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simKNNv2.py
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simKNNv2.py
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import numpy as np
import pandas as pd
import pickle
from warnings import warn
from tqdm.notebook import tqdm
class SimKNN:
def __init__(self, k, rho=0.4, beta=0.7, gamma=0.3, \
sim=["cosine", "idf", "cosine"], verbose=True):
'''
k : int \\
rho : float; 0.4(default) only for idf \\
alpha, beta, gamma : float
sim : list of length 3; ["cosine", "idf", "cosine"](default) \\
"idf", "amplification", "cosine", "position", etc or function \\
verbose : boolean
'''
self.id = None
self.title = None
self.songs = None
self.tags = None
self.freq = None # numpy.ndarray
self.X = None
# hyperparameter
self.k = k
self.rho = rho
self.beta = beta
self.gamma = gamma
# ---
self.sim = sim
self.verbose = verbose
self.__version__ = "2.0"
self._check()
def fit(self, x):
'''
x : pandas.DataFrame (columns=['id', 'plylst_title', 'songs', 'tags'])
'''
self.id = x['id'] # pandas.Series of int
# self.title = x['plylst_title'] # pandas.Series of string
self.songs = x['songs'].to_numpy() # pandas.Series of list
self.tags = x['tags'].to_numpy() # pandas.Series of list
del x
if self.sim[1] == "idf":
self.freq = np.zeros(707989, dtype=np.int64)
_playlist = tqdm(self.songs) if self.verbose else self.songs
for _songs in _playlist:
self.freq[_songs] += 1
def predict(self, X, start=0, end=None, inter=True, save_fname=None, save_interval=1000):
'''
parameters \\
X : pandas.DataFrame (columns=['id', 'plylst_title', 'songs', 'tags']) \\
start : int \\
end : int \\
inter : boolean; if predict songs and tags together or not
returns \\
pandas.DataFrame (columns=['id', 'songs', 'rel_songs', 'tags', 'rel_tags'])
'''
self.X_id = X['id'] # pandas.Series of int
# self.X_title = X['plylst_title'] # pandas.Series of string
self.X_songs = X['songs'].to_numpy() # pandas.Series of list
self.X_tags = X['tags'].to_numpy() # pandas.Series of list
del X
pred = pd.DataFrame(index=self.X_id.index, columns=["id", "songs", "rel_songs", "tags", "rel_tags"])
pred['id'] = pred['id'].astype('object')
pred['songs'] = pred['songs'].astype('object')
pred['rel_songs'] = pred['rel_songs'].astype('object')
pred['tags'] = pred['tags'].astype('object')
pred['rel_tags'] = pred['rel_tags'].astype('object')
if end:
_range = tqdm(range(start, end)) if self.verbose else range(start, end)
else:
_range = tqdm(self.X_id.index) if self.verbose else self.X_id.index
for uth in _range:
# interconnection check
if inter:
S = np.array([self._sim(uth, vth) for vth in self.id.index]) # similarities
else:
self.beta = 1
S = np.array([self._sim(uth, vth, target='songs') for vth in self.id.index])
top = S.argsort()[-self.k:]
norm = S[top].sum()
# predict songs
tracks = np.unique(np.concatenate(self.songs[top])) # all tracks in top k playlists
tracks = np.setdiff1d(tracks, self.X_songs[uth], assume_unique=True) # remove common songs
if tracks.size < 100:
print(f"{uth}(songs) {tracks.size}")
R = np.array([( track, np.sum([S[vth] if track in self.songs[vth] else 0 for vth in top]) / norm) \
for track in tracks])
del tracks
R = R[R[:, 1].argsort()][::-1][:100]
pred.at[uth, "id"] = self.X_id[uth]
pred.at[uth, "songs"] = R[:, 0].astype(np.int64)
pred.at[uth, "rel_songs"] = R[:, 1]
del R
# interconnection check
if inter: pass
else:
del S, top, norm
self.gamma = 1
S = np.array([self._sim(uth, vth, target='tags') for vth in self.id.index])
top = S.argsort()[-self.k:]
norm = S[top].sum()
# predict tags
stickers = np.unique(np.concatenate(self.tags[top]))
stickers = np.setdiff1d(stickers, self.X_tags[uth])
if stickers.size < 10:
print(f"{uth}(tags) {stickers.size}")
R = np.array([( sticker, np.sum([S[vth] if sticker in self.tags[vth] else 0 for vth in top]) / norm) \
for sticker in stickers])
del stickers
try:
R = R[R[:, 1].argsort()][::-1][:10]
pred.at[uth, "tags"] = R[:, 0]
pred.at[uth, "rel_tags"] = R[:, 1]
del S, top, norm, R
except Exception as e:
print(f"{uth} : {e}")
# temporary save
if save_fname and (uth + 1) % save_interval == 0:
self._save(pred, save_fname)
return pred
def _sim(self, uth, vth, target=None):
'''
uth : int; u is index of playlist in test.json \\
vth : int; v is index of playlist in train.json
'''
if hasattr(self.sim, '__call__'):
return self.sim(uth, vth)
# title
# title = 0 # FIXME
# if self.X_title[uth] == '':
# title = 0
# songs
if target == 'songs' or target == None:
if self.X_songs[uth] == []:
songs = 0
elif self.sim[1] == "idf":
u = self.X_songs[uth]
v = self.songs[vth]
freq = self.freq[np.intersect1d(u, v)]
freq = 1 / (((freq - 1) ** self.rho) + 1) # numpy!
songs = freq.sum() / ((len(u) ** 0.5) * (len(v) ** 0.5))
elif self.sim[1] == "cosine":
u = self.X_songs[uth]
v = self.songs[vth]
songs = np.intersect1d(u, v).size / ((len(u) ** 0.5) * (len(v).size ** 0.5))
# {{ add other similarities here }}
else:
songs = 0
# tags
if target == 'tags' or target == None:
if self.X_tags[uth] == [] or self.tags[vth] == []:
tags = 0
elif self.sim[2] == "idf":
tags = None
elif self.sim[2] == "cosine":
u = self.X_tags[uth] # list
v = self.tags[vth] # list
tags = np.intersect1d(u, v).size / ((len(u) ** 0.5) * (len(v) ** 0.5))
# {{ add other similarities here }}
else:
tags = 0
return (self.beta * songs) + (self.gamma * tags)
def _check(self):
if self.beta + self.gamma != 1:
warn("beta + gamma == 1 is recommended.")
if type(self.k) == type(1):
pass
else:
raise TypeError(self.k)
sim_keys = ["idf", "cosine"]
if type(self.sim) == list:
for sim in self.sim:
if not (sim in sim_keys):
raise KeyError(sim)
elif hasattr(self.sim, '__call__'):
pass
else:
raise KeyError(self.sim)
def _save(self, pred, save_fname):
with open(save_fname, 'wb') as f:
pickle.dump(pred, f)
def load(self, fname):
with open(fname, 'rb') as f:
saved = pickle.load(f)
return saved
if __name__=="__main__":
import pickle
with open("bin/Xs.p", 'rb') as f:
Xs = pickle.load(f)
simknn = SimKNN(k=200, sim=["cosine", "idf", "cosine"], beta=0.5, gamma=0.5)
simknn.fit(x=Xs[0])
start, end = 19, 22
pred = simknn.predict(X=Xs[1], start=start, end=end)
print(pred.loc[[i for i in range(start, end)], ["songs", "tags", "rel_tags"]])
pred = simknn.predict(X=Xs[1], start=start, end=end, inter=False)
print(pred.loc[[i for i in range(start, end)], ["songs", "tags", "rel_tags"]])