def needleman_wunsch(s1, s2):
"""
This function computes the Needleman-Wunsch measure between the two input
strings.
Args:
s1,s2 (string): The input strings for which the similarity measure should
be computed.
Returns:
The Needleman-Wunsch measure if both the strings are not missing (i.e
NaN), else returns NaN.
"""
if s1 is None or s2 is None:
return pd.np.NaN
if pd.isnull(s1) or pd.isnull(s2):
return pd.np.NaN
# if isinstance(s1, six.string_types):
# s1 = gh.remove_non_ascii(s1)
# if isinstance(s2, six.string_types):
# s2 = gh.remove_non_ascii(s2)
# Create the similarity measure object
measure = sm.NeedlemanWunsch()
if not (isinstance(s1, six.string_types) or isinstance(s1, bytes)):
s1 = str(s1)
if not (isinstance(s2, six.string_types) or isinstance(s2, bytes)):
s2 = str(s2)
# Call the function to compute the similarity measure
return measure.get_raw_score(s1, s2)
def needleman_wunsch(s1, s2):
if s1 is None or s2 is None:
return pd.np.NaN
if pd.isnull(s1) or pd.isnull(s2):
return pd.np.NaN
s1 = helper.convert_to_str_unicode(s1)
s2 = helper.convert_to_str_unicode(s2)
measure = sm.NeedlemanWunsch()
return measure.get_raw_score(s1, s2)
def needleman_wunsch(s1, s2):
if s1 is None or s2 is None:
return pd.np.NaN
if pd.isnull(s1) or pd.isnull(s2):
return pd.np.NaN
# if isinstance(s1, six.string_types):
# s1 = gh.remove_non_ascii(s1)
# if isinstance(s2, six.string_types):
# s2 = gh.remove_non_ascii(s2)
# Create the similarity measure object
measure = sm.NeedlemanWunsch()
if not(isinstance(s1, six.string_types) or isinstance(s1, bytes)):
s1 = str(s1)
if not(isinstance(s2, six.string_types) or isinstance(s2, bytes)):
s2 = str(s2)
# Call the function to compute the similarity measure
return nw_norm(s1, s2)
def needleman_wunsch(s1, s2):
"""
This function computes the Needleman-Wunsch measure between the two input
strings.
Args:
s1,s2 (string): The input strings for which the similarity measure should
be computed.
Returns:
The Needleman-Wunsch measure if both the strings are not missing (i.e
NaN), else returns NaN.
Examples:
>>> import py_entitymatching as em
>>> em.needleman_wunsch('dva', 'deeva')
1.0
>>> em.needleman_wunsch('dva', None)
nan
"""
if s1 is None or s2 is None:
return pd.np.NaN
if pd.isnull(s1) or pd.isnull(s2):
return pd.np.NaN
# Create the similarity measure object
measure = sm.NeedlemanWunsch()
s1 = gh.convert_to_str_unicode(s1)
s2 = gh.convert_to_str_unicode(s2)
# Call the function to compute the similarity measure
return measure.get_raw_score(s1, s2)
jaro = sm.Jaro()
df['Jaro'] = df.apply(
lambda x: jaro.get_sim_score(x['Sequence1'], x['Sequence2']), axis=1)
df.head()
# In[42]:
lev = sm.Levenshtein()
df['Levenshtein'] = df.apply(
lambda x: lev.get_sim_score(x['Sequence1'], x['Sequence2']), axis=1)
df.head()
# In[43]:
nw = sm.NeedlemanWunsch()
df['NeedlemanWunsch'] = df.apply(
lambda x: nw.get_raw_score(x['Sequence1'], x['Sequence2']), axis=1)
df.head()
# In[44]:
sw = sm.SmithWaterman()
df['SmithWaterman'] = df.apply(
lambda x: sw.get_raw_score(x['Sequence1'], x['Sequence2']), axis=1)
df.head()
# ### The following data is used to quickly establish the performance of models (NOT THE FINAL CODE)
# In[45]:
True)
jac = FVC.stringMatchExcerpts('Jacc', sm.Jaccard(),
sm.WhitespaceTokenizer(return_set=True))
jacq3 = FVC.stringMatchExcerpts('FuzzJacc', sm.Jaccard(),
sm.QgramTokenizer(qval=3, return_set=True))
dice = FVC.stringMatchExcerpts('Dice', sm.Dice(),
sm.WhitespaceTokenizer(return_set=True))
diceq3 = FVC.stringMatchExcerpts('Dice', sm.Dice(),
sm.QgramTokenizer(qval=3, return_set=True))
cosM = FVC.stringMatchExcerpts('CosMeasure', sm.Cosine(),
sm.WhitespaceTokenizer(return_set=True))
cosMq3 = FVC.stringMatchExcerpts('FuzzCosMeasure', sm.Cosine(),
sm.QgramTokenizer(return_set=True))
LVdist = FVC.stringMatchTitles('LVDist', sm.Levenshtein())
sw = FVC.stringMatchTitles('SW', sm.SmithWaterman())
nw = FVC.stringMatchTitles('NW', sm.NeedlemanWunsch())
jw = FVC.stringMatchTitles('JW', sm.JaroWinkler())
def writeToCSV(fileName, header, tableList):
wr = csv.writer(open(fileName, 'wb'), quoting=csv.QUOTE_ALL)
wr.writerow(header)
for row in tableList:
wr.writerow(row)
# Given a set of feature vector components, records precision and recall over several
# classifiers. Records output to a table and vertical bar plot.
def modelExperiment(insampleData,