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‎docs/architecture/apps/index.rst‎

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@@ -31,6 +31,8 @@ TrustedFlow内置了多种可信APP，每一个可信APP在执行计算逻辑之
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lr_train
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xgb_predict
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lr_predict
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lgbm_train
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lgbm_predict
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binary_evaluation
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prediction_bias_eval
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‎docs/architecture/apps/lgbm_predict.md‎

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# LightGBM预测
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使用给定的LightGBM模型对数据进行预测。
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## 组件定义
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1. 参数
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(1) pred_name: 预测值的列名。
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(2) save_label: 输出结果是否包含标签列，true表示保存。
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(3) label_name: 标签列的名称，默认为“label”。
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(4) save_id: 输出结果是否保存ID列，true表示保存。
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(5) id_name： ID列的名称。
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(6) col_names: 可选，输出指定的列到结果中，默认为空。
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2. 输入：待预测的数据以及LightGBM模型。
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3. 输出：预测结果。
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```json
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{
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"domain": "ml.predict",
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"name": "lgbm_predict",
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"desc": "Predict using the lgbm model.",
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"version": "0.0.1",
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"attrs": [
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{
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"name": "pred_name",
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"desc": "Column name for predictions.",
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"type": "AT_STRING",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"s": "pred"
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}
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}
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},
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{
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"name": "save_label",
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"desc": "Whether or not to save real label column into output pred table. If true, input feature_dataset must contain label column.",
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"type": "AT_BOOL",
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"atomic": {
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"is_optional": true,
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"default_value": {}
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}
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},
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{
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"name": "label_name",
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"desc": "Column name for label.",
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"type": "AT_STRING",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"s": "label"
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}
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}
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},
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{
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"name": "save_id",
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"desc": "Whether to save id column into output pred table. If true, input feature_dataset must contain id column.",
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"type": "AT_BOOL",
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"atomic": {
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"is_optional": true,
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"default_value": {}
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}
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},
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{
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"name": "id_name",
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"desc": "Column name for id.",
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"type": "AT_STRING",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"s": "id"
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}
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}
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},
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{
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"name": "col_names",
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"desc": "Extra column names into output pred table.",
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"type": "AT_STRINGS",
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"atomic": {
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"list_max_length_inclusive": "-1",
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"is_optional": true
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}
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}
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],
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"inputs": [
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{
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"name": "feature_dataset",
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"desc": "Input feature dataset.",
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"types": [
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"sf.table.individual"
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],
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"attrs": [
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{
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"name": "ids",
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"desc": "Id columns.",
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"col_max_cnt_inclusive": "1"
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},
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{
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"name": "label",
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"desc": "Label column.",
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"col_max_cnt_inclusive": "1"
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}
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]
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},
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{
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"name": "model",
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"desc": "Input model.",
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"types": [
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"sf.model.lgbm"
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]
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}
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],
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"outputs": [
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{
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"name": "pred",
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"desc": "Output prediction.",
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"types": [
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"sf.table.individual"
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]
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}
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]
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}
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```

‎docs/architecture/apps/lgbm_train.md‎

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# LightGBM训练
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使用LightGBM对数据集进行训练，得到LightGBM模型，支持二分类和线性回归。
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## 组件定义
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```json
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{
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"domain": "ml.train",
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"name": "lgbm_train",
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"desc": "LightGBM train component for individual dataset.",
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"version": "0.0.1",
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"attrs": [
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{
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"name": "n_estimators",
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"desc": "Number of boosted trees to fit.",
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"type": "AT_INT",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"i64": "10"
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},
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"lower_bound_enabled": true,
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"lower_bound": {
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"i64": "1"
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},
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"lower_bound_inclusive": true,
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"upper_bound_enabled": true,
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"upper_bound": {
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"i64": "1024"
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},
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"upper_bound_inclusive": true
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}
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},
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{
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"name": "objective",
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"desc": "Specify the learning objective.",
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"type": "AT_STRING",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"s": "binary"
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},
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"allowed_values": {
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"ss": [
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"regression",
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"binary"
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]
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}
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}
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},
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{
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"name": "boosting_type",
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"desc": "Boosting type.",
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"type": "AT_STRING",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"s": "gbdt"
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},
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"allowed_values": {
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"ss": [
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"gbdt",
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"rf",
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"dart"
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]
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}
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}
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},
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{
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"name": "learning_rate",
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"desc": "Learning rate.",
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"type": "AT_FLOAT",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"f": 0.1
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},
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"lower_bound_enabled": true,
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"lower_bound": {},
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"upper_bound_enabled": true,
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"upper_bound": {
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"f": 1
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},
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"upper_bound_inclusive": true
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}
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},
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{
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"name": "num_leaves",
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"desc": "Max number of leaves in one tree.",
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"type": "AT_INT",
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"atomic": {
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"is_optional": true,
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"default_value": {
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"i64": "31"
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},
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"lower_bound_enabled": true,
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"lower_bound": {
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"i64": "2"
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},
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"lower_bound_inclusive": true,
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"upper_bound_enabled": true,
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"upper_bound": {
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"i64": "1024"
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},
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"upper_bound_inclusive": true
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}
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}
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],
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"inputs": [
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{
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"name": "train_dataset",
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"desc": "Input table.",
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"types": [
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"sf.table.individual"
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],
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"attrs": [
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{
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"name": "ids",
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"desc": "Id columns will not be trained."
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},
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{
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"name": "label",
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"desc": "Label column.",
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"col_min_cnt_inclusive": "1",
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"col_max_cnt_inclusive": "1"
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}
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]
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}
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],
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"outputs": [
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{
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"name": "output_model",
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"desc": "Output model.",
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"types": [
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"sf.model.lgbm"
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]
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}
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]
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}
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```

‎docs/architecture/index.rst‎

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核心功能
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架构设计
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========================
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想了解TrustedFlow原理和功能，欢迎阅读下列文章！
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