analysis.py

import yaml
import subprocess
import nltk
from nltk import word_tokenize
from nltk.corpus import cmudict, stopwords
import spacy
import torch
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import matplotlib.pyplot as plt
import numpy as np

from matplotlib.patches import Circle, RegularPolygon
from matplotlib.path import Path
from matplotlib.projections import register_projection
from matplotlib.projections.polar import PolarAxes
from matplotlib.spines import Spine
from matplotlib.transforms import Affine2D
from writing_analysis import (
    estimated_slightly_difficult_words_ratio,
    entity_density,
    determiners_frequency,
    punctuation_diversity,
    type_token_ratio,
    calculate_perplexity,
    calculate_syntactic_tree_depth,
    hapax_legomena_ratio,
    mtld,
)

nltk.download("cmudict")
nltk.download("punkt")
nltk.download("stopwords")
nltk.download("wordnet")
d = cmudict.dict()
command = ["python3", "-m", "spacy", "download", "en_core_web_sm"]
subprocess.run(command)
nlp = spacy.load("en_core_web_sm")


with open("config.yaml", "r") as file:
    params = yaml.safe_load(file)
device = "cuda" if torch.cuda.is_available() else "cpu"
readability_model_id = params["READABILITY_MODEL_ID"]
gpt2_model = GPT2LMHeadModel.from_pretrained(readability_model_id).to(device)
gpt2_tokenizer = GPT2TokenizerFast.from_pretrained(readability_model_id)


def normalize(value, min_value, max_value):
    normalized_value = ((value - min_value) * 100) / (max_value - min_value)
    return max(0, min(100, normalized_value))


def depth_analysis(input_text):

    usual_ranges = {
        "estimated_slightly_difficult_words_ratio": (
            0.2273693623058005,
            0.557383692351033,
        ),
        "entity_density": (-0.07940776754145815, 0.23491038179986615),
        "determiners_frequency": (0.012461059190031154, 0.15700934579439252),
        "punctuation_diversity": (-0.21875, 0.53125),
        "type_token_ratio": (0.33002482852189063, 1.0894414982357028),
        "calculate_perplexity": (-25.110544681549072, 82.4620680809021),
        "calculate_syntactic_tree_depth": (1.8380681818181812, 10.997159090909092),
        "hapax_legomena_ratio": (0.0830971690138207, 1.0302715687215778),
        "mtld": (-84.03125000000001, 248.81875000000002),
    }

    vocabulary_level = estimated_slightly_difficult_words_ratio(input_text, d)
    entity_ratio = entity_density(input_text, nlp)
    determiner_use = determiners_frequency(input_text, nlp)
    punctuation_variety = punctuation_diversity(input_text)
    sentence_depth = calculate_syntactic_tree_depth(input_text, nlp)
    perplexity = calculate_perplexity(input_text, gpt2_model, gpt2_tokenizer, device)
    lexical_diversity = type_token_ratio(input_text)
    unique_words = hapax_legomena_ratio(input_text)
    vocabulary_stability = mtld(input_text)

    # normalize between 0 and 100
    vocabulary_level_norm = normalize(
        vocabulary_level, *usual_ranges["estimated_slightly_difficult_words_ratio"]
    )
    entity_ratio_norm = normalize(entity_ratio, *usual_ranges["entity_density"])
    determiner_use_norm = normalize(
        determiner_use, *usual_ranges["determiners_frequency"]
    )
    punctuation_variety_norm = normalize(
        punctuation_variety, *usual_ranges["punctuation_diversity"]
    )
    lexical_diversity_norm = normalize(
        lexical_diversity, *usual_ranges["type_token_ratio"]
    )
    unique_words_norm = normalize(unique_words, *usual_ranges["hapax_legomena_ratio"])
    vocabulary_stability_norm = normalize(vocabulary_stability, *usual_ranges["mtld"])
    sentence_depth_norm = normalize(
        sentence_depth, *usual_ranges["calculate_syntactic_tree_depth"]
    )
    perplexity_norm = normalize(perplexity, *usual_ranges["calculate_perplexity"])

    features = {
        "Lexical Diversity": lexical_diversity_norm,
        "Vocabulary Level": vocabulary_level_norm,
        "Unique Words": unique_words_norm,
        "Determiner Use": determiner_use_norm,
        "Punctuation Variety": punctuation_variety_norm,
        "Sentence Depth": sentence_depth_norm,
        "Vocabulary Stability": vocabulary_stability_norm,
        "Entity Ratio": entity_ratio_norm,
        "Perplexity": perplexity_norm,
    }

    def radar_factory(num_vars, frame="circle"):
        theta = np.linspace(0, 2 * np.pi, num_vars, endpoint=False)

        class RadarTransform(PolarAxes.PolarTransform):
            def transform_path_non_affine(self, path):
                if path._interpolation_steps > 1:
                    path = path.interpolated(num_vars)
                return Path(self.transform(path.vertices), path.codes)

        class RadarAxes(PolarAxes):
            name = "radar"
            PolarTransform = RadarTransform

            def __init__(self, *args, **kwargs):
                super().__init__(*args, **kwargs)
                self.set_theta_zero_location("N")

            def fill(self, *args, closed=True, **kwargs):
                return super().fill(closed=closed, *args, **kwargs)

            def plot(self, *args, **kwargs):
                lines = super().plot(*args, **kwargs)
                for line in lines:
                    self._close_line(line)

            def _close_line(self, line):
                x, y = line.get_data()
                if x[0] != x[-1]:
                    x = np.append(x, x[0])
                    y = np.append(y, y[0])
                    line.set_data(x, y)

            def set_varlabels(self, labels):
                self.set_thetagrids(np.degrees(theta), labels)

            def _gen_axes_patch(self):
                if frame == "circle":
                    return Circle((0.5, 0.5), 0.5)
                elif frame == "polygon":
                    return RegularPolygon(
                        (0.5, 0.5), num_vars, radius=0.5, edgecolor="k"
                    )

            def _gen_axes_spines(self):
                if frame == "polygon":
                    spine = Spine(
                        axes=self,
                        spine_type="circle",
                        path=Path.unit_regular_polygon(num_vars),
                    )
                    spine.set_transform(
                        Affine2D().scale(0.5).translate(0.5, 0.5) + self.transAxes
                    )
                    return {"polar": spine}

        register_projection(RadarAxes)
        return theta

    N = 9
    theta = radar_factory(N, frame="polygon")
    data = features.values()
    labels = features.keys()
    fig, ax = plt.subplots(subplot_kw=dict(projection="radar"), figsize=(7.5, 5))
    ax.plot(theta, data)
    ax.fill(theta, data, alpha=0.4)
    ax.set_varlabels(labels)

    rgrids = np.linspace(0, 100, num=6)
    ax.set_rgrids(
        rgrids, labels=[f"{round(r)}%" for r in rgrids], fontsize=8, color="black"
    )
    ax.grid(True, color="black", linestyle="-", linewidth=0.5, alpha=0.5)

    for dd, (label, value) in enumerate(zip(labels, data)):
        ax.text(
            theta[dd] + 0.1,
            value + 5,
            f"{value:.0f}",
            horizontalalignment="left",
            verticalalignment="bottom",
            fontsize=8,
        )

    return fig