namebot.get_adjective_verb_or_noun() - Code Metrics - Inspection of "Add stubbed function and test, update docstrings,..." - christabor/namebot - Measure and Improve Code Quality continuously with Scrutinizer

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by Chris

created 2016-03-08 05:19 UTC

namebot.get_adjective_verb_or_noun() B

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Metric	Value
cc	4
dl	0
loc	24
rs	8.6845

"""Metrics for measuring various aspects of words.

Conventions used in this utility:
1.  All functions return a dictionary,
    with key 'data' and/or 'summary':
    return {
        'data': data,
        'summary': summary or None
    }
"""

from __future__ import division
import re
from pattern.en import parse
from pattern.web import sort
from nltk import pos_tag


def prep_file(file_name):
    """Take a file, extracts items line-by-line, and returns a list of them.

    Args:
        file_name (str): The file name to open

    Returns:
        items (list): A list of items extracted from the file
    """
    items = []
    with open(file_name) as files:
        for newline in files:
            items.append(newline)
    return items


def get_named_numbers_1_10(words):
    """Return a summary of words spelled out (e.g. one, two).

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    matches = []
    numbers = re.compile(
        r'\Aone |two |three |four |five |six |seven |eight |nine |ten',
        re.IGNORECASE)
    for word in words:
        if re.findall(numbers, word):
            matches.append(word)
    return {
        'data': matches,
        'summary': 'Of {} words, {} matched'.format(len(words), len(matches))
    }


def name_length(words):
    """Check the length of each word and an average.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    names_length = []
    for val in words:
        names_length.append(len(val))
    summary = 'Of {} words, the average length of names is...{}'.format(
        len(words),
        round(sum(names_length) / len(names_length)))
    return {
        'data': names_length,
        'summary': summary
    }


def name_vowel_count(words):
    """Check the number of times vowels occurs, and total the results.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    num_count = {'a': 0, 'e': 0, 'i': 0, 'o': 0, 'u': 0}
    try:
        for word in words:
            num_count['a'] += word.count('a')
            num_count['e'] += word.count('e')
            num_count['i'] += word.count('i')
            num_count['o'] += word.count('o')
            num_count['u'] += word.count('u')
    except AttributeError:
        pass
    finally:
        return {
            'data': num_count,
            'summary': None
        }


def name_starts_with_vowel(words):
    """Check the number of times a list of words starts with a vowel.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    vowelcount = 0
    vowels = re.compile(r'\A[aeiou]')
    for name in words:
        if re.match(vowels, name):
            vowelcount += 1
    summary = 'Of {} words, {} or {}% are vowels as the first letter.'.format(
        len(words), vowelcount,
        round(float(vowelcount) / len(words) * 100))
    return {
        'data': None,
        'summary': summary
    }


def get_digits_frequency(words):
    """Look for and count the digits in names, e.g. 7-11, 3M, etc...

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    new_words = []
    count = 0
    digits = re.compile(r'[0-9]+')
    for name in words:
        if re.findall(digits, name):
            count += 1
            matches = re.findall(digits, name)
            new_words += matches
    return {
        'data': new_words,
        'summary': ('Of {} words, {} have numbers in them, '
                    'with a total of {} numbers found.').format(
                        len(words), count, len(new_words))
    }


def get_first_letter_frequency(words):
    """Add the frequency of first letters e.g. [C]at, [C]law, c = 2.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    letters = {}
    # populate keys
    for name in words:
        letters[name[0]] = 0

    # add counts
    for name in words:
        letters[name[0]] += 1

    return {
        'data': letters,
        'summary': None
    }


def get_special_chars(words):
    """Find occurrences of special characters (non-alphabetical characters).

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    data = []
    chars = re.compile(r'[^a-z]', re.IGNORECASE)
    for word in words:
        if re.findall(chars, word):
            data += re.findall(chars, word)
    return {
        'data': data,
        'summary': ('{} occurrences of special characters were'
                    ' found in {} words.').format(len(data), len(words))
    }


def get_word_types(words):
    """Determine the occurrences of pos types.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    new_arr = []
    for val in words:
        try:
            val = parse(
                val,
                encoding='utf-8',
                tokenize=False,
                light=False,
                tags=True,
                chunks=False,
                relations=False,
                lemmata=False)
            new_arr.append(val)
        except IndexError:
            continue
    return {
        'data': new_arr,
        'summary': None
    }


def get_name_spaces(words):
    """Check number of spaces for a given set of words.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    results = [{'word': word, 'spaces': len(word.split(r' '))}
               for word in words]
    return {
        'data': results,
        'summary': None
    }


def get_consonant_repeat_frequency(words):
    """Check for repeating consonant frequency for a given set of words.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    count = 0
    cons = re.compile(r'[^a|e|i|o|u{6}]')
    for val in words:
        if re.match(cons, val):
            count += 1
    return {
        'data': count,
        'summary': None
    }


def get_consonant_duplicate_repeat_frequency(words):
    """Check for duplicate repeating consonant frequency.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    count = 0
    cons_dup = re.compile(r'[^a|e|i|o|u]{1,}')
    for name in words:
        if re.match(cons_dup, name):
            count += 1
    return {
        'data': count,
        'summary': None
    }


def get_vowel_repeat_frequency(words):
    """Check for repeating vowel frequency for a given set of words.

    Args:
        words (list): A list of words

    Returns:
        dict: The data and summary results.
    """
    count = 0
    cons_vowel = re.compile(r'[aeiou{3}]')
    for val in words:
        if re.match(cons_vowel, val):
            count += 1
    return {
        'data': count,
        'summary': None
    }


def get_adjective_verb_or_noun(words):
    """Get the number of words that are classified as verbs or nouns.

    Args:
        words (TYPE): Description

    Returns:
        dict: The data and summary results.
    """
    total = len(words)
    data = {'verbs': 0, 'nouns': 0}
    verby = ['VBP', 'VB', 'RB', 'VBG']
    nouns = ['NN', 'NNP']
    for word, tag in pos_tag(words):
        if tag in nouns:
            data['nouns'] += 1
        elif tag in verby:
            data['verbs'] += 1
    remainder = total - (data['verbs'] + data['nouns'])
    return {
        'data': data,
        'summary': ('Of {0} words, {1} were nouns, {2} were verbs, '
                    'and {3} were everything else.').format(
                        total, data['nouns'], data['verbs'], remainder)
    }


def get_keyword_relevancy_map(words, n_list, terms, sortcontext,
                              enginetype='BING',
                              license=None):
    """http://www.clips.ua.ac.be/pages/pattern-web#sort."""
    results_list = []
    results = sort(
        terms=[],
        context=sortcontext,   # Term used for sorting.
        service=enginetype,    # GOOGLE, YAHOO, BING, ...
        license=None,          # You should supply your own API license key
        strict=True,           # Wraps query in quotes: 'mac sweet'.
        reverse=True,          # Reverse: 'sweet mac' <=> 'mac sweet'.
        cached=True)

    for weight, term in results:
        results.append("%5.2f" % (weight * 100) + "%", term)
    return {
        'data': results_list,
        'summary': None
    }


def check_trademark_registration(words):
    # TODO
    """Search the USTM office and return the number of results."""
    return {
        'data': None,
        'summary': None
    }


def check_domain_searches(words):
    # TODO
    """Check domain search results for each name."""
    raise NotImplemented


def get_search_result_count(words):
    # TODO
    """
    Check google results and return the number of results.

    http://www.clips.ua.ac.be/pages/pattern-web#DOM
    """
    raise NotImplemented


def categorize_word_type(words):
    """Get the common naming strategy 'category' of a name, based on precedence.

    Categories are derived from
    http://www.thenameinspector.com/10-name-types/,
    so it is important to note there is no agreed upon standard,
    meaning it is ultimately a little arbitrary.

    Since it is a bit challenging to actually determine its type,
    we give a weighting for each word based on a few known metrics.
    This can be updated in the future so that weightings are binary
    (e.g. 0.0 and 100.0), giving traditional False/True.

    Categories ====

    1. Real Words
     1a. Misspelled words
     1b. Foreign words
    2. Compounds
    3. Phrases
    4. Blends
    5. Tweaked
    6. Affixed
    7. Fake/obscure
    8. Puns
    9. People's names
    10. Initials and Acronyms

    Args:
        words (list): A list of words

    Returns:
        new_words (list) - A list of lists, with each word
                           and its distribution by word "type"
    """
    new_words = []

    def _get_distribution(word):
        # TODO:
        # misspelled, foreign, tweaked, affixed, fake_obscure,
        # initials_acronym, blend, puns, person, compound
        """Return the distribution for all categories, given a single word."""
        categories = {
            'real': 0,
            'misspelled': 0,
            'foreign': 0,
            'compound': 0,
            'phrase': 0,
            'blend': 0,
            'tweaked': 0,
            'affixed': 0,
            'fake_obscure': 0,
            'puns': 0,
            'person': 0,
            'initials_acronym': 0,
        }
        if len(word.split(' ')) == 1:
            # Real words are single
            categories['real'] = 50
        else:
            # Phrases are not
            categories['phrase'] = 50
        # If word cannot be tagged,
        # it's very likely fake_obscure
        if pos_tag([word])[0][1] == '-NONE-':
            categories['real'] = 0
            categories['fake_obscure'] = 75
        return categories

    for word in words:
        new_words.append([word, _get_distribution(word)])
    return new_words


def get_word_ranking(words):
    """Use google results and get a quality of ranking.

    This is based on other metrics such as domain name availability,
    google results and others.
    """
    results = []
    for name in words:
        results = get_search_result_count(words)
        domains = check_domain_searches(words)
        results.append(results / domains)
    return {
        'data': results,
        'summary': None
    }


def generate_all_metrics(filename=None, words=None):
    """Generate all metrics in this module in one place.

    Args:
        filename (str, optional): A filename to load words from.
        words (TYPE, optional): Words to use, if file is not specified.

    Returns:
        dict: All metrics results, keyed by name.
    """
    if not filename and not words:
        return None
    if filename:
        allnames = prep_file(filename)
    else:
        allnames = words
    return {
        'names': allnames,
        'metrics': {
            'digits_freq': get_digits_frequency(allnames),
            'length': name_length(allnames),
            'vowel_beginning': name_starts_with_vowel(allnames),
            'vowel_count': name_vowel_count(allnames),
            'name_length': name_length(allnames),
            'name_spaces': get_name_spaces(allnames),
            'consonant_repeat_freq': get_consonant_repeat_frequency(allnames),
            'consonant_dup_repeat_freq': get_consonant_duplicate_repeat_frequency(allnames),
            'vowel_repeat_freq': get_vowel_repeat_frequency(allnames),
            'special_characters': get_special_chars(allnames),
            'name_numbers': get_named_numbers_1_10(allnames),
            'adj_verb_noun': get_adjective_verb_or_noun(allnames),
            'first_letter_freq': get_first_letter_frequency(allnames),
            'word_types': get_word_types(allnames)
        }
    }


1			"""Metrics for measuring various aspects of words.
2
3			Conventions used in this utility:
4			1. All functions return a dictionary,
5			with key 'data' and/or 'summary':
6			return {
7			'data': data,
8			'summary': summary or None
9			}
10			"""
11
12			from __future__ import division
13			import re
14			from pattern.en import parse
15			from pattern.web import sort
16			from nltk import pos_tag
17
18
19			def prep_file(file_name):
20			"""Take a file, extracts items line-by-line, and returns a list of them.
21
22			Args:
23			file_name (str): The file name to open
24
25			Returns:
26			items (list): A list of items extracted from the file
27			"""
28			items = []
29			with open(file_name) as files:
30			for newline in files:
31			items.append(newline)
32			return items
33
34
35			def get_named_numbers_1_10(words):
36			"""Return a summary of words spelled out (e.g. one, two).
37
38			Args:
39			words (list): A list of words
40
41			Returns:
42			dict: The data and summary results.
43			"""
44			matches = []
45			numbers = re.compile(
46			r'\Aone \|two \|three \|four \|five \|six \|seven \|eight \|nine \|ten',
47			re.IGNORECASE)
48			for word in words:
49			if re.findall(numbers, word):
50			matches.append(word)
51			return {
52			'data': matches,
53			'summary': 'Of {} words, {} matched'.format(len(words), len(matches))
54			}
55
56
57			def name_length(words):
58			"""Check the length of each word and an average.
59
60			Args:
61			words (list): A list of words
62
63			Returns:
64			dict: The data and summary results.
65			"""
66			names_length = []
67			for val in words:
68			names_length.append(len(val))
69			summary = 'Of {} words, the average length of names is...{}'.format(
70			len(words),
71			round(sum(names_length) / len(names_length)))
72			return {
73			'data': names_length,
74			'summary': summary
75			}
76
77
78			def name_vowel_count(words):
79			"""Check the number of times vowels occurs, and total the results.
80
81			Args:
82			words (list): A list of words
83
84			Returns:
85			dict: The data and summary results.
86			"""
87			num_count = {'a': 0, 'e': 0, 'i': 0, 'o': 0, 'u': 0}
88			try:
89			for word in words:
90			num_count['a'] += word.count('a')
91			num_count['e'] += word.count('e')
92			num_count['i'] += word.count('i')
93			num_count['o'] += word.count('o')
94			num_count['u'] += word.count('u')
95			except AttributeError:
96			pass
97			finally:
98			return {
99			'data': num_count,
100			'summary': None
101			}
102
103
104			def name_starts_with_vowel(words):
105			"""Check the number of times a list of words starts with a vowel.
106
107			Args:
108			words (list): A list of words
109
110			Returns:
111			dict: The data and summary results.
112			"""
113			vowelcount = 0
114			vowels = re.compile(r'\A[aeiou]')
115			for name in words:
116			if re.match(vowels, name):
117			vowelcount += 1
118			summary = 'Of {} words, {} or {}% are vowels as the first letter.'.format(
119			len(words), vowelcount,
120			round(float(vowelcount) / len(words) * 100))
121			return {
122			'data': None,
123			'summary': summary
124			}
125
126
127			def get_digits_frequency(words):
128			"""Look for and count the digits in names, e.g. 7-11, 3M, etc...
129
130			Args:
131			words (list): A list of words
132
133			Returns:
134			dict: The data and summary results.
135			"""
136			new_words = []
137			count = 0
138			digits = re.compile(r'[0-9]+')
139			for name in words:
140			if re.findall(digits, name):
141			count += 1
142			matches = re.findall(digits, name)
143			new_words += matches
144			return {
145			'data': new_words,
146			'summary': ('Of {} words, {} have numbers in them, '
147			'with a total of {} numbers found.').format(
148			len(words), count, len(new_words))
149			}
150
151
152			def get_first_letter_frequency(words):
153			"""Add the frequency of first letters e.g. [C]at, [C]law, c = 2.
154
155			Args:
156			words (list): A list of words
157
158			Returns:
159			dict: The data and summary results.
160			"""
161			letters = {}
162			# populate keys
163			for name in words:
164			letters[name[0]] = 0
165
166			# add counts
167			for name in words:
168			letters[name[0]] += 1
169
170			return {
171			'data': letters,
172			'summary': None
173			}
174
175
176			def get_special_chars(words):
177			"""Find occurrences of special characters (non-alphabetical characters).
178
179			Args:
180			words (list): A list of words
181
182			Returns:
183			dict: The data and summary results.
184			"""
185			data = []
186			chars = re.compile(r'[^a-z]', re.IGNORECASE)
187			for word in words:
188			if re.findall(chars, word):
189			data += re.findall(chars, word)
190			return {
191			'data': data,
192			'summary': ('{} occurrences of special characters were'
193			' found in {} words.').format(len(data), len(words))
194			}
195
196
197			def get_word_types(words):
198			"""Determine the occurrences of pos types.
199
200			Args:
201			words (list): A list of words
202
203			Returns:
204			dict: The data and summary results.
205			"""
206			new_arr = []
207			for val in words:
208			try:
209			val = parse(
210			val,
211			encoding='utf-8',
212			tokenize=False,
213			light=False,
214			tags=True,
215			chunks=False,
216			relations=False,
217			lemmata=False)
218			new_arr.append(val)
219			except IndexError:
220			continue
221			return {
222			'data': new_arr,
223			'summary': None
224			}
225
226
227			def get_name_spaces(words):
228			"""Check number of spaces for a given set of words.
229
230			Args:
231			words (list): A list of words
232
233			Returns:
234			dict: The data and summary results.
235			"""
236			results = [{'word': word, 'spaces': len(word.split(r' '))}
237			for word in words]
238			return {
239			'data': results,
240			'summary': None
241			}
242
243
244			def get_consonant_repeat_frequency(words):
245			"""Check for repeating consonant frequency for a given set of words.
246
247			Args:
248			words (list): A list of words
249
250			Returns:
251			dict: The data and summary results.
252			"""
253			count = 0
254			cons = re.compile(r'[^a\|e\|i\|o\|u{6}]')
255			for val in words:
256			if re.match(cons, val):
257			count += 1
258			return {
259			'data': count,
260			'summary': None
261			}
262
263
264			def get_consonant_duplicate_repeat_frequency(words):
265			"""Check for duplicate repeating consonant frequency.
266
267			Args:
268			words (list): A list of words
269
270			Returns:
271			dict: The data and summary results.
272			"""
273			count = 0
274			cons_dup = re.compile(r'[^a\|e\|i\|o\|u]{1,}')
275			for name in words:
276			if re.match(cons_dup, name):
277			count += 1
278			return {
279			'data': count,
280			'summary': None
281			}
282
283
284			def get_vowel_repeat_frequency(words):
285			"""Check for repeating vowel frequency for a given set of words.
286
287			Args:
288			words (list): A list of words
289
290			Returns:
291			dict: The data and summary results.
292			"""
293			count = 0
294			cons_vowel = re.compile(r'[aeiou{3}]')
295			for val in words:
296			if re.match(cons_vowel, val):
297			count += 1
298			return {
299			'data': count,
300			'summary': None
301			}
302
303
304			def get_adjective_verb_or_noun(words):
305			"""Get the number of words that are classified as verbs or nouns.
306
307			Args:
308			words (TYPE): Description
309
310			Returns:
311			dict: The data and summary results.
312			"""
313			total = len(words)
314			data = {'verbs': 0, 'nouns': 0}
315			verby = ['VBP', 'VB', 'RB', 'VBG']
316			nouns = ['NN', 'NNP']
317			for word, tag in pos_tag(words):
318			if tag in nouns:
319			data['nouns'] += 1
320			elif tag in verby:
321			data['verbs'] += 1
322			remainder = total - (data['verbs'] + data['nouns'])
323			return {
324			'data': data,
325			'summary': ('Of {0} words, {1} were nouns, {2} were verbs, '
326			'and {3} were everything else.').format(
327			total, data['nouns'], data['verbs'], remainder)
328			}
329
330
331			def get_keyword_relevancy_map(words, n_list, terms, sortcontext,
332			enginetype='BING',
333			license=None):
334			"""http://www.clips.ua.ac.be/pages/pattern-web#sort."""
335			results_list = []
336			results = sort(
337			terms=[],
338			context=sortcontext, # Term used for sorting.
339			service=enginetype, # GOOGLE, YAHOO, BING, ...
340			license=None, # You should supply your own API license key
341			strict=True, # Wraps query in quotes: 'mac sweet'.
342			reverse=True, # Reverse: 'sweet mac' <=> 'mac sweet'.
343			cached=True)
344
345			for weight, term in results:
346			results.append("%5.2f" % (weight * 100) + "%", term)
347			return {
348			'data': results_list,
349			'summary': None
350			}
351
352
353			def check_trademark_registration(words):
354			# TODO
355			"""Search the USTM office and return the number of results."""
356			return {
357			'data': None,
358			'summary': None
359			}
360
361
362			def check_domain_searches(words):
363			# TODO
364			"""Check domain search results for each name."""
365			raise NotImplemented
366
367
368			def get_search_result_count(words):
369			# TODO
370			"""
371			Check google results and return the number of results.
372
373			http://www.clips.ua.ac.be/pages/pattern-web#DOM
374			"""
375			raise NotImplemented
376
377
378			def categorize_word_type(words):
379			"""Get the common naming strategy 'category' of a name, based on precedence.
380
381			Categories are derived from
382			http://www.thenameinspector.com/10-name-types/,
383			so it is important to note there is no agreed upon standard,
384			meaning it is ultimately a little arbitrary.
385
386			Since it is a bit challenging to actually determine its type,
387			we give a weighting for each word based on a few known metrics.
388			This can be updated in the future so that weightings are binary
389			(e.g. 0.0 and 100.0), giving traditional False/True.
390
391			Categories ====
392
393			1. Real Words
394			1a. Misspelled words
395			1b. Foreign words
396			2. Compounds
397			3. Phrases
398			4. Blends
399			5. Tweaked
400			6. Affixed
401			7. Fake/obscure
402			8. Puns
403			9. People's names
404			10. Initials and Acronyms
405
406			Args:
407			words (list): A list of words
408
409			Returns:
410			new_words (list) - A list of lists, with each word
411			and its distribution by word "type"
412			"""
413			new_words = []
414
415			def _get_distribution(word):
416			# TODO:
417			# misspelled, foreign, tweaked, affixed, fake_obscure,
418			# initials_acronym, blend, puns, person, compound
419			"""Return the distribution for all categories, given a single word."""
420			categories = {
421			'real': 0,
422			'misspelled': 0,
423			'foreign': 0,
424			'compound': 0,
425			'phrase': 0,
426			'blend': 0,
427			'tweaked': 0,
428			'affixed': 0,
429			'fake_obscure': 0,
430			'puns': 0,
431			'person': 0,
432			'initials_acronym': 0,
433			}
434			if len(word.split(' ')) == 1:
435			# Real words are single
436			categories['real'] = 50
437			else:
438			# Phrases are not
439			categories['phrase'] = 50
440			# If word cannot be tagged,
441			# it's very likely fake_obscure
442			if pos_tag([word])[0][1] == '-NONE-':
443			categories['real'] = 0
444			categories['fake_obscure'] = 75
445			return categories
446
447			for word in words:
448			new_words.append([word, _get_distribution(word)])
449			return new_words
450
451
452			def get_word_ranking(words):
453			"""Use google results and get a quality of ranking.
454
455			This is based on other metrics such as domain name availability,
456			google results and others.
457			"""
458			results = []
459			for name in words:
460			results = get_search_result_count(words)
461			domains = check_domain_searches(words)
462			results.append(results / domains)
463			return {
464			'data': results,
465			'summary': None
466			}
467
468
469			def generate_all_metrics(filename=None, words=None):
470			"""Generate all metrics in this module in one place.
471
472			Args:
473			filename (str, optional): A filename to load words from.
474			words (TYPE, optional): Words to use, if file is not specified.
475
476			Returns:
477			dict: All metrics results, keyed by name.
478			"""
479			if not filename and not words:
480			return None
481			if filename:
482			allnames = prep_file(filename)
483			else:
484			allnames = words
485			return {
486			'names': allnames,
487			'metrics': {
488			'digits_freq': get_digits_frequency(allnames),
489			'length': name_length(allnames),
490			'vowel_beginning': name_starts_with_vowel(allnames),
491			'vowel_count': name_vowel_count(allnames),
492			'name_length': name_length(allnames),
493			'name_spaces': get_name_spaces(allnames),
494			'consonant_repeat_freq': get_consonant_repeat_frequency(allnames),
495			'consonant_dup_repeat_freq': get_consonant_duplicate_repeat_frequency(allnames),
496			'vowel_repeat_freq': get_vowel_repeat_frequency(allnames),
497			'special_characters': get_special_chars(allnames),
498			'name_numbers': get_named_numbers_1_10(allnames),
499			'adj_verb_noun': get_adjective_verb_or_noun(allnames),
500			'first_letter_freq': get_first_letter_frequency(allnames),
501			'word_types': get_word_types(allnames)
502			}
503			}
504

christabor / namebot

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namebot.get_adjective_verb_or_noun() B

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