tsne_plot.py

import argparse
import logging
from typing import Any, Optional

import bokeh
import numpy as np
import pandas as pd
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure, output_file, save
from bokeh.transform import factor_cmap
from bokeh.palettes import Cividis256 as Pallete
from sklearn.manifold import TSNE


logging.basicConfig(level = logging.INFO)
logger = logging.getLogger(__name__)
SEED = 0

def get_tsne_embeddings(embeddings: np.ndarray, perplexity: int=30, n_components: int=2, init: str='pca', n_iter: int=5000, random_state: int=SEED) -> np.ndarray:
    tsne = TSNE(perplexity=perplexity, n_components=n_components, init=init, n_iter=n_iter, random_state=random_state)
    return tsne.fit_transform(embeddings)

def draw_interactive_scatter_plot(texts: np.ndarray, xs: np.ndarray, ys: np.ndarray, values: np.ndarray) -> Any:
    # Normalize values to range between 0-255, to assign a color for each value
    max_value = values.max()
    min_value = values.min()
    values_color = ((values - min_value) / (max_value - min_value) * 255).round().astype(int).astype(str)
    values_color_set = sorted(values_color)

    values_list = values.astype(str).tolist()
    values_set = sorted(values_list)

    source = ColumnDataSource(data=dict(x=xs, y=ys, text=texts, perplexity=values_list))
    hover = HoverTool(tooltips=[('Sentence', '@text{safe}'), ('Perplexity', '@perplexity')])
    p = figure(plot_width=1200, plot_height=1200, tools=[hover], title='Sentences')
    p.circle(
        'x', 'y', size=10, source=source, fill_color=factor_cmap('perplexity', palette=[Pallete[int(id_)] for id_ in values_color_set], factors=values_set))
    return p

def generate_plot(tsv: str, output_file_name: str, sample: Optional[int]):
    logger.info("Loading dataset in memory")
    df = pd.read_csv(tsv, sep="\t")
    if sample:
        df = df.sample(sample, random_state=SEED)
    logger.info(f"Dataset contains {df.shape[0]} sentences")
    embeddings = df[sorted([col for col in df.columns if col.startswith("dim")], key=lambda x: int(x.split("_")[-1]))].values
    logger.info(f"Running t-SNE")
    tsne_embeddings = get_tsne_embeddings(embeddings)
    logger.info(f"Generating figure")
    plot = draw_interactive_scatter_plot(df["sentence"].values, tsne_embeddings[:, 0], tsne_embeddings[:, 1], df["perplexity"].values)
    output_file(output_file_name)
    save(plot)




if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Embeddings t-SNE plot")
    parser.add_argument("--tsv", type=str, help="Path to tsv file with columns 'text', 'perplexity' and N 'dim_<i> columns for each embdeding dimension.'")
    parser.add_argument("--output_file", type=str, help="Path to the output HTML file for the interactive plot.", default="perplexity_colored_embeddings.html")
    parser.add_argument("--sample", type=int, help="Number of sentences to use", default=None)

    args = parser.parse_args()
    generate_plot(args.tsv, args.output_file, args.sample)