recommenders | TensorFlow Recommenders is a library for building | Recommender System library
kandi X-RAY | recommenders Summary
kandi X-RAY | recommenders Summary
TensorFlow Recommenders is a library for building recommender system models using TensorFlow. It helps with the full workflow of building a recommender system: data preparation, model formulation, training, evaluation, and deployment. It's built on Keras and aims to have a gentle learning curve while still giving you the flexibility to build complex models.
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Top functions reviewed by kandi - BETA
- Compute the query and indices
- Wrap tf errors
- Compute the tensorflow score
- Generates a random dataset from a given rating dataset
- Sample a list of movie titles
- Call the layer
- Performs the embedding lookup
- Apply gradients
- Check if the given strategy is a TPU strategy
- Create a copy of the given feature config
- Translates a Keras optimizer
- Normalize the optimizer
- Ensures that the given optimizer s supported parameters
- Adds candidates
- Reset tf function cache
- Index the candidates from the candidates
- Returns the index of the candidates
- Check that candidate embeddings are the same
- Create a TPUEmbedding API
- Calculate the indices of logits
- Index the candidates
- Builds TensorFlow docs
- Perform a training step
- Perform the query against the query model
- Compute metrics for each training step
- Specify the top k candidates from the given dataset
recommenders Key Features
recommenders Examples and Code Snippets
{
text: "© Fido Dido",
img: "http://p2.zhimg.com/10/7b/107bb4894b46d75a892da6fa80ef504a.jpg"
}
{
date: "20140523",
stories: [
{
title: "中国古代家具发展到今天有两个高峰,一个两宋一个明末(多图)",
ga_prefix: "052321",
images: [
"http://p1.zhimg.com/45/b9/45b9f057fc19
# in csv format
data/nowplaying_subset.csv
# or in tar.gz format
data/nowplaying_subset.csv.tar.gz
Python 2.7.x - required by GraphLab Create
GraphLab-Create
git clone https://github.com/YOUR-USERNAME/ucl-irdm-collaborative-filtering
# navigate t
import numpy as np
from spotlight.cross_validation import random_train_test_split
from spotlight.datasets.movielens import get_movielens_dataset
from spotlight.factorization import explicit, implicit
from spotlight.evaluation import mrr_score, rmse_
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#
# This script contains functions that appear in the tutorial jupyter notebooks (step_1 to step_5).
# You can find their usage in the corresponding cells f
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import os
import numpy as np
import pandas as pd
from tempfile import TemporaryDirectory
import surprise
import cornac
try:
from pyspark.ml.recommendat
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#
# MicrosoftAcademicGraph class to read MAG streams for Pandas
#
# Note:
# MAG streams do not have header
#
import numpy as np
import pandas as pd
Community Discussions
Trending Discussions on recommenders
QUESTION
if we check below documentation https://www.tensorflow.org/recommenders/examples/basic_retrieval it uses underscore (_) in the integer values like for batch size . What does this underscore signifies is it same as decimal point.
...ANSWER
Answered 2022-Mar-21 at 08:37The underscores are for improved readability, but do not have any deeper meaning. See this for more information. Here is an example:
QUESTION
I have a pandas dataframe like this:
...ANSWER
Answered 2022-Mar-21 at 15:56The 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)
QUESTION
NOTE - Since I do need to stream loading the data instead of in memory, Please show the example using tf.data.experimental.make_csv_dataset
. Also, please show an example using my exact dataset.
I'm trying to replicate this TensorFlow Recommenders tutorial with a toy dataset. However, I'm getting this below error:
...ANSWER
Answered 2021-Nov-19 at 17:50You seem to be preprocessing your data incorrectly. For example, you cannot use tf.strings.to_number
to convert 00001446-da5f-4d17
into some number. It will throw an error, since the string contains more than just numbers. Also, each sample in your dataset was an array instead of a single sample: Channel 1, for example, was not 1, but [1]. This was the cause of the original problem in your question. Here is a simplified working example based on your code:
QUESTION
Done
I am just trying to run and replicate the following project: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ . Basically until this point I have done everything as it is in the linked project but than I got the following issue:
My Own Dataset - I have tried with the dataframe:
- I have tried with his original dataset fully 100% his code but I still have the same error
- A.) having the 2 columns (1st column date and 2nd column target values),
- B.) time code in to the index and dataframe only containing the target value.
INPUT CODE:
...ANSWER
Answered 2021-Jun-22 at 15:36Solution
- I switched to AWS EC2 SageMaker "Python [conda env:tensorflow2_p36] " so this is the exact pre made environment "tensorflow2_p36"
- As I ahev read it in some palces it is probably library collision maybe with NumPy.
QUESTION
I am trying to build one recommendation model similar to this example. But this example uses Tensorflow v2.4.0 and for my work, I need to use v2.1.0. It seems that the StringLookUp
layer does not exist in v2.1.0. Is there any equivalent way to achieve the exact same thing in 2.1.0? I need to use this in such a model:
ANSWER
Answered 2021-Feb-21 at 13:48You can use tf.strings.to_hash_bucket_strong to hash your strings to indices, as long as you don't care about the mapping order.
Example:
QUESTION
I am trying to prepare a VM Resizing Recommendations Report using a python3.7 script My code is as follows:
import datetime
...ANSWER
Answered 2020-Dec-11 at 14:27I have checked the code on my end an it seems to be a syntax error when calling the method. As per the library documentation the first parameter should be a request object or None and the parent parameter has to be passed by name. Changing the line as follows I didn't have the error:
QUESTION
Here is my query. I have two recommenders with their own ranking. I need to convert the rank into a number using the CASE statement and then average both numbers. When I run the query I get "Invalid column name errors". If I comment out the averageScore field, the CASE statement works as expected.
...ANSWER
Answered 2020-Oct-23 at 13:16I assume that you are using MS SQL based on your Syntax. In that case you can't reference a column_alias in another column:
column_alias can be used in an ORDER BY clause. However, it cannot be used in a WHERE, GROUP BY, or HAVING clause. If the query expression is part of a DECLARE CURSOR statement, column_alias cannot be used in the FOR UPDATE clause.
I think it's relatively easy to work around this with an CTE:
QUESTION
My ant build stopped working sometime recently. I don't know exactly when, since I only do ant builds occasionally in this project. I get the following popup:
The error is only for this project. I can run a build from another project no problem.
The build.xml file can be extremely simple, and still cause the error:
...ANSWER
Answered 2020-Oct-16 at 07:42This seems to be related to the Java used to run the Ant script.
In the run configuration, in the JRE tab, make sure a JRE/JDK is selected (for an existing Ant run configuration it can happen that the previously selected JRE/JDK is lost due to changes in the preferences Java > Installed JREs or when updating Java). If a JRE/JDK is already selected, try another JRE/JDK setting here (if there are VM arguments specify, try it without them, since they might be illegal for the chosen VM).
QUESTION
I am using Surprise to evaluate various recommender system algorithms. I would like to calculate predictions and prediction coverage on all possible user and item permutations. My data is loaded in from predefined splits.
My strategy to calculate prediction coverage is to
- build a full trainset and fit
- get lists of all users and items
- iterate through the list and make predictions
- count exceptions where predictions are impossible to calculate prediction coverage.
Trying to call data.build_full_trainset())
yields the following error:
ANSWER
Answered 2020-Aug-13 at 23:10TLDR; The model_selection documentation in Surprise indicates a "refit" method, that will fit data on a full trainset, however it explicitly doesn't work with predefined folds.
Another major issue: oyyablokov's comment on this issue suggests you cannot fit a model with data that has NaNs. So even if you have a full trainset, how does one create a full prediction matrix to calculate things like prediction coverage, which requires all users and item combinations with or without ratings?
My workaround was to create 3 Surprise datasets.
- The dataset from predefined folds to compute best_params
- The full dataset of ratings (combining all folds outside of Surprise)
- The full prediction matrix dataset including all possible combinations of users and items (with or without ratings).
After you find your best paramaters with grid search cross validation, you can find your predictions and coverage with something like this:
QUESTION
I have a following models.py for my Django blog, I made a following views.py to pass the value of the slug for my URL parameter.
However I am struggling to create a model in views to get other data(person & description) from Category class.
I have tried some patterns by myself but can not pass them to HTML. (always Error or not showing)
Can you please give me some idea of how to solve this.
models.py
ANSWER
Answered 2020-Jun-03 at 17:13I would look at this example.
Namely, if you render the template like it is shown in the example, you should be able to do
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Building a factorization model for the Movielens 100K dataset is very simple (Colab):.
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