Merge GaussianCopula Model

docs/develop/single_table_GAN.md

@@ -1,4 +1,4 @@
-# SDG 模型开发文档
+# SDG 神经网络模型开发文档
 
 ## 为 SDG 开发可运行的单表模型模块
 

docs/develop/single_table_GaussianCopula.md

@@ -0,0 +1 @@
+# SDG 统计学模型开发文档

example/2_guassian_copula_example.py

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+# 运行该例子，可使用：
+# ipython -i  example/2_guassian_copula_example.py
+# 并查看 sampled_data 变量
+
+from sdgx.statistics.single_table.copula import GaussianCopulaSynthesizer
+from sdgx.utils.io.csv_utils import *
+
+# 针对 csv 格式的小规模数据
+# 目前我们以 df 作为输入的数据的格式
+demo_data, discrete_cols = get_demo_single_table()
+# print(demo_data)
+# print(discrete_cols)
+
+model = GaussianCopulaSynthesizer(discrete_cols)
+model.fit(demo_data)
+
+# sampled
+sampled_data = model.sample(10)
+print(sampled_data)

sdgx/errors.py

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+# TBD 
+# 主要用于存放 sdg 中特有的的报错信息
+
+class SdgxError(Exception):
+    """Base class for exceptions in this module."""
+    pass    
+
+class NonParametricError(Exception):
+    """Exception to indicate that a model is not parametric."""

sdgx/statistics/base.py

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+from typing import List, Optional
+
+import numpy as np
+import torch
+
+
+class BaseSynthesizerModel:
+    random_states = None
+
+    def __init__(self, transformer=None, sampler=None) -> None:
+        # 以下几个变量都需要在初始化 model 时进行更改
+        self.model = None  # 存放模型
+        self.status = "UNFINED"
+        self.model_type = "MODEL_TYPE_UNDEFINED"
+        # self.epochs = epochs
+        self._device = "CPU"
+
+    def fit(self, input_df, discrete_cols: Optional[List] = None):
+        raise NotImplementedError
+
+    def set_device(self, device):
+        """Set the `device` to be used ('GPU' or 'CPU')."""
+        self._device = device
+        if self._generator is not None:
+            self._generator.to(self._device)
+
+    def __getstate__(self):
+        device_backup = self._device
+        self.set_device(torch.device("cpu"))
+        state = self.__dict__.copy()
+        self.set_device(device_backup)
+        if (
+            isinstance(self.random_states, tuple)
+            and isinstance(self.random_states[0], np.random.RandomState)
+            and isinstance(self.random_states[1], torch.Generator)
+        ):
+            state["_numpy_random_state"] = self.random_states[0].get_state()
+            state["_torch_random_state"] = self.random_states[1].get_state()
+            state.pop("random_states")
+        return state
+
+    def __setstate__(self, state):
+        if "_numpy_random_state" in state and "_torch_random_state" in state:
+            np_state = state.pop("_numpy_random_state")
+            torch_state = state.pop("_torch_random_state")
+            current_torch_state = torch.Generator()
+            current_torch_state.set_state(torch_state)
+            current_numpy_state = np.random.RandomState()
+            current_numpy_state.set_state(np_state)
+            state["random_states"] = (current_numpy_state, current_torch_state)
+        self.__dict__ = state
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.set_device(device)
+
+    def save(self, path):
+        device_backup = self._device
+        self.set_device(torch.device("cpu"))
+        torch.save(self, path)
+        self.set_device(device_backup)
+
+    @classmethod
+    def load(cls, path):
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        model = torch.load(path)
+        model.set_device(device)
+        return model
+
+    def set_random_state(self, random_state):
+        if random_state is None:
+            self.random_states = random_state
+        elif isinstance(random_state, int):
+            self.random_states = (
+                np.random.RandomState(seed=random_state),
+                torch.Generator().manual_seed(random_state),
+            )
+        elif (
+            isinstance(random_state, tuple)
+            and isinstance(random_state[0], np.random.RandomState)
+            and isinstance(random_state[1], torch.Generator)
+        ):
+            self.random_states = random_state
+        else:
+            raise TypeError(
+                f"`random_state` {random_state} expected to be an int or a tuple of "
+                "(`np.random.RandomState`, `torch.Generator`)"
+            )