ranking | Learning to Rank in TensorFlow | Machine Learning library
kandi X-RAY | ranking Summary
kandi X-RAY | ranking Summary
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. It contains the following components:. We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications.
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Top functions reviewed by kandi - BETA
- Creates a function that returns a function that returns the metric_fn
- Calculate the mean cumulative gain
- Calculate the mean of binary predictions
- Calculates the average elevation position
- Parse an example from an example in Example
- Converts a serialized example
- Parse a tensor
- Reads a batched sequence example dataset
- Build a ranking dataset
- Creates a estimatorSpec
- Define noise
- Compute logits
- Calculate the indices of a tensor
- Computes the reduced loss
- Encodes the given features
- Sorts a logits of the given logits
- Creates estimatorSpec
- Makes a DNN ranking estimator
- Make a GAM ranking estimator
- Encodes a list of features
- Train and validate the model
- Convert a keras model to an Estimator
- Parse a sequence example from a sequence example
- Parse a feature list
- Creates a loss function
- Train and evaluation function
ranking Key Features
ranking Examples and Code Snippets
Lines of Code : 42
License : No License
CopyThis feature is only available with `clip_server>=0.3.0`.
```python
from docarray import Document
d = Document(
uri='.github/README-img/rerank.png',
matches=[
Document(text=f'a photo of a {p}')
for p in (
'co """
This scripts demonstrates how to train a sentence embedding model for Information Retrieval.
As dataset, we use Quora Duplicates Questions, where we have pairs of duplicate questions.
As loss function, we use MultipleNegativesRankingLoss. Here, import datetime
import math
from peewee import *
from peewee import query_to_string
db = SqliteDatabase(':memory:')
@db.func('log')
def log(n, b):
return math.log(n, b)
class Base(Model):
class Meta:
database = db
class Post(Bas from pathlib import Path
import dask.array as da
import numpy as np
from distributed import Client, LocalCluster
from sklearn.datasets import load_svmlight_file
import lightgbm as lgb
if __name__ == "__main__":
print("loading data")
rank_ filenames = ['/tmp/train.tfrecords']
raw_dataset = tf.data.TFRecordDataset(filenames)
for e in raw_dataset.take(1):
ELWC = input_pb2.ExampleListWithContext()
v = ELWC.FromString(e.numpy())
print(v.context)
for e in v.examplpip uninstall -y google-cloud-dataflow
pip install apache-beam[gcp]
0 qid:10 1:53156 2:6456 3:700
1 qid:10 1:48112 2:3535 3:700
2 qid:10 1:48112 2:3655 3:16500
3 qid:10 1:51641 2:8871 3:1200
4 qid:10 1:13207 2:2790 3:400
5 qid:10 1:8175 2:1656 3:700
6 qid:21 1:8175 2:1776 3:2700
7 qid:21 1:9620 2:2424 3Community Discussions
Trending Discussions on ranking
QUESTION
I went through a lengthy process removing mod_ssl and replaced it with gnutls because I wanted to use TLS 1.3.
The difference I’ve noticed is gnutls doesn’t support older, legacy methods, only new ones.
OpenSSL supports them all. Is that a better choice? I could find very little support configuring with gnutls and Apache other than the RTFM pages.
Cons pro? Should I move back to mod_ssl? I'm leaning towards, I made a mistake with GnuTLS just for TLS 1.3 and to get a good security ranking score.
...ANSWER
Answered 2022-Mar-22 at 03:56Most people use OpenSSL, and it has been updated to work with newer versions of TLS as well as older versions (backwards compatibility).
As far as which is better, I don't know, but most use OpenSSL.
And regarding "Server Fault" in the comment, the user could have been more informative, and likely was referring to the domain, serverfault.com, which they did not elaborate to, making it confusing.
QUESTION
I have a database with columns M1, M2 and M3. These M values correspond to the values obtained by each method. My idea is now to make a rank column for each of them. For M1 and M2, the rank will be from the highest value to the lowest value and M3 in reverse. I made the output table for you to see.
ANSWER
Answered 2022-Mar-07 at 14:15Using rank and relocate:
QUESTION
Problem description:
You and Peter are talking about
nmovies, which are represented by integers[1,n]. You have made a ranking list for the movies according to your preference. Now, Peter tells you his ranking list. You want to know how similar your and Peter's tastes are. For 2 moviesi, j, if you and Peter both rank movieibefore moviej, You will get11 similarity. Please output the total similarity.
I know that I can solve this problem in brute force way. Its Java code is like this:
...ANSWER
Answered 2022-Mar-05 at 15:19public void similarity(int[] me, int[] peter){
int[] peterTemp = new int[peter.length];
Map map = new HashMap<>();
for(int i = 0; i < me.length; i++){
map.put(me[i], i);
}
for(int i = 0; i < peter.length; i++){
peterTemp[peterTemp.length - (i + 1)] = map.get(peter[i]);
}
// as David Eisenstat pointed out we are going to count inversion in array, invCount method copied from here
// https://stackoverflow.com/questions/337664/counting-inversions-in-an-array
System.out.println(invCount(peterTemp));
}
long merge(int[] arr, int[] left, int[] right) {
int i = 0, j = 0, count = 0;
while (i < left.length || j < right.length) {
if (i == left.length) {
arr[i+j] = right[j];
j++;
} else if (j == right.length) {
arr[i+j] = left[i];
i++;
} else if (left[i] <= right[j]) {
arr[i+j] = left[i];
i++;
} else {
arr[i+j] = right[j];
count += left.length-i;
j++;
}
}
return count;
}
long invCount(int[] arr) {
if (arr.length < 2)
return 0;
int m = (arr.length + 1) / 2;
int left[] = Arrays.copyOfRange(arr, 0, m);
int right[] = Arrays.copyOfRange(arr, m, arr.length);
return invCount(left) + invCount(right) + merge(arr, left, right);
}
QUESTION
I have an array of positive integers. For example:
...ANSWER
Answered 2022-Feb-27 at 22:44This problem has a fun O(n) solution.
If you draw a graph of cumulative sum vs index, then:
The average value in the subarray between any two indexes is the slope of the line between those points on the graph.
The first highest-average-prefix will end at the point that makes the highest angle from 0. The next highest-average-prefix must then have a smaller average, and it will end at the point that makes the highest angle from the first ending. Continuing to the end of the array, we find that...
These segments of highest average are exactly the segments in the upper convex hull of the cumulative sum graph.
Find these segments using the monotone chain algorithm. Since the points are already sorted, it takes O(n) time.
QUESTION
ANSWER
Answered 2022-Feb-26 at 11:51In order to use the unique values instead of the "Var" names with fmsb::radarchart, we need to reformat the data into a dataframe which has these values as column names, and the respective values per group as rows, which can be done e.g. using the tidyverse:
QUESTION
I have been trying to get the names of the batsmen from the page but Selenium is throwing
...ANSWER
Answered 2022-Feb-02 at 20:24To extract names of the batsmen from the webpage you need to induce WebDriverWait for visibility_of_all_elements_located() and you can use either of the following Locator Strategies:
Using CSS_SELECTOR:
QUESTION
I have followed structure:
...ANSWER
Answered 2022-Jan-18 at 20:47Using data.table, we can split the 'words', unlist the column, while replicate the 'rating' based on the lengths, get the mean of 'rating' by 'words', paste the 'words', by the rank and then order the 'rank'
QUESTION
I've been struggling to restructure this particular object;
...ANSWER
Answered 2022-Jan-05 at 21:04There's a few steps to do:
- Group by
userId, which you've already done. - Sort the values for each user by
rank. - Build a map of counts for each array of ranking items. I'm assuming the ranking numbers don't matter, only the string names.
- Prune any extra elements with
count > 1in the map. (I think this is the logic you want; if it isn't, you can remove thedelete byUser[userId]line) - Iterate and attach the final counts to each object in the result array.
Here's one approach to all of this:
QUESTION
I am hoping to have some low code model using Azure AutoML, which is really just going to the AutoML tab, running a classification experiment with my dataset, after it's done, I deploy the best selected model.
The model kinda works (meaning, I publish the endpoint and then I do some manual validation, seems accurate), however, I am not confident enough, because when I am looking at the explanation, I can see something like this:
4 top features are not really closely important. The most "important" one is really not the one I prefer it to use. I am hoping it will use the Title feature more.
Is there such a thing I can adjust the importance of individual features, like ranking all features before it starts the experiment?
I would love to do more reading, but I only found this:
The only answer seems to be about how to measure if a feature is important.
Hence, does it mean, if I want to customize the experiment, such as selecting which features to "focus", I should learn how to use the "designer" part in Azure ML? Or is it something I can't do, even with the designer. I guess my confusion is, with ML being such a big topic, I am looking for a direction of learning, in this case of what I am having, so I can improve my current model.
...ANSWER
Answered 2022-Jan-03 at 11:55Here is link to the document for feature customization.
Using the SDK you can specify "feauturization": 'auto' / 'off' / 'FeaturizationConfig' in your AutoMLConfig object. Learn more about enabling featurization.
Automated ML tries out different ML models that have different settings which control for overfitting. Automated ML will pick which overfitting parameter configuration is best based on the best score (e.g. accuracy) it gets from hold-out data. The kind of overfitting settings these models has includes:
- Explicitly penalizing overly-complex models in the loss function that the ML model is optimizing
- Limiting model complexity before training, for example by limiting the size of trees in an ensemble tree learning model (e.g. gradient boosting trees or random forest)
https://docs.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls
QUESTION
I have this code to order a group of teams based on their scores, just like a soccer ranking and the code works fine when implemented like this (btw I defined "NEQS" to 18):
...ANSWER
Answered 2021-Dec-30 at 11:54The function trocar_equipas receives a pointer as an argument, so you can just pass it like this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ranking
You can use ranking 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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