classification_algorithms:
  LogisticRegression:
    parameters:
      - name: "penalty"
        type: "str"
        default: "l2"
        description: "用于正则化的惩罚项，可选 'l1', 'l2', 'elasticnet', 'none'。"
      - name: "C"
        type: "float"
        default: "1.0"
        description: "正则化强度的倒数，较小的值表示更强的正则化。"
      - name: "solver"
        type: "str"
        default: "lbfgs"
        description: "优化算法，可选 'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'。"
      - name: "max_iter"
        type: "int"
        default: "100"
        description: "最大迭代次数，控制优化收敛速度。"

  SVC:
    parameters:
      - name: "C"
        type: "float"
        default: "1.0"
        description: "正则化参数，控制决策边界的松弛程度。"
      - name: "kernel"
        type: "str"
        default: "rbf"
        description: "核函数类型，可选 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'。"
      - name: "degree"
        type: "int"
        default: "3"
        description: "多项式核函数的维度，仅当 kernel='poly' 时有效。"
      - name: "gamma"
        type: "str" 
        default: "scale"
        description: "核函数系数，可选 'scale', 'auto' 或浮点数。"
      - name: "max_iter"
        type: "int"
        default: "-1"
        description: "最大迭代次数，-1 表示无限制。"

  DecisionTreeClassifier:
    parameters:
      - name: "criterion"
        type: "str"
        default: "gini"
        description: "用于划分的准则，可选 'gini' 或 'entropy'。"
      - name: "max_depth"
        type: "int"
        default: "None"
        description: "树的最大深度，None 表示不限制。"
      - name: "min_samples_split"
        type: "int"
        default: "2"
        description: "内部节点再划分所需的最小样本数。"
      - name: "min_samples_leaf"
        type: "int"
        default: "1"
        description: "叶子节点包含的最小样本数。"

  RandomForestClassifier:
    parameters:
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "森林中树的数量。"
      - name: "criterion"
        type: "str"
        default: "gini"
        description: "用于划分的准则，可选 'gini' 或 'entropy'。"
      - name: "max_depth"
        type: "int"
        default: "None"
        description: "树的最大深度，None 表示不限制。"
      - name: "bootstrap"
        type: "bool"
        default: "True"
        description: "是否进行自助采样。"

  XGBClassifier:
    parameters:
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "树的数量。"
      - name: "learning_rate"
        type: "float"
        default: "0.1"
        description: "学习率，控制每棵树的贡献程度。"
      - name: "max_depth"
        type: "int"
        default: "6"
        description: "每棵树的最大深度。"
      - name: "gamma"
        type: "float"
        default: "0"
        description: "节点分裂所需的最小损失减少量。"

  AdaBoostClassifier:
    parameters:
      - name: "n_estimators"
        type: "int"
        default: "50"
        description: "弱分类器的数量。"
      - name: "learning_rate"
        type: "float"
        default: "1.0"
        description: "更新权重时的缩放因子。"
      - name: "algorithm"
        type: "str"
        default: "SAMME.R"
        description: "提升算法，可选 'SAMME' 或 'SAMME.R'。"

  CatBoostClassifier:
    parameters:
      - name: "iterations"
        type: "int"
        default: "1000"
        description: "训练的迭代次数。"
      - name: "learning_rate"
        type: "float"
        default: "0.03"
        description: "学习率，控制更新步长。"
      - name: "depth"
        type: "int"
        default: "6"
        description: "树的深度。"

  LGBMClassifier:
    parameters:
      - name: "num_leaves"
        type: "int"
        default: "31"
        description: "树的最大叶子数。"
      - name: "learning_rate"
        type: "float"
        default: "0.1"
        description: "学习率。"
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "树的数量。"

  GaussianNB:
    parameters:
      - name: "var_smoothing"
        type: "float"
        default: "1e-9"
        description: "加在方差上的小数，防止零方差问题。"

  KNeighborsClassifier:
    parameters:
      - name: "n_neighbors"
        type: "int"
        default: "5"
        description: "最近邻的数量。"
      - name: "weights"
        type: "str"
        default: "uniform"
        description: "权重分配策略，可选 'uniform' 或 'distance'。"
      - name: "algorithm"
        type: "str"
        default: "auto"
        description: "搜索最近邻的算法。"

  MLPClassifier:
    parameters:
      - name: "hidden_layer_sizes"
        type: "tuple"
        default: "(100,)"
        description: "隐藏层的神经元数量。"
      - name: "activation"
        type: "str"
        default: "relu"
        description: "激活函数，可选 'identity', 'logistic', 'tanh', 'relu'。"
      - name: "solver"
        type: "str"
        default: "adam"
        description: "优化算法，可选 'lbfgs', 'sgd', 'adam'。"
      - name: "max_iter"
        type: "int"
        default: "200"
        description: "最大迭代次数。"

  GradientBoostingClassifier:
    parameters:
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "树的数量。"
      - name: "learning_rate"
        type: "float"
        default: "0.1"
        description: "学习率。"
      - name: "max_depth"
        type: "int"
        default: "3"
        description: "每棵树的最大深度。"


regression_algorithms:
  LinearRegression:
  parameters:
    - name: "fit_intercept"
      type: "bool"
      default: "True"
      description: "是否计算截距。"
    - name: "normalize"
      type: "bool"
      default: "False"
      description: "是否对数据进行归一化处理。（已弃用）"
    - name: "copy_X"
      type: "bool"
      default: "True"
      description: "是否复制输入数据。"
    - name: "n_jobs"
      type: "int"
      default: "None"
      description: "用于计算的并行作业数。"

  PolynomialRegression:
    parameters:
      - name: "degree"
        type: "int"
        default: "2"
        description: "多项式的最高次数。"
      - name: "interaction_only"
        type: "bool"
        default: "False"
        description: "是否仅考虑特征之间的交互项。"
      - name: "include_bias"
        type: "bool"
        default: "True"
        description: "是否包含偏置项。"

  Ridge:
    parameters:
      - name: "alpha"
        type: "float"
        default: "1.0"
        description: "正则化力度。"
      - name: "fit_intercept"
        type: "bool"
        default: "True"
        description: "是否计算截距。"
      - name: "max_iter"
        type: "int"
        default: "None"
        description: "最大迭代次数。"
      - name: "tol"
        type: "float"
        default: "0.001"
        description: "容忍误差。"
      - name: "solver"
        type: "str"
        default: "auto"
        description: "求解器选择。"

  Lasso:
    parameters:
      - name: "alpha"
        type: "float"
        default: "1.0"
        description: "正则化参数。"
      - name: "fit_intercept"
        type: "bool"
        default: "True"
        description: "是否计算截距。"
      - name: "max_iter"
        type: "int"
        default: "1000"
        description: "最大迭代次数。"
      - name: "tol"
        type: "float"
        default: "0.0001"
        description: "收敛容忍度。"
      - name: "selection"
        type: "str"
        default: "cyclic"
        description: "特征选择方式，可选 'cyclic' 或 'random'。"

  ElasticNet:
    parameters:
      - name: "alpha"
        type: "float"
        default: "1.0"
        description: "正则化参数。"
      - name: "l1_ratio"
        type: "float"
        default: "0.5"
        description: "L1正则化比例，控制L1和L2的混合比例。"
      - name: "fit_intercept"
        type: "bool"
        default: "True"
        description: "是否计算截距。"
      - name: "max_iter"
        type: "int"
        default: "1000"
        description: "最大迭代次数。"
      - name: "tol"
        type: "float"
        default: "0.0001"
        description: "收敛容忍度。"

  SVR:
    parameters:
      - name: "kernel"
        type: "str"
        default: "rbf"
        description: "核函数类型，可选 'linear', 'poly', 'rbf', 'sigmoid'。"
      - name: "C"
        type: "float"
        default: "1.0"
        description: "惩罚参数。"
      - name: "epsilon"
        type: "float"
        default: "0.1"
        description: "epsilon不敏感损失中的参数。"
      - name: "degree"
        type: "int"
        default: "3"
        description: "多项式核函数的度数，仅当 kernel='poly' 时有效。"
      - name: "gamma"
        type: "str"
        default: "scale"
        description: "核函数系数。"

  DecisionTreeRegressor:
    parameters:
      - name: "criterion"
        type: "str"
        default: "squared_error"
        description: "衡量分裂质量的指标。"
      - name: "splitter"
        type: "str"
        default: "best"
        description: "划分策略，可选 'best' 或 'random'。"
      - name: "max_depth"
        type: "int"
        default: "None"
        description: "树的最大深度。"
      - name: "min_samples_split"
        type: "int/float"
        default: "2"
        description: "内部节点再划分所需的最小样本数。"
      - name: "min_samples_leaf"
        type: "int/float"
        default: "1"
        description: "叶子节点最少样本数。"

  RandomForestRegressor:
    parameters:
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "森林中树的数量。"
      - name: "criterion"
        type: "str"
        default: "squared_error"
        description: "衡量分裂质量的指标。"
      - name: "max_depth"
        type: "int"
        default: "None"
        description: "树的最大深度。"
      - name: "min_samples_split"
        type: "int"
        default: "2"
        description: "内部节点再划分所需的最小样本数。"
      - name: "n_jobs"
        type: "int"
        default: "None"
        description: "用于计算的并行作业数。"

  XGBRegressor:
    parameters:
      - name: "max_depth"
        type: "int"
        default: "3"
        description: "树的最大深度。"
      - name: "learning_rate"
        type: "float"
        default: "0.1"
        description: "学习率。"
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "树的数量。"
      - name: "objective"
        type: "str"
        default: "reg:squarederror"
        description: "损失函数。"
      - name: "subsample"
        type: "float"
        default: "1"
        description: "采样比例。"

  AdaBoostRegressor:
    parameters:
      - name: "n_estimators"
        type: "int"
        default: "50"
        description: "基学习器的数量。"
      - name: "learning_rate"
        type: "float"
        default: "1.0"
        description: "学习率。"
      - name: "loss"
        type: "str"
        default: "linear"
        description: "损失函数类型，可选 'linear', 'square', 'exponential'。"

  CatBoostRegressor:
    parameters:
      - name: "iterations"
        type: "int"
        default: "1000"
        description: "迭代次数。"
      - name: "learning_rate"
        type: "float"
        default: "0.03"
        description: "学习率。"
      - name: "depth"
        type: "int"
        default: "6"
        description: "树的深度。"
      - name: "l2_leaf_reg"
        type: "float"
        default: "3.0"
        description: "L2正则化系数。"
      - name: "loss_function"
        type: "str"
        default: "RMSE"
        description: "损失函数。"

  LGBMRegressor:
    parameters:
      - name: "num_leaves"
        type: "int"
        default: "31"
        description: "叶子节点数量。"
      - name: "learning_rate"
        type: "float"
        default: "0.1"
        description: "学习率。"
      - name: "n_estimators"
        type: "int"
        default: "100"
        description: "树的数量。"
      - name: "objective"
        type: "str"
        default: "regression"
        description: "目标函数。"
      - name: "subsample"
        type: "float"
        default: "1.0"
        description: "采样比例。"

  MLPRegressor:
    parameters:
      - name: "hidden_layer_sizes"
        type: "tuple"
        default: "(100,)"
        description: "隐藏层的神经元数量和层数。"
      - name: "activation"
        type: "str"
        default: "relu"
        description: "激活函数，可选 'identity', 'logistic', 'tanh', 'relu'。"
      - name: "solver"
        type: "str"
        default: "adam"
        description: "权重优化算法。"
      - name: "alpha"
        type: "float"
        default: "0.0001"
        description: "L2正则化参数。"
      - name: "max_iter"
        type: "int"
        default: "200"
        description: "最大迭代次数。"


clustering_algorithms:
  KMeans:
    parameters:
      - name: "n_clusters"
        type: "int"
        default: "8"
        description: "簇的数量，K均值聚类的核心参数。"
      - name: "init"
        type: "str"
        default: "k-means++"
        description: "初始化方法，可选 'k-means++'、'random' 或数组类型。"
      - name: "n_init"
        type: "int"
        default: "10"
        description: "初始化次数，K均值算法会运行该次数，选择最优结果。"
      - name: "max_iter"
        type: "int"
        default: "300"
        description: "每次运行的最大迭代次数。"
      - name: "tol"
        type: "float"
        default: "1e-4"
        description: "算法收敛的容忍误差，当目标函数变化小于此值时，认为算法收敛。"
      - name: "precompute_distances"
        type: "bool"
        default: "True"
        description: "是否预计算距离矩阵（会增加计算量）。"

  KMeansPlusPlus:
    parameters:
      - name: "n_clusters"
        type: "int"
        default: "8"
        description: "簇的数量。"
      - name: "n_init"
        type: "int"
        default: "10"
        description: "初始化次数。"
      - name: "max_iter"
        type: "int"
        default: "300"
        description: "最大迭代次数。"
      - name: "tol"
        type: "float"
        default: "1e-4"
        description: "容忍误差，用于判断算法是否收敛。"

  AgglomerativeClustering:
    parameters:
      - name: "n_clusters"
        type: "int"
        default: "2"
        description: "簇的数量。"
      - name: "affinity"
        type: "str"
        default: "euclidean"
        description: "衡量样本间距离的方式，可选 'euclidean'、'manhattan'、'cosine'。"
      - name: "memory"
        type: "str/None"
        default: "None"
        description: "缓存路径，存储树状结构。"
      - name: "linkage"
        type: "str"
        default: "ward"
        description: "聚类方式，可选 'ward'（最小化平方误差）、'average'、'complete'。"
      - name: "compute_full_tree"
        type: "str"
        default: "auto"
        description: "是否计算完全树结构，'auto'为自动，'True'为计算，'False'为不计算。"

  FCM:
    parameters:
      - name: "n_clusters"
        type: "int"
        default: "2"
        description: "簇的数量。"
      - name: "m"
        type: "float"
        default: "2.0"
        description: "模糊指数，控制隶属度的模糊程度，通常设置为2。"
      - name: "max_iter"
        type: "int"
        default: "100"
        description: "最大迭代次数。"
      - name: "tol"
        type: "float"
        default: "1e-5"
        description: "收敛容忍度，当隶属度变化小于此值时，认为算法收敛。"
      - name: "random_state"
        type: "int"
        default: "None"
        description: "随机种子，用于初始化隶属度矩阵。"

  DBSCAN:
    parameters:
      - name: "eps"
        type: "float"
        default: "0.5"
        description: "邻域的最大距离，决定样本是否在同一簇中。"
      - name: "min_samples"
        type: "int"
        default: "5"
        description: "形成簇的最小样本数。"
      - name: "metric"
        type: "str"
        default: "euclidean"
        description: "计算距离的方式，可选 'euclidean', 'manhattan', 'cosine' 等。"
      - name: "algorithm"
        type: "str"
        default: "auto"
        description: "计算最近邻的方法，可选 'auto', 'ball_tree', 'kd_tree', 'brute'。"
      - name: "leaf_size"
        type: "int"
        default: "30"
        description: "对于BallTree或KDTree的叶子大小，影响效率。"

  GaussianMixture:
    parameters:
      - name: "n_components"
        type: "int"
        default: "1"
        description: "高斯混合分布的组件数，表示簇的数量。"
      - name: "covariance_type"
        type: "str"
        default: "full"
        description: "协方差类型，可选 'full', 'tied', 'diag', 'spherical'。"
      - name: "tol"
        type: "float"
        default: "1e-3"
        description: "收敛容忍度，当对数似然变化小于此值时停止算法。"
      - name: "max_iter"
        type: "int"
        default: "100"
        description: "最大迭代次数。"
      - name: "random_state"
        type: "int"
        default: "None"
        description: "随机种子，用于初始化高斯混合模型的参数。"

  SpectralClustering:
    parameters:
      - name: "n_clusters"
        type: "int"
        default: "8"
        description: "簇的数量。"
      - name: "affinity"
        type: "str"
        default: "rbf"
        description: "计算相似度的方式，可选 'rbf'、'nearest_neighbors'、'precomputed'。"
      - name: "n_neighbors"
        type: "int"
        default: "10"
        description: "对于 'nearest_neighbors' 相似度，使用的邻居数。"
      - name: "gamma"
        type: "float"
        default: "1.0"
        description: "用于计算径向基函数的gamma参数，影响相似度的计算。"
      - name: "eigen_solver"
        type: "str"
        default: "auto"
        description: "求解特征值的方式，可选 'arpack', 'lobpcg', 'auto'。"
      - name: "random_state"
        type: "int"
        default: "None"
        description: "随机种子，用于初始化谱聚类的计算。"