-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_singleview_pool.py
133 lines (118 loc) · 7.18 KB
/
train_singleview_pool.py
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
import yaml
import argparse
import os
import sys
import time
import gc
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.utils import class_weight
from src.training.utils import preprocess_views
from src.training.learn_pipeline import PoolEnsemble_train
from src.datasets.views_structure import DataViews, load_structure
from src.datasets.utils import _to_loader
OVERWRITE=True
def main_run(config_file):
start_time = time.time()
input_dir_folder = config_file["input_dir_folder"]
output_dir_folder = config_file["output_dir_folder"]
data_name = config_file["data_name"]
view_names = config_file["view_names"]
runs = config_file["experiment"]["runs"]
preprocess_args = config_file["experiment"]["preprocess"]
val_size = config_file["experiment"]["val_size"]
mlflow_runs_exp = config_file["experiment"]["mlflow_runs_exp"]
method_name = "Pool"
if config_file.get("additional_method_name"):
method_name += config_file.get("additional_method_name")
if config_file["training"].get("early_stop_args"):
if config_file["training"].get("early_stop_args").get("min_delta"):
config_file["training"]["early_stop_args"]["min_delta"] *= len(view_names)
try:
data_views_tr = load_structure(f"{input_dir_folder}/{data_name}_train")
except:
data_views_tr = load_structure(f"{input_dir_folder}/{data_name}")
raw_dims = {}
for view_name in data_views_tr.get_view_names():
aux_data = data_views_tr.get_view_data(view_name)["views"]
raw_dims[view_name] = {"raw": list(aux_data.shape[1:]), "flatten": int(np.prod(aux_data.shape[1:]))}
funcs = preprocess_views(data_views_tr, **preprocess_args)
views_tr = data_views_tr.generate_full_view_data(views_first=True, view_names=view_names)
data_views_te = load_structure(f"{input_dir_folder}/{data_name}_test")
preprocess_views(data_views_te, train=False, funcs=funcs, **preprocess_args)
views_te = data_views_te.generate_full_view_data(views_first=True, view_names=view_names)
if "loss_args" not in config_file["training"]:
config_file["training"]["loss_args"] = {}
config_file["training"]["loss_args"]["name"] = "ce" if "name" not in config_file["training"]["loss_args"] else config_file["training"]["loss_args"]["name"]
train_data_target = views_tr["target"].astype(int).flatten()
config_file["training"]["loss_args"]["weight"]=class_weight.compute_class_weight(class_weight='balanced',classes= np.unique(train_data_target), y=train_data_target)
run_id_mlflow = None
metadata_r = {"epoch_runs":[], "prediction_time":[], "training_time":[], "best_score":[]}
for r in range(runs):
if os.path.isfile(f"{output_dir_folder}/pred/{data_name}/test/{method_name}/out_run-{r:02d}.csv") and not OVERWRITE:
print(f"run {r} already created.. so skipping")
continue
if mlflow_runs_exp:
run_id_mlflow = "ind"
print(f"Executing model on run {r}")
if val_size!= 0:
mask_train = np.random.rand(len(data_views_tr.get_all_identifiers())) <= (1-val_size)
indx_train = np.arange(len(mask_train))[~mask_train]
data_views_tr.set_test_mask(indx_train, reset=True)
train_data = data_views_tr.generate_full_view_data(train = True, views_first=True, view_names=view_names)
val_data = data_views_tr.generate_full_view_data(train = False, views_first=True, view_names=view_names)
else:
train_data = views_tr
val_data = None
start_aux = time.time()
method, trainer = PoolEnsemble_train(train_data, val_data=val_data,run_id=r,
method_name=method_name, run_id_mlflow = run_id_mlflow, **config_file)
mlf_logger, run_id_mlflow = trainer.loggers[0], trainer.loggers[0].run_id
mlf_logger.experiment.log_dict(run_id_mlflow, raw_dims, "original_data_dim.yaml")
mlf_logger.experiment.log_dict(run_id_mlflow, config_file, "config_file.yaml")
metadata_r["training_time"].append(time.time()-start_aux)
metadata_r["epoch_runs"].append(trainer.callbacks[0].stopped_epoch)
metadata_r["best_score"].append(trainer.callbacks[0].best_score.cpu())
print("Training done")
pred_time_Start = time.time()
BS = config_file["training"]["batch_size"]
outputs_tr = method.transform(_to_loader(views_tr, batch_size=BS, train=False), output=True, out_norm=True)
outputs_te = method.transform(_to_loader(views_te, batch_size=BS, train=False), output=True, out_norm=True)
metadata_r["prediction_time"].append(time.time()-pred_time_Start)
for view_n, values in outputs_tr["views:prediction"].items():
data_save_tr = DataViews([values], identifiers=views_tr["identifiers"], view_names=[f"out_run-{r:02d}"])
data_save_tr.save(f"{output_dir_folder}/pred/{data_name}/train/{method_name}_{view_n}", ind_views=True, xarray=False)
mlf_logger.experiment.log_artifact(run_id_mlflow, f"{output_dir_folder}/pred/{data_name}/train/{method_name}_{view_n}/out_run-{r:02d}.csv",
artifact_path=f"preds/train/{method_name}_{view_n}")
for view_n, values in outputs_te["views:prediction"].items():
data_save_te = DataViews([values], identifiers=views_te["identifiers"], view_names=[f"out_run-{r:02d}"])
data_save_te.save(f"{output_dir_folder}/pred/{data_name}/test/{method_name}_{view_n}", ind_views=True, xarray=False)
mlf_logger.experiment.log_artifact(run_id_mlflow, f"{output_dir_folder}/pred/{data_name}/test/{method_name}_{view_n}/out_run-{r:02d}.csv",
artifact_path=f"preds/test/{method_name}_{view_n}")
print(f"Run {r:02d} of {method_name} finished...")
if type(run_id_mlflow) != type(None):
mlf_logger.experiment.log_metric(run_id_mlflow, "mean_tr_time", np.mean(metadata_r["training_time"]))
mlf_logger.experiment.log_metric(run_id_mlflow, "mean_pred_time", np.mean(metadata_r["prediction_time"]))
mlf_logger.experiment.log_metric(run_id_mlflow, "mean_epoch_runs", np.mean(metadata_r["epoch_runs"]))
mlf_logger.experiment.log_metric(run_id_mlflow, "mean_best_score", np.mean(metadata_r["best_score"]))
pd.DataFrame(metadata_r).to_csv(f"{output_dir_folder}/metadata_runs.csv")
mlf_logger.experiment.log_artifact(run_id_mlflow, f"{output_dir_folder}/metadata_runs.csv",)
os.remove(f"{output_dir_folder}/metadata_runs.csv")
print("Epochs for %s runs on average for %.2f epochs +- %.3f"%(method_name,np.mean(metadata_r["epoch_runs"]),np.std(metadata_r["epoch_runs"])))
print(f"Finished whole execution of {runs} runs in {time.time()-start_time:.2f} secs")
if __name__ == "__main__":
arg_parser = argparse.ArgumentParser()
arg_parser.add_argument(
"--settings_file",
"-s",
action="store",
dest="settings_file",
required=True,
type=str,
help="path of the settings file",
)
args = arg_parser.parse_args()
with open(args.settings_file) as fd:
config_file = yaml.load(fd, Loader=yaml.SafeLoader)
main_run(config_file)