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Jufel Librarian, Philippines
$3 /hr
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I am a librarian profession and a freelancer at the same time. I am a virtual assistant for 5 years and a social media manager for 3 years. I can do a...Read More
Prabhas K.Software Developer, India
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Highly motivated and focused with experience in java and micro services. Proficient in object-oriented design, data structure, problem solving and deb...Read More
Subhasis P.Senior Java Developer, India
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8 Years Exp.
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I have total experience of 8 years in java, spring, microservice. I am certified AWS slution architect.
Arun Kumar R Software Consultant, India
$4 /hr
6 Years Exp.
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An Associate Software development Consultant at a German technological company having 6 years experience in software development. Currently I'm p...Read More
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If you’re considering hiring a ghostwriter for the first time, you’ll want to keep the following in mind.

Understand your needs:

Before hiring a ghostwriters, first discover your needs and expectations for them. Ask yourself the following questions:

  • Do I want to hire the ghostwriter for one project or multiple?
  • What type of text do I want a book, article, blog, anthology, or e-book?
  • How many pages or words do I want my text to be?
  • What is my deadline?
  • What type of writing do I want, formal, casual, or academic?
  • What is my budget?
  • How much experience should the ghostwriter have?

Search freelance ghostwriter:

Toogit freelance ghostwriter provide ghostwriting services at Toogit.com. Creating a job post on Toogit include information such as your needs and requirements and your budget. Keep the tone friendly to attract possible candidates. Searching best freelance ghostwriters on Toogit can ensure that you get a high quality ghostwriter who best fits your specific project. Ghostwriters on Toogit are highly skilled and talented. Hiring Ghostwriters on Toogit is quite affordable as compared to a full-time employee and you can save up to 50% in business cost by hiring Ghostwriters on Toogit.

Take interview of shortlisted candidates:

Once you’ve shortlisted a ghostwriter candidates have a conversation about your expectations, needs, and how the person envisions the collaboration. Ask any questions you have and answer any questions the candidate may have for you. An interview can help you figure out if you and the potential ghostwriter have right chemistry to work together. It can even offer you a way of the person’s communication skills.

Review his previous work and ask for writing sample on your project:

Request a freelancer to provide several writing samples of works they’ve ghostwritten. Reviewing his previous work will minimize the risk of hiring a ghostwriter with very little experience or who doesn’t have a style you prefer. After that ask freelancer to provide a one page writing sample on your project within a specific period of time. Review every text to see if it’s customized to your project and meets your expectations. This may ensure you hire the best ghostwriter for your specific project.

Agree on project details:

Most ghostwriters have standard contracts they use. That’s fine, you probably don’t need to involve lawyers, but there are many deal terms you would like to make positive you discuss properly.

  1. Price and payment terms: Make sure you're very clear on how much you're paying, and when those payments are due. There are a lot of ways to pay, however the vast majority of excellent ghostwriters can use a flat fee structure that means you're paying a set amount for a defined book of a defined size or scope. There are some that use flat fee + hourly for revisions beyond a certain point. That’s fine too. On payment terms, confirm that you are paying in installments and not paying all the money up front. Those installments can be time based, or tied to specific deliverable.
  2. Deliverables: Part of the payment discussion will be what the deliverables are. The very best and most expensive ghostwriters will be somewhat loose with this, they can afford to be when they are charging $100k+ for a manuscript. You are paying them to just handle everything, whatever it takes, and for that price, they will. Do not let a ghostwriter just go with a straight hourly fee, unless you are cool with them potentially running up their bill. Most of the good ones will have a set price for a specific set of deliverables, and then an hourly rate for time beyond that.
  3. Rights and Royalties: Make sure you retain 100% of all the rights to your book. Not only copyright, but the print license, all film, rights, TV, foreign, and adaptations rights. Do not let any ghostwriter try to keep any rights to your book, ever.
  4. Plagiarism protection: This won’t be a concern with a good ghostwriter, but make sure you are indemnified against them stealing someone else’s content. No good ghostwriter would ever think of doing this, so they’ll have no issue putting in the contract.

The importance of extracting information from the web is becoming increasingly loud and clear. Every few weeks, I realize myself in a situation where we need to extract information from the web to create a machine learning model. We have to pull or extract a large amount of information from websites and we would like to do it as quickly as possible. How would we do it without manually going to every web site and getting the data? Web Scraping simply makes this job easier and faster.

Why is web scraping needed?

Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? Let’s look at the applications of web scraping: 

  1. Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products.
  2. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending.
  3. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails.
  4. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc.) from websites, which are analyzed and used to carry out Surveys or for R&D.
  5. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the user.

 

Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code.

Why Python is best for Web Scraping

Features of Python which makes it more suitable for web scraping:

  1. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use.
  2. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data.
  3. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster.
  4. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code.
  5. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code.
  6. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help from.

How does web scraping work

To extract data using web scraping with python, you need to follow these basic steps:

  1. Find the URL that you want to scrape
  2. Inspecting the Page
  3. Find the data you want to extract
  4. Write the code
  5. Run the code and extract the data
  6. Store the data in the required format

Example: Scraping a website to get product details

Pre-requisite:

  • Python 2.x or Python 3.x
  • Selenium Library
  • BeautifulSoup Library
  • Pandas Library
  1. We are going scrape online shopping website to extract the Price, Name, and rating of products, go to products URL
  2. The data is usually nested in tags. So, we inspect the page to examine, under which tag the information we would like to scrape is nested. To inspect the page, just right click on the element and click on “Inspect”. When you click on the “Inspect” tab, you will see a “Browser Inspector Box” open.
  3. Let’s extract the Price, Name, and Rating which is nested in the “div” tag respectively.
  4. Write code:

#Let us import all the necessary libraries

from selenium import webdriver

from BeautifulSoup import BeautifulSoup

import pandas as pd

driver = webdriver.Chrome("/usr/lib/chromium-browser/chromedriver")

products=[] #List to store name of the product

prices=[] #List to store price of the product

ratings=[] #List to store rating of the product

driver.get("Product_URL")

content = driver.page_source

soup = BeautifulSoup(content)

for a in soup.findAll('a',href=True, attrs={'class':'.…'}):

name=a.find('div', attrs={'class': '….'})

price=a.find('div', attrs={'class':'….'})

rating=a.find('div', attrs={'class':'….'})

products.append(name.text)

ratings.append(rating.text)

df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings})

df.to_csv('products.csv', index=False, encoding='utf-8')

 

To run the code, a file name “products.csv” is created and this file contains the extracted data.

Optimization deals with selecting the simplest option among a number of possible choices that are feasible or do not violate constraints. Python is used to optimize parameters in a model to best fit data, increase profitability of a possible engineering style, or meet another form of objective which will be described mathematically with variables and equations.

 

pyOpt is a Python-based package for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. Python programming uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem.

 

All optimisation downside solvers inherit from the Optimizer abstract category. The category attributes include the solver name (name), an optimizer kind symbol (category), and dictionaries that contain the solver setup parameters (options) and message output settings (informs). The class provides ways to check and alter default solver parameters (getOption, setOption), as well as a method that runs the solver for a given optimisation problem (solve).

 

Optimization solver

A number of constrained optimization solvers are designed to solve the general nonlinear optimization problem.

  1. PSQP: This optimizer is a preconditioned sequential quadratic programming algorithm. This optimizer implements a sequential quadratic programming method with a BFGS variable metric update.
  2. SLSQP: This optimizer is a sequential least squares programming algorithm. SLSQP uses the Han–Powell quasi-Newton method with a BFGS update of the B-matrix and an L1-test function in the step-length algorithm. The optimizer uses a slightly modified version of Lawson and Hanson’s NNLS nonlinear least-squares solver.
  3. CONMIN: This optimizer implements the method of feasible directions. CONMIN solves the nonlinear programming problem by moving from one feasible point to an improved one by choosing at each iteration a feasible direction and step size that improves the objective function.
  4. COBYLA: It is an implementation of Powell’s nonlinear derivative–free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust–region algorithm that employs linear approximations to the objective and constraint functions.
  5. SOLVOPT: SOLVOPT is a modified version of Shor’s r–algorithm with space dilation to find a local minimum of nonlinear and non–smooth problems.
  6. KSOPT: This code reformulates the constrained problem into an unconstrained one using a composite Kreisselmeier–Steinhauser objective function to create an envelope of the objective function and set of constraints. The envelope function is then optimized using a sequential unconstrained minimization technique.
  7. NSGA2: This optimizer is a non-dominating sorting genetic algorithm that solves non-convex and non-smooth single and multiobjective optimization problems.
  8. ALGENCAN: It solves the general non-linear constrained optimization problem without resorting to the use of matrix manipulations. It uses instead an Augmented Lagrangian approach which is able to solve extremely large problems with moderate computer time.
  9. FILTERSD: It use of a Ritz values approach Linear Constraint Problem solver. Second derivatives and storage of an approximate reduced Hessian matrix is avoided using a limited memory spectral gradient approach based on Ritz values.

 

To solve an optimization problem with pyOpt an optimizer must be initialized. The initialization of one or more optimizers is independent of the initialization of any number of optimization problems. To initialize SLSQP, which is an open-source, sequential least squares programming algorithm that comes as part of the pyOpt package, use:

>>> slsqp = pyOpt.SLSQP()

This initializes an instance of SLSQP with the default options. The setOption method can be used to change any optimizer specific option, for example the internal output flag of SLSQP:

>>> slsqp.setOption('IPRINT', -1)

Now Schittkowski’s constrained problem can be solved using SLSQP and for example, pyOpt’s automatic finite difference for the gradients:

>>> [fstr, xstr, inform] = slsqp(opt_prob,sens_type='FD')

By default, the solution information of an optimizer is also stored in the specific optimization problem. To output solution to the screen one can use:

>>> print opt_prob.solution(0)

 

Example:

The problem is taken from the set of nonlinear programming examples by Hock and Schittkowski and it is defined as

=======================================================================

      min            − x1x2x3

     x1,x2,x3

 

subjected to     x1 + 2x2 + 2x3 − 72 ≤ 0

                        − x1 − 2x2 − 2x3 ≤ 0

 

                        0 ≤ x1 ≤ 42

                        0 ≤ x2 ≤ 42

                        0 ≤ x3 ≤ 42

 

The optimum of this problem is at (x1∗ , x2∗ , x3* ) = (24, 12, 12), with an objective function value of f ∗ = −3456, and constraint values g (x∗ ) = (0, −72).

 

#======================================================================

# Standard Python modules

#======================================================================

import os, sys, time

import pdb

#======================================================================

# Extension modules

#======================================================================

#from pyOpt import *

from pyOpt import Optimization

from pyOpt import PSQP

from pyOpt import SLSQP

from pyOpt import CONMIN

from pyOpt import COBYLA

from pyOpt import SOLVOPT

from pyOpt import KSOPT

from pyOpt import NSGA2

from pyOpt import ALGENCAN

from pyOpt import FILTERSD

 

#======================================================================

def objfunc(x):

   

    f = -x[0]*x[1]*x[2]

    g = [0.0]*2

    g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0

    g[1] = -x[0] - 2.*x[1] - 2.*x[2]

   

    fail = 0

    return f,g, fail  

 

#======================================================================

# Instantiate Optimization Problem

opt_prob = Optimization('Hock and Schittkowski Constrained Problem',objfunc)

opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)

opt_prob.addObj('f')

opt_prob.addCon('g1','i')

opt_prob.addCon('g2','i')

print opt_prob

 

# Instantiate Optimizer (PSQP) & Solve Problem

psqp = PSQP()

psqp.setOption('IPRINT',0)

psqp(opt_prob,sens_type='FD')

print opt_prob.solution(0)

 

# Instantiate Optimizer (SLSQP) & Solve Problem

slsqp = SLSQP()

slsqp.setOption('IPRINT',-1)

slsqp(opt_prob,sens_type='FD')

print opt_prob.solution(1)

 

# Instantiate Optimizer (CONMIN) & Solve Problem

conmin = CONMIN()

conmin.setOption('IPRINT',0)

conmin(opt_prob,sens_type='CS')

print opt_prob.solution(2)

 

# Instantiate Optimizer (COBYLA) & Solve Problem

cobyla = COBYLA()

cobyla.setOption('IPRINT',0)

cobyla(opt_prob)

print opt_prob.solution(3)

 

# Instantiate Optimizer (SOLVOPT) & Solve Problem

solvopt = SOLVOPT()

solvopt.setOption('iprint',-1)

solvopt(opt_prob,sens_type='FD')

print opt_prob.solution(4)

 

# Instantiate Optimizer (KSOPT) & Solve Problem

ksopt = KSOPT()

ksopt.setOption('IPRINT',0)

ksopt(opt_prob,sens_type='FD')

print opt_prob.solution(5)

 

# Instantiate Optimizer (NSGA2) & Solve Problem

nsga2 = NSGA2()

nsga2.setOption('PrintOut',0)

nsga2(opt_prob)

print opt_prob.solution(6)

 

# Instantiate Optimizer (ALGENCAN) & Solve Problem

algencan = ALGENCAN()

algencan.setOption('iprint',0)

algencan(opt_prob)

print opt_prob.solution(7)

 

# Instantiate Optimizer (FILTERSD) & Solve Problem

filtersd = FILTERSD()

filtersd.setOption('iprint',0)

filtersd(opt_prob)

print opt_prob.solution(8)

 

Solving non-linear global optimization problems could be tedious task sometimes. If the problem is not that complex then general purpose solvers could work. However, as the complexity of problem increases, general purpose global optimizers start to take time. That is when need to create your problem specific fast and direct global optimizer’s need arises.

 

We have an specialized team with PHD holders and coders to design and develop customized global optimizers. If you need help with one, please feel free to send your queries to us.

 

We first understand the problem and data by visualizing it. After that we create a solution to your needs.

 

Please do read to understand what a solver is and how it works - If you want to create your own simple solver. This is not exactly how every solver works, however, this will give you a pretty solid idea of what is a solver and how it is supposed to work.

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Jufel


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