def get_clean_stats(self):
"""Get clean stats of the line
Returns:
Statistics: Statistics of the clean line
"""
if self.stats[
"clean"] is None: # Compute clean stats if it is not already done
self.stats["clean"] = Statistics(
["lw_char", "up_char", "nb_char", "sp_char"])
tmp_line = re.sub(r'[a-z]', 'a',
self.get_clean_line()) # Lower chars replacement
tmp_line = re.sub(r'[A-Z]', 'A',
tmp_line) # Upper chars replacement
tmp_line = re.sub(r'[0-9]', '0', tmp_line) # Numbers replacement
tmp_line = re.sub(r'[^a-zA-Z0-9 ]', '#',
tmp_line) # Special chars replacement
line_stats = Counter(tmp_line)
self.stats["clean"].set_stat("lw_char", line_stats["a"])
self.stats["clean"].set_stat("up_char", line_stats["A"])
self.stats["clean"].set_stat("nb_char", line_stats["0"])
self.stats["clean"].set_stat("sp_char", line_stats["#"])
return self.stats["clean"]
def __init__(self, string, result=None):
self.tokens = [[tkn, clean_head_tail(tkn), None]
for tkn in tokenize(string)]
self.pos_string = string # String containing the position of each token (e.g. "%0 %1%2 ... %n")
for index, token in enumerate(self.tokens):
self.pos_string = self.pos_string.replace(token[0],
"%" + str(index), 1)
self.result = None
if result is not None:
self.result = int(result)
if sum([len(t[1]) for t in self.tokens if not t[1] is None]) == 0:
self.grade = 0
else:
self.grade = 3
self.stats = {
"orig": Statistics(["lw_char", "up_char", "nb_char", "sp_char"]),
"clean": None
}
tmp_line = re.sub(r'[a-z]', 'a',
self.get_orig_line()) # Lower chars replacement
tmp_line = re.sub(r'[A-Z]', 'A', tmp_line) # Upper chars replacement
tmp_line = re.sub(r'[0-9]', '0', tmp_line) # Numbers replacement
tmp_line = re.sub(r'[^a-zA-Z0-9 ]', '#',
tmp_line) # Special chars replacement
line_stats = Counter(tmp_line)
self.stats["orig"].set_stat("lw_char", line_stats["a"])
self.stats["orig"].set_stat("up_char", line_stats["A"])
self.stats["orig"].set_stat("nb_char", line_stats["0"])
self.stats["orig"].set_stat("sp_char", line_stats["#"])
def __init__(self, fname):
self.filename = fname
self.text = []
self.contains_training_data = False
self.stats = Statistics([
"line_nb", "line_avg_length", "line_total_length",
"word_avg_length", "word_total_length", "word_avg_nb",
"word_total_nb"
])
self.stats.set_stat("line_nb", 0)
self.stats.set_stat("line_avg_length", 0)
self.stats.set_stat("line_total_length", 0)
self.stats.set_stat("word_avg_length", 0)
self.stats.set_stat("word_total_length", 0)
self.stats.set_stat("word_avg_nb", 0)
self.stats.set_stat("word_total_nb", 0)
def __init__(self, fname):
self.filename = fname
self.text = []
self.contains_training_data = False
self.stats = Statistics(["line_nb", "line_avg_length", "line_total_length", "word_avg_length",
"word_total_length", "word_avg_nb", "word_total_nb"])
self.stats.set_stat("line_nb", 0)
self.stats.set_stat("line_avg_length", 0)
self.stats.set_stat("line_total_length", 0)
self.stats.set_stat("word_avg_length", 0)
self.stats.set_stat("word_total_length", 0)
self.stats.set_stat("word_avg_nb", 0)
self.stats.set_stat("word_total_nb", 0)
class Text(object):
"""Stores the the text from a filename given in parameters
Args:
fname (str): Path to the file.
Attributes:
filename (:func:`str`): Name of the file.
text (:func:`list`): List of paragraphs. Every paragraph is a list of :class:`.Line`.
stats (:class:`.Statistics`): Statistics object.
"""
def __init__(self, fname):
self.filename = fname
self.text = []
self.contains_training_data = False
self.stats = Statistics([
"line_nb", "line_avg_length", "line_total_length",
"word_avg_length", "word_total_length", "word_avg_nb",
"word_total_nb"
])
self.stats.set_stat("line_nb", 0)
self.stats.set_stat("line_avg_length", 0)
self.stats.set_stat("line_total_length", 0)
self.stats.set_stat("word_avg_length", 0)
self.stats.set_stat("word_total_length", 0)
self.stats.set_stat("word_avg_nb", 0)
self.stats.set_stat("word_total_nb", 0)
def read_csv(self):
"""Read a CSV file and build the associated text object
Returns:
`Text`
"""
self.contains_training_data = True
with open(self.filename, "r") as f:
csv_reader = csv.reader(f)
paragraph = []
for row in csv_reader:
if len(row) != 2:
if len(paragraph) != 0:
self.text.append(paragraph)
paragraph = []
continue
line = unicode(row[0].decode("utf-8"))
line = line.strip(" \t\r\n")
if len(line) == 0:
if len(paragraph) != 0:
self.text.append(paragraph)
paragraph = []
continue
line_object = Line(line, row[1])
paragraph.append(line_object)
self.stats.set_stat("line_nb",
self.stats.get_stat("line_nb") + 1)
self.stats.set_stat(
"line_total_length",
self.stats.get_stat("line_total_length") +
len(line_object))
self.stats.set_stat(
"word_total_nb",
self.stats.get_stat("word_total_nb") +
len(line_object.tokens))
words_len = sum([len(tkn) for tkn in line_object.tokens])
self.stats.set_stat(
"word_total_length",
self.stats.get_stat("word_total_length") + words_len)
if len(paragraph) != 0:
self.text.append(paragraph)
self.stats.set_stat(
"line_avg_length",
self.stats.get_stat("line_total_length") /
self.stats.get_stat("line_nb"))
self.stats.set_stat(
"word_avg_length",
self.stats.get_stat("word_total_length") /
self.stats.get_stat("word_total_nb"))
self.stats.set_stat(
"word_avg_nb",
self.stats.get_stat("word_total_nb") /
self.stats.get_stat("line_nb"))
logging.debug(self.filename + " read")
def read_txt(self):
"""Read a text file and build the associated text object
Returns:
`Text`
"""
self.contains_training_data = False
with codecs.open(self.filename, "rb", encoding="utf-8") as f:
paragraph = []
for line in f:
line = line.strip(" \t\r\n")
if len(line) == 0:
if len(paragraph) != 0:
self.text.append(paragraph)
paragraph = []
continue
line_object = Line(line)
paragraph.append(line_object)
self.stats.set_stat("line_nb",
self.stats.get_stat("line_nb") + 1)
self.stats.set_stat(
"line_total_length",
self.stats.get_stat("line_total_length") +
len(line_object))
self.stats.set_stat(
"word_total_nb",
self.stats.get_stat("word_total_nb") +
len(line_object.tokens))
words_len = sum([len(tkn) for tkn in line_object.tokens])
self.stats.set_stat(
"word_total_length",
self.stats.get_stat("word_total_length") + words_len)
if len(paragraph) != 0:
self.text.append(paragraph)
self.stats.set_stat(
"line_avg_length",
self.stats.get_stat("line_total_length") /
self.stats.get_stat("line_nb"))
self.stats.set_stat(
"word_avg_length",
self.stats.get_stat("word_total_length") /
self.stats.get_stat("word_total_nb"))
self.stats.set_stat(
"word_avg_nb",
self.stats.get_stat("word_total_nb") /
self.stats.get_stat("line_nb"))
logging.debug(self.filename + " read")
def get_clean_lines(self):
"""Returns cleans line from the text object
Returns:
list: List of clean lines
"""
lines = []
for paragraph in self.text:
for line in paragraph:
if line.grade == 5:
lines.append(line.get_clean_line())
if len(lines) > 0 and lines[-1] != "":
lines.append("")
return lines
def get_garbage_lines(self):
"""Returns garbage lines from the text object
Returns:
list: List of garbage lines
"""
lines = []
for paragraph in self.text:
for line in paragraph:
if line.grade == 0:
lines.append(line.get_orig_line())
if len(lines) > 0 and lines[-1] != "":
lines.append("")
return lines
def get_unclassified_lines(self):
"""Returns unclassified lines from the text object
Returns:
list: List of unclassified lines
"""
lines = []
for paragraph in self.text:
for line in paragraph:
if line.grade % 5 != 0: # Grade is not 0 nor 5
lines.append(line.get_orig_line())
if len(lines) > 0 and lines[-1] != "":
lines.append("")
return lines
def retrieve_text_score(self):
"""Returns some stats and score regarding classification
Returns:
dict: Dictionary containing the results
"""
# True positive is a garbage string detected as such
score_stats = {"FP": 0, "TP": 0, "FN": 0, "TN": 0}
class_stats = {"classified": 0, "unclassified": 0, "unrated": 0}
for paragraph in self.text:
for line in paragraph:
if line.grade != 0 and line.grade != 5:
class_stats["unclassified"] += 1
continue
if line.result is None or line.result < 0:
class_stats["unrated"] += 1
continue
class_stats["classified"] += 1
if line.grade == 0: # Line detected as garbage
if line.result == 1: # Line is clean
score_stats["FP"] += 1 # False positive
else: # Line is garbage
score_stats["TP"] += 1 # True postive
else: # Line detected as clean
if line.result == 1: # Line is clean
score_stats["TN"] += 1 # True negative
else: # Line is garbage
score_stats["FN"] += 1 # False negative
# Precision
divider_pr = score_stats["TP"] + score_stats["FP"]
if divider_pr != 0:
precision = score_stats["TP"] / divider_pr
else:
precision = 0
# Recall
divider_rc = score_stats["TP"] + score_stats["FN"]
if divider_rc != 0:
recall = score_stats["TP"] / divider_rc
else:
recall = 0
# F1 score
if precision + recall != 0:
f1 = 2 * precision * recall / (precision + recall)
else:
f1 = 0
return {
"class": class_stats,
"score": {
"precision": precision,
"recall": recall,
"f1": f1
},
"raw": score_stats
}
class Text(object):
"""Stores the the text from a filename given in parameters
Args:
fname (str): Path to the file.
Attributes:
filename (:func:`str`): Name of the file.
text (:func:`list`): List of paragraphs. Every paragraph is a list of :class:`.Line`.
stats (:class:`.Statistics`): Statistics object.
"""
def __init__(self, fname):
self.filename = fname
self.text = []
self.contains_training_data = False
self.stats = Statistics(["line_nb", "line_avg_length", "line_total_length", "word_avg_length",
"word_total_length", "word_avg_nb", "word_total_nb"])
self.stats.set_stat("line_nb", 0)
self.stats.set_stat("line_avg_length", 0)
self.stats.set_stat("line_total_length", 0)
self.stats.set_stat("word_avg_length", 0)
self.stats.set_stat("word_total_length", 0)
self.stats.set_stat("word_avg_nb", 0)
self.stats.set_stat("word_total_nb", 0)
def read_csv(self):
"""Read a CSV file and build the associated text object
Returns:
`Text`
"""
self.contains_training_data = True
with open(self.filename, "r") as f:
csv_reader = csv.reader(f)
paragraph = []
for row in csv_reader:
if len(row) != 2:
if len(paragraph) != 0:
self.text.append(paragraph)
paragraph = []
continue
line = unicode(row[0].decode("utf-8"))
line = line.strip(" \t\r\n")
if len(line) == 0:
if len(paragraph) != 0:
self.text.append(paragraph)
paragraph = []
continue
line_object = Line(line, row[1])
paragraph.append(line_object)
self.stats.set_stat("line_nb", self.stats.get_stat("line_nb")+1)
self.stats.set_stat("line_total_length", self.stats.get_stat("line_total_length")+len(line_object))
self.stats.set_stat("word_total_nb", self.stats.get_stat("word_total_nb") + len(line_object.tokens))
words_len = sum([len(tkn) for tkn in line_object.tokens])
self.stats.set_stat("word_total_length", self.stats.get_stat("word_total_length") + words_len)
if len(paragraph) != 0:
self.text.append(paragraph)
self.stats.set_stat("line_avg_length",
self.stats.get_stat("line_total_length") / self.stats.get_stat("line_nb"))
self.stats.set_stat("word_avg_length",
self.stats.get_stat("word_total_length") / self.stats.get_stat("word_total_nb"))
self.stats.set_stat("word_avg_nb",
self.stats.get_stat("word_total_nb") / self.stats.get_stat("line_nb"))
logging.debug(self.filename+" read")
def read_txt(self):
"""Read a text file and build the associated text object
Returns:
`Text`
"""
self.contains_training_data = False
with codecs.open(self.filename, "rb", encoding="utf-8") as f:
paragraph = []
for line in f:
line = line.strip(" \t\r\n")
if len(line) == 0:
if len(paragraph) != 0:
self.text.append(paragraph)
paragraph = []
continue
line_object = Line(line)
paragraph.append(line_object)
self.stats.set_stat("line_nb", self.stats.get_stat("line_nb")+1)
self.stats.set_stat("line_total_length", self.stats.get_stat("line_total_length")+len(line_object))
self.stats.set_stat("word_total_nb", self.stats.get_stat("word_total_nb") + len(line_object.tokens))
words_len = sum([len(tkn) for tkn in line_object.tokens])
self.stats.set_stat("word_total_length", self.stats.get_stat("word_total_length") + words_len)
if len(paragraph) != 0:
self.text.append(paragraph)
self.stats.set_stat("line_avg_length",
self.stats.get_stat("line_total_length") / self.stats.get_stat("line_nb"))
self.stats.set_stat("word_avg_length",
self.stats.get_stat("word_total_length") / self.stats.get_stat("word_total_nb"))
self.stats.set_stat("word_avg_nb",
self.stats.get_stat("word_total_nb") / self.stats.get_stat("line_nb"))
logging.debug(self.filename+" read")
def get_clean_lines(self):
"""Returns cleans line from the text object
Returns:
list: List of clean lines
"""
lines = []
for paragraph in self.text:
for line in paragraph:
if line.grade == 5:
lines.append(line.get_clean_line())
if len(lines) > 0 and lines[-1] != "":
lines.append("")
return lines
def get_garbage_lines(self):
"""Returns garbage lines from the text object
Returns:
list: List of garbage lines
"""
lines = []
for paragraph in self.text:
for line in paragraph:
if line.grade == 0:
lines.append(line.get_orig_line())
if len(lines) > 0 and lines[-1] != "":
lines.append("")
return lines
def get_unclassified_lines(self):
"""Returns unclassified lines from the text object
Returns:
list: List of unclassified lines
"""
lines = []
for paragraph in self.text:
for line in paragraph:
if line.grade % 5 != 0: # Grade is not 0 nor 5
lines.append(line.get_orig_line())
if len(lines) > 0 and lines[-1] != "":
lines.append("")
return lines
def retrieve_text_score(self):
"""Returns some stats and score regarding classification
Returns:
dict: Dictionary containing the results
"""
# True positive is a garbage string detected as such
score_stats = {"FP": 0, "TP": 0, "FN": 0, "TN": 0}
class_stats = {"classified": 0, "unclassified": 0, "unrated": 0}
for paragraph in self.text:
for line in paragraph:
if line.grade != 0 and line.grade != 5:
class_stats["unclassified"] += 1
continue
if line.result is None or line.result < 0:
class_stats["unrated"] += 1
continue
class_stats["classified"] += 1
if line.grade == 0: # Line detected as garbage
if line.result == 1: # Line is clean
score_stats["FP"] += 1 # False positive
else: # Line is garbage
score_stats["TP"] += 1 # True postive
else: # Line detected as clean
if line.result == 1: # Line is clean
score_stats["TN"] += 1 # True negative
else: # Line is garbage
score_stats["FN"] += 1 # False negative
# Precision
divider_pr = score_stats["TP"] + score_stats["FP"]
if divider_pr != 0:
precision = score_stats["TP"] / divider_pr
else:
precision = 0
# Recall
divider_rc = score_stats["TP"] + score_stats["FN"]
if divider_rc != 0:
recall = score_stats["TP"] / divider_rc
else:
recall = 0
# F1 score
if precision + recall != 0:
f1 = 2 * precision * recall / (precision + recall)
else:
f1 = 0
return {
"class": class_stats,
"score": {
"precision": precision,
"recall": recall,
"f1": f1
},
"raw": score_stats
}
