def run_model_params(params):
run_model({
"obj_function": params["obj_function"],
"search_parameters": {
"pop_max_size": 1,
"k_to_replace": 1,
},
"action_space_parameters": {
"atoms": params["atoms"]
},
"io_parameters": {
"model_path": join(output_results_prefix_path, params["name"]),
"save_n_steps": 1,
}
})
"count_N"),
{"type": "linear_combination",
"functions": [(count_nitrogen, "count_N"), "qed", "clscore"],
"coef": [0.5, 0.5, 0]},
{"type": "product_sigm_lin",
"functions": ["h**o", "clscore"],
"a": [-1, -1],
"b": [-7, 1.5],
"lambda": [1, 10]},
]
for i, eval_fun in enumerate(eval_functions):
run_model({
"obj_function": eval_fun,
"optimization_parameters": {
"pop_max_size": 10,
"k_to_replace": 2,
"max_steps": 10 if eval_fun == "h**o" or eval_fun == "lumo" or isinstance(eval_fun, dict) else 50,
"problem_type": "min" if eval_fun == "sascore" or eval_fun == "lumo" else "max"
},
"action_space_parameters": {
"atoms": "C,N,O,F" if eval_fun == "h**o" or eval_fun == "lumo" or isinstance(eval_fun,
dict) else "C,N,O,F,P,S,Cl,Br"
},
"io_parameters": {
"model_path": '1_test_evaluation_functions/' + str(i) + "_" + str(eval_fun),
"dft_working_dir": "/home/jleguy/dft_comput/",
"dft_cache_files": ["/home/jleguy/Documents/these/prod/data/00_datasets/DFT/cache_OPT.json"]
}
})
from evomol import run_model
run_model({
"obj_function": "entropy_shg_1",
"optimization_parameters": {
"max_steps": 100,
"pop_max_size": 1000
},
"io_parameters": {
"model_path": "4_entropy/"
},
})
from evomol import run_model
run_model({
"obj_function": "guacamol",
"optimization_parameters": {
"max_steps": 100,
"pop_max_size": 1000,
"guacamol_init_top_100": False
},
"io_parameters": {
"model_path": "2_guacamol/"
},
})
# "record_history": True
# }
# })
#
# exploration_graph(model_path=model_path, layout="neato")
#
# Plotting small exploration tree with images and actions
model_path = "3_plot_exploration_tree_images/"
run_model({
"obj_function": "qed",
"optimization_parameters": {
"max_steps": 10,
"pop_max_size": 10,
"k_to_replace": 2,
"mutable_init_pop": False
},
"io_parameters": {
"model_path": model_path,
"record_history": True,
"smiles_list_init_path": "acetylsalicylic_acid.smi"
}
})
exploration_graph(model_path=model_path, layout="dot", draw_actions=True, plot_images=True, draw_scores=True,
root_node="O=C(C)Oc1ccccc1C(=O)O", legend_scores_keys_strat=["total"], mol_size=0.3,
legend_offset=(-0.007, -0.05), figsize=(20, 20/1.5), legends_font_size=13)