dask xgboost で irisの分類までの一連の流れ

iris-dask-xgb.py

"""
# pip install pandas sklearn dask_xgboost
"""

from sklearn import datasets

#--- dataset
iris = datasets.load_iris()
print(iris.feature_names)
X = iris.data

print(iris.target_names)
y = iris.target

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

#--- train
"""
# start scheduler on another shell
`dask-scheduler`

# start worker on another shell
`dask-worker 127.0.0.1:8786`

2 workers on below example.
"""

from dask.distributed import Client
client = Client('127.0.0.1:8786')
print(client)

import dask.dataframe as dd
X_train_dd = dd.from_array(X_train, columns=iris.feature_names, chunksize=5)
y_train_dd = dd.from_array(y_train, chunksize=5)
print(X_train_dd)
print(y_train_dd)

params = {
    'max_depth': 3,
    'eta': 0.3,
    'objective': 'multi:softprob',
    'num_class': 3}

import dask_xgboost as dxgb
bst = dxgb.train(client, params, X_train_dd, y_train_dd)
bst.save_model('model.xgb')


#--- predict
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder

clf = xgb.XGBClassifier()

booster = xgb.Booster()
booster.load_model('./model.xgb')
clf._Booster = booster

clf._le = LabelEncoder().fit(iris.target_names)
print(clf)

import pandas as pd

X_test_dd = pd.DataFrame(data=X_test, columns=iris.feature_names)
print(clf.predict(X_test_dd))