mirror of
https://github.com/vale981/ray
synced 2025-03-06 18:41:40 -05:00
0ec198acc2
Removes unnecessary pandas usage from AIR examples. Helps ensure users do not follow bad practices.
523 lines
55 KiB
Text
523 lines
55 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0d385409",
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"metadata": {},
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"source": [
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"# Training a model with distributed LightGBM\n",
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"In this example we will train a model in Ray AIR using distributed LightGBM."
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]
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},
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{
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"cell_type": "markdown",
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"id": "07d92cee",
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"metadata": {},
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"source": [
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"Let's start with installing our dependencies:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "86131abe",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -qU \"ray[tune]\" lightgbm_ray"
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]
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},
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{
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"cell_type": "markdown",
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"id": "135fc884",
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"metadata": {},
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"source": [
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"Then we need some imports:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "102ef1ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Tuple\n",
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"\n",
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"import ray\n",
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"from ray.train.batch_predictor import BatchPredictor\n",
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"from ray.train.lightgbm import LightGBMPredictor\n",
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"from ray.data.preprocessors.chain import Chain\n",
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"from ray.data.preprocessors.encoder import Categorizer\n",
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"from ray.train.lightgbm import LightGBMTrainer\n",
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"from ray.data.dataset import Dataset\n",
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"from ray.air.result import Result\n",
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"from ray.air.util.datasets import train_test_split\n",
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"from ray.data.preprocessors import StandardScaler"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c7d102bd",
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"metadata": {},
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"source": [
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"Next we define a function to load our train, validation, and test datasets."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "f1f35cd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepare_data() -> Tuple[Dataset, Dataset, Dataset]:\n",
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" dataset = ray.data.read_csv(\"s3://air-example-data/breast_cancer_with_categorical.csv\")\n",
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" train_dataset, valid_dataset = train_test_split(dataset, test_size=0.3)\n",
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" test_dataset = valid_dataset.map_batches(lambda df: df.drop(\"target\", axis=1), batch_format=\"pandas\")\n",
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" return train_dataset, valid_dataset, test_dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f7afbce",
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"metadata": {},
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"source": [
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"The following function will create a LightGBM trainer, train it, and return the result."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "fefcbc8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_lightgbm(num_workers: int, use_gpu: bool = False) -> Result:\n",
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" train_dataset, valid_dataset, _ = prepare_data()\n",
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"\n",
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" # Scale some random columns, and categorify the categorical_column,\n",
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" # allowing LightGBM to use its built-in categorical feature support\n",
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" columns_to_scale = [\"mean radius\", \"mean texture\"]\n",
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" preprocessor = Chain(\n",
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" Categorizer([\"categorical_column\"]), StandardScaler(columns=columns_to_scale)\n",
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" )\n",
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"\n",
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" # LightGBM specific params\n",
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" params = {\n",
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" \"objective\": \"binary\",\n",
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" \"metric\": [\"binary_logloss\", \"binary_error\"],\n",
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" }\n",
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"\n",
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" trainer = LightGBMTrainer(\n",
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" scaling_config={\n",
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" \"num_workers\": num_workers,\n",
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" \"use_gpu\": use_gpu,\n",
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" },\n",
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" label_column=\"target\",\n",
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" params=params,\n",
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" datasets={\"train\": train_dataset, \"valid\": valid_dataset},\n",
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" preprocessor=preprocessor,\n",
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" num_boost_round=100,\n",
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" )\n",
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" result = trainer.fit()\n",
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" print(result.metrics)\n",
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"\n",
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" return result"
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]
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},
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{
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"cell_type": "markdown",
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"id": "04d278ae",
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"metadata": {},
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"source": [
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"Once we have the result, we can do batch inference on the obtained model. Let's define a utility function for this."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "3f1d0c19",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_lightgbm(result: Result):\n",
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" _, _, test_dataset = prepare_data()\n",
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" batch_predictor = BatchPredictor.from_checkpoint(\n",
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" result.checkpoint, LightGBMPredictor\n",
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" )\n",
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"\n",
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" predicted_labels = (\n",
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" batch_predictor.predict(test_dataset)\n",
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" .map_batches(lambda df: (df > 0.5).astype(int), batch_format=\"pandas\")\n",
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" )\n",
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" print(f\"PREDICTED LABELS\")\n",
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" predicted_labels.show()\n",
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"\n",
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" shap_values = batch_predictor.predict(test_dataset, pred_contrib=True)\n",
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" print(f\"SHAP VALUES\")\n",
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" shap_values.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2bb0e5df",
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"metadata": {},
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"source": [
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"Now we can run the training:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "8244ff3c",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-06-22 17:26:41,346\tWARNING read_api.py:260 -- The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n",
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"Map_Batches: 100%|██████████| 1/1 [00:00<00:00, 46.26it/s]\n"
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]
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},
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{
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"data": {
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"text/html": [
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"== Status ==<br>Current time: 2022-06-22 17:26:56 (running for 00:00:14.07)<br>Memory usage on this node: 10.0/31.0 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/8 CPUs, 0/0 GPUs, 0.0/13.32 GiB heap, 0.0/6.66 GiB objects<br>Result logdir: /home/ubuntu/ray_results/LightGBMTrainer_2022-06-22_17-26-41<br>Number of trials: 1/1 (1 TERMINATED)<br><table>\n",
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"<thead>\n",
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"<tr><th>Trial name </th><th>status </th><th>loc </th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> train-binary_logloss</th><th style=\"text-align: right;\"> train-binary_error</th><th style=\"text-align: right;\"> valid-binary_logloss</th></tr>\n",
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"</thead>\n",
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"<tbody>\n",
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"<tr><td>LightGBMTrainer_7b049_00000</td><td>TERMINATED</td><td>172.31.43.110:1491578</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.9726</td><td style=\"text-align: right;\"> 0.000574522</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0.171898</td></tr>\n",
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"</tbody>\n",
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"</table><br><br>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"UserWarning: cpus_per_actor is set to less than 2. Distributed LightGBM needs at least 2 CPUs per actor to train efficiently. This may lead to a degradation of performance during training.\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491578)\u001b[0m from pandas import MultiIndex, Int64Index\n",
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"\u001b[2m\u001b[36m(LightGBMTrainer pid=1491578)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n",
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"\u001b[2m\u001b[36m(LightGBMTrainer pid=1491578)\u001b[0m UserWarning: Dataset 'valid' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n",
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"\u001b[2m\u001b[36m(LightGBMTrainer pid=1491578)\u001b[0m UserWarning: cpus_per_actor is set to less than 2. Distributed LightGBM needs at least 2 CPUs per actor to train efficiently. This may lead to a degradation of performance during training.\n",
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"\u001b[2m\u001b[36m(pid=1491651)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491651)\u001b[0m from pandas import MultiIndex, Int64Index\n",
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"\u001b[2m\u001b[36m(pid=1491651)\u001b[0m FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491651)\u001b[0m FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491651)\u001b[0m FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491653)\u001b[0m from pandas import MultiIndex, Int64Index\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(pid=1491652)\u001b[0m from pandas import MultiIndex, Int64Index\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m 2022-06-22 17:26:50,509\tWARNING __init__.py:190 -- DeprecationWarning: `ray.worker.get_resource_ids` is a private attribute and access will be removed in a future Ray version.\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m 2022-06-22 17:26:50,658\tWARNING __init__.py:190 -- DeprecationWarning: `ray.worker.get_resource_ids` is a private attribute and access will be removed in a future Ray version.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m [LightGBM] [Info] Trying to bind port 59039...\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m [LightGBM] [Info] Binding port 59039 succeeded\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m [LightGBM] [Info] Listening...\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Info] Trying to bind port 46955...\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Info] Binding port 46955 succeeded\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Info] Listening...\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Warning] Connecting to rank 1 failed, waiting for 200 milliseconds\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m UserWarning: Overriding the parameters from Reference Dataset.\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m UserWarning: categorical_column in param dict is overridden.\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m UserWarning: Overriding the parameters from Reference Dataset.\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m UserWarning: categorical_column in param dict is overridden.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m [LightGBM] [Info] Connected to rank 0\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m [LightGBM] [Info] Local rank: 1, total number of machines: 2\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491653)\u001b[0m [LightGBM] [Warning] num_threads is set=1, n_jobs=-1 will be ignored. Current value: num_threads=1\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Info] Connected to rank 1\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Info] Local rank: 0, total number of machines: 2\n",
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"\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=1491652)\u001b[0m [LightGBM] [Warning] num_threads is set=1, n_jobs=-1 will be ignored. Current value: num_threads=1\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
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"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(_QueueActor pid=1491650)\u001b[0m from pandas import MultiIndex, Int64Index\n"
|
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]
|
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},
|
|
{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Result for LightGBMTrainer_7b049_00000:\n",
|
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" date: 2022-06-22_17-26-53\n",
|
|
" done: false\n",
|
|
" experiment_id: b4a87c26a7604a43baf895755d4f16b3\n",
|
|
" hostname: ip-172-31-43-110\n",
|
|
" iterations_since_restore: 1\n",
|
|
" node_ip: 172.31.43.110\n",
|
|
" pid: 1491578\n",
|
|
" should_checkpoint: true\n",
|
|
" time_since_restore: 8.369545459747314\n",
|
|
" time_this_iter_s: 8.369545459747314\n",
|
|
" time_total_s: 8.369545459747314\n",
|
|
" timestamp: 1655918813\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" train-binary_error: 0.5175879396984925\n",
|
|
" train-binary_logloss: 0.6302848981539763\n",
|
|
" training_iteration: 1\n",
|
|
" trial_id: 7b049_00000\n",
|
|
" valid-binary_error: 0.2\n",
|
|
" valid-binary_logloss: 0.558752017793943\n",
|
|
" warmup_time: 0.008721590042114258\n",
|
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" \n",
|
|
"Result for LightGBMTrainer_7b049_00000:\n",
|
|
" date: 2022-06-22_17-26-56\n",
|
|
" done: true\n",
|
|
" experiment_id: b4a87c26a7604a43baf895755d4f16b3\n",
|
|
" experiment_tag: '0'\n",
|
|
" hostname: ip-172-31-43-110\n",
|
|
" iterations_since_restore: 100\n",
|
|
" node_ip: 172.31.43.110\n",
|
|
" pid: 1491578\n",
|
|
" should_checkpoint: true\n",
|
|
" time_since_restore: 10.972588300704956\n",
|
|
" time_this_iter_s: 0.027977466583251953\n",
|
|
" time_total_s: 10.972588300704956\n",
|
|
" timestamp: 1655918816\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" train-binary_error: 0.0\n",
|
|
" train-binary_logloss: 0.0005745220956391456\n",
|
|
" training_iteration: 100\n",
|
|
" trial_id: 7b049_00000\n",
|
|
" valid-binary_error: 0.058823529411764705\n",
|
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" valid-binary_logloss: 0.17189847605331432\n",
|
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" warmup_time: 0.008721590042114258\n",
|
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" \n"
|
|
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
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"2022-06-22 17:26:56,406\tINFO tune.py:734 -- Total run time: 14.73 seconds (14.06 seconds for the tuning loop).\n"
|
|
]
|
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},
|
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'train-binary_logloss': 0.0005745220956391456, 'train-binary_error': 0.0, 'valid-binary_logloss': 0.17189847605331432, 'valid-binary_error': 0.058823529411764705, 'time_this_iter_s': 0.027977466583251953, 'should_checkpoint': True, 'done': True, 'timesteps_total': None, 'episodes_total': None, 'training_iteration': 100, 'trial_id': '7b049_00000', 'experiment_id': 'b4a87c26a7604a43baf895755d4f16b3', 'date': '2022-06-22_17-26-56', 'timestamp': 1655918816, 'time_total_s': 10.972588300704956, 'pid': 1491578, 'hostname': 'ip-172-31-43-110', 'node_ip': '172.31.43.110', 'config': {}, 'time_since_restore': 10.972588300704956, 'timesteps_since_restore': 0, 'iterations_since_restore': 100, 'warmup_time': 0.008721590042114258, 'experiment_tag': '0'}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"result = train_lightgbm(num_workers=2, use_gpu=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d7155d9b",
|
|
"metadata": {},
|
|
"source": [
|
|
"And perform inference on the obtained model:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 17,
|
|
"id": "871c9be6",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2022-06-22 17:26:57,517\tWARNING read_api.py:260 -- The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n",
|
|
"Map_Batches: 100%|██████████| 1/1 [00:00<00:00, 50.96it/s]\n",
|
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"Map_Batches: 0%| | 0/1 [00:00<?, ?it/s]\u001b[2m\u001b[36m(pid=1491998)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1491998)\u001b[0m from pandas import MultiIndex, Int64Index\n",
|
|
"Map Progress (1 actors 1 pending): 100%|██████████| 1/1 [00:02<00:00, 2.05s/it]\n",
|
|
"Map_Batches: 100%|██████████| 1/1 [00:00<00:00, 75.07it/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"PREDICTED LABELS\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 0}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 0}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 0}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 1}\n",
|
|
"{'predictions': 0}\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
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"Map_Batches: 0%| | 0/1 [00:00<?, ?it/s]\u001b[2m\u001b[36m(pid=1492031)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492031)\u001b[0m from pandas import MultiIndex, Int64Index\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492033)\u001b[0m from pandas import MultiIndex, Int64Index\n",
|
|
"Map Progress (1 actors 1 pending): 100%|██████████| 1/1 [00:02<00:00, 2.09s/it]\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"SHAP VALUES\n",
|
|
"{'predictions_0': 0.006121974664714535, 'predictions_1': 0.8940294162424869, 'predictions_2': -0.013623909529011522, 'predictions_3': -0.26580572803883, 'predictions_4': 0.2897686828261492, 'predictions_5': -0.03784232120648852, 'predictions_6': 0.021865334852359534, 'predictions_7': 1.1753326094382734, 'predictions_8': -0.02525466292349231, 'predictions_9': 0.0733463992354119, 'predictions_10': 0.09191922035401615, 'predictions_11': -0.0035196096494634313, 'predictions_12': 0.20211476104388482, 'predictions_13': 0.7813488658944929, 'predictions_14': 0.10000464816891827, 'predictions_15': 0.11543593649642907, 'predictions_16': -0.009732477634862284, 'predictions_17': 0.19117650484758314, 'predictions_18': -0.17600075102817322, 'predictions_19': 0.5829434737180024, 'predictions_20': 1.4220773445509465, 'predictions_21': 0.6086211783805069, 'predictions_22': 2.0031654232526925, 'predictions_23': 0.3090376110779834, 'predictions_24': -0.21156467772251453, 'predictions_25': 0.14122943819731193, 'predictions_26': -0.1324700025487787, 'predictions_27': 0.8280650504246968, 'predictions_28': 0.03147457104755769, 'predictions_29': 0.00029604737237433516, 'predictions_30': 0.024336487839325866, 'predictions_31': 1.5201632854544105}\n",
|
|
"{'predictions_0': 0.00565090216762466, 'predictions_1': 0.7173247917145018, 'predictions_2': -0.01352989648419376, 'predictions_3': -0.204508963539279, 'predictions_4': -0.11703564338083555, 'predictions_5': 0.059858710083059874, 'predictions_6': 0.06974454296095976, 'predictions_7': 1.5952991804773315, 'predictions_8': 0.30494490847895245, 'predictions_9': 0.03770331660034111, 'predictions_10': 0.08779844216179675, 'predictions_11': 0.0001818669974550241, 'predictions_12': -0.10871732001356472, 'predictions_13': 0.49872871949407244, 'predictions_14': 0.16083030838859202, 'predictions_15': 0.4071487385487001, 'predictions_16': -0.00920287075428388, 'predictions_17': 0.21519060265555054, 'predictions_18': -0.24141319659570365, 'predictions_19': -0.19394859165532527, 'predictions_20': 1.2358452648954865, 'predictions_21': 0.16127531717942642, 'predictions_22': 1.3397755121893355, 'predictions_23': 0.24271016133964965, 'predictions_24': -0.11296858156987878, 'predictions_25': 0.21775788278030012, 'predictions_26': 0.8594002204044787, 'predictions_27': 1.0571631081079365, 'predictions_28': 0.06338809094380635, 'predictions_29': 0.14952090064808415, 'predictions_30': -0.020191656254497082, 'predictions_31': 1.5201632854544105}\n",
|
|
"{'predictions_0': 0.0011410972769028797, 'predictions_1': -0.023112580054428615, 'predictions_2': 0.0015007474035067395, 'predictions_3': -0.3960490192373774, 'predictions_4': -0.30646108596137317, 'predictions_5': -0.015606280874156383, 'predictions_6': -0.10875176916234583, 'predictions_7': -2.3253286264519457, 'predictions_8': 0.2758843675860649, 'predictions_9': 0.029091310311824298, 'predictions_10': -0.057950348636255644, 'predictions_11': -0.00017555498393944432, 'predictions_12': -0.4136454204716676, 'predictions_13': -0.3629735922139978, 'predictions_14': 0.04232756741319012, 'predictions_15': 0.06936920198392876, 'predictions_16': 0.010307144611165166, 'predictions_17': -0.4063116213440989, 'predictions_18': -0.07826460708005233, 'predictions_19': 0.28668914680505037, 'predictions_20': -2.0034181076720015, 'predictions_21': -0.4289092806234529, 'predictions_22': -2.059807308089095, 'predictions_23': -0.2625534917898286, 'predictions_24': -1.0607560950436483, 'predictions_25': -0.13241418825219023, 'predictions_26': -0.46713657128877134, 'predictions_27': -2.0707325110237127, 'predictions_28': -0.0212343580603297, 'predictions_29': -0.11761200100287779, 'predictions_30': 0.03805635018946682, 'predictions_31': 1.5201632854544105}\n",
|
|
"{'predictions_0': 0.003501920356918757, 'predictions_1': 0.9649889446638613, 'predictions_2': -0.011627077584939034, 'predictions_3': -0.33201537640627937, 'predictions_4': 0.2626117060870051, 'predictions_5': -0.0420997182498785, 'predictions_6': 0.05656763216450521, 'predictions_7': 1.076092179662977, 'predictions_8': -0.1396182169782879, 'predictions_9': -0.09872952353947571, 'predictions_10': 0.04378766056466948, 'predictions_11': 0.002478996394296549, 'predictions_12': 0.25042183813566526, 'predictions_13': 0.8751692867530225, 'predictions_14': 0.18679133739736484, 'predictions_15': 0.046846715006450504, 'predictions_16': -0.009211815518670832, 'predictions_17': 0.22485983912144494, 'predictions_18': -0.2861737431801593, 'predictions_19': -0.2533929278067911, 'predictions_20': 1.316951719635302, 'predictions_21': 1.1964971086769494, 'predictions_22': 1.2740098717427248, 'predictions_23': 0.25042580055967084, 'predictions_24': -0.4015257176668039, 'predictions_25': 0.17935395324361414, 'predictions_26': 1.126933988937795, 'predictions_27': 0.8031626612897146, 'predictions_28': 0.0771850514731471, 'predictions_29': 0.03755423306624511, 'predictions_30': -0.016833253240925238, 'predictions_31': 1.5201632854544105}\n",
|
|
"{'predictions_0': -0.0034560551402153003, 'predictions_1': 0.5230708630376469, 'predictions_2': -0.015562114219360572, 'predictions_3': -0.1196402194436373, 'predictions_4': 0.4106482044292619, 'predictions_5': 0.06220233147046589, 'predictions_6': 0.12716114707514065, 'predictions_7': 1.3356455912614509, 'predictions_8': 0.1447514882444872, 'predictions_9': 0.12370386736447751, 'predictions_10': 0.07410456355721864, 'predictions_11': 0.012016763274156357, 'predictions_12': -0.10513441936331262, 'predictions_13': 0.7484191363603289, 'predictions_14': 0.18707788149117566, 'predictions_15': 0.3327881147491029, 'predictions_16': -0.009219336794413353, 'predictions_17': -0.10065740008750416, 'predictions_18': 0.16625881614886867, 'predictions_19': 0.23084551369873454, 'predictions_20': 1.358717098613538, 'predictions_21': 0.19175435277095332, 'predictions_22': 1.3375643842995248, 'predictions_23': 0.2926283902278477, 'predictions_24': 0.1146310032943002, 'predictions_25': 0.23343399483643015, 'predictions_26': 0.6034462734909513, 'predictions_27': 0.9230214841058666, 'predictions_28': 0.029594344165258104, 'predictions_29': 0.04913153000099999, 'predictions_30': 0.02341707352913655, 'predictions_31': 1.5201632854544105}\n",
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"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/dask/dataframe/backends.py:181: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m /home/ubuntu/ray/venv/lib/python3.8/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
|
|
"\u001b[2m\u001b[36m(pid=1492090)\u001b[0m from pandas import MultiIndex, Int64Index\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"predict_lightgbm(result)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"jupytext": {
|
|
"cell_metadata_filter": "-all",
|
|
"main_language": "python",
|
|
"notebook_metadata_filter": "-all"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.10 ('venv': venv)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.10"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "3c0d54d489a08ae47a06eae2fd00ff032d6cddb527c382959b7b2575f6a8167f"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|