- CONDA INSTALL PACKAGE PYTHON TWITTER HOW TO
- CONDA INSTALL PACKAGE PYTHON TWITTER SOFTWARE
- CONDA INSTALL PACKAGE PYTHON TWITTER DOWNLOAD
CONDA INSTALL PACKAGE PYTHON TWITTER DOWNLOAD
This will download the miniconda package Miniconda3-latest-Linux-x86_64.sh wget Head to your terminal, and a preferable location. Both are package managers, but Miniconda is the simpler & lighter version, while Anaconda conda pre-installed with tons of useful libraries – which you may need on not. But before you let your horses run to that Google again, you might have to know what is a Miniconda vs Anaconda.
CONDA INSTALL PACKAGE PYTHON TWITTER HOW TO
So today we will see how to get started with conda on a CentOS machine. Unlike pip, conda can install package in any language. Pip works most of the time, but certain tasks are just left to conda. So you can google that – and find there are two major package managers for Python – pip & conda.
CONDA INSTALL PACKAGE PYTHON TWITTER SOFTWARE
Read Wikipedia – A package manager or package-management system is a collection of software tools that automates the process of installing, upgrading, configuring, and removing computer programs for a computer’s operating system in a consistent manner. Let’s introduce environments – fun way to maintain libraries. Python has amazing libraries for most of the tasks – creating a server, data analysis work & other tasks like Image Processing too, YAY !! But a coder must not get bogged with the versions of libraries, we know it takes ages to set them up. If you use alibi in your research, please consider citing it.I love using Python, it’s simplicity has won my attention since I stopped working with Java about 3 years back. Tree Shapley Additive Explanations ( Lundberg et al., 2020) Multinomial logistic regression with continous data, Kernel Shapley Additive Explanations ( Lundberg et al., 2017) Integrated Gradients ( Sundararajan et al., 2017) Model-agnostic Counterfactual Explanations via RL( Samoilescu et al., 2021) (2) - may require dimensionality reductionĪccumulated Local Effects (ALE, Apley and Zhu, 2016)Īnchor explanations ( Ribeiro et al., 2018)Ĭontrastive Explanation Method (CEM, Dhurandhar et al., 2018)Ĭounterfactual Explanations (extension ofĬounterfactual Explanations Guided by Prototypes ( Van Looveren and Klaise, 2019).Global - explains the model with respect to a set of instances.Local - instance specific explanation, why was this prediction made?.TF/Keras - TensorFlow models via the Keras API.There may be limitations on models supported BB* - black-box but assume model is differentiable.BB - black-box (only require a prediction function).These algorithms provide instance-specific scores measuring the model confidence for making a The following tables summarize the possible use cases for each method. The exact details of available fields variesįrom method to method so we encourage the reader to become familiar with the Via explanation.data (or explanation.anchor). For example, for the Anchor algorithm the explanation can be accessed meta is a dictionaryĬontaining the explainer metadata and any hyperparameters and data is a dictionary containing everything The explanation returned is an Explanation object with attributes meta and data. fit ( X_train ) # explain an instance explanation = explainer. We will use the AnchorTabularĮxplainer to illustrate the API: from alibi.explainers import AnchorTabular # initialize and fit explainer by passing a prediction function and any other required arguments explainer = AnchorTabular ( predict_fn, feature_names = feature_names, category_map = category_map ) explainer. The alibi explanation API takes inspiration from scikit-learn, consisting of distinct initialize,įit and explain steps. Which can be installed to the base conda enviroment with: conda install mamba -n base -c conda-forgeįor the standard Alibi install: mamba install -c conda-forge alibiįor distributed computating support: mamba install -c conda-forge alibi rayįor SHAP support: mamba install -c conda-forge alibi shap To install from conda-forge it is recommended to use mamba, To take advantage of distributed computation of explanations, install alibi with ray: pip install alibi įor SHAP support, install alibi as follows: pip install alibi
If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project alibi-detect.Īlibi can be installed from PyPI: pip install alibiĪlternatively, the development version can be installed: pip install git+ The focus of the library is to provide high-quality implementations of black-box, white-box, local and globalĮxplanation methods for classification and regression models. Alibi is an open source Python library aimed at machine learning model inspection and interpretation.
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