### Batch Process Documents with Whichlang

Source: https://context7.com/quickwit-oss/whichlang/llms.txt

Process multiple text documents efficiently and count them by language. This example demonstrates filtering and grouping documents based on their detected language.

```rust
use whichlang::{detect_language, Lang};
use std::collections::HashMap;

fn main() {
    // Process a batch of documents and count by language
    let documents = vec![
        "Hello world, this is an English document",
        "Bonjour, ceci est un document français",
        "Hallo, dies ist ein deutsches Dokument",
        "Another English text here",
        "Ciao, questo è un documento italiano",
        "Hola, este es un documento en español",
        "More English content follows",
        "日本語のドキュメントです",
        "这是一个中文文档",
        "한국어 문서입니다",
    ];

    // Count documents per language
    let mut lang_counts: HashMap<Lang, usize> = HashMap::new();
    for doc in &documents {
        let lang = detect_language(doc);
        *lang_counts.entry(lang).or_insert(0) += 1;
    }

    // Print statistics
    println!("Language distribution:");
    for (lang, count) in &lang_counts {
        println!("  {{}}: {{}} documents", lang.three_letter_code(), count);
    }
    // Output:
    // Language distribution:
    //   eng: 3 documents
    //   fra: 1 documents
    //   deu: 1 documents
    //   ita: 1 documents
    //   spa: 1 documents
    //   jpn: 1 documents
    //   cmn: 1 documents
    //   kor: 1 documents

    // Filter documents by language
    let english_docs: Vec<&&str> = documents.iter()
        .filter(|doc| detect_language(doc) == Lang::Eng)
        .collect();

    println!("\nEnglish documents: {{}}", english_docs.len()); // Output: 3

    // Group documents by language
    let mut grouped: HashMap<Lang, Vec<&str>> = HashMap::new();
    for doc in &documents {
        let lang = detect_language(doc);
        grouped.entry(lang).or_default().push(doc);
    }

    for (lang, docs) in &grouped {
        println!("\n{{}} documents:", lang.three_letter_code().to_uppercase());
        for doc in docs {
            println!("  - {{}}", &doc[..doc.len().min(50)]);
        }
    }
}
```

--------------------------------

### Detect Language in Rust Project

Source: https://context7.com/quickwit-oss/whichlang/llms.txt

Example usage in your project to detect the language of multiple text documents. It maps each text to its detected language using `detect_language` and returns a vector of tuples.

```rust
use whichlang::{detect_language, Lang};

fn process_multilingual_content(texts: &[&str]) -> Vec<(String, Lang)> {
    texts.iter()
        .map(|text| (text.to_string(), detect_language(text)))
        .collect()
}

fn main() {
    let documents = vec![
        "The quick brown fox jumps over the lazy dog",
        "Le renard brun rapide saute par-dessus le chien paresseux",
        "Der schnelle braune Fuchs springt über den faulen Hund",
        "日本語のテキストです",
        "Привет мир",
    ];

    for (text, lang) in process_multilingual_content(&documents) {
        println!("[{{}}] {{}}", lang.three_letter_code(), &text[..text.len().min(40)]);
    }
    // Output:
    // [eng] The quick brown fox jumps over the lazy
    // [fra] Le renard brun rapide saute par-dessus
    // [deu] Der schnelle braune Fuchs springt über
    // [jpn] 日本語のテキストです
    // [rus] Привет мир
}
```

--------------------------------

### Lang Enum and Supported Languages

Source: https://context7.com/quickwit-oss/whichlang/llms.txt

The `Lang` enum represents all 16 supported languages with ISO 639-3 codes. Use the `three_letter_code()` method to get the string representation. The `LANGUAGES` constant provides a convenient way to iterate over all supported languages.

```rust
use whichlang::{Lang, LANGUAGES};

fn main() {
    // All supported language variants
    let languages = [
        Lang::Ara,  // Arabic
        Lang::Cmn,  // Mandarin Chinese
        Lang::Deu,  // German
        Lang::Eng,  // English
        Lang::Fra,  // French
        Lang::Hin,  // Hindi
        Lang::Ita,  // Italian
        Lang::Jpn,  // Japanese
        Lang::Kor,  // Korean
        Lang::Nld,  // Dutch
        Lang::Por,  // Portuguese
        Lang::Rus,  // Russian
        Lang::Spa,  // Spanish
        Lang::Swe,  // Swedish
        Lang::Tur,  // Turkish
        Lang::Vie,  // Vietnamese
    ];

    // Convert to three-letter code strings
    for lang in languages {
        let code = lang.three_letter_code();
        println!("{:?} -> {}", lang, code);
    }
    // Output:
    // Ara -> ara
    // Cmn -> cmn
    // Deu -> deu
    // Eng -> eng
    // Fra -> fra
    // Hin -> hin
    // Ita -> ita
    // Jpn -> jpn
    // Kor -> kor
    // Nld -> nld
    // Por -> por
    // Rus -> rus
    // Spa -> spa
    // Swe -> swe
    // Tur -> tur
    // Vie -> vie

    // Use LANGUAGES constant to iterate all supported languages
    println!("Total supported languages: {}", LANGUAGES.len()); // Output: 16

    // Lang implements Copy, Clone, Debug, Eq, PartialEq
    let lang1 = Lang::Eng;
    let lang2 = lang1; // Copy
    assert_eq!(lang1, lang2);

    // Use in match expressions
    let detected = whichlang::detect_language("Bonjour le monde");
    match detected {
        Lang::Fra => println!("French detected!"),
        Lang::Eng => println!("English detected!"),
        _ => println!("Other language: {}", detected.three_letter_code()),
    }
    // Output: French detected!
}
```

--------------------------------

### Generate Rust Weights File

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This script generates a Rust file ('src/weights.rs') containing language enum, constants for languages, weights, and intercepts from a trained model. Ensure 'model', 'np', and 'coef' are properly defined.

```python
from sklearn import metrics
print(model.classes_)
print(sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['deu', 'eng', 'fra',  'ita','nld', 'por', 'rus', 'spa']))
```

```python
sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['kor', 'jpn', 'cmn'])
```

```python
from sklearn import metrics
print(model.classes_)
print(sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['deu', 'eng', 'fra',  'ita','nld', 'por', 'rus', 'spa']))
```

```python
sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['kor', 'jpn', 'cmn'])
```

```python
from sklearn import metrics
print(model.classes_)
print(sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['deu', 'eng', 'fra',  'ita','nld', 'por', 'rus', 'spa']))
```

```python
sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['kor', 'jpn', 'cmn'])
```

```python
(LANG, DIM) = model.coef_.shape
print(model.coef_.shape)
coef = np.float32(model.coef_)

print(coef[0,0])
print(model.coef_[:,0])

f = open("src/weights.rs", "w")

f.write("#[derive(Clone, Copy, Debug, Eq, PartialEq)]\n")
f.write("pub enum Lang {
")
for lang in model.classes_:
    f.write("	%s,\n" % lang.capitalize())
f.write("}

")

f.write("\nimpl Lang {\n    pub fn three_letter_code(self)-> &'static str {\n        match self {\n")
for lang in model.classes_:
    f.write("			Lang::%s => \"%s\",\n" % (lang.capitalize(), lang))
f.write("		}	}
}


")


f.write("pub const LANGUAGES: [Lang; %d] = [\n\t" % LANG)
for lang in model.classes_:
    f.write("Lang::%s, " % lang.capitalize())
f.write("];\n\n")

f.write("pub const WEIGHTS: [f32; %d] = [\n" % (LANG * DIM))
for i in range(DIM):
    f.write("	")
    for val in coef[:, i]:
        f.write("%f, " % val)
    f.write("\n")
f.write("];\n\n")


f.write("pub const INTERCEPTS: [f32; %d] = [\n\t" % LANG)
for val in model.intercept_:
    f.write("%f, " % val)
f.write("];\n\n")


f.flush()
f.close()

```

--------------------------------

### Logistic Regression Model Training with Scikit-learn

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This Python snippet demonstrates training a logistic regression model using scikit-learn. It configures the model with specific parameters like max_iter, C, and class_weight, and then fits it to the training data. The output shows the convergence status and accuracy on training and testing sets.

```python
for C in [64]:
    model = sklearn.linear_model.LogisticRegression(max_iter=500, penalty='l2', multi_class='multinomial', C=C, verbose=1, class_weight='balanced') #, l1_ratio=0.1,) # penalty='elasticnet', solver='saga') 
    model.fit(X_train, y_train)
    print("\n\n\n\n\n----------")
    print(C)
    print((model.predict(X_train) == y_train).mean())
    print((model.predict(X_test) == y_test).mean())
```

--------------------------------

### Split Data into Training and Testing Sets

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

Splits the preprocessed feature matrix (X) and label vector (y) into training and testing sets without shuffling. This is often used when the order of data is important, such as in time-series analysis.

```python
(X_train, X_test, y_train, y_test) = train_test_split(X, y, shuffle=False)
del X
del y
n_train = X_train.shape[0]
print(X_train.shape, X_test.shape)
```

--------------------------------

### Generate Confusion Matrix with Specific Labels

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

Use this snippet to generate a confusion matrix for specific language labels. Ensure 'y_test', 'model', and 'X_test' are defined.

```python
from sklearn import metrics
print(model.classes_)
print(sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['deu', 'eng', 'fra',  'ita','nld', 'por', 'rus', 'spa']))
```

```python
sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['kor', 'jpn', 'cmn'])
```

--------------------------------

### Import Libraries for Data Processing

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

Imports necessary libraries including NumPy, Scikit-learn, Polars, and SciPy for data manipulation and machine learning tasks.

```python
import numpy as np
import sklearn
from sklearn import linear_model
import polars
from sklearn.model_selection import train_test_split
from scipy import sparse
```

--------------------------------

### Load and Preprocess CSV Data in Batches

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

Reads a CSV file in batches using Polars, extracts features and labels, converts them to NumPy arrays and sparse matrices, normalizes features, and concatenates batches. This is useful for handling large datasets that do not fit into memory.

```python
df_it = polars.read_csv_batched("train.csv", has_header=True)
ys = []
Xs = []
idxs = []
for i in range(12):
    df = df_it.next_batches(1)[0]
    df[0, 0:10]
    print(df.shape)
    y = df[:, 1].to_numpy()
    ys.append(y)
    X = sparse.csr_matrix(np.float32(df[:, 2:].to_numpy()))
    X = sklearn.preprocessing.normalize(X)
    Xs.append(X)
    idx = df[:, 0]
    idxs.append(idx)

X = sparse.vstack(Xs)
y = np.hstack(ys)

del Xs
del ys

print(X.shape)
print(y.shape)
```

--------------------------------

### Scikit-learn Logistic Regression Convergence Warning

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This output indicates a ConvergenceWarning from scikit-learn's Logistic Regression, suggesting that the optimization algorithm (L-BFGS-B) did not converge within the specified number of iterations. It recommends increasing max_iter or scaling the data.

```text
/home/fulmicoton/miniconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  8.0min finished
```

--------------------------------

### Add Whichlang Dependency to Cargo.toml

Source: https://context7.com/quickwit-oss/whichlang/llms.txt

Add whichlang as a dependency in your Rust project's Cargo.toml file. The library has no external dependencies and compiles quickly.

```toml
[dependencies]
whichlang = "0.1"
```

--------------------------------

### Re-evaluation of Confusion Matrix with Specific Labels

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This Python code snippet re-calculates and prints the confusion matrix using scikit-learn's metrics module. It specifies the labels for the matrix, ensuring the output corresponds to the desired language order.

```python
from sklearn import metrics
print(model.classes_)
print(sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['deu', 'eng', 'fra',  'ita','nld', 'por', 'rus', 'spa']))

```

--------------------------------

### Language Identification Model Evaluation with Confusion Matrix

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This Python code snippet uses scikit-learn's metrics module to print the predicted classes and generate a confusion matrix for a text classification task. It evaluates the model's performance by comparing true labels (y_test) against predicted labels, with specific language labels provided for the matrix.

```python
import time
time.sleep(100)
print("start")from sklearn import metrics
print(model.classes_)
print(sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['deu', 'eng', 'fra',  'ita','nld', 'por', 'rus', 'spa']))

sklearn.metrics.confusion_matrix(y_test, model.predict(X_test), labels=['kor', 'jpn', 'cmn'])
```

--------------------------------

### Character Frequency Analysis for Language Identification

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This Python script analyzes character frequencies in a dataset of sentences to identify common non-ASCII characters across several languages. It's useful for understanding character distribution and potential features for language identification models.

```python
from collections import Counter
counter = Counter()

c = 0
for line in open("dataset/archive/sentences.prepared.csv"):
    (rid, lang, sentence) = line.strip().split("\t", 2)
    if lang not in {"fra","eng", "ita", "deu", "esp", "por"}:
        continue
    c += 1
    if c > 100_000:
        break
    for chr in sentence:
        if ord(chr) > 128:
            counter[chr] += 1
letters = sorted(ord(letter) for (letter, count) in counter.most_common(100) if count >= 10)
print(", ".join(map(str, letters)))
#print(letters)
```

--------------------------------

### Analyze Misclassified Samples

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This code snippet helps identify and display details of misclassified samples. It prints the shape of misclassified samples and iterates through the first 10 to show true label, predicted label, and an index.

```python
y_predict = model.predict(X_test)
print(np.where((y_predict == y_test) == False)[0].shape)
print(y_predict.shape)
i = 0 
for row in list(np.where((y_predict == y_test) == False))[0]:
    i += 1
    print(y_test[row], y_predict[row], idx[int(n_train + row)])
    if i == 10:
        break
```

--------------------------------

### Detect Language of Text String

Source: https://context7.com/quickwit-oss/whichlang/llms.txt

Use the `detect_language` function to identify the language of a given text. Empty strings default to English. This function returns a `Lang` enum variant.

```rust
use whichlang::{detect_language, Lang};

fn main() {
    // English detection
    let text = "Hello, happy tax payer";
    let lang = detect_language(text);
    assert_eq!(lang, Lang::Eng);
    println!("Detected language: {:?}", lang); // Output: Detected language: Eng

    // French detection
    let french_text = "Bonjour joyeux contribuable";
    let french_lang = detect_language(french_text);
    assert_eq!(french_lang, Lang::Fra);
    println!("French: {:?}", french_lang); // Output: French: Fra

    // German detection
    let german_text = "Hallo glücklicher Steuerzahler";
    let german_lang = detect_language(german_text);
    assert_eq!(german_lang, Lang::Deu);
    println!("German: {:?}", german_lang); // Output: German: Deu

    // Japanese detection
    let japanese_text = "こんにちは幸せな税金納め";
    let japanese_lang = detect_language(japanese_text);
    assert_eq!(japanese_lang, Lang::Jpn);
    println!("Japanese: {:?}", japanese_lang); // Output: Japanese: Jpn

    // Mandarin Chinese detection
    let chinese_text = "你好幸福的纳税人";
    let chinese_lang = detect_language(chinese_text);
    assert_eq!(chinese_lang, Lang::Cmn);
    println!("Mandarin: {:?}", chinese_lang); // Output: Mandarin: Cmn

    // Korean detection
    let korean_text = "안녕하세요 행복한 납세자입니다";
    let korean_lang = detect_language(korean_text);
    assert_eq!(korean_lang, Lang::Kor);
    println!("Korean: {:?}", korean_lang); // Output: Korean: Kor

    // Spanish detection
    let spanish_text = "Hola feliz contribuyente";
    let spanish_lang = detect_language(spanish_text);
    assert_eq!(spanish_lang, Lang::Spa);
    println!("Spanish: {:?}", spanish_lang); // Output: Spanish: Spa

    // Empty string defaults to English
    let empty_lang = detect_language("");
    assert_eq!(empty_lang, Lang::Eng);
}
```

--------------------------------

### Confusion Matrix for Specific Languages

Source: https://github.com/quickwit-oss/whichlang/blob/main/train.ipynb

This output represents a confusion matrix generated by scikit-learn for a multi-class classification problem. The rows typically represent the true labels and columns represent the predicted labels for the specified languages (e.g., 'kor', 'jpn', 'cmn').

```text
Result:
array([[ 209,    0,    0],
       [   0, 5127,    3],
       [   0,    1, 1704]])
```

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