Python 中的 Dask 并行编程
James Fulton
Climate Informatics Researcher
# 载入表格数据集 import dask.dataframe as dd dask_df = dd.read_parquet("dataset_parquet")
X = dask_df[['feature1', 'feature2', 'feature3']] y = dask_df['target_column']
from dask_ml.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X) # 非惰性 standardized_X = scaler.transform(X) # 惰性
from dask_ml.preprocessing import StandardScaler scaler = StandardScaler()
scaler.fit(X) # 非惰性
standardized_X = scaler.transform(X) # 惰性
from dask_ml.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, test_size=0.2) print(X_train)
from dask_ml.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, test_size=0.2)
print(X_train)
Dask DataFrame 结构: feature1 feature2 feature3 npartitions=7 int64 float64 float64 ... ... ...
# 在训练集上评估已拟合模型 train_score = dask_model.score(X_train, y_train) # 非惰性 print(train_score)
# 在训练集上评估已拟合模型 train_score = dask_model.score(X_train, y_train) # 非惰性
print(train_score)
-0.12321
# 在测试集上评估已拟合模型 test_score = dask_model.score(X_test, y_test) # 非惰性 print(test_score)
# 在测试集上评估已拟合模型 test_score = dask_model.score(X_test, y_test) # 非惰性
print(test_score)
-0.23453
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