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import logging
import pathlib
import gradio as gr
import pandas as pd
from gt4sd.algorithms.conditional_generation.regression_transformer import (
RegressionTransformer,
)
from gt4sd.algorithms.registry import ApplicationsRegistry
from utils import (
draw_grid_generate,
draw_grid_predict,
get_application,
get_inference_dict,
get_rt_name,
)
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
def regression_transformer(
algorithm: str,
task: str,
target: str,
number_of_samples: int,
search: str,
temperature: float,
tolerance: int,
wrapper: bool,
fraction_to_mask: float,
property_goal: str,
tokens_to_mask: str,
substructures_to_mask: str,
substructures_to_keep: str,
):
if task == "Predict" and wrapper:
logger.warning(
f"For prediction, no sampling_wrapper will be used, ignoring: fraction_to_mask: {fraction_to_mask}, "
f"tokens_to_mask: {tokens_to_mask}, substructures_to_mask={substructures_to_mask}, "
f"substructures_to_keep: {substructures_to_keep}."
)
sampling_wrapper = {}
elif not wrapper:
sampling_wrapper = {}
else:
substructures_to_mask = (
[]
if substructures_to_mask == ""
else substructures_to_mask.replace(" ", "").split(",")
)
substructures_to_keep = (
[]
if substructures_to_keep == ""
else substructures_to_keep.replace(" ", "").split(",")
)
tokens_to_mask = [] if tokens_to_mask == "" else tokens_to_mask.split(",")
property_goals = {}
if property_goal == "":
raise ValueError(
"For conditional generation you have to specify `property_goal`."
)
for line in property_goal.split(","):
property_goals[line.split(":")[0].strip()] = float(line.split(":")[1])
sampling_wrapper = {
"substructures_to_keep": substructures_to_keep,
"substructures_to_mask": substructures_to_mask,
"text_filtering": False,
"fraction_to_mask": fraction_to_mask,
"property_goal": property_goals,
}
algorithm_application = get_application(algorithm.split(":")[0])
algorithm_version = algorithm.split(" ")[-1].lower()
config = algorithm_application(
algorithm_version=algorithm_version,
search=search.lower(),
temperature=temperature,
tolerance=tolerance,
sampling_wrapper=sampling_wrapper,
)
model = RegressionTransformer(configuration=config, target=target)
samples = list(model.sample(number_of_samples))
if task == "Predict":
return draw_grid_predict(samples[0], target, domain=algorithm.split(":")[0])
else:
return draw_grid_generate(samples, domain=algorithm.split(":")[0])
if __name__ == "__main__":
# Preparation (retrieve all available algorithms)
all_algos = ApplicationsRegistry.list_available()
rt_algos = list(
filter(lambda x: "RegressionTransformer" in x["algorithm_name"], all_algos)
)
rt_names = list(map(get_rt_name, rt_algos))
properties = {}
for algo in rt_algos:
application = get_application(
algo["algorithm_application"].split("Transformer")[-1]
)
data = get_inference_dict(
application=application, algorithm_version=algo["algorithm_version"]
)
properties[get_rt_name(algo)] = data
properties
# Load metadata
metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
examples = pd.read_csv(
metadata_root.joinpath("regression_transformer_examples.csv"), header=None
).fillna("")
with open(metadata_root.joinpath("regression_transformer_article.md"), "r") as f:
article = f.read()
with open(
metadata_root.joinpath("regression_transformer_description.md"), "r"
) as f:
description = f.read()
demo = gr.Interface(
fn=regression_transformer,
title="Regression Transformer",
inputs=[
gr.Dropdown(rt_names, label="Algorithm version", value="Molecules: Qed"),
gr.Radio(choices=["Predict", "Generate"], label="Task", value="Generate"),
gr.Textbox(
label="Input", placeholder="CC(C#C)N(C)C(=O)NC1=CC=C(Cl)C=C1", lines=1
),
gr.Slider(
minimum=1, maximum=50, value=10, label="Number of samples", step=1
),
gr.Radio(choices=["Sample", "Greedy"], label="Search", value="Sample"),
gr.Slider(minimum=0.5, maximum=2, value=1, label="Decoding temperature"),
gr.Slider(minimum=5, maximum=100, value=30, label="Tolerance", step=1),
gr.Radio(choices=[True, False], label="Sampling Wrapper", value=True),
gr.Slider(minimum=0, maximum=1, value=0.5, label="Fraction to mask"),
gr.Textbox(label="Property goal", placeholder="<qed>:0.75", lines=1),
gr.Textbox(label="Tokens to mask", placeholder="N, C", lines=1),
gr.Textbox(
label="Substructures to mask", placeholder="C(=O), C#C", lines=1
),
gr.Textbox(
label="Substructures to keep", placeholder="C1=CC=C(Cl)C=C1", lines=1
),
],
outputs=gr.HTML(label="Output"),
article=article,
description=description,
examples=examples.values.tolist(),
)
demo.launch(debug=True, show_error=True)
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