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ray/doc/source/ray-air/examples/xgboost_example.ipynb
Kai Fricke 9a8c8f4889
[air/docs] Move some examples from ml/examples to docs (#24959) 
This moves the basic LightGBM, Sklearn, and XGBoost examples from the examples/ folder to the docs. We keep a symlink in the examples folder.
2022-05-19 14:01:49 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "5fb89b3d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Training a model with distributed XGBoost\n",
"In this example we will train a model in Ray Air using distributed XGBoost."
]
},
{
"cell_type": "markdown",
"id": "53d57c1f",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Let's start with installing our dependencies:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "41f20cc1",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"!pip install -qU \"ray[tune]\" xgboost_ray"
]
},
{
"cell_type": "markdown",
"id": "d2fe8d4a",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Then we need some imports:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7232303d",
"metadata": {},
"outputs": [],
"source": [
"import argparse\n",
"from typing import Tuple\n",
"\n",
"import pandas as pd\n",
"\n",
"import ray\n",
"from ray.ml.batch_predictor import BatchPredictor\n",
"from ray.ml.predictors.integrations.xgboost import XGBoostPredictor\n",
"from ray.ml.train.integrations.xgboost import XGBoostTrainer\n",
"from ray.data.dataset import Dataset\n",
"from ray.ml.result import Result\n",
"from ray.ml.preprocessors import StandardScaler\n",
"from sklearn.datasets import load_breast_cancer\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "markdown",
"id": "1c75b5ca",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Next we define a function to load our train, validation, and test datasets."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "37c4f38f",
"metadata": {},
"outputs": [],
"source": [
"def prepare_data() -> Tuple[Dataset, Dataset, Dataset]:\n",
" data_raw = load_breast_cancer()\n",
" dataset_df = pd.DataFrame(data_raw[\"data\"], columns=data_raw[\"feature_names\"])\n",
" dataset_df[\"target\"] = data_raw[\"target\"]\n",
" train_df, test_df = train_test_split(dataset_df, test_size=0.3)\n",
" train_dataset = ray.data.from_pandas(train_df)\n",
" valid_dataset = ray.data.from_pandas(test_df)\n",
" test_dataset = ray.data.from_pandas(test_df.drop(\"target\", axis=1))\n",
" return train_dataset, valid_dataset, test_dataset"
]
},
{
"cell_type": "markdown",
"id": "9b2850dd",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"The following function will create a XGBoost trainer, train it, and return the result."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dae8998d",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def train_xgboost(num_workers: int, use_gpu: bool = False) -> Result:\n",
" train_dataset, valid_dataset, _ = prepare_data()\n",
"\n",
" # Scale some random columns\n",
" columns_to_scale = [\"mean radius\", \"mean texture\"]\n",
" preprocessor = StandardScaler(columns=columns_to_scale)\n",
"\n",
" # XGBoost specific params\n",
" params = {\n",
" \"tree_method\": \"approx\",\n",
" \"objective\": \"binary:logistic\",\n",
" \"eval_metric\": [\"logloss\", \"error\"],\n",
" }\n",
"\n",
" trainer = XGBoostTrainer(\n",
" scaling_config={\n",
" \"num_workers\": num_workers,\n",
" \"use_gpu\": use_gpu,\n",
" },\n",
" label_column=\"target\",\n",
" params=params,\n",
" datasets={\"train\": train_dataset, \"valid\": valid_dataset},\n",
" preprocessor=preprocessor,\n",
" num_boost_round=100,\n",
" )\n",
" result = trainer.fit()\n",
" print(result.metrics)\n",
"\n",
" return result"
]
},
{
"cell_type": "markdown",
"id": "ce05af87",
"metadata": {},
"source": [
"Once we have the result, we can do batch inference on the obtained model. Let's define a utility function for this."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5b8076d3",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def predict_xgboost(result: Result):\n",
" _, _, test_dataset = prepare_data()\n",
"\n",
" batch_predictor = BatchPredictor.from_checkpoint(\n",
" result.checkpoint, XGBoostPredictor\n",
" )\n",
"\n",
" predicted_labels = (\n",
" batch_predictor.predict(test_dataset)\n",
" .map_batches(lambda df: (df > 0.5).astype(int), batch_format=\"pandas\")\n",
" .to_pandas(limit=float(\"inf\"))\n",
" )\n",
" print(f\"PREDICTED LABELS\\n{predicted_labels}\")\n",
"\n",
" shap_values = batch_predictor.predict(test_dataset, pred_contribs=True).to_pandas(\n",
" limit=float(\"inf\")\n",
" )\n",
" print(f\"SHAP VALUES\\n{shap_values}\")\n"
]
},
{
"cell_type": "markdown",
"id": "7e172f66",
"metadata": {},
"source": [
"Now we can run the training:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0f96d62b",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-19 11:44:42,413\tINFO services.py:1483 -- View the Ray dashboard at \u001B[1m\u001B[32mhttp://127.0.0.1:8265\u001B[39m\u001B[22m\n"
]
},
{
"data": {
"text/html": [
"== Status ==<br>Current time: 2022-05-19 11:45:00 (running for 00:00:13.93)<br>Memory usage on this node: 10.3/16.0 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/4.5 GiB heap, 0.0/2.0 GiB objects<br>Result logdir: /Users/kai/ray_results/XGBoostTrainer_2022-05-19_11-44-45<br>Number of trials: 1/1 (1 TERMINATED)<br><table>\n",
"<thead>\n",
"<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-logloss</th><th style=\"text-align: right;\"> train-error</th><th style=\"text-align: right;\"> valid-logloss</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>XGBoostTrainer_b273b_00000</td><td>TERMINATED</td><td>127.0.0.1:11036</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 9.03935</td><td style=\"text-align: right;\"> 0.005949</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 0.07483</td></tr>\n",
"</tbody>\n",
"</table><br><br>"
],
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"<IPython.core.display.HTML object>"
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"text": [
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:47,554\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=16 --runtime-env-hash=-2010331134\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:51,603\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=17 --runtime-env-hash=-2010331069\n",
"\u001B[2m\u001B[36m(GBDTTrainable pid=11036)\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",
"\u001B[2m\u001B[36m(GBDTTrainable pid=11036)\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",
"\u001B[2m\u001B[36m(GBDTTrainable pid=11036)\u001B[0m 2022-05-19 11:44:53,035\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:54,085\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=18 --runtime-env-hash=-2010331069\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:54,106\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=19 --runtime-env-hash=-2010331069\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:54,252\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=21 --runtime-env-hash=-2010331134\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:54,266\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=23 --runtime-env-hash=-2010331134\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:54,266\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=20 --runtime-env-hash=-2010331134\n",
"\u001B[2m\u001B[33m(raylet)\u001B[0m 2022-05-19 11:44:54,271\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=54067 --object-store-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-44-39_813259_10959/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=61242 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61017 --redis-password=5241590000000000 --startup-token=22 --runtime-env-hash=-2010331134\n",
"\u001B[2m\u001B[36m(GBDTTrainable pid=11036)\u001B[0m 2022-05-19 11:44:56,874\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n",
"\u001B[2m\u001B[36m(_RemoteRayXGBoostActor pid=11104)\u001B[0m [11:44:56] task [xgboost.ray]:4517180944 got new rank 1\n",
"\u001B[2m\u001B[36m(_RemoteRayXGBoostActor pid=11103)\u001B[0m [11:44:56] task [xgboost.ray]:4655847056 got new rank 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for XGBoostTrainer_b273b_00000:\n",
" date: 2022-05-19_11-44-57\n",
" done: false\n",
" experiment_id: 991235d8b76649398688695ca70a08e4\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 1\n",
" node_ip: 127.0.0.1\n",
" pid: 11036\n",
" should_checkpoint: true\n",
" time_since_restore: 7.17207407951355\n",
" time_this_iter_s: 7.17207407951355\n",
" time_total_s: 7.17207407951355\n",
" timestamp: 1652957097\n",
" timesteps_since_restore: 0\n",
" train-error: 0.020101\n",
" train-logloss: 0.465715\n",
" training_iteration: 1\n",
" trial_id: b273b_00000\n",
" valid-error: 0.052632\n",
" valid-logloss: 0.480831\n",
" warmup_time: 0.003935098648071289\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(GBDTTrainable pid=11036)\u001B[0m 2022-05-19 11:44:59,796\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=398 in 6.80 seconds (2.92 pure XGBoost training time).\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for XGBoostTrainer_b273b_00000:\n",
" date: 2022-05-19_11-44-59\n",
" done: true\n",
" experiment_id: 991235d8b76649398688695ca70a08e4\n",
" experiment_tag: '0'\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 100\n",
" node_ip: 127.0.0.1\n",
" pid: 11036\n",
" should_checkpoint: true\n",
" time_since_restore: 9.03934907913208\n",
" time_this_iter_s: 0.018042802810668945\n",
" time_total_s: 9.03934907913208\n",
" timestamp: 1652957099\n",
" timesteps_since_restore: 0\n",
" train-error: 0.0\n",
" train-logloss: 0.005949\n",
" training_iteration: 100\n",
" trial_id: b273b_00000\n",
" valid-error: 0.017544\n",
" valid-logloss: 0.07483\n",
" warmup_time: 0.003935098648071289\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-19 11:45:00,535\tINFO tune.py:753 -- Total run time: 15.30 seconds (13.91 seconds for the tuning loop).\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'train-logloss': 0.005949, 'train-error': 0.0, 'valid-logloss': 0.07483, 'valid-error': 0.017544, 'time_this_iter_s': 0.018042802810668945, 'should_checkpoint': True, 'done': True, 'timesteps_total': None, 'episodes_total': None, 'training_iteration': 100, 'trial_id': 'b273b_00000', 'experiment_id': '991235d8b76649398688695ca70a08e4', 'date': '2022-05-19_11-44-59', 'timestamp': 1652957099, 'time_total_s': 9.03934907913208, 'pid': 11036, 'hostname': 'Kais-MacBook-Pro.local', 'node_ip': '127.0.0.1', 'config': {}, 'time_since_restore': 9.03934907913208, 'timesteps_since_restore': 0, 'iterations_since_restore': 100, 'warmup_time': 0.003935098648071289, 'experiment_tag': '0'}\n"
]
}
],
"source": [
"result = train_xgboost(num_workers=2, use_gpu=False)"
]
},
{
"cell_type": "markdown",
"id": "7055ad1b",
"metadata": {},
"source": [
"And perform inference on the obtained model:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "283b1dba",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.96s/it]\n",
"Map_Batches: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 87.81it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PREDICTED LABELS\n",
" predictions\n",
"0 0\n",
"1 0\n",
"2 1\n",
"3 1\n",
"4 0\n",
".. ...\n",
"166 1\n",
"167 1\n",
"168 0\n",
"169 1\n",
"170 0\n",
"\n",
"[171 rows x 1 columns]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.78s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SHAP VALUES\n",
" predictions_0 predictions_1 predictions_2 predictions_3 \\\n",
"0 -0.139882 -0.748878 0.0 -1.143079 \n",
"1 0.013840 -1.053747 0.0 0.361219 \n",
"2 -0.082575 0.952107 0.0 0.396908 \n",
"3 0.016314 0.916166 0.0 0.535740 \n",
"4 -0.087534 1.317693 0.0 -0.631737 \n",
".. ... ... ... ... \n",
"166 0.016314 1.006091 0.0 0.535740 \n",
"167 0.010002 0.948294 0.0 0.529942 \n",
"168 -0.084481 0.766085 0.0 -0.582221 \n",
"169 0.010002 0.846374 0.0 0.502846 \n",
"170 -0.108186 -1.032712 0.0 -0.737255 \n",
"\n",
" predictions_4 predictions_5 predictions_6 predictions_7 \\\n",
"0 0.228545 0.074653 -0.033109 -1.680274 \n",
"1 -0.386373 0.030964 -0.026341 -1.796480 \n",
"2 0.294464 0.142708 0.151952 1.859482 \n",
"3 0.224681 -0.013640 0.062032 0.909347 \n",
"4 -0.123310 -0.008267 -0.081633 -1.907682 \n",
".. ... ... ... ... \n",
"166 0.224681 -0.013640 0.062032 0.890978 \n",
"167 -0.107441 0.143260 0.062032 1.149335 \n",
"168 -0.164466 0.088426 -0.081633 -1.767637 \n",
"169 -0.112530 0.029944 -0.074865 0.963479 \n",
"170 -0.250381 0.034186 -0.033109 -1.654185 \n",
"\n",
" predictions_8 predictions_9 ... predictions_21 predictions_22 \\\n",
"0 -0.173504 -0.027610 ... -0.373735 -1.117443 \n",
"1 0.153518 0.018295 ... -0.798841 0.277471 \n",
"2 0.153518 0.029338 ... 1.314059 -0.455756 \n",
"3 0.153518 0.015659 ... 0.816392 0.683619 \n",
"4 -0.173504 0.009200 ... 1.207632 -0.945986 \n",
".. ... ... ... ... ... \n",
"166 -0.173504 0.015659 ... 0.856858 0.704448 \n",
"167 0.153518 0.010089 ... 1.203512 0.708437 \n",
"168 0.153518 0.014880 ... -0.418931 -1.201489 \n",
"169 0.153518 0.010089 ... 1.211174 0.600757 \n",
"170 0.153518 0.016329 ... -0.556651 -1.009517 \n",
"\n",
" predictions_23 predictions_24 predictions_25 predictions_26 \\\n",
"0 -1.207984 0.349734 0.018222 -0.725013 \n",
"1 0.075934 -0.990557 -0.012509 -0.863824 \n",
"2 0.137665 0.668639 -0.042249 -0.684045 \n",
"3 0.766776 0.575949 0.022816 1.013024 \n",
"4 -0.577419 -0.454616 0.051755 -0.861906 \n",
".. ... ... ... ... \n",
"166 0.754576 0.573718 0.022816 0.948516 \n",
"167 1.066871 0.487933 0.056155 -0.601421 \n",
"168 -1.310177 -0.386367 0.018222 -0.837832 \n",
"169 1.009837 0.694783 -0.042249 -0.626939 \n",
"170 -1.149971 -0.386467 -0.006737 -0.750287 \n",
"\n",
" predictions_27 predictions_28 predictions_29 predictions_30 \n",
"0 -1.149301 0.374839 0.0 1.046286 \n",
"1 -2.501725 -0.492608 0.0 1.046286 \n",
"2 0.077563 -0.106669 0.0 1.046286 \n",
"3 0.757272 0.341423 0.0 1.046286 \n",
"4 -0.800213 0.400311 0.0 1.046286 \n",
".. ... ... ... ... \n",
"166 0.757272 0.061695 0.0 1.046286 \n",
"167 0.610080 -0.339797 0.0 1.046286 \n",
"168 -1.300907 -0.474622 0.0 1.046286 \n",
"169 0.238948 -0.361304 0.0 1.046286 \n",
"170 -1.241549 -0.370570 0.0 1.046286 \n",
"\n",
"[171 rows x 31 columns]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"predict_xgboost(result)"
]
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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