Strange Error On Some CSV Inputs

**Describe the bug**
Ran into out of range error when using tool with this CSV input:
```
"id","truth_value","family_name","given_name","gender","birth_date","phone","street_address","city","state","postal_code","SSN"
"4_Arlene_Oliver.xml","","oliver","arlene","female","05/26/1972","434-228-8487","845 Collier Pike Center","North Katheryn","WA","99033",""
"21_Raul_Waters.xml","","waters","raul","male","10/31/1972","(878)360-4765","61035 Adell Ranch Wall","Kerlukeburgh","NC","28885",""
"22_Raul_Waters.xml","","waters","raul","male","10/31/1972","(878)360-4765","61035 Adell Ranch Wall","Kerlukeburgh","NC","28885",""
"29_Terrance_Weber.xml","","weber","terrance","male","09/19/1960","903-814-2082","88790 Lemke Trail Road","South Quinton","NE","68843",""
"30_Terrance_Weber.xml","","weber","terrance","male","09/12/1960","903-814-2082","88790 Lemke Trail Road","South Quinton","NE","68843",""
```
This input has no SSN or truth value which may have something to do with the error.

However when i run the script:
python3.11 cli/ecqm_dedupe.py dedupe-data --fmt CSV /tmp/x.csv /tmp/out.csv

Things get very bad (please note that running with the same data i initially sent is working fine) :

```
Format is CSV
Data dir is /tmp/x.csv
----- Estimating u probabilities using random sampling -----
u probability not trained for postal_code - Exact match Postcode District (comparison vector value: 2). This usually means the comparison level was never observed in the training data.
u probability not trained for postal_code - Exact match Postcode Area (comparison vector value: 1). This usually means the comparison level was never observed in the training data.
u probability not trained for postal_code - All other comparisons (comparison vector value: 0). This usually means the comparison level was never observed in the training data.

Estimated u probabilities using random sampling

Your model is not yet fully trained. Missing estimates for:
- given_name (no m values are trained).
- family_name (no m values are trained).
- birth_date (no m values are trained).
- postal_code (some u values are not trained, no m values are trained).
- street_address (no m values are trained).
- phone (no m values are trained).

----- Starting EM training session -----

Estimating the m probabilities of the model by blocking on:
l."ssn" = r."ssn"

Parameter estimates will be made for the following comparison(s):
- given_name
- family_name
- birth_date
- postal_code
- street_address
- phone

Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules:

WARNING:
Level Within 1 month on comparison birth_date not observed in dataset, unable to train m value

WARNING:
Level Within 10 years on comparison birth_date not observed in dataset, unable to train m value

WARNING:
Level Exact match Postcode District on comparison postal_code not observed in dataset, unable to train m value

WARNING:
Level Exact match Postcode Area on comparison postal_code not observed in dataset, unable to train m value

WARNING:
Level All other comparisons on comparison postal_code not observed in dataset, unable to train m value

Iteration 1: Largest change in params was 0.904 in the m_probability of street_address, level `All other comparisons`
Iteration 2: Largest change in params was 0.197 in probability_two_random_records_match
Iteration 3: Largest change in params was 1.06e-09 in probability_two_random_records_match

EM converged after 3 iterations
m probability not trained for birth_date - Within 1 month (comparison vector value: 3). This usually means the comparison level was never observed in the training data.
m probability not trained for birth_date - Within 10 years (comparison vector value: 1). This usually means the comparison level was never observed in the training data.
m probability not trained for postal_code - Exact match Postcode District (comparison vector value: 2). This usually means the comparison level was never observed in the training data.
m probability not trained for postal_code - Exact match Postcode Area (comparison vector value: 1). This usually means the comparison level was never observed in the training data.
m probability not trained for postal_code - All other comparisons (comparison vector value: 0). This usually means the comparison level was never observed in the training data.

Your model is not yet fully trained. Missing estimates for:
- birth_date (some m values are not trained).
- postal_code (some u values are not trained, some m values are not trained).

----- Starting EM training session -----

Estimating the m probabilities of the model by blocking on:
l."birth_date" = r."birth_date"

Parameter estimates will be made for the following comparison(s):
- given_name
- family_name
- postal_code
- street_address
- phone

Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules:
- birth_date

Traceback (most recent call last):
File "/usr/local/lib/python3.11/site-packages/splink/linker.py", line 691, in _log_and_run_sql_execution
return self._run_sql_execution(final_sql, templated_name, physical_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/splink/duckdb/linker.py", line 225, in _run_sql_execution
self._con.sql(final_sql)
duckdb.duckdb.OutOfRangeException: Out of Range Error: cannot take logarithm of zero

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/home/cage/public_html/mdinteractive/extraApps/dedupliFHIR-main/cli/ecqm_dedupe.py", line 190, in
cli()
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1688, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/cage/public_html/mdinteractive-03/extraApps/dedupliFHIR-main/cli/deduplifhirLib/utils.py", line 150, in wrapper
return func(*args,**kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/cage/public_html/mdinteractive/extraApps/dedupliFHIR-main/cli/ecqm_dedupe.py", line 35, in dedupe_data
linker.estimate_parameters_using_expectation_maximisation(
File "/usr/local/lib/python3.11/site-packages/splink/linker.py", line 1682, in estimate_parameters_using_expectation_maximisation
em_training_session._train()
File "/usr/local/lib/python3.11/site-packages/splink/em_training_session.py", line 197, in _train
expectation_maximisation(self, cvv)
File "/usr/local/lib/python3.11/site-packages/splink/expectation_maximisation.py", line 256, in expectation_maximisation
df_params = linker._execute_sql_pipeline([df_comparison_vector_values])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/splink/linker.py", line 629, in _execute_sql_pipeline
raise e
File "/usr/local/lib/python3.11/site-packages/splink/linker.py", line 623, in _execute_sql_pipeline
dataframe = self._sql_to_splink_dataframe_checking_cache(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/splink/linker.py", line 889, in _sql_to_splink_dataframe_checking_cache
splink_dataframe = self._execute_sql_against_backend(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/splink/duckdb/linker.py", line 220, in _execute_sql_against_backend
self._log_and_run_sql_execution(sql, templated_name, physical_name)
File "/usr/local/lib/python3.11/site-packages/splink/linker.py", line 703, in _log_and_run_sql_execution
raise SplinkException(
splink.exceptions.SplinkException: Error executing the following sql for table `__splink__m_u_counts`(__splink__m_u_counts_26a1779fb):
CREATE TABLE __splink__m_u_counts_26a1779fb AS
(
WITH __splink__df_comparison_vectors AS (
SELECT
*
FROM __splink__df_comparison_vectors_a7cd2b9d8
), __splink__df_match_weight_parts AS (
SELECT
"unique_id_l",
"unique_id_r",
gamma_given_name,
CASE
WHEN gamma_given_name = -1
THEN CAST(1.0 AS DOUBLE)
WHEN gamma_given_name = 4
THEN CAST(111.89189187998129 AS DOUBLE)
WHEN gamma_given_name = 3
THEN CAST(81.88130481588395 AS DOUBLE)
WHEN gamma_given_name = 2
THEN CAST(92.09886357253461 AS DOUBLE)
WHEN gamma_given_name = 1
THEN CAST(48.06629076272102 AS DOUBLE)
WHEN gamma_given_name = 0
THEN CAST(4.009431336940681e-229 AS DOUBLE)
END AS bf_given_name,
CASE
WHEN gamma_given_name = -1
THEN CAST(1 AS DOUBLE)
WHEN gamma_given_name = 4
THEN (
CASE
WHEN NOT COALESCE("tf_given_name_l", "tf_given_name_r") IS NULL
THEN POWER(
CAST(0.0014492753623188406 AS DOUBLE) / NULLIF(
(
CASE
WHEN COALESCE("tf_given_name_l", "tf_given_name_r") >= COALESCE("tf_given_name_r", "tf_given_name_l")
THEN COALESCE("tf_given_name_l", "tf_given_name_r")
ELSE COALESCE("tf_given_name_r", "tf_given_name_l")
END
),
0
),
CAST(1.0 AS DOUBLE)
)
ELSE CAST(1 AS DOUBLE)
END
)
WHEN gamma_given_name = 3
THEN CAST(1 AS DOUBLE)
WHEN gamma_given_name = 2
THEN CAST(1 AS DOUBLE)
WHEN gamma_given_name = 1
THEN CAST(1 AS DOUBLE)
WHEN gamma_given_name = 0
THEN CAST(1 AS DOUBLE)
END AS bf_tf_adj_given_name,
gamma_family_name,
CASE
WHEN gamma_family_name = -1
THEN CAST(1.0 AS DOUBLE)
WHEN gamma_family_name = 4
THEN CAST(139.19707503507163 AS DOUBLE)
WHEN gamma_family_name = 3
THEN CAST(115.4071885370217 AS DOUBLE)
WHEN gamma_family_name = 2
THEN CAST(165.4130726624298 AS DOUBLE)
WHEN gamma_family_name = 1
THEN CAST(57.22386641775578 AS DOUBLE)
WHEN gamma_family_name = 0
THEN CAST(3.992587823932763e-229 AS DOUBLE)
END AS bf_family_name,
CASE
WHEN gamma_family_name = -1
THEN CAST(1 AS DOUBLE)
WHEN gamma_family_name = 4
THEN (
CASE
WHEN NOT COALESCE("tf_family_name_l", "tf_family_name_r") IS NULL
THEN POWER(
CAST(0.0011649825407125862 AS DOUBLE) / NULLIF(
(
CASE
WHEN COALESCE("tf_family_name_l", "tf_family_name_r") >= COALESCE("tf_family_name_r", "tf_family_name_l")
THEN COALESCE("tf_family_name_l", "tf_family_name_r")
ELSE COALESCE("tf_family_name_r", "tf_family_name_l")
END
),
0
),
CAST(1.0 AS DOUBLE)
)
ELSE CAST(1 AS DOUBLE)
END
)
WHEN gamma_family_name = 3
THEN CAST(1 AS DOUBLE)
WHEN gamma_family_name = 2
THEN CAST(1 AS DOUBLE)
WHEN gamma_family_name = 1
THEN CAST(1 AS DOUBLE)
WHEN gamma_family_name = 0
THEN CAST(1 AS DOUBLE)
END AS bf_tf_adj_family_name,
gamma_postal_code,
CASE
WHEN gamma_postal_code = -1
THEN CAST(1.0 AS DOUBLE)
WHEN gamma_postal_code = 4
THEN CAST(266.5650741350906 AS DOUBLE)
WHEN gamma_postal_code = 3
THEN CAST(3.970841325158504e-229 AS DOUBLE)
WHEN gamma_postal_code = 2
THEN CAST(1.259921049894873 AS DOUBLE)
WHEN gamma_postal_code = 1
THEN CAST(0.19842513149602492 AS DOUBLE)
WHEN gamma_postal_code = 0
THEN CAST(0.03125 AS DOUBLE)
END AS bf_postal_code,
gamma_street_address,
CASE
WHEN gamma_street_address = -1
THEN CAST(1.0 AS DOUBLE)
WHEN gamma_street_address = 1
THEN CAST(259.14714720358563 AS DOUBLE)
WHEN gamma_street_address = 0
THEN CAST(0.9640980278331669 AS DOUBLE)
END AS bf_street_address,
CASE
WHEN gamma_street_address = -1
THEN CAST(1 AS DOUBLE)
WHEN gamma_street_address = 1
THEN (
CASE
WHEN NOT COALESCE("tf_street_address_l", "tf_street_address_r") IS NULL
THEN POWER(
CAST(0.00013905627143784185 AS DOUBLE) / NULLIF(
(
CASE
WHEN COALESCE("tf_street_address_l", "tf_street_address_r") >= COALESCE("tf_street_address_r", "tf_street_address_l")
THEN COALESCE("tf_street_address_l", "tf_street_address_r")
ELSE COALESCE("tf_street_address_r", "tf_street_address_l")
END
),
0
),
CAST(1.0 AS DOUBLE)
)
ELSE CAST(1 AS DOUBLE)
END
)
WHEN gamma_street_address = 0
THEN CAST(1 AS DOUBLE)
END AS bf_tf_adj_street_address,
gamma_phone,
CASE
WHEN gamma_phone = -1
THEN CAST(1.0 AS DOUBLE)
WHEN gamma_phone = 1
THEN CAST(1.7515202196470399 AS DOUBLE)
WHEN gamma_phone = 0
THEN CAST(0.5579969710188165 AS DOUBLE)
END AS bf_phone,
CASE
WHEN gamma_phone = -1
THEN CAST(1 AS DOUBLE)
WHEN gamma_phone = 1
THEN (
CASE
WHEN NOT COALESCE("tf_phone_l", "tf_phone_r") IS NULL
THEN POWER(
CAST(0.3703346620932604 AS DOUBLE) / NULLIF(
(
CASE
WHEN COALESCE("tf_phone_l", "tf_phone_r") >= COALESCE("tf_phone_r", "tf_phone_l")
THEN COALESCE("tf_phone_l", "tf_phone_r")
ELSE COALESCE("tf_phone_r", "tf_phone_l")
END
),
0
),
CAST(1.0 AS DOUBLE)
)
ELSE CAST(1 AS DOUBLE)
END
)
WHEN gamma_phone = 0
THEN CAST(1 AS DOUBLE)
END AS bf_tf_adj_phone,
"ssn_l",
"ssn_r",
match_key
FROM __splink__df_comparison_vectors
), __splink__df_predict AS (
SELECT
LOG(
2,
CAST(0.02611780678018568 AS DOUBLE) * bf_given_name * bf_tf_adj_given_name * bf_family_name * bf_tf_adj_family_name * bf_postal_code * bf_street_address * bf_tf_adj_street_address * bf_phone * bf_tf_adj_phone
) AS match_weight,
CASE
WHEN bf_given_name = CAST('infinity' AS DOUBLE)
OR bf_tf_adj_given_name = CAST('infinity' AS DOUBLE)
OR bf_family_name = CAST('infinity' AS DOUBLE)
OR bf_tf_adj_family_name = CAST('infinity' AS DOUBLE)
OR bf_postal_code = CAST('infinity' AS DOUBLE)
OR bf_street_address = CAST('infinity' AS DOUBLE)
OR bf_tf_adj_street_address = CAST('infinity' AS DOUBLE)
OR bf_phone = CAST('infinity' AS DOUBLE)
OR bf_tf_adj_phone = CAST('infinity' AS DOUBLE)
THEN 1.0
ELSE (
CAST(0.02611780678018568 AS DOUBLE) * bf_given_name * bf_tf_adj_given_name * bf_family_name * bf_tf_adj_family_name * bf_postal_code * bf_street_address * bf_tf_adj_street_address * bf_phone * bf_tf_adj_phone
) / NULLIF(
(
1 + (
CAST(0.02611780678018568 AS DOUBLE) * bf_given_name * bf_tf_adj_given_name * bf_family_name * bf_tf_adj_family_name * bf_postal_code * bf_street_address * bf_tf_adj_street_address * bf_phone * bf_tf_adj_phone
)
),
0
)
END AS match_probability,
"unique_id_l",
"unique_id_r",
gamma_given_name,
gamma_family_name,
gamma_postal_code,
gamma_street_address,
gamma_phone,
"ssn_l",
"ssn_r",
match_key
FROM __splink__df_match_weight_parts
ORDER BY
1 NULLS LAST
)
SELECT
gamma_given_name AS comparison_vector_value,
SUM(match_probability * 1) AS m_count,
SUM((
1 - match_probability
) * 1) AS u_count,
'given_name' AS output_column_name
FROM __splink__df_predict
GROUP BY
gamma_given_name
UNION ALL
SELECT
gamma_family_name AS comparison_vector_value,
SUM(match_probability * 1) AS m_count,
SUM((
1 - match_probability
) * 1) AS u_count,
'family_name' AS output_column_name
FROM __splink__df_predict
GROUP BY
gamma_family_name
UNION ALL
SELECT
gamma_postal_code AS comparison_vector_value,
SUM(match_probability * 1) AS m_count,
SUM((
1 - match_probability
) * 1) AS u_count,
'postal_code' AS output_column_name
FROM __splink__df_predict
GROUP BY
gamma_postal_code
UNION ALL
SELECT
gamma_street_address AS comparison_vector_value,
SUM(match_probability * 1) AS m_count,
SUM((
1 - match_probability
) * 1) AS u_count,
'street_address' AS output_column_name
FROM __splink__df_predict
GROUP BY
gamma_street_address
UNION ALL
SELECT
gamma_phone AS comparison_vector_value,
SUM(match_probability * 1) AS m_count,
SUM((
1 - match_probability
) * 1) AS u_count,
'phone' AS output_column_name
FROM __splink__df_predict
GROUP BY
gamma_phone
UNION ALL
SELECT
0 AS comparison_vector_value,
SUM(match_probability * 1) / NULLIF(SUM(1), 0) AS m_count,
SUM((
1 - match_probability
) * 1) / NULLIF(SUM(1), 0) AS u_count,
'_probability_two_random_records_match' AS output_column_name
FROM __splink__df_predict
)

Error was: Out of Range Error: cannot take logarithm of zero
```
**To Reproduce**
Use the above input with this CLI call:
`python3.11 cli/ecqm_dedupe.py dedupe-data --fmt CSV /tmp/x.csv /tmp/out.csv`

**Expected behavior**
The CLI should output the deduplicated data normally.

**Actual behavior**
The tool throws an out of range error