การสร้างแบบจำลองการตัดสินใจและโครงข่ายประสาทเทียม

ดูบน TensorFlow.org ทำงานใน Google Colab ดูบน GitHub ดาวน์โหลดโน๊ตบุ๊ค Keras Functional API

บทนำ

ยินดีต้อนรับสู่รูปแบบการกวดวิชาองค์ประกอบสำหรับป่า TensorFlow ตัดสินใจ (TF-DF) โน๊ตบุ๊คนี้แสดงให้เห็นถึงวิธีการแต่งป่าตัดสินใจหลาย ๆ และรูปแบบเครือข่ายประสาทด้วยกันโดยใช้ชั้น preprocessing ทั่วไปและ Keras ทำงาน API

คุณอาจต้องการรวมโมเดลเข้าด้วยกันเพื่อปรับปรุงประสิทธิภาพการคาดการณ์ (ensembling) เพื่อให้ได้เทคโนโลยีการสร้างแบบจำลองที่แตกต่างกันมากที่สุด (การรวมโมเดลที่แตกต่างกัน) เพื่อฝึกส่วนต่างๆ ของโมเดลในชุดข้อมูลต่างๆ (เช่น ก่อนการฝึกอบรม) หรือเพื่อสร้าง แบบจำลองแบบเรียงซ้อน (เช่น แบบจำลองดำเนินการตามการคาดการณ์ของแบบจำลองอื่น)

บทช่วยสอนนี้ครอบคลุมกรณีการใช้งานขั้นสูงขององค์ประกอบโมเดลโดยใช้ Functional API คุณสามารถค้นหาตัวอย่างสำหรับสถานการณ์ที่เรียบง่ายขององค์ประกอบของรูปแบบใน "คุณลักษณะ preprocessing" ส่วนนี้ของ การกวดวิชา และใน "การใช้ข้อความ pretrained ฝัง" ส่วนนี้ของ การกวดวิชา

นี่คือโครงสร้างของแบบจำลองที่คุณจะสร้าง:

svg

โมเดลที่คุณแต่งมีสามขั้นตอน:

  1. ขั้นตอนแรกคือเลเยอร์การประมวลผลล่วงหน้าที่ประกอบด้วยโครงข่ายประสาทเทียม และพบได้ทั่วไปในโมเดลทั้งหมดในระยะถัดไป ในทางปฏิบัติ เลเยอร์การประมวลผลล่วงหน้าดังกล่าวอาจเป็นการฝังแบบฝึกล่วงหน้าเพื่อปรับแต่ง หรืออาจเป็นโครงข่ายประสาทเทียมที่เตรียมข้อมูลเบื้องต้นแบบสุ่ม
  2. ขั้นตอนที่สองคือชุดของฟอเรสต์การตัดสินใจสองแบบและแบบจำลองโครงข่ายประสาทเทียมสองแบบ
  3. ขั้นตอนสุดท้ายจะเฉลี่ยการคาดการณ์ของแบบจำลองในขั้นตอนที่สอง ไม่มีน้ำหนักที่เรียนรู้ได้

เครือข่ายประสาทมีการฝึกอบรมโดยใช้ ขั้นตอนวิธีการแพร่กระจายย้อนกลับ และการไล่ระดับสีโคตร อัลกอริธึมนี้มีคุณสมบัติที่สำคัญสองประการ: (1) เลเยอร์ของโครงข่ายประสาทเทียมสามารถฝึกได้หากได้รับการไล่ระดับการสูญเสีย (แม่นยำกว่านั้น การไล่ระดับสีของการสูญเสียตามผลลัพธ์ของเลเยอร์) และ (2) อัลกอริธึม "ส่ง" การไล่ระดับการสูญเสียจากเอาต์พุตของเลเยอร์ไปยังอินพุตของเลเยอร์ (นี่คือ "กฎลูกโซ่") ด้วยเหตุผลสองประการนี้ Backpropagation สามารถฝึกโครงข่ายประสาทเทียมหลายชั้นซ้อนกันซ้อนกันได้

ในตัวอย่างนี้ป่าการตัดสินใจที่ได้รับการฝึกฝนกับ สุ่มป่า (RF) อัลกอริทึม แตกต่างจาก Backpropagation การฝึกอบรม RF ไม่ได้ "ส่ง" การไล่ระดับการสูญเสียจากเอาต์พุตไปยังอินพุต ด้วยเหตุผลนี้ จึงไม่สามารถใช้อัลกอริธึม RF แบบคลาสสิกเพื่อฝึกหรือปรับแต่งโครงข่ายประสาทเทียมที่อยู่ด้านล่างได้ กล่าวอีกนัยหนึ่ง ไม่สามารถใช้ขั้นตอน "ฟอเรสต์การตัดสินใจ" เพื่อฝึก "บล็อกการประมวลผลล่วงหน้า NN ที่เรียนรู้ได้"

  1. ฝึกขั้นตอนก่อนการประมวลผลและโครงข่ายประสาทเทียม
  2. ฝึกขั้นตอนของป่าตัดสินใจ

ติดตั้ง TensorFlow Decision Forests

ติดตั้ง TF-DF โดยเรียกใช้เซลล์ต่อไปนี้

pip install tensorflow_decision_forests -U --quiet

ติดตั้ง Wurlitzer เพื่อแสดงรายละเอียดบันทึกการฝึกอบรม สิ่งนี้จำเป็นในโน้ตบุ๊กเท่านั้น

pip install wurlitzer -U --quiet

นำเข้าห้องสมุด

import tensorflow_decision_forests as tfdf

import os
import numpy as np
import pandas as pd
import tensorflow as tf
import math
import matplotlib.pyplot as plt

try:
  from wurlitzer import sys_pipes
except:
  from colabtools.googlelog import CaptureLog as sys_pipes

from IPython.core.magic import register_line_magic
from IPython.display import Javascript
WARNING:root:Failure to load the custom c++ tensorflow ops. This error is likely caused the version of TensorFlow and TensorFlow Decision Forests are not compatible.
WARNING:root:TF Parameter Server distributed training not available.

ชุดข้อมูล

คุณจะใช้ชุดข้อมูลสังเคราะห์อย่างง่ายในบทช่วยสอนนี้ เพื่อให้ง่ายต่อการตีความแบบจำลองสุดท้าย

def make_dataset(num_examples, num_features, seed=1234):
  np.random.seed(seed)
  features = np.random.uniform(-1, 1, size=(num_examples, num_features))
  noise = np.random.uniform(size=(num_examples))

  left_side = np.sqrt(
      np.sum(np.multiply(np.square(features[:, 0:2]), [1, 2]), axis=1))
  right_side = features[:, 2] * 0.7 + np.sin(
      features[:, 3] * 10) * 0.5 + noise * 0.0 + 0.5

  labels = left_side <= right_side
  return features, labels.astype(int)

สร้างตัวอย่างบางส่วน:

make_dataset(num_examples=5, num_features=4)
(array([[-0.6169611 ,  0.24421754, -0.12454452,  0.57071717],
        [ 0.55995162, -0.45481479, -0.44707149,  0.60374436],
        [ 0.91627871,  0.75186527, -0.28436546,  0.00199025],
        [ 0.36692587,  0.42540405, -0.25949849,  0.12239237],
        [ 0.00616633, -0.9724631 ,  0.54565324,  0.76528238]]),
 array([0, 0, 0, 1, 0]))

คุณยังสามารถพล็อตพวกมันเพื่อให้เข้าใจถึงรูปแบบสังเคราะห์:

plot_features, plot_label = make_dataset(num_examples=50000, num_features=4)

plt.rcParams["figure.figsize"] = [8, 8]
common_args = dict(c=plot_label, s=1.0, alpha=0.5)

plt.subplot(2, 2, 1)
plt.scatter(plot_features[:, 0], plot_features[:, 1], **common_args)

plt.subplot(2, 2, 2)
plt.scatter(plot_features[:, 1], plot_features[:, 2], **common_args)

plt.subplot(2, 2, 3)
plt.scatter(plot_features[:, 0], plot_features[:, 2], **common_args)

plt.subplot(2, 2, 4)
plt.scatter(plot_features[:, 0], plot_features[:, 3], **common_args)
<matplotlib.collections.PathCollection at 0x7f6b78d20e90>

png

โปรดทราบว่ารูปแบบนี้เรียบและไม่อยู่ในแนวแกน สิ่งนี้จะเป็นประโยชน์ต่อโมเดลโครงข่ายประสาทเทียม นี่เป็นเพราะว่าโครงข่ายประสาทเทียมง่ายกว่าโครงสร้างการตัดสินใจที่มีขอบเขตการตัดสินใจที่กลมและไม่สอดคล้องกัน

ในทางกลับกัน เราจะฝึกโมเดลในชุดข้อมูลขนาดเล็กที่มีตัวอย่าง 2,500 ตัวอย่าง สิ่งนี้จะเป็นประโยชน์ต่อโมเดลการตัดสินใจของฟอเรสต์ เนื่องจากฟอเรสต์การตัดสินใจมีประสิทธิภาพมากกว่ามาก โดยใช้ข้อมูลที่มีอยู่ทั้งหมดจากตัวอย่าง (ฟอเรสต์การตัดสินใจคือ "ตัวอย่างที่มีประสิทธิภาพ")

โครงข่ายประสาทเทียมและป่าการตัดสินใจทั้งหมดของเราจะใช้สิ่งที่ดีที่สุดของทั้งสองโลก

ขอสร้างรถไฟและการทดสอบ tf.data.Dataset :

def make_tf_dataset(batch_size=64, **args):
  features, labels = make_dataset(**args)
  return tf.data.Dataset.from_tensor_slices(
      (features, labels)).batch(batch_size)


num_features = 10

train_dataset = make_tf_dataset(
    num_examples=2500, num_features=num_features, batch_size=64, seed=1234)
test_dataset = make_tf_dataset(
    num_examples=10000, num_features=num_features, batch_size=64, seed=5678)

โครงสร้างโมเดล

กำหนดโครงสร้างโมเดลดังนี้:

# Input features.
raw_features = tf.keras.layers.Input(shape=(num_features,))

# Stage 1
# =======

# Common learnable pre-processing
preprocessor = tf.keras.layers.Dense(10, activation=tf.nn.relu6)
preprocess_features = preprocessor(raw_features)

# Stage 2
# =======

# Model #1: NN
m1_z1 = tf.keras.layers.Dense(5, activation=tf.nn.relu6)(preprocess_features)
m1_pred = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(m1_z1)

# Model #2: NN
m2_z1 = tf.keras.layers.Dense(5, activation=tf.nn.relu6)(preprocess_features)
m2_pred = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(m2_z1)


def seed_advanced_argument(seed):
  """Create a seed argument for a TF-DF model.

  TODO(gbm): Surface the "seed" argument to the model constructor directly.
  """
  return tfdf.keras.AdvancedArguments(
      yggdrasil_training_config=tfdf.keras.core.YggdrasilTrainingConfig(
          random_seed=seed))


# Model #3: DF
model_3 = tfdf.keras.RandomForestModel(
    num_trees=1000, advanced_arguments=seed_advanced_argument(1234))
m3_pred = model_3(preprocess_features)

# Model #4: DF
model_4 = tfdf.keras.RandomForestModel(
    num_trees=1000,
    #split_axis="SPARSE_OBLIQUE", # Uncomment this line to increase the quality of this model
    advanced_arguments=seed_advanced_argument(4567))
m4_pred = model_4(preprocess_features)

# Since TF-DF uses deterministic learning algorithms, you should set the model's
# training seed to different values otherwise both
# `tfdf.keras.RandomForestModel` will be exactly the same.

# Stage 3
# =======

mean_nn_only = tf.reduce_mean(tf.stack([m1_pred, m2_pred], axis=0), axis=0)
mean_nn_and_df = tf.reduce_mean(
    tf.stack([m1_pred, m2_pred, m3_pred, m4_pred], axis=0), axis=0)

# Keras Models
# ============

ensemble_nn_only = tf.keras.models.Model(raw_features, mean_nn_only)
ensemble_nn_and_df = tf.keras.models.Model(raw_features, mean_nn_and_df)
WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f6ba21b62f0>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:absl:The model was called directly (i.e. using `model(data)` instead of using `model.predict(data)`) before being trained. The model will only return zeros until trained. The output shape might change after training Tensor("inputs:0", shape=(None, 10), dtype=float32)
WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f6ba21b62f0>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7f6ba21b62f0>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:absl:The model was called directly (i.e. using `model(data)` instead of using `model.predict(data)`) before being trained. The model will only return zeros until trained. The output shape might change after training Tensor("inputs:0", shape=(None, 10), dtype=float32)

ก่อนที่คุณจะฝึกโมเดล คุณสามารถพล็อตโมเดลเพื่อตรวจสอบว่าคล้ายกับไดอะแกรมเริ่มต้นหรือไม่

from keras.utils.vis_utils import plot_model

plot_model(ensemble_nn_and_df, to_file="/tmp/model.png", show_shapes=True)

png

การฝึกโมเดล

ขั้นแรก ฝึกการประมวลผลล่วงหน้าและเลเยอร์เครือข่ายประสาทสองชั้นโดยใช้อัลกอริธึมการแพร่กระจายกลับ

%%time
ensemble_nn_only.compile(
        optimizer=tf.keras.optimizers.Adam(),
        loss=tf.keras.losses.BinaryCrossentropy(),
        metrics=["accuracy"])

ensemble_nn_only.fit(train_dataset, epochs=20, validation_data=test_dataset)
Epoch 1/20
40/40 [==============================] - 1s 13ms/step - loss: 0.6115 - accuracy: 0.7308 - val_loss: 0.5857 - val_accuracy: 0.7407
Epoch 2/20
40/40 [==============================] - 0s 9ms/step - loss: 0.5645 - accuracy: 0.7484 - val_loss: 0.5487 - val_accuracy: 0.7391
Epoch 3/20
40/40 [==============================] - 0s 9ms/step - loss: 0.5310 - accuracy: 0.7496 - val_loss: 0.5237 - val_accuracy: 0.7392
Epoch 4/20
40/40 [==============================] - 0s 9ms/step - loss: 0.5074 - accuracy: 0.7500 - val_loss: 0.5055 - val_accuracy: 0.7391
Epoch 5/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4887 - accuracy: 0.7496 - val_loss: 0.4901 - val_accuracy: 0.7397
Epoch 6/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4725 - accuracy: 0.7520 - val_loss: 0.4763 - val_accuracy: 0.7440
Epoch 7/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4585 - accuracy: 0.7584 - val_loss: 0.4644 - val_accuracy: 0.7542
Epoch 8/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4470 - accuracy: 0.7700 - val_loss: 0.4544 - val_accuracy: 0.7682
Epoch 9/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4374 - accuracy: 0.7804 - val_loss: 0.4462 - val_accuracy: 0.7789
Epoch 10/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4297 - accuracy: 0.7848 - val_loss: 0.4395 - val_accuracy: 0.7865
Epoch 11/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4232 - accuracy: 0.7904 - val_loss: 0.4339 - val_accuracy: 0.7933
Epoch 12/20
40/40 [==============================] - 0s 10ms/step - loss: 0.4176 - accuracy: 0.7952 - val_loss: 0.4289 - val_accuracy: 0.7963
Epoch 13/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4126 - accuracy: 0.7992 - val_loss: 0.4243 - val_accuracy: 0.8010
Epoch 14/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4078 - accuracy: 0.8052 - val_loss: 0.4199 - val_accuracy: 0.8033
Epoch 15/20
40/40 [==============================] - 0s 9ms/step - loss: 0.4029 - accuracy: 0.8096 - val_loss: 0.4155 - val_accuracy: 0.8067
Epoch 16/20
40/40 [==============================] - 0s 9ms/step - loss: 0.3981 - accuracy: 0.8132 - val_loss: 0.4109 - val_accuracy: 0.8099
Epoch 17/20
40/40 [==============================] - 0s 9ms/step - loss: 0.3932 - accuracy: 0.8152 - val_loss: 0.4061 - val_accuracy: 0.8129
Epoch 18/20
40/40 [==============================] - 0s 9ms/step - loss: 0.3883 - accuracy: 0.8208 - val_loss: 0.4012 - val_accuracy: 0.8149
Epoch 19/20
40/40 [==============================] - 0s 9ms/step - loss: 0.3832 - accuracy: 0.8232 - val_loss: 0.3963 - val_accuracy: 0.8168
Epoch 20/20
40/40 [==============================] - 0s 10ms/step - loss: 0.3783 - accuracy: 0.8276 - val_loss: 0.3912 - val_accuracy: 0.8203
CPU times: user 12.1 s, sys: 2.14 s, total: 14.2 s
Wall time: 8.54 s
<keras.callbacks.History at 0x7f6b181d7450>

มาประเมินกันก่อนการประมวลผลและส่วนที่มีสองโครงข่ายประสาทเทียมเท่านั้น:

evaluation_nn_only = ensemble_nn_only.evaluate(test_dataset, return_dict=True)
print("Accuracy (NN #1 and #2 only): ", evaluation_nn_only["accuracy"])
print("Loss (NN #1 and #2 only): ", evaluation_nn_only["loss"])
157/157 [==============================] - 0s 2ms/step - loss: 0.3912 - accuracy: 0.8203
Accuracy (NN #1 and #2 only):  0.8202999830245972
Loss (NN #1 and #2 only):  0.39124569296836853

มาฝึกองค์ประกอบ Decision Forest ทั้งสองแบบกันเถอะ

%%time
train_dataset_with_preprocessing = train_dataset.map(lambda x,y: (preprocessor(x), y))
test_dataset_with_preprocessing = test_dataset.map(lambda x,y: (preprocessor(x), y))

model_3.fit(train_dataset_with_preprocessing)
model_4.fit(train_dataset_with_preprocessing)
WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7f6b86bc3dd0> and will run it as-is.
Cause: could not parse the source code of <function <lambda> at 0x7f6b86bc3dd0>: no matching AST found among candidates:

To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7f6b86bc3dd0> and will run it as-is.
Cause: could not parse the source code of <function <lambda> at 0x7f6b86bc3dd0>: no matching AST found among candidates:

To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <function <lambda> at 0x7f6b86bc3dd0> and will run it as-is.
Cause: could not parse the source code of <function <lambda> at 0x7f6b86bc3dd0>: no matching AST found among candidates:

To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7f6b783a9320> and will run it as-is.
Cause: could not parse the source code of <function <lambda> at 0x7f6b783a9320>: no matching AST found among candidates:

To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7f6b783a9320> and will run it as-is.
Cause: could not parse the source code of <function <lambda> at 0x7f6b783a9320>: no matching AST found among candidates:

To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <function <lambda> at 0x7f6b783a9320> and will run it as-is.
Cause: could not parse the source code of <function <lambda> at 0x7f6b783a9320>: no matching AST found among candidates:

To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
23/40 [================>.............] - ETA: 0s
[INFO kernel.cc:736] Start Yggdrasil model training
[INFO kernel.cc:737] Collect training examples
[INFO kernel.cc:392] Number of batches: 40
[INFO kernel.cc:393] Number of examples: 2500
[INFO kernel.cc:759] Dataset:
Number of records: 2500
Number of columns: 11

Number of columns by type:
    NUMERICAL: 10 (90.9091%)
    CATEGORICAL: 1 (9.09091%)

Columns:

NUMERICAL: 10 (90.9091%)
    0: "data:0.0" NUMERICAL mean:0.356465 min:0 max:2.37352 sd:0.451418
    1: "data:0.1" NUMERICAL mean:0.392088 min:0 max:2.3411 sd:0.470499
    2: "data:0.2" NUMERICAL mean:0.382386 min:0 max:2.11809 sd:0.483672
    3: "data:0.3" NUMERICAL mean:0.290395 min:0 max:2.27481 sd:0.400102
    4: "data:0.4" NUMERICAL mean:0.210684 min:0 max:1.35897 sd:0.281379
    5: "data:0.5" NUMERICAL mean:0.4008 min:0 max:2.06561 sd:0.453018
    6: "data:0.6" NUMERICAL mean:0.289166 min:0 max:2.0263 sd:0.407337
    7: "data:0.7" NUMERICAL mean:0.277971 min:0 max:1.77561 sd:0.363215
    8: "data:0.8" NUMERICAL mean:0.41254 min:0 max:2.79804 sd:0.553333
    9: "data:0.9" NUMERICAL mean:0.197082 min:0 max:1.60773 sd:0.298194

CATEGORICAL: 1 (9.09091%)
    10: "__LABEL" CATEGORICAL integerized vocab-size:3 no-ood-item

Terminology:
    nas: Number of non-available (i.e. missing) values.
    ood: Out of dictionary.
    manually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.
    tokenized: The attribute value is obtained through tokenization.
    has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
    vocab-size: Number of unique values.

[INFO kernel.cc:762] Configure learner
[INFO kernel.cc:787] Training config:
learner: "RANDOM_FOREST"
features: "data:0\\.0"
features: "data:0\\.1"
features: "data:0\\.2"
features: "data:0\\.3"
features: "data:0\\.4"
features: "data:0\\.5"
features: "data:0\\.6"
features: "data:0\\.7"
features: "data:0\\.8"
features: "data:0\\.9"
label: "__LABEL"
task: CLASSIFICATION
random_seed: 1234
[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {
  num_trees: 1000
  decision_tree {
    max_depth: 16
    min_examples: 5
    in_split_min_examples_check: true
    missing_value_policy: GLOBAL_IMPUTATION
    allow_na_conditions: false
    categorical_set_greedy_forward {
      sampling: 0.1
      max_num_items: -1
      min_item_frequency: 1
    }
    growing_strategy_local {
    }
    categorical {
      cart {
      }
    }
    num_candidate_attributes_ratio: -1
    axis_aligned_split {
    }
    internal {
      sorting_strategy: PRESORTED
    }
  }
  winner_take_all_inference: true
  compute_oob_performances: true
  compute_oob_variable_importances: false
  adapt_bootstrap_size_ratio_for_maximum_training_duration: false
}

[INFO kernel.cc:790] Deployment config:
num_threads: 6

[INFO kernel.cc:817] Train model
[INFO random_forest.cc:315] Training random forest on 2500 example(s) and 10 feature(s).
[INFO random_forest.cc:628] Training of tree  1/1000 (tree index:1) done accuracy:0.781996 logloss:7.85767
[INFO random_forest.cc:628] Training of tree  11/1000 (tree index:8) done accuracy:0.79895 logloss:2.7263
[INFO random_forest.cc:628] Training of tree  21/1000 (tree index:20) done accuracy:0.8012 logloss:1.26831
[INFO random_forest.cc:628] Training of tree  31/1000 (tree index:30) done accuracy:0.8076 logloss:0.898323
[INFO random_forest.cc:628] Training of tree  41/1000 (tree index:37) done accuracy:0.8084 logloss:0.736323
[INFO random_forest.cc:628] Training of tree  51/1000 (tree index:51) done accuracy:0.8072 logloss:0.612984
[INFO random_forest.cc:628] Training of tree  61/1000 (tree index:63) done accuracy:0.8104 logloss:0.55782
[INFO random_forest.cc:628] Training of tree  71/1000 (tree index:69) done accuracy:0.81 logloss:0.544938
[INFO random_forest.cc:628] Training of tree  81/1000 (tree index:80) done accuracy:0.814 logloss:0.532167
[INFO random_forest.cc:628] Training of tree  91/1000 (tree index:89) done accuracy:0.8144 logloss:0.530892
[INFO random_forest.cc:628] Training of tree  101/1000 (tree index:100) done accuracy:0.814 logloss:0.516588
[INFO random_forest.cc:628] Training of tree  111/1000 (tree index:108) done accuracy:0.8128 logloss:0.490739
[INFO random_forest.cc:628] Training of tree  121/1000 (tree index:118) done accuracy:0.8124 logloss:0.490544
[INFO random_forest.cc:628] Training of tree  131/1000 (tree index:134) done accuracy:0.8112 logloss:0.451653
[INFO random_forest.cc:628] Training of tree  141/1000 (tree index:140) done accuracy:0.8136 logloss:0.437757
[INFO random_forest.cc:628] Training of tree  151/1000 (tree index:150) done accuracy:0.8144 logloss:0.424328
[INFO random_forest.cc:628] Training of tree  161/1000 (tree index:159) done accuracy:0.8132 logloss:0.42426
[INFO random_forest.cc:628] Training of tree  171/1000 (tree index:168) done accuracy:0.814 logloss:0.411061
[INFO random_forest.cc:628] Training of tree  181/1000 (tree index:184) done accuracy:0.8136 logloss:0.411324
[INFO random_forest.cc:628] Training of tree  191/1000 (tree index:190) done accuracy:0.8148 logloss:0.410002
[INFO random_forest.cc:628] Training of tree  201/1000 (tree index:200) done accuracy:0.8144 logloss:0.409526
[INFO random_forest.cc:628] Training of tree  211/1000 (tree index:208) done accuracy:0.814 logloss:0.40944
[INFO random_forest.cc:628] Training of tree  221/1000 (tree index:218) done accuracy:0.8152 logloss:0.409039
[INFO random_forest.cc:628] Training of tree  231/1000 (tree index:234) done accuracy:0.8144 logloss:0.409254
[INFO random_forest.cc:628] Training of tree  241/1000 (tree index:242) done accuracy:0.8144 logloss:0.40879
[INFO random_forest.cc:628] Training of tree  251/1000 (tree index:251) done accuracy:0.8152 logloss:0.395703
[INFO random_forest.cc:628] Training of tree  261/1000 (tree index:259) done accuracy:0.8168 logloss:0.395747
[INFO random_forest.cc:628] Training of tree  271/1000 (tree index:268) done accuracy:0.814 logloss:0.394959
[INFO random_forest.cc:628] Training of tree  281/1000 (tree index:283) done accuracy:0.8148 logloss:0.395202
[INFO random_forest.cc:628] Training of tree  291/1000 (tree index:292) done accuracy:0.8136 logloss:0.395536
[INFO random_forest.cc:628] Training of tree  301/1000 (tree index:300) done accuracy:0.8128 logloss:0.39472
[INFO random_forest.cc:628] Training of tree  311/1000 (tree index:308) done accuracy:0.8124 logloss:0.394763
[INFO random_forest.cc:628] Training of tree  321/1000 (tree index:318) done accuracy:0.8132 logloss:0.394732
[INFO random_forest.cc:628] Training of tree  331/1000 (tree index:334) done accuracy:0.8136 logloss:0.394822
[INFO random_forest.cc:628] Training of tree  341/1000 (tree index:343) done accuracy:0.812 logloss:0.395051
[INFO random_forest.cc:628] Training of tree  351/1000 (tree index:350) done accuracy:0.8132 logloss:0.39492
[INFO random_forest.cc:628] Training of tree  361/1000 (tree index:358) done accuracy:0.8132 logloss:0.395054
[INFO random_forest.cc:628] Training of tree  371/1000 (tree index:368) done accuracy:0.812 logloss:0.395588
[INFO random_forest.cc:628] Training of tree  381/1000 (tree index:384) done accuracy:0.8104 logloss:0.395576
[INFO random_forest.cc:628] Training of tree  391/1000 (tree index:390) done accuracy:0.8132 logloss:0.395713
[INFO random_forest.cc:628] Training of tree  401/1000 (tree index:400) done accuracy:0.8088 logloss:0.383693
[INFO random_forest.cc:628] Training of tree  411/1000 (tree index:408) done accuracy:0.8088 logloss:0.383575
[INFO random_forest.cc:628] Training of tree  421/1000 (tree index:417) done accuracy:0.8096 logloss:0.383934
[INFO random_forest.cc:628] Training of tree  431/1000 (tree index:434) done accuracy:0.81 logloss:0.384001
[INFO random_forest.cc:628] Training of tree  441/1000 (tree index:442) done accuracy:0.808 logloss:0.384118
[INFO random_forest.cc:628] Training of tree  451/1000 (tree index:450) done accuracy:0.8096 logloss:0.384076
[INFO random_forest.cc:628] Training of tree  461/1000 (tree index:458) done accuracy:0.8104 logloss:0.383208
[INFO random_forest.cc:628] Training of tree  471/1000 (tree index:468) done accuracy:0.812 logloss:0.383298
[INFO random_forest.cc:628] Training of tree  481/1000 (tree index:482) done accuracy:0.81 logloss:0.38358
[INFO random_forest.cc:628] Training of tree  491/1000 (tree index:492) done accuracy:0.812 logloss:0.383453
[INFO random_forest.cc:628] Training of tree  501/1000 (tree index:500) done accuracy:0.8128 logloss:0.38317
[INFO random_forest.cc:628] Training of tree  511/1000 (tree index:508) done accuracy:0.812 logloss:0.383369
[INFO random_forest.cc:628] Training of tree  521/1000 (tree index:518) done accuracy:0.8132 logloss:0.383461
[INFO random_forest.cc:628] Training of tree  531/1000 (tree index:532) done accuracy:0.8124 logloss:0.38342
[INFO random_forest.cc:628] Training of tree  541/1000 (tree index:542) done accuracy:0.8128 logloss:0.383376
[INFO random_forest.cc:628] Training of tree  551/1000 (tree index:550) done accuracy:0.8128 logloss:0.383663
[INFO random_forest.cc:628] Training of tree  561/1000 (tree index:558) done accuracy:0.812 logloss:0.383574
[INFO random_forest.cc:628] Training of tree  571/1000 (tree index:568) done accuracy:0.8116 logloss:0.383529
[INFO random_forest.cc:628] Training of tree  581/1000 (tree index:580) done accuracy:0.8128 logloss:0.383624
[INFO random_forest.cc:628] Training of tree  591/1000 (tree index:592) done accuracy:0.814 logloss:0.383599
[INFO random_forest.cc:628] Training of tree  601/1000 (tree index:601) done accuracy:0.8148 logloss:0.383524
[INFO random_forest.cc:628] Training of tree  611/1000 (tree index:608) done accuracy:0.8156 logloss:0.383555
[INFO random_forest.cc:628] Training of tree  621/1000 (tree index:619) done accuracy:0.8132 logloss:0.382847
[INFO random_forest.cc:628] Training of tree  631/1000 (tree index:632) done accuracy:0.8124 logloss:0.382872
[INFO random_forest.cc:628] Training of tree  641/1000 (tree index:641) done accuracy:0.8144 logloss:0.382728
[INFO random_forest.cc:628] Training of tree  651/1000 (tree index:648) done accuracy:0.8132 logloss:0.382554
[INFO random_forest.cc:628] Training of tree  661/1000 (tree index:658) done accuracy:0.8128 logloss:0.382705
[INFO random_forest.cc:628] Training of tree  671/1000 (tree index:670) done accuracy:0.8136 logloss:0.38288
[INFO random_forest.cc:628] Training of tree  681/1000 (tree index:682) done accuracy:0.8152 logloss:0.383007
[INFO random_forest.cc:628] Training of tree  691/1000 (tree index:690) done accuracy:0.8144 logloss:0.382971
[INFO random_forest.cc:628] Training of tree  701/1000 (tree index:698) done accuracy:0.8152 logloss:0.382869
[INFO random_forest.cc:628] Training of tree  711/1000 (tree index:708) done accuracy:0.8152 logloss:0.382792
[INFO random_forest.cc:628] Training of tree  721/1000 (tree index:722) done accuracy:0.8136 logloss:0.38274
[INFO random_forest.cc:628] Training of tree  731/1000 (tree index:732) done accuracy:0.8144 logloss:0.38268
[INFO random_forest.cc:628] Training of tree  741/1000 (tree index:740) done accuracy:0.814 logloss:0.382835
[INFO random_forest.cc:628] Training of tree  751/1000 (tree index:751) done accuracy:0.8152 logloss:0.38297
[INFO random_forest.cc:628] Training of tree  761/1000 (tree index:758) done accuracy:0.8152 logloss:0.382917
[INFO random_forest.cc:628] Training of tree  771/1000 (tree index:770) done accuracy:0.8156 logloss:0.370596
[INFO random_forest.cc:628] Training of tree  781/1000 (tree index:782) done accuracy:0.816 logloss:0.370687
[INFO random_forest.cc:628] Training of tree  791/1000 (tree index:789) done accuracy:0.8164 logloss:0.37068
[INFO random_forest.cc:628] Training of tree  801/1000 (tree index:798) done accuracy:0.8172 logloss:0.370535
[INFO random_forest.cc:628] Training of tree  811/1000 (tree index:809) done accuracy:0.816 logloss:0.370674
[INFO random_forest.cc:628] Training of tree  821/1000 (tree index:821) done accuracy:0.816 logloss:0.370929
[INFO random_forest.cc:628] Training of tree  831/1000 (tree index:829) done accuracy:0.8148 logloss:0.370904
[INFO random_forest.cc:628] Training of tree  841/1000 (tree index:841) done accuracy:0.8164 logloss:0.371016
[INFO random_forest.cc:628] Training of tree  851/1000 (tree index:849) done accuracy:0.8168 logloss:0.370914
[INFO random_forest.cc:628] Training of tree  861/1000 (tree index:860) done accuracy:0.8164 logloss:0.371043
[INFO random_forest.cc:628] Training of tree  871/1000 (tree index:871) done accuracy:0.8168 logloss:0.371094
[INFO random_forest.cc:628] Training of tree  881/1000 (tree index:878) done accuracy:0.8152 logloss:0.371054
[INFO random_forest.cc:628] Training of tree  891/1000 (tree index:888) done accuracy:0.8156 logloss:0.370908
[INFO random_forest.cc:628] Training of tree  901/1000 (tree index:900) done accuracy:0.8156 logloss:0.370831
[INFO random_forest.cc:628] Training of tree  911/1000 (tree index:910) done accuracy:0.8152 logloss:0.370775
[INFO random_forest.cc:628] Training of tree  921/1000 (tree index:922) done accuracy:0.814 logloss:0.370804
[INFO random_forest.cc:628] Training of tree  931/1000 (tree index:929) done accuracy:0.8148 logloss:0.370495
[INFO random_forest.cc:628] Training of tree  941/1000 (tree index:941) done accuracy:0.816 logloss:0.370443
[INFO random_forest.cc:628] Training of tree  951/1000 (tree index:948) done accuracy:0.8156 logloss:0.370486
[INFO random_forest.cc:628] Training of tree  961/1000 (tree index:960) done accuracy:0.8152 logloss:0.370519
[INFO random_forest.cc:628] Training of tree  971/1000 (tree index:971) done accuracy:0.8144 logloss:0.370543
[INFO random_forest.cc:628] Training of tree  981/1000 (tree index:983) done accuracy:0.8144 logloss:0.370629
[INFO random_forest.cc:628] Training of tree  991/1000 (tree index:991) done accuracy:0.814 logloss:0.370625
[INFO random_forest.cc:628] Training of tree  1000/1000 (tree index:998) done accuracy:0.8144 logloss:0.370667
[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.8144 logloss:0.370667
[INFO kernel.cc:828] Export model in log directory: /tmp/tmp9izglk4r
[INFO kernel.cc:836] Save model in resources
[INFO kernel.cc:988] Loading model from path
40/40 [==============================] - 6s 66ms/step
[INFO decision_forest.cc:590] Model loaded with 1000 root(s), 324508 node(s), and 10 input feature(s).
[INFO abstract_model.cc:993] Engine "RandomForestOptPred" built
[INFO kernel.cc:848] Use fast generic engine
24/40 [=================>............] - ETA: 0s
[INFO kernel.cc:736] Start Yggdrasil model training
[INFO kernel.cc:737] Collect training examples
[INFO kernel.cc:392] Number of batches: 40
[INFO kernel.cc:393] Number of examples: 2500
[INFO kernel.cc:759] Dataset:
Number of records: 2500
Number of columns: 11

Number of columns by type:
    NUMERICAL: 10 (90.9091%)
    CATEGORICAL: 1 (9.09091%)

Columns:

NUMERICAL: 10 (90.9091%)
    0: "data:0.0" NUMERICAL mean:0.356465 min:0 max:2.37352 sd:0.451418
    1: "data:0.1" NUMERICAL mean:0.392088 min:0 max:2.3411 sd:0.470499
    2: "data:0.2" NUMERICAL mean:0.382386 min:0 max:2.11809 sd:0.483672
    3: "data:0.3" NUMERICAL mean:0.290395 min:0 max:2.27481 sd:0.400102
    4: "data:0.4" NUMERICAL mean:0.210684 min:0 max:1.35897 sd:0.281379
    5: "data:0.5" NUMERICAL mean:0.4008 min:0 max:2.06561 sd:0.453018
    6: "data:0.6" NUMERICAL mean:0.289166 min:0 max:2.0263 sd:0.407337
    7: "data:0.7" NUMERICAL mean:0.277971 min:0 max:1.77561 sd:0.363215
    8: "data:0.8" NUMERICAL mean:0.41254 min:0 max:2.79804 sd:0.553333
    9: "data:0.9" NUMERICAL mean:0.197082 min:0 max:1.60773 sd:0.298194

CATEGORICAL: 1 (9.09091%)
    10: "__LABEL" CATEGORICAL integerized vocab-size:3 no-ood-item

Terminology:
    nas: Number of non-available (i.e. missing) values.
    ood: Out of dictionary.
    manually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.
    tokenized: The attribute value is obtained through tokenization.
    has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
    vocab-size: Number of unique values.

[INFO kernel.cc:762] Configure learner
[INFO kernel.cc:787] Training config:
learner: "RANDOM_FOREST"
features: "data:0\\.0"
features: "data:0\\.1"
features: "data:0\\.2"
features: "data:0\\.3"
features: "data:0\\.4"
features: "data:0\\.5"
features: "data:0\\.6"
features: "data:0\\.7"
features: "data:0\\.8"
features: "data:0\\.9"
label: "__LABEL"
task: CLASSIFICATION
random_seed: 4567
[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {
  num_trees: 1000
  decision_tree {
    max_depth: 16
    min_examples: 5
    in_split_min_examples_check: true
    missing_value_policy: GLOBAL_IMPUTATION
    allow_na_conditions: false
    categorical_set_greedy_forward {
      sampling: 0.1
      max_num_items: -1
      min_item_frequency: 1
    }
    growing_strategy_local {
    }
    categorical {
      cart {
      }
    }
    num_candidate_attributes_ratio: -1
    axis_aligned_split {
    }
    internal {
      sorting_strategy: PRESORTED
    }
  }
  winner_take_all_inference: true
  compute_oob_performances: true
  compute_oob_variable_importances: false
  adapt_bootstrap_size_ratio_for_maximum_training_duration: false
}

[INFO kernel.cc:790] Deployment config:
num_threads: 6

[INFO kernel.cc:817] Train model
[INFO random_forest.cc:315] Training random forest on 2500 example(s) and 10 feature(s).
[INFO random_forest.cc:628] Training of tree  1/1000 (tree index:1) done accuracy:0.783262 logloss:7.81204
[INFO random_forest.cc:628] Training of tree  11/1000 (tree index:9) done accuracy:0.801127 logloss:2.73187
[INFO random_forest.cc:628] Training of tree  21/1000 (tree index:19) done accuracy:0.811449 logloss:1.1286
[INFO random_forest.cc:628] Training of tree  31/1000 (tree index:32) done accuracy:0.8132 logloss:0.910787
[INFO random_forest.cc:628] Training of tree  41/1000 (tree index:42) done accuracy:0.812 logloss:0.745694
[INFO random_forest.cc:628] Training of tree  51/1000 (tree index:48) done accuracy:0.8144 logloss:0.690226
[INFO random_forest.cc:628] Training of tree  61/1000 (tree index:59) done accuracy:0.8136 logloss:0.659137
[INFO random_forest.cc:628] Training of tree  71/1000 (tree index:72) done accuracy:0.8176 logloss:0.577357
[INFO random_forest.cc:628] Training of tree  81/1000 (tree index:79) done accuracy:0.814 logloss:0.565115
[INFO random_forest.cc:628] Training of tree  91/1000 (tree index:91) done accuracy:0.8156 logloss:0.56459
[INFO random_forest.cc:628] Training of tree  101/1000 (tree index:99) done accuracy:0.8148 logloss:0.564104
[INFO random_forest.cc:628] Training of tree  111/1000 (tree index:109) done accuracy:0.8172 logloss:0.537417
[INFO random_forest.cc:628] Training of tree  121/1000 (tree index:120) done accuracy:0.8156 logloss:0.524543
[INFO random_forest.cc:628] Training of tree  131/1000 (tree index:132) done accuracy:0.8152 logloss:0.511111
[INFO random_forest.cc:628] Training of tree  141/1000 (tree index:141) done accuracy:0.816 logloss:0.498209
[INFO random_forest.cc:628] Training of tree  151/1000 (tree index:150) done accuracy:0.8192 logloss:0.485477
[INFO random_forest.cc:628] Training of tree  161/1000 (tree index:160) done accuracy:0.8196 logloss:0.472341
[INFO random_forest.cc:628] Training of tree  171/1000 (tree index:171) done accuracy:0.818 logloss:0.459903
[INFO random_forest.cc:628] Training of tree  181/1000 (tree index:182) done accuracy:0.8172 logloss:0.459812
[INFO random_forest.cc:628] Training of tree  191/1000 (tree index:190) done accuracy:0.8192 logloss:0.459588
[INFO random_forest.cc:628] Training of tree  201/1000 (tree index:199) done accuracy:0.818 logloss:0.459855
[INFO random_forest.cc:628] Training of tree  211/1000 (tree index:209) done accuracy:0.8176 logloss:0.459088
[INFO random_forest.cc:628] Training of tree  221/1000 (tree index:221) done accuracy:0.8168 logloss:0.43377
[INFO random_forest.cc:628] Training of tree  231/1000 (tree index:233) done accuracy:0.8196 logloss:0.433567
[INFO random_forest.cc:628] Training of tree  241/1000 (tree index:241) done accuracy:0.8208 logloss:0.434371
[INFO random_forest.cc:628] Training of tree  251/1000 (tree index:250) done accuracy:0.8192 logloss:0.434301
[INFO random_forest.cc:628] Training of tree  261/1000 (tree index:260) done accuracy:0.8172 logloss:0.43402
[INFO random_forest.cc:628] Training of tree  271/1000 (tree index:271) done accuracy:0.818 logloss:0.433583
[INFO random_forest.cc:628] Training of tree  281/1000 (tree index:283) done accuracy:0.8184 logloss:0.420657
[INFO random_forest.cc:628] Training of tree  291/1000 (tree index:291) done accuracy:0.8168 logloss:0.420481
[INFO random_forest.cc:628] Training of tree  301/1000 (tree index:299) done accuracy:0.82 logloss:0.419901
[INFO random_forest.cc:628] Training of tree  311/1000 (tree index:312) done accuracy:0.8188 logloss:0.419881
[INFO random_forest.cc:628] Training of tree  321/1000 (tree index:319) done accuracy:0.8172 logloss:0.419582
[INFO random_forest.cc:628] Training of tree  331/1000 (tree index:332) done accuracy:0.8176 logloss:0.419608
[INFO random_forest.cc:628] Training of tree  341/1000 (tree index:341) done accuracy:0.816 logloss:0.419608
[INFO random_forest.cc:628] Training of tree  351/1000 (tree index:352) done accuracy:0.8152 logloss:0.419729
[INFO random_forest.cc:628] Training of tree  361/1000 (tree index:361) done accuracy:0.8152 logloss:0.419264
[INFO random_forest.cc:628] Training of tree  371/1000 (tree index:369) done accuracy:0.8148 logloss:0.418932
[INFO random_forest.cc:628] Training of tree  381/1000 (tree index:379) done accuracy:0.8156 logloss:0.419148
[INFO random_forest.cc:628] Training of tree  391/1000 (tree index:391) done accuracy:0.8164 logloss:0.419344
[INFO random_forest.cc:628] Training of tree  401/1000 (tree index:398) done accuracy:0.8156 logloss:0.419051
[INFO random_forest.cc:628] Training of tree  411/1000 (tree index:408) done accuracy:0.8168 logloss:0.406486
[INFO random_forest.cc:628] Training of tree  421/1000 (tree index:420) done accuracy:0.8168 logloss:0.406477
[INFO random_forest.cc:628] Training of tree  431/1000 (tree index:430) done accuracy:0.816 logloss:0.406362
[INFO random_forest.cc:628] Training of tree  441/1000 (tree index:440) done accuracy:0.8172 logloss:0.406377
[INFO random_forest.cc:628] Training of tree  451/1000 (tree index:448) done accuracy:0.8176 logloss:0.406083
[INFO random_forest.cc:628] Training of tree  461/1000 (tree index:458) done accuracy:0.8172 logloss:0.406205
[INFO random_forest.cc:628] Training of tree  471/1000 (tree index:474) done accuracy:0.8168 logloss:0.406437
[INFO random_forest.cc:628] Training of tree  481/1000 (tree index:482) done accuracy:0.8184 logloss:0.406287
[INFO random_forest.cc:628] Training of tree  491/1000 (tree index:490) done accuracy:0.8172 logloss:0.40588
[INFO random_forest.cc:628] Training of tree  501/1000 (tree index:498) done accuracy:0.816 logloss:0.406036
[INFO random_forest.cc:628] Training of tree  511/1000 (tree index:508) done accuracy:0.8164 logloss:0.406053
[INFO random_forest.cc:628] Training of tree  521/1000 (tree index:524) done accuracy:0.8168 logloss:0.405945
[INFO random_forest.cc:628] Training of tree  531/1000 (tree index:530) done accuracy:0.816 logloss:0.405778
[INFO random_forest.cc:628] Training of tree  541/1000 (tree index:540) done accuracy:0.8156 logloss:0.405737
[INFO random_forest.cc:628] Training of tree  551/1000 (tree index:552) done accuracy:0.8156 logloss:0.406028
[INFO random_forest.cc:628] Training of tree  561/1000 (tree index:559) done accuracy:0.8164 logloss:0.406081
[INFO random_forest.cc:628] Training of tree  571/1000 (tree index:569) done accuracy:0.8152 logloss:0.405734
[INFO random_forest.cc:628] Training of tree  581/1000 (tree index:579) done accuracy:0.8172 logloss:0.393451
[INFO random_forest.cc:628] Training of tree  591/1000 (tree index:591) done accuracy:0.816 logloss:0.393428
[INFO random_forest.cc:628] Training of tree  601/1000 (tree index:603) done accuracy:0.8156 logloss:0.393545
[INFO random_forest.cc:628] Training of tree  611/1000 (tree index:609) done accuracy:0.8156 logloss:0.3934
[INFO random_forest.cc:628] Training of tree  621/1000 (tree index:620) done accuracy:0.8148 logloss:0.393539
[INFO random_forest.cc:628] Training of tree  631/1000 (tree index:629) done accuracy:0.8156 logloss:0.393731
[INFO random_forest.cc:628] Training of tree  641/1000 (tree index:641) done accuracy:0.8164 logloss:0.39383
[INFO random_forest.cc:628] Training of tree  651/1000 (tree index:649) done accuracy:0.8152 logloss:0.393724
[INFO random_forest.cc:628] Training of tree  661/1000 (tree index:659) done accuracy:0.8152 logloss:0.393764
[INFO random_forest.cc:628] Training of tree  671/1000 (tree index:670) done accuracy:0.816 logloss:0.393834
[INFO random_forest.cc:628] Training of tree  681/1000 (tree index:680) done accuracy:0.8156 logloss:0.393894
[INFO random_forest.cc:628] Training of tree  691/1000 (tree index:689) done accuracy:0.8152 logloss:0.393746
[INFO random_forest.cc:628] Training of tree  701/1000 (tree index:698) done accuracy:0.814 logloss:0.393743
[INFO random_forest.cc:628] Training of tree  711/1000 (tree index:708) done accuracy:0.8152 logloss:0.393294
[INFO random_forest.cc:628] Training of tree  721/1000 (tree index:721) done accuracy:0.816 logloss:0.393451
[INFO random_forest.cc:628] Training of tree  731/1000 (tree index:733) done accuracy:0.8164 logloss:0.393486
[INFO random_forest.cc:628] Training of tree  741/1000 (tree index:739) done accuracy:0.8156 logloss:0.393553
[INFO random_forest.cc:628] Training of tree  751/1000 (tree index:751) done accuracy:0.816 logloss:0.393731
[INFO random_forest.cc:628] Training of tree  761/1000 (tree index:758) done accuracy:0.8172 logloss:0.393635
[INFO random_forest.cc:628] Training of tree  771/1000 (tree index:769) done accuracy:0.8164 logloss:0.393584
[INFO random_forest.cc:628] Training of tree  781/1000 (tree index:779) done accuracy:0.8184 logloss:0.393728
[INFO random_forest.cc:628] Training of tree  791/1000 (tree index:789) done accuracy:0.8192 logloss:0.393858
[INFO random_forest.cc:628] Training of tree  801/1000 (tree index:800) done accuracy:0.8184 logloss:0.381756
[INFO random_forest.cc:628] Training of tree  811/1000 (tree index:813) done accuracy:0.82 logloss:0.38174
[INFO random_forest.cc:628] Training of tree  821/1000 (tree index:819) done accuracy:0.8196 logloss:0.381865
[INFO random_forest.cc:628] Training of tree  831/1000 (tree index:829) done accuracy:0.8172 logloss:0.381929
[INFO random_forest.cc:628] Training of tree  841/1000 (tree index:838) done accuracy:0.8164 logloss:0.382007
[INFO random_forest.cc:628] Training of tree  851/1000 (tree index:850) done accuracy:0.8172 logloss:0.382099
[INFO random_forest.cc:628] Training of tree  861/1000 (tree index:863) done accuracy:0.8172 logloss:0.381937
[INFO random_forest.cc:628] Training of tree  871/1000 (tree index:869) done accuracy:0.8168 logloss:0.382131
[INFO random_forest.cc:628] Training of tree  881/1000 (tree index:879) done accuracy:0.8188 logloss:0.381963
[INFO random_forest.cc:628] Training of tree  891/1000 (tree index:889) done accuracy:0.8192 logloss:0.382052
[INFO random_forest.cc:628] Training of tree  901/1000 (tree index:901) done accuracy:0.8184 logloss:0.382174
[INFO random_forest.cc:628] Training of tree  911/1000 (tree index:913) done accuracy:0.8192 logloss:0.382273
[INFO random_forest.cc:628] Training of tree  921/1000 (tree index:919) done accuracy:0.82 logloss:0.382407
[INFO random_forest.cc:628] Training of tree  931/1000 (tree index:929) done accuracy:0.8216 logloss:0.382277
[INFO random_forest.cc:628] Training of tree  941/1000 (tree index:939) done accuracy:0.8204 logloss:0.382434
[INFO random_forest.cc:628] Training of tree  951/1000 (tree index:951) done accuracy:0.8192 logloss:0.382444
[INFO random_forest.cc:628] Training of tree  961/1000 (tree index:959) done accuracy:0.8192 logloss:0.382497
[INFO random_forest.cc:628] Training of tree  971/1000 (tree index:969) done accuracy:0.8188 logloss:0.382592
[INFO random_forest.cc:628] Training of tree  981/1000 (tree index:979) done accuracy:0.8192 logloss:0.382657
[INFO random_forest.cc:628] Training of tree  991/1000 (tree index:989) done accuracy:0.8188 logloss:0.382671
[INFO random_forest.cc:628] Training of tree  1000/1000 (tree index:997) done accuracy:0.8192 logloss:0.38269
[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.8192 logloss:0.38269
[INFO kernel.cc:828] Export model in log directory: /tmp/tmp0r9hhl7d
[INFO kernel.cc:836] Save model in resources
[INFO kernel.cc:988] Loading model from path
40/40 [==============================] - 3s 64ms/step
[INFO decision_forest.cc:590] Model loaded with 1000 root(s), 324942 node(s), and 10 input feature(s).
[INFO kernel.cc:848] Use fast generic engine
CPU times: user 21.5 s, sys: 755 ms, total: 22.2 s
Wall time: 10.5 s
<keras.callbacks.History at 0x7f6b7874c4d0>

และมาประเมิน Decision Forests กันเป็นรายบุคคล

model_3.compile(["accuracy"])
model_4.compile(["accuracy"])

evaluation_df3_only = model_3.evaluate(
    test_dataset_with_preprocessing, return_dict=True)
evaluation_df4_only = model_4.evaluate(
    test_dataset_with_preprocessing, return_dict=True)

print("Accuracy (DF #3 only): ", evaluation_df3_only["accuracy"])
print("Accuracy (DF #4 only): ", evaluation_df4_only["accuracy"])
157/157 [==============================] - 2s 8ms/step - loss: 0.0000e+00 - accuracy: 0.8218
157/157 [==============================] - 1s 8ms/step - loss: 0.0000e+00 - accuracy: 0.8223
Accuracy (DF #3 only):  0.8217999935150146
Accuracy (DF #4 only):  0.8223000168800354

มาประเมินองค์ประกอบแบบจำลองทั้งหมดกัน:

ensemble_nn_and_df.compile(
    loss=tf.keras.losses.BinaryCrossentropy(), metrics=["accuracy"])

evaluation_nn_and_df = ensemble_nn_and_df.evaluate(
    test_dataset, return_dict=True)

print("Accuracy (2xNN and 2xDF): ", evaluation_nn_and_df["accuracy"])
print("Loss (2xNN and 2xDF): ", evaluation_nn_and_df["loss"])
157/157 [==============================] - 2s 8ms/step - loss: 0.3707 - accuracy: 0.8236
Accuracy (2xNN and 2xDF):  0.8235999941825867
Loss (2xNN and 2xDF):  0.3706760108470917

เสร็จสิ้น มาปรับแต่งเลเยอร์โครงข่ายประสาทเทียมเพิ่มเติมอีกหน่อย โปรดทราบว่าเราไม่ได้ปรับแต่งการฝังที่ฝึกไว้ล่วงหน้า เนื่องจากโมเดล DF นั้นขึ้นอยู่กับมัน (เว้นแต่เราจะฝึกพวกมันใหม่ภายหลังด้วย)

โดยสรุปคุณมี:

Accuracy (NN #1 and #2 only): 0.820300
Accuracy (DF #3 only):        0.821800
Accuracy (DF #4 only):        0.822300
----------------------------------------
Accuracy (2xNN and 2xDF): 0.823600
                  +0.003300 over NN #1 and #2 only
                  +0.001800 over DF #3 only
                  +0.001300 over DF #4 only

ที่นี่ คุณจะเห็นว่าแบบจำลองที่ประกอบขึ้นแล้วทำงานได้ดีกว่าชิ้นส่วนแต่ละชิ้น นี่คือเหตุผลที่ตระการตาทำงานได้ดี

อะไรต่อไป?

ในตัวอย่างนี้ คุณได้เห็นวิธีรวมฟอเรสต์การตัดสินใจเข้ากับโครงข่ายประสาทเทียม ขั้นตอนเพิ่มเติมคือการฝึกอบรมโครงข่ายประสาทเทียมและฟอเรสต์การตัดสินใจร่วมกัน

นอกจากนี้ เพื่อความชัดเจน ฟอเรสต์การตัดสินใจได้รับเฉพาะอินพุตที่ประมวลผลล่วงหน้าเท่านั้น อย่างไรก็ตาม โดยทั่วไปแล้ว ฟอเรสต์การตัดสินใจนั้นยอดเยี่ยมมากคือการบริโภคข้อมูลดิบ โมเดลจะได้รับการปรับปรุงโดยการป้อนคุณสมบัติดิบให้กับแบบจำลองฟอเรสต์การตัดสินใจ

ในตัวอย่างนี้ โมเดลสุดท้ายคือค่าเฉลี่ยของการทำนายของแต่ละโมเดล วิธีแก้ปัญหานี้ใช้ได้ดีหากโมเดลทั้งหมดทำงานน้อยลงโดยมีค่าเท่ากัน อย่างไรก็ตาม หากโมเดลย่อยตัวใดตัวหนึ่งดีมาก การรวมเข้ากับโมเดลอื่นๆ อาจเป็นผลเสียจริง (หรือในทางกลับกัน ตัวอย่างเช่น พยายามลดจำนวนตัวอย่างจาก 1k และดูว่ามันทำร้ายโครงข่ายประสาทเทียมมากอย่างไร หรือเปิดใช้ SPARSE_OBLIQUE แยกในสองรูปแบบการสุ่มต์)