recommenders | TensorFlow Recommenders is a library for building | Recommender System library

 by   tensorflow Python Version: v0.7.3 License: Apache-2.0

kandi X-RAY | recommenders Summary

kandi X-RAY | recommenders Summary

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

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.

            kandi-support Support

              recommenders has a medium active ecosystem.
              It has 1575 star(s) with 234 fork(s). There are 47 watchers for this library.
              It had no major release in the last 12 months.
              There are 196 open issues and 175 have been closed. On average issues are closed in 6 days. There are 28 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of recommenders is v0.7.3

            kandi-Quality Quality

              recommenders has 0 bugs and 0 code smells.

            kandi-Security Security

              recommenders has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              recommenders code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              recommenders 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.

            kandi-Reuse Reuse

              recommenders 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.
              recommenders saves you 589 person hours of effort in developing the same functionality from scratch.
              It has 2654 lines of code, 168 functions and 44 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed recommenders and discovered the below as its top functions. This is intended to give you an instant insight into recommenders implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            recommenders Key Features

            No Key Features are available at this moment for recommenders.

            recommenders Examples and Code Snippets

            #API 分析
            Swiftdot img1Lines of Code : 250dot img1no licencesLicense : No License
            copy iconCopy
            	text: "© Fido Dido",
            	img: ""
            	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: [
            Pythondot img2Lines of Code : 165dot img2no licencesLicense : No License
            copy iconCopy
            # in csv format
            # or in tar.gz format
            Python 2.7.x - required by GraphLab Create
            git clone
            # navigate t  
            Explicit vs implicit feedback models
            Jupyter Notebookdot img3Lines of Code : 37dot img3License : Permissive (MIT)
            copy iconCopy
            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_  
            recommenders - task helper
            Pythondot img4Lines of Code : 806dot img4License : Permissive (MIT License)
            copy iconCopy
            # 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  
            recommenders - benchmark utils
            Pythondot img5Lines of Code : 335dot img5License : Permissive (MIT License)
            copy iconCopy
            # 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
            recommenders - Pandas Mag Class
            Pythondot img6Lines of Code : 220dot img6License : Permissive (MIT License)
            copy iconCopy
            # 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


            Use of underscore in interger values or Number values in Tensorflow documentation
            Asked 2022-Mar-29 at 04:47

            if we check below documentation it uses underscore (_) in the integer values like for batch size . What does this underscore signifies is it same as decimal point.



            Answered 2022-Mar-21 at 08:37

            The underscores are for improved readability, but do not have any deeper meaning. See this for more information. Here is an example:



            DataError: No numeric types to aggregate pandas pivot
            Asked 2022-Mar-21 at 15:56

            I have a pandas dataframe like this:



            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:

            1. 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".
            2. 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)



            TensorFlow Recommenders - ValueError: Shape must be rank 2 but is rank 3
            Asked 2021-Nov-19 at 17:50

            NOTE - Since I do need to stream loading the data instead of in memory, Please show the example using 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:



            Answered 2021-Nov-19 at 17:50

            You 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:



            ValueError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes:
            Asked 2021-Jun-22 at 15:36


            I am just trying to run and replicate the following project: . 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:



            Answered 2021-Jun-22 at 15:36


            • 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.



            StringLookup equivalent for tensorflow v2.1.0
            Asked 2021-Feb-21 at 13:48

            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:



            Answered 2021-Feb-21 at 13:48

            You 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.




            ListRecommendationsRequest constructor error in python script
            Asked 2020-Dec-11 at 14:27

            I am trying to prepare a VM Resizing Recommendations Report using a python3.7 script My code is as follows:

            import datetime



            Answered 2020-Dec-11 at 14:27

            I 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:



            SQL How to average two fields formed by a Case Statement
            Asked 2020-Oct-23 at 13:16

            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.



            Answered 2020-Oct-23 at 13:16

            I 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:



            Cannot Run Ant Build in Eclipse
            Asked 2020-Oct-16 at 07:42

            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:



            Answered 2020-Oct-16 at 07:42

            This 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).



            How to build a full trainset when loading data from predefined folds in Surprise?
            Asked 2020-Aug-13 at 23:10

            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

            1. build a full trainset and fit
            2. get lists of all users and items
            3. iterate through the list and make predictions
            4. count exceptions where predictions are impossible to calculate prediction coverage.

            Trying to call data.build_full_trainset()) yields the following error:



            Answered 2020-Aug-13 at 23:10

            TLDR; 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.

            1. The dataset from predefined folds to compute best_params
            2. The full dataset of ratings (combining all folds outside of Surprise)
            3. 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:



            How to pass multiple values from to HTML via in Django
            Asked 2020-Jun-03 at 18:27

            I have a following for my Django blog, I made a following 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.




            Answered 2020-Jun-03 at 17:13

            I would look at this example.

            Namely, if you render the template like it is shown in the example, you should be able to do


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


            No vulnerabilities reported

            Install recommenders

            Make sure you have TensorFlow 2.x installed, and install from pip:.
            Building a factorization model for the Movielens 100K dataset is very simple (Colab):.


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