Plotting & Visualization Maryam Tavakol Machine Learning Group - - PowerPoint PPT Presentation

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Plotting & Visualization Maryam Tavakol Machine Learning Group - - PowerPoint PPT Presentation

Jan 14, 2023 390 likes •677 views

Plotting & Visualization Maryam Tavakol Machine Learning Group Winter semester 2016/17 1 Motivation Making plots and static or interactive visualizations is one of the most important tasks in data analysis Helping identify outliers

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Plotting & Visualization

Maryam Tavakol

Machine Learning Group

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Winter semester 2016/17

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Motivation

  • Making plots and static or interactive visualizations

is one of the most important tasks in data analysis

  • Helping identify outliers
  • Needed data transformations
  • Coming up with ideas for models
  • etc.

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matplotlib

  • Desktop plotting package designed for creating

publication-quality plots

  • Does have interactive features like zooming and

panning

  • Supports many different GUI backends on all
  • perating systems
  • Can export graphics to all of the common vector

and raster graphics formats

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Figures

  • Plots in matplotlib reside within a Figure object
  • Creates a new empty window

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Subplots

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Subplot Options

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Plotting

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Formatting

  • Line style, color, and marker

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Formatting

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Titles & Labels

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Adding Legends

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Example

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Annotations

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Annotations

  • Annotations and text can be added using the text,

arrow, and annotate functions

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Adding Shapes

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Saving Options

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Histogram

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Histogram

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Box Plot

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Box Plot

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Profiling & Optimization

Maryam Tavakol

Machine Learning Group

21

Winter semester 2016/17

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Introduction

  • Python is generally known (a bit unfairly) as a slow

language, but it is possible to achieve very good performance with the right methods

  • Profile: To evaluate what makes a program slow
  • Optimize: To make the code more efficient

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%timeit

  • The %timeit allow you to quickly evaluate the time

taken by one or several Python statements in IPython

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%timeit

  • The %timeit command accepts several optional

parameters, e.g.:

  • Using -r or -n to set number of statement

evaluations

  • Inside your script: timeit.timeit()

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cProfile

  • Creates a detailed report of the execution time of
  • ur code, function by function
  • The total number of calls, total time, etc.

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line_profiler

  • Analyzing of code performance by a line-by-line

report

  • Need the package kernprof
  • We decorate the functions we wish to profile with

@profile


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Memory Usage

  • Memory is also a critical factor
  • Using memory_profiler
  • We decorate the functions we wish to profile with

@profile

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Optimization Tips

  • Choosing the right data structure
  • Using NumPy over native Python code
  • Avoiding unnecessary array copying
  • Using in-place operations instead of implicit copy
  • Parallel computing and much more…

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