decomp | Universal Decompositional Semantics dataset | Graph Database library
kandi X-RAY | decomp Summary
kandi X-RAY | decomp Summary
Decomp is a toolkit for working with the Universal Decompositional Semantics (UDS) dataset, which is a collection of directed acyclic semantic graphs with real-valued node and edge attributes pointing into Universal Dependencies syntactic dependency trees.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Serve the parser
- Prepare the graph
- Create a new UDSVisualization object from a dictionary
- Returns a list of subspaces
- Return the index of an item
- Create a UDSAnnotation object from a json file
- Create a new UDSDataType from a dictionary
- Create a new UDSPropertyMetadata object from a dictionary
- Construct a new UDSAnnotationMetadata from a dictionary
- Add the semantics nodes to the graph
- Return the maxima of the given nodes
- Processes a CONLL file
- Create a new UDSCorpus from a given corpus
- Return the span of the given node
- Convert a networkx graph into an rdf graph
- Process node data
- Add an annotation to the graph
- Process edge data
- Create a PredPattGraphBuilder from the given graphid
- Return the head edges for the given nodeid
- Return a dictionary of semantic edge edges
- Build the graph
- Loads a single split
- Validate raw_UDSAnnotation
- Validate the normalized UDSAnnotation
- Process incoming data
- Return the index of an item in the list
decomp Key Features
decomp Examples and Code Snippets
Lines of Code : 23License : Permissive (MIT)
Copyfrom decomp import UDSCorpus
uds = UDSCorpus()
uds["ewt-train-12"]
for graphid in uds:
print(graphid)
for graphid, graph in uds.items():
print(graphid)
print(graph.sentence)
# a list of the graph identifiers in the corpus
uds.graphid @inproceedings{white-etal-2020-universal,
title = "The Universal Decompositional Semantics Dataset and Decomp Toolkit",
author = "White, Aaron Steven and
Stengel-Eskin, Elias and
Vashishtha, Siddharth and
Govindarajan, Ve git clone git://github.com/decompositional-semantics-initiative/decomp.git
cd decomp
docker build -t decomp .
docker run -it decomp python
pip install --user git+git://github.com/decompositional-semantics-initiative/decomp.git
git clone git://githu Community Discussions
Trending Discussions on decomp
QUESTION
I'm trying to implement a 32bits checksum macro written in masm32 to the Dart language. Here is what I understood: the checksum function takes a String as input and returns the checksum in a 4 bytes integer. But I don't get the same result. Does anyone see my errors please?
...ANSWER
Answered 2021-May-23 at 18:20The transcription of the checksum algorithm is wrong.
Here's how I'd do it:
QUESTION
ANSWER
Answered 2021-Apr-13 at 14:59It's not completely clear to me what your exact expectations are..., but I would use the plotting style with boxxyerror (check help boxxyerror).
Code:
QUESTION
I have a dataset with 7 columns - level,Time_30,Time_60,Time_90,Time_120,Time_150 and Time_180
My main goal is to do a time-series anomaly detection using cell count in a 30-minute interval.
I want to do the following data preparation steps:
(I) melt/reshape the df into the appropriate time-series format (from wide to long)- consolidate the columns time_30, time_60 ,....., time_180 into one column time with 6 levels (30,60,.....,180)
(II) since the result from (I) comes out as 30,60,.....180, I want to set the time column as the appropriate time or date format for time-series (something like this '%H:%M:%S')
(III) use a for-loop to plot the time-series plot for each level - A, B,...., F) for comparison purposes.
(IV) Anomaly detection
...ANSWER
Answered 2021-Feb-15 at 03:47I made a few small edits to your sample dataframe based on my comment above:
QUESTION
I'm trying to run copy of data processing pipeline, that correctly working on cluster, on local machine with hadoop and hbase working in standalone mode. Pipeline contains few mapreduce jobs starting one after another and one of these jobs has mapper that does not write anything in output (depends on input, but it does not write anything in my test), but has reducer. I receive this exception during this job running:
...ANSWER
Answered 2021-Jan-24 at 11:48I couldn't find an explanation for this problem, but I solved it by turning off compression of mapper output:
QUESTION
I'm using the svars package to generate some IRF plots. The plots are rendered using ggplot2, however I need some help with changing some of the aesthetics.
Is there any way I can change the fill and alpha of the shaded confidence bands, as well as the color of the solid line? I know in ggplot2 you can pass fill and alpha arguments to geom_ribbon (and col to geom_line), just unsure of how to do the same within the plot function of this package's source code.
ANSWER
Answered 2021-Jan-20 at 18:53Your first desired result is easily achieved by resetting the aes_params after calling plot. For your second goal. There is probably an approach to manipulate the ggplot object. Instead my approach below constructs the plot from scratch. Basically I copy and pasted the data wrangling code from vars:::plot.hd and filtered the prepared dataset for the desired series:
QUESTION
I want to move on the device the whole while loop in the main. The problems emerges when I add #pragma acc host_data use_device(err) to MPI_Allreduce (&err, &err, 1, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);.
The error is that the reduction on err doesn't work so that the code exit after one step from the loop.
After the MPI_Allreduce(), even using #pragma acc update self(err), err is still equal to zero.
I'm compiling with mpicc -acc -ta=tesla:managed -Minfo=accel -w jacobi.c
And running with mpirun -np 2 -mca pml ^ucx ./a.out
Could you help me to find the error?
...ANSWER
Answered 2020-Oct-17 at 14:48Thanks for updating the example. There's a few issues here.
First, for "err" and "err_glob". At the beginning of the loop, you set "err=0" on the host but don't update it on the device. Then after the MPI_AllReduce call, you set "err=err_glob", again on the host, so need to update "err_glob".
The second issue is that the code is getting partially present errors for "y" when run with multiple ranks. The problem being you're using the global size not the local size for "x" and "y" so when you copy "y" it overlaps with "x" due to the offsets. I fixed this by copying "xg" and "yg" to the device instead.
As for performance relative to the CPU, the main problem here is that the size is small so the code severly under utilizes the GPU. I increased the GLOB sizes to 4096 and see better relative performance, though the code converges much faster.
I also took the liberty of adding some boiler plate code that I use for rank to device assignment so the code can take advantage of multiple GPUs.
QUESTION
library(data.table)
var = fread("Q:\\Electricity\\Analysis\\6 Working\\NZ Power\\Jack Perry\\Rainfall\\Clutha R (V01).csv")
var.ts = ts(var$Rainfall, start = c(2008,5),end = c(2020), frequency = 53)
n = length(var.ts)
n
print(var.ts)
plot(var.ts);
var.decomp = stl(var.ts,s.window = 'periodic', t.window = 500)
plot(var.decomp)
ANSWER
Answered 2020-Sep-09 at 03:22The structure is a list of elements. So, we extract the 'time.series' component and get the 'seasonal' column
QUESTION
I have a dataset of tiff images that needs to be decomposed. Each file has 50 frames and currently i'm decomposing one by one, but compared to the amount of images that i have, it'll take a long long time to decompose every single one of them. My goal is for every tiff file inside a folder, i'd like to decompose and store them inside a separate folder, where every tiff image will always have 50 frames, for example:
inside C:\Dataset\tiff-images\
i have tiff-image1, tiff-image2, tiffimage3, tiffimage4.
Still inside the same directory i have the folders: tiff-image1, tiff-image2, tiff-image3, tiff-image4.
Basically, what i would like is to simply iterate through as many tiff-images inside the directory and decompose them inside their respective folder by creating a folder in-case there isn't.
The way i am trying right now, isnt exactly optimal and will take me a long time to do this process:
...ANSWER
Answered 2020-Aug-16 at 10:48You can use os module for such kind of automation.
Check this:
QUESTION
I'm writing an OpenCL kernel which will involve solving a linear system. Currently my kernel is simply too slow, and improving the performance of the linear system portion seemed like a good place to start.
I should also note that I'm not trying make my linear solver parallel, the problem I'm working on is already embarassingly parallel at a macroscopic level.
The following is C code I wrote for solving Ax=b using Gaussian elimination with partial pivoting,
...ANSWER
Answered 2020-Jul-05 at 08:19TL;DR: The current C code is inefficient on a modern hardware. Moreover, using OpenCL on dedicated GPUs or CUDA will only be fast for quite big matrices here (ie. not 50x50 ones).
The biggest problem in the C code comes from the line A[K * l + (i + 1)] += c * A[K * l + j];. Indeed, as the loop iterator is l, the memory access pattern is not contiguous but strided. Strided memory access pattern is much more inefficient than a contiguous ones on modern hardware architectures (due to code vectorization, cache lines, memory prefetching, etc.). This is especially true on GPUs.
You can fix this problem by transposing the A matrix. Here is the modified version:
QUESTION
I'm relatively new to Polysemy, and I'm trying to wrap my head around how to use NonDet correctly. Specifically, let's say I've got this computation
ANSWER
Answered 2020-Jun-28 at 22:46Now attempt1 and attempt2 succeed, since we simply forcibly exit the program after success. But, aside from feeling incredibly sloppy, this doesn't generalize either. I want to stop running the current computation after finding 100, not the whole program.
Rather than exitSuccess, a closely related idea is to throw an exception that you can catch in the interpreter.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install decomp
The UDS corpus can be read by directly importing it. This imports a UDSCorpus object uds, which contains all graphs across all splits in the data. If you would like a corpus, e.g., containing only a particular split, see other loading options in the tutorial on reading the corpus for details. The first time you read UDS, it will take several minutes to complete while the dataset is built from the Universal Dependencies English Web Treebank, which is not shipped with the package (but is downloaded automatically on import in the background), and the UDS annotations, which are shipped with the package. Subsequent uses will be faster, since the dataset is cached on build.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
