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))
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_sparse_matrix_regression(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": 'mae',
"task": 'regression',
"log_file_name": "test/sparse_regression.log",
"model_history": True
}
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.fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
**automl_settings)
assert automl_experiment.X_val.shape == X_val.shape
print(automl_experiment.predict(X_train))
print(automl_experiment.model)
print(automl_experiment.config_history)
print(automl_experiment.model_history)
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_training_log(self):
with TemporaryDirectory() as d:
filename = os.path.join(d, 'test_training_log.log')
# Run a simple job.
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": 'mse',
"task": 'regression',
"log_file_name": filename,
"log_training_metric": True,
"mem_thres": 1024*1024,
"n_jobs": 1,
"model_history": True
}
X_train, y_train = load_boston(return_X_y=True)
automl_experiment.fit(X_train=X_train, y_train=y_train,
**automl_settings)
# Check if the training log file is populated.
self.assertTrue(os.path.exists(filename))
with training_log_reader(filename) as reader:
count = 0
for record in reader.records():
print(record)
count += 1
self.assertGreater(count, 0)
def test_regression(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": 'mse',
"task": 'regression',
"log_file_name": "test/boston.log",
"log_training_metric": True,
"model_history": True
}
X_train, y_train = load_boston(return_X_y=True)
n = len(y_train)
automl_experiment.fit(X_train=X_train[:n >> 1],
y_train=y_train[:n >> 1],
X_val=X_train[n >> 1:],
y_val=y_train[n >> 1:],
**automl_settings)
assert automl_experiment.y_val.shape[0] == n - (n >> 1)
assert automl_experiment.eval_method == 'holdout'
print(automl_experiment.predict(X_train))
print(automl_experiment.model)
print(automl_experiment.config_history)
print(automl_experiment.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
print(get_output_from_log(automl_settings["log_file_name"], 1))
def _test_custom_data():
from flaml import AutoML
import requests
import pandas as pd
try:
train_dataset = pd.read_csv("data/input/train.tsv",
delimiter="\t",
quoting=3)
dev_dataset = pd.read_csv("data/input/dev.tsv",
delimiter="\t",
quoting=3)
test_dataset = pd.read_csv("data/input/test.tsv",
delimiter="\t",
quoting=3)
except requests.exceptions.HTTPError:
return
custom_sent_keys = ["#1 String", "#2 String"]
label_key = "Quality"
X_train = train_dataset[custom_sent_keys]
y_train = train_dataset[label_key]
X_val = dev_dataset[custom_sent_keys]
y_val = dev_dataset[label_key]
X_test = test_dataset[custom_sent_keys]
automl = AutoML()
automl_settings = {
"gpu_per_trial": 0,
"max_iter": 3,
"time_budget": 5,
"task": "seq-classification",
"metric": "accuracy",
}
automl_settings["custom_hpo_args"] = {
"model_path": "google/electra-small-discriminator",
"output_dir": "data/output/",
"ckpt_per_epoch": 1,
}
automl.fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
**automl_settings)
automl.predict(X_test)
automl.predict(["test test"])
automl.predict([
["test test", "test test"],
["test test", "test test"],
["test test", "test test"],
])
def _test_ray_classification():
from sklearn.datasets import make_classification
X, y = make_classification(1000, 10)
automl = AutoML()
automl.fit(X,
y,
time_budget=10,
task="classification",
n_concurrent_trials=2)
def test_classification(self, as_frame=False):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 4,
"metric": 'accuracy',
"task": 'classification',
"log_file_name": "test/iris.log",
"log_training_metric": True,
"model_history": True
}
X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
automl_experiment.fit(X_train=X_train,
y_train=y_train,
**automl_settings)
print(automl_experiment.classes_)
print(automl_experiment.predict_proba(X_train)[:5])
print(automl_experiment.model)
print(automl_experiment.config_history)
print(automl_experiment.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
del automl_settings["metric"]
del automl_settings["model_history"]
del automl_settings["log_training_metric"]
automl_experiment = AutoML()
duration = automl_experiment.retrain_from_log(
log_file_name=automl_settings["log_file_name"],
X_train=X_train,
y_train=y_train,
train_full=True,
record_id=0)
print(duration)
print(automl_experiment.model)
print(automl_experiment.predict_proba(X_train)[:5])
def test_roc_auc_ovo(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 1,
"metric": "roc_auc_ovo",
"task": "classification",
"log_file_name": "test/roc_auc_ovo.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
}
X_train, y_train = load_iris(return_X_y=True)
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
def test_custom_metric(self):
df, y = load_iris(return_X_y=True, as_frame=True)
df["label"] = y
automl_experiment = AutoML()
automl_settings = {
"dataframe": df,
"label": "label",
"time_budget": 5,
"eval_method": "cv",
"metric": custom_metric,
"task": "classification",
"log_file_name": "test/iris_custom.log",
"log_training_metric": True,
"log_type": "all",
"n_jobs": 1,
"model_history": True,
"sample_weight": np.ones(len(y)),
"pred_time_limit": 1e-5,
"ensemble": True,
}
automl_experiment.fit(**automl_settings)
print(automl_experiment.classes_)
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)
automl_experiment = AutoML()
estimator = automl_experiment.get_estimator_from_log(
automl_settings["log_file_name"], record_id=0, task="multi"
)
print(estimator)
(
time_history,
best_valid_loss_history,
valid_loss_history,
config_history,
metric_history,
) = get_output_from_log(
filename=automl_settings["log_file_name"], time_budget=6
)
print(metric_history)
try:
import ray
df = ray.put(df)
automl_settings["dataframe"] = df
automl_settings["use_ray"] = True
automl_experiment.fit(**automl_settings)
except ImportError:
pass
def test_cv():
from flaml import AutoML
import pandas as pd
import requests
train_data = {
"sentence1": [
'Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .',
"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .",
"They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .",
"Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .",
],
"sentence2": [
'Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .',
"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .",
"On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .",
"Tab shares jumped 20 cents , or 4.6 % , to set a record closing high at A $ 4.57 .",
],
"label": [1, 0, 1, 0],
"idx": [0, 1, 2, 3],
}
train_dataset = pd.DataFrame(train_data)
custom_sent_keys = ["sentence1", "sentence2"]
label_key = "label"
X_train = train_dataset[custom_sent_keys]
y_train = train_dataset[label_key]
automl = AutoML()
automl_settings = {
"gpu_per_trial": 0,
"max_iter": 3,
"time_budget": 5,
"task": "seq-classification",
"metric": "accuracy",
"n_splits": 3,
}
automl_settings["custom_hpo_args"] = {
"model_path": "google/electra-small-discriminator",
"output_dir": "test/data/output/",
"ckpt_per_epoch": 1,
"fp16": False,
}
try:
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
except requests.exceptions.HTTPError:
return
def test_custom_metric(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 10,
'eval_method': 'holdout',
"metric": custom_metric,
"task": 'classification',
"log_file_name": "test/iris_custom.log",
"log_training_metric": True,
'log_type': 'all',
"model_history": True
}
X_train, y_train = load_iris(return_X_y=True)
automl_experiment.fit(X_train=X_train,
y_train=y_train,
**automl_settings)
print(automl_experiment.classes_)
print(automl_experiment.predict_proba(X_train))
print(automl_experiment.model)
print(automl_experiment.config_history)
print(automl_experiment.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
automl_experiment = AutoML()
estimator = automl_experiment.get_estimator_from_log(
automl_settings["log_file_name"], record_id=0, objective='multi')
print(estimator)
time_history, best_valid_loss_history, valid_loss_history, \
config_history, train_loss_history = get_output_from_log(
filename=automl_settings['log_file_name'], time_budget=6)
print(train_loss_history)
def test_sparse_matrix_lr(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 3,
"metric": "f1",
"task": "classification",
"log_file_name": "test/sparse_classification.log",
"estimator_list": ["lrl1", "lrl2"],
"log_type": "all",
"n_jobs": 1,
}
X_train = scipy.sparse.random(3000, 3000, density=0.1)
y_train = np.random.randint(2, size=3000)
automl_experiment.fit(X_train=X_train,
y_train=y_train,
train_time_limit=1,
**automl_settings)
automl_settings["time_budget"] = 5
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("lrl2"))
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
def test_random_skip_oom(self):
automl_experiment = AutoML()
automl_experiment.add_learner(learner_name="large_lgbm",
learner_class=MyLargeLGBM)
automl_settings = {
"time_budget": 2,
"task": "classification",
"log_file_name": "test/sparse_classification_oom.log",
"estimator_list": ["large_lgbm"],
"log_type": "all",
"n_jobs": 1,
"hpo_method": "random",
"n_concurrent_trials": 2,
}
X_train = scipy.sparse.eye(900000)
y_train = np.random.randint(2, size=900000)
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("large_lgbm"))
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
except ImportError:
print("skipping concurrency test as ray is not installed")
return
def test_micro_macro_f1(self):
automl_experiment_micro = AutoML()
automl_experiment_macro = AutoML()
automl_settings = {
"time_budget": 2,
"task": "classification",
"log_file_name": "test/micro_macro_f1.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
}
X_train, y_train = load_iris(return_X_y=True)
automl_experiment_micro.fit(
X_train=X_train, y_train=y_train, metric="micro_f1", **automl_settings
)
automl_experiment_macro.fit(
X_train=X_train, y_train=y_train, metric="macro_f1", **automl_settings
)
estimator = automl_experiment_macro.model
y_pred = estimator.predict(X_train)
y_pred_proba = estimator.predict_proba(X_train)
from flaml.ml import norm_confusion_matrix, multi_class_curves
print(norm_confusion_matrix(y_train, y_pred))
from sklearn.metrics import roc_curve, precision_recall_curve
print(multi_class_curves(y_train, y_pred_proba, roc_curve))
print(multi_class_curves(y_train, y_pred_proba, precision_recall_curve))
def test_binary(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 1,
"task": "binary",
"log_file_name": "test/breast_cancer.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
}
X_train, y_train = load_breast_cancer(return_X_y=True)
automl_experiment.fit(X_train=X_train,
y_train=y_train,
**automl_settings)
_ = automl_experiment.predict(X_train)
def test_roc_auc_ovr(self):
automl_experiment = AutoML()
X_train, y_train = load_iris(return_X_y=True)
automl_settings = {
"time_budget": 1,
"metric": "roc_auc_ovr",
"task": "classification",
"log_file_name": "test/roc_auc_ovr.log",
"log_training_metric": True,
"n_jobs": 1,
"sample_weight": np.ones(len(y_train)),
"eval_method": "holdout",
"model_history": True,
}
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
def test_numpy_large():
import numpy as np
import pandas as pd
from flaml import AutoML
X_train = pd.date_range("2017-01-01", periods=70000, freq="T")
y_train = pd.DataFrame(np.random.randint(6500, 7500, 70000))
automl = AutoML()
automl.fit(
X_train=X_train[:-10].values, # a single column of timestamp
y_train=y_train[:-10].values, # value for each timestamp
period=10, # time horizon to forecast, e.g., 12 months
task="ts_forecast",
time_budget=10, # time budget in seconds
)
def test_fit_w_starting_point(self, as_frame=True):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 3,
"metric": "accuracy",
"task": "classification",
"log_file_name": "test/iris.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
}
X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
if as_frame:
# test drop column
X_train.columns = range(X_train.shape[1])
X_train[X_train.shape[1]] = np.zeros(len(y_train))
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
automl_val_accuracy = 1.0 - automl_experiment.best_loss
print("Best ML leaner:", automl_experiment.best_estimator)
print("Best hyperparmeter config:", automl_experiment.best_config)
print("Best accuracy on validation data: {0:.4g}".format(automl_val_accuracy))
print(
"Training duration of best run: {0:.4g} s".format(
automl_experiment.best_config_train_time
)
)
starting_points = automl_experiment.best_config_per_estimator
print("starting_points", starting_points)
print("loss of the starting_points", automl_experiment.best_loss_per_estimator)
automl_settings_resume = {
"time_budget": 2,
"metric": "accuracy",
"task": "classification",
"log_file_name": "test/iris_resume.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
"log_type": "all",
"starting_points": starting_points,
}
new_automl_experiment = AutoML()
new_automl_experiment.fit(
X_train=X_train, y_train=y_train, **automl_settings_resume
)
new_automl_val_accuracy = 1.0 - new_automl_experiment.best_loss
print("Best ML leaner:", new_automl_experiment.best_estimator)
print("Best hyperparmeter config:", new_automl_experiment.best_config)
print(
"Best accuracy on validation data: {0:.4g}".format(new_automl_val_accuracy)
)
print(
"Training duration of best run: {0:.4g} s".format(
new_automl_experiment.best_config_train_time
)
)
def test_logging_level(self):
from flaml import logger, logger_formatter
with tempfile.TemporaryDirectory() as d:
training_log = os.path.join(d, "training.log")
# Configure logging for the FLAML logger
# and add a handler that outputs to a buffer.
logger.setLevel(logging.INFO)
buf = io.StringIO()
ch = logging.StreamHandler(buf)
ch.setFormatter(logger_formatter)
logger.addHandler(ch)
# Run a simple job.
automl = AutoML()
automl_settings = {
"time_budget": 1,
"metric": 'mse',
"task": 'regression',
"log_file_name": training_log,
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
}
X_train, y_train = load_boston(return_X_y=True)
n = len(y_train) >> 1
automl.fit(X_train=X_train[:n],
y_train=y_train[:n],
X_val=X_train[n:],
y_val=y_train[n:],
**automl_settings)
# Check if the log buffer is populated.
self.assertTrue(len(buf.getvalue()) > 0)
import pickle
with open('automl.pkl', 'wb') as f:
pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)
print(automl.__version__)
def test_regression(self):
automl_experiment = 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_experiment.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_experiment._state.eval_method == "holdout"
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)
print(get_output_from_log(automl_settings["log_file_name"], 1))
automl_experiment.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_experiment.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=0,
)
def test_sparse_matrix_regression_cv(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
'eval_method': 'cv',
"task": 'regression',
"log_file_name": "test/sparse_regression.log",
"model_history": True
}
X_train = scipy.sparse.random(100, 100)
y_train = np.random.uniform(size=100)
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.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
def test_parallel_xgboost(self, hpo_method=None):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 10,
"metric": "ap",
"task": "classification",
"log_file_name": "test/sparse_classification.log",
"estimator_list": ["xgboost"],
"log_type": "all",
"n_jobs": 1,
"n_concurrent_trials": 2,
"hpo_method": hpo_method,
}
X_train = scipy.sparse.eye(900000)
y_train = np.random.randint(2, size=900000)
try:
import ray
X_train_ref = ray.put(X_train)
automl_experiment.fit(X_train=X_train_ref,
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_ray_classification(self):
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25)
automl = AutoML()
try:
automl.fit(
X_train,
y_train,
X_val=X_test,
y_val=y_test,
time_budget=10,
task="classification",
use_ray=True,
)
automl.fit(
X_train,
y_train,
X_val=X_test,
y_val=y_test,
time_budget=10,
task="classification",
n_concurrent_trials=2,
)
except ImportError:
return
def test_mlflow():
import subprocess
import sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "mlflow"])
import mlflow
from flaml.data import load_openml_task
try:
X_train, X_test, y_train, y_test = load_openml_task(
task_id=7592, data_dir="test/"
)
except (OpenMLServerException, ChunkedEncodingError) as e:
print(e)
return
""" import AutoML class from flaml package """
from flaml import AutoML
automl = AutoML()
settings = {
"time_budget": 5, # total running time in seconds
"metric": "accuracy", # primary metrics can be chosen from: ['accuracy','roc_auc','roc_auc_ovr','roc_auc_ovo','f1','log_loss','mae','mse','r2']
"estimator_list": ["lgbm", "rf", "xgboost"], # list of ML learners
"task": "classification", # task type
"sample": False, # whether to subsample training data
"log_file_name": "adult.log", # flaml log file
}
mlflow.set_experiment("flaml")
with mlflow.start_run() as run:
automl.fit(X_train=X_train, y_train=y_train, **settings)
mlflow.sklearn.log_model(automl, "automl")
loaded_model = mlflow.pyfunc.load_model(f"{run.info.artifact_uri}/automl")
print(loaded_model.predict(X_test))
automl._mem_thres = 0
print(automl.trainable(automl.points_to_evaluate[0]))
settings["use_ray"] = True
try:
with mlflow.start_run() as run:
automl.fit(X_train=X_train, y_train=y_train, **settings)
mlflow.sklearn.log_model(automl, "automl")
automl = mlflow.sklearn.load_model(f"{run.info.artifact_uri}/automl")
print(automl.predict_proba(X_test))
except ImportError:
pass
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_experiment = 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_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("rf"))
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_datetime_columns(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": 'mse',
"task": 'regression',
"log_file_name": "test/datetime_columns.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True
}
fake_df = pd.DataFrame({
'A': [
datetime(1900, 2, 3),
datetime(1900, 3, 4),
datetime(1900, 3, 4),
datetime(1900, 3, 4),
datetime(1900, 7, 2),
datetime(1900, 8, 9)
],
'B': [
datetime(1900, 1, 1),
datetime(1900, 1, 1),
datetime(1900, 1, 1),
datetime(1900, 1, 1),
datetime(1900, 1, 1),
datetime(1900, 1, 1)
],
'year_A': [
datetime(1900, 1, 2),
datetime(1900, 8, 1),
datetime(1900, 1, 4),
datetime(1900, 6, 1),
datetime(1900, 1, 5),
datetime(1900, 4, 1)
]
})
y = np.array([0, 1, 0, 1, 0, 0])
automl_experiment.fit(X_train=fake_df, y_train=y, **automl_settings)
_ = automl_experiment.predict(fake_df)
def test_xgboost():
from flaml import AutoML
from sklearn.datasets import make_moons
import scipy.sparse
import numpy as np
from xgboost.core import XGBoostError
try:
X_train = scipy.sparse.eye(900000)
y_train = np.random.randint(2, size=900000)
automl = AutoML()
automl.fit(
X_train,
y_train,
estimator_list=["xgb_limitdepth", "xgboost"],
time_budget=5,
gpu_per_trial=1,
)
train, label = make_moons(
n_samples=300000, shuffle=True, noise=0.3, random_state=None
)
automl = AutoML()
automl.fit(
train,
label,
estimator_list=["xgb_limitdepth", "xgboost"],
time_budget=5,
gpu_per_trial=1,
)
automl.fit(
train,
label,
estimator_list=["xgb_limitdepth", "xgboost"],
time_budget=5,
)
except XGBoostError:
# No visible GPU is found for XGBoost.
return
def test_package_minimum():
# Initialize an AutoML instance
automl = AutoML()
# Specify automl goal and constraint
automl_settings = {
"time_budget": 10, # in seconds
"metric": "accuracy",
"task": "classification",
"log_file_name": "iris.log",
}
X_train, y_train = load_iris(return_X_y=True)
# Train with labeled input data
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
# Check that `best_config` is created, the log was created and best model is accessible
assert hasattr(automl, "best_config")
assert Path("iris.log").exists()
assert automl.model is not None
print(automl.model)
# Predict and check that the prediction shape is as expected
preds = automl.predict_proba(X_train)
assert preds.shape == (150, 3)
print(preds)
def test_sparse_matrix_xgboost(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": 'ap',
"task": 'classification',
"log_file_name": "test/sparse_classification.log",
"estimator_list": ["xgboost"],
"log_type": "all",
}
X_train = scipy.sparse.eye(900000)
y_train = np.random.randint(2, size=900000)
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.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
