#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 27 11:55:46 2025 Load parameters and evaluate likelihoods for an markovian site class model """ # general python import os import pandas as pd pd.options.mode.chained_assignment = None # this is annoying import pickle from functools import partial import platform import argparse import json from tqdm import tqdm from datetime import datetime # jax/flax stuff import jax import jax.numpy as jnp import flax # pytorch imports from torch.utils.data import DataLoader # custom function/classes imports (in order of appearance) from utils.build_optimizer import build_optimizer from utils.edit_argparse import enforce_valid_defaults from train_eval_fns.general_training_wrapper.training_wrapper_helpers import jit_compile_determine_alignlen_bin from utils.end_training import write_final_eval_results # specific to this model from utils.edit_argparse import pairhmm_frag_and_site_classes_fill_with_default_values as fill_with_default_values from utils.edit_argparse import pairhmms_share_top_level_args as share_top_level_args from latent_class_mixtures.initializers import init_pairhmm_transit_mixes as init_pairhmm from train_eval_fns.transit_mixes_training_fns import ( eval_one_batch, final_eval_wrapper ) def eval_pairhmm_transit_mixes( args, dataloader_dict: dict, training_argparse, **kwargs ): ########################################################################### ### 0: CHECK CONFIG; IMPORT APPROPRIATE MODULES ######################### ########################################################################### # final where model pickles, previous argparses are err = (f"Pred model type: {training_argparse.pred_model_type}; "+ f"this is the eval script for pairHMM with mixtures of transit classes!") assert training_argparse.pred_model_type in ['pairhmm_frag_and_site_classes', 'pairhmm_nested_tkf'], err del err prev_model_ckpts_dir = f'{os.getcwd()}/{args.training_wkdir}/model_ckpts' pairhmm_savemodel_filename = prev_model_ckpts_dir + '/'+ f'FINAL_PRED_BEST.pkl' fill_with_default_values(training_argparse) enforce_valid_defaults(training_argparse) share_top_level_args(training_argparse) ########################################################################### ### 1: SETUP ############################################################ ########################################################################### ### create the eval working directory, if it doesn't exist args.logfile_dir = f'{args.eval_wkdir}/logfiles' args.logfile_name = f'{args.logfile_dir}/PROGRESS.log' args.out_arrs_dir = f'{args.eval_wkdir}/out_arrs' args.model_ckpts_dir = f'{args.eval_wkdir}/model_ckpts' if args.eval_wkdir not in os.listdir(): os.mkdir(args.eval_wkdir) os.mkdir(args.logfile_dir) os.mkdir(args.out_arrs_dir) os.mkdir(args.model_ckpts_dir) # new place to save final pred outputs finalpred_save_model_filename = args.model_ckpts_dir + '/'+ f'FINAL_PRED.pkl' # create a new logfile with open(args.logfile_name,'w') as g: g.write(f"{datetime.now()}\n\n") g.write( f'Loading from {training_argparse.training_wkdir} to eval new data\n' ) # standard header g.write( f'PairHMM TKF92 with mixtures of transit classes: {training_argparse.pred_model_type}\n' ) g.write( f'Substitution model: {training_argparse.pred_config["subst_model_type"]}\n' ) g.write( f'Indel model: TKF92\n\n' ) g.write( f'Number of domain mixes: {training_argparse.pred_config["num_domain_mixtures"]}\n' ) g.write( f'Number of fragment mixes: {training_argparse.pred_config["num_fragment_mixtures"]}\n' ) g.write( f'Number of site mixes: {training_argparse.pred_config["num_site_mixtures"]}\n' ) g.write( f'Number of rate multipliers: {training_argparse.pred_config["k_rate_mults"]}\n' ) # note if rates are independent if training_argparse.pred_config['indp_rate_mults']: g.write( f' - Rates are independent of site class label: ( P(k | c) = P(k) )\n' ) elif not training_argparse.pred_config['indp_rate_mults']: g.write( f' - Rates depend on class labels\n' ) # how to normalize reported metrics (usually by descendant length) g.write(f' - When reporting, normalizing losses by: descendant length\n') # write source of times g.write( f'Times from: {training_argparse.pred_config["times_from"]}\n' ) ### extract data from dataloader_dict test_dset = dataloader_dict['test_dset'] test_dl = dataloader_dict['test_dl'] t_array_for_all_samples = dataloader_dict['t_array_for_all_samples'] ########################################################################### ### 2: INITIALIZE MODEL PARTS, OPTIMIZER ################################# ########################################################################### print('MODEL INIT') with open(args.logfile_name,'a') as g: g.write('\n') g.write(f'1: model init\n') # need to intialize an optimizer for compatibility when restoring the state, # but we're not training so this doesn't really matter? tx = build_optimizer(training_argparse) ### determine shapes for init # time if t_array_for_all_samples is not None: dummy_t_array_for_all_samples = jnp.empty( (t_array_for_all_samples.shape[0], ) ) dummy_t_for_each_sample = None else: dummy_t_array_for_all_samples = None dummy_t_for_each_sample = jnp.empty( (args.batch_size,) ) ### init sizes # (B, L, 3) max_dim1 = test_dset.global_align_max_length largest_aligns = jnp.empty( (args.batch_size, max_dim1, 3), dtype=int ) del max_dim1 ### initialize functions seq_shapes = [largest_aligns, dummy_t_for_each_sample] out = init_pairhmm( pred_model_type = training_argparse.pred_model_type, seq_shapes = seq_shapes, dummy_t_array = dummy_t_array_for_all_samples, tx = tx, model_init_rngkey = jax.random.key(0), pred_config = training_argparse.pred_config, tabulate_file_loc = args.model_ckpts_dir) blank_tstate, pairhmm_instance = out del out # load values with open(pairhmm_savemodel_filename, 'rb') as f: state_dict = pickle.load(f) best_pairhmm_trainstate = flax.serialization.from_state_dict( blank_tstate, state_dict ) del blank_tstate, state_dict ### part+jit functions # manage sequence lengths jitted_determine_alignlen_bin = jit_compile_determine_alignlen_bin(training_argparse) no_outputs = {k: False for k in training_argparse.interms_for_tboard.keys()} parted_eval_fn = partial( eval_one_batch, t_array = t_array_for_all_samples, all_trainstates = [best_pairhmm_trainstate], pairhmm_instance = pairhmm_instance, interms_for_tboard = no_outputs, return_all_loglikes = True ) eval_fn_jitted = jax.jit(parted_eval_fn, static_argnames = ['max_align_len']) del parted_eval_fn ### un-transform parameters and write to numpy arrays if args.save_arrs: # if using a set grid, write for all values in the grid if t_array_for_all_samples is not None: best_pairhmm_trainstate.apply_fn( variables = best_pairhmm_trainstate.params, t_array = t_array_for_all_samples, prefix = 't_grid', out_folder = args.out_arrs_dir, write_time_static_objs = True, method = pairhmm_instance.write_params ) # if using one branch length per sample, write arrays with t=1.0 elif t_array_for_all_samples is None: best_pairhmm_trainstate.apply_fn( variables = best_pairhmm_trainstate.params, t_array = jnp.array([1.0]), prefix = 't=1', out_folder = args.out_arrs_dir, write_time_static_objs = True, method = pairhmm_instance.write_params ) ########################################################################### ### 3: EVAL ############################################################# ########################################################################### print(f'BEGIN EVAL') # write to logfile with open(args.logfile_name,'a') as g: g.write('\n') g.write(f'BEGIN EVAL\n') test_summary_stats = final_eval_wrapper(dataloader = test_dl, dataset = test_dset, eval_fn_jitted = eval_fn_jitted, save_per_sample_losses = args.save_per_sample_losses, jitted_determine_alignlen_bin = jitted_determine_alignlen_bin, logfile_dir = args.logfile_dir, out_arrs_dir = args.out_arrs_dir, outfile_prefix = f'test-dset') ########################################### ### update the logfile with final losses # ########################################### # save the trainstate again with open(f'{args.model_ckpts_dir}/FINAL_PRED.pkl', 'wb') as g: model_state_dict = flax.serialization.to_state_dict(best_pairhmm_trainstate) pickle.dump(model_state_dict, g) write_final_eval_results(args = args, summary_stats = test_summary_stats, filename = 'AVE-LOSSES.tsv')