def K_exact_match_Roja():
k_layer_ids = anchor.load_row_from_csv(k_layer_ids_file, 1)
k_layer_ids = anchor.cleaning_list(
k_layer_ids) #assigning Zero at - places
k_layer_ids[0] = "K_exact_match"
return k_layer_ids
def A_layer():
a_layer_ids = anchor.load_row_from_csv(k_layer_ids_file, 0)
a_layer_ids = anchor.cleaning_list(
a_layer_ids) #Not needed but still added
a_layer_ids[0] = "English_word_ids"
return a_layer_ids
def K_exact_mwe_word_align_csv():
k_mwe = []
try:
k_mwe_csv_file = sent_dir + '/K_exact_mwe_word_align.csv'
k_mwe = anchor.load_row_from_csv(k_mwe_csv_file, 0)
k_mwe = anchor.cleaning_list(k_mwe) #
k_mwe = convert_words_to_ids_in_list(k_mwe, h2w)
print(k_mwe)
#k_mwe.insert(0,"K_exact_mwe_word_align.csv")
except FileNotFoundError:
k_mwe = ['0'] * (no_of_eng_words + 1)
k_mwe[0] = 'K_exact_mwe_word_align.csv'
log.write("FILE MISSING: " + k_mwe_csv_file + "\n")
return k_mwe
def Kishori_exact_match_WSD_modulo():
dict_new = []
try:
kishori_csv = sent_dir + '/Exact_match_dict.csv'
dict_new = anchor.load_row_from_csv(kishori_csv, 2)
dict_new = convert_words_to_ids_in_list(dict_new, h2w)
dict_new.insert(0, "WSD_modulo")
except FileNotFoundError:
dict_new = ['0'] * (no_of_eng_words + 1)
dict_new[0] = "WSD_modulo"
log.write("FILE MISSING: " + kishori_csv + "\n")
return dict_new
def Transliteration_Dict():
roja_transliterate_list = []
try:
transl_csv = sent_dir + '/Transliterate1.csv'
dict_new = anchor.load_row_from_csv(transl_csv, 2)
dict_new = convert_words_to_ids_in_list(dict_new, h2w)
dict_new.insert(0, "Transliteration")
except FileNotFoundError:
dict_new = ['0'] * (no_of_eng_words + 1)
dict_new[0] = "Transliteration"
log.write("FILE MISSING: " + transl_csv + "\n")
return dict_new
def K_1st_letter_capital_word():
k_prop_list = []
try:
k_prop_csv_file = sent_dir + '/K_1st_letter_capital_word.csv'
k_prop = anchor.load_row_from_csv(k_prop_csv_file, 0)
k_prop = anchor.cleaning_list(k_prop)
k_prop = convert_words_to_ids_in_list(k_prop, h2w)
except FileNotFoundError:
k_prop = ['0'] * (no_of_eng_words + 1)
k_prop[0] = 'K_1st_letter_capital_word'
log.write("FILE MISSING: " + k_prop_csv_file + "\n")
return k_prop
def Domain_Specific_Alignment_Dict():
dict_new = []
try:
nupur_csv = sent_dir + '/Domain_Specific_Align_Dict.csv'
dict_new = anchor.load_row_from_csv(nupur_csv, 2)
dict_new = convert_words_to_ids_in_list(dict_new, h2w)
dict_new.insert(0, "Preprocessing")
except FileNotFoundError:
dict_new = ['0'] * (no_of_eng_words + 1)
dict_new[0] = "Preprocessing"
log.write("FILE MISSING: " + nupur_csv + "\n")
return dict_new
def K_dict_Roja():
k_dict_ids = anchor.load_row_from_csv(k_layer_ids_file, 5)
k_dit_ids = anchor.cleaning_list(k_dict_ids)
k_dict_ids[0] = "K_dict"
return (k_dict_ids)
def K_partial_Roja():
k_layer_partial_ids = anchor.load_row_from_csv(k_layer_ids_file, 3)
k_layer_partial_ids = anchor.cleaning_list(k_layer_partial_ids)
k_layer_partial_ids[0] = "K_partial"
return k_layer_partial_ids
def K_exact_without_vib_Roja():
k_exact_wo_vib_ids = anchor.load_row_from_csv(k_layer_ids_file, 2)
k_exact_wo_vib_ids = anchor.cleaning_list(k_exact_wo_vib_ids)
k_exact_wo_vib_ids[0] = "K_exact_without_vib"
return k_exact_wo_vib_ids
stdout=subprocess.PIPE,
shell=True).stdout.read()
# x1 = x.decode(encoding="utf-8", errors="strict")
x1 = x.decode("utf-8")
x2 = x1.split(")")
while "" in x2:
x2.remove("")
print(x2, len(x2), type(x2))
title = ["Resources"] + list(x2[:-1])
#------------------------------------------------------------------------------------
#Prashant's and Apratim's module
try:
prashant_csv = path_tmp + '/new_N1.csv'
# print(open(prashant_csv,'r').read())
N1_layer = a.load_row_from_csv(prashant_csv, 1)
print(N1_layer)
N1_layer.insert(0, "N1_layer")
# print(N1_layer)
print(N1_layer)
dfs.loc[dfs.index[-1] + 1] = N1_layer
potential_anchor = a.extract_row_from_df_as_list_by_column_name_and_cell_value(
dfs, 'Resources', 'Potential anchors (exact)')
starting_anchor = a.extract_row_from_df_as_list_by_column_name_and_cell_value(
dfs, 'Resources', 'Starting anchor')
potential_anchor_partial = a.extract_row_from_df_as_list_by_column_name_and_cell_value(
dfs, 'Resources', 'Potential anchors (partial)')
potential_anchor_guess = a.extract_row_from_df_as_list_by_column_name_and_cell_value(
dfs, 'Resources', 'Potential anchors (guess)')
import sys, os, anchor, csv
tmp_path = os.getenv('HOME_anu_tmp') + '/tmp/'
eng_file_name = sys.argv[1]
sent_no = sys.argv[2]
sent_dir = tmp_path + eng_file_name + "_tmp/" + sent_no
file_ = sent_dir + "/All_Resources.csv"
facts = sent_dir + "/facts.dat"
print(file_)
k_dict = anchor.load_row_from_csv(file_, 11)
wsd_modulo = anchor.load_row_from_csv(file_, 10)
print(k_dict)
print(wsd_modulo)
print('================')
with open(facts, 'w') as f:
for i in range(1, len(k_dict)):
print("(E_id-k_dict-mfs-wsd_modulo\t" + str(i) + "\t" +
k_dict[i].replace("_", '0') + '\tmfs\t' + wsd_modulo[i] + ')')
f.write("(E_id-k_dict-mfs-wsd_modulo\t" + str(i) + "\t" +
k_dict[i].replace("_", '0') + '\tmfs\t' + wsd_modulo[i] +
')\n')
