Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By : Cyrille Rossant
Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By: Cyrille Rossant

Overview of this book

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Table of Contents (19 chapters)
IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
Preface
Index

Using support vector machines for classification tasks


In this recipe, we introduce support vector machines, or SVMs. These models can be used for classification and regression. Here, we illustrate how to use linear and nonlinear SVMs on a simple classification task. This recipe is inspired by an example in the scikit-learn documentation (see http://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html).

How to do it...

  1. Let's import the packages:

    >>> import numpy as np
        import pandas as pd
        import sklearn
        import sklearn.datasets as ds
        import sklearn.model_selection as ms
        import sklearn.svm as svm
        import matplotlib.pyplot as plt
        %matplotlib inline
  2. We generate 2D points and assign a binary label according to a linear operation on the coordinates:

    >>> X = np.random.randn(200, 2)
        y = X[:, 0] + X[:, 1] > 1
  3. We now fit a linear Support Vector Classifier (SVC). This classifier tries to separate the two groups of points with a linear boundary...