How to run PySpark 2.4.0 in Jupyter Notebook on Mac

Install Jupyter notebook

$ pip3 install jupyter

Install PySpark

Make sure you have Java 8 or higher installed on your computer and visit the Spark download page

Select the latest Spark release, a prebuilt package for Hadoop, and download it directly.

Unzip it and move it to your /opt folder:

$ tar -xzf spark-2.4.0-bin-hadoop2.7.tgz
$ sudo mv spark-2.4.0-bin-hadoop2.7 /opt/spark-2.4.0

A symbolic link is like a shortcut from one file to another. The contents of a symbolic link are the address of the actual file or folder that is being linked to.

Create a symbolic link (this will let you have multiple spark versions):

$ sudo ln -s /opt/spark-2.4.0 /opt/spark̀

Check that the link was indeed created

$ ls -l /opt/spark̀

lrwxr-xr-x  1 root  wheel  16 Dec 26 15:08 /opt/spark̀ -> /opt/spark-2.4.0

Finally, tell your bash where to find Spark. To find what shell you are using, type:

$ echo $SHELL
/bin/bash

To do so, edit your bash file:

$ nano ~/.bash_profile

configure your $PATH variables by adding the following lines to your ~/.bash_profile file:

export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH
# For python 3, You have to add the line below or you will get an error
export PYSPARK_PYTHON=python3

Now to run PySpark in Jupyter you’ll need to update the PySpark driver environment variables. Just add these lines to your ~/.bash_profile file:

export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS='notebook'

Your ~/.bash_profile file may look like this:

Restart (our just source) your terminal and launch PySpark:

$ pyspark

This command should start a Jupyter Notebook in your web browser. Create a new notebook by clicking on ‘New’ > ‘Notebooks Python [default]’.

Running PySpark in Jupyter Notebook

The PySpark context can be 

sc = SparkContext.getOrCreate()

To check if your notebook is initialized with SparkContext, you could try the following codes in your notebook:

sc = SparkContext.getOrCreate()
import numpy as np
TOTAL = 10000
dots = sc.parallelize([2.0 * np.random.random(2) - 1.0 for i in range(TOTAL)]).cache()
print("Number of random points:", dots.count())
stats = dots.stats()
print('Mean:', stats.mean())
print('stdev:', stats.stdev())

The result:

Running PySpark in your favorite IDE

Sometimes you need a full IDE to create more complex code, and PySpark isn’t on sys.path by default, but that doesn’t mean it can’t be used as a regular library. You can address this by adding PySpark to sys.path at runtime. The package findspark does that for you.

To install findspark just type:

$ pip3 install findspark

And then on your IDE (I use Eclipse and Pydev) to initialize PySpark, just call:

import findspark
findspark.init()
import pyspark
sc = pyspark.SparkContext(appName="myAppName")

Here is a full example of a standalone application to test PySpark locally 

import findspark
findspark.init()
import random
from pyspark import SparkContext
sc = SparkContext(appName="EstimatePi")
def inside(p):
x, y = random.random(), random.random()
return x<em>x + y</em>y &lt; 1
NUM_SAMPLES = 1000000
count = sc.parallelize(range(0, NUM_SAMPLES)) \
.filter(inside).count()
print("Pi is roughly %f" % (4.0 * count / NUM_SAMPLES))
sc.stop()

The result:

Enjoy!

Based on this article and on this article

Error when executing `jupyter notebook` (-bash: jupiter: command not found) [Mac]

After installing jupyter

pip3 install --upgrade pip
pip3 install jupyter

and trying to launch 

jupyter notebook

the following error message appeared

-bash: jupyter: command not found

The solution:  

pip3 install --upgrade --force-reinstall --no-cache-dir jupyter

Fighting Digital Payments Fraud with Deep Learning

Interesting presentation today at the DataScience SG meet-up

Conventional fraud prevention methods are rule based, expansive and slow to implement

Q1 2016: $5 of every $100 subject to fraud attack!

Key fraud types: account takeover, friendly fraud & fraud due to stolen card information

Consumers want: easy, instant, customized, mobile and dynamic options to suit their personal situation. Consumers do NOT want to be part of the fraud detection process.

Key technology enablers:

Historically fraud detection systems have relied on rues hand-curated by fraud experts to catch fraudulent activity.

An auto-encoder is a neural network trained to reconstruct its inputs, which forces a hidden layer to try and to learn good representations of the input

Kaggle dataset:

Train Autoencoder on normal transactions and using the Autoencoder transformation there is now a clear separation between the normal and the fraudulent transactions.

The Secret Recipe Behind GO-FOOD’s Recommendations (PyData Meetup)

The December PyData Meetup started with Luis Smith, Data Scientist at GO-JEK, sharing the Secret Recipe Behind GO-FOOD’s Recommendations:

“For GO-FOOD, we believe the key to unlocking good recommendations is to derive vector representations for our users, dishes, and merchants. This way we are able to capture our users’ food preferences and recommend them the most relevant merchants and dishes.”

How do people think about the food?

  • Flavor profile
  • Trendy
  • Value for money
  • Portion size
  • Ingredients

… and much more

The preferred approach is to let the transactional data discover the pattern.

A sample ETL workflow:

Using StarSpace to learn the vector representations:

Go-Jek formulation of the problem:

User-to-dish similarity is surfaced in the app via the “dishes you might like”. The average vector of customer’s purchases represents the recommended dish.

Due to data sparsity, item-based collaborative filtering is used for merchant recommendation.

The cold start problem is still an issue, for inactive users or users that purchase infrequently.

(published here)

Understanding the Unpacking Operators (* and **) in Python 3.x

The * operator unpack the arguments out of a list or tuple.
> args = [3, 6]
> list(range(*args))
[3, 4, 5]

As an example, when we have a list of three arguments, we can use the * operator inside a function call to unpack it into the three arguments:

def f(a,b,c):
    print('a={},b={},c={}'.format(a,b,c))

> z = ['I','like','Python']
> f(*z)
a=I,b=like,c=Python

> z = [['I','really'],'like','Python']
> f(*z)
a=['I', 'really'],b=like,c=Python

In Python 3 it is possible to use the operator * on the left side of an assignment, allowing to specify a “catch-all” name which will be assigned a list of all items not assigned to a “regular” name:

> a, *b, c = range(5)
> a
0
> c
4
> b
[1, 2, 3]

The ** operator can be used to unpack a dictionary of arguments as a collection of keyword arguments. Calling the same function f that we defined above:

 

> d = {'c':'Python','b':'like', 'a':'I'}
> f(**d)
a=I,b=like,c=Python

and when there is a missing argument in the dictionary (‘a’ in this example),  the following error message will be printed:


> d2 = {'c':'Python','b':'like'}
> f(**d2)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: f() missing 1 required positional argument: 'a'

 
Tried with: Python 3.6.5

Highlights of the 2018 Singapore Symposium on Natural Language Processing (SSNLP)

What a great symposium! Thank you Dr. Linlin Li, Prof. Ido Dagan, Prof. Noah Smith and the rest of the speakers for the interesting talks and thank you Singapore University of Technology and Design (SUTD) for hosting this event. Here is a quick summary of the first half of the symposium, you can learn more by looking for the papers published by these research groups:

Linlin Li: The text processing engine that powers Alibaba’s business applications

Dr. Linlin Li from Alibaba presented the mission of Alibaba’s NLP group and spoke about AliNLP, a large scale NLP technology platform for the entire Alibaba Eco-system, dealing with data collection and multilingual algorithms for lexical, syntactic, semantic, discourse analysis and distributed representation of text.

Alibaba is also helping to improve the quality of the Electronic Medical Records (EMRs) in China, traditionally done by labour intensive methods.

Ido Dagan: Consolidating Textual Information

Prof. Ido Dagan gave an excellent presentation on Natural Knowledge Consolidating Textual Information. Texts come in large multitudes, such as news story, search results, and product reviews. Search interfaces hasn’t changed much in decades, which make them accessible, but hard to consume. For example, the news tweets illustration in the slide below shows that here is a lot of redundancy and complementary information, so there is a need to consolidate the knowledge within multiple texts.

Generic knowledge representation via structured knowledge graphs and semantic representation are often being used, where both approaches require an expert to annotate the dataset, which is expansive and hard to replicate.

The structure of a single sentence will look like this:

The information can be consolidated across the various data sources via Coreference

To conclude

Noah A. Smith: Syncretizing Structured and Learned Representation

Prof. Noah described new ways to use representation learning for NLP

Some promising results

Prof. Noah presented different approaches to solve backpropagation with structure in the middle, where the intermediate representation is non-differentiable.

See you all the the next conference!

Installing Wand (0.4) and ImageMagick v6 on Mac (macOS High Sierra v 10.13.5)

wizard

ImageMagick® is used to create, edit, compose, or convert bitmap images. It can read and write images in a variety of formats (over 200) including PNG, JPEG, GIF, HEIC, TIFF, DPX, EXR, WebP, Postscript, PDF, and SVG. Use ImageMagick to resize, flip, mirror, rotate, distort, shear and transform images, adjust image colors, apply various special effects, or draw text, lines, polygons, ellipses and Bézier curves.

Wand is a ctypes-based simple ImageMagick binding for Python, so go through the step-by-step guide on how to install it.

Let’s start by installing ImageMagic:

brew install imagemagick@6

Next, create a symbolic link, with the following command (replace <your specific 6 version> with your specific version):

ln -s /usr/local/Cellar/imagemagick@6/<your specific 6 version>/lib/libMagickWand-6.Q16.dylib /usr/local/lib/libMagickWand.dylib

In my case, it was:

ln -s /usr/local/Cellar/imagemagick@6/6.9.10-0/lib/libMagickWand-6.Q16.dylib /usr/local/lib/libMagickWand.dylib

Let’s install Wand

pip3 install Wand

Now, let’s try to run the code

from wand.image import Image

…

with Image(filename=sourceFullPathFilename) as img:
img.save(filename=targetFilenameFull)

Unfortunately, I got the following error message:

wand.exceptions.DelegateError: FailedToExecuteCommand `’gs’ -sstdout=%stderr -dQUIET -dSAFER -dBATCH -dNOPAUSE -dNOPROMPT -dMaxBitmap=500000000 -dAlignToPixels=0 -dGridFitTT=2 ‘-sDEVICE=pngalpha’ -dTextAlphaBits=4 -dGraphicsAlphaBits=4 ‘-r72x72’ ‘-sOutputFile=/var/folders/n7/9xyh2rj14qvf3hrmr7g9b4gm0000gp/T/magick-31607l23fY21KEi6b%d’ ‘-f/var/folders/n7/9xyh2rj14qvf3hrmr7g9b4gm0000gp/T/magick-31607_nNNZjiBBusp’ ‘-f/var/folders/n7/9xyh2rj14qvf3hrmr7g9b4gm0000gp/T/magick-31607Zfemn9tWrdiY” (1) @ error/pdf.c/InvokePDFDelegate/292
Exception ignored in: <bound method Resource.__del__ of <wand.image.Image: (empty)>>

It seems that ghostscript is not installed by default, so let’s install it:

brew install ghostscript

Now we will need to create a soft link to /usr/bin, but /usr/bin/ in OS X 10.11+ is protected.

Just follow these steps:

1. Reboot to Recovery Mode. Reboot and hold “Cmd + R” after start sound.
2. In Recovery Mode go to Utilities -> Terminal.
3. Run: csrutil disable
4. Reboot in Normal Mode.
5. Do the “sudo ln -s /usr/local/bin/gs /usr/bin/gs” in terminal.
6. Do the 1 and 2 step. In terminal enable back csrutil by run: csrutil enable

(based on this)

Now it works – Enjoy!

 

How to copy full file or folder path on your Mac

Step 1: Launch a new Finder window by choosing New Finder Window under the Finder’s File menu.

Step 2: Navigate to a desired file or folder and click the item in the Finder window while holding the Control (⌃) key, which will bring up a contextual menu populated with various file-related operations.

Step 3: Now hold down the Option (⌥) key to reveal a hidden option in the contextual menu, labeled “Copy (file/folder name) as Pathname”.

Step 4: Selecting this option will copy the complete, not relative, pathname of your item into the system clipboard.

Based on this

MICE is Nice, but why should you care?

Multiple Imputation by Chained Equations (MICE) 

mouse-303588_1280

As every data scientist will witness, it is rarely that your data is 100% complete. We are often taught to “ignore” missing data. In practice, however, ignoring or inappropriately handling the missing data may lead to biased estimates, incorrect standard errors and incorrect inferences.

But first we need to think about what led to this missing data, or what was the mechanism by which some values were missing and some were observed?

There are three different mechanisms to describe what led to the missing values:

  • Missing Completely At Random (MCAR): the missing observations are just a random subset of all observations, so there are no systematic differences between the missing and observed data. In this case, analysis using only complete cases will not be biased, but may have lower power.
  • Missing At Random (MAR): there might be systematic differences between the missing and observed data, but these can be entirely explained by other observed variables. For example, a case where you observe gender and you see that women are more likely to respond than men. Including a lot of predictors in the imputation model can make this assumption more plausible.
  • Not Missing At Random (NMAR): the probability of a variable being missing might depend on itself on other unobserved values. For example, the probability of someone reporting their income depends on what their income is.

MICE operates under the assumption that given the variables used in the imputation procedure, the missing data are Missing At Random (MAR), which means that the probability that a value is missing depends only on observed values and not on unobserved values

Multiple imputation by chained equations (MICE) has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds.

The chained equation process can be broken down into the following general steps:

  • Step 1: A simple imputation, such as imputing the mean, is performed for every missing value in the dataset. These mean imputations can be thought of as “place holders.”
  • Step 2: Start Step 2 with the variable with the fewest number of missing  values. The “place holder” mean imputations for one variable (“var”) are set back to missing.
  • Step 3: “var” is the dependent variable in a regression model and all the other variables are independent variables in the regression model.
  • Step 4: The missing values for “var” are then replaced with predictions (imputations) from the regression model. When “var” is subsequently used as an independent variable in the regression models for other variables, both the observed and these imputed values will be used.
  • Step 5: Moving on to the next variable with the next fewest missing values, steps 2–4 are then repeated for each variable that has missing data. The cycling through each of the variables constitutes one iteration or “cycle.” At the end of one cycle all of the missing values have been replaced with predictions from regressions that reflect the relationships observed in the data.
  • Step 6: Steps 2 through 4 are repeated for a number of cycles, with the imputations being updated at each cycle. The idea is that by the end of the cycles the distribution of the parameters governing the imputations (e.g., the coefficients in the regression models) should have converged in the sense of becoming stable.

To make the chained equation approach more concrete, imagine a simple example where we have 3 variables in our dataset: age, income, and gender, and all 3 have at least some missing values. I created this animation as a way to visualize the details of the following example, so let’s get started.

MICE Animation

The initial dataset is given below, where missing values are marked as N.A.

Step00

In step 1 of the MICE process, each variable would first be imputed using, e.g., mean imputation, temporarily setting any missing value equal to the mean observed value for that variable.

Step01

Then in the next step the imputed mean values of age would be set back to missing (N.A).

Step02

In the next step Bayesian linear regression of age predicted by income and gender would be run using all cases where age was observed.

Step03

In the next step, prediction of the missing age value would be obtained from that regression equation and imputed. At this point, age does not have any missingness.

Step04

The previous steps would then be repeated for the income variable. The originally missing values of income would be set back to missing (N.A).

Step05

A linear regression of income predicted by age and gender would be run using all cases with income observed.

Step06

Imputations (predictions) would be obtained from that regression equation for the missing income value.

Step07

Then, the previous steps would again be repeated for the variable gender. The originally missing values of gender would be set back to missing and a logistic regression of gender on age and income would be run using all cases with gender observed. Predictions from that logistic regression model would be used to impute the missing gender values.

Step08

This entire process of iterating through the three variables would be repeated until some measure of convergence, where the imputations are stable; the observed data and the final set of imputed values would then constitute one “complete” data set.

We then repeat this whole process multiple times in order to get multiple imputations.

* Let’s connect on Twitter (@ofirdi), LinkedIn or my Blog

Resources

What is the difference between missing completely at random and missing at random? Bhaskaran et al https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4121561/

A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models, E. Raghunathan et al http://www.statcan.gc.ca/pub/12-001-x/2001001/article/5857-eng.pdf

Multiple Imputation by Chained Equations: What is it and how does it work? Azur et al https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

Recent Advances in missing Data Methods: Imputation and Weighting – Elizabeth Stuart https://www.youtube.com/watch?v=xnQ17bbSeEk

Change the default directory when SSH to server (.bashrc)

In order to load your preferences, bash runs the contents of the .bashrc file at each launch. This shell script is found in each user’s home directory. It’s used to save and load your terminal preferences and environmental variables.

Show hidden files in Terminal

ls -la
nano ~/.bashrc

Any changes you make to bashrc will be applied next time you launch terminal. If you want to apply them immediately, run the command below:

source ~/.bashrc

Add to the end of your .bashrc

cd $HOME/[FolderName]