# relnCollFilt.py - Latent Property-based Collaborative Filtering # AIFCA Python3 code Version 0.9.0 Documentation at http://aipython.org # Artificial Intelligence: Foundations of Computational Agents # http://artint.info # Copyright David L Poole and Alan K Mackworth 2017-2020. # This work is licensed under a Creative Commons # Attribution-NonCommercial-ShareAlike 4.0 International License. # See: http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en import random import matplotlib.pyplot as plt import urllib.request from learnProblem import Learner from display import Displayable class CF_learner(Learner): def __init__(self, rating_set, # a Rating_set object rating_subset = None, # subset of ratings to be used as training ratings test_subset = None, # subset of ratings to be used as test ratings step_size = 0.01, # gradient descent step size reglz = 1.0, # the weight for the regularization terms num_properties = 10, # number of hidden properties property_range = 0.02 # properties are initialized to be between # -property_range and property_range ): self.rating_set = rating_set self.ratings = rating_subset or rating_set.training_ratings # whichever is not empty if test_subset is None: self.test_ratings = self.rating_set.test_ratings else: self.test_ratings = test_subset self.step_size = step_size self.reglz = reglz self.num_properties = num_properties self.num_ratings = len(self.ratings) self.ave_rating = (sum(r for (u,i,r,t) in self.ratings) /self.num_ratings) self.users = {u for (u,i,r,t) in self.ratings} self.items = {i for (u,i,r,t) in self.ratings} self.user_bias = {u:0 for u in self.users} self.item_bias = {i:0 for i in self.items} self.user_prop = {u:[random.uniform(-property_range,property_range) for p in range(num_properties)] for u in self.users} self.item_prop = {i:[random.uniform(-property_range,property_range) for p in range(num_properties)] for i in self.items} self.zeros = [0 for p in range(num_properties)] self.iter=0 def stats(self): self.display(1,"ave sumsq error of mean for training=", sum((self.ave_rating-rating)**2 for (user,item,rating,timestamp) in self.ratings)/len(self.ratings)) self.display(1,"ave sumsq error of mean for test=", sum((self.ave_rating-rating)**2 for (user,item,rating,timestamp) in self.test_ratings)/len(self.test_ratings)) self.display(1,"error on training set", self.evaluate(self.ratings)) self.display(1,"error on test set", self.evaluate(self.test_ratings)) def prediction(self,user,item): """Returns prediction for this user on this item. The use of .get() is to handle users or items not in the training set. """ return (self.ave_rating + self.user_bias.get(user,0) #self.user_bias[user] + self.item_bias.get(item,0) #self.item_bias[item] + sum([self.user_prop.get(user,self.zeros)[p]*self.item_prop.get(item,self.zeros)[p] for p in range(self.num_properties)])) def learn(self, num_iter = 50): """ do num_iter iterations of gradient descent.""" for i in range(num_iter): self.iter += 1 abs_error=0 sumsq_error=0 for (user,item,rating,timestamp) in random.sample(self.ratings,len(self.ratings)): error = self.prediction(user,item) - rating abs_error += abs(error) sumsq_error += error * error self.user_bias[user] -= self.step_size*error self.item_bias[item] -= self.step_size*error for p in range(self.num_properties): self.user_prop[user][p] -= self.step_size*error*self.item_prop[item][p] self.item_prop[item][p] -= self.step_size*error*self.user_prop[user][p] for user in self.users: self.user_bias[user] -= self.step_size*self.reglz* self.user_bias[user] for p in range(self.num_properties): self.user_prop[user][p] -= self.step_size*self.reglz*self.user_prop[user][p] for item in self.items: self.item_bias[item] -= self.step_size*self.reglz*self.item_bias[item] for p in range(self.num_properties): self.item_prop[item][p] -= self.step_size*self.reglz*self.item_prop[item][p] self.display(1,"Iteration",self.iter, "(Ave Abs,AveSumSq) training =",self.evaluate(self.ratings), "test =",self.evaluate(self.test_ratings)) def evaluate(self,ratings): """returns (avergage_absolute_error, average_sum_squares_error) for ratings """ abs_error = 0 sumsq_error = 0 if not ratings: return (0,0) for (user,item,rating,timestamp) in ratings: error = self.prediction(user,item) - rating abs_error += abs(error) sumsq_error += error * error return abs_error/len(ratings), sumsq_error/len(ratings) def plot_predictions(self, examples="test"): """ examples is either "test" or "training" or the actual examples """ if examples == "test": examples = self.test_ratings elif examples == "training": examples = self.ratings plt.ion() plt.xlabel("prediction") plt.ylabel("cumulative proportion") self.actuals = [[] for r in range(0,6)] for (user,item,rating,timestamp) in examples: self.actuals[rating].append(self.prediction(user,item)) for rating in range(1,6): self.actuals[rating].sort() numrat=len(self.actuals[rating]) yvals = [i/numrat for i in range(numrat)] plt.plot(self.actuals[rating], yvals, label="rating="+str(rating)) plt.legend() plt.draw() def plot_property(self, p, # property plot_all=False, # true if all points should be plotted num_points=200 # number of random points plotted if not all ): """plot some of the user-movie ratings, if plot_all is true num_points is the number of points selected at random plotted. the plot has the users on the x-axis sorted by their value on property p and with the items on the y-axis sorted by their value on property p and the ratings plotted at the corresponding x-y position. """ plt.ion() plt.xlabel("users") plt.ylabel("items") user_vals = [self.user_prop[u][p] for u in self.users] item_vals = [self.item_prop[i][p] for i in self.items] plt.axis([min(user_vals)-0.02, max(user_vals)+0.05, min(item_vals)-0.02, max(item_vals)+0.05]) if plot_all: for (u,i,r,t) in self.ratings: plt.text(self.user_prop[u][p], self.item_prop[i][p], str(r)) else: for i in range(num_points): (u,i,r,t) = random.choice(self.ratings) plt.text(self.user_prop[u][p], self.item_prop[i][p], str(r)) plt.show() class Rating_set(Displayable): def __init__(self, date_split=892000000, local_file=False, url="http://files.grouplens.org/datasets/movielens/ml-100k/u.data", file_name="u.data"): self.display(1,"reading...") if local_file: lines = open(file_name,'r') else: lines = (line.decode('utf-8') for line in urllib.request.urlopen(url)) all_ratings = (tuple(int(e) for e in line.strip().split('\t')) for line in lines) self.training_ratings = [] self.training_stats = {1:0, 2:0, 3:0, 4:0 ,5:0} self.test_ratings = [] self.test_stats = {1:0, 2:0, 3:0, 4:0 ,5:0} for rate in all_ratings: if rate[3] < date_split: # rate[3] is timestamp self.training_ratings.append(rate) self.training_stats[rate[2]] += 1 else: self.test_ratings.append(rate) self.test_stats[rate[2]] += 1 self.display(1,"...read:", len(self.training_ratings),"training ratings and", len(self.test_ratings),"test ratings") tr_users = {user for (user,item,rating,timestamp) in self.training_ratings} test_users = {user for (user,item,rating,timestamp) in self.test_ratings} self.display(1,"users:",len(tr_users),"training,",len(test_users),"test,", len(tr_users & test_users),"in common") tr_items = {item for (user,item,rating,timestamp) in self.training_ratings} test_items = {item for (user,item,rating,timestamp) in self.test_ratings} self.display(1,"items:",len(tr_items),"training,",len(test_items),"test,", len(tr_items & test_items),"in common") self.display(1,"Rating statistics for training set: ",self.training_stats) self.display(1,"Rating statistics for test set: ",self.test_stats) def create_top_subset(self, num_items = 30, num_users = 30): """Returns a subset of the ratings by picking the most rated items, and then the users that have most ratings on these, and then all of the ratings that involve these users and items. """ items = {item for (user,item,rating,timestamp) in self.training_ratings} item_counts = {i:0 for i in items} for (user,item,rating,timestamp) in self.training_ratings: item_counts[item] += 1 items_sorted = sorted((item_counts[i],i) for i in items) top_items = items_sorted[-num_items:] set_top_items = set(item for (count, item) in top_items) users = {user for (user,item,rating,timestamp) in self.training_ratings} user_counts = {u:0 for u in users} for (user,item,rating,timestamp) in self.training_ratings: if item in set_top_items: user_counts[user] += 1 users_sorted = sorted((user_counts[u],u) for u in users) top_users = users_sorted[-num_users:] set_top_users = set(user for (count, user) in top_users) used_ratings = [ (user,item,rating,timestamp) for (user,item,rating,timestamp) in self.training_ratings if user in set_top_users and item in set_top_items] return used_ratings movielens = Rating_set() learner1 = CF_learner(movielens, num_properties = 1) #learner1.learn(50) # learner1.plot_predictions(examples = "training") # learner1.plot_predictions(examples = "test") #learner1.plot_property(0) #movielens_subset = movielens.create_top_subset(num_items = 20, num_users = 20) #learner_s = CF_learner(movielens, rating_subset=movielens_subset, test_subset=[], num_properties=1) #learner_s.learn(1000) #learner_s.plot_property(0,plot_all=True)