X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
automl = AutoML(
#results_path="AutoML_30",
algorithms=["Random Forest"],
total_time_limit=20,
explain_level=0,
# validation={"validation_type": "split"},
mode="Explain",
# validation={"validation_type": "split"}
validation={
"validation_type": "kfold",
"k_folds": 2,
"shuffle": True,
"stratify": True,
},
golden_features=True,
feature_selection=True)
automl.set_advanced(start_random_models=20,
hill_climbing_steps=10,
top_models_to_improve=3)
automl.fit(X_train, y_train)
predictions = automl.predict(X_test)
print(predictions.head())
print(predictions.tail())
print(X_test.shape, predictions.shape)
print("LogLoss", log_loss(y_test, predictions["prediction_>50K"]))
import pandas as pd
import numpy as np
from supervised.automl import AutoML
# df = pd.read_csv("tests/data/iris_classes_missing_values_missing_target.csv")
df = pd.read_csv("tests/data/iris_missing_values_missing_target.csv")
X = df[["feature_1", "feature_2", "feature_3", "feature_4"]]
y = df["class"]
automl = AutoML(
# results_path="AutoML_100",
algorithms=[
"Linear",
# "Xgboost",
# "Random Forest"
],
model_time_limit=1,
tuning_mode="Normal",
)
automl.set_advanced(start_random_models=1)
automl.fit(X, y)
predictions = automl.predict(X)
print(predictions.head())
print(predictions.tail())