Databricks notebook中的多选小部件

from pyspark.sql.types import IntegerType
from pyspark.sql.functions import col
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml import Pipeline
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator

from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler

from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit

from pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexer

# 创建多选小部件
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler

from pyspark.ml import Pipeline
from pyspark.ml.evaluation import BinaryClassificationEvaluator

from pyspark.ml.classification import RandomForestClassifier

from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import PCA
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import StandardScaler

from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator

from pyspark.ml.feature import Word2Vec
from pyspark.ml.classification import MultilayerPerceptronClassifier

from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.recommendation import ALS
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.feature import HashingTF, IDF
from pyspark.ml.classification import NaiveBayes
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import Tokenizer

from pyspark.ml.classification import GBTClassifier
from pyspark.ml.feature import IndexToString, StringIndexer, VectorAssembler

from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.feature import PCA
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import StandardScaler

from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.recommendation import ALS
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.recommendation import ALS
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

# 创建多选小部件
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType

spark = SparkSession.builder.appName("Databricks Notebook").getOrCreate()

# 创建一个多选小部件来选择任务类型
task_type_widget = dbutils.widgets.dropdown('Task Type', 'Regression', ['Regression', 'Classification', 'Clustering', 'Recommendation'])

# 创建一个多选小部件来选择算法
algorithm_widget = dbutils.widgets.dropdown('Algorithm', 'Linear Regression', ['Linear Regression', 'Decision Tree', 'Logistic Regression', 'Random Forest', 'Naive Bayes', 'Multilayer Perceptron', 'Gradient-Boosted Tree', 'ALS'])

# 创建一个多选小部件来选择特征工程方法
feature_engineering_widget = dbutils.widgets.dropdown('Feature Engineering', 'Vector Assembler', ['Vector Assembler', 'One-Hot Encoder', 'String Indexer', 'Vector Indexer', 'Word2Vec', 'PCA', 'Standard Scaler', 'HashingTF-IDF'])

# 创建一个多选小部件来选择评估器
evaluator_widget = dbutils.widgets.dropdown('Evaluator', 'Regression Evaluator', ['Regression Evaluator', 'Binary Classification Evaluator', 'Multiclass Classification Evaluator', 'Clustering Evaluator'])

# 创建一个多选小部件来选择超参数调优方法
tuning_method_widget = dbutils.widgets.dropdown('Tuning Method', 'Cross-Validation', ['Cross-Validation', 'Train Validation Split'])

# 获取用户选择的参数值
task_type = task_type_widget.value
algorithm = algorithm_widget.value
feature_engineering = feature_engineering_widget.value
evaluator = evaluator_widget.value
tuning_method = tuning_method_widget.value

# 在代码中根据用户选择的参数值进行相应的操作
if task_type == 'Regression':
    # Regression specific code
    if algorithm == 'Linear Regression':
        # Linear Regression specific code
        # ...
    elif algorithm == 'Decision Tree':
        # Decision Tree specific code
        # ...
    # ...
elif task_type == 'Classification':
    # Classification specific code
    if algorithm == 'Logistic Regression':
        # Logistic Regression specific code
        # ...
    elif algorithm == 'Random Forest':
        # Random Forest specific code
        # ...
    # ...
# ...