spark ML做智能菜品推荐代码

好的，以下是使用Spark ML中的分类算法实现智能菜品推荐的代码示例：

# 导入必要的库
from pyspark.ml.feature import VectorAssembler, StringIndexer, OneHotEncoder
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml import Pipeline
from pyspark.sql.functions import col, udf
from pyspark.sql.types import DoubleType

# 读取数据集
data = spark.read.format("csv").option("header", "true").load("data/cuisine.csv")

# 数据预处理，将类别特征转换成数值特征
cuisineIndexer = StringIndexer(inputCol="cuisine", outputCol="label")
encoder = OneHotEncoder(inputCol="cuisineIndex", outputCol="cuisineVec")
assembler = VectorAssembler(inputCols=["cuisineVec", "ingredients"], outputCol="features")

# 划分训练集和测试集
(trainingData, testData) = data.randomSplit([0.7, 0.3])

# 使用随机森林算法训练模型
rf = RandomForestClassifier(labelCol="label", featuresCol="features", numTrees=10)
pipeline = Pipeline(stages=[cuisineIndexer, encoder, assembler, rf])
model = pipeline.fit(trainingData)

# 预测测试集中的菜品类别
predictions = model.transform(testData)

# 评估模型
evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction",
                                              metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))

# 定义udf函数，将类别特征转换成数值特征
cuisineIndex = StringIndexer(inputCol="cuisine", outputCol="cuisineIndex").fit(data)
encoder = OneHotEncoder(inputCol="cuisineIndex", outputCol="cuisineVec")

def cuisine_to_vec(cuisine):
    return float(cuisineIndex.transform([(cuisine,)])[0][0])

cuisine_to_vec_udf = udf(cuisine_to_vec, DoubleType())

# 输入用户喜好的菜品类别和食材列表，输出推荐菜品的类别和食材列表
def recommend_cuisine(cuisine, ingredients):
    df = spark.createDataFrame([(cuisine, ingredients)], ["cuisine", "ingredients"])
    df = df.withColumn("cuisineIndex", cuisine_to_vec_udf(col("cuisine")))
    df = encoder.transform(df)
    df = assembler.transform(df)
    predictions = model.transform(df).select("prediction").collect()
    return cuisineIndex.labels[int(predictions[0][0])]

# 示例调用
recommend_cuisine("italian", ["tomato", "pasta", "olive oil"])

以上代码是基于Spark ML中的随机森林算法实现的智能菜品推荐，您可以根据自己的需求修改数据集和参数，以及调整模型的算法和参数。