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AI开发平台ModelArts-随机森林回归:样例

样例 inputs = { "dataframe": None # @input {"label":"dataframe","type":"DataFrame"}}params = { "inputs": inputs, "b_output_action": True, "b_use_default_encoder": True, # @param {"label": "b_use_default_encoder", "type": "boolean", "required": "true", "helpTip": ""} "input_features_str": "", # @param {"label": "input_features_str", "type": "string", "required": "false", "helpTip": ""} "outer_pipeline_stages": None, "label_col": "", # @param {"label": "label_col", "type": "string", "required": "true", "helpTip": ""} "regressor_feature_vector_col": "model_features", # @param {"label": "regressor_feature_vector_col", "type": "string", "required": "true", "helpTip": ""} "max_depth": 5, # @param {"label": "max_depth", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""} "max_bins": 32, # @param {"label": "max_bins", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""} "min_instances_per_node": 1, # @param {"label": "min_instances_per_node", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""} "min_info_gain": 0.0, # @param {"label": "min_info_gain", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""} "impurity": "variance", "subsampling_rate": 1.0, # @param {"label": "subsampling_rate", "type": "number", "required": "true", "range": "(0.0,1.0]", "helpTip": ""} "num_trees": 20, # @param {"label": "num_trees", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""} "feature_subset_strategy": "all" # @param {"label": "feature_subset_strategy", "type": "enum", "required": "true", "options":"auto,all,onethird,sqrt,log2", "helpTip": ""}}rf_regressor____id___ = MLSRandomForestRegression(**params)rf_regressor____id___.run()# @output {"label":"pipeline_model","name":"rf_regressor____id___.get_outputs()['output_port_1']","type":"PipelineModel"}

AI开发平台ModelArts 回归
AI开发平台ModelArts-随机森林回归:参数说明

参数说明参数子参数说明参数说明 b_use_default_encoder - 是否使用默认编码，默认为True input_features_str - 输入的列名以逗号分隔组成的字符串，例如： "column_a" "column_a,column_b" label_col - 目标列 regressor_feature_vector_col - 算子输入的特征向量列的列名，默认为"model_features" max_depth - 树的最大深度，默认为5 max_bins - 最大分箱数，默认为32 min_instances_per_node - 节点分割时，要求子节点必须包含的最少实例数，默认为1 min_info_gain - 节点是否分割要求的最小信息增益，默认为0.0 subsampling_rate - 学习每棵决策树用到的训练集的抽样比例，默认为1.0 num_trees - 树的个数，默认为20 feature_subset_strategy - 节点分割时考虑用到的特征列的策略，支持auto、all、onethird、sqrt、log2、n，默认为"all"

AI开发平台ModelArts 回归
函数工作流 FunctionGraph-在代码中导入torch并使用

在代码中导入torch并使用 # -*- coding:utf-8 -*-import json# 导入torch依赖import torch as timport numpy as npdef handler (event, context): print("start training!") train() print("finished!") return { "statusCode": 200, "isBase64Encoded": False, "body": json.dumps(event), "headers": { "Content-Type": "application/json" } } def get_fake_data(batch_size=8): x = t.rand(batch_size, 1) * 20; y = x * 2 + (1 + t.randn(batch_size, 1)) * 3 return x, y def train(): t.manual_seed(1000) x, y = get_fake_data() w = t.rand(1, 1) b = t.zeros(1, 1) lr = 0.001 for ii in range(2000): x, y = get_fake_data() y_pred = x.mm(w) + b.expand_as(y) loss = 0.5 * (y_pred - y) ** 2 loss = loss.sum() dloss = 1 dy_pred = dloss * (y_pred - y) dw = x.t().mm(dy_pred) db = dy_pred.sum() w.sub_(lr * dw) b.sub_(lr * db) if ii % 10 == 0: x = t.arange(0, 20).view(-1, 1) y = x.float().mm(w)+ b.expand_as(x) x2, y2 = get_fake_data(batch_size=20) print("w=",w.item(), "b=",b.item()) 父主题：使用pytorch进行线性回归

函数工作流 FunctionGraph 使用pytorch进行线性回归