def main(): src_path = os.path.join(os.getcwd(), 'ratings.csv') res_path = os.path.join(os.getcwd(), 'preRatings.csv') predicted_data = pd.read_csv(res_path, header = 0, index_col = 0) int_col = [] for col in predicted_data.columns: icol = int(col) int_col.append(icol) predicted_data.columns = int_col movie_rated_num = pd.Series(index = predicted_data.columns) for i in predicted_data.columns.values: movie_rated_num[i] = predicted_data[i].dropna().count() movie_rated_num.sort() cuted_data = predicted_data.loc[ : , movie_rated_num[8500: ].index] print cuted_data.shape data_matrix = cuted_data.fillna(0).values for i in range(0, len(data_matrix)): for j in range(0, len(data_matrix[i])): if data_matrix[i][j]>3.5: data_matrix[i][j] = 2 elif data_matrix[i][j]<2.5: data_matrix[i][j] = 0 else: data_matrix[i][j] = 1 print data_matrix E_matrix = cs.getEMatrix(data_matrix) labels = spectral_clustering(E_matrix, n_clusters = 20) print labels ''' init_data = pd.read_csv(src_path, header = 0, index_col = 0) # Cause the type of columns that read in csv is str, we need to convert it into int int_col = [int(col) for col in init_data.columns] init_data.columns = int_col init_data = init_data.loc[predicted_data.index, predicted_data.columns] init_data_matrix = init_data.fillna(0).values ''' columns = ['userID', 'movieID', 'rating', 'timestamp'] ratings = pd.read_csv(src_path, header = 1, names = columns) data = ratings.pivot(index = 'userID', columns = 'movieID', values = 'rating') init_data = data.loc[cuted_data.index, cuted_data.columns] init_data_matrix = init_data.fillna(0).values dp.drawPicture(init_data_matrix, labels) '''
def main(): # Use predicted data to do clustering # Predicted_data = cf.CollaborativeFiltering(srcPath) src_path = os.path.join(os.getcwd(), 'datatestcv_3.csv') res_path = os.path.join(os.getcwd(), 'pretestDatacv_3.csv') predicted_data = pd.read_csv(res_path, header = 0, index_col = 0) # Cause the type of columns that read in csv is str, we need to convert it into int int_col = [] for col in predicted_data.columns: icol = int(col) int_col.append(icol) predicted_data.columns = int_col data_matrix = predicted_data.fillna(0).values print data_matrix # Change the data_matrix, we define that if user love this movie, he will rate this movie more than 3.5; # If user doesn't love this movie, he will rate this movie less than 2.5; # If user rates one movie 3, we define he think this movie is normal. for i in range(0, len(data_matrix)): for j in range(0, len(data_matrix[i])): if data_matrix[i][j]>3.5: data_matrix[i][j] = 2 elif data_matrix[i][j]<2.5: data_matrix[i][j] = 0 else: data_matrix[i][j] = 1 print data_matrix res = SpectralClustering(data_matrix, 6) result = res[0] print result[:100] print result[100:] init_data = pd.read_csv(src_path, header = 0, index_col = 0) # Cause the type of columns that read in csv is str, we need to convert it into int int_col = [int(col) for col in init_data.columns] init_data.columns = int_col init_data = init_data.loc[predicted_data.index, predicted_data.columns] init_data_matrix = init_data.fillna(0).values dp.drawPicture(init_data_matrix, result)
