alibi-detect | Algorithms for outlier , adversarial and drift detection | Predictive Analytics library
kandi X-RAY | alibi-detect Summary
kandi X-RAY | alibi-detect Summary
Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. For more background on the importance of monitoring outliers and distributions in a production setting, check out this talk from the Challenges in Deploying and Monitoring Machine Learning Systems ICML 2020 workshop, based on the paper Monitoring and explainability of models in production and referencing Alibi Detect. For a thorough introduction to drift detection, check out Protecting Your Machine Learning Against Drift: An Introduction. The talk covers what drift is and why it pays to detect it, the different types of drift, how it can be detected in a principled manner and also describes the anatomy of a drift detector.
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Top functions reviewed by kandi - BETA
- Builds the convolution layer
- Make a kernel constraint for a kernel
- Apply a sigmoid gate
- Build and apply h projection
- Inject categorical variables into the given cols
- Perform multidimization on a multi - dimensional dataset
- Calculate the BDD for each category
- Discretize data
- Fit the model
- Train a keras model
- Clone the original model
- Logs the probability of the given value
- Samples from the input n
- Fetch a specific detector
- Fetch genome and test data
- Compute the fitness score for the given data
- Runs the model
- Compute kernel matrix
- Configures the thresholds for each feature
- Save model configuration
- Compute the kernel for the given context
- Configure the thresholds for bootstrapping
- Compute predictions for each fold
- Calculate the score of each fold
- Transform x into an AffineTransform
- Save a detector to file
alibi-detect Key Features
alibi-detect Examples and Code Snippets
Lines of Code : 25License : Permissive (MIT)
Copy# no version requirements
Cliver.detect('subl')
# => '/Users/yaauie/.bin/subl'
# one version requirement
Cliver.detect('bzip2', '~> 1.0.6')
# => '/usr/bin/bzip2'
# many version requirements
Cliver.detect('racc', '>= 1.0', '< 1.4.9')
License : No LicenseCopyvar operaDetect = {
isOpera: 1
isExtremeMode: 1
results: {
mode: "Extreme Savings"
platform: "Mobile/Tablet"
browser: "Opera Mini"
OS: "Android"
}
}
### detect edges ###
#choose values for te Canny Edge Detection Filter
#for the differentioal value threshold chosen is 150 which is pretty high given that the max
#difference between black and white is 255
#low threshold of 50 w def plot_detections(
detection: Dict,
data: "DetectionDataset" = None,
idx: int = None,
keypoint_meta: Dict = None,
ax: plt.axes = None,
text_size: int = None,
rect_th: int = None,
keypoint_th = None,
) -> PIL.Image function loopDetection() {
let slow = this.head;
let fast = this.head;
// first collision will happen k nodes before the beginning of the loop.
// k it also happen to be the distance from the head to the beginning of the loop
while(slow &a Community Discussions
Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
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Install alibi-detect
PyPI or GitHub source (with pip)
Anaconda (with conda/mamba)
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