mirror of
https://github.com/vale981/ray
synced 2025-03-09 04:46:38 -04:00
4b9a89ad90
The package "ml" should be renamed to "air". Main question: Keep a `ml.py` with `from ray.air import *` for some level of backwards compatibility? I'd go for no to force people to use the new structure.
74 lines
2 KiB
Python
74 lines
2 KiB
Python
# flake8: noqa
|
|
|
|
# __air_xgb_preprocess_start__
|
|
import ray
|
|
from ray.air.preprocessors import StandardScaler
|
|
|
|
import pandas as pd
|
|
|
|
from sklearn.datasets import load_breast_cancer
|
|
from sklearn.model_selection import train_test_split
|
|
|
|
data_raw = load_breast_cancer()
|
|
dataset_df = pd.DataFrame(data_raw["data"], columns=data_raw["feature_names"])
|
|
dataset_df["target"] = data_raw["target"]
|
|
train_df, test_df = train_test_split(dataset_df, test_size=0.3)
|
|
train_dataset = ray.data.from_pandas(train_df)
|
|
valid_dataset = ray.data.from_pandas(test_df)
|
|
test_dataset = ray.data.from_pandas(test_df.drop("target", axis=1))
|
|
|
|
|
|
# Create a preprocessor to scale some columns
|
|
columns_to_scale = ["mean radius", "mean texture"]
|
|
preprocessor = StandardScaler(columns=columns_to_scale)
|
|
# __air_xgb_preprocess_end__
|
|
|
|
|
|
# __air_xgb_train_start__
|
|
from ray.air.train.integrations.xgboost import XGBoostTrainer
|
|
|
|
# XGBoost specific params
|
|
params = {
|
|
"tree_method": "approx",
|
|
"objective": "binary:logistic",
|
|
"eval_metric": ["logloss", "error"],
|
|
}
|
|
|
|
num_workers = 2
|
|
use_gpu = False # use GPUs if detected.
|
|
|
|
trainer = XGBoostTrainer(
|
|
scaling_config={
|
|
"num_workers": num_workers,
|
|
"use_gpu": use_gpu,
|
|
},
|
|
label_column="target",
|
|
params=params,
|
|
datasets={"train": train_dataset, "valid": valid_dataset},
|
|
preprocessor=preprocessor,
|
|
num_boost_round=20,
|
|
)
|
|
result = trainer.fit()
|
|
print(result.metrics)
|
|
# __air_xgb_train_end__
|
|
|
|
# __air_xgb_batchpred_start__
|
|
from ray.air.batch_predictor import BatchPredictor
|
|
from ray.air.predictors.integrations.xgboost import XGBoostPredictor
|
|
|
|
batch_predictor = BatchPredictor.from_checkpoint(result.checkpoint, XGBoostPredictor)
|
|
|
|
predicted_labels = (
|
|
batch_predictor.predict(test_dataset)
|
|
.map_batches(lambda df: (df > 0.5).astype(int), batch_format="pandas")
|
|
.to_pandas(limit=float("inf"))
|
|
)
|
|
print("PREDICTED LABELS")
|
|
print(f"{predicted_labels}")
|
|
|
|
shap_values = batch_predictor.predict(test_dataset, pred_contribs=True).to_pandas(
|
|
limit=float("inf")
|
|
)
|
|
print("SHAP VALUES")
|
|
print(f"{shap_values}")
|
|
# __air_xgb_batchpred_end__
|