""" Gradient Boosted Decision Tree (GBDT).

Implement a Gradient Boosted Decision tree with TensorFlow to classify
handwritten digit images. This example is using the MNIST database of
handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).

Links:
    [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
"""

from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier
from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner

# Ignore all GPUs (current TF GBDT does not support GPU).
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""

# Import MNIST data
# Set verbosity to display errors only (Remove this line for showing warnings)
tf.logging.set_verbosity(tf.logging.ERROR)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False,
                                  source_url='http://yann.lecun.com/exdb/mnist/')

# Parameters
batch_size = 4096 # The number of samples per batch
num_classes = 10 # The 10 digits
num_features = 784 # Each image is 28x28 pixels
max_steps = 10000

# GBDT Parameters
learning_rate = 0.1
l1_regul = 0.
l2_regul = 1.
examples_per_layer = 1000
num_trees = 10
max_depth = 16

# Fill GBDT parameters into the config proto
learner_config = gbdt_learner.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate
learner_config.regularization.l1 = l1_regul
learner_config.regularization.l2 = l2_regul / examples_per_layer
learner_config.constraints.max_tree_depth = max_depth
growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER
learner_config.growing_mode = growing_mode
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
learner_config.multi_class_strategy = (
    gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\

# Create a TensorFlor GBDT Estimator
gbdt_model = GradientBoostedDecisionTreeClassifier(
    model_dir=None, # No save directory specified
    learner_config=learner_config,
    n_classes=num_classes,
    examples_per_layer=examples_per_layer,
    num_trees=num_trees,
    center_bias=False,
    config=run_config)

# Display TF info logs
tf.logging.set_verbosity(tf.logging.INFO)

# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': mnist.train.images}, y=mnist.train.labels,
    batch_size=batch_size, num_epochs=None, shuffle=True)
# Train the Model
gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)

# Evaluate the Model
# Define the input function for evaluating
input_fn = tf.estimator.inputs.numpy_input_fn(
    x={'images': mnist.test.images}, y=mnist.test.labels,
    batch_size=batch_size, shuffle=False)
# Use the Estimator 'evaluate' method
e = gbdt_model.evaluate(input_fn=input_fn)

print("Testing Accuracy:", e['accuracy'])