def recommend( usr, pwd ):
userInfo, allWeibos = Weibo.authentication( usr, pwd )
label = Recommendation.recommend( allWeibos )
labelIdx = int(label[0])
tag = labels[labelIdx]
books = Douban.getRecommondBooks(tag)
for book in books:
print book
return books, userInfo
def changeUser():
global currentUser
currentUser = Recommendation.getRandomUserID()
#rest the sentiment score
SentimentAnalysis.reset()
currentSentimentScore = SentimentAnalysis.initSentimentScore()
trackObjs = getUserRecommendation()
htmlSnippet = render_template('recommendation.html', trackObjs=trackObjs)
data = jsonify({'datax': htmlSnippet, 'currentUser': str(currentUser)})
return data
class Environnement():
def __init__(self,
N_items,
N_recommended,
behaviour="random",
rewardType='Similarity',
rewardParameters=[1, 1],
proba_p=0.7,
specific_items=None,
name='envi_01'): #proba_p for ramdom choice (customer)
self.items = Items(N_items)
self.recommendation = Recommendation(self.items, N_recommended)
self.customer = Customer(self.items, self.recommendation, behaviour,
proba_p, specific_items)
self.name = name
self.rewardType = rewardType
self.rewardParameters = rewardParameters
def endEpisode(self):
self.customer.endEpisode()
self.recommendation.endEpisode()
#self.step to simulate new step of the environnement
def step(
self, agentRecommendation
): #here agentRecommendation is the items recommended by the agent
self.recommendation.recommend(
agentRecommendation) #We have set the new recommendations
self.customer.choice()
reward = self.computeReward()
return reward
def computeReward(
self
): #This function will be refined to get more realistic rewards
if self.rewardType == 'Similarity':
reward = -self.items.items[
self.customer.choice_id].cost * self.rewardParameters[0]
reward += self.items.similarities[
self.customer.previous_choice_id][
self.customer.choice_id] * self.rewardParameters[1]
elif self.rewardType == 'Trust':
reward = -(self.items.items[self.customer.choice_id].cost
) * self.rewardParameters[0]
reward += self.customer.trust_recommendation * self.rewardParameters[
1]
else:
print("Error : wrong reward type")
return None
return reward
def display(self, print_item=False):
print('---------ENVIRONNEMENT DISPLAY--------')
self.items.display(print_item)
self.recommendation.display()
self.customer.display()
def WeekTweet(self):
tempTweet = ""
for x in range(1, 6):
self.weatherObj = Weather(GPSLOCATION, AIRPORT, x)
self.rec = Recommendation.Recommendation(self.weatherObj)
tempTweet += self.weatherObj.dayofWeek["short"] + "- " + str(
self.rec.rating) + "/"
tempTweet += self.weatherObj.weatherStatus + "/Morn " + str(
math.ceil(self.weatherObj.mornTemp)) + "°F/Eve " + str(
math.ceil(self.weatherObj.eveTemp)) + "°F"
if (self.rec.weatherEmoji):
tempTweet += self.rec.weatherEmoji + "\n"
self.tweetContents = "THIS WEEK'S CONDITIONS:\n" + tempTweet
self.addHashTag()
TweetPublisher(self.tweetContents)
def main():
if request.method == 'GET':
return render_template('index.html')
if request.method == 'POST':
movie_name = request.form['movie_name']
movie_name = movie_name.title()
print(movie_name)
if movie_name not in data['original_title'].unique():
return render_template('negative.html')
else:
res = Recommendation.results(movie_name)
return render_template('positive.html',
movie_names=res['Title'].tolist(),
director=res['Director'].tolist(),
search_name=movie_name)
def slider_response( self ):
Property_dict[ "Melee" ] = self.Melee.value()
Property_dict[ "Physical" ] = self.Physical.value()
Property_dict[ "Damage" ] = self.Damage.value()
Property_dict[ "Solo" ] = self.Solo.value()
Property_dict[ "Team" ] = self.Team.value()
Property_dict[ "Range" ] = self.Range.value()
Property_dict[ "Magical" ] = self.Magical.value()
Property_dict[ "Tank" ] = self.Tank.value()
Property_dict[ "Support" ] = self.Support.value()
#self.career_recommendation = []
#self.friend_recommendation = []
self.career_recommendation, self.friend_recommendation = Recommendation.getInput( Property_dict )
self.comboBox.clear()
self.comboBox.addItems( self.friend_recommendation )
pic = QtGui.QPixmap(self.pic_path + self.career_recommendation[0]+'.png')
pic = pic.scaled(self.graphicsView.size())
self.QGraphicsScene.clear()
self.QGraphicsScene.addPixmap(pic)
self.commandLinkButton.setText(self.career_recommendation[0])
pic = QtGui.QPixmap(self.pic_path + self.career_recommendation[1]+'.png')
pic = pic.scaled(self.graphicsView_2.size())
self.QGraphicsScene_2.clear()
self.QGraphicsScene_2.addPixmap(pic)
self.commandLinkButton_2.setText(self.career_recommendation[1])
pic = QtGui.QPixmap(self.pic_path + self.career_recommendation[2]+'.png')
pic = pic.scaled(self.graphicsView_3.size())
self.QGraphicsScene_3.clear()
self.QGraphicsScene_3.addPixmap(pic)
self.commandLinkButton_3.setText(self.career_recommendation[2])
pic = QtGui.QPixmap(self.pic_path + self.career_recommendation[3]+'.png')
pic = pic.scaled(self.graphicsView_4.size())
self.QGraphicsScene_4.clear()
self.QGraphicsScene_4.addPixmap(pic)
self.commandLinkButton_4.setText(self.career_recommendation[3])
pic = QtGui.QPixmap(self.pic_path + self.career_recommendation[4]+'.png')
pic = pic.scaled(self.graphicsView_5.size())
self.QGraphicsScene_5.clear()
self.QGraphicsScene_5.addPixmap(pic)
self.commandLinkButton_5.setText(self.career_recommendation[4])
def note(request, heading=""):
if heading:
try:
new_note = Ribbit.objects.get(heading=heading)
form = RibbitForm(request.POST or None, instance=new_note)
print 'line 142'
global_heading = heading
except Ribbit.DoesNotExist:
redirect('/')
return render(request, 'detailed_note.html', {
'ribbit': new_note,
'form': form
})
if request.POST.get('extract'):
extractor = rake.Rake(
'/Users/seda/Xtrify/ribbit/ribbit_app/SmartStoplist.txt')
note = Ribbit.objects.get(heading=request.POST['heading'])
keywords = extractor.run(note.content)
keywords = [str(keywords[x][0]) for x in range(len(keywords))]
keywords = str(keywords)[1:-1]
note.keywords = keywords
note.save()
h = request.POST['heading']
return redirect('/note/' + h)
if request.POST.get('recommend'):
rec = Recommendation.Recommendation()
note = Ribbit.objects.get(heading=request.POST['heading'])
keywords = note.keywords
keywords = re.sub('[\']', '', keywords)
keywords = keywords.split(',')
results = rec.search(keywords)
results.append('asdasd')
results.append('adasda')
print results
return render(request, 'recommendation.html', {'results': results})
if request.method == "POST":
ribbit_form = RibbitForm(
request.POST,
instance=Ribbit.objects.get(heading=request.POST['heading']))
#ribbit_form = RibbitForm(request.POST, instance=Ribbit.objects.get(heading=global_heading))
if ribbit_form.is_valid():
ribbit_form.save()
return redirect('/')
def validate(job_title, df_job_post, df_course, course_num=5):
'''
check the accuracy of first 5 recommended courses if the job title of the poster is in the suggested user for the courses
'''
related = 0
irrelated = 0
df_course2 = df_course[pd.notnull(df_course.checked_job)]
for job_post in df_job_post.iterrows():
df_recommed_course = rmd.recommendation(job_post, df_course2,
course_num)
df_recommed_course['relevancy'] = df_recommed_course.checked_job.apply(
lambda x: True if job_title in x else False)
a = df_recommed_course.relevancy.sum()
related += a
irrelated += course_num - a
accuracy = related / (related + irrelated)
return accuracy
def note(request, heading=""):
if heading:
try:
new_note = Note.objects.get(heading=heading)
form = NoteForm(request.POST or None, instance=new_note)
print 'line 142'
global_heading = heading
except Note.DoesNotExist:
redirect('/')
return render(request, 'detailed_note.html', {
'note': new_note,
'form': form
})
if request.POST.get('extract'):
extractor = rake.Rake(os.getcwd() + '/note_app/SmartStoplist.txt')
note = Note.objects.get(heading=request.POST['heading'])
keywords = extractor.run(note.content)
keywords = [str(keywords[x][0]) for x in range(len(keywords))]
keywords = str(keywords)[1:-1]
note.keywords = keywords
note.save()
h = request.POST['heading']
return redirect('/note/' + h)
if request.POST.get('recommend'):
rec = Recommendation.Recommendation()
note = Note.objects.get(heading=request.POST['heading'])
keywords = note.keywords
keywords = re.sub('[\']', '', keywords)
keywords = keywords.split(',')
results = rec.recommend(keywords)
print keywords
return render(request, 'recommendation.html', {'results': results})
if request.method == "POST":
note_form = NoteForm(
request.POST,
instance=Note.objects.get(heading=request.POST['heading']))
#note_form = NoteForm(request.POST, instance=Note.objects.get(heading=global_heading))
if note_form.is_valid():
note_form.save()
h = request.POST['heading']
return redirect('/note/' + h)
def WeekendTweet(self):
tempTweet = ""
for x in range(1, 3):
self.weatherObj = Weather(GPSLOCATION, AIRPORT, x)
self.rec = Recommendation.Recommendation(self.weatherObj)
tempTweet += self.weatherObj.dayofWeek["long"] + "- " + str(
self.rec.rating) + "/"
tempTweet += self.weatherObj.weatherStatus + "/Morn " + str(
math.ceil(self.weatherObj.mornTemp)) + "°F/Eve " + str(
math.ceil(self.weatherObj.eveTemp)) + "°F"
if ((self.weatherObj.rainText)
and (self.weatherObj.weatherStatus != "trace rain")):
tempTweet += "/" + self.weatherObj.rainText
if (self.rec.weatherEmoji):
tempTweet += self.rec.weatherEmoji + "\n"
#Get the emoji here
self.tweetContents = "THIS WEEKEND: \n" + tempTweet
self.addHashTag()
TweetPublisher(self.tweetContents)
def main(argv):
args = parser.parse_args(argv[1:])
# train_set, test_set = split_test_set(get_ratings(), frac=0.3, random=True)
# data = get_ratings('data/ratings_test.csv')
data = get_ratings(args.train)
# data = hide_rating(data, frac=0.3)
k, pred_user, pred_item = 10, '140756691', ''
if args.method == 'cf':
item_based = model_recomm.ItemBased(data)
recommend_list = item_based.top_k(user=pred_user, k=k, sim_fn=item_based.sim_cos)
elif args.method == 'mf':
model = train_mf(data, num_steps=args.num_steps)
recommend_list = model.top_k(user=pred_user, k=k)
else:
raise Exception('Unsupport method %s' % args.method)
# 140756691
print(recommend_list)
def train_mf(df, num_steps=10000):
if 'real_rating' in df.columns:
train_df = df[df['rating'] > 0]
else:
train_df = df
def _set_index(df, keys):
"""
Set index of given key (after drop duplicates)
:param df:
:param keys:
:return:
"""
for key in keys:
unique_df = df.drop_duplicates([key]).reset_index()
df.insert(len(df.columns), key + '_index', np.zeros(len(df), dtype=np.int32))
for i, row in unique_df.iterrows():
origin_index = list(df[df[key] == row[key]].index)
df.loc[origin_index, key + '_index'] = i
_set_index(train_df, ['item', 'user'])
# code.interact(local=locals())
total_item, total_user = len(train_df.drop_duplicates(['item'])), len(train_df.drop_duplicates(['user']))
estimator = model_recomm.MatrixFactorizationEstimator(
R_shape=(total_item, total_user),
num_features=10,
model_dir="models/mf/"
)
input_fn = tf.estimator.inputs.pandas_input_fn(
x=train_df, y=train_df['rating'], batch_size=2, num_epochs=None, shuffle=True
)
estimator.train(input_fn=input_fn, steps=num_steps)
return estimator
def generateRecommdation(name, email, phoneno):
data = Recommendation.RecommendationDataMap()
data.name = name
data.email = email
data.phoneno = phoneno
data.tier_city = tier_city_map[get_int_random(1, 3)]
data.count_travel = get_int_random(1, 20)
data.count_pol = get_int_random(1, 20)
data.count_auto = get_int_random(1, 20)
data.count_shop = get_int_random(1, 20)
like = []
random_no = get_int_random(5, 10)
choosen_array = []
while len(choosen_array) != random_no:
y = get_int_random(0, len(likes_insight) - 1)
if y not in choosen_array:
choosen_array.append(y)
like.append(likes_insight[y])
x = get_int_random(2, 15)
like.append(x)
data.likes_insights = like
genre = []
random_no = get_int_random(2, 8)
choosen_array = []
while len(choosen_array) != random_no:
y = get_int_random(0, len(movie_genre) - 1)
if y not in choosen_array:
choosen_array.append(y)
genre.append(movie_genre[y])
x = get_int_random(2, 15)
genre.append(x)
data.movie_genre = genre
data.purchased_car = cars[get_int_random(0, len(cars) - 1)]
return data
def hide_rating(data, frac=0.3):
"""
Hide some ratings for evaluate
:param data:
:param frac: how many ratings should be hidden
:return:
DataFrame with additional column `real_rating` for origin ratings,
and some `rating` assigned by 0
"""
df = data
if 'rating' not in df:
raise model_recomm.ColumnNotFoundError('rating')
if 'real_rating' in df:
df['real_rating'] = np.array(df['rating'])
else:
df.insert(len(df.columns), 'real_rating', np.array(df['rating']))
if frac > 0:
# hide top `frac` ratings for every user
def _hide(df, frac):
for i, user in df['user'].drop_duplicates().items():
user_ratings = df[df['user'] == user]
hide_len = int(len(user_ratings) * 0.3)
if hide_len == 0:
continue
index = user_ratings.index[:hide_len]
df.loc[index, 'rating'] = 0
return df
df = _hide(df, frac)
df = df.sample(frac=1).reset_index(drop=True)
return df
count = 5
distancia = 5
Desenha().desenhar(movies)
for f in movies:
if f == src:
print(f)
for similar in f.similar:
print('\t', similar)
print('*' * 100)
print('A partir do filme:')
print('\t', src)
print('Foram achados os seguintes filmes recomendados: ')
r = Recommendation.recommend(src, count)
for movie in r:
print('\t', movie)
Desenha().recomendados(src, r)
print('*' * 100)
print('A partir do filme:')
print('\t', src)
print('Com o gênero: ', genero)
print('Foram achados os seguintes filmes recomendados: ')
r2 = Recommendation.recommendInGenre(src, genero, count)
for movie in r2:
print('\t', movie)
Desenha().recomendados(src, r2)
print('*' * 100)
def __init__(self, user):
super(MainWindow, self).__init__() # 使用super函数可以实现子类使用父类的方法
self.setWindowTitle("豆瓣电影推荐系统")
self.setWindowIcon(QIcon('../douban.jpg')) # 设置窗口图标
self.resize(1400,800)
self.user_df = pd.read_csv('../data/douban_users.csv')
self.user_df = self.user_df.iloc[:,[1,2,3]]
self.movies_df = pd.read_csv('../data/doubanMovies.csv', encoding='utf-8')
self.movies_df = self.movies_df.iloc[:,[0,1,6,15,16]]
self.movies_df = self.movies_df.drop_duplicates(subset='url')
self.movies_df = self.movies_df.rename(columns={'Unnamed: 0': 'Movie_ID'})
self.user = user
self.user_movie_num = len(list(self.user_df[self.user_df['user_id'] == user].movie_id))
self.user_movie_num_show = str(self.user_movie_num)
self.USER_PWD = dict()
self.USER_Label = dict()
f = open('../data/users_info.csv', 'a+', encoding='utf-8')
f.seek(0) # 文件指针指向开头
for line in f:
self.USER_PWD[line.split(',')[0]] = line.split(',')[1].strip() # 存入用户名密码
try:
self.USER_Label[line.split(',')[0]] = line.split(',')[2].strip() # 存入用户标签
except:
self.USER_Label[line.split(',')[0]] = None
f.close()
self.user_label = self.USER_Label[user]
self.welcome_label = QLabel(self)
self.welcome_label.setText("<h1>欢迎您! " + self.user + "</h1>")
self.recommend_label = QLabel("<h1>根据您的喜好为您推荐以下电影:</h1>", self)
self.recommend_table = QTableWidget(self)
self.hot_movies_label = QLabel("<h1>热门电影:</h1>", self)
self.hot_movies_table = QTableWidget(self)
self.user_center = QPushButton("个人主页", self)
self.user_center.clicked.connect(lambda: self.show_user_center(user, self.user_movie_num_show, self.user_label))
self.movie_detailed_button = QPushButton("电影详细页面", self)
self.movie_detailed_button.clicked.connect(self.movie_detailed)
self.search_button = QPushButton("电影搜索", self)
self.search_button.clicked.connect(self.search)
self.hot_rec_movies = Recommendation.rec_hot_movies() # 热门电影
self.rec_movies = Recommendation.find_user_like(self.user) # 推荐电影
self.v1_layout = QVBoxLayout()
self.v2_layout = QVBoxLayout()
self.h1_layout = QHBoxLayout()
self.h2_layout = QHBoxLayout()
self.h_layout = QHBoxLayout()
self.h1_layout.addWidget(self.welcome_label)
self.h1_layout.addWidget(self.user_center)
self.v1_layout.addLayout(self.h1_layout)
self.v1_layout.addWidget(self.recommend_label)
self.v1_layout.addWidget(self.recommend_table)
self.h2_layout.addWidget(self.hot_movies_label)
self.h2_layout.addWidget(self.movie_detailed_button)
self.h2_layout.addWidget(self.search_button)
self.v2_layout.addLayout(self.h2_layout)
self.v2_layout.addWidget(self.hot_movies_table)
self.h_layout.addLayout(self.v1_layout)
self.h_layout.addLayout(self.v2_layout)
self.setLayout(self.h_layout)
self.rec_movies_table_init()
self.hot_movies_table_init()
self.set_hot_movie_table(self.hot_rec_movies)
self.set_rec_movies_table(self.rec_movies)
def getUserRecommendation():
global currentUser
trackObjs = Recommendation.getUserReccomendation(currentUser,
currentSentimentScore)
return trackObjs
1) Created Iterative Version of the Kosaraju Algoritihim
"""
kso = Kosaraju(tEdge)
kso.adjancency_list()
kso.dfsiLoop()
kso.transpose_graph()
print(kso.dfsiRev())
"""
Task 4: Recommendation Engine
1) Using tiny directed graph, due to memory constraints for repl.it
"""
Karan = Recommendation(E, 3)
Karan.adjacency_matrix()
Karan.matrix_multiplication()
print(Karan.length_k_chains(0, 3))
"""
Bonus Material: Detect Cycles in directed Graphs
"""
cycles_in_graph = cycle_DFS_wrapper(tEdge)
nocyc = 0
cyc = 0
for key in cycles_in_graph:
if cycles_in_graph[key] == 'Cycles Exist at the specified Node':
cyc = cyc + 1
print('Number of users who have rated 20 or more items =', len(df_final))
print('Number of unique USERS in final data = ', df_final['UserId'].nunique())
print('Number of unique ITEMS in final data = ', df_final['ProductId'].nunique())
stream = df_final.Score.to_list()
L, counter = MisraGries.misra_gries(stream)
print('Final Counter for Stream is :', counter)
print('Final location/label for Stream is :', L)
train_data, test_data = train_test_split(df_final, test_size = 0.3, random_state=0)
print("Test data shape: ", test_data.shape)
print("Train data shape: ", train_data.shape)
print("The top 5 popular recommendations:")
print(Recommendation.recommend(df_final))
df_CF = pd.concat([train_data, test_data]).reset_index()
p_df = pd.pivot_table(df_CF, index=['UserId'], columns='ProductId', values="Score")
p_df.fillna(0, inplace=True)
p_df['user_index'] = np.arange(0, p_df.shape[0], 1)
p_df.set_index(['user_index'], inplace=True)
# SVD
U, sigma, Vt = svds(p_df, k=20)
sigma = np.diag(sigma)
all_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt)
# Predicted ratings
preds_df = pd.DataFrame(all_user_predicted_ratings, columns = p_df.columns)
from elasticsearch import Elasticsearch
#TODO: Configuration
host = "http://*****:*****@fantasticsearch.route('/')
def index():
term = "*"
results = performQuery(term, "", 0)
return render_template('index.html', results=results, term=term, aggregationFields = aggregationFields, page=1, listSelection = listSelection)
@fantasticsearch.route('/search')
def search():
def train( train_data_dir, labels, labels_num ):
Recommendation.train( train_data_dir, labels, labels_num )
def recommendation(self):
self.rec = Recommendation.Recommendation(self.weatherObj)
def PMTweet(self):
self.weatherObj = Weather(GPSLOCATION, AIRPORT, 1)
self.rec = Recommendation.Recommendation(self.weatherObj)
self.formTweet()
TweetPublisher(self.tweetContents)
