텍스트 및 신경망 기능 사용

TensorFlow 의사 결정 숲의 중간 Colab (TF-DF)에 오신 것을 환영합니다. 이 colab에서는 자연 언어 기능을 처리하는 방법을 포함하여 TF-DF 좀 더 고급 기능에 대해 배우게됩니다.

이 colab는 개념이 제시된 당신이 잘 알고있는 가정 초급 colab을 특히 TF-DF에 대한 설치에 대해.

이 공동 작업에서는 다음을 수행합니다.

1. 기본적으로 범주 집합으로 텍스트 기능을 사용하는 랜덤 포레스트를 훈련합니다.

2. 소비하는 텍스트가 사용하는 기능을하는 임의의 숲 훈련 TensorFlow 허브 모듈을. 이 설정(전이 학습)에서 모듈은 이미 큰 텍스트 말뭉치에 대해 사전 학습되었습니다.

3. GBDT(Gradient Boosted Decision Trees)와 신경망을 함께 훈련시킵니다. GBDT는 신경망의 출력을 소비합니다.

설정

# Install TensorFlow Dececision Forests
pip install tensorflow_decision_forests

설치 월 리처를 . 자세한 훈련 로그를 표시하는 데 사용할 수 있습니다. 이것은 colab에서만 필요합니다.

pip install wurlitzer

필요한 라이브러리를 가져옵니다.

import tensorflow_decision_forests as tfdf

import os
import numpy as np
import pandas as pd
import tensorflow as tf
import math

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.

숨겨진 코드 셀은 colab에서 출력 높이를 제한합니다.

토글 코드

# Some of the model training logs can cover the full
# screen if not compressed to a smaller viewport.
# This magic allows setting a max height for a cell.
@register_line_magic
def set_cell_height(size):
  display(
      Javascript("google.colab.output.setIframeHeight(0, true, {maxHeight: " +
                 str(size) + "})"))

원시 텍스트를 기능으로 사용

TF-DF가 소비 할 수있는 범주 집합이 기본적으로 제공합니다. 범주 집합은 텍스트 기능을 단어 모음(또는 n-그램)으로 나타냅니다.

예를 들면 : "The little blue dog" → {"the", "little", "blue", "dog"}

이 예제에서는에 임의의 숲을 훈련 할 것이다 스탠포드 심리 Treebank (SST) 데이터 세트. 이 데이터 집합의 목적은 긍정적 또는 부정적 감정을 운반로 분류 문장이다. 당신의 큐레이터 데이터 세트의 이진 분류 버전을 사용할 수 있습니다 TensorFlow 데이터 집합을 .

주 : 범주가 설정 한 기능 훈련에 비용이 많이들 수 있습니다. 이 colab에서 우리는 20그루의 나무가 있는 작은 랜덤 포레스트를 훈련할 것입니다.

# Install the nighly TensorFlow Datasets package
# TODO: Remove when the release package is fixed.
pip install tfds-nightly -U --quiet

# Load the dataset
import tensorflow_datasets as tfds
all_ds = tfds.load("glue/sst2")

# Display the first 3 examples of the test fold.
for example in all_ds["test"].take(3):
  print({attr_name: attr_tensor.numpy() for attr_name, attr_tensor in example.items()})

{'idx': 163, 'label': -1, 'sentence': b'not even the hanson brothers can save it'}
{'idx': 131, 'label': -1, 'sentence': b'strong setup and ambitious goals fade as the film descends into unsophisticated scare tactics and b-film thuggery .'}
{'idx': 1579, 'label': -1, 'sentence': b'too timid to bring a sense of closure to an ugly chapter of the twentieth century .'}
2021-11-08 12:12:01.807072: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

데이터세트는 다음과 같이 수정됩니다.

1. 원시 레이블의 정수 {-1, 1} 하지만, 학습 알고리즘은 양의 정수 라벨 등을 기대 {0, 1} . 다음 따라서, 라벨은 변형되어 new_labels = (original_labels + 1) / 2 .
2. 데이터 세트를 보다 효율적으로 읽기 위해 배치 크기 64가 적용됩니다.
3. sentence 속성 요구 토큰 화되는, 즉 "hello world" -> ["hello", "world"] .

참고 :이 예에서는 사용하지 않는 test 으로 라벨을 가지고 있지 않는 한 데이터 세트의 분할. 경우 test 분할 라벨을했다, 당신은 연결할 수있는 validation 에 접어 train 하나 (예 : all_ds["train"].concatenate(all_ds["validation"]) ).

상세 사항 : 일부 결정 숲 학습 알고리즘 (일부의 경우 예를 들어 그라데이션 밀어주는 나무) 검증 데이터 세트 (예를 들어, 임의의 숲을) 다른 사람이 할 때 필요하지 않습니다. TF-DF의 각 학습 알고리즘은 유효성 검사 데이터를 다르게 사용할 수 있으므로 TF-DF는 내부적으로 훈련/검증 분할을 처리합니다. 결과적으로 훈련 및 검증 세트가 있는 경우 항상 학습 알고리즘에 대한 입력으로 연결될 수 있습니다.

def prepare_dataset(example):
  label = (example["label"] + 1) // 2
  return {"sentence" : tf.strings.split(example["sentence"])}, label

train_ds = all_ds["train"].batch(64).map(prepare_dataset)
test_ds = all_ds["validation"].batch(64).map(prepare_dataset)

마지막으로 평소와 같이 모델을 훈련하고 평가합니다. TF-DF는 다중 값 범주 기능을 범주 집합으로 자동 감지합니다.

%set_cell_height 300

# Specify the model.
model_1 = tfdf.keras.RandomForestModel(num_trees=30)

# Optionally, add evaluation metrics.
model_1.compile(metrics=["accuracy"])

# Train the model.
with sys_pipes():
  model_1.fit(x=train_ds)

<IPython.core.display.Javascript object>
1027/1053 [============================>.] - ETA: 0s
[INFO kernel.cc:736] Start Yggdrasil model training
[INFO kernel.cc:737] Collect training examples
[INFO kernel.cc:392] Number of batches: 1053
[INFO kernel.cc:393] Number of examples: 67349
[INFO data_spec_inference.cc:290] 12816 item(s) have been pruned (i.e. they are considered out of dictionary) for the column sentence (2000 item(s) left) because min_value_count=5 and max_number_of_unique_values=2000
[INFO kernel.cc:759] Dataset:
Number of records: 67349
Number of columns: 2

Number of columns by type:
    CATEGORICAL_SET: 1 (50%)
    CATEGORICAL: 1 (50%)

Columns:

CATEGORICAL_SET: 1 (50%)
    0: "sentence" CATEGORICAL_SET has-dict vocab-size:2001 num-oods:3595 (5.33787%) most-frequent:"the" 27205 (40.3941%)

CATEGORICAL: 1 (50%)
    1: "__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: "sentence"
label: "__LABEL"
task: CLASSIFICATION
[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {
  num_trees: 30
  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 67349 example(s) and 1 feature(s).
[INFO random_forest.cc:628] Training of tree  1/30 (tree index:1) done accuracy:0.7412 logloss:9.32811
[INFO random_forest.cc:628] Training of tree  4/30 (tree index:2) done accuracy:0.75669 logloss:5.54597
[INFO random_forest.cc:628] Training of tree  7/30 (tree index:7) done accuracy:0.779932 logloss:3.76263
[INFO random_forest.cc:628] Training of tree  9/30 (tree index:8) done accuracy:0.788283 logloss:3.14015
[INFO random_forest.cc:628] Training of tree  13/30 (tree index:13) done accuracy:0.803553 logloss:1.6681
[INFO random_forest.cc:628] Training of tree  15/30 (tree index:18) done accuracy:0.809139 logloss:1.48232
[INFO random_forest.cc:628] Training of tree  21/30 (tree index:20) done accuracy:0.817067 logloss:0.997885
[INFO random_forest.cc:628] Training of tree  23/30 (tree index:23) done accuracy:0.81845 logloss:0.944225
[INFO random_forest.cc:628] Training of tree  27/30 (tree index:26) done accuracy:0.821066 logloss:0.877389
[INFO random_forest.cc:628] Training of tree  29/30 (tree index:29) done accuracy:0.821571 logloss:0.861307
[INFO random_forest.cc:628] Training of tree  30/30 (tree index:28) done accuracy:0.821274 logloss:0.854486
[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.821274 logloss:0.854486
[INFO kernel.cc:828] Export model in log directory: /tmp/tmpab1ap3d5
[INFO kernel.cc:836] Save model in resources
[INFO kernel.cc:988] Loading model from path
[INFO decision_forest.cc:590] Model loaded with 30 root(s), 43180 node(s), and 1 input feature(s).
[INFO abstract_model.cc:993] Engine "RandomForestGeneric" built
[INFO kernel.cc:848] Use fast generic engine
1053/1053 [==============================] - 233s 217ms/step

이전 로그에 유의 sentence A는 CATEGORICAL_SET 기능입니다.

모델은 평소와 같이 평가됩니다.

evaluation = model_1.evaluate(test_ds)

print(f"BinaryCrossentropyloss: {evaluation[0]}")
print(f"Accuracy: {evaluation[1]}")

14/14 [==============================] - 1s 3ms/step - loss: 0.0000e+00 - accuracy: 0.7638
BinaryCrossentropyloss: 0.0
Accuracy: 0.7637614607810974

훈련 로그는 다음과 같습니다.

import matplotlib.pyplot as plt

logs = model_1.make_inspector().training_logs()
plt.plot([log.num_trees for log in logs], [log.evaluation.accuracy for log in logs])
plt.xlabel("Number of trees")
plt.ylabel("Out-of-bag accuracy")
pass

더 많은 나무가 도움이 될 것입니다 (시도했기 때문에 확신합니다 :p).

사전 훈련된 텍스트 임베딩 사용

이전 예제에서는 원시 텍스트 기능을 사용하여 랜덤 포레스트를 훈련했습니다. 이 예제는 사전 훈련된 TF-Hub 임베딩을 사용하여 텍스트 기능을 조밀한 임베딩으로 변환한 다음 그 위에 Random Forest를 훈련합니다. 이 상황에서 Random Forest는 임베딩의 숫자 출력만 "볼" 것입니다(즉, 원시 텍스트는 볼 수 없음).

이 실험에서 사용하는 범용 - 문장 - 인코더 . 다른 사전 훈련된 임베딩은 다른 유형의 텍스트(예: 다른 언어, 다른 작업)에 적합할 수 있지만 다른 유형의 구조화된 기능(예: 이미지)에도 적합할 수 있습니다.

참고 :이 삽입은 (고전 의사 결정 나무 추론에 비해) (1GB) 크고, 따라서 최종 모델은 실행 속도가 느린 것입니다.

임베딩 모듈은 다음 두 위치 중 하나에 적용할 수 있습니다.

1. 데이터세트 준비 중.
2. 모델의 전처리 단계에서.

두 번째 옵션이 선호되는 경우가 많습니다. 임베딩을 모델에 패키징하면 모델을 더 쉽게 사용할 수 있고 오용하기 더 어려워집니다.

먼저 TF-Hub를 설치합니다.

pip install --upgrade tensorflow-hub

이전과 달리 텍스트를 토큰화할 필요가 없습니다.

def prepare_dataset(example):
  label = (example["label"] + 1) // 2
  return {"sentence" : example["sentence"]}, label

train_ds = all_ds["train"].batch(64).map(prepare_dataset)
test_ds = all_ds["validation"].batch(64).map(prepare_dataset)

%set_cell_height 300

import tensorflow_hub as hub
# NNLM (https://tfhub.dev/google/nnlm-en-dim128/2) is also a good choice.
hub_url = "http://tfhub.dev/google/universal-sentence-encoder/4"
embedding = hub.KerasLayer(hub_url)

sentence = tf.keras.layers.Input(shape=(), name="sentence", dtype=tf.string)
embedded_sentence = embedding(sentence)

raw_inputs = {"sentence": sentence}
processed_inputs = {"embedded_sentence": embedded_sentence}
preprocessor = tf.keras.Model(inputs=raw_inputs, outputs=processed_inputs)

model_2 = tfdf.keras.RandomForestModel(
    preprocessing=preprocessor,
    num_trees=100)
model_2.compile(metrics=["accuracy"])

with sys_pipes():
  model_2.fit(x=train_ds)

<IPython.core.display.Javascript object>
1053/1053 [==============================] - ETA: 0s
[INFO kernel.cc:736] Start Yggdrasil model training
[INFO kernel.cc:737] Collect training examples
[INFO kernel.cc:392] Number of batches: 1053
[INFO kernel.cc:393] Number of examples: 67349
[INFO kernel.cc:759] Dataset:
Number of records: 67349
Number of columns: 513

Number of columns by type:
    NUMERICAL: 512 (99.8051%)
    CATEGORICAL: 1 (0.194932%)

Columns:

NUMERICAL: 512 (99.8051%)
    0: "embedded_sentence.0" NUMERICAL mean:-0.00405803 min:-0.110598 max:0.113378 sd:0.0382544
    1: "embedded_sentence.1" NUMERICAL mean:0.0020755 min:-0.120324 max:0.106003 sd:0.0434171
    2: "embedded_sentence.10" NUMERICAL mean:0.0143459 min:-0.1118 max:0.118193 sd:0.039633
    3: "embedded_sentence.100" NUMERICAL mean:0.003884 min:-0.104019 max:0.127238 sd:0.0431
    4: "embedded_sentence.101" NUMERICAL mean:-0.0132592 min:-0.133774 max:0.125128 sd:0.0465773
    5: "embedded_sentence.102" NUMERICAL mean:0.00732224 min:-0.114158 max:0.135181 sd:0.0462208
    6: "embedded_sentence.103" NUMERICAL mean:-0.00316622 min:-0.115661 max:0.110651 sd:0.0393422
    7: "embedded_sentence.104" NUMERICAL mean:-0.000406039 min:-0.115186 max:0.115727 sd:0.0404569
    8: "embedded_sentence.105" NUMERICAL mean:0.01286 min:-0.10478 max:0.116059 sd:0.0408527
    9: "embedded_sentence.106" NUMERICAL mean:-0.0200857 min:-0.112344 max:0.115696 sd:0.0348447
    10: "embedded_sentence.107" NUMERICAL mean:-0.000881199 min:-0.117538 max:0.128118 sd:0.0397207
    11: "embedded_sentence.108" NUMERICAL mean:-0.0153816 min:-0.119853 max:0.111478 sd:0.0408014
    12: "embedded_sentence.109" NUMERICAL mean:0.0226631 min:-0.115775 max:0.109245 sd:0.0344709
    13: "embedded_sentence.11" NUMERICAL mean:7.16192e-05 min:-0.10631 max:0.107239 sd:0.0399338
    14: "embedded_sentence.110" NUMERICAL mean:-0.0117186 min:-0.12628 max:0.0972872 sd:0.043443
    15: "embedded_sentence.111" NUMERICAL mean:-0.0195 min:-0.138677 max:0.111032 sd:0.0530712
    16: "embedded_sentence.112" NUMERICAL mean:-0.00883525 min:-0.125434 max:0.115491 sd:0.039556
    17: "embedded_sentence.113" NUMERICAL mean:-0.0004395 min:-0.106039 max:0.1141 sd:0.0441183
    18: "embedded_sentence.114" NUMERICAL mean:-0.00404027 min:-0.131798 max:0.106558 sd:0.040391
    19: "embedded_sentence.115" NUMERICAL mean:0.0164961 min:-0.137229 max:0.11088 sd:0.0396261
    20: "embedded_sentence.116" NUMERICAL mean:-0.0163338 min:-0.109692 max:0.115104 sd:0.0396108
    21: "embedded_sentence.117" NUMERICAL mean:-0.000866382 min:-0.111258 max:0.110021 sd:0.0413076
    22: "embedded_sentence.118" NUMERICAL mean:0.00925641 min:-0.117275 max:0.109073 sd:0.0392531
    23: "embedded_sentence.119" NUMERICAL mean:0.0111224 min:-0.108271 max:0.11018 sd:0.0438516
    24: "embedded_sentence.12" NUMERICAL mean:-0.0115011 min:-0.115238 max:0.115996 sd:0.039107
    25: "embedded_sentence.120" NUMERICAL mean:-0.0109583 min:-0.117243 max:0.113314 sd:0.03753
    26: "embedded_sentence.121" NUMERICAL mean:0.0143342 min:-0.109885 max:0.121471 sd:0.0401907
    27: "embedded_sentence.122" NUMERICAL mean:-0.00603129 min:-0.111126 max:0.106422 sd:0.0401383
    28: "embedded_sentence.123" NUMERICAL mean:-0.00175511 min:-0.115219 max:0.103571 sd:0.0388962
    29: "embedded_sentence.124" NUMERICAL mean:-0.0119755 min:-0.119062 max:0.122632 sd:0.0447561
    30: "embedded_sentence.125" NUMERICAL mean:0.00210507 min:-0.116783 max:0.125758 sd:0.0469827
    31: "embedded_sentence.126" NUMERICAL mean:-0.0166424 min:-0.109771 max:0.13027 sd:0.0399639
    32: "embedded_sentence.127" NUMERICAL mean:-0.0462275 min:-0.137916 max:0.106133 sd:0.0478679
    33: "embedded_sentence.128" NUMERICAL mean:0.0101449 min:-0.134851 max:0.118003 sd:0.0415072
    34: "embedded_sentence.129" NUMERICAL mean:0.0119622 min:-0.106398 max:0.122529 sd:0.047894
    35: "embedded_sentence.13" NUMERICAL mean:-0.0179365 min:-0.133052 max:0.120982 sd:0.0461472
    36: "embedded_sentence.130" NUMERICAL mean:-0.0109302 min:-0.127096 max:0.102555 sd:0.0407236
    37: "embedded_sentence.131" NUMERICAL mean:-2.30421e-05 min:-0.0958128 max:0.116109 sd:0.0393919
    38: "embedded_sentence.132" NUMERICAL mean:0.00622466 min:-0.118524 max:0.171935 sd:0.0435631
    39: "embedded_sentence.133" NUMERICAL mean:0.00537511 min:-0.0999398 max:0.143991 sd:0.0431652
    40: "embedded_sentence.134" NUMERICAL mean:0.0111946 min:-0.101547 max:0.105716 sd:0.0365295
    41: "embedded_sentence.135" NUMERICAL mean:-0.0123165 min:-0.118347 max:0.113619 sd:0.0422525
    42: "embedded_sentence.136" NUMERICAL mean:0.00882626 min:-0.118642 max:0.115052 sd:0.0393646
    43: "embedded_sentence.137" NUMERICAL mean:0.0106701 min:-0.108036 max:0.109746 sd:0.0405698
    44: "embedded_sentence.138" NUMERICAL mean:-0.0130655 min:-0.148064 max:0.118745 sd:0.047092
    45: "embedded_sentence.139" NUMERICAL mean:0.00256777 min:-0.108547 max:0.102547 sd:0.0388182
    46: "embedded_sentence.14" NUMERICAL mean:0.00090757 min:-0.124092 max:0.111964 sd:0.0393761
    47: "embedded_sentence.140" NUMERICAL mean:-0.00255201 min:-0.113298 max:0.120327 sd:0.0469564
    48: "embedded_sentence.141" NUMERICAL mean:-0.0123127 min:-0.124039 max:0.110528 sd:0.047218
    49: "embedded_sentence.142" NUMERICAL mean:0.00659571 min:-0.106909 max:0.126327 sd:0.0444828
    50: "embedded_sentence.143" NUMERICAL mean:0.00838607 min:-0.121819 max:0.108286 sd:0.0409403
    51: "embedded_sentence.144" NUMERICAL mean:-0.00504916 min:-0.117741 max:0.109832 sd:0.0402179
    52: "embedded_sentence.145" NUMERICAL mean:-0.0135 min:-0.112358 max:0.108238 sd:0.0393695
    53: "embedded_sentence.146" NUMERICAL mean:-0.00551706 min:-0.108132 max:0.103118 sd:0.0375181
    54: "embedded_sentence.147" NUMERICAL mean:0.00226707 min:-0.109358 max:0.117688 sd:0.0416268
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    56: "embedded_sentence.149" NUMERICAL mean:-0.0029158 min:-0.104327 max:0.10898 sd:0.0394245
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    60: "embedded_sentence.152" NUMERICAL mean:-0.0220037 min:-0.122602 max:0.105503 sd:0.0429071
    61: "embedded_sentence.153" NUMERICAL mean:-0.00103943 min:-0.109326 max:0.112115 sd:0.0413219
    62: "embedded_sentence.154" NUMERICAL mean:-0.010306 min:-0.106116 max:0.112624 sd:0.0392094
    63: "embedded_sentence.155" NUMERICAL mean:-0.0128503 min:-0.133511 max:0.129721 sd:0.0417087
    64: "embedded_sentence.156" NUMERICAL mean:-0.00796017 min:-0.10801 max:0.111555 sd:0.0401771
    65: "embedded_sentence.157" NUMERICAL mean:-0.0263644 min:-0.135057 max:0.131898 sd:0.0473006
    66: "embedded_sentence.158" NUMERICAL mean:0.0157188 min:-0.109795 max:0.13194 sd:0.0423631
    67: "embedded_sentence.159" NUMERICAL mean:0.00616692 min:-0.0996693 max:0.121898 sd:0.0405747
    68: "embedded_sentence.16" NUMERICAL mean:0.0122186 min:-0.132531 max:0.112023 sd:0.0412513
    69: "embedded_sentence.160" NUMERICAL mean:0.00140896 min:-0.125797 max:0.10415 sd:0.0422833
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    71: "embedded_sentence.162" NUMERICAL mean:0.0174977 min:-0.102559 max:0.117249 sd:0.0394065
    72: "embedded_sentence.163" NUMERICAL mean:-0.01559 min:-0.117529 max:0.132716 sd:0.0422287
    73: "embedded_sentence.164" NUMERICAL mean:0.0103332 min:-0.131635 max:0.117116 sd:0.0432647
    74: "embedded_sentence.165" NUMERICAL mean:0.0164754 min:-0.111395 max:0.106868 sd:0.03591
    75: "embedded_sentence.166" NUMERICAL mean:-0.0300909 min:-0.110079 max:0.138071 sd:0.0393771
    76: "embedded_sentence.167" NUMERICAL mean:-0.00284721 min:-0.113047 max:0.1113 sd:0.0402787
    77: "embedded_sentence.168" NUMERICAL mean:0.0128449 min:-0.123295 max:0.101678 sd:0.035443
    78: "embedded_sentence.169" NUMERICAL mean:-0.0018307 min:-0.113497 max:0.108755 sd:0.0385736
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    82: "embedded_sentence.172" NUMERICAL mean:-0.00305818 min:-0.108149 max:0.125287 sd:0.0416153
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    84: "embedded_sentence.174" NUMERICAL mean:-0.00547071 min:-0.106778 max:0.107318 sd:0.0412694
    85: "embedded_sentence.175" NUMERICAL mean:0.00303105 min:-0.114183 max:0.11671 sd:0.037753
    86: "embedded_sentence.176" NUMERICAL mean:0.0200632 min:-0.119154 max:0.12262 sd:0.0449386
    87: "embedded_sentence.177" NUMERICAL mean:0.00830421 min:-0.106867 max:0.108159 sd:0.04212
    88: "embedded_sentence.178" NUMERICAL mean:0.00879771 min:-0.119236 max:0.0975505 sd:0.0365596
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    92: "embedded_sentence.181" NUMERICAL mean:0.015665 min:-0.123784 max:0.117493 sd:0.0423638
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    99: "embedded_sentence.188" NUMERICAL mean:0.0375059 min:-0.107009 max:0.147423 sd:0.0457626
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    437: "embedded_sentence.492" NUMERICAL mean:-0.00805514 min:-0.112223 max:0.118394 sd:0.0409569
    438: "embedded_sentence.493" NUMERICAL mean:-0.00381922 min:-0.109779 max:0.113538 sd:0.0375221
    439: "embedded_sentence.494" NUMERICAL mean:0.0192517 min:-0.108658 max:0.118238 sd:0.0414103
    440: "embedded_sentence.495" NUMERICAL mean:-0.00252727 min:-0.118617 max:0.100404 sd:0.0398346
    441: "embedded_sentence.496" NUMERICAL mean:-0.000870086 min:-0.10941 max:0.119059 sd:0.043479
    442: "embedded_sentence.497" NUMERICAL mean:-0.00296294 min:-0.123757 max:0.109776 sd:0.0420959
    443: "embedded_sentence.498" NUMERICAL mean:0.0127804 min:-0.138546 max:0.154906 sd:0.0511673
    444: "embedded_sentence.499" NUMERICAL mean:-0.00481274 min:-0.104637 max:0.112387 sd:0.0419786
    445: "embedded_sentence.5" NUMERICAL mean:0.0120099 min:-0.120963 max:0.118971 sd:0.041685
    446: "embedded_sentence.50" NUMERICAL mean:0.0382225 min:-0.0980938 max:0.129267 sd:0.0373726
    447: "embedded_sentence.500" NUMERICAL mean:-0.012455 min:-0.109502 max:0.102241 sd:0.0402451
    448: "embedded_sentence.501" NUMERICAL mean:-0.0236005 min:-0.117228 max:0.124977 sd:0.0464432
    449: "embedded_sentence.502" NUMERICAL mean:0.00916425 min:-0.128705 max:0.110148 sd:0.0412428
    450: "embedded_sentence.503" NUMERICAL mean:-0.0099854 min:-0.179229 max:0.112813 sd:0.0666002
    451: "embedded_sentence.504" NUMERICAL mean:0.0140659 min:-0.124558 max:0.131239 sd:0.0459631
    452: "embedded_sentence.505" NUMERICAL mean:0.00529723 min:-0.119894 max:0.104362 sd:0.0399805
    453: "embedded_sentence.506" NUMERICAL mean:-0.00319069 min:-0.111178 max:0.108562 sd:0.040611
    454: "embedded_sentence.507" NUMERICAL mean:-0.00332249 min:-0.108088 max:0.118358 sd:0.0396039
    455: "embedded_sentence.508" NUMERICAL mean:-0.00396023 min:-0.11048 max:0.107852 sd:0.0375341
    456: "embedded_sentence.509" NUMERICAL mean:-0.00917504 min:-0.116661 max:0.100524 sd:0.0361387
    457: "embedded_sentence.51" NUMERICAL mean:-0.0244919 min:-0.143322 max:0.151466 sd:0.0569238
    458: "embedded_sentence.510" NUMERICAL mean:0.037723 min:-0.0965472 max:0.140981 sd:0.0479428
    459: "embedded_sentence.511" NUMERICAL mean:0.00788656 min:-0.116457 max:0.102988 sd:0.0402552
    460: "embedded_sentence.52" NUMERICAL mean:0.0137383 min:-0.119567 max:0.149818 sd:0.0480009
    461: "embedded_sentence.53" NUMERICAL mean:-0.00754001 min:-0.119613 max:0.139327 sd:0.0441231
    462: "embedded_sentence.54" NUMERICAL mean:-0.00119265 min:-0.117568 max:0.0984011 sd:0.0386896
    463: "embedded_sentence.55" NUMERICAL mean:-0.00382799 min:-0.113112 max:0.107257 sd:0.0435431
    464: "embedded_sentence.56" NUMERICAL mean:0.00818074 min:-0.145547 max:0.123275 sd:0.0429192
    465: "embedded_sentence.57" NUMERICAL mean:-0.00208038 min:-0.126433 max:0.101673 sd:0.0393041
    466: "embedded_sentence.58" NUMERICAL mean:0.00506083 min:-0.118728 max:0.13801 sd:0.0459501
    467: "embedded_sentence.59" NUMERICAL mean:-0.00110454 min:-0.111315 max:0.10866 sd:0.0384711
    468: "embedded_sentence.6" NUMERICAL mean:0.00266504 min:-0.107839 max:0.108908 sd:0.0381836
    469: "embedded_sentence.60" NUMERICAL mean:-0.00560149 min:-0.126673 max:0.142958 sd:0.0476651
    470: "embedded_sentence.61" NUMERICAL mean:-0.010492 min:-0.116135 max:0.117787 sd:0.0398593
    471: "embedded_sentence.62" NUMERICAL mean:-0.0196407 min:-0.143423 max:0.104133 sd:0.0483823
    472: "embedded_sentence.63" NUMERICAL mean:0.0072672 min:-0.134359 max:0.115527 sd:0.0442733
    473: "embedded_sentence.64" NUMERICAL mean:-0.00813338 min:-0.104328 max:0.11042 sd:0.0378631
    474: "embedded_sentence.65" NUMERICAL mean:0.0252276 min:-0.134246 max:0.126575 sd:0.0404105
    475: "embedded_sentence.66" NUMERICAL mean:0.0121496 min:-0.121565 max:0.115153 sd:0.0399014
    476: "embedded_sentence.67" NUMERICAL mean:0.000328628 min:-0.108976 max:0.10698 sd:0.0409231
    477: "embedded_sentence.68" NUMERICAL mean:0.0209823 min:-0.111598 max:0.12123 sd:0.0391018
    478: "embedded_sentence.69" NUMERICAL mean:0.00544792 min:-0.108988 max:0.126124 sd:0.0422695
    479: "embedded_sentence.7" NUMERICAL mean:-0.00274169 min:-0.104539 max:0.13168 sd:0.0381854
    480: "embedded_sentence.70" NUMERICAL mean:-0.000593016 min:-0.119492 max:0.113604 sd:0.0415354
    481: "embedded_sentence.71" NUMERICAL mean:-0.000604193 min:-0.128741 max:0.107355 sd:0.0426992
    482: "embedded_sentence.72" NUMERICAL mean:-0.00433507 min:-0.113435 max:0.102836 sd:0.0414469
    483: "embedded_sentence.73" NUMERICAL mean:-0.0101648 min:-0.10628 max:0.119432 sd:0.0400882
    484: "embedded_sentence.74" NUMERICAL mean:0.0132994 min:-0.123574 max:0.103854 sd:0.0381882
    485: "embedded_sentence.75" NUMERICAL mean:-0.00154112 min:-0.135068 max:0.106161 sd:0.0393081
    486: "embedded_sentence.76" NUMERICAL mean:-0.0107704 min:-0.106198 max:0.106547 sd:0.0380247
    487: "embedded_sentence.77" NUMERICAL mean:0.0151205 min:-0.0985188 max:0.107297 sd:0.0381537
    488: "embedded_sentence.78" NUMERICAL mean:0.00829679 min:-0.102936 max:0.116536 sd:0.0410818
    489: "embedded_sentence.79" NUMERICAL mean:0.00578581 min:-0.156252 max:0.125833 sd:0.0489822
    490: "embedded_sentence.8" NUMERICAL mean:0.0078143 min:-0.1422 max:0.125118 sd:0.0480273
    491: "embedded_sentence.80" NUMERICAL mean:-0.00466792 min:-0.10975 max:0.118669 sd:0.0422673
    492: "embedded_sentence.81" NUMERICAL mean:0.00499065 min:-0.0934409 max:0.115151 sd:0.0382445
    493: "embedded_sentence.82" NUMERICAL mean:-0.0120384 min:-0.115119 max:0.109741 sd:0.039712
    494: "embedded_sentence.83" NUMERICAL mean:-0.0116498 min:-0.107953 max:0.113206 sd:0.0408114
    495: "embedded_sentence.84" NUMERICAL mean:-0.0210408 min:-0.108707 max:0.0992159 sd:0.0386516
    496: "embedded_sentence.85" NUMERICAL mean:-0.00273396 min:-0.12944 max:0.12272 sd:0.0449487
    497: "embedded_sentence.86" NUMERICAL mean:0.00658216 min:-0.113506 max:0.112219 sd:0.039801
    498: "embedded_sentence.87" NUMERICAL mean:-0.00378743 min:-0.117676 max:0.109386 sd:0.0402421
    499: "embedded_sentence.88" NUMERICAL mean:-0.0205237 min:-0.107587 max:0.103141 sd:0.040405
    500: "embedded_sentence.89" NUMERICAL mean:-0.000411177 min:-0.119937 max:0.109877 sd:0.0421414
    501: "embedded_sentence.9" NUMERICAL mean:0.0295029 min:-0.128134 max:0.118291 sd:0.0394542
    502: "embedded_sentence.90" NUMERICAL mean:-0.00181531 min:-0.117795 max:0.106343 sd:0.0421115
    503: "embedded_sentence.91" NUMERICAL mean:-0.00550051 min:-0.127822 max:0.113907 sd:0.0399804
    504: "embedded_sentence.92" NUMERICAL mean:-0.00547455 min:-0.126723 max:0.119811 sd:0.0431932
    505: "embedded_sentence.93" NUMERICAL mean:0.014195 min:-0.105489 max:0.118567 sd:0.0413103
    506: "embedded_sentence.94" NUMERICAL mean:0.0188997 min:-0.104824 max:0.132286 sd:0.0497162
    507: "embedded_sentence.95" NUMERICAL mean:0.00497901 min:-0.108731 max:0.124192 sd:0.0414468
    508: "embedded_sentence.96" NUMERICAL mean:-0.0179242 min:-0.125507 max:0.10199 sd:0.0383211
    509: "embedded_sentence.97" NUMERICAL mean:0.00327183 min:-0.122499 max:0.123037 sd:0.0419092
    510: "embedded_sentence.98" NUMERICAL mean:0.0216785 min:-0.10081 max:0.116099 sd:0.0479454
    511: "embedded_sentence.99" NUMERICAL mean:0.019005 min:-0.125922 max:0.117505 sd:0.0429193

CATEGORICAL: 1 (0.194932%)
    512: "__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: "embedded_sentence\\.0"
features: "embedded_sentence\\.1"
features: "embedded_sentence\\.10"
features: "embedded_sentence\\.100"
features: "embedded_sentence\\.101"
features: "embedded_sentence\\.102"
features: "embedded_sentence\\.103"
features: "embedded_sentence\\.104"
features: "embedded_sentence\\.105"
features: "embedded_sentence\\.106"
features: "embedded_sentence\\.107"
features: "embedded_sentence\\.108"
features: "embedded_sentence\\.109"
features: "embedded_sentence\\.11"
features: "embedded_sentence\\.110"
features: "embedded_sentence\\.111"
features: "embedded_sentence\\.112"
features: "embedded_sentence\\.113"
features: "embedded_sentence\\.114"
features: "embedded_sentence\\.115"
features: "embedded_sentence\\.116"
features: "embedded_sentence\\.117"
features: "embedded_sentence\\.118"
features: "embedded_sentence\\.119"
features: "embedded_sentence\\.12"
features: "embedded_sentence\\.120"
features: "embedded_sentence\\.121"
features: "embedded_sentence\\.122"
features: "embedded_sentence\\.123"
features: "embedded_sentence\\.124"
features: "embedded_sentence\\.125"
features: "embedded_sentence\\.126"
features: "embedded_sentence\\.127"
features: "embedded_sentence\\.128"
features: "embedded_sentence\\.129"
features: "embedded_sentence\\.13"
features: "embedded_sentence\\.130"
features: "embedded_sentence\\.131"
features: "embedded_sentence\\.132"
features: "embedded_sentence\\.133"
features: "embedded_sentence\\.134"
features: "embedded_sentence\\.135"
features: "embedded_sentence\\.136"
features: "embedded_sentence\\.137"
features: "embedded_sentence\\.138"
features: "embedded_sentence\\.139"
features: "embedded_sentence\\.14"
features: "embedded_sentence\\.140"
features: "embedded_sentence\\.141"
features: "embedded_sentence\\.142"
features: "embedded_sentence\\.143"
features: "embedded_sentence\\.144"
features: "embedded_sentence\\.145"
features: "embedded_sentence\\.146"
features: "embedded_sentence\\.147"
features: "embedded_sentence\\.148"
features: "embedded_sentence\\.149"
features: "embedded_sentence\\.15"
features: "embedded_sentence\\.150"
features: "embedded_sentence\\.151"
features: "embedded_sentence\\.152"
features: "embedded_sentence\\.153"
features: "embedded_sentence\\.154"
features: "embedded_sentence\\.155"
features: "embedded_sentence\\.156"
features: "embedded_sentence\\.157"
features: "embedded_sentence\\.158"
features: "embedded_sentence\\.159"
features: "embedded_sentence\\.16"
features: "embedded_sentence\\.160"
features: "embedded_sentence\\.161"
features: "embedded_sentence\\.162"
features: "embedded_sentence\\.163"
features: "embedded_sentence\\.164"
features: "embedded_sentence\\.165"
features: "embedded_sentence\\.166"
features: "embedded_sentence\\.167"
features: "embedded_sentence\\.168"
features: "embedded_sentence\\.169"
features: "embedded_sentence\\.17"
features: "embedded_sentence\\.170"
features: "embedded_sentence\\.171"
features: "embedded_sentence\\.172"
features: "embedded_sentence\\.173"
features: "embedded_sentence\\.174"
features: "embedded_sentence\\.175"
features: "embedded_sentence\\.176"
features: "embedded_sentence\\.177"
features: "embedded_sentence\\.178"
features: "embedded_sentence\\.179"
features: "embedded_sentence\\.18"
features: "embedded_sentence\\.180"
features: "embedded_sentence\\.181"
features: "embedded_sentence\\.182"
features: "embedded_sentence\\.183"
features: "embedded_sentence\\.184"
features: "embedded_sentence\\.185"
features: "embedded_sentence\\.186"
features: "embedded_sentence\\.187"
features: "embedded_sentence\\.188"
features: "embedded_sentence\\.189"
features: "embedded_sentence\\.19"
features: "embedded_sentence\\.190"
features: "embedded_sentence\\.191"
features: "embedded_sentence\\.192"
features: "embedded_sentence\\.193"
features: "embedded_sentence\\.194"
features: "embedded_sentence\\.195"
features: "embedded_sentence\\.196"
features: "embedded_sentence\\.197"
features: "embedded_sentence\\.198"
features: "embedded_sentence\\.199"
features: "embedded_sentence\\.2"
features: "embedded_sentence\\.20"
features: "embedded_sentence\\.200"
features: "embedded_sentence\\.201"
features: "embedded_sentence\\.202"
features: "embedded_sentence\\.203"
features: "embedded_sentence\\.204"
features: "embedded_sentence\\.205"
features: "embedded_sentence\\.206"
features: "embedded_sentence\\.207"
features: "embedded_sentence\\.208"
features: "embedded_sentence\\.209"
features: "embedded_sentence\\.21"
features: "embedded_sentence\\.210"
features: "embedded_sentence\\.211"
features: "embedded_sentence\\.212"
features: "embedded_sentence\\.213"
features: "embedded_sentence\\.214"
features: "embedded_sentence\\.215"
features: "embedded_sentence\\.216"
features: "embedded_sentence\\.217"
features: "embedded_sentence\\.218"
features: "embedded_sentence\\.219"
features: "embedded_sentence\\.22"
features: "embedded_sentence\\.220"
features: "embedded_sentence\\.221"
features: "embedded_sentence\\.222"
features: "embedded_sentence\\.223"
features: "embedded_sentence\\.224"
features: "embedded_sentence\\.225"
features: "embedded_sentence\\.226"
features: "embedded_sentence\\.227"
features: "embedded_sentence\\.228"
features: "embedded_sentence\\.229"
features: "embedded_sentence\\.23"
features: "embedded_sentence\\.230"
features: "embedded_sentence\\.231"
features: "embedded_sentence\\.232"
features: "embedded_sentence\\.233"
features: "embedded_sentence\\.234"
features: "embedded_sentence\\.235"
features: "embedded_sentence\\.236"
features: "embedded_sentence\\.237"
features: "embedded_sentence\\.238"
features: "embedded_sentence\\.239"
features: "embedded_sentence\\.24"
features: "embedded_sentence\\.240"
features: "embedded_sentence\\.241"
features: "embedded_sentence\\.242"
features: "embedded_sentence\\.243"
features: "embedded_sentence\\.244"
features: "embedded_sentence\\.245"
features: "embedded_sentence\\.246"
features: "embedded_sentence\\.247"
features: "embedded_sentence\\.248"
features: "embedded_sentence\\.249"
features: "embedded_sentence\\.25"
features: "embedded_sentence\\.250"
features: "embedded_sentence\\.251"
features: "embedded_sentence\\.252"
features: "embedded_sentence\\.253"
features: "embedded_sentence\\.254"
features: "embedded_sentence\\.255"
features: "embedded_sentence\\.256"
features: "embedded_sentence\\.257"
features: "embedded_sentence\\.258"
features: "embedded_sentence\\.259"
features: "embedded_sentence\\.26"
features: "embedded_sentence\\.260"
features: "embedded_sentence\\.261"
features: "embedded_sentence\\.262"
features: "embedded_sentence\\.263"
features: "embedded_sentence\\.264"
features: "embedded_sentence\\.265"
features: "embedded_sentence\\.266"
features: "embedded_sentence\\.267"
features: "embedded_sentence\\.268"
features: "embedded_sentence\\.269"
features: "embedded_sentence\\.27"
features: "embedded_sentence\\.270"
features: "embedded_sentence\\.271"
features: "embedded_sentence\\.272"
features: "embedded_sentence\\.273"
features: "embedded_sentence\\.274"
features: "embedded_sentence\\.275"
features: "embedded_sentence\\.276"
features: "embedded_sentence\\.277"
features: "embedded_sentence\\.278"
features: "embedded_sentence\\.279"
features: "embedded_sentence\\.28"
features: "embedded_sentence\\.280"
features: "embedded_sentence\\.281"
features: "embedded_sentence\\.282"
features: "embedded_sentence\\.283"
features: "embedded_sentence\\.284"
features: "embedded_sentence\\.285"
features: "embedded_sentence\\.286"
features: "embedded_sentence\\.287"
features: "embedded_sentence\\.288"
features: "embedded_sentence\\.289"
features: "embedded_sentence\\.29"
features: "embedded_sentence\\.290"
features: "embedded_sentence\\.291"
features: "embedded_sentence\\.292"
features: "embedded_sentence\\.293"
features: "embedded_sentence\\.294"
features: "embedded_sentence\\.295"
features: "embedded_sentence\\.296"
features: "embedded_sentence\\.297"
features: "embedded_sentence\\.298"
features: "embedded_sentence\\.299"
features: "embedded_sentence\\.3"
features: "embedded_sentence\\.30"
features: "embedded_sentence\\.300"
features: "embedded_sentence\\.301"
features: "embedded_sentence\\.302"
features: "embedded_sentence\\.303"
features: "embedded_sentence\\.304"
features: "embedded_sentence\\.305"
features: "embedded_sentence\\.306"
features: "embedded_sentence\\.307"
features: "embedded_sentence\\.308"
features: "embedded_sentence\\.309"
features: "embedded_sentence\\.31"
features: "embedded_sentence\\.310"
features: "embedded_sentence\\.311"
features: "embedded_sentence\\.312"
features: "embedded_sentence\\.313"
features: "embedded_sentence\\.314"
features: "embedded_sentence\\.315"
features: "embedded_sentence\\.316"
features: "embedded_sentence\\.317"
features: "embedded_sentence\\.318"
features: "embedded_sentence\\.319"
features: "embedded_sentence\\.32"
features: "embedded_sentence\\.320"
features: "embedded_sentence\\.321"
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features: "embedded_sentence\\.326"
features: "embedded_sentence\\.327"
features: "embedded_sentence\\.328"
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features: "embedded_sentence\\.337"
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features: "embedded_sentence\\.4"
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features: "embedded_sentence\\.447"
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features: "embedded_sentence\\.449"
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features: "embedded_sentence\\.450"
features: "embedded_sentence\\.451"
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label: "__LABEL"
task: CLASSIFICATION
[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {
  num_trees: 100
  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 67349 example(s) and 512 feature(s).
[INFO random_forest.cc:628] Training of tree  1/100 (tree index:1) done accuracy:0.743339 logloss:9.25099
[INFO random_forest.cc:628] Training of tree  11/100 (tree index:10) done accuracy:0.788438 logloss:1.97592
[INFO random_forest.cc:628] Training of tree  21/100 (tree index:20) done accuracy:0.82798 logloss:0.687896
[INFO random_forest.cc:628] Training of tree  31/100 (tree index:28) done accuracy:0.8427 logloss:0.466909
[INFO random_forest.cc:628] Training of tree  41/100 (tree index:40) done accuracy:0.851327 logloss:0.403339
[INFO random_forest.cc:628] Training of tree  51/100 (tree index:53) done accuracy:0.856553 logloss:0.379845
[INFO random_forest.cc:628] Training of tree  61/100 (tree index:59) done accuracy:0.859998 logloss:0.369493
[INFO random_forest.cc:628] Training of tree  71/100 (tree index:69) done accuracy:0.862864 logloss:0.365896
[INFO random_forest.cc:628] Training of tree  81/100 (tree index:79) done accuracy:0.864556 logloss:0.363075
[INFO random_forest.cc:628] Training of tree  91/100 (tree index:91) done accuracy:0.865596 logloss:0.361243
[INFO random_forest.cc:628] Training of tree  100/100 (tree index:99) done accuracy:0.866991 logloss:0.360368
[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.866991 logloss:0.360368
[INFO kernel.cc:828] Export model in log directory: /tmp/tmpw2g04fbi
[INFO kernel.cc:836] Save model in resources
[INFO kernel.cc:988] Loading model from path
[INFO decision_forest.cc:590] Model loaded with 100 root(s), 561666 node(s), and 512 input feature(s).
[INFO abstract_model.cc:993] Engine "RandomForestOptPred" built
[INFO kernel.cc:848] Use fast generic engine
1053/1053 [==============================] - 75s 66ms/step

evaluation = model_2.evaluate(test_ds)

print(f"BinaryCrossentropyloss: {evaluation[0]}")
print(f"Accuracy: {evaluation[1]}")

14/14 [==============================] - 2s 16ms/step - loss: 0.0000e+00 - accuracy: 0.7821
BinaryCrossentropyloss: 0.0
Accuracy: 0.7821100950241089

범주형 집합은 조밀한 임베딩과 다르게 텍스트를 나타내므로 두 전략을 함께 사용하는 것이 유용할 수 있습니다.

의사 결정 트리와 신경망을 함께 훈련

이전 예제에서는 사전 훈련된 신경망(NN)을 사용하여 텍스트 기능을 랜덤 포레스트에 전달하기 전에 처리했습니다. 이 예제는 신경망과 랜덤 포레스트를 처음부터 훈련할 것입니다.

(TF-DF의 의사 결정 산림하지 백 전파 그라데이션을 이 지속적인 연구의 대상이지만 ). 따라서 훈련은 두 단계로 진행됩니다.

1. 신경망을 표준 분류 작업으로 훈련:

example → [Normalize] → [Neural Network*] → [classification head] → prediction
*: Training.

1. 신경망의 헤드(마지막 레이어와 소프트맥스)를 랜덤 포레스트로 교체합니다. 평소와 같이 랜덤 포레스트 훈련:

example → [Normalize] → [Neural Network] → [Random Forest*] → prediction
*: Training.

데이터세트 준비

이 예는 사용 팔머의 펭귄 데이터 집합을. 참고 항목 초급 colab 자세한 내용을.

먼저 원시 데이터를 다운로드합니다.

wget -q https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins.csv -O /tmp/penguins.csv

Pandas Dataframe에 데이터세트를 로드합니다.

dataset_df = pd.read_csv("/tmp/penguins.csv")

# Display the first 3 examples.
dataset_df.head(3)

훈련을 위해 데이터세트를 준비합니다.

label = "species"

# Replaces numerical NaN (representing missing values in Pandas Dataframe) with 0s.
# ...Neural Nets don't work well with numerical NaNs.
for col in dataset_df.columns:
  if dataset_df[col].dtype not in [str, object]:
    dataset_df[col] = dataset_df[col].fillna(0)

# Split the dataset into a training and testing dataset.

def split_dataset(dataset, test_ratio=0.30):
  """Splits a panda dataframe in two."""
  test_indices = np.random.rand(len(dataset)) < test_ratio
  return dataset[~test_indices], dataset[test_indices]

train_ds_pd, test_ds_pd = split_dataset(dataset_df)
print("{} examples in training, {} examples for testing.".format(
    len(train_ds_pd), len(test_ds_pd)))

# Convert the datasets into tensorflow datasets
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label)
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=label)

252 examples in training, 92 examples for testing.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_decision_forests/keras/core.py:1612: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  features_dataframe = dataframe.drop(label, 1)

모델 구축

다음은 사용 신경망 모델을 만들 Keras '기능 스타일을 .

예제를 단순하게 유지하기 위해 이 모델은 두 개의 입력만 사용합니다.

input_1 = tf.keras.Input(shape=(1,), name="bill_length_mm", dtype="float")
input_2 = tf.keras.Input(shape=(1,), name="island", dtype="string")

nn_raw_inputs = [input_1, input_2]

사용 전처리 층을 입력은 신경 netrwork에 대한 apropriate에 원시 입력을 변환 할 수 있습니다.

# Normalization.
Normalization = tf.keras.layers.Normalization
CategoryEncoding = tf.keras.layers.CategoryEncoding
StringLookup = tf.keras.layers.StringLookup

values = train_ds_pd["bill_length_mm"].values[:, tf.newaxis]
input_1_normalizer = Normalization()
input_1_normalizer.adapt(values)

values = train_ds_pd["island"].values
input_2_indexer = StringLookup(max_tokens=32)
input_2_indexer.adapt(values)

input_2_onehot = CategoryEncoding(output_mode="binary", max_tokens=32)

normalized_input_1 = input_1_normalizer(input_1)
normalized_input_2 = input_2_onehot(input_2_indexer(input_2))

nn_processed_inputs = [normalized_input_1, normalized_input_2]

WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.
WARNING:tensorflow:max_tokens is deprecated, please use num_tokens instead.

신경망의 본체를 구축합니다.

y = tf.keras.layers.Concatenate()(nn_processed_inputs)
y = tf.keras.layers.Dense(16, activation=tf.nn.relu6)(y)
last_layer = tf.keras.layers.Dense(8, activation=tf.nn.relu, name="last")(y)

# "3" for the three label classes. If it were a binary classification, the
# output dim would be 1.
classification_output = tf.keras.layers.Dense(3)(y)

nn_model = tf.keras.models.Model(nn_raw_inputs, classification_output)

이 nn_model 직접 분류 logits을 생산하고 있습니다.

다음으로 의사 결정 포리스트 모델을 만듭니다. 이것은 분류 헤드 이전의 마지막 레이어에서 신경망이 추출하는 높은 수준의 기능에서 작동합니다.

# To reduce the risk of mistakes, group both the decision forest and the
# neural network in a single keras model.
nn_without_head = tf.keras.models.Model(inputs=nn_model.inputs, outputs=last_layer)
df_and_nn_model = tfdf.keras.RandomForestModel(preprocessing=nn_without_head)

모델 학습 및 평가

모델은 2단계로 학습됩니다. 먼저 자체 분류 헤드로 신경망을 훈련합니다.

%set_cell_height 300

nn_model.compile(
  optimizer=tf.keras.optimizers.Adam(),
  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
  metrics=["accuracy"])

nn_model.fit(x=train_ds, validation_data=test_ds, epochs=10)
nn_model.summary()

<IPython.core.display.Javascript object>
Epoch 1/10
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/engine/functional.py:559: UserWarning: Input dict contained keys ['bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex', 'year'] which did not match any model input. They will be ignored by the model.
  inputs = self._flatten_to_reference_inputs(inputs)
4/4 [==============================] - 0s 53ms/step - loss: 1.0232 - accuracy: 0.3730 - val_loss: 1.0186 - val_accuracy: 0.3587
Epoch 2/10
4/4 [==============================] - 0s 7ms/step - loss: 1.0107 - accuracy: 0.3810 - val_loss: 1.0096 - val_accuracy: 0.3587
Epoch 3/10
4/4 [==============================] - 0s 7ms/step - loss: 1.0006 - accuracy: 0.3889 - val_loss: 1.0006 - val_accuracy: 0.3696
Epoch 4/10
4/4 [==============================] - 0s 7ms/step - loss: 0.9909 - accuracy: 0.3968 - val_loss: 0.9915 - val_accuracy: 0.3696
Epoch 5/10
4/4 [==============================] - 0s 7ms/step - loss: 0.9813 - accuracy: 0.3968 - val_loss: 0.9825 - val_accuracy: 0.3696
Epoch 6/10
4/4 [==============================] - 0s 7ms/step - loss: 0.9717 - accuracy: 0.4008 - val_loss: 0.9735 - val_accuracy: 0.3696
Epoch 7/10
4/4 [==============================] - 0s 7ms/step - loss: 0.9621 - accuracy: 0.4048 - val_loss: 0.9645 - val_accuracy: 0.4457
Epoch 8/10
4/4 [==============================] - 0s 7ms/step - loss: 0.9525 - accuracy: 0.6111 - val_loss: 0.9555 - val_accuracy: 0.6522
Epoch 9/10
4/4 [==============================] - 0s 8ms/step - loss: 0.9430 - accuracy: 0.7262 - val_loss: 0.9465 - val_accuracy: 0.6848
Epoch 10/10
4/4 [==============================] - 0s 7ms/step - loss: 0.9335 - accuracy: 0.7460 - val_loss: 0.9374 - val_accuracy: 0.7283
Model: "model_1"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 island (InputLayer)            [(None, 1)]          0           []                               
                                                                                                  
 bill_length_mm (InputLayer)    [(None, 1)]          0           []                               
                                                                                                  
 string_lookup (StringLookup)   (None, 1)            0           ['island[0][0]']                 
                                                                                                  
 normalization (Normalization)  (None, 1)            3           ['bill_length_mm[0][0]']         
                                                                                                  
 category_encoding (CategoryEnc  (None, 32)          0           ['string_lookup[0][0]']          
 oding)                                                                                           
                                                                                                  
 concatenate (Concatenate)      (None, 33)           0           ['normalization[0][0]',          
                                                                  'category_encoding[0][0]']      
                                                                                                  
 dense (Dense)                  (None, 16)           544         ['concatenate[0][0]']            
                                                                                                  
 dense_1 (Dense)                (None, 3)            51          ['dense[0][0]']                  
                                                                                                  
==================================================================================================
Total params: 598
Trainable params: 595
Non-trainable params: 3
__________________________________________________________________________________________________

신경망 계층은 두 모델 간에 공유됩니다. 이제 신경망이 훈련되었으므로 의사 결정 포리스트 모델은 신경망 계층의 훈련된 출력에 적합합니다.

%set_cell_height 300

df_and_nn_model.compile(metrics=["accuracy"])
with sys_pipes():
  df_and_nn_model.fit(x=train_ds)

<IPython.core.display.Javascript object>
1/4 [======>.......................] - ETA: 0s
[INFO kernel.cc:736] Start Yggdrasil model training
[INFO kernel.cc:737] Collect training examples
[INFO kernel.cc:392] Number of batches: 4
[INFO kernel.cc:393] Number of examples: 252
[INFO kernel.cc:759] Dataset:
Number of records: 252
Number of columns: 9

Number of columns by type:
    NUMERICAL: 8 (88.8889%)
    CATEGORICAL: 1 (11.1111%)

Columns:

NUMERICAL: 8 (88.8889%)
    0: "model_2/last/Relu:0.0" NUMERICAL mean:0.0612511 min:0 max:1.05271 sd:0.1172
    1: "model_2/last/Relu:0.1" NUMERICAL mean:0.145744 min:0 max:0.357441 sd:0.140661
    2: "model_2/last/Relu:0.2" NUMERICAL mean:0.114429 min:0 max:0.527097 sd:0.0945893
    3: "model_2/last/Relu:0.3" NUMERICAL mean:0.0132481 min:0 max:0.124071 sd:0.0305115
    4: "model_2/last/Relu:0.4" NUMERICAL mean:0.0538435 min:0 max:0.446979 sd:0.110693
    5: "model_2/last/Relu:0.5" NUMERICAL mean:0.000560531 min:0 max:0.0364899 sd:0.00370266
    6: "model_2/last/Relu:0.6" NUMERICAL mean:0.0278776 min:0 max:0.449398 sd:0.0592763
    7: "model_2/last/Relu:0.7" NUMERICAL mean:0.0485136 min:0 max:0.319197 sd:0.104035

CATEGORICAL: 1 (11.1111%)
    8: "__LABEL" CATEGORICAL integerized vocab-size:4 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: "model_2/last/Relu:0\\.0"
features: "model_2/last/Relu:0\\.1"
features: "model_2/last/Relu:0\\.2"
features: "model_2/last/Relu:0\\.3"
features: "model_2/last/Relu:0\\.4"
features: "model_2/last/Relu:0\\.5"
features: "model_2/last/Relu:0\\.6"
features: "model_2/last/Relu:0\\.7"
label: "__LABEL"
task: CLASSIFICATION
[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {
  num_trees: 300
  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 252 example(s) and 8 feature(s).
[INFO random_forest.cc:628] Training of tree  1/300 (tree index:0) done accuracy:0.944444 logloss:2.00243
[INFO random_forest.cc:628] Training of tree  11/300 (tree index:10) done accuracy:0.948207 logloss:1.04535
[INFO random_forest.cc:628] Training of tree  21/300 (tree index:20) done accuracy:0.956349 logloss:0.763534
[INFO random_forest.cc:628] Training of tree  31/300 (tree index:30) done accuracy:0.952381 logloss:0.633103
[INFO random_forest.cc:628] Training of tree  41/300 (tree index:40) done accuracy:0.952381 logloss:0.634035
[INFO random_forest.cc:628] Training of tree  51/300 (tree index:49) done accuracy:0.952381 logloss:0.63407
[INFO random_forest.cc:628] Training of tree  61/300 (tree index:60) done accuracy:0.952381 logloss:0.632213
[INFO random_forest.cc:628] Training of tree  71/300 (tree index:69) done accuracy:0.952381 logloss:0.634892
[INFO random_forest.cc:628] Training of tree  81/300 (tree index:80) done accuracy:0.948413 logloss:0.634806
[INFO random_forest.cc:628] Training of tree  91/300 (tree index:90) done accuracy:0.948413 logloss:0.634308
[INFO random_forest.cc:628] Training of tree  101/300 (tree index:100) done accuracy:0.944444 logloss:0.63434
[INFO random_forest.cc:628] Training of tree  111/300 (tree index:110) done accuracy:0.944444 logloss:0.63474
[INFO random_forest.cc:628] Training of tree  121/300 (tree index:120) done accuracy:0.944444 logloss:0.634896
[INFO random_forest.cc:628] Training of tree  131/300 (tree index:130) done accuracy:0.948413 logloss:0.634515
[INFO random_forest.cc:628] Training of tree  141/300 (tree index:138) done accuracy:0.944444 logloss:0.635284
[INFO random_forest.cc:628] Training of tree  151/300 (tree index:150) done accuracy:0.944444 logloss:0.634902
[INFO random_forest.cc:628] Training of tree  161/300 (tree index:160) done accuracy:0.944444 logloss:0.633816
[INFO random_forest.cc:628] Training of tree  171/300 (tree index:170) done accuracy:0.944444 logloss:0.632936
[INFO random_forest.cc:628] Training of tree  181/300 (tree index:180) done accuracy:0.944444 logloss:0.632445
[INFO random_forest.cc:628] Training of tree  191/300 (tree index:189) done accuracy:0.944444 logloss:0.632614
[INFO random_forest.cc:628] Training of tree  201/300 (tree index:199) done accuracy:0.944444 logloss:0.632688
[INFO random_forest.cc:628] Training of tree  211/300 (tree index:206) done accuracy:0.944444 logloss:0.633056
[INFO random_forest.cc:628] Training of tree  221/300 (tree index:220) done accuracy:0.944444 logloss:0.633952
[INFO random_forest.cc:628] Training of tree  231/300 (tree index:231) done accuracy:0.944444 logloss:0.634217
[INFO random_forest.cc:628] Training of tree  241/300 (tree index:240) done accuracy:0.944444 logloss:0.634271
[INFO random_forest.cc:628] Training of tree  251/300 (tree index:244) done accuracy:0.944444 logloss:0.634761
[INFO random_forest.cc:628] Training of tree  261/300 (tree index:261) done accuracy:0.944444 logloss:0.634685
[INFO random_forest.cc:628] Training of tree  271/300 (tree index:268) done accuracy:0.944444 logloss:0.634395
[INFO random_forest.cc:628] Training of tree  281/300 (tree index:280) done accuracy:0.944444 logloss:0.633878
[INFO random_forest.cc:628] Training of tree  291/300 (tree index:291) done accuracy:0.944444 logloss:0.633605
[INFO random_forest.cc:628] Training of tree  300/300 (tree index:299) done accuracy:0.944444 logloss:0.633627
[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.944444 logloss:0.633627
[INFO kernel.cc:828] Export model in log directory: /tmp/tmpb92rvbmj
[INFO kernel.cc:836] Save model in resources
[INFO kernel.cc:988] Loading model from path
[INFO decision_forest.cc:590] Model loaded with 300 root(s), 4148 node(s), and 8 input feature(s).
[INFO kernel.cc:848] Use fast generic engine
4/4 [==============================] - 0s 18ms/step

이제 구성된 모델을 평가합니다.

print("Evaluation:", df_and_nn_model.evaluate(test_ds))

2/2 [==============================] - 0s 5ms/step - loss: 0.0000e+00 - accuracy: 0.9565
Evaluation: [0.0, 0.95652174949646]

신경망 단독으로 비교:

print("Evaluation :", nn_model.evaluate(test_ds))

2/2 [==============================] - 0s 4ms/step - loss: 0.9374 - accuracy: 0.7283
Evaluation : [0.9373641610145569, 0.72826087474823]