 Voir sur TensorFlow.org |  Exécuter dans Google Colab |  Voir sur GitHub |  Télécharger le cahier |
Dans ce colab, vous apprendrez à inspecter et à créer directement la structure d'un modèle. Nous supposons que vous êtes familiarisé avec les concepts introduits dans le débutant et intermédiaire colabs.
Dans cette collaboration, vous allez :
Entraînez un modèle de forêt aléatoire et accédez à sa structure par programmation.
Créez un modèle de forêt aléatoire à la main et utilisez-le comme un modèle classique.
Installer
# 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.
La cellule de code cachée limite la hauteur de sortie dans colab.
# 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) + "})"))
Former une forêt aléatoire simple
Nous formons une forêt au hasard comme dans le colab débutant :
# Download the dataset
!wget -q https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins.csv -O /tmp/penguins.csv
# Load a dataset into a Pandas Dataframe.
dataset_df = pd.read_csv("/tmp/penguins.csv")
# Show the first three examples.
print(dataset_df.head(3))
# 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"
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: true
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 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 kernel.cc:988] Loading model from path
[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>
Notez le compute_oob_variable_importances=True hyper-paramètre dans le constructeur de modèle. Cette option calcule l'importance de la variable Out-of-bag (OOB) pendant l'entraînement. C'est une populaire importance variable de permutation pour les modèles Random Forest.
Le calcul de l'importance de la variable OOB n'a pas d'impact sur le modèle final, cela ralentira l'apprentissage sur de grands ensembles de données.
Consultez le résumé du modèle :
%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"
Task: CLASSIFICATION
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
Notez les multiples importances variables avec le nom MEAN_DECREASE_IN_* .
Tracer le modèle
Ensuite, tracez le modèle.
Une forêt aléatoire est un grand modèle (ce modèle a 300 arbres et environ 5 000 nœuds ; voir le résumé ci-dessus). Par conséquent, ne tracez que le premier arbre et limitez les nœuds à la profondeur 3.
tfdf.model_plotter.plot_model_in_colab(model, tree_idx=0, max_depth=3)
Inspecter la structure du modèle
La structure du modèle et méta-données sont disponibles par l'inspecteur créé par make_inspector() .
inspector = model.make_inspector()
Pour notre modèle, les champs d'inspecteur disponibles sont :
[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', 'specialized_header', 'task', 'training_logs', 'variable_importances', 'winner_take_all_inference']
Rappelez - vous de voir l'API référence ou l' utilisation ? pour la documentation intégrée.
?inspector.model_type
Certaines des métadonnées du modèle :
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() est l'évaluation du modèle calculé au cours de la formation. L'ensemble de données utilisé pour cette évaluation dépend de l'algorithme. Par exemple, il peut s'agir du jeu de données de validation ou du jeu de données out-of-bag .
inspector.evaluation()
Evaluation(num_examples=344, accuracy=0.9767441860465116, loss=0.06894904488784283, rmse=None, ndcg=None, aucs=None)
Les importances variables sont :
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
Différentes importances variables ont une sémantique différente. Par exemple, une caractéristique avec une diminution moyenne auc de 0.05 signifie que la suppression de cette fonction de l'ensemble de données de formation réduirait / mal de 5% de l'AUC.
# 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)]
Enfin, accédez à l'arborescence actuelle :
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})
L'extraction d'un arbre n'est pas efficace. Si la vitesse est importante, l'inspection du modèle peut être fait avec les iterate_on_nodes() méthode à la place. Cette méthode est un itérateur de parcours de pré-ordre en profondeur sur tous les nœuds du modèle.
L'exemple suivant calcule combien de fois chaque fonctionnalité est utilisée (c'est une sorte d'importance de variable structurelle) :
# 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
Création d'un modèle à la main
Dans cette section, vous allez créer à la main un petit modèle de forêt aléatoire. Pour le rendre encore plus facile, le modèle ne contiendra qu'un seul arbre simple :
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"]))
Chaque arbre est ajouté un par un.
# 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
builder.add_tree(
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),
mask=["cat", "dog"],
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)))))
Conclure l'écriture de l'arbre
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
Vous pouvez maintenant ouvrir le modèle en tant que modèle Keras normal et faire des prédictions :
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]]
Accéder à la structure :
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)]
Et bien sûr, vous pouvez tracer ce modèle construit manuellement :
tfdf.model_plotter.plot_model_in_colab(manual_model)