if lgb.compat.MATPLOTLIB_INSTALLED:
import matplotlib.pyplot as plt
else:
raise ImportError("You need to install matplotlib for plot_demo.py")
# -------------------------
# data
# -------------------------
print("Loading data....")
train_path = os.path.join(data_path, "lgb_data/regression/regression.train")
test_path = os.path.join(data_path, "lgb_data/regression/regression.test")
X_train, y_train, \
X_test, y_test, \
lgb_train, lgb_test = get_lgb_train_test_data(
train_path,
test_path,
weight_paths = []
)
# -------------------------
# model parameters
# -------------------------
params = {
"num_leaves": 5,
"metric": ("l1", "l2"),
"verbose": 0,
}
# -------------------------
# model train
# -------------------------
# ------------------------------
# data
# ------------------------------
print("Loading data...")
train_path = os.path.join(data_path,
"lgb_data/binary_classification/binary.train")
test_path = os.path.join(data_path,
"lgb_data/binary_classification/binary.test")
weight_path = [
os.path.join(data_path,
"lgb_data/binary_classification/binary.train.weight"),
os.path.join(data_path,
"lgb_data/binary_classification/binary.train.weight")
]
W_train, W_test, X_train, y_train, X_test, y_test, lgb_train, lgb_eval = get_lgb_train_test_data(
train_path, test_path, weight_path)
num_train, num_feature = X_train.shape
feature_name = ["feature_" + str(col) for col in range(num_feature)]
print(f"W_train.head():\n {W_train.head()}")
print()
print(f"W.train.shape:\n {W_train.shape}")
print()
print(f"X_train.head():\n {X_train.head()}")
print()
print(f"X_train.shape:\n {X_train.shape}")
print()
print(f"num_train:\n {num_train}")
print()
print(f"num_feature:\n {num_feature}")
print()
print(f"feature_name:\n {feature_name}")