Skip to content

Documentation for ComparisonLevel object

Each ComparisonLevel defines a gradation (category) of similarity within a Comparison.

For example, a Comparison that uses the first_name and surname columns may define three ComparisonLevels: An exact match on first name and surname First name and surname have a JaroWinkler score of above 0.95 All other comparisons

The method used to assess similarity will depend on the type of data - for instance, the method used to assess similarity of a company's turnover would be different to the method used to assess the similarity of a person's first name.

To summarise:

Data Linking Model
├─-- Comparison: Name
│    ├─-- ComparisonLevel: Exact match on first_name and surname
│    ├─-- ComparisonLevel: first_name and surname have JaroWinkler > 0.95
│    ├─-- ComparisonLevel: All other
├─-- Comparison: Date of birth
│    ├─-- ComparisonLevel: Exact match
│    ├─-- ComparisonLevel: One character difference
│    ├─-- ComparisonLevel: All other
├─-- etc.
Source code in splink/comparison_level.py
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
class ComparisonLevel:
    """Each ComparisonLevel defines a gradation (category) of similarity within a
    `Comparison`.

    For example, a `Comparison` that uses the first_name and surname columns may
    define three `ComparisonLevel`s:
        An exact match on first name and surname
        First name and surname have a JaroWinkler score of above 0.95
        All other comparisons

    The method used to assess similarity will depend on the type of data - for
    instance, the method used to assess similarity of a company's turnover would be
    different to the method used to assess the similarity of a person's first name.

    To summarise:

    ```
    Data Linking Model
    ├─-- Comparison: Name
    │    ├─-- ComparisonLevel: Exact match on first_name and surname
    │    ├─-- ComparisonLevel: first_name and surname have JaroWinkler > 0.95
    │    ├─-- ComparisonLevel: All other
    ├─-- Comparison: Date of birth
    │    ├─-- ComparisonLevel: Exact match
    │    ├─-- ComparisonLevel: One character difference
    │    ├─-- ComparisonLevel: All other
    ├─-- etc.
    ```
    """

    def __init__(
        self,
        level_dict,
        comparison: "Comparison" = None,
        sql_dialect: str = None,
    ):

        # Protected, because we don't want to modify the original dict
        self._level_dict = level_dict

        self.comparison: "Comparison" = comparison
        self._sql_dialect = sql_dialect

        self._sql_condition = self._level_dict["sql_condition"]
        self._is_null_level = self._level_dict_val_else_default("is_null_level")
        self._tf_adjustment_weight = self._level_dict_val_else_default(
            "tf_adjustment_weight"
        )

        self._tf_minimum_u_value = self._level_dict_val_else_default(
            "tf_minimum_u_value"
        )

        # Private values controlled with getter/setter
        self._m_probability = self._level_dict.get("m_probability")
        self._u_probability = self._level_dict.get("u_probability")

        # These will be set when the ComparisonLevel is passed into a Comparison
        self._comparison_vector_value: int = None
        self._max_level: bool = None

        # Enable the level to 'know' when it's been trained
        self._trained_m_probabilities: list = []
        self._trained_u_probabilities: list = []

        self._validate()

    def _level_dict_val_else_default(self, key):
        val = self._level_dict.get(key)
        if not val:
            val = default_value_from_schema(key, "comparison_level")
        return val

    @property
    def _tf_adjustment_input_column(self):

        val = self._level_dict_val_else_default("tf_adjustment_column")
        if val:
            return InputColumn(val, sql_dialect=self._sql_dialect)
        else:
            return None

    @property
    def _tf_adjustment_input_column_name(self):
        input_column = self._tf_adjustment_input_column
        if input_column:
            return input_column.unquote().name()

    @property
    def _has_comparison(self):
        from .comparison import Comparison

        return isinstance(self.comparison, Comparison)

    @property
    def m_probability(self):
        if self._is_null_level:
            return None
        if self._m_probability == "level not observed in training dataset":
            return 1e-6
        if self._m_probability is None and self._has_comparison:
            vals = _default_m_values(self.comparison._num_levels)
            return vals[self._comparison_vector_value]
        return self._m_probability

    @m_probability.setter
    def m_probability(self, value):
        if self._is_null_level:
            raise AttributeError("Cannot set m_probability when is_null_level is true")
        if value == "level not observed in training dataset":
            cc_n = self.comparison._output_column_name
            cl_n = self._label_for_charts
            logger.warning(
                "\nWARNING:\n"
                f"Level {cl_n} on comparison {cc_n} not observed in dataset, "
                "unable to train m value"
            )

        self._m_probability = value

    @property
    def u_probability(self):
        if self._is_null_level:
            return None
        if self._u_probability == "level not observed in training dataset":
            return 1e-6
        if self._u_probability is None:
            vals = _default_u_values(self.comparison._num_levels)
            return vals[self._comparison_vector_value]
        return self._u_probability

    @u_probability.setter
    def u_probability(self, value):
        if self._is_null_level:
            raise AttributeError("Cannot set u_probability when is_null_level is true")
        if value == "level not observed in training dataset":
            cc_n = self.comparison._output_column_name
            cl_n = self._label_for_charts
            logger.warning(
                "\nWARNING:\n"
                f"Level {cl_n} on comparison {cc_n} not observed in dataset, "
                "unable to train u value"
            )
        self._u_probability = value

    @property
    def _m_probability_description(self):
        if self.m_probability is not None:
            return (
                "Amongst matching record comparisons, "
                f"{self.m_probability:.2%} of records are in the "
                f"{self._label_for_charts.lower()} comparison level"
            )

    @property
    def _u_probability_description(self):
        if self.u_probability is not None:
            return (
                "Amongst non-matching record comparisons, "
                f"{self.u_probability:.2%} of records are in the "
                f"{self._label_for_charts.lower()} comparison level"
            )

    def _add_trained_u_probability(self, val, desc="no description given"):

        self._trained_u_probabilities.append(
            {"probability": val, "description": desc, "m_or_u": "u"}
        )

    def _add_trained_m_probability(self, val, desc="no description given"):

        self._trained_m_probabilities.append(
            {"probability": val, "description": desc, "m_or_u": "m"}
        )

    @property
    def _has_estimated_u_values(self):
        if self._is_null_level:
            return True
        vals = [r["probability"] for r in self._trained_u_probabilities]
        vals = [v for v in vals if isinstance(v, (int, float))]
        return len(vals) > 0

    @property
    def _has_estimated_m_values(self):
        if self._is_null_level:
            return True
        vals = [r["probability"] for r in self._trained_m_probabilities]
        vals = [v for v in vals if isinstance(v, (int, float))]
        return len(vals) > 0

    @property
    def _has_estimated_values(self):
        return self._has_estimated_m_values and self._has_estimated_u_values

    @property
    def _trained_m_median(self):
        vals = [r["probability"] for r in self._trained_m_probabilities]
        vals = [v for v in vals if isinstance(v, (int, float))]
        if len(vals) == 0:
            return None
        return median(vals)

    @property
    def _trained_u_median(self):
        vals = [r["probability"] for r in self._trained_u_probabilities]
        vals = [v for v in vals if isinstance(v, (int, float))]
        if len(vals) == 0:
            return None
        return median(vals)

    @property
    def _m_is_trained(self):
        if self._is_null_level:
            return True
        if self._m_probability == "level not observed in data":
            return False
        if self._m_probability is None:
            return False
        return True

    @property
    def _u_is_trained(self):
        if self._is_null_level:
            return True
        if self._u_probability == "level not observed in data":
            return False
        if self._u_probability is None:
            return False
        return True

    @property
    def _is_trained(self):
        return self._m_is_trained and self._u_is_trained

    @property
    def _bayes_factor(self):
        if self._is_null_level:
            return 1.0
        if self.m_probability is None or self.u_probability is None:
            return None
        else:
            return self.m_probability / self.u_probability

    @property
    def _log2_bayes_factor(self):
        if self._is_null_level:
            return 0.0
        else:
            return math.log2(self._bayes_factor)

    @property
    def _bayes_factor_description(self):
        text = (
            f"If comparison level is `{self._label_for_charts.lower()}` "
            "then comparison is"
        )
        if self._bayes_factor >= 1.0:
            return f"{text} {self._bayes_factor:,.2f} times more likely to be a match"
        else:
            mult = 1 / self._bayes_factor
            return f"{text}  {mult:,.2f} times less likely to be a match"

    @property
    def _label_for_charts(self):
        return self._level_dict.get(
            "label_for_charts", str(self._comparison_vector_value)
        )

    @property
    def _is_else_level(self):
        if self._sql_condition.strip().upper() == "ELSE":
            return True

    @property
    def _has_tf_adjustments(self):
        col = self._level_dict.get("tf_adjustment_column")
        return col is not None

    def _validate_sql(self):
        sql = self._sql_condition
        if self._is_else_level:
            return True

        try:
            sqlglot.parse_one(sql, read=self._sql_dialect)
        except sqlglot.ParseError as e:
            raise ValueError(f"Error parsing sql_statement:\n{sql}") from e

        return True

    @property
    def _input_columns_used_by_sql_condition(self) -> List[InputColumn]:
        # returns e.g. InputColumn(first_name), InputColumn(surname)

        if self._is_else_level:
            return []

        cols = get_columns_used_from_sql(self._sql_condition, dialect=self._sql_dialect)
        # Parsed order seems to be roughly in reverse order of apearance
        cols = cols[::-1]

        cols = [re.sub(r"_L$|_R$", "", c, flags=re.IGNORECASE) for c in cols]
        cols = dedupe_preserving_order(cols)

        input_cols = []
        for c in cols:

            # We could have tf adjustments for surname on a dmeta_surname column
            # If so, we want to set the tf adjustments against the surname col,
            # not the dmeta_surname one

            input_cols.append(InputColumn(c, sql_dialect=self._sql_dialect))

        return input_cols

    @property
    def _columns_to_select_for_blocking(self):
        # e.g. l.first_name as first_name_l, r.first_name as first_name_r
        output_cols = []
        cols = self._input_columns_used_by_sql_condition

        for c in cols:
            output_cols.extend(c.l_r_names_as_l_r())
            if self._tf_adjustment_input_column:
                output_cols.extend(c.l_r_tf_names_as_l_r())

        return dedupe_preserving_order(output_cols)

    @property
    def _when_then_comparison_vector_value_sql(self):
        # e.g. when first_name_l = first_name_r then 1
        if not hasattr(self, "_comparison_vector_value"):
            raise ValueError(
                "Cannot get the 'when .. then ...' sql expression because "
                "this comparison level does not belong to a parent Comparison. "
                "The comparison_vector_value is only defined in the "
                "context of a list of ComparisonLevels within a Comparison."
            )
        if self._is_else_level:
            return f"{self._sql_condition} {self._comparison_vector_value}"
        else:
            return f"WHEN {self._sql_condition} THEN {self._comparison_vector_value}"

    @property
    def _is_exact_match(self):
        if self._is_else_level:
            return False

        sqls = re.split(r" and ", self._sql_condition, flags=re.IGNORECASE)
        for sql in sqls:
            if not _is_exact_match(sql, self._sql_dialect):
                return False
        return True

    @property
    def _exact_match_colnames(self):

        sqls = re.split(r" and ", self._sql_condition, flags=re.IGNORECASE)
        for sql in sqls:
            if not _is_exact_match(sql):
                raise ValueError(
                    "sql_cond not an exact match so can't get exact match column name"
                )

        cols = []
        for sql in sqls:
            col = _exact_match_colname(sql, sql_dialect=self._sql_dialect)
            cols.append(col)
        return cols

    @property
    def _u_probability_corresponding_to_exact_match(self):
        levels = self.comparison.comparison_levels

        # Find a level with a single exact match colname
        # which is equal to the tf adjustment input colname

        for level in levels:
            if not level._is_exact_match:
                continue
            colnames = level._exact_match_colnames
            if len(colnames) != 1:
                continue
            if colnames[0] == self._tf_adjustment_input_column_name.lower():
                return level.u_probability
        raise ValueError(
            "Could not find an exact match level for "
            f"{self._tf_adjustment_input_column_name}."
            "\nAn exact match level is required to make a term frequency adjustment "
            "on a comparison level that is not an exact match."
        )

    @property
    def _bayes_factor_sql(self):
        sql = f"""
        WHEN
        {self.comparison._gamma_column_name} = {self._comparison_vector_value}
        THEN cast({self._bayes_factor} as double)
        """
        return dedent(sql)

    @property
    def _tf_adjustment_sql(self):
        gamma_column_name = self.comparison._gamma_column_name
        gamma_colname_value_is_this_level = (
            f"{gamma_column_name} = {self._comparison_vector_value}"
        )

        # A tf adjustment of 1D is a multiplier of 1.0, i.e. no adjustment
        if self._comparison_vector_value == -1:
            sql = f"WHEN  {gamma_colname_value_is_this_level} then cast(1 as double)"
        elif not self._has_tf_adjustments:
            sql = f"WHEN  {gamma_colname_value_is_this_level} then cast(1 as double)"
        elif self._tf_adjustment_weight == 0:
            sql = f"WHEN  {gamma_colname_value_is_this_level} then cast(1 as double)"
        elif self._is_else_level:
            sql = f"WHEN  {gamma_colname_value_is_this_level} then cast(1 as double)"
        else:
            tf_adj_col = self._tf_adjustment_input_column

            coalesce_l_r = (
                f"coalesce({tf_adj_col.tf_name_l()}, {tf_adj_col.tf_name_r()})"
            )
            coalesce_r_l = (
                f"coalesce({tf_adj_col.tf_name_r()}, {tf_adj_col.tf_name_l()})"
            )

            tf_adjustment_exists = f"{coalesce_l_r} is not null"
            u_prob_exact_match = self._u_probability_corresponding_to_exact_match

            # Using coalesce protects against one of the tf adjustments being null
            # Which would happen if the user provided their own tf adjustment table
            # That didn't contain some of the values in this data

            # In this case rather than taking the greater of the two, we take
            # whichever value exists

            if self._tf_minimum_u_value == 0.0:
                divisor_sql = f"""
                (CASE
                    WHEN {coalesce_l_r} >= {coalesce_r_l}
                    THEN {coalesce_l_r}
                    ELSE {coalesce_r_l}
                END)
                """
            else:
                # This sql works correctly even when the tf_minimum_u_value is 0.0
                # but is less efficient to execute, hence the above if statement
                divisor_sql = f"""
                (CASE
                    WHEN {coalesce_l_r} >= {coalesce_r_l}
                    AND {coalesce_l_r} > cast({self._tf_minimum_u_value} as double)
                        THEN {coalesce_l_r}
                    WHEN {coalesce_r_l}  > cast({self._tf_minimum_u_value} as double)
                        THEN {coalesce_r_l}
                    ELSE cast({self._tf_minimum_u_value} as double)
                END)
                """

            sql = f"""
            WHEN  {gamma_colname_value_is_this_level} then
                (CASE WHEN {tf_adjustment_exists}
                THEN
                POW(
                    cast({u_prob_exact_match} as double) /{divisor_sql},
                    cast({self._tf_adjustment_weight} as double)
                )
                ELSE cast(1 as double)
                END)
            """
        return dedent(sql).strip()

    def as_dict(self):
        "The minimal representation of this level to use as an input to Splink"
        output = {}

        output["sql_condition"] = self._sql_condition

        if self._level_dict.get("label_for_charts"):
            output["label_for_charts"] = self._label_for_charts

        if self._m_probability and self._m_is_trained:
            output["m_probability"] = self.m_probability

        if self._u_probability and self._u_is_trained:
            output["u_probability"] = self.u_probability

        if self._has_tf_adjustments:
            output["tf_adjustment_column"] = self._tf_adjustment_input_column.input_name
            if self._tf_adjustment_weight != 0:
                output["tf_adjustment_weight"] = self._tf_adjustment_weight

        if self._is_null_level:
            output["is_null_level"] = True
        return output

    def _as_completed_dict(self):
        comp_dict = self.as_dict()
        comp_dict["comparison_vector_value"] = self._comparison_vector_value
        return comp_dict

    @property
    def _as_detailed_record(self):
        "A detailed representation of this level to describe it in charting outputs"
        output = {}
        output["sql_condition"] = self._sql_condition
        output["label_for_charts"] = self._label_for_charts

        output["m_probability"] = self.m_probability
        output["u_probability"] = self.u_probability

        output["m_probability_description"] = self._m_probability_description
        output["u_probability_description"] = self._u_probability_description

        output["has_tf_adjustments"] = self._has_tf_adjustments
        if self._has_tf_adjustments:
            output["tf_adjustment_column"] = self._tf_adjustment_input_column.input_name
        else:
            output["tf_adjustment_column"] = None
        output["tf_adjustment_weight"] = self._tf_adjustment_weight

        output["is_null_level"] = self._is_null_level
        output["bayes_factor"] = self._bayes_factor
        output["log2_bayes_factor"] = self._log2_bayes_factor
        output["comparison_vector_value"] = self._comparison_vector_value
        output["max_comparison_vector_value"] = self.comparison._num_levels - 1
        output["bayes_factor_description"] = self._bayes_factor_description

        return output

    @property
    def _parameter_estimates_as_records(self):

        output_records = []

        cl_record = self._as_detailed_record
        trained_values = self._trained_u_probabilities + self._trained_m_probabilities
        for trained_value in trained_values:
            record = {}
            record["m_or_u"] = trained_value["m_or_u"]
            p = trained_value["probability"]
            record["estimated_probability"] = p
            record["estimate_description"] = trained_value["description"]
            if p is not None and p > 0.0:
                record["estimated_probability_as_log_odds"] = math.log2(p / (1 - p))
            else:
                record["estimated_probability_as_log_odds"] = None

            record["sql_condition"] = cl_record["sql_condition"]
            record["comparison_level_label"] = cl_record["label_for_charts"]
            record["comparison_vector_value"] = cl_record["comparison_vector_value"]
            output_records.append(record)

        return output_records

    def _validate(self):
        self._validate_sql()

    def _abbreviated_sql(self, cutoff=75):
        sql = self._sql_condition
        sql = (sql[:75] + "...") if len(sql) > 75 else sql
        return sql

    def __repr__(self):

        return f"<{self._human_readable_succinct}>"

    @property
    def _human_readable_succinct(self):
        sql = self._abbreviated_sql(75)
        return f"Comparison level '{self._label_for_charts}' using SQL rule: {sql}"

    @property
    def human_readable_description(self):

        input_cols = join_list_with_commas_final_and(
            [c.name() for c in self._input_columns_used_by_sql_condition]
        )
        desc = (
            f"Comparison level: {self._label_for_charts} of {input_cols}\n"
            "Assesses similarity between pairwise comparisons of the input columns "
            f"using the following rule\n{self._sql_condition}"
        )

        return desc

__init__(level_dict, comparison=None, sql_dialect=None)

Source code in splink/comparison_level.py
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
def __init__(
    self,
    level_dict,
    comparison: "Comparison" = None,
    sql_dialect: str = None,
):

    # Protected, because we don't want to modify the original dict
    self._level_dict = level_dict

    self.comparison: "Comparison" = comparison
    self._sql_dialect = sql_dialect

    self._sql_condition = self._level_dict["sql_condition"]
    self._is_null_level = self._level_dict_val_else_default("is_null_level")
    self._tf_adjustment_weight = self._level_dict_val_else_default(
        "tf_adjustment_weight"
    )

    self._tf_minimum_u_value = self._level_dict_val_else_default(
        "tf_minimum_u_value"
    )

    # Private values controlled with getter/setter
    self._m_probability = self._level_dict.get("m_probability")
    self._u_probability = self._level_dict.get("u_probability")

    # These will be set when the ComparisonLevel is passed into a Comparison
    self._comparison_vector_value: int = None
    self._max_level: bool = None

    # Enable the level to 'know' when it's been trained
    self._trained_m_probabilities: list = []
    self._trained_u_probabilities: list = []

    self._validate()

m_probability() property writable

Source code in splink/comparison_level.py
189
190
191
192
193
194
195
196
197
198
@property
def m_probability(self):
    if self._is_null_level:
        return None
    if self._m_probability == "level not observed in training dataset":
        return 1e-6
    if self._m_probability is None and self._has_comparison:
        vals = _default_m_values(self.comparison._num_levels)
        return vals[self._comparison_vector_value]
    return self._m_probability

u_probability() property writable

Source code in splink/comparison_level.py
215
216
217
218
219
220
221
222
223
224
@property
def u_probability(self):
    if self._is_null_level:
        return None
    if self._u_probability == "level not observed in training dataset":
        return 1e-6
    if self._u_probability is None:
        vals = _default_u_values(self.comparison._num_levels)
        return vals[self._comparison_vector_value]
    return self._u_probability