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Code Example
Here is an example of a well-documented script.
Key things to notice:
- Docstring for the script at the top of the file
- Imports organized: standard library, general imports, custom imports
- Each function has type hints and docstrings
- Most of the function explanation is in the docstring with inline comments added for further clarification
- The “sample usage” section in the docstring has doctests. While we do not require doctests for the clinic this type of testing is standard in practice.
```python “"”Module for performing record linkage on state campaign finance dataset”””
import re from collections.abc import Callable
import numpy as np import pandas as pd import textdistance as td import usaddress from splink.duckdb.linker import DuckDBLinker
from utils.constants import BASE_FILEPATH, COMPANY_TYPES, suffixes, titles
def get_address_line_1_from_full_address(address: str) -> str: “"”Given a full address, return the first line of the formatted address
Address line 1 usually includes street address or PO Box information.
Uses the usaddress libray which splits an address string into components,
and labels each component.
https://usaddress.readthedocs.io/en/latest/
Args:
address: raw string representing full address
Returns:
address_line_1 as a string
Sample Usage:
>>> get_address_line_1_from_full_address('6727 W. Corrine Dr. Peoria,AZ 85381')
'6727 W. Corrine Dr.'
>>> get_address_line_1_from_full_address('P.O. Box 5456 Sun City West ,AZ 85375')
'P.O. Box 5456'
>>> get_address_line_1_from_full_address('119 S 5th St Niles,MI 49120')
'119 S 5th St'
>>> get_address_line_1_from_full_address(
... '1415 PARKER STREET APT 251 DETROIT MI 48214-0000'
... )
'1415 PARKER STREET'
"""
parsed_address = usaddress.parse(address)
line1_components = [
value
for value, key in parsed_address
if key
in (
"AddressNumber",
"StreetNamePreDirectional",
"StreetName",
"StreetNamePostType",
"USPSBoxType",
"USPSBoxID",
)
]
# halting at first occurrence of "PlaceName" or continue until end if not found
place_name_index = next(
(i for i, (_, key) in enumerate(parsed_address) if key == "PlaceName"), None
)
if place_name_index is not None:
line1_components = line1_components[:place_name_index]
return " ".join(line1_components)
def calculate_string_similarity(string1: str, string2: str) -> float: “"”Returns how similar two strings are on a scale of 0 to 1
This version utilizes Jaro-Winkler distance, which is a metric of
edit distance. Jaro-Winkler specially prioritizes the early
characters in a string.
Since the ends of strings are often more valuable in matching names
and addresses, we reverse the strings before matching them.
https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance
https://github.com/Yomguithereal/talisman/blob/master/src/metrics/jaro-winkler.js
The exact meaning of the metric is open, but the following must hold true:
1. equivalent strings must return 1
2. strings with no similar characters must return 0
3. strings with higher intuitive similarity must return higher scores
similarity score
Args:
string1: any string
string2: any string
Returns:
similarity score
Sample Usage:
>>> calculate_string_similarity("exact match", "exact match")
1.0
>>> calculate_string_similarity("aaaaaa", "bbbbbbbbbbb")
0.0
>>> similar_score = calculate_string_similarity("very similar", "vary similar")
>>> different_score = calculate_string_similarity("very similar", "very not close")
>>> similar_score > different_score
True
"""
return float(td.jaro_winkler(string1.lower()[::-1], string2.lower()[::-1]))
def calculate_row_similarity( row1: pd.DataFrame, row2: pd.DataFrame, weights: np.array, comparison_func: Callable ) -> float: “"”Find weighted similarity of two rows in a dataframe
The length of the weights vector must be the same as
the number of selected columns.
This version is slow and not optimized, and will be
revised in order to make it more efficient. It
exists as to provide basic functionality. Once we have
the comparison function locked in, using .apply will
likely be easier and more efficient.
"""
row_length = len(weights)
if not (row1.shape[1] == row2.shape[1] == row_length):
raise ValueError("Number of columns and weights must be the same")
similarity = np.zeros(row_length)
for i in range(row_length):
similarity[i] = comparison_func(
row1.reset_index().drop(columns="index").iloc[:, i][0],
row2.reset_index().drop(columns="index").iloc[:, i][0],
)
return sum(similarity * weights)
def row_matches( df: pd.DataFrame, weights: np.array, threshold: float, comparison_func: Callable ) -> dict: “"”Get weighted similarity score of two rows
Run through the rows using indices: if two rows have a comparison score
greater than a threshold, we assign the later row to the former. Any
row which is matched to any other row is not examined again. Matches are
stored in a dictionary object, with each index appearing no more than once.
This is not optimized. Not presently sure how to make a good test case
for this, will submit and ask in mentor session.
"""
all_indices = np.array(list(df.index))
index_dict = {}
[index_dict.setdefault(x, []) for x in all_indices]
discard_indices = []
end = max(all_indices)
for i in all_indices:
# Skip indices that have been stored in the discard_indices list
if i in discard_indices:
continue
# Iterate through the remaining numbers
for j in range(i + 1, end):
if j in discard_indices:
continue
# Our conditional
if (
calculate_row_similarity(
df.iloc[[i]], df.iloc[[j]], weights, comparison_func
)
> threshold
):
# Store the other index and mark it for skipping in future iterations
discard_indices.append(j)
index_dict[i].append(j)
return index_dict
def match_confidence( confidences: np.ndarray, weights: np.ndarray, weights_toggle: bool ) -> float: “"”Combine confidences for row matches into a final confidence
This is a weighted log-odds based combination of row match confidences
originating from various record linkage methods. Weights will be applied
to the linkage methods in order and must be of the same length.
weights_toggle allows one to turn weights on and off when calling the
function. False cancels the use of weights.
Since log-odds have undesirable behaviors at 0 and 1, we truncate at
+-5, which corresponds to around half a percent probability or
1 - the same.
>>> match_confidence(np.array([.6, .9, .0001]), np.array([2,5.7,8]), True)
2.627759082143462e-12
>>> match_confidence(np.array([.6, .9, .0001]), np.array([2,5.7,8]), False)
0.08337802853594725
"""
if not (0 <= confidences).all() and (confidences <= 1).all():
raise ValueError("Probabilities must be bounded on [0, 1]")
log_odds = np.clip(
np.log(confidences / (1 - confidences)), -5, 5
) # specified max logit = 5
if weights_toggle:
log_odds *= weights
return np.exp(log_odds.sum()) / (1 + np.exp(log_odds.sum()))
def determine_comma_role(name: str) -> str: “"”Given a name, determine purpose of comma (“last, first”, “first last, jr.”, etc)
Some assumptions are made:
* If a suffix is included in the name and the name is not just the last
name(i.e "Doe, Jr), the format is
(last_name suffix, first and middle name) i.e Doe iv, Jane Elisabeth
* If a comma is used anywhere else, it is in the format of
(last_name, first and middle name) i.e Doe, Jane Elisabeth
Args:
name: a string representing a name/names of individuals
Returns:
the name with or without a comma based on some conditions
Sample Usage:
>>> determine_comma_role("Jane Doe, Jr")
'Jane Doe, Jr'
>>> determine_comma_role("Doe, Jane Elisabeth")
' Jane Elisabeth Doe'
>>> determine_comma_role("Jane Doe,")
'Jane Doe'
>>> determine_comma_role("DOe, Jane")
' Jane Doe'
"""
name_parts = name.split(",")
last_name, remainder = name_parts[0], " ".join(name_parts[1:]).strip()
if not remainder:
return name.title()
if remainder.lower() in suffixes:
return name.title()
return f"{remainder.title()} {last_name.title()}".strip()
def get_likely_name(first_name: str, last_name: str, full_name: str) -> str: “"”Given name related columns, return a person’s likely name
Given different formatting used accross states, errors in data entry
and missing data, it can be difficult to determine someone's actual
name. For example, some states have a last name column with values like
"Doe, Jane", where the person's first name appears to have been erroneously
included.
Args:
first_name: raw value of first name column
last_name: raw value last name column
full_name: raw value of name or full_name column
Returns:
The most likely full name of the person listed
Sample Usage:
>>> get_likely_name("Jane", "Doe", "")
'Jane Doe'
>>> get_likely_name("", "", "Jane Doe")
'Jane Doe'
>>> get_likely_name("", "Doe, Jane", "")
'Jane Doe'
>>> get_likely_name("Jane Doe", "Doe", "Jane Doe")
'Jane Doe'
>>> get_likely_name("Jane","","Doe, Sr")
'Jane Doe, Sr'
>>> get_likely_name("Jane Elisabeth Doe, IV","Elisabeth","Doe, IV")
'Jane Elisabeth Doe, Iv'
>>> get_likely_name("","","Jane Elisabeth Doe, IV")
'Jane Elisabeth Doe, Iv'
>>> get_likely_name("Jane","","Doe, Jane, Elisabeth")
'Jane Elisabeth Doe'
"""
# first, convert any Nans to empty strings ''
first_name, last_name, full_name = (
"" if x is np.NAN else x for x in [first_name, last_name, full_name]
)
# second, ensure clean input by deleting spaces:
first_name, last_name, full_name = (
x.lower().strip() for x in [first_name, last_name, full_name]
)
# if data is clean:
if first_name + " " + last_name == full_name:
return full_name.title()
# remove titles or professions from the name
names = [first_name, last_name, full_name]
for i in range(len(names)):
# if there is a ',' deal with it accordingly
if "," in names[i]:
names[i] = determine_comma_role(names[i])
names[i] = names[i].replace(".", "").split(" ")
names[i] = [name_part for name_part in names[i] if name_part not in titles]
names[i] = " ".join(names[i])
# one last check to remove any pieces that might add extra whitespace
names = list(filter(lambda x: x != "", names))
names = " ".join(names)
names = names.title().replace(" ", " ").split(" ")
final_name = []
[final_name.append(x) for x in names if x not in final_name]
return " ".join(final_name).strip()
def get_street_from_address_line_1(address_line_1: str) -> str: “"”Given an address line 1, return the street name
Uses the usaddress libray which splits an address string into components,
and labels each component.
https://usaddress.readthedocs.io/en/latest/
Args:
address_line_1: either street information or PO box as a string
Returns:
street name as a string
Raises:
ValueError: if string is malformed and no street can be reasonably
found.
>>> get_street_from_address_line_1("5645 N. UBER ST")
'UBER ST'
>>> get_street_from_address_line_1("")
Traceback (most recent call last):
...
ValueError: address_line_1 must have whitespace
>>> get_street_from_address_line_1("PO Box 1111")
Traceback (most recent call last):
...
ValueError: address_line_1 is PO Box
>>> get_street_from_address_line_1("300 59 St.")
'59 St.'
>>> get_street_from_address_line_1("Uber St.")
'Uber St.'
>>> get_street_from_address_line_1("3NW 59th St")
'59th St'
"""
if not address_line_1.strip():
raise ValueError("address_line_1 must have content")
parsed_address = usaddress.parse(address_line_1)
street_components = [
value
for value, key in parsed_address
if key in ["StreetName", "StreetNamePostType"]
]
if not street_components or "po box" in address_line_1.lower():
raise ValueError("Valid street name not found or address_line_1 is a PO Box")
return " ".join(street_components)
def convert_duplicates_to_dict(df_with_matches: pd.DataFrame) -> None: “"”Map each uuid to all other uuids for which it has been deemed a match
Given a dataframe where the uuids of all rows deemed similar are stored in a
list and all but the first row of each paired uuid is dropped, this function
maps the matched uuids to a single uuid.
Args:
df_with_matches: A pandas df containing a column called 'duplicated',
where each row is a list of all uuids deemed a match. In each list,
all uuids but the first have their rows already dropped.
Returns:
None. However it outputs a file to the output directory, with 2
columns. The first lists all the uuids in df, and is labeled
'original_uuids.' The 2nd shows the uuids to which each entry is mapped
to, and is labeled 'mapped_uuids'.
"""
deduped_dict = {}
for i in range(len(df_with_matches)):
deduped_uudis = df_with_matches.iloc[i]["duplicated"]
for j in range(len(deduped_uudis)):
deduped_dict.update({deduped_uudis[j]: df_with_matches.iloc[i]["id"]})
# now convert dictionary into a csv file
deduped_df = pd.DataFrame.from_dict(deduped_dict, "index")
deduped_df = deduped_df.reset_index().rename(
columns={"index": "original_uuids", 0: "mapped_uuid"}
)
deduped_df.to_csv(
BASE_FILEPATH / "output" / "deduplicated_UUIDs.csv",
index=False,
mode="a",
)
def deduplicate_perfect_matches(df: pd.DataFrame) -> pd.DataFrame: “"”Return a dataframe with duplicated entries removed.
Given a dataframe, combines rows that have identical data beyond their
UUIDs, keeps the first UUID amond the similarly grouped UUIDs, and saves the
rest of the UUIDS to a file in the "output" directory linking them to the
first selected UUID.
Args:
df: a pandas dataframe containing contribution data
Returns:
a deduplicated pandas dataframe containing contribution data
"""
# first remove all duplicate entries:
new_df = df.drop_duplicates()
# find the duplicates along all columns but the id
new_df = (
new_df.groupby(df.columns.difference(["id"]).tolist(), dropna=False)["id"]
.agg(list)
.reset_index()
.rename(columns={"id": "duplicated"})
)
new_df.index = new_df["duplicated"].str[0].tolist()
# convert the duplicated column into a dictionary that can will be
# an output by only feeding the entries with duplicates
new_df = new_df.reset_index().rename(columns={"index": "id"})
convert_duplicates_to_dict(new_df[["id", "duplicated"]])
new_df = new_df.drop(["duplicated"], axis=1)
return new_df
def cleaning_company_column(company_entry: str) -> str: “"”Check if string contains abbreviation of common employment state
Args:
company_entry: string of inputted company names
Returns:
standardized for retired, self employed, and unemployed,
or original string if no match or empty string
Sample Usage:
>>> cleaning_company_column("Retireed")
'Retired'
>>> cleaning_company_column("self")
'Self Employed'
>>> cleaning_company_column("None")
'Unemployed'
>>> cleaning_company_column("N/A")
'Unemployed'
"""
if not company_entry:
return company_entry
company_edited = company_entry.lower()
if company_edited == "n/a":
return "Unemployed"
company_edited = re.sub(r"[^\w\s]", "", company_edited)
if (
company_edited == "retired"
or company_edited == "retiree"
or company_edited == "retire"
or "retiree" in company_edited
):
return "Retired"
elif (
"self employe" in company_edited
or "freelance" in company_edited
or company_edited == "self"
or company_edited == "independent contractor"
):
return "Self Employed"
elif (
"unemploye" in company_edited
or company_edited == "none"
or company_edited == "not employed"
or company_edited == "nan"
):
return "Unemployed"
else:
return company_edited
def standardize_corp_names(company_name: str) -> str: “"”Given an employer name, return the standardized version
Args:
company_name: corporate name
Returns:
standardized company name
Sample Usage:
>>> standardize_corp_names('MI BEER WINE WHOLESALERS ASSOC')
'MI BEER WINE WHOLESALERS ASSOCIATION'
>>> standardize_corp_names('MI COMMUNITY COLLEGE ASSOCIATION')
'MI COMMUNITY COLLEGE ASSOCIATION'
>>> standardize_corp_names('STEPHANIES CHANGEMAKER FUND')
'STEPHANIES CHANGEMAKER FUND'
"""
company_name_split = company_name.upper().split(" ")
for i in range(len(company_name_split)):
if company_name_split[i] in list(COMPANY_TYPES.keys()):
hold = company_name_split[i]
company_name_split[i] = COMPANY_TYPES[hold]
new_company_name = " ".join(company_name_split)
return new_company_name
def get_address_number_from_address_line_1(address_line_1: str) -> str: “"”Given an address line 1, return the building number or po box
Uses the usaddress libray which splits an address string into components,
and labels each component.
https://usaddress.readthedocs.io/en/latest/
Args:
address_line_1: either street information or PO box
Returns:
address or po box number
Sample Usage:
>>> get_address_number_from_address_line_1('6727 W. Corrine Dr. Peoria,AZ 85381')
'6727'
>>> get_address_number_from_address_line_1('P.O. Box 5456 Sun City West ,AZ 85375')
'5456'
>>> get_address_number_from_address_line_1('119 S 5th St Niles,MI 49120')
'119'
>>> get_address_number_from_address_line_1(
... '1415 PARKER STREET APT 251 DETROIT MI 48214-0000'
... )
'1415'
"""
address_line_1_components = usaddress.parse(address_line_1)
for i in range(len(address_line_1_components)):
if address_line_1_components[i][1] == "AddressNumber":
return address_line_1_components[i][0]
elif address_line_1_components[i][1] == "USPSBoxID":
return address_line_1_components[i][0]
raise ValueError("Cannot find Address Number")
def splink_dedupe(df: pd.DataFrame, settings: dict, blocking: list) -> pd.DataFrame: “"”Use splink to deduplicate dataframe based on settings
Configuration settings and blocking can be found in constants.py as
individuals_settings, indivduals_blocking, organizations_settings,
organizations_blocking
Uses the splink library which employs probabilistic matching for
record linkage
https://moj-analytical-services.github.io/splink/index.html
Args:
df: dataframe
settings: configuration settings
(based on splink documentation and dataframe columns)
blocking: list of columns to block on for the table
(cuts dataframe into parts based on columns labeled blocks)
Returns:
deduplicated version of initial dataframe with column 'matching_id'
that holds list of matching unique_ids
"""
linker = DuckDBLinker(df, settings)
linker.estimate_probability_two_random_records_match(
blocking, recall=0.6
) # default
linker.estimate_u_using_random_sampling(max_pairs=5e6)
for i in blocking:
linker.estimate_parameters_using_expectation_maximisation(i)
df_predict = linker.predict()
clusters = linker.cluster_pairwise_predictions_at_threshold(
df_predict, threshold_match_probability=0.7
) # default
clusters_df = clusters.as_pandas_dataframe()
match_list_df = (
clusters_df.groupby("cluster_id")["unique_id"].agg(list).reset_index()
) # dataframe where cluster_id maps unique_id to initial instance of row
match_list_df = match_list_df.rename(columns={"unique_id": "duplicated"})
first_instance_df = clusters_df.drop_duplicates(subset="cluster_id")
col_names = np.append("cluster_id", df.columns)
first_instance_df = first_instance_df[col_names]
deduped_df = first_instance_df.merge(
match_list_df[["cluster_id", "duplicated"]],
on="cluster_id",
how="left",
)
deduped_df = deduped_df.rename(columns={"cluster_id": "unique_id"})
deduped_df["duplicated"] = deduped_df["duplicated"].apply(
lambda x: x if isinstance(x, list) else [x]
)
convert_duplicates_to_dict(deduped_df)
deduped_df = deduped_df.drop(columns=["duplicated"])
return deduped_df