Factorization machines in python

Related tags

Machine LearningpyFM
Overview

Factorization Machines in Python

This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive regularization as a learning method, which adapts the regularization automatically while training the model parameters. See [2] for details. From libfm.org: "Factorization machines (FM) are a generic approach that allows to mimic most factorization models by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain."

[1] Steffen Rendle (2012): Factorization Machines with libFM, in ACM Trans. Intell. Syst. Technol., 3(3), May. [2] Steffen Rendle: Learning recommender systems with adaptive regularization. WSDM 2012: 133-142

Installation

pip install git+https://github.com/coreylynch/pyFM

Dependencies

  • numpy
  • sklearn

Training Representation

The easiest way to use this class is to represent your training data as lists of standard Python dict objects, where the dict elements map each instance's categorical and real valued variables to its values. Then use a sklearn DictVectorizer to convert them to a design matrix with a one-of-K or “one-hot” coding.

Here's a toy example

from pyfm import pylibfm
from sklearn.feature_extraction import DictVectorizer
import numpy as np
train = [
	{"user": "1", "item": "5", "age": 19},
	{"user": "2", "item": "43", "age": 33},
	{"user": "3", "item": "20", "age": 55},
	{"user": "4", "item": "10", "age": 20},
]
v = DictVectorizer()
X = v.fit_transform(train)
print(X.toarray())
[[ 19.   0.   0.   0.   1.   1.   0.   0.   0.]
 [ 33.   0.   0.   1.   0.   0.   1.   0.   0.]
 [ 55.   0.   1.   0.   0.   0.   0.   1.   0.]
 [ 20.   1.   0.   0.   0.   0.   0.   0.   1.]]
y = np.repeat(1.0,X.shape[0])
fm = pylibfm.FM()
fm.fit(X,y)
fm.predict(v.transform({"user": "1", "item": "10", "age": 24}))

Getting Started

Here's an example on some real movie ratings data.

First get the smallest movielens ratings dataset from http://www.grouplens.org/system/files/ml-100k.zip. ml-100k contains the files u.item (list of movie ids and titles) and u.data (list of user_id, movie_id, rating, timestamp).

import numpy as np
from sklearn.feature_extraction import DictVectorizer
from pyfm import pylibfm

# Read in data
def loadData(filename,path="ml-100k/"):
    data = []
    y = []
    users=set()
    items=set()
    with open(path+filename) as f:
        for line in f:
            (user,movieid,rating,ts)=line.split('\t')
            data.append({ "user_id": str(user), "movie_id": str(movieid)})
            y.append(float(rating))
            users.add(user)
            items.add(movieid)

    return (data, np.array(y), users, items)

(train_data, y_train, train_users, train_items) = loadData("ua.base")
(test_data, y_test, test_users, test_items) = loadData("ua.test")
v = DictVectorizer()
X_train = v.fit_transform(train_data)
X_test = v.transform(test_data)

# Build and train a Factorization Machine
fm = pylibfm.FM(num_factors=10, num_iter=100, verbose=True, task="regression", initial_learning_rate=0.001, learning_rate_schedule="optimal")

fm.fit(X_train,y_train)
Creating validation dataset of 0.01 of training for adaptive regularization
-- Epoch 1
Training MSE: 0.59477
-- Epoch 2
Training MSE: 0.51841
-- Epoch 3
Training MSE: 0.49125
-- Epoch 4
Training MSE: 0.47589
-- Epoch 5
Training MSE: 0.46571
-- Epoch 6
Training MSE: 0.45852
-- Epoch 7
Training MSE: 0.45322
-- Epoch 8
Training MSE: 0.44908
-- Epoch 9
Training MSE: 0.44557
-- Epoch 10
Training MSE: 0.44278
...
-- Epoch 98
Training MSE: 0.41863
-- Epoch 99
Training MSE: 0.41865
-- Epoch 100
Training MSE: 0.41874

# Evaluate
preds = fm.predict(X_test)
from sklearn.metrics import mean_squared_error
print("FM MSE: %.4f" % mean_squared_error(y_test,preds))
FM MSE: 0.9227

Classification example

import numpy as np
from sklearn.feature_extraction import DictVectorizer
from sklearn.cross_validation import train_test_split
from pyfm import pylibfm

from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000,n_features=100, n_clusters_per_class=1)
data = [ {v: k for k, v in dict(zip(i, range(len(i)))).items()}  for i in X]

X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.1, random_state=42)

v = DictVectorizer()
X_train = v.fit_transform(X_train)
X_test = v.transform(X_test)

fm = pylibfm.FM(num_factors=50, num_iter=10, verbose=True, task="classification", initial_learning_rate=0.0001, learning_rate_schedule="optimal")

fm.fit(X_train,y_train)

Creating validation dataset of 0.01 of training for adaptive regularization
-- Epoch 1
Training log loss: 1.91885
-- Epoch 2
Training log loss: 1.62022
-- Epoch 3
Training log loss: 1.36736
-- Epoch 4
Training log loss: 1.15562
-- Epoch 5
Training log loss: 0.97961
-- Epoch 6
Training log loss: 0.83356
-- Epoch 7
Training log loss: 0.71208
-- Epoch 8
Training log loss: 0.61108
-- Epoch 9
Training log loss: 0.52705
-- Epoch 10
Training log loss: 0.45685

# Evaluate
from sklearn.metrics import log_loss
print "Validation log loss: %.4f" % log_loss(y_test,fm.predict(X_test))
Validation log loss: 1.5025
Owner
Corey Lynch
Research Engineer, Robotics @ Google Brain
Corey Lynch
Time-series momentum for momentum investing strategy

Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an

Victor Caldeira 3 Jun 18, 2022
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the effici

Jina AI 17k Jan 01, 2023
Provide an input CSV and a target field to predict, generate a model + code to run it.

automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn

Max Woolf 1.8k Jan 04, 2023
Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan

Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.

Abid Ali Awan 1 Dec 31, 2021
Library for machine learning stacking generalization.

stacked_generalization Implemented machine learning *stacking technic[1]* as handy library in Python. Feature weighted linear stacking is also availab

114 Jul 19, 2022
Predicting diabetes over a five year period using logistic regression and the Pima First-Nation dataset

Diabetes This script uses the Pima First Nations dataset to create a model to predict whether or not an individual will develop Diabetes Mellitus Type

1 Mar 28, 2022
A Collection of Conference & School Notes in Machine Learning 🦄📝🎉

Machine Learning Conference & Summer School Notes. 🦄📝🎉

558 Dec 28, 2022
Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks.

Databricks Certification Spark Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along

19 Dec 13, 2022
🌊 River is a Python library for online machine learning.

River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on strea

OnlineML 4k Jan 03, 2023
K-Means clusternig example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se

Emin 1 Dec 13, 2021
whylogs: A Data and Machine Learning Logging Standard

whylogs: A Data and Machine Learning Logging Standard whylogs is an open source standard for data and ML logging whylogs logging agent is the easiest

WhyLabs 2k Jan 06, 2023
Management of exclusive GPU access for distributed machine learning workloads

TensorHive is an open source tool for managing computing resources used by multiple users across distributed hosts. It focuses on granting

Paweł Rościszewski 131 Dec 12, 2022
inding a method to objectively quantify skill versus chance in games, using reinforcement learning

Skill-vs-chance-games-analysis - Finding a method to objectively quantify skill versus chance in games, using reinforcement learning

Marcus Chiam 4 Nov 19, 2022
pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

pywFM pywFM is a Python wrapper for Steffen Rendle's libFM. libFM is a Factorization Machine library: Factorization machines (FM) are a generic approa

João Ferreira Loff 251 Sep 23, 2022
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

neurodata 3 Dec 16, 2022
Uses WiFi signals :signal_strength: and machine learning to predict where you are

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Pascal van Kooten 5k Jan 09, 2023
Lightweight Machine Learning Experiment Logging 📖

Simple logging of statistics, model checkpoints, plots and other objects for your Machine Learning Experiments (MLE). Furthermore, the MLELogger comes with smooth multi-seed result aggregation and co

Robert Lange 65 Dec 08, 2022
Machine Learning Techniques using python.

👋 Hi, I’m Fahad from TEXAS TECH. 👀 I’m interested in Optimization / Machine Learning/ Statistics 🌱 I’m currently learning Machine Learning and Stat

FAHAD MOSTAFA 1 Jan 19, 2022
Toolss - Automatic installer of hacking tools (ONLY FOR TERMUKS!)

Tools Автоматический установщик хакерских утилит (ТОЛЬКО ДЛЯ ТЕРМУКС!) Оригиналь

14 Jan 05, 2023