Metadata-Version: 2.1
Name: MLProto
Version: 0.0.31
Summary: Modular Neural Network Protyping for Stock Market Prediction
Home-page: https://github.com/CSLukeW/MLProto
Author: Luke Williams
Author-email: williams.luke.2001@gmail.com
License: UNKNOWN
Description: # MLProto: Modular Prototyping Tool for LSTM Machine Learning Models
        
        ## Usage
        
        ### Video overview coming
        
        ### The ProtoMake Script
        
        The ProtoMake script combines the Proto and Data modules into one to create an easy, convenient, and modular neural network prototyping tool for LSTM machine learning models. The script will take the user's desired parameters and create, train, and evaluate a model fitting said parameters. This allows the user to quickly analyze model prototypes, make adjustments, and iterate on model designs.
        
        #### Arguments:
            Positional:
                key ---- User's Alpha_Vantage API key
                identifier ---- Ticker symbols to create models for
                target ---- Column number of target values
        
            Optional:
                -depth ---- number of layers to include in the neural network (def: 1)
                -node_counts ---- list of node counts for layers (len(node_counts) must equal depth)
                -batch ---- batch size of input data set (def: [100])
                -test_size ---- proportion of dataset to use as validation (def: .2)
                -loss ---- identifier string of keras-supported loss function to be used in training (def: mse)
                -learning_rate ---- learning rate to be used by the Adam optimizer
                -epochs ---- maximum number of epochs to train the model (def: 100)
                -model_in ---- file path of pre-made model to load
                --early_stop ---- flag deciding whether to apply early stopping (patience 5) to the training phase
                --plots ---- flag deciding whether to save loss, input, and prediction graphs
                --normalize ---- flag deciding whether or not to normalize input data
        
        #### Usage Example:
            ProtoMake test.csv test_model --early_stop --plots
        
        The above command will create, train, and evaluate a model for the data in test.csv. It saves a model test_model.h5 in directory ./models/ and input, loss, and prediction graphs in the directory ./plots/ for analysis.
        
        ### The Stocker Module
        
        If you would like to use your own data pipelines as inputs, the Stocker and data helper modules can be used separately from the main script.
        
        ```Python
        from MLProto import *
        
        """ Data operations (assign data, pred_data)
        ______________________________________________
        """
        
        stkr = Proto(data)
        stkr.train(25, True, True))
        stkr.evaluate()
        stkr.predict_data(pred_data)
        ```
        The above code will take prepared data, create a stocker instance for the FORD ticker, train for 25 epochs, save the model to the models folder as FORD.h5 and predict a data point based on the user's prepared prediction data.
        
        ### The Data module
        
        This module includes the data operation helper functions used by Stocker.
        
        single_step_data takes a full dataset and creates a single-step timeseries dataset from it for input into an LSTM model.
        
        ## Contributions
        
        Please send pull requests! I am a full-time student, so development and support for Stocker will likely be slow with me working alone. I welcome any and all efforts to contribute!
        
        ## License
        
        [GNU LGPLv3](https://choosealicense.com/licenses/lgpl-3.0/)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.6
Description-Content-Type: text/markdown
