lightfm | Python implementation of LightFM , a hybrid recommendation | Recommender System library

by lyst Python Version: 1.17 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | lightfm Summary

lightfm is a Python library typically used in Artificial Intelligence, Recommender System applications. lightfm has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install lightfm' or download it from GitHub, PyPI.

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features). For more details, see the Documentation. Need help? Contact me via email, Twitter, or Gitter.

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Support

lightfm has a highly active ecosystem.

It has 4348 star(s) with 668 fork(s). There are 93 watchers for this library.

It had no major release in the last 12 months.

There are 130 open issues and 354 have been closed. On average issues are closed in 50 days. There are 12 open pull requests and 0 closed requests.

It has a negative sentiment in the developer community.

The latest version of lightfm is 1.17

Quality

lightfm has 0 bugs and 0 code smells.

Security

lightfm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

lightfm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

lightfm is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

lightfm releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

lightfm saves you 1057 person hours of effort in developing the same functionality from scratch.

It has 2488 lines of code, 156 functions and 22 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed lightfm and discovered the below as its top functions. This is intended to give you an instant insight into lightfm implemented functionality, and help decide if they suit your requirements.

Downloadovielens
Convert the matrix to a coo_matrix object
Calculate the dimensions of the dataset
Build interaction matrix
Return metadata for a movie item
Return the path to movielens zip files
Download the movielens dataset
Get the raw metadata
Calculate the recall of a given model
Constructs a matrix representation of the feature matrix
Checks that the test interaction between test interactions
Predict the rank of the graph
Gets the covariance matrix fromovielens data
Get the raw movielens data
Run the lightfm
Generate code for pyx
Compute reciprocal rank
Define the list of supported extensions

Get all kandi verified functions for this library.

lightfm Key Features

No Key Features are available at this moment for lightfm.

lightfm Examples and Code Snippets

LightFM Dataset helper,Example,Creating the helper instance

Python

Lines of Code : 34

License : Permissive (MIT)

Copy

dataset_helper_instance = DatasetHelper(
    users_dataframe=users,
    items_dataframe=books,
    interactions_dataframe=ratings,
    item_id_column=items_column,
    items_feature_columns=items_feature_columns,
    user_id_column=user_column,
    u

Pre-train LightFM,Method

Python

Lines of Code : 28

License : No License

Copy

def __init__(self, no_components=10, k=5, n=10,
                 learning_schedule='adagrad',
                 loss='logistic',
                 learning_rate=0.05, rho=0.95, epsilon=1e-6,
                 item_alpha=0.0, user_alpha=0.0, max_sampled=

LightFM Dataset helper,Example,Preparing the Dataframe and the required info

Python

Lines of Code : 26

License : Permissive (MIT)

Copy

# using pandas to load csv files
import pandas as pd

def read_csv(filename):
    return pd.read_csv(filename, sep=";", error_bad_lines=False, encoding="latin-1", low_memory=False)

books = read_csv("Data/BX-Books.csv")
users = read_csv("Data/BX-User

Community Discussions

Trending Discussions on lightfm

LightFM how to make predictions for new users (cold start) - user id 8 not in user id mappings

DataError: No numeric types to aggregate pandas pivot

predict new user using lightfm

Scipy coo_matrix.max() alters data attribute

Lightfm Incorrect number of features in user_features

Import rasterio failed. Reason: image not found

QUESTION

LightFM how to make predictions for new users (cold start) - user id 8 not in user id mappings

Asked 2022-Mar-26 at 12:28

I am building a recommendation system in order to recommend training to employees based on user features and item features which LightFM according to the documentation its a great algorithm.

my user dataframe:

...

ANSWER

Answered 2022-Mar-26 at 12:28

I cannot test it, but I think the problem is when you write:

Source https://stackoverflow.com/questions/71524594

QUESTION

DataError: No numeric types to aggregate pandas pivot

Asked 2022-Mar-21 at 15:56

I have a pandas dataframe like this:

...

ANSWER

Answered 2022-Mar-21 at 15:56

The Pandas Documentation states:

While pivot() provides general purpose pivoting with various data types (strings, numerics, etc.), pandas also provides pivot_table() for pivoting with aggregation of numeric data

Make sure the column is numeric. Without seeing how you create trainingtaken I can't provide more specific guidance. However the following may help:

Make sure you handle "empty" values in that column. The Pandas guide is a very good place to start. Pandas points out that "a column of integers with even one missing values is cast to floating-point dtype".
If working with a dataframe, the column can be cast to a specific type via your_df.your_col.astype(int) or for your example, pd.trainingtaken.astype(int)

Source https://stackoverflow.com/questions/71559906

QUESTION

predict new user using lightfm

Asked 2021-Sep-21 at 10:15

I want to give a recommendation to a new user using lightfm.

Hi, I've got model, interactions, item_features. The new user is not in interactions and the only information of the new user is their ratings.(list of book_id and rating pairs)

I tried to use predict() or predict_rank(), but I failed to figure out how. Could you please give me some advice?

Below is my screenshot which raised ValueError..

...

ANSWER

Answered 2021-Sep-21 at 10:15

I was having the same problem, What I did was

Created a user_features matrix (based on their preferences) using Dataset class

Source https://stackoverflow.com/questions/68857138

QUESTION

Scipy coo_matrix.max() alters data attribute

Asked 2020-Dec-30 at 23:18

I am building a recommendation system using an open source library, LightFM. This library requires certain pieces of data to be in a sparse matrix format, specifically the scipy coo_matrix. It is here that I am encountering strange behavior. It seems like a bug, but it's more likely that I am doing something wrong.

Basically, I let LightFM.Dataset build me a sparse matrix, like so:

...

ANSWER

Answered 2020-Dec-30 at 23:18

A 'raw' coo_matrix can have duplicate elements (repeats of the same row and col values), but when converted to csr format for calculations those duplicates are summed. It must be doing the same, but in-place, in order to find that max.

Source https://stackoverflow.com/questions/65513251

QUESTION

Lightfm Incorrect number of features in user_features

Asked 2020-Dec-15 at 16:03

I am building recommender system - hybrid in Lightfm. My data has 39326 unique users and 2569 unique game titles(items). My train interaction sparce matrix has shape: <39326x2569 sparse matrix of type '' with 758931 stored elements in Compressed Sparse Row format> My test interaction sparce matrix has shape:<39323x2569 sparse matrix of type '' with 194622 stored elements in Compressed Sparse Row format>

I train model: model1 = LightFM(learning_rate=0.01, loss='warp') model1.fit(train_interactions,
epochs=20) which creates object: But when I try to check accuracy by: train_precision = precision_at_k(model1, train_interactions, k=10).mean() test_precision = precision_at_k(model1, test_interactions, k=10).mean()

I get error message: Incorrect number of features in user_features WHY??? Clearly the shapes are compatible? What am I missing?

...

ANSWER

Answered 2020-Dec-15 at 16:03

Your test sparse matrix is of dimension 39323x2569 while your train sparse matrix is of dimension 39326x2569. You are missing 3 users in your test set.

I suggest you use the lightfm built-in train/test split function to avoid errors : https://making.lyst.com/lightfm/docs/cross_validation.html

If you want to split your data in your own way, you can also transform your user_id and item_id to consecutive integers starting from 0. And then use this :

Source https://stackoverflow.com/questions/65298197

QUESTION

Import rasterio failed. Reason: image not found

Asked 2020-Sep-22 at 05:37

I'm going to use rasterio in python. I downloaded rasterio via

...

ANSWER

Answered 2020-Sep-22 at 05:37

I've got some experience with rasterio, but I am not nearly a master with it. If I remember correctly, rasterio requires you to have installed the program GDAL(both binaries and python utilities), and some other dependencies listed on the PyPi page. I don't use conda at the moment, I like to use the regular python 3.8 installer with pip. Given what I'm seeing with your installation, I would uninstall rasterio and follow a different installation procedure.

I follow the instructions listed here: https://rasterio.readthedocs.io/en/latest/installation.html
This page also has separate instructions for those using Anaconda.

The GDAL installation is by far the most annoying but once it's done, the hard part is over. The python utilities for both rasterio and gdal can be found here:
https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal
The second link is also provided on the PyPi page but I like to keep it bookmarked because there's a lot of good resources there!

Source https://stackoverflow.com/questions/64002714

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install lightfm

Fitting an implicit feedback model on the MovieLens 100k dataset is very easy:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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