| import os | |
| import numpy as np | |
| import math | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import warnings | |
| class Helper(): | |
| def __init__(self, train, test, songs, ds): | |
| self.ds = ds | |
| self.train = train | |
| self.test = test | |
| self.songs = songs | |
| self.m2v_songs = self.songs.m2v.tolist() | |
| self.sm2v_songs = self.songs.sm2v.tolist() | |
| self.songs_ix = { v:k for k,v in enumerate(songs.index, 0) } | |
| self.ix_songs = { k:v for k,v in enumerate(songs.index, 0) } | |
| self.ix_users = { v:k for k,v in enumerate(np.concatenate([train.index.values, test.index.values]).tolist(), 0) } | |
| self.num_users = len(self.ix_users) | |
| self.num_songs = len(songs.index) | |
| self.ix_pref = { v:self.u_pref(k) for (k,v) in self.ix_users.items() } | |
| self.ix_u_songs = { v:self.unique_songs(k) for (k,v) in self.ix_users.items() } | |
| def user_sessions(self, user): | |
| history = self.test.loc[user, 'history'] | |
| return [(s[:len(s)//2], s[len(s)//2:]) for s in history] | |
| def song_ix(self, song): | |
| return self.songs_ix[song] | |
| def ix_user(self, ix): | |
| return self.ix_users[ix] | |
| def unique_songs(self, user): | |
| if user in self.train.index: | |
| history = self.train[self.train.index == user]['history'].values[0] | |
| if user in self.test.index: | |
| history = self.test[self.test.index == user]['history'].values[0] | |
| flat_history = [song for session in history for song in session] | |
| unique_songs = list(set(flat_history)) | |
| return unique_songs | |
| def u_pref(self, user): | |
| if user in self.train.index: | |
| history = self.train[self.train.index == user]['history'].values[0] | |
| if user in self.test.index: | |
| history = self.test[self.test.index == user]['history'].values[0] | |
| history = [s[:len(s)//2] for s in history] | |
| flat_history = [song for session in history for song in session] | |
| flat_history = [self.songs.loc[song, 'm2v'] for song in flat_history] | |
| mean = np.mean(flat_history, axis=0) | |
| return mean | |
| def c_pref(self, songs): | |
| flat_vecs = self.songs.loc[songs, 'sm2v'].tolist() | |
| return np.mean(np.array(flat_vecs), axis=0) | |
| def get_n_largest(self, cos,n): | |
| songs = self.songs.index.values | |
| index = np.argpartition(cos, -n)[-n:] | |
| return songs[index] | |
| def uu_matrix(self): | |
| if os.path.isfile('tmp/{}/matrix_users.npy'.format(self.ds)): | |
| return np.load('tmp/{}/matrix_users.npy'.format(self.ds)) | |
| matrix_users = np.zeros((self.num_users, self.num_users)) | |
| for ix in range(self.num_users): | |
| u_array = np.array([self.ix_pref[i] for i in range(self.num_users)]) | |
| y_array = np.zeros(self.num_users) | |
| for j in range(self.num_users): | |
| y_array[j] = math.sqrt(len(self.ix_u_songs[ix]) + len(self.ix_u_songs[j])) | |
| cos = cosine_similarity(self.ix_pref[ix].reshape(1, -1), u_array) | |
| val = np.sum([cos, y_array], axis=0) | |
| matrix_users[ix] = np.divide(np.ones(val.shape), val) | |
| np.save('tmp/{}/matrix_users'.format(self.ds), matrix_users) | |
| return matrix_users | |
| def us_matrix(self): | |
| if os.path.isfile('tmp/{}/matrix_user_songs.npy'.format(self.ds)): | |
| return np.load('tmp/{}/matrix_user_songs.npy'.format(self.ds)) | |
| matrix_u_songs = np.zeros((self.num_users, self.num_songs)) | |
| for u in list(self.ix_u_songs.keys()): | |
| songs = self.ix_u_songs[u] | |
| songs_ids = [self.songs_ix[s] for s in songs] | |
| y_array = np.zeros(self.num_songs) | |
| y_array[songs_ids] = 1 | |
| matrix_u_songs[u] = y_array | |
| np.save('tmp/{}/matrix_user_songs'.format(self.ds), matrix_u_songs) | |
| return matrix_u_songs | |