# Memeriksa dan men-debug model hutan keputusan

Dalam colab ini, Anda akan mempelajari cara memeriksa dan membuat struktur model secara langsung. Kami menganggap Anda sudah familiar dengan konsep yang diperkenalkan di pemula dan menengah colabs.

Dalam kolab ini, Anda akan:

1. Latih model Hutan Acak dan akses strukturnya secara terprogram.

2. Buat model Hutan Acak dengan tangan dan gunakan sebagai model klasik.

## Mempersiapkan

````# Install TensorFlow Dececision Forests.`
`pip install tensorflow_decision_forests`

`# Use wurlitzer to capture training logs.`
`pip install wurlitzer`
```
``````import tensorflow_decision_forests as tfdf

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

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.
```

Sel kode tersembunyi membatasi tinggi keluaran dalam colab.

## Latih Hutan Acak sederhana

Kami melatih Acak Hutan seperti di colab pemula :

``````# Download the dataset

# Load a dataset into a Pandas Dataframe.

# Show the first three examples.

# Convert the pandas dataframe into a tf dataset.
dataset_tf = tfdf.keras.pd_dataframe_to_tf_dataset(dataset_df, label="species")

# Train the Random Forest
model = tfdf.keras.RandomForestModel(compute_oob_variable_importances=True)
model.fit(x=dataset_tf)
``````
```species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \
0  Adelie  Torgersen            39.1           18.7              181.0
1  Adelie  Torgersen            39.5           17.4              186.0
2  Adelie  Torgersen            40.3           18.0              195.0

body_mass_g     sex  year
0       3750.0    male  2007
1       3800.0  female  2007
2       3250.0  female  2007
/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)
6/6 [==============================] - 4s 17ms/step
[INFO kernel.cc:736] Start Yggdrasil model training
[INFO kernel.cc:737] Collect training examples
[INFO kernel.cc:392] Number of batches: 6
[INFO kernel.cc:393] Number of examples: 344
[INFO kernel.cc:759] Dataset:
Number of records: 344
Number of columns: 8

Number of columns by type:
NUMERICAL: 5 (62.5%)
CATEGORICAL: 3 (37.5%)

Columns:

NUMERICAL: 5 (62.5%)
0: "bill_depth_mm" NUMERICAL num-nas:2 (0.581395%) mean:17.1512 min:13.1 max:21.5 sd:1.9719
1: "bill_length_mm" NUMERICAL num-nas:2 (0.581395%) mean:43.9219 min:32.1 max:59.6 sd:5.4516
2: "body_mass_g" NUMERICAL num-nas:2 (0.581395%) mean:4201.75 min:2700 max:6300 sd:800.781
3: "flipper_length_mm" NUMERICAL num-nas:2 (0.581395%) mean:200.915 min:172 max:231 sd:14.0411
6: "year" NUMERICAL mean:2008.03 min:2007 max:2009 sd:0.817166

CATEGORICAL: 3 (37.5%)
4: "island" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:"Biscoe" 168 (48.8372%)
5: "sex" CATEGORICAL num-nas:11 (3.19767%) has-dict vocab-size:3 zero-ood-items most-frequent:"male" 168 (50.4505%)
7: "__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: "bill_depth_mm"
features: "bill_length_mm"
features: "body_mass_g"
features: "flipper_length_mm"
features: "island"
features: "sex"
features: "year"
label: "__LABEL"
[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: true
}

[INFO kernel.cc:790] Deployment config:

[INFO kernel.cc:817] Train model
[INFO random_forest.cc:315] Training random forest on 344 example(s) and 7 feature(s).
[INFO random_forest.cc:628] Training of tree  1/300 (tree index:0) done accuracy:0.964286 logloss:1.28727
[INFO random_forest.cc:628] Training of tree  11/300 (tree index:10) done accuracy:0.956268 logloss:0.584301
[INFO random_forest.cc:628] Training of tree  22/300 (tree index:21) done accuracy:0.965116 logloss:0.378823
[INFO random_forest.cc:628] Training of tree  35/300 (tree index:34) done accuracy:0.968023 logloss:0.178185
[INFO random_forest.cc:628] Training of tree  46/300 (tree index:45) done accuracy:0.973837 logloss:0.170304
[INFO random_forest.cc:628] Training of tree  58/300 (tree index:57) done accuracy:0.973837 logloss:0.171223
[INFO random_forest.cc:628] Training of tree  70/300 (tree index:69) done accuracy:0.979651 logloss:0.169564
[INFO random_forest.cc:628] Training of tree  83/300 (tree index:82) done accuracy:0.976744 logloss:0.17074
[INFO random_forest.cc:628] Training of tree  96/300 (tree index:95) done accuracy:0.976744 logloss:0.0736925
[INFO random_forest.cc:628] Training of tree  106/300 (tree index:105) done accuracy:0.976744 logloss:0.0748649
[INFO random_forest.cc:628] Training of tree  117/300 (tree index:116) done accuracy:0.976744 logloss:0.074671
[INFO random_forest.cc:628] Training of tree  130/300 (tree index:129) done accuracy:0.976744 logloss:0.0736275
[INFO random_forest.cc:628] Training of tree  140/300 (tree index:139) done accuracy:0.976744 logloss:0.0727718
[INFO random_forest.cc:628] Training of tree  152/300 (tree index:151) done accuracy:0.976744 logloss:0.0715068
[INFO random_forest.cc:628] Training of tree  162/300 (tree index:161) done accuracy:0.976744 logloss:0.0708994
[INFO random_forest.cc:628] Training of tree  173/300 (tree index:172) done accuracy:0.976744 logloss:0.069447
[INFO random_forest.cc:628] Training of tree  184/300 (tree index:183) done accuracy:0.976744 logloss:0.0695926
[INFO random_forest.cc:628] Training of tree  195/300 (tree index:194) done accuracy:0.976744 logloss:0.0690138
[INFO random_forest.cc:628] Training of tree  205/300 (tree index:204) done accuracy:0.976744 logloss:0.0694597
[INFO random_forest.cc:628] Training of tree  217/300 (tree index:216) done accuracy:0.976744 logloss:0.068122
[INFO random_forest.cc:628] Training of tree  229/300 (tree index:228) done accuracy:0.976744 logloss:0.0687641
[INFO random_forest.cc:628] Training of tree  239/300 (tree index:238) done accuracy:0.976744 logloss:0.067988
[INFO random_forest.cc:628] Training of tree  250/300 (tree index:249) done accuracy:0.976744 logloss:0.0690187
[INFO random_forest.cc:628] Training of tree  260/300 (tree index:259) done accuracy:0.976744 logloss:0.0690134
[INFO random_forest.cc:628] Training of tree  270/300 (tree index:269) done accuracy:0.976744 logloss:0.0689877
[INFO random_forest.cc:628] Training of tree  280/300 (tree index:279) done accuracy:0.976744 logloss:0.0689845
[INFO random_forest.cc:628] Training of tree  290/300 (tree index:288) done accuracy:0.976744 logloss:0.0690742
[INFO random_forest.cc:628] Training of tree  300/300 (tree index:299) done accuracy:0.976744 logloss:0.068949
[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.976744 logloss:0.068949
[INFO kernel.cc:828] Export model in log directory: /tmp/tmpoqki9pfl
[INFO kernel.cc:836] Save model in resources
[INFO decision_forest.cc:590] Model loaded with 300 root(s), 5080 node(s), and 7 input feature(s).
[INFO abstract_model.cc:993] Engine "RandomForestGeneric" built
[INFO kernel.cc:848] Use fast generic engine
<keras.callbacks.History at 0x7f09eaa9cb90>
```

Perhatikan `compute_oob_variable_importances=True` hiper-parameter dalam model konstruktor. Opsi ini menghitung kepentingan variabel Out-of-bag (OOB) selama pelatihan. Ini adalah populer variabel permutasi pentingnya untuk model Acak Forest.

Menghitung pentingnya Variabel OOB tidak berdampak pada model akhir, ini akan memperlambat pelatihan pada kumpulan data besar.

Periksa ringkasan model:

``````%set_cell_height 300

model.summary()
``````
```<IPython.core.display.Javascript object>
Model: "random_forest_model"
_________________________________________________________________
Layer (type)                Output Shape              Param #
=================================================================
=================================================================
Total params: 1
Trainable params: 0
Non-trainable params: 1
_________________________________________________________________
Type: "RANDOM_FOREST"
Label: "__LABEL"

Input Features (7):
bill_depth_mm
bill_length_mm
body_mass_g
flipper_length_mm
island
sex
year

No weights

Variable Importance: MEAN_DECREASE_IN_ACCURACY:

1.    "bill_length_mm"  0.151163 ################
2.            "island"  0.008721 #
3.     "bill_depth_mm"  0.000000
4.       "body_mass_g"  0.000000
5.               "sex"  0.000000
6.              "year"  0.000000
7. "flipper_length_mm" -0.002907

Variable Importance: MEAN_DECREASE_IN_AP_1_VS_OTHERS:

1.    "bill_length_mm"  0.083305 ################
2.            "island"  0.007664 #
3. "flipper_length_mm"  0.003400
4.     "bill_depth_mm"  0.002741
5.       "body_mass_g"  0.000722
6.               "sex"  0.000644
7.              "year"  0.000000

Variable Importance: MEAN_DECREASE_IN_AP_2_VS_OTHERS:

1.    "bill_length_mm"  0.508510 ################
2.            "island"  0.023487
3.     "bill_depth_mm"  0.007744
4. "flipper_length_mm"  0.006008
5.       "body_mass_g"  0.003017
6.               "sex"  0.001537
7.              "year" -0.000245

Variable Importance: MEAN_DECREASE_IN_AP_3_VS_OTHERS:

1.            "island"  0.002192 ################
2.    "bill_length_mm"  0.001572 ############
3.     "bill_depth_mm"  0.000497 #######
4.               "sex"  0.000000 ####
5.              "year"  0.000000 ####
6.       "body_mass_g" -0.000053 ####
7. "flipper_length_mm" -0.000890

Variable Importance: MEAN_DECREASE_IN_AUC_1_VS_OTHERS:

1.    "bill_length_mm"  0.071306 ################
2.            "island"  0.007299 #
3. "flipper_length_mm"  0.004506 #
4.     "bill_depth_mm"  0.002124
5.       "body_mass_g"  0.000548
6.               "sex"  0.000480
7.              "year"  0.000000

Variable Importance: MEAN_DECREASE_IN_AUC_2_VS_OTHERS:

1.    "bill_length_mm"  0.108642 ################
2.            "island"  0.014493 ##
3.     "bill_depth_mm"  0.007406 #
4. "flipper_length_mm"  0.005195
5.       "body_mass_g"  0.001012
6.               "sex"  0.000480
7.              "year" -0.000053

Variable Importance: MEAN_DECREASE_IN_AUC_3_VS_OTHERS:

1.            "island"  0.002126 ################
2.    "bill_length_mm"  0.001393 ###########
3.     "bill_depth_mm"  0.000293 #####
4.               "sex"  0.000000 ###
5.              "year"  0.000000 ###
6.       "body_mass_g" -0.000037 ###
7. "flipper_length_mm" -0.000550

Variable Importance: MEAN_DECREASE_IN_PRAUC_1_VS_OTHERS:

1.    "bill_length_mm"  0.083122 ################
2.            "island"  0.010887 ##
3. "flipper_length_mm"  0.003425
4.     "bill_depth_mm"  0.002731
5.       "body_mass_g"  0.000719
6.               "sex"  0.000641
7.              "year"  0.000000

Variable Importance: MEAN_DECREASE_IN_PRAUC_2_VS_OTHERS:

1.    "bill_length_mm"  0.497611 ################
2.            "island"  0.024045
3.     "bill_depth_mm"  0.007734
4. "flipper_length_mm"  0.006017
5.       "body_mass_g"  0.003000
6.               "sex"  0.001528
7.              "year" -0.000243

Variable Importance: MEAN_DECREASE_IN_PRAUC_3_VS_OTHERS:

1.            "island"  0.002187 ################
2.    "bill_length_mm"  0.001568 ############
3.     "bill_depth_mm"  0.000495 #######
4.               "sex"  0.000000 ####
5.              "year"  0.000000 ####
6.       "body_mass_g" -0.000053 ####
7. "flipper_length_mm" -0.000886

Variable Importance: MEAN_MIN_DEPTH:

1.           "__LABEL"  3.479602 ################
2.              "year"  3.463891 ###############
3.               "sex"  3.430498 ###############
4.       "body_mass_g"  2.898112 ###########
5.            "island"  2.388925 ########
6.     "bill_depth_mm"  2.336100 #######
7.    "bill_length_mm"  1.282960
8. "flipper_length_mm"  1.270079

Variable Importance: NUM_AS_ROOT:

1. "flipper_length_mm" 157.000000 ################
2.    "bill_length_mm" 76.000000 #######
3.     "bill_depth_mm" 52.000000 #####
4.            "island" 12.000000
5.       "body_mass_g"  3.000000

Variable Importance: NUM_NODES:

1.    "bill_length_mm" 778.000000 ################
2.     "bill_depth_mm" 463.000000 #########
3. "flipper_length_mm" 414.000000 ########
4.            "island" 342.000000 ######
5.       "body_mass_g" 338.000000 ######
6.               "sex" 36.000000
7.              "year" 19.000000

Variable Importance: SUM_SCORE:

1.    "bill_length_mm" 36515.793787 ################
2. "flipper_length_mm" 35120.434174 ###############
3.            "island" 14669.408395 ######
4.     "bill_depth_mm" 14515.446617 ######
5.       "body_mass_g" 3485.330881 #
6.               "sex" 354.201073
7.              "year" 49.737758

Winner take all: true
Out-of-bag evaluation: accuracy:0.976744 logloss:0.068949
Number of trees: 300
Total number of nodes: 5080

Number of nodes by tree:
Count: 300 Average: 16.9333 StdDev: 3.10197
Min: 11 Max: 31 Ignored: 0
----------------------------------------------
[ 11, 12)  6   2.00%   2.00% #
[ 12, 13)  0   0.00%   2.00%
[ 13, 14) 46  15.33%  17.33% #####
[ 14, 15)  0   0.00%  17.33%
[ 15, 16) 70  23.33%  40.67% ########
[ 16, 17)  0   0.00%  40.67%
[ 17, 18) 84  28.00%  68.67% ##########
[ 18, 19)  0   0.00%  68.67%
[ 19, 20) 46  15.33%  84.00% #####
[ 20, 21)  0   0.00%  84.00%
[ 21, 22) 30  10.00%  94.00% ####
[ 22, 23)  0   0.00%  94.00%
[ 23, 24) 13   4.33%  98.33% ##
[ 24, 25)  0   0.00%  98.33%
[ 25, 26)  2   0.67%  99.00%
[ 26, 27)  0   0.00%  99.00%
[ 27, 28)  2   0.67%  99.67%
[ 28, 29)  0   0.00%  99.67%
[ 29, 30)  0   0.00%  99.67%
[ 30, 31]  1   0.33% 100.00%

Depth by leafs:
Count: 2690 Average: 3.53271 StdDev: 1.06789
Min: 2 Max: 7 Ignored: 0
----------------------------------------------
[ 2, 3) 545  20.26%  20.26% ######
[ 3, 4) 747  27.77%  48.03% ########
[ 4, 5) 888  33.01%  81.04% ##########
[ 5, 6) 444  16.51%  97.55% #####
[ 6, 7)  62   2.30%  99.85% #
[ 7, 7]   4   0.15% 100.00%

Number of training obs by leaf:
Count: 2690 Average: 38.3643 StdDev: 44.8651
Min: 5 Max: 155 Ignored: 0
----------------------------------------------
[   5,  12) 1474  54.80%  54.80% ##########
[  12,  20)  124   4.61%  59.41% #
[  20,  27)   48   1.78%  61.19%
[  27,  35)   74   2.75%  63.94% #
[  35,  42)   58   2.16%  66.10%
[  42,  50)   85   3.16%  69.26% #
[  50,  57)   96   3.57%  72.83% #
[  57,  65)   87   3.23%  76.06% #
[  65,  72)   49   1.82%  77.88%
[  72,  80)   23   0.86%  78.74%
[  80,  88)   30   1.12%  79.85%
[  88,  95)   23   0.86%  80.71%
[  95, 103)   42   1.56%  82.27%
[ 103, 110)   62   2.30%  84.57%
[ 110, 118)  115   4.28%  88.85% #
[ 118, 125)  115   4.28%  93.12% #
[ 125, 133)   98   3.64%  96.77% #
[ 133, 140)   49   1.82%  98.59%
[ 140, 148)   31   1.15%  99.74%
[ 148, 155]    7   0.26% 100.00%

Attribute in nodes:
778 : bill_length_mm [NUMERICAL]
463 : bill_depth_mm [NUMERICAL]
414 : flipper_length_mm [NUMERICAL]
342 : island [CATEGORICAL]
338 : body_mass_g [NUMERICAL]
36 : sex [CATEGORICAL]
19 : year [NUMERICAL]

Attribute in nodes with depth <= 0:
157 : flipper_length_mm [NUMERICAL]
76 : bill_length_mm [NUMERICAL]
52 : bill_depth_mm [NUMERICAL]
12 : island [CATEGORICAL]
3 : body_mass_g [NUMERICAL]

Attribute in nodes with depth <= 1:
250 : bill_length_mm [NUMERICAL]
244 : flipper_length_mm [NUMERICAL]
183 : bill_depth_mm [NUMERICAL]
170 : island [CATEGORICAL]
53 : body_mass_g [NUMERICAL]

Attribute in nodes with depth <= 2:
462 : bill_length_mm [NUMERICAL]
320 : flipper_length_mm [NUMERICAL]
310 : bill_depth_mm [NUMERICAL]
287 : island [CATEGORICAL]
162 : body_mass_g [NUMERICAL]
9 : sex [CATEGORICAL]
5 : year [NUMERICAL]

Attribute in nodes with depth <= 3:
669 : bill_length_mm [NUMERICAL]
410 : bill_depth_mm [NUMERICAL]
383 : flipper_length_mm [NUMERICAL]
328 : island [CATEGORICAL]
286 : body_mass_g [NUMERICAL]
32 : sex [CATEGORICAL]
10 : year [NUMERICAL]

Attribute in nodes with depth <= 5:
778 : bill_length_mm [NUMERICAL]
462 : bill_depth_mm [NUMERICAL]
413 : flipper_length_mm [NUMERICAL]
342 : island [CATEGORICAL]
338 : body_mass_g [NUMERICAL]
36 : sex [CATEGORICAL]
19 : year [NUMERICAL]

Condition type in nodes:
2012 : HigherCondition
378 : ContainsBitmapCondition
Condition type in nodes with depth <= 0:
288 : HigherCondition
12 : ContainsBitmapCondition
Condition type in nodes with depth <= 1:
730 : HigherCondition
170 : ContainsBitmapCondition
Condition type in nodes with depth <= 2:
1259 : HigherCondition
296 : ContainsBitmapCondition
Condition type in nodes with depth <= 3:
1758 : HigherCondition
360 : ContainsBitmapCondition
Condition type in nodes with depth <= 5:
2010 : HigherCondition
378 : ContainsBitmapCondition
Node format: NOT_SET

Training OOB:
trees: 1, Out-of-bag evaluation: accuracy:0.964286 logloss:1.28727
trees: 11, Out-of-bag evaluation: accuracy:0.956268 logloss:0.584301
trees: 22, Out-of-bag evaluation: accuracy:0.965116 logloss:0.378823
trees: 35, Out-of-bag evaluation: accuracy:0.968023 logloss:0.178185
trees: 46, Out-of-bag evaluation: accuracy:0.973837 logloss:0.170304
trees: 58, Out-of-bag evaluation: accuracy:0.973837 logloss:0.171223
trees: 70, Out-of-bag evaluation: accuracy:0.979651 logloss:0.169564
trees: 83, Out-of-bag evaluation: accuracy:0.976744 logloss:0.17074
trees: 96, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0736925
trees: 106, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0748649
trees: 117, Out-of-bag evaluation: accuracy:0.976744 logloss:0.074671
trees: 130, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0736275
trees: 140, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0727718
trees: 152, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0715068
trees: 162, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0708994
trees: 173, Out-of-bag evaluation: accuracy:0.976744 logloss:0.069447
trees: 184, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0695926
trees: 195, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0690138
trees: 205, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0694597
trees: 217, Out-of-bag evaluation: accuracy:0.976744 logloss:0.068122
trees: 229, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0687641
trees: 239, Out-of-bag evaluation: accuracy:0.976744 logloss:0.067988
trees: 250, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0690187
trees: 260, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0690134
trees: 270, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0689877
trees: 280, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0689845
trees: 290, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0690742
trees: 300, Out-of-bag evaluation: accuracy:0.976744 logloss:0.068949
```

Perhatikan beberapa pentingnya peranan variabel dengan nama `MEAN_DECREASE_IN_*` .

## Merencanakan model

Selanjutnya, plot modelnya.

Hutan Acak adalah model besar (model ini memiliki 300 pohon dan ~5k node; lihat ringkasan di atas). Oleh karena itu, hanya plot pohon pertama, dan batasi node hingga kedalaman 3.

``````tfdf.model_plotter.plot_model_in_colab(model, tree_idx=0, max_depth=3)
``````

## Periksa struktur model

Struktur model dan meta-data yang tersedia melalui inspektur yang diciptakan oleh `make_inspector()` .

``````inspector = model.make_inspector()
``````

Untuk model kami, bidang inspektur yang tersedia adalah:

``````[field for field in dir(inspector) if not field.startswith("_")]
``````
```['MODEL_NAME',
'dataspec',
'evaluation',
'export_to_tensorboard',
'extract_all_trees',
'extract_tree',
'features',
'iterate_on_nodes',
'label',
'label_classes',
'model_type',
'num_trees',
'objective',
'training_logs',
'variable_importances',
'winner_take_all_inference']
```

Ingat untuk melihat API-referensi atau penggunaan `?` untuk dokumentasi bawaan.

``````?inspector.model_type
``````

Beberapa model meta-data:

``````print("Model type:", inspector.model_type())
print("Number of trees:", inspector.num_trees())
print("Objective:", inspector.objective())
print("Input features:", inspector.features())
``````
```Model type: RANDOM_FOREST
Number of trees: 300
Objective: Classification(label=__LABEL, class=None, num_classes=3)
Input features: ["bill_depth_mm" (1; #0), "bill_length_mm" (1; #1), "body_mass_g" (1; #2), "flipper_length_mm" (1; #3), "island" (4; #4), "sex" (4; #5), "year" (1; #6)]
```

`evaluate()` adalah evaluasi model dihitung selama pelatihan. Dataset yang digunakan untuk evaluasi ini bergantung pada algoritme. Misalnya, itu bisa berupa kumpulan data validasi atau kumpulan data yang sudah habis.

``````inspector.evaluation()
``````
```Evaluation(num_examples=344, accuracy=0.9767441860465116, loss=0.06894904488784283, rmse=None, ndcg=None, aucs=None)
```

``````print(f"Available variable importances:")
for importance in inspector.variable_importances().keys():
print("\t", importance)
``````
```Available variable importances:
MEAN_DECREASE_IN_AUC_3_VS_OTHERS
NUM_AS_ROOT
MEAN_DECREASE_IN_AUC_2_VS_OTHERS
MEAN_DECREASE_IN_AP_2_VS_OTHERS
MEAN_DECREASE_IN_ACCURACY
SUM_SCORE
MEAN_DECREASE_IN_PRAUC_2_VS_OTHERS
MEAN_DECREASE_IN_PRAUC_3_VS_OTHERS
MEAN_DECREASE_IN_AP_3_VS_OTHERS
MEAN_DECREASE_IN_AUC_1_VS_OTHERS
MEAN_MIN_DEPTH
MEAN_DECREASE_IN_PRAUC_1_VS_OTHERS
NUM_NODES
MEAN_DECREASE_IN_AP_1_VS_OTHERS
```

Kepentingan variabel yang berbeda memiliki semantik yang berbeda. Sebagai contoh, sebuah fitur dengan penurunan rata-rata di AUC `0.05` berarti bahwa menghapus fitur ini dari dataset pelatihan akan mengurangi / menyakiti AUC dengan 5%.

``````# Mean decrease in AUC of the class 1 vs the others.
inspector.variable_importances()["MEAN_DECREASE_IN_AUC_1_VS_OTHERS"]
``````
```[("bill_length_mm" (1; #1), 0.0713061951754389),
("island" (4; #4), 0.007298519736842035),
("flipper_length_mm" (1; #3), 0.004505893640351366),
("bill_depth_mm" (1; #0), 0.0021244517543865804),
("body_mass_g" (1; #2), 0.0005482456140351033),
("sex" (4; #5), 0.00047971491228060437),
("year" (1; #6), 0.0)]
```

Terakhir, akses struktur pohon yang sebenarnya:

``````inspector.extract_tree(tree_idx=0)
``````
```Tree(NonLeafNode(condition=(bill_length_mm >= 43.25; miss=True), pos_child=NonLeafNode(condition=(island in ['Biscoe']; miss=True), pos_child=NonLeafNode(condition=(bill_depth_mm >= 17.225584030151367; miss=False), pos_child=LeafNode(value=ProbabilityValue([0.16666666666666666, 0.0, 0.8333333333333334],n=6.0)), neg_child=LeafNode(value=ProbabilityValue([0.0, 0.0, 1.0],n=104.0)), value=ProbabilityValue([0.00909090909090909, 0.0, 0.990909090909091],n=110.0)), neg_child=LeafNode(value=ProbabilityValue([0.0, 1.0, 0.0],n=61.0)), value=ProbabilityValue([0.005847953216374269, 0.3567251461988304, 0.6374269005847953],n=171.0)), neg_child=NonLeafNode(condition=(bill_depth_mm >= 15.100000381469727; miss=True), pos_child=NonLeafNode(condition=(flipper_length_mm >= 187.5; miss=True), pos_child=LeafNode(value=ProbabilityValue([1.0, 0.0, 0.0],n=104.0)), neg_child=NonLeafNode(condition=(bill_length_mm >= 42.30000305175781; miss=True), pos_child=LeafNode(value=ProbabilityValue([0.0, 1.0, 0.0],n=5.0)), neg_child=NonLeafNode(condition=(bill_length_mm >= 40.55000305175781; miss=True), pos_child=LeafNode(value=ProbabilityValue([0.8, 0.2, 0.0],n=5.0)), neg_child=LeafNode(value=ProbabilityValue([1.0, 0.0, 0.0],n=53.0)), value=ProbabilityValue([0.9827586206896551, 0.017241379310344827, 0.0],n=58.0)), value=ProbabilityValue([0.9047619047619048, 0.09523809523809523, 0.0],n=63.0)), value=ProbabilityValue([0.9640718562874252, 0.03592814371257485, 0.0],n=167.0)), neg_child=LeafNode(value=ProbabilityValue([0.0, 0.0, 1.0],n=6.0)), value=ProbabilityValue([0.930635838150289, 0.03468208092485549, 0.03468208092485549],n=173.0)), value=ProbabilityValue([0.47093023255813954, 0.19476744186046513, 0.33430232558139533],n=344.0)),label_classes={self.label_classes})
```

Mengekstrak pohon tidak efisien. Jika kecepatan adalah penting, pemeriksaan model dapat dilakukan dengan `iterate_on_nodes()` metode sebagai gantinya. Metode ini merupakan iterator traversal Depth First Pre-order pada semua node model.

Untuk contoh berikut menghitung berapa kali setiap fitur digunakan (ini adalah semacam variabel struktural yang penting):

``````# number_of_use[F] will be the number of node using feature F in its condition.
number_of_use = collections.defaultdict(lambda: 0)

# Iterate over all the nodes in a Depth First Pre-order traversals.
for node_iter in inspector.iterate_on_nodes():

if not isinstance(node_iter.node, tfdf.py_tree.node.NonLeafNode):
# Skip the leaf nodes
continue

# Iterate over all the features used in the condition.
# By default, models are "oblique" i.e. each node tests a single feature.
for feature in node_iter.node.condition.features():
number_of_use[feature] += 1

print("Number of condition nodes per features:")
for feature, count in number_of_use.items():
print("\t", feature.name, ":", count)
``````
```Number of condition nodes per features:
bill_length_mm : 778
bill_depth_mm : 463
flipper_length_mm : 414
island : 342
body_mass_g : 338
year : 19
sex : 36
```

## Membuat model dengan tangan

Di bagian ini Anda akan membuat model Hutan Acak kecil dengan tangan. Untuk membuatnya lebih mudah, model hanya akan berisi satu pohon sederhana:

``````3 label classes: Red, blue and green.
2 features: f1 (numerical) and f2 (string categorical)

f1>=1.5
├─(pos)─ f2 in ["cat","dog"]
│         ├─(pos)─ value: [0.8, 0.1, 0.1]
│         └─(neg)─ value: [0.1, 0.8, 0.1]
└─(neg)─ value: [0.1, 0.1, 0.8]
``````
``````# Create the model builder
builder = tfdf.builder.RandomForestBuilder(
path="/tmp/manual_model",
objective=tfdf.py_tree.objective.ClassificationObjective(
label="color", classes=["red", "blue", "green"]))
``````

Setiap pohon ditambahkan satu per satu.

``````# So alias
Tree = tfdf.py_tree.tree.Tree
SimpleColumnSpec = tfdf.py_tree.dataspec.SimpleColumnSpec
ColumnType = tfdf.py_tree.dataspec.ColumnType
# Nodes
NonLeafNode = tfdf.py_tree.node.NonLeafNode
LeafNode = tfdf.py_tree.node.LeafNode
# Conditions
NumericalHigherThanCondition = tfdf.py_tree.condition.NumericalHigherThanCondition
CategoricalIsInCondition = tfdf.py_tree.condition.CategoricalIsInCondition
# Leaf values
ProbabilityValue = tfdf.py_tree.value.ProbabilityValue

Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(name="f1", type=ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=NonLeafNode(
condition=CategoricalIsInCondition(
feature=SimpleColumnSpec(name="f2",type=ColumnType.CATEGORICAL),
missing_evaluation=False),
pos_child=LeafNode(value=ProbabilityValue(probability=[0.8, 0.1, 0.1], num_examples=10)),
neg_child=LeafNode(value=ProbabilityValue(probability=[0.1, 0.8, 0.1], num_examples=20))),
neg_child=LeafNode(value=ProbabilityValue(probability=[0.1, 0.1, 0.8], num_examples=30)))))
``````

Akhiri tulisan pohon

``````builder.close()
``````
```[INFO kernel.cc:988] Loading model from path
[INFO decision_forest.cc:590] Model loaded with 1 root(s), 5 node(s), and 2 input feature(s).
[INFO kernel.cc:848] Use fast generic engine
2021-11-08 12:19:14.555155: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/manual_model/assets
INFO:tensorflow:Assets written to: /tmp/manual_model/assets
```

Sekarang Anda dapat membuka model sebagai model keras biasa, dan membuat prediksi:

``````manual_model = tf.keras.models.load_model("/tmp/manual_model")
``````
```[INFO kernel.cc:988] Loading model from path
[INFO decision_forest.cc:590] Model loaded with 1 root(s), 5 node(s), and 2 input feature(s).
[INFO kernel.cc:848] Use fast generic engine
```
``````examples = tf.data.Dataset.from_tensor_slices({
"f1": [1.0, 2.0, 3.0],
"f2": ["cat", "cat", "bird"]
}).batch(2)

predictions = manual_model.predict(examples)

print("predictions:\n",predictions)
``````
```predictions:
[[0.1 0.1 0.8]
[0.8 0.1 0.1]
[0.1 0.8 0.1]]
```

Akses struktur:

``````yggdrasil_model_path = manual_model.yggdrasil_model_path_tensor().numpy().decode("utf-8")
print("yggdrasil_model_path:",yggdrasil_model_path)

inspector = tfdf.inspector.make_inspector(yggdrasil_model_path)
print("Input features:", inspector.features())
``````
```yggdrasil_model_path: /tmp/manual_model/assets/
Input features: ["f1" (1; #1), "f2" (4; #2)]
```

Dan tentu saja, Anda dapat memplot model yang dibuat secara manual ini:

``````tfdf.model_plotter.plot_model_in_colab(manual_model)
``````
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Informasi yang saya butuhkan tidak ada" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Terlalu rumit/langkahnya terlalu banyak" },{ "type": "thumb-down", "id": "outOfDate", "label":"Sudah usang" },{ "type": "thumb-down", "id": "translationIssue", "label":"Masalah terjemahan" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Masalah kode / contoh" },{ "type": "thumb-down", "id": "otherDown", "label":"Lainnya" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Mudah dipahami" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Memecahkan masalah saya" },{ "type": "thumb-up", "id": "otherUp", "label":"Lainnya" }]