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run_with_plot.py
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run_with_plot.py
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from kingrec import KingRec
from kingrec.evaluation import precision_at_k
from kingrec.evaluation import auc_score
from kingrec.dataset import init_movielens
import matplotlib.pyplot as plt
import numpy as np
k = 10
threads = 16
final_test_auc = []
final_test_precision = []
dataset = '../king-rec-dataset/ml-latest-small'
def load_auc_params(optimized_for=None):
if optimized_for is None:
print('Optimized for non features')
optimal_epochs = 300
optimal_learning_rate = 0.013125743984880447
optimal_no_components = 169
optimal_alpha = 2.6154143367150727e-06
optimal_scaling = 0.04382333041868763
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres':
print('Optimized for genres')
optimal_epochs = 300
optimal_learning_rate = 0.026238747910509397
optimal_no_components = 193
optimal_alpha = 0.0027085249085071626
optimal_scaling = 0.07322973067589604
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 100
optimal_learning_rate = 0.0570326091236193
optimal_no_components = 68
optimal_alpha = 0.0029503539747277366
optimal_scaling = 0.02563602355611453
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 200
optimal_learning_rate = 0.027730397776550147
optimal_no_components = 189
optimal_alpha = 0.0011133373244076297
optimal_scaling = 0.4922360335772573
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
def load_precision_params(optimized_for=None):
if optimized_for is None:
print('Optimized for non features')
optimal_epochs = 141
optimal_learning_rate = 0.043040683676705736
optimal_no_components = 21
optimal_alpha = 0.00541554967720231
optimal_scaling = 0.014726505321746962
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres':
print('Optimized for genres')
optimal_epochs = 136
optimal_learning_rate = 0.075490395178898
optimal_no_components = 82
optimal_alpha = 0.007065549151367718
optimal_scaling = 0.00799962475267643
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 63
optimal_learning_rate = 0.05647434188275842
optimal_no_components = 98
optimal_alpha = 0.0031993742820159436
optimal_scaling = 0.0933642796909375
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 96
optimal_learning_rate = 0.1703221223672566
optimal_no_components = 22
optimal_alpha = 0.004206346506337412
optimal_scaling = 0.041303781930858034
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
def load_precision_params_clusters(optimized_for='clusters', model='vgg19'):
print('Model:', model)
if model == 'vgg19':
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 232
optimal_learning_rate = 0.07171978672352887
optimal_no_components = 42
optimal_alpha = 0.006517845577815826
optimal_scaling = 0.016142300018137722
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 218
optimal_learning_rate = 0.12470857345083873
optimal_no_components = 73
optimal_alpha = 0.005478316990150038
optimal_scaling = 0.04637764141484815
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
elif model == 'inception_v3':
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 119
optimal_learning_rate = 0.00852211930222011
optimal_no_components = 192
optimal_alpha = 7.276515301192984e-05
optimal_scaling = 0.027052254503857717
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 245
optimal_learning_rate = 0.028963892665938032
optimal_no_components = 43
optimal_alpha = 0.0006238083410955659
optimal_scaling = 0.36579038826022736
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
elif model == 'resnet50':
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 88
optimal_learning_rate = 0.07492160698420884
optimal_no_components = 21
optimal_alpha = 0.004634987385145838
optimal_scaling = 0.028198967823831238
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 224
optimal_learning_rate = 0.04214027912721876
optimal_no_components = 186
optimal_alpha = 0.008676073688466915
optimal_scaling = 0.0024915458462563605
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
def load_auc_params_clusters(optimized_for='clusters', model='vgg19'):
print('Model:', model)
if model == 'vgg19':
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 89
optimal_learning_rate = 0.018841927704689492
optimal_no_components = 139
optimal_alpha = 0.0008662511914237855
optimal_scaling = 0.2864763834214625
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 236
optimal_learning_rate = 0.031860755009764305
optimal_no_components = 139
optimal_alpha = 0.0010930770083784052
optimal_scaling = 0.8362665749306415
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
elif model == 'inception_v3':
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 232
optimal_learning_rate = 0.02981041359364386
optimal_no_components = 84
optimal_alpha = 0.004287524090264805
optimal_scaling = 0.040501994149651166
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 250
optimal_learning_rate = 0.019411170816577752
optimal_no_components = 136
optimal_alpha = 0.0008323333176050233
optimal_scaling = 0.4767783602102349
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
elif model == 'resnet50':
if optimized_for == 'clusters':
print('Optimized for clusters')
optimal_epochs = 198
optimal_learning_rate = 0.016780379637566917
optimal_no_components = 169
optimal_alpha = 0.0012939223653296507
optimal_scaling = 0.6692069103186539
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
if optimized_for == 'genres_clusters':
print('Optimized for genres and clusters')
optimal_epochs = 211
optimal_learning_rate = 0.09767064566975311
optimal_no_components = 48
optimal_alpha = 0.003428832598553235
optimal_scaling = 0.11239835090728653
return optimal_epochs, optimal_learning_rate, optimal_no_components, optimal_alpha, optimal_scaling
def general_models(params_to_load=load_auc_params):
for features in [None, ['genres'], ['clusters'], ['genres', 'clusters']]:
movielens = init_movielens(dataset, min_rating=3.5, k=k, item_features=features)
if features is None:
epochs, learning_rate, no_components, alpha, scaling = params_to_load(optimized_for=features)
elif len(features) == 1:
epochs, learning_rate, no_components, alpha, scaling = params_to_load(optimized_for=features[0])
elif len(features) == 2:
epochs, learning_rate, no_components, alpha, scaling = params_to_load(optimized_for=features[0] + '_' + features[1])
train = movielens['train']
test = movielens['test']
item_features = movielens['item_features']
king_rec = KingRec(no_components=no_components, learning_rate=learning_rate, alpha=alpha, scale=scaling,
loss='warp')
model = king_rec.model
train_auc_scores = []
test_auc_scores = []
train_precision_scores = []
test_precision_scores = []
for epoch in range(epochs):
print('Epoch:', epoch)
model.fit_partial(train, item_features=item_features, epochs=1)
train_precision = precision_at_k(model, train, item_features=item_features, k=k, num_threads=threads).mean()
test_precision = precision_at_k(model, test, item_features=item_features, k=k, num_threads=threads).mean()
train_auc = auc_score(model, train, item_features=item_features, num_threads=threads).mean()
test_auc = auc_score(model, test, item_features=item_features, num_threads=threads).mean()
train_auc_scores.append(train_auc)
test_auc_scores.append(test_auc)
train_precision_scores.append(train_precision)
test_precision_scores.append(test_precision)
final_test_auc.append(test_auc_scores)
final_test_precision.append(test_precision_scores)
# plot results
plt.figure()
for auc_scores in final_test_auc:
x = np.arange(len(auc_scores))
max_value = max(auc_scores)
max_index = auc_scores.index(max_value)
plt.plot(x, auc_scores, '-D', markevery=[max_index])
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.title('Accuracy per model')
plt.legend(['without features: ' + str(np.round(max(final_test_auc[0]) * 100, decimals=2)) + '%',
'genres: ' + str(np.round(max(final_test_auc[1]) * 100, decimals=2)) + '%',
'clusters: ' + str(np.round(max(final_test_auc[2]) * 100, decimals=2)) + '%',
'genres + clusters: ' + str(np.round(max(final_test_auc[3]) * 100, decimals=2)) + '%'
])
plt.savefig('auc-comparison.jpg')
plt.show()
plt.figure()
for precision_scores in final_test_precision:
x = np.arange(len(precision_scores))
max_value = max(precision_scores)
max_index = precision_scores.index(max_value)
plt.plot(x, precision_scores, '-D', markevery=[max_index])
plt.ylabel('Precision')
plt.xlabel('Epochs')
plt.title('Precision per model')
plt.legend(['without features: ' + str(np.round(max(final_test_precision[0]) * 100, decimals=2)) + '%',
'genres: ' + str(np.round(max(final_test_precision[1]) * 100, decimals=2)) + '%',
'clusters: ' + str(np.round(max(final_test_precision[2]) * 100, decimals=2)) + '%',
'genres + clusters: ' + str(np.round(max(final_test_precision[3]) * 100, decimals=2)) + '%'
])
plt.savefig('precision-comparison.jpg')
plt.show()
def clusters_models(params_to_load_clusters=load_auc_params_clusters):
for model in ['vgg19', 'inception_v3', 'resnet50']:
# features = ['clusters']
features = ['genres', 'clusters']
movielens = init_movielens(dataset, min_rating=3.5, k=k, item_features=features, model=model)
if len(features) == 1:
epochs, learning_rate, no_components, alpha, scaling = params_to_load_clusters(optimized_for=features[0], model=model)
elif len(features) == 2:
epochs, learning_rate, no_components, alpha, scaling = params_to_load_clusters(optimized_for=features[0] + '_' + features[1], model=model)
train = movielens['train']
test = movielens['test']
item_features = movielens['item_features']
king_rec = KingRec(no_components=no_components, learning_rate=learning_rate, alpha=alpha, scale=scaling, loss='warp')
model = king_rec.model
train_auc_scores = []
test_auc_scores = []
train_precision_scores = []
test_precision_scores = []
for epoch in range(epochs):
print('Epoch:', epoch)
model.fit_partial(train, item_features=item_features, epochs=1)
train_precision = precision_at_k(model, train, item_features=item_features, k=k, num_threads=threads).mean()
test_precision = precision_at_k(model, test, item_features=item_features, k=k, num_threads=threads).mean()
train_precision_scores.append(train_precision)
test_precision_scores.append(test_precision)
# train_auc = auc_score(model, train, item_features=item_features, num_threads=threads).mean()
# test_auc = auc_score(model, test, item_features=item_features, num_threads=threads).mean()
# train_auc_scores.append(train_auc)
# test_auc_scores.append(test_auc)
# final_test_auc.append(test_auc_scores)
final_test_precision.append(test_precision_scores)
# plot results
plt.figure()
for auc_scores in final_test_auc:
x = np.arange(len(auc_scores))
max_value = max(auc_scores)
max_index = auc_scores.index(max_value)
# plt.plot(x, auc_scores, '-D', markevery=[max_index])
plt.plot(x, auc_scores)
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.title('Accuracy per model with genres and clusters metadata')
plt.legend(['vgg19: ' + str(np.round(max(final_test_auc[0]) * 100, decimals=2)) + '%',
'inception_v3: ' + str(np.round(max(final_test_auc[1]) * 100, decimals=2)) + '%',
'resnet50: ' + str(np.round(max(final_test_auc[2]) * 100, decimals=2)) + '%',
])
plt.savefig('auc-comparison.jpg')
plt.show()
plt.figure()
for precision_scores in final_test_precision:
x = np.arange(len(precision_scores))
max_value = max(precision_scores)
max_index = precision_scores.index(max_value)
# plt.plot(x, precision_scores, '-D', markevery=[max_index])
plt.plot(x, precision_scores)
plt.ylabel('Precision')
plt.xlabel('Epochs')
plt.title('Precision per model with genres and clusters metadata')
plt.legend(['vgg19: ' + str(np.round(max(final_test_precision[0]) * 100, decimals=2)) + '%',
'inception_v3: ' + str(np.round(max(final_test_precision[1]) * 100, decimals=2)) + '%',
'resnet50: ' + str(np.round(max(final_test_precision[2]) * 100, decimals=2)) + '%',
])
plt.savefig('precision-comparison.jpg')
plt.show()
general_models(load_precision_params)
# clusters_models(load_precision_params_clusters)