How to fix “Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA”

After installing Tensorflow using pip3 install:

sudo pip3 install tensorflow

I’ve received the following warning message:

I tensorflow/core/platform/] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

Advanced Vector Extensions (AVX) are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD proposed by Intel in March 2008 and first supported by Intel with the Sandy Bridge processor shipping in Q1 2011 and later on by AMD with the Bulldozer processor shipping in Q3 2011. AVX provides new features, new instructions, and a new coding scheme.

AVX introduces fused multiply-accumulate (FMA) operations, which speed up linear algebra computation, namely dot-product, matrix multiply, convolution, etc. Almost every machine-learning training involves a great deal of these operations, hence will be faster on a CPU that supports AVX and FMA (up to 300%).

We won’t ignore the warning message and we will compile TF from source.

We will start with uninstalling the default version of  Tensorflow:

sudo pip3 uninstall protobuf
sudo pip3 uninstall tensorflow

In a temp folder, clone Tensorflow:

git clone 
git checkout r2.0

Install the TensorFlow pip package dependencies:

pip3 install -U --user pip six numpy wheel setuptools mock future>=0.17.1
pip3 install -U --user keras_applications==1.0.6 --no-deps
pip3 install -U --user keras_preprocessing==1.0.5 --no-deps

Install Bazel, the build tool used to compile TensorFlow. In my case, after downloading

chmod +x ./ --user export PATH="$PATH:$HOME/bin" bazel version

Configure your system build by running the following at the root of your TensorFlow source tree:


The Tensorflow build options expose flags to enable building for platform-specific CPU instruction sets:

Use bazel to make the TensorFlow package builder with CPU-only support:

bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse4.2 //tensorflow/tools/pip_package:build_pip_package

The bazel build command creates an executable named build_pip_package—this is the program that builds the pip package. Run the executable as shown below to build a .whl package in the /tmp/tensorflow_pkg directory.

To build from a release branch:

./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

Output wheel file is in: /tmp/tensorflow_pkg

You can download the file from here, and try to install it directly

pip3 install /tmp/tensorflow_pkg/tensorflow-2.0.0b1-cp37-cp37m-macosx_10_14_x86_64.whl 

cd out of that directory, and now running this should not produce any warning:

python3 -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"


Learn the Basics of Git and Version Control


There are four fundamental elements in the Git Workflow.
Working Directory, Staging Area, Local Repository and Remote Repository.

If you consider a file in your Working Directory, it can be in three possible states.

  1. It can be staged. Which means the files with with the updated changes are marked to be committed to the local repository but not yet committed.
  2. It can be modified. Which means the files with the updated changes are not yet stored in the local repository.
  3. It can be committed. Which means that the changes you made to your file are safely stored in the local repository.
  • git add is a command used to add a file that is in the working directory to the staging area.
  • git commit is a command used to add all files that are staged to the local repository.
  • git push is a command used to add all committed files in the local repository to the remote repository. So in the remote repository, all files and changes will be visible to anyone with access to the remote repository.
  • git fetch is a command used to get files from the remote repository to the local repository but not into the working directory.
  • git merge is a command used to get the files from the local repository into the working directory.
  • git pull is command used to get files from the remote repository directly into the working directory. It is equivalent to a git fetch and a git merge .
git --version
git config --global --list

Check your machine for existing SSH keys:

ls -al ~/.ssh

If you already have a SSH key, you can skip the next step of generating a new SSH key

Generating a new SSH key and adding it to the ssh-agent

ssh-keygen -t rsa -b 4096 -C ""

When adding your SSH key to the agent, use the default macOS ssh-add command. Start the ssh-agent in the background:

Adding a new SSH key to your GitHub account

To add a new SSH key to your GitHub account, copy the SSH key to your clipboard:

pbcopy < ~/.ssh/

Copy the In the “Title” field, add a descriptive label for the new key. For example, if you’re using a personal Mac, you might call this key “Personal MacBook Air”.

Paste your key into the “Key” field.

After you’ve set up your SSH key and added it to your GitHub account, you can test your connection:

ssh -T

Let’s Git

Create a new repository on GitHub. Follow this link.
Now, locate to the folder you want to place under git in your terminal.

echo "# testGit" >>

Now to add the files to the git repository for commit:

git add . 
git status

Now to commit files you added to your git repo:

git commit -m "First commit"
git status

Add a remote origin and Push:

Now each time you make changes in your files and save it, it won’t be automatically updated on GitHub. All the changes we made in the file are updated in the local repository.

To add a new remote, use the git remote add command on the terminal, in the directory your repository is stored at.

The git remote add command takes two arguments:

Now to update the changes to the master:

git remote add origin
git remote -v

Now the git push command pushes the changes in your local repository up to the remote repository you specified as the origin.

git push -u origin master

And now if we go and check our repository page on GitHub it should look something like this:

See the Changes you made to your file:

Once you start making changes on your files and you save them, the file won’t match the last version that was committed to git.

Let’s modify to include the following text:

To see the changes you just made:

git diff

Markers for changes

--- a/
+++ b/

These lines are a legend that assigns symbols to each diff input source. In this case, changes from a/ are marked with a --- and the changes from b/ are marked with the +++ symbol.

Diff chunks

The remaining diff output is a list of diff ‘chunks’. A diff only displays the sections of the file that have changes. In our current example, we only have one chunk as we are working with a simple scenario. Chunks have their own granular output semantics.

Revert back to the last committed version to the Git Repo:

Now you can choose to revert back to the last committed version by entering:

git checkout .

View Commit History:

You can use the git log command to see the history of commit you made to your files:

$ git log
echo 'testGit #2' > 
git add .
git commit -m 'second commit'
git push origin master

Pushing Changes to the Git Repo:

Now you can work on the files you want and commit to changes locally. If you want to push changes to that repository you either have to be added as a collaborator for the repository or you have create something known as pull request. Go and check out how to do one here and give me a pull request with your code file.

So to make sure that changes are reflected on my local copy of the repo:

git pull origin master

Two more useful command:

git fetch
git merge

In the simplest terms, git fetch followed by a git merge equals a git pull. But then why do these exist?

When you use git pull, Git tries to automatically do your work for you. It is context sensitive, so Git will merge any pulled commits into the branch you are currently working in. git pull automatically merges the commits without letting you review them first.

When you git fetch, Git gathers any commits from the target branch that do not exist in your current branch and stores them in your local repository. However, it does not merge them with your current branch. This is particularly useful if you need to keep your repository up to date, but are working on something that might break if you update your files. To integrate the commits into your master branch, you use git merge.

Pull Request

Pull requests let you tell others about changes you’ve pushed to a GitHub repository. Once a pull request is sent, interested parties can review the set of changes, discuss potential modifications, and even push follow-up commits if necessary.


Pull requests are GitHub’s way of modeling that you’ve made commits to a copy of a repository, and you’d like to have them incorporated in someone else’s copy. Usually the way this works is like so:

  1. Lady Ada publishes a repository of code to GitHub.
  2. Brennen uses Lady Ada’s repo, and decides to fix a bug or add a feature.
  3. Brennen forks the repo, which means copying it to his GitHub account, and clones that fork to his computer.
  4. Brennen changes his copy of the repo, makes commits, and pushes them up to GitHub.
  5. Brennen submits a pull request to the original repo, which includes a human-readable description of the changes.
  6. Lady Ada decides whether or not to merge the changes into her copy.

Creating a Pull Request

There are 2 main work flows when dealing with pull requests:

  1. Pull Request from a forked repository
  2. Pull Request from a branch within a repository

Here we are going to focus on 2.

Creating a Topical Branch

First, we will need to create a branch from the latest commit on master. Make sure your repository is up to date first using

git pull origin master

To create a branch, use git checkout -b <new-branch-name> [<base-branch-name>], where base-branch-name is optional and defaults to master. I’m going to create a new branch called pull-request-demo from the master branch and push it to github.

git checkout -b pull-request-demo
git status
git push origin pull-request-demo

Now you can see two branches:


make some changes to

echo "test git #3 pull-request-demo" >>

Commit the changes:

git add
git commit -m 'commit to pull-request-demo'

…and push your new commit back up to your copy of the repo on GitHub:

git push --set-upstream origin pull-request-demo

Back to the web interface:

You can press the “Compare”, and now you can create the pull request:

Go ahead and click the big green “Create Pull Request” button. You’ll get a form with space for a title and longer description:

Like most text inputs on GitHub, the description can be written in GitHub Flavored Markdown. Fill it out with a description of your changes. If you especially want a user’s attention in the pull request, you can use the “@username” syntax to mention them (just like on Twitter).

GitHub has a handy guide to writing the perfect pull request that you may want to read before submitting work to other repositories, but for now a description like the one I wrote should be ok. You can see my example pull request here.

Pressing the green “Create pull request”:

And now, pressing the “Merge pull request” button:

Confirm merge:

Switching you local repo back to master:

git checkout master
git pull origin master

And now the local repo is pointing to master and contains the merged files.


BTW please find below a nice Git cheat sheet


Learn the Basics of Git in Under 10 Minutes

Pull Request Tutorial

Submitting a Pull Request on GitHub

Get started developing workflows with Apache Airflow



Airflow was started in October 2014 by Maxime Beauchemin at Airbnb. It was open source from the very first commit and officially brought under the Airbnb GitHub and announced in June 2015.

The project joined the Apache Software Foundation’s Incubator program in March 2016 and the Foundation announced Apache Airflow as a Top-Level Project in January 2019.

Apache Airflow is in use at more than 200 organizations, including Adobe, Airbnb, Astronomer, Etsy, Google, ING, Lyft, NYC City Planning, Paypal, Polidea, Qubole, Quizlet, Reddit, Reply, Solita, Square, Twitter, and United Airlines, among others.


Airflow is a platform to programmatically author, schedule and monitor workflows.

Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.


We’ll create a workflow by specifying actions as a Directed Acyclic Graph (DAG) in Python. The tasks of a workflow make up a Graph; the graph is Directed because the tasks are ordered; and we don’t want to get stuck in an eternal loop so the graph also has to be Acyclic.

The figure below shows an example of a DAG:


pip3 install apache-airflow
airflow version

AIRFLOW_HOME is the directory where you store your DAG definition files and Airflow plugins

mkdir Airflow
export AIRFLOW_HOME=`pwd`/Airflow

Airflow requires a database to be initiated before you can run tasks. If you’re just experimenting and learning Airflow, you can stick with the default SQLite option. 

airflow initdb
ls -l Airflow/

The database airflow.db is created

You can start Airflow UI by issuing the following command:

airflow webserver


Your first Airflow DAG

We’ll start by creating a Hello World workflow, which does nothing other then sending “Hello world!” to the log.

Create your dags folder, that is the directory where your DAG definition files will be stored in AIRFLOW_HOME/dags. Inside that directory create a file named

mkdir dags

Add the following code to dags/

from datetime import datetime
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.python_operator import PythonOperator

def print_hello():
    return 'Hello world!'

dag = DAG('hello_world', description='Simple tutorial DAG',
          schedule_interval='0 12 * * *',
          start_date=datetime(2019, 5, 29), catchup=False)

dummy_operator = DummyOperator(task_id='dummy_task', retries=3, dag=dag)

hello_operator = PythonOperator(task_id='hello_task', python_callable=print_hello, dag=dag)

dummy_operator >> hello_operator

This file creates a simple DAG with just two operators, the DummyOperator, which does nothing and a PythonOperator which calls the print_hello function when its task is executed.

Running your DAG

Open a second terminal, go to the AIRFLOW_HOME folder and start the Airflow scheduler by issuing :

export AIRFLOW_HOME=`pwd`
airflow scheduler

When you reload the Airflow UI in your browser, you should see your hello_world DAG listed in Airflow UI.

In order to start a DAG Run, first turn the workflow on, then click the Trigger Dag button and finally, click on the Graph View to see the progress of the run.

After clicking the Graph View:

You can reload the graph view until both tasks reach the status Success.

When they are done, you can click on the hello_task and then click View Log.

If everything worked as expected, the log should show a number of lines and among them something like this:

Your first Airflow Operator

Let’s start writing our own Airflow operators. An Operator is an atomic block of workflow logic, which performs a single action. Operators are written as Python classes (subclasses of BaseOperator), where the __init__ function can be used to configure settings for the task and a method named execute is called when the task instance is executed.

Any value that the execute method returns is saved as an Xcom message under the key return_value. We’ll cover this topic later.

The execute method may also raise the AirflowSkipException from airflow.exceptions. In such a case the task instance would transition to the Skipped status.

If another exception is raised, the task will be retried until the maximum number of retries is reached.

Remember that since the execute method can retry many times, it should be idempotent [it can be applied multiple times without changing the result beyond the initial application]

We’ll create your first operator in an Airflow plugin file named plugins/ First create the /plugins directory, then add the file with the following content:

import logging

from airflow.models import BaseOperator
from airflow.plugins_manager import AirflowPlugin
from airflow.utils.decorators import apply_defaults

log = logging.getLogger(__name__)

class MyFirstOperator(BaseOperator):

    def __init__(self, my_operator_param, *args, **kwargs):
        self.operator_param = my_operator_param
        super(MyFirstOperator, self).__init__(*args, **kwargs)

    def execute(self, context):"Hello World!")'operator_param: %s', self.operator_param)

class MyFirstPlugin(AirflowPlugin):
    name = "my_first_plugin"
    operators = [MyFirstOperator]

In this file we are defining a new operator named MyFirstOperator. Its execute method is very simple, all it does is log “Hello World!” and the value of its own single parameter. The parameter is set in the __init__ function.

We are also defining an Airflow plugin named MyFirstPlugin. By defining a plugin in a file stored in the /plugins directory, we’re providing Airflow the ability to pick up our plugin and all the operators it defines. We’ll be able to import these operators later using the line from airflow.operators import MyFirstOperator.

Now, we’ll need to create a new DAG to test our operator. Create a dags/ file and fill it with the following content:

from datetime import datetime
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators import MyFirstOperator

dag = DAG('my_test_dag', description='Another tutorial DAG',
          schedule_interval='0 12 * * *',
          start_date=datetime(2019, 5, 29), catchup=False)

dummy_task = DummyOperator(task_id='dummy_task', dag=dag)

operator_task = MyFirstOperator(my_operator_param='This is a test.',
                                task_id='my_first_operator_task', dag=dag)

dummy_task >> operator_task

Here we just created a simple DAG named my_test_dag with a DummyOperator task and another task using our new MyFirstOperator. Notice how we pass the configuration value for my_operator_param here during DAG definition.

At this stage your source tree looks like this:

To test your new operator, you should stop (CTRL-C) and restart your Airflow web server and scheduler. Afterwards, go back to the Airflow UI, turn on the my_test_dag DAG and trigger a run. Take a look at the logs for my_first_operator_task.

Your first Airflow Sensor

An Airflow Sensor is a special type of Operator, typically used to monitor a long running task on another system.

To create a Sensor, we define a subclass of BaseSensorOperator and override its poke function. The poke function will be called over and over every poke_interval seconds until one of the following happens:

  • poke returns True – if it returns False it will be called again.
  • poke raises an AirflowSkipException from airflow.exceptions – the Sensor task instance’s status will be set to Skipped.
  • poke raises another exception, in which case it will be retried until the maximum number of retries is reached.

As an example, SqlSensor runs a sql statement until a criteria is met, HdfsSensor waits for a file or folder to land in HDFS, S3KeySensor waits for a key (a file-like instance on S3) to be present in a S3 bucket), S3PrefixSensor waits for a prefix to exist and HttpSensor executes a HTTP get statement and returns False on failure.

To add a new Sensor to your file, add the following code:

from datetime import datetime
from airflow.operators.sensors import BaseSensorOperator

class MyFirstSensor(BaseSensorOperator):

    def __init__(self, *args, **kwargs):
        super(MyFirstSensor, self).__init__(*args, **kwargs)

    def poke(self, context):
        current_minute =
        if current_minute % 3 != 0:
  "Current minute (%s) not is divisible by 3, sensor will retry.", current_minute)
            return False"Current minute (%s) is divisible by 3, sensor finishing.", current_minute)
        return True

Here we created a very simple sensor, which will wait until the the current minute is a number divisible by 3. When this happens, the sensor’s condition will be satisfied and it will exit. This is a contrived example, in a real case you would probably check something more unpredictable than just the time.

Remember to also change the plugin class, to add the new sensor to the operators it exports:

class MyFirstPlugin(AirflowPlugin):
    name = "my_first_plugin"
    operators = [MyFirstOperator, MyFirstSensor]

The final file is:

import logging

from airflow.models import BaseOperator
from airflow.plugins_manager import AirflowPlugin
from airflow.utils.decorators import apply_defaults

from datetime import datetime
from airflow.operators.sensors import BaseSensorOperator

log = logging.getLogger(__name__)

class MyFirstOperator(BaseOperator):

    def __init__(self, my_operator_param, *args, **kwargs):
        self.operator_param = my_operator_param
        super(MyFirstOperator, self).__init__(*args, **kwargs)

    def execute(self, context):"Hello World!")'operator_param: %s', self.operator_param)

class MyFirstSensor(BaseSensorOperator):

    def __init__(self, *args, **kwargs):
        super(MyFirstSensor, self).__init__(*args, **kwargs)

    def poke(self, context):
        current_minute =
        if current_minute % 3 != 0:
  "Current minute (%s) not is divisible by 3, sensor will retry.", current_minute)
            return False"Current minute (%s) is divisible by 3, sensor finishing.", current_minute)
        return True

class MyFirstPlugin(AirflowPlugin):
    name = "my_first_plugin"
    operators = [MyFirstOperator, MyFirstSensor]

You can now place the operator in your DAG, so the new file looks like:

from datetime import datetime
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators import MyFirstOperator, MyFirstSensor

dag = DAG('my_test_dag', description='Another tutorial DAG',
          schedule_interval='0 12 * * *',
          start_date=datetime(2019, 5, 29), catchup=False)

dummy_task = DummyOperator(task_id='dummy_task', dag=dag)

sensor_task = MyFirstSensor(task_id='my_sensor_task', poke_interval=30, dag=dag)

operator_task = MyFirstOperator(my_operator_param='This is a test.',
                                task_id='my_first_operator_task', dag=dag)

dummy_task >> sensor_task >> operator_task

Restart your webserver and scheduler and try out your new workflow. The Graph View looks like:

If you click View log of the my_sensor_task task, you should see something similar to this:

Have fun developing data pipelines!


PyCon.DE 2017 Tamara Mendt – Modern ETL-ing with Python and Airflow (and Spark)

Recall, Precision, F1, ROC, AUC, and everything

Your boss asked you to build a fraud detection classifier, so you’ve created one.

The output of your fraud detection model is the probability [0.0-1.0] that a transaction is fraudulent. If this probability is below 0.5, you classify the transaction as non-fraudulent; otherwise, you classify the transaction as fraudulent.

To evaluate the performance of your model, you collect 10,000 manually classified transactions, with 300 fraudulent transaction and 9,700 non-fraudulent transactions. You run your classifier on every transaction, predict the class label (fraudulent or non-fraudulent) and summarise the results in the following confusion matrix:

A True Positive (TP=100) is an outcome where the model correctly predicts the positive (fraudulent) class. Similarly, a True Negative (TN=9,000) is an outcome where the model correctly predicts the negative (non-fraudulent) class.

False Positive (FP=700) is an outcome where the model incorrectly predicts the positive  (fraudulent) class. And a False Negative (FN=200) is an outcome where the model incorrectly predicts the negative (non-fraudulent) class.

Asking yourself what percent of your predictions were correct, you calculate the accuracy:

Accuracy = \frac{True}{True+False} = \frac{TP+TN}{TP+TN+FP+FN} = \frac{100+9,000}{100+9,000+700+200} = \frac{9,100}{10,000} = 0.91

Wow, 91% accuracy! Just before sharing the great news with your boss, you notice that out of the 300 fraudulent transactions, only 100 fraudulent transactions are classified correctly. Your classifier missed 200 out of the 300 fraudulent transactions!

Your colleague, hardly hiding her simile, suggests a “better” classifier. Her classifier predicts every transaction as non-fraudulent (negative), with a staggering 97% accuracy!

Accuracy =   \frac{True}{True+False} = \frac{TP+TN}{TP+TN+FP+FN} = \frac{0+9,700}{100+9,000+700+200} = \frac{9,700}{10,000} = 0.97

While 97% accuracy may seem excellent at first glance, you’ve soon realized the catch: your boss asked you to build a fraud detection classifier, and with the always-return-non-fraudulent classifier you will miss all the fraudulent transactions.

“Nothing travels faster than the speed of light, with the possible exception of bad news, which obeys its own special laws.” 

Douglas Adams

You learned the hard-way that accuracy can be misleading and that for problems like this, additional measures are required to evaluate your classifier.

You start by asking yourself what percent of the positive (fraudulent) cases did you catch? You go back to the confusion matrix and divide the True Positive (TP – blue oval) by the overall number of true fraudulent transactions (red rectangle)

Recall ( True Positive Rate ) = \frac{TP}{TP+FN} = \frac{100}{100+200} \approx 0.333

So the classier caught 33.3% of the fraudulent transactions.

Next, you ask yourself what percent of positive (fraudulent) predictions were correct? You go back to the confusion matrix and divide the True Positive (TP – blue oval) by the overall number of predicted fraudulent transactions (red rectangle)

Precision =   \frac{TP}{TP+FP} = \frac{100}{100+700} = 0.125

So now you know that when your classifier predicts that a transaction is fraudulent, only 12.5% of the time your classifier is correct.

F1 Score combines Recall and Precision to one performance metric. F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. F1 is usually more useful than Accuracy, especially if you have an uneven class distribution.

F1 = 2*\frac{Recall * Precision}{ Recall + Precision}=2*\frac{0.333 * 0.125}{ 0.333 + 0.125}\approx 0.182

Finally, you ask yourself what percent of negative (non-fraudulent) predictions were incorrect? You go back to the confusion matrix and divide the False Positive (FP – blue oval) by the overall number of true non-fraudulent transactions (red rectangle)

False Positive Rate = \frac{FP}{FP+TN} = \frac{700}{700+9,000} \approx 0.072

7.2% of the non-fraudulent transactions were classified incorrectly as fraudulent transactions.

ROC (Receiver Operating Characteristics)

You soon learn that you must examine both Precision and Recall. Unfortunately, Precision and Recall are often in tension. That is, improving Precision typically reduces Recall and vice versa.

The overall performance of a classifier, summarized over all possible thresholds, is given by the Receiver Operating Characteristics (ROC) curve. The name “ROC” is historical and comes from communications theory. ROC Curves are used to see how well your classifier can separate positive and negative examples and to identify the best threshold for separating them.

To be able to use the ROC curve, your classifier should be able to rank examples such that the ones with higher rank are more likely to be positive (fraudulent). As an example, Logistic Regression outputs probabilities, which is a score that you can use for ranking.

You train a new model and you use it to predict the outcome of 10 new test transactions, summarizing the result in the following table: the values of the middle column (True Label) are either zero (0) for non-fraudulent transactions or one (1) for fraudulent transactions, and the last column (Fraudulent Prob) is the probability that the transaction is fraudulent:

Remember the 0.5 threshold? If you are concerned about missing the two fraudulent transactions (red circles), then you may consider lowering this threshold.

For instance, you might lower the threshold and label any transaction with a probability below 0.1 to the non-fraudulent class, catching the two fraudulent transactions that you previously missed.

To derive the ROC curve, you calculate the True Positive Rate (TPR) and the False Positive Rate (FPR), starting by setting the threshold to 1.0, where every transaction with a Fraudulent Prob of less than 1.0 is classified as non-fraudulent (0). The column “T=1.0” shows the predicted class labels when the threshold is 1.0:

The confusion matrix for the Threshold=1.0 case:

The ROC curve is created by plotting the True Positive Pate (TPR) against the False Positive Rate (FPR) at various threshold settings, so you calculate both:

True Positive Rate (Recall) = \frac{TP}{TP+FN} = \frac{0}{0+5} =0

False Positive Rate = \frac{FP}{FP+TN} = \frac{0}{0+5} =0

You summarize it in the following table:

Now you can finally plot the first point on your ROC graph! A random guess would give a point along the dotted diagonal line (the so-called line of no-discrimination) from the left bottom to the top right corners

You now lower the threshold to 0.9, and recalculate the FPR and the TPR:

The confusion matrix for Threshold=0.9:

True Positive Rate (Recall) = \frac{TP}{TP+FN} = \frac{1}{1+4} =0.2

False Positive Rate = \frac{FP}{FP+TN} = \frac{0}{0+5} =0

Adding a new row to your summary table:

You continue and plot the True Positive Pate (TPR) against the False Positive Rate (FPR) at various threshold settings:

Receiver Operating Characteristics (ROC) curve

And voila, here is your ROC curve!

AUC (Area Under the Curve)

The model performance is determined by looking at the area under the ROC curve (or AUC). An excellent model has AUC near to the 1.0, which means it has a good measure of separability. For your model, the AUC is the combined are of the blue, green and purple rectangles, so the AUC = 0.4 x 0.6 + 0.2 x 0.8 + 0.4 x 1.0 = 0.80.

You can validate this result by calling roc_auc_score, and the result is indeed 0.80.


  • Accuracy will not always be the metric.
  • Precision and recall are often in tension. That is, improving precision typically reduces recall and vice versa.
  • AUC-ROC curve is one of the most commonly used metrics to evaluate the performance of machine learning algorithms.
  • ROC Curves summarize the trade-off between the true positive rate and false positive rate for a predictive model using different probability thresholds.
  • The ROC curve can be used to choose the best operating point.

Thanks for Reading! You can reach me at LinkedIn and Twitter.


[1] An Introduction to Statistical Learning [James, Witten, Hastie, and Tibshirani]

How to debug and test your regular expression (regex)

Regular expressions are such an incredibly convenient tool, available across so many languages that most developers will learn them sooner or later.

But regular expressions can become quite complex. The syntax is terse, subtle, and subject to combinatorial explosion.

The best way to improve your skills is to write a regular expression, test it on some real data, debug the expression, improve it and repeat this process again and again.

This is why regex101 ( is such a great tool.

Not only does it let you test out your regexes on a sample set, color coding your match groups:

But it also gives you a full explanation of what’s going on under the hood.

You can review the match information:

And even choose your favorite flavor (PHP, JavaScript, Python or Golan)

How can I create email notifications for when an Amazon EMR cluster or step changes state?

Create an SNS topic. This topic is the target for the CloudWatch Events rule.

Open the AWS Identity and Access Management (IAM) console, and then choose Roles in the navigation pane.

Click on the name of the role that is attached to your cluster’s Amazon Elastic Compute Cloud (Amazon EC2) instances (for example, EMR_EC2_DefaultRole) and click Attach policies

Attach the AmazonSNSFullAccess policy to the role. This policy allows SNS to send notifications based on state changes in your Amazon EMR cluster.

A summary page is presented with the message “Policy AmazonSNSFullAccess has been attached for the EMR_EC2_DefaultRole.”

Open the CloudWatch console at and create a CloudWatch Events Rule That Triggers on an Event

Choose Event PatternBuild event pattern to match events by service. For Service Name, choose the service that emits the event to trigger the rule. For Event Type, choose the specific event that is to trigger the rule.

For Targets, choose Add Target and choose the AWS service that is to act when an event of the selected type is detected

Choose Configure details. For Rule definition, type a name and description for the rule.

Once created, the message “Rule emr_state_change_SNS was created.” will be presented

based on

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

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
# 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:


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

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

import findspark
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)) \
print("Pi is roughly %f" % (4.0 * count / NUM_SAMPLES))

The result:


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)