新しい！スプレッドシート用のシンプルな ML を使用して、Google スプレッドシートのデータに機械学習を適用します続きを読む

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デシジョンフォレストモデルとニューラルネットワークモデルの構成

序章

TensorFlowディシジョン森（TF-DF）のためのモデル構成のチュートリアルにようこそ。共通の前処理層と一緒に使用して、複数の判定森とニューラルネットワークモデルを構成する方法これは、ノートブックのショーあなたKeras機能APIを。

モデルを一緒に構成して、予測パフォーマンスを向上させたり（アンサンブル）、さまざまなモデリングテクノロジーを最大限に活用したり（異種モデルのアンサンブル）、モデルのさまざまな部分をさまざまなデータセットでトレーニングしたり（事前トレーニングなど）、スタックモデル（たとえば、モデルが別のモデルの予測に基づいて動作する）。

このチュートリアルでは、FunctionalAPIを使用したモデル構成の高度なユースケースについて説明します。あなたは、このセクションの「前処理機能」でモデル組成物の単純なシナリオの例を見つけることができるチュートリアルと、このセクションの「埋め込むpretrainedテキスト使用」のチュートリアルを。

作成するモデルの構造は次のとおりです。

!pip install graphviz -U --quiet

from graphviz import Source

Source("""
digraph G {
  raw_data [label="Input features"];
  preprocess_data [label="Learnable NN pre-processing", shape=rect];

  raw_data -> preprocess_data

  subgraph cluster_0 {
    color=grey;
    a1[label="NN layer", shape=rect];
    b1[label="NN layer", shape=rect];
    a1 -> b1;
    label = "Model #1";
  }

   subgraph cluster_1 {
    color=grey;
    a2[label="NN layer", shape=rect];
    b2[label="NN layer", shape=rect];
    a2 -> b2;
    label = "Model #2";
  }

  subgraph cluster_2 {
    color=grey;
    a3[label="Decision Forest", shape=rect];
    label = "Model #3";
  }

  subgraph cluster_3 {
    color=grey;
    a4[label="Decision Forest", shape=rect];
    label = "Model #4";
  }

  preprocess_data -> a1;
  preprocess_data -> a2;
  preprocess_data -> a3;
  preprocess_data -> a4;

  b1  -> aggr;
  b2  -> aggr;
  a3 -> aggr;
  a4 -> aggr;

  aggr [label="Aggregation (mean)", shape=rect]
  aggr -> predictions
}
""")

svg

構成されたモデルには3つの段階があります。

最初のステージは、ニューラルネットワークで構成され、次のステージのすべてのモデルに共通の前処理レイヤーです。実際には、そのような前処理層は、微調整するための事前に訓練された埋め込み、またはランダムに初期化されたニューラルネットワークのいずれかである可能性があります。
第2段階は、2つの決定フォレストと2つのニューラルネットワークモデルのアンサンブルです。
最終段階では、第2段階のモデルの予測を平均します。学習可能な重みは含まれていません。

ニューラルネットワークを使用して訓練されているバックプロパゲーションアルゴリズムと勾配降下を。このアルゴリズムには2つの重要な特性があります。（1）ニューラルネットワークの層は、損失勾配（より正確には、層の出力に応じた損失の勾配）を受信した場合にトレーニングでき、（2）アルゴリズムはレイヤーの出力からレイヤーの入力への損失勾配（これが「連鎖律」です）。これらの2つの理由により、バックプロパゲーションは、互いに積み重ねられたニューラルネットワークの複数の層を一緒にトレーニングできます。

この例では、意思決定の森林はで訓練されているランダムフォレスト（RF）アルゴリズム。バックプロパゲーションとは異なり、RFのトレーニングでは、損失勾配を出力から入力に「送信」しません。このため、従来のRFアルゴリズムを使用して、その下のニューラルネットワークをトレーニングまたは微調整することはできません。つまり、「決定フォレスト」ステージを使用して「学習可能なNN前処理ブロック」をトレーニングすることはできません。

前処理とニューラルネットワークの段階をトレーニングします。
決定フォレストステージをトレーニングします。

TensorFlow意思決定フォレストをインストールする

次のセルを実行してTF-DFをインストールします。

pip install tensorflow_decision_forests -U --quiet

インストールワーリッツァーを、詳細なトレーニングログを示すこと。これはノートブックでのみ必要です。

pip install wurlitzer -U --quiet

ライブラリをインポートする

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.

データセット

このチュートリアルでは、単純な合成データセットを使用して、最終モデルの解釈を容易にします。

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)

いくつかの例を生成します。

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]))

それらをプロットして、合成パターンのアイデアを得ることができます。

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

このパターンは滑らかで、軸が整列していないことに注意してください。これはニューラルネットワークモデルに有利になります。これは、決定木よりもニューラルネットワークの方が丸くて整列していない決定境界を持つ方が簡単だからです。

一方、2500の例を使用して、小さなデータセットでモデルをトレーニングします。これは、決定フォレストモデルに有利になります。これは、例から入手可能なすべての情報を使用して、意思決定フォレストがはるかに効率的であるためです（意思決定フォレストは「サンプル効率的」です）。

ニューラルネットワークと意思決定フォレストのアンサンブルは、両方の長所を使用します。

のは、電車やテスト作成してみましょう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)

モデル構造

モデル構造を次のように定義します。

# 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)

モデルをトレーニングする前に、モデルをプロットして、最初の図と類似しているかどうかを確認できます。

from keras.utils.vis_utils import plot_model

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

png

モデルトレーニング

最初に、バックプロパゲーションアルゴリズムを使用して、前処理層と2つのニューラルネットワーク層をトレーニングします。

%%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>

前処理と2つのニューラルネットワークのみを使用した部分を評価してみましょう。

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

2つのDecisionForestコンポーネントを（次々に）トレーニングしてみましょう。

%%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>

そして、DecisionForestsを個別に評価してみましょう。

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

モデル構成全体を評価してみましょう。

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

最後に、ニューラルネットワーク層をもう少し微調整しましょう。 DFモデルはそれに依存しているため、事前にトレーニングされた埋め込みを微調整しないことに注意してください（後で再トレーニングする場合を除く）。

要約すると、次のようになります。

print(f"Accuracy (NN #1 and #2 only):\t{evaluation_nn_only['accuracy']:.6f}")
print(f"Accuracy (DF #3 only):\t\t{evaluation_df3_only['accuracy']:.6f}")
print(f"Accuracy (DF #4 only):\t\t{evaluation_df4_only['accuracy']:.6f}")
print("----------------------------------------")
print(f"Accuracy (2xNN and 2xDF):\t{evaluation_nn_and_df['accuracy']:.6f}")


def delta_percent(src_eval, key):
  src_acc = src_eval["accuracy"]
  final_acc = evaluation_nn_and_df["accuracy"]
  increase = final_acc - src_acc
  print(f"\t\t\t\t  {increase:+.6f} over {key}")


delta_percent(evaluation_nn_only, "NN #1 and #2 only")
delta_percent(evaluation_df3_only, "DF #3 only")
delta_percent(evaluation_df4_only, "DF #4 only")

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

ここでは、構成されたモデルのパフォーマンスが個々のパーツよりも優れていることがわかります。これが、アンサンブルが非常にうまく機能する理由です。

次は何ですか？

この例では、意思決定フォレストをニューラルネットワークと組み合わせる方法を説明しました。追加のステップは、ニューラルネットワークと意思決定フォレストを一緒にさらにトレーニングすることです。

さらに、わかりやすくするために、決定フォレストは前処理された入力のみを受け取りました。ただし、意思決定フォレストは一般的に優れており、生データを消費しています。モデルは、生の特徴を決定フォレストモデルにもフィードすることによって改善されます。

この例では、最終モデルは個々のモデルの予測の平均です。このソリューションは、すべてのモデルが同じものでより少ないパフォーマンスを発揮する場合にうまく機能します。ただし、サブモデルの1つが非常に優れている場合、それを他のモデルと集約すると、実際には有害になる可能性があります（または、その逆。たとえば、例の数を1kから減らして、ニューラルネットワークに大きな悪影響を与えることを確認してください。または有効SPARSE_OBLIQUE ）は、第2のランダムフォレストモデルで分割。