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tpot | Python Automated Machine Learning tool | Machine Learning library

 by   EpistasisLab Python Version: v0.11.7 License: LGPL-3.0

 by   EpistasisLab Python Version: v0.11.7 License: LGPL-3.0

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kandi X-RAY | tpot Summary

tpot is a Python library typically used in Artificial Intelligence, Machine Learning applications. tpot has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has high support. You can install using 'pip install tpot' or download it from GitHub, PyPI.
TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.
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kandi-support Support

  • tpot has a highly active ecosystem.
  • It has 8209 star(s) with 1420 fork(s). There are 292 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 226 open issues and 600 have been closed. On average issues are closed in 23 days. There are 8 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of tpot is v0.11.7
tpot Support
Best in #Machine Learning
Average in #Machine Learning
tpot Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • tpot has 0 bugs and 0 code smells.
tpot Quality
Best in #Machine Learning
Average in #Machine Learning
tpot Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • tpot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • tpot code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
tpot Security
Best in #Machine Learning
Average in #Machine Learning
tpot Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • tpot is licensed under the LGPL-3.0 License. This license is Weak Copyleft.
  • Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.
tpot License
Best in #Machine Learning
Average in #Machine Learning
tpot License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • tpot 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.
  • tpot saves you 5516 person hours of effort in developing the same functionality from scratch.
  • It has 11556 lines of code, 382 functions and 68 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
tpot Reuse
Best in #Machine Learning
Average in #Machine Learning
tpot Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed tpot and discovered the below as its top functions. This is intended to give you an instant insight into tpot implemented functionality, and help decide if they suit your requirements.

  • Create a TOTP operator class
    • Decode source code
    • Create an ARGType subclass
    • Check if estimator is a selector
  • Sets up the input tensor
    • Add terminal terminals
    • Import the given operator and add it to the graph
    • Add operators to the pipeline
  • Create TPOT classifier
    • Check if the features are consistent
    • Compute the score for the given features
    • Impute missing values in feature set
  • Decorator for pre test tests
    • Convert an expression into a tree
    • Generate the code for the pipeline
    • Update the progress bar
  • Setup the config dictionary
    • Reads the specified TPOT operator config file
    • Return whether or not the module is installed
  • Fit X to X
    • Replace all values in X
  • Compile the pipeline into a sklearn pipeline
    • Recursively sets a parameter
  • Return an argument parser
    • Replace the features in X
      • Calculate version number

        Get all kandi verified functions for this library.

        Get all kandi verified functions for this library.

        tpot Key Features

        A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

        Classification

        copy iconCopydownload iconDownload
        from tpot import TPOTClassifier
        from sklearn.datasets import load_digits
        from sklearn.model_selection import train_test_split
        
        digits = load_digits()
        X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
                                                            train_size=0.75, test_size=0.25, random_state=42)
        
        tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2, random_state=42)
        tpot.fit(X_train, y_train)
        print(tpot.score(X_test, y_test))
        tpot.export('tpot_digits_pipeline.py')
        

        Regression

        copy iconCopydownload iconDownload
        from tpot import TPOTRegressor
        from sklearn.datasets import load_boston
        from sklearn.model_selection import train_test_split
        
        housing = load_boston()
        X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target,
                                                            train_size=0.75, test_size=0.25, random_state=42)
        
        tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2, random_state=42)
        tpot.fit(X_train, y_train)
        print(tpot.score(X_test, y_test))
        tpot.export('tpot_boston_pipeline.py')
        

        Citing TPOT

        copy iconCopydownload iconDownload
        @article{le2020scaling,
          title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
          author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
          journal={Bioinformatics},
          volume={36},
          number={1},
          pages={250--256},
          year={2020},
          publisher={Oxford University Press}
        }
        

        How to find which model is selected by TPOT

        copy iconCopydownload iconDownload
        my_tpot = TPOTClassifier()
        my_tpot.fit(…)
        print(my_tpot.fitted_pipeline_)
        

        TPOT error in python cannot set using a slice indexer with a different length

        copy iconCopydownload iconDownload
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.model_selection import RandomizedSearchCV
        from sklearn.model_selection import train_test_split
        from tpot import TPOTClassifier
        from sklearn import datasets
        iris = datasets.load_iris()
        X = iris.data 
        y = iris.target
        
        X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)
        
        model_parameters = {'n_estimators': [100,200],
                              "max_depth" : [None, 5, 10],
                              "max_features" : [len(X_train[0])]}  
        
        
        model_tuned = TPOTClassifier(generations= 2, 
                                     population_size= 2, 
                                     offspring_size= 2,
                                     verbosity= 2, 
                                     early_stop= 10, 
                                     config_dict={'sklearn.ensemble.RandomForestClassifier': 
                                     model_parameters}, 
                                     cv = 5)
         
        
        model_tuned.fit(X_train,y_train)
        
        X = X.to_numpy
        y = y.to_numpy
        
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.model_selection import RandomizedSearchCV
        from sklearn.model_selection import train_test_split
        from tpot import TPOTClassifier
        from sklearn import datasets
        iris = datasets.load_iris()
        X = iris.data 
        y = iris.target
        
        X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)
        
        model_parameters = {'n_estimators': [100,200],
                              "max_depth" : [None, 5, 10],
                              "max_features" : [len(X_train[0])]}  
        
        
        model_tuned = TPOTClassifier(generations= 2, 
                                     population_size= 2, 
                                     offspring_size= 2,
                                     verbosity= 2, 
                                     early_stop= 10, 
                                     config_dict={'sklearn.ensemble.RandomForestClassifier': 
                                     model_parameters}, 
                                     cv = 5)
         
        
        model_tuned.fit(X_train,y_train)
        
        X = X.to_numpy
        y = y.to_numpy
        

        Looking for some guidance on combinatorics in python

        copy iconCopydownload iconDownload
        a = np.arange(0.5, -0.01, -0.01)
        for i in range(len(a)):
            first_element = round(a[i], 2) # this is the 1st element, rounded to 2 digit
            for j in range(i, len(a)):
                second_element = round(a[j], 2) # same with 2nd element
                print(first_element, second_element) # you can replace this with your function
        

        Can't solve the errorr message "Expected 2D array, got 1D array instead"?

        copy iconCopydownload iconDownload
        results = exported_pipeline.predict(x_test)
        

        Usage of LSTM/GRU and Flatten throws dimensional incompatibility error

        copy iconCopydownload iconDownload
        from tensorflow.keras import Sequential
        from tensorflow.keras.layers import Conv1D, GRU, Bidirectional, AveragePooling1D, Dense, Flatten, Dropout
        import numpy as np
        
        
        def twds_model(layer1=32, layer2=32, layer3=16, dropout_rate=0.5, optimizer='Adam',
                       learning_rate=0.001, activation='relu', loss='mse'):
            model = Sequential()
            model.add(Bidirectional(GRU(layer1, return_sequences=True), input_shape=(X_train.shape[1], X_train.shape[2])))
            model.add(AveragePooling1D(2))
            model.add(Conv1D(layer2, 3, activation=activation, padding='same',
                             name='extractor'))
            model.add(Flatten())
            model.add(Dense(layer3, activation=activation))
            model.add(Dropout(dropout_rate))
            model.add(Dense(1))
            model.compile(optimizer=optimizer, loss=loss)
            return model
        
        
        if __name__ == '__main__':
            X_train = np.random.rand(1200, 18, 15)
            y_train = np.random.rand(1200, 18, 1)
        
            twds_model = twds_model()
            print(twds_model.summary())
        
            twds_model.fit(X_train, y_train, epochs=20)
        
        
        Tensorflow 2.3.0
        Keras 2.4.2
        CUDA 10.1
        cuDNN 7.6.5
        
        import tensorflow as tf
        import os
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"] = '0' # Set to -1 if CPU should be used CPU = -1 , GPU = 0
        
        gpus = tf.config.experimental.list_physical_devices('GPU')
        cpus = tf.config.experimental.list_physical_devices('CPU')
        
        if gpus:
            try:
                # Currently, memory growth needs to be the same across GPUs
                for gpu in gpus:
                    tf.config.experimental.set_memory_growth(gpu, True)
                logical_gpus = tf.config.experimental.list_logical_devices('GPU')
                print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
            except RuntimeError as e:
                # Memory growth must be set before GPUs have been initialized
                print(e)
        elif cpus:
            try:
                # Currently, memory growth needs to be the same across GPUs
                logical_cpus= tf.config.experimental.list_logical_devices('CPU')
                print(len(cpus), "Physical CPU,", len(logical_cpus), "Logical CPU")
            except RuntimeError as e:
                # Memory growth must be set before GPUs have been initialized
                print(e)
        
        Tensorflow 2.3.0
        Keras 2.4.2
        CUDA 10.1
        cuDNN 7.6.5
        
        import tensorflow as tf
        import os
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        os.environ["CUDA_VISIBLE_DEVICES"] = '0' # Set to -1 if CPU should be used CPU = -1 , GPU = 0
        
        gpus = tf.config.experimental.list_physical_devices('GPU')
        cpus = tf.config.experimental.list_physical_devices('CPU')
        
        if gpus:
            try:
                # Currently, memory growth needs to be the same across GPUs
                for gpu in gpus:
                    tf.config.experimental.set_memory_growth(gpu, True)
                logical_gpus = tf.config.experimental.list_logical_devices('GPU')
                print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
            except RuntimeError as e:
                # Memory growth must be set before GPUs have been initialized
                print(e)
        elif cpus:
            try:
                # Currently, memory growth needs to be the same across GPUs
                logical_cpus= tf.config.experimental.list_logical_devices('CPU')
                print(len(cpus), "Physical CPU,", len(logical_cpus), "Logical CPU")
            except RuntimeError as e:
                # Memory growth must be set before GPUs have been initialized
                print(e)
        

        Terraform azurerm vm creation with cloudinit

        copy iconCopydownload iconDownload
        cloud_init_template=data.template_cloudinit_config.config.rendered
        

        Community Discussions

        Trending Discussions on tpot
        • How to find which model is selected by TPOT
        • TPOT taking too long to train
        • Explanation of pipeline generated by tpot
        • Dask aws cluster error when initializing: User data is limited to 16384 bytes
        • Does the "tpot" model object automatically apply any scaling or other transformations when .score or .predict is called on new out-of-sample data?
        • TPOT error in python cannot set using a slice indexer with a different length
        • Looking for some guidance on combinatorics in python
        • Import rasterio failed. Reason: image not found
        • Can't solve the errorr message "Expected 2D array, got 1D array instead"?
        • Usage of LSTM/GRU and Flatten throws dimensional incompatibility error
        Trending Discussions on tpot

        QUESTION

        How to find which model is selected by TPOT

        Asked 2022-Feb-18 at 06:34

        Hi am using TPOT for machine learning I am getting 99% accuracy but I am not sure to which model did it predict can someone help me with this also does it do SMOTE?

        ANSWER

        Answered 2022-Feb-18 at 06:34

        If you stored the TPOTClassifier in the variable my_tpot, then you can access the final trained pipeline by accessing the fitted_pipeline_ attribute:

        my_tpot = TPOTClassifier()
        my_tpot.fit(…)
        print(my_tpot.fitted_pipeline_)
        

        Source https://stackoverflow.com/questions/71154137

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

        Vulnerabilities

        No vulnerabilities reported

        Install tpot

        We maintain the TPOT installation instructions in the documentation. TPOT requires a working installation of Python.

        Support

        We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to TPOT, please file a new issue so we can discuss it. Before submitting any contributions, please review our contribution guidelines.

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