# Julio Cárdenas-Rodríguez

# A simple code to calculate the performance metrics of a binary classifier

### Specificity, Sensitivity, PPV, and NPV

### 1. Code

```
import numpy as np
def perf_metrics_2X2(yobs, yhat):
"""
Returns the specificity, sensitivity, positive predictive value, and negeative predictive value
of a 2X2 table.
where:
0 = negative case
1 = positive case
Parameters
----------
yobs : array of . . .
```

# How to create an alias in Windows Powershell to launch Jupyter and Python

### - Why am I doing this?

Running `Jupyter`

and `PIP`

becomes painful if you don't have admin privileges for your computer; A solution to this is to enter the entire path to `PIP`

and/or `Jupyter`

but it can very time consuming and inefficient. A way around this is to create and alias for the terminal to interpret a short command as if you . . .

# Tuning scikit-learn parameters using optimization instead of random search

### Work smart not hard

## The problem

On previous posts I described how to perform non-linear curve fitting in Ptyhon and Julia. At their core non-linear and linear curve fitting (or regression) are optimization problems in which we find the parameters that minimize an objective function. The entire field of mathematical optimization is concerned . . .

# Data Science at CyVerse: 3) Using Python and Atmosphere (VM) for data analysis

### WORK IN PROGRESS

No content...

# Data Science at CyVerse: 2) Installing Python in Atmosphere (VM)

# Why am I doing this?

This is the **second installment on a blog series** about how to use Python for Data Analysis at CyVerse. If you are not familiar with CyVerse, please read my previous post.

## What you will need

- An Instance (Virtual Machine) in Atmosphere. Read the previous blog on this series if you don't . . .

# Data Science at CyVerse: 1) Setting Up Atmosphere (Virtual Machine)

### A simple guide for new comers to high performance computing

# Why am I doing this?

The current workflow for data analysis and scientific computing in medical imaging requires the storage and processing of tens of millions of voxels per patient. Unfortunately, a personal PC/laptop is inadequate for such tasks.

## What is CyVerse?

"OUR MISSION TO DESIGN, DEPLOY, . . .

# Non Linear regression in Julia is ~10X faster than Python and ~635X faster than Matlab.

### But also equally accurate

## Why Julia ?

I gave a talk a couple weeks ago at the Tucson Python MeetUp about how Julia and Python can

be used to analyze medical images. It turns out that for a simple processing task of calculating a T1 map of a lemon Julia is ~**10X** faster than Python and ~**635X** faster than Matlab. Besides speed, Julia offers great features: