rosetta | data science with a concentration on text processing

 by   columbia-applied-data-science Jupyter Notebook Version: 0.3 License: Non-SPDX

kandi X-RAY | rosetta Summary

kandi X-RAY | rosetta Summary

rosetta is a Jupyter Notebook library typically used in Data Science applications. rosetta has no bugs, it has no vulnerabilities and it has low support. However rosetta has a Non-SPDX License. You can download it from GitHub.

Tools, wrappers, etc... for data science with a concentration on text processing
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              rosetta has a low active ecosystem.
              It has 203 star(s) with 46 fork(s). There are 22 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 14 open issues and 7 have been closed. On average issues are closed in 29 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of rosetta is 0.3

            kandi-Quality Quality

              rosetta has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              rosetta has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              rosetta releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of rosetta
            Get all kandi verified functions for this library.

            rosetta Key Features

            No Key Features are available at this moment for rosetta.

            rosetta Examples and Code Snippets

            ImportError when importing psycopg2 on M1
            Pythondot img1Lines of Code : 2dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pip3.9 install psycopg2-binary --force-reinstall --no-cache-dir
            
            arch x86_64 and arm64e is available but python3 is saying incompatible architecture on Mac M1
            Pythondot img2Lines of Code : 4dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            python3 make_keras_charnet_model.py
            
            arch -arm64 python3 make_keras_charnet_model.py
            
            How can I create a script to switch between my ARM conda and x84 conda?
            Pythondot img3Lines of Code : 23dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            x86 () {
            
            conda deactivate
            
            # >>> conda initialize >>>
            # !! Contents within this block are managed by 'conda init' !!
            __conda_setup="$('/Users/$USERNAME/miniforge3_x86_64/bin/conda' 'shell.zsh' 'hook' 2> /dev/null)"
            if
            Django can't migrate to a new db
            Pythondot img4Lines of Code : 3dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            arch -x86_64 python manage.py makemigrations 
            arch -x86_64 python manage.py migrate
            
            pip fails to find brew installed libs with M1 chip
            Pythondot img5Lines of Code : 2dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            CFLAGS="-I/opt/homebrew/include -L/opt/homebrew/lib" python3 -m pip install plyvel
            
            MacOS M1 system is detected as ARM by Python package even though I'm using Rosetta
            Pythondot img6Lines of Code : 3dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            > python -c 'import platform; print(platform.platform())'
            macOS-12.0.1-arm64-i386-64bit
            
            Problems installing python packages on Mac M1
            Pythondot img7Lines of Code : 12dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # create environment in conda
            conda create -n venv python=3.8 # with your python version
            
            # activate
            conda activate venv
            
            # config channel
            conda config --append channels conda-forge # available channel name
            
            # then 
            copy iconCopy
            ./script.py
            
            arch -x86_64 ./script.py
            arch -arm64 ./script.py
            
            ModuleNotFoundError: No module named 'google.cloud' jupyter notbook python 3.8.0
            Pythondot img9Lines of Code : 4dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            !pip install google-cloud-storage
            
            from google.cloud import storage
            
            copy iconCopy
            export LDFLAGS="-L$(brew --prefix openssl@1.1)/lib"
            export CFLAGS="-I$(brew --prefix openssl@1.1)/include"
            

            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 rosetta

            You can download it from GitHub.

            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 .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            Install
          • PyPI

            pip install rosetta

          • CLONE
          • HTTPS

            https://github.com/columbia-applied-data-science/rosetta.git

          • CLI

            gh repo clone columbia-applied-data-science/rosetta

          • sshUrl

            git@github.com:columbia-applied-data-science/rosetta.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link