benchmarking | Results from the reproducibility | Machine Learning library
kandi X-RAY | benchmarking Summary
kandi X-RAY | benchmarking Summary
This repository contains the results from the reproducibility and benchmarking studies described in. Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. Ali, M., Berrendorf, M., Hoyt, C. T., Vermue, L., Galkin, M., Sharifzadeh, S., Fischer, A., Tresp, V., & Lehmann, J. (2020). arXiv, 2006.13365. This repository itself is archived on Zenodo at .
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Top functions reviewed by kandi - BETA
- Write 1d summaries in the given dataframe
- Make plots from dataframe
- Read data from a csv file
- Make a sizeplotplot for a given dataframe
- Return a pandas DataFrame with only those that satisfy the given criteria
- Make a barplot of model loss bar plot
- Write dataset optimizer summaries
- Make a config index
- Make sizeplots for each dataset
- Write dataset optimizer bar plot
- Make dataset plots
- Get model size
- Collates the results of each experiment
- Read the experiment collation
- Make a 2 - way box plot
- Plot a 3d bar chart
- Write the results to a PDF file
- Compute the gold table
- Collate experiment labels into a dataframe
- Write a 2D summary of the model
- Generate results table
- Return a filtered version of a dictionary
- Make sizeplis
- Runs the top experiments
- Convert a pandas checklist to a pandas DataFrame
- Generate a table of size and model sizes
benchmarking Key Features
benchmarking Examples and Code Snippets
Lines of Code : 39
License : No License
Copyimport unittest
from selenium import webdriver
import time
class TestThree(unittest.TestCase):
def setUp(self):
self.startTime = time.time()
def test_url_fire(self):
time.sleep(2)
self.driver = webdriver.Firefox()
def run_benchmark(self,
dataset,
num_elements,
iters=1,
warmup=True,
apply_default_optimizations=False,
session_config=None):
def run_and_report_benchmark(self,
dataset,
num_elements,
name,
iters=5,
extras=None,
def _run_graph_benchmark(self,
iterable,
iters,
warmup,
session_config,
initializer=None):
"""Benchmarks the it Community Discussions
Trending Discussions on benchmarking
QUESTION
I'm learning that Golang channels are actually slower than many alternatives provided by the language. Of course, they are really easy to grasp but because they are a high level structure, they come with some overhead.
Reading some articles about it, I found someone benchmarking the channels here. He basically says that the channels can transfer 10 MB/s, which of course must be dependant on his hardware. He then says something that I haven't completely understood:
If you just want to move data quickly using channels then moving it 1 byte at a time is not sensible. What you really do with a channel is move ownership of the data, in which case the data rate can be effectively infinite, depending on the size of data block you transfer.
I've seen this "move ownership of data" in several places but I haven't seen a solid example illustrating how to do it instead of moving the data itself.
I wanted to see an example in order to understand this best practice.
...ANSWER
Answered 2021-Jun-14 at 03:22Moving data over a channel:
QUESTION
In a heatmap, how could I create a three-color gradient, with blue for negative values, red for positive values and white for zero, such that with many zero values, much of the heatmap would be white (and not light red as with the default gradient).
...ANSWER
Answered 2021-Jun-10 at 22:07You can compute the maximum absolute value in your array, then use it to set the clims argument. c.f. http://docs.juliaplots.org/latest/generated/attributes_subplot/
QUESTION
when i run
...ANSWER
Answered 2021-Jun-08 at 07:48-t doesn't works with SCAN and HSCAN for some reason.
this works -->
QUESTION
Thanks to both of you for suggesting elegant solutions! Both solutions worked for me, but only the melt() and back-join solution worked for a data.table with dates instead of numeric values.
EDIT
I implemented the proposed data.table solution through melting and joining back with the obtained results from Wimpel as his/her solution also works with dates stored in the date columns instead of the intial toy data that was all integer values.
I prefered the readability of Peace Wang's solution though using data.table assignments and IMO it is much clearer syntax than the melt() solution, however (at least for me), it does not work with columns of type date.
Benchmarking both solutions for numeric/integer data, saw the melt() solution as clear winner.
EDIT 2 To replicate the NA-values through conversion that I get if I implement the solution proposed by Peace Wang, see below for the corrected version of the input data.table.
I have sth like this: Image a list of patient records with measurements taken at various dates. The colnames of the date columns would be sth like "2020-12-15" / "2021-01-15" etc.
...ANSWER
Answered 2021-Mar-01 at 14:02The following code can work.
QUESTION
I wrote a function to add up all the elements of a double[] array using SIMD (System.Numerics.Vector) and the performance is worse than the naïve method.
On my computer Vector.Count is 4 which means I could create an accumulator of 4 values and run through the array adding up the elements by groups.
For example a 10 element array, with a 4 element accumulator and 2 remaining elements I would get
...ANSWER
Answered 2021-May-19 at 18:28I would suggest you take a look at this article exploring SIMD performance in .Net.
The overall algorithm looks identical for summing using regular vectorization. One difference is that the multiplication can be avoided when slicing the array:
QUESTION
Having an intent to study a sort algorithm (of my own), I decided to compare its performance with the classical quicksort and to my great surprise I've discovered that the time taken by my implementation of quicksort is far not proportional to N log(N). I thoroughly tried to find an error in my quicksort but unsuccessfully. It is a simple version of the sort algorithm working with arrays of Integer of different sizes, filled with random numbers, and I have no idea, where the error can sneak in. I have even counted all the comparisons and swaps executed by my code, and their number was rather fairly proportional to N log(N). I am completely confused and can't understand the reality I observe. Here are the benchkmark results for sorting arrays of 1,000, 2,000, 4,000, 8,000 and 16,000 random values (measured with JMH):
ANSWER
Answered 2021-May-18 at 21:03Three points work together against your implementation:
- Quicksort has a worst case complexity of O(n^2)
- Picking the leftmost element as pivot gives worst case behavior on already sorted arrays (https://en.wikipedia.org/wiki/Quicksort#Choice_of_pivot):
In the very early versions of quicksort, the leftmost element of the partition would often be chosen as the pivot element. Unfortunately, this causes worst-case behavior on already sorted arrays
- Your algorithm sorts the arrays in place, meaning that after the first pass the "random" array is sorted. (To calculate average times JMH does several passes over the data).
To fix this, you could change your benchmark methods. For example, you could change sortArray01000() to
QUESTION
I have a data file in CSV (Comma-Separated-Value) format that has about 50 million lines in it.
Each line is read into a string, parsed, and then used to fill in the fields of an object of type FOO. The object then gets added to a List(of FOO) that ultimately has 50 million items.
That all works, and fits in memory (at least on an x64 machine), but its SLOW. It takes like 5 minutes every time load and parse the file into the list. I would like to make it faster. How can I make it faster?
The important parts of the code are shown below.
...ANSWER
Answered 2021-May-14 at 14:15I have a lot of experience with CSV, and the bad news is that you aren't going to be able to make this a whole lot faster. CSV libraries aren't going to be of much assistance here. The difficult problem with CSV, that libraries attempt to handle, is dealing with fields that have embedded commas, or newlines, which require quoting and escaping. Your dataset doesn't have this issue, since none of the columns are strings.
As you have discovered, the bulk of the time is spent in the parse methods. Andrew Morton had a good suggestion, using TryParseExact for DateTime values can be a quite a bit faster than TryParse. My own CSV library, Sylvan.Data.Csv (which is the fastest available for .NET), uses an optimization where it parses primitive values directly out of the stream read buffer without converting to string first (only when running on .NET core), that can also speed things up a bit. However, I wouldn't expect it to be possible to cut the processing time in half while sticking with CSV.
Here is an example of using my library, Sylvan.Data.Csv to process the CSV in C#.
QUESTION
Say I have a vector A (or a variable A in a dataframe say df) with following values
ANSWER
Answered 2021-May-11 at 14:40Anil, perhaps this might be something to help in moving forward. It isn't elegant. You can create a loop checking for changes in values, while tracking prior thresholds.
QUESTION
I have a benchmarking tool that has an output looking like this:
...ANSWER
Answered 2021-May-10 at 12:19here is an alternative
QUESTION
I am fairly new to pyodbc and ran in a problem where executemany takes a considerable long time. When benchmarking the script it took about 15 min to insert 962 rows into a table. I would like to speed this query up if possible.
I run the following script:
...ANSWER
Answered 2021-May-10 at 10:46I made several attempts to speed up the query and gathered some insights which in wanted to share with everybody who may encounters the same issue:
Takeaways:
- When using Azure SQL Server always try to use the
INSERT INTO ... VALUES (...)Statement instead ofINSERT INTO ... SELECT ..., as it performs about 350% faster (when benchmarked for the described problem and used syntax).- The main reason why i used
INSERT INTO ... SELECT ...was because of the specificDATEADD()Cast, as you can't do that without explicitly declaring variables in Azure SQL Server.
- The main reason why i used
- You can skip the
DATEADD()in the given example if you cast the provided time to pythondatetime. If you choose this option make sure to not use literal strings when inserting the data to your SQL Table. Besides bad practice as adressed by @Charlieface,PYODBChas no built-in logic for that datatype when using the string-literal input (sequence-of-sequence input structure has no problem here) - The
IF NOT EXISTSStatement is really expensive. Try to omit it if possible. A simple workaround, if you depend on preserving your table historically, is to create a second table that is newly created and then insert from that table to your original where no match was found. Here you can depend on your native SQL implementation instead of the PYODBC implementation. This way was by far the fastest.
The different design choices resulted in the following performance improvements:
INSERT INTO ... SELECT ...vsINSERT INTO ... VALUES (...): 350%- Leveraging a second table and native SQL support: 560%
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install benchmarking
You can use benchmarking like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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