3月18日

进行ai验收项目

 

完成了聚类和分类算法的代码

from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml import Pipeline

# 初始化Spark会话
spark = SparkSession.builder.appName("MLlibExample").getOrCreate()

# 加载数据集（以鸢尾花数据集为例）
# 假设数据集包含特征列：sepal_length, sepal_width, petal_length, petal_width 和标签列：label
data = spark.read.csv("iris.csv", header=True, inferSchema=True)

# 将特征列组合为一个向量
assembler = VectorAssembler(inputCols=["sepal_length", "sepal_width", "petal_length", "petal_width"],
                            outputCol="features")

# 聚类分析：K-Means
kmeans = KMeans(featuresCol="features", k=3, seed=42)

# 分类分析：随机森林
rf = RandomForestClassifier(featuresCol="features", labelCol="label", seed=42)

# 构建Pipeline
pipeline = Pipeline(stages=[assembler, kmeans, rf])

# 拆分数据集为训练集和测试集
train_data, test_data = data.randomSplit([0.7, 0.3], seed=42)

# 训练模型
model = pipeline.fit(train_data)

# 预测
predictions = model.transform(test_data)

# 评估分类模型性能
evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print(f"Accuracy: {accuracy:.4f}")

evaluator.setMetricName("f1")
f1_score = evaluator.evaluate(predictions)
print(f"F1 Score: {f1_score:.4f}")

evaluator.setMetricName("weightedRecall")
recall = evaluator.evaluate(predictions)
print(f"Weighted Recall: {recall:.4f}")

# 评估聚类模型性能（可选）
# 由于K-Means是无监督学习，通常使用轮廓系数等指标评估聚类效果
# 这里仅展示分类模型的评估指标

# 停止Spark会话
spark.stop()