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intrinsic_eval_run.py
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intrinsic_eval_run.py
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import os
from ParentChildEvaluate import ParentChildEvaluate
from PlotEmbTypes import PlotEmbTypes
from ParseInterPro2GO import ParseInterPro2GO
from GOEvaluate import GOEvaluate
from GOSimEvaluate import GOSimEvaluate
from DomainXMLParser import DomainXMLParser
from EC_SCOP_Evaluate import EC_SCOP_Evaluate
from utils import write_random_vectors, create_dir
import itertools
import matplotlib.pyplot as plt
import numpy as np
print("=== Intrinsic Evaluation ===")
### ###
### Random domain vectors ###
### ###
"""
print("=== Create random domain vectors to test significance of results ===")
window = [2]
is_skipgram = [0] #[0,1]
emb_dim = [50, 100, 200] #[50,100,200]
epochs = [5]
all_emb_instances = list(itertools.product(window, is_skipgram,emb_dim, epochs))
### Embedding type###
### Linear ###
# model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap" # no redundant domains with gap
model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap" #no overlap
rand_model_path = os.path.join(model_path, "random")
create_dir(rand_model_path)
for emb_instance in all_emb_instances:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(
emb_instance[1]) + "_hierSoft0" + "_dim" + str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
model_file = os.path.join(model_path, model_file_name)
write_random_vectors(rand_model_path,model_file)
print("=== ===")
"""
### ###
### ParentChild ###
### ###
"""
print("1. Evaluate embeddings: Precision for ParentChild relation")
# create all trained embeddings possible instances
###dom2vec###
window = [2] # [2, 5]
is_skipgram = [1] # [0, 1] #0: CBOW, 1: Skipgram
emb_dim = [200] # [50, 100, 200]
epochs = [5] # [e for e in range(5, 55, 5)]
###random vec###
#window = [2]
#is_skipgram = [0]
#emb_dim = [50, 100, 200]
#epochs = [5]
all_emb_instances = list(itertools.product(window, is_skipgram, emb_dim, epochs))
### Embedding type###
### Linear ###
model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap" # no redundant domains with gap
# model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap" #no overlap with gap
load_rand_vec = False #True # load dom2vec or random vectors
if load_rand_vec:
model_path = os.path.join(model_path, "random")
data_path = model_path
parent_child_eval = ParentChildEvaluate(data_path)
# create the tree
interpro_tree_path = "/home/damian/Desktop/domains/interpro_tree"
interpro_tree_file_name = "ParentChildTreeFile.txt"
interpro_tree_file = os.path.join(interpro_tree_path, interpro_tree_file_name)
parsed_tree_file_name = "interpro_parsed_tree.txt" # Output file
save_parsed_tree = False
parent_child_eval.parse_parent_child_file(interpro_tree_file, interpro_tree_path, parsed_tree_file_name, save_parsed_tree)
is_model_bin = False
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w2" + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" + str(
emb_instance[2]) + "_e5" + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(
emb_instance[1]) + "_hierSoft0" + "_dim" + str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
model_file = os.path.join(model_path, model_file_name)
parent_child_eval.load_emb_model(model_file, is_model_bin)
plot_histograms = True
save_diagnostics = False
parent_child_eval.get_nn_calculate_precision_recall_atN(100, plot_histograms, save_diagnostics)
print("---")
print("--- ---")
"""
### ###
### GOEvaluate ###
### ###
### ###
### Preprocess interpro2go.txt for GO ###
### ###
"""
print("interpro2GO -> interpro2GO tabs")
interpro2go = "/home/damian/Desktop/domains/go/interpro2go.txt"
# data_path = "/home/damian/Desktop/domains/go/human"
# species_domains = "interpro_dom_human.txt"
# species_name = "human"
# data_path = "/home/damian/Desktop/domains/go/ecolik12"
# species_domains = "interpro_dom_ecolik12.txt"
# species_name = "ecolik12"
data_path = "/home/damian/Desktop/domains/go/yeast"
species_domains = "interpro_dom_yeast.txt"
species_name = "yeast"
# data_path = "/home/damian/Desktop/domains/go/malaria"
# species_domains = "interpro_dom_malaria.txt"
# species_name = "malaria"
prepareInterPro2GO = ParseInterPro2GO(data_path, interpro2go, species_domains, species_name)
prepareInterPro2GO.convert_go_labels(keep_only_MF=True)#get first level GO annotations as labels
"""
### ###
### Evaluate embeddings with GO molecular function ###
### ###
# print("Predict 1-level GO annotation using k-NN -> evaluate embeddings")
# create all trained embeddings possible instances
### dom2vec ###
# window = [2] #[2, 5]#[2, 5]
# is_skipgram = [0] #[0, 1] #[0, 1] #0: CBOW, 1: Skipgram
# emb_dim = [50] #[50, 100, 200]
# epochs = [5] #[e for e in range(5, 55, 5)]
### random vec ###
# window = [2]
# is_skipgram = [0]
# emb_dim = [50, 100, 200]
# epochs = [5]
#
# all_emb_instances = list(itertools.product(window, is_skipgram, emb_dim, epochs))
# is_model_bin = False
### ###
### human ###
### ###
"""
print("=== human ===")
data_path_human = "/home/damian/Desktop/domains/go/human"
domains_go_file = "interpro2go_human_MF_labels.csv"
go_evaluate_human = GOEvaluate(data_path_human, domains_go_file)
print("=== No overlap ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_human.run_classification(use_shortest, emb_file, is_model_bin)
print("---")
print("=== No redundant ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap"
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
# True: use label of parent with lowest level (= 1) immediate child of root,
# False: use label of all parents with lowest level (=1)
use_shortest = False
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_human.run_classification(use_shortest, emb_file, is_model_bin)
print("---")
print("=== ===")
"""
### ###
### yeast ###
### ###
"""
print("=== yeast ===")
data_path_yeast = "/home/damian/Desktop/domains/go/yeast"
domains_go_file = "interpro2go_yeast_MF_labels.csv"
go_evaluate_yeast = GOEvaluate(data_path_yeast, domains_go_file)
print("=== No overlap ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) +"_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_yeast.run_classification(use_shortest, emb_file, is_model_bin)
print("=== No redundant ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap"
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_yeast.run_classification(use_shortest, emb_file, is_model_bin)
print("=== ===")
"""
### ###
### ecolik12 ###
### ###
"""
print("=== ecolik12 ===")
data_path_ecoli = "/home/damian/Desktop/domains/go/ecolik12"
domains_go_file = "interpro2go_ecolik12_MF_labels.csv"
go_evaluate_ecoli = GOEvaluate(data_path_ecoli, domains_go_file)
print("=== No overlap ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_ecoli.run_classification(use_shortest, emb_file, is_model_bin)
print("=== No redundant ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap"
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_ecoli.run_classification(use_shortest, emb_file, is_model_bin)
print("=== ===")
"""
### ###
### malaria ###
### ###
"""
print("=== malaria ===")
data_path_malaria = "/home/damian/Desktop/domains/go/malaria"
domains_go_file = "interpro2go_malaria_MF_labels.csv"
go_evaluate_malaria = GOEvaluate(data_path_malaria, domains_go_file)
print("=== No overlap ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_malaria.run_classification(use_shortest, emb_file, is_model_bin)
print("=== No redundant ===")
emb_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap"
if load_rand_vec:
emb_path = os.path.join(emb_path, "random")
use_shortest = False # use label of parent with shortest level
for emb_instance in all_emb_instances:
print(emb_instance)
# set up the embedding file
if load_rand_vec:
print("Load random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
emb_file = os.path.join(emb_path, model_file_name)
go_evaluate_malaria.run_classification(use_shortest, emb_file, is_model_bin)
print("=== ===")
"""
### ###
### EC & SCOPe ###
### ###
### ###
### Preprocess interpro.xml for EC and SCOPe ###
### ###
"""
#get association id -> EC and SCOPe
print("EC and SCOPe -> k-NN Performance")
### DomainXMLParser - Parse EC and SCOPe for interpro ###
print("1. Get interpro_id -> EC and SCOPe")
data_path = "/home/damian/Desktop/domains"
interpro_xml = "interpro.xml"
out_name = "interpro2EC_SCOPe.tab"
domainXMLParser = DomainXMLParser(data_path, interpro_xml, out_name)
domainXMLParser.parse_and_save_EC_SCOP()
"""
### ###
### Evaluate embeddings using EC & SCOPe ###
### ###
"""
data_path = "/home/damian/Desktop/domains"
dom_ec_scope_file = "interpro2EC_SCOPe.tab"
### Embedding type###
# create all trained embeddings possible instances
###dom2vec###
# window = [2,5] #[2, 5]
# is_skipgram = [0,1] #[0, 1] #0: CBOW, 1: Skipgram
# emb_dim = [50, 100, 200]
# epochs = [e for e in range(5, 55, 5)]
###random vec###
window = [2]
is_skipgram = [0]
emb_dim = [50, 100, 200]
epochs = [5]
all_emb_instances = list(itertools.product(window, is_skipgram, emb_dim, epochs))
"""
### ###
### SCOPe ###
### ###
"""
print("3. domain embeddings labeled by SCOPe -> k-NN")
### Linear ###
print("=== no overlap ===")
model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
model_path = os.path.join(model_path, "random")
for emb_instance in all_emb_instances:
print(emb_instance)
out_name = "scop_clusters.png"
use_ec = False
SCOP_ClusterEvaluate = EC_SCOP_Evaluate(data_path, dom_ec_scope_file, use_ec, out_name)
is_model_bin = False
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
model_file = os.path.join(model_path, model_file_name)
SCOP_ClusterEvaluate.run_classification(model_file, is_model_bin, dim_reduction_algo="", low_dim_size=10,
classifier_name="NN")
print("---")
print("=== no red gap ===")
model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
model_path = os.path.join(model_path, "random")
for emb_instance in all_emb_instances:
print(emb_instance)
out_name = "scop_clusters.png"
use_ec = False
SCOP_ClusterEvaluate = EC_SCOP_Evaluate(data_path, dom_ec_scope_file, use_ec, out_name)
is_model_bin = False
# set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + ".txt"
model_file = os.path.join(model_path, model_file_name)
SCOP_ClusterEvaluate.run_classification(model_file, is_model_bin, dim_reduction_algo="", low_dim_size=10,
classifier_name="NN") # run lle, pca,t-sne
print("---")
"""
### ###
### EC ###
### ###
"""
print("2. domain embeddings labeled by EC -> k-NN")
print("=== no overlap ===")
### Embedding type###
### Linear ###
model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_overlap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
model_path = os.path.join(model_path, "random")
for emb_instance in all_emb_instances:
print(emb_instance)
out_name = "scop_clusters.png"
use_ec = True
EC_ClusterEvaluate_no_overlap = EC_SCOP_Evaluate(data_path, dom_ec_scope_file, use_ec, out_name)
is_model_bin = False
#set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0"+"_dim" \
+ str(emb_instance[2])+"_e" + str(emb_instance[3]) + ".txt"
model_file = os.path.join(model_path, model_file_name)
mode_file = os.path.join(model_path, model_file_name)
EC_ClusterEvaluate_no_overlap.run_classification(model_file, is_model_bin, dim_reduction_algo="", low_dim_size=10,
classifier_name="NN") # run lle, pca,t-sne
print("---")
print("=== no redundant ===")
### Embedding type###
### Linear ###
model_path = "/home/damian/Documents/L3S/projects/linear_gap/no_red_gap"
load_rand_vec = True # load dom2vec or random vectors
if load_rand_vec:
model_path = os.path.join(model_path, "random")
for emb_instance in all_emb_instances:
print(emb_instance)
out_name = "ec_clusters.png"
use_ec = True
EC_ClusterEvaluate_no_red_gap = EC_SCOP_Evaluate(data_path, dom_ec_scope_file, use_ec, out_name)
is_model_bin = False
#set up the embedding file
if load_rand_vec:
print("Loading random vectors")
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0" + "_dim" \
+ str(emb_instance[2]) + "_e" + str(emb_instance[3]) + "_rand" + ".txt"
else:
model_file_name = "dom2vec" + "_w" + str(emb_instance[0]) + "_sg" + str(emb_instance[1]) + "_hierSoft0"+"_dim" \
+ str(emb_instance[2])+"_e" + str(emb_instance[3]) + ".txt"
model_file = os.path.join(model_path, model_file_name)
mode_file = os.path.join(model_path, model_file_name)
EC_ClusterEvaluate_no_red_gap.run_classification(model_file, is_model_bin, dim_reduction_algo="", low_dim_size=10,
classifier_name="NN") # run lle, pca,t-sne
print("---")
"""
print("=== * ===")
print("== *** ==")