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Introducción
Bienvenido al modelo de composición tutorial para los Bosques de decisión TensorFlow (TF-DF). Este portátil se muestra cómo componer bosque decisión múltiples y modelos de redes neuronales juntos utilizando una capa de procesamiento previo común y la API funcional Keras .
Es posible que desee componer modelos juntos para mejorar el rendimiento predictivo (ensamblaje), obtener lo mejor de las diferentes tecnologías de modelado (ensamblaje de modelos heterogéneos), entrenar diferentes partes del modelo en diferentes conjuntos de datos (por ejemplo, entrenamiento previo) o crear un modelo apilado (por ejemplo, un modelo opera sobre las predicciones de otro modelo).
Este tutorial cubre un caso de uso avanzado de la composición de modelos utilizando la API funcional. Puede encontrar ejemplos de escenarios simples de la composición modelo de la "función de pre-procesamiento" de este tutorial y en la sección "utilizando un texto pretrained incrustación" de este tutorial .
Aquí está la estructura del modelo que creará:
!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
}
""")
Su modelo compuesto tiene tres etapas:
- La primera etapa es una capa de preprocesamiento compuesta por una red neuronal y común a todos los modelos en la siguiente etapa. En la práctica, dicha capa de preprocesamiento podría ser una incrustación preentrenada para ajustarla o una red neuronal inicializada aleatoriamente.
- La segunda etapa es un conjunto de dos bosques de decisiones y dos modelos de redes neuronales.
- La última etapa promedia las predicciones de los modelos en la segunda etapa. No contiene ningún peso que se pueda aprender.
Las redes neuronales se forman utilizando el algoritmo de propagación hacia atrás y descenso de gradiente. Este algoritmo tiene dos propiedades importantes: (1) la capa de la red neuronal se puede entrenar si recibe un gradiente de pérdida (más precisamente, el gradiente de la pérdida de acuerdo con la salida de la capa), y (2) el algoritmo "transmite" la gradiente de pérdida de la salida de la capa a la entrada de la capa (esta es la "regla de la cadena"). Por estas dos razones, la retropropagación puede entrenar juntas múltiples capas de redes neuronales apiladas una encima de la otra.
En este ejemplo, los bosques de decisión se entrenan con el Bosque aleatoria algoritmo (RF). A diferencia de la retropropagación, el entrenamiento de RF no "transmite" el gradiente de pérdida desde su salida hasta su entrada. Por esta razón, el algoritmo de RF clásico no se puede utilizar para entrenar o ajustar una red neuronal subyacente. En otras palabras, las etapas de "bosque de decisiones" no se pueden utilizar para entrenar el "bloque de preprocesamiento de NN que se puede aprender".
- Entrenar la etapa de preprocesamiento y redes neuronales.
- Entrene las etapas del bosque de decisiones.
Instalar bosques de decisiones de TensorFlow
Instale TF-DF ejecutando la siguiente celda.
pip install tensorflow_decision_forests -U --quiet
Instalar Wurlitzer para mostrar los registros detallados de la formación. Esto solo es necesario en los cuadernos.
pip install wurlitzer -U --quiet
Importar bibliotecas
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.
Conjunto de datos
Utilizará un conjunto de datos sintético simple en este tutorial para facilitar la interpretación del modelo final.
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)
Genera algunos ejemplos:
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]))
También puede trazarlos para tener una idea del patrón sintético:
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>
Tenga en cuenta que este patrón es suave y no está alineado con el eje. Esto aprovechará los modelos de redes neuronales. Esto se debe a que es más fácil para una red neuronal que para un árbol de decisión tener límites de decisión redondos y no alineados.
Por otro lado, entrenaremos el modelo en pequeños conjuntos de datos con 2500 ejemplos. Esto aprovechará los modelos de bosque de decisiones. Esto se debe a que los bosques de decisiones son mucho más eficientes y utilizan toda la información disponible de los ejemplos (los bosques de decisiones son "eficientes en la muestra").
Nuestro conjunto de redes neuronales y bosques de decisiones utilizará lo mejor de ambos mundos.
Vamos a crear un tren y prueba 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)
Estructura del modelo
Defina la estructura del modelo de la siguiente manera:
# 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)
Antes de entrenar el modelo, puede trazarlo para verificar si es similar al diagrama inicial.
from keras.utils.vis_utils import plot_model
plot_model(ensemble_nn_and_df, to_file="/tmp/model.png", show_shapes=True)
Entrenamiento de modelos
Primero entrene el preprocesamiento y las dos capas de la red neuronal utilizando el algoritmo de retropropagación.
%%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>
Evaluemos el preprocesamiento y la parte con las dos redes neuronales únicamente:
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
Entrenemos los dos componentes de Decision Forest (uno tras otro).
%%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>
Y evaluemos los bosques de decisión individualmente.
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
Evaluemos toda la composición del modelo:
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
Para terminar, afinemos un poco más la capa de red neuronal. Tenga en cuenta que no ajustamos la incrustación preentrenada ya que los modelos DF dependen de ella (a menos que también los volvamos a entrenar después).
En resumen, tienes:
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
Aquí, puede ver que el modelo compuesto funciona mejor que sus partes individuales. Por eso los conjuntos funcionan tan bien.
¿Que sigue?
En este ejemplo, vio cómo combinar bosques de decisión con redes neuronales. Un paso adicional sería entrenar aún más la red neuronal y los bosques de decisión.
Además, en aras de la claridad, los bosques de decisión recibieron solo la entrada preprocesada. Sin embargo, los bosques de decisiones son generalmente excelentes y consumen datos sin procesar. El modelo se mejoraría al incorporar también las características sin procesar a los modelos de bosque de decisiones.
En este ejemplo, el modelo final es el promedio de las predicciones de los modelos individuales. Esta solución funciona bien si todos los modelos funcionan más o menos con lo mismo. Sin embargo, si uno de los submodelos es muy bueno, agregarlo con otros modelos podría ser perjudicial (o viceversa; por ejemplo, intente reducir el número de ejemplos de 1k y vea cómo daña mucho las redes neuronales; o habilitar el SPARSE_OBLIQUE escisión en el segundo modelo Random Forest).