diff --git a/documentation/public/docs/image/tutorial/compose_with_tf_ensemble.png b/documentation/public/docs/image/tutorial/compose_with_tf_ensemble.png new file mode 100644 index 00000000..3b7aad35 Binary files /dev/null and b/documentation/public/docs/image/tutorial/compose_with_tf_ensemble.png differ diff --git a/documentation/public/docs/image/tutorial/compose_with_tf_stack.png b/documentation/public/docs/image/tutorial/compose_with_tf_stack.png new file mode 100644 index 00000000..2f5a4ce9 Binary files /dev/null and b/documentation/public/docs/image/tutorial/compose_with_tf_stack.png differ diff --git a/documentation/public/docs/tutorial/compose_with_tf.ipynb b/documentation/public/docs/tutorial/compose_with_tf.ipynb new file mode 100644 index 00000000..fa195422 --- /dev/null +++ b/documentation/public/docs/tutorial/compose_with_tf.ipynb @@ -0,0 +1,3670 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "076a8af5-2019-4794-9100-980d1ebaeb81", + "metadata": {}, + "source": [ + "# With TensorFlow\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/compose_with_tf.ipynb)\n", + "\n", + "## About this tutorial\n", + "\n", + "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.\n", + "\n", + "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.\n", + "\n", + "![compose_with_tf_stack](../../image/tutorial/compose_with_tf_stack.png)\n", + "\n", + "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.\n", + "\n", + "![compose_with_tf_ensemble](../../image/tutorial/compose_with_tf_ensemble.png)" + ] + }, + { + "cell_type": "markdown", + "id": "0e66bc33-c8be-4e42-8970-ee20e4a5b38f", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c55819c-44e0-484c-8e8b-23dfa4e85280", + "metadata": {}, + "outputs": [], + "source": [ + "# Install YDF and TensorFlow\n", + "!pip install ydf -U -q\n", + "!pip install tensorflow -U -q" + ] + }, + { + "cell_type": "markdown", + "id": "46070c5e-709f-41f9-b452-5b9ecc66f5b4", + "metadata": {}, + "source": [ + "## Stacking a decision forest and a pre-trained neural network model\n", + "\n", + "For this example, we train a model on the [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/index.html) text classification dataset.\n", + "The prediction task is to classify movie reviews as positive (label 1) or negative (label 0).\n", + "The dataset is available in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/glue#gluesst2)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b11a182-0fdc-4c5c-ae80-c00529674e47", + "metadata": {}, + "outputs": [], + "source": [ + "# Install TensorFlow Dataset\n", + "!pip install tensorflow-datasets -U -q" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5675b737-5e64-479b-b039-2ad0035f5e82", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label: [0]\n", + "sentence: [b'a valueless kiddie paean to pro basketball underwritten by the nba . ']\n", + "\n", + "label: [1]\n", + "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 . \"]\n", + "\n", + "label: [0]\n", + "sentence: [b'i am sorry that i was unable to get the full brunt of the comedy . ']\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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.\n", + "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\n" + ] + } + ], + "source": [ + "# Load the Stanford Sentiment Treebank dataset\n", + "\n", + "import tensorflow_datasets as tfds\n", + "raw_dataset = tfds.load(\"glue/sst2\")\n", + "raw_train_dataset = raw_dataset[\"train\"].batch(200)\n", + "raw_test_dataset = raw_dataset[\"validation\"].batch(200)\n", + "# Note: The `raw_dataset[\"test\"]` fold does not have labels. Therefore, we use the\n", + "# \"validation\" fold for testing.\n", + "\n", + "# Display the first 3 test examples.\n", + "for example in raw_test_dataset.rebatch(1).take(3):\n", + " print(\"label:\", example[\"label\"].numpy())\n", + " print(\"sentence:\", example[\"sentence\"].numpy())\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "733f81c3-54e9-40cc-9663-e1d36e96b00e", + "metadata": {}, + "source": [ + "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](https://tfhub.dev/google/universal-sentence-encoder/4) 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.\n", + "\n", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f02eb08d-5c08-49f1-9207-fad3a71e8e11", + "metadata": {}, + "outputs": [], + "source": [ + "# Install TensorFlow Hub\n", + "!pip install tensorflow-hub -U -q" + ] + }, + { + "cell_type": "markdown", + "id": "6703cbc9-2853-41a6-82e6-4cafc42fbe8c", + "metadata": {}, + "source": [ + "We load the Universal-Sentence-Encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0c74e75f-8d32-489a-9982-c8ba2627c78b", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow_hub as hub\n", + "\n", + "pretrained_neural_network_model = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder/4\")" + ] + }, + { + "cell_type": "markdown", + "id": "16c49ba1-58fe-41f9-8739-87764a5bbe50", + "metadata": {}, + "source": [ + "We apply the Universal-Sentence-Encoder to convert the raw text into embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3c104a67-2b46-4f11-b1fa-a3baf92330c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label: [0]\n", + "embedding: [[ 0.0325069 0.02350717 0.0928923 ... -0.05117338 -0.0959709\n", + " 0.01819227]]\n", + "\n", + "label: [1]\n", + "embedding: [[-0.02955496 0.04451125 0.03023855 ... 0.02529242 0.06243655\n", + " 0.0004012 ]]\n", + "\n", + "label: [0]\n", + "embedding: [[-0.02897574 -0.0021273 0.02524822 ... -0.00667133 0.07221754\n", + " 0.05386261]]\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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.\n", + "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\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "def apply_neural_network_model(data):\n", + " return {\n", + " \"embedding\": pretrained_neural_network_model(data[\"sentence\"]),\n", + " \"label\": data[\"label\"],\n", + " }\n", + " \n", + "processed_train_dataset = raw_train_dataset.map(apply_neural_network_model)\n", + "processed_test_dataset = raw_test_dataset.map(apply_neural_network_model)\n", + "\n", + "# Display the first 3 processed test examples.\n", + "for example in processed_test_dataset.rebatch(1).take(3):\n", + " print(\"label:\", example[\"label\"].numpy())\n", + " print(\"embedding:\", np.array2string(example[\"embedding\"].numpy(), threshold = 10))\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "bfd11bc9-2ff0-4f2e-9c88-94ba042a4cc6", + "metadata": {}, + "source": [ + "Then, we train a gradient-boosted trees model on the Neural Network output." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "80b41fac-7fda-47cc-a0bc-f73a390c536a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train model on 67349 examples\n", + "Model trained in 0:00:54.704927\n" + ] + } + ], + "source": [ + "import ydf\n", + "\n", + "decision_forest_model = ydf.GradientBoostedTreesLearner(label=\"label\", num_trees=100).train(processed_train_dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "82e6655c-252c-4ee6-9a6d-463acbe22042", + "metadata": {}, + "source": [ + "Notice that the YDF model is trained on a TensorFlow Dataset directly.\n", + "\n", + "Looking at the more is important." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f117c0b8-06d9-4628-aadb-370d8b083e93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "
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mean:0.00578581 min:-0.156252 max:0.125833 sd:0.0489822 dtype:DTYPE_FLOAT32\n", + "\t81: "embedding.080_of_512" NUMERICAL mean:-0.00466792 min:-0.10975 max:0.118669 sd:0.0422673 dtype:DTYPE_FLOAT32\n", + "\t82: "embedding.081_of_512" NUMERICAL mean:0.00499065 min:-0.0934409 max:0.115151 sd:0.0382445 dtype:DTYPE_FLOAT32\n", + "\t83: "embedding.082_of_512" NUMERICAL mean:-0.0120384 min:-0.115119 max:0.109741 sd:0.039712 dtype:DTYPE_FLOAT32\n", + "\t84: "embedding.083_of_512" NUMERICAL mean:-0.0116498 min:-0.107953 max:0.113206 sd:0.0408114 dtype:DTYPE_FLOAT32\n", + "\t85: "embedding.084_of_512" NUMERICAL mean:-0.0210408 min:-0.108707 max:0.0992159 sd:0.0386516 dtype:DTYPE_FLOAT32\n", + "\t86: "embedding.085_of_512" NUMERICAL mean:-0.00273396 min:-0.12944 max:0.12272 sd:0.0449487 dtype:DTYPE_FLOAT32\n", + "\t87: "embedding.086_of_512" NUMERICAL mean:0.00658216 min:-0.113506 max:0.112219 sd:0.039801 dtype:DTYPE_FLOAT32\n", + "\t88: "embedding.087_of_512" NUMERICAL mean:-0.00378743 min:-0.117676 max:0.109386 sd:0.0402421 dtype:DTYPE_FLOAT32\n", + "\t89: "embedding.088_of_512" NUMERICAL mean:-0.0205237 min:-0.107587 max:0.103141 sd:0.040405 dtype:DTYPE_FLOAT32\n", + "\t90: "embedding.089_of_512" NUMERICAL mean:-0.000411177 min:-0.119937 max:0.109877 sd:0.0421414 dtype:DTYPE_FLOAT32\n", + "\t91: "embedding.090_of_512" NUMERICAL mean:-0.00181531 min:-0.117795 max:0.106343 sd:0.0421115 dtype:DTYPE_FLOAT32\n", + "\t92: "embedding.091_of_512" NUMERICAL mean:-0.00550051 min:-0.127822 max:0.113907 sd:0.0399804 dtype:DTYPE_FLOAT32\n", + "\t93: "embedding.092_of_512" NUMERICAL mean:-0.00547455 min:-0.126723 max:0.119811 sd:0.0431932 dtype:DTYPE_FLOAT32\n", + "\t94: "embedding.093_of_512" NUMERICAL mean:0.014195 min:-0.105489 max:0.118567 sd:0.0413103 dtype:DTYPE_FLOAT32\n", + "\t95: "embedding.094_of_512" NUMERICAL mean:0.0188997 min:-0.104824 max:0.132286 sd:0.0497162 dtype:DTYPE_FLOAT32\n", + "\t96: "embedding.095_of_512" NUMERICAL mean:0.00497901 min:-0.108731 max:0.124192 sd:0.0414468 dtype:DTYPE_FLOAT32\n", + "\t97: "embedding.096_of_512" NUMERICAL mean:-0.0179242 min:-0.125507 max:0.10199 sd:0.0383211 dtype:DTYPE_FLOAT32\n", + "\t98: "embedding.097_of_512" NUMERICAL mean:0.00327183 min:-0.122499 max:0.123037 sd:0.0419092 dtype:DTYPE_FLOAT32\n", + "\t99: "embedding.098_of_512" NUMERICAL mean:0.0216785 min:-0.10081 max:0.116099 sd:0.0479454 dtype:DTYPE_FLOAT32\n", + "\t100: "embedding.099_of_512" NUMERICAL mean:0.019005 min:-0.125922 max:0.117505 sd:0.0429193 dtype:DTYPE_FLOAT32\n", + "\t101: "embedding.100_of_512" NUMERICAL mean:0.003884 min:-0.104019 max:0.127238 sd:0.0431 dtype:DTYPE_FLOAT32\n", + "\t102: "embedding.101_of_512" NUMERICAL mean:-0.0132592 min:-0.133774 max:0.125128 sd:0.0465773 dtype:DTYPE_FLOAT32\n", + "\t103: "embedding.102_of_512" NUMERICAL mean:0.00732224 min:-0.114158 max:0.135181 sd:0.0462208 dtype:DTYPE_FLOAT32\n", + "\t104: "embedding.103_of_512" NUMERICAL mean:-0.00316622 min:-0.115661 max:0.110651 sd:0.0393422 dtype:DTYPE_FLOAT32\n", + "\t105: "embedding.104_of_512" NUMERICAL mean:-0.000406039 min:-0.115186 max:0.115727 sd:0.0404569 dtype:DTYPE_FLOAT32\n", + "\t106: "embedding.105_of_512" NUMERICAL mean:0.01286 min:-0.10478 max:0.116059 sd:0.0408527 dtype:DTYPE_FLOAT32\n", + "\t107: "embedding.106_of_512" NUMERICAL mean:-0.0200857 min:-0.112344 max:0.115696 sd:0.0348447 dtype:DTYPE_FLOAT32\n", + "\t108: "embedding.107_of_512" NUMERICAL mean:-0.0008812 min:-0.117538 max:0.128118 sd:0.0397207 dtype:DTYPE_FLOAT32\n", + "\t109: "embedding.108_of_512" NUMERICAL mean:-0.0153816 min:-0.119853 max:0.111478 sd:0.0408014 dtype:DTYPE_FLOAT32\n", + "\t110: "embedding.109_of_512" NUMERICAL mean:0.0226631 min:-0.115775 max:0.109245 sd:0.0344709 dtype:DTYPE_FLOAT32\n", + "\t111: "embedding.110_of_512" NUMERICAL mean:-0.0117186 min:-0.12628 max:0.0972872 sd:0.043443 dtype:DTYPE_FLOAT32\n", + "\t112: "embedding.111_of_512" NUMERICAL mean:-0.0195 min:-0.138677 max:0.111032 sd:0.0530712 dtype:DTYPE_FLOAT32\n", 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NUMERICAL mean:0.0128449 min:-0.123295 max:0.101678 sd:0.035443 dtype:DTYPE_FLOAT32\n", + "\t170: "embedding.169_of_512" NUMERICAL mean:-0.0018307 min:-0.113497 max:0.108755 sd:0.0385736 dtype:DTYPE_FLOAT32\n", + "\t171: "embedding.170_of_512" NUMERICAL mean:-0.0154471 min:-0.123997 max:0.0995884 sd:0.039095 dtype:DTYPE_FLOAT32\n", + "\t172: "embedding.171_of_512" NUMERICAL mean:-0.0115266 min:-0.135629 max:0.111586 sd:0.0564499 dtype:DTYPE_FLOAT32\n", + "\t173: "embedding.172_of_512" NUMERICAL mean:-0.00305818 min:-0.108148 max:0.125287 sd:0.0416153 dtype:DTYPE_FLOAT32\n", + "\t174: "embedding.173_of_512" NUMERICAL mean:-0.0192183 min:-0.128661 max:0.111586 sd:0.0445312 dtype:DTYPE_FLOAT32\n", + "\t175: "embedding.174_of_512" NUMERICAL mean:-0.00547071 min:-0.106778 max:0.107318 sd:0.0412694 dtype:DTYPE_FLOAT32\n", + "\t176: "embedding.175_of_512" NUMERICAL mean:0.00303105 min:-0.114183 max:0.11671 sd:0.037753 dtype:DTYPE_FLOAT32\n", + "\t177: "embedding.176_of_512" NUMERICAL mean:0.0200632 min:-0.119154 max:0.12262 sd:0.0449386 dtype:DTYPE_FLOAT32\n", + "\t178: "embedding.177_of_512" NUMERICAL mean:0.00830421 min:-0.106867 max:0.108159 sd:0.04212 dtype:DTYPE_FLOAT32\n", + "\t179: "embedding.178_of_512" NUMERICAL mean:0.00879771 min:-0.119236 max:0.0975505 sd:0.0365596 dtype:DTYPE_FLOAT32\n", + "\t180: "embedding.179_of_512" NUMERICAL mean:-0.0224472 min:-0.141699 max:0.121597 sd:0.0451563 dtype:DTYPE_FLOAT32\n", + "\t181: "embedding.180_of_512" NUMERICAL mean:0.00700458 min:-0.122243 max:0.106828 sd:0.0406674 dtype:DTYPE_FLOAT32\n", + "\t182: "embedding.181_of_512" NUMERICAL mean:0.015665 min:-0.123784 max:0.117493 sd:0.0423638 dtype:DTYPE_FLOAT32\n", + "\t183: "embedding.182_of_512" NUMERICAL mean:0.00455087 min:-0.130433 max:0.129947 sd:0.0468312 dtype:DTYPE_FLOAT32\n", + "\t184: "embedding.183_of_512" NUMERICAL mean:0.00469912 min:-0.105513 max:0.115268 sd:0.0422015 dtype:DTYPE_FLOAT32\n", + "\t185: "embedding.184_of_512" NUMERICAL mean:0.00118913 min:-0.132085 max:0.119005 sd:0.0425006 dtype:DTYPE_FLOAT32\n", + "\t186: "embedding.185_of_512" NUMERICAL mean:-0.0091211 min:-0.105384 max:0.107321 sd:0.0394833 dtype:DTYPE_FLOAT32\n", + "\t187: "embedding.186_of_512" NUMERICAL mean:0.00847289 min:-0.100142 max:0.11416 sd:0.0354507 dtype:DTYPE_FLOAT32\n", + "\t188: "embedding.187_of_512" NUMERICAL mean:0.00401229 min:-0.0997345 max:0.0985512 sd:0.0330015 dtype:DTYPE_FLOAT32\n", + "\t189: "embedding.188_of_512" NUMERICAL mean:0.0375059 min:-0.107009 max:0.147423 sd:0.0457626 dtype:DTYPE_FLOAT32\n", + "\t190: "embedding.189_of_512" NUMERICAL mean:-0.0108558 min:-0.158798 max:0.124698 sd:0.0429543 dtype:DTYPE_FLOAT32\n", + "\t191: "embedding.190_of_512" NUMERICAL mean:0.0055649 min:-0.102637 max:0.112907 sd:0.0428818 dtype:DTYPE_FLOAT32\n", + "\t192: "embedding.191_of_512" NUMERICAL mean:0.0115727 min:-0.0992453 max:0.114756 sd:0.0385606 dtype:DTYPE_FLOAT32\n", + "\t193: "embedding.192_of_512" NUMERICAL mean:0.0188207 min:-0.10799 max:0.126446 sd:0.0480458 dtype:DTYPE_FLOAT32\n", + "\t194: "embedding.193_of_512" NUMERICAL mean:-0.0231128 min:-0.125829 max:0.098485 sd:0.0413616 dtype:DTYPE_FLOAT32\n", + "\t195: "embedding.194_of_512" NUMERICAL mean:-0.0125518 min:-0.118983 max:0.111524 sd:0.0394032 dtype:DTYPE_FLOAT32\n", + "\t196: "embedding.195_of_512" NUMERICAL mean:-0.00734374 min:-0.140773 max:0.124731 sd:0.048662 dtype:DTYPE_FLOAT32\n", + "\t197: "embedding.196_of_512" NUMERICAL mean:0.0147101 min:-0.109208 max:0.114207 sd:0.0392372 dtype:DTYPE_FLOAT32\n", + "\t198: "embedding.197_of_512" NUMERICAL mean:0.00382817 min:-0.0960263 max:0.109744 sd:0.0343786 dtype:DTYPE_FLOAT32\n", + "\t199: "embedding.198_of_512" NUMERICAL mean:0.0148358 min:-0.121261 max:0.137886 sd:0.0396124 dtype:DTYPE_FLOAT32\n", + "\t200: "embedding.199_of_512" NUMERICAL mean:0.0139377 min:-0.133057 max:0.129123 sd:0.0434494 dtype:DTYPE_FLOAT32\n", + "\t201: "embedding.200_of_512" NUMERICAL mean:-0.022174 min:-0.135182 max:0.0998059 sd:0.0447171 dtype:DTYPE_FLOAT32\n", + "\t202: "embedding.201_of_512" NUMERICAL mean:0.00918432 min:-0.129768 max:0.104146 sd:0.0407455 dtype:DTYPE_FLOAT32\n", + "\t203: "embedding.202_of_512" NUMERICAL mean:6.68976e-05 min:-0.108528 max:0.112123 sd:0.039669 dtype:DTYPE_FLOAT32\n", + "\t204: "embedding.203_of_512" NUMERICAL mean:-0.0211792 min:-0.138447 max:0.151201 sd:0.0475548 dtype:DTYPE_FLOAT32\n", + "\t205: "embedding.204_of_512" NUMERICAL mean:0.0149458 min:-0.114192 max:0.121993 sd:0.0451805 dtype:DTYPE_FLOAT32\n", + "\t206: "embedding.205_of_512" NUMERICAL mean:-0.000877425 min:-0.106281 max:0.110069 sd:0.0399283 dtype:DTYPE_FLOAT32\n", + "\t207: "embedding.206_of_512" NUMERICAL mean:0.00135042 min:-0.122458 max:0.133155 sd:0.0490798 dtype:DTYPE_FLOAT32\n", + "\t208: "embedding.207_of_512" NUMERICAL mean:-0.00564687 min:-0.0980346 max:0.124534 sd:0.0381495 dtype:DTYPE_FLOAT32\n", + "\t209: "embedding.208_of_512" NUMERICAL mean:-0.0137386 min:-0.104712 max:0.116268 sd:0.0380542 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NUMERICAL mean:-0.0216578 min:-0.124605 max:0.115269 sd:0.0434055 dtype:DTYPE_FLOAT32\n", + "\t323: "embedding.322_of_512" NUMERICAL mean:0.00985385 min:-0.100306 max:0.1268 sd:0.0390696 dtype:DTYPE_FLOAT32\n", + "\t324: "embedding.323_of_512" NUMERICAL mean:0.00628717 min:-0.0997496 max:0.119355 sd:0.0396103 dtype:DTYPE_FLOAT32\n", + "\t325: "embedding.324_of_512" NUMERICAL mean:-0.00196284 min:-0.121922 max:0.120337 sd:0.0459949 dtype:DTYPE_FLOAT32\n", + "\t326: "embedding.325_of_512" NUMERICAL mean:-0.00537022 min:-0.110575 max:0.123165 sd:0.0455996 dtype:DTYPE_FLOAT32\n", + "\t327: "embedding.326_of_512" NUMERICAL mean:0.00455174 min:-0.115791 max:0.104665 sd:0.0401681 dtype:DTYPE_FLOAT32\n", + "\t328: "embedding.327_of_512" NUMERICAL mean:-0.00533296 min:-0.130506 max:0.112283 sd:0.0453555 dtype:DTYPE_FLOAT32\n", + "\t329: "embedding.328_of_512" NUMERICAL mean:-0.00440578 min:-0.126272 max:0.103891 sd:0.041464 dtype:DTYPE_FLOAT32\n", + "\t330: "embedding.329_of_512" NUMERICAL mean:-0.0101936 min:-0.108874 max:0.111676 sd:0.0395482 dtype:DTYPE_FLOAT32\n", + "\t331: "embedding.330_of_512" NUMERICAL mean:0.00703036 min:-0.108652 max:0.103578 sd:0.0400975 dtype:DTYPE_FLOAT32\n", + "\t332: "embedding.331_of_512" NUMERICAL mean:0.000541923 min:-0.109862 max:0.10999 sd:0.0408574 dtype:DTYPE_FLOAT32\n", + "\t333: "embedding.332_of_512" NUMERICAL mean:0.0188891 min:-0.112872 max:0.118079 sd:0.0397373 dtype:DTYPE_FLOAT32\n", + "\t334: "embedding.333_of_512" NUMERICAL mean:-0.012192 min:-0.133506 max:0.13836 sd:0.0512842 dtype:DTYPE_FLOAT32\n", + "\t335: "embedding.334_of_512" NUMERICAL mean:-0.0265024 min:-0.126857 max:0.097852 sd:0.0420318 dtype:DTYPE_FLOAT32\n", + "\t336: "embedding.335_of_512" NUMERICAL mean:0.00215234 min:-0.111504 max:0.116062 sd:0.038159 dtype:DTYPE_FLOAT32\n", + "\t337: "embedding.336_of_512" NUMERICAL mean:-0.00825738 min:-0.125886 max:0.10212 sd:0.0376238 dtype:DTYPE_FLOAT32\n", + "\t338: "embedding.337_of_512" NUMERICAL mean:-0.0055194 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max:0.101491 sd:0.0404394 dtype:DTYPE_FLOAT32\n", + "\t347: "embedding.346_of_512" NUMERICAL mean:-0.00118125 min:-0.14804 max:0.11391 sd:0.0423848 dtype:DTYPE_FLOAT32\n", + "\t348: "embedding.347_of_512" NUMERICAL mean:-0.00893261 min:-0.125488 max:0.109213 sd:0.0498338 dtype:DTYPE_FLOAT32\n", + "\t349: "embedding.348_of_512" NUMERICAL mean:-0.0112279 min:-0.119783 max:0.106986 sd:0.039883 dtype:DTYPE_FLOAT32\n", + "\t350: "embedding.349_of_512" NUMERICAL mean:0.00921196 min:-0.108645 max:0.124486 sd:0.0427417 dtype:DTYPE_FLOAT32\n", + "\t351: "embedding.350_of_512" NUMERICAL mean:-0.0064119 min:-0.11853 max:0.108147 sd:0.0396107 dtype:DTYPE_FLOAT32\n", + "\t352: "embedding.351_of_512" NUMERICAL mean:0.00046816 min:-0.133059 max:0.106031 sd:0.0419676 dtype:DTYPE_FLOAT32\n", + "\t353: "embedding.352_of_512" NUMERICAL mean:0.00143986 min:-0.119083 max:0.0987319 sd:0.0358907 dtype:DTYPE_FLOAT32\n", + "\t354: "embedding.353_of_512" NUMERICAL mean:0.00247002 min:-0.109389 max:0.118887 sd:0.0416032 dtype:DTYPE_FLOAT32\n", + "\t355: "embedding.354_of_512" NUMERICAL mean:0.00010288 min:-0.139157 max:0.0995683 sd:0.0394998 dtype:DTYPE_FLOAT32\n", + "\t356: "embedding.355_of_512" NUMERICAL mean:-0.00525663 min:-0.146684 max:0.104288 sd:0.0406929 dtype:DTYPE_FLOAT32\n", + "\t357: "embedding.356_of_512" NUMERICAL mean:-0.0884722 min:-0.132905 max:0.0538598 sd:0.0108211 dtype:DTYPE_FLOAT32\n", + "\t358: "embedding.357_of_512" NUMERICAL mean:0.00677648 min:-0.110339 max:0.110136 sd:0.0402613 dtype:DTYPE_FLOAT32\n", + "\t359: "embedding.358_of_512" NUMERICAL mean:0.00630266 min:-0.111695 max:0.115859 sd:0.0427588 dtype:DTYPE_FLOAT32\n", + "\t360: "embedding.359_of_512" NUMERICAL mean:0.00225805 min:-0.126003 max:0.117678 sd:0.0444635 dtype:DTYPE_FLOAT32\n", + "\t361: "embedding.360_of_512" NUMERICAL mean:-0.00827234 min:-0.133543 max:0.115376 sd:0.0466799 dtype:DTYPE_FLOAT32\n", + "\t362: "embedding.361_of_512" NUMERICAL mean:-0.00625222 min:-0.10512 max:0.123856 sd:0.0418715 dtype:DTYPE_FLOAT32\n", + "\t363: "embedding.362_of_512" NUMERICAL mean:0.0651293 min:-0.11562 max:0.153915 sd:0.0359 dtype:DTYPE_FLOAT32\n", + "\t364: "embedding.363_of_512" NUMERICAL mean:0.00968887 min:-0.115793 max:0.11435 sd:0.0422501 dtype:DTYPE_FLOAT32\n", + "\t365: "embedding.364_of_512" NUMERICAL mean:0.00449241 min:-0.132071 max:0.103237 sd:0.0373741 dtype:DTYPE_FLOAT32\n", + "\t366: "embedding.365_of_512" NUMERICAL mean:-0.016221 min:-0.113495 max:0.106975 sd:0.0425689 dtype:DTYPE_FLOAT32\n", + "\t367: "embedding.366_of_512" NUMERICAL mean:0.0112515 min:-0.154925 max:0.151612 sd:0.0513015 dtype:DTYPE_FLOAT32\n", + "\t368: "embedding.367_of_512" NUMERICAL mean:-5.21381e-05 min:-0.11585 max:0.112307 sd:0.0391906 dtype:DTYPE_FLOAT32\n", + "\t369: "embedding.368_of_512" NUMERICAL mean:-0.00112394 min:-0.121213 max:0.126588 sd:0.044652 dtype:DTYPE_FLOAT32\n", + "\t370: "embedding.369_of_512" NUMERICAL mean:0.00485578 min:-0.106476 max:0.115632 sd:0.041426 dtype:DTYPE_FLOAT32\n", 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"embedding.402_of_512" NUMERICAL mean:-0.00798588 min:-0.114245 max:0.109355 sd:0.0417294 dtype:DTYPE_FLOAT32\n", + "\t404: "embedding.403_of_512" NUMERICAL mean:-0.00715776 min:-0.110958 max:0.109412 sd:0.0426725 dtype:DTYPE_FLOAT32\n", + "\t405: "embedding.404_of_512" NUMERICAL mean:0.0421547 min:-0.0936097 max:0.14341 sd:0.0449053 dtype:DTYPE_FLOAT32\n", + "\t406: "embedding.405_of_512" NUMERICAL mean:0.0138744 min:-0.101141 max:0.110993 sd:0.0409959 dtype:DTYPE_FLOAT32\n", + "\t407: "embedding.406_of_512" NUMERICAL mean:0.0221997 min:-0.10012 max:0.12351 sd:0.0512918 dtype:DTYPE_FLOAT32\n", + "\t408: "embedding.407_of_512" NUMERICAL mean:0.00840243 min:-0.100731 max:0.108785 sd:0.0385815 dtype:DTYPE_FLOAT32\n", + "\t409: "embedding.408_of_512" NUMERICAL mean:-0.00995255 min:-0.119931 max:0.107382 sd:0.0397331 dtype:DTYPE_FLOAT32\n", + "\t410: "embedding.409_of_512" NUMERICAL mean:0.00122281 min:-0.123687 max:0.110221 sd:0.0419264 dtype:DTYPE_FLOAT32\n", + "\t411: "embedding.410_of_512" NUMERICAL mean:0.00722765 min:-0.120324 max:0.118298 sd:0.0397953 dtype:DTYPE_FLOAT32\n", + "\t412: "embedding.411_of_512" NUMERICAL mean:0.00372596 min:-0.110838 max:0.104775 sd:0.0397102 dtype:DTYPE_FLOAT32\n", + "\t413: "embedding.412_of_512" NUMERICAL mean:0.00750692 min:-0.105861 max:0.113608 sd:0.0404272 dtype:DTYPE_FLOAT32\n", + "\t414: "embedding.413_of_512" NUMERICAL mean:0.00702045 min:-0.100497 max:0.109256 sd:0.040414 dtype:DTYPE_FLOAT32\n", + "\t415: "embedding.414_of_512" NUMERICAL mean:0.0129925 min:-0.104637 max:0.129069 sd:0.0476144 dtype:DTYPE_FLOAT32\n", + "\t416: "embedding.415_of_512" NUMERICAL mean:0.00895771 min:-0.103221 max:0.131867 sd:0.0416565 dtype:DTYPE_FLOAT32\n", + "\t417: "embedding.416_of_512" NUMERICAL mean:-0.0113754 min:-0.108457 max:0.108912 sd:0.039076 dtype:DTYPE_FLOAT32\n", + "\t418: "embedding.417_of_512" NUMERICAL mean:-0.00972072 min:-0.108896 max:0.120041 sd:0.039969 dtype:DTYPE_FLOAT32\n", + "\t419: "embedding.418_of_512" NUMERICAL mean:0.0103305 min:-0.115689 max:0.117791 sd:0.0438928 dtype:DTYPE_FLOAT32\n", + "\t420: "embedding.419_of_512" NUMERICAL mean:-0.011858 min:-0.110158 max:0.112286 sd:0.0405172 dtype:DTYPE_FLOAT32\n", + "\t421: "embedding.420_of_512" NUMERICAL mean:0.0113019 min:-0.117355 max:0.110719 sd:0.0390142 dtype:DTYPE_FLOAT32\n", + "\t422: "embedding.421_of_512" NUMERICAL mean:-0.00325833 min:-0.11971 max:0.0998387 sd:0.0386342 dtype:DTYPE_FLOAT32\n", + "\t423: "embedding.422_of_512" NUMERICAL mean:-0.0175019 min:-0.121014 max:0.108533 sd:0.0430717 dtype:DTYPE_FLOAT32\n", + "\t424: "embedding.423_of_512" NUMERICAL mean:0.00661466 min:-0.121052 max:0.104438 sd:0.0401472 dtype:DTYPE_FLOAT32\n", + "\t425: "embedding.424_of_512" NUMERICAL mean:0.0157025 min:-0.119043 max:0.121705 sd:0.0455012 dtype:DTYPE_FLOAT32\n", + "\t426: "embedding.425_of_512" NUMERICAL mean:0.00671776 min:-0.119955 max:0.135544 sd:0.046337 dtype:DTYPE_FLOAT32\n", + "\t427: "embedding.426_of_512" NUMERICAL mean:0.00625655 min:-0.110938 max:0.120801 sd:0.0434661 dtype:DTYPE_FLOAT32\n", + "\t428: "embedding.427_of_512" NUMERICAL mean:0.0204839 min:-0.112639 max:0.12859 sd:0.0461795 dtype:DTYPE_FLOAT32\n", + "\t429: "embedding.428_of_512" NUMERICAL mean:-0.00954845 min:-0.131481 max:0.103867 sd:0.0409481 dtype:DTYPE_FLOAT32\n", + "\t430: "embedding.429_of_512" NUMERICAL mean:0.0227497 min:-0.114759 max:0.128784 sd:0.0461912 dtype:DTYPE_FLOAT32\n", + "\t431: "embedding.430_of_512" NUMERICAL mean:-0.0143054 min:-0.116372 max:0.0982788 sd:0.0397653 dtype:DTYPE_FLOAT32\n", + "\t432: "embedding.431_of_512" NUMERICAL mean:0.00108119 min:-0.10975 max:0.113431 sd:0.0395805 dtype:DTYPE_FLOAT32\n", + "\t433: "embedding.432_of_512" NUMERICAL mean:-0.0124634 min:-0.128303 max:0.122121 sd:0.043612 dtype:DTYPE_FLOAT32\n", + "\t434: "embedding.433_of_512" NUMERICAL mean:-0.000974066 min:-0.127452 max:0.143975 sd:0.0512878 dtype:DTYPE_FLOAT32\n", + "\t435: "embedding.434_of_512" NUMERICAL mean:-0.000695708 min:-0.117519 max:0.132419 sd:0.048299 dtype:DTYPE_FLOAT32\n", + "\t436: "embedding.435_of_512" NUMERICAL mean:-0.00800422 min:-0.11716 max:0.106095 sd:0.0385783 dtype:DTYPE_FLOAT32\n", + "\t437: "embedding.436_of_512" NUMERICAL mean:-0.00449899 min:-0.119801 max:0.13136 sd:0.0450766 dtype:DTYPE_FLOAT32\n", + "\t438: "embedding.437_of_512" NUMERICAL mean:0.00152719 min:-0.101368 max:0.111586 sd:0.0373092 dtype:DTYPE_FLOAT32\n", + "\t439: "embedding.438_of_512" NUMERICAL mean:-0.00746199 min:-0.110446 max:0.107505 sd:0.0409118 dtype:DTYPE_FLOAT32\n", + "\t440: "embedding.439_of_512" NUMERICAL mean:-0.000542517 min:-0.126726 max:0.150725 sd:0.0498822 dtype:DTYPE_FLOAT32\n", + "\t441: "embedding.440_of_512" NUMERICAL mean:0.0162618 min:-0.110413 max:0.112766 sd:0.039636 dtype:DTYPE_FLOAT32\n", + "\t442: "embedding.441_of_512" NUMERICAL mean:-0.0252852 min:-0.140847 max:0.123998 sd:0.045552 dtype:DTYPE_FLOAT32\n", + "\t443: "embedding.442_of_512" NUMERICAL mean:-0.00971423 min:-0.14093 max:0.115633 sd:0.0430468 dtype:DTYPE_FLOAT32\n", + "\t444: "embedding.443_of_512" NUMERICAL mean:-0.00171618 min:-0.130186 max:0.122902 sd:0.0446095 dtype:DTYPE_FLOAT32\n", + "\t445: "embedding.444_of_512" NUMERICAL mean:0.0108986 min:-0.114492 max:0.110956 sd:0.0418642 dtype:DTYPE_FLOAT32\n", + "\t446: "embedding.445_of_512" NUMERICAL mean:-0.00650931 min:-0.106713 max:0.126819 sd:0.0394136 dtype:DTYPE_FLOAT32\n", + "\t447: "embedding.446_of_512" NUMERICAL mean:7.68803e-05 min:-0.107121 max:0.104196 sd:0.0371536 dtype:DTYPE_FLOAT32\n", + "\t448: "embedding.447_of_512" NUMERICAL mean:-0.00166973 min:-0.106304 max:0.113193 sd:0.0417721 dtype:DTYPE_FLOAT32\n", + "\t449: "embedding.448_of_512" NUMERICAL mean:0.00143107 min:-0.112879 max:0.117707 sd:0.0438514 dtype:DTYPE_FLOAT32\n", + "\t450: "embedding.449_of_512" NUMERICAL mean:0.00577755 min:-0.114301 max:0.116267 sd:0.0413021 dtype:DTYPE_FLOAT32\n", + "\t451: "embedding.450_of_512" NUMERICAL mean:0.00523777 min:-0.121324 max:0.109753 sd:0.0422962 dtype:DTYPE_FLOAT32\n", + "\t452: "embedding.451_of_512" NUMERICAL mean:0.00232381 min:-0.107421 max:0.116006 sd:0.0411045 dtype:DTYPE_FLOAT32\n", + "\t453: "embedding.452_of_512" NUMERICAL mean:0.0131371 min:-0.119915 max:0.110052 sd:0.0388742 dtype:DTYPE_FLOAT32\n", + "\t454: "embedding.453_of_512" NUMERICAL mean:0.00384022 min:-0.113448 max:0.103866 sd:0.0399839 dtype:DTYPE_FLOAT32\n", + "\t455: "embedding.454_of_512" NUMERICAL mean:0.00746132 min:-0.11867 max:0.107229 sd:0.0393659 dtype:DTYPE_FLOAT32\n", + "\t456: "embedding.455_of_512" NUMERICAL mean:0.0217711 min:-0.130108 max:0.130266 sd:0.0457751 dtype:DTYPE_FLOAT32\n", + "\t457: "embedding.456_of_512" NUMERICAL mean:-0.00486573 min:-0.125269 max:0.103216 sd:0.0417326 dtype:DTYPE_FLOAT32\n", + "\t458: "embedding.457_of_512" NUMERICAL mean:-0.00370284 min:-0.152411 max:0.118391 sd:0.0475716 dtype:DTYPE_FLOAT32\n", + "\t459: "embedding.458_of_512" NUMERICAL mean:-0.0252088 min:-0.129244 max:0.110772 sd:0.0447074 dtype:DTYPE_FLOAT32\n", + "\t460: "embedding.459_of_512" NUMERICAL mean:0.0196455 min:-0.107007 max:0.110025 sd:0.0371162 dtype:DTYPE_FLOAT32\n", + "\t461: "embedding.460_of_512" NUMERICAL mean:0.00486969 min:-0.133702 max:0.117438 sd:0.0404765 dtype:DTYPE_FLOAT32\n", + "\t462: "embedding.461_of_512" NUMERICAL mean:0.00879324 min:-0.123721 max:0.109769 sd:0.0418885 dtype:DTYPE_FLOAT32\n", + "\t463: "embedding.462_of_512" NUMERICAL mean:0.00541842 min:-0.103881 max:0.115937 sd:0.040526 dtype:DTYPE_FLOAT32\n", + "\t464: "embedding.463_of_512" NUMERICAL mean:0.0112013 min:-0.129965 max:0.125135 sd:0.0445652 dtype:DTYPE_FLOAT32\n", + "\t465: "embedding.464_of_512" NUMERICAL mean:-0.00978469 min:-0.112536 max:0.136367 sd:0.0432779 dtype:DTYPE_FLOAT32\n", + "\t466: "embedding.465_of_512" NUMERICAL mean:-0.00372292 min:-0.132975 max:0.107404 sd:0.0434915 dtype:DTYPE_FLOAT32\n", + "\t467: "embedding.466_of_512" NUMERICAL mean:0.000832962 min:-0.106678 max:0.109534 sd:0.041454 dtype:DTYPE_FLOAT32\n", + "\t468: "embedding.467_of_512" NUMERICAL mean:0.0128707 min:-0.123202 max:0.108301 sd:0.036966 dtype:DTYPE_FLOAT32\n", + "\t469: "embedding.468_of_512" NUMERICAL mean:0.00143448 min:-0.109754 max:0.115596 sd:0.0410802 dtype:DTYPE_FLOAT32\n", + "\t470: "embedding.469_of_512" NUMERICAL mean:0.00821259 min:-0.0968573 max:0.116681 sd:0.037229 dtype:DTYPE_FLOAT32\n", + "\t471: "embedding.470_of_512" NUMERICAL mean:-0.0126405 min:-0.11236 max:0.104975 sd:0.0410705 dtype:DTYPE_FLOAT32\n", + "\t472: "embedding.471_of_512" NUMERICAL mean:0.00967789 min:-0.114741 max:0.113365 sd:0.0415494 dtype:DTYPE_FLOAT32\n", + "\t473: "embedding.472_of_512" NUMERICAL mean:0.0051147 min:-0.116287 max:0.123708 sd:0.038196 dtype:DTYPE_FLOAT32\n", + "\t474: "embedding.473_of_512" NUMERICAL mean:0.00460656 min:-0.117806 max:0.116034 sd:0.0417151 dtype:DTYPE_FLOAT32\n", + "\t475: "embedding.474_of_512" NUMERICAL mean:-0.00244138 min:-0.103319 max:0.116585 sd:0.0374234 dtype:DTYPE_FLOAT32\n", + "\t476: "embedding.475_of_512" NUMERICAL mean:-0.00797766 min:-0.112168 max:0.110854 sd:0.043268 dtype:DTYPE_FLOAT32\n", + "\t477: "embedding.476_of_512" NUMERICAL mean:-0.0123356 min:-0.118527 max:0.110389 sd:0.0415487 dtype:DTYPE_FLOAT32\n", + "\t478: "embedding.477_of_512" NUMERICAL mean:-0.00891097 min:-0.109911 max:0.114824 sd:0.0409558 dtype:DTYPE_FLOAT32\n", + "\t479: "embedding.478_of_512" NUMERICAL mean:0.0531792 min:-0.123494 max:0.14429 sd:0.0446525 dtype:DTYPE_FLOAT32\n", + "\t480: "embedding.479_of_512" NUMERICAL mean:0.00310177 min:-0.126525 max:0.135642 sd:0.0508086 dtype:DTYPE_FLOAT32\n", + "\t481: "embedding.480_of_512" NUMERICAL mean:0.00178777 min:-0.101512 max:0.111535 sd:0.0393616 dtype:DTYPE_FLOAT32\n", + "\t482: "embedding.481_of_512" NUMERICAL mean:0.000436977 min:-0.141595 max:0.116526 sd:0.0498771 dtype:DTYPE_FLOAT32\n", + "\t483: "embedding.482_of_512" NUMERICAL mean:0.0139387 min:-0.109079 max:0.125151 sd:0.0395955 dtype:DTYPE_FLOAT32\n", + "\t484: "embedding.483_of_512" NUMERICAL mean:-0.0190178 min:-0.116579 max:0.12211 sd:0.0404221 dtype:DTYPE_FLOAT32\n", + "\t485: "embedding.484_of_512" NUMERICAL mean:0.0111983 min:-0.115318 max:0.114151 sd:0.0407415 dtype:DTYPE_FLOAT32\n", + "\t486: "embedding.485_of_512" NUMERICAL mean:-0.0210413 min:-0.12817 max:0.102505 sd:0.0409111 dtype:DTYPE_FLOAT32\n", + "\t487: "embedding.486_of_512" NUMERICAL mean:0.00291598 min:-0.136717 max:0.132649 sd:0.0483605 dtype:DTYPE_FLOAT32\n", + "\t488: "embedding.487_of_512" NUMERICAL mean:0.0258506 min:-0.118507 max:0.139141 sd:0.0476916 dtype:DTYPE_FLOAT32\n", + "\t489: "embedding.488_of_512" NUMERICAL mean:0.00950834 min:-0.117085 max:0.104573 sd:0.0394689 dtype:DTYPE_FLOAT32\n", + "\t490: "embedding.489_of_512" NUMERICAL mean:-0.00655678 min:-0.113501 max:0.116317 sd:0.0412641 dtype:DTYPE_FLOAT32\n", + "\t491: "embedding.490_of_512" NUMERICAL mean:0.0025444 min:-0.0976397 max:0.133059 sd:0.0391231 dtype:DTYPE_FLOAT32\n", + "\t492: "embedding.491_of_512" NUMERICAL mean:-0.00116524 min:-0.115012 max:0.108975 sd:0.0373331 dtype:DTYPE_FLOAT32\n", + "\t493: "embedding.492_of_512" NUMERICAL mean:-0.00805514 min:-0.112223 max:0.118394 sd:0.0409569 dtype:DTYPE_FLOAT32\n", + "\t494: "embedding.493_of_512" NUMERICAL mean:-0.00381922 min:-0.109779 max:0.113538 sd:0.0375221 dtype:DTYPE_FLOAT32\n", + "\t495: "embedding.494_of_512" NUMERICAL mean:0.0192517 min:-0.108658 max:0.118238 sd:0.0414103 dtype:DTYPE_FLOAT32\n", + "\t496: "embedding.495_of_512" NUMERICAL mean:-0.00252727 min:-0.118617 max:0.100404 sd:0.0398346 dtype:DTYPE_FLOAT32\n", + "\t497: "embedding.496_of_512" NUMERICAL mean:-0.000870086 min:-0.10941 max:0.119059 sd:0.043479 dtype:DTYPE_FLOAT32\n", + "\t498: "embedding.497_of_512" NUMERICAL mean:-0.00296294 min:-0.123757 max:0.109776 sd:0.0420959 dtype:DTYPE_FLOAT32\n", + "\t499: "embedding.498_of_512" NUMERICAL mean:0.0127804 min:-0.138546 max:0.154906 sd:0.0511673 dtype:DTYPE_FLOAT32\n", + "\t500: "embedding.499_of_512" NUMERICAL mean:-0.00481274 min:-0.104637 max:0.112387 sd:0.0419786 dtype:DTYPE_FLOAT32\n", + "\t501: "embedding.500_of_512" NUMERICAL mean:-0.012455 min:-0.109502 max:0.102241 sd:0.0402451 dtype:DTYPE_FLOAT32\n", + "\t502: "embedding.501_of_512" NUMERICAL mean:-0.0236005 min:-0.117228 max:0.124977 sd:0.0464432 dtype:DTYPE_FLOAT32\n", + "\t503: "embedding.502_of_512" NUMERICAL mean:0.00916425 min:-0.128705 max:0.110148 sd:0.0412428 dtype:DTYPE_FLOAT32\n", + "\t504: "embedding.503_of_512" NUMERICAL mean:-0.0099854 min:-0.179229 max:0.112813 sd:0.0666002 dtype:DTYPE_FLOAT32\n", + "\t505: "embedding.504_of_512" NUMERICAL mean:0.0140659 min:-0.124558 max:0.131239 sd:0.0459631 dtype:DTYPE_FLOAT32\n", + "\t506: "embedding.505_of_512" NUMERICAL mean:0.00529723 min:-0.119894 max:0.104362 sd:0.0399805 dtype:DTYPE_FLOAT32\n", + "\t507: "embedding.506_of_512" NUMERICAL mean:-0.00319069 min:-0.111178 max:0.108562 sd:0.040611 dtype:DTYPE_FLOAT32\n", + "\t508: "embedding.507_of_512" NUMERICAL mean:-0.00332249 min:-0.108088 max:0.118358 sd:0.0396039 dtype:DTYPE_FLOAT32\n", + "\t509: "embedding.508_of_512" NUMERICAL mean:-0.00396023 min:-0.11048 max:0.107852 sd:0.0375341 dtype:DTYPE_FLOAT32\n", + "\t510: "embedding.509_of_512" NUMERICAL mean:-0.00917504 min:-0.116661 max:0.100524 sd:0.0361387 dtype:DTYPE_FLOAT32\n", + "\t511: "embedding.510_of_512" NUMERICAL mean:0.037723 min:-0.0965471 max:0.140981 sd:0.0479428 dtype:DTYPE_FLOAT32\n", + "\t512: "embedding.511_of_512" NUMERICAL mean:0.00788656 min:-0.116457 max:0.102988 sd:0.0402552 dtype:DTYPE_FLOAT32\n", + "\n", + "CATEGORICAL: 1 (0.194932%)\n", + "\t0: "label" CATEGORICAL has-dict vocab-size:3 zero-ood-items most-frequent:"1" 37569 (55.7826%) dtype:DTYPE_INT64\n", + "\n", + "Terminology:\n", + "\tnas: Number of non-available (i.e. missing) values.\n", + "\tood: Out of dictionary.\n", + "\tmanually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.\n", + "\ttokenized: The attribute value is obtained through tokenization.\n", + "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n", + "\tvocab-size: Number of unique values.\n", + "
The following evaluation is computed on the validation or out-of-bag dataset.
Task: CLASSIFICATION\n", + "Label: label\n", + "Loss (BINOMIAL_LOG_LIKELIHOOD): 0.738938\n", + "\n", + "Accuracy: 0.837441 CI95[W][0 1]\n", + "ErrorRate: : 0.162559\n", + "\n", + "\n", + "Confusion Table:\n", + "truth\\prediction\n", + " 1 0\n", + " 1 3230 559\n", + " 0 536 2411\n", + "Total: 6736\n", + "\n", + "
Variable importances measure the importance of an input feature for a model.
1. "embedding.171_of_512" 0.209350 ################\n", + " 2. "embedding.111_of_512" 0.204045 #############\n", + " 3. "embedding.384_of_512" 0.178102 ####\n", + " 4. "embedding.291_of_512" 0.177804 ###\n", + " 5. "embedding.408_of_512" 0.177389 ###\n", + " 6. "embedding.365_of_512" 0.176330 ###\n", + " 7. "embedding.188_of_512" 0.175333 ###\n", + " 8. "embedding.276_of_512" 0.175256 ##\n", + " 9. "embedding.036_of_512" 0.174208 ##\n", + " 10. "embedding.350_of_512" 0.174185 ##\n", + " 11. "embedding.343_of_512" 0.174146 ##\n", + " 12. "embedding.022_of_512" 0.173972 ##\n", + " 13. "embedding.333_of_512" 0.173661 ##\n", + " 14. "embedding.332_of_512" 0.173290 ##\n", + " 15. "embedding.307_of_512" 0.172651 ##\n", + " 16. "embedding.058_of_512" 0.172588 #\n", + " 17. "embedding.167_of_512" 0.172248 #\n", + " 18. "embedding.184_of_512" 0.172189 #\n", + " 19. "embedding.041_of_512" 0.172093 #\n", + " 20. "embedding.118_of_512" 0.171974 #\n", + " 21. "embedding.063_of_512" 0.171966 #\n", + " 22. "embedding.065_of_512" 0.171925 #\n", + " 23. "embedding.132_of_512" 0.171918 #\n", + " 24. "embedding.346_of_512" 0.171654 #\n", + " 25. "embedding.092_of_512" 0.171654 #\n", + " 26. "embedding.182_of_512" 0.171428 #\n", + " 27. "embedding.043_of_512" 0.171327 #\n", + " 28. "embedding.329_of_512" 0.171311 #\n", + " 29. "embedding.342_of_512" 0.171305 #\n", + " 30. "embedding.140_of_512" 0.171297 #\n", + " 31. "embedding.302_of_512" 0.171015 #\n", + " 32. "embedding.297_of_512" 0.170923 #\n", + " 33. "embedding.478_of_512" 0.170846 #\n", + " 34. "embedding.511_of_512" 0.170711 #\n", + " 35. "embedding.227_of_512" 0.170582 #\n", + " 36. "embedding.175_of_512" 0.170326 #\n", + " 37. "embedding.237_of_512" 0.170321 #\n", + " 38. "embedding.292_of_512" 0.170164 #\n", + " 39. "embedding.363_of_512" 0.170138 #\n", + " 40. "embedding.319_of_512" 0.169955 \n", + " 41. "embedding.424_of_512" 0.169759 \n", + " 42. "embedding.079_of_512" 0.169743 \n", + " 43. "embedding.306_of_512" 0.169673 \n", + " 44. "embedding.368_of_512" 0.169648 \n", + " 45. "embedding.494_of_512" 0.169495 \n", + " 46. "embedding.052_of_512" 0.169474 \n", + " 47. "embedding.194_of_512" 0.169375 \n", + " 48. "embedding.483_of_512" 0.169328 \n", + " 49. "embedding.240_of_512" 0.169305 \n", + " 50. "embedding.046_of_512" 0.169244 \n", + " 51. "embedding.243_of_512" 0.169234 \n", + " 52. "embedding.215_of_512" 0.169210 \n", + " 53. "embedding.152_of_512" 0.169186 \n", + " 54. "embedding.502_of_512" 0.169168 \n", + " 55. "embedding.471_of_512" 0.169095 \n", + " 56. "embedding.376_of_512" 0.169079 \n", + " 57. "embedding.244_of_512" 0.169073 \n", + " 58. "embedding.219_of_512" 0.169046 \n", + " 59. "embedding.308_of_512" 0.169035 \n", + " 60. "embedding.287_of_512" 0.169025 \n", + " 61. "embedding.044_of_512" 0.169018 \n", + " 62. "embedding.195_of_512" 0.168966 \n", + " 63. "embedding.130_of_512" 0.168943 \n", + " 64. "embedding.469_of_512" 0.168916 \n", + " 65. "embedding.352_of_512" 0.168860 \n", + " 66. "embedding.358_of_512" 0.168809 \n", + " 67. "embedding.289_of_512" 0.168801 \n", + " 68. "embedding.142_of_512" 0.168721 \n", + " 69. "embedding.503_of_512" 0.168673 \n", + " 70. "embedding.205_of_512" 0.168658 \n", + " 71. "embedding.126_of_512" 0.168630 \n", + " 72. "embedding.116_of_512" 0.168582 \n", + " 73. "embedding.185_of_512" 0.168548 \n", + " 74. "embedding.228_of_512" 0.168537 \n", + " 75. "embedding.225_of_512" 0.168512 \n", + " 76. "embedding.427_of_512" 0.168510 \n", + " 77. "embedding.443_of_512" 0.168502 \n", + " 78. "embedding.267_of_512" 0.168497 \n", + " 79. "embedding.399_of_512" 0.168497 \n", + " 80. "embedding.050_of_512" 0.168446 \n", + " 81. "embedding.158_of_512" 0.168424 \n", + " 82. "embedding.449_of_512" 0.168423 \n", + " 83. "embedding.202_of_512" 0.168404 \n", + " 84. "embedding.066_of_512" 0.168397 \n", + " 85. "embedding.155_of_512" 0.168383 \n", + " 86. "embedding.012_of_512" 0.168377 \n", + " 87. "embedding.268_of_512" 0.168339 \n", + " 88. "embedding.327_of_512" 0.168327 \n", + " 89. "embedding.359_of_512" 0.168288 \n", + " 90. "embedding.072_of_512" 0.168259 \n", + " 91. "embedding.416_of_512" 0.168239 \n", + " 92. "embedding.093_of_512" 0.168229 \n", + " 93. "embedding.169_of_512" 0.168224 \n", + " 94. "embedding.433_of_512" 0.168222 \n", + " 95. "embedding.233_of_512" 0.168221 \n", + " 96. "embedding.068_of_512" 0.168220 \n", + " 97. "embedding.098_of_512" 0.168219 \n", + " 98. "embedding.415_of_512" 0.168218 \n", + " 99. "embedding.076_of_512" 0.168206 \n", + " 100. "embedding.200_of_512" 0.168200 \n", + " 101. "embedding.314_of_512" 0.168172 \n", + " 102. "embedding.197_of_512" 0.168162 \n", + " 103. "embedding.030_of_512" 0.168141 \n", + " 104. "embedding.141_of_512" 0.168119 \n", + " 105. "embedding.498_of_512" 0.168115 \n", + " 106. "embedding.402_of_512" 0.168106 \n", + " 107. "embedding.321_of_512" 0.168090 \n", + " 108. "embedding.479_of_512" 0.168059 \n", + " 109. "embedding.146_of_512" 0.168056 \n", + " 110. "embedding.370_of_512" 0.168052 \n", + " 111. "embedding.051_of_512" 0.168045 \n", + " 112. "embedding.262_of_512" 0.168041 \n", + " 113. "embedding.163_of_512" 0.168039 \n", + " 114. "embedding.187_of_512" 0.168037 \n", + " 115. "embedding.247_of_512" 0.168030 \n", + " 116. "embedding.390_of_512" 0.168029 \n", + " 117. "embedding.204_of_512" 0.168024 \n", + " 118. "embedding.179_of_512" 0.168023 \n", + " 119. "embedding.235_of_512" 0.168017 \n", + " 120. "embedding.378_of_512" 0.168014 \n", + " 121. "embedding.209_of_512" 0.168010 \n", + " 122. "embedding.057_of_512" 0.168008 \n", + " 123. "embedding.505_of_512" 0.168005 \n", + " 124. "embedding.286_of_512" 0.168004 \n", + " 125. "embedding.296_of_512" 0.167997 \n", + " 126. "embedding.313_of_512" 0.167978 \n", + " 127. "embedding.356_of_512" 0.167977 \n", + " 128. "embedding.437_of_512" 0.167975 \n", + " 129. "embedding.489_of_512" 0.167952 \n", + " 130. "embedding.338_of_512" 0.167946 \n", + " 131. "embedding.283_of_512" 0.167939 \n", + " 132. "embedding.071_of_512" 0.167930 \n", + " 133. "embedding.173_of_512" 0.167921 \n", + " 134. "embedding.405_of_512" 0.167918 \n", + " 135. "embedding.114_of_512" 0.167901 \n", + " 136. "embedding.136_of_512" 0.167900 \n", + " 137. "embedding.281_of_512" 0.167900 \n", + " 138. "embedding.064_of_512" 0.167894 \n", + " 139. "embedding.284_of_512" 0.167878 \n", + " 140. "embedding.060_of_512" 0.167857 \n", + " 141. "embedding.061_of_512" 0.167852 \n", + " 142. "embedding.375_of_512" 0.167851 \n", + " 143. "embedding.039_of_512" 0.167847 \n", + " 144. "embedding.042_of_512" 0.167837 \n", + " 145. "embedding.293_of_512" 0.167836 \n", + " 146. "embedding.218_of_512" 0.167832 \n", + " 147. "embedding.255_of_512" 0.167831 \n", + " 148. "embedding.067_of_512" 0.167826 \n", + " 149. "embedding.326_of_512" 0.167825 \n", + " 150. "embedding.110_of_512" 0.167818 \n", + " 151. "embedding.191_of_512" 0.167814 \n", + " 152. "embedding.263_of_512" 0.167808 \n", + " 153. "embedding.020_of_512" 0.167807 \n", + " 154. "embedding.115_of_512" 0.167807 \n", + " 155. "embedding.265_of_512" 0.167801 \n", + " 156. "embedding.324_of_512" 0.167796 \n", + " 157. "embedding.011_of_512" 0.167792 \n", + " 158. "embedding.090_of_512" 0.167789 \n", + " 159. "embedding.180_of_512" 0.167769 \n", + " 160. "embedding.381_of_512" 0.167767 \n", + " 161. "embedding.362_of_512" 0.167762 \n", + " 162. "embedding.054_of_512" 0.167759 \n", + " 163. "embedding.382_of_512" 0.167751 \n", + " 164. "embedding.325_of_512" 0.167749 \n", + " 165. "embedding.454_of_512" 0.167739 \n", + " 166. "embedding.251_of_512" 0.167737 \n", + " 167. "embedding.331_of_512" 0.167733 \n", + " 168. "embedding.480_of_512" 0.167732 \n", + " 169. "embedding.221_of_512" 0.167730 \n", + " 170. "embedding.217_of_512" 0.167729 \n", + " 171. "embedding.354_of_512" 0.167722 \n", + " 172. "embedding.391_of_512" 0.167720 \n", + " 173. "embedding.444_of_512" 0.167708 \n", + " 174. "embedding.216_of_512" 0.167708 \n", + " 175. "embedding.232_of_512" 0.167694 \n", + " 176. "embedding.143_of_512" 0.167693 \n", + " 177. "embedding.119_of_512" 0.167688 \n", + " 178. "embedding.123_of_512" 0.167683 \n", + " 179. "embedding.095_of_512" 0.167677 \n", + " 180. "embedding.018_of_512" 0.167676 \n", + " 181. "embedding.144_of_512" 0.167676 \n", + " 182. "embedding.062_of_512" 0.167672 \n", + " 183. "embedding.021_of_512" 0.167666 \n", + " 184. "embedding.165_of_512" 0.167664 \n", + " 185. "embedding.208_of_512" 0.167661 \n", + " 186. "embedding.078_of_512" 0.167659 \n", + " 187. "embedding.309_of_512" 0.167658 \n", + " 188. "embedding.113_of_512" 0.167654 \n", + " 189. "embedding.080_of_512" 0.167653 \n", + " 190. "embedding.386_of_512" 0.167653 \n", + " 191. "embedding.447_of_512" 0.167653 \n", + " 192. 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"embedding.216_of_512" 26.833603 \n", + " 148. "embedding.011_of_512" 26.743176 \n", + " 149. "embedding.236_of_512" 26.659612 \n", + " 150. "embedding.158_of_512" 26.437004 \n", + " 151. "embedding.197_of_512" 26.104442 \n", + " 152. "embedding.480_of_512" 25.937455 \n", + " 153. "embedding.135_of_512" 25.826829 \n", + " 154. "embedding.202_of_512" 25.767675 \n", + " 155. "embedding.256_of_512" 25.752772 \n", + " 156. "embedding.404_of_512" 25.730176 \n", + " 157. "embedding.057_of_512" 25.713334 \n", + " 158. "embedding.263_of_512" 25.631016 \n", + " 159. "embedding.482_of_512" 25.280783 \n", + " 160. "embedding.062_of_512" 25.175564 \n", + " 161. "embedding.284_of_512" 25.075373 \n", + " 162. "embedding.470_of_512" 25.049711 \n", + " 163. "embedding.136_of_512" 25.014152 \n", + " 164. "embedding.040_of_512" 24.838080 \n", + " 165. "embedding.418_of_512" 24.201707 \n", + " 166. "embedding.324_of_512" 23.976690 \n", + " 167. "embedding.109_of_512" 23.801806 \n", + " 168. "embedding.391_of_512" 23.550975 \n", + " 169. "embedding.290_of_512" 23.394580 \n", + " 170. "embedding.187_of_512" 23.258433 \n", + " 171. "embedding.405_of_512" 23.122473 \n", + " 172. "embedding.221_of_512" 23.117095 \n", + " 173. "embedding.119_of_512" 22.902634 \n", + " 174. "embedding.025_of_512" 22.826385 \n", + " 175. "embedding.345_of_512" 22.597737 \n", + " 176. "embedding.122_of_512" 22.411945 \n", + " 177. "embedding.039_of_512" 22.320384 \n", + " 178. "embedding.060_of_512" 22.304902 \n", + " 179. "embedding.262_of_512" 22.021096 \n", + " 180. "embedding.381_of_512" 21.759861 \n", + " 181. "embedding.165_of_512" 21.728633 \n", + " 182. "embedding.499_of_512" 21.633024 \n", + " 183. "embedding.074_of_512" 21.594031 \n", + " 184. "embedding.051_of_512" 21.301531 \n", + " 185. "embedding.237_of_512" 21.156419 \n", + " 186. "embedding.371_of_512" 21.131870 \n", + " 187. "embedding.508_of_512" 21.000655 \n", + " 188. "embedding.317_of_512" 20.679063 \n", + " 189. "embedding.421_of_512" 20.666468 \n", + " 190. "embedding.235_of_512" 20.389141 \n", + " 191. "embedding.239_of_512" 19.915094 \n", + " 192. "embedding.027_of_512" 19.772421 \n", + " 193. "embedding.095_of_512" 19.266230 \n", + " 194. "embedding.492_of_512" 18.949160 \n", + " 195. "embedding.070_of_512" 18.817319 \n", + " 196. "embedding.370_of_512" 18.771609 \n", + " 197. "embedding.354_of_512" 18.714025 \n", + " 198. "embedding.089_of_512" 18.706402 \n", + " 199. "embedding.064_of_512" 18.472540 \n", + " 200. "embedding.006_of_512" 18.288014 \n", + " 201. "embedding.212_of_512" 18.248336 \n", + " 202. "embedding.018_of_512" 18.210169 \n", + " 203. "embedding.372_of_512" 18.089667 \n", + " 204. "embedding.046_of_512" 17.988413 \n", + " 205. "embedding.200_of_512" 17.920917 \n", + " 206. "embedding.141_of_512" 17.893822 \n", + " 207. "embedding.462_of_512" 17.821465 \n", + " 208. "embedding.081_of_512" 17.694298 \n", + " 209. "embedding.107_of_512" 17.566795 \n", + " 210. "embedding.305_of_512" 17.504576 \n", + " 211. "embedding.261_of_512" 17.480185 \n", + " 212. "embedding.087_of_512" 17.274760 \n", + " 213. "embedding.220_of_512" 17.223686 \n", + " 214. "embedding.000_of_512" 17.153120 \n", + " 215. "embedding.496_of_512" 17.089169 \n", + " 216. "embedding.210_of_512" 17.079963 \n", + " 217. "embedding.320_of_512" 17.063082 \n", + " 218. "embedding.134_of_512" 16.808995 \n", + " 219. "embedding.359_of_512" 16.806446 \n", + " 220. "embedding.293_of_512" 16.792584 \n", + " 221. "embedding.133_of_512" 16.714364 \n", + " 222. "embedding.138_of_512" 16.382440 \n", + " 223. "embedding.452_of_512" 16.213482 \n", + " 224. "embedding.075_of_512" 15.913921 \n", + " 225. "embedding.059_of_512" 15.839770 \n", + " 226. "embedding.415_of_512" 15.773601 \n", + " 227. "embedding.174_of_512" 15.639605 \n", + " 228. "embedding.024_of_512" 15.292290 \n", + " 229. "embedding.105_of_512" 15.232853 \n", + " 230. "embedding.287_of_512" 15.135759 \n", + " 231. "embedding.336_of_512" 15.119497 \n", + " 232. "embedding.510_of_512" 15.114059 \n", + " 233. "embedding.161_of_512" 14.983866 \n", + " 234. "embedding.209_of_512" 14.911226 \n", + " 235. "embedding.078_of_512" 14.860597 \n", + " 236. "embedding.123_of_512" 14.738864 \n", + " 237. "embedding.228_of_512" 14.704528 \n", + " 238. "embedding.280_of_512" 14.700699 \n", + " 239. "embedding.250_of_512" 14.622368 \n", + " 240. "embedding.412_of_512" 14.549791 \n", + " 241. "embedding.084_of_512" 14.457128 \n", + " 242. "embedding.224_of_512" 14.325537 \n", + " 243. "embedding.323_of_512" 14.301669 \n", + " 244. "embedding.458_of_512" 14.175333 \n", + " 245. "embedding.385_of_512" 14.152160 \n", + " 246. "embedding.411_of_512" 14.127793 \n", + " 247. "embedding.361_of_512" 14.030305 \n", + " 248. "embedding.484_of_512" 13.869317 \n", + " 249. "embedding.257_of_512" 13.863068 \n", + " 250. "embedding.179_of_512" 13.750212 \n", + " 251. "embedding.442_of_512" 13.720752 \n", + " 252. "embedding.223_of_512" 13.591060 \n", + " 253. "embedding.451_of_512" 13.567758 \n", + " 254. "embedding.163_of_512" 13.495614 \n", + " 255. "embedding.341_of_512" 13.326650 \n", + " 256. "embedding.474_of_512" 13.315612 \n", + " 257. "embedding.005_of_512" 13.226211 \n", + " 258. "embedding.286_of_512" 13.069766 \n", + " 259. "embedding.264_of_512" 13.052572 \n", + " 260. "embedding.479_of_512" 12.980791 \n", + " 261. "embedding.014_of_512" 12.954876 \n", + " 262. "embedding.110_of_512" 12.938494 \n", + " 263. "embedding.131_of_512" 12.828036 \n", + " 264. "embedding.489_of_512" 12.754188 \n", + " 265. "embedding.461_of_512" 12.693593 \n", + " 266. "embedding.288_of_512" 12.610884 \n", + " 267. "embedding.328_of_512" 12.424237 \n", + " 268. "embedding.213_of_512" 12.373543 \n", + " 269. "embedding.160_of_512" 12.324912 \n", + " 270. "embedding.125_of_512" 12.296855 \n", + " 271. "embedding.476_of_512" 12.159989 \n", + " 272. "embedding.049_of_512" 12.135087 \n", + " 273. "embedding.440_of_512" 12.127148 \n", + " 274. "embedding.096_of_512" 12.009432 \n", + " 275. "embedding.318_of_512" 11.805688 \n", + " 276. "embedding.038_of_512" 11.743252 \n", + " 277. "embedding.390_of_512" 11.634256 \n", + " 278. "embedding.211_of_512" 11.608288 \n", + " 279. "embedding.294_of_512" 11.542138 \n", + " 280. "embedding.348_of_512" 11.415023 \n", + " 281. "embedding.454_of_512" 11.385024 \n", + " 282. "embedding.144_of_512" 11.308329 \n", + " 283. "embedding.431_of_512" 11.241699 \n", + " 284. "embedding.326_of_512" 11.238328 \n", + " 285. "embedding.475_of_512" 11.232105 \n", + " 286. "embedding.400_of_512" 11.220325 \n", + " 287. "embedding.164_of_512" 11.212532 \n", + " 288. "embedding.373_of_512" 11.167605 \n", + " 289. "embedding.303_of_512" 11.137827 \n", + " 290. "embedding.466_of_512" 10.996574 \n", + " 291. "embedding.322_of_512" 10.987503 \n", + " 292. "embedding.104_of_512" 10.878272 \n", + " 293. "embedding.338_of_512" 10.680139 \n", + " 294. "embedding.278_of_512" 10.661013 \n", + " 295. "embedding.439_of_512" 10.625585 \n", + " 296. "embedding.493_of_512" 10.580044 \n", + " 297. "embedding.386_of_512" 10.416147 \n", + " 298. "embedding.001_of_512" 10.330815 \n", + " 299. "embedding.477_of_512" 10.215354 \n", + " 300. "embedding.274_of_512" 10.195050 \n", + " 301. "embedding.189_of_512" 10.034064 \n", + " 302. "embedding.501_of_512" 9.712892 \n", + " 303. "embedding.023_of_512" 9.704768 \n", + " 304. "embedding.337_of_512" 9.698276 \n", + " 305. "embedding.438_of_512" 9.515732 \n", + " 306. "embedding.238_of_512" 9.445766 \n", + " 307. "embedding.425_of_512" 9.411107 \n", + " 308. "embedding.208_of_512" 9.390540 \n", + " 309. "embedding.315_of_512" 9.389285 \n", + " 310. "embedding.010_of_512" 9.344792 \n", + " 311. "embedding.434_of_512" 9.321555 \n", + " 312. "embedding.344_of_512" 9.305979 \n", + " 313. "embedding.151_of_512" 9.257123 \n", + " 314. "embedding.330_of_512" 9.217059 \n", + " 315. "embedding.406_of_512" 9.214288 \n", + " 316. "embedding.340_of_512" 9.188421 \n", + " 317. "embedding.159_of_512" 9.052036 \n", + " 318. "embedding.137_of_512" 9.038237 \n", + " 319. "embedding.056_of_512" 8.905088 \n", + " 320. "embedding.459_of_512" 8.895027 \n", + " 321. "embedding.007_of_512" 8.830927 \n", + " 322. "embedding.353_of_512" 8.829274 \n", + " 323. "embedding.388_of_512" 8.813356 \n", + " 324. "embedding.313_of_512" 8.809157 \n", + " 325. "embedding.456_of_512" 8.672220 \n", + " 326. "embedding.229_of_512" 8.619430 \n", + " 327. "embedding.321_of_512" 8.604794 \n", + " 328. "embedding.266_of_512" 8.576367 \n", + " 329. "embedding.379_of_512" 8.476270 \n", + " 330. "embedding.100_of_512" 8.439626 \n", + " 331. "embedding.053_of_512" 8.413065 \n", + " 332. "embedding.468_of_512" 8.388147 \n", + " 333. "embedding.352_of_512" 8.382706 \n", + " 334. "embedding.009_of_512" 8.268669 \n", + " 335. "embedding.234_of_512" 8.223046 \n", + " 336. "embedding.447_of_512" 8.218239 \n", + " 337. "embedding.269_of_512" 8.212989 \n", + " 338. "embedding.301_of_512" 8.075671 \n", + " 339. "embedding.422_of_512" 8.063095 \n", + " 340. "embedding.156_of_512" 8.042654 \n", + " 341. "embedding.428_of_512" 8.035930 \n", + " 342. "embedding.153_of_512" 8.029318 \n", + " 343. "embedding.003_of_512" 7.987826 \n", + " 344. "embedding.106_of_512" 7.969378 \n", + " 345. "embedding.507_of_512" 7.824125 \n", + " 346. "embedding.312_of_512" 7.728918 \n", + " 347. "embedding.258_of_512" 7.615799 \n", + " 348. "embedding.226_of_512" 7.458633 \n", + " 349. "embedding.430_of_512" 7.401733 \n", + " 350. "embedding.168_of_512" 7.393336 \n", + " 351. "embedding.035_of_512" 7.387460 \n", + " 352. "embedding.487_of_512" 7.330754 \n", + " 353. "embedding.309_of_512" 7.330402 \n", + " 354. "embedding.033_of_512" 7.309564 \n", + " 355. "embedding.304_of_512" 7.307759 \n", + " 356. "embedding.091_of_512" 7.254614 \n", + " 357. "embedding.102_of_512" 7.214308 \n", + " 358. "embedding.357_of_512" 7.210662 \n", + " 359. "embedding.157_of_512" 7.165839 \n", + " 360. "embedding.094_of_512" 7.165105 \n", + " 361. "embedding.472_of_512" 7.144578 \n", + " 362. "embedding.139_of_512" 7.064992 \n", + " 363. "embedding.377_of_512" 7.042943 \n", + " 364. "embedding.380_of_512" 7.041339 \n", + " 365. "embedding.272_of_512" 7.041162 \n", + " 366. "embedding.004_of_512" 7.035992 \n", + " 367. "embedding.085_of_512" 6.984889 \n", + " 368. "embedding.230_of_512" 6.945414 \n", + " 369. "embedding.432_of_512" 6.943797 \n", + " 370. "embedding.389_of_512" 6.881828 \n", + " 371. "embedding.218_of_512" 6.842948 \n", + " 372. "embedding.008_of_512" 6.803792 \n", + " 373. "embedding.097_of_512" 6.738676 \n", + " 374. "embedding.445_of_512" 6.670537 \n", + " 375. "embedding.037_of_512" 6.596426 \n", + " 376. "embedding.002_of_512" 6.565671 \n", + " 377. "embedding.271_of_512" 6.314382 \n", + " 378. "embedding.207_of_512" 6.268816 \n", + " 379. "embedding.103_of_512" 6.197685 \n", + " 380. "embedding.331_of_512" 6.188369 \n", + " 381. "embedding.460_of_512" 6.159943 \n", + " 382. "embedding.347_of_512" 6.139413 \n", + " 383. "embedding.206_of_512" 6.100772 \n", + " 384. "embedding.351_of_512" 5.963090 \n", + " 385. "embedding.441_of_512" 5.958451 \n", + " 386. "embedding.032_of_512" 5.841272 \n", + " 387. "embedding.464_of_512" 5.748941 \n", + " 388. "embedding.172_of_512" 5.668636 \n", + " 389. "embedding.045_of_512" 5.624897 \n", + " 390. "embedding.495_of_512" 5.606474 \n", + " 391. "embedding.231_of_512" 5.488857 \n", + " 392. "embedding.457_of_512" 5.412411 \n", + " 393. "embedding.360_of_512" 5.408576 \n", + " 394. "embedding.124_of_512" 5.398018 \n", + " 395. "embedding.198_of_512" 5.389353 \n", + " 396. "embedding.021_of_512" 5.353755 \n", + " 397. "embedding.277_of_512" 5.305103 \n", + " 398. "embedding.467_of_512" 5.292973 \n", + " 399. "embedding.120_of_512" 5.268761 \n", + " 400. "embedding.506_of_512" 5.188110 \n", + " 401. "embedding.500_of_512" 5.162792 \n", + " 402. "embedding.403_of_512" 5.110633 \n", + " 403. "embedding.083_of_512" 5.093338 \n", + " 404. "embedding.048_of_512" 5.090771 \n", + " 405. "embedding.259_of_512" 4.962671 \n", + " 406. "embedding.491_of_512" 4.955279 \n", + " 407. "embedding.426_of_512" 4.930060 \n", + " 408. "embedding.170_of_512" 4.810002 \n", + " 409. "embedding.162_of_512" 4.763983 \n", + " 410. "embedding.335_of_512" 4.745229 \n", + " 411. "embedding.199_of_512" 4.703493 \n", + " 412. "embedding.260_of_512" 4.625093 \n", + " 413. "embedding.275_of_512" 4.350696 \n", + " 414. "embedding.364_of_512" 4.328313 \n", + " 415. "embedding.183_of_512" 4.213411 \n", + " 416. "embedding.108_of_512" 4.180671 \n", + " 417. "embedding.117_of_512" 4.159432 \n", + " 418. "embedding.300_of_512" 4.080444 \n", + " 419. "embedding.409_of_512" 4.074465 \n", + " 420. "embedding.178_of_512" 4.040530 \n", + " 421. "embedding.310_of_512" 3.940741 \n", + " 422. "embedding.016_of_512" 3.898330 \n", + " 423. "embedding.148_of_512" 3.897144 \n", + " 424. "embedding.419_of_512" 3.813937 \n", + " 425. "embedding.077_of_512" 3.810567 \n", + " 426. "embedding.453_of_512" 3.758238 \n", + " 427. "embedding.019_of_512" 3.755729 \n", + " 428. "embedding.270_of_512" 3.727955 \n", + " 429. "embedding.490_of_512" 3.595743 \n", + " 430. "embedding.398_of_512" 3.510094 \n", + " 431. "embedding.127_of_512" 3.494055 \n", + " 432. "embedding.203_of_512" 3.392086 \n", + " 433. "embedding.446_of_512" 3.358459 \n", + " 434. "embedding.413_of_512" 3.340526 \n", + " 435. "embedding.145_of_512" 3.282636 \n", + " 436. "embedding.128_of_512" 3.217526 \n", + " 437. "embedding.166_of_512" 3.124052 \n", + " 438. "embedding.394_of_512" 3.113460 \n", + " 439. "embedding.316_of_512" 3.064561 \n", + " 440. "embedding.149_of_512" 3.053578 \n", + " 441. "embedding.369_of_512" 2.945368 \n", + " 442. "embedding.028_of_512" 2.810905 \n", + " 443. "embedding.181_of_512" 2.743388 \n", + " 444. "embedding.196_of_512" 2.742419 \n", + " 445. "embedding.407_of_512" 2.738602 \n", + " 446. "embedding.273_of_512" 2.672149 \n", + " 447. "embedding.015_of_512" 2.670484 \n", + " 448. "embedding.295_of_512" 2.441439 \n", + " 449. "embedding.285_of_512" 2.377308 \n", + " 450. "embedding.395_of_512" 2.374672 \n", + " 451. "embedding.485_of_512" 2.341882 \n", + " 452. "embedding.392_of_512" 2.339278 \n", + " 453. "embedding.252_of_512" 2.329295 \n", + " 454. "embedding.154_of_512" 2.317964 \n", + " 455. "embedding.017_of_512" 2.229209 \n", + " 456. "embedding.073_of_512" 2.228032 \n", + " 457. "embedding.129_of_512" 2.217764 \n", + " 458. "embedding.481_of_512" 2.177979 \n", + " 459. "embedding.367_of_512" 2.111253 \n", + " 460. "embedding.383_of_512" 2.013000 \n", + " 461. "embedding.282_of_512" 2.006441 \n", + " 462. "embedding.115_of_512" 1.967250 \n", + " 463. "embedding.374_of_512" 1.892543 \n", + " 464. "embedding.055_of_512" 1.868395 \n", + " 465. "embedding.121_of_512" 1.431630 \n", + " 466. "embedding.047_of_512" 1.068393 \n", + " 467. "embedding.029_of_512" 1.015992 \n", + " 468. "embedding.031_of_512" 0.881612 \n", + " 469. "embedding.299_of_512" 0.785336 \n", + " 470. "embedding.401_of_512" 0.655422 \n", + " 471. "embedding.214_of_512" 0.349836 \n", + " 472. "embedding.248_of_512" 0.313830 \n", + " 473. "embedding.486_of_512" 0.227126 \n", + " 474. "embedding.509_of_512" 0.131360 \n", + " 475. "embedding.435_of_512" 0.051429 \n", + " 476. "embedding.034_of_512" 0.029603 \n", + "
Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.
Only printing the first tree.
Tree #0:\n", + " "embedding.171_of_512">=-0.0184792 [s:0.0574827 n:60613 np:32412 miss:1] ; pred:1.68723e-08\n", + " ├─(pos)─ "embedding.111_of_512">=-0.00413349 [s:0.0157348 n:32412 np:21552 miss:0] ; pred:-0.0906464\n", + " | ├─(pos)─ "embedding.291_of_512">=0.0227038 [s:0.00645317 n:21552 np:15994 miss:1] ; pred:-0.126738\n", + " | | ├─(pos)─ "embedding.171_of_512">=0.0260582 [s:0.00247006 n:15994 np:11830 miss:0] ; pred:-0.145932\n", + " | | | ├─(pos)─ "embedding.291_of_512">=0.070725 [s:0.00102745 n:11830 np:7037 miss:0] ; pred:-0.157884\n", + " | | | | ├─(pos)─ pred:-0.168606\n", + " | | | | └─(neg)─ pred:-0.142141\n", + " | | | └─(neg)─ "embedding.022_of_512">=-0.029009 [s:0.00552812 n:4164 np:3265 miss:1] ; pred:-0.111978\n", + " | | | ├─(pos)─ pred:-0.127791\n", + " | | | └─(neg)─ pred:-0.054546\n", + " | | └─(neg)─ "embedding.111_of_512">=0.0393142 [s:0.00708494 n:5558 np:2244 miss:0] ; pred:-0.0715036\n", + " | | ├─(pos)─ "embedding.404_of_512">=0.10271 [s:0.00525161 n:2244 np:91 miss:0] ; pred:-0.112964\n", + " | | | ├─(pos)─ pred:0.0299088\n", + " | | | └─(neg)─ pred:-0.119003\n", + " | | └─(neg)─ "embedding.171_of_512">=0.0234795 [s:0.00580773 n:3314 np:1817 miss:0] ; pred:-0.0434295\n", + " | | ├─(pos)─ pred:-0.071467\n", + " | | └─(neg)─ pred:-0.0093987\n", + " | └─(neg)─ "embedding.291_of_512">=0.0634367 [s:0.0133273 n:10860 np:4737 miss:0] ; pred:-0.0190217\n", + " | ├─(pos)─ "embedding.384_of_512">=0.0238699 [s:0.0101704 n:4737 np:966 miss:0] ; pred:-0.0722208\n", + " | | ├─(pos)─ "embedding.276_of_512">=-0.0597052 [s:0.0231348 n:966 np:722 miss:1] ; pred:0.00854195\n", + " | | | ├─(pos)─ pred:-0.0272976\n", + " | | | └─(neg)─ pred:0.114592\n", + " | | └─(neg)─ "embedding.111_of_512">=-0.0612384 [s:0.00659412 n:3771 np:3147 miss:1] ; pred:-0.0929095\n", + " | | ├─(pos)─ pred:-0.107566\n", + " | | └─(neg)─ pred:-0.0189937\n", + " | └─(neg)─ "embedding.111_of_512">=-0.0621904 [s:0.00782627 n:6123 np:5273 miss:1] ; pred:0.0221354\n", + " | ├─(pos)─ "embedding.289_of_512">=0.0119708 [s:0.00516395 n:5273 np:2436 miss:1] ; pred:0.00773873\n", + " | | ├─(pos)─ pred:-0.0236942\n", + " | | └─(neg)─ pred:0.0347287\n", + " | └─(neg)─ "embedding.140_of_512">=0.0595422 [s:0.0123933 n:850 np:263 miss:0] ; pred:0.111445\n", + " | ├─(pos)─ pred:0.0440332\n", + " | └─(neg)─ pred:0.141649\n", + " └─(neg)─ "embedding.111_of_512">=-0.047889 [s:0.0143104 n:28201 np:9723 miss:1] ; pred:0.104182\n", + " ├─(pos)─ "embedding.291_of_512">=0.0518959 [s:0.0110193 n:9723 np:4373 miss:0] ; pred:0.0373387\n", + " | ├─(pos)─ "embedding.384_of_512">=0.0086953 [s:0.0114093 n:4373 np:2134 miss:0] ; pred:-0.00972295\n", + " | | ├─(pos)─ "embedding.132_of_512">=-0.0165455 [s:0.0113251 n:2134 np:1244 miss:1] ; pred:0.0346238\n", + " | | | ├─(pos)─ pred:-0.00186079\n", + " | | | └─(neg)─ pred:0.0856202\n", + " | | └─(neg)─ "embedding.368_of_512">=0.0546857 [s:0.0104556 n:2239 np:323 miss:0] ; pred:-0.05199\n", + " | | ├─(pos)─ pred:0.0489525\n", + " | | └─(neg)─ pred:-0.0690069\n", + " | └─(neg)─ "embedding.111_of_512">=0.0176729 [s:0.00952959 n:5350 np:879 miss:0] ; pred:0.0758061\n", + " | ├─(pos)─ "embedding.257_of_512">=0.0422597 [s:0.0121365 n:879 np:273 miss:0] ; pred:-0.0134314\n", + " | | ├─(pos)─ pred:-0.0799594\n", + " | | └─(neg)─ pred:0.0165391\n", + " | └─(neg)─ "embedding.171_of_512">=-0.0514695 [s:0.00725224 n:4471 np:2448 miss:1] ; pred:0.0933503\n", + " | ├─(pos)─ pred:0.0619719\n", + " | └─(neg)─ pred:0.131321\n", + " └─(neg)─ "embedding.384_of_512">=8.67305e-05 [s:0.00620107 n:18478 np:14151 miss:1] ; pred:0.139354\n", + " ├─(pos)─ "embedding.111_of_512">=-0.0819011 [s:0.00399313 n:14151 np:6809 miss:1] ; pred:0.157004\n", + " | ├─(pos)─ "embedding.140_of_512">=0.0381925 [s:0.00527311 n:6809 np:1738 miss:0] ; pred:0.130407\n", + " | | ├─(pos)─ pred:0.0801316\n", + " | | └─(neg)─ pred:0.147638\n", + " | └─(neg)─ "embedding.365_of_512">=0.0224651 [s:0.00220593 n:7342 np:772 miss:0] ; pred:0.18167\n", + " | ├─(pos)─ pred:0.126134\n", + " | └─(neg)─ pred:0.188195\n", + " └─(neg)─ "embedding.022_of_512">=0.0520979 [s:0.00928281 n:4327 np:880 miss:0] ; pred:0.0816329\n", + " ├─(pos)─ "embedding.483_of_512">=-0.0492134 [s:0.0154152 n:880 np:453 miss:1] ; pred:0.00434318\n", + " | ├─(pos)─ pred:0.0532019\n", + " | └─(neg)─ pred:-0.0474906\n", + " └─(neg)─ "embedding.177_of_512">=0.0136228 [s:0.00564147 n:3447 np:1675 miss:0] ; pred:0.101365\n", + " ├─(pos)─ pred:0.0700516\n", + " └─(neg)─ pred:0.130963\n", + "
Label \\ Pred | \n", + "1 | \n", + "0 | \n", + "
---|---|---|
1 | \n", + "365 | \n", + "102 | \n", + "
0 | \n", + "79 | \n", + "326 | \n", + "