pycaret | An open-source , low-code machine learning library in Python | Machine Learning library

 by   pycaret Jupyter Notebook Version: 3.2.0 License: MIT

kandi X-RAY | pycaret Summary

kandi X-RAY | pycaret Summary

pycaret is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. pycaret has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.

            kandi-support Support

              pycaret has a medium active ecosystem.
              It has 7392 star(s) with 1604 fork(s). There are 127 watchers for this library.
              There were 4 major release(s) in the last 6 months.
              There are 279 open issues and 1805 have been closed. On average issues are closed in 80 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pycaret is 3.2.0

            kandi-Quality Quality

              pycaret has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              pycaret is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pycaret releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              It has 23953 lines of code, 781 functions and 89 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of pycaret
            Get all kandi verified functions for this library.

            pycaret Key Features

            No Key Features are available at this moment for pycaret.

            pycaret Examples and Code Snippets

            get a string without ""
            Pythondot img1Lines of Code : 21dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> a = "feature_selection = True"
            >>> a
            "feature_selection = True"
            >>> import re
            >>> d = dict([re.split('\s*=\s*', a)])
            {'feature_selection': 'True'}
            Unable to install sklearn 0.23.2 with pip 20.2.4 and python 3.9
            Pythondot img2Lines of Code : 12dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            C:\Users\WDAGUtilityAccount>conda create --name py38 python=3.8

            C:\Users\WDAGUtilityAccount>activate py38

            (py38) C:\Users\WDAGUtilityAccount>pip install -U setuptools

            (py38) C:\Users\WDAGUtilityAccount>pip insta

            Pycaret.regression.compare_models: Evaluation table neither returned nor shown
            Pythondot img3Lines of Code : 7dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            best_regression_models = regression.compare_models()
            regression_results = pull()
            How to deal with Pycaret's adding extra features while modelling? (For reusing the model)
            Pythondot img4Lines of Code : 8dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            lr = create_model('lr')
            final_lr = finalize_model(lr)
            predictions = predict_model(final_lr, data = mynewdfwithouttarget)
            How to determine number of neighbors in knn in pycaret
            Pythondot img5Lines of Code : 14dot img5License : Strong Copyleft (CC BY-SA 4.0)
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            import pycaret
            from pycaret.anomaly import *
            from pycaret.datasets import get_data
            anomaly = get_data('anomaly')
            exp_name = setup(data = anomaly)
            knn = create_model('knn') 
            TypeError: unsupported operand type(s) for -: ‘str’ and ‘int’ in PyCaret regression
            Pythondot img6Lines of Code : 6dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # Check correlation
            cor = df[features].corr()
            cor.loc[:,:] = np.tril(cor, k=-1) 
            cor = cor.stack()
            cor[(cor > 0.7) | (cor < -0.7)]
            copy iconCopy
            def predict(data: Data = Depends()):
            Build API using FastAPI for Classification Model produced using pycaret
            Pythondot img8Lines of Code : 26dot img8License : Strong Copyleft (CC BY-SA 4.0)
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            def predict(input_dict: Data):
            input_dict = Data
            app = FastAPI()
            model = load_model('catboost_cm_creditable')
            class Data(BaseModel):
                age: float
                live_province: str
                live_city: s
            `ValueError: The least populated class in y has only 1 member, which is too few` in PyCaret
            Pythondot img9Lines of Code : 2dot img9License : Strong Copyleft (CC BY-SA 4.0)
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            from pycaret.classification import *

            Community Discussions


            is test data used in Pycaret time series(beta) completely unseen by the model(s)?
            Asked 2022-Apr-04 at 10:51

            Post checking official documentation and example, I am still confused if test data passed to the setup function is completely unseen by the model???



            Answered 2022-Apr-04 at 10:51

            If you notice the cv splits, they do not use the test data at all. So any step such as create_model, tune_model, blend_model, compare_models that use Cross-Validation, will not use the test data at all for training.

            Once you are happy with the models from these steps, you can finalize the model using finalize_model. In this case, whatever model you pass to finalize_model is trained on the complete dataset (train + test) so that you can make true future predictions.



            get a string without ""
            Asked 2021-Dec-25 at 16:51

            I am trying to automate the setup-process in PyCaret Library. The goal is to create a list of "experiments" and the script should iretate over the list of elements.

            In the setup could be following preprocessing steps chosen: feature_selection = True, remove_outliers = True, etc.

            All such pre-processing steps i would like to store in a list and then run a loop in order to find the best setting (best performance) for the model.

            For example:



            Answered 2021-Dec-23 at 23:28

            Say you have a string like this:



            Pycaret - 'Make_Time_Features' object has no attribute 'list_of_features'
            Asked 2021-Oct-22 at 13:02

            I am trying to create a model using pycaret just as:



            Answered 2021-Sep-23 at 08:38

            You mentioned my previous question here.

            I just faced the same issue as you on Colab. It is 100% issue with libraries.

            Initially, I got the error for SMOTE:

            • `AttributeError: 'SMOTE' object has no attribute '_validate_data'

            After installing/reinstalling libraries I got exactly your error.

            How did I resolve it?

            • Started to run Colab and imported all common libraries (pd, np, scikit, etc).
            • Installed PyCaret via pip install. Then import pycaret and from pycaret.classification import *
            • Colab reacted: you have issues with scipy, sklearn, lightgbm, please restart your runtime.
            • Restarted my runtime on Colab
            • Imported all libraries again as I did in step 1
            • Ran import pycaret and from pycaret.classification import * only

            My final code:



            Pycaret anomaly detection setup: ValueError: Setting a random_state has no effect since shuffle is False
            Asked 2021-Sep-09 at 12:22

            I have recently transitioned from R to python, and I am not sure how to problem solve the following.

            When I run the setup for pycaret anomaly detection, following the instructions that can be found here, on my own data I get the following error.



            Answered 2021-Sep-09 at 12:22

            The answer to this question is that the environment had a library versions such as numpy that were too new for pycaret to work with, for example, pycaret need numpy (1.19.5 and will not work with newer).

            My solution was to create a new environment in anaconda, which used pip install pycaret[full], and added nothing else to the environment. It worked after this.



            Weird Time-Series Graph Using Pycaret and plotly
            Asked 2021-Sep-09 at 10:35

            I am trying to visualize Air Quality Data as time-series charts using pycaret and plotly dash python libraries , but i am getting very weird graphs, below is my code:



            Answered 2021-Sep-09 at 10:35
            • data has not been provided in a usable way. Sought out publicly available similar data. found:
            • using this data, with a couple of cleanups of your code, no issues with plots. I suspect your data has one of these issues
              1. date is not datetime64[ns] in your data frame
              2. date is not sorted, leading to lines being drawn in way you have noted
            • by refactoring way moving average is calculated, you can use animation instead of lots of separate figures
            get some data



            Creating 12 month MA in PyCaret time series gives a column with NA values
            Asked 2021-Aug-19 at 23:02

            I am trying to use PyCaret for time series, according to this tutorial. My analysis did not work. When I created a new column

            data['MA12'] = data['variable'].rolling(12).mean()

            I got this new MA12 column with NA values only.

            As a resulted I decided to replicate the code from the tutorial, using AirPassangers dataset, but got the same issue.

            When I print data, I get



            Answered 2021-Aug-19 at 23:02

            Since you want the previous 12 reads, the first 11 will be NaN. You need more rows than 12 before you get a moving average of 12. You can see this on the link you provided. The chart of MA doesn't start up right away.



            What does the Anomaly_Score in Pycaret.anomaly library mean?
            Asked 2021-Aug-12 at 15:14

            If we use the assign_model() function from pycaret.anomaly library we get a dataframe with two additional columns Anomaly and Anomaly_Score as output. What does the Anomaly_Score column mean in this dataframe and how is its value calculated?



            Answered 2021-Aug-12 at 15:14

            PyCaret uses the PyOd library for anomaly detection. The anomaly score of an input sample is computed based on different detector algorithms. For consistency, outliers are assigned with larger anomaly scores. Wow it is calculated depends on the algorithm used for anomaly detection. Check out the documentation: and search for: "anomaly score".



            How to install PyCaret in AWS Glue
            Asked 2021-Jul-08 at 17:01

            How can I properly install PyCaret in AWS Glue?

            Methods I tried:

            I am using Glue Version 2.0. I used --additional-python-modules and set to pycaret as shown in the picture.

            Then I got this error log.



            Answered 2021-Jul-08 at 17:01

            I reached out to AWS support. Meghana was in charge of this case.

            Here is the reply:



            Unable to install sklearn 0.23.2 with pip 20.2.4 and python 3.9
            Asked 2021-Jun-07 at 17:47

            I cant use higher version of pip other than 20.2.4, since some SSL certification errors are occuring in higher versions and cannot reinstall any settings I have now (because of some office setup). Now I am using Pycaret and currently it supports only sklearn 0.23.2.

            But my sklearn version is 0.24.1 and I am unable to downgrade it with 20.2.4 version of pip.

            I also tried manual installation using file and it is also not success.

            I am having Winpython and unable to install anaconda too.

            Can someone help me to sort this problem? I am having python 3.9 . May be that is the problem?




            Answered 2021-Jun-07 at 17:47

            I tried various things, and the best advice I can give you is don't bother with python 3.9 for this library; It's just not supported yet. Dependencies are not sorted out yet for pycaret yet on python 3.9, and they should probably make note of that on their github, but here's the process I went through to get it installed from a completely fresh computer (windows sandbox).

          • Get miniconda to handle environments (or full fat anaconda if you want). Conda treats python itself as a package making it easy to create env's with different python versions. The version doesn't matter too much, as you'll be creating an env with python 3.8. I used `Miniconda3 Windows 64-bit`.
          • Get the Microsoft visual studio c++ build tools. It's possible you won't need them now that we're moving back to 3.8, but it's just good to have for installing any libraries which aren't distributed as pre-built binaries.
          • Create and activate your env:
          • Source


            Pycaret.regression.compare_models: Evaluation table neither returned nor shown
            Asked 2021-May-30 at 04:31

            pycaret is a very compact tool to compare models that I wanted to use for model selection. Unfortunately, the method compare_models does not show the typical output table that you see everywhere. I am using pycaret in PyCharm and not Jupyter Notebook which seems to be the typical approach. I do get the best model as a return value, but am really aiming for the overview table. It also doesn't seem to make a difference whether the parameter silent is set to True or False appart from being asked for the confirmation of whether the derived datatypes are correct.

            Thank you very much!

            The system: Python 3.6 pycaret 2.3 CentOS 7 PyCharm 2020.1 Community Edition

            My code:



            Answered 2021-May-30 at 04:31

            Running PyCaret from a terminal/command line has different behavior compared to running from a Jupyter notebook. In your case, if you want to display the comparison output table, add these 2 lines after your compare_models() function call:


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


            No vulnerabilities reported

            Install pycaret

            PyCaret's default installation only installs hard dependencies as listed in the requirements.txt file.


            With PyCaret >= 2.2, you can train models on GPU and speed up your workflow by 10x. To train models on GPU simply pass use_gpu = True in the setup function. There is no change in the use of the API, however, in some cases, additional libraries have to be installed as they are not installed with the default version or the full version. As of the latest release, the following models can be trained on GPU:.
            Find more information at:

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            pip install pycaret

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            gh repo clone pycaret/pycaret

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