Spaces:
Running
Running
File size: 5,171 Bytes
5984d9a e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 4b5d582 e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 621ac03 e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 973616f 5984d9a e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 5984d9a e83e5dc 5984d9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
import logging
import pathlib
from typing import List
import gradio as gr
import pandas as pd
from gt4sd.algorithms.controlled_sampling.paccmann_gp import (
PaccMannGPGenerator,
PaccMannGP,
)
from gt4sd.algorithms.controlled_sampling.paccmann_gp.implementation import (
MINIMIZATION_FUNCTIONS,
)
from gt4sd.algorithms.registry import ApplicationsRegistry
from utils import draw_grid_generate
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
MINIMIZATION_FUNCTIONS.pop("callable", None)
MINIMIZATION_FUNCTIONS.pop("molwt", None)
def run_inference(
algorithm_version: str,
targets: List[str],
protein_target: str,
temperature: float,
length: float,
number_of_samples: int,
limit: int,
number_of_steps: int,
number_of_initial_points: int,
number_of_optimization_rounds: int,
sampling_variance: float,
samples_for_evaluation: int,
maximum_number_of_sampling_steps: int,
seed: int,
):
config = PaccMannGPGenerator(
algorithm_version=algorithm_version.split("_")[-1],
batch_size=32,
temperature=temperature,
generated_length=length,
limit=limit,
acquisition_function="EI",
number_of_steps=number_of_steps,
number_of_initial_points=number_of_initial_points,
initial_point_generator="random",
number_of_optimization_rounds=number_of_optimization_rounds,
sampling_variance=sampling_variance,
samples_for_evaluation=samples_for_evaluation,
maximum_number_of_sampling_steps=maximum_number_of_sampling_steps,
seed=seed,
)
target = {i: {} for i in targets}
if "affinity" in targets:
if protein_target == "" or not isinstance(protein_target, str):
raise ValueError(
f"Protein target must be specified for affinity prediction, not ={protein_target}"
)
target["affinity"]["protein"] = protein_target
else:
protein_target = ""
model = PaccMannGP(config, target=target)
samples = list(model.sample(number_of_samples))
return draw_grid_generate(
samples=samples,
n_cols=5,
properties=set(target.keys()),
protein_target=protein_target,
)
if __name__ == "__main__":
# Preparation (retrieve all available algorithms)
all_algos = ApplicationsRegistry.list_available()
algos = [
x["algorithm_version"]
for x in list(filter(lambda x: "PaccMannGP" in x["algorithm_name"], all_algos))
]
# Load metadata
metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
examples = pd.read_csv(
metadata_root.joinpath("examples.csv"), header=None, sep="|"
).fillna("")
examples[1] = examples[1].apply(eval)
with open(metadata_root.joinpath("article.md"), "r") as f:
article = f.read()
with open(metadata_root.joinpath("description.md"), "r") as f:
description = f.read()
demo = gr.Interface(
fn=run_inference,
title="PaccMannGP",
inputs=[
gr.Dropdown(algos, label="Algorithm version", value="v0"),
gr.CheckboxGroup(
choices=list(MINIMIZATION_FUNCTIONS.keys()),
value=["qed"],
multiselect=True,
label="Property goals",
),
gr.Textbox(
label="Protein target",
placeholder="MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT",
lines=1,
),
gr.Slider(minimum=0.5, maximum=2, value=1, label="Decoding temperature"),
gr.Slider(
minimum=5,
maximum=400,
value=100,
label="Maximal sequence length",
step=1,
),
gr.Slider(
minimum=1, maximum=50, value=10, label="Number of samples", step=1
),
gr.Slider(minimum=1, maximum=8, value=4.0, label="Limit"),
gr.Slider(minimum=1, maximum=32, value=8, label="Number of steps", step=1),
gr.Slider(
minimum=1, maximum=32, value=4, label="Number of initial points", step=1
),
gr.Slider(
minimum=1,
maximum=4,
value=1,
label="Number of optimization rounds",
step=1,
),
gr.Slider(minimum=0.01, maximum=1, value=0.1, label="Sampling variance"),
gr.Slider(
minimum=1,
maximum=10,
value=1,
label="Samples used for evaluation",
step=1,
),
gr.Slider(
minimum=1,
maximum=64,
value=4,
label="Maximum number of sampling steps",
step=1,
),
gr.Number(value=42, label="Seed", precision=0),
],
outputs=gr.HTML(label="Output"),
article=article,
description=description,
examples=examples.values.tolist(),
)
demo.launch(debug=True, show_error=True)
|