LightGBM + Optuna: no brainer
- auto train lightgbm directly from CSV files
- auto tune lightgbm using optuna
- auto serve best lightgbm model using fastapi
NOTE: PRs are currently
- not accepted. If there are issues/problems, please create an issue.
- accepted. If there are issues/problems, please solve with a PR.
Inspired by Abhishek Thakur's AutoXGB.
Install using pip
pip install autolgbm
Training a model using AutoLGBM is a piece of cake. All you need is some tabular data.
###############################################################################
### required parameters
###############################################################################
# path to training data
train_filename = "data_samples/binary_classification.csv"
# path to output folder to store artifacts
output = "output"
###############################################################################
### optional parameters
###############################################################################
# path to test data. if specified, the model will be evaluated on the test data
# and test_predictions.csv will be saved to the output folder
# if not specified, only OOF predictions will be saved
# test_filename = "test.csv"
test_filename = None
# task: classification or regression
# if not specified, the task will be inferred automatically
# task = "classification"
# task = "regression"
task = None
# an id column
# if not specified, the id column will be generated automatically with the name `id`
# idx = "id"
idx = None
# target columns are list of strings
# if not specified, the target column be assumed to be named `target`
# and the problem will be treated as one of: binary classification, multiclass classification,
# or single column regression
# targets = ["target"]
# targets = ["target1", "target2"]
targets = ["income"]
# features columns are list of strings
# if not specified, all columns except `id`, `targets` & `kfold` columns will be used
# features = ["col1", "col2"]
features = None
# categorical_features are list of strings
# if not specified, categorical columns will be inferred automatically
# categorical_features = ["col1", "col2"]
categorical_features = None
# use_gpu is boolean
# if not specified, GPU is not used
# use_gpu = True
# use_gpu = False
use_gpu = True
# number of folds to use for cross-validation
# default is 5
num_folds = 5
# random seed for reproducibility
# default is 42
seed = 42
# number of optuna trials to run
# default is 1000
# num_trials = 1000
num_trials = 100
# time_limit for optuna trials in seconds
# if not specified, timeout is not set and all trials are run
# time_limit = None
time_limit = 360
# if fast is set to True, the hyperparameter tuning will use only one fold
# however, the model will be trained on all folds in the end
# to generate OOF predictions and test predictions
# default is False
# fast = False
fast = FalseTo train a new model, you can run:
from autolgbm import AutoLGBM
# required parameters:
train_filename = "data_samples/binary_classification.csv"
output = "output"
# optional parameters
test_filename = None
task = None
idx = None
targets = ["income"]
features = None
categorical_features = None
use_gpu = True
num_folds = 5
seed = 42
num_trials = 100
time_limit = 360
fast = False
# Now its time to train the model!
algbm = AutoLGBM(
train_filename=train_filename,
output=output,
test_filename=test_filename,
task=task,
idx=idx,
targets=targets,
features=features,
categorical_features=categorical_features,
use_gpu=use_gpu,
num_folds=num_folds,
seed=seed,
num_trials=num_trials,
time_limit=time_limit,
fast=fast,
)
algbm.train()Train the model using the autolgbm train command. The parameters are same as above.
autolgbm train \
--train_filename datasets/30train.csv \
--output outputs/30days \
--test_filename datasets/30test.csv \
--use_gpu
You can also serve the trained model using the autolgbm serve command.
autolgbm serve --model_path outputs/mll --host 0.0.0.0 --debugTo know more about a command, run:
`autolgbm <command> --help`
autolgbm train --help
usage: autolgbm <command> [<args>] train [-h] --train_filename TRAIN_FILENAME [--test_filename TEST_FILENAME] --output
OUTPUT [--task {classification,regression}] [--idx IDX] [--targets TARGETS]
[--num_folds NUM_FOLDS] [--features FEATURES] [--use_gpu] [--fast]
[--seed SEED] [--time_limit TIME_LIMIT]
optional arguments:
-h, --help show this help message and exit
--train_filename TRAIN_FILENAME
Path to training file
--test_filename TEST_FILENAME
Path to test file
--output OUTPUT Path to output directory
--task {classification,regression}
User defined task type
--idx IDX ID column
--targets TARGETS Target column(s). If there are multiple targets, separate by ';'
--num_folds NUM_FOLDS
Number of folds to use
--features FEATURES Features to use, separated by ';'
--use_gpu Whether to use GPU for training
--fast Whether to use fast mode for tuning params. Only one fold will be used if fast mode is set
--seed SEED Random seed
--time_limit TIME_LIMIT
Time limit for optimization
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent