给出上述机器学习的代码
时间: 2023-06-21 17:23:35 浏览: 59
好的,以下是一个简单的二分类模型的代码,用于预测居民收入是否超过五万美元:
```python
# 导入必要的库和模块
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler, StringIndexer
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml import Pipeline
# 创建SparkSession
spark = SparkSession.builder.appName('income_prediction').getOrCreate()
# 读取数据集
data = spark.read.csv('income.csv', header=True, inferSchema=True)
# 数据预处理
categorical_cols = ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'gender', 'native_country', 'income']
indexers = [StringIndexer(inputCol=col, outputCol=col+'_indexed').fit(data) for col in categorical_cols]
assembler = VectorAssembler(inputCols=[col+'_indexed' for col in categorical_cols[:-1]], outputCol='features')
preprocessor = Pipeline(stages=indexers+[assembler])
data_processed = preprocessor.fit(data).transform(data)
# 模型构建和训练
train_data, test_data = data_processed.randomSplit([0.7, 0.3])
lr = LogisticRegression(featuresCol='features', labelCol='income_indexed')
model = lr.fit(train_data)
# 模型评估
evaluator = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction', labelCol='income_indexed')
predictions = model.transform(test_data)
auc = evaluator.evaluate(predictions)
# 模型预测
new_data = spark.createDataFrame([('Private', 'HS-grad', 'Married-civ-spouse', 'Craft-repair', 'Husband', 'White', 'Male', 'United-States')], ['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'gender', 'native_country'])
new_data_processed = preprocessor.transform(new_data)
prediction = model.transform(new_data_processed).select('prediction').collect()[0][0]
print('AUC:', auc)
print('Prediction:', prediction)
```
需要注意的是,这只是一个简单的示例代码,具体的实现会根据数据集的特点和模型的需求而有所不同。