neo4j-playlist-builder | A tool to dynamically generate Spotify playlists

 by   nielsdejong Python Version: Current License: Apache-2.0

kandi X-RAY | neo4j-playlist-builder Summary

kandi X-RAY | neo4j-playlist-builder Summary

neo4j-playlist-builder is a Python library typically used in Data Science applications. neo4j-playlist-builder has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A tool to dynamically generate Spotify playlists using Neo4j graph data science.
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            kandi-support Support

              neo4j-playlist-builder has a low active ecosystem.
              It has 4 star(s) with 1 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              neo4j-playlist-builder has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of neo4j-playlist-builder is current.

            kandi-Quality Quality

              neo4j-playlist-builder has 0 bugs and 0 code smells.

            kandi-Security Security

              neo4j-playlist-builder has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              neo4j-playlist-builder code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              neo4j-playlist-builder 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

              neo4j-playlist-builder releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 277 lines of code, 14 functions and 1 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed neo4j-playlist-builder and discovered the below as its top functions. This is intended to give you an instant insight into neo4j-playlist-builder implemented functionality, and help decide if they suit your requirements.
            • Loads the graph from the spotify API
            • Cluster genres with gds
            • Make a list of playlists for a given supergenre
            • Get all album info
            • Get artist information
            • Rename playlists based on keywords
            • Return a list of tracks
            • Creates Spotify playlist in Spotify
            • Get audio features
            • Recursively recreate constraints
            • Generate playlists
            • Get a set of all the genres in the songs
            • Create a playlist for a small super genre
            • Create a Neo4j session
            Get all kandi verified functions for this library.

            neo4j-playlist-builder Key Features

            No Key Features are available at this moment for neo4j-playlist-builder.

            neo4j-playlist-builder Examples and Code Snippets

            No Code Snippets are available at this moment for neo4j-playlist-builder.

            Community Discussions

            QUESTION

            What does stopping the runtime while uploading a dataset to Hub cause?
            Asked 2022-Mar-24 at 01:06

            I am getting the following error while trying to upload a dataset to Hub (dataset format for AI) S3SetError: Connection was closed before we received a valid response from endpoint URL: "<...>".

            So, I tried to delete the dataset and it is throwing this error below.

            CorruptedMetaError: 'boxes/tensor_meta.json' and 'boxes/chunks_index/unsharded' have a record of different numbers of samples. Got 0 and 6103 respectively.

            Using Hub version: v2.3.1

            ...

            ANSWER

            Answered 2022-Mar-24 at 01:06

            Seems like when you were uploading the dataset the runtime got interrupted which led to the corruption of the data you were trying to upload. Using force=True while deleting should allow you to delete it.

            For more information feel free to check out the Hub API basics docs for details on how to delete datasets in Hub.

            If you stop uploading a Hub dataset midway through your dataset will be only partially uploaded to Hub. So, you will need to restart the upload. If you would like to re-create the dataset, you can use the overwrite = True flag in hub.empty(overwrite = True). If you are making updates to an existing dataset, you should use version control to checkpoint the states that are in good shape.

            Source https://stackoverflow.com/questions/71595867

            QUESTION

            Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?
            Asked 2022-Mar-19 at 16:28

            I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.

            I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.

            I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).

            ...

            ANSWER

            Answered 2022-Mar-19 at 16:28

            Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.

            You can find more information about storage authentication in the Hub docs.

            Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.

            Source https://stackoverflow.com/questions/71539946

            QUESTION

            split geometric progression efficiently in Python (Pythonic way)
            Asked 2022-Jan-22 at 10:09

            I am trying to achieve a calculation involving geometric progression (split). Is there any effective/efficient way of doing it. The data set has millions of rows. I need the column "Traded_quantity"

            Marker Action Traded_quantity 2019-11-05 09:25 0 0 09:35 2 BUY 3 09:45 0 0 09:55 1 BUY 4 10:05 0 0 10:15 3 BUY 56 10:24 6 BUY 8128

            turtle = 2 (User defined)

            base_quantity = 1 (User defined)

            ...

            ANSWER

            Answered 2022-Jan-22 at 10:09

            QUESTION

            is there any effective or efficient way to find net position of numbers from a data frame in python
            Asked 2022-Jan-21 at 01:04

            I have a multi index df, with column "Turtle"

            ...

            ANSWER

            Answered 2022-Jan-21 at 01:02

            There is a simple formula that maps Turtle to Net Pos. The calculation can be expressed as a sum of geometric series times base_quantity, yielding the function f below.

            Source https://stackoverflow.com/questions/70795029

            QUESTION

            Is there a way to return float or integer from a conditional True/False
            Asked 2022-Jan-16 at 14:28
            n_level = range(1, steps + 2)
            
            ...

            ANSWER

            Answered 2022-Jan-16 at 14:22

            this can be achieved easily using binary search, there are many ways to apply that(NumPy, bisect). I would recommend the library bisect.

            Added Buu for the Crest and See for the Trough, so that code and differentiate the segments. You can choose anything

            Source https://stackoverflow.com/questions/70601323

            QUESTION

            Generate the all possible unique peptides (permutants) in Python/Biopython
            Asked 2021-Dec-01 at 07:07

            I have a scenario in which I have a peptide frame having 9 AA. I want to generate all possible peptides by replacing a maximum of 3 AA on this frame ie by replacing only 1 or 2 or 3 AA.

            The frame is CKASGFTFS and I want to see all the mutants by replacing a maximum of 3 AA from the pool of 20 AA.

            we have a pool of 20 different AA (A,R,N,D,E,G,C,Q,H,I,L,K,M,F,P,S,T,W,Y,V).

            I am new to coding so Can someone help me out with how to code for this in Python or Biopython.

            output is supposed to be a list of unique sequences like below:

            CKASGFTFT, CTTSGFTFS, CTASGKTFS, CTASAFTWS, CTRSGFTFS, CKASEFTFS ....so on so forth getting 1, 2, or 3 substitutions from the pool of AA without changing the existing frame.

            ...

            ANSWER

            Answered 2021-Dec-01 at 07:07

            Ok, so after my code finished, I worked the calculations backwards,

            Case1, is 9c1 x 19 = 171

            Case2, is 9c2 x 19 x 19 = 12,996

            Case3, is 9c3 x 19 x 19 x 19 = 576,156

            That's a total of 589,323 combinations.

            Here is the code for all 3 cases, you can run them sequentially.

            You also requested to join the array into a single string, I have updated my code to reflect that.

            Source https://stackoverflow.com/questions/70178355

            QUESTION

            Getting Error 524 while running jupyter lab in google cloud platform
            Asked 2021-Oct-15 at 02:14

            I am not able to access jupyter lab created on google cloud

            I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

            I tried the diagnostic tool, which gave the following result:

            but didn't fix it.

            Thanks in advance.

            ...

            ANSWER

            Answered 2021-Aug-20 at 14:00

            QUESTION

            TypeError: import_optional_dependency() got an unexpected keyword argument 'errors'
            Asked 2021-Oct-08 at 03:00

            I am trying to work with Featuretools to develop an automated feature engineering workflow for the customer churn dataset. The end outcome is a function that takes in a dataset and label times for customers and builds a feature matrix that can be used to train a machine learning model.

            As part of this exercise I am trying to execute the below code for plotting a histogram and got "TypeError: import_optional_dependency() got an unexpected keyword argument 'errors' ". Please help resolve this TypeError.

            ...

            ANSWER

            Answered 2021-Sep-14 at 20:32

            Try to upgrade pandas:

            Source https://stackoverflow.com/questions/69148495

            QUESTION

            HUGGINGFACE TypeError: '>' not supported between instances of 'NoneType' and 'int'
            Asked 2021-Sep-12 at 16:55

            I am working on Fine-Tuning Pretrained Model on custom (using HuggingFace) dataset I will copy all code correctly from the one youtube video everything is ok but in this cell/code:

            ...

            ANSWER

            Answered 2021-Sep-12 at 16:55

            Seems to be an issue with the new version of transformers.

            Installing version 4.6.0 worked for me.

            Source https://stackoverflow.com/questions/68875496

            QUESTION

            How to identify what features affect predictions result?
            Asked 2021-Aug-11 at 15:55

            I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

            Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

            Table example:

            ...

            ANSWER

            Answered 2021-Aug-11 at 15:55

            You could build a model like this.

            x = features you have. y = true_lable

            from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).

            Source https://stackoverflow.com/questions/68744565

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

            Vulnerabilities

            No vulnerabilities reported

            Install neo4j-playlist-builder

            This project uses a single Python script: neo4j_spotify_playlist_builder.py. In here, you specify the parameters needed to connect to your spotify account using the Spotify API:.
            Your user_id can be found by using the web version of spotify and going to your profile overview. A spotify user id will look something like this: 111306XXXXX
            You need the Spotify developer dashboard to obtain a client id/secret. You can access it here: https://developer.spotify.com/dashboard/login. Next, create an app to obtain a client_id and a client_secret.
            Your public playlist_uri can be found using the spotify application. Right click a playlist, select 'Share' and click 'Copy Spotify URI'. Your URI will have the following format: spotify:playlist:XXXXXXXXXXXXXXXXXX
            Ensure that your Spotify developer app has the right redirect_url configured. For this tool to work, go to the Spotify developer dashboard and open the app you created. Click 'edit settings', and add the following url to "Redirect URIs": http://localhost:8888/callback.
            Set up your Neo4j connection in neo4j_spotify_playlist_builder.py: neo4j_url = "bolt://localhost:7687" neo4j_username = "neo4j" neo4j_password = "neo" Keep in mind this application clears your database, so best use a fresh DB.
            Install python dependencies in requirements.txt.
            Run neo4j_spotify_playlist_builder.py and watch the magic happen!

            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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            gh repo clone nielsdejong/neo4j-playlist-builder

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            git@github.com:nielsdejong/neo4j-playlist-builder.git

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