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update config if n_estimators is modified (#225)

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* update config if n_estimators is modified

* prediction as int

* handle the case n_estimators <= 0

* if trained and no budget to train more, return the trained model

* split_type=group for classification & regression
This commit is contained in:
Chi Wang
2021-09-27 21:30:49 -07:00
committed by GitHub
parent 7d9e28f02d
commit a99e939404
6 changed files with 163 additions and 108 deletions
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37
test/test_split.py
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@@ -17,7 +17,7 @@ def _test(split_type):
automl_settings = {
"time_budget": 2,
# "metric": 'accuracy',
"task": 'classification',
"task": "classification",
"log_file_name": "test/{}.log".format(dataset),
"model_history": True,
"log_training_metric": True,
@@ -28,13 +28,16 @@ def _test(split_type):
X, y = fetch_openml(name=dataset, return_X_y=True)
except (ArffException, ValueError):
from sklearn.datasets import load_wine
X, y = load_wine(return_X_y=True)
if split_type != 'time':
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
random_state=42)
if split_type != "time":
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42
)
else:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
shuffle=False)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, shuffle=False
)
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
pred = automl.predict(X_test)
@@ -53,36 +56,45 @@ def test_time():
def test_groups():
from sklearn.externals._arff import ArffException
try:
X, y = fetch_openml(name=dataset, return_X_y=True)
except (ArffException, ValueError):
from sklearn.datasets import load_wine
X, y = load_wine(return_X_y=True)
import numpy as np
automl = AutoML()
automl_settings = {
"time_budget": 2,
"task": 'classification',
"task": "classification",
"log_file_name": "test/{}.log".format(dataset),
"model_history": True,
"eval_method": "cv",
"groups": np.random.randint(low=0, high=10, size=len(y)),
"estimator_list": ['lgbm', 'rf', 'xgboost', 'kneighbor'], # list of ML learners
"estimator_list": ["lgbm", "rf", "xgboost", "kneighbor"],
"learner_selector": "roundrobin",
}
automl.fit(X, y, **automl_settings)
automl_settings["eval_method"] = "holdout"
automl.fit(X, y, **automl_settings)
def test_rank():
from sklearn.externals._arff import ArffException
try:
X, y = fetch_openml(name=dataset, return_X_y=True)
except (ArffException, ValueError):
from sklearn.datasets import load_wine
X, y = load_wine(return_X_y=True)
y = y.cat.codes
import numpy as np
automl = AutoML()
automl_settings = {
"time_budget": 2,
@@ -90,8 +102,9 @@ def test_rank():
"log_file_name": "test/{}.log".format(dataset),
"model_history": True,
"eval_method": "cv",
"groups": np.array( # group labels
[0] * 200 + [1] * 200 + [2] * 200 + [3] * 200 + [4] * 100 + [5] * 100),
"groups": np.array( # group labels
[0] * 200 + [1] * 200 + [2] * 200 + [3] * 200 + [4] * 100 + [5] * 100
),
"learner_selector": "roundrobin",
}
automl.fit(X, y, **automl_settings)
@@ -100,10 +113,10 @@ def test_rank():
automl_settings = {
"time_budget": 2,
"task": "rank",
"metric": "ndcg@5", # 5 can be replaced by any number
"metric": "ndcg@5", # 5 can be replaced by any number
"log_file_name": "test/{}.log".format(dataset),
"model_history": True,
"groups": [200] * 4 + [100] * 2, # alternative way: group counts
"groups": [200] * 4 + [100] * 2, # alternative way: group counts
# "estimator_list": ['lgbm', 'xgboost'], # list of ML learners
"learner_selector": "roundrobin",
}