RecommenderSystem.py

from LatentFactorModel import LatentFactorModel
from BaselineRecommendations import BaselineRecommendations
from DataPreprocessing import DataPreprocessing
from CollaborativeFiltering import CollaborativeFiltering


if __name__ == '__main__':
    print "Welcome to the Anime Recommender System."

    #***************Data Preprocessing***************
    print "If this is your first time running the program? You'll need to create the necessary matrices and" \
          "remapped rating file if it is."
    initialization = raw_input("Create matrices and remapped rating file if they don't already exist? (yes or no) ")
    while initialization != 'yes' and initialization != 'no':
        initialization = raw_input("Create matrices and remapped rating file if they don't already exist? (yes or no) ")
    if initialization == 'yes':
        preprocess = DataPreprocessing()
        preprocess.run_random_split()
        preprocess.run_arbitrary_split()

    #***************Baseline Recommendation***************
    baseline = raw_input("Do you want to run the baseline recommendation? (yes or no) ")
    while baseline != 'yes' and baseline != 'no':
        baseline = raw_input("Do you want to run the baseline recommendation? (yes or no) ")
    if baseline == 'yes':
        sample_type = raw_input("Do you want to run the randomly sampled data, arbitrarily sampled data or both? (r, a, b) ")
        while sample_type != 'r' and sample_type != 'a' and sample_type != 'b':
            sample_type = raw_input("Do you want to run the randomly sampled data, arbitrarily sampled data or both? (r, a, b) ")
        if sample_type == 'r':
            print "Calculating RMSE for random dataset split."
            baseline_recommend = BaselineRecommendations('random')
            baseline_recommend.run_baseline()
        elif sample_type == 'a':
            print "Calculating RMSE for arbitrary dataset split."
            baseline_recommend = BaselineRecommendations('arbitrary')
            baseline_recommend.run_baseline()
        elif sample_type == 'b':
            print "Calculating RMSE for random dataset split."
            baseline_recommend = BaselineRecommendations('random')
            baseline_recommend.run_baseline()
            print "Calculating RMSE for arbitrary dataset split."
            baseline_recommend = BaselineRecommendations('arbitrary')
            baseline_recommend.run_baseline()


    #***************Item-Item Collaborative Filtering Recommendation***************
    collab_filt = raw_input("Do you want to run the collaborative filtering recommendation? (yes or no) ")
    while collab_filt != 'yes' and collab_filt != 'no':
        collab_filt = raw_input("Do you want to run the collaborative filtering recommendation? (yes or no) ")
    if collab_filt == 'yes':
        sample_type = raw_input("Do you want to run the randomly sampled data, arbitrarily sampled data or both? (r, a, b) ")
        while sample_type != 'r' and sample_type != 'a' and sample_type != 'b':
            sample_type = raw_input("Do you want to run the randomly sampled data, arbitrarily sampled data or both? (r, a, b) ")
        print "Note: this is a fairly time consuming process--please allow for approximately 2+ hours to calculate RMSE on a single dataset."
        if sample_type == 'r':
            print "Calculating RMSE for random dataset split."
            collab_recommend = CollaborativeFiltering()
            collab_recommend.collaborative_filter()
        elif sample_type == 'a':
            print "Calculating RMSE for arbitrary dataset split."
            collab_recommend = CollaborativeFiltering(dataset='arbitrary')
            collab_recommend.collaborative_filter()
        elif sample_type == 'b':
            print "Calculating RMSE for random dataset split."
            collab_recommend = CollaborativeFiltering()
            collab_recommend.collaborative_filter()
            print "Calculating RMSE for arbitrary dataset split."
            collab_recommend = CollaborativeFiltering(dataset='arbitrary')
            collab_recommend.collaborative_filter()


    #***************Latent Factor Model Recommendation***************
    run = raw_input("Do you want to run the latent factor model recommendation? (yes or no) ")
    while run != 'yes' and run != 'no':
        run = raw_input("Do you want to run the latent factor model recommendation? (yes or no) ")
    if run == 'yes':
        model_type = raw_input("Do you want to start a new model or load an old model? (old or new)\nWarning: "
                               "training a new model is very slow, it's recommended that you use the default model provided. ")
        while model_type != 'new' and model_type != 'old':
            model_type = raw_input("Do you want to start a new model or load an old model? (old or new) ")
        if model_type == 'new':
            parameters = raw_input("Do you want to use the default parameters? (yes or no) ")
            while parameters != 'yes' and parameters != 'no':
                parameters = raw_input("Do you want to use the default parameters? (yes or no) ")
            if parameters == 'yes':
                print "Initializing the latent factor model."
                latent_model = LatentFactorModel(epochs=15, k=10, learning_rate=0.006, lambda_reg=0.06)
                print "Beginning the long training process..."
                latent_model.run_new_model()
            elif parameters == 'no':
                epochs = input("Enter the number of epochs to train for: ")
                k = input("Enter the number of latent factors: ")
                learning_rate = input("Enter the learning rate: ")
                lambda_reg = input("Enter the lambda regularization value: ")
                print "Initializing the latent factor model."
                latent_model = LatentFactorModel(epochs=epochs, k=k, learning_rate=learning_rate, lambda_reg=lambda_reg)
                print "Beginning the long training process..."
                latent_model.run_new_model()
        elif model_type == 'old':
            default_model = raw_input("Do you want to load the default model? (yes or no)\nNote: it's recommended "
                                          "to load the default model. ")
            while default_model != 'yes' and default_model != 'no':
                default_model = raw_input("Do you want to load the default model? (yes or no)\nNote: it's recommended "
                                          "to load the default model. ")
            if default_model == 'yes':
                print "Initializing the latent factor model."
                latent_model = LatentFactorModel(epochs=15, k=10, learning_rate=0.006, lambda_reg=0.06)
                latent_model.load_model(model_directory='optimization/2017-11-23_16-28-35/')
                print "Calculating the random split test RMSE."
                test_rmse = latent_model.calculate_test_rmse()
                print "Random split test RMSE is: " + str(test_rmse)
            elif default_model == 'no':
                directory = raw_input("Enter the model directory (e.g. 'optimization/2017-11-23_16-28-35/'): ")
                print "Initializing the latent factor model."
                latent_model = LatentFactorModel(epochs=15, k=10, learning_rate=0.006, lambda_reg=0.06)
                latent_model.load_model(model_directory=directory)
                print "Calculating the random split test RMSE."
                test_rmse = latent_model.calculate_test_rmse()
                print "Random split test RMSE is: " + str(test_rmse)