# coding: utf-8
from pathlib import Path

import pandas as pd

import lightgbm as lgb

if lgb.compat.MATPLOTLIB_INSTALLED:
    import matplotlib.pyplot as plt
else:
    raise ImportError("You need to install matplotlib and restart your session for plot_example.py.")

print("Loading data...")
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] / "regression"
df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t")
df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t")

y_train = df_train[0]
y_test = df_test[0]
X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)

# create dataset for lightgbm
lgb_train = lgb.Dataset(X_train, y_train)
lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)

# specify your configurations as a dict
params = {"num_leaves": 5, "metric": ("l1", "l2"), "verbose": 0}

evals_result = {}  # to record eval results for plotting

print("Starting training...")
# train
gbm = lgb.train(
    params,
    lgb_train,
    num_boost_round=100,
    valid_sets=[lgb_train, lgb_test],
    feature_name=[f"f{i + 1}" for i in range(X_train.shape[-1])],
    categorical_feature=[21],
    callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)],
)

print("Plotting metrics recorded during training...")
ax = lgb.plot_metric(evals_result, metric="l1")
plt.show()

print("Plotting feature importances...")
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()

print("Plotting split value histogram...")
ax = lgb.plot_split_value_histogram(gbm, feature="f26", bins="auto")
plt.show()

print("Plotting 54th tree...")  # one tree use categorical feature to split
ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=["split_gain"])
plt.show()

print("Plotting 54th tree with graphviz...")
graph = lgb.create_tree_digraph(gbm, tree_index=53, name="Tree54")
graph.render(view=True)

# coding: utf-8
from pathlib import Path

import pandas as pd

import lightgbm as lgb

if lgb.compat.MATPLOTLIB_INSTALLED:
    import matplotlib.pyplot as plt
else:
    raise ImportError('You need to install matplotlib and restart your session for plot_example.py.')

print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
df_train = pd.read_csv(str(regression_example_dir / 'regression.train'), header=None, sep='\t')
df_test = pd.read_csv(str(regression_example_dir / 'regression.test'), header=None, sep='\t')

y_train = df_train[0]
y_test = df_test[0]
X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)

# create dataset for lightgbm
lgb_train = lgb.Dataset(X_train, y_train)
lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)

# specify your configurations as a dict
params = {
    'num_leaves': 5,
    'metric': ('l1', 'l2'),
    'verbose': 0
}

evals_result = {}  # to record eval results for plotting

print('Starting training...')
# train
gbm = lgb.train(
    params,
    lgb_train,
    num_boost_round=100,
    valid_sets=[lgb_train, lgb_test],
    feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],
    categorical_feature=[21],
    callbacks=[
        lgb.log_evaluation(10),
        lgb.record_evaluation(evals_result)
    ]
)

print('Plotting metrics recorded during training...')
ax = lgb.plot_metric(evals_result, metric='l1')
plt.show()

print('Plotting feature importances...')
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()

print('Plotting split value histogram...')
ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
plt.show()

print('Plotting 54th tree...')  # one tree use categorical feature to split
ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=['split_gain'])
plt.show()

print('Plotting 54th tree with graphviz...')
graph = lgb.create_tree_digraph(gbm, tree_index=53, name='Tree54')
graph.render(view=True)
