tindetheus 0.2.0

tindetheus

Build personalized machine learning models for Tinder based on your historical preference using Python.

There are three parts to this: 1. A function to build a database which records everything about the profiles you’ve liked and disliked. 2. A function to train a model to your database. 3. A function to use the trained model to automatically like and dislike new profiles.

How it works

Essentially you can use the last layer a CNN trained for facial classification as a feature set that describes an individual’s face. It just so happens that this feature set is related to facial attractiveness.

tindetheus let’s you build a database based on the profiles that you like and dislike. You can then train a classification model to your database. The model training first uses a MTCNN to detect and box the faces in your database. Then a facenet model is run on the faces to extract the embeddings (last layer of the CNN). A logistic regression model is then fit to the embeddings. The logistic regression model is saved, and this processes is repeated in automation to automatically like and dislike profiles based on your historical preference.

Example usage

tindetheus browse

build a database by liking and disliking profiles on Tinder. The database contains all the profile information as a numpy array, while the profile images are saved in a different folder.

tindethus browse --distance=20

by default tindetheus starts with a 5 mile radius, but you can specify a search distance by specifying –distance. The above example is to start with a 20 mile search radius. It is important to note that when you run out of nearby users, tindethesus will ask you if you’d like to increase the search distance by 5 miles.

tindetheus train

Use machine learning to build a personalized model of who you like and dislike based on your database. The more profiles you’ve browsed, the better your model will be.

tindetheus like

Use your personalized model to automatically like and dislike profiles. The profiles which you have automatically liked and disliked are stored in al_database. By default this will start with a 5 mile search radius, which increases by 5 miles until you’ve used 100 likes. You can change the default search radius by using

tindetheus like --distance=20

which would start with a 20 mile search radius.

Installation

1. Install pynder from source (pynder on pip has not been updated)

   git clone https://github.com/charliewolf/pynder.git
   [sudo] pip install ./pynder

2. Install tindetheus

   [sudo] pip install tindetheus

Getting started

1. After you have installed tindetheus. Create a new folder that will be your Tinder database.

   mkdir my_tinder_data
   cd my_tinder_data

2. You need your facebook id and facebook auth token. There are many discussions on this on the internet to find this. If you are still lost, perhaps check out this or this.

3. Create a config.txt file that contains the following two lines exactly

   facebook_token = YYYY
   facebook_id = XXXX

   where YYYY and XXXX are replaced with your token and id in order to login using pynder.

4. Download a pretrained facenet model. I recommend using this model 20170512-110547 mirror. You must download 20170512-110547.zip and extract the contents in your my_tinder_data folder. The contents will be a folder named 20170512-110547. You can use other pretrained facenet models as long as you rename the folder to 20170512-110547.

5. You need to initialize git in your my_tinder_data folder which is used to track revision history. Run the following commands to initialize git.

   git init
   git add .
   git commit -m "first commit"

6. Start building your database. Manually reviewing 20-40 profiles will be a good starting point, but you can do it with less. Before you start training a model you have to be sure that you’ve liked and disliked at leach one profile.

   tindetheus browse

7. After browsing profiles you can train your personalized classifcation model at any time. Just run

   tindetheus train

   to build your personalized model. With more profiles you can build a more accurate model, so feel free to browse more profiles at any time and build to your database. Newly browsed profiles aren’t automatically added to the model, so you must manually run tinetheus train to update your model.

8. You can automatically like and dislike profiles based on your trained model. To do this simply run

   tindetheus like

   which will use your latest trained model to automatically like and dislike profiles. The application will start with a 5 mile search radius, and automatically like and dislike the people in this radius. After running out of people, the search radius is increased by 5 miles and the processes repeats. This goes on until you’ve used 100 likes, at which point the application stops.

9. This is all in the early stages, so after each session I highly recommend you backup your my_tinder_data folder by creating an archvie of the folder.

Open source libraries

tindetheus uses the following open source libraries:

• pynder

• facenet

• numpy

• matplotlib

• scikit-learn

• tensorflow

• imageio

• pandas

About the name

Tindetheus is a combination of Tinder (the popular online dating application) and the Greek Titans: Prometheus and Epimetheus. Prometheus signifies “forethought,” while his brother Epimetheus denotes “afterthought”. In synergy they serve to improve your Tinder experience.

Epimetheus creates a database from all of the profiles you review on Tinder.

Prometheus learns from your historical preferences to automatically like new Tinder profiles.