With TensorFlow¶
About this tutorial¶
This tutorial shows how to combine YDF decision forest models with TensorFlow neural network models. Such composed models are typically used to improve model quality and consume unstructured data.
First, we show how to "stack" a decision forest model on top of a pre-trained neural network model. This approach is particularly useful for the "pre-trained embedding" method: Instead of training a model directly on the features, we train or use an existing model trained on abundant similar-looking data. The output of this first model then serves as the input for a second model trained on our limited dataset. The "pre-trained embedding" approach also simplifies model development for text or images. While text or images require some effort to be fed into a model, embeddings, also known as intermediate representations, are easier to train on.
In the second part, we show how to ensemble multiple decision forest and neural network models. Ensembling with different model types can leverage their complementary strengths as decision forests excel in some examples, while neural networks are superior in others.
Setup¶
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# Install YDF and TensorFlow
!pip install ydf -U -q
!pip install tensorflow -U -q
# Install YDF and TensorFlow
!pip install ydf -U -q
!pip install tensorflow -U -q
Stacking a decision forest and a pre-trained neural network model¶
For this example, we train a model on the Stanford Sentiment Treebank text classification dataset. The prediction task is to classify movie reviews as positive (label 1) or negative (label 0). The dataset is available in TensorFlow Datasets.
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# Install TensorFlow Dataset
!pip install tensorflow-datasets -U -q
# Install TensorFlow Dataset
!pip install tensorflow-datasets -U -q
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# Load the Stanford Sentiment Treebank dataset
import tensorflow_datasets as tfds
raw_dataset = tfds.load("glue/sst2")
raw_train_dataset = raw_dataset["train"].batch(200)
raw_test_dataset = raw_dataset["validation"].batch(200)
# Note: The `raw_dataset["test"]` fold does not have labels. Therefore, we use the
# "validation" fold for testing.
# Display the first 3 test examples.
for example in raw_test_dataset.rebatch(1).take(3):
print("label:", example["label"].numpy())
print("sentence:", example["sentence"].numpy())
print("")
# Load the Stanford Sentiment Treebank dataset
import tensorflow_datasets as tfds
raw_dataset = tfds.load("glue/sst2")
raw_train_dataset = raw_dataset["train"].batch(200)
raw_test_dataset = raw_dataset["validation"].batch(200)
# Note: The `raw_dataset["test"]` fold does not have labels. Therefore, we use the
# "validation" fold for testing.
# Display the first 3 test examples.
for example in raw_test_dataset.rebatch(1).take(3):
print("label:", example["label"].numpy())
print("sentence:", example["sentence"].numpy())
print("")
label: [0] sentence: [b'a valueless kiddie paean to pro basketball underwritten by the nba . '] label: [1] sentence: [b"featuring a dangerously seductive performance from the great daniel auteuil , `` sade '' covers the same period as kaufmann 's `` quills '' with more unsettlingly realistic results . "] label: [0] sentence: [b'i am sorry that i was unable to get the full brunt of the comedy . ']
2024-04-12 10:19:56.151619: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. 2024-04-12 10:19:56.151977: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
The Stanford Sentiment Treebank dataset is relatively small. So instead of training directly on it, we will use an already trained text model. For instance we will use the Universal-Sentence-Encoder neural network which has been trained on a much larger dataset. By reusing USE, we can train a text model quickly and without the need for large datasets.
We use the Universal-Sentence-Encoder available on the TensorFlow Hub website. For other types of unstructured data (e.g., images or audio), corresponding pre-trained models can be found on the TensorFlow Hub website.
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# Install TensorFlow Hub
!pip install tensorflow-hub -U -q
# Install TensorFlow Hub
!pip install tensorflow-hub -U -q
We load the Universal-Sentence-Encoder.
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import tensorflow_hub as hub
pretrained_neural_network_model = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
import tensorflow_hub as hub
pretrained_neural_network_model = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
We apply the Universal-Sentence-Encoder to convert the raw text into embeddings.
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import numpy as np
def apply_neural_network_model(data):
return {
"embedding": pretrained_neural_network_model(data["sentence"]),
"label": data["label"],
}
processed_train_dataset = raw_train_dataset.map(apply_neural_network_model)
processed_test_dataset = raw_test_dataset.map(apply_neural_network_model)
# Display the first 3 processed test examples.
for example in processed_test_dataset.rebatch(1).take(3):
print("label:", example["label"].numpy())
print("embedding:", np.array2string(example["embedding"].numpy(), threshold = 10))
print("")
import numpy as np
def apply_neural_network_model(data):
return {
"embedding": pretrained_neural_network_model(data["sentence"]),
"label": data["label"],
}
processed_train_dataset = raw_train_dataset.map(apply_neural_network_model)
processed_test_dataset = raw_test_dataset.map(apply_neural_network_model)
# Display the first 3 processed test examples.
for example in processed_test_dataset.rebatch(1).take(3):
print("label:", example["label"].numpy())
print("embedding:", np.array2string(example["embedding"].numpy(), threshold = 10))
print("")
label: [0] embedding: [[ 0.0325069 0.02350717 0.0928923 ... -0.05117338 -0.0959709 0.01819227]] label: [1] embedding: [[-0.02955496 0.04451125 0.03023855 ... 0.02529242 0.06243655 0.0004012 ]] label: [0] embedding: [[-0.02897574 -0.0021273 0.02524822 ... -0.00667133 0.07221754 0.05386261]]
2024-04-12 10:20:03.889224: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. 2024-04-12 10:20:03.889676: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
Then, we train a gradient-boosted trees model on the Neural Network output.
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import ydf
decision_forest_model = ydf.GradientBoostedTreesLearner(label="label", num_trees=100).train(processed_train_dataset)
import ydf
decision_forest_model = ydf.GradientBoostedTreesLearner(label="label", num_trees=100).train(processed_train_dataset)
Train model on 67349 examples Model trained in 0:00:54.704927
Notice that the YDF model is trained on a TensorFlow Dataset directly.
Looking at the more is important.
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decision_forest_model.describe()
decision_forest_model.describe()
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Name : GRADIENT_BOOSTED_TREES
Task : CLASSIFICATION
Label : label
Features (512) : embedding.000_of_512 embedding.001_of_512 embedding.002_of_512 embedding.003_of_512 embedding.004_of_512 embedding.005_of_512 embedding.006_of_512 embedding.007_of_512 embedding.008_of_512 embedding.009_of_512 embedding.010_of_512 embedding.011_of_512 embedding.012_of_512 embedding.013_of_512 embedding.014_of_512 embedding.015_of_512 embedding.016_of_512 embedding.017_of_512 embedding.018_of_512 embedding.019_of_512 embedding.020_of_512 embedding.021_of_512 embedding.022_of_512 embedding.023_of_512 embedding.024_of_512 embedding.025_of_512 embedding.026_of_512 embedding.027_of_512 embedding.028_of_512 embedding.029_of_512 embedding.030_of_512 embedding.031_of_512 embedding.032_of_512 embedding.033_of_512 embedding.034_of_512 embedding.035_of_512 embedding.036_of_512 embedding.037_of_512 embedding.038_of_512 embedding.039_of_512 embedding.040_of_512 embedding.041_of_512 embedding.042_of_512 embedding.043_of_512 embedding.044_of_512 embedding.045_of_512 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Weights : None
Trained with tuner : No
Model size : 1643 kB
Task : CLASSIFICATION
Label : label
Features (512) : embedding.000_of_512 embedding.001_of_512 embedding.002_of_512 embedding.003_of_512 embedding.004_of_512 embedding.005_of_512 embedding.006_of_512 embedding.007_of_512 embedding.008_of_512 embedding.009_of_512 embedding.010_of_512 embedding.011_of_512 embedding.012_of_512 embedding.013_of_512 embedding.014_of_512 embedding.015_of_512 embedding.016_of_512 embedding.017_of_512 embedding.018_of_512 embedding.019_of_512 embedding.020_of_512 embedding.021_of_512 embedding.022_of_512 embedding.023_of_512 embedding.024_of_512 embedding.025_of_512 embedding.026_of_512 embedding.027_of_512 embedding.028_of_512 embedding.029_of_512 embedding.030_of_512 embedding.031_of_512 embedding.032_of_512 embedding.033_of_512 embedding.034_of_512 embedding.035_of_512 embedding.036_of_512 embedding.037_of_512 embedding.038_of_512 embedding.039_of_512 embedding.040_of_512 embedding.041_of_512 embedding.042_of_512 embedding.043_of_512 embedding.044_of_512 embedding.045_of_512 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Weights : None
Trained with tuner : No
Model size : 1643 kB
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402: "embedding.401_of_512" NUMERICAL mean:-0.0120933 min:-0.110716 max:0.107581 sd:0.0391564 dtype:DTYPE_FLOAT32 403: "embedding.402_of_512" NUMERICAL mean:-0.00798588 min:-0.114245 max:0.109355 sd:0.0417294 dtype:DTYPE_FLOAT32 404: "embedding.403_of_512" NUMERICAL mean:-0.00715776 min:-0.110958 max:0.109412 sd:0.0426725 dtype:DTYPE_FLOAT32 405: "embedding.404_of_512" NUMERICAL mean:0.0421547 min:-0.0936097 max:0.14341 sd:0.0449053 dtype:DTYPE_FLOAT32 406: "embedding.405_of_512" NUMERICAL mean:0.0138744 min:-0.101141 max:0.110993 sd:0.0409959 dtype:DTYPE_FLOAT32 407: "embedding.406_of_512" NUMERICAL mean:0.0221997 min:-0.10012 max:0.12351 sd:0.0512918 dtype:DTYPE_FLOAT32 408: "embedding.407_of_512" NUMERICAL mean:0.00840243 min:-0.100731 max:0.108785 sd:0.0385815 dtype:DTYPE_FLOAT32 409: "embedding.408_of_512" NUMERICAL mean:-0.00995255 min:-0.119931 max:0.107382 sd:0.0397331 dtype:DTYPE_FLOAT32 410: "embedding.409_of_512" NUMERICAL mean:0.00122281 min:-0.123687 max:0.110221 sd:0.0419264 dtype:DTYPE_FLOAT32 411: "embedding.410_of_512" NUMERICAL mean:0.00722765 min:-0.120324 max:0.118298 sd:0.0397953 dtype:DTYPE_FLOAT32 412: "embedding.411_of_512" NUMERICAL mean:0.00372596 min:-0.110838 max:0.104775 sd:0.0397102 dtype:DTYPE_FLOAT32 413: "embedding.412_of_512" NUMERICAL mean:0.00750692 min:-0.105861 max:0.113608 sd:0.0404272 dtype:DTYPE_FLOAT32 414: "embedding.413_of_512" NUMERICAL mean:0.00702045 min:-0.100497 max:0.109256 sd:0.040414 dtype:DTYPE_FLOAT32 415: "embedding.414_of_512" NUMERICAL mean:0.0129925 min:-0.104637 max:0.129069 sd:0.0476144 dtype:DTYPE_FLOAT32 416: "embedding.415_of_512" NUMERICAL mean:0.00895771 min:-0.103221 max:0.131867 sd:0.0416565 dtype:DTYPE_FLOAT32 417: "embedding.416_of_512" NUMERICAL mean:-0.0113754 min:-0.108457 max:0.108912 sd:0.039076 dtype:DTYPE_FLOAT32 418: "embedding.417_of_512" NUMERICAL mean:-0.00972072 min:-0.108896 max:0.120041 sd:0.039969 dtype:DTYPE_FLOAT32 419: "embedding.418_of_512" NUMERICAL mean:0.0103305 min:-0.115689 max:0.117791 sd:0.0438928 dtype:DTYPE_FLOAT32 420: "embedding.419_of_512" NUMERICAL mean:-0.011858 min:-0.110158 max:0.112286 sd:0.0405172 dtype:DTYPE_FLOAT32 421: "embedding.420_of_512" NUMERICAL mean:0.0113019 min:-0.117355 max:0.110719 sd:0.0390142 dtype:DTYPE_FLOAT32 422: "embedding.421_of_512" NUMERICAL mean:-0.00325833 min:-0.11971 max:0.0998387 sd:0.0386342 dtype:DTYPE_FLOAT32 423: "embedding.422_of_512" NUMERICAL mean:-0.0175019 min:-0.121014 max:0.108533 sd:0.0430717 dtype:DTYPE_FLOAT32 424: "embedding.423_of_512" NUMERICAL mean:0.00661466 min:-0.121052 max:0.104438 sd:0.0401472 dtype:DTYPE_FLOAT32 425: "embedding.424_of_512" NUMERICAL mean:0.0157025 min:-0.119043 max:0.121705 sd:0.0455012 dtype:DTYPE_FLOAT32 426: "embedding.425_of_512" NUMERICAL mean:0.00671776 min:-0.119955 max:0.135544 sd:0.046337 dtype:DTYPE_FLOAT32 427: "embedding.426_of_512" NUMERICAL mean:0.00625655 min:-0.110938 max:0.120801 sd:0.0434661 dtype:DTYPE_FLOAT32 428: "embedding.427_of_512" NUMERICAL mean:0.0204839 min:-0.112639 max:0.12859 sd:0.0461795 dtype:DTYPE_FLOAT32 429: "embedding.428_of_512" NUMERICAL mean:-0.00954845 min:-0.131481 max:0.103867 sd:0.0409481 dtype:DTYPE_FLOAT32 430: "embedding.429_of_512" NUMERICAL mean:0.0227497 min:-0.114759 max:0.128784 sd:0.0461912 dtype:DTYPE_FLOAT32 431: "embedding.430_of_512" NUMERICAL mean:-0.0143054 min:-0.116372 max:0.0982788 sd:0.0397653 dtype:DTYPE_FLOAT32 432: "embedding.431_of_512" NUMERICAL mean:0.00108119 min:-0.10975 max:0.113431 sd:0.0395805 dtype:DTYPE_FLOAT32 433: "embedding.432_of_512" NUMERICAL mean:-0.0124634 min:-0.128303 max:0.122121 sd:0.043612 dtype:DTYPE_FLOAT32 434: "embedding.433_of_512" NUMERICAL mean:-0.000974066 min:-0.127452 max:0.143975 sd:0.0512878 dtype:DTYPE_FLOAT32 435: "embedding.434_of_512" NUMERICAL mean:-0.000695708 min:-0.117519 max:0.132419 sd:0.048299 dtype:DTYPE_FLOAT32 436: "embedding.435_of_512" NUMERICAL mean:-0.00800422 min:-0.11716 max:0.106095 sd:0.0385783 dtype:DTYPE_FLOAT32 437: "embedding.436_of_512" NUMERICAL mean:-0.00449899 min:-0.119801 max:0.13136 sd:0.0450766 dtype:DTYPE_FLOAT32 438: "embedding.437_of_512" NUMERICAL mean:0.00152719 min:-0.101368 max:0.111586 sd:0.0373092 dtype:DTYPE_FLOAT32 439: "embedding.438_of_512" NUMERICAL mean:-0.00746199 min:-0.110446 max:0.107505 sd:0.0409118 dtype:DTYPE_FLOAT32 440: "embedding.439_of_512" NUMERICAL mean:-0.000542517 min:-0.126726 max:0.150725 sd:0.0498822 dtype:DTYPE_FLOAT32 441: "embedding.440_of_512" NUMERICAL mean:0.0162618 min:-0.110413 max:0.112766 sd:0.039636 dtype:DTYPE_FLOAT32 442: "embedding.441_of_512" NUMERICAL mean:-0.0252852 min:-0.140847 max:0.123998 sd:0.045552 dtype:DTYPE_FLOAT32 443: "embedding.442_of_512" NUMERICAL mean:-0.00971423 min:-0.14093 max:0.115633 sd:0.0430468 dtype:DTYPE_FLOAT32 444: "embedding.443_of_512" NUMERICAL mean:-0.00171618 min:-0.130186 max:0.122902 sd:0.0446095 dtype:DTYPE_FLOAT32 445: "embedding.444_of_512" NUMERICAL mean:0.0108986 min:-0.114492 max:0.110956 sd:0.0418642 dtype:DTYPE_FLOAT32 446: "embedding.445_of_512" NUMERICAL mean:-0.00650931 min:-0.106713 max:0.126819 sd:0.0394136 dtype:DTYPE_FLOAT32 447: "embedding.446_of_512" NUMERICAL mean:7.68803e-05 min:-0.107121 max:0.104196 sd:0.0371536 dtype:DTYPE_FLOAT32 448: "embedding.447_of_512" NUMERICAL mean:-0.00166973 min:-0.106304 max:0.113193 sd:0.0417721 dtype:DTYPE_FLOAT32 449: "embedding.448_of_512" NUMERICAL mean:0.00143107 min:-0.112879 max:0.117707 sd:0.0438514 dtype:DTYPE_FLOAT32 450: "embedding.449_of_512" NUMERICAL mean:0.00577755 min:-0.114301 max:0.116267 sd:0.0413021 dtype:DTYPE_FLOAT32 451: "embedding.450_of_512" NUMERICAL mean:0.00523777 min:-0.121324 max:0.109753 sd:0.0422962 dtype:DTYPE_FLOAT32 452: "embedding.451_of_512" NUMERICAL mean:0.00232381 min:-0.107421 max:0.116006 sd:0.0411045 dtype:DTYPE_FLOAT32 453: "embedding.452_of_512" NUMERICAL mean:0.0131371 min:-0.119915 max:0.110052 sd:0.0388742 dtype:DTYPE_FLOAT32 454: "embedding.453_of_512" NUMERICAL mean:0.00384022 min:-0.113448 max:0.103866 sd:0.0399839 dtype:DTYPE_FLOAT32 455: "embedding.454_of_512" NUMERICAL mean:0.00746132 min:-0.11867 max:0.107229 sd:0.0393659 dtype:DTYPE_FLOAT32 456: "embedding.455_of_512" NUMERICAL mean:0.0217711 min:-0.130108 max:0.130266 sd:0.0457751 dtype:DTYPE_FLOAT32 457: "embedding.456_of_512" NUMERICAL mean:-0.00486573 min:-0.125269 max:0.103216 sd:0.0417326 dtype:DTYPE_FLOAT32 458: "embedding.457_of_512" NUMERICAL mean:-0.00370284 min:-0.152411 max:0.118391 sd:0.0475716 dtype:DTYPE_FLOAT32 459: "embedding.458_of_512" NUMERICAL mean:-0.0252088 min:-0.129244 max:0.110772 sd:0.0447074 dtype:DTYPE_FLOAT32 460: "embedding.459_of_512" NUMERICAL mean:0.0196455 min:-0.107007 max:0.110025 sd:0.0371162 dtype:DTYPE_FLOAT32 461: "embedding.460_of_512" NUMERICAL mean:0.00486969 min:-0.133702 max:0.117438 sd:0.0404765 dtype:DTYPE_FLOAT32 462: "embedding.461_of_512" NUMERICAL mean:0.00879324 min:-0.123721 max:0.109769 sd:0.0418885 dtype:DTYPE_FLOAT32 463: "embedding.462_of_512" NUMERICAL mean:0.00541842 min:-0.103881 max:0.115937 sd:0.040526 dtype:DTYPE_FLOAT32 464: "embedding.463_of_512" NUMERICAL mean:0.0112013 min:-0.129965 max:0.125135 sd:0.0445652 dtype:DTYPE_FLOAT32 465: "embedding.464_of_512" NUMERICAL mean:-0.00978469 min:-0.112536 max:0.136367 sd:0.0432779 dtype:DTYPE_FLOAT32 466: "embedding.465_of_512" NUMERICAL mean:-0.00372292 min:-0.132975 max:0.107404 sd:0.0434915 dtype:DTYPE_FLOAT32 467: "embedding.466_of_512" NUMERICAL mean:0.000832962 min:-0.106678 max:0.109534 sd:0.041454 dtype:DTYPE_FLOAT32 468: "embedding.467_of_512" NUMERICAL mean:0.0128707 min:-0.123202 max:0.108301 sd:0.036966 dtype:DTYPE_FLOAT32 469: "embedding.468_of_512" NUMERICAL mean:0.00143448 min:-0.109754 max:0.115596 sd:0.0410802 dtype:DTYPE_FLOAT32 470: "embedding.469_of_512" NUMERICAL mean:0.00821259 min:-0.0968573 max:0.116681 sd:0.037229 dtype:DTYPE_FLOAT32 471: "embedding.470_of_512" NUMERICAL mean:-0.0126405 min:-0.11236 max:0.104975 sd:0.0410705 dtype:DTYPE_FLOAT32 472: "embedding.471_of_512" NUMERICAL mean:0.00967789 min:-0.114741 max:0.113365 sd:0.0415494 dtype:DTYPE_FLOAT32 473: "embedding.472_of_512" NUMERICAL mean:0.0051147 min:-0.116287 max:0.123708 sd:0.038196 dtype:DTYPE_FLOAT32 474: "embedding.473_of_512" NUMERICAL mean:0.00460656 min:-0.117806 max:0.116034 sd:0.0417151 dtype:DTYPE_FLOAT32 475: "embedding.474_of_512" NUMERICAL mean:-0.00244138 min:-0.103319 max:0.116585 sd:0.0374234 dtype:DTYPE_FLOAT32 476: "embedding.475_of_512" NUMERICAL mean:-0.00797766 min:-0.112168 max:0.110854 sd:0.043268 dtype:DTYPE_FLOAT32 477: "embedding.476_of_512" NUMERICAL mean:-0.0123356 min:-0.118527 max:0.110389 sd:0.0415487 dtype:DTYPE_FLOAT32 478: "embedding.477_of_512" NUMERICAL mean:-0.00891097 min:-0.109911 max:0.114824 sd:0.0409558 dtype:DTYPE_FLOAT32 479: "embedding.478_of_512" NUMERICAL mean:0.0531792 min:-0.123494 max:0.14429 sd:0.0446525 dtype:DTYPE_FLOAT32 480: "embedding.479_of_512" NUMERICAL mean:0.00310177 min:-0.126525 max:0.135642 sd:0.0508086 dtype:DTYPE_FLOAT32 481: "embedding.480_of_512" NUMERICAL mean:0.00178777 min:-0.101512 max:0.111535 sd:0.0393616 dtype:DTYPE_FLOAT32 482: "embedding.481_of_512" NUMERICAL mean:0.000436977 min:-0.141595 max:0.116526 sd:0.0498771 dtype:DTYPE_FLOAT32 483: "embedding.482_of_512" NUMERICAL mean:0.0139387 min:-0.109079 max:0.125151 sd:0.0395955 dtype:DTYPE_FLOAT32 484: "embedding.483_of_512" NUMERICAL mean:-0.0190178 min:-0.116579 max:0.12211 sd:0.0404221 dtype:DTYPE_FLOAT32 485: "embedding.484_of_512" NUMERICAL mean:0.0111983 min:-0.115318 max:0.114151 sd:0.0407415 dtype:DTYPE_FLOAT32 486: "embedding.485_of_512" NUMERICAL mean:-0.0210413 min:-0.12817 max:0.102505 sd:0.0409111 dtype:DTYPE_FLOAT32 487: "embedding.486_of_512" NUMERICAL mean:0.00291598 min:-0.136717 max:0.132649 sd:0.0483605 dtype:DTYPE_FLOAT32 488: "embedding.487_of_512" NUMERICAL mean:0.0258506 min:-0.118507 max:0.139141 sd:0.0476916 dtype:DTYPE_FLOAT32 489: "embedding.488_of_512" NUMERICAL mean:0.00950834 min:-0.117085 max:0.104573 sd:0.0394689 dtype:DTYPE_FLOAT32 490: "embedding.489_of_512" NUMERICAL mean:-0.00655678 min:-0.113501 max:0.116317 sd:0.0412641 dtype:DTYPE_FLOAT32 491: "embedding.490_of_512" NUMERICAL mean:0.0025444 min:-0.0976397 max:0.133059 sd:0.0391231 dtype:DTYPE_FLOAT32 492: "embedding.491_of_512" NUMERICAL mean:-0.00116524 min:-0.115012 max:0.108975 sd:0.0373331 dtype:DTYPE_FLOAT32 493: "embedding.492_of_512" NUMERICAL mean:-0.00805514 min:-0.112223 max:0.118394 sd:0.0409569 dtype:DTYPE_FLOAT32 494: "embedding.493_of_512" NUMERICAL mean:-0.00381922 min:-0.109779 max:0.113538 sd:0.0375221 dtype:DTYPE_FLOAT32 495: "embedding.494_of_512" NUMERICAL mean:0.0192517 min:-0.108658 max:0.118238 sd:0.0414103 dtype:DTYPE_FLOAT32 496: "embedding.495_of_512" NUMERICAL mean:-0.00252727 min:-0.118617 max:0.100404 sd:0.0398346 dtype:DTYPE_FLOAT32 497: "embedding.496_of_512" NUMERICAL mean:-0.000870086 min:-0.10941 max:0.119059 sd:0.043479 dtype:DTYPE_FLOAT32 498: "embedding.497_of_512" NUMERICAL mean:-0.00296294 min:-0.123757 max:0.109776 sd:0.0420959 dtype:DTYPE_FLOAT32 499: "embedding.498_of_512" NUMERICAL mean:0.0127804 min:-0.138546 max:0.154906 sd:0.0511673 dtype:DTYPE_FLOAT32 500: "embedding.499_of_512" NUMERICAL mean:-0.00481274 min:-0.104637 max:0.112387 sd:0.0419786 dtype:DTYPE_FLOAT32 501: "embedding.500_of_512" NUMERICAL mean:-0.012455 min:-0.109502 max:0.102241 sd:0.0402451 dtype:DTYPE_FLOAT32 502: "embedding.501_of_512" NUMERICAL mean:-0.0236005 min:-0.117228 max:0.124977 sd:0.0464432 dtype:DTYPE_FLOAT32 503: "embedding.502_of_512" NUMERICAL mean:0.00916425 min:-0.128705 max:0.110148 sd:0.0412428 dtype:DTYPE_FLOAT32 504: "embedding.503_of_512" NUMERICAL mean:-0.0099854 min:-0.179229 max:0.112813 sd:0.0666002 dtype:DTYPE_FLOAT32 505: "embedding.504_of_512" NUMERICAL mean:0.0140659 min:-0.124558 max:0.131239 sd:0.0459631 dtype:DTYPE_FLOAT32 506: "embedding.505_of_512" NUMERICAL mean:0.00529723 min:-0.119894 max:0.104362 sd:0.0399805 dtype:DTYPE_FLOAT32 507: "embedding.506_of_512" NUMERICAL mean:-0.00319069 min:-0.111178 max:0.108562 sd:0.040611 dtype:DTYPE_FLOAT32 508: "embedding.507_of_512" NUMERICAL mean:-0.00332249 min:-0.108088 max:0.118358 sd:0.0396039 dtype:DTYPE_FLOAT32 509: "embedding.508_of_512" NUMERICAL mean:-0.00396023 min:-0.11048 max:0.107852 sd:0.0375341 dtype:DTYPE_FLOAT32 510: "embedding.509_of_512" NUMERICAL mean:-0.00917504 min:-0.116661 max:0.100524 sd:0.0361387 dtype:DTYPE_FLOAT32 511: "embedding.510_of_512" NUMERICAL mean:0.037723 min:-0.0965471 max:0.140981 sd:0.0479428 dtype:DTYPE_FLOAT32 512: "embedding.511_of_512" NUMERICAL mean:0.00788656 min:-0.116457 max:0.102988 sd:0.0402552 dtype:DTYPE_FLOAT32 CATEGORICAL: 1 (0.194932%) 0: "label" CATEGORICAL has-dict vocab-size:3 zero-ood-items most-frequent:"1" 37569 (55.7826%) dtype:DTYPE_INT64 Terminology: nas: Number of non-available (i.e. missing) values. ood: Out of dictionary. manually-defined: Attribute whose 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.
The following evaluation is computed on the validation or out-of-bag dataset.
Task: CLASSIFICATION
Label: label
Loss (BINOMIAL_LOG_LIKELIHOOD): 0.738938
Accuracy: 0.837441 CI95[W][0 1]
ErrorRate: : 0.162559
Confusion Table:
truth\prediction
1 0
1 3230 559
0 536 2411
Total: 6736
Variable importances measure the importance of an input feature for a model.
1. "embedding.171_of_512" 0.209350 ################
2. "embedding.111_of_512" 0.204045 #############
3. "embedding.384_of_512" 0.178102 ####
4. "embedding.291_of_512" 0.177804 ###
5. "embedding.408_of_512" 0.177389 ###
6. "embedding.365_of_512" 0.176330 ###
7. "embedding.188_of_512" 0.175333 ###
8. "embedding.276_of_512" 0.175256 ##
9. "embedding.036_of_512" 0.174208 ##
10. "embedding.350_of_512" 0.174185 ##
11. "embedding.343_of_512" 0.174146 ##
12. "embedding.022_of_512" 0.173972 ##
13. "embedding.333_of_512" 0.173661 ##
14. "embedding.332_of_512" 0.173290 ##
15. "embedding.307_of_512" 0.172651 ##
16. "embedding.058_of_512" 0.172588 #
17. "embedding.167_of_512" 0.172248 #
18. "embedding.184_of_512" 0.172189 #
19. "embedding.041_of_512" 0.172093 #
20. "embedding.118_of_512" 0.171974 #
21. "embedding.063_of_512" 0.171966 #
22. "embedding.065_of_512" 0.171925 #
23. "embedding.132_of_512" 0.171918 #
24. "embedding.346_of_512" 0.171654 #
25. "embedding.092_of_512" 0.171654 #
26. "embedding.182_of_512" 0.171428 #
27. "embedding.043_of_512" 0.171327 #
28. "embedding.329_of_512" 0.171311 #
29. "embedding.342_of_512" 0.171305 #
30. "embedding.140_of_512" 0.171297 #
31. "embedding.302_of_512" 0.171015 #
32. "embedding.297_of_512" 0.170923 #
33. "embedding.478_of_512" 0.170846 #
34. "embedding.511_of_512" 0.170711 #
35. "embedding.227_of_512" 0.170582 #
36. "embedding.175_of_512" 0.170326 #
37. "embedding.237_of_512" 0.170321 #
38. "embedding.292_of_512" 0.170164 #
39. "embedding.363_of_512" 0.170138 #
40. "embedding.319_of_512" 0.169955
41. "embedding.424_of_512" 0.169759
42. "embedding.079_of_512" 0.169743
43. "embedding.306_of_512" 0.169673
44. "embedding.368_of_512" 0.169648
45. "embedding.494_of_512" 0.169495
46. "embedding.052_of_512" 0.169474
47. "embedding.194_of_512" 0.169375
48. "embedding.483_of_512" 0.169328
49. "embedding.240_of_512" 0.169305
50. "embedding.046_of_512" 0.169244
51. "embedding.243_of_512" 0.169234
52. "embedding.215_of_512" 0.169210
53. "embedding.152_of_512" 0.169186
54. "embedding.502_of_512" 0.169168
55. "embedding.471_of_512" 0.169095
56. "embedding.376_of_512" 0.169079
57. "embedding.244_of_512" 0.169073
58. "embedding.219_of_512" 0.169046
59. "embedding.308_of_512" 0.169035
60. "embedding.287_of_512" 0.169025
61. "embedding.044_of_512" 0.169018
62. "embedding.195_of_512" 0.168966
63. "embedding.130_of_512" 0.168943
64. "embedding.469_of_512" 0.168916
65. "embedding.352_of_512" 0.168860
66. "embedding.358_of_512" 0.168809
67. "embedding.289_of_512" 0.168801
68. "embedding.142_of_512" 0.168721
69. "embedding.503_of_512" 0.168673
70. "embedding.205_of_512" 0.168658
71. "embedding.126_of_512" 0.168630
72. "embedding.116_of_512" 0.168582
73. "embedding.185_of_512" 0.168548
74. "embedding.228_of_512" 0.168537
75. "embedding.225_of_512" 0.168512
76. "embedding.427_of_512" 0.168510
77. "embedding.443_of_512" 0.168502
78. "embedding.267_of_512" 0.168497
79. "embedding.399_of_512" 0.168497
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438. "embedding.394_of_512" 3.113460
439. "embedding.316_of_512" 3.064561
440. "embedding.149_of_512" 3.053578
441. "embedding.369_of_512" 2.945368
442. "embedding.028_of_512" 2.810905
443. "embedding.181_of_512" 2.743388
444. "embedding.196_of_512" 2.742419
445. "embedding.407_of_512" 2.738602
446. "embedding.273_of_512" 2.672149
447. "embedding.015_of_512" 2.670484
448. "embedding.295_of_512" 2.441439
449. "embedding.285_of_512" 2.377308
450. "embedding.395_of_512" 2.374672
451. "embedding.485_of_512" 2.341882
452. "embedding.392_of_512" 2.339278
453. "embedding.252_of_512" 2.329295
454. "embedding.154_of_512" 2.317964
455. "embedding.017_of_512" 2.229209
456. "embedding.073_of_512" 2.228032
457. "embedding.129_of_512" 2.217764
458. "embedding.481_of_512" 2.177979
459. "embedding.367_of_512" 2.111253
460. "embedding.383_of_512" 2.013000
461. "embedding.282_of_512" 2.006441
462. "embedding.115_of_512" 1.967250
463. "embedding.374_of_512" 1.892543
464. "embedding.055_of_512" 1.868395
465. "embedding.121_of_512" 1.431630
466. "embedding.047_of_512" 1.068393
467. "embedding.029_of_512" 1.015992
468. "embedding.031_of_512" 0.881612
469. "embedding.299_of_512" 0.785336
470. "embedding.401_of_512" 0.655422
471. "embedding.214_of_512" 0.349836
472. "embedding.248_of_512" 0.313830
473. "embedding.486_of_512" 0.227126
474. "embedding.509_of_512" 0.131360
475. "embedding.435_of_512" 0.051429
476. "embedding.034_of_512" 0.029603
Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.
Num trees : 100
Only printing the first tree.
Tree #0:
"embedding.171_of_512">=-0.0184792 [s:0.0574827 n:60613 np:32412 miss:1] ; pred:1.68723e-08
├─(pos)─ "embedding.111_of_512">=-0.00413349 [s:0.0157348 n:32412 np:21552 miss:0] ; pred:-0.0906464
| ├─(pos)─ "embedding.291_of_512">=0.0227038 [s:0.00645317 n:21552 np:15994 miss:1] ; pred:-0.126738
| | ├─(pos)─ "embedding.171_of_512">=0.0260582 [s:0.00247006 n:15994 np:11830 miss:0] ; pred:-0.145932
| | | ├─(pos)─ "embedding.291_of_512">=0.070725 [s:0.00102745 n:11830 np:7037 miss:0] ; pred:-0.157884
| | | | ├─(pos)─ pred:-0.168606
| | | | └─(neg)─ pred:-0.142141
| | | └─(neg)─ "embedding.022_of_512">=-0.029009 [s:0.00552812 n:4164 np:3265 miss:1] ; pred:-0.111978
| | | ├─(pos)─ pred:-0.127791
| | | └─(neg)─ pred:-0.054546
| | └─(neg)─ "embedding.111_of_512">=0.0393142 [s:0.00708494 n:5558 np:2244 miss:0] ; pred:-0.0715036
| | ├─(pos)─ "embedding.404_of_512">=0.10271 [s:0.00525161 n:2244 np:91 miss:0] ; pred:-0.112964
| | | ├─(pos)─ pred:0.0299088
| | | └─(neg)─ pred:-0.119003
| | └─(neg)─ "embedding.171_of_512">=0.0234795 [s:0.00580773 n:3314 np:1817 miss:0] ; pred:-0.0434295
| | ├─(pos)─ pred:-0.071467
| | └─(neg)─ pred:-0.0093987
| └─(neg)─ "embedding.291_of_512">=0.0634367 [s:0.0133273 n:10860 np:4737 miss:0] ; pred:-0.0190217
| ├─(pos)─ "embedding.384_of_512">=0.0238699 [s:0.0101704 n:4737 np:966 miss:0] ; pred:-0.0722208
| | ├─(pos)─ "embedding.276_of_512">=-0.0597052 [s:0.0231348 n:966 np:722 miss:1] ; pred:0.00854195
| | | ├─(pos)─ pred:-0.0272976
| | | └─(neg)─ pred:0.114592
| | └─(neg)─ "embedding.111_of_512">=-0.0612384 [s:0.00659412 n:3771 np:3147 miss:1] ; pred:-0.0929095
| | ├─(pos)─ pred:-0.107566
| | └─(neg)─ pred:-0.0189937
| └─(neg)─ "embedding.111_of_512">=-0.0621904 [s:0.00782627 n:6123 np:5273 miss:1] ; pred:0.0221354
| ├─(pos)─ "embedding.289_of_512">=0.0119708 [s:0.00516395 n:5273 np:2436 miss:1] ; pred:0.00773873
| | ├─(pos)─ pred:-0.0236942
| | └─(neg)─ pred:0.0347287
| └─(neg)─ "embedding.140_of_512">=0.0595422 [s:0.0123933 n:850 np:263 miss:0] ; pred:0.111445
| ├─(pos)─ pred:0.0440332
| └─(neg)─ pred:0.141649
└─(neg)─ "embedding.111_of_512">=-0.047889 [s:0.0143104 n:28201 np:9723 miss:1] ; pred:0.104182
├─(pos)─ "embedding.291_of_512">=0.0518959 [s:0.0110193 n:9723 np:4373 miss:0] ; pred:0.0373387
| ├─(pos)─ "embedding.384_of_512">=0.0086953 [s:0.0114093 n:4373 np:2134 miss:0] ; pred:-0.00972295
| | ├─(pos)─ "embedding.132_of_512">=-0.0165455 [s:0.0113251 n:2134 np:1244 miss:1] ; pred:0.0346238
| | | ├─(pos)─ pred:-0.00186079
| | | └─(neg)─ pred:0.0856202
| | └─(neg)─ "embedding.368_of_512">=0.0546857 [s:0.0104556 n:2239 np:323 miss:0] ; pred:-0.05199
| | ├─(pos)─ pred:0.0489525
| | └─(neg)─ pred:-0.0690069
| └─(neg)─ "embedding.111_of_512">=0.0176729 [s:0.00952959 n:5350 np:879 miss:0] ; pred:0.0758061
| ├─(pos)─ "embedding.257_of_512">=0.0422597 [s:0.0121365 n:879 np:273 miss:0] ; pred:-0.0134314
| | ├─(pos)─ pred:-0.0799594
| | └─(neg)─ pred:0.0165391
| └─(neg)─ "embedding.171_of_512">=-0.0514695 [s:0.00725224 n:4471 np:2448 miss:1] ; pred:0.0933503
| ├─(pos)─ pred:0.0619719
| └─(neg)─ pred:0.131321
└─(neg)─ "embedding.384_of_512">=8.67305e-05 [s:0.00620107 n:18478 np:14151 miss:1] ; pred:0.139354
├─(pos)─ "embedding.111_of_512">=-0.0819011 [s:0.00399313 n:14151 np:6809 miss:1] ; pred:0.157004
| ├─(pos)─ "embedding.140_of_512">=0.0381925 [s:0.00527311 n:6809 np:1738 miss:0] ; pred:0.130407
| | ├─(pos)─ pred:0.0801316
| | └─(neg)─ pred:0.147638
| └─(neg)─ "embedding.365_of_512">=0.0224651 [s:0.00220593 n:7342 np:772 miss:0] ; pred:0.18167
| ├─(pos)─ pred:0.126134
| └─(neg)─ pred:0.188195
└─(neg)─ "embedding.022_of_512">=0.0520979 [s:0.00928281 n:4327 np:880 miss:0] ; pred:0.0816329
├─(pos)─ "embedding.483_of_512">=-0.0492134 [s:0.0154152 n:880 np:453 miss:1] ; pred:0.00434318
| ├─(pos)─ pred:0.0532019
| └─(neg)─ pred:-0.0474906
└─(neg)─ "embedding.177_of_512">=0.0136228 [s:0.00564147 n:3447 np:1675 miss:0] ; pred:0.101365
├─(pos)─ pred:0.0700516
└─(neg)─ pred:0.130963
The decision forest model use 512 numerical features as input. These are the 512 dimensions of the embedding generated by the neural network.
Then, we evaluate the model quality.
In [7]:
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decision_forest_model.evaluate(processed_test_dataset)
decision_forest_model.evaluate(processed_test_dataset)
Out[7]:
accuracy:
0.792431
AUC: '0' vs others:
0.881988
PR-AUC: '0' vs others:
0.870899
loss:
0.43559
872
872
| Label \ Pred | 1 | 0 |
|---|---|---|
| 1 | 365 | 102 |
| 0 | 79 | 326 |
To simplify productionization, it is often good to save together the decision forest model and the preprocessing neural network together in a single model.
In [8]:
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# Converts the YDF decision forest model into a TensorFlow function.
tf_decision_forest_model = decision_forest_model.to_tensorflow_function()
# Converts the YDF decision forest model into a TensorFlow function.
tf_decision_forest_model = decision_forest_model.to_tensorflow_function()
[INFO 24-04-12 10:21:03.6602 CEST kernel.cc:1233] Loading model from path /tmp/tmpicaxyl1d/ with prefix a15dba1c_ [INFO 24-04-12 10:21:03.6767 CEST quick_scorer_extended.cc:911] The binary was compiled without AVX2 support, but your CPU supports it. Enable it for faster model inference. [INFO 24-04-12 10:21:03.6774 CEST abstract_model.cc:1344] Engine "GradientBoostedTreesQuickScorerExtended" built [INFO 24-04-12 10:21:03.6775 CEST kernel.cc:1061] Use fast generic engine
In [9]:
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# Save both the decision forests and pre-processing neural network to a TensorFlow SaveModel.
import tensorflow as tf
# The stacked model applies the neural network and decision forest models in sequence.
class StackedModel(tf.Module):
def __init__(self, nn, df):
self._nn = nn
self._df = df
@tf.function
def __call__(self, raw_features):
sentence = raw_features["sentence"]
embedding = self._nn(sentence)
processed_features = {"embedding": embedding}
return self._df(processed_features)
stacked_model = StackedModel(pretrained_neural_network_model, tf_decision_forest_model)
# Run the stacked model on the first 3 test examples.
for example in raw_test_dataset.rebatch(1).take(3):
print("label:", example["label"].numpy())
print("sentence:", example["sentence"].numpy())
prediction = stacked_model({"sentence": example["sentence"]})
print("predictions:", prediction)
print("")
# Save both the decision forests and pre-processing neural network to a TensorFlow SaveModel.
import tensorflow as tf
# The stacked model applies the neural network and decision forest models in sequence.
class StackedModel(tf.Module):
def __init__(self, nn, df):
self._nn = nn
self._df = df
@tf.function
def __call__(self, raw_features):
sentence = raw_features["sentence"]
embedding = self._nn(sentence)
processed_features = {"embedding": embedding}
return self._df(processed_features)
stacked_model = StackedModel(pretrained_neural_network_model, tf_decision_forest_model)
# Run the stacked model on the first 3 test examples.
for example in raw_test_dataset.rebatch(1).take(3):
print("label:", example["label"].numpy())
print("sentence:", example["sentence"].numpy())
prediction = stacked_model({"sentence": example["sentence"]})
print("predictions:", prediction)
print("")
label: [0] sentence: [b'a valueless kiddie paean to pro basketball underwritten by the nba . '] predictions: tf.Tensor([0.8391822], shape=(1,), dtype=float32) label: [1] sentence: [b"featuring a dangerously seductive performance from the great daniel auteuil , `` sade '' covers the same period as kaufmann 's `` quills '' with more unsettlingly realistic results . "] predictions: tf.Tensor([0.23475255], shape=(1,), dtype=float32) label: [0] sentence: [b'i am sorry that i was unable to get the full brunt of the comedy . '] predictions: tf.Tensor([0.8494433], shape=(1,), dtype=float32)
2024-04-12 10:21:05.527045: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
We can then save this stacked model for later.
In [10]:
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tf.saved_model.save(stacked_model, "/tmp/my_stacked_model")
tf.saved_model.save(stacked_model, "/tmp/my_stacked_model")
The model can then be reloaded and reused.
In [11]:
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loaded_stacked_model = tf.saved_model.load("/tmp/my_stacked_model")
loaded_stacked_model({"sentence":["This is a great movie"]}).numpy()
loaded_stacked_model = tf.saved_model.load("/tmp/my_stacked_model")
loaded_stacked_model({"sentence":["This is a great movie"]}).numpy()
[INFO 24-04-12 10:21:12.4042 CEST kernel.cc:1233] Loading model from path /tmp/my_stacked_model/assets/ with prefix a15dba1c_ [INFO 24-04-12 10:21:12.4235 CEST kernel.cc:1061] Use fast generic engine
Out[11]:
array([0.02682637], dtype=float32)
The model saved in "/tmp/my_stacked_model" above accepts raw tensor values as input. This is ideal for Python model development.
Although such models can be used in TensorFlow Serving or Vertex AI, it is more common for models in these platforms to use a serialized TensorFlow Example as input and output a dictionary. Here's how to create such a model:
In [12]:
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@tf.function(input_signature=[tf.TensorSpec([None], dtype=tf.string, name="inputs")])
def predict_with_with_proto_input(serialized_examples: tf.Tensor):
# Extract the features
feature_spec = {"sentence": tf.io.FixedLenFeature(shape=[], dtype=tf.string)}
features = tf.io.parse_example(serialized_examples, feature_spec)
# Make predictions
predictions = stacked_model(features)
return {"scores": predictions}
tf.saved_model.save(stacked_model, "/tmp/my_stacked_model_with_proto_input",
signatures = {tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: predict_with_with_proto_input})
@tf.function(input_signature=[tf.TensorSpec([None], dtype=tf.string, name="inputs")])
def predict_with_with_proto_input(serialized_examples: tf.Tensor):
# Extract the features
feature_spec = {"sentence": tf.io.FixedLenFeature(shape=[], dtype=tf.string)}
features = tf.io.parse_example(serialized_examples, feature_spec)
# Make predictions
predictions = stacked_model(features)
return {"scores": predictions}
tf.saved_model.save(stacked_model, "/tmp/my_stacked_model_with_proto_input",
signatures = {tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: predict_with_with_proto_input})
Training an ensemble of neural network and decision forests models¶
For this example, we train and ensemble together a random forest model, a gradient-boosted trees model, and a neural network model on a simple synthetic dataset.
In [13]:
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# A function to generate a synthetic dataset
def make_dataset(num_examples=10000, seed=1234):
np.random.seed(seed)
features = np.random.uniform(-1, 1, size=(num_examples, 4))
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": features, "label": labels.astype(int) }
# A function to generate a synthetic dataset
def make_dataset(num_examples=10000, seed=1234):
np.random.seed(seed)
features = np.random.uniform(-1, 1, size=(num_examples, 4))
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": features, "label": labels.astype(int) }
We generate and plot some examples:
In [14]:
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make_dataset(num_examples=5)
make_dataset(num_examples=5)
Out[14]:
{'features': 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]]),
'label': array([0, 0, 0, 1, 0])}
In [15]:
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import matplotlib.pyplot as plt
plot_dataset = make_dataset(num_examples=50000)
plot_label = plot_dataset["label"]
plot_features = plot_dataset["features"]
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)
import matplotlib.pyplot as plt
plot_dataset = make_dataset(num_examples=50000)
plot_label = plot_dataset["label"]
plot_features = plot_dataset["features"]
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)
Out[15]:
<matplotlib.collections.PathCollection at 0x7f1928582e90>