set of requirements was created with 18 lines of RxJS code. The code is not only reader-friendly, but it is also much cleaner than a standalone JavaScript implementation. This means that your code will be easier to understand, update, and maintain in the future.

      To read more about using RxJS, check out the official API documentation.



      Source link