mirror of
https://github.com/microsoft/autogen.git
synced 2025-09-11 01:06:08 +00:00
44ddf9e104
* Refactor into automl subpackage Moved some of the packages into an automl subpackage to tidy before the task-based refactor. This is in response to discussions with the group and a comment on the first task-based PR. Only changes here are moving subpackages and modules into the new automl, fixing imports to work with this structure and fixing some dependencies in setup.py. * Fix doc building post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Fix broken links in website post automl subpackage refactor * Remove vw from test deps as this is breaking the build * Move default back to the top-level I'd moved this to automl as that's where it's used internally, but had missed that this is actually part of the public interface so makes sense to live where it was. * Re-add top level modules with deprecation warnings flaml.data, flaml.ml and flaml.model are re-added to the top level, being re-exported from flaml.automl for backwards compatability. Adding a deprecation warning so that we can have a planned removal later. * Fix model.py line-endings * Pin pytorch-lightning to less than 1.8.0 We're seeing strange lightning related bugs from pytorch-forecasting since the release of lightning 1.8.0. Going to try constraining this to see if we have a fix. * Fix the lightning version pin Was optimistic with setting it in the 1.7.x range, but that isn't compatible with python 3.6 * Remove lightning version pin * Revert dependency version changes * Minor change to retrigger the build * Fix line endings in ml.py and model.py Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: EgorKraevTransferwise <egor.kraev@transferwise.com>
255 lines
8.7 KiB
Python
255 lines
8.7 KiB
Python
import unittest
|
|
import numpy as np
|
|
import scipy.sparse
|
|
from sklearn.datasets import (
|
|
fetch_california_housing,
|
|
)
|
|
|
|
from flaml import AutoML
|
|
from flaml.automl.data import get_output_from_log
|
|
from flaml.automl.model import XGBoostEstimator
|
|
|
|
|
|
def logregobj(preds, dtrain):
|
|
labels = dtrain.get_label()
|
|
preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight
|
|
grad = preds - labels
|
|
hess = preds * (1.0 - preds)
|
|
return grad, hess
|
|
|
|
|
|
class MyXGB1(XGBoostEstimator):
|
|
"""XGBoostEstimator with logregobj as the objective function"""
|
|
|
|
def __init__(self, **config):
|
|
super().__init__(objective=logregobj, **config)
|
|
|
|
|
|
class MyXGB2(XGBoostEstimator):
|
|
"""XGBoostEstimator with 'reg:squarederror' as the objective function"""
|
|
|
|
def __init__(self, **config):
|
|
super().__init__(objective="reg:squarederror", **config)
|
|
|
|
|
|
class TestRegression(unittest.TestCase):
|
|
def test_regression(self):
|
|
automl = AutoML()
|
|
automl_settings = {
|
|
"time_budget": 2,
|
|
"task": "regression",
|
|
"log_file_name": "test/california.log",
|
|
"log_training_metric": True,
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
}
|
|
X_train, y_train = fetch_california_housing(return_X_y=True)
|
|
n = int(len(y_train) * 9 // 10)
|
|
automl.fit(
|
|
X_train=X_train[:n],
|
|
y_train=y_train[:n],
|
|
X_val=X_train[n:],
|
|
y_val=y_train[n:],
|
|
**automl_settings
|
|
)
|
|
assert automl._state.eval_method == "holdout"
|
|
y_pred = automl.predict(X_train)
|
|
print(y_pred)
|
|
print(automl.model.estimator)
|
|
n_iter = automl.model.estimator.get_params("n_estimators")
|
|
print(automl.config_history)
|
|
print(automl.best_model_for_estimator("xgboost"))
|
|
print(automl.best_iteration)
|
|
print(automl.best_estimator)
|
|
print(get_output_from_log(automl_settings["log_file_name"], 1))
|
|
automl.retrain_from_log(
|
|
task="regression",
|
|
log_file_name=automl_settings["log_file_name"],
|
|
X_train=X_train,
|
|
y_train=y_train,
|
|
train_full=True,
|
|
time_budget=1,
|
|
)
|
|
automl.retrain_from_log(
|
|
task="regression",
|
|
log_file_name=automl_settings["log_file_name"],
|
|
X_train=X_train,
|
|
y_train=y_train,
|
|
time_budget=0,
|
|
)
|
|
automl = AutoML()
|
|
automl.retrain_from_log(
|
|
task="regression",
|
|
log_file_name=automl_settings["log_file_name"],
|
|
X_train=X_train[:n],
|
|
y_train=y_train[:n],
|
|
train_full=True,
|
|
)
|
|
print(automl.model.estimator)
|
|
y_pred2 = automl.predict(X_train)
|
|
# In some rare case, the last config is early stopped and it's the best config. But the logged config's n_estimator is not reduced.
|
|
assert (
|
|
n_iter != automl.model.estimator.get_params("n_estimator")
|
|
or (y_pred == y_pred2).all()
|
|
)
|
|
|
|
def test_sparse_matrix_regression(self):
|
|
X_train = scipy.sparse.random(300, 900, density=0.0001)
|
|
y_train = np.random.uniform(size=300)
|
|
X_val = scipy.sparse.random(100, 900, density=0.0001)
|
|
y_val = np.random.uniform(size=100)
|
|
automl = AutoML()
|
|
settings = {
|
|
"time_budget": 2,
|
|
"metric": "mae",
|
|
"task": "regression",
|
|
"log_file_name": "test/sparse_regression.log",
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
"keep_search_state": True,
|
|
"verbose": 0,
|
|
"early_stop": True,
|
|
}
|
|
automl.fit(
|
|
X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **settings
|
|
)
|
|
assert automl._state.X_val.shape == X_val.shape
|
|
print(automl.predict(X_train))
|
|
print(automl.model)
|
|
print(automl.config_history)
|
|
print(automl.best_model_for_estimator("rf"))
|
|
print(automl.best_iteration)
|
|
print(automl.best_estimator)
|
|
print(automl.best_config)
|
|
print(automl.best_loss)
|
|
print(automl.best_config_train_time)
|
|
|
|
settings.update(
|
|
{
|
|
"estimator_list": ["catboost"],
|
|
"keep_search_state": False,
|
|
"model_history": False,
|
|
"use_best_model": False,
|
|
"time_budget": None,
|
|
"max_iter": 2,
|
|
"custom_hp": {"catboost": {"n_estimators": {"domain": 100}}},
|
|
}
|
|
)
|
|
automl.fit(
|
|
X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **settings
|
|
)
|
|
|
|
def test_parallel(self, hpo_method=None):
|
|
automl_experiment = AutoML()
|
|
automl_settings = {
|
|
"time_budget": 10,
|
|
"task": "regression",
|
|
"log_file_name": "test/california.log",
|
|
"log_type": "all",
|
|
"n_jobs": 1,
|
|
"n_concurrent_trials": 10,
|
|
"hpo_method": hpo_method,
|
|
}
|
|
X_train, y_train = fetch_california_housing(return_X_y=True)
|
|
try:
|
|
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.best_model_for_estimator("xgboost"))
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
except ImportError:
|
|
return
|
|
|
|
def test_sparse_matrix_regression_holdout(self):
|
|
X_train = scipy.sparse.random(8, 100)
|
|
y_train = np.random.uniform(size=8)
|
|
automl_experiment = AutoML()
|
|
automl_settings = {
|
|
"time_budget": 1,
|
|
"eval_method": "holdout",
|
|
"task": "regression",
|
|
"log_file_name": "test/sparse_regression.log",
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
"metric": "mse",
|
|
"sample_weight": np.ones(len(y_train)),
|
|
"early_stop": True,
|
|
}
|
|
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.best_model_for_estimator("rf"))
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
|
|
def test_regression_xgboost(self):
|
|
X_train = scipy.sparse.random(300, 900, density=0.0001)
|
|
y_train = np.random.uniform(size=300)
|
|
X_val = scipy.sparse.random(100, 900, density=0.0001)
|
|
y_val = np.random.uniform(size=100)
|
|
automl_experiment = AutoML()
|
|
automl_experiment.add_learner(learner_name="my_xgb1", learner_class=MyXGB1)
|
|
automl_experiment.add_learner(learner_name="my_xgb2", learner_class=MyXGB2)
|
|
automl_settings = {
|
|
"time_budget": 2,
|
|
"estimator_list": ["my_xgb1", "my_xgb2"],
|
|
"task": "regression",
|
|
"log_file_name": "test/regression_xgboost.log",
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
"keep_search_state": True,
|
|
"early_stop": True,
|
|
}
|
|
automl_experiment.fit(
|
|
X_train=X_train,
|
|
y_train=y_train,
|
|
X_val=X_val,
|
|
y_val=y_val,
|
|
**automl_settings
|
|
)
|
|
assert automl_experiment._state.X_val.shape == X_val.shape
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.best_model_for_estimator("my_xgb2"))
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
print(automl_experiment.best_config)
|
|
print(automl_experiment.best_loss)
|
|
print(automl_experiment.best_config_train_time)
|
|
|
|
|
|
def test_multioutput():
|
|
from sklearn.datasets import make_regression
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.multioutput import MultiOutputRegressor, RegressorChain
|
|
|
|
# create regression data
|
|
X, y = make_regression(n_targets=3)
|
|
|
|
# split into train and test data
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
X, y, test_size=0.30, random_state=42
|
|
)
|
|
|
|
# train the model
|
|
model = MultiOutputRegressor(AutoML(task="regression", time_budget=1))
|
|
model.fit(X_train, y_train)
|
|
|
|
# predict
|
|
print(model.predict(X_test))
|
|
|
|
# train the model
|
|
model = RegressorChain(AutoML(task="regression", time_budget=1))
|
|
model.fit(X_train, y_train)
|
|
|
|
# predict
|
|
print(model.predict(X_test))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|