Metadata-Version: 2.4
Name: sparpartner
Version: 1.5.2
Summary: Deterministic, benchmark-driven stratified sampler for train/test prep.
Author: Henry
Author-email: Henry <osas2henry@gmail.com>
License: All Rights Reserved
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3
Dynamic: author
Dynamic: license-file
Dynamic: requires-python

# sparpartner

A deterministic stratified sampler for train/test prep. It doesn't
split your data randomly, it deliberately finds the rows that most
resemble a benchmark case, so you can hold those out as a genuine
test set and train on everything else.

## Why this exists

A random train/test split assumes the test set should look
statistically like the train set. That's the wrong question if
what you actually want to know is: **does the model generalize past
one specific profile, or did it just memorize the neighborhood
around it?**

`sparpartner` answers that by ranking every row in your data by how
closely it resembles a benchmark ("bench_marks") you define, then
sorting closest-to-benchmark first. You then slice the sorted frame
yourself:

- **Lookalikes as test, the rest as train**: the rows most like the
  benchmark are always at the top of the sorted result, so `head()`
  gives you the test set and `tail()` (or everything past your cut)
  gives you the train set. This is the harder, more honest check: it
  tells you whether the model actually learned something general, or
  only performs well near cases it's already seen a lot of.
- **Just the lookalikes, nothing else**: pass `sparring_n` and skip
  the manual slice entirely. `sample()` hands you back only the top
  `sparring_n` rows, already ranked, plus a report on exactly that
  set (see the sparring report section below).

`sparpartner` only produces the ranking (and, optionally, the
top-N slice). The actual train/test cut beyond that is a plain
slice on your side (see the usage examples below).

## Where the idea comes from

A fighter in camp doesn't spar with whoever's free in the gym, they
specifically look for a sparring partner who moves, reaches, and
hits like the opponent they're about to face. Training against a
random partner tells you nothing about how you'll actually do;
training against someone who resembles the real threat does.
`sparpartner` applies that same logic to a model: instead of a
random holdout, it finds the rows that resemble the toughest, most
relevant "opponent" profile and holds those back as the real test,
so what's left to train on is everything *unlike* that opponent,
and the test genuinely checks whether the model can handle the
match it's actually walking into.

## How the scoring works

You give it:

- `df`: your data
- `bench_marks`: a dict of `{column_name: benchmark_value}`, one
  entry per signal you care about
- `custom_weights`: a **dict** of `{column_name: weight}`, saying
  which columns matter and how much. Insertion order is preserved
  and drives both the `show_progress` readout order and, combined
  with descending weight, the tie-break cascade order (see below).

For each weighted column, `sparpartner` auto-detects the column's
type and scores every row's distance to the benchmark on a 0–1
scale (1.0 = exact match, 0.0 = as far as possible):

| Detected type | How distance is measured |
|---|---|
| **numeric** | `abs(value - bench)`, capped by the column's own max observed distance from bench |
| **date** | both sides converted to "age in days" relative to the benchmark date, capped by the column's own max observed age |
| **string** | exact match = 1, anything else = 0 |

Date detection is automatic. A column is only treated as a date if
its values look date-shaped (contain a separator like `-`, `/`, `.`
or a recognizable month name) **and** parse successfully at least
98% of the time. Bare numeric-looking strings (e.g. `"12345"`) never
even reach the date-parsing attempt, and object-dtype columns of
digit strings fall through to the exact-match string path instead of
being mistaken for numbers. Only a real numeric dtype gets the
numeric path.

Each column's 0–1 score is multiplied by its weight and summed into
one raw score per row. That raw sum is divided by the total weight
to get the final `_score`, **but only if the total weight is > 0**.
In that normal case `_score` always lands in the 0–1 range,
however many signals or weights you used. If the weights sum to
`<= 0`, normalization is skipped entirely and `_score` is left as
the raw, unnormalized weighted sum (not guaranteed to fall in 0–1).

The result is always sorted `_score` descending, the row closest
to the benchmark is always first.

### Tie-breaking

Rows that land on the exact same `_score` aren't left to random or
arbitrary order. Ties are broken by the per-signal score of the
**highest-weight** signal first (higher wins), then the next-highest,
cascading down the weight-sorted signal list until the tie resolves.
Signals that share the same weight are compared in the order they
appear in `custom_weights` (dict insertion order). Only if every
signal is exhausted and rows are still tied does it fall back to
pandas' stable sort (original row order).

## The sparring report

`sample()` doesn't just return the ranked frame, it returns a
`(df, sparring_report)` tuple. `sparring_report` is a flat dict
summarizing match quality, e.g.:

```python
{
    "age": 55.0, "income": 57.5,        # one key per signal, avg score
    "min_score": 0.0,
    "max_score": 91.93,
    "mean_score": 56.45,
}
```

Every value is on a 0–100 scale (100 = perfect match to benchmark)
and rounded to 2 decimal places, regardless of `return_score`.

By default (`sparring_n=None`) both the returned `df` and the report
cover **all** rows. Pass an int and `sample()` slices the sorted
result down to just the top `sparring_n` rows (the ones closest to
the benchmark) **before** anything else happens. That slice is what
you get back as `df`, and it's also exactly what `sparring_report`
and the score distribution are computed on. There's no separate
"full set" kept around once `sparring_n` is set; if you need the
rest of the rows too (e.g. to build the train set), take them from
your original `df` yourself, or call `sample()` again with
`sparring_n=None`.

## Usage

### Sample usage

```python
import pandas as pd
from sparpartner import sample

df = pd.DataFrame({
    "id": [1, 2, 3, 4, 5],
    "age": [25, 30, 47, 52, 33],
    "signup_date": ["2023-01-15", "2023-03-02", "2022-11-20", "2023-01-10", "2023-06-01"],
    "country": ["US", "US", "CA", "US", "MX"],
})

bench_marks = {
    "age": 30,
    "signup_date": "2023-01-01",
    "country": "US",
}

custom_weights = {
    "age": 2,
    "signup_date": 1,
    "country": 1,
}

result, sparring_report = sample(
    df,
    bench_marks,
    custom_weights,
    sparring_n=None,      # None = every row scored, sorted, and returned
    drop_nan=True,
    show_progress=True,   # prints the full scoring breakdown
    return_score=True,    # keep _score columns in the output
)

print(result)
print(sparring_report)
```

`age=30`, `signup_date="2023-01-01"`, and `country="US"` closely
match row `id=1` (age 25, close date, US), so that row lands at or
near the top of the sorted output.

```python
# --- post-sample: turn the ranking into an actual train/test split ---
# Result is always sorted closest-to-benchmark first.
ranked, _ = sample(df, bench_marks, custom_weights)

# Slice however large you want the test set to be, e.g. the top 30%:
cut = int(len(ranked) * 0.3)
test = ranked.iloc[:cut]    # lookalikes, the harder, honest test set
train = ranked.iloc[cut:]   # everything unlike the benchmark

# Or skip the manual slice and get the test set directly:
test_only, report = sample(df, bench_marks, custom_weights, sparring_n=cut)
```

### Parameters

| Name | Type | Default | What it does |
|---|---|---|---|
| `df` | DataFrame | required | Must contain a column for every name (key) in `custom_weights` |
| `bench_marks` | dict | required | `{column_name: benchmark_value}` |
| `custom_weights` | dict of `{name: weight}` | required | Which columns to score and how much each contributes |
| `sparring_n` | int or `None` | `None` | `None` = every row scored, sorted, and returned, report covers all of them. An int slices the sorted result down to the top `sparring_n` rows **before** anything else. That slice is what's returned as `df`, and what the report/distribution are computed on |
| `drop_nan` | bool | `True` | If `True`, drops any row with a NaN in a per-signal `_score_<name>` column **or** in `_score` itself, after printing (if `show_progress`) a sanity check of what was dropped and why |
| `show_progress` | bool | `False` | Prints a full readout, in run order: header, per-column type/cap/sample scores, drop_nan check (if `drop_nan=True`), normalize check, sort-apply readout, sparring_n slice readout (if `sparring_n` is set), top N ranked rows with weighted contributions, and the sparring report itself, flagging any signal contributing zero separation |
| `return_score` | bool | `False` | If `True`, keeps `_score` and `_score_<name>` columns in the returned df instead of dropping them |

### Returns

`sample()` returns a `(df, sparring_report)` tuple, not just a
DataFrame. See the sparring report section above.

### Validation

Input validation runs upfront, before any scoring starts, in two
passes: general parameter checks, then `custom_weights` checks.

Raises `TypeError` if:
- `df` isn't a pandas DataFrame
- `bench_marks` isn't a dict
- `custom_weights` isn't a dict
- `drop_nan`, `show_progress`, or `return_score` isn't a bool
- `sparring_n` isn't an int or `None` (bools are rejected too)

Raises `ValueError` if:
- `df` has no rows
- `custom_weights` is an empty dict
- `sparring_n` isn't a positive integer
- a `custom_weights` key isn't a string, or doesn't match a column
  in `df`
- a `custom_weights` key has no matching entry in `bench_marks`
- a weight isn't numeric (bools are rejected too, a `bool` is
  technically an `int` in Python but was never meant as a weight)
- a weight is `NaN`, `inf`, or `-inf`

**Note on duplicate signal names:** since `custom_weights` is now a
dict, keys are inherently unique, so a repeated column name can no
longer be passed in the first place, Python itself resolves a
repeated key in a dict literal (keeping only the last value) before
`sample()` ever sees it. There's nothing left for validation to
catch here.

## A couple of things worth knowing

- **Object-dtype numeric strings**: a column of strings like
  `"100"`, `"200"` (object dtype, no separator) is scored as an
  exact-match string column, *not* auto-converted to numeric. Only
  genuine numeric dtypes (`int`, `float`) get the numeric distance
  path.
- **Positional calls**: if you call `sample()` positionally beyond
  `custom_weights`, the 4th argument is now `sparring_n`, not
  `show_progress` or `drop_nan`. Use keyword arguments for anything
  past the first three positional params to avoid ambiguity.
