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Add CLI and refactor
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import logging
from functools import partial
from typing import Optional
import typer
from bokeh.plotting import output_file as bokeh_output_file
from bokeh.plotting import save
from embedding_lenses.data import uploaded_file_to_dataframe
from embedding_lenses.dimensionality_reduction import get_tsne_embeddings, get_umap_embeddings
from embedding_lenses.embedding import load_model
from perplexity_lenses.data import documents_df_to_sentences_df, hub_dataset_to_dataframe
from perplexity_lenses.engine import DIMENSIONALITY_REDUCTION_ALGORITHMS, DOCUMENT_TYPES, EMBEDDING_MODELS, LANGUAGES, SEED, generate_plot
from perplexity_lenses.perplexity import KenlmModel
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = typer.Typer()
@app.command()
def main(
dataset: str = typer.Option("mc4", help="The name of the hub dataset or local csv/tsv file."),
dataset_config: Optional[str] = typer.Option("es", help="The configuration of the hub dataset, if any. Does not apply to local csv/tsv files."),
dataset_split: Optional[str] = typer.Option("train", help="The dataset split. Does not apply to local csv/tsv files."),
text_column: str = typer.Option("text", help="The text field name."),
language: str = typer.Option("es", help=f"The language of the text. Options: {LANGUAGES}"),
doc_type: str = typer.Option("sentence", help=f"Whether to embed at the sentence or document level. Options: {DOCUMENT_TYPES}."),
sample: int = typer.Option(1000, help="Maximum number of examples to use."),
dimensionality_reduction: str = typer.Option(
DIMENSIONALITY_REDUCTION_ALGORITHMS[0],
help=f"Whether to use UMAP or t-SNE for dimensionality reduction. Options: {DIMENSIONALITY_REDUCTION_ALGORITHMS}.",
),
model_name: str = typer.Option(EMBEDDING_MODELS[0], help=f"The sentence embedding model to use. Options: {EMBEDDING_MODELS}"),
output_file: str = typer.Option("perplexity.html", help="The name of the output visualization HTML file."),
):
"""
Perplexity Lenses: Visualize text embeddings in 2D using colors to represent perplexity values.
"""
logger.info("Loading embedding model...")
model = load_model(model_name)
dimensionality_reduction_function = (
partial(get_umap_embeddings, random_state=SEED) if dimensionality_reduction.lower() == "umap" else partial(get_tsne_embeddings, random_state=SEED)
)
logger.info("Loading KenLM model...")
kenlm_model = KenlmModel.from_pretrained(language)
logger.info("Loading dataset...")
if dataset.endswith(".csv") or dataset.endswith(".tsv"):
df = uploaded_file_to_dataframe(dataset)
if doc_type.lower() == "sentence":
df = documents_df_to_sentences_df(df, text_column, sample, seed=SEED)
df["perplexity"] = df[text_column].map(kenlm_model.get_perplexity)
else:
df = hub_dataset_to_dataframe(dataset, dataset_config, dataset_split, sample, text_column, kenlm_model, seed=SEED, doc_type=doc_type)
# Round perplexity
df["perplexity"] = df["perplexity"].round().astype(int)
logger.info(f"Perplexity range: {df['perplexity'].min()} - {df['perplexity'].max()}")
plot = generate_plot(df, text_column, "perplexity", None, dimensionality_reduction_function, model, seed=SEED)
logger.info("Saving plot")
bokeh_output_file(output_file)
save(plot)
logger.info("Done")
if __name__ == "__main__":
app()