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kandi X-RAY | datasets Summary
kandi X-RAY | datasets Summary
Datasets also provides access to +15 evaluation metrics and is designed to let the community easily add and share new datasets and evaluation metrics.
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Top functions reviewed by kandi - BETA
- Download and prepare and prepare files
- Download and prepare data for all splits
- Check if manual data requires manual data
- Check if the filesystem is a remote file system
- Push shard shards to hub
- Create a repository
- Sharded dataset
- Push parquet shards to hub
- Add a FAiss index
- Align the labels with the given mapping
- Sort the Dataset
- Return a Dataset based on a function
- Run the builder
- Shuffle dataset
- Renames a column
- Renames columns
- Returns an iterator over the examples in the dataset
- Sort dataset by column
- Add an elasticsearch index
- Build a single dataset
- Return a YAML representation of the feature
- Encodes a column
- Shuffle the dataset
- Save the dataset to disk
- Return a new Dataset with the given function
- Runs the tool
datasets Key Features
datasets Examples and Code Snippets
'images': [
{
'file_name': 'COCO_val2014_000000001268.jpg',
'height': 427,
'width': 640,
'id': 1268
},
...
],
'annotations': [
{
'segmentation': [[192.81,
247.09,
...
#
000001.jpg
1280 720
2
10 20 40 60 1
20 40 50 60 2
#
000002.jpg
1280 720
3
50 20 40 60 2
20 40 30 45 2
30 40 50 60 3
import mmcv
import numpy as np
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
│ ├── cityscapes
│ │ ├── annotations
│ │ ├── leftImg8bit
│ │ │ ├── train
│ │
def distribute_datasets_from_function(self, dataset_fn, options=None):
# pylint: disable=line-too-long
"""Distributes `tf.data.Dataset` instances created by calls to `dataset_fn`.
The argument `dataset_fn` that users pass in is an input
def get_distributed_datasets_from_function(dataset_fn,
input_workers,
input_contexts,
strategy,
def sample_from_datasets_v2(datasets,
weights=None,
seed=None,
stop_on_empty_dataset=False):
"""Samples elements at random from the datasets in `datasets`.
Creat
ner = pipeline("ner", aggregation_strategy="simple", model="dbmdz/bert-large-cased-finetuned-conll03-english") # Named Entity Recognition (NER)
df1['Total']=df1.groupby('State')['Product'].transform(lambda x: x.count())
plt.imshow(ds.pixel_array[75])
for i, slice in enumerate(ds.pixel_array):
plt.imshow(slice)
plt.savefig(f'slice_{i:03n}.png')
## install and load package:
install.packages('fma')
library('fma')
## list example data of package fma:
data(package = 'fma')
## export single data as csv:
write.csv(cement, file = 'cement.csv')
## bulk export:
## data names are in `[,
Community Discussions
Trending Discussions on datasets
QUESTION
When displaying summary_plot, the color bar does not show.
...ANSWER
Answered 2021-Dec-26 at 21:17I had the same problem as you did, and I found that the solution was to downgrade matplotlib to 3.4.3.. It appears SHAP isn't optimized for matplotlib 3.5.1 yet.
QUESTION
I am working on a React app where i want to display charts. I tried to use react-chartjs-2 but i can't find a way to make it work. when i try to use Pie component, I get the error: Error: "arc" is not a registered element.
I did a very simple react app:
- npx create-react-app my-app
- npm install --save react-chartjs-2 chart.js
Here is my package.json:
...ANSWER
Answered 2021-Nov-24 at 15:13Chart.js is treeshakable since chart.js V3 so you will need to import and register all elements you are using.
QUESTION
I was using pyspark on AWS EMR (4 r5.xlarge as 4 workers, each has one executor and 4 cores), and I got AttributeError: Can't get attribute 'new_block' on . Below is a snippet of the code that threw this error:
...
ANSWER
Answered 2021-Aug-26 at 14:53I had the same error using pandas 1.3.2 in the server while 1.2 in my client. Downgrading pandas to 1.2 solved the problem.
QUESTION
For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda
, miniconda
, virtualenv
but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?
Following are the steps I am using to install tensorflow
in RStudio.
Since RStudio simply crashes each time I run tensorflow::tf_config()
I have no way to check what is going wrong.
ANSWER
Answered 2022-Jan-16 at 00:08Perhaps my failed attempts will help someone else solve this problem; my approach:
- boot up a clean CentOS 7 vm
- install R and some dependencies
QUESTION
I'm trying to use packages that require Rcpp
in R on my M1 Mac, which I was never able to get up and running after purchasing this computer. I updated it to Monterey in the hope that this would fix some installation issues but it hasn't. I tried running the Rcpp
check from this page but I get the following error:
ANSWER
Answered 2022-Feb-10 at 21:07Currently (2022-02-05), CRAN builds R binaries for Apple silicon using Apple clang
(from Command Line Tools for Xcode 12.4) and an experimental build of gfortran
.
If you obtain R from CRAN (i.e., here), then you need to replicate CRAN's compiler setup on your system before building R packages that contain C/C++/Fortran code from their sources (and before using Rcpp
, etc.). This requirement ensures that your package builds are compatible with R itself.
A further complication is the fact that Apple clang
doesn't support OpenMP, so you need to do even more work to compile programs that make use of multithreading. You could circumvent the issue by building R itself and all R packages from sources with LLVM clang
, which does support OpenMP, but this approach is onerous and "for experts only". There is another approach that has been tested by a few people, including Simon Urbanek, the maintainer of R for macOS. It is experimental and also "for experts only", but seems to work on my machine and is simpler than trying to build R yourself.
Warning: These instructions come with no warranty and could break at any time. They assume some level of familiarity with C/C++/Fortran program compilation, Makefile syntax, and Unix shells. As usual, sudo
at your own risk.
I will try to address compilers and OpenMP support at the same time. I am going to assume that you are starting from nothing. Feel free to skip steps you've already taken, though you might find a fresh start helpful.
I've tested these instructions on a machine running Big Sur, and at least one person has tested them on a machine running Monterey. I would be glad to hear from others.
Download an R binary from CRAN here and install. Be sure to select the binary built for Apple silicon.
Run
QUESTION
I've built this new ggplot2
geom layer I'm calling geom_triangles
(see https://github.com/ctesta01/ggtriangles/) that plots isosceles triangles given aesthetics including x, y, z
where z
is the height of the triangle and
the base of the isosceles triangle has midpoint (x,y) on the graph.
What I want is for the geom_triangles()
layer to automatically provide legend components for the height and width of the triangles, but I am not sure how to do that.
I understand based on this reference that I may need to adjust the draw_key
argument in the ggproto
StatTriangles
object, but I'm not sure how I would do that and can't seem to find examples online of how to do it. I've been looking at the source code in ggplot2
for the draw_key
functions, but I'm not sure how I would introduce multiple legend components (one for each of height and width) in a single draw_key
argument in the StatTriangles
ggproto
.
ANSWER
Answered 2022-Jan-30 at 18:08I think you might be slightly overcomplicating things. Ideally, you'd just want a single key drawing method for the whole layer. However, because you're using a Stat
to do the majority of calculations, this becomes hairy to implement. In my answer, I'm avoiding this.
Let's say I'd want to use a geom-only implementation of such a layer. I can make the following (simplified) class/constructor pair. Below, I haven't bothered width_scale
or height_scale
parameters, just for simplicity.
QUESTION
I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:
...ANSWER
Answered 2021-Dec-16 at 10:18If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.
I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).
Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.
That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).
Some further ideas:
- If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
- If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.
QUESTION
I am stuck with a problem on chart js while creating line chart. I want to create a chart with the specified data and also need to have horizontal and vertical line while I hover on intersection point. I am able to create vertical line on hover but can not find any solution where I can draw both the line. Here is my code to draw vertical line on hover.
...ANSWER
Answered 2021-Dec-06 at 04:46I have done exactly this (but vertical line only) in a previous version of one of my projects. Unfortunately this feature has been removed but the older source code file can still be accessed via my github.
The key is this section of the code:
QUESTION
I want to add fill to a line chart using the react-chartjs-2
package. I'm passing fill: true
to the dataset but that doesn't work as expected. Any suggestions?
ANSWER
Answered 2021-Dec-07 at 09:30This is because you are using treeshaking and not importing/registering the filler plugin.
QUESTION
I work for an org that has a number of internal packages that were created many years ago. These are in the form of package zip archives that were compiled on Windows on R 3.x
. Therefore, they can't be installed on R 4.x
, and can't be used on Macs or Linux either without being recompiled. So everyone in the entire org is stuck on R 3.6
until this is resolved. I don't have access to the original package source files. They are lost to time....
I want to take these packages, extract the code and data, and update them for modern best practices (roxygen
, GitHub repos, testthat
etc.). What is the best way of doing this? I have a fair amount of experience with package development. I have already tackled one. I started a new RStudio package project, and going function by function, copying the function code to a new script file, getting and reformatting the help from the help browser as roxygen docs. I've done the same for any internal hidden functions that i could find (via pkg_name:::
mostly) , and also the internal datasets. That is all fairly straightforward, but very time consuming. It builds ok, but I haven't yet tested the actual functionality of the code.
I'm currently stuck because there are a couple of standardGeneric
method functions for custom S4 class objects. I am completely unfamiliar with these and haven't been able to figure out how to copy them over. Viewing the source code they are wrapped in new()
with "standardGeneric"
as the first argument (plus a lot more obviously), as opposed to just being a simple function
definition for all the other functions. Any help with how to recreate or copy these over would be very welcome.
But maybe I am going about this the wrong way in the first place. I haven't been able to find any helpful suggestions about how to "back engineer" R package source files from a compiled version.
Anyone any ideas?
...ANSWER
Answered 2021-Nov-15 at 15:23Check out if this works in R 3.6
.
Below script can automate least part of your problem by writing all function sources into separate and appropriately named .R
files. This code will also take care of hidden functions.
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