from pyspark.sql.types import *
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import VectorAssembler
from pyspark.ml import Pipeline
from pyspark.sql.functions import udf, col
from pyspark.sql import DataFrame


def to_array(col):
    def to_array_(v):
        return v.toArray().tolist()

    return udf(to_array_, ArrayType(DoubleType())).asNondeterministic()(col)


def main_func(train_df: DataFrame, test_df: DataFrame, spark):
    feat_cols = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6', 'feature7', 'feature8',
                 'feature9']

    vector_assembler = VectorAssembler().setInputCols(feat_cols).setOutputCol("features")

    #### 训练 ####
    print("step 1")
    lr = LogisticRegression(regParam=0.01, maxIter=100)  # regParam 正则项参数
    pipeline = Pipeline(stages=[vector_assembler, lr])
    model = pipeline.fit(train_df)

    # 打印参数
    print("\n-------------------------------------------------------------------------")
    print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
    print("-------------------------------------------------------------------------\n")

    #### 预测, 保存结果 ####
    print("step 2")
    labels_and_preds = model.transform(test_df).withColumn("probability_xj", to_array(col("probability"))[1]) \
        .select("uuid", "label", "prediction", "probability_xj")

    return [labels_and_preds]