def __init__(self, data_filename, docs_mask_dict, pick_method='sum', ngram_range=(1, 3), text_header='abstract',
cached_folder_name=None, max_df=0.1, user_ngrams=None, prefilter_terms=0,
terms_threshold=None, output_name=None, calculate_timeseries=None, m_steps_ahead=5,
emergence_index='porter', exponential=False, nterms=50, patents_per_quarter_threshold=20, sma=None):
self.__emergence_index = emergence_index
# load data
self.__data_filename = data_filename
self.__date_dict = docs_mask_dict['date']
self.__timeseries_date_dict = docs_mask_dict['timeseries_date']
self.__timeseries_data = []
self.__emergence_list = []
self.__pick_method = pick_method
# calculate or fetch tf-idf mat
if cached_folder_name is None:
dataframe = data_factory.get(data_filename)
utils.checkdf(dataframe, calculate_timeseries, docs_mask_dict, text_header)
utils.remove_empty_documents(dataframe, text_header)
self.__tfidf_obj = tfidf_from_text(text_series=dataframe[text_header],
ngram_range=ngram_range,
max_document_frequency=max_df,
tokenizer=LemmaTokenizer())
tfidf_mask_obj = TfidfMask(self.__tfidf_obj, ngram_range=ngram_range, uni_factor=0.8, unbias=True)
self.__tfidf_obj.apply_weights(tfidf_mask_obj.tfidf_mask)
if prefilter_terms != 0:
tfidf_reduce_obj = TfidfReduce(self.__tfidf_obj.tfidf_matrix, self.__tfidf_obj.feature_names)
term_score_tuples = tfidf_reduce_obj.extract_ngrams_from_docset(pick_method)
num_tuples_to_retain = min(prefilter_terms, len(term_score_tuples))
feature_subset = sorted([x[1] for x in term_score_tuples[:num_tuples_to_retain]])
number_of_ngrams_before = len(self.__tfidf_obj.feature_names)
self.__tfidf_obj = tfidf_subset_from_features(self.__tfidf_obj, feature_subset)
number_of_ngrams_after = len(self.__tfidf_obj.feature_names)
print(f'Reduced number of terms by pre-filtering from {number_of_ngrams_before:,} '
f'to {number_of_ngrams_after:,}')
self.__cpc_dict = utils.cpc_dict(dataframe)
if docs_mask_dict['date_header'] is None:
self.__cached_folder_name = path.join('cached', output_name + f'-mdf-{max_df}')
self.__dates = None
else:
self.__dates = scripts.utils.date_utils.generate_year_week_dates(dataframe,
docs_mask_dict['date_header'])
min_date = min(self.__dates)
max_date = max(self.__dates)
self.__cached_folder_name = path.join('cached', output_name + f'-mdf-{max_df}-{min_date}-{max_date}')
utils.pickle_object('tfidf', self.__tfidf_obj, self.__cached_folder_name)
utils.pickle_object('dates', self.__dates, self.__cached_folder_name)
utils.pickle_object('cpc_dict', self.__cpc_dict, self.__cached_folder_name)
else:
print(f'Reading document and TFIDF from pickle {cached_folder_name}')
self.__cached_folder_name = path.join('cached', cached_folder_name)
self.__tfidf_obj = utils.unpickle_object('tfidf', self.__cached_folder_name)
self.__dates = utils.unpickle_object('dates', self.__cached_folder_name)
self.__cpc_dict = utils.unpickle_object('cpc_dict', self.__cached_folder_name)
if self.__dates is not None:
min_date = min(self.__dates)
max_date = max(self.__dates)
print(f'Document year-week dates range from {min_date // 100}-{(min_date % 100):02d} '
f'to {max_date // 100}-{(max_date % 100):02d}')
WordAnalyzer.init(
tokenizer=LemmaTokenizer(),
preprocess=lowercase_strip_accents_and_ownership,
ngram_range=ngram_range)
# todo: pipeline is now a one-way trip of data, slowly collapsing / shrinking it as we don't need to keep
# the original. We're really just filtering down.
# todo: build up a list of functions to apply as document filters. all filters to have common args (c/o
# partialfunc if required) so we can then call them in sequence...
# from a combiner.
# each func just returns an array of bool (or 0/1)
# if union - create union combiner, else create intersection combiner. combiner = union if... else intersection
# weights = combiner(list of funcs, data set)
# combiner: if list is empty, return [1] * size; if single entry, return its array
# union: if more entries after single, add / or
# intersection: if more entries after single, multiple / and
# then apply mask to tfidf object and df (i.e. remove rows with false or 0); do this in place
print(f'Applying documents filter...')
# docs weights( column, dates subset + time, citations etc.)
doc_filters = DocumentsFilter(self.__dates, docs_mask_dict, self.__cpc_dict,
self.__tfidf_obj.tfidf_matrix.shape[0]).doc_filters
# todo: build up list of weight functions (left with single remaining arg etc via partialfunc)
# combine(list, tfidf) => multiplies weights together, then multiplies across tfidf (if empty, no side effect)
# todo: this is another weight function...
# term weights - embeddings
print(f'Applying terms filter...')
filter_terms_obj = FilterTerms(self.__tfidf_obj.feature_names, user_ngrams, threshold=terms_threshold)
term_weights = filter_terms_obj.ngram_weights_vec
# todo: replace tfidf_mask with isolated functions: clean_unigrams, unbias_ngrams;
# these operate directly on tfidf
# Hence return nothing - operate in place on tfidf.
print(f'Creating a masked tfidf matrix from filters...')
# tfidf mask ( doc_ids, doc_weights, embeddings_filter will all merge to a single mask in the future)
tfidf_mask_obj = TfidfMask(self.__tfidf_obj, ngram_range=ngram_range, uni_factor=0.8)
tfidf_mask_obj.update_mask(doc_filters, term_weights)
tfidf_mask = tfidf_mask_obj.tfidf_mask
# todo: this mutiply and remove null will disappear - maybe put weight combiner last so it can remove 0 weights
# mask the tfidf matrix
tfidf_masked = tfidf_mask.multiply(self.__tfidf_obj.tfidf_matrix)
tfidf_masked, self.__dates = utils.remove_all_null_rows_global(tfidf_masked, self.__dates)
print(f'Processing TFIDF matrix of {tfidf_masked.shape[0]:,}'
f' / {self.__tfidf_obj.tfidf_matrix.shape[0]:,} documents')
# todo: no advantage in classes - just create term_count and extract_ngrams as functions
self.__tfidf_reduce_obj = TfidfReduce(tfidf_masked, self.__tfidf_obj.feature_names)
self.__timeseries_data = None
# if other outputs
self.__term_score_tuples = self.__tfidf_reduce_obj.extract_ngrams_from_docset(pick_method)
self.__term_score_tuples = utils.stop_tup(self.__term_score_tuples, WordAnalyzer.stemmed_stop_word_set_uni,
WordAnalyzer.stemmed_stop_word_set_n)
# todo: no output method; just if statements to call output functions...?
# Only supply what they each directly require
# todo: hence Pipeline then becomes a single function
if not calculate_timeseries:
return
# TODO: offer timeseries cache as an option. Then filter dates and terms after reading the cached matrix
print(f'Creating timeseries matrix...')
if cached_folder_name is None or not (
path.isfile(utils.pickle_name('weekly_series_terms', self.__cached_folder_name))
and path.isfile(utils.pickle_name('weekly_series_global', self.__cached_folder_name))
and path.isfile(utils.pickle_name('weekly_isodates', self.__cached_folder_name))):
self.__timeseries_data = self.__tfidf_reduce_obj.create_timeseries_data(self.__dates)
[self.__term_counts_per_week, self.__term_ngrams, self.__number_of_patents_per_week,
self.__weekly_iso_dates] = self.__timeseries_data
utils.pickle_object('weekly_series_terms', self.__term_counts_per_week, self.__cached_folder_name)
utils.pickle_object('weekly_series_global', self.__number_of_patents_per_week, self.__cached_folder_name)
utils.pickle_object('weekly_isodates', self.__weekly_iso_dates, self.__cached_folder_name)
else:
self.__term_counts_per_week = utils.unpickle_object('weekly_series_terms', self.__cached_folder_name)
self.__number_of_patents_per_week = utils.unpickle_object('weekly_series_global', self.__cached_folder_name)
self.__weekly_iso_dates = utils.unpickle_object('weekly_isodates', self.__cached_folder_name)
self.__term_ngrams = self.__tfidf_obj.feature_names
self.__M = m_steps_ahead
# TODO: define period from command line, then cascade through the code
term_counts_per_week_csc = self.__term_counts_per_week.tocsc()
self.__timeseries_quarterly = []
self.__timeseries_intercept = []
self.__timeseries_derivatives = []
self.__timeseries_quarterly_smoothed = []
self.__term_nonzero_dates = []
all_quarters, all_quarterly_values = self.__x = scripts.utils.date_utils.timeseries_weekly_to_quarterly(
self.__weekly_iso_dates, self.__number_of_patents_per_week)
# find indexes for date-range
min_date = max_date = None
if self.__timeseries_date_dict is not None:
min_date = self.__timeseries_date_dict['from']
max_date = self.__timeseries_date_dict['to']
min_i = 0
max_i = len(all_quarters)
for i, quarter in enumerate(all_quarters):
if min_date is not None and min_date < quarter:
break
min_i = i
for i, quarter in enumerate(all_quarters):
if max_date is not None and max_date < quarter:
break
max_i = i
self.__lims = [min_i, max_i]
self.__timeseries_quarterly_smoothed = None if sma is None else []
for term_index in tqdm(range(self.__term_counts_per_week.shape[1]), unit='term',
desc='Calculating quarterly timeseries',
leave=False, unit_scale=True):
row_indices, row_values = utils.get_row_indices_and_values(term_counts_per_week_csc, term_index)
weekly_iso_dates = [self.__weekly_iso_dates[x] for x in row_indices]
non_zero_dates, quarterly_values = scripts.utils.date_utils.timeseries_weekly_to_quarterly(weekly_iso_dates,
row_values)
non_zero_dates, quarterly_values = utils.fill_missing_zeros(quarterly_values, non_zero_dates, all_quarters)
self.__timeseries_quarterly.append(quarterly_values)
if emergence_index == 'gradients' or sma == 'kalman':
if cached_folder_name is None or not (
path.isfile(utils.pickle_name('smooth_series_s', self.__cached_folder_name))
and path.isfile(utils.pickle_name('derivatives', self.__cached_folder_name))):
for term_index, quarterly_values in tqdm(enumerate(self.__timeseries_quarterly), unit='term',
desc='smoothing quarterly timeseries with kalman filter',
leave=False, unit_scale=True,
total=len(self.__timeseries_quarterly)):
_, _1, smooth_series_s, _intercept = StateSpaceModel(quarterly_values).run_smoothing()
smooth_series = smooth_series_s[0].tolist()[0]
smooth_series_no_negatives = np.clip(smooth_series, a_min=0, a_max=None)
self.__timeseries_quarterly_smoothed.append(smooth_series_no_negatives.tolist())
derivatives = smooth_series_s[1].tolist()[0]
self.__timeseries_derivatives.append(derivatives)
utils.pickle_object('smooth_series_s', self.__timeseries_quarterly_smoothed, self.__cached_folder_name)
utils.pickle_object('derivatives', self.__timeseries_derivatives, self.__cached_folder_name)
else:
self.__timeseries_quarterly_smoothed = utils.unpickle_object('smooth_series_s',
self.__cached_folder_name)
self.__timeseries_derivatives = utils.unpickle_object('derivatives', self.__cached_folder_name)
if sma == 'savgol':
for quarterly_values in tqdm(self.__timeseries_quarterly, unit='term',
desc='savgol smoothing quarterly timeseries',
leave=False, unit_scale=True):
smooth_series = savgol_filter(quarterly_values, 9, 2, mode='nearest')
smooth_series_no_negatives = np.clip(smooth_series, a_min=0, a_max=None)
self.__timeseries_quarterly_smoothed.append(smooth_series_no_negatives.tolist())
em = Emergence(all_quarterly_values[min_i:max_i])
for term_index in tqdm(range(self.__term_counts_per_week.shape[1]), unit='term', desc='Calculating eScore',
leave=False, unit_scale=True):
if term_weights[term_index] == 0.0:
continue
term_ngram = self.__term_ngrams[term_index]
if self.__timeseries_quarterly_smoothed is not None:
quarterly_values = list(self.__timeseries_quarterly_smoothed[term_index])[min_i:max_i]
else:
quarterly_values = list(self.__timeseries_quarterly[term_index])[min_i:max_i]
if len(quarterly_values) == 0 or max(list(self.__timeseries_quarterly[term_index][min_i:max_i])) < float(
patents_per_quarter_threshold):
continue
if emergence_index == 'quadratic':
escore = em.escore2(quarterly_values)
elif emergence_index == 'porter':
if not em.is_emergence_candidate(quarterly_values):
continue
escore = em.calculate_escore(quarterly_values)
elif emergence_index == 'gradients':
derivatives = self.__timeseries_derivatives[term_index][min_i:max_i]
escore = em.net_growth(quarterly_values, derivatives)
else:
weekly_values = term_counts_per_week_csc.getcol(term_index).todense().ravel().tolist()[0]
escore = em.escore_exponential(weekly_values)
self.__emergence_list.append((term_ngram, escore))
nterms2 = min(nterms, len(self.__emergence_list))
self.__emergence_list.sort(key=lambda emergence: -emergence[1])
self.__emergent = [x[0] for x in self.__emergence_list[:nterms2]]
self.__declining = [x[0] for x in self.__emergence_list[-nterms2:]]
self.__declining.reverse()
self.__stationary = [x[0] for x in utils.stationary_terms(self.__emergence_list, nterms2)]
def __init__(self,
term_counts_data,
m_steps_ahead=5,
curves=True,
nterms=50,
minimum_patents_per_quarter=20,
outname=None):
self.__M = m_steps_ahead
[
self.__term_counts_per_week, self.__term_ngrams,
self.__number_of_patents_per_week, self.__weekly_iso_dates
] = term_counts_data
term_counts_per_week_csc = self.__term_counts_per_week.tocsc()
em = Emergence(self.__number_of_patents_per_week)
self.__emergence_list = []
for term_index in tqdm(range(self.__term_counts_per_week.shape[1]),
unit='term',
desc='Calculating eScore',
leave=False,
unit_scale=True):
term_ngram = self.__term_ngrams[term_index]
row_indices, row_values = utils.get_row_indices_and_values(
term_counts_per_week_csc, term_index)
if len(row_values) == 0:
continue
weekly_iso_dates = [
self.__weekly_iso_dates[x] for x in row_indices
]
_, quarterly_values = utils.timeseries_weekly_to_quarterly(
weekly_iso_dates, row_values)
if max(quarterly_values) < minimum_patents_per_quarter:
continue
if em.init_vars(row_indices, row_values):
escore = em.calculate_escore() if not curves else em.escore2()
self.__emergence_list.append((term_ngram, escore))
if len(self.__emergence_list) == 0:
self.__emergent = []
self.__declining = []
self.__stationary = []
return
self.__emergence_list.sort(key=lambda emergence: -emergence[1])
# for tup in self.__emergence_list:
# print(tup[0] + ": " + str(tup[1]))
self.__emergent = [x[0] for x in self.__emergence_list[:nterms]]
self.__declining = [x[0] for x in self.__emergence_list[-nterms:]]
zero_pivot_emergence = None
last_emergence = self.__emergence_list[0][1]
for index, value in enumerate(self.__emergence_list[1:]):
if value[1] <= 0.0 < last_emergence:
zero_pivot_emergence = index
break
last_emergence = value[1]
stationary_start_index = zero_pivot_emergence - nterms // 2
stationary_end_index = zero_pivot_emergence + nterms // 2
self.__stationary = [
x[0] for x in
self.__emergence_list[stationary_start_index:stationary_end_index]
]
filename_and_path = path.join('outputs', 'reports',
outname + '_emergence.txt')
with open(filename_and_path, 'w') as file:
print()
print('Emergent')
file.write('Emergent\n')
for tup in self.__emergence_list[:nterms]:
print(tup[0] + ": " + str(tup[1]))
file.write(tup[0] + ": " + str(tup[1]) + '\n')
print()
file.write('\n')
print('Stationary')
file.write('Stationary\n')
for tup in self.__emergence_list[
stationary_start_index:stationary_end_index]:
print(tup[0] + ": " + str(tup[1]))
file.write(tup[0] + ": " + str(tup[1]) + '\n')
print()
file.write('\n')
print('Declining')
file.write('Declining' + '\n')
for tup in self.__emergence_list[-nterms:]:
print(tup[0] + ": " + str(tup[1]))
file.write(tup[0] + ": " + str(tup[1]) + '\n')
print()
file.write('\n')
def __init__(
self,
data_filename,
docs_mask_dict,
pick_method='sum',
ngram_range=(1, 3),
text_header='abstract',
pickled_tfidf_folder_name=None,
max_df=0.1,
user_ngrams=None,
prefilter_terms=0,
terms_threshold=None,
output_name=None,
calculate_timeseries=None,
m_steps_ahead=5,
curves=True,
nterms=50,
minimum_patents_per_quarter=20,
):
# load data
self.__data_filename = data_filename
self.__date_dict = docs_mask_dict['date']
self.__timeseries_data = []
self.__emergence_list = []
self.__pick_method = pick_method
# calculate or fetch tf-idf mat
if pickled_tfidf_folder_name is None:
dataframe = data_factory.get(data_filename)
utils.checkdf(dataframe, calculate_timeseries, docs_mask_dict,
text_header)
utils.remove_empty_documents(dataframe, text_header)
self.__tfidf_obj = tfidf_from_text(
text_series=dataframe[text_header],
ngram_range=ngram_range,
max_document_frequency=max_df,
tokenizer=LemmaTokenizer())
tfidf_mask_obj = TfidfMask(self.__tfidf_obj,
ngram_range=ngram_range,
uni_factor=0.8,
unbias=True)
self.__tfidf_obj.apply_weights(tfidf_mask_obj.tfidf_mask)
if prefilter_terms != 0:
tfidf_reduce_obj = TfidfReduce(self.__tfidf_obj.tfidf_matrix,
self.__tfidf_obj.feature_names)
term_score_tuples = tfidf_reduce_obj.extract_ngrams_from_docset(
pick_method)
num_tuples_to_retain = min(prefilter_terms,
len(term_score_tuples))
feature_subset = sorted(
[x[1] for x in term_score_tuples[:num_tuples_to_retain]])
number_of_ngrams_before = len(self.__tfidf_obj.feature_names)
self.__tfidf_obj = tfidf_subset_from_features(
self.__tfidf_obj, feature_subset)
number_of_ngrams_after = len(self.__tfidf_obj.feature_names)
print(
f'Reduced number of terms by pre-filtering from {number_of_ngrams_before:,} '
f'to {number_of_ngrams_after:,}')
self.__cpc_dict = utils.cpc_dict(dataframe)
self.__dates = scripts.utils.date_utils.generate_year_week_dates(
dataframe, docs_mask_dict['date_header'])
base_pickle_path = path.join('outputs', 'tfidf')
makedirs(base_pickle_path, exist_ok=True)
def pickle_object(short_name, obj):
folder_name = path.join(base_pickle_path,
output_name + f'-mdf-{max_df}')
makedirs(folder_name, exist_ok=True)
file_name = path.join(
folder_name,
output_name + f'-mdf-{max_df}-{short_name}.pkl.bz2')
with bz2.BZ2File(file_name, 'wb') as pickle_file:
pickle.dump(obj,
pickle_file,
protocol=4,
fix_imports=False)
pickle_object('tfidf', self.__tfidf_obj)
pickle_object('dates', self.__dates)
pickle_object('cpc_dict', self.__cpc_dict)
else:
print(
f'Reading document and TFIDF from pickle {pickled_tfidf_folder_name}'
)
base_folder = path.basename(pickled_tfidf_folder_name)
pickled_base_file_name = path.join(pickled_tfidf_folder_name,
base_folder)
self.__tfidf_obj = read_pickle(pickled_base_file_name +
'-tfidf.pkl.bz2')
self.__dates = read_pickle(pickled_base_file_name +
'-dates.pkl.bz2')
self.__cpc_dict = read_pickle(pickled_base_file_name +
'-cpc_dict.pkl.bz2')
if self.__dates is not None:
min_date = min(self.__dates)
max_date = max(self.__dates)
print(
f'Document year-week dates range from {min_date // 100}-{(min_date % 100):02d} '
f'to {max_date // 100}-{(max_date % 100):02d}')
WordAnalyzer.init(tokenizer=LemmaTokenizer(),
preprocess=lowercase_strip_accents_and_ownership,
ngram_range=ngram_range)
# todo: pipeline is now a one-way trip of data, slowly collapsing / shrinking it as we don't need to keep
# the original. We're really just filtering down.
# todo: build up a list of functions to apply as document filters. all filters to have common args (c/o
# partialfunc if required) so we can then call them in sequence...
# from a combiner.
# each func just returns an array of bool (or 0/1)
# if union - create union combiner, else create intersection combiner. combiner = union if... else intersection
# weights = combiner(list of funcs, data set)
# combiner: if list is empty, return [1] * size; if single entry, return its array
# union: if more entries after single, add / or
# intersection: if more entries after single, multiple / and
# then apply mask to tfidf object and df (i.e. remove rows with false or 0); do this in place
print(f'Applying documents filter...')
# docs weights( column, dates subset + time, citations etc.)
doc_filters = DocumentsFilter(
self.__dates, docs_mask_dict, self.__cpc_dict,
self.__tfidf_obj.tfidf_matrix.shape[0]).doc_filters
# todo: build up list of weight functions (left with single remaining arg etc via partialfunc)
# combine(list, tfidf) => multiplies weights together, then multiplies across tfidf (if empty, no side effect)
# todo: this is another weight function...
# term weights - embeddings
print(f'Applying terms filter...')
filter_terms_obj = FilterTerms(self.__tfidf_obj.feature_names,
user_ngrams,
threshold=terms_threshold)
term_weights = filter_terms_obj.ngram_weights_vec
# todo: replace tfidf_mask with isolated functions: clean_unigrams, unbias_ngrams;
# these operate directly on tfidf
# Hence return nothing - operate in place on tfidf.
print(f'Creating a masked tfidf matrix from filters...')
# tfidf mask ( doc_ids, doc_weights, embeddings_filter will all merge to a single mask in the future)
tfidf_mask_obj = TfidfMask(self.__tfidf_obj,
ngram_range=ngram_range,
uni_factor=0.8)
tfidf_mask_obj.update_mask(doc_filters, term_weights)
tfidf_mask = tfidf_mask_obj.tfidf_mask
# todo: this mutiply and remove null will disappear - maybe put weight combiner last so it can remove 0 weights
# mask the tfidf matrix
tfidf_masked = tfidf_mask.multiply(self.__tfidf_obj.tfidf_matrix)
tfidf_masked, self.__dates = utils.remove_all_null_rows_global(
tfidf_masked, self.__dates)
print(f'Processing TFIDF matrix of {tfidf_masked.shape[0]:,}'
f' / {self.__tfidf_obj.tfidf_matrix.shape[0]:,} documents')
# todo: no advantage in classes - just create term_count and extract_ngrams as functions
self.__tfidf_reduce_obj = TfidfReduce(tfidf_masked,
self.__tfidf_obj.feature_names)
self.__timeseries_data = None
# if other outputs
self.__term_score_tuples = self.__tfidf_reduce_obj.extract_ngrams_from_docset(
pick_method)
self.__term_score_tuples = utils.stop_tup(
self.__term_score_tuples, WordAnalyzer.stemmed_stop_word_set_uni,
WordAnalyzer.stemmed_stop_word_set_n)
# todo: no output method; just if statements to call output functions...?
# Only supply what they each directly require
# todo: hence Pipeline then becomes a single function
if not calculate_timeseries:
return
print(f'Creating timeseries matrix...')
self.__timeseries_data = self.__tfidf_reduce_obj.create_timeseries_data(
self.__dates)
[
self.__term_counts_per_week, self.__term_ngrams,
self.__number_of_patents_per_week, self.__weekly_iso_dates
] = self.__timeseries_data
self.__M = m_steps_ahead
term_counts_per_week_csc = self.__term_counts_per_week.tocsc()
em = Emergence(self.__number_of_patents_per_week)
for term_index in tqdm(range(self.__term_counts_per_week.shape[1]),
unit='term',
desc='Calculating eScore',
leave=False,
unit_scale=True):
term_ngram = self.__term_ngrams[term_index]
row_indices, row_values = utils.get_row_indices_and_values(
term_counts_per_week_csc, term_index)
if len(row_values) == 0:
continue
weekly_iso_dates = [
self.__weekly_iso_dates[x] for x in row_indices
]
_, quarterly_values = scripts.utils.date_utils.timeseries_weekly_to_quarterly(
weekly_iso_dates, row_values)
if max(quarterly_values) < minimum_patents_per_quarter:
continue
if em.init_vars(row_indices, row_values, porter=not curves):
escore = em.calculate_escore() if not curves else em.escore2()
self.__emergence_list.append((term_ngram, escore))
nterms2 = min(nterms, len(self.__emergence_list))
self.__emergence_list.sort(key=lambda emergence: -emergence[1])
self.__emergent = [x[0] for x in self.__emergence_list[:nterms2]]
self.__declining = [x[0] for x in self.__emergence_list[-nterms2:]]
self.__stationary = utils.stationary_terms(self.__emergence_list,
nterms2)
class EmergenceTests(unittest.TestCase):
def find_term_index(self, term):
for term_index in range(0, len(self.term_ngrams)):
if self.term_ngrams[term_index] == term:
return term_index
self.fail(f'Failed to find term {term}')
@classmethod
def setUpClass(cls):
[
cls.term_counts_per_week, cls.term_ngrams,
cls.num_patents_per_week, cls.week_iso_dates
] = pd.read_pickle(
os.path.join('..', 'data',
'USPTO-random-500000-term_counts.pkl.bz2'))
cls.all_yearly_dates, cls.all_yearly_values = timeseries_weekly_to_yearly(
cls.week_iso_dates, cls.num_patents_per_week)
cls.em = Emergence(cls.all_yearly_values)
cls.term_counts_per_week_csc_common = cls.term_counts_per_week.tocsc()
def extract_yearly_values(self, term_index, all_yearly_dates):
row_indices, row_values = get_row_indices_and_values(
self.term_counts_per_week_csc, term_index)
weekly_iso_dates = [self.week_iso_dates[x] for x in row_indices]
non_zero_dates, yearly_values = timeseries_weekly_to_yearly(
weekly_iso_dates, row_values)
non_zero_dates, yearly_values = fill_missing_zeros(
yearly_values, non_zero_dates, all_yearly_dates)
return yearly_values
def set_up_emergent_term(self):
# Aim:
# escore = 2 * active period trend + recent trend + mid-year to last year slope
# active period trend = (term counts 5+6+7)/(sqrt(total 5) + sqrt(total 6) + sqrt(total 7)
# - (term counts 1+2+3)/(sqrt(total 1) + sqrt(total 2) + sqrt(total 3))
# recent trend = 10 * (term count 6+7)/(sqrt(total 6) + sqrt(total 7))
# - (term counts 4 + 5)/(sqrt(total 4) + sqrt(total 5))
# mid-year to last year slope = 10 * ((term counts 7 / sqrt(total 7)) - (term counts 4/sqrt(total 4))) / (7-4)
#
# Also: emergent if:
# term present for >3 years
# >7 docs with term
# # term records in active / # term records in base
# # base term records / # base all records < 15%
# single author set...
weeks_per_period = 52
self.term_counts_matrix = np.zeros(shape=(weeks_per_period * 10, 9),
dtype=np.int)
# [0: emergent term, 1: non-emergent due to base, 2: non-emergent constant count,
# 3: non-emergent decreasing count, 4: not 10 years data, 5: background, 6: background,
# 7: two occurrences over 10 years, 8: all but one term occurs in base]
# period 1 - base
self.term_counts_matrix[0, :] = [1, 0, 1, 1, 0, 0, 0, 1, 1]
self.term_counts_matrix[2, :] = [0, 1, 0, 1, 0, 1, 0, 0, 1]
# period 2 - base
self.term_counts_matrix[0 + (1 * weeks_per_period), :] = [
0, 0, 0, 1, 0, 0, 0, 0, 1
]
self.term_counts_matrix[3 + (1 * weeks_per_period), :] = [
0, 0, 1, 1, 0, 1, 1, 0, 1
]
self.term_counts_matrix[7 + (1 * weeks_per_period), :] = [
0, 1, 0, 1, 1, 0, 0, 0, 1
]
# period 3 - base
self.term_counts_matrix[12 + (2 * weeks_per_period), :] = [
0, 0, 0, 1, 0, 0, 0, 0, 1
]
self.term_counts_matrix[20 + (2 * weeks_per_period), :] = [
0, 0, 1, 1, 0, 1, 1, 0, 1
]
self.term_counts_matrix[30 + (2 * weeks_per_period), :] = [
0, 1, 0, 0, 1, 0, 0, 0, 1
]
# period 4 - active
self.term_counts_matrix[5 + (3 * weeks_per_period), :] = [
1, 0, 0, 1, 0, 0, 0, 0, 0
]
self.term_counts_matrix[8 + (3 * weeks_per_period), :] = [
0, 0, 1, 0, 0, 1, 1, 0, 0
]
self.term_counts_matrix[9 + (3 * weeks_per_period), :] = [
0, 1, 0, 1, 1, 0, 0, 0, 0
]
# period 5 - active
self.term_counts_matrix[20 + (4 * weeks_per_period), :] = [
1, 0, 0, 1, 0, 0, 0, 0, 0
]
self.term_counts_matrix[40 + (4 * weeks_per_period), :] = [
0, 0, 1, 1, 0, 1, 1, 0, 0
]
self.term_counts_matrix[51 + (4 * weeks_per_period), :] = [
1, 1, 0, 0, 1, 1, 0, 0, 0
]
# period 6 - active
self.term_counts_matrix[10 + (5 * weeks_per_period), :] = [
1, 0, 0, 1, 0, 0, 0, 0, 0
]
self.term_counts_matrix[11 + (5 * weeks_per_period), :] = [
1, 1, 1, 0, 0, 1, 1, 0, 0
]
self.term_counts_matrix[12 + (5 * weeks_per_period), :] = [
0, 1, 0, 0, 1, 0, 0, 0, 0
]
# period 7 - active
self.term_counts_matrix[21 + (6 * weeks_per_period), :] = [
1, 0, 0, 1, 0, 0, 0, 0, 0
]
self.term_counts_matrix[32 + (6 * weeks_per_period), :] = [
0, 1, 1, 0, 0, 1, 1, 0, 0
]
self.term_counts_matrix[43 + (6 * weeks_per_period), :] = [
1, 1, 0, 0, 1, 0, 0, 0, 0
]
# period 8 - active
self.term_counts_matrix[12 + (7 * weeks_per_period), :] = [
1, 1, 0, 0, 0, 0, 0, 0, 0
]
self.term_counts_matrix[13 + (7 * weeks_per_period), :] = [
1, 0, 1, 1, 0, 1, 1, 0, 0
]
self.term_counts_matrix[14 + (7 * weeks_per_period), :] = [
1, 1, 0, 0, 1, 0, 0, 0, 0
]
# period 9 - active
self.term_counts_matrix[28 + (8 * weeks_per_period), :] = [
1, 1, 0, 0, 0, 0, 0, 0, 0
]
self.term_counts_matrix[29 + (8 * weeks_per_period), :] = [
1, 1, 1, 0, 0, 1, 1, 0, 0
]
self.term_counts_matrix[51 + (8 * weeks_per_period), :] = [
1, 1, 0, 0, 1, 0, 0, 0, 0
]
# period 10 - active
self.term_counts_matrix[49 + (9 * weeks_per_period), :] = [
1, 1, 0, 1, 0, 0, 0, 0, 0
]
self.term_counts_matrix[50 + (9 * weeks_per_period), :] = [
1, 1, 1, 0, 0, 1, 1, 0, 0
]
self.term_counts_matrix[51 + (9 * weeks_per_period), :] = [
1, 1, 0, 0, 1, 0, 0, 1, 0
]
self.term_counts_per_week_csc = csc_matrix(self.term_counts_matrix)
self.num_patents_per_week = self.term_counts_matrix.sum(axis=1) > 0
self.num_patents_per_week = self.num_patents_per_week.astype(
dtype=np.int32)
yearly_dates, yearly_values = timeseries_weekly_to_yearly(
self.week_iso_dates, self.num_patents_per_week)
self.em = Emergence(yearly_values)
return yearly_dates
def assert_term_escore(self, em_expected, escore_expected, term):
term_index = self.find_term_index(term)
self.term_counts_per_week_csc = self.term_counts_per_week_csc_common
yearly_values = self.extract_yearly_values(term_index,
self.all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
escore_actual = self.em.calculate_escore(yearly_values)
self.assertEqual(em_expected, potentially_emergent_actual,
term + ": em failed")
self.assertEqual(escore_expected, escore_actual,
term + ": escore failed")
def test_term_with_less_than_10_years_data(self):
all_yearly_dates = self.set_up_emergent_term()
yearly_values = self.extract_yearly_values(4, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
self.assertFalse(potentially_emergent_actual)
def test_term_with_less_than_7_occurrences(self):
all_yearly_dates = self.set_up_emergent_term()
yearly_values = self.extract_yearly_values(7, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
self.assertFalse(potentially_emergent_actual)
def test_term_counts_base2all_over_threshold_and_emergent(self):
escore_expected = 6.35
all_yearly_dates = self.set_up_emergent_term()
self.em.TERM_BASE_RECS_THRESHOLD = 1
yearly_values = self.extract_yearly_values(0, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
escore_actual = self.em.calculate_escore(yearly_values)
self.assertTrue(potentially_emergent_actual)
self.assertAlmostEqual(escore_expected, escore_actual, places=2)
def test_term_counts_base2all_over_threshold_but_not_emergent(self):
all_yearly_dates = self.set_up_emergent_term()
self.em.TERM_BASE_RECS_THRESHOLD = 1
yearly_values = self.extract_yearly_values(1, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
self.assertFalse(potentially_emergent_actual)
def test_term_with_base_but_no_emergent_instances(self):
all_yearly_dates = self.set_up_emergent_term()
yearly_values = self.extract_yearly_values(8, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
self.assertFalse(potentially_emergent_actual)
def test_non_emergent_with_constant_usage_term(self):
escore_expected = 0
all_yearly_dates = self.set_up_emergent_term()
yearly_values = self.extract_yearly_values(2, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
escore_actual = self.em.calculate_escore(yearly_values)
self.assertEqual(potentially_emergent_actual.dtype, np.dtype('bool'))
self.assertAlmostEqual(escore_expected, escore_actual, places=2)
def test_non_emergent_with_decreasing_usage_term(self):
escore_expected = -4.04
all_yearly_dates = self.set_up_emergent_term()
self.em.BASE_TERM2ALL_RATIO_THRESHOLD = 1
self.em.ACTIVE2BASE_RATIO_THRESHOLD = 0
yearly_values = self.extract_yearly_values(3, all_yearly_dates)
potentially_emergent_actual = self.em.is_emergence_candidate(
yearly_values)
escore_actual = self.em.calculate_escore(yearly_values)
self.assertTrue(potentially_emergent_actual)
self.assertAlmostEqual(escore_expected, escore_actual, places=2)
def test_3d_image(self):
term = '3d image'
escore_expected = -1.3383140739474317
em_expected = True
self.assert_term_escore(em_expected, escore_expected, term)
def test_3d_display(self):
term = '3d display'
escore_expected = -0.17674809905755776
em_expected = True
self.assert_term_escore(em_expected, escore_expected, term)
def test_ac_power_supply(self):
term = 'ac power supply'
escore_expected = -0.19543452810736667
em_expected = True
self.assert_term_escore(em_expected, escore_expected, term)
def test_acid_molecule(self):
term = 'acid molecule'
escore_expected = -0.633235199024181
em_expected = False
self.assert_term_escore(em_expected, escore_expected, term)
def test_acid_molecule_encoding(self):
term = 'acid molecule encoding'
escore_expected = -0.3469298902297481
em_expected = False
self.assert_term_escore(em_expected, escore_expected, term)