Untitled

import os
import re
import pandas as pd
import spacy

# Carica il modello di SpaCy per l'italiano
nlp = spacy.load("it_core_news_sm")


# Funzione per calcolare le percentuali e i rapporti
def calculate_percentages_and_ratios(text):
    # Analizza il testo con SpaCy
    doc = nlp(text)
    # Trova tutte le parole etichettate
    words = re.findall(r'\b\w+:\w+\b', text)
    # Filtra solo le parole di contenuto (sostantivi, verbi, aggettivi, avverbi)
    content_words = [
        word for word in words if any(
            token.text == word.split(':')[0] and token.pos_ in {'NOUN', 'VERB', 'ADJ', 'ADV'}
            for token in doc
        )
    ]
    total_labeled_words = len(content_words)
    simple_words = sum(1 for word in content_words if word.endswith(':SIMPLE'))
    complex_words = sum(1 for word in content_words if word.endswith(':COMPLEX'))

    # Totale parole nel testo
    total_words = len(doc)

    # Calcola le percentuali
    simple_percentage = (simple_words / total_labeled_words) * 100 if total_labeled_words > 0 else 0
    complex_percentage = (complex_words / total_labeled_words) * 100 if total_labeled_words > 0 else 0
    simple_total_percentage = (simple_words / total_words) * 100 if total_words > 0 else 0
    complex_total_percentage = (complex_words / total_words) * 100 if total_words > 0 else 0

    # Calcola i rapporti
    simple_to_complex_ratio = (simple_words / complex_words) if complex_words > 0 else 0
    complex_to_simple_ratio = (complex_words / simple_words) if simple_words > 0 else 0

    return simple_percentage, complex_percentage, simple_to_complex_ratio, complex_to_simple_ratio, simple_total_percentage, complex_total_percentage


# Leggi i testi dal corpus (per esempio da una directory)
corpus_dir = '/Users/lauraocchipinti/PycharmProjects/phd_thesis/labeled_corpus'  # Sostituire con il percorso corretto
texts = []

for root, dirs, files in os.walk(corpus_dir):
    for file in files:
        if file.endswith('.txt'):  # Assume che i file di testo siano con estensione .txt
            with open(os.path.join(root, file), 'r', encoding='utf-8') as f:
                texts.append(f.read())

# Calcola le percentuali e i rapporti per ogni testo
results = []
for text in texts:
    simple_percentage, complex_percentage, simple_to_complex_ratio, complex_to_simple_ratio, simple_total_percentage, complex_total_percentage = calculate_percentages_and_ratios(
        text)
    results.append((simple_percentage, complex_percentage, simple_to_complex_ratio, complex_to_simple_ratio,
                    simple_total_percentage, complex_total_percentage))

# Calcola la media delle percentuali e dei rapporti
simple_mean = sum(result[0] for result in results) / len(results) if results else 0
complex_mean = sum(result[1] for result in results) / len(results) if results else 0
simple_to_complex_ratio_mean = sum(result[2] for result in results) / len(results) if results else 0
complex_to_simple_ratio_mean = sum(result[3] for result in results) / len(results) if results else 0
simple_total_mean = sum(result[4] for result in results) / len(results) if results else 0
complex_total_mean = sum(result[5] for result in results) / len(results) if results else 0

# Mostra i risultati
df = pd.DataFrame(results,
                  columns=['Simple Percentage', 'Complex Percentage', 'Simple/Complex Ratio', 'Complex/Simple Ratio',
                           'Simple Total Percentage', 'Complex Total Percentage'])
df.loc['Mean'] = [simple_mean, complex_mean, simple_to_complex_ratio_mean, complex_to_simple_ratio_mean,
                  simple_total_mean, complex_total_mean]

print(df)