Merge pull request #38 from hitsz-ids/bugfix-requirements

Update `requirements.txt`

README.md

@@ -14,9 +14,9 @@
 
 Synthetic Data Generator (SDG) is a framework focused on quickly generating high-quality structured tabular data. It supports more than 10 single-table and multi-table data synthesis algorithms, achieving up to 120 times performance improvement, and supports differential privacy and other methods to enhance the security of synthesized data.
 
-Synthetic data is generated by machines based on real data and algorithms, it does not contain sensitive information, but can retain the characteristics of real data. 
-There is no correspondence between synthetic data and real data, and it is not subject to privacy regulations such as GDPR and ADPPA. 
-In practical applications, there is no need to worry about the risk of privacy leakage. 
+Synthetic data is generated by machines based on real data and algorithms, it does not contain sensitive information, but can retain the characteristics of real data.
+There is no correspondence between synthetic data and real data, and it is not subject to privacy regulations such as GDPR and ADPPA.
+In practical applications, there is no need to worry about the risk of privacy leakage.
 High-quality synthetic data can also be used in various fields such as data opening, model training and debugging, system development and testing, etc.
 
 
@@ -138,4 +138,3 @@ The SDG project was initiated by **Institute of Data Security, Harbin Institute
 ## 📄 License
 
 The SDG open source project uses Apache-2.0 license, please refer to the [LICENSE].(https://github.com/hitsz-ids/synthetic-data-generator/blob/main/LICENSE)。
-

docs/develop/single_table_GaussianCopula.md

@@ -1 +1 @@
-# SDG 统计学模型开发文档
+# SDG 统计学模型开发文档

docs/readme.md

@@ -160,4 +160,3 @@ sampled = model.generate(num_rows=10)
 
 [10 rows x 10 columns]}}
 ```
-

example/1_ctgan_example.py

@@ -3,6 +3,7 @@
 # 并查看 sampled_data 变量
 
 from sdgx.models.single_table.ctgan import CTGAN
+
 # from sdgx.transform.sampler import DataSamplerCTGAN
 # from sdgx.transform.transformer import DataTransformerCTGAN
 from sdgx.utils.io.csv_utils import *

requirements.txt

@@ -1,14 +1,15 @@
-# using python 3.9
-setproctitle==1.2.3
-PyMySQL==1.0.2
-pandas>=1.2.4
+copulas==0.9.1
+dython==0.5.1
+joblib>=1.2.0
 numpy>=1.21.5
-scikit-learn==1.2.2
-torch==1.12.0
-torchvision==0.13.0
+pandas>=1.2.4
+PyMySQL>=1.0.2
+pytest>=7.4.3
 rdt==1.6.0
-joblib==1.2.0
-dython==0.5.1
+scikit-learn==1.2.2
 seaborn==0.11.1
-table-evaluator==1.4.2
-copulas==0.9.1
+# using python 3.8 3.9, 3.10
+setproctitle>=1.2.3
+table-evaluator>=1.4.2
+torch>=2.1.0
+torchvision

sdgx/errors.py

@@ -1,9 +1,12 @@
-# TBD 
+# 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    
+
+    pass
+
 
 class NonParametricError(Exception):
-    """Exception to indicate that a model is not parametric."""
+    """Exception to indicate that a model is not parametric."""

sdgx/models/single_table/ctgan.py

@@ -160,8 +160,8 @@ class CTGAN(BaseSynthesizerModel):
             设计上这个参数需要是 str 类型或者 bool 类型 可以是如下数值：
                 - True：根据实际情况进行自动化内存优化，保证在能跑完的情况下，尽量多地利用内存，同时不报错
                 - False：不采用任何内存优化策略
-                - None：同 False 
-            该参数默认设为 False 
+                - None：同 False
+            该参数默认设为 False
     """
 
     def __init__(
@@ -180,7 +180,7 @@ def __init__(
         epochs=300,
         pac=10,
         cuda=True,
-        memory_optimize = False
+        memory_optimize=False,
     ):
         assert batch_size % 2 == 0
 
@@ -216,7 +216,6 @@ def __init__(
         #   - False：不采用任何内存优化策略
         self.memory_optimize = memory_optimize
 
-
     @staticmethod
     def _gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=-1):
         """Deals with the instability of the gumbel_softmax for older versions of torch.
@@ -315,14 +314,11 @@ def _validate_discrete_columns(self, train_data, discrete_columns):
 
         if invalid_columns:
             raise ValueError(f"Invalid columns found: {invalid_columns}")
-
-
+
     # OPTIMIZE 新增方法，完成在内存受限情况下的 CTGAN 训练
-    @random_state 
-    def fit_optimze(self, train_data_iterator,\
-                    discrete_columns: Optional[List] = [],\
-                    epoches = 10):
-        ''' OPTIMIZE 新增方法，在内存受限情况下完成 CTGAN 训练
+    @random_state
+    def fit_optimze(self, train_data_iterator, discrete_columns: Optional[List] = [], epoches=10):
+        """OPTIMIZE 新增方法，在内存受限情况下完成 CTGAN 训练
 
         参数列表：
             train_data_iterator (iterator)：
@@ -337,18 +333,20 @@ def fit_optimze(self, train_data_iterator,\
             epoches(int-like)：
                 CTGAN 模型的迭代次数，
                 选填参数，默认为10，但严肃应用中应必须填写。
-        '''
+        """
         if epochs is None:
             epochs = self._epochs
-
 
         pass
 
     @random_state
-    def fit(self, train_data,
-            discrete_columns: Optional[List] = None,\
-            train_data_iterator = None,
-            epochs=None):
+    def fit(
+        self,
+        train_data,
+        discrete_columns: Optional[List] = None,
+        train_data_iterator=None,
+        epochs=None,
+    ):
         """Fit the CTGAN Synthesizer models to the training data.
 
         Args:
@@ -367,13 +365,13 @@ def fit(self, train_data,
         if not discrete_columns:
             discrete_columns = []
 
-        # OPTIMIZE 检查 optimize 以及 train_data_iterator 
+        # OPTIMIZE 检查 optimize 以及 train_data_iterator
         if self.memory_optimize and train_data_iterator is None:
             raise ValueError("train_data_iterator should not be None.")
         # 如果符合 optimize 的需求，则转到新实现的 optimize 方法，这样也不干扰老方法的顺利执行
         if self.memory_optimize and train_data_iterator is not None:
             self.fit_optimze(train_data_iterator, discrete_columns, epochs)
-            return 
+            return
         # OPTIMIZE 改动结束
 
         # 以下为原始的 fit 方法