Package: autoprototype
autoprototypeThis library is designed for optimization of hyper-parameters in Deep Learning and Machine Learning Models. This module aims at
giving the user the best set of hyper-parameters for their DL/ML models.
Along with suggesting the hyper-parameters, this module
aims at returning to the user the best suggestive model framework/architecture based on the user input data. The user has
to just provide the dataset and the module will return :The best model architecture based on the input data.The best hyper-parameters for the model architecture.In this way, the whole process of experimenting with the architecture is narrowed down. This is easy and requires only few lines of codes.
Apart from the input data, only few other parameters are required by the module. All, the processes in rapid prototyping is automated thereafter, through this module.
The structure is based on some default values for parameters spaces required for the optimization. However, user would also have freedom to
dynamically construct the search spaces for the hyperparameters.This module is a wrap around the popular Hyper-parameter Optimization Tool calledOptuna.
Optuna is used for the optimization process using iterative trails. This module takes the data as the primary input and
suggests the user the model based on this Optuna trials. Optuna enables efficient hyperparameter optimization by adopting state-of-the-art
algorithms for sampling hyper-parameters and pruning efficiently unpromising trials.Some key features of Optuna, that we used are:1.Lightweight, versatile, and platform agnostic architecturePythonic search spacesEfficient Optimization AlgorithmsThis module, includiong all examples, is entirely written on Python.Installation GuideUsers can use pip install to install the packages to start working on the prototyping.
Below is given the pip command.pip install autoprototypeThe usage or the API calls for each supported ML/DL libraries are shown in the next sections.#Supported Integrations Libraries##SKLearn
At this point, the following models and their respective hyper-parameters are supporteda. Decision Treeb. SVCc. Linear Regressiond. Ridge and Lasso Regressione. Random Forestf. Logistic RegressionStarting the prototyping process consists of just two line of codefrom autoprototype.sklearn import sklearnopt
    hpo = sklearnopt(X_train,y_train)
    trial , params , value = hpo.get_best_params(n_trials=trials)This would return to us, the best trail, the set of best parameters including the model and the best objective value based on which the optimization is done.To run the example navigate toexamplesand use:python iris.pytf.kerasAt this point this supports, the following models and their hyper-parameters are supported:Artificial Neural NetworksThis also requires few lines of codes as shown below.from autoprototype.tf_keras import kerasopt
    hpo = kerasopt(x_train,y_train,EPOCHS=10,classes=CLASSES)
    trial , params , value = hpo.get_best_params(n_trials=n_trials)By default it would run the trials and suggest you:a.n_layers: Number of hidden layers in the model.b.units_i: Number of units in each layersc.dropout_rate: the dropout rated.lr: Learning rate of optimizerse.optimizer_name: Name of the best optimizerThe loss criterion and the maximum number of layers, is set tosparse_categorical_crossentropyand5respectively, by default.
User can also provide any other loss function based on requirement as follows:hpo = HyperparamOpt(x_train,y_train,EPOCHS=10,classes=CLASSES,loss="your_loss_function",n_ann_layers=number_of layers)To run the ANN example, navigate toexamplesand run :python ann_tfkeras.pyConvolution Neural NetworkThe API for CNN model is fairly the same as that of the above ANN. The user, must pass few other optional parameters to construct the suggestive CNN architecture. All other syntaxes are same.hpo = kerasopt(x_train,y_train,EPOCHS=10,classes=120,
                   max_units_fcl=400, max_conv_filters=1000,
                   arch="cnn",input_shape=(128,128,3),steps_per_epoch=10)Two mandatory arguments to run CNN:archrefers to the specification of architecture to 'cnn',input_shapemust be provided for any CNN models.Other optional argumentsmax_units_fcl: max units in the fully connected layers of CNN, default1000max_nconv: max number of convolution layers, default5max_fully_conn_layers: max number of fully connected layers. default2steps_per_epoch: Number of steps to be taken in an epoch, default216max_conv_filters: maximum filters in the convolution layers, default256To run the CNN example [tf_keras/examples] and run :Download the data from :https://www.kaggle.com/c/dog-breed-identification/dataNavigate to examples foldercd examplesMake an empty directory called data.mkdir datamove the downloaded data into thedatafolder. Please check the path to data provided in thecnn_tfkeras.pyscript!Run:python cnn_tfkeras.py