def calculate_metrics(df):
""" Calculates metrics at different k (1 to 10 + 20,30,40,50)"""
#print(df.columns)
klist = list(range(1, 11))
klist.extend([20, 30, 40, 50, 100, 200, 300, 500])
print(klist)
# 14 x 3 x 4 columns added for each
for k in tqdm(klist):
df['average_precision_p2v_{}'.format(k)] = df['p2v_binary'].apply(lambda x: average_precision(x, k))
df['recall_p2v_{}'.format(k)] = df[['p2v_binary', 'ground_truth']].apply(
lambda x: recall_at_k(x.p2v_binary, x.ground_truth, k), axis=1)
df['reciprocal_rank_p2v_{}'.format(k)] = df['p2v_binary'].apply(lambda x: reciprocal_rank(x, k))
df['ndcg_p2v_{}'.format(k)] = df['p2v_binary'].apply(lambda x: ndcg(x, k))
df['average_precision_d2v_{}'.format(k)] = df['d2v_binary'].apply(lambda x: average_precision(x, k))
df['recall_d2v_{}'.format(k)] = df[['d2v_binary', 'ground_truth']].apply(
lambda x: recall_at_k(x.d2v_binary, x.ground_truth, k), axis=1)
df['reciprocal_rank_D2v_{}'.format(k)] = df['d2v_binary'].apply(lambda x: reciprocal_rank(x, k))
df['ndcg_d2v_{}'.format(k)] = df['d2v_binary'].apply(lambda x: ndcg(x, k))
df.to_pickle('/home/ashwath/Programs/MAGCS/Pickles/paperwisemetrics_mag50_d2v_p2v_may23_df.pickle')
print("METRICS CALCULATED, time to calculate the means")
# Get the mean of all the index columns
df = df.drop(['p2v_recommendations', 'p2v_binary', 'd2v_recommendations', 'd2v_binary', 'ground_truth'], axis=1)
mean_series = df.mean()
mean_series.to_csv('/home/ashwath/Programs/MAGCS/Evaluation/meanmetrics_mag50_d2v_p2v_may21.tsv', sep='\t', index=True, header=False)
print("C'est fini.")
def get_scores(ds_bios, plays_full, plays_train, norm_plays_full, norm_plays_train,cf_model, tfIdfRecommender,artist_index, index_artist, methodKeys,kk):
NUSERS,NARTISTS = plays_full.shape
global the_user_id
completed = 0
new_completed = 0
lightUsers = get_rnd_rank(NUSERS,[],100)
ranks = {}
for user_id in lightUsers: #range(the_user_id,NUSERS):
the_user_id = user_id
print_progress(completed, user_id, NUSERS)
# Colaborative filtering rank
ranks['cf'] =[i for i,x in cf_model.recommend(user_id, norm_plays_train,N=max(kk) ) ]
# get history of artistid
user_history_indexs = (plays_train[user_id] > 1).nonzero()[1]
# mapped to artistnames from user artist history
user_history = [index_artist[artistid] for artistid in user_history_indexs]
# Content based rank
ranks['cb'] = get_cb_rank(ds_bios, user_history, tfIdfRecommender, artist_index,max(kk))
# Hybrid mixed rank
ranks['hb'] = mix(ranks['cf'], ranks['cb'])[:max(kk)]
# Random baseline rank
rnd_rank = get_rnd_rank(NARTISTS,user_history_indexs, max(kk))
scores = {}
relevants={}
rnd_relevants = []
upper_bound = 0
# Calculate relevants and scores for each method
for method in methodKeys:
scores[method] = []
relevants[method] = []
for artist_id in ranks[method]:
ground_truth = plays_full[user_id,artist_id]
relevants[method].append(1 if ground_truth > 1 else 0)
norm_ground_truth = norm_plays_full[user_id,artist_id]
scores[method].append(norm_ground_truth)
# Rnd Baseline
for artist_id in rnd_rank:
try:
ground_truth = plays_full[user_id,artist_id]
except:
ground_truth = 0
finally:
rnd_relevants.append(1 if ground_truth > 1 else 0)
# Upper Bound
x, nonzero = plays_full[user_id].nonzero()
for artist_id in nonzero:
ground_truth = plays_full[user_id,artist_id]
try:
train = plays_train[user_id,artist_id]
except:
train = 0
finally:
if(train == 0 and ground_truth > 1):
upper_bound += 1
# save user metrics
for k in kk:
rnd_baselines[k].append(sum(rnd_relevants[:k])/k)
upper_bounds[k].append(1 if upper_bound/k > 1 else upper_bound/k)
for method in methodKeys:
for k in kk:
diversities[method][k].update(ranks[method][:k])
precisions[method][k].append(sum(relevants[method][:k])/k)
ndcgs[method][k].append(metrics.ndcg_at_k(scores[method][:k], k))
mrrs[method][k].append(metrics.reciprocal_rank(relevants[method][:k]))
return rnd_baselines, upper_bounds, diversities, precisions, mrrs, ndcgs