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figure_6.py
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################################################################################
# ______ ______ __ #
# / _/ /____ ____ ___ / ____/___ ____ ________ ____ / /_ #
# / // __/ _ \/ __ `__ \ / / / __ \/ __ \/ ___/ _ \/ __ \/ __/ #
# _/ // /_/ __/ / / / / / / /___/ /_/ / / / / /__/ __/ /_/ / /_ #
# /___/\__/\___/_/ /_/ /_/ \____/\____/_/ /_/\___/\___/ .___/\__/ #
# /_/ #
# ______ __ __ ___ #
# / ____/___ ___ / /_ ___ ____/ /___/ (_)___ ____ _ #
# / __/ / __ `__ \/ __ \/ _ \/ __ / __ / / __ \/ __ `/ #
# / /___/ / / / / / /_/ / __/ /_/ / /_/ / / / / / /_/ / #
# /_____/_/ /_/ /_/_.___/\___/\__,_/\__,_/_/_/ /_/\__, / #
# /____/ credit: patorjk #
################################################################################
# Proj: Item Concept Embedding (ICE)
# File: figure_6.py
# Date: 06/02/2017
from ice_lib.utility import *
from ice_lib.preprocess import *
from ice_lib.retrieve import *
from ice_lib.evaluate import *
title = "Word-to-Song Retrieval Task (Concept-similar Word)"
xl = "$|W|$"
xt_list = ['1', '3', '5', '8', '10']
style_list = ['c*-', 'gd-', 'bo-', 'ys-', 'r^-']
# SECTION 1: Setup.
graph_path = "/home/LyricsRec/datasplit-700030/graph/"
lyric_path = "/home/LyricsRec/sourcefile/lyrics-cut.json"
latest_song_path = graph_path + "top1/post_uniq/sl_top1.edge" # top1 has the most abundant songs
lyric_dict = load_lyric_dict(lyric_path, 's')
avail_song_list = list(load_el_by_tag(latest_song_path, 's', True).keys())
# Step 0: Declare constant components.
mod_list = ["rand", "km", "w2v_", "sl_", "sll_exp_"]
top_list = ["top1", "top3", "top5", "top8", "top10"]
quo_list = [10, 50, 100]
query_list = ['w失落', 'w心痛', 'w想念', 'w深愛', 'w難過', 'w回家', 'w房間',
'w海邊', 'w火車', 'w花園', 'w夕陽', 'w日出', 'w日落', 'w月亮', 'w黑夜']
# Step 1: Generate plot label.
label_list = ["RAND", "KBR", "AVGEMB", "BPT", "ICE (exp-3)"]
label_dict = dict()
label_dict["plot/synonym_label.pdf"] = (label_list, style_list)
save_json_obj(label_dict, "plot/figure_6_label.json")
# Step 2:
stat_dict = dict()
rand_done = False
km_done = False
rand_mi_p = 0
km_mi_p = 0
for quo in quo_list:
print("At quota=", quo)
yl = "Precision@" + str(quo)
path = "plot/synonym_p@" + str(quo) + ".pdf"
yl_list = []
for mod in mod_list:
print("At quota:mod=%s:%s" % (quo, mod))
y_list = []
for top in top_list:
print("At quota:mod:top=%s:%s:%s" % (quo, mod, top))
# Step 2: Set survey & representation paths.
if mod=="rand":
if rand_done:
continue
else:
rand_done = True
fname = mod
elif mod=="km":
if km_done:
continue
else:
km_done = True
fname = mod
elif mod=="w2v_":
fname = mod + top
elif mod=="sl_":
fname = "unorm_2-undir_" + mod + top
elif mod=="sll_":
fname = "unorm_2-undir_" + mod + top + "x3"
else:
fname = "unorm_2-dir_" + mod + top + "x3"
repr_path = graph_path + "all_repr_api/" + fname + ".embd"
# SECTION 2: Load resources.
# Step 1: Set paths.
synonym_path = graph_path + top + "/" + "post_uniq/" + "ll_" + top + "x3.edge"
syn_dict = load_el_by_tag(synonym_path, 'w', True) # load LL edge list
# Load repr dicts.
if mod!="rand" and mod!="km":
if mod=="sll_exp_": # SLL exp
repr_tag = 'exp_w'
else:
repr_tag = 'w'
lyric_repr_dict = load_repr_by_tag(repr_path, repr_tag, 1)
song_repr_dict = load_repr_by_tag(repr_path, 's', 1)
rec_mat = generate_indexed_matrix(list(song_repr_dict.keys()), song_repr_dict)
# SECTION 3: Conduct the retrieval task.
rl_list = [] # list of rec list
for query in query_list:
# Step 1: Random.
if mod == "rand":
songs = retrieve_by_random(quo, avail_song_list)
# Step 2: Keyword-matching.
elif mod == "km":
songs = retrieve_by_keyword(quo, [query], avail_song_list, lyric_dict)
# Step 3: Representation, i.e. w2v, sl, sll, sll_exp, sll_nxm, w2v_exp
else:
if mod=="sll_exp_":
query = "exp_w" + query[1:] # replace 'w' tag with 'exp_w' tag
if query in lyric_repr_dict: # Rec ONLY if repr exists.
songs = retrieve_by_repr(quo, [query], lyric_repr_dict, rec_mat)
else:
songs = []
rl_list.append(songs)
# SECTION 4: Evaluate results.
query_num = len(query_list)
avail_sl_list = [avail_song_list]*query_num # list of lists of available songs
tp_fp_list = [quo]*query_num
copy_friendly = False
# 5-1: Evaluation query relevance.
ql_list = []
for q in query_list:
try:
ql_list.append(syn_dict[q])
except KeyError:
print("Keyword:" + q + "does NOT have synonym!", end="\n")
tp_list = count_keyword_containment(ql_list, rl_list, lyric_dict, "w")
mi_p = calculate_micro_precision(tp_list, tp_fp_list)
y_list.append(mi_p) # top
if mod=="rand":
rand_mi_p = mi_p
if mod=="km":
km_mi_p = mi_p
# Random and keyword-matching baselines are run only ONCE.
if mod=="rand":
y_list = len(top_list)*[rand_mi_p]
if mod=="km":
y_list = len(top_list)*[km_mi_p]
yl_list.append(y_list) # mod
stat_dict[path] = (title, xl, yl, xt_list, yl_list, style_list) # quo
save_json_obj(stat_dict, "plot/figure_6_stat.json")