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from pathlib import Path
from collections import Counter, defaultdict
from itertools import islice
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib as mplt
MAX_STR = 50
class MatchGroups():
"""A class for grouping strings based on set membership. Supports splitting and uniting of groups."""
def __init__(self, strings=None):
"""
Initialize MatchGroups object.
Parameters
----------
strings : list, optional
List of strings to add to the match groups object, by default None
"""
self.counts = Counter()
self.labels = {}
self.groups = {}
if strings is not None:
self.add_strings(strings, inplace=True)
def __len__(self):
"""Return the number of strings in the match groups object."""
return len(self.labels)
def __repr__(self):
"""Return a string representation of the MatchGroups object."""
return f'<nama.MatchGroups containing {len(self)} strings in {len(self.groups)} groups>'
def __str__(self):
"""Return a string representation of the groups of a MatchGroups object."""
output = self.__repr__()
remaining = MAX_STR
for group in self.groups.values():
for s in group:
if remaining:
output += '\n' + s
remaining -= 1
else:
output += f'...\n(Output truncated at {MAX_STR} strings)'
return output
output += '\n'
return output
def __contains__(self, s):
"""Return True if string is in the match groups object, False otherwise."""
return s in self.labels
def __getitem__(self, strings):
"""Return the group label for a single string or a list of strings."""
if isinstance(strings, str):
return self.labels[strings]
else:
return [self.labels[s] for s in strings]
def __add__(self, match_obj):
"""Add two match groups objects together and return the result."""
result = self.add_strings(match_obj)
result.unite(match_obj, inplace=True)
return result
def items(self):
"""Return an iterator of strings and their group labels."""
for i, g in self.labels.items():
yield i, g
def copy(self):
"""Return a copy of the MatchGroups object."""
new_match_obj = MatchGroups()
new_match_obj.counts = self.counts.copy()
new_match_obj.labels = self.labels.copy()
new_match_obj.groups = self.groups.copy()
return new_match_obj
def strings(self):
"""Return a list of strings in the match groups object. Order is not guaranteed."""
return list(self.labels.keys())
def matches(self, string):
"""Return the group of strings that match the given string."""
return self.groups[self.labels[string]]
def add_strings(self, arg, inplace=False):
"""Add new strings to the match groups object.
Parameters
----------
arg : str, Counter, MatchGroups, Iterable
String or group of strings to add to the match groups object
inplace : bool, optional
If True, add strings to the existing MatchGroups object, by default False
Returns
-------
MatchGroups
The updated MatchGroups object
"""
if isinstance(arg, str):
counts = {arg: 1}
elif isinstance(arg, Counter):
counts = arg
elif isinstance(arg, MatchGroups):
counts = arg.counts
elif hasattr(arg, '__next__') or hasattr(arg, '__iter__'):
counts = Counter(arg)
if not inplace:
self = self.copy()
for s in counts.keys():
if s not in self.labels:
self.labels[s] = s
self.groups[s] = [s]
self.counts += counts
return self
def drop(self, strings, inplace=False):
"""Remove strings from the match groups object.
Parameters
----------
strings : list or str
String or list of strings to remove from the match groups object
inplace : bool, optional
If True, remove strings from the existing MatchGroups object, by default False
Returns
-------
MatchGroups
The updated MatchGroups object
"""
if isinstance(strings, str):
strings = [strings]
strings = set(strings)
if not inplace:
self = self.copy()
# Remove strings from their groups
affected_group_labels = {self[s] for s in strings}
for old_label in affected_group_labels:
old_group = self.groups[old_label]
new_group = [s for s in old_group if s not in strings]
if new_group:
counts = self.counts
new_label = min((-counts[s], s) for s in new_group)[1]
if new_label != old_label:
del self.groups[old_label]
self.groups[new_label] = new_group
for s in new_group:
self.labels[s] = new_label
else:
del self.groups[old_label]
# Remove strings from counts and labels
for s in strings:
del self.counts[s]
del self.labels[s]
return self
def keep(self, strings, inplace=False):
"""Drop all strings from the match groups object except the passed strings.
Parameters
----------
strings : list
List of strings to keep in the match groups object
inplace : bool, optional
If True, drop strings from the existing MatchGroups object, by default False
Returns
-------
MatchGroups
The updated MatchGroups object
"""
strings = set(strings)
to_drop = [s for s in self.strings() if s not in strings]
return self.drop(to_drop, inplace=inplace)
def _unite_strings(self, strings):
"""
Unite strings in the match groups object without checking argument type.
Intended as a low-level function called by self.unite()
Parameters
----------
strings : list
List of strings to unite in the match groups object
Returns
-------
None
"""
strings = {s for s in strings if s in self.labels}
if len(strings) > 1:
# Identify groups that will be united
old_labels = set(self[strings])
# Only need to do the merge if the strings span multiple groups
if len(old_labels) > 1:
# Identify the new group label
counts = self.counts
new_label = min((-counts[s], s) for s in old_labels)[1]
# Identify the groups which need to be modified
old_labels.remove(new_label)
for old_label in old_labels:
# Update the string group labels
for s in self.groups[old_label]:
self.labels[s] = new_label
# Update group dict
self.groups[new_label] = self.groups[new_label] + \
self.groups[old_label]
del self.groups[old_label]
def unite(self, arg, inplace=False, **kwargs):
"""
Merge groups containing the passed strings. Groups can be passed as:
- A list of strings to unite
- A nested list to unite each set of strings
- A dictionary mapping strings to labels to unite by label
- A function mapping strings to labels to unite by label
- A MatchGroups instance to unite by MatchGroups groups
Parameters
----------
arg : list, dict, function or MatchGroups instance
Argument representing the strings or labels to merge.
inplace : bool, optional
Whether to perform the operation in place or return a new MatchGroups.
kwargs : dict, optional
Additional arguments to be passed to predict_groupings method if arg
is a similarity model with a predict_groupings method.
Returns
-------
MatchGroups
The updated MatchGroups object. If `inplace` is True, the updated object
is returned, else a new MatchGroups object with the updates is returned.
"""
if not inplace:
self = self.copy()
if isinstance(arg, str):
raise ValueError('Cannot unite a single string')
elif isinstance(arg, MatchGroups):
self.unite(arg.groups.values(), inplace=True)
elif hasattr(arg, 'unite_similar'):
# Unite can accept a similarity model if it has a unite_similar
# method
self.unite(arg.unite_similar(self, **kwargs))
elif callable(arg):
# Assume arg is a mapping from strings to labels and unite by label
groups = {s: arg(s) for s in self.strings()}
self.unite(groups, inplace=True)
elif isinstance(arg, dict):
# Assume arg is a mapping from strings to labels and unite by label
# groups = {label:[] for label in arg.values()}
groups = defaultdict(list)
for string, label in arg.items():
groups[label].append(string)
for group in groups.values():
self._unite_strings(group)
elif hasattr(arg, '__next__'):
# Assume arg is an iterator of groups to unite
# (This needs to be checked early to avoid consuming the first group)
for group in arg:
self._unite_strings(group)
elif all(isinstance(s, str) for s in arg):
# Main case: Unite group of strings
self._unite_strings(arg)
elif hasattr(arg, '__iter__'):
# Assume arg is an iterable of groups to unite
for group in arg:
self._unite_strings(group)
else:
raise ValueError('Unknown input type')
if not inplace:
return self
def split(self, strings, inplace=False):
"""
Split strings into singleton groups. Strings can be passed as:
- A single string to isolate into a singleton group
- A list or iterator of strings to split
Parameters
----------
strings : str or list of str
The string(s) to split into singleton groups.
inplace : bool, optional
Whether to perform the operation in place or return a new MatchGroups.
Returns
-------
MatchGroups
The updated MatchGroups object. If `inplace` is True, the updated object
is returned, else a new MatchGroups object with the updates is returned.
"""
if not inplace:
self = self.copy()
if isinstance(strings, str):
strings = [strings]
strings = set(strings)
# Remove strings from their groups
affected_group_labels = {self[s] for s in strings}
for old_label in affected_group_labels:
old_group = self.groups[old_label]
if len(old_group) > 1:
new_group = [s for s in old_group if s not in strings]
if new_group:
counts = self.counts
new_label = min((-counts[s], s) for s in new_group)[1]
if new_label != old_label:
del self.groups[old_label]
self.groups[new_label] = new_group
for s in new_group:
self.labels[s] = new_label
# Update labels and add singleton groups
for s in strings:
self.labels[s] = s
self.groups[s] = [s]
return self
def split_all(self, inplace=False):
"""
Split all strings into singleton groups.
Parameters
----------
inplace : bool, optional
Whether to perform the operation in place or return a new MatchGroups.
Returns
-------
MatchGroups
The updated MatchGroups object. If `inplace` is True, the updated object
is returned, else a new MatchGroups object with the updates is returned.
"""
if not inplace:
self = self.copy()
self.labels = {s: s for s in self.strings()}
self.groups = {s: [s] for s in self.strings()}
return self
def separate(
self,
strings,
similarity_model,
inplace=False,
threshold=0,
**kwargs):
"""
Separate the strings in according to the prediction of the similarity_model.
Parameters
----------
strings: list
List of strings to be separated.
similarity_model: Model
Model used to predict similarity between strings.
inplace: bool, optional
If True, the separation operation is performed in-place. Otherwise, a copy is created.
threshold: float, optional
Threshold value for prediction.
kwargs: dict, optional
Additional keyword arguments passed to the prediction function.
Returns
-------
self: MatchGroups
Returns the MatchGroups object after the separation operation.
"""
if not inplace:
self = self.copy()
# Identify which groups contain the strings to separate
group_map = defaultdict(list)
for s in set(strings):
group_map[self[s]].append(s)
for g, g_sep in group_map.items():
# If group contains strings to separate...
if len(g_sep) > 1:
group_strings = self.groups[g]
# Split the group strings
self.split(group_strings, inplace=True)
# Re-unite with new prediction that enforces separation
try:
embeddings = similarity_model[group_strings]
except Exception as e:
print(f'{g=} {g_sep} {group_strings}')
raise e
predicted = embeddings.predict(
threshold=threshold,
separate_strings=strings,
**kwargs)
self.unite(predicted, inplace=True)
return self
# def refine(self,similarity_model)
def top_scored_pairs_df(self, similarity_model,
n=10000, buffer_n=100000,
by_group=True,
sort_by=['impact', 'score'], ascending=False,
skip_pairs=None, **kwargs):
"""
Return the DataFrame containing the n most important pairs of strings, according to the score generated by the `similarity_model`.
Parameters
----------
similarity_model: Model
Model used to predict similarity between strings.
n: int, optional
Number of most important pairs to return. Default is 10000.
buffer_n: int, optional
Size of buffer to iterate through the scored pairs. Default is 100000.
by_group: bool, optional
If True, only the most important pair will be returned for each unique group combination.
sort_by: list, optional
A list of column names by which to sort the dataframe. Default is ['impact','score'].
ascending: bool, optional
Whether the sort order should be ascending or descending. Default is False.
skip_pairs: list, optional
List of string pairs to ignore when constructing the ranking.
If by_group=True, any group combination represented in the skip_pairs list will be ignored
kwargs: dict, optional
Additional keyword arguments passed to the `iter_scored_pairs` function.
Returns
-------
top_df: pandas.DataFrame
The DataFrame containing the n most important pairs of strings.
"""
top_df = pd.DataFrame(
columns=[
'string0',
'string1',
'group0',
'group1',
'impact',
'score',
'loss'])
pair_iterator = similarity_model.iter_scored_pairs(self, **kwargs)
def group_size(g):
return len(self.groups[g])
if skip_pairs is not None:
if by_group:
skip_pairs = {tuple(sorted([self[s0], self[s1]]))
for s0, s1 in skip_pairs}
else:
skip_pairs = {tuple(sorted([s0, s1])) for s0, s1 in skip_pairs}
while True:
df = pd.DataFrame(islice(pair_iterator, buffer_n))
if len(df):
for i in 0, 1:
df[f'group{i}'] = [self[s] for s in df[f'string{i}']]
df['impact'] = df['group0'].apply(
group_size) * df['group1'].apply(group_size)
if by_group:
df['group_pair'] = [tuple(sorted([g0, g1])) for g0, g1 in df[[
'group0', 'group1']].values]
if skip_pairs:
if by_group:
df = df[~df['group_pair'].isin(skip_pairs)]
else:
string_pairs = [tuple(sorted([s0, s1])) for s0, s1 in df[[
'string0', 'string1']].values]
df = df[~string_pairs.isin(skip_pairs)]
if len(df):
top_df = pd.concat([top_df, df]) \
.sort_values(sort_by, ascending=ascending)
if by_group:
top_df = top_df \
.groupby('group_pair') \
.first() \
.reset_index()
top_df = top_df \
.sort_values(sort_by, ascending=ascending) \
.head(n)
else:
break
if len(top_df) and by_group:
top_df = top_df \
.drop('group_pair', axis=1) \
.reset_index()
return top_df
def reset_counts(self, inplace=False):
"""
Reset the counts of strings in the MatchGroups object.
Parameters
----------
inplace: bool, optional
If True, the operation is performed in-place. Otherwise, a copy is created.
Returns
-------
self: MatchGroups
Returns the MatchGroups object after the reset operation.
"""
if not inplace:
self = self.copy()
self.counts = Counter(self.strings())
return self
def to_df(self, singletons=True, sort_groups=True):
"""
Convert the match groups object to a dataframe with string, count and group columns.
Parameters
----------
singletons: bool, optional
If True, the resulting DataFrame will include singleton groups. Default is True.
...
Returns
-------
df: pandas.DataFrame
The resulting DataFrame.
"""
strings = self.strings()
if singletons:
df = pd.DataFrame([(s, self.counts[s], self.labels[s]) for s in strings],
columns=['string', 'count', 'group'])
else:
df = pd.DataFrame([(s, self.counts[s], self.labels[s]) for s in strings
if len(self.groups[self[s]]) > 1],
columns=['string', 'count', 'group'])
if sort_groups:
df['group_count'] = df.groupby('group')['count'].transform('sum')
df = df.sort_values(['group_count', 'group', 'count', 'string'], ascending=[
False, True, False, True])
df = df.drop('group_count', axis=1)
df = df.reset_index(drop=True)
return df
def to_csv(self, filename, singletons=True, **pandas_args):
"""
Save the match groups object as a csv file with string, count and group columns.
Parameters
----------
filename : str
Path to file to save the data.
singletons : bool, optional
If True, include singleton groups in the saved file, by default True.
pandas_args : dict
Additional keyword arguments to pass to the pandas.DataFrame.to_csv method.
"""
df = self.to_df(singletons=singletons)
df.to_csv(filename, index=False, **pandas_args)
def merge_dfs(self, left_df, right_df, how='inner',
on=None, left_on=None, right_on=None,
group_column_name='match_group', suffixes=('_x', '_y'),
**merge_args):
"""
Replicated pandas.merge() functionality, except that dataframes are merged by match group instead of directly on the strings in the "on" columns.
Parameters
----------
left_df : pandas.DataFrame
The left dataframe to merge.
right_df : pandas.DataFrame
The right dataframe to merge.
how : str, optional
How to merge the dataframes. Possible values are 'left', 'right', 'outer', 'inner', by default 'inner'.
on : str, optional
Columns in both left and right dataframes to merge on.
left_on : str, optional
Columns in the left dataframe to merge on.
right_on : str, optional
Columns in the right dataframe to merge on.
group_column_name : str, optional
Column name for the merged match group, by default 'match_group'.
suffixes : tuple of str, optional
Suffix to apply to overlapping column names in the left and right dataframes, by default ('_x','_y').
**merge_args : dict
Additional keyword arguments to pass to the pandas.DataFrame.merge method.
Returns
-------
pandas.DataFrame
The merged dataframe.
Raises
------
ValueError
If 'on', 'left_on', and 'right_on' are all None.
ValueError
If `group_column_name` already exists in one of the dataframes.
"""
if ((left_on is None) or (right_on is None)) and (on is None):
raise ValueError('Must provide column(s) to merge on')
left_df = left_df.copy()
right_df = right_df.copy()
if on is not None:
left_on = on + suffixes[0]
right_on = on + suffixes[1]
left_df = left_df.rename(columns={on:left_on})
right_df = right_df.rename(columns={on:right_on})
group_map = lambda s: self[s] if s in self.labels else np.nan
left_group = left_df[left_on].apply(group_map)
right_group = right_df[right_on].apply(group_map)
if group_column_name:
if group_column_name in list(left_df.columns) + list(right_df.columns):
raise ValueError('f{group_column_name=} already exists in one of the dataframes.')
else:
left_df[group_column_name] = left_group
merged_df = pd.merge(left_df,right_df,left_on=left_group,right_on=right_group,how=how,suffixes=suffixes,**merge_args)
merged_df = merged_df[[c for c in merged_df.columns if c in list(left_df.columns) + list(right_df.columns)]]
return merged_df
def from_df(
df,
match_format='detect',
pair_columns=[
'string0',
'string1'],
string_column='string',
group_column='group',
count_column='count'):
"""
Construct a new match groups object from a pandas DataFrame.
Parameters
----------
df : pandas.DataFrame
The input dataframe.
match_format : str, optional
The format of the dataframe, by default "detect".
It can be one of ['unmatched', 'groups', 'pairs', 'detect'].
pair_columns : list of str, optional
The columns names containing the string pairs, by default ['string0','string1'].
string_column : str, optional
The column name containing the strings, by default 'string'.
group_column : str, optional
The column name containing the groups, by default 'group'.
count_column : str, optional
The column name containing the counts, by default 'count'.
Returns
-------
MatchGroups
The constructed MatchGroups object.
Raises
------
ValueError
If the input `match_format` is not one of ['unmatched', 'groups', 'pairs', 'detect'].
ValueError
If the `match_format` is 'detect' and the input dataframe format could not be inferred.
Notes
-----
The function accepts two formats of the input dataframe:
- "groups": The standard format for a match groups object dataframe. It includes a
string column, and a "group" column that contains group labels, and an
optional "count" column. These three columns completely describe a
match groups object, allowing lossless match groups object -> dataframe -> match groups object
conversion (though the specific group labels in the dataframe will be
ignored and rebuilt in the new match groups object).
- "pairs": The dataframe includes two string columns, and each row indicates
a link between a pair of strings. A new match groups object will be constructed by
uniting each pair of strings.
"""
if match_format not in ['unmatched', 'groups', 'pairs', 'detect']:
raise ValueError(
'match_format must be one of "unmatched", "groups", "pairs", or "detect"')
# Create an empty match groups object
match_obj = MatchGroups()
if match_format == 'detect':
if (string_column in df.columns):
if group_column is None:
match_format = 'unmatched'
elif (group_column in df.columns):
match_format = 'groups'
elif set(df.columns) == set(pair_columns):
match_format = 'pairs'
if match_format == 'detect':
raise ValueError('Could not infer valid dataframe format from input')
if count_column in df.columns:
counts = df[count_column].values
else:
counts = np.ones(len(df))
if match_format == 'unmatched':
strings = df[string_column].values
# Build the match groups object
match_obj.counts = Counter({s: int(c) for s, c in zip(strings, counts)})
match_obj.labels = {s: s for s in strings}
match_obj.groups = {s: [s] for s in strings}
elif match_format == 'groups':
strings = df[string_column].values
group_ids = df[group_column].values
# Sort by group and string count
g_sort = np.lexsort((counts, group_ids))
group_ids = group_ids[g_sort]
strings = strings[g_sort]
counts = counts[g_sort]
# Identify group boundaries and split locations
split_locs = np.nonzero(group_ids[1:] != group_ids[:-1])[0] + 1
# Get grouped strings as separate arrays
groups = np.split(strings, split_locs)
# Build the match groups object
match_obj.counts = Counter({s: int(c) for s, c in zip(strings, counts)})
match_obj.labels = {s: g[-1] for g in groups for s in g}
match_obj.groups = {g[-1]: list(g) for g in groups}
elif match_format == 'pairs':
# TODO: Allow pairs data to use counts
for pair_column in pair_columns:
match_obj.add_strings(df[pair_column].values, inplace=True)
# There are several ways to unite pairs
# Guessing it is most efficient to "group by" one of the string columns
groups = {s: pair[1] for pair in df[pair_columns].values for s in pair}
match_obj.unite(groups, inplace=True)
return match_obj
def read_csv(
filename,
match_format='detect',
pair_columns=[
'string0',
'string1'],
string_column='string',
group_column='group',
count_column='count',
**pandas_args):
"""
Read a csv file and construct a new match groups object.
Parameters
----------
filename : str
The path to the csv file.
match_format : str, optional (default='detect')
One of "unmatched", "groups", "pairs", or "detect".
pair_columns : list of str, optional (default=['string0', 'string1'])
Two string columns to use if match_format='pairs'.
string_column : str, optional (default='string')
Column name for string values in match_format='unmatched' or 'groups'.
group_column : str, optional (default='group')
Column name for group values in match_format='groups'.
count_column : str, optional (default='count')
Column name for count values in match_format='unmatched' or 'groups'.
**pandas_args : optional
Optional arguments to pass to `pandas.read_csv`.
Returns
-------
MatchGroups
A new match groups object built from the csv file.
"""
df = pd.read_csv(filename, **pandas_args, na_filter=False)
df = df.astype(str)
return from_df(df, match_format=match_format, pair_columns=pair_columns,
string_column=string_column, group_column=group_column,
count_column=count_column) |