LncLOOMv1 | based framework | Natural Language Processing library
kandi X-RAY | LncLOOMv1 Summary
kandi X-RAY | LncLOOMv1 Summary
LncLOOM is a graph-based framework that uses integer programming to identify combinations of short motifs that are deeply conserved in rapidly evolving sequences. This version is implemented in Python 2 and is supported on Linux/Unix-based systems.
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Community Discussions
Trending Discussions on Natural Language Processing
QUESTION
For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.
As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.
...ANSWER
Answered 2022-Apr-14 at 13:32Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.
QUESTION
I'm trying to figure out why Apple's Natural Language API returns unexpected results.
What am I doing wrong? Is it a grammar issue?
I have the following four strings, and I want to extract each word's "stem form."
...ANSWER
Answered 2022-Apr-01 at 20:30As for why the tagger doesn't find "accredit" from "accreditation", this is because the scheme .lemma finds the lemma of words, not actually the stems. See the difference between stem and lemma on Wikipedia.
The stem is the part of the word that never changes even when morphologically inflected; a lemma is the base form of the word. For example, from "produced", the lemma is "produce", but the stem is "produc-". This is because there are words such as production and producing In linguistic analysis, the stem is defined more generally as the analyzed base form from which all inflected forms can be formed.
The documentation uses the word "stem", but I do think that the lemma is what is intended here, and getting "accreditation" is the expected behaviour. See the Usage section of the Wikipedia article for "Word stem" for more info. The lemma is the dictionary form of a word, and "accreditation" has a dictionary entry, whereas something like "accredited" doesn't. Whatever you call these things, the point is that there are two distinct concepts, and the tagger gets you one of them, but you are expecting the other one.
As for why the order of the words matters, this is because the tagger tries to analyse your words as "natural language", rather than each one individually. Naturally, word order matters. If you use .lexicalClass, you'll see that it thinks the third word in text2 is an adjective, which explains why it doesn't think its dictionary form is "accredit", because adjectives don't conjugate like that. Note that accredited is an adjective in the dictionary. So "is it a grammar issue?" Exactly.
QUESTION
I am trying to clean up text using a pre-processing function. I want to remove all non-alpha characters such as punctuation and digits, but I would like to retain compound words that use a dash without splitting them (e.g. pre-tender, pre-construction).
...ANSWER
Answered 2022-Mar-29 at 09:14To remove all non-alpha characters but - between letters, you can use
QUESTION
Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!
List of bigrams:
...ANSWER
Answered 2022-Feb-16 at 20:28You could use a regex and extractall:
QUESTION
Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.
Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.
ANSWER
Answered 2022-Feb-03 at 09:29I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081
QUESTION
I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:
Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help
Dear customer.... etc
The highlighted sentence would be:
However, this morning the door does not lock properly.
- What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
- What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
- What resources are available to research how to implement this in Python (using tensorflow or pytorch)
I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.
...ANSWER
Answered 2022-Feb-07 at 10:21This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.
The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.
I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).
This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.
QUESTION
My current data-frame is:
...ANSWER
Answered 2022-Jan-06 at 12:13try
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
...ANSWER
Answered 2021-Dec-25 at 21:51There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.
As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:
QUESTION
I am working on some sentence formation like this:
...ANSWER
Answered 2021-Dec-12 at 17:53You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.
QUESTION
We can create a model from AutoModel(TFAutoModel) function:
...ANSWER
Answered 2021-Dec-05 at 09:07The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install LncLOOMv1
Download the lncLOOM repository. git clone https://github.com/lncLOOM/LncLOOMv1.git cd LncLOOMv1
Install Python 2 (If needed) LncLOOM is supported on Linux/Unix-based systems. It is run via the command line. Python 2 is available for download at: here
Install LncLOOM as an executable using pip. Firstly ensure that pip is installed: sudo apt-get install python-pip if you are using macOS: sudo easy_install pip Install LncLOOMv1 using pip (the following command ensures that it is setup to run with python2) python2 -m pip install --user -e ./LncLOOM_v1
Add LncLOOM to your $PATH. pip creates a LncLOOM executable. Depending on your OS, this executable will be saved to certain directory, which needs to be added to your $PATH: For Linux systems, LncLOOM will be saved in ~/.local/bin/ (or /home/ /.local/bin) export PATH="~/.local/bin:$PATH" For macOS, LncLOOM will be saved in /Users/ /Library/Python/ /bin eg: /Users/Mac/Library/Python/2.7/bin export PATH="/Users/Mac/Library/Python/2.7/bin:$PATH" *Note: the above paths to LncLOOM may vary depending on your directories
Install LncLOOM dependencies LncLOOM requires several packages to be installed. Most of these would have already been installed when you installed LncLOOMv1 (see last section of this page for a list of these packages) However the following additional programs must be installed individually: BLASTN sudo apt-get install ncbi-blast+ Mafft To download and setup follow the steps given here
Set paths to genome files and eCLIP data that LncLOOM will use for annotations and generation of a custom track for the UCSC Genome Browser In the LncLOOM_v1/LncLOOM_v1/src/ directory there is a file called for_eclip_annotation.txt. This file tells LncLOOM where to find data needed for annotations. The file looks as follows: Query Layer: 1 Blat: src/hg19.fa eCLIP: Data 1: src/eCLIP_narrowPeakApr2019/ Currently the paths have been set to use data that is located in the LncLOOM_v1/LncLOOM_v1/src/ folder. However, these files are too large to be stored on GitHub and need to be downloaded from hg19.fa and eCLIP_narrowPeakApr2019 To use this data, download and extract the files into the LncLOOM_v1/LncLOOM_v1/src/ folder. The eCLIP data consists of BigBed files retrieved from ENCODE in 2019. If you choose to run the eCLIP annotation option with Blat, by default your query sequence must have at least 95% similarity to the target genome for a match to be considered. This can be adjusted using the --blatID paramater. tar xvzf eCLIP_narrowPeakApr2019.tar.gz mv eCLIP_narrowPeakApr2019 LncLOOMv2/LncLOOMv2/src/ tar xvzf hg19.tgz mv hg19.fa LncLOOMv2/LncLOOMv2/src/ Alternatively, if you have your own data you can update these paths in for_eclip_annotation.txt to the full paths to your genome file and eCLIP data. For example: Query Layer: 1 Blat: /home/MySpace/MyGenomeFiles/hg19.fa eCLIP: Data 1: /home/MySpace/My_eCLIP_Data/ To annotate motifs found with eCLIP data specified in for_eclip_annotation.txt use the --eclip option when running LncLOOM. Explanation: The query layer specifies which sequence you would like annotate. By default this will be the top sequence (layer 1) in your input file. Note that LncLOOM always sets the first sequence in your file to the top sequence, but may reorder the other sequences to improve motif discovery. To retain your original order of sequences use the --inputorder command is used. Blat: specifies the full path to a genome file eCLIP: specifies the full paths to eCLIP data. Note: you can add multiple paths as follows: Query Layer: 1 Blat: <specify path to genome fasta file> eCLIP: Data 1: <specify path to eCLIP data> eCLIP: Data 2: <specify path to eCLIP data> eCLIP: Data 3: <specify path to eCLIP data> Alternatively you can upload a bedfile instead of running Blat Query Layer: 1 Bed: <specify path to bedfile> eCLIP: Data 1: <specify path to eCLIP data>
Make sure that the blat executable has the correct executable permissions: chmod 755 LncLOOM_v1/LncLOOM_v1/src/blat
Make sure that the blat executable is compatible with your machine type: The blat executable in the src folder is compatible with linux.x86_64 machines. If needed download the correct executable for your machine type from Genome Browser software and replace the current blat executable.
OPTIONAL: Install the Gurobi Solver - although not required it allows much faster performance on larger datasets There are two possible ways to install Gurobi: Option 1: Install through Anaconda. If needed download and install Anaconda Add the gurobi channel to the Ananconda search list conda config --add channels http://conda.anaconda.org/gurobi install gurobi conda install gurobi Initialise Gurobi License A free academic license can be obtained from: [https://www.gurobi.com/downloads/end-user-license-agreement-academic/] First register an account Verify your account from a link sent to your email, this will take you to a home page Click on Licenses (you may be askd to login again) On the top Navigation bar, select Academia... and Licenses Click on a link: Free Academic License page,this will issue you a license Scroll to the bottom of the page to Installation: you will see a command similar to this, but specific to your key: grbgetkey YOUR_KEY Copy and run this command in your terminal Option 2: Download Gurobi Once you have downloaded your version of Gurobi copy the folder to /opt sudo cp -r gurobi9.0.2_linux64.tar.gz /opt Extract the file into /opt cd /opt/ sudo tar xvfz gurobi9.0.2_linux64.tar.gz Set environment variables Users of the bash shell should add the following lines to their .bashrc files: export GUROBI_HOME="/opt/gurobi902/linux64" export PATH="${PATH}:${GUROBI_HOME}/bin" export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${GUROBI_HOME}/lib" Users of the csh shell should add the following lines to their .cshrc files: setenv GUROBI_HOME /opt/gurobi902/linux64 setenv PATH ${PATH}:${GUROBI_HOME}/bin setenv LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:${GUROBI_HOME}/lib If LD_LIBRARY_PATH is not already set, use the following instead: export LD_LIBRARY_PATH="${GUROBI_HOME}/lib" or setenv LD_LIBRARY_PATH ${GUROBI_HOME}/lib Initialise Gurobi License A free academic license can be obtained from: https://www.gurobi.com/downloads/end-user-license-agreement-academic/ First register an account Verify your account from a link sent to your email, this will take you to a home page Click on Licenses (you may be askd to login again) On the top Navigation bar, select Academia... and Licenses Click on a link: Free Academic License page,this will issue you a license Scroll to the bottom of the page to Installation: you will a command similar to this, but specific to your key: grbgetkey YOUR_KEY copy and run this command in your terminal
Note: The following packages should have been automatically installed in step 3. However, if this was unsuccessful each package must be installed individually:.
You may need to upgrade to a newer version of pip
networkx pip install networkx
PulP pip install pulp You may need to run the pulp tests to ensure that the Linear Programming solvers are available to pulp: sudo pulptest If PULP_CBC_CMD is unavailable, reinstall pulp using the following command, and then rerun the pulptest: python2 -m pip install -U git+https://github.com/coin-or/pulp If you are using Gurobi, ensure that you have configured the solver correctly with PulP
NumPy pip install numpy
pyBigWig pip install pyBigWig
Biopython (Version 1.76 is the last release to support python 2.7, later versions are supported by Python 3) pip install biopython==1.76
Run lncLOOM from within the downloaded LncLOOM_v1 directory. This is important as LncLOOM uses files stored in the src folder.
Change your directory to LncLOOM_v1/LncLOOM_v1: cd ./lncLOOM/LncLOOM_v1/LncLOOM_v1
Basic command to run LncLOOM which invokes all default options: python LncLOOM.py --fasta <path to file of sequences>
To save output in a specified directory use the --pname command: python LncLOOM.py --fasta <path to file of sequences> --targetscan --pname <name of directory>
To annotate motifs with conserved miRNA binding sites from TargetScan, invoke the --targetscan option: python LncLOOM.py --fasta <path to file of sequences> --pname <name of directory> --targetscan
To annotate motifs based on eCLIP data according to the parameters set in src/for_eclip_annotation.txt, use the --eclip option: python LncLOOM.py --fasta <path to file of sequences> --pname <name of directory> --targetscan --eclip
To perform an empirical statistical analysis, run multiple iterations on random datasets generated by LncLOOM: python LncLOOM.py --fasta <path to file of sequences> --pname <name of directory> --targetscan --eclip --iterations 100
To speed things up you can run the statistical iterations in parallel python LncLOOM.py --fasta <path to file of sequences> --pname <name of directory> --targetscan --eclip --iterations 100 --multiprocess 10
To generate a custom track of your conserved motifs,coloured by conservation, that can be viewed in Genome Browser, use the --track command: Output will be in bedfile format python LncLOOM.py --fasta <path to file of sequences> --pname <name of directory> --targetscan --eclip --iterations 100 --multiprocess 6 --track
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