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update docs.
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‎docs/cn/Pipeline 组件/回归/线性SVR (LinearSvr).md‎

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@@ -53,7 +53,7 @@ df = pd.DataFrame([
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[1, 2, 1],
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[5, 3, 3]])
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batchData = BatchOperator.fromDataframe(df, schemaStr='f0 int, f1 int, label int', op_type='batch')
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batchData = BatchOperator.fromDataframe(df, schemaStr='f0 int, f1 int, label int')
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colnames = ["f0","f1"]
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‎docs/cn/Pipeline 组件/图像/图片转张量 (ReadImageToTensor).md‎

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@@ -32,7 +32,7 @@ df_data = pd.DataFrame([
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batch_data = BatchOperator.fromDataframe(df_data, schemaStr = 'path string')
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ReadImageToTensor()\
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.setRootFilePath("https://pytorch.org/vision/stable/_images/")\
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.setRootFilePath("http://alink-test-datatset.oss-cn-hangzhou-zmf.aliyuncs.com/images/")\
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.setRelativeFilePathCol("path")\
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.setOutputCol("tensor")\
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.transform(batch_data)\

‎docs/cn/Pipeline 组件/推荐/Swing推荐 (SwingSimilarItemsRecommender).md‎

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@@ -52,6 +52,7 @@ data = BatchOperator.fromDataframe(df_data, schemaStr='user string, item string,
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model = SwingTrainBatchOp()\
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.setUserCol("user")\
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.setItemCol("item")\
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.setMinUserItems(2)\
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.linkFrom(data)
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predictor = SwingSimilarItemsRecommender()\
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BatchOperator <?> model = new SwingTrainBatchOp()
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.setUserCol("user")
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.setItemCol("item")
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.setMinUserItems(2)
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.linkFrom(data);
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SwingSimilarItemsRecommender <?> predictor = new SwingSimilarItemsRecommender()
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.setItemCol("item")

‎docs/cn/Pipeline 组件/模型选择和调参/Bayes搜索CV (BayesSearchCV).md‎

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@@ -17,9 +17,9 @@ cv为交叉验证，将数据切分为k-folds，对每k-1份数据做训练，
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| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 默认值 |
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| --- | --- | --- | --- | --- | --- |
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| NumFolds | 折数 | 交叉验证的参数，数据的折数（大于等于2） | Integer | | 10 |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | --- |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | --- |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | --- |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | |
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## 代码示例
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‎docs/cn/Pipeline 组件/模型选择和调参/Bayes搜索TV (BayesSearchTVSplit).md‎

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@@ -17,9 +17,9 @@ tv为训练验证，将数据按照比例切分为两份，对其中一份数据
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| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 默认值 |
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| --- | --- | --- | --- | --- | --- |
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| trainRatio | 训练集比例 | 训练集与验证集的划分比例，取值范围为(0, 1]。 | Double | | 0.8 |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | --- |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | --- |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | --- |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | |
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## 代码示例
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‎docs/cn/Pipeline 组件/模型选择和调参/网格搜索CV (GridSearchCV).md‎

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| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 默认值 |
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| --- | --- | --- | --- | --- | --- |
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| NumFolds | 折数 | 交叉验证的参数，数据的折数（大于等于2） | Integer | | 10 |
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| ParamGrid | 参数网格 | 指定参数的网格 | ParamGrid | ✓ | --- |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | --- |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | --- |
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| ParamGrid | 参数网格 | 指定参数的网格 | ParamGrid | ✓ | |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | |
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## 代码示例
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### Python 代码

‎docs/cn/Pipeline 组件/模型选择和调参/网格搜索TV (GridSearchTVSplit).md‎

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| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 默认值 |
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| --- | --- | --- | --- | --- | --- |
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| trainRatio | 训练集比例 | 训练集与验证集的划分比例，取值范围为(0, 1]。 | Double | | 0.8 |
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| ParamGrid | 参数网格 | 指定参数的网格 | ParamGrid | ✓ | --- |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | --- |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | --- |
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| ParamGrid | 参数网格 | 指定参数的网格 | ParamGrid | ✓ | |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | |
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## 代码示例
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### Python 代码

‎docs/cn/Pipeline 组件/模型选择和调参/随机搜索CV (RandomSearchCV).md‎

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| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 默认值 |
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| --- | --- | --- | --- | --- | --- |
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| NumFolds | 折数 | 交叉验证的参数，数据的折数（大于等于2） | Integer | | 10 |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | --- |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | --- |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | --- |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | |
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## 代码示例
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### Python 代码

‎docs/cn/Pipeline 组件/模型选择和调参/随机搜索TV (RandomSearchTVSplit).md‎

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| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 默认值 |
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| --- | --- | --- | --- | --- | --- |
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| trainRatio | 训练集比例 | 训练集与验证集的划分比例，取值范围为(0, 1]。 | Double | | 0.8 |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | --- |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | --- |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | --- |
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| ParamDist | 参数分布 | 指定搜索的参数的分布 | ParamDist | ✓ | |
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| Estimator | Estimator | 用于调优的Estimator | Estimator | ✓ | |
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| TuningEvaluator | 评估指标 | 用于选择最优模型的评估指标 | TuningEvaluator | ✓ | |
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## 代码示例
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### Python 代码

‎docs/cn/Pipeline 组件/聚类/Kmodes训练 (KModes).md‎

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useLocalEnv(1)
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df = pd.DataFrame([
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["pc", "Hp.com", 1],
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["camera", "Hp.com", 1],
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["digital camera", "Hp.com", 1],
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["camera", "BestBuy.com", 1],
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["digital camera", "BestBuy.com", 1],
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["tv", "BestBuy.com", 1],
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["flower", "Teleflora.com", 1],
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["flower", "Orchids.com", 1]
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["pc", "Hp.com"],
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["camera", "Hp.com"],
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["digital camera", "Hp.com"],
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["camera", "BestBuy.com"],
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["digital camera", "BestBuy.com"],
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["tv", "BestBuy.com"],
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["flower", "Teleflora.com"],
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["flower", "Orchids.com"]
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])
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inOp1 = BatchOperator.fromDataframe(df, schemaStr='f0 string, f1 string')

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