# Usage and examples

## Creating a stemmer

```python
from radixor import Stemmer

s = Stemmer("en")                      # by language code (bundled model)
s = Stemmer("us-uk-default")           # by full model ID
s = Stemmer(path="my_dictionary.gz")   # a custom gzipped TSV source dictionary
s = Stemmer(compiled="en.rxc")         # a pre-compiled binary (instant load)
```

The traversal direction is derived from the language (right-to-left `fa`/`he`/`yi`
use FORWARD, all others BACKWARD); override with `backward=True|False` for a
custom `path=`.

## Stemming a single word

```python
s.stem("running")     # 'run'
s.stem("cats")        # 'cat'
s.stem("zzzzz")       # None  -> not reducible / unknown
```

`stem()` returns the single **dominant** stem, or `None`.

!!! info "Why a known word may return itself"
    A surface form that is *also* a canonical headword (e.g. an English word
    that is both its own lemma and an inflection of another lemma) returns
    itself, because the dominant sense is “this word is its own stem”. The
    inflectional reading is still available via `stem_all()`.

## Batch stemming — the fast path

For anything beyond a handful of words, use the batch API. It crosses the
Python↔native boundary **once** for the whole list, which is the dominant cost
when stemming from Python.

```python
words = ["running", "cats", "stemming", "quickly"]
s.stem_batch(words)          # ['run', 'cat', 'stem', 'quick'] (None for unknowns)
```

```python
# Multiple candidate stems per word (ambiguity preserved):
s.stem_all("running")               # e.g. ['run', 'runn']
s.stem_all_batch(["running", "cats"])
```

## PyStemmer-compatible methods

Radixor also exposes PyStemmer's scalar and batch method names. They differ
from the native Radixor methods only when the trie cannot find a patch command:

| Method | Recognized word | Word without a patch command | Return type |
| --- | --- | --- | --- |
| `stem(word)` | dominant stem | `None` | `str | None` |
| `stem_batch(words)` | dominant stem at the same position | `None` at the same position | `list[str | None]` |
| `stemWord(word)` | dominant stem | original input word | `str` |
| `stemWords(words)` | dominant stem at the same position | original input word at the same position | `list[str]` |

Use `stemWord()` and `stemWords()` when migrating code that expects
PyStemmer's no-`None` contract:

```python
import radixor as Stemmer

# The rest of this common PyStemmer call pattern remains unchanged.
s = Stemmer.Stemmer("english")

s.stemWord("running")                  # 'run'
s.stemWord("unknown_word")             # 'unknown_word'
s.stemWords(["running", "unknown_word"])
# ['run', 'unknown_word']
```

`stemWords()` retains input order and makes one Python-to-Rust call for the
whole list. PyStemmer's full language names, such as `"english"` and
`"czech"`, are accepted for bundled Radixor languages alongside two-letter
codes and full model IDs.

The compatibility contract covers these method names, full language aliases,
and unmatched-word fallback behavior. Radixor configuration keywords remain
Radixor-specific: use `cache_size`, not PyStemmer's `maxCacheSize`. Both
libraries default to a cache capacity of 10,000 entries.

## Bounded result cache

Real text repeats tokens. The default bounded cache returns the already-built
result object on a recognized-word hit (a reference-count bump — no
re-stemming, no new result string). Unknown words are cached as misses, so
`stemWord()` and `stemWords()` still create their required original-word
result. Its default capacity is **10,000 entries**, matching PyStemmer:

```python
s = Stemmer("en")                       # cache up to 10,000 distinct input words
s = Stemmer("en", cache_size=50_000)    # choose a custom capacity
s = Stemmer("en", cache_size=0)         # explicitly disable caching
```

One cache is shared by `stem()`, `stemWord()`, `stem_batch()`, and
`stemWords()`. The `stem_all()` and `stem_all_batch()` methods are not cached.
Caching never changes results; it only avoids recomputation. Entries are
inserted until the configured capacity is reached; there is no eviction. For a
high-cardinality stream without useful token repetition, use `cache_size=0`.

## Skipping lowercasing for pre-normalized input

By default lookups lowercase the input (`LOWERCASE_WITH_LOCALE_ROOT`). If your
pipeline already lowercases tokens, skip the redundant work:

```python
s = Stemmer("en", lowercase=False)     # assume already-lowercased input
s.stem("running")                      # 'run'
s.stem("Running")                      # None  -> not lowercased, so no match
```

The model's keys are always lowercase; `lowercase=False` only turns off
per-lookup normalization. On already-lowercased input the results are identical.

## Compile once, load instantly

Compiling a trie from text costs a few seconds for large languages. Compile it
once to Radixor's binary format and load it directly afterwards:

```python
import radixor

radixor.compile("stemmer.gz", "en.rxc", language="en")
s = radixor.Stemmer(compiled="en.rxc")
```

See [Compiling Dictionaries in Python](model-compilation.md) for the source
format, traversal and normalization options, deployment guidance, Java
interoperability, and the controls that remain Java-only.

## Using a custom dictionary

A source dictionary is a gzipped (or plain) TSV file, one entry per line, the
first column the canonical stem and the rest its variants; `#` and `//` start
line remarks:

```
run	running	runs	ran
cat	cats
```

```python
s = Stemmer(path="custom.gz", backward=True, store_original=True)
```

`store_original=True` (default) maps each stem to itself (a no-op patch) so the
stem is recognised. See [Dictionary Format](../dictionary-format.md) for the
authoritative specification shared with the Java project.

## Thread-safety

A `Stemmer` is safe to share across threads. The bounded cache is guarded
internally; the compiled trie is immutable after construction.

## API summary

| Call | Returns | Notes |
|---|---|---|
| `Stemmer(lang \| path= \| compiled=, *, backward, store_original, lowercase, cache_size=10_000)` | stemmer | auto-detects compiled vs textual for `path=`; `cache_size=0` disables caching |
| `stem(word)` | `str \| None` | dominant stem |
| `stem_batch(words)` | `list[str \| None]` | **preferred** for many words |
| `stemWord(word)` | `str` | PyStemmer-compatible; returns an unmatched word unchanged |
| `stemWords(words)` | `list[str]` | PyStemmer-compatible batch call; preserves unmatched words and input order |
| `stem_all(word)` | `list[str]` | all candidate stems, best first |
| `stem_all_batch(words)` | `list[list[str]]` | |
| `radixor.compile(source, out, *, language, backward, store_original, lowercase)` | `None` | writes a v7 binary |
