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# coding: utf-8
# # Creating a Spanish Lemma-Frequency list from Open Source corpora
# In[1]:
import re
import os
import gzip
import time
import operator
import json
import logging
import pandas as pd
from pprint import pformat
import xml.etree.ElementTree as ET
# In[2]:
# set up logging
logging.basicConfig(filename="lemma_freq.log", format="%(asctime)s %(message)s", level=logging.DEBUG)"================= STARTED RUNNING =================")"")
# measure process time
t_proc = time.clock()
# measure real time elapsed
t_wall = time.time()
# ## Fetch many files
# In[3]:
base_dir = "corpora/OPUS_es"
subfolders = os.listdir(base_dir)
dig_deeper = "xml/es"
# In[ ]:
# fetches every OPUS file in all the subdirectories
file_list = []
for root, dirs, files in os.walk("corpora/OPUS_es"):
for file in files:
if file.endswith(".gz"):
file_list.append(os.path.join(root, file))
# In[6]:
# short list for testing
#test_list = file_list[:5]
# ## Initialize all things
# In[7]:
# the dicts that will hold all the info
lemma_freq = dict()
what_lems = dict()
# a regex to exclude punctuation
non_words = re.compile(r"[\W\d]+")
# In[8]:
def fetch_frequency(tree, lemma_freq, what_lems, non_words):
# initialize some stats
start_time = time.time()
s_count = 0
t_count = 0
# root the tree
root = tree.getroot()
# root[1] accesses the <body> of the XML file, where the text resides
for sent in root[1]:
# count introduced for keeping track
s_count += 1
if sent.tag == "s":
for word in sent:
t_count += 1
# get the current word token and discard capitalization
token = word.text.lower()
# avoid adding sentence delimiters to the word dictionary
if not, token):
lemma = word.get("lem")
# not every word has a lemma form!
if type(lemma) == type(None):
# use the word itself as dict key instead
# check whether the key exists (initialize it with 0 if not) and add 1 to the count
lemma_freq[token] = lemma_freq.get(token, 0) + 1
# create a set of tokens to the lemma key (for controlling the lemmatization work)
what_lems[token] = what_lems.get(token, set()).union({token})
# otherwise we collect the lemma info
lemma_freq[lemma] = lemma_freq.get(lemma, 0) + 1
# add to word list to retrace what words the lemmas came from
what_lems[lemma] = what_lems.get(lemma, set()).union({token})"--- worked for {0} seconds ---".format(time.time() - start_time))"processed {0} sentences with all together {1} word tokens".format(s_count, t_count))
return lemma_freq, what_lems
# ## Execute the program
# In[9]:
#for zip_file in test_list:
for zip_file in file_list:
with, "rb") as xml_file:
tree = ET.parse(xml_file)"processing {0}...".format(zip_file))
lemma_freq, what_lems = fetch_frequency(tree, lemma_freq, what_lems, non_words)"current length of lemma freq dict: {0}".format(len(lemma_freq)))"")
logging.warning("*****ATTENTION***** Mistake in the following file *****ATTENTION*****")"{0}".format(zip_file))"")
# In[10]:"================= FINISH LINE =================")"seconds process time: {0}".format(time.clock() - t_proc))"seconds real time: {0}".format(time.time() - t_wall))"")
# start a new timer to time the calculations
t_stats = time.time()
# In[11]:
# how many of the processed words have inflections?
count = 0
for key, value in what_lems.items():
if len(value) == 1:
count += 1"================= STATS, DUDE! =================")"from the {0} lemmas {1} have inflections and {2} are just single words".format(len(what_lems), len(what_lems) - count, count))"YO!")
# In[12]:
# start a new timer to time the calculations
t_files = time.time()
# define the directory to save the outputs
output_dir = "outputs"
# In[13]:
# check up on how the lemmatizations went
with open(output_dir + "/check_lems.json", "w") as f:
json.dump(what_lems, f)
logging.warning("*****ATTENTION***** failed to write file *****ATTENTION*****")
with open(output_dir + "/check_lems.txt", "w") as f:
# In[14]:
# save also the full frequency dict
with open(output_dir + "/full_frequency.json", "w") as f:
json.dump(lemma_freq, f)
logging.warning("*****ATTENTION***** failed to write file *****ATTENTION*****")
with open(output_dir + "/full_frequency.txt", "w") as f:
# In[15]:
# sort the dict values according to frequency and keep the top 5000
fdf = pd.DataFrame(list(lemma_freq.items()), columns=["word", "frequency"])
# In[17]:
sorted_fdf = fdf.sort_values(by=["frequency"], ascending=[False])
top_5000_fdf = sorted_fdf[:5000]
top_5000_fdf.to_csv(output_dir + "/top_5000.csv", encoding="utf-8")
# In[18]:"================= ******* THE END ******** =================")"seconds process time: {0}".format(time.clock() - t_proc))"seconds real time: {0}".format(time.time() - t_wall))
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