kandi X-RAY | lightfm Summary
kandi X-RAY | lightfm Summary
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.
Top functions reviewed by kandi - BETA
- 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
lightfm Key Features
lightfm Examples and Code Snippets
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
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=
# 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
Trending Discussions on lightfm
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:...
ANSWERAnswered 2022-Mar-26 at 12:28
I cannot test it, but I think the problem is when you write:
I have a pandas dataframe like this:...
ANSWERAnswered 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,
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.....
ANSWERAnswered 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
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:...
ANSWERAnswered 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.
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')
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?...
ANSWERAnswered 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 :
I'm going to use rasterio in python. I downloaded rasterio via...
ANSWERAnswered 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:
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!
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