निर्णय वन मॉडल का निरीक्षण और डिबग करें

TensorFlow.org पर देखें Google Colab में चलाएं गिटहब पर देखें नोटबुक डाउनलोड करें

इस कोलाब में, आप सीखेंगे कि सीधे मॉडल की संरचना का निरीक्षण और निर्माण कैसे करें। हम मानते हैं आप अवधारणाओं में शुरू से परिचित हैं शुरुआत और मध्यवर्ती colabs।

इस कोलाब में, आप:

  1. एक यादृच्छिक वन मॉडल को प्रशिक्षित करें और इसकी संरचना को प्रोग्रामिक रूप से एक्सेस करें।

  2. हाथ से एक यादृच्छिक वन मॉडल बनाएं और इसे शास्त्रीय मॉडल के रूप में उपयोग करें।

सेट अप

# 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.

छुपा कोड सेल कोलाब में आउटपुट ऊंचाई को सीमित करता है।

एक साधारण यादृच्छिक वन को प्रशिक्षित करें

हम में की तरह एक यादृच्छिक वन प्रशिक्षित शुरुआत colab :

# 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>

नोट compute_oob_variable_importances=True मॉडल निर्माता में अति पैरामीटर। यह विकल्प प्रशिक्षण के दौरान आउट-ऑफ-बैग (OOB) चर महत्व की गणना करता है। यह एक लोकप्रिय है क्रमचय चर महत्व रैंडम वन मॉडल के लिए।

OOB चर महत्व की गणना अंतिम मॉडल को प्रभावित नहीं करती है, यह बड़े डेटासेट पर प्रशिक्षण को धीमा कर देगी।

मॉडल सारांश की जाँच करें:

%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

नोट नाम के साथ कई चर importances MEAN_DECREASE_IN_*

मॉडल प्लॉट करना

अगला, मॉडल को प्लॉट करें।

एक रैंडम फ़ॉरेस्ट एक बड़ा मॉडल है (इस मॉडल में 300 पेड़ और ~ 5k नोड्स हैं; ऊपर सारांश देखें)। इसलिए, केवल पहले पेड़ को प्लॉट करें, और नोड्स को गहराई 3 तक सीमित करें।

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

मॉडल संरचना का निरीक्षण करें

मॉडल संरचना और मेटा डेटा के द्वारा बनाई गई निरीक्षक के माध्यम से उपलब्ध है make_inspector()

inspector = model.make_inspector()

हमारे मॉडल के लिए, उपलब्ध निरीक्षक क्षेत्र हैं:

[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']

देखने के लिए याद रखें एपीआई-संदर्भ या उपयोग ? बिल्टिन दस्तावेज़ीकरण के लिए।

?inspector.model_type

कुछ मॉडल मेटा-डेटा:

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() मॉडल प्रशिक्षण के दौरान अभिकलन के मूल्यांकन है। इस मूल्यांकन के लिए उपयोग किया जाने वाला डेटासेट एल्गोरिथम पर निर्भर करता है। उदाहरण के लिए, यह सत्यापन डेटासेट या आउट-ऑफ-बैग-डेटासेट हो सकता है।

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

परिवर्तनशील महत्व हैं:

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

विभिन्न चर महत्वों के अलग-अलग शब्दार्थ हैं। उदाहरण के लिए, की एयूसी में एक मतलब कमी के साथ एक फीचर 0.05 अर्थ यह है कि प्रशिक्षण डाटासेट से इस विशेषता को हटा कम होगी / 5% से एयूसी चोट लगी है।

# 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)]

अंत में, वास्तविक वृक्ष संरचना तक पहुँचें:

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})

एक पेड़ निकालना कुशल नहीं है। यदि गति महत्वपूर्ण है, मॉडल निरीक्षण के साथ किया जा सकता है iterate_on_nodes() के बजाय विधि। यह विधि मॉडल के सभी नोड्स पर डेप्थ फर्स्ट प्री-ऑर्डर ट्रैवर्सल इटरेटर है।

निम्नलिखित उदाहरण के लिए गणना करता है कि प्रत्येक सुविधा का कितनी बार उपयोग किया जाता है (यह एक प्रकार का संरचनात्मक परिवर्तनशील महत्व है):

# 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

हाथ से एक मॉडल बनाना

इस खंड में आप हाथ से एक छोटा रैंडम फ़ॉरेस्ट मॉडल बनाएंगे। इसे और आसान बनाने के लिए, मॉडल में केवल एक साधारण पेड़ होगा:

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"]))

प्रत्येक पेड़ को एक-एक करके जोड़ा जाता है।

# 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)))))

वृक्ष लेखन समाप्त करें

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

अब आप मॉडल को नियमित केरस मॉडल के रूप में खोल सकते हैं, और भविष्यवाणियां कर सकते हैं:

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]]

संरचना तक पहुँचें:

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)]

और निश्चित रूप से, आप इस मैन्युअल रूप से निर्मित मॉडल को प्लॉट कर सकते हैं:

tfdf.model_plotter.plot_model_in_colab(manual_model)