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

After installing jupyter

pip3 install --upgrade pippip3 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)

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.

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)

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.

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

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.

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

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

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.

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).

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

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

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.

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.

### 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

cd \$HOME/[FolderName]`

# Introduction to Survival Analysis

## Introduction

Survival analysis is generally defined as a set of methods for analysing data where the outcome variable is the time until the occurrence of an event of interest. For example, if the event of interest is heart attack, then the survival time can be the time in years until a person develops a heart attack. For simplicity, we will adopt the terminology of survival analysis, referring to the event of interest as ‘death’ and to the waiting time as ‘survival’ time, but this technique has much wider applicability. The event can be death, occurrence of a disease, marriage, divorce, etc. The time to event or survival time can be measured in days, weeks, years, etc.

The specific difficulties relating to survival analysis arise largely from the fact that only some individuals have experienced the event and, subsequently, survival times will be unknown for a subset of the study group. This phenomenon is called censoring.

In longitudinal studies exact survival time is only known for those individuals who show the event of interest during the follow-up period. For others (those who are disease free at the end of the observation period or those that were lost) all we can say is that they did not show the event of interest during the follow-up period. These individuals are called censored observations. An attractive feature of survival analysis is that we are able to include the data contributed by censored observations right up until they are removed from the risk set.

## Survival and Hazard

T  –  a non-negative random variable representing the waiting time until the occurrence of an event.

The survival function, S(t), of an individual is the probability that they survive until at least time t, where t is a time of interest and T is the time of event.

The survival curve is non-increasing (the event may not reoccur for an individual) and is limited within [0,1].

F(t) – the probability that the event has occurred by duration t:

the probability density function (p.d.f.) f(t):

An alternative characterisation of the distribution of T is given by the hazard function, or instantaneous rate of occurrence of the event, defined as

The numerator of this expression is the conditional probability that the event will occur in the interval [t,t+dt] given that it has not occurred before, and the denominator is the width of the interval. Dividing one by the other we obtain a rate of event occurrence per unit of time. Taking the limit as the width of the interval goes down to zero, we obtain an instantaneous rate of occurrence.

Applying Bayes’ Rule

on the numerator of the hazard function:

Given that the event happened between time t to t+dt, the conditional probability of this event happening after time t is 1:

Dividing by dt and passing to the limit gives the useful result:

In words, the rate of occurrence of the event at duration t equals the density of events at t, divided by the probability of surviving to that duration without experiencing the event.

We will soon show that there is a one-to-one relation between the hazard and the survival function.

The derivative of S(t) is:

We will now show that the hazard function is the derivative of -log S(t):

If we now integrate from 0 to time t:

and introduce the boundary condition S(0) = 1 (since the event is sure not to have occurred by duration 0):

we can solve the above expression to obtain a formula for the probability of surviving to duration t as a function of the hazard at all durations up to t:

One approach to estimating the survival probabilities is to assume that the hazard function follow a specific mathematical distribution. Models with increasing hazard rates may arise when there is natural aging or wear. Decreasing hazard functions are much less common but find occasional use when there is a very early likelihood of failure, such as in certain types of electronic devices or in patients experiencing certain types of transplants. Most often, a bathtub-shaped hazard is appropriate in populations followed from birth.

The figure below hows the relationship between four parametrically specified hazards and the corresponding survival probabilities. It illustrates (a) a constant hazard rate over time (e.g. healthy persons) which is analogous to an exponential distribution of survival times, (b) strictly increasing (c) decreasing hazard rates based on a Weibull model, and (d) a combination of decreasing and increasing hazard rates using a log-Normal model. These curves are illustrative examples and other shapes are possible.

### Example

The simplest possible survival distribution is obtained by assuming a constant risk over time:

## Censoring and truncation

One of the distinguishing feature of the field of survival analysis is censoring: observations are called censored when the information about their survival time is incomplete; the most commonly encountered form is right censoring.

Right censoring occurs when a subject leaves the study before an event occurs, or the study ends before the event has occurred. For example, we consider patients in a clinical trial to study the effect of treatments on stroke occurrence. The study ends after 5 years. Those patients who have had no strokes by the end of the year are censored. Another example of right censoring is when a person drops out of the study before the end of the study observation time and did not experience the event. This person’s survival time is said to be censored, since we know that the event of interest did not happen while this person was under observation.

Left censoring is when the event of interest has already occurred before enrolment. This is very rarely encountered.

In a truncated sample, we do not even “pick up” observations that lie outside a certain range.

Unlike ordinary regression models, survival methods correctly incorporate information from both censored and uncensored observations in estimating important model parameters

## Non-parametric Models

The very simplest survival models are really just tables of event counts: non-parametric, easily computed and a good place to begin modelling to check assumptions, data quality and end-user requirements etc. When no event times are censored, a non-parametric estimator of S(t) is 1 − F(t), where F(t) is the empirical cumulative distribution function.

### Kaplan–Meier

When some observations are censored, we can estimate S(t) using the Kaplan-Meier product-limit estimator. An important advantage of the Kaplan–Meier curve is that the method can take into account some types of censored data, particularly right-censoring, which occurs if a patient withdraws from a study, is lost to follow-up, or is alive without event occurrence at last follow-up.

Suppose that 100 subjects of a certain type were tracked over a period of time to determine how many survived for one year, two years, three years, and so forth. If all the subjects remained accessible throughout the entire length of the study, the estimation of year-by-year survival probabilities for subjects of this type in general would be an easy matter. The survival of 87 subjects at the end of the first year would give a one-year survival probability estimate of 87/100=0.87; the survival of 76 subjects at the end of the second year would yield a two-year estimate of 76/100=0.76; and so forth.

But in real-life longitudinal research it rarely works out this neatly. Typically there are subjects lost along the way (censored) for reasons unrelated to the focus of the study.

Suppose that 100 subjects of a certain type were tracked over a period of two years determine how many survived for one year and for two years. Of the 100 subjects who are “at risk” at the beginning of the study, 3 become unavailable (censored) during the first year and 3 are known to have died by the end of the first year. Another 2 become unavailable during the second year and another 10 are known to have died by the end of the second year.

Kaplan and Meier proposed that subjects who become unavailable during a given time period be counted among those who survive through the end of that period, but then deleted from the number who are at risk for the next time period.

The table below shows how these conventions would work out for the present example. Of the 100 subjects who are at risk at the beginning of the study, 3 become unavailable during the first year and 3 die. The number surviving the first year (Year 1) is therefore 100 (at risk) – 3 (died) = 97 and the number at risk at the beginning of the second year (Year 2) is 100 (at risk) – 3 (died) – 3 (unavailable) = 94. Another 2 subjects become unavailable during the second year and another 10 die. So the number surviving Year 2 is 94 (at risk) – 10 (died) = 84.

As illustrated in the next table, the Kaplan-Meier procedure then calculates the survival probability estimate for each of the t time periods, except the first, as a compound conditional probability.

The estimate for surviving through Year 1 is simply 97/100=0.97. And if one does survive through Year 1, the conditional probability of then surviving through Year 2 is 84/94=0.8936. The estimated probability of surviving through both Year 1 and Year 2 is therefore (97/100) x (84/94)=0.8668.

## Incorporating covariates: proportional hazards models

Up to now we have not had information for each individual other than the survival time and censoring status ie. we have not considered information such as the weight, age, or smoking status of individuals, for example. These are referred to as covariates or explanatory variables.

### Cox Proportional Hazards Modelling

The most interesting survival-analysis research examines the relationship between survival — typically in the form of the hazard function — and one or more explanatory variables (or covariates).

where λ0(t) is the non-parametric baseline hazard function and βx is a linear parametric model using features of the individuals, transformed by an exponential function. The baseline hazard function λ0(t) does not need to be specified for the Cox model, making it semi-parametric. The baseline hazard function is appropriately named because it describes the risk at a certain time when x = 0, which is when the features are not incorporated. The hazard function describes the relationship between the baseline hazard and features of a specific sample to quantify the hazard or risk at a certain time.

The model only needs to satisfy the proportional hazard assumption, which is that the hazard of one sample is proportional to the hazard of another sample. Two samples xi and xj satisfy this assumption when the ratio is not dependent on time as shown below:

The parameters can be estimated by maximizing the partial likelihood.