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RecommenderSystem.py
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RecommenderSystem.py
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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)
# Uncomment only one dataset
# dataset = 'random'
# dataset = 'arbitrary'
# learning_rate_trials = [0.015, 0.006, 0.002]
# lambda_reg_trials = [0.06, 0.02]
# k_trials = [5, 10, 20]
#start_whole = time.time()
#recommender = RecommenderSystems(dataset=dataset)
#recommender.initialize_program()
# baseline = BaselineRecommendations(dataset)
# baseline.run_baseline()
#latent_model = LatentFactorModel(epochs=50, k=k_trials[2], learning_rate=learning_rate_trials[0], lambda_reg=lambda_reg_trials[0])
# Overrides default parameters passed in constructor
#latent_model.run_new_model()
#latent_model.run_old_model(model_directory='optimization/2017-11-23_16-28-35/')
#recommender.fill_similarity_matrix(centered_training_filepath, similarity_filepath)
#recommender.collaborative_filter(similarity_filepath)
#end = time.time()
#print "Time to run program: " + str((end - start_whole))