拟议系统通过在旅游领域提供更具体的食物、酒店和旅游地点.zip

# -*- encoding='utf-8-sig' -*- import numpy as np from recommendations.models import Restaurant import pandas as pd import scipy from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn import tree from sklearn.feature_extraction import DictVectorizer from sklearn.cluster import KMeans from sklearn.metrics.pairwise import euclidean_distances def find_rating(nearby_rid, cuisine): print("I am starting to find rating algo") #print nearby_rid # First read csv files and store it in dataframe # Second convert dataframe to array df_restaurant = pd.read_csv('data/restaurant.csv', header=0) array_restaurant = df_restaurant.values #print array_restaurant df_cuisine = pd.read_csv('data/cuisine.csv', header=0) array_cuisine = df_cuisine.values # # Perform natural join on cuisine and restaurant based on key 'rid' and store it in dataframe # # convert that dataframe into an array # combine = df_cuisine.set_index('rid').join(df_restaurant.set_index('id')) # array_combine = combine.values # #print array_combine #--------------------------------------------------------------------------- # Select only those restaurant from all which are nearby # Convert 2d numpy array to 1d array. For eg. [[1, 2, 3]] into [1, 2, 3] nearby_rid = nearby_rid.ravel() filter_nearby = df_restaurant.loc[df_restaurant['id'].isin(nearby_rid)] array_filter_nearby = filter_nearby.values #print array_filter_nearby filter_cuisine_id = array_cuisine[array_cuisine[:,2] == 'Italian'] #print "I WANT THISSSSSSSSSS" #print filter_cuisine_id filter_cuisine_id = filter_cuisine_id[:,1] #print filter_cuisine_id.astype(int) filter_cuisine = filter_nearby.loc[filter_nearby['id'].isin(filter_cuisine_id.astype(int))] print(filter_cuisine) filter_cuisine = filter_cuisine.values #--------------------------------------------------------------------------- # Extract latitude and longitude of above filtered restaurant lat_long = filter_cuisine[:,2:4] #print lat_long # Apply clustering algo on filtered restaurant data kmeans = KMeans(n_clusters=3, random_state=0).fit(lat_long) # Cluster number for all the above filtered restaurant in which cluster they fall print(kmeans.labels_) #print kmeans.predict([[18.95618666,72.81199761], [18.99120402, 72.81458057]]) print("Clustering centre") print(kmeans.cluster_centers_) #---------------------------------------------------------------------------- # calculate distance of each cluster from user's current location distance = euclidean_distances([[19.044497, 72.8204535]], kmeans.cluster_centers_) print(np.transpose(distance)) print(len(distance)) # append cluster number with above distance array, for knowing which cluster distance is that # because after we are sorting these distances distance_cluster_centre = np.insert(np.transpose(distance), 1, np.array([0, 1, 2]), axis=1) print(distance_cluster_centre) # sorted distances print("sorted distance") arr = distance_cluster_centre[distance_cluster_centre[:,0].argsort()] #------------------------------------------------------------------------------ # make numpy array with columns [id, lat, long, rating, cid] # cid = cluster id id_after_cuisine = filter_cuisine[:,0] id_lat_long = np.insert(lat_long, 0, id_after_cuisine, axis=1) id_lat_long_cid = np.insert(id_lat_long, 3, kmeans.labels_ , axis=1) id_lat_long_rating_cid = np.insert(id_lat_long_cid, 3, filter_cuisine[:,8] , axis=1) print(id_lat_long_rating_cid) # convert above array to dataframe columns=['id','latitude','longitude','rating','cid'] df = pd.DataFrame(id_lat_long_rating_cid ,columns=columns) #----------------------------------------------------------------------------------------------- # SORT CLUSTER ACCORDING TO CLUSTER CENTRE DISTANCES FROM USER'S LOCATION # select [[12.313, 12.375843, 24.7364],[0, 2, 1]] - [[centre distances][cluster id]] print(np.array(arr[:,1][0])) #initialize empty dataframe sorted_cluster = pd.DataFrame() # sort cluster according to cluster centre distance for i in range(0, len(arr[:,1])): # dataframe of single cluster single_cluster = df.loc[df['cid'].isin(np.array( arr[:,1][i] ).ravel())] single_cluster = single_cluster.sort_values(by='rating', ascending=False) sorted_cluster = sorted_cluster.append(single_cluster) print(sorted_cluster) #df_groupby = sorted_cluster.groupby('cid') #print len(df_groupby) #for group in df_groupby: # print group #print df_groupby.sort_values('rating', ascending=False) #print df_groupby.get_group(0) # convert dataframe to array and extract only rid sorted_cluster_rid = sorted_cluster.as_matrix(columns=None)[:,0] # convert long datatype of rid into int return sorted_cluster_rid.astype(int) #df_groupby = df.groupby('cid') #print len(df_groupby) #for group in df_groupby: # print group #print df_groupby.sort_values('rating', ascending=False) #print df_groupby.get_group(0) #-------------------------------------------------------------------------------------------------- # featureset_all = np.delete(filter_cuisine, np.s_[2:10], axis=1) # print "CONVERT THIS ARRAY TO DATFRAMEEEEEEEEEEEEEE" # print featureset_all # #featureset_all = featureset_all[0:6,:] # featureset_X = np.delete(featureset_all, np.s_[0], axis=1) # print featureset_X # featureset_Y = np.delete(featureset_all, np.s_[1:], axis=1) # print featureset_Y # columns=['cuisine','homedelivery','smoking','alcohol','wifi', 'valetparking','rooftop'] # df = pd.DataFrame(featureset_X ,columns=columns) # print "CONVERTEDDDDDDDDDDDDDDDDDDD" # print df # cols_to_retain = ['cuisine', 'homedelivery', 'smoking', 'alcohol', 'wifi', 'valetparking', 'rooftop'] # #cols_to_retain = ['homedelivery', 'smoking', 'alcohol', 'wifi'] # feature = df[cols_to_retain].to_dict( orient = 'records' ) # print "DICTIONARYYYYYYYYY" # print feature # vec = DictVectorizer() # X = vec.fit_transform(feature).toarray() # print X # columns=['id'] # df = pd.DataFrame(featureset_Y ,columns=columns) # cols_to_retain = ['id'] # Y = df[cols_to_retain].to_dict( orient = 'records' ) # vec = DictVectorizer() # Y = vec.fit_transform(Y).toarray() # print Y # X_train, X_test, Y_train_labels, Y_test_labels = train_test_split(X, Y, test_size=0.3, random_state=100) # print "-----------Training feature---------------" # print X_train # print "------------Testing feature--------------" # print X_test # print "------------Training label--------------" # print Y_train_labels # print "-----------Testing label---------------" # print Y_test_labels # print "--------------------------" # clf_entropy = DecisionTreeClassifier(criterion = "gini", random_state = 100, max_depth=3, min_samples_leaf=5) # clf_entropy.fit(X_train, Y_train_labels) # print "Fitting done" # # Make predictions # y_pred_en = clf_entropy.predict(X_test) # print y_pred_en # # columns=['cuisine','homedelivery','smoking','alcohol','wifi', 'valetparking','rooftop'] # # df = pd.DataFrame([['Italian', 'yes', 'no', 'yes', 'no', 'no', 'no'], ['Italian', 'yes', 'no', 'yes', 'no', 'no', 'no']] ,columns=columns) # # cols_to_retain = ['cuisine', 'homedelivery', 'smoking', 'alcohol', 'wifi', 'valetparking', 'rooftop'] # # feature = df[cols_to_retain].to_dict( orient = 'records' ) # # print feature # # vec = DictVectorizer() # # user_input = vec.fit_transform(feature).toarray() #