def process(args):
# Create a graph from the training set
nodedict = graph.records_to_graph()
# Build the model using DeepWalk and Word2Vec
G = graph.load_adjacencylist("out.adj", undirected=True)
walks = graph.build_deepwalk_corpus(G,
num_paths=args.number_walks,
path_length=args.walk_length,
alpha=0,
rand=random.Random(args.seed))
model = Word2Vec(walks,
size=args.representation_size,
window=args.window_size,
min_count=0,
workers=args.workers)
# Perform some evaluation of the model on the test dataset
with open("./data/test_user_ratings.dat") as fin:
fin.readline()
groundtruth = [line.strip().split("\t")[:3]
for line in fin] # (user, movie, rating)
tr = [int(round(float(g[2]))) for g in groundtruth]
pr = [
predict_rating(model, nodedict, "u" + g[0], "m" + g[1])
for g in groundtruth
]
print("MSE = %f" % mean_squared_error(tr, pr))
print("accuracy = %f" % accuracy_score(tr, pr))
cm = confusion_matrix(tr, pr, labels=range(1, 6))
print(cm)
def process(args):
# Create a graph from the training set
nodedict = graph.records_to_graph()
# Build the model using DeepWalk and Word2Vec
G = graph.load_adjacencylist("out.adj", undirected=True)
####################################################################################################################################################################
# Code Written for BI Project : Author : Himangshu Ranjan Borah(hborah)
####################################################################################################################################################################
# call the build_deepwalk_corpus function
# Take and populate the arguments from the command lines.
generated_walks = graph.build_deepwalk_corpus(G = G, num_paths = args.number_walks, path_length = args.walk_length, alpha=0, rand=random.Random(0))
# Call word2vec to build the model.
# print generated_walks
# The structure Looks like ['32173', '32168'], ['124010', '22676'], ['17792', '72925'],
model = Word2Vec(generated_walks, size=args.representation_size, window=args.window_size, min_count=0, workers=args.workers)
####################################################################################################################################################################
# Code Written for BI Project : Author : Himangshu Ranjan Borah(hborah)
####################################################################################################################################################################
# Perform some evaluation of the model on the test dataset
with open("./data/test_user_ratings.dat") as fin:
fin.next()
groundtruth = [line.strip().split("\t")[:3] for line in fin] # (user, movie, rating)
tr = [int(round(float(g[2]))) for g in groundtruth]
pr = [predict_rating(model, nodedict, "u"+g[0], "m"+g[1]) for g in groundtruth]
print "MSE = %f" % mean_squared_error(tr, pr)
print "accuracy = %f" % accuracy_score(tr, pr)
cm = confusion_matrix(tr, pr, labels=range(1,6))
print cm
def process(args):
# Create a graph from the training set
nodedict = graph.records_to_graph()
# Build the model using DeepWalk and Word2Vec
G = graph.load_adjacencylist("out.adj", undirected=True)
walks = graph.build_deepwalk_corpus(G, int(args.number_walks),
int(args.walk_length))
model = Word2Vec(walks, 100, 5, 5, 1)
# Perform some evaluation of the model on the test dataset
with open("./data/test_user_ratings.dat") as fin:
fin.next()
groundtruth = [line.strip().split("\t")[:3]
for line in fin] # (user, movie, rating)
tr = [int(round(float(g[2]))) for g in groundtruth]
pr = [
predict_rating(model, nodedict, "u" + g[0], "m" + g[1])
for g in groundtruth
]
print "MSE = %f" % mean_squared_error(tr, pr)
print "accuracy = %f" % accuracy_score(tr, pr)
cm = confusion_matrix(tr, pr, labels=range(1, 6))
print cm
def process(args):
# Create a graph from the training set
nodedict = graph.records_to_graph()
# Build the model using DeepWalk and Word2Vec
G = graph.load_adjacencylist("out.adj", undirected=True)
# YOUR CODE HERE
no_walks = int(args.number_walks)
walk_len = int(args.walk_length)
def process(args):
# Create a graph from the training set
nodedict = graph.records_to_graph()
# Build the model using DeepWalk and Word2Vec
G = graph.load_adjacencylist("out.adj", undirected=True)
####################################################################################################################################################################
# Code Written for BI Project : Author : Himangshu Ranjan Borah(hborah)
####################################################################################################################################################################
# call the build_deepwalk_corpus function
# Take and populate the arguments from the command lines.
generated_walks = graph.build_deepwalk_corpus(G=G,
num_paths=args.number_walks,
path_length=args.walk_length,
alpha=0,
rand=random.Random(0))
# Call word2vec to build the model.
# print generated_walks
# The structure Looks like ['32173', '32168'], ['124010', '22676'], ['17792', '72925'],
model = Word2Vec(generated_walks,
size=args.representation_size,
window=args.window_size,
min_count=0,
workers=args.workers)
####################################################################################################################################################################
# Code Written for BI Project : Author : Himangshu Ranjan Borah(hborah)
####################################################################################################################################################################
# Perform some evaluation of the model on the test dataset
with open("./data/test_user_ratings.dat") as fin:
fin.next()
groundtruth = [line.strip().split("\t")[:3]
for line in fin] # (user, movie, rating)
tr = [int(round(float(g[2]))) for g in groundtruth]
pr = [
predict_rating(model, nodedict, "u" + g[0], "m" + g[1])
for g in groundtruth
]
print "MSE = %f" % mean_squared_error(tr, pr)
print "accuracy = %f" % accuracy_score(tr, pr)
cm = confusion_matrix(tr, pr, labels=range(1, 6))
print cm
def process(args):
# Create a graph from the training set
nodedict = graph.records_to_graph()
# Build the model using DeepWalk and Word2Vec
G = graph.load_adjacencylist("out.adj", undirected=True)
# YOUR CODE HERE
# Perform some evaluation of the model on the test dataset
with open("./data/test_user_ratings.dat") as fin:
fin.next()
groundtruth = [line.strip().split("\t")[:3] for line in fin] # (user, movie, rating)
tr = [int(round(float(g[2]))) for g in groundtruth]
pr = [predict_rating(model, nodedict, "u"+g[0], "m"+g[1]) for g in groundtruth]
print "MSE = %f" % mean_squared_error(tr, pr)
print "accuracy = %f" % accuracy_score(tr, pr)
cm = confusion_matrix(tr, pr, labels=range(1,6))
print cm