autogen/test/test_automl.py

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import unittest
import numpy as np
import scipy.sparse
from sklearn.datasets import (
fetch_california_housing,
load_iris,
load_wine,
load_breast_cancer,
)
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import pandas as pd
from datetime import datetime
from flaml import AutoML
from flaml.data import get_output_from_log
from flaml.model import LGBMEstimator, SKLearnEstimator, XGBoostEstimator
from rgf.sklearn import RGFClassifier, RGFRegressor
from flaml import tune
from flaml.training_log import training_log_reader
class MyRegularizedGreedyForest(SKLearnEstimator):
def __init__(self, task="binary", **config):
super().__init__(task, **config)
if task in ("binary", "multi"):
self.estimator_class = RGFClassifier
else:
self.estimator_class = RGFRegressor
@classmethod
def search_space(cls, data_size, task):
space = {
"max_leaf": {
"domain": tune.lograndint(lower=4, upper=data_size),
"init_value": 4,
},
"n_iter": {
"domain": tune.lograndint(lower=1, upper=data_size),
"init_value": 1,
},
"n_tree_search": {
"domain": tune.lograndint(lower=1, upper=32768),
"init_value": 1,
},
"opt_interval": {
"domain": tune.lograndint(lower=1, upper=10000),
"init_value": 100,
},
"learning_rate": {"domain": tune.loguniform(lower=0.01, upper=20.0)},
"min_samples_leaf": {
"domain": tune.lograndint(lower=1, upper=20),
"init_value": 20,
},
}
return space
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@classmethod
def size(cls, config):
max_leaves = int(round(config["max_leaf"]))
n_estimators = int(round(config["n_iter"]))
return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8
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@classmethod
def cost_relative2lgbm(cls):
return 1.0
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 MyLargeLGBM(LGBMEstimator):
@classmethod
def search_space(cls, **params):
return {
"n_estimators": {
"domain": tune.lograndint(lower=4, upper=32768),
"init_value": 32768,
"low_cost_init_value": 4,
},
"num_leaves": {
"domain": tune.lograndint(lower=4, upper=32768),
"init_value": 32768,
"low_cost_init_value": 4,
},
}
def custom_metric(
X_test,
y_test,
estimator,
labels,
X_train,
y_train,
weight_test=None,
weight_train=None,
config=None,
groups_test=None,
groups_train=None,
):
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from sklearn.metrics import log_loss
import time
start = time.time()
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y_pred = estimator.predict_proba(X_test)
pred_time = (time.time() - start) / len(X_test)
test_loss = log_loss(y_test, y_pred, labels=labels, sample_weight=weight_test)
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y_pred = estimator.predict_proba(X_train)
train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
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alpha = 0.5
return test_loss * (1 + alpha) - alpha * train_loss, {
"test_loss": test_loss,
"train_loss": train_loss,
"pred_time": pred_time,
}
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class TestAutoML(unittest.TestCase):
def test_custom_learner(self):
automl = AutoML()
automl.add_learner(learner_name="RGF", learner_class=MyRegularizedGreedyForest)
X_train, y_train = load_wine(return_X_y=True)
settings = {
"time_budget": 8, # total running time in seconds
"estimator_list": ["RGF", "lgbm", "rf", "xgboost"],
"task": "classification", # task type
"sample": True, # whether to subsample training data
"log_file_name": "test/wine.log",
"log_training_metric": True, # whether to log training metric
"n_jobs": 1,
}
"""The main flaml automl API"""
automl.fit(X_train=X_train, y_train=y_train, **settings)
# print the best model found for RGF
print(automl.best_model_for_estimator("RGF"))
MyRegularizedGreedyForest.search_space = lambda data_size, task: {}
automl.fit(X_train=X_train, y_train=y_train, **settings)
def test_ensemble(self):
automl = AutoML()
automl.add_learner(learner_name="RGF", learner_class=MyRegularizedGreedyForest)
X_train, y_train = load_wine(return_X_y=True)
settings = {
"time_budget": 5, # total running time in seconds
"estimator_list": ["rf", "xgboost", "catboost"],
"task": "classification", # task type
"sample": True, # whether to subsample training data
"log_file_name": "test/wine.log",
"log_training_metric": True, # whether to log training metric
"ensemble": {
"final_estimator": MyRegularizedGreedyForest(),
"passthrough": False,
},
"n_jobs": 1,
}
"""The main flaml automl API"""
automl.fit(X_train=X_train, y_train=y_train, **settings)
def test_preprocess(self):
automl = AutoML()
X = pd.DataFrame(
{
"f1": [1, -2, 3, -4, 5, -6, -7, 8, -9, -10, -11, -12, -13, -14],
"f2": [
3.0,
16.0,
10.0,
12.0,
3.0,
14.0,
11.0,
12.0,
5.0,
14.0,
20.0,
16.0,
15.0,
11.0,
],
"f3": [
"a",
"b",
"a",
"c",
"c",
"b",
"b",
"b",
"b",
"a",
"b",
1.0,
1.0,
"a",
],
"f4": [
True,
True,
False,
True,
True,
False,
False,
False,
True,
True,
False,
False,
True,
True,
],
}
)
y = pd.Series([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
automl = AutoML()
automl_settings = {
"time_budget": 6,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["catboost", "lrl2"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
"verbose": 4,
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
automl = AutoML()
automl_settings = {
"time_budget": 2,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["lrl2", "kneighbor"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
"verbose": 4,
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
automl = AutoML()
automl_settings = {
"time_budget": 3,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["xgboost", "catboost", "kneighbor"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
"verbose": 4,
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
automl = AutoML()
automl_settings = {
"time_budget": 3,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["lgbm", "catboost", "kneighbor"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
"verbose": 4,
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
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def test_dataframe(self):
self.test_classification(True)
def test_custom_metric(self):
df, y = load_iris(return_X_y=True, as_frame=True)
df["label"] = y
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automl_experiment = AutoML()
automl_settings = {
"dataframe": df,
"label": "label",
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"time_budget": 5,
"eval_method": "cv",
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"metric": custom_metric,
"task": "classification",
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"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,
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}
automl_experiment.fit(**automl_settings)
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print(automl_experiment.classes_)
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, task="multi"
)
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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)
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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)
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def test_classification(self, as_frame=False):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 4,
"metric": "accuracy",
"task": "classification",
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"log_file_name": "test/iris.log",
"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
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}
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)
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print(automl_experiment.classes_)
print(automl_experiment.predict(X_train)[:5])
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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,
)
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print(duration)
print(automl_experiment.model)
print(automl_experiment.predict_proba(X_train)[:5])
def test_datetime_columns(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"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_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_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_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)
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def test_regression(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"task": "regression",
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"log_file_name": "test/california.log",
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"log_training_metric": True,
"n_jobs": 1,
"model_history": True,
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}
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"
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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))
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,
)
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def test_sparse_matrix_classification(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": "auto",
"task": "classification",
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"log_file_name": "test/sparse_classification.log",
"split_type": "uniform",
"n_jobs": 1,
"model_history": True,
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}
X_train = scipy.sparse.random(1554, 21, dtype=int)
y_train = np.random.randint(3, size=1554)
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
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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)
def test_sparse_matrix_regression(self):
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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)
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automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": "mae",
"task": "regression",
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"log_file_name": "test/sparse_regression.log",
"n_jobs": 1,
"model_history": True,
"keep_search_state": True,
"verbose": 0,
"early_stop": True,
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}
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
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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_sparse_matrix_xgboost(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 3,
"metric": "ap",
"task": "classification",
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"log_file_name": "test/sparse_classification.log",
"estimator_list": ["xgboost"],
"log_type": "all",
"n_jobs": 1,
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}
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)
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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(self, hpo_method=None):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 10,
"task": "regression",
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"log_file_name": "test/california.log",
"log_type": "all",
"n_jobs": 1,
"n_concurrent_trials": 2,
"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.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
except ImportError:
return
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:
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)
except ImportError:
return
def test_parallel_xgboost_others(self):
# use random search as the hpo_method
self.test_parallel_xgboost(hpo_method="random")
def test_random_out_of_memory(self):
automl_experiment = AutoML()
automl_experiment.add_learner(
learner_name="large_lgbm", learner_class=MyLargeLGBM
)
automl_settings = {
"time_budget": 2,
"metric": "ap",
"task": "classification",
"log_file_name": "test/sparse_classification_oom.log",
"estimator_list": ["large_lgbm"],
"log_type": "all",
"n_jobs": 1,
"n_concurrent_trials": 2,
"hpo_method": "random",
}
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.model_history)
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
except ImportError:
return
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def test_sparse_matrix_lr(self):
automl_experiment = AutoML()
automl_settings = {
"time_budget": 2,
"metric": "f1",
"task": "classification",
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"log_file_name": "test/sparse_classification.log",
"estimator_list": ["lrl1", "lrl2"],
"log_type": "all",
"n_jobs": 1,
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}
X_train = scipy.sparse.random(3000, 900, density=0.1)
y_train = np.random.randint(2, size=3000)
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
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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_sparse_matrix_regression_holdout(self):
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X_train = scipy.sparse.random(8, 100)
y_train = np.random.uniform(size=8)
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automl_experiment = AutoML()
automl_settings = {
"time_budget": 1,
"eval_method": "holdout",
"task": "regression",
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"log_file_name": "test/sparse_regression.log",
"n_jobs": 1,
"model_history": True,
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"metric": "mse",
"sample_weight": np.ones(len(y_train)),
"early_stop": True,
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}
automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
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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_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.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_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)
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_fit_w_starting_points_list(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 = {}
log_file_name = automl_settings["log_file_name"]
with training_log_reader(log_file_name) as reader:
for record in reader.records():
config = record.config
learner = record.learner
if learner not in starting_points:
starting_points[learner] = []
starting_points[learner].append(config)
max_iter = sum([len(s) for k, s in starting_points.items()])
automl_settings_resume = {
"time_budget": 2,
"metric": "accuracy",
"task": "classification",
"log_file_name": "test/iris_resume_all.log",
"log_training_metric": True,
"n_jobs": 1,
"max_iter": max_iter,
"model_history": True,
"log_type": "all",
"starting_points": starting_points,
"append_log": True,
}
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))
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if __name__ == "__main__":
unittest.main()