ModAL: A Modular Active Learning Framework for Python

ModAL

Modular Active Learning framework for Python3 

Introduction

modAL is an active learning framework for Python3, designed with modularity, flexibility and extensibility in mind. Built on top of scikit-learn, it allows you to rapidly create active learning workflows with nearly complete freedom. What is more, you can easily replace parts with your custom built solutions, allowing you to design novel algorithms with ease.

Active learning from bird's-eye view

With the recent explosion of available data, you have can have millions of unlabelled examples with a high cost to obtain labels. For instance, when trying to predict the sentiment of tweets, obtaining a training set can require immense manual labour. But worry not, active learning comes to the rescue! In general, AL is a framework allowing you to increase classification performance by intelligently querying you to label the most informative instances. To give an example, suppose that you have the following data and classifier with shaded regions signifying the classification probability.

Suppose that you can query the label of an unlabelled instance, but it costs you a lot. Which one would you choose? By querying an instance in the uncertain region, surely you obtain more information than querying by random. Active learning gives you a set of tools to handle problems like this. In general, an active learning workflow looks like the following.

The key components of any workflow are the model you choose, the uncertainty measure you use and the query strategy you apply to request labels. With modAL, instead of choosing from a small set of built-in components, you have the freedom to seamlessly integrate scikit-learn or Keras models into your algorithm and easily tailor your custom query strategies and uncertainty measures.

modAL in action

Let's see what modAL can do for you!

From zero to one in a few lines of code

Active learning with a scikit-learn classifier, for instance RandomForestClassifier, can be as simple as the following.

from modAL.models import ActiveLearner
from sklearn.ensemble import RandomForestClassifier

# initializing the learner
learner = ActiveLearner(
    estimator=RandomForestClassifier(),
    X_training=X_training, y_training=y_training
)

# query for labels
query_idx, query_inst = learner.query(X_pool)

# ...obtaining new labels from the Oracle...

# supply label for queried instance
learner.teach(X_pool[query_idx], y_new)

Replacing parts quickly

If you would like to use different uncertainty measures and query strategies than the default uncertainty sampling, you can either replace them with several built-in strategies or you can design your own by following a few very simple design principles. For instance, replacing the default uncertainty measure to classification entropy looks the following.

from modAL.models import ActiveLearner
from modAL.uncertainty import entropy_sampling
from sklearn.ensemble import RandomForestClassifier

learner = ActiveLearner(
    estimator=RandomForestClassifier(),
    query_strategy=entropy_sampling,
    X_training=X_training, y_training=y_training
)

Replacing parts with your own solutions

modAL was designed to make it easy for you to implement your own query strategy. For example, implementing and using a simple random sampling strategy is as easy as the following.

import numpy as np

def random_sampling(classifier, X_pool):
    n_samples = len(X_pool)
    query_idx = np.random.choice(range(n_samples))
    return query_idx, X_pool[query_idx]

learner = ActiveLearner(
    estimator=RandomForestClassifier(),
    query_strategy=random_sampling,
    X_training=X_training, y_training=y_training
)

For more details on how to implement your custom strategies, visit the page Extending modAL!

An example with active regression

To see modAL in real action, let's consider an active regression problem with Gaussian Processes! In this example, we shall try to learn the noisy sine function:

import numpy as np

X = np.random.choice(np.linspace(0, 20, 10000), size=200, replace=False).reshape(-1, 1)
y = np.sin(X) + np.random.normal(scale=0.3, size=X.shape)

For active learning, we shall define a custom query strategy tailored to Gaussian processes. In a nutshell, a query stategy in modAL is a function taking (at least) two arguments (an estimator object and a pool of examples), outputting the index of the queried instance. In our case, the arguments are regressor and X.

def GP_regression_std(regressor, X):
    _, std = regressor.predict(X, return_std=True)
    return np.argmax(std)

After setting up the query strategy and the data, the active learner can be initialized.

from modAL.models import ActiveLearner
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import WhiteKernel, RBF

n_initial = 5
initial_idx = np.random.choice(range(len(X)), size=n_initial, replace=False)
X_training, y_training = X[initial_idx], y[initial_idx]

kernel = RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) \
         + WhiteKernel(noise_level=1, noise_level_bounds=(1e-10, 1e+1))

regressor = ActiveLearner(
    estimator=GaussianProcessRegressor(kernel=kernel),
    query_strategy=GP_regression_std,
    X_training=X_training.reshape(-1, 1), y_training=y_training.reshape(-1, 1)
)

The initial regressor is not very accurate.

The blue band enveloping the regressor represents the standard deviation of the Gaussian process at the given point. Now we are ready to do active learning!

# active learning
n_queries = 10
for idx in range(n_queries):
    query_idx, query_instance = regressor.query(X)
    regressor.teach(X[query_idx].reshape(1, -1), y[query_idx].reshape(1, -1))

After a few queries, we can see that the prediction is much improved.

Additional examples

Including this, many examples are available:

• Pool-based sampling
• Stream-based sampling
• Active regression
• Ensemble regression
• Bayesian optimization
• Query by committee
• Bootstrapping and bagging
• Keras integration

Installation

modAL requires

• Python >= 3.5
• NumPy >= 1.13
• SciPy >= 0.18
• scikit-learn >= 0.18

You can install modAL directly with pip:

pip install modAL

Alternatively, you can install modAL directly from source:

pip install git+https://github.com/modAL-python/modAL.git

Documentation

You can find the documentation of modAL at https://modAL-python.github.io, where several tutorials and working examples are available, along with a complete API reference. For running the examples, Matplotlib >= 2.0 is recommended.

Citing

If you use modAL in your projects, you can cite it as

@article{modAL2018,
    title={mod{AL}: {A} modular active learning framework for {P}ython},
    author={Tivadar Danka and Peter Horvath},
    url={https://github.com/modAL-python/modAL},
    note={available on arXiv at \url{https://arxiv.org/abs/1805.00979}}
}

About the developer

modAL is developed by me, Tivadar Danka (aka cosmic-cortex in GitHub). I have a PhD in pure mathematics, but I fell in love with biology and machine learning right after I finished my PhD. I have changed fields and now I work in the Bioimage Analysis and Machine Learning Group of Peter Horvath, where I am working to develop active learning strategies for intelligent sample analysis in biology. During my work I realized that in Python, creating and prototyping active learning workflows can be made really easy and fast with scikit-learn, so I ended up developing a general framework for this. The result is modAL :) If you have any questions, requests or suggestions, you can contact me at 85a5187a@opayq.com! I hope you'll find modAL useful!

Download Details:

Author: ModAL-python
Source Code: https://github.com/modAL-python/modAL 
License: MIT license

#machinelearning #python #scikitlearn 

What is GEEK

Buddha Community

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips