{ "cells": [ { "cell_type": "markdown", "id": "0d385409", "metadata": {}, "source": [ "# Training a model with distributed LightGBM\n", "In this example we will train a model in Ray Air using distributed LightGBM." ] }, { "cell_type": "markdown", "id": "07d92cee", "metadata": {}, "source": [ "Let's start with installing our dependencies:" ] }, { "cell_type": "code", "execution_count": 1, "id": "86131abe", "metadata": {}, "outputs": [], "source": [ "!pip install -qU \"ray[tune]\" lightgbm_ray" ] }, { "cell_type": "markdown", "id": "135fc884", "metadata": {}, "source": [ "Then we need some imports:" ] }, { "cell_type": "code", "execution_count": 2, "id": "102ef1ac", "metadata": {}, "outputs": [], "source": [ "import argparse\n", "import math\n", "from typing import Tuple\n", "\n", "import pandas as pd\n", "\n", "import ray\n", "from ray.air.batch_predictor import BatchPredictor\n", "from ray.air.predictors.integrations.lightgbm import LightGBMPredictor\n", "from ray.air.preprocessors.chain import Chain\n", "from ray.air.preprocessors.encoder import Categorizer\n", "from ray.air.train.integrations.lightgbm import LightGBMTrainer\n", "from ray.data.dataset import Dataset\n", "from ray.air.result import Result\n", "from ray.air.preprocessors import StandardScaler\n", "from sklearn.datasets import load_breast_cancer\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "id": "c7d102bd", "metadata": {}, "source": [ "Next we define a function to load our train, validation, and test datasets." ] }, { "cell_type": "code", "execution_count": 3, "id": "f1f35cd7", "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", " # add a random categorical column\n", " num_samples = len(dataset_df)\n", " dataset_df[\"categorical_column\"] = pd.Series(\n", " ([\"A\", \"B\"] * math.ceil(num_samples / 2))[:num_samples]\n", " )\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": "8f7afbce", "metadata": {}, "source": [ "The following function will create a LightGBM trainer, train it, and return the result." ] }, { "cell_type": "code", "execution_count": 4, "id": "fefcbc8a", "metadata": {}, "outputs": [], "source": [ "def train_lightgbm(num_workers: int, use_gpu: bool = False) -> Result:\n", " train_dataset, valid_dataset, _ = prepare_data()\n", "\n", " # Scale some random columns, and categorify the categorical_column,\n", " # allowing LightGBM to use its built-in categorical feature support\n", " columns_to_scale = [\"mean radius\", \"mean texture\"]\n", " preprocessor = Chain(\n", " Categorizer([\"categorical_column\"]), StandardScaler(columns=columns_to_scale)\n", " )\n", "\n", " # LightGBM specific params\n", " params = {\n", " \"objective\": \"binary\",\n", " \"metric\": [\"binary_logloss\", \"binary_error\"],\n", " }\n", "\n", " trainer = LightGBMTrainer(\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": "04d278ae", "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": "3f1d0c19", "metadata": {}, "outputs": [], "source": [ "def predict_lightgbm(result: Result):\n", " _, _, test_dataset = prepare_data()\n", " batch_predictor = BatchPredictor.from_checkpoint(\n", " result.checkpoint, LightGBMPredictor\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_contrib=True).to_pandas(\n", " limit=float(\"inf\")\n", " )\n", " print(f\"SHAP VALUES\\n{shap_values}\")" ] }, { "cell_type": "markdown", "id": "2bb0e5df", "metadata": {}, "source": [ "Now we can run the training:" ] }, { "cell_type": "code", "execution_count": 6, "id": "8244ff3c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-05-19 11:18:27,652\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 ==
Current time: 2022-05-19 11:18:47 (running for 00:00:15.19)
Memory usage on this node: 10.2/16.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/4.86 GiB heap, 0.0/2.0 GiB objects
Result logdir: /Users/kai/ray_results/LightGBMTrainer_2022-05-19_11-18-30
Number of trials: 1/1 (1 TERMINATED)
\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Trial name status loc iter total time (s) train-binary_logloss train-binary_error valid-binary_logloss
LightGBMTrainer_07bf3_00000TERMINATED127.0.0.1:9219 100 10.4622 0.000197893 0 0.289033


" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "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", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:32,940\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=16 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:36,664\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=17 --runtime-env-hash=-2010331069\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=9219)\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=9219)\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=9219)\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", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:38,980\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=18 --runtime-env-hash=-2010331069\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:38,997\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=19 --runtime-env-hash=-2010331069\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:39,091\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=21 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:39,095\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=20 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:39,107\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=23 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 11:18:39,107\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=51840 --object-store-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_11-18-25_114449_9132/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=56443 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:58688 --redis-password=5241590000000000 --startup-token=22 --runtime-env-hash=-2010331134\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Info] Trying to bind port 52127...\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Info] Binding port 52127 succeeded\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Info] Listening...\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Warning] Connecting to rank 1 failed, waiting for 200 milliseconds\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m [LightGBM] [Info] Trying to bind port 52128...\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m [LightGBM] [Info] Binding port 52128 succeeded\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m [LightGBM] [Info] Listening...\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Info] Connected to rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Info] Local rank: 0, total number of machines: 2\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m [LightGBM] [Warning] num_threads is set=1, n_jobs=-1 will be ignored. Current value: num_threads=1\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m [LightGBM] [Info] Connected to rank 0\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m [LightGBM] [Info] Local rank: 1, total number of machines: 2\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m [LightGBM] [Warning] num_threads is set=1, n_jobs=-1 will be ignored. Current value: num_threads=1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m UserWarning: Overriding the parameters from Reference Dataset.\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9243)\u001b[0m UserWarning: categorical_column in param dict is overridden.\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m UserWarning: Overriding the parameters from Reference Dataset.\n", "\u001b[2m\u001b[36m(_RemoteRayLightGBMActor pid=9242)\u001b[0m UserWarning: categorical_column in param dict is overridden.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Result for LightGBMTrainer_07bf3_00000:\n", " date: 2022-05-19_11-18-44\n", " done: false\n", " experiment_id: 1d3640d1c3a743aeae7274a0ce253107\n", " hostname: Kais-MacBook-Pro.local\n", " iterations_since_restore: 1\n", " node_ip: 127.0.0.1\n", " pid: 9219\n", " should_checkpoint: true\n", " time_since_restore: 8.41084909439087\n", " time_this_iter_s: 8.41084909439087\n", " time_total_s: 8.41084909439087\n", " timestamp: 1652955524\n", " timesteps_since_restore: 0\n", " train-binary_error: 0.36683417085427134\n", " train-binary_logloss: 0.5804693664919086\n", " training_iteration: 1\n", " trial_id: 07bf3_00000\n", " valid-binary_error: 0.36470588235294116\n", " valid-binary_logloss: 0.5868466345817073\n", " warmup_time: 0.004106044769287109\n", " \n", "Result for LightGBMTrainer_07bf3_00000:\n", " date: 2022-05-19_11-18-46\n", " done: true\n", " experiment_id: 1d3640d1c3a743aeae7274a0ce253107\n", " experiment_tag: '0'\n", " hostname: Kais-MacBook-Pro.local\n", " iterations_since_restore: 100\n", " node_ip: 127.0.0.1\n", " pid: 9219\n", " should_checkpoint: true\n", " time_since_restore: 10.46218204498291\n", " time_this_iter_s: 0.025421857833862305\n", " time_total_s: 10.46218204498291\n", " timestamp: 1652955526\n", " timesteps_since_restore: 0\n", " train-binary_error: 0.0\n", " train-binary_logloss: 0.00019789273681613937\n", " training_iteration: 100\n", " trial_id: 07bf3_00000\n", " valid-binary_error: 0.058823529411764705\n", " valid-binary_logloss: 0.2890328865004496\n", " warmup_time: 0.004106044769287109\n", " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-05-19 11:18:47,218\tINFO tune.py:753 -- Total run time: 16.87 seconds (15.17 seconds for the tuning loop).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'train-binary_logloss': 0.00019789273681613937, 'train-binary_error': 0.0, 'valid-binary_logloss': 0.2890328865004496, 'valid-binary_error': 0.058823529411764705, 'time_this_iter_s': 0.025421857833862305, 'should_checkpoint': True, 'done': True, 'timesteps_total': None, 'episodes_total': None, 'training_iteration': 100, 'trial_id': '07bf3_00000', 'experiment_id': '1d3640d1c3a743aeae7274a0ce253107', 'date': '2022-05-19_11-18-46', 'timestamp': 1652955526, 'time_total_s': 10.46218204498291, 'pid': 9219, 'hostname': 'Kais-MacBook-Pro.local', 'node_ip': '127.0.0.1', 'config': {}, 'time_since_restore': 10.46218204498291, 'timesteps_since_restore': 0, 'iterations_since_restore': 100, 'warmup_time': 0.004106044769287109, '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", "execution_count": 7, "id": "871c9be6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.21s/it]\n", "Map_Batches: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 93.04it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PREDICTED LABELS\n", " predictions\n", "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", ".. ...\n", "166 1\n", "167 0\n", "168 1\n", "169 1\n", "170 1\n", "\n", "[171 rows x 1 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.20s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "SHAP VALUES\n", " predictions_0 predictions_1 predictions_2 predictions_3 \\\n", "0 0.089310 -0.909119 0.042819 0.002084 \n", "1 0.080590 -0.961430 0.043946 -0.014364 \n", "2 0.080606 -0.778903 0.067703 0.005561 \n", "3 0.095129 -0.281614 0.083395 0.005946 \n", "4 0.085822 0.470362 0.106340 -0.014601 \n", ".. ... ... ... ... \n", "166 0.095845 0.217879 0.131259 0.045455 \n", "167 -0.369657 -1.825973 -0.270361 -0.005203 \n", "168 0.078703 0.142254 0.069414 0.002620 \n", "169 0.069391 0.226548 0.035343 -0.014900 \n", "170 0.088893 0.216221 0.068534 -0.015672 \n", "\n", " predictions_4 predictions_5 predictions_6 predictions_7 \\\n", "0 -0.243459 0.536332 -1.275628 0.490998 \n", "1 0.888434 -0.666081 0.541587 1.229128 \n", "2 -0.444282 -0.886527 0.615167 1.113725 \n", "3 0.292466 -0.205161 0.606795 0.971005 \n", "4 0.549158 -0.405526 0.601660 0.438182 \n", ".. ... ... ... ... \n", "166 -0.177095 0.418596 0.649067 0.853719 \n", "167 0.248983 0.327855 0.028472 -0.396260 \n", "168 0.398207 0.217577 0.655739 0.779261 \n", "169 -0.395022 -1.123284 0.555980 0.367841 \n", "170 0.478937 0.223053 -1.091246 0.506502 \n", "\n", " predictions_8 predictions_9 ... predictions_22 predictions_23 \\\n", "0 0.245076 0.145549 ... 0.635400 1.961269 \n", "1 -0.344460 0.013988 ... 0.676673 1.151546 \n", "2 0.179933 0.109160 ... 0.689301 1.877165 \n", "3 0.172837 0.195020 ... 0.718073 1.592259 \n", "4 0.182619 -0.127179 ... 0.682158 1.121333 \n", ".. ... ... ... ... ... \n", "166 0.243637 0.070938 ... 0.744151 1.771634 \n", "167 0.124145 -0.493628 ... -0.541835 -2.201714 \n", "168 0.179598 0.192144 ... 0.754423 1.300675 \n", "169 -0.270785 0.072316 ... 0.531150 0.803271 \n", "170 0.098758 0.163663 ... 0.545371 1.010722 \n", "\n", " predictions_24 predictions_25 predictions_26 predictions_27 \\\n", "0 0.251278 0.089320 -0.598509 1.552746 \n", "1 0.284339 0.053013 0.818982 1.595459 \n", "2 0.564781 -0.043412 0.613467 1.649626 \n", "3 0.532693 0.070353 0.544844 1.746157 \n", "4 0.311470 0.115707 1.179717 1.670017 \n", ".. ... ... ... ... \n", "166 -0.375872 0.127995 0.817884 1.686068 \n", "167 0.625508 0.039757 -0.539761 0.908562 \n", "168 0.398768 0.118736 0.736576 1.693371 \n", "169 0.266529 -0.005190 0.850678 1.707049 \n", "170 0.877337 0.080191 -0.588513 1.725231 \n", "\n", " predictions_28 predictions_29 predictions_30 predictions_31 \n", "0 -0.025840 0.106845 -0.037509 0.636929 \n", "1 0.035559 -0.119623 -0.016733 0.636929 \n", "2 0.043654 0.070261 -0.012863 0.636929 \n", "3 0.082365 0.098007 0.024749 0.636929 \n", "4 0.079768 -0.008935 0.035418 0.636929 \n", ".. ... ... ... ... \n", "166 0.043644 -0.044403 -0.038294 0.636929 \n", "167 0.005756 -0.192442 -0.012280 0.636929 \n", "168 0.054687 0.095692 -0.012863 0.636929 \n", "169 0.086557 -0.104739 -0.016732 0.636929 \n", "170 -0.319243 0.137260 0.023966 0.636929 \n", "\n", "[171 rows x 32 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "predict_lightgbm(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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