category_encoders | A library of sklearn compatible categorical variable | Machine Learning library

 by   scikit-learn-contrib Python Version: 2.6.1 License: BSD-3-Clause

kandi X-RAY | category_encoders Summary

kandi X-RAY | category_encoders Summary

category_encoders is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. category_encoders has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install category_encoders' or download it from GitHub, PyPI.

[DOI] A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques.
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              category_encoders has a medium active ecosystem.
              It has 2227 star(s) with 383 fork(s). There are 38 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 42 open issues and 227 have been closed. On average issues are closed in 574 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of category_encoders is 2.6.1

            kandi-Quality Quality

              category_encoders has 0 bugs and 0 code smells.

            kandi-Security Security

              category_encoders has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              category_encoders code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              category_encoders is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              category_encoders releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              category_encoders saves you 2727 person hours of effort in developing the same functionality from scratch.
              It has 5908 lines of code, 364 functions and 61 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed category_encoders and discovered the below as its top functions. This is intended to give you an instant insight into category_encoders implemented functionality, and help decide if they suit your requirements.
            • Fit the model
            • Convert cols to a list
            • Get fit columns
            • Return a list of categorical columns
            • Transform the input data using the encoder
            • Reverse missing values
            • Performs multiprocessing
            • Require data from the data
            • Transform inputs into integers
            • Convert columns to integers
            • Transform X into quantiles
            • Transform X into WOE values
            • Transform X using the decoder
            • Transform a dataframe
            • Fit the model to the given data
            • Encodes the given data
            • Transform X into X
            • Extract data from mushroom
            • Fit the categorical encoder
            • Train the model
            • Train the model on the given folds
            • Performs the transformation on input X
            • Transform X and Y
            • Transform X
            • Compute the mean and standard deviation for each fold
            • Transform the data
            Get all kandi verified functions for this library.

            category_encoders Key Features

            No Key Features are available at this moment for category_encoders.

            category_encoders Examples and Code Snippets

            Table of Contents,Install
            Jupyter Notebookdot img1Lines of Code : 11dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            conda install -c conda-forge lazytransform
            
            pip install lazytransform 
            
            pip install lazytransform --ignore-installed --no-deps
            pip install category-encoders --ignore-installed --no-deps
            
            
            cd 
            git clone git@github.com:AutoViML/lazytransform.git
            
            conda  
            Table of Contents,API
            Jupyter Notebookdot img2Lines of Code : 9dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            from sklearn import set_config
            set_config(display="text")
            lazy.xformer
            
            from sklearn import set_config
            set_config(display="diagram")
            lazy.xformer
            # If you have a model in the pipeline, do:
            lazy.modelformer
            
            lazy.plot_importance()
              
            pylint and astroid AttributeError: 'Module' object has no attribute 'col_offset'
            Pythondot img3Lines of Code : 3dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pip install pylint==2.9.3
            pip install git+git://github.com/PyCQA/astroid.git@c37b6fd47b62486fd6cbe77b913b568b809f1a6d#egg=astroid
            
            Understanding FeatureHasher, collisions and vector size trade-off
            Pythondot img4Lines of Code : 12dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def hash_trick(features, n_features):
                 for f in features:
                     res = np.zero_like(features)
                     h = usual_hash_function(f) # just the usual hashing
                     index = h % n_features  # find the modulo to get index to place f in 
            Performance One Hot Encoding
            Pythondot img5Lines of Code : 6dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import multiprocessing
            inputs = range(sample_size)
            num_cores = multiprocessing.cpu_count()
            print("number of available cores:", num_cores)
            results = Parallel(n_jobs=num_cores)(delayed(My_Fun)(i) for i in inputs)
            
            How to handle categorical data for preprocessing in Machine Learning
            Pythondot img6Lines of Code : 2dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df['T-size'].astype(pd.api.types.CategoricalDtype(['S','M','L'],ordered=True)).
            

            Community Discussions

            QUESTION

            Get support and ranking attributes for RFE using Pipeline in Python 3
            Asked 2022-Mar-04 at 12:46

            The code I have so far is below and it works perfectly. However, I would like to print the following RFE attributes for each number of features tested: "rfe.support_[i]", "rfe.ranking_[i]" and the name of the selected features since "i" refers to the index, the first attribute returns True or False (if the columns were selected or not) and the second one returns their respective rankings.

            In other words, I would like to print the columns considered in each RFE and that they do not remain as something abstract.

            ...

            ANSWER

            Answered 2022-Feb-26 at 22:29

            Point is that you haven't explicitly fitted the 'DecisionTreeRegressor_2' pipeline.

            Indeed, though cross_val_score already takes care of fitting the estimator as you might see here, cross_val_score does not return the estimator instance, as .fit() method does. Therefore you're not able to access the RFE instance attributes.

            Here's a toy example from your setting:

            Source https://stackoverflow.com/questions/71279499

            QUESTION

            How to use Binary Encoding of Categorical Columns to predict labels in Python?
            Asked 2022-Jan-26 at 09:44

            I have 2 files test.csv and train.csv. The attribute values are categorical and I am trying to convert them into numerical values.

            I am doing the following:

            ...

            ANSWER

            Answered 2022-Jan-26 at 09:44

            You just do the transform() on the test data (and do not fit the encoder again). The values that don't occur in "training" datasets would be encoded as 0 in all of the categories (as long as you won't change the handle_unknown parameter). For example:

            Source https://stackoverflow.com/questions/70860886

            QUESTION

            ValueError on inverse transform using OrdinalEncoder with dictionary
            Asked 2021-Nov-24 at 13:17

            I can transform the target column to desired ordered numerical value using categorical encoding and ordinal encoding. But I am unable to perform inverse_transform as an error is showing which is written below.

            ...

            ANSWER

            Answered 2021-Nov-24 at 13:17

            QUESTION

            How to inverse TargetEncoder post-training with XGB for Feature Importance?
            Asked 2021-Nov-15 at 10:58

            I used TargetEncoder on all my categorical, nominal features in my dataset. After splitting the df into train and test, I am fitting a XGB on the dataset.

            After the model is trained, I am looking to plot feature importance, however, the features are showing up in an "encoded" state. How can I reverse the features, so the importance plot is interpretable?

            ...

            ANSWER

            Answered 2021-Nov-15 at 10:58

            As stated in the documentation: you can get return the encoded column/feature names by using get_feature_names() and then drop the original feature names.

            Also, do you need to encode your target features (y)?

            In the below example, I assumed that you only need to encode features that corresponded to the 'object' datatype in your X_train dataset.

            Finally, it is good practice to first split your dataset into train and test and then do fit_transform on the training set and only fit on the test set. In this way you prevent leakage.

            Source https://stackoverflow.com/questions/69970789

            QUESTION

            How to perform target guided encoding on a particular feature excluding 'nan' values?
            Asked 2021-Oct-22 at 06:58
            from category_encoders import TargetEncoder
            encoder=TargetEncoder()
            
            for i in df['gender']:
            df['gender']=np.where(df[i]!='nan',encoder.fit_transform(data['gender'],data['target']),'nan')
            
            ...

            ANSWER

            Answered 2021-Oct-22 at 06:58

            After a lot of Google search, I found out that there is already an in-built method. Try this:

            Source https://stackoverflow.com/questions/69661736

            QUESTION

            How to retrieve the mapping generated from a category_encoder in python?
            Asked 2021-Sep-20 at 14:43

            I'm using the category encoder package in Python to use the Weight of Evidence encoder.

            After I define an encoder object and fit it to data, the columns I wanted to encode are correctly replaced by their Weight of Evidence (WoE) values, according to which category they belong to.

            So my question is, how can I obtain the mapping defined by the encoder? For example, let's say I have a variable with categories "A", "B" and "C". The respective WoE values could be 0.2, -0.4 and 0.02. But how can I know that 0.2 corresponds to the category "A"?

            I tried acessing the "mapping" attribute, by using:

            ...

            ANSWER

            Answered 2021-Sep-20 at 14:43

            From the source, you can see that an OrdinalEncoder (the category_encoder version, not sklearn) is used to convert from categories to integers before doing the WoE-encoding. That object is available through the attribute ordinal_encoder. And those themselves have an attribute mapping (or category_mapping) that is a dictionary with the appropriate mapping.

            The format of those mapping attributes isn't particularly pleasant, but here's a stab at "composing" the two for a given feature:

            Source https://stackoverflow.com/questions/69228024

            QUESTION

            XGBoostError: Check failed: typestr.size() == 3 (2 vs. 3) : `typestr' should be of format
            Asked 2021-May-02 at 14:44

            I'm having a weird issue with a new installation of xgboost. Under normal circumstances it works fine. However, when I use the model in the following function it gives the error in the title.

            The dataset I'm using is borrowed from kaggle, and can be seen here: https://www.kaggle.com/kemical/kickstarter-projects

            The function I use to fit my model is the following:

            ...

            ANSWER

            Answered 2021-May-02 at 14:44

            xgboost library is currently under updating to fix this bug, so the current solution is to downgrade the library to older versions, for me I have solved this problem by downgrading to xgboost v0.90

            Try to check your xgboost version by cmd:

            Source https://stackoverflow.com/questions/67095097

            QUESTION

            Multi-output regression using skorch & sklearn pipeline gives runtime error due to dtype
            Asked 2021-Apr-12 at 16:40

            I want to use skorch to do multi-output regression. I've created a small toy example as can be seen below. In the example, the NN should predict 5 outputs. I also want to use a preprocessing step that is incorporated using sklearn pipelines (in this example PCA is used, but it could be any other preprocessor). When executing this example I get the following error in the Variable._execution_engine.run_backward step of torch:

            ...

            ANSWER

            Answered 2021-Apr-12 at 16:05

            By default OneHotEncoder returns numpy array of dtype=float64. So one could simply cast the input-data X when being fed into forward() of the model:

            Source https://stackoverflow.com/questions/67004312

            QUESTION

            Feature Extraction for multiple text columns for classification problem
            Asked 2021-Mar-02 at 09:57

            which is the correct way to extract features from multiple text columns and apply any classification algorithm on it? please suggest me, if i am going wrong

            example dataset

            Independent Variables : Description1,Description2, State, NumericCol1,NumericCol2

            Dependent Variable : TargetCategory

            Code:

            ...

            ANSWER

            Answered 2021-Mar-02 at 09:57

            The way to use multiple columns as input in scikit-learn is by using the ColumnTransformer.

            Here is an example on how to use it with heterogeneous data.

            Source https://stackoverflow.com/questions/66436192

            QUESTION

            Understanding FeatureHasher, collisions and vector size trade-off
            Asked 2020-Dec-09 at 14:36

            I'm preprocessing my data before implementing a machine learning model. Some of the features are with high cardinality, like country and language.

            Since encoding those features as one-hot-vector can produce sparse data, I've decided to look into the hashing trick and used python's category_encoders like so:

            ...

            ANSWER

            Answered 2020-Dec-09 at 14:36

            Is that the way to use the library in order to encode high categorical values?

            Yes. There is nothing wrong with your implementation.

            You can think about the hashing trick as a "reduced size one-hot encoding with a small risk of collision, that you won't need to use if you can tolerate the original feature dimension".

            This idea was first introduced by Kilian Weinberger. You can find in their paper the whole analysis of the algorithm theoretically and practically/empirically.

            Why are some values negative?

            To avoid collision, a signed hash function is used. That is, the strings are hashed by using the usual hash function first (e.g. a string is converted to its corresponding numerical value by summing ASCII value of each char, then modulo n_feature to get an index in (0, n_features]). Then another single-bit output hash function is used. The latter produces +1 or -1 by definition, where it's added to the index resulted from the first hashing function.

            Pseudo code (it looks like Python, though):

            Source https://stackoverflow.com/questions/65108407

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install category_encoders

            The package requires: numpy, statsmodels, and scipy.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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