Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- .hydra/config.yaml +122 -0
- .hydra/hydra.yaml +155 -0
- .hydra/overrides.yaml +1 -0
- .hydra/used_configs/hparam_search_20250307_081058.yaml +117 -0
- .hydra/used_configs/hparam_search_20250307_081243.yaml +117 -0
- log.log +250 -0
- model.pkl +3 -0
- model_summary.csv +2 -0
- optuna_study_forest_v1_0_mlogloss_lr.db +3 -0
- optuna_study_log.csv +66 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
optuna_study_forest_v1_0_mlogloss_lr.db filter=lfs diff=lfs merge=lfs -text
|
.hydra/config.yaml
ADDED
|
@@ -0,0 +1,122 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset:
|
| 2 |
+
parent_dir: ~/Developer/Projects/limonade/data
|
| 3 |
+
name: forest_v1_0
|
| 4 |
+
num_classes: 7
|
| 5 |
+
target_column: Cover_Type
|
| 6 |
+
train_path: ${dataset.parent_dir}/${dataset.name}/train.csv
|
| 7 |
+
test_path: ${dataset.parent_dir}/${dataset.name}/test.csv
|
| 8 |
+
val_path: ${dataset.parent_dir}/${dataset.name}/val.csv
|
| 9 |
+
model:
|
| 10 |
+
type: lr
|
| 11 |
+
params:
|
| 12 |
+
xgb:
|
| 13 |
+
tree_method:
|
| 14 |
+
type: categorical
|
| 15 |
+
values:
|
| 16 |
+
- approx
|
| 17 |
+
- hist
|
| 18 |
+
max_depth:
|
| 19 |
+
type: int
|
| 20 |
+
range:
|
| 21 |
+
- 3
|
| 22 |
+
- 12
|
| 23 |
+
min_child_weight:
|
| 24 |
+
type: int
|
| 25 |
+
range:
|
| 26 |
+
- 1
|
| 27 |
+
- 250
|
| 28 |
+
subsample:
|
| 29 |
+
type: float
|
| 30 |
+
range:
|
| 31 |
+
- 0.1
|
| 32 |
+
- 1.0
|
| 33 |
+
colsample_bynode:
|
| 34 |
+
type: float
|
| 35 |
+
range:
|
| 36 |
+
- 0.1
|
| 37 |
+
- 1.0
|
| 38 |
+
reg_lambda:
|
| 39 |
+
type: float
|
| 40 |
+
range:
|
| 41 |
+
- 0.005
|
| 42 |
+
- 25
|
| 43 |
+
log: true
|
| 44 |
+
learning_rate:
|
| 45 |
+
type: float
|
| 46 |
+
range:
|
| 47 |
+
- 0.01
|
| 48 |
+
- 0.2
|
| 49 |
+
log: true
|
| 50 |
+
early_stopping_rounds:
|
| 51 |
+
type: fixed
|
| 52 |
+
value: 10
|
| 53 |
+
num_boost_round:
|
| 54 |
+
type: int
|
| 55 |
+
range:
|
| 56 |
+
- 500
|
| 57 |
+
- 1000
|
| 58 |
+
lr:
|
| 59 |
+
C:
|
| 60 |
+
type: float
|
| 61 |
+
range:
|
| 62 |
+
- 1.0e-05
|
| 63 |
+
- 100
|
| 64 |
+
log: true
|
| 65 |
+
penalty_solver:
|
| 66 |
+
type: categorical
|
| 67 |
+
values:
|
| 68 |
+
- l1:liblinear
|
| 69 |
+
- l1:saga
|
| 70 |
+
- l2:lbfgs
|
| 71 |
+
- l2:liblinear
|
| 72 |
+
- l2:sag
|
| 73 |
+
- l2:saga
|
| 74 |
+
- elasticnet:saga
|
| 75 |
+
l1_ratio:
|
| 76 |
+
type: float
|
| 77 |
+
range:
|
| 78 |
+
- 0.0
|
| 79 |
+
- 1.0
|
| 80 |
+
max_iter:
|
| 81 |
+
type: int
|
| 82 |
+
range:
|
| 83 |
+
- 100
|
| 84 |
+
- 1000
|
| 85 |
+
tol:
|
| 86 |
+
type: float
|
| 87 |
+
range:
|
| 88 |
+
- 1.0e-05
|
| 89 |
+
- 0.001
|
| 90 |
+
log: true
|
| 91 |
+
optuna:
|
| 92 |
+
time_limit: 43200
|
| 93 |
+
n_trials: 1000
|
| 94 |
+
use_cross_validation: false
|
| 95 |
+
cv_folds: 5
|
| 96 |
+
metric: mlogloss
|
| 97 |
+
metric_direction:
|
| 98 |
+
logloss: minimize
|
| 99 |
+
mlogloss: minimize
|
| 100 |
+
accuracy: maximize
|
| 101 |
+
auc: maximize
|
| 102 |
+
sampler:
|
| 103 |
+
name: TPESampler
|
| 104 |
+
params:
|
| 105 |
+
seed: 42
|
| 106 |
+
study_name: optuna_study_${dataset.name}_${optuna.metric}_${model.type}
|
| 107 |
+
n_jobs: -1
|
| 108 |
+
use_gpu: false
|
| 109 |
+
prune: false
|
| 110 |
+
paths:
|
| 111 |
+
study_folder: /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/${optuna.study_name}
|
| 112 |
+
sqlite_db_path: ${paths.study_folder}/${optuna.study_name}.db
|
| 113 |
+
storage_url: sqlite:///${paths.sqlite_db_path}
|
| 114 |
+
model_save_path: ${paths.study_folder}/model.pkl
|
| 115 |
+
summary_save_path: ${paths.study_folder}/model_summary.csv
|
| 116 |
+
study_log_csv: ${paths.study_folder}/optuna_study_log.csv
|
| 117 |
+
config_save_dir: ${paths.study_folder}/.hydra/used_configs
|
| 118 |
+
huggingface:
|
| 119 |
+
upload_model: true
|
| 120 |
+
repo_id: zokaityte/${dataset.name}_${model.type}
|
| 121 |
+
final_training:
|
| 122 |
+
train: true
|
.hydra/hydra.yaml
ADDED
|
@@ -0,0 +1,155 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
hydra:
|
| 2 |
+
run:
|
| 3 |
+
dir: ${paths.study_folder}
|
| 4 |
+
sweep:
|
| 5 |
+
dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
|
| 6 |
+
subdir: ${hydra.job.num}
|
| 7 |
+
launcher:
|
| 8 |
+
_target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
|
| 9 |
+
sweeper:
|
| 10 |
+
_target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
|
| 11 |
+
max_batch_size: null
|
| 12 |
+
params: null
|
| 13 |
+
help:
|
| 14 |
+
app_name: ${hydra.job.name}
|
| 15 |
+
header: '${hydra.help.app_name} is powered by Hydra.
|
| 16 |
+
|
| 17 |
+
'
|
| 18 |
+
footer: 'Powered by Hydra (https://hydra.cc)
|
| 19 |
+
|
| 20 |
+
Use --hydra-help to view Hydra specific help
|
| 21 |
+
|
| 22 |
+
'
|
| 23 |
+
template: '${hydra.help.header}
|
| 24 |
+
|
| 25 |
+
== Configuration groups ==
|
| 26 |
+
|
| 27 |
+
Compose your configuration from those groups (group=option)
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
$APP_CONFIG_GROUPS
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
== Config ==
|
| 34 |
+
|
| 35 |
+
Override anything in the config (foo.bar=value)
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
$CONFIG
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
${hydra.help.footer}
|
| 42 |
+
|
| 43 |
+
'
|
| 44 |
+
hydra_help:
|
| 45 |
+
template: 'Hydra (${hydra.runtime.version})
|
| 46 |
+
|
| 47 |
+
See https://hydra.cc for more info.
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
== Flags ==
|
| 51 |
+
|
| 52 |
+
$FLAGS_HELP
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
== Configuration groups ==
|
| 56 |
+
|
| 57 |
+
Compose your configuration from those groups (For example, append hydra/job_logging=disabled
|
| 58 |
+
to command line)
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
$HYDRA_CONFIG_GROUPS
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
Use ''--cfg hydra'' to Show the Hydra config.
|
| 65 |
+
|
| 66 |
+
'
|
| 67 |
+
hydra_help: ???
|
| 68 |
+
hydra_logging:
|
| 69 |
+
version: 1
|
| 70 |
+
formatters:
|
| 71 |
+
simple:
|
| 72 |
+
format: '[%(asctime)s][HYDRA] %(message)s'
|
| 73 |
+
handlers:
|
| 74 |
+
console:
|
| 75 |
+
class: logging.StreamHandler
|
| 76 |
+
formatter: simple
|
| 77 |
+
stream: ext://sys.stdout
|
| 78 |
+
root:
|
| 79 |
+
level: INFO
|
| 80 |
+
handlers:
|
| 81 |
+
- console
|
| 82 |
+
loggers:
|
| 83 |
+
logging_example:
|
| 84 |
+
level: DEBUG
|
| 85 |
+
disable_existing_loggers: false
|
| 86 |
+
job_logging:
|
| 87 |
+
version: 1
|
| 88 |
+
formatters:
|
| 89 |
+
simple:
|
| 90 |
+
format: '[%(asctime)s] [%(levelname)s] [%(filename)s:%(lineno)d] - %(message)s'
|
| 91 |
+
handlers:
|
| 92 |
+
console:
|
| 93 |
+
class: logging.StreamHandler
|
| 94 |
+
formatter: simple
|
| 95 |
+
stream: ext://sys.stdout
|
| 96 |
+
file:
|
| 97 |
+
class: logging.FileHandler
|
| 98 |
+
formatter: simple
|
| 99 |
+
filename: ${paths.study_folder}/log.log
|
| 100 |
+
mode: a
|
| 101 |
+
root:
|
| 102 |
+
level: INFO
|
| 103 |
+
handlers:
|
| 104 |
+
- file
|
| 105 |
+
- console
|
| 106 |
+
disable_existing_loggers: false
|
| 107 |
+
env: {}
|
| 108 |
+
mode: RUN
|
| 109 |
+
searchpath: []
|
| 110 |
+
callbacks: {}
|
| 111 |
+
output_subdir: .hydra
|
| 112 |
+
overrides:
|
| 113 |
+
hydra:
|
| 114 |
+
- hydra.mode=RUN
|
| 115 |
+
task: []
|
| 116 |
+
job:
|
| 117 |
+
name: final_model_training
|
| 118 |
+
chdir: null
|
| 119 |
+
override_dirname: ''
|
| 120 |
+
id: ???
|
| 121 |
+
num: ???
|
| 122 |
+
config_name: hparam_search
|
| 123 |
+
env_set: {}
|
| 124 |
+
env_copy: []
|
| 125 |
+
config:
|
| 126 |
+
override_dirname:
|
| 127 |
+
kv_sep: '='
|
| 128 |
+
item_sep: ','
|
| 129 |
+
exclude_keys: []
|
| 130 |
+
runtime:
|
| 131 |
+
version: 1.3.2
|
| 132 |
+
version_base: '1.2'
|
| 133 |
+
cwd: /Users/gintare/Developer/Projects/limonade/scripts/models_training
|
| 134 |
+
config_sources:
|
| 135 |
+
- path: hydra.conf
|
| 136 |
+
schema: pkg
|
| 137 |
+
provider: hydra
|
| 138 |
+
- path: /Users/gintare/Developer/Projects/limonade/scripts/models_training
|
| 139 |
+
schema: file
|
| 140 |
+
provider: main
|
| 141 |
+
- path: ''
|
| 142 |
+
schema: structured
|
| 143 |
+
provider: schema
|
| 144 |
+
output_dir: /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/optuna_study_forest_v1_0_mlogloss_lr
|
| 145 |
+
choices:
|
| 146 |
+
hydra/env: default
|
| 147 |
+
hydra/callbacks: null
|
| 148 |
+
hydra/job_logging: default
|
| 149 |
+
hydra/hydra_logging: default
|
| 150 |
+
hydra/hydra_help: default
|
| 151 |
+
hydra/help: default
|
| 152 |
+
hydra/sweeper: basic
|
| 153 |
+
hydra/launcher: basic
|
| 154 |
+
hydra/output: default
|
| 155 |
+
verbose: false
|
.hydra/overrides.yaml
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
[]
|
.hydra/used_configs/hparam_search_20250307_081058.yaml
ADDED
|
@@ -0,0 +1,117 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset:
|
| 2 |
+
parent_dir: ~/Developer/Projects/limonade/data
|
| 3 |
+
name: forest_v1_0
|
| 4 |
+
num_classes: 7
|
| 5 |
+
target_column: Cover_Type
|
| 6 |
+
train_path: ${dataset.parent_dir}/${dataset.name}/train.csv
|
| 7 |
+
test_path: ${dataset.parent_dir}/${dataset.name}/test.csv
|
| 8 |
+
val_path: ${dataset.parent_dir}/${dataset.name}/val.csv
|
| 9 |
+
model:
|
| 10 |
+
type: lr
|
| 11 |
+
params:
|
| 12 |
+
xgb:
|
| 13 |
+
tree_method:
|
| 14 |
+
type: categorical
|
| 15 |
+
values:
|
| 16 |
+
- approx
|
| 17 |
+
- hist
|
| 18 |
+
max_depth:
|
| 19 |
+
type: int
|
| 20 |
+
range:
|
| 21 |
+
- 3
|
| 22 |
+
- 12
|
| 23 |
+
min_child_weight:
|
| 24 |
+
type: int
|
| 25 |
+
range:
|
| 26 |
+
- 1
|
| 27 |
+
- 250
|
| 28 |
+
subsample:
|
| 29 |
+
type: float
|
| 30 |
+
range:
|
| 31 |
+
- 0.1
|
| 32 |
+
- 1.0
|
| 33 |
+
colsample_bynode:
|
| 34 |
+
type: float
|
| 35 |
+
range:
|
| 36 |
+
- 0.1
|
| 37 |
+
- 1.0
|
| 38 |
+
reg_lambda:
|
| 39 |
+
type: float
|
| 40 |
+
range:
|
| 41 |
+
- 0.005
|
| 42 |
+
- 25
|
| 43 |
+
log: true
|
| 44 |
+
learning_rate:
|
| 45 |
+
type: float
|
| 46 |
+
range:
|
| 47 |
+
- 0.01
|
| 48 |
+
- 0.2
|
| 49 |
+
log: true
|
| 50 |
+
early_stopping_rounds:
|
| 51 |
+
type: fixed
|
| 52 |
+
value: 10
|
| 53 |
+
num_boost_round:
|
| 54 |
+
type: int
|
| 55 |
+
range:
|
| 56 |
+
- 500
|
| 57 |
+
- 1000
|
| 58 |
+
lr:
|
| 59 |
+
C:
|
| 60 |
+
type: float
|
| 61 |
+
range:
|
| 62 |
+
- 1.0e-05
|
| 63 |
+
- 100
|
| 64 |
+
log: true
|
| 65 |
+
penalty_solver:
|
| 66 |
+
type: categorical
|
| 67 |
+
values:
|
| 68 |
+
- l1:liblinear
|
| 69 |
+
- l1:saga
|
| 70 |
+
- l2:lbfgs
|
| 71 |
+
- l2:liblinear
|
| 72 |
+
- l2:sag
|
| 73 |
+
- l2:saga
|
| 74 |
+
- elasticnet:saga
|
| 75 |
+
l1_ratio:
|
| 76 |
+
type: float
|
| 77 |
+
range:
|
| 78 |
+
- 0.0
|
| 79 |
+
- 1.0
|
| 80 |
+
max_iter:
|
| 81 |
+
type: int
|
| 82 |
+
range:
|
| 83 |
+
- 100
|
| 84 |
+
- 1000
|
| 85 |
+
tol:
|
| 86 |
+
type: float
|
| 87 |
+
range:
|
| 88 |
+
- 1.0e-05
|
| 89 |
+
- 0.001
|
| 90 |
+
log: true
|
| 91 |
+
optuna:
|
| 92 |
+
time_limit: 43200
|
| 93 |
+
n_trials: 1000
|
| 94 |
+
use_cross_validation: false
|
| 95 |
+
cv_folds: 5
|
| 96 |
+
metric: mlogloss
|
| 97 |
+
metric_direction:
|
| 98 |
+
logloss: minimize
|
| 99 |
+
mlogloss: minimize
|
| 100 |
+
accuracy: maximize
|
| 101 |
+
auc: maximize
|
| 102 |
+
sampler:
|
| 103 |
+
name: TPESampler
|
| 104 |
+
params:
|
| 105 |
+
seed: 42
|
| 106 |
+
study_name: optuna_study_${dataset.name}_${optuna.metric}_${model.type}
|
| 107 |
+
n_jobs: -1
|
| 108 |
+
use_gpu: false
|
| 109 |
+
prune: false
|
| 110 |
+
paths:
|
| 111 |
+
study_folder: /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/${optuna.study_name}
|
| 112 |
+
sqlite_db_path: ${paths.study_folder}/${optuna.study_name}.db
|
| 113 |
+
storage_url: sqlite:///${paths.sqlite_db_path}
|
| 114 |
+
model_save_path: ${paths.study_folder}/model.pkl
|
| 115 |
+
summary_save_path: ${paths.study_folder}/model_summary.csv
|
| 116 |
+
study_log_csv: ${paths.study_folder}/optuna_study_log.csv
|
| 117 |
+
config_save_dir: ${paths.study_folder}/.hydra/used_configs
|
.hydra/used_configs/hparam_search_20250307_081243.yaml
ADDED
|
@@ -0,0 +1,117 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset:
|
| 2 |
+
parent_dir: ~/Developer/Projects/limonade/data
|
| 3 |
+
name: forest_v1_0
|
| 4 |
+
num_classes: 7
|
| 5 |
+
target_column: Cover_Type
|
| 6 |
+
train_path: ${dataset.parent_dir}/${dataset.name}/train.csv
|
| 7 |
+
test_path: ${dataset.parent_dir}/${dataset.name}/test.csv
|
| 8 |
+
val_path: ${dataset.parent_dir}/${dataset.name}/val.csv
|
| 9 |
+
model:
|
| 10 |
+
type: lr
|
| 11 |
+
params:
|
| 12 |
+
xgb:
|
| 13 |
+
tree_method:
|
| 14 |
+
type: categorical
|
| 15 |
+
values:
|
| 16 |
+
- approx
|
| 17 |
+
- hist
|
| 18 |
+
max_depth:
|
| 19 |
+
type: int
|
| 20 |
+
range:
|
| 21 |
+
- 3
|
| 22 |
+
- 12
|
| 23 |
+
min_child_weight:
|
| 24 |
+
type: int
|
| 25 |
+
range:
|
| 26 |
+
- 1
|
| 27 |
+
- 250
|
| 28 |
+
subsample:
|
| 29 |
+
type: float
|
| 30 |
+
range:
|
| 31 |
+
- 0.1
|
| 32 |
+
- 1.0
|
| 33 |
+
colsample_bynode:
|
| 34 |
+
type: float
|
| 35 |
+
range:
|
| 36 |
+
- 0.1
|
| 37 |
+
- 1.0
|
| 38 |
+
reg_lambda:
|
| 39 |
+
type: float
|
| 40 |
+
range:
|
| 41 |
+
- 0.005
|
| 42 |
+
- 25
|
| 43 |
+
log: true
|
| 44 |
+
learning_rate:
|
| 45 |
+
type: float
|
| 46 |
+
range:
|
| 47 |
+
- 0.01
|
| 48 |
+
- 0.2
|
| 49 |
+
log: true
|
| 50 |
+
early_stopping_rounds:
|
| 51 |
+
type: fixed
|
| 52 |
+
value: 10
|
| 53 |
+
num_boost_round:
|
| 54 |
+
type: int
|
| 55 |
+
range:
|
| 56 |
+
- 500
|
| 57 |
+
- 1000
|
| 58 |
+
lr:
|
| 59 |
+
C:
|
| 60 |
+
type: float
|
| 61 |
+
range:
|
| 62 |
+
- 1.0e-05
|
| 63 |
+
- 100
|
| 64 |
+
log: true
|
| 65 |
+
penalty_solver:
|
| 66 |
+
type: categorical
|
| 67 |
+
values:
|
| 68 |
+
- l1:liblinear
|
| 69 |
+
- l1:saga
|
| 70 |
+
- l2:lbfgs
|
| 71 |
+
- l2:liblinear
|
| 72 |
+
- l2:sag
|
| 73 |
+
- l2:saga
|
| 74 |
+
- elasticnet:saga
|
| 75 |
+
l1_ratio:
|
| 76 |
+
type: float
|
| 77 |
+
range:
|
| 78 |
+
- 0.0
|
| 79 |
+
- 1.0
|
| 80 |
+
max_iter:
|
| 81 |
+
type: int
|
| 82 |
+
range:
|
| 83 |
+
- 100
|
| 84 |
+
- 1000
|
| 85 |
+
tol:
|
| 86 |
+
type: float
|
| 87 |
+
range:
|
| 88 |
+
- 1.0e-05
|
| 89 |
+
- 0.001
|
| 90 |
+
log: true
|
| 91 |
+
optuna:
|
| 92 |
+
time_limit: 43200
|
| 93 |
+
n_trials: 1000
|
| 94 |
+
use_cross_validation: false
|
| 95 |
+
cv_folds: 5
|
| 96 |
+
metric: mlogloss
|
| 97 |
+
metric_direction:
|
| 98 |
+
logloss: minimize
|
| 99 |
+
mlogloss: minimize
|
| 100 |
+
accuracy: maximize
|
| 101 |
+
auc: maximize
|
| 102 |
+
sampler:
|
| 103 |
+
name: TPESampler
|
| 104 |
+
params:
|
| 105 |
+
seed: 42
|
| 106 |
+
study_name: optuna_study_${dataset.name}_${optuna.metric}_${model.type}
|
| 107 |
+
n_jobs: -1
|
| 108 |
+
use_gpu: false
|
| 109 |
+
prune: false
|
| 110 |
+
paths:
|
| 111 |
+
study_folder: /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/${optuna.study_name}
|
| 112 |
+
sqlite_db_path: ${paths.study_folder}/${optuna.study_name}.db
|
| 113 |
+
storage_url: sqlite:///${paths.sqlite_db_path}
|
| 114 |
+
model_save_path: ${paths.study_folder}/model.pkl
|
| 115 |
+
summary_save_path: ${paths.study_folder}/model_summary.csv
|
| 116 |
+
study_log_csv: ${paths.study_folder}/optuna_study_log.csv
|
| 117 |
+
config_save_dir: ${paths.study_folder}/.hydra/used_configs
|
log.log
ADDED
|
@@ -0,0 +1,250 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2025-03-07 08:10:58,172] [INFO] [hparam_search.py:214] - Configuration saved to /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/optuna_study_forest_v1_0_mlogloss_lr/.hydra/used_configs/hparam_search_20250307_081058.yaml
|
| 2 |
+
[2025-03-07 08:10:58,172] [INFO] [hparam_search.py:167] - Starting hyperparameter search...
|
| 3 |
+
[2025-03-07 08:10:58,172] [INFO] [data_loading.py:16] - Loading dataset: train=~/Developer/Projects/limonade/data/forest_v1_0/train.csv, test=~/Developer/Projects/limonade/data/forest_v1_0/test.csv, val=~/Developer/Projects/limonade/data/forest_v1_0/val.csv
|
| 4 |
+
[2025-03-07 08:10:58,540] [INFO] [data_loading.py:20] - Train dataset loaded successfully: (464808, 55)
|
| 5 |
+
[2025-03-07 08:10:58,587] [INFO] [data_loading.py:23] - Test dataset loaded successfully: (58102, 55)
|
| 6 |
+
[2025-03-07 08:10:58,636] [INFO] [data_loading.py:28] - Validation dataset loaded successfully: (58102, 55)
|
| 7 |
+
[2025-03-07 08:10:58,637] [INFO] [data_loading.py:51] - Dataset verification passed: 7 classes detected.
|
| 8 |
+
[2025-03-07 08:10:58,662] [INFO] [data_loading.py:57] - Data split completed. Shapes: Train (464808, 54), Val (58102, 54), Test (58102, 54)
|
| 9 |
+
[2025-03-07 08:10:58,662] [INFO] [hparam_search.py:171] - Dataset loaded: Train (464808, 54), Test (58102, 54), Val (58102, 54)
|
| 10 |
+
[2025-03-07 08:10:58,854] [INFO] [hparam_search.py:186] - Optuna study created. Starting optimization...
|
| 11 |
+
[2025-03-07 08:10:58,897] [INFO] [hparam_search.py:82] - Starting trial 2
|
| 12 |
+
[2025-03-07 08:10:58,898] [INFO] [hparam_search.py:82] - Starting trial 1
|
| 13 |
+
[2025-03-07 08:10:58,901] [INFO] [hparam_search.py:82] - Starting trial 5
|
| 14 |
+
[2025-03-07 08:10:58,902] [INFO] [hparam_search.py:82] - Starting trial 4
|
| 15 |
+
[2025-03-07 08:10:58,920] [INFO] [hparam_search.py:85] - Suggested parameters for trial 1: {'C': 4.7875359532895424e-05, 'penalty_solver': 'l2:saga', 'l1_ratio': 0.018221830648489812, 'max_iter': 802, 'tol': 0.0002727712487415353}
|
| 16 |
+
[2025-03-07 08:10:58,926] [INFO] [hparam_search.py:82] - Starting trial 6
|
| 17 |
+
[2025-03-07 08:10:58,928] [INFO] [hparam_search.py:82] - Starting trial 10
|
| 18 |
+
[2025-03-07 08:10:58,939] [INFO] [hparam_search.py:82] - Starting trial 7
|
| 19 |
+
[2025-03-07 08:10:58,941] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 1
|
| 20 |
+
[2025-03-07 08:10:58,943] [INFO] [hparam_search.py:85] - Suggested parameters for trial 2: {'C': 34.13344131182989, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.271735709526303, 'max_iter': 653, 'tol': 0.00012121404273410466}
|
| 21 |
+
[2025-03-07 08:10:58,978] [INFO] [hparam_search.py:82] - Starting trial 0
|
| 22 |
+
[2025-03-07 08:10:58,979] [INFO] [hparam_search.py:82] - Starting trial 11
|
| 23 |
+
[2025-03-07 08:10:58,981] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 2
|
| 24 |
+
[2025-03-07 08:10:58,983] [INFO] [hparam_search.py:82] - Starting trial 9
|
| 25 |
+
[2025-03-07 08:10:58,983] [INFO] [hparam_search.py:82] - Starting trial 3
|
| 26 |
+
[2025-03-07 08:10:58,991] [INFO] [hparam_search.py:85] - Suggested parameters for trial 6: {'C': 3.6181077651453326, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.382582098762437, 'max_iter': 109, 'tol': 0.00041392892542484915}
|
| 27 |
+
[2025-03-07 08:10:58,995] [INFO] [hparam_search.py:82] - Starting trial 8
|
| 28 |
+
[2025-03-07 08:10:58,998] [INFO] [hparam_search.py:82] - Starting trial 12
|
| 29 |
+
[2025-03-07 08:10:59,005] [INFO] [hparam_search.py:85] - Suggested parameters for trial 10: {'C': 3.304723016424694e-05, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.4558487897247905, 'max_iter': 219, 'tol': 0.0009913557730329046}
|
| 30 |
+
[2025-03-07 08:10:59,011] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 6
|
| 31 |
+
[2025-03-07 08:10:59,019] [INFO] [hparam_search.py:85] - Suggested parameters for trial 11: {'C': 3.099102714379317, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.42263550165274577, 'max_iter': 968, 'tol': 8.666421709771902e-05}
|
| 32 |
+
[2025-03-07 08:10:59,024] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 11
|
| 33 |
+
[2025-03-07 08:10:59,032] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 10
|
| 34 |
+
[2025-03-07 08:10:59,044] [INFO] [hparam_search.py:85] - Suggested parameters for trial 9: {'C': 0.00010445552123911683, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.8421592738725264, 'max_iter': 605, 'tol': 5.5691220302481965e-05}
|
| 35 |
+
[2025-03-07 08:10:59,058] [INFO] [hparam_search.py:85] - Suggested parameters for trial 7: {'C': 16.550757089177576, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.32581243064306664, 'max_iter': 616, 'tol': 0.0001275690555421488}
|
| 36 |
+
[2025-03-07 08:10:59,067] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 7
|
| 37 |
+
[2025-03-07 08:10:59,075] [INFO] [hparam_search.py:85] - Suggested parameters for trial 12: {'C': 1.8383875772825157e-05, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.6069954638911863, 'max_iter': 129, 'tol': 1.0796652256635228e-05}
|
| 38 |
+
[2025-03-07 08:10:59,079] [INFO] [hparam_search.py:82] - Starting trial 13
|
| 39 |
+
[2025-03-07 08:10:59,083] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 12
|
| 40 |
+
[2025-03-07 08:10:59,102] [INFO] [hparam_search.py:85] - Suggested parameters for trial 5: {'C': 1.6326522359899025, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.003021686292953185, 'max_iter': 484, 'tol': 0.0005025604309899143}
|
| 41 |
+
[2025-03-07 08:10:59,109] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 9
|
| 42 |
+
[2025-03-07 08:10:59,121] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 5
|
| 43 |
+
[2025-03-07 08:10:59,136] [INFO] [hparam_search.py:85] - Suggested parameters for trial 4: {'C': 2.589570571232358, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9426076326524999, 'max_iter': 935, 'tol': 0.0002836660823633425}
|
| 44 |
+
[2025-03-07 08:10:59,141] [INFO] [hparam_search.py:85] - Suggested parameters for trial 13: {'C': 94.72138074987807, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.9808293489208364, 'max_iter': 838, 'tol': 0.0001191094556785542}
|
| 45 |
+
[2025-03-07 08:10:59,145] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 13
|
| 46 |
+
[2025-03-07 08:10:59,155] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 4
|
| 47 |
+
[2025-03-07 08:10:59,159] [INFO] [hparam_search.py:85] - Suggested parameters for trial 0: {'C': 1.9632861206224492e-05, 'penalty_solver': 'l1:saga', 'l1_ratio': 0.9761918972004304, 'max_iter': 880, 'tol': 1.9341489273261066e-05}
|
| 48 |
+
[2025-03-07 08:10:59,163] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 0
|
| 49 |
+
[2025-03-07 08:10:59,167] [INFO] [hparam_search.py:85] - Suggested parameters for trial 3: {'C': 0.005228496985183292, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.8678453897787207, 'max_iter': 887, 'tol': 0.0002752128277082581}
|
| 50 |
+
[2025-03-07 08:10:59,177] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 3
|
| 51 |
+
[2025-03-07 08:10:59,216] [INFO] [hparam_search.py:85] - Suggested parameters for trial 8: {'C': 0.014745693420318746, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.5824976035235025, 'max_iter': 357, 'tol': 0.00016913126055906019}
|
| 52 |
+
[2025-03-07 08:10:59,221] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 8
|
| 53 |
+
[2025-03-07 08:12:43,280] [INFO] [hparam_search.py:214] - Configuration saved to /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/optuna_study_forest_v1_0_mlogloss_lr/.hydra/used_configs/hparam_search_20250307_081243.yaml
|
| 54 |
+
[2025-03-07 08:12:43,280] [INFO] [hparam_search.py:167] - Starting hyperparameter search...
|
| 55 |
+
[2025-03-07 08:12:43,281] [INFO] [data_loading.py:16] - Loading dataset: train=~/Developer/Projects/limonade/data/forest_v1_0/train.csv, test=~/Developer/Projects/limonade/data/forest_v1_0/test.csv, val=~/Developer/Projects/limonade/data/forest_v1_0/val.csv
|
| 56 |
+
[2025-03-07 08:12:43,645] [INFO] [data_loading.py:20] - Train dataset loaded successfully: (464808, 55)
|
| 57 |
+
[2025-03-07 08:12:43,695] [INFO] [data_loading.py:23] - Test dataset loaded successfully: (58102, 55)
|
| 58 |
+
[2025-03-07 08:12:43,744] [INFO] [data_loading.py:28] - Validation dataset loaded successfully: (58102, 55)
|
| 59 |
+
[2025-03-07 08:12:43,745] [INFO] [data_loading.py:51] - Dataset verification passed: 7 classes detected.
|
| 60 |
+
[2025-03-07 08:12:43,770] [INFO] [data_loading.py:57] - Data split completed. Shapes: Train (464808, 54), Val (58102, 54), Test (58102, 54)
|
| 61 |
+
[2025-03-07 08:12:43,770] [INFO] [hparam_search.py:171] - Dataset loaded: Train (464808, 54), Test (58102, 54), Val (58102, 54)
|
| 62 |
+
[2025-03-07 08:12:43,943] [INFO] [hparam_search.py:186] - Optuna study created. Starting optimization...
|
| 63 |
+
[2025-03-07 08:12:43,996] [INFO] [hparam_search.py:82] - Starting trial 18
|
| 64 |
+
[2025-03-07 08:12:43,996] [INFO] [hparam_search.py:82] - Starting trial 17
|
| 65 |
+
[2025-03-07 08:12:44,004] [INFO] [hparam_search.py:82] - Starting trial 21
|
| 66 |
+
[2025-03-07 08:12:44,005] [INFO] [hparam_search.py:82] - Starting trial 14
|
| 67 |
+
[2025-03-07 08:12:44,005] [INFO] [hparam_search.py:82] - Starting trial 15
|
| 68 |
+
[2025-03-07 08:12:44,005] [INFO] [hparam_search.py:82] - Starting trial 16
|
| 69 |
+
[2025-03-07 08:12:44,010] [INFO] [hparam_search.py:82] - Starting trial 19
|
| 70 |
+
[2025-03-07 08:12:44,015] [INFO] [hparam_search.py:82] - Starting trial 20
|
| 71 |
+
[2025-03-07 08:12:44,026] [INFO] [hparam_search.py:85] - Suggested parameters for trial 18: {'C': 0.10753794163930472, 'penalty_solver': 'l1:saga', 'l1_ratio': 0.8119533855607183, 'max_iter': 251, 'tol': 7.058855209127119e-05}
|
| 72 |
+
[2025-03-07 08:12:44,034] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 18
|
| 73 |
+
[2025-03-07 08:12:44,046] [INFO] [hparam_search.py:85] - Suggested parameters for trial 21: {'C': 66.88943514438645, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.7104256340067888, 'max_iter': 862, 'tol': 2.8484414321600405e-05}
|
| 74 |
+
[2025-03-07 08:12:44,075] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 21
|
| 75 |
+
[2025-03-07 08:12:44,094] [INFO] [hparam_search.py:85] - Suggested parameters for trial 15: {'C': 0.08290917029064834, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.8780724901444901, 'max_iter': 597, 'tol': 6.516962587992502e-05}
|
| 76 |
+
[2025-03-07 08:12:44,181] [INFO] [hparam_search.py:85] - Suggested parameters for trial 17: {'C': 5.2959004321466774e-05, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.976635868194475, 'max_iter': 880, 'tol': 0.0001995998413593255}
|
| 77 |
+
[2025-03-07 08:12:44,187] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 15
|
| 78 |
+
[2025-03-07 08:12:44,189] [INFO] [hparam_search.py:82] - Starting trial 22
|
| 79 |
+
[2025-03-07 08:12:44,216] [INFO] [hparam_search.py:85] - Suggested parameters for trial 14: {'C': 0.0005228109721193718, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.8093658562012899, 'max_iter': 493, 'tol': 3.874147467551387e-05}
|
| 80 |
+
[2025-03-07 08:12:44,217] [INFO] [hparam_search.py:82] - Starting trial 27
|
| 81 |
+
[2025-03-07 08:12:44,223] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 17
|
| 82 |
+
[2025-03-07 08:12:44,227] [INFO] [hparam_search.py:82] - Starting trial 23
|
| 83 |
+
[2025-03-07 08:12:44,229] [INFO] [hparam_search.py:82] - Starting trial 24
|
| 84 |
+
[2025-03-07 08:12:44,229] [INFO] [hparam_search.py:82] - Starting trial 26
|
| 85 |
+
[2025-03-07 08:12:44,236] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 14
|
| 86 |
+
[2025-03-07 08:12:44,265] [INFO] [hparam_search.py:85] - Suggested parameters for trial 19: {'C': 0.04365867127670072, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.18857030681906028, 'max_iter': 936, 'tol': 1.2253441001829851e-05}
|
| 87 |
+
[2025-03-07 08:12:44,271] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 19
|
| 88 |
+
[2025-03-07 08:12:44,276] [INFO] [hparam_search.py:85] - Suggested parameters for trial 16: {'C': 0.002235922974214041, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.1749696370372762, 'max_iter': 849, 'tol': 2.491622379628421e-05}
|
| 89 |
+
[2025-03-07 08:12:44,280] [INFO] [hparam_search.py:82] - Starting trial 25
|
| 90 |
+
[2025-03-07 08:12:44,317] [INFO] [hparam_search.py:85] - Suggested parameters for trial 22: {'C': 4.725676373184278, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.37209617568379727, 'max_iter': 819, 'tol': 5.832433478492103e-05}
|
| 91 |
+
[2025-03-07 08:12:44,322] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 22
|
| 92 |
+
[2025-03-07 08:12:44,329] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 16
|
| 93 |
+
[2025-03-07 08:12:44,385] [INFO] [hparam_search.py:85] - Suggested parameters for trial 20: {'C': 0.0002470789370081118, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.07256628067642645, 'max_iter': 174, 'tol': 0.000856655026152043}
|
| 94 |
+
[2025-03-07 08:12:44,416] [INFO] [hparam_search.py:85] - Suggested parameters for trial 25: {'C': 0.0019454839024506696, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.430701449464998, 'max_iter': 136, 'tol': 0.0005352514125792776}
|
| 95 |
+
[2025-03-07 08:12:44,478] [INFO] [hparam_search.py:85] - Suggested parameters for trial 26: {'C': 14.494867724886827, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.7872642427767997, 'max_iter': 491, 'tol': 1.695513599231274e-05}
|
| 96 |
+
[2025-03-07 08:12:44,502] [INFO] [hparam_search.py:85] - Suggested parameters for trial 23: {'C': 0.9852329490911322, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.6199056558332002, 'max_iter': 803, 'tol': 0.00015033229675130488}
|
| 97 |
+
[2025-03-07 08:12:44,557] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 23
|
| 98 |
+
[2025-03-07 08:12:44,565] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 26
|
| 99 |
+
[2025-03-07 08:12:44,585] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 20
|
| 100 |
+
[2025-03-07 08:12:44,594] [INFO] [hparam_search.py:85] - Suggested parameters for trial 27: {'C': 1.1034005898853718e-05, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.049352468371193, 'max_iter': 476, 'tol': 0.0004147242955185673}
|
| 101 |
+
[2025-03-07 08:12:44,601] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 27
|
| 102 |
+
[2025-03-07 08:12:44,609] [INFO] [hparam_search.py:85] - Suggested parameters for trial 24: {'C': 0.31320459939088413, 'penalty_solver': 'l2:saga', 'l1_ratio': 0.8007694096230313, 'max_iter': 750, 'tol': 7.719707411861858e-05}
|
| 103 |
+
[2025-03-07 08:12:44,615] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 24
|
| 104 |
+
[2025-03-07 08:12:44,624] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 25
|
| 105 |
+
[2025-03-07 08:27:43,884] [INFO] [hparam_search.py:82] - Starting trial 28
|
| 106 |
+
[2025-03-07 08:27:43,912] [INFO] [hparam_search.py:85] - Suggested parameters for trial 28: {'C': 0.0002585775051143828, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.28161698468553553, 'max_iter': 965, 'tol': 0.00036057203643168013}
|
| 107 |
+
[2025-03-07 08:27:43,936] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 28
|
| 108 |
+
[2025-03-07 08:31:59,875] [INFO] [hparam_search.py:82] - Starting trial 29
|
| 109 |
+
[2025-03-07 08:31:59,884] [INFO] [hparam_search.py:85] - Suggested parameters for trial 29: {'C': 0.01872557405037317, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.29858404227811497, 'max_iter': 972, 'tol': 0.0005317911737119296}
|
| 110 |
+
[2025-03-07 08:31:59,890] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 29
|
| 111 |
+
[2025-03-07 08:32:01,140] [INFO] [hparam_search.py:148] - Trial 23 is the best so far.
|
| 112 |
+
[2025-03-07 08:32:57,026] [INFO] [hparam_search.py:148] - Trial 25 is the best so far.
|
| 113 |
+
[2025-03-07 08:34:15,107] [INFO] [hparam_search.py:82] - Starting trial 30
|
| 114 |
+
[2025-03-07 08:34:15,137] [INFO] [hparam_search.py:85] - Suggested parameters for trial 30: {'C': 0.899854995828966, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.6279205105919434, 'max_iter': 212, 'tol': 0.00037650298191910155}
|
| 115 |
+
[2025-03-07 08:34:15,157] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 30
|
| 116 |
+
[2025-03-07 08:34:15,515] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.927823830715556, 'val_mlogloss': 0.6765094087409561, 'val_accuracy': 0.7045712712126949, 'val_precision_micro': 0.7045712712126949, 'val_recall_micro': 0.7045712712126949, 'val_f1_micro': 0.7045712712126949, 'val_precision_weighted': 0.6886994293672783, 'val_recall_weighted': 0.7045712712126949, 'val_f1_weighted': 0.6868623111817979, 'val_precision_macro': 0.5555950175823064, 'val_recall_macro': 0.4105432040857316, 'val_f1_macro': 0.4280160645470881, 'val_cm': [[14460, 6560, 13, 0, 1, 0, 150], [4939, 22683, 617, 1, 9, 60, 21], [9, 366, 3016, 29, 0, 155, 1], [0, 0, 191, 49, 0, 35, 0], [21, 860, 61, 0, 1, 5, 1], [0, 632, 958, 0, 0, 147, 0], [1451, 12, 7, 0, 0, 0, 581]]}
|
| 117 |
+
[2025-03-07 08:34:15,581] [INFO] [hparam_search.py:82] - Starting trial 31
|
| 118 |
+
[2025-03-07 08:34:15,605] [INFO] [hparam_search.py:85] - Suggested parameters for trial 31: {'C': 2.4394692876817174, 'penalty_solver': 'l1:saga', 'l1_ratio': 0.24101962705857216, 'max_iter': 452, 'tol': 3.2310248682653125e-05}
|
| 119 |
+
[2025-03-07 08:34:15,615] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 31
|
| 120 |
+
[2025-03-07 08:35:20,984] [INFO] [hparam_search.py:82] - Starting trial 32
|
| 121 |
+
[2025-03-07 08:35:21,041] [INFO] [hparam_search.py:85] - Suggested parameters for trial 32: {'C': 0.00028829650171758236, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.5250608314672751, 'max_iter': 341, 'tol': 2.3173018226579656e-05}
|
| 122 |
+
[2025-03-07 08:35:21,058] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 32
|
| 123 |
+
[2025-03-07 08:36:51,732] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9140457539215289, 'val_mlogloss': 0.7226542102409799, 'val_accuracy': 0.6873601597191147, 'val_precision_micro': 0.6873601597191147, 'val_recall_micro': 0.6873601597191147, 'val_f1_micro': 0.6873601597191147, 'val_precision_weighted': 0.6618312777021531, 'val_recall_weighted': 0.6873601597191147, 'val_f1_weighted': 0.6595545116452762, 'val_precision_macro': 0.429545300361069, 'val_recall_macro': 0.32058378353907635, 'val_f1_macro': 0.31942924983510756, 'val_cm': [[14204, 6942, 0, 0, 0, 0, 38], [4832, 23179, 271, 0, 0, 32, 16], [8, 1107, 2383, 0, 0, 77, 1], [0, 0, 265, 0, 0, 10, 0], [4, 944, 0, 0, 0, 0, 1], [0, 746, 927, 0, 0, 64, 0], [1920, 24, 0, 0, 0, 0, 107]]}
|
| 124 |
+
[2025-03-07 08:36:51,786] [INFO] [hparam_search.py:82] - Starting trial 33
|
| 125 |
+
[2025-03-07 08:36:51,796] [INFO] [hparam_search.py:85] - Suggested parameters for trial 33: {'C': 0.36264409286899085, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.39534316068814, 'max_iter': 845, 'tol': 4.699941049120226e-05}
|
| 126 |
+
[2025-03-07 08:36:51,805] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 33
|
| 127 |
+
[2025-03-07 08:36:52,197] [INFO] [hparam_search.py:82] - Starting trial 34
|
| 128 |
+
[2025-03-07 08:36:52,207] [INFO] [hparam_search.py:85] - Suggested parameters for trial 34: {'C': 2.335829803388853, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.7564518562376523, 'max_iter': 809, 'tol': 0.0008596551415818968}
|
| 129 |
+
[2025-03-07 08:36:52,212] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 34
|
| 130 |
+
[2025-03-07 08:40:01,451] [INFO] [hparam_search.py:82] - Starting trial 35
|
| 131 |
+
[2025-03-07 08:40:01,464] [INFO] [hparam_search.py:85] - Suggested parameters for trial 35: {'C': 0.010368737038077025, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.7965917940036776, 'max_iter': 655, 'tol': 6.827644851696467e-05}
|
| 132 |
+
[2025-03-07 08:40:01,473] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 35
|
| 133 |
+
[2025-03-07 08:42:35,456] [INFO] [hparam_search.py:82] - Starting trial 36
|
| 134 |
+
[2025-03-07 08:42:35,474] [INFO] [hparam_search.py:85] - Suggested parameters for trial 36: {'C': 0.04537225302234671, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.8686747315831244, 'max_iter': 439, 'tol': 0.0002606702849897083}
|
| 135 |
+
[2025-03-07 08:42:35,487] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 36
|
| 136 |
+
[2025-03-07 08:43:44,799] [INFO] [hparam_search.py:82] - Starting trial 37
|
| 137 |
+
[2025-03-07 08:43:44,832] [INFO] [hparam_search.py:85] - Suggested parameters for trial 37: {'C': 55.06359926215541, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.4800306308532137, 'max_iter': 997, 'tol': 0.00017761040892822977}
|
| 138 |
+
[2025-03-07 08:43:44,838] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 37
|
| 139 |
+
[2025-03-07 08:48:44,507] [INFO] [hparam_search.py:148] - Trial 15 is the best so far.
|
| 140 |
+
[2025-03-07 08:48:45,047] [INFO] [hparam_search.py:82] - Starting trial 38
|
| 141 |
+
[2025-03-07 08:48:45,092] [INFO] [hparam_search.py:85] - Suggested parameters for trial 38: {'C': 0.003953269302320486, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.3337185702329081, 'max_iter': 956, 'tol': 1.187462388273072e-05}
|
| 142 |
+
[2025-03-07 08:48:45,106] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 38
|
| 143 |
+
[2025-03-07 08:49:26,494] [INFO] [hparam_search.py:82] - Starting trial 39
|
| 144 |
+
[2025-03-07 08:49:26,555] [INFO] [hparam_search.py:85] - Suggested parameters for trial 39: {'C': 0.006223789453814822, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.28295662768112523, 'max_iter': 995, 'tol': 1.0531311758109589e-05}
|
| 145 |
+
[2025-03-07 08:49:26,567] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 39
|
| 146 |
+
[2025-03-07 08:49:50,766] [INFO] [hparam_search.py:82] - Starting trial 40
|
| 147 |
+
[2025-03-07 08:49:50,838] [INFO] [hparam_search.py:85] - Suggested parameters for trial 40: {'C': 0.006795954284842855, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.38591787059598587, 'max_iter': 683, 'tol': 0.0001740758502675412}
|
| 148 |
+
[2025-03-07 08:49:50,851] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 40
|
| 149 |
+
[2025-03-07 08:51:22,122] [INFO] [hparam_search.py:82] - Starting trial 41
|
| 150 |
+
[2025-03-07 08:51:22,207] [INFO] [hparam_search.py:85] - Suggested parameters for trial 41: {'C': 0.006657422627218748, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.35910598144145023, 'max_iter': 645, 'tol': 0.0001631441853705579}
|
| 151 |
+
[2025-03-07 08:51:22,216] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 41
|
| 152 |
+
[2025-03-07 08:52:10,056] [INFO] [hparam_search.py:82] - Starting trial 42
|
| 153 |
+
[2025-03-07 08:52:10,127] [INFO] [hparam_search.py:85] - Suggested parameters for trial 42: {'C': 2.8150645392060993, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.38663190201830494, 'max_iter': 704, 'tol': 0.00015863909944384581}
|
| 154 |
+
[2025-03-07 08:52:10,148] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 42
|
| 155 |
+
[2025-03-07 08:52:10,245] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9293868124824156, 'val_mlogloss': 0.6627201442714479, 'val_accuracy': 0.7135382603008502, 'val_precision_micro': 0.7135382603008502, 'val_recall_micro': 0.7135382603008502, 'val_f1_micro': 0.7135382603008502, 'val_precision_weighted': 0.6998268051864504, 'val_recall_weighted': 0.7135382603008502, 'val_f1_weighted': 0.6988748305830644, 'val_precision_macro': 0.5867426285340545, 'val_recall_macro': 0.46211251321075913, 'val_f1_macro': 0.47582394484780915, 'val_cm': [[14555, 6223, 14, 0, 2, 0, 390], [5045, 22506, 678, 1, 31, 38, 31], [0, 358, 3080, 30, 0, 108, 0], [0, 0, 162, 74, 0, 39, 0], [9, 862, 68, 0, 10, 0, 0], [0, 627, 979, 2, 2, 127, 0], [927, 11, 7, 0, 0, 0, 1106]]}
|
| 156 |
+
[2025-03-07 08:52:10,363] [INFO] [hparam_search.py:82] - Starting trial 43
|
| 157 |
+
[2025-03-07 08:52:10,445] [INFO] [hparam_search.py:85] - Suggested parameters for trial 43: {'C': 1.523096978056629, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.9814411176687381, 'max_iter': 673, 'tol': 0.0001297660208678293}
|
| 158 |
+
[2025-03-07 08:52:10,458] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 43
|
| 159 |
+
[2025-03-07 08:53:05,502] [INFO] [hparam_search.py:82] - Starting trial 44
|
| 160 |
+
[2025-03-07 08:54:02,908] [INFO] [hparam_search.py:85] - Suggested parameters for trial 44: {'C': 2.0736029621023846, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9458333900370979, 'max_iter': 642, 'tol': 0.00018518303207849614}
|
| 161 |
+
[2025-03-07 08:54:02,924] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 44
|
| 162 |
+
[2025-03-07 08:54:03,092] [INFO] [hparam_search.py:82] - Starting trial 45
|
| 163 |
+
[2025-03-07 08:54:03,113] [INFO] [hparam_search.py:85] - Suggested parameters for trial 45: {'C': 6.276211534727921, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.5913958548997577, 'max_iter': 644, 'tol': 0.00015164810044100748}
|
| 164 |
+
[2025-03-07 08:54:03,121] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 45
|
| 165 |
+
[2025-03-07 08:55:52,499] [INFO] [hparam_search.py:82] - Starting trial 46
|
| 166 |
+
[2025-03-07 08:55:52,525] [INFO] [hparam_search.py:85] - Suggested parameters for trial 46: {'C': 13.780618131926744, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9880070758449144, 'max_iter': 647, 'tol': 0.00015748967510050693}
|
| 167 |
+
[2025-03-07 08:55:52,535] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 46
|
| 168 |
+
[2025-03-07 08:57:42,640] [INFO] [hparam_search.py:82] - Starting trial 47
|
| 169 |
+
[2025-03-07 08:57:42,667] [INFO] [hparam_search.py:85] - Suggested parameters for trial 47: {'C': 0.2564296272715108, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9972448417654862, 'max_iter': 623, 'tol': 0.00016282735033191673}
|
| 170 |
+
[2025-03-07 08:57:42,674] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 47
|
| 171 |
+
[2025-03-07 08:59:35,488] [INFO] [hparam_search.py:82] - Starting trial 48
|
| 172 |
+
[2025-03-07 08:59:35,513] [INFO] [hparam_search.py:85] - Suggested parameters for trial 48: {'C': 0.01261827585323151, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9943397238138628, 'max_iter': 666, 'tol': 0.00011473280412276607}
|
| 173 |
+
[2025-03-07 08:59:35,520] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 48
|
| 174 |
+
[2025-03-07 09:00:31,721] [INFO] [final_model_training.py:127] - Uploaded study folder to zokaityte/forest_v1_0_lr
|
| 175 |
+
[2025-03-07 09:03:33,487] [INFO] [hparam_search.py:82] - Starting trial 49
|
| 176 |
+
[2025-03-07 09:03:33,508] [INFO] [hparam_search.py:85] - Suggested parameters for trial 49: {'C': 0.6272340733811133, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9703361065154678, 'max_iter': 652, 'tol': 5.6021602591641575e-05}
|
| 177 |
+
[2025-03-07 09:03:33,512] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 49
|
| 178 |
+
[2025-03-07 09:04:20,831] [INFO] [hparam_search.py:82] - Starting trial 50
|
| 179 |
+
[2025-03-07 09:04:20,845] [INFO] [hparam_search.py:85] - Suggested parameters for trial 50: {'C': 0.2959185431524882, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9859223553659934, 'max_iter': 720, 'tol': 0.00011576009919516169}
|
| 180 |
+
[2025-03-07 09:04:20,849] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 50
|
| 181 |
+
[2025-03-07 09:06:36,614] [INFO] [hparam_search.py:148] - Trial 42 is the best so far.
|
| 182 |
+
[2025-03-07 09:08:55,358] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9293755856729281, 'val_mlogloss': 0.6620885715445897, 'val_accuracy': 0.7137275825272796, 'val_precision_micro': 0.7137275825272796, 'val_recall_micro': 0.7137275825272796, 'val_f1_micro': 0.7137275825272796, 'val_precision_weighted': 0.6994044510837508, 'val_recall_weighted': 0.7137275825272796, 'val_f1_weighted': 0.6989529512015351, 'val_precision_macro': 0.5824675877633627, 'val_recall_macro': 0.46859487330351607, 'val_f1_macro': 0.48129753834632644, 'val_cm': [[14556, 6216, 14, 0, 3, 0, 395], [5053, 22499, 673, 1, 29, 43, 32], [0, 356, 3091, 31, 0, 98, 0], [0, 0, 153, 87, 0, 35, 0], [11, 861, 68, 0, 9, 0, 0], [0, 627, 981, 8, 3, 118, 0], [924, 11, 7, 0, 0, 0, 1109]]}
|
| 183 |
+
[2025-03-07 09:09:01,136] [INFO] [hparam_search.py:82] - Starting trial 51
|
| 184 |
+
[2025-03-07 09:09:01,153] [INFO] [hparam_search.py:85] - Suggested parameters for trial 51: {'C': 8.108865453423824, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.610294064942541, 'max_iter': 587, 'tol': 0.00011542896302122851}
|
| 185 |
+
[2025-03-07 09:09:01,164] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 51
|
| 186 |
+
[2025-03-07 09:15:54,125] [INFO] [hparam_search.py:82] - Starting trial 52
|
| 187 |
+
[2025-03-07 09:15:54,145] [INFO] [hparam_search.py:85] - Suggested parameters for trial 52: {'C': 0.2614478349260976, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.5999406664335546, 'max_iter': 576, 'tol': 4.255905709858595e-05}
|
| 188 |
+
[2025-03-07 09:15:54,151] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 52
|
| 189 |
+
[2025-03-07 09:19:45,732] [INFO] [hparam_search.py:82] - Starting trial 53
|
| 190 |
+
[2025-03-07 09:19:45,759] [INFO] [hparam_search.py:85] - Suggested parameters for trial 53: {'C': 9.618631751361129, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5881462517633972, 'max_iter': 584, 'tol': 0.00010469135555879149}
|
| 191 |
+
[2025-03-07 09:19:45,765] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 53
|
| 192 |
+
[2025-03-07 09:32:04,097] [INFO] [hparam_search.py:148] - Trial 53 is the best so far.
|
| 193 |
+
[2025-03-07 09:32:26,597] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9294522467074238, 'val_mlogloss': 0.6617867217985753, 'val_accuracy': 0.7137620047502667, 'val_precision_micro': 0.7137620047502667, 'val_recall_micro': 0.7137620047502667, 'val_f1_micro': 0.7137620047502667, 'val_precision_weighted': 0.6995911075629004, 'val_recall_weighted': 0.7137620047502667, 'val_f1_weighted': 0.699120638063175, 'val_precision_macro': 0.5825050746791766, 'val_recall_macro': 0.46826596888076333, 'val_f1_macro': 0.48143998075538535, 'val_cm': [[14558, 6215, 14, 0, 3, 0, 394], [5049, 22501, 675, 1, 30, 42, 32], [0, 356, 3086, 31, 0, 103, 0], [0, 0, 153, 86, 0, 36, 0], [11, 861, 68, 0, 9, 0, 0], [0, 626, 975, 8, 3, 125, 0], [927, 11, 7, 0, 0, 0, 1106]]}
|
| 194 |
+
[2025-03-07 09:32:27,614] [INFO] [hparam_search.py:82] - Starting trial 54
|
| 195 |
+
[2025-03-07 09:32:28,566] [INFO] [hparam_search.py:85] - Suggested parameters for trial 54: {'C': 10.558278130280515, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.570497284516704, 'max_iter': 576, 'tol': 0.0001010654222999762}
|
| 196 |
+
[2025-03-07 09:32:28,575] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 54
|
| 197 |
+
[2025-03-07 09:44:47,742] [INFO] [hparam_search.py:148] - Trial 54 is the best so far.
|
| 198 |
+
[2025-03-07 09:45:08,557] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9294520475880966, 'val_mlogloss': 0.6617819876997856, 'val_accuracy': 0.7137275825272796, 'val_precision_micro': 0.7137275825272796, 'val_recall_micro': 0.7137275825272796, 'val_f1_micro': 0.7137275825272796, 'val_precision_weighted': 0.6993730887041032, 'val_recall_weighted': 0.7137275825272796, 'val_f1_weighted': 0.6990656117165808, 'val_precision_macro': 0.5811113222667654, 'val_recall_macro': 0.468101481669644, 'val_f1_macro': 0.4811462513699717, 'val_cm': [[14558, 6215, 14, 0, 3, 0, 394], [5048, 22501, 675, 1, 31, 42, 32], [0, 355, 3086, 31, 0, 104, 0], [0, 0, 153, 86, 0, 36, 0], [11, 861, 68, 0, 9, 0, 0], [0, 626, 977, 8, 3, 123, 0], [927, 11, 7, 0, 0, 0, 1106]]}
|
| 199 |
+
[2025-03-07 09:45:09,501] [INFO] [hparam_search.py:82] - Starting trial 55
|
| 200 |
+
[2025-03-07 09:45:10,612] [INFO] [hparam_search.py:85] - Suggested parameters for trial 55: {'C': 10.237632198036332, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5771596969169119, 'max_iter': 562, 'tol': 0.00010993659881135612}
|
| 201 |
+
[2025-03-07 09:45:10,627] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 55
|
| 202 |
+
[2025-03-07 09:53:26,421] [INFO] [hparam_search.py:82] - Starting trial 56
|
| 203 |
+
[2025-03-07 09:53:29,049] [INFO] [hparam_search.py:85] - Suggested parameters for trial 56: {'C': 7.459724193401961, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5559225692027648, 'max_iter': 565, 'tol': 4.528152611802195e-05}
|
| 204 |
+
[2025-03-07 09:53:29,058] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 56
|
| 205 |
+
[2025-03-07 10:00:46,356] [INFO] [hparam_search.py:82] - Starting trial 57
|
| 206 |
+
[2025-03-07 10:00:46,380] [INFO] [hparam_search.py:85] - Suggested parameters for trial 57: {'C': 11.655472295612883, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5673558522790473, 'max_iter': 550, 'tol': 4.4695182580481785e-05}
|
| 207 |
+
[2025-03-07 10:00:46,388] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 57
|
| 208 |
+
[2025-03-07 10:03:59,272] [INFO] [hparam_search.py:82] - Starting trial 58
|
| 209 |
+
[2025-03-07 10:04:18,140] [INFO] [hparam_search.py:85] - Suggested parameters for trial 58: {'C': 8.393389900978752, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5733018061701951, 'max_iter': 551, 'tol': 0.00011387818326576763}
|
| 210 |
+
[2025-03-07 10:04:18,155] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 58
|
| 211 |
+
[2025-03-07 10:20:21,213] [INFO] [hparam_search.py:148] - Trial 56 is the best so far.
|
| 212 |
+
[2025-03-07 10:20:21,334] [INFO] [hparam_search.py:82] - Starting trial 59
|
| 213 |
+
[2025-03-07 10:20:21,358] [INFO] [hparam_search.py:85] - Suggested parameters for trial 59: {'C': 12.587414498877646, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.587182254159709, 'max_iter': 568, 'tol': 0.00010290496011707416}
|
| 214 |
+
[2025-03-07 10:20:21,372] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 59
|
| 215 |
+
[2025-03-07 10:25:49,491] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9295577302110238, 'val_mlogloss': 0.6614428973570167, 'val_accuracy': 0.7139685380881897, 'val_precision_micro': 0.7139685380881897, 'val_recall_micro': 0.7139685380881897, 'val_f1_micro': 0.7139685380881897, 'val_precision_weighted': 0.7000216688216877, 'val_recall_weighted': 0.7139685380881897, 'val_f1_weighted': 0.6995278387643221, 'val_precision_macro': 0.5842054429638457, 'val_recall_macro': 0.4682777170647164, 'val_f1_macro': 0.48205887275933434, 'val_cm': [[14570, 6206, 14, 0, 3, 0, 391], [5050, 22501, 672, 1, 27, 47, 32], [0, 355, 3077, 31, 0, 113, 0], [0, 0, 154, 85, 0, 36, 0], [11, 861, 68, 0, 9, 0, 0], [0, 625, 967, 8, 3, 134, 0], [926, 11, 7, 0, 0, 0, 1107]]}
|
| 216 |
+
[2025-03-07 10:25:57,124] [INFO] [hparam_search.py:82] - Starting trial 60
|
| 217 |
+
[2025-03-07 10:25:57,161] [INFO] [hparam_search.py:85] - Suggested parameters for trial 60: {'C': 12.171604969704727, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.6697124766906287, 'max_iter': 564, 'tol': 4.634066356964722e-05}
|
| 218 |
+
[2025-03-07 10:25:57,167] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 60
|
| 219 |
+
[2025-03-07 10:27:28,946] [INFO] [hparam_search.py:82] - Starting trial 61
|
| 220 |
+
[2025-03-07 10:27:28,962] [INFO] [hparam_search.py:85] - Suggested parameters for trial 61: {'C': 12.625185196589724, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.6995586189884887, 'max_iter': 557, 'tol': 4.846841583410714e-05}
|
| 221 |
+
[2025-03-07 10:27:28,967] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 61
|
| 222 |
+
[2025-03-07 10:38:06,966] [INFO] [hparam_search.py:148] - Trial 57 is the best so far.
|
| 223 |
+
[2025-03-07 10:45:10,578] [INFO] [hparam_search.py:151] - Sneakpeak. Full validation results for best model: {'val_roc_auc': 0.9295653890657446, 'val_mlogloss': 0.6614160855772266, 'val_accuracy': 0.7140029603111769, 'val_precision_micro': 0.7140029603111769, 'val_recall_micro': 0.7140029603111769, 'val_f1_micro': 0.7140029603111769, 'val_precision_weighted': 0.6999668135610365, 'val_recall_weighted': 0.7140029603111769, 'val_f1_weighted': 0.6996098366410228, 'val_precision_macro': 0.5836258179500499, 'val_recall_macro': 0.4688393322478623, 'val_f1_macro': 0.48273987571939475, 'val_cm': [[14570, 6206, 14, 0, 3, 0, 391], [5047, 22503, 672, 1, 27, 48, 32], [0, 354, 3074, 31, 1, 116, 0], [0, 0, 154, 86, 0, 35, 0], [11, 861, 68, 0, 9, 0, 0], [0, 625, 966, 8, 3, 135, 0], [925, 11, 7, 0, 0, 0, 1108]]}
|
| 224 |
+
[2025-03-07 10:45:10,712] [INFO] [hparam_search.py:82] - Starting trial 62
|
| 225 |
+
[2025-03-07 10:45:36,543] [INFO] [hparam_search.py:85] - Suggested parameters for trial 62: {'C': 22.761017380313884, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.7013164516803247, 'max_iter': 373, 'tol': 4.737215120463014e-05}
|
| 226 |
+
[2025-03-07 10:45:36,551] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 62
|
| 227 |
+
[2025-03-07 10:50:03,979] [INFO] [hparam_search.py:82] - Starting trial 63
|
| 228 |
+
[2025-03-07 10:50:03,996] [INFO] [hparam_search.py:85] - Suggested parameters for trial 63: {'C': 24.4879916982142, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.6890386437865847, 'max_iter': 381, 'tol': 4.561160164255161e-05}
|
| 229 |
+
[2025-03-07 10:50:04,003] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 63
|
| 230 |
+
[2025-03-07 10:59:51,656] [INFO] [hparam_search.py:82] - Starting trial 64
|
| 231 |
+
[2025-03-07 10:59:51,691] [INFO] [hparam_search.py:85] - Suggested parameters for trial 64: {'C': 34.243025953023185, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.7058689812014265, 'max_iter': 357, 'tol': 4.216425664309419e-05}
|
| 232 |
+
[2025-03-07 10:59:51,697] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 64
|
| 233 |
+
[2025-03-07 11:01:23,320] [INFO] [hparam_search.py:82] - Starting trial 65
|
| 234 |
+
[2025-03-07 11:01:23,342] [INFO] [hparam_search.py:85] - Suggested parameters for trial 65: {'C': 91.65426524400961, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.6758871040976917, 'max_iter': 413, 'tol': 3.8746376716868805e-05}
|
| 235 |
+
[2025-03-07 11:01:23,349] [INFO] [hparam_search.py:123] - Training without cross-validation for trial 65
|
| 236 |
+
[2025-03-07 12:30:19,238] [INFO] [final_model_training.py:35] - Training final model...
|
| 237 |
+
[2025-03-07 12:30:19,238] [INFO] [final_model_training.py:37] - Loading dataset for final training: forest_v1_0
|
| 238 |
+
[2025-03-07 12:30:19,239] [INFO] [data_loading.py:16] - Loading dataset: train=~/Developer/Projects/limonade/data/forest_v1_0/train.csv, test=~/Developer/Projects/limonade/data/forest_v1_0/test.csv, val=~/Developer/Projects/limonade/data/forest_v1_0/val.csv
|
| 239 |
+
[2025-03-07 12:30:19,630] [INFO] [data_loading.py:20] - Train dataset loaded successfully: (464808, 55)
|
| 240 |
+
[2025-03-07 12:30:19,682] [INFO] [data_loading.py:23] - Test dataset loaded successfully: (58102, 55)
|
| 241 |
+
[2025-03-07 12:30:19,734] [INFO] [data_loading.py:28] - Validation dataset loaded successfully: (58102, 55)
|
| 242 |
+
[2025-03-07 12:30:19,736] [INFO] [data_loading.py:51] - Dataset verification passed: 7 classes detected.
|
| 243 |
+
[2025-03-07 12:30:19,779] [INFO] [data_loading.py:57] - Data split completed. Shapes: Train (464808, 54), Val (58102, 54), Test (58102, 54)
|
| 244 |
+
[2025-03-07 12:30:19,853] [INFO] [final_model_training.py:45] - Dataset loaded: Train (522910, 54), Test (58102, 54)
|
| 245 |
+
[2025-03-07 12:30:20,131] [INFO] [final_model_training.py:51] - Best trial found: 57 with params {'C': 11.655472295612883, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5673558522790473, 'max_iter': 550, 'tol': 4.4695182580481785e-05}
|
| 246 |
+
[2025-03-07 12:49:23,637] [INFO] [final_model_training.py:63] - Training completed in 1143.51 seconds.
|
| 247 |
+
[2025-03-07 12:49:23,641] [INFO] [final_model_training.py:67] - Model saved to /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/optuna_study_forest_v1_0_mlogloss_lr/model.pkl
|
| 248 |
+
[2025-03-07 12:49:25,577] [INFO] [final_model_training.py:83] - Final model evaluation: {'dataset': 'forest_v1_0', 'model_type': 'lr', 'best_trial_number': 57, 'best_trial_params': {'C': 11.655472295612883, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5673558522790473, 'max_iter': 550, 'tol': 4.4695182580481785e-05}, 'train_time_s': 1143.5055191516876, 'train_roc_auc': 0.930531735775758, 'train_mlogloss': 0.6590944770995031, 'train_accuracy': 0.7158421143217762, 'train_precision_micro': 0.7158421143217762, 'train_recall_micro': 0.7158421143217762, 'train_f1_micro': 0.7158421143217762, 'train_precision_weighted': 0.7033694746983873, 'train_recall_weighted': 0.7158421143217762, 'train_f1_weighted': 0.7015261167207598, 'train_precision_macro': 0.5978167007678554, 'train_recall_macro': 0.4694481833803115, 'train_f1_macro': 0.4845387544907457, 'train_cm': [[131547, 55278, 161, 0, 12, 8, 3650], [44836, 203256, 5850, 7, 255, 426, 340], [0, 3360, 27539, 274, 4, 1002, 0], [0, 0, 1441, 784, 0, 247, 0], [151, 7519, 755, 0, 112, 7, 0], [0, 5560, 8715, 58, 21, 1276, 0], [8472, 109, 71, 0, 0, 0, 9807]], 'test_roc_auc': 0.9290632207651718, 'test_mlogloss': 0.6636865217104727, 'test_accuracy': 0.712935871398575, 'test_precision_micro': 0.712935871398575, 'test_recall_micro': 0.712935871398575, 'test_f1_micro': 0.712935871398575, 'test_precision_weighted': 0.6994871846273708, 'test_recall_weighted': 0.712935871398575, 'test_f1_weighted': 0.6986838175658986, 'test_precision_macro': 0.58456365042707, 'test_recall_macro': 0.4669114486333375, 'test_f1_macro': 0.48289797979276866, 'test_cm': [[14489, 6299, 21, 0, 0, 0, 375], [4961, 22586, 667, 0, 30, 50, 37], [0, 399, 3034, 33, 0, 109, 0], [0, 0, 148, 89, 0, 38, 0], [14, 833, 94, 0, 7, 1, 0], [0, 597, 977, 6, 2, 155, 0], [967, 14, 7, 0, 0, 0, 1063]]}
|
| 249 |
+
[2025-03-07 12:49:25,582] [INFO] [final_model_training.py:87] - Training ended. Summary saved to /Users/gintare/Developer/Projects/limonade/scripts/models_training/_outputs/optuna_study_forest_v1_0_mlogloss_lr/model_summary.csv
|
| 250 |
+
[2025-03-07 12:49:26,513] [INFO] [final_model_training.py:104] - Created repo zokaityte/forest_v1_0_lr
|
model.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:05ba45eae1b35221b102350f44b4a6fdc5155548e0e77ac2f54a325a42a1a391
|
| 3 |
+
size 4019
|
model_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
dataset,model_type,best_trial_number,best_trial_params,train_time_s,train_roc_auc,train_mlogloss,train_accuracy,train_precision_micro,train_recall_micro,train_f1_micro,train_precision_weighted,train_recall_weighted,train_f1_weighted,train_precision_macro,train_recall_macro,train_f1_macro,train_cm,test_roc_auc,test_mlogloss,test_accuracy,test_precision_micro,test_recall_micro,test_f1_micro,test_precision_weighted,test_recall_weighted,test_f1_weighted,test_precision_macro,test_recall_macro,test_f1_macro,test_cm
|
| 2 |
+
forest_v1_0,lr,57,"{'C': 11.655472295612883, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5673558522790473, 'max_iter': 550, 'tol': 4.4695182580481785e-05}",1143.5055191516876,0.930531735775758,0.6590944770995031,0.7158421143217762,0.7158421143217762,0.7158421143217762,0.7158421143217762,0.7033694746983873,0.7158421143217762,0.7015261167207598,0.5978167007678554,0.4694481833803115,0.4845387544907457,"[[131547, 55278, 161, 0, 12, 8, 3650], [44836, 203256, 5850, 7, 255, 426, 340], [0, 3360, 27539, 274, 4, 1002, 0], [0, 0, 1441, 784, 0, 247, 0], [151, 7519, 755, 0, 112, 7, 0], [0, 5560, 8715, 58, 21, 1276, 0], [8472, 109, 71, 0, 0, 0, 9807]]",0.9290632207651718,0.6636865217104727,0.712935871398575,0.712935871398575,0.712935871398575,0.712935871398575,0.6994871846273708,0.712935871398575,0.6986838175658986,0.58456365042707,0.4669114486333375,0.48289797979276866,"[[14489, 6299, 21, 0, 0, 0, 375], [4961, 22586, 667, 0, 30, 50, 37], [0, 399, 3034, 33, 0, 109, 0], [0, 0, 148, 89, 0, 38, 0], [14, 833, 94, 0, 7, 1, 0], [0, 597, 977, 6, 2, 155, 0], [967, 14, 7, 0, 0, 0, 1063]]"
|
optuna_study_forest_v1_0_mlogloss_lr.db
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fe5f47890f8b6d8cf1cbb50cfcfb445745f5094c07fad49c364cf450d7df5c00
|
| 3 |
+
size 233472
|
optuna_study_log.csv
ADDED
|
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
number,value,datetime_start,datetime_complete,duration,params_C,params_l1_ratio,params_max_iter,params_penalty_solver,params_tol,user_attrs_cv_folds,user_attrs_data_shape,user_attrs_dataset,user_attrs_eval_metric,user_attrs_is_best_so_far,user_attrs_model_type,user_attrs_params,user_attrs_train_score_mean,user_attrs_train_score_std,user_attrs_use_cv,user_attrs_val_results,user_attrs_val_score_mean,user_attrs_val_score_std,state
|
| 2 |
+
0,,2025-03-07 08:10:58.858436,2025-03-07 08:10:59.172813,0 days 00:00:00.314377,1.9632861206224492e-05,0.9761918972004304,880,l1:saga,1.9341489273261066e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 3 |
+
1,,2025-03-07 08:10:58.858853,2025-03-07 08:10:59.002959,0 days 00:00:00.144106,4.7875359532895424e-05,0.018221830648489812,802,l2:saga,0.0002727712487415353,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 4 |
+
2,,2025-03-07 08:10:58.868107,2025-03-07 08:10:59.117243,0 days 00:00:00.249136,34.13344131182989,0.271735709526303,653,l2:sag,0.00012121404273410466,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 5 |
+
3,,2025-03-07 08:10:58.871504,2025-03-07 08:10:59.178693,0 days 00:00:00.307189,0.005228496985183292,0.8678453897787207,887,elasticnet:saga,0.0002752128277082581,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 6 |
+
4,,2025-03-07 08:10:58.874918,2025-03-07 08:10:59.170912,0 days 00:00:00.295994,2.589570571232358,0.9426076326524999,935,l2:lbfgs,0.0002836660823633425,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 7 |
+
5,,2025-03-07 08:10:58.878199,2025-03-07 08:10:59.130584,0 days 00:00:00.252385,1.6326522359899025,0.003021686292953185,484,l2:sag,0.0005025604309899143,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 8 |
+
6,,2025-03-07 08:10:58.884029,2025-03-07 08:10:59.052870,0 days 00:00:00.168841,3.6181077651453326,0.382582098762437,109,l1:liblinear,0.00041392892542484915,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 9 |
+
7,,2025-03-07 08:10:58.887265,2025-03-07 08:10:59.097582,0 days 00:00:00.210317,16.550757089177576,0.32581243064306664,616,l2:liblinear,0.0001275690555421488,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 10 |
+
8,,2025-03-07 08:10:58.893325,2025-03-07 08:10:59.222468,0 days 00:00:00.329143,0.014745693420318746,0.5824976035235025,357,l2:sag,0.00016913126055906019,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 11 |
+
9,,2025-03-07 08:10:58.899775,2025-03-07 08:10:59.127979,0 days 00:00:00.228204,0.00010445552123911683,0.8421592738725264,605,l2:liblinear,5.5691220302481965e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 12 |
+
10,,2025-03-07 08:10:58.905100,2025-03-07 08:10:59.083708,0 days 00:00:00.178608,3.304723016424694e-05,0.4558487897247905,219,l1:liblinear,0.0009913557730329046,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 13 |
+
11,,2025-03-07 08:10:58.910701,2025-03-07 08:10:59.052474,0 days 00:00:00.141773,3.099102714379317,0.42263550165274577,968,l2:liblinear,8.666421709771902e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 14 |
+
12,,2025-03-07 08:10:58.919651,2025-03-07 08:10:59.125737,0 days 00:00:00.206086,1.8383875772825157e-05,0.6069954638911863,129,l2:liblinear,1.0796652256635228e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 15 |
+
13,,2025-03-07 08:10:59.035107,2025-03-07 08:10:59.146754,0 days 00:00:00.111647,94.72138074987807,0.9808293489208364,838,elasticnet:saga,0.0001191094556785542,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,FAIL
|
| 16 |
+
14,0.7375303037064548,2025-03-07 08:12:43.954555,2025-03-07 08:43:44.741703,0 days 00:31:00.787148,0.0005228109721193718,0.8093658562012899,493,l1:liblinear,3.874147467551387e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.0005228109721193718, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.8093658562012899, 'max_iter': 493, 'tol': 3.874147467551387e-05}",0.7475125270011029,0.0,False,,0.7375303037064548,0.0,COMPLETE
|
| 17 |
+
15,0.6674505574233229,2025-03-07 08:12:43.956534,2025-03-07 08:52:10.270579,0 days 00:39:26.314045,0.08290917029064834,0.8780724901444901,597,l1:liblinear,6.516962587992502e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 0.08290917029064834, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.8780724901444901, 'max_iter': 597, 'tol': 6.516962587992502e-05}",0.7007940259406257,0.0,False,"{'val_roc_auc': 0.9293868124824156, 'val_mlogloss': 0.6627201442714479, 'val_accuracy': 0.7135382603008502, 'val_precision_micro': 0.7135382603008502, 'val_recall_micro': 0.7135382603008502, 'val_f1_micro': 0.7135382603008502, 'val_precision_weighted': 0.6998268051864504, 'val_recall_weighted': 0.7135382603008502, 'val_f1_weighted': 0.6988748305830644, 'val_precision_macro': 0.5867426285340545, 'val_recall_macro': 0.46211251321075913, 'val_f1_macro': 0.47582394484780915, 'val_cm': [[14555, 6223, 14, 0, 2, 0, 390], [5045, 22506, 678, 1, 31, 38, 31], [0, 358, 3080, 30, 0, 108, 0], [0, 0, 162, 74, 0, 39, 0], [9, 862, 68, 0, 10, 0, 0], [0, 627, 979, 2, 2, 127, 0], [927, 11, 7, 0, 0, 0, 1106]]}",0.6674505574233229,0.0,COMPLETE
|
| 18 |
+
16,0.6948408701697837,2025-03-07 08:12:43.966292,2025-03-07 08:42:35.413596,0 days 00:29:51.447304,0.002235922974214041,0.1749696370372762,849,l1:liblinear,2.491622379628421e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.002235922974214041, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.1749696370372762, 'max_iter': 849, 'tol': 2.491622379628421e-05}",0.7209085754033068,0.0,False,,0.6948408701697837,0.0,COMPLETE
|
| 19 |
+
17,0.7718790504604023,2025-03-07 08:12:43.969762,2025-03-07 08:34:16.129482,0 days 00:21:32.159720,5.2959004321466774e-05,0.976635868194475,880,elasticnet:saga,0.0001995998413593255,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 5.2959004321466774e-05, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.976635868194475, 'max_iter': 880, 'tol': 0.0001995998413593255}",0.8696944624945989,0.0,False,,0.7718790504604023,0.0,COMPLETE
|
| 20 |
+
18,1.0325186464647587,2025-03-07 08:12:43.973063,2025-03-07 08:31:59.817494,0 days 00:19:15.844431,0.10753794163930472,0.8119533855607183,251,l1:saga,7.058855209127119e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.10753794163930472, 'penalty_solver': 'l1:saga', 'l1_ratio': 0.8119533855607183, 'max_iter': 251, 'tol': 7.058855209127119e-05}",0.7616308651436822,0.0,False,,1.0325186464647587,0.0,COMPLETE
|
| 21 |
+
19,0.7341396959227684,2025-03-07 08:12:43.976263,2025-03-07 08:49:42.862773,0 days 00:36:58.886510,0.04365867127670072,0.18857030681906028,936,elasticnet:saga,1.2253441001829851e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.04365867127670072, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.18857030681906028, 'max_iter': 936, 'tol': 1.2253441001829851e-05}",0.746630548369173,0.0,False,,0.7341396959227684,0.0,COMPLETE
|
| 22 |
+
20,0.7500045741897678,2025-03-07 08:12:43.982023,2025-03-07 08:27:43.829992,0 days 00:14:59.847969,0.0002470789370081118,0.07256628067642645,174,l2:sag,0.000856655026152043,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.0002470789370081118, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.07256628067642645, 'max_iter': 174, 'tol': 0.000856655026152043}",0.8188587305350093,0.0,False,,0.7500045741897678,0.0,COMPLETE
|
| 23 |
+
21,0.7165582292789365,2025-03-07 08:12:43.987768,2025-03-07 08:49:26.412966,0 days 00:36:42.425198,66.88943514438645,0.7104256340067888,862,elasticnet:saga,2.8484414321600405e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 66.88943514438645, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.7104256340067888, 'max_iter': 862, 'tol': 2.8484414321600405e-05}",0.7785643928123143,0.0,False,,0.7165582292789365,0.0,COMPLETE
|
| 24 |
+
22,,2025-03-07 08:12:43.991079,,,4.725676373184278,0.37209617568379727,819,l2:lbfgs,5.832433478492103e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 25 |
+
23,0.6765094087409561,2025-03-07 08:12:43.997878,2025-03-07 08:34:15.528286,0 days 00:21:31.530408,0.9852329490911322,0.6199056558332002,803,l2:liblinear,0.00015033229675130488,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 0.9852329490911322, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.6199056558332002, 'max_iter': 803, 'tol': 0.00015033229675130488}",0.7278664648162343,0.0,False,"{'val_roc_auc': 0.927823830715556, 'val_mlogloss': 0.6765094087409561, 'val_accuracy': 0.7045712712126949, 'val_precision_micro': 0.7045712712126949, 'val_recall_micro': 0.7045712712126949, 'val_f1_micro': 0.7045712712126949, 'val_precision_weighted': 0.6886994293672783, 'val_recall_weighted': 0.7045712712126949, 'val_f1_weighted': 0.6868623111817979, 'val_precision_macro': 0.5555950175823064, 'val_recall_macro': 0.4105432040857316, 'val_f1_macro': 0.4280160645470881, 'val_cm': [[14460, 6560, 13, 0, 1, 0, 150], [4939, 22683, 617, 1, 9, 60, 21], [9, 366, 3016, 29, 0, 155, 1], [0, 0, 191, 49, 0, 35, 0], [21, 860, 61, 0, 1, 5, 1], [0, 632, 958, 0, 0, 147, 0], [1451, 12, 7, 0, 0, 0, 581]]}",0.6765094087409561,0.0,COMPLETE
|
| 26 |
+
24,0.726392564254706,2025-03-07 08:12:44.000118,2025-03-07 08:40:01.405987,0 days 00:27:17.405869,0.31320459939088413,0.8007694096230313,750,l2:saga,7.719707411861858e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.31320459939088413, 'penalty_solver': 'l2:saga', 'l1_ratio': 0.8007694096230313, 'max_iter': 750, 'tol': 7.719707411861858e-05}",0.856174173185501,0.0,False,,0.726392564254706,0.0,COMPLETE
|
| 27 |
+
25,0.7226542102409799,2025-03-07 08:12:44.007717,2025-03-07 08:36:51.745544,0 days 00:24:07.737827,0.0019454839024506696,0.430701449464998,136,l2:liblinear,0.0005352514125792776,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 0.0019454839024506696, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.430701449464998, 'max_iter': 136, 'tol': 0.0005352514125792776}",0.8062878532598768,0.0,False,"{'val_roc_auc': 0.9140457539215289, 'val_mlogloss': 0.7226542102409799, 'val_accuracy': 0.6873601597191147, 'val_precision_micro': 0.6873601597191147, 'val_recall_micro': 0.6873601597191147, 'val_f1_micro': 0.6873601597191147, 'val_precision_weighted': 0.6618312777021531, 'val_recall_weighted': 0.6873601597191147, 'val_f1_weighted': 0.6595545116452762, 'val_precision_macro': 0.429545300361069, 'val_recall_macro': 0.32058378353907635, 'val_f1_macro': 0.31942924983510756, 'val_cm': [[14204, 6942, 0, 0, 0, 0, 38], [4832, 23179, 271, 0, 0, 32, 16], [8, 1107, 2383, 0, 0, 77, 1], [0, 0, 265, 0, 0, 10, 0], [4, 944, 0, 0, 0, 0, 1], [0, 746, 927, 0, 0, 64, 0], [1920, 24, 0, 0, 0, 0, 107]]}",0.7226542102409799,0.0,COMPLETE
|
| 28 |
+
26,0.9182156666498725,2025-03-07 08:12:44.016274,2025-03-07 08:36:52.135343,0 days 00:24:08.119069,14.494867724886827,0.7872642427767997,491,elasticnet:saga,1.695513599231274e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 14.494867724886827, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.7872642427767997, 'max_iter': 491, 'tol': 1.695513599231274e-05}",0.7390724724104352,0.0,False,,0.9182156666498725,0.0,COMPLETE
|
| 29 |
+
27,0.7556824505267641,2025-03-07 08:12:44.168599,2025-03-07 08:33:38.853163,0 days 00:20:54.684564,1.1034005898853718e-05,0.049352468371193,476,elasticnet:saga,0.0004147242955185673,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 1.1034005898853718e-05, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.049352468371193, 'max_iter': 476, 'tol': 0.0004147242955185673}",1.0301464530204367,0.0,False,,0.7556824505267641,0.0,COMPLETE
|
| 30 |
+
28,,2025-03-07 08:27:43.880693,,,0.0002585775051143828,0.28161698468553553,965,l2:lbfgs,0.00036057203643168013,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 31 |
+
29,0.7185333419147909,2025-03-07 08:31:59.872497,2025-03-07 08:51:38.327277,0 days 00:19:38.454780,0.01872557405037317,0.29858404227811497,972,elasticnet:saga,0.0005317911737119296,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.01872557405037317, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.29858404227811497, 'max_iter': 972, 'tol': 0.0005317911737119296}",0.7380884544970436,0.0,False,,0.7185333419147909,0.0,COMPLETE
|
| 32 |
+
30,0.7225786539681572,2025-03-07 08:33:38.946378,2025-03-07 08:48:44.945980,0 days 00:15:05.999602,0.899854995828966,0.6279205105919434,212,l2:sag,0.00037650298191910155,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.899854995828966, 'penalty_solver': 'l2:sag', 'l1_ratio': 0.6279205105919434, 'max_iter': 212, 'tol': 0.00037650298191910155}",1.0051365203518514,0.0,False,,0.7225786539681572,0.0,COMPLETE
|
| 33 |
+
31,1.173955484753526,2025-03-07 08:34:15.575530,2025-03-07 08:54:03.037928,0 days 00:19:47.462398,2.4394692876817174,0.24101962705857216,452,l1:saga,3.2310248682653125e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 2.4394692876817174, 'penalty_solver': 'l1:saga', 'l1_ratio': 0.24101962705857216, 'max_iter': 452, 'tol': 3.2310248682653125e-05}",0.9849242544953916,0.0,False,,1.173955484753526,0.0,COMPLETE
|
| 34 |
+
32,0.7475587256079398,2025-03-07 08:34:16.241361,2025-03-07 08:51:08.445150,0 days 00:16:52.203789,0.00028829650171758236,0.5250608314672751,341,elasticnet:saga,2.3173018226579656e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.00028829650171758236, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.5250608314672751, 'max_iter': 341, 'tol': 2.3173018226579656e-05}",0.9355301999476298,0.0,False,,0.7475587256079398,0.0,COMPLETE
|
| 35 |
+
33,1.9459101490553148,2025-03-07 08:36:51.782801,2025-03-07 09:19:44.862447,0 days 00:42:53.079646,0.36264409286899085,0.39534316068814,845,l2:lbfgs,4.699941049120226e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.36264409286899085, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.39534316068814, 'max_iter': 845, 'tol': 4.699941049120226e-05}",1.9459101490553121,0.0,False,,1.9459101490553148,0.0,COMPLETE
|
| 36 |
+
34,0.7235394556588833,2025-03-07 08:36:52.183989,2025-03-07 08:55:10.403278,0 days 00:18:18.219289,2.335829803388853,0.7564518562376523,809,elasticnet:saga,0.0008596551415818968,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 2.335829803388853, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.7564518562376523, 'max_iter': 809, 'tol': 0.0008596551415818968}",0.7584857036043211,0.0,False,,0.7235394556588833,0.0,COMPLETE
|
| 37 |
+
35,0.6774620240618197,2025-03-07 08:40:01.448283,2025-03-07 08:53:05.437367,0 days 00:13:03.989084,0.010368737038077025,0.7965917940036776,655,l2:liblinear,6.827644851696467e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.010368737038077025, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.7965917940036776, 'max_iter': 655, 'tol': 6.827644851696467e-05}",0.717979760497629,0.0,False,,0.6774620240618197,0.0,COMPLETE
|
| 38 |
+
36,0.7221966144630454,2025-03-07 08:42:35.452951,2025-03-07 08:59:35.455866,0 days 00:17:00.002915,0.04537225302234671,0.8686747315831244,439,elasticnet:saga,0.0002606702849897083,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.04537225302234671, 'penalty_solver': 'elasticnet:saga', 'l1_ratio': 0.8686747315831244, 'max_iter': 439, 'tol': 0.0002606702849897083}",0.8661995959781275,0.0,False,,0.7221966144630454,0.0,COMPLETE
|
| 39 |
+
37,0.6935771539995703,2025-03-07 08:43:44.795710,2025-03-07 08:57:42.606295,0 days 00:13:57.810585,55.06359926215541,0.4800306308532137,997,l2:liblinear,0.00017761040892822977,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 55.06359926215541, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.4800306308532137, 'max_iter': 997, 'tol': 0.00017761040892822977}",0.7444462152547522,0.0,False,,0.6935771539995703,0.0,COMPLETE
|
| 40 |
+
38,0.6858766389782672,2025-03-07 08:48:45.039203,2025-03-07 09:15:52.783187,0 days 00:27:07.743984,0.003953269302320486,0.3337185702329081,956,l1:liblinear,1.187462388273072e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.003953269302320486, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.3337185702329081, 'max_iter': 956, 'tol': 1.187462388273072e-05}",0.6834208716461103,0.0,False,,0.6858766389782672,0.0,COMPLETE
|
| 41 |
+
39,,2025-03-07 08:49:26.491074,,,0.006223789453814822,0.28295662768112523,995,l2:lbfgs,1.0531311758109589e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 42 |
+
40,1.0084539331029476,2025-03-07 08:49:50.754398,2025-03-07 10:00:46.325033,0 days 01:10:55.570635,0.006795954284842855,0.38591787059598587,683,l2:lbfgs,0.0001740758502675412,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.006795954284842855, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.38591787059598587, 'max_iter': 683, 'tol': 0.0001740758502675412}",1.0259737969972798,0.0,False,,1.0084539331029476,0.0,COMPLETE
|
| 43 |
+
41,0.6829691829788447,2025-03-07 08:51:22.110995,2025-03-07 09:03:33.448856,0 days 00:12:11.337861,0.006657422627218748,0.35910598144145023,645,l2:liblinear,0.0001631441853705579,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.006657422627218748, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.35910598144145023, 'max_iter': 645, 'tol': 0.0001631441853705579}",0.8244458539393832,0.0,False,,0.6829691829788447,0.0,COMPLETE
|
| 44 |
+
42,0.6620885715445897,2025-03-07 08:52:10.052430,2025-03-07 09:08:55.369926,0 days 00:16:45.317496,2.8150645392060993,0.38663190201830494,704,l1:liblinear,0.00015863909944384581,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 2.8150645392060993, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.38663190201830494, 'max_iter': 704, 'tol': 0.00015863909944384581}",0.6595389979837466,0.0,False,"{'val_roc_auc': 0.9293755856729281, 'val_mlogloss': 0.6620885715445897, 'val_accuracy': 0.7137275825272796, 'val_precision_micro': 0.7137275825272796, 'val_recall_micro': 0.7137275825272796, 'val_f1_micro': 0.7137275825272796, 'val_precision_weighted': 0.6994044510837508, 'val_recall_weighted': 0.7137275825272796, 'val_f1_weighted': 0.6989529512015351, 'val_precision_macro': 0.5824675877633627, 'val_recall_macro': 0.46859487330351607, 'val_f1_macro': 0.48129753834632644, 'val_cm': [[14556, 6216, 14, 0, 3, 0, 395], [5053, 22499, 673, 1, 29, 43, 32], [0, 356, 3091, 31, 0, 98, 0], [0, 0, 153, 87, 0, 35, 0], [11, 861, 68, 0, 9, 0, 0], [0, 627, 981, 8, 3, 118, 0], [924, 11, 7, 0, 0, 0, 1109]]}",0.6620885715445897,0.0,COMPLETE
|
| 45 |
+
43,0.6771984635229982,2025-03-07 08:52:10.354665,2025-03-07 09:04:20.812316,0 days 00:12:10.457651,1.523096978056629,0.9814411176687381,673,l2:liblinear,0.0001297660208678293,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 1.523096978056629, 'penalty_solver': 'l2:liblinear', 'l1_ratio': 0.9814411176687381, 'max_iter': 673, 'tol': 0.0001297660208678293}",0.6752532743229848,0.0,False,,0.6771984635229982,0.0,COMPLETE
|
| 46 |
+
44,,2025-03-07 08:53:05.496770,,,2.0736029621023846,0.9458333900370979,642,l2:lbfgs,0.00018518303207849614,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 47 |
+
45,,2025-03-07 08:54:03.090461,,,6.276211534727921,0.5913958548997577,644,l2:lbfgs,0.00015164810044100748,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 48 |
+
46,,2025-03-07 08:55:52.497676,,,13.780618131926744,0.9880070758449144,647,l2:lbfgs,0.00015748967510050693,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 49 |
+
47,0.9984565218581871,2025-03-07 08:57:42.636724,2025-03-07 09:53:26.381195,0 days 00:55:43.744471,0.2564296272715108,0.9972448417654862,623,l2:lbfgs,0.00016282735033191673,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.2564296272715108, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9972448417654862, 'max_iter': 623, 'tol': 0.00016282735033191673}",1.034209498284577,0.0,False,,0.9984565218581871,0.0,COMPLETE
|
| 50 |
+
48,,2025-03-07 08:59:35.486471,,,0.01261827585323151,0.9943397238138628,666,l2:lbfgs,0.00011473280412276607,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 51 |
+
49,,2025-03-07 09:03:33.485439,,,0.6272340733811133,0.9703361065154678,652,l2:lbfgs,5.6021602591641575e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 52 |
+
50,0.9979804230043228,2025-03-07 09:04:20.829308,2025-03-07 10:27:28.923936,0 days 01:23:08.094628,0.2959185431524882,0.9859223553659934,720,l2:lbfgs,0.00011576009919516169,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 0.2959185431524882, 'penalty_solver': 'l2:lbfgs', 'l1_ratio': 0.9859223553659934, 'max_iter': 720, 'tol': 0.00011576009919516169}",1.0260032003958257,0.0,False,,0.9979804230043228,0.0,COMPLETE
|
| 53 |
+
51,,2025-03-07 09:09:01.133467,,,8.108865453423824,0.610294064942541,587,l2:lbfgs,0.00011542896302122851,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 54 |
+
52,,2025-03-07 09:15:53.768617,,,0.2614478349260976,0.5999406664335546,576,l2:lbfgs,4.255905709858595e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 55 |
+
53,0.6617867217985753,2025-03-07 09:19:44.887081,2025-03-07 09:32:26.610751,0 days 00:12:41.723670,9.618631751361129,0.5881462517633972,584,l1:liblinear,0.00010469135555879149,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 9.618631751361129, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5881462517633972, 'max_iter': 584, 'tol': 0.00010469135555879149}",0.659185737941418,0.0,False,"{'val_roc_auc': 0.9294522467074238, 'val_mlogloss': 0.6617867217985753, 'val_accuracy': 0.7137620047502667, 'val_precision_micro': 0.7137620047502667, 'val_recall_micro': 0.7137620047502667, 'val_f1_micro': 0.7137620047502667, 'val_precision_weighted': 0.6995911075629004, 'val_recall_weighted': 0.7137620047502667, 'val_f1_weighted': 0.699120638063175, 'val_precision_macro': 0.5825050746791766, 'val_recall_macro': 0.46826596888076333, 'val_f1_macro': 0.48143998075538535, 'val_cm': [[14558, 6215, 14, 0, 3, 0, 394], [5049, 22501, 675, 1, 30, 42, 32], [0, 356, 3086, 31, 0, 103, 0], [0, 0, 153, 86, 0, 36, 0], [11, 861, 68, 0, 9, 0, 0], [0, 626, 975, 8, 3, 125, 0], [927, 11, 7, 0, 0, 0, 1106]]}",0.6617867217985753,0.0,COMPLETE
|
| 56 |
+
54,0.6617819876997856,2025-03-07 09:32:27.611903,2025-03-07 09:45:08.566500,0 days 00:12:40.954597,10.558278130280515,0.570497284516704,576,l1:liblinear,0.0001010654222999762,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 10.558278130280515, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.570497284516704, 'max_iter': 576, 'tol': 0.0001010654222999762}",0.6591842878028286,0.0,False,"{'val_roc_auc': 0.9294520475880966, 'val_mlogloss': 0.6617819876997856, 'val_accuracy': 0.7137275825272796, 'val_precision_micro': 0.7137275825272796, 'val_recall_micro': 0.7137275825272796, 'val_f1_micro': 0.7137275825272796, 'val_precision_weighted': 0.6993730887041032, 'val_recall_weighted': 0.7137275825272796, 'val_f1_weighted': 0.6990656117165808, 'val_precision_macro': 0.5811113222667654, 'val_recall_macro': 0.468101481669644, 'val_f1_macro': 0.4811462513699717, 'val_cm': [[14558, 6215, 14, 0, 3, 0, 394], [5048, 22501, 675, 1, 31, 42, 32], [0, 355, 3086, 31, 0, 104, 0], [0, 0, 153, 86, 0, 36, 0], [11, 861, 68, 0, 9, 0, 0], [0, 626, 977, 8, 3, 123, 0], [927, 11, 7, 0, 0, 0, 1106]]}",0.6617819876997856,0.0,COMPLETE
|
| 57 |
+
55,0.6617863338889969,2025-03-07 09:45:09.498393,2025-03-07 10:03:59.245195,0 days 00:18:49.746802,10.237632198036332,0.5771596969169119,562,l1:liblinear,0.00010993659881135612,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 10.237632198036332, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5771596969169119, 'max_iter': 562, 'tol': 0.00010993659881135612}",0.6591906588767609,0.0,False,,0.6617863338889969,0.0,COMPLETE
|
| 58 |
+
56,0.6614428973570167,2025-03-07 09:53:26.420036,2025-03-07 10:25:49.519230,0 days 00:32:23.099194,7.459724193401961,0.5559225692027648,565,l1:liblinear,4.528152611802195e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 7.459724193401961, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5559225692027648, 'max_iter': 565, 'tol': 4.528152611802195e-05}",0.6587983252821884,0.0,False,"{'val_roc_auc': 0.9295577302110238, 'val_mlogloss': 0.6614428973570167, 'val_accuracy': 0.7139685380881897, 'val_precision_micro': 0.7139685380881897, 'val_recall_micro': 0.7139685380881897, 'val_f1_micro': 0.7139685380881897, 'val_precision_weighted': 0.7000216688216877, 'val_recall_weighted': 0.7139685380881897, 'val_f1_weighted': 0.6995278387643221, 'val_precision_macro': 0.5842054429638457, 'val_recall_macro': 0.4682777170647164, 'val_f1_macro': 0.48205887275933434, 'val_cm': [[14570, 6206, 14, 0, 3, 0, 391], [5050, 22501, 672, 1, 27, 47, 32], [0, 355, 3077, 31, 0, 113, 0], [0, 0, 154, 85, 0, 36, 0], [11, 861, 68, 0, 9, 0, 0], [0, 625, 967, 8, 3, 134, 0], [926, 11, 7, 0, 0, 0, 1107]]}",0.6614428973570167,0.0,COMPLETE
|
| 59 |
+
57,0.6614160855772266,2025-03-07 10:00:46.352906,2025-03-07 10:45:10.682273,0 days 00:44:24.329367,11.655472295612883,0.5673558522790473,550,l1:liblinear,4.4695182580481785e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,True,lr,"{'C': 11.655472295612883, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5673558522790473, 'max_iter': 550, 'tol': 4.4695182580481785e-05}",0.6587626681491918,0.0,False,"{'val_roc_auc': 0.9295653890657446, 'val_mlogloss': 0.6614160855772266, 'val_accuracy': 0.7140029603111769, 'val_precision_micro': 0.7140029603111769, 'val_recall_micro': 0.7140029603111769, 'val_f1_micro': 0.7140029603111769, 'val_precision_weighted': 0.6999668135610365, 'val_recall_weighted': 0.7140029603111769, 'val_f1_weighted': 0.6996098366410228, 'val_precision_macro': 0.5836258179500499, 'val_recall_macro': 0.4688393322478623, 'val_f1_macro': 0.48273987571939475, 'val_cm': [[14570, 6206, 14, 0, 3, 0, 391], [5047, 22503, 672, 1, 27, 48, 32], [0, 354, 3074, 31, 1, 116, 0], [0, 0, 154, 86, 0, 35, 0], [11, 861, 68, 0, 9, 0, 0], [0, 625, 966, 8, 3, 135, 0], [925, 11, 7, 0, 0, 0, 1108]]}",0.6614160855772266,0.0,COMPLETE
|
| 60 |
+
58,0.6618920249512533,2025-03-07 10:03:59.270285,2025-03-07 10:20:21.297059,0 days 00:16:22.026774,8.393389900978752,0.5733018061701951,551,l1:liblinear,0.00011387818326576763,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 8.393389900978752, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.5733018061701951, 'max_iter': 551, 'tol': 0.00011387818326576763}",0.6592985488570391,0.0,False,,0.6618920249512533,0.0,COMPLETE
|
| 61 |
+
59,0.6618337575804186,2025-03-07 10:20:21.328380,2025-03-07 10:50:03.950849,0 days 00:29:42.622469,12.587414498877646,0.587182254159709,568,l1:liblinear,0.00010290496011707416,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 12.587414498877646, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.587182254159709, 'max_iter': 568, 'tol': 0.00010290496011707416}",0.6592304611936718,0.0,False,,0.6618337575804186,0.0,COMPLETE
|
| 62 |
+
60,0.6614597272265049,2025-03-07 10:25:57.119538,2025-03-07 10:59:28.775164,0 days 00:33:31.655626,12.171604969704727,0.6697124766906287,564,l1:liblinear,4.634066356964722e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 12.171604969704727, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.6697124766906287, 'max_iter': 564, 'tol': 4.634066356964722e-05}",0.6588125265761676,0.0,False,,0.6614597272265049,0.0,COMPLETE
|
| 63 |
+
61,0.6614250420107123,2025-03-07 10:27:28.944334,2025-03-07 11:01:23.289201,0 days 00:33:54.344867,12.625185196589724,0.6995586189884887,557,l1:liblinear,4.846841583410714e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,False,lr,"{'C': 12.625185196589724, 'penalty_solver': 'l1:liblinear', 'l1_ratio': 0.6995586189884887, 'max_iter': 557, 'tol': 4.846841583410714e-05}",0.658772991340384,0.0,False,,0.6614250420107123,0.0,COMPLETE
|
| 64 |
+
62,,2025-03-07 10:45:10.710219,,,22.761017380313884,0.7013164516803247,373,l1:liblinear,4.737215120463014e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 65 |
+
63,,2025-03-07 10:50:03.977001,,,24.4879916982142,0.6890386437865847,381,l1:liblinear,4.561160164255161e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|
| 66 |
+
64,,2025-03-07 10:59:51.647396,,,34.243025953023185,0.7058689812014265,357,l1:liblinear,4.216425664309419e-05,5,"{'train': [464808, 54], 'val': [58102, 54], 'test': [58102, 54]}",forest_v1_0,mlogloss,,lr,,,,False,,,,RUNNING
|