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syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model copy/ctabgan.py

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+"""
+Generative model training algorithm based on the CTABGANSynthesiser
+
+"""
+import pandas as pd
+import time
+from model.pipeline.data_preparation import DataPrep
+from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+class CTABGAN():
+
+    def __init__(self,
+                 df,
+                 test_ratio = 0.20,
+                 categorical_columns = [ 'workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country', 'income'], 
+                 log_columns = [],
+                 mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]},
+                 general_columns = ["age"],
+                 non_categorical_columns = [],
+                 integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'],
+                 problem_type= {"Classification": "income"},
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=100,
+                 num_channels=64,
+                 l2scale=1e-5,
+                 batch_size=500,
+                 epochs=150,
+                 device="cpu"):
+
+        self.__name__ = 'CTABGAN'
+              
+        self.synthesizer = CTABGANSynthesizer(
+                class_dim=class_dim,
+                random_dim=random_dim,
+                num_channels=num_channels,
+                l2scale=l2scale,
+                batch_size=batch_size,
+                epochs=epochs,
+                device=device
+        )
+        self.raw_df = df
+        self.test_ratio = test_ratio
+        self.categorical_columns = categorical_columns
+        self.log_columns = log_columns
+        self.mixed_columns = mixed_columns
+        self.general_columns = general_columns
+        self.non_categorical_columns = non_categorical_columns
+        self.integer_columns = integer_columns
+        self.problem_type = problem_type
+                
+    def fit(self):
+        
+        start_time = time.time()
+        self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.general_columns,self.non_categorical_columns,self.integer_columns,self.problem_type,self.test_ratio)
+        self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], mixed = self.data_prep.column_types["mixed"],
+        general = self.data_prep.column_types["general"], non_categorical = self.data_prep.column_types["non_categorical"], type=self.problem_type)
+        end_time = time.time()
+        print('Finished training in',end_time-start_time," seconds.")
+
+
+    def generate_samples(self, seed=0):
+        
+        sample = self.synthesizer.sample(len(self.raw_df), seed) 
+        sample_df = self.data_prep.inverse_prep(sample)
+        
+        return sample_df

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model copy/eval/evaluation.py

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+import numpy as np
+import pandas as pd 
+from sklearn import metrics
+from sklearn import model_selection
+from sklearn.preprocessing import MinMaxScaler,StandardScaler
+from sklearn.neural_network import MLPClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn import svm,tree
+from sklearn.ensemble import RandomForestClassifier
+from dython.nominal import compute_associations
+from scipy.stats import wasserstein_distance
+from scipy.spatial import distance
+import warnings
+
+warnings.filterwarnings("ignore")
+
+def supervised_model_training(x_train, y_train, x_test, 
+                              y_test, model_name):
+  
+     
+  if model_name == 'lr':
+    model  = LogisticRegression(random_state=42,max_iter=500) 
+  elif model_name == 'svm':
+    model  = svm.SVC(random_state=42,probability=True)
+  elif model_name == 'dt':
+    model  = tree.DecisionTreeClassifier(random_state=42)
+  elif model_name == 'rf':      
+    model = RandomForestClassifier(random_state=42)
+  elif model_name == "mlp":
+    model = MLPClassifier(random_state=42,max_iter=100)
+  
+  model.fit(x_train, y_train)
+  pred = model.predict(x_test)
+
+  if len(np.unique(y_train))>2:
+    predict = model.predict_proba(x_test)        
+    acc = metrics.accuracy_score(y_test,pred)*100
+    auc = metrics.roc_auc_score(y_test, predict,average="weighted",multi_class="ovr")
+    f1_score = metrics.precision_recall_fscore_support(y_test, pred,average="weighted")[2]
+    return [acc, auc,f1_score] 
+
+  else:
+    predict = model.predict_proba(x_test)[:,1]    
+    acc = metrics.accuracy_score(y_test,pred)*100
+    auc = metrics.roc_auc_score(y_test, predict)
+    f1_score = metrics.precision_recall_fscore_support(y_test,pred)[2].mean()
+    return [acc, auc,f1_score] 
+
+
+def get_utility_metrics(real_path,fake_paths,scaler="MinMax",classifiers=["lr","dt","rf","mlp"],test_ratio=.20):
+
+    data_real = pd.read_csv(real_path).to_numpy()
+    data_dim = data_real.shape[1]
+
+    data_real_y = data_real[:,-1]
+    data_real_X = data_real[:,:data_dim-1]
+    X_train_real, X_test_real, y_train_real, y_test_real = model_selection.train_test_split(data_real_X ,data_real_y, test_size=test_ratio, stratify=data_real_y,random_state=42) 
+
+    if scaler=="MinMax":
+        scaler_real = MinMaxScaler()
+    else:
+        scaler_real = StandardScaler()
+        
+    scaler_real.fit(data_real_X)
+    X_train_real_scaled = scaler_real.transform(X_train_real)
+    X_test_real_scaled = scaler_real.transform(X_test_real)
+
+    all_real_results = []
+    for classifier in classifiers:
+      real_results = supervised_model_training(X_train_real_scaled,y_train_real,X_test_real_scaled,y_test_real,classifier)
+      all_real_results.append(real_results)
+      
+    all_fake_results_avg = []
+    
+    for fake_path in fake_paths:
+      data_fake  = pd.read_csv(fake_path).to_numpy()
+      data_fake_y = data_fake[:,-1]
+      data_fake_X = data_fake[:,:data_dim-1]
+      X_train_fake, _ , y_train_fake, _ = model_selection.train_test_split(data_fake_X ,data_fake_y, test_size=test_ratio, stratify=data_fake_y,random_state=42) 
+
+      if scaler=="MinMax":
+        scaler_fake = MinMaxScaler()
+      else:
+        scaler_fake = StandardScaler()
+      
+      scaler_fake.fit(data_fake_X)
+      
+      X_train_fake_scaled = scaler_fake.transform(X_train_fake)
+      
+      all_fake_results = []
+      for classifier in classifiers:
+        fake_results = supervised_model_training(X_train_fake_scaled,y_train_fake,X_test_real_scaled,y_test_real,classifier)
+        all_fake_results.append(fake_results)
+
+      all_fake_results_avg.append(all_fake_results)
+    
+    diff_results = np.array(all_real_results)- np.array(all_fake_results_avg).mean(axis=0)
+
+    return diff_results
+
+def stat_sim(real_path,fake_path,cat_cols=None):
+    
+    Stat_dict={}
+    
+    real = pd.read_csv(real_path)
+    fake = pd.read_csv(fake_path)
+
+    really = real.copy()
+    fakey = fake.copy()
+
+    real_corr = compute_associations(real, nominal_columns=cat_cols)
+
+    fake_corr = compute_associations(fake, nominal_columns=cat_cols)
+
+    corr_dist = np.linalg.norm(real_corr - fake_corr)
+    
+    cat_stat = []
+    num_stat = []
+    
+    for column in real.columns:
+        
+        if column in cat_cols:
+
+            real_pdf=(really[column].value_counts()/really[column].value_counts().sum())
+            fake_pdf=(fakey[column].value_counts()/fakey[column].value_counts().sum())
+            categories = (fakey[column].value_counts()/fakey[column].value_counts().sum()).keys().tolist()
+            sorted_categories = sorted(categories)
+            
+            real_pdf_values = [] 
+            fake_pdf_values = []
+
+            for i in sorted_categories:
+                real_pdf_values.append(real_pdf[i])
+                fake_pdf_values.append(fake_pdf[i])
+            
+            if len(real_pdf)!=len(fake_pdf):
+                zero_cats = set(really[column].value_counts().keys())-set(fakey[column].value_counts().keys())
+                for z in zero_cats:
+                    real_pdf_values.append(real_pdf[z])
+                    fake_pdf_values.append(0)
+            Stat_dict[column]=(distance.jensenshannon(real_pdf_values,fake_pdf_values, 2.0))
+            cat_stat.append(Stat_dict[column])        
+        else:
+            scaler = MinMaxScaler()
+            scaler.fit(real[column].values.reshape(-1,1))
+            l1 = scaler.transform(real[column].values.reshape(-1,1)).flatten()
+            l2 = scaler.transform(fake[column].values.reshape(-1,1)).flatten()
+            Stat_dict[column]= (wasserstein_distance(l1,l2))
+            num_stat.append(Stat_dict[column])
+
+    return [np.mean(num_stat),np.mean(cat_stat),corr_dist]
+
+def privacy_metrics(real_path,fake_path,data_percent=15):
+    
+    real = pd.read_csv(real_path).drop_duplicates(keep=False)
+    fake = pd.read_csv(fake_path).drop_duplicates(keep=False)
+
+    real_refined = real.sample(n=int(len(real)*(.01*data_percent)), random_state=42).to_numpy()
+    fake_refined = fake.sample(n=int(len(fake)*(.01*data_percent)), random_state=42).to_numpy()
+
+    scalerR = StandardScaler()
+    scalerR.fit(real_refined)
+    scalerF = StandardScaler()
+    scalerF.fit(fake_refined)
+    df_real_scaled = scalerR.transform(real_refined)
+    df_fake_scaled = scalerF.transform(fake_refined)
+    
+    dist_rf = metrics.pairwise_distances(df_real_scaled, Y=df_fake_scaled, metric='minkowski', n_jobs=-1)
+    dist_rr = metrics.pairwise_distances(df_real_scaled, Y=None, metric='minkowski', n_jobs=-1)
+    rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1)
+    dist_ff = metrics.pairwise_distances(df_fake_scaled, Y=None, metric='minkowski', n_jobs=-1)
+    rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) 
+    smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))]
+    smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))]       
+    smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))]
+    smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))]
+    smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))]
+    smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))]
+    nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr])
+    nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff])
+    nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf])
+    nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5)
+    nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5)
+    nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5)
+
+    min_dist_rf = np.array([i[0] for i in smallest_two_rf])
+    fifth_perc_rf = np.percentile(min_dist_rf,5)
+    min_dist_rr = np.array([i[0] for i in smallest_two_rr])
+    fifth_perc_rr = np.percentile(min_dist_rr,5)
+    min_dist_ff = np.array([i[0] for i in smallest_two_ff])
+    fifth_perc_ff = np.percentile(min_dist_ff,5)
+    
+    return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6)    

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model copy/pipeline/data_preparation.py

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+import numpy as np
+import pandas as pd
+from sklearn import preprocessing
+from sklearn import model_selection
+
+class DataPrep(object):
+  
+    def __init__(self, raw_df: pd.DataFrame, categorical: list, log:list, mixed:dict, general:list, non_categorical:list, integer:list, type:dict, test_ratio:float):
+        
+        
+        self.categorical_columns = categorical
+        self.log_columns = log
+        self.mixed_columns = mixed
+        self.general_columns = general
+        self.non_categorical_columns = non_categorical
+        self.integer_columns = integer
+        self.column_types = dict()
+        self.column_types["categorical"] = []
+        self.column_types["mixed"] = {}
+        self.column_types["general"] = []
+        self.column_types["non_categorical"] = []
+        self.lower_bounds = {}
+        self.label_encoder_list = []
+        
+        target_col = list(type.values())[0]
+        if target_col is not None:
+            y_real = raw_df[target_col]
+            X_real = raw_df.drop(columns=[target_col])
+            X_train_real, _, y_train_real, _ = model_selection.train_test_split(X_real ,y_real, test_size=test_ratio, stratify=y_real,random_state=42)
+        
+            X_train_real[target_col]= y_train_real
+
+            self.df = X_train_real
+        else:
+            self.df = raw_df
+
+        self.df = self.df.replace(r' ', np.nan)
+        self.df = self.df.fillna('empty')
+       
+        all_columns= set(self.df.columns)
+        irrelevant_missing_columns = set(self.categorical_columns)
+        relevant_missing_columns = list(all_columns - irrelevant_missing_columns)
+        
+        for i in relevant_missing_columns:
+            if i in self.log_columns:
+                if "empty" in list(self.df[i].values):
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x)
+                    self.mixed_columns[i] = [-9999999]
+            elif i in list(self.mixed_columns.keys()):
+                if "empty" in list(self.df[i].values):
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x )
+                    self.mixed_columns[i].append(-9999999)
+            else:
+                if "empty" in list(self.df[i].values):   
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x)
+                    self.mixed_columns[i] = [-9999999]
+        
+        if self.log_columns:
+            for log_column in self.log_columns:
+                valid_indices = []
+                for idx,val in enumerate(self.df[log_column].values):
+                    if val!=-9999999:
+                        valid_indices.append(idx)
+                eps = 1
+                lower = np.min(self.df[log_column].iloc[valid_indices].values)
+                self.lower_bounds[log_column] = lower
+                if lower>0: 
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x) if x!=-9999999 else -9999999)
+                elif lower == 0:
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x+eps) if x!=-9999999 else -9999999) 
+                else:
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x-lower+eps) if x!=-9999999 else -9999999)
+
+        for column_index, column in enumerate(self.df.columns):            
+            if column in self.categorical_columns:        
+                label_encoder = preprocessing.LabelEncoder()
+                self.df[column] = self.df[column].astype(str)
+                label_encoder.fit(self.df[column])
+                current_label_encoder = dict()
+                current_label_encoder['column'] = column
+                current_label_encoder['label_encoder'] = label_encoder
+                transformed_column = label_encoder.transform(self.df[column])
+                self.df[column] = transformed_column
+                self.label_encoder_list.append(current_label_encoder)
+                self.column_types["categorical"].append(column_index)
+
+                if column in self.general_columns:
+                    self.column_types["general"].append(column_index)
+            
+                if column in self.non_categorical_columns:
+                    self.column_types["non_categorical"].append(column_index)
+            
+            elif column in self.mixed_columns:
+                self.column_types["mixed"][column_index] = self.mixed_columns[column]
+            
+            elif column in self.general_columns:
+                self.column_types["general"].append(column_index)
+            
+
+        super().__init__()
+        
+    def inverse_prep(self, data, eps=1):
+        
+        df_sample = pd.DataFrame(data,columns=self.df.columns)
+     
+        for i in range(len(self.label_encoder_list)):
+            le = self.label_encoder_list[i]["label_encoder"]
+            df_sample[self.label_encoder_list[i]["column"]] = df_sample[self.label_encoder_list[i]["column"]].astype(int)
+            df_sample[self.label_encoder_list[i]["column"]] = le.inverse_transform(df_sample[self.label_encoder_list[i]["column"]])
+
+        if self.log_columns:
+            for i in df_sample:
+                if i in self.log_columns:
+                    lower_bound = self.lower_bounds[i]
+                    if lower_bound>0:
+                        df_sample[i].apply(lambda x: np.exp(x)) 
+                    elif lower_bound==0:
+                        df_sample[i] = df_sample[i].apply(lambda x: np.ceil(np.exp(x)-eps) if (np.exp(x)-eps) < 0 else (np.exp(x)-eps))
+                    else: 
+                        df_sample[i] = df_sample[i].apply(lambda x: np.exp(x)-eps+lower_bound)
+
+        if self.integer_columns:
+            for column in self.integer_columns:
+                df_sample[column]= (np.round(df_sample[column].values))
+                df_sample[column] = df_sample[column].astype(int)
+
+        df_sample.replace(-9999999, np.nan,inplace=True)
+        df_sample.replace('empty', np.nan,inplace=True)
+
+        return df_sample

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model copy/privacy_utils/rdp_accountant.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import sys
+
+import numpy as np
+from scipy import special
+import six
+
+########################
+# LOG-SPACE ARITHMETIC #
+########################
+
+
+def _log_add(logx, logy):
+  """Add two numbers in the log space."""
+  a, b = min(logx, logy), max(logx, logy)
+  if a == -np.inf:  # adding 0
+    return b
+  # Use exp(a) + exp(b) = (exp(a - b) + 1) * exp(b)
+  return math.log1p(math.exp(a - b)) + b  # log1p(x) = log(x + 1)
+
+
+def _log_sub(logx, logy):
+  """Subtract two numbers in the log space. Answer must be non-negative."""
+  if logx < logy:
+    raise ValueError("The result of subtraction must be non-negative.")
+  if logy == -np.inf:  # subtracting 0
+    return logx
+  if logx == logy:
+    return -np.inf  # 0 is represented as -np.inf in the log space.
+
+  try:
+    # Use exp(x) - exp(y) = (exp(x - y) - 1) * exp(y).
+    return math.log(math.expm1(logx - logy)) + logy  # expm1(x) = exp(x) - 1
+  except OverflowError:
+    return logx
+
+
+def _log_print(logx):
+  """Pretty print."""
+  if logx < math.log(sys.float_info.max):
+    return "{}".format(math.exp(logx))
+  else:
+    return "exp({})".format(logx)
+
+
+def _compute_log_a_int(q, sigma, alpha):
+  """Compute log(A_alpha) for integer alpha. 0 < q < 1."""
+  assert isinstance(alpha, six.integer_types)
+
+  # Initialize with 0 in the log space.
+  log_a = -np.inf
+
+  for i in range(alpha + 1):
+    log_coef_i = (
+        math.log(special.binom(alpha, i)) + i * math.log(q) +
+        (alpha - i) * math.log(1 - q))
+
+    s = log_coef_i + (i * i - i) / (2 * (sigma**2))
+    log_a = _log_add(log_a, s)
+
+  return float(log_a)
+
+
+def _compute_log_a_frac(q, sigma, alpha):
+  """Compute log(A_alpha) for fractional alpha. 0 < q < 1."""
+  # The two parts of A_alpha, integrals over (-inf,z0] and [z0, +inf), are
+  # initialized to 0 in the log space:
+  log_a0, log_a1 = -np.inf, -np.inf
+  i = 0
+
+  z0 = sigma**2 * math.log(1 / q - 1) + .5
+
+  while True:  # do ... until loop
+    coef = special.binom(alpha, i)
+    log_coef = math.log(abs(coef))
+    j = alpha - i
+
+    log_t0 = log_coef + i * math.log(q) + j * math.log(1 - q)
+    log_t1 = log_coef + j * math.log(q) + i * math.log(1 - q)
+
+    log_e0 = math.log(.5) + _log_erfc((i - z0) / (math.sqrt(2) * sigma))
+    log_e1 = math.log(.5) + _log_erfc((z0 - j) / (math.sqrt(2) * sigma))
+
+    log_s0 = log_t0 + (i * i - i) / (2 * (sigma**2)) + log_e0
+    log_s1 = log_t1 + (j * j - j) / (2 * (sigma**2)) + log_e1
+
+    if coef > 0:
+      log_a0 = _log_add(log_a0, log_s0)
+      log_a1 = _log_add(log_a1, log_s1)
+    else:
+      log_a0 = _log_sub(log_a0, log_s0)
+      log_a1 = _log_sub(log_a1, log_s1)
+
+    i += 1
+    if max(log_s0, log_s1) < -30:
+      break
+
+  return _log_add(log_a0, log_a1)
+
+
+def _compute_log_a(q, sigma, alpha):
+  """Compute log(A_alpha) for any positive finite alpha."""
+  if float(alpha).is_integer():
+    return _compute_log_a_int(q, sigma, int(alpha))
+  else:
+    return _compute_log_a_frac(q, sigma, alpha)
+
+
+def _log_erfc(x):
+  """Compute log(erfc(x)) with high accuracy for large x."""
+  try:
+    return math.log(2) + special.log_ndtr(-x * 2**.5)
+  except NameError:
+    # If log_ndtr is not available, approximate as follows:
+    r = special.erfc(x)
+    if r == 0.0:
+      # Using the Laurent series at infinity for the tail of the erfc function:
+      #     erfc(x) ~ exp(-x^2-.5/x^2+.625/x^4)/(x*pi^.5)
+      # To verify in Mathematica:
+      #     Series[Log[Erfc[x]] + Log[x] + Log[Pi]/2 + x^2, {x, Infinity, 6}]
+      return (-math.log(math.pi) / 2 - math.log(x) - x**2 - .5 * x**-2 +
+              .625 * x**-4 - 37. / 24. * x**-6 + 353. / 64. * x**-8)
+    else:
+      return math.log(r)
+
+
+def _compute_delta(orders, rdp, eps):
+  """Compute delta given a list of RDP values and target epsilon.
+
+  Args:
+    orders: An array (or a scalar) of orders.
+    rdp: A list (or a scalar) of RDP guarantees.
+    eps: The target epsilon.
+
+  Returns:
+    Pair of (delta, optimal_order).
+
+  Raises:
+    ValueError: If input is malformed.
+
+  """
+  orders_vec = np.atleast_1d(orders)
+  rdp_vec = np.atleast_1d(rdp)
+
+  if len(orders_vec) != len(rdp_vec):
+    raise ValueError("Input lists must have the same length.")
+
+  deltas = np.exp((rdp_vec - eps) * (orders_vec - 1))
+  idx_opt = np.argmin(deltas)
+  return min(deltas[idx_opt], 1.), orders_vec[idx_opt]
+
+
+def _compute_eps(orders, rdp, delta):
+  """Compute epsilon given a list of RDP values and target delta.
+
+  Args:
+    orders: An array (or a scalar) of orders.
+    rdp: A list (or a scalar) of RDP guarantees.
+    delta: The target delta.
+
+  Returns:
+    Pair of (eps, optimal_order).
+
+  Raises:
+    ValueError: If input is malformed.
+
+  """
+  orders_vec = np.atleast_1d(orders)
+  rdp_vec = np.atleast_1d(rdp)
+
+  if len(orders_vec) != len(rdp_vec):
+    raise ValueError("Input lists must have the same length.")
+
+  eps = rdp_vec - math.log(delta) / (orders_vec - 1)
+
+  idx_opt = np.nanargmin(eps)  # Ignore NaNs
+  return eps[idx_opt], orders_vec[idx_opt]
+
+
+def _compute_rdp(q, sigma, alpha):
+  """Compute RDP of the Sampled Gaussian mechanism at order alpha.
+
+  Args:
+    q: The sampling rate.
+    sigma: The std of the additive Gaussian noise.
+    alpha: The order at which RDP is computed.
+
+  Returns:
+    RDP at alpha, can be np.inf.
+  """
+  if q == 0:
+    return 0
+
+  if q == 1.:
+    return alpha / (2 * sigma**2)
+
+  if np.isinf(alpha):
+    return np.inf
+
+  return _compute_log_a(q, sigma, alpha) / (alpha - 1)
+
+
+def compute_rdp(q, noise_multiplier, steps, orders):
+  """Compute RDP of the Sampled Gaussian Mechanism.
+
+  Args:
+    q: The sampling rate.
+    noise_multiplier: The ratio of the standard deviation of the Gaussian noise
+        to the l2-sensitivity of the function to which it is added.
+    steps: The number of steps.
+    orders: An array (or a scalar) of RDP orders.
+
+  Returns:
+    The RDPs at all orders, can be np.inf.
+  """
+  if np.isscalar(orders):
+    rdp = _compute_rdp(q, noise_multiplier, orders)
+  else:
+    rdp = np.array([_compute_rdp(q, noise_multiplier, order)
+                    for order in orders])
+
+  return rdp * steps
+
+
+def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None):
+  """Compute delta (or eps) for given eps (or delta) from RDP values.
+
+  Args:
+    orders: An array (or a scalar) of RDP orders.
+    rdp: An array of RDP values. Must be of the same length as the orders list.
+    target_eps: If not None, the epsilon for which we compute the corresponding
+              delta.
+    target_delta: If not None, the delta for which we compute the corresponding
+              epsilon. Exactly one of target_eps and target_delta must be None.
+
+  Returns:
+    eps, delta, opt_order.
+
+  Raises:
+    ValueError: If target_eps and target_delta are messed up.
+  """
+  if target_eps is None and target_delta is None:
+    raise ValueError(
+        "Exactly one out of eps and delta must be None. (Both are).")
+
+  if target_eps is not None and target_delta is not None:
+    raise ValueError(
+        "Exactly one out of eps and delta must be None. (None is).")
+
+  if target_eps is not None:
+    delta, opt_order = _compute_delta(orders, rdp, target_eps)
+    return target_eps, delta, opt_order
+  else:
+    eps, opt_order = _compute_eps(orders, rdp, target_delta)
+    return eps, target_delta, opt_order
+
+
+def compute_rdp_from_ledger(ledger, orders):
+  """Compute RDP of Sampled Gaussian Mechanism from ledger.
+
+  Args:
+    ledger: A formatted privacy ledger.
+    orders: An array (or a scalar) of RDP orders.
+
+  Returns:
+    RDP at all orders, can be np.inf.
+  """
+  total_rdp = np.zeros_like(orders, dtype=float)
+  for sample in ledger:
+    # Compute equivalent z from l2_clip_bounds and noise stddevs in sample.
+    # See https://arxiv.org/pdf/1812.06210.pdf for derivation of this formula.
+    effective_z = sum([
+        (q.noise_stddev / q.l2_norm_bound)**-2 for q in sample.queries])**-0.5
+    total_rdp += compute_rdp(
+        sample.selection_probability, effective_z, 1, orders)
+  return total_rdp

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/ctabgan_synthesizer.py

@@ -0,0 +1,601 @@
+import numpy as np
+import pandas as pd
+import torch
+import torch.utils.data
+import torch.optim as optim
+from torch.optim import Adam
+from torch.nn import functional as F
+from torch.nn import (Dropout, LeakyReLU, Linear, Module, ReLU, Sequential,
+Conv2d, ConvTranspose2d, Sigmoid, init, BCELoss, CrossEntropyLoss,SmoothL1Loss,LayerNorm)
+from model.synthesizer.transformer import ImageTransformer,DataTransformer
+from model.privacy_utils.rdp_accountant import compute_rdp, get_privacy_spent
+from tqdm import tqdm
+
+
+class Classifier(Module):
+    def __init__(self,input_dim, dis_dims,st_ed):
+        super(Classifier,self).__init__()
+        dim = input_dim-(st_ed[1]-st_ed[0])
+        seq = []
+        self.str_end = st_ed
+        for item in list(dis_dims):
+            seq += [
+                Linear(dim, item),
+                LeakyReLU(0.2),
+                Dropout(0.5)
+            ]
+            dim = item
+        
+        if (st_ed[1]-st_ed[0])==1:
+            seq += [Linear(dim, 1)]
+        
+        elif (st_ed[1]-st_ed[0])==2:
+            seq += [Linear(dim, 1),Sigmoid()]
+        else:
+            seq += [Linear(dim,(st_ed[1]-st_ed[0]))] 
+        
+        self.seq = Sequential(*seq)
+
+    def forward(self, input):
+        
+        label=None
+        
+        if (self.str_end[1]-self.str_end[0])==1:
+            label = input[:, self.str_end[0]:self.str_end[1]]
+        else:
+            label = torch.argmax(input[:, self.str_end[0]:self.str_end[1]], axis=-1)
+        
+        new_imp = torch.cat((input[:,:self.str_end[0]],input[:,self.str_end[1]:]),1)
+        
+        if ((self.str_end[1]-self.str_end[0])==2) | ((self.str_end[1]-self.str_end[0])==1):
+            return self.seq(new_imp).view(-1), label
+        else:
+            return self.seq(new_imp), label
+
+def apply_activate(data, output_info):
+    data_t = []
+    st = 0
+    for item in output_info:
+        if item[1] == 'tanh':
+            ed = st + item[0]
+            data_t.append(torch.tanh(data[:, st:ed]))
+            st = ed
+        elif item[1] == 'softmax':
+            ed = st + item[0]
+            data_t.append(F.gumbel_softmax(data[:, st:ed], tau=0.2))
+            st = ed
+    return torch.cat(data_t, dim=1)
+
+def get_st_ed(target_col_index,output_info):
+    st = 0
+    c= 0
+    tc= 0
+
+    for item in output_info:
+        if c==target_col_index:
+            break
+        if item[1]=='tanh':
+            st += item[0]
+            if item[2] == 'yes_g':
+                c+=1
+        elif item[1] == 'softmax':
+            st += item[0]
+            c+=1
+        tc+=1    
+    
+    ed= st+output_info[tc][0] 
+
+    return (st,ed)
+
+def random_choice_prob_index_sampling(probs,col_idx):
+    option_list = []
+    for i in col_idx:
+        pp = probs[i]
+        option_list.append(np.random.choice(np.arange(len(probs[i])), p=pp))
+    
+    return np.array(option_list).reshape(col_idx.shape)
+
+def random_choice_prob_index(a, axis=1):
+    r = np.expand_dims(np.random.rand(a.shape[1 - axis]), axis=axis)
+    return (a.cumsum(axis=axis) > r).argmax(axis=axis)
+
+def maximum_interval(output_info):
+    max_interval = 0
+    for item in output_info:
+        max_interval = max(max_interval, item[0])
+    return max_interval
+
+class Cond(object):
+    def __init__(self, data, output_info):
+       
+        self.model = []
+        st = 0
+        counter = 0
+        for item in output_info:
+           
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':
+                ed = st + item[0]
+                counter += 1
+                self.model.append(np.argmax(data[:, st:ed], axis=-1))
+                st = ed
+            
+        self.interval = []
+        self.n_col = 0  
+        self.n_opt = 0  
+        st = 0
+        self.p = np.zeros((counter, maximum_interval(output_info)))  
+        self.p_sampling = []
+        for item in output_info:
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax': 
+                ed = st + item[0]
+                tmp = np.sum(data[:, st:ed], axis=0)  
+                tmp_sampling = np.sum(data[:, st:ed], axis=0)     
+                tmp = np.log(tmp + 1)  
+                tmp = tmp / np.sum(tmp) 
+                tmp_sampling = tmp_sampling / np.sum(tmp_sampling)
+                self.p_sampling.append(tmp_sampling)
+                self.p[self.n_col, :item[0]] = tmp 
+                self.interval.append((self.n_opt, item[0]))
+                self.n_opt += item[0]
+                self.n_col += 1
+                st = ed
+                
+        self.interval = np.asarray(self.interval)
+        
+    def sample_train(self, batch):
+        if self.n_col == 0:
+            return None
+        batch = batch
+
+        idx = np.random.choice(np.arange(self.n_col), batch)
+
+        vec = np.zeros((batch, self.n_opt), dtype='float32')
+        mask = np.zeros((batch, self.n_col), dtype='float32')
+        mask[np.arange(batch), idx] = 1  
+        opt1prime = random_choice_prob_index(self.p[idx]) 
+        for i in np.arange(batch):
+            vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1
+            
+        return vec, mask, idx, opt1prime
+
+    def sample(self, batch):
+        if self.n_col == 0:
+            return None
+        batch = batch
+      
+        idx = np.random.choice(np.arange(self.n_col), batch)
+
+        vec = np.zeros((batch, self.n_opt), dtype='float32')
+        opt1prime = random_choice_prob_index_sampling(self.p_sampling,idx)
+        
+        for i in np.arange(batch):
+            vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1
+            
+        return vec
+
+def cond_loss(data, output_info, c, m):
+    loss = []
+    st = 0
+    st_c = 0
+    for item in output_info:
+        if item[1] == 'tanh':
+            st += item[0]
+            continue
+
+        elif item[1] == 'softmax':
+            ed = st + item[0]
+            ed_c = st_c + item[0]
+            tmp = F.cross_entropy(
+            data[:, st:ed],
+            torch.argmax(c[:, st_c:ed_c], dim=1),
+            reduction='none')
+            loss.append(tmp)
+            st = ed
+            st_c = ed_c
+
+    loss = torch.stack(loss, dim=1)
+    return (loss * m).sum() / data.size()[0]
+
+class Sampler(object):
+    def __init__(self, data, output_info):
+        super(Sampler, self).__init__()
+        self.data = data
+        self.model = []
+        self.n = len(data)
+        st = 0
+        for item in output_info:
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':
+                ed = st + item[0]
+                tmp = []
+                for j in range(item[0]):
+                    tmp.append(np.nonzero(data[:, st + j])[0])
+                self.model.append(tmp)
+                st = ed
+                
+    def sample(self, n, col, opt):
+        if col is None:
+            idx = np.random.choice(np.arange(self.n), n)
+            return self.data[idx]
+        idx = []
+        for c, o in zip(col, opt):
+            idx.append(np.random.choice(self.model[c][o]))
+        return self.data[idx]
+
+class Discriminator(Module):
+    def __init__(self, side, layers):
+        super(Discriminator, self).__init__()
+        self.side = side
+        info = len(layers)-2
+        self.seq = Sequential(*layers)
+        self.seq_info = Sequential(*layers[:info])
+
+    def forward(self, input):
+        return (self.seq(input)), self.seq_info(input)
+
+class Generator(Module):
+    def __init__(self, side, layers):
+        super(Generator, self).__init__()
+        self.side = side
+        self.seq = Sequential(*layers)
+
+    def forward(self, input_):
+        return self.seq(input_)
+
+def determine_layers_disc(side, num_channels):
+    assert side >= 4 and side <= 64
+
+    layer_dims = [(1, side), (num_channels, side // 2)]
+
+    while layer_dims[-1][1] > 3 and len(layer_dims) < 4:
+        layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2))
+
+    layerNorms = []
+    num_c = num_channels
+    num_s = side / 2
+    for l in range(len(layer_dims) - 1):
+        layerNorms.append([int(num_c), int(num_s), int(num_s)])
+        num_c = num_c * 2
+        num_s = num_s / 2
+
+    layers_D = []
+
+    for prev, curr, ln in zip(layer_dims, layer_dims[1:], layerNorms):
+        layers_D += [
+            Conv2d(prev[0], curr[0], 4, 2, 1, bias=False),
+            LayerNorm(ln),
+            LeakyReLU(0.2, inplace=True),
+        ]
+
+    layers_D += [Conv2d(layer_dims[-1][0], 1, layer_dims[-1][1], 1, 0), ReLU(True)] 
+
+    return layers_D
+
+def determine_layers_gen(side, random_dim, num_channels):
+    assert side >= 4 and side <= 64
+
+    layer_dims = [(1, side), (num_channels, side // 2)]
+
+    while layer_dims[-1][1] > 3 and len(layer_dims) < 4:
+        layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2))
+
+    layerNorms = []
+
+    num_c = num_channels * (2 ** (len(layer_dims) - 2))
+    num_s = int(side / (2 ** (len(layer_dims) - 1)))
+    for l in range(len(layer_dims) - 1):
+        layerNorms.append([int(num_c), int(num_s), int(num_s)])
+        num_c = num_c / 2
+        num_s = num_s * 2
+
+    layers_G = [ConvTranspose2d(random_dim, layer_dims[-1][0], layer_dims[-1][1], 1, 0, output_padding=0, bias=False)]
+
+    for prev, curr, ln in zip(reversed(layer_dims), reversed(layer_dims[:-1]), layerNorms):
+        layers_G += [LayerNorm(ln), ReLU(True), ConvTranspose2d(prev[0], curr[0], 4, 2, 1, output_padding=0, bias=True)]
+    return layers_G
+
+def slerp(val, low, high):
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm*high_norm).sum(1)).view(val.size(0), 1)
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so)*low + (torch.sin(val*omega)/so) * high
+    
+    return res
+
+def calc_gradient_penalty_slerp(netD, real_data, fake_data, transformer, device='cpu', lambda_=10):
+    batchsize = real_data.shape[0]
+    alpha = torch.rand(batchsize, 1,  device=device)
+    interpolates = slerp(alpha, real_data, fake_data)
+    interpolates = interpolates.to(device)
+    interpolates = transformer.transform(interpolates)
+    interpolates = torch.autograd.Variable(interpolates, requires_grad=True)
+    disc_interpolates,_ = netD(interpolates) 
+
+    gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolates,
+                                  grad_outputs=torch.ones(disc_interpolates.size()).to(device),
+                                  create_graph=True, retain_graph=True, only_inputs=True)[0] 
+    
+    gradients_norm = gradients.norm(2, dim=1)
+    gradient_penalty = ((gradients_norm - 1) ** 2).mean() * lambda_
+    
+    return gradient_penalty
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    
+    if classname.find('Conv') != -1:
+        init.normal_(m.weight.data, 0.0, 0.02)
+
+    elif classname.find('BatchNorm') != -1:
+        init.normal_(m.weight.data, 1.0, 0.02)
+        init.constant_(m.bias.data, 0)
+
+class CTABGANSynthesizer:
+    def __init__(self,
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=100,
+                 num_channels=64,
+                 l2scale=1e-5,
+                 batch_size=500,
+                 epochs=150,
+                 device="cpu"):
+                 
+
+        self.random_dim = random_dim
+        self.class_dim = class_dim
+        self.num_channels = num_channels
+        self.dside = None
+        self.gside = None
+        self.l2scale = l2scale
+        self.batch_size = batch_size
+        self.epochs = epochs
+        self.device = torch.device(device)
+
+    def fit(self, train_data=pd.DataFrame, categorical=[], mixed={}, general=[], non_categorical=[], type={}):
+
+        problem_type = None
+        target_index=None
+        if type:
+            problem_type = list(type.keys())[0]
+            if problem_type:
+                target_index = train_data.columns.get_loc(type[problem_type])
+
+        self.transformer = DataTransformer(train_data=train_data, categorical_list=categorical, mixed_dict=mixed, general_list=general, non_categorical_list=non_categorical)
+        self.transformer.fit() 
+        train_data = self.transformer.transform(train_data.values)
+        data_sampler = Sampler(train_data, self.transformer.output_info)
+        data_dim = self.transformer.output_dim
+        self.cond_generator = Cond(train_data, self.transformer.output_info)
+        		
+        sides = [4, 8, 16, 24, 64]
+        col_size_d = data_dim + self.cond_generator.n_opt
+        for i in sides:
+            if i * i >= col_size_d:
+                self.dside = i
+                break
+        
+        sides = [4, 8, 16, 24, 64]
+        col_size_g = data_dim
+        for i in sides:
+            if i * i >= col_size_g:
+                self.gside = i
+                break
+		
+
+        layers_G = determine_layers_gen(self.gside, self.random_dim+self.cond_generator.n_opt, self.num_channels)
+        layers_D = determine_layers_disc(self.dside, self.num_channels)
+        
+        self.generator = Generator(self.gside, layers_G).to(self.device)
+        discriminator = Discriminator(self.dside, layers_D).to(self.device)
+        optimizer_params = dict(lr=2e-4, betas=(0.5, 0.9), eps=1e-3, weight_decay=self.l2scale)
+        optimizerG = Adam(self.generator.parameters(), **optimizer_params)
+        optimizerD = Adam(discriminator.parameters(), **optimizer_params)
+
+        st_ed = None
+        classifier=None
+        optimizerC= None
+        if target_index != None:
+            st_ed= get_st_ed(target_index,self.transformer.output_info)
+            classifier = Classifier(data_dim,self.class_dim,st_ed).to(self.device)
+            optimizerC = optim.Adam(classifier.parameters(),**optimizer_params)
+        
+        
+        self.generator.apply(weights_init)
+        discriminator.apply(weights_init)
+
+        self.Gtransformer = ImageTransformer(self.gside)       
+        self.Dtransformer = ImageTransformer(self.dside)
+        
+        epsilon = 0
+        epoch = 0
+        steps = 0
+        ci = 1
+        
+        for i in tqdm(range(self.epochs)):
+				
+            
+            for _ in range(ci):
+                noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+                condvec = self.cond_generator.sample_train(self.batch_size)
+
+                c, m, col, opt = condvec
+                c = torch.from_numpy(c).to(self.device)
+                m = torch.from_numpy(m).to(self.device)
+                noisez = torch.cat([noisez, c], dim=1)
+                noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                
+                perm = np.arange(self.batch_size)
+                np.random.shuffle(perm)
+                real = data_sampler.sample(self.batch_size, col[perm], opt[perm])
+                c_perm = c[perm]
+                
+                real = torch.from_numpy(real.astype('float32')).to(self.device)
+                
+                fake = self.generator(noisez)
+                faket = self.Gtransformer.inverse_transform(fake)
+                fakeact = apply_activate(faket, self.transformer.output_info)
+                
+                fake_cat = torch.cat([fakeact, c], dim=1)
+                real_cat = torch.cat([real, c_perm], dim=1)
+                
+                real_cat_d = self.Dtransformer.transform(real_cat)
+                fake_cat_d = self.Dtransformer.transform(fake_cat)
+                
+                optimizerD.zero_grad()
+                
+                d_real,_ = discriminator(real_cat_d)
+                
+
+                d_real = -torch.mean(d_real)
+                d_real.backward() 
+                
+
+                d_fake,_ = discriminator(fake_cat_d)
+                
+                d_fake = torch.mean(d_fake)
+
+                d_fake.backward() 
+                
+                pen = calc_gradient_penalty_slerp(discriminator, real_cat, fake_cat,  self.Dtransformer , self.device)
+
+                pen.backward()
+            
+                optimizerD.step()
+                
+            noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+            
+            condvec = self.cond_generator.sample_train(self.batch_size)
+
+            c, m, col, opt = condvec
+            c = torch.from_numpy(c).to(self.device)
+            m = torch.from_numpy(m).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+
+            optimizerG.zero_grad()
+
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket, self.transformer.output_info)
+
+            fake_cat = torch.cat([fakeact, c], dim=1) 
+            fake_cat = self.Dtransformer.transform(fake_cat)
+                
+            y_fake,info_fake = discriminator(fake_cat)
+            
+            cross_entropy = cond_loss(faket, self.transformer.output_info, c, m)
+
+            _,info_real = discriminator(real_cat_d)
+            
+
+            g = -torch.mean(y_fake) + cross_entropy
+            g.backward(retain_graph=True)
+            loss_mean = torch.norm(torch.mean(info_fake.view(self.batch_size,-1), dim=0) - torch.mean(info_real.view(self.batch_size,-1), dim=0), 1)
+            loss_std = torch.norm(torch.std(info_fake.view(self.batch_size,-1), dim=0) - torch.std(info_real.view(self.batch_size,-1), dim=0), 1)
+            loss_info = loss_mean + loss_std 
+            loss_info.backward()
+            optimizerG.step()
+
+
+            if problem_type:
+                        
+                fake = self.generator(noisez)
+                
+                faket = self.Gtransformer.inverse_transform(fake)
+                
+                fakeact = apply_activate(faket, self.transformer.output_info)
+                
+                real_pre, real_label = classifier(real)
+                fake_pre, fake_label = classifier(fakeact)
+                    
+                c_loss = CrossEntropyLoss() 
+                
+                if (st_ed[1] - st_ed[0])==1:
+                    c_loss= SmoothL1Loss()
+                    real_label = real_label.type_as(real_pre)
+                    fake_label = fake_label.type_as(fake_pre)
+                    real_label = torch.reshape(real_label,real_pre.size())
+                    fake_label = torch.reshape(fake_label,fake_pre.size())
+                    
+                
+                elif (st_ed[1] - st_ed[0])==2:
+                    c_loss = BCELoss()
+                    real_label = real_label.type_as(real_pre)
+                    fake_label = fake_label.type_as(fake_pre)
+
+                loss_cc = c_loss(real_pre, real_label)
+                loss_cg = c_loss(fake_pre, fake_label)
+
+                optimizerG.zero_grad()
+                loss_cg.backward()
+                optimizerG.step()
+
+                optimizerC.zero_grad()
+                loss_cc.backward()
+                optimizerC.step()
+                            
+
+            
+   
+    @torch.no_grad()
+    def sample(self, n, seed=0):
+
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        sample_batch_size = 8092
+        self.generator.eval()
+
+        output_info = self.transformer.output_info
+        steps = n // sample_batch_size + 1
+
+        data = []
+        
+        for i in range(steps):
+            noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+            condvec = self.cond_generator.sample(self.batch_size)
+            c = condvec
+            c = torch.from_numpy(c).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket,output_info)
+            data.append(fakeact.detach().cpu().numpy())
+
+        data = np.concatenate(data, axis=0)
+        result,resample = self.transformer.inverse_transform(data)
+        
+        while len(result) < n:
+            data_resample = []    
+            steps_left = resample// self.batch_size + 1
+            
+            for i in range(steps_left):
+                noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+                condvec = self.cond_generator.sample(self.batch_size)
+                c = condvec
+                c = torch.from_numpy(c).to(self.device)
+                noisez = torch.cat([noisez, c], dim=1)
+                noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                    
+                fake = self.generator(noisez)
+                faket = self.Gtransformer.inverse_transform(fake)
+                fakeact = apply_activate(faket, output_info)
+                data_resample.append(fakeact.detach().cpu().numpy())
+
+            data_resample = np.concatenate(data_resample, axis=0)
+
+            res,resample = self.transformer.inverse_transform(data_resample)
+            result  = np.concatenate([result,res],axis=0)
+        
+        return result[0:n]
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model copy/synthesizer/transformer.py

@@ -0,0 +1,429 @@
+import numpy as np
+import pandas as pd
+import torch
+from sklearn.mixture import BayesianGaussianMixture
+
+class DataTransformer():
+    
+    def __init__(self, train_data=pd.DataFrame, categorical_list=[], mixed_dict={}, general_list=[], non_categorical_list=[], n_clusters=10, eps=0.005):
+        self.meta = None
+        self.n_clusters = n_clusters
+        self.eps = eps
+        self.train_data = train_data
+        self.categorical_columns= categorical_list
+        self.mixed_columns= mixed_dict
+        self.general_columns = general_list
+        self.non_categorical_columns= non_categorical_list
+        
+    def get_metadata(self):
+        
+        meta = []
+    
+        for index in range(self.train_data.shape[1]):
+            column = self.train_data.iloc[:,index]
+            if index in self.categorical_columns:
+                if index in self.non_categorical_columns:
+                    meta.append({
+                      "name": index,
+                      "type": "continuous",
+                      "min": column.min(),
+                      "max": column.max(),
+                    })
+                else:
+                    mapper = column.value_counts().index.tolist()
+                    meta.append({
+                        "name": index,
+                        "type": "categorical",
+                        "size": len(mapper),
+                        "i2s": mapper
+                    })
+
+            elif index in self.mixed_columns.keys():
+                meta.append({
+                    "name": index,
+                    "type": "mixed",
+                    "min": column.min(),
+                    "max": column.max(),
+                    "modal": self.mixed_columns[index]
+                })
+            else:
+                meta.append({
+                    "name": index,
+                    "type": "continuous",
+                    "min": column.min(),
+                    "max": column.max(),
+                })            
+
+        return meta
+
+    def fit(self):
+        data = self.train_data.values
+        self.meta = self.get_metadata()
+        model = []
+        self.ordering = []
+        self.output_info = []
+        self.output_dim = 0
+        self.components = []
+        self.filter_arr = []
+        for id_, info in enumerate(self.meta):
+            if info['type'] == "continuous":
+                if id_ not in self.general_columns:
+                  gm = BayesianGaussianMixture(
+                      n_components = self.n_clusters, 
+                      weight_concentration_prior_type='dirichlet_process',
+                      weight_concentration_prior=0.001, 
+                      max_iter=100,n_init=1, random_state=42)
+                  gm.fit(data[:, id_].reshape([-1, 1]))
+                  mode_freq = (pd.Series(gm.predict(data[:, id_].reshape([-1, 1]))).value_counts().keys())
+                  model.append(gm)
+                  old_comp = gm.weights_ > self.eps
+                  comp = []
+                  for i in range(self.n_clusters):
+                      if (i in (mode_freq)) & old_comp[i]:
+                          comp.append(True)
+                      else:
+                          comp.append(False)
+                  self.components.append(comp) 
+                  self.output_info += [(1, 'tanh','no_g'), (np.sum(comp), 'softmax')]
+                  self.output_dim += 1 + np.sum(comp)
+                else:
+                  model.append(None)
+                  self.components.append(None)
+                  self.output_info += [(1, 'tanh','yes_g')]
+                  self.output_dim += 1
+            
+            elif info['type'] == "mixed":
+                
+                gm1 = BayesianGaussianMixture(
+                    n_components = self.n_clusters, 
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, max_iter=100,
+                    n_init=1,random_state=42)
+                gm2 = BayesianGaussianMixture(
+                    n_components = self.n_clusters,
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, max_iter=100,
+                    n_init=1,random_state=42)
+                
+                gm1.fit(data[:, id_].reshape([-1, 1]))
+                
+                filter_arr = []
+                for element in data[:, id_]:
+                    if element not in info['modal']:
+                        filter_arr.append(True)
+                    else:
+                        filter_arr.append(False)
+               
+                gm2.fit(data[:, id_][filter_arr].reshape([-1, 1]))
+                mode_freq = (pd.Series(gm2.predict(data[:, id_][filter_arr].reshape([-1, 1]))).value_counts().keys())
+                self.filter_arr.append(filter_arr)
+                model.append((gm1,gm2))
+               
+                old_comp = gm2.weights_ > self.eps
+                  
+                comp = []
+                  
+                for i in range(self.n_clusters):
+                    if (i in (mode_freq)) & old_comp[i]:
+                        comp.append(True)
+                    else:
+                        comp.append(False)
+
+                self.components.append(comp)
+
+                self.output_info += [(1, 'tanh',"no_g"), (np.sum(comp) + len(info['modal']), 'softmax')]
+                self.output_dim += 1 + np.sum(comp) + len(info['modal'])
+            else:
+                model.append(None)
+                self.components.append(None)
+                self.output_info += [(info['size'], 'softmax')]
+                self.output_dim += info['size']
+        self.model = model
+
+    def transform(self, data, ispositive = False, positive_list = None):
+        values = []
+        mixed_counter = 0
+        for id_, info in enumerate(self.meta):
+            current = data[:, id_]
+            if info['type'] == "continuous":
+                if id_ not in self.general_columns: 
+                  current = current.reshape([-1, 1])
+                  means = self.model[id_].means_.reshape((1, self.n_clusters))
+                  stds = np.sqrt(self.model[id_].covariances_).reshape((1, self.n_clusters))
+                  features = np.empty(shape=(len(current),self.n_clusters))
+                  if ispositive == True:
+                      if id_ in positive_list:
+                          features = np.abs(current - means) / (4 * stds)
+                  else:
+                      features = (current - means) / (4 * stds)
+
+                  probs = self.model[id_].predict_proba(current.reshape([-1, 1]))
+                  n_opts = sum(self.components[id_])
+                  features = features[:, self.components[id_]]
+                  probs = probs[:, self.components[id_]]
+
+                  opt_sel = np.zeros(len(data), dtype='int')
+                  for i in range(len(data)):
+                      pp = probs[i] + 1e-6
+                      pp = pp / sum(pp)
+                      opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)
+
+                  idx = np.arange((len(features)))
+                  features = features[idx, opt_sel].reshape([-1, 1])
+                  features = np.clip(features, -.99, .99) 
+                  probs_onehot = np.zeros_like(probs)
+                  probs_onehot[np.arange(len(probs)), opt_sel] = 1
+
+                  re_ordered_phot = np.zeros_like(probs_onehot)
+                  
+                  col_sums = probs_onehot.sum(axis=0)
+                  
+
+                  n = probs_onehot.shape[1]
+                  largest_indices = np.argsort(-1*col_sums)[:n]
+                  self.ordering.append(largest_indices)
+                  for id,val in enumerate(largest_indices):
+                      re_ordered_phot[:,id] = probs_onehot[:,val]
+                
+                  
+                  values += [features, re_ordered_phot]
+                  
+                else:
+                  
+                  self.ordering.append(None)
+                  
+                  if id_ in self.non_categorical_columns:
+                    info['min'] = -1e-3
+                    info['max'] = info['max'] + 1e-3
+                    
+                  current = (current - (info['min'])) / (info['max'] - info['min'])
+                  current = current * 2 - 1 
+                  current = current.reshape([-1, 1])
+                  values.append(current)
+
+            elif info['type'] == "mixed":
+                    
+                means_0 = self.model[id_][0].means_.reshape([-1])
+                stds_0 = np.sqrt(self.model[id_][0].covariances_).reshape([-1])
+
+                zero_std_list = []
+                means_needed = []
+                stds_needed = []
+
+                for mode in info['modal']:
+                    if mode!=-9999999:
+                        dist = []
+                        for idx,val in enumerate(list(means_0.flatten())):
+                            dist.append(abs(mode-val))
+                        index_min = np.argmin(np.array(dist))
+                        zero_std_list.append(index_min)
+                    else: continue
+
+                for idx in zero_std_list:
+                    means_needed.append(means_0[idx])
+                    stds_needed.append(stds_0[idx])
+                
+                
+                mode_vals = []
+
+                for i,j,k in zip(info['modal'],means_needed,stds_needed):
+                    this_val  = np.abs(i - j) / (4*k)
+                    mode_vals.append(this_val)
+                
+                if -9999999 in info["modal"]:
+                    mode_vals.append(0)
+                
+                current = current.reshape([-1, 1])
+                filter_arr = self.filter_arr[mixed_counter]
+                current = current[filter_arr]
+                
+                means = self.model[id_][1].means_.reshape((1, self.n_clusters))
+                stds = np.sqrt(self.model[id_][1].covariances_).reshape((1, self.n_clusters))
+                features = np.empty(shape=(len(current),self.n_clusters))
+                if ispositive == True:
+                    if id_ in positive_list:
+                        features = np.abs(current - means) / (4 * stds)
+                else:
+                    features = (current - means) / (4 * stds)
+
+                probs = self.model[id_][1].predict_proba(current.reshape([-1, 1]))
+
+                n_opts = sum(self.components[id_]) # 8
+                features = features[:, self.components[id_]]
+                probs = probs[:, self.components[id_]]
+                
+                opt_sel = np.zeros(len(current), dtype='int')
+                for i in range(len(current)):
+                    pp = probs[i] + 1e-6
+                    pp = pp / sum(pp)
+                    opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)
+                idx = np.arange((len(features)))
+                features = features[idx, opt_sel].reshape([-1, 1])
+                features = np.clip(features, -.99, .99)
+                probs_onehot = np.zeros_like(probs)
+                probs_onehot[np.arange(len(probs)), opt_sel] = 1
+                extra_bits = np.zeros([len(current), len(info['modal'])])
+                temp_probs_onehot = np.concatenate([extra_bits,probs_onehot], axis = 1)
+                final = np.zeros([len(data), 1 + probs_onehot.shape[1] + len(info['modal'])])
+                features_curser = 0
+                for idx, val in enumerate(data[:, id_]):
+                    if val in info['modal']:
+                        category_ = list(map(info['modal'].index, [val]))[0]
+                        final[idx, 0] = mode_vals[category_]
+                        final[idx, (category_+1)] = 1
+                    
+                    else:
+                        final[idx, 0] = features[features_curser]
+                        final[idx, (1+len(info['modal'])):] = temp_probs_onehot[features_curser][len(info['modal']):]
+                        features_curser = features_curser + 1
+               
+                just_onehot = final[:,1:]
+                re_ordered_jhot= np.zeros_like(just_onehot)
+                n = just_onehot.shape[1]
+                col_sums = just_onehot.sum(axis=0)
+                largest_indices = np.argsort(-1*col_sums)[:n]
+                self.ordering.append(largest_indices)
+                for id,val in enumerate(largest_indices):
+                      re_ordered_jhot[:,id] = just_onehot[:,val]
+                final_features = final[:,0].reshape([-1, 1])
+                values += [final_features, re_ordered_jhot]
+                mixed_counter = mixed_counter + 1
+    
+            else:
+                self.ordering.append(None)
+                col_t = np.zeros([len(data), info['size']])
+                idx = list(map(info['i2s'].index, current))
+                col_t[np.arange(len(data)), idx] = 1
+                values.append(col_t)
+                
+        return np.concatenate(values, axis=1)
+
+    def inverse_transform(self, data):
+        data_t = np.zeros([len(data), len(self.meta)])
+        invalid_ids = []
+        st = 0
+        for id_, info in enumerate(self.meta):
+            if info['type'] == "continuous":
+                if id_ not in self.general_columns:
+                  u = data[:, st]
+                  v = data[:, st + 1:st + 1 + np.sum(self.components[id_])]
+                  order = self.ordering[id_] 
+                  v_re_ordered = np.zeros_like(v)
+
+                  for id,val in enumerate(order):
+                      v_re_ordered[:,val] = v[:,id]
+                  
+                  v = v_re_ordered
+
+                  u = np.clip(u, -1, 1)
+                  v_t = np.ones((data.shape[0], self.n_clusters)) * -100
+                  v_t[:, self.components[id_]] = v
+                  v = v_t
+                  st += 1 + np.sum(self.components[id_])
+                  means = self.model[id_].means_.reshape([-1])
+                  stds = np.sqrt(self.model[id_].covariances_).reshape([-1])
+                  p_argmax = np.argmax(v, axis=1)
+                  std_t = stds[p_argmax]
+                  mean_t = means[p_argmax]
+                  tmp = u * 4 * std_t + mean_t
+                                    
+                  for idx,val in enumerate(tmp):
+                     if (val < info["min"]) | (val > info['max']):
+                         invalid_ids.append(idx)
+                  
+                  if id_ in self.non_categorical_columns:
+                    
+                    tmp = np.round(tmp)
+                  
+                  data_t[:, id_] = tmp
+
+                else:
+                  u = data[:, st]
+                  u = (u + 1) / 2
+                  u = np.clip(u, 0, 1)
+                  u = u * (info['max'] - info['min']) + info['min']
+                  if id_ in self.non_categorical_columns:
+                    data_t[:, id_] = np.round(u)
+                  else: data_t[:, id_] = u
+
+                  st += 1
+
+            elif info['type'] == "mixed":
+
+                u = data[:, st]
+                full_v = data[:,(st+1):(st+1)+len(info['modal'])+np.sum(self.components[id_])]
+                order = self.ordering[id_]
+                full_v_re_ordered = np.zeros_like(full_v)
+
+                for id,val in enumerate(order):
+                    full_v_re_ordered[:,val] = full_v[:,id]
+                  
+                full_v = full_v_re_ordered
+
+                
+                mixed_v = full_v[:,:len(info['modal'])]
+                v = full_v[:,-np.sum(self.components[id_]):]
+
+                u = np.clip(u, -1, 1)
+                v_t = np.ones((data.shape[0], self.n_clusters)) * -100
+                v_t[:, self.components[id_]] = v
+                v = np.concatenate([mixed_v,v_t], axis=1)
+
+                st += 1 + np.sum(self.components[id_]) + len(info['modal'])
+                means = self.model[id_][1].means_.reshape([-1]) 
+                stds = np.sqrt(self.model[id_][1].covariances_).reshape([-1]) 
+                p_argmax = np.argmax(v, axis=1)
+
+                result = np.zeros_like(u)
+
+                for idx in range(len(data)):
+                    if p_argmax[idx] < len(info['modal']):
+                        argmax_value = p_argmax[idx]
+                        result[idx] = float(list(map(info['modal'].__getitem__, [argmax_value]))[0])
+                    else:
+                        std_t = stds[(p_argmax[idx]-len(info['modal']))]
+                        mean_t = means[(p_argmax[idx]-len(info['modal']))]
+                        result[idx] = u[idx] * 4 * std_t + mean_t
+                        
+                for idx,val in enumerate(result):
+                     if (val < info["min"]) | (val > info['max']):
+                         invalid_ids.append(idx)
+
+                data_t[:, id_] = result
+
+            else:
+                current = data[:, st:st + info['size']]
+                st += info['size']
+                idx = np.argmax(current, axis=1)
+                data_t[:, id_] = list(map(info['i2s'].__getitem__, idx))
+            
+            
+        invalid_ids = np.unique(np.array(invalid_ids)) 
+        all_ids = np.arange(0,len(data))
+        valid_ids = list(set(all_ids) - set(invalid_ids))
+            
+        return data_t[valid_ids],len(invalid_ids)
+
+
+class ImageTransformer():
+
+    def __init__(self, side):
+    
+        self.height = side
+            
+    def transform(self, data):
+
+        if self.height * self.height > len(data[0]):
+            
+            padding = torch.zeros((len(data), self.height * self.height - len(data[0]))).to(data.device)
+            data = torch.cat([data, padding], axis=1)
+
+        return data.view(-1, 1, self.height, self.height)
+
+    def inverse_transform(self, data):
+        
+        data = data.view(-1, self.height * self.height)
+
+        return data
+
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model/ctabgan.py

@@ -0,0 +1,72 @@
+"""
+Generative model training algorithm based on the CTABGANSynthesiser
+
+"""
+import pandas as pd
+import time
+from model.pipeline.data_preparation import DataPrep
+from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+class CTABGAN():
+
+    def __init__(self,
+                 df,
+                 test_ratio = 0.20,
+                 categorical_columns = [],
+                 log_columns = [],
+                 mixed_columns= {},
+                 general_columns = [],
+                 non_categorical_columns = [],
+                 integer_columns = [],
+                 problem_type= {},
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=100,
+                 num_channels=64,
+                 l2scale=1e-5,
+                 batch_size=500,
+                 epochs=150,
+                 lr=2e-4,
+                 device="cpu"):
+
+        self.__name__ = 'CTABGAN'
+              
+        self.synthesizer = CTABGANSynthesizer(
+                class_dim=class_dim,
+                random_dim=random_dim,
+                num_channels=num_channels,
+                l2scale=l2scale,
+                lr=lr,
+                batch_size=batch_size,
+                epochs=epochs,
+                device=device
+        )
+        self.raw_df = df
+        self.test_ratio = test_ratio
+        self.categorical_columns = categorical_columns
+        self.log_columns = log_columns
+        self.mixed_columns = mixed_columns
+        self.general_columns = general_columns
+        self.non_categorical_columns = non_categorical_columns
+        self.integer_columns = integer_columns
+        self.problem_type = problem_type
+                
+    def fit(self):
+        
+        start_time = time.time()
+        self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.general_columns,self.non_categorical_columns,self.integer_columns,self.problem_type,self.test_ratio)
+        self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], mixed = self.data_prep.column_types["mixed"],
+        general = self.data_prep.column_types["general"], non_categorical = self.data_prep.column_types["non_categorical"], type=self.problem_type)
+        end_time = time.time()
+        print('Finished training in',end_time-start_time," seconds.")
+
+
+    def generate_samples(self, num_samples, seed=0):
+        
+        sample = self.synthesizer.sample(num_samples, seed) 
+        sample_df = self.data_prep.inverse_prep(sample)
+        
+        return sample_df

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model/eval/evaluation.py

@@ -0,0 +1,193 @@
+import numpy as np
+import pandas as pd 
+from sklearn import metrics
+from sklearn import model_selection
+from sklearn.preprocessing import MinMaxScaler,StandardScaler
+from sklearn.neural_network import MLPClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn import svm,tree
+from sklearn.ensemble import RandomForestClassifier
+from dython.nominal import compute_associations
+from scipy.stats import wasserstein_distance
+from scipy.spatial import distance
+import warnings
+
+warnings.filterwarnings("ignore")
+
+def supervised_model_training(x_train, y_train, x_test, 
+                              y_test, model_name):
+  
+     
+  if model_name == 'lr':
+    model  = LogisticRegression(random_state=42,max_iter=500) 
+  elif model_name == 'svm':
+    model  = svm.SVC(random_state=42,probability=True)
+  elif model_name == 'dt':
+    model  = tree.DecisionTreeClassifier(random_state=42)
+  elif model_name == 'rf':      
+    model = RandomForestClassifier(random_state=42)
+  elif model_name == "mlp":
+    model = MLPClassifier(random_state=42,max_iter=100)
+  
+  model.fit(x_train, y_train)
+  pred = model.predict(x_test)
+
+  if len(np.unique(y_train))>2:
+    predict = model.predict_proba(x_test)        
+    acc = metrics.accuracy_score(y_test,pred)*100
+    auc = metrics.roc_auc_score(y_test, predict,average="weighted",multi_class="ovr")
+    f1_score = metrics.precision_recall_fscore_support(y_test, pred,average="weighted")[2]
+    return [acc, auc,f1_score] 
+
+  else:
+    predict = model.predict_proba(x_test)[:,1]    
+    acc = metrics.accuracy_score(y_test,pred)*100
+    auc = metrics.roc_auc_score(y_test, predict)
+    f1_score = metrics.precision_recall_fscore_support(y_test,pred)[2].mean()
+    return [acc, auc,f1_score] 
+
+
+def get_utility_metrics(real_path,fake_paths,scaler="MinMax",classifiers=["lr","dt","rf","mlp"],test_ratio=.20):
+
+    data_real = pd.read_csv(real_path).to_numpy()
+    data_dim = data_real.shape[1]
+
+    data_real_y = data_real[:,-1]
+    data_real_X = data_real[:,:data_dim-1]
+    X_train_real, X_test_real, y_train_real, y_test_real = model_selection.train_test_split(data_real_X ,data_real_y, test_size=test_ratio, stratify=data_real_y,random_state=42) 
+
+    if scaler=="MinMax":
+        scaler_real = MinMaxScaler()
+    else:
+        scaler_real = StandardScaler()
+        
+    scaler_real.fit(data_real_X)
+    X_train_real_scaled = scaler_real.transform(X_train_real)
+    X_test_real_scaled = scaler_real.transform(X_test_real)
+
+    all_real_results = []
+    for classifier in classifiers:
+      real_results = supervised_model_training(X_train_real_scaled,y_train_real,X_test_real_scaled,y_test_real,classifier)
+      all_real_results.append(real_results)
+      
+    all_fake_results_avg = []
+    
+    for fake_path in fake_paths:
+      data_fake  = pd.read_csv(fake_path).to_numpy()
+      data_fake_y = data_fake[:,-1]
+      data_fake_X = data_fake[:,:data_dim-1]
+      X_train_fake, _ , y_train_fake, _ = model_selection.train_test_split(data_fake_X ,data_fake_y, test_size=test_ratio, stratify=data_fake_y,random_state=42) 
+
+      if scaler=="MinMax":
+        scaler_fake = MinMaxScaler()
+      else:
+        scaler_fake = StandardScaler()
+      
+      scaler_fake.fit(data_fake_X)
+      
+      X_train_fake_scaled = scaler_fake.transform(X_train_fake)
+      
+      all_fake_results = []
+      for classifier in classifiers:
+        fake_results = supervised_model_training(X_train_fake_scaled,y_train_fake,X_test_real_scaled,y_test_real,classifier)
+        all_fake_results.append(fake_results)
+
+      all_fake_results_avg.append(all_fake_results)
+    
+    diff_results = np.array(all_real_results)- np.array(all_fake_results_avg).mean(axis=0)
+
+    return diff_results
+
+def stat_sim(real_path,fake_path,cat_cols=None):
+    
+    Stat_dict={}
+    
+    real = pd.read_csv(real_path)
+    fake = pd.read_csv(fake_path)
+
+    really = real.copy()
+    fakey = fake.copy()
+
+    real_corr = compute_associations(real, nominal_columns=cat_cols)
+
+    fake_corr = compute_associations(fake, nominal_columns=cat_cols)
+
+    corr_dist = np.linalg.norm(real_corr - fake_corr)
+    
+    cat_stat = []
+    num_stat = []
+    
+    for column in real.columns:
+        
+        if column in cat_cols:
+
+            real_pdf=(really[column].value_counts()/really[column].value_counts().sum())
+            fake_pdf=(fakey[column].value_counts()/fakey[column].value_counts().sum())
+            categories = (fakey[column].value_counts()/fakey[column].value_counts().sum()).keys().tolist()
+            sorted_categories = sorted(categories)
+            
+            real_pdf_values = [] 
+            fake_pdf_values = []
+
+            for i in sorted_categories:
+                real_pdf_values.append(real_pdf[i])
+                fake_pdf_values.append(fake_pdf[i])
+            
+            if len(real_pdf)!=len(fake_pdf):
+                zero_cats = set(really[column].value_counts().keys())-set(fakey[column].value_counts().keys())
+                for z in zero_cats:
+                    real_pdf_values.append(real_pdf[z])
+                    fake_pdf_values.append(0)
+            Stat_dict[column]=(distance.jensenshannon(real_pdf_values,fake_pdf_values, 2.0))
+            cat_stat.append(Stat_dict[column])        
+        else:
+            scaler = MinMaxScaler()
+            scaler.fit(real[column].values.reshape(-1,1))
+            l1 = scaler.transform(real[column].values.reshape(-1,1)).flatten()
+            l2 = scaler.transform(fake[column].values.reshape(-1,1)).flatten()
+            Stat_dict[column]= (wasserstein_distance(l1,l2))
+            num_stat.append(Stat_dict[column])
+
+    return [np.mean(num_stat),np.mean(cat_stat),corr_dist]
+
+def privacy_metrics(real_path,fake_path,data_percent=15):
+    
+    real = pd.read_csv(real_path).drop_duplicates(keep=False)
+    fake = pd.read_csv(fake_path).drop_duplicates(keep=False)
+
+    real_refined = real.sample(n=int(len(real)*(.01*data_percent)), random_state=42).to_numpy()
+    fake_refined = fake.sample(n=int(len(fake)*(.01*data_percent)), random_state=42).to_numpy()
+
+    scalerR = StandardScaler()
+    scalerR.fit(real_refined)
+    scalerF = StandardScaler()
+    scalerF.fit(fake_refined)
+    df_real_scaled = scalerR.transform(real_refined)
+    df_fake_scaled = scalerF.transform(fake_refined)
+    
+    dist_rf = metrics.pairwise_distances(df_real_scaled, Y=df_fake_scaled, metric='minkowski', n_jobs=-1)
+    dist_rr = metrics.pairwise_distances(df_real_scaled, Y=None, metric='minkowski', n_jobs=-1)
+    rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1)
+    dist_ff = metrics.pairwise_distances(df_fake_scaled, Y=None, metric='minkowski', n_jobs=-1)
+    rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) 
+    smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))]
+    smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))]       
+    smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))]
+    smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))]
+    smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))]
+    smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))]
+    nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr])
+    nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff])
+    nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf])
+    nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5)
+    nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5)
+    nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5)
+
+    min_dist_rf = np.array([i[0] for i in smallest_two_rf])
+    fifth_perc_rf = np.percentile(min_dist_rf,5)
+    min_dist_rr = np.array([i[0] for i in smallest_two_rr])
+    fifth_perc_rr = np.percentile(min_dist_rr,5)
+    min_dist_ff = np.array([i[0] for i in smallest_two_ff])
+    fifth_perc_ff = np.percentile(min_dist_ff,5)
+    
+    return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6)    

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model/pipeline/data_preparation.py

@@ -0,0 +1,131 @@
+import numpy as np
+import pandas as pd
+from sklearn import preprocessing
+from sklearn import model_selection
+
+class DataPrep(object):
+  
+    def __init__(self, raw_df: pd.DataFrame, categorical: list, log:list, mixed:dict, general:list, non_categorical:list, integer:list, type:dict, test_ratio:float):
+        
+        
+        self.categorical_columns = categorical
+        self.log_columns = log
+        self.mixed_columns = mixed
+        self.general_columns = general
+        self.non_categorical_columns = non_categorical
+        self.integer_columns = integer
+        self.column_types = dict()
+        self.column_types["categorical"] = []
+        self.column_types["mixed"] = {}
+        self.column_types["general"] = []
+        self.column_types["non_categorical"] = []
+        self.lower_bounds = {}
+        self.label_encoder_list = []
+        
+        target_col = list(type.values())[0]
+        if target_col is not None:
+            y_real = raw_df[target_col]
+            X_real = raw_df.drop(columns=[target_col])
+            # X_train_real, _, y_train_real, _ = model_selection.train_test_split(X_real ,y_real, test_size=test_ratio, stratify=y_real,random_state=42)
+            X_train_real, y_train_real = X_real, y_real
+        
+            X_train_real[target_col]= y_train_real
+
+            self.df = X_train_real
+        else:
+            self.df = raw_df
+
+        self.df = self.df.replace(r' ', np.nan)
+        self.df = self.df.fillna('empty')
+       
+        all_columns= set(self.df.columns)
+        irrelevant_missing_columns = set(self.categorical_columns)
+        relevant_missing_columns = list(all_columns - irrelevant_missing_columns)
+        
+        for i in relevant_missing_columns:
+            if i in self.log_columns:
+                if "empty" in list(self.df[i].values):
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x)
+                    self.mixed_columns[i] = [-9999999]
+            elif i in list(self.mixed_columns.keys()):
+                if "empty" in list(self.df[i].values):
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x )
+                    self.mixed_columns[i].append(-9999999)
+            else:
+                if "empty" in list(self.df[i].values):   
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x)
+                    self.mixed_columns[i] = [-9999999]
+        
+        if self.log_columns:
+            for log_column in self.log_columns:
+                valid_indices = []
+                for idx,val in enumerate(self.df[log_column].values):
+                    if val!=-9999999:
+                        valid_indices.append(idx)
+                eps = 1
+                lower = np.min(self.df[log_column].iloc[valid_indices].values)
+                self.lower_bounds[log_column] = lower
+                if lower>0: 
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x) if x!=-9999999 else -9999999)
+                elif lower == 0:
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x+eps) if x!=-9999999 else -9999999) 
+                else:
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x-lower+eps) if x!=-9999999 else -9999999)
+
+        for column_index, column in enumerate(self.df.columns):            
+            if column in self.categorical_columns:        
+                label_encoder = preprocessing.LabelEncoder()
+                self.df[column] = self.df[column].astype(str)
+                label_encoder.fit(self.df[column])
+                current_label_encoder = dict()
+                current_label_encoder['column'] = column
+                current_label_encoder['label_encoder'] = label_encoder
+                transformed_column = label_encoder.transform(self.df[column])
+                self.df[column] = transformed_column
+                self.label_encoder_list.append(current_label_encoder)
+                self.column_types["categorical"].append(column_index)
+
+                if column in self.general_columns:
+                    self.column_types["general"].append(column_index)
+            
+                if column in self.non_categorical_columns:
+                    self.column_types["non_categorical"].append(column_index)
+            
+            elif column in self.mixed_columns:
+                self.column_types["mixed"][column_index] = self.mixed_columns[column]
+            
+            elif column in self.general_columns:
+                self.column_types["general"].append(column_index)
+            
+
+        super().__init__()
+        
+    def inverse_prep(self, data, eps=1):
+        
+        df_sample = pd.DataFrame(data,columns=self.df.columns)
+     
+        for i in range(len(self.label_encoder_list)):
+            le = self.label_encoder_list[i]["label_encoder"]
+            df_sample[self.label_encoder_list[i]["column"]] = df_sample[self.label_encoder_list[i]["column"]].astype(int)
+            df_sample[self.label_encoder_list[i]["column"]] = le.inverse_transform(df_sample[self.label_encoder_list[i]["column"]])
+
+        if self.log_columns:
+            for i in df_sample:
+                if i in self.log_columns:
+                    lower_bound = self.lower_bounds[i]
+                    if lower_bound>0:
+                        df_sample[i].apply(lambda x: np.exp(x)) 
+                    elif lower_bound==0:
+                        df_sample[i] = df_sample[i].apply(lambda x: np.ceil(np.exp(x)-eps) if (np.exp(x)-eps) < 0 else (np.exp(x)-eps))
+                    else: 
+                        df_sample[i] = df_sample[i].apply(lambda x: np.exp(x)-eps+lower_bound)
+
+        if self.integer_columns:
+            for column in self.integer_columns:
+                df_sample[column]= (np.round(df_sample[column].values))
+                df_sample[column] = df_sample[column].astype(int)
+
+        df_sample.replace(-9999999, np.nan,inplace=True)
+        df_sample.replace('empty', np.nan,inplace=True)
+
+        return df_sample

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model/privacy_utils/rdp_accountant.py

@@ -0,0 +1,280 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import sys
+
+import numpy as np
+from scipy import special
+import six
+
+########################
+# LOG-SPACE ARITHMETIC #
+########################
+
+
+def _log_add(logx, logy):
+  """Add two numbers in the log space."""
+  a, b = min(logx, logy), max(logx, logy)
+  if a == -np.inf:  # adding 0
+    return b
+  # Use exp(a) + exp(b) = (exp(a - b) + 1) * exp(b)
+  return math.log1p(math.exp(a - b)) + b  # log1p(x) = log(x + 1)
+
+
+def _log_sub(logx, logy):
+  """Subtract two numbers in the log space. Answer must be non-negative."""
+  if logx < logy:
+    raise ValueError("The result of subtraction must be non-negative.")
+  if logy == -np.inf:  # subtracting 0
+    return logx
+  if logx == logy:
+    return -np.inf  # 0 is represented as -np.inf in the log space.
+
+  try:
+    # Use exp(x) - exp(y) = (exp(x - y) - 1) * exp(y).
+    return math.log(math.expm1(logx - logy)) + logy  # expm1(x) = exp(x) - 1
+  except OverflowError:
+    return logx
+
+
+def _log_print(logx):
+  """Pretty print."""
+  if logx < math.log(sys.float_info.max):
+    return "{}".format(math.exp(logx))
+  else:
+    return "exp({})".format(logx)
+
+
+def _compute_log_a_int(q, sigma, alpha):
+  """Compute log(A_alpha) for integer alpha. 0 < q < 1."""
+  assert isinstance(alpha, six.integer_types)
+
+  # Initialize with 0 in the log space.
+  log_a = -np.inf
+
+  for i in range(alpha + 1):
+    log_coef_i = (
+        math.log(special.binom(alpha, i)) + i * math.log(q) +
+        (alpha - i) * math.log(1 - q))
+
+    s = log_coef_i + (i * i - i) / (2 * (sigma**2))
+    log_a = _log_add(log_a, s)
+
+  return float(log_a)
+
+
+def _compute_log_a_frac(q, sigma, alpha):
+  """Compute log(A_alpha) for fractional alpha. 0 < q < 1."""
+  # The two parts of A_alpha, integrals over (-inf,z0] and [z0, +inf), are
+  # initialized to 0 in the log space:
+  log_a0, log_a1 = -np.inf, -np.inf
+  i = 0
+
+  z0 = sigma**2 * math.log(1 / q - 1) + .5
+
+  while True:  # do ... until loop
+    coef = special.binom(alpha, i)
+    log_coef = math.log(abs(coef))
+    j = alpha - i
+
+    log_t0 = log_coef + i * math.log(q) + j * math.log(1 - q)
+    log_t1 = log_coef + j * math.log(q) + i * math.log(1 - q)
+
+    log_e0 = math.log(.5) + _log_erfc((i - z0) / (math.sqrt(2) * sigma))
+    log_e1 = math.log(.5) + _log_erfc((z0 - j) / (math.sqrt(2) * sigma))
+
+    log_s0 = log_t0 + (i * i - i) / (2 * (sigma**2)) + log_e0
+    log_s1 = log_t1 + (j * j - j) / (2 * (sigma**2)) + log_e1
+
+    if coef > 0:
+      log_a0 = _log_add(log_a0, log_s0)
+      log_a1 = _log_add(log_a1, log_s1)
+    else:
+      log_a0 = _log_sub(log_a0, log_s0)
+      log_a1 = _log_sub(log_a1, log_s1)
+
+    i += 1
+    if max(log_s0, log_s1) < -30:
+      break
+
+  return _log_add(log_a0, log_a1)
+
+
+def _compute_log_a(q, sigma, alpha):
+  """Compute log(A_alpha) for any positive finite alpha."""
+  if float(alpha).is_integer():
+    return _compute_log_a_int(q, sigma, int(alpha))
+  else:
+    return _compute_log_a_frac(q, sigma, alpha)
+
+
+def _log_erfc(x):
+  """Compute log(erfc(x)) with high accuracy for large x."""
+  try:
+    return math.log(2) + special.log_ndtr(-x * 2**.5)
+  except NameError:
+    # If log_ndtr is not available, approximate as follows:
+    r = special.erfc(x)
+    if r == 0.0:
+      # Using the Laurent series at infinity for the tail of the erfc function:
+      #     erfc(x) ~ exp(-x^2-.5/x^2+.625/x^4)/(x*pi^.5)
+      # To verify in Mathematica:
+      #     Series[Log[Erfc[x]] + Log[x] + Log[Pi]/2 + x^2, {x, Infinity, 6}]
+      return (-math.log(math.pi) / 2 - math.log(x) - x**2 - .5 * x**-2 +
+              .625 * x**-4 - 37. / 24. * x**-6 + 353. / 64. * x**-8)
+    else:
+      return math.log(r)
+
+
+def _compute_delta(orders, rdp, eps):
+  """Compute delta given a list of RDP values and target epsilon.
+
+  Args:
+    orders: An array (or a scalar) of orders.
+    rdp: A list (or a scalar) of RDP guarantees.
+    eps: The target epsilon.
+
+  Returns:
+    Pair of (delta, optimal_order).
+
+  Raises:
+    ValueError: If input is malformed.
+
+  """
+  orders_vec = np.atleast_1d(orders)
+  rdp_vec = np.atleast_1d(rdp)
+
+  if len(orders_vec) != len(rdp_vec):
+    raise ValueError("Input lists must have the same length.")
+
+  deltas = np.exp((rdp_vec - eps) * (orders_vec - 1))
+  idx_opt = np.argmin(deltas)
+  return min(deltas[idx_opt], 1.), orders_vec[idx_opt]
+
+
+def _compute_eps(orders, rdp, delta):
+  """Compute epsilon given a list of RDP values and target delta.
+
+  Args:
+    orders: An array (or a scalar) of orders.
+    rdp: A list (or a scalar) of RDP guarantees.
+    delta: The target delta.
+
+  Returns:
+    Pair of (eps, optimal_order).
+
+  Raises:
+    ValueError: If input is malformed.
+
+  """
+  orders_vec = np.atleast_1d(orders)
+  rdp_vec = np.atleast_1d(rdp)
+
+  if len(orders_vec) != len(rdp_vec):
+    raise ValueError("Input lists must have the same length.")
+
+  eps = rdp_vec - math.log(delta) / (orders_vec - 1)
+
+  idx_opt = np.nanargmin(eps)  # Ignore NaNs
+  return eps[idx_opt], orders_vec[idx_opt]
+
+
+def _compute_rdp(q, sigma, alpha):
+  """Compute RDP of the Sampled Gaussian mechanism at order alpha.
+
+  Args:
+    q: The sampling rate.
+    sigma: The std of the additive Gaussian noise.
+    alpha: The order at which RDP is computed.
+
+  Returns:
+    RDP at alpha, can be np.inf.
+  """
+  if q == 0:
+    return 0
+
+  if q == 1.:
+    return alpha / (2 * sigma**2)
+
+  if np.isinf(alpha):
+    return np.inf
+
+  return _compute_log_a(q, sigma, alpha) / (alpha - 1)
+
+
+def compute_rdp(q, noise_multiplier, steps, orders):
+  """Compute RDP of the Sampled Gaussian Mechanism.
+
+  Args:
+    q: The sampling rate.
+    noise_multiplier: The ratio of the standard deviation of the Gaussian noise
+        to the l2-sensitivity of the function to which it is added.
+    steps: The number of steps.
+    orders: An array (or a scalar) of RDP orders.
+
+  Returns:
+    The RDPs at all orders, can be np.inf.
+  """
+  if np.isscalar(orders):
+    rdp = _compute_rdp(q, noise_multiplier, orders)
+  else:
+    rdp = np.array([_compute_rdp(q, noise_multiplier, order)
+                    for order in orders])
+
+  return rdp * steps
+
+
+def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None):
+  """Compute delta (or eps) for given eps (or delta) from RDP values.
+
+  Args:
+    orders: An array (or a scalar) of RDP orders.
+    rdp: An array of RDP values. Must be of the same length as the orders list.
+    target_eps: If not None, the epsilon for which we compute the corresponding
+              delta.
+    target_delta: If not None, the delta for which we compute the corresponding
+              epsilon. Exactly one of target_eps and target_delta must be None.
+
+  Returns:
+    eps, delta, opt_order.
+
+  Raises:
+    ValueError: If target_eps and target_delta are messed up.
+  """
+  if target_eps is None and target_delta is None:
+    raise ValueError(
+        "Exactly one out of eps and delta must be None. (Both are).")
+
+  if target_eps is not None and target_delta is not None:
+    raise ValueError(
+        "Exactly one out of eps and delta must be None. (None is).")
+
+  if target_eps is not None:
+    delta, opt_order = _compute_delta(orders, rdp, target_eps)
+    return target_eps, delta, opt_order
+  else:
+    eps, opt_order = _compute_eps(orders, rdp, target_delta)
+    return eps, target_delta, opt_order
+
+
+def compute_rdp_from_ledger(ledger, orders):
+  """Compute RDP of Sampled Gaussian Mechanism from ledger.
+
+  Args:
+    ledger: A formatted privacy ledger.
+    orders: An array (or a scalar) of RDP orders.
+
+  Returns:
+    RDP at all orders, can be np.inf.
+  """
+  total_rdp = np.zeros_like(orders, dtype=float)
+  for sample in ledger:
+    # Compute equivalent z from l2_clip_bounds and noise stddevs in sample.
+    # See https://arxiv.org/pdf/1812.06210.pdf for derivation of this formula.
+    effective_z = sum([
+        (q.noise_stddev / q.l2_norm_bound)**-2 for q in sample.queries])**-0.5
+    total_rdp += compute_rdp(
+        sample.selection_probability, effective_z, 1, orders)
+  return total_rdp

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/ctabgan_synthesizer.py

@@ -0,0 +1,605 @@
+import numpy as np
+import pandas as pd
+import torch
+import torch.utils.data
+import torch.optim as optim
+from torch.optim import Adam
+from torch.nn import functional as F
+from torch.nn import (Dropout, LeakyReLU, Linear, Module, ReLU, Sequential,
+Conv2d, ConvTranspose2d, Sigmoid, init, BCELoss, CrossEntropyLoss,SmoothL1Loss,LayerNorm)
+from model.synthesizer.transformer import ImageTransformer,DataTransformer
+from model.privacy_utils.rdp_accountant import compute_rdp, get_privacy_spent
+from tqdm import tqdm, trange
+import time
+
+
+class Classifier(Module):
+    def __init__(self,input_dim, dis_dims,st_ed):
+        super(Classifier,self).__init__()
+        dim = input_dim-(st_ed[1]-st_ed[0])
+        seq = []
+        self.str_end = st_ed
+        for item in list(dis_dims):
+            seq += [
+                Linear(dim, item),
+                LeakyReLU(0.2),
+                Dropout(0.5)
+            ]
+            dim = item
+        
+        if (st_ed[1]-st_ed[0])==1:
+            seq += [Linear(dim, 1)]
+        
+        elif (st_ed[1]-st_ed[0])==2:
+            seq += [Linear(dim, 1),Sigmoid()]
+        else:
+            seq += [Linear(dim,(st_ed[1]-st_ed[0]))] 
+        
+        self.seq = Sequential(*seq)
+
+    def forward(self, input):
+        
+        label=None
+        
+        if (self.str_end[1]-self.str_end[0])==1:
+            label = input[:, self.str_end[0]:self.str_end[1]]
+        else:
+            label = torch.argmax(input[:, self.str_end[0]:self.str_end[1]], axis=-1)
+        
+        new_imp = torch.cat((input[:,:self.str_end[0]],input[:,self.str_end[1]:]),1)
+        
+        if ((self.str_end[1]-self.str_end[0])==2) | ((self.str_end[1]-self.str_end[0])==1):
+            return self.seq(new_imp).view(-1), label
+        else:
+            return self.seq(new_imp), label
+
+def apply_activate(data, output_info):
+    data_t = []
+    st = 0
+    for item in output_info:
+        if item[1] == 'tanh':
+            ed = st + item[0]
+            data_t.append(torch.tanh(data[:, st:ed]))
+            st = ed
+        elif item[1] == 'softmax':
+            ed = st + item[0]
+            data_t.append(F.gumbel_softmax(data[:, st:ed], tau=0.2))
+            st = ed
+    return torch.cat(data_t, dim=1)
+
+def get_st_ed(target_col_index,output_info):
+    st = 0
+    c= 0
+    tc= 0
+
+    for item in output_info:
+        if c==target_col_index:
+            break
+        if item[1]=='tanh':
+            st += item[0]
+            if item[2] == 'yes_g':
+                c+=1
+        elif item[1] == 'softmax':
+            st += item[0]
+            c+=1
+        tc+=1    
+    
+    ed= st+output_info[tc][0] 
+
+    return (st,ed)
+
+def random_choice_prob_index_sampling(probs,col_idx):
+    option_list = []
+    for i in col_idx:
+        pp = probs[i]
+        option_list.append(np.random.choice(np.arange(len(probs[i])), p=pp))
+    
+    return np.array(option_list).reshape(col_idx.shape)
+
+def random_choice_prob_index(a, axis=1):
+    r = np.expand_dims(np.random.rand(a.shape[1 - axis]), axis=axis)
+    return (a.cumsum(axis=axis) > r).argmax(axis=axis)
+
+def maximum_interval(output_info):
+    max_interval = 0
+    for item in output_info:
+        max_interval = max(max_interval, item[0])
+    return max_interval
+
+class Cond(object):
+    def __init__(self, data, output_info):
+       
+        self.model = []
+        st = 0
+        counter = 0
+        for item in output_info:
+           
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':
+                ed = st + item[0]
+                counter += 1
+                self.model.append(np.argmax(data[:, st:ed], axis=-1))
+                st = ed
+            
+        self.interval = []
+        self.n_col = 0  
+        self.n_opt = 0  
+        st = 0
+        self.p = np.zeros((counter, maximum_interval(output_info)))  
+        self.p_sampling = []
+        for item in output_info:
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax': 
+                ed = st + item[0]
+                tmp = np.sum(data[:, st:ed], axis=0)  
+                tmp_sampling = np.sum(data[:, st:ed], axis=0)     
+                tmp = np.log(tmp + 1)  
+                tmp = tmp / np.sum(tmp) 
+                tmp_sampling = tmp_sampling / np.sum(tmp_sampling)
+                self.p_sampling.append(tmp_sampling)
+                self.p[self.n_col, :item[0]] = tmp 
+                self.interval.append((self.n_opt, item[0]))
+                self.n_opt += item[0]
+                self.n_col += 1
+                st = ed
+                
+        self.interval = np.asarray(self.interval)
+        
+    def sample_train(self, batch):
+        if self.n_col == 0:
+            return None
+        batch = batch
+
+        idx = np.random.choice(np.arange(self.n_col), batch)
+
+        vec = np.zeros((batch, self.n_opt), dtype='float32')
+        mask = np.zeros((batch, self.n_col), dtype='float32')
+        mask[np.arange(batch), idx] = 1  
+        opt1prime = random_choice_prob_index(self.p[idx]) 
+        for i in np.arange(batch):
+            vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1
+            
+        return vec, mask, idx, opt1prime
+
+    def sample(self, batch):
+        if self.n_col == 0:
+            return None
+        batch = batch
+      
+        idx = np.random.choice(np.arange(self.n_col), batch)
+
+        vec = np.zeros((batch, self.n_opt), dtype='float32')
+        opt1prime = random_choice_prob_index_sampling(self.p_sampling,idx)
+        
+        for i in np.arange(batch):
+            vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1
+            
+        return vec
+
+def cond_loss(data, output_info, c, m):
+    loss = []
+    st = 0
+    st_c = 0
+    for item in output_info:
+        if item[1] == 'tanh':
+            st += item[0]
+            continue
+
+        elif item[1] == 'softmax':
+            ed = st + item[0]
+            ed_c = st_c + item[0]
+            tmp = F.cross_entropy(
+            data[:, st:ed],
+            torch.argmax(c[:, st_c:ed_c], dim=1),
+            reduction='none')
+            loss.append(tmp)
+            st = ed
+            st_c = ed_c
+
+    loss = torch.stack(loss, dim=1)
+    return (loss * m).sum() / data.size()[0]
+
+class Sampler(object):
+    def __init__(self, data, output_info):
+        super(Sampler, self).__init__()
+        self.data = data
+        self.model = []
+        self.n = len(data)
+        st = 0
+        for item in output_info:
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':
+                ed = st + item[0]
+                tmp = []
+                for j in range(item[0]):
+                    tmp.append(np.nonzero(data[:, st + j])[0])
+                self.model.append(tmp)
+                st = ed
+                
+    def sample(self, n, col, opt):
+        if col is None:
+            idx = np.random.choice(np.arange(self.n), n)
+            return self.data[idx]
+        idx = []
+        for c, o in zip(col, opt):
+            idx.append(np.random.choice(self.model[c][o]))
+        return self.data[idx]
+
+class Discriminator(Module):
+    def __init__(self, side, layers):
+        super(Discriminator, self).__init__()
+        self.side = side
+        info = len(layers)-2
+        self.seq = Sequential(*layers)
+        self.seq_info = Sequential(*layers[:info])
+
+    def forward(self, input):
+        return (self.seq(input)), self.seq_info(input)
+
+class Generator(Module):
+    def __init__(self, side, layers):
+        super(Generator, self).__init__()
+        self.side = side
+        self.seq = Sequential(*layers)
+
+    def forward(self, input_):
+        return self.seq(input_)
+
+def determine_layers_disc(side, num_channels):
+    assert side >= 4 and side <= 64
+
+    layer_dims = [(1, side), (num_channels, side // 2)]
+
+    while layer_dims[-1][1] > 3 and len(layer_dims) < 4:
+        layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2))
+
+    layerNorms = []
+    num_c = num_channels
+    num_s = side / 2
+    for l in range(len(layer_dims) - 1):
+        layerNorms.append([int(num_c), int(num_s), int(num_s)])
+        num_c = num_c * 2
+        num_s = num_s / 2
+
+    layers_D = []
+
+    for prev, curr, ln in zip(layer_dims, layer_dims[1:], layerNorms):
+        layers_D += [
+            Conv2d(prev[0], curr[0], 4, 2, 1, bias=False),
+            LayerNorm(ln),
+            LeakyReLU(0.2, inplace=True),
+        ]
+
+    layers_D += [Conv2d(layer_dims[-1][0], 1, layer_dims[-1][1], 1, 0), ReLU(True)] 
+
+    return layers_D
+
+def determine_layers_gen(side, random_dim, num_channels):
+    assert side >= 4 and side <= 64
+
+    layer_dims = [(1, side), (num_channels, side // 2)]
+
+    while layer_dims[-1][1] > 3 and len(layer_dims) < 4:
+        layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2))
+
+    layerNorms = []
+
+    num_c = num_channels * (2 ** (len(layer_dims) - 2))
+    num_s = int(side / (2 ** (len(layer_dims) - 1)))
+    for l in range(len(layer_dims) - 1):
+        layerNorms.append([int(num_c), int(num_s), int(num_s)])
+        num_c = num_c / 2
+        num_s = num_s * 2
+
+    layers_G = [ConvTranspose2d(random_dim, layer_dims[-1][0], layer_dims[-1][1], 1, 0, output_padding=0, bias=False)]
+
+    for prev, curr, ln in zip(reversed(layer_dims), reversed(layer_dims[:-1]), layerNorms):
+        layers_G += [LayerNorm(ln), ReLU(True), ConvTranspose2d(prev[0], curr[0], 4, 2, 1, output_padding=0, bias=True)]
+    return layers_G
+
+def slerp(val, low, high):
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm*high_norm).sum(1)).view(val.size(0), 1)
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so)*low + (torch.sin(val*omega)/so) * high
+    
+    return res
+
+def calc_gradient_penalty_slerp(netD, real_data, fake_data, transformer, device='cpu', lambda_=10):
+    batchsize = real_data.shape[0]
+    alpha = torch.rand(batchsize, 1,  device=device)
+    interpolates = slerp(alpha, real_data, fake_data)
+    interpolates = interpolates.to(device)
+    interpolates = transformer.transform(interpolates)
+    interpolates = torch.autograd.Variable(interpolates, requires_grad=True)
+    disc_interpolates,_ = netD(interpolates) 
+
+    gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolates,
+                                  grad_outputs=torch.ones(disc_interpolates.size()).to(device),
+                                  create_graph=True, retain_graph=True, only_inputs=True)[0] 
+    
+    gradients_norm = gradients.norm(2, dim=1)
+    gradient_penalty = ((gradients_norm - 1) ** 2).mean() * lambda_
+    
+    return gradient_penalty
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    
+    if classname.find('Conv') != -1:
+        init.normal_(m.weight.data, 0.0, 0.02)
+
+    elif classname.find('BatchNorm') != -1:
+        init.normal_(m.weight.data, 1.0, 0.02)
+        init.constant_(m.bias.data, 0)
+
+class CTABGANSynthesizer:
+    def __init__(self,
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=100,
+                 num_channels=64,
+                 l2scale=1e-5,
+                 batch_size=500,
+                 epochs=150,
+                 lr=2e-4,
+                 device="cpu"):
+                 
+
+        self.random_dim = random_dim
+        self.class_dim = class_dim
+        self.num_channels = num_channels
+        self.dside = None
+        self.gside = None
+        self.l2scale = l2scale
+        self.lr = lr
+        self.batch_size = batch_size
+        self.epochs = epochs
+        self.device = torch.device(device)
+
+    def fit(self, train_data=pd.DataFrame, categorical=[], mixed={}, general=[], non_categorical=[], type={}):
+
+        problem_type = None
+        target_index=None
+        if type:
+            problem_type = list(type.keys())[0]
+            if problem_type:
+                target_index = train_data.columns.get_loc(type[problem_type])
+
+        self.transformer = DataTransformer(train_data=train_data, categorical_list=categorical, mixed_dict=mixed, general_list=general, non_categorical_list=non_categorical)
+        self.transformer.fit() 
+        train_data = self.transformer.transform(train_data.values)
+        data_sampler = Sampler(train_data, self.transformer.output_info)
+        data_dim = self.transformer.output_dim
+        self.cond_generator = Cond(train_data, self.transformer.output_info)
+        		
+        sides = [4, 8, 16, 24, 32]
+        col_size_d = data_dim + self.cond_generator.n_opt
+        for i in sides:
+            if i * i >= col_size_d:
+                self.dside = i
+                break
+        
+        sides = [4, 8, 16, 24, 32]
+        col_size_g = data_dim
+        for i in sides:
+            if i * i >= col_size_g:
+                self.gside = i
+                break
+		
+
+        layers_G = determine_layers_gen(self.gside, self.random_dim+self.cond_generator.n_opt, self.num_channels)
+        layers_D = determine_layers_disc(self.dside, self.num_channels)
+        
+        self.generator = Generator(self.gside, layers_G).to(self.device)
+        discriminator = Discriminator(self.dside, layers_D).to(self.device)
+        optimizer_params = dict(lr=self.lr, betas=(0.5, 0.9), eps=1e-3, weight_decay=self.l2scale)
+        optimizerG = Adam(self.generator.parameters(), **optimizer_params)
+        optimizerD = Adam(discriminator.parameters(), **optimizer_params)
+
+        st_ed = None
+        classifier=None
+        optimizerC= None
+        if target_index != None:
+            st_ed= get_st_ed(target_index,self.transformer.output_info)
+            classifier = Classifier(data_dim,self.class_dim,st_ed).to(self.device)
+            optimizerC = optim.Adam(classifier.parameters(),**optimizer_params)
+        
+        
+        self.generator.apply(weights_init)
+        discriminator.apply(weights_init)
+
+        self.Gtransformer = ImageTransformer(self.gside)       
+        self.Dtransformer = ImageTransformer(self.dside)
+        
+        epsilon = 0
+        epoch = 0
+        steps = 0
+        ci = 1
+        
+        for i in tqdm(range(self.epochs)):
+				
+            
+            for _ in range(ci):
+                noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+                condvec = self.cond_generator.sample_train(self.batch_size)
+
+                c, m, col, opt = condvec
+                c = torch.from_numpy(c).to(self.device)
+                m = torch.from_numpy(m).to(self.device)
+                noisez = torch.cat([noisez, c], dim=1)
+                noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                
+                perm = np.arange(self.batch_size)
+                np.random.shuffle(perm)
+                real = data_sampler.sample(self.batch_size, col[perm], opt[perm])
+                c_perm = c[perm]
+                
+                real = torch.from_numpy(real.astype('float32')).to(self.device)
+                
+                fake = self.generator(noisez)
+                faket = self.Gtransformer.inverse_transform(fake)
+                fakeact = apply_activate(faket, self.transformer.output_info)
+                
+                fake_cat = torch.cat([fakeact, c], dim=1)
+                real_cat = torch.cat([real, c_perm], dim=1)
+                
+                real_cat_d = self.Dtransformer.transform(real_cat)
+                fake_cat_d = self.Dtransformer.transform(fake_cat)
+                
+                optimizerD.zero_grad()
+                
+                d_real,_ = discriminator(real_cat_d)
+                
+
+                d_real = -torch.mean(d_real)
+                d_real.backward() 
+                
+
+                d_fake,_ = discriminator(fake_cat_d)
+                
+                d_fake = torch.mean(d_fake)
+
+                d_fake.backward() 
+                
+                pen = calc_gradient_penalty_slerp(discriminator, real_cat, fake_cat,  self.Dtransformer , self.device)
+
+                pen.backward()
+            
+                optimizerD.step()
+                
+            noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+            
+            condvec = self.cond_generator.sample_train(self.batch_size)
+
+            c, m, col, opt = condvec
+            c = torch.from_numpy(c).to(self.device)
+            m = torch.from_numpy(m).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+
+            optimizerG.zero_grad()
+
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket, self.transformer.output_info)
+
+            fake_cat = torch.cat([fakeact, c], dim=1) 
+            fake_cat = self.Dtransformer.transform(fake_cat)
+                
+            y_fake,info_fake = discriminator(fake_cat)
+            
+            cross_entropy = cond_loss(faket, self.transformer.output_info, c, m)
+
+            _,info_real = discriminator(real_cat_d)
+            
+
+            g = -torch.mean(y_fake) + cross_entropy
+            g.backward(retain_graph=True)
+            loss_mean = torch.norm(torch.mean(info_fake.view(self.batch_size,-1), dim=0) - torch.mean(info_real.view(self.batch_size,-1), dim=0), 1)
+            loss_std = torch.norm(torch.std(info_fake.view(self.batch_size,-1), dim=0) - torch.std(info_real.view(self.batch_size,-1), dim=0), 1)
+            loss_info = loss_mean + loss_std 
+            loss_info.backward()
+            optimizerG.step()
+
+
+            if problem_type:
+                        
+                fake = self.generator(noisez)
+                
+                faket = self.Gtransformer.inverse_transform(fake)
+                
+                fakeact = apply_activate(faket, self.transformer.output_info)
+                
+                real_pre, real_label = classifier(real)
+                fake_pre, fake_label = classifier(fakeact)
+                    
+                c_loss = CrossEntropyLoss() 
+                
+                if (st_ed[1] - st_ed[0])==1:
+                    c_loss= SmoothL1Loss()
+                    real_label = real_label.type_as(real_pre)
+                    fake_label = fake_label.type_as(fake_pre)
+                    real_label = torch.reshape(real_label,real_pre.size())
+                    fake_label = torch.reshape(fake_label,fake_pre.size())
+                    
+                
+                elif (st_ed[1] - st_ed[0])==2:
+                    c_loss = BCELoss()
+                    real_label = real_label.type_as(real_pre)
+                    fake_label = fake_label.type_as(fake_pre)
+
+                loss_cc = c_loss(real_pre, real_label)
+                loss_cg = c_loss(fake_pre, fake_label)
+
+                optimizerG.zero_grad()
+                loss_cg.backward()
+                optimizerG.step()
+
+                optimizerC.zero_grad()
+                loss_cc.backward()
+                optimizerC.step()
+                            
+
+            
+   
+    @torch.no_grad()
+    def sample(self, n, seed=0):
+        print(n)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        sample_batch_size = 8092
+        self.generator.eval()
+
+        output_info = self.transformer.output_info
+        steps = n // sample_batch_size + 1
+
+        data = []
+        
+        for i in range(steps):
+            noisez = torch.randn(sample_batch_size, self.random_dim, device=self.device)
+            condvec = self.cond_generator.sample(sample_batch_size)
+            c = condvec
+            c = torch.from_numpy(c).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(sample_batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket,output_info)
+            data.append(fakeact.detach().cpu().numpy())
+
+        data = np.concatenate(data, axis=0)
+        result,resample = self.transformer.inverse_transform(data)
+
+        t0 = time.time()
+        while len(result) < n and (time.time() - t0) <= 600:
+            data_resample = []    
+            steps_left = resample// sample_batch_size + 1
+            # print(f"Sampling: {len(result)}/{n}")
+            for i in range(steps_left):
+                noisez = torch.randn(sample_batch_size, self.random_dim, device=self.device)
+                condvec = self.cond_generator.sample(sample_batch_size)
+                c = condvec
+                c = torch.from_numpy(c).to(self.device)
+                noisez = torch.cat([noisez, c], dim=1)
+                noisez =  noisez.view(sample_batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                    
+                fake = self.generator(noisez)
+                faket = self.Gtransformer.inverse_transform(fake)
+                fakeact = apply_activate(faket, output_info)
+                data_resample.append(fakeact.detach().cpu().numpy())
+
+            data_resample = np.concatenate(data_resample, axis=0)
+
+            res,resample = self.transformer.inverse_transform(data_resample)
+            result  = np.concatenate([result,res],axis=0)
+        
+        return result[0:n]
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN-Plus/model/synthesizer/transformer.py

@@ -0,0 +1,429 @@
+import numpy as np
+import pandas as pd
+import torch
+from sklearn.mixture import BayesianGaussianMixture
+
+class DataTransformer():
+    
+    def __init__(self, train_data=pd.DataFrame, categorical_list=[], mixed_dict={}, general_list=[], non_categorical_list=[], n_clusters=10, eps=0.005):
+        self.meta = None
+        self.n_clusters = n_clusters
+        self.eps = eps
+        self.train_data = train_data
+        self.categorical_columns= categorical_list
+        self.mixed_columns= mixed_dict
+        self.general_columns = general_list
+        self.non_categorical_columns= non_categorical_list
+        
+    def get_metadata(self):
+        
+        meta = []
+    
+        for index in range(self.train_data.shape[1]):
+            column = self.train_data.iloc[:,index]
+            if index in self.categorical_columns:
+                if index in self.non_categorical_columns:
+                    meta.append({
+                      "name": index,
+                      "type": "continuous",
+                      "min": column.min(),
+                      "max": column.max(),
+                    })
+                else:
+                    mapper = column.value_counts().index.tolist()
+                    meta.append({
+                        "name": index,
+                        "type": "categorical",
+                        "size": len(mapper),
+                        "i2s": mapper
+                    })
+
+            elif index in self.mixed_columns.keys():
+                meta.append({
+                    "name": index,
+                    "type": "mixed",
+                    "min": column.min(),
+                    "max": column.max(),
+                    "modal": self.mixed_columns[index]
+                })
+            else:
+                meta.append({
+                    "name": index,
+                    "type": "continuous",
+                    "min": column.min(),
+                    "max": column.max(),
+                })            
+
+        return meta
+
+    def fit(self):
+        data = self.train_data.values
+        self.meta = self.get_metadata()
+        model = []
+        self.ordering = []
+        self.output_info = []
+        self.output_dim = 0
+        self.components = []
+        self.filter_arr = []
+        for id_, info in enumerate(self.meta):
+            if info['type'] == "continuous":
+                if id_ not in self.general_columns:
+                  gm = BayesianGaussianMixture(
+                      n_components = self.n_clusters, 
+                      weight_concentration_prior_type='dirichlet_process',
+                      weight_concentration_prior=0.001, 
+                      max_iter=100,n_init=1, random_state=42)
+                  gm.fit(data[:, id_].reshape([-1, 1]))
+                  mode_freq = (pd.Series(gm.predict(data[:, id_].reshape([-1, 1]))).value_counts().keys())
+                  model.append(gm)
+                  old_comp = gm.weights_ > self.eps
+                  comp = []
+                  for i in range(self.n_clusters):
+                      if (i in (mode_freq)) & old_comp[i]:
+                          comp.append(True)
+                      else:
+                          comp.append(False)
+                  self.components.append(comp) 
+                  self.output_info += [(1, 'tanh','no_g'), (np.sum(comp), 'softmax')]
+                  self.output_dim += 1 + np.sum(comp)
+                else:
+                  model.append(None)
+                  self.components.append(None)
+                  self.output_info += [(1, 'tanh','yes_g')]
+                  self.output_dim += 1
+            
+            elif info['type'] == "mixed":
+                
+                gm1 = BayesianGaussianMixture(
+                    n_components = self.n_clusters, 
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, max_iter=100,
+                    n_init=1,random_state=42)
+                gm2 = BayesianGaussianMixture(
+                    n_components = self.n_clusters,
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, max_iter=100,
+                    n_init=1,random_state=42)
+                
+                gm1.fit(data[:, id_].reshape([-1, 1]))
+                
+                filter_arr = []
+                for element in data[:, id_]:
+                    if element not in info['modal']:
+                        filter_arr.append(True)
+                    else:
+                        filter_arr.append(False)
+               
+                gm2.fit(data[:, id_][filter_arr].reshape([-1, 1]))
+                mode_freq = (pd.Series(gm2.predict(data[:, id_][filter_arr].reshape([-1, 1]))).value_counts().keys())
+                self.filter_arr.append(filter_arr)
+                model.append((gm1,gm2))
+               
+                old_comp = gm2.weights_ > self.eps
+                  
+                comp = []
+                  
+                for i in range(self.n_clusters):
+                    if (i in (mode_freq)) & old_comp[i]:
+                        comp.append(True)
+                    else:
+                        comp.append(False)
+
+                self.components.append(comp)
+
+                self.output_info += [(1, 'tanh',"no_g"), (np.sum(comp) + len(info['modal']), 'softmax')]
+                self.output_dim += 1 + np.sum(comp) + len(info['modal'])
+            else:
+                model.append(None)
+                self.components.append(None)
+                self.output_info += [(info['size'], 'softmax')]
+                self.output_dim += info['size']
+        self.model = model
+
+    def transform(self, data, ispositive = False, positive_list = None):
+        values = []
+        mixed_counter = 0
+        for id_, info in enumerate(self.meta):
+            current = data[:, id_]
+            if info['type'] == "continuous":
+                if id_ not in self.general_columns: 
+                  current = current.reshape([-1, 1])
+                  means = self.model[id_].means_.reshape((1, self.n_clusters))
+                  stds = np.sqrt(self.model[id_].covariances_).reshape((1, self.n_clusters))
+                  features = np.empty(shape=(len(current),self.n_clusters))
+                  if ispositive == True:
+                      if id_ in positive_list:
+                          features = np.abs(current - means) / (4 * stds)
+                  else:
+                      features = (current - means) / (4 * stds)
+
+                  probs = self.model[id_].predict_proba(current.reshape([-1, 1]))
+                  n_opts = sum(self.components[id_])
+                  features = features[:, self.components[id_]]
+                  probs = probs[:, self.components[id_]]
+
+                  opt_sel = np.zeros(len(data), dtype='int')
+                  for i in range(len(data)):
+                      pp = probs[i] + 1e-6
+                      pp = pp / sum(pp)
+                      opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)
+
+                  idx = np.arange((len(features)))
+                  features = features[idx, opt_sel].reshape([-1, 1])
+                  features = np.clip(features, -.99, .99) 
+                  probs_onehot = np.zeros_like(probs)
+                  probs_onehot[np.arange(len(probs)), opt_sel] = 1
+
+                  re_ordered_phot = np.zeros_like(probs_onehot)
+                  
+                  col_sums = probs_onehot.sum(axis=0)
+                  
+
+                  n = probs_onehot.shape[1]
+                  largest_indices = np.argsort(-1*col_sums)[:n]
+                  self.ordering.append(largest_indices)
+                  for id,val in enumerate(largest_indices):
+                      re_ordered_phot[:,id] = probs_onehot[:,val]
+                
+                  
+                  values += [features, re_ordered_phot]
+                  
+                else:
+                  
+                  self.ordering.append(None)
+                  
+                  if id_ in self.non_categorical_columns:
+                    info['min'] = -1e-3
+                    info['max'] = info['max'] + 1e-3
+                    
+                  current = (current - (info['min'])) / (info['max'] - info['min'])
+                  current = current * 2 - 1 
+                  current = current.reshape([-1, 1])
+                  values.append(current)
+
+            elif info['type'] == "mixed":
+                    
+                means_0 = self.model[id_][0].means_.reshape([-1])
+                stds_0 = np.sqrt(self.model[id_][0].covariances_).reshape([-1])
+
+                zero_std_list = []
+                means_needed = []
+                stds_needed = []
+
+                for mode in info['modal']:
+                    if mode!=-9999999:
+                        dist = []
+                        for idx,val in enumerate(list(means_0.flatten())):
+                            dist.append(abs(mode-val))
+                        index_min = np.argmin(np.array(dist))
+                        zero_std_list.append(index_min)
+                    else: continue
+
+                for idx in zero_std_list:
+                    means_needed.append(means_0[idx])
+                    stds_needed.append(stds_0[idx])
+                
+                
+                mode_vals = []
+
+                for i,j,k in zip(info['modal'],means_needed,stds_needed):
+                    this_val  = np.abs(i - j) / (4*k)
+                    mode_vals.append(this_val)
+                
+                if -9999999 in info["modal"]:
+                    mode_vals.append(0)
+                
+                current = current.reshape([-1, 1])
+                filter_arr = self.filter_arr[mixed_counter]
+                current = current[filter_arr]
+                
+                means = self.model[id_][1].means_.reshape((1, self.n_clusters))
+                stds = np.sqrt(self.model[id_][1].covariances_).reshape((1, self.n_clusters))
+                features = np.empty(shape=(len(current),self.n_clusters))
+                if ispositive == True:
+                    if id_ in positive_list:
+                        features = np.abs(current - means) / (4 * stds)
+                else:
+                    features = (current - means) / (4 * stds)
+
+                probs = self.model[id_][1].predict_proba(current.reshape([-1, 1]))
+
+                n_opts = sum(self.components[id_]) # 8
+                features = features[:, self.components[id_]]
+                probs = probs[:, self.components[id_]]
+                
+                opt_sel = np.zeros(len(current), dtype='int')
+                for i in range(len(current)):
+                    pp = probs[i] + 1e-6
+                    pp = pp / sum(pp)
+                    opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)
+                idx = np.arange((len(features)))
+                features = features[idx, opt_sel].reshape([-1, 1])
+                features = np.clip(features, -.99, .99)
+                probs_onehot = np.zeros_like(probs)
+                probs_onehot[np.arange(len(probs)), opt_sel] = 1
+                extra_bits = np.zeros([len(current), len(info['modal'])])
+                temp_probs_onehot = np.concatenate([extra_bits,probs_onehot], axis = 1)
+                final = np.zeros([len(data), 1 + probs_onehot.shape[1] + len(info['modal'])])
+                features_curser = 0
+                for idx, val in enumerate(data[:, id_]):
+                    if val in info['modal']:
+                        category_ = list(map(info['modal'].index, [val]))[0]
+                        final[idx, 0] = mode_vals[category_]
+                        final[idx, (category_+1)] = 1
+                    
+                    else:
+                        final[idx, 0] = features[features_curser]
+                        final[idx, (1+len(info['modal'])):] = temp_probs_onehot[features_curser][len(info['modal']):]
+                        features_curser = features_curser + 1
+               
+                just_onehot = final[:,1:]
+                re_ordered_jhot= np.zeros_like(just_onehot)
+                n = just_onehot.shape[1]
+                col_sums = just_onehot.sum(axis=0)
+                largest_indices = np.argsort(-1*col_sums)[:n]
+                self.ordering.append(largest_indices)
+                for id,val in enumerate(largest_indices):
+                      re_ordered_jhot[:,id] = just_onehot[:,val]
+                final_features = final[:,0].reshape([-1, 1])
+                values += [final_features, re_ordered_jhot]
+                mixed_counter = mixed_counter + 1
+    
+            else:
+                self.ordering.append(None)
+                col_t = np.zeros([len(data), info['size']])
+                idx = list(map(info['i2s'].index, current))
+                col_t[np.arange(len(data)), idx] = 1
+                values.append(col_t)
+                
+        return np.concatenate(values, axis=1)
+
+    def inverse_transform(self, data):
+        data_t = np.zeros([len(data), len(self.meta)])
+        invalid_ids = []
+        st = 0
+        for id_, info in enumerate(self.meta):
+            if info['type'] == "continuous":
+                if id_ not in self.general_columns:
+                  u = data[:, st]
+                  v = data[:, st + 1:st + 1 + np.sum(self.components[id_])]
+                  order = self.ordering[id_] 
+                  v_re_ordered = np.zeros_like(v)
+
+                  for id,val in enumerate(order):
+                      v_re_ordered[:,val] = v[:,id]
+                  
+                  v = v_re_ordered
+
+                  u = np.clip(u, -1, 1)
+                  v_t = np.ones((data.shape[0], self.n_clusters)) * -100
+                  v_t[:, self.components[id_]] = v
+                  v = v_t
+                  st += 1 + np.sum(self.components[id_])
+                  means = self.model[id_].means_.reshape([-1])
+                  stds = np.sqrt(self.model[id_].covariances_).reshape([-1])
+                  p_argmax = np.argmax(v, axis=1)
+                  std_t = stds[p_argmax]
+                  mean_t = means[p_argmax]
+                  tmp = u * 4 * std_t + mean_t
+                                    
+                  for idx,val in enumerate(tmp):
+                     if (val < info["min"]) | (val > info['max']):
+                         invalid_ids.append(idx)
+                  
+                  if id_ in self.non_categorical_columns:
+                    
+                    tmp = np.round(tmp)
+                  
+                  data_t[:, id_] = tmp
+
+                else:
+                  u = data[:, st]
+                  u = (u + 1) / 2
+                  u = np.clip(u, 0, 1)
+                  u = u * (info['max'] - info['min']) + info['min']
+                  if id_ in self.non_categorical_columns:
+                    data_t[:, id_] = np.round(u)
+                  else: data_t[:, id_] = u
+
+                  st += 1
+
+            elif info['type'] == "mixed":
+
+                u = data[:, st]
+                full_v = data[:,(st+1):(st+1)+len(info['modal'])+np.sum(self.components[id_])]
+                order = self.ordering[id_]
+                full_v_re_ordered = np.zeros_like(full_v)
+
+                for id,val in enumerate(order):
+                    full_v_re_ordered[:,val] = full_v[:,id]
+                  
+                full_v = full_v_re_ordered
+
+                
+                mixed_v = full_v[:,:len(info['modal'])]
+                v = full_v[:,-np.sum(self.components[id_]):]
+
+                u = np.clip(u, -1, 1)
+                v_t = np.ones((data.shape[0], self.n_clusters)) * -100
+                v_t[:, self.components[id_]] = v
+                v = np.concatenate([mixed_v,v_t], axis=1)
+
+                st += 1 + np.sum(self.components[id_]) + len(info['modal'])
+                means = self.model[id_][1].means_.reshape([-1]) 
+                stds = np.sqrt(self.model[id_][1].covariances_).reshape([-1]) 
+                p_argmax = np.argmax(v, axis=1)
+
+                result = np.zeros_like(u)
+
+                for idx in range(len(data)):
+                    if p_argmax[idx] < len(info['modal']):
+                        argmax_value = p_argmax[idx]
+                        result[idx] = float(list(map(info['modal'].__getitem__, [argmax_value]))[0])
+                    else:
+                        std_t = stds[(p_argmax[idx]-len(info['modal']))]
+                        mean_t = means[(p_argmax[idx]-len(info['modal']))]
+                        result[idx] = u[idx] * 4 * std_t + mean_t
+                        
+                for idx,val in enumerate(result):
+                     if (val < info["min"]) | (val > info['max']):
+                         invalid_ids.append(idx)
+
+                data_t[:, id_] = result
+
+            else:
+                current = data[:, st:st + info['size']]
+                st += info['size']
+                idx = np.argmax(current, axis=1)
+                data_t[:, id_] = list(map(info['i2s'].__getitem__, idx))
+            
+            
+        invalid_ids = np.unique(np.array(invalid_ids)) 
+        all_ids = np.arange(0,len(data))
+        valid_ids = list(set(all_ids) - set(invalid_ids))
+            
+        return data_t[valid_ids],len(invalid_ids)
+
+
+class ImageTransformer():
+
+    def __init__(self, side):
+    
+        self.height = side
+            
+    def transform(self, data):
+
+        if self.height * self.height > len(data[0]):
+            
+            padding = torch.zeros((len(data), self.height * self.height - len(data[0]))).to(data.device)
+            data = torch.cat([data, padding], axis=1)
+
+        return data.view(-1, 1, self.height, self.height)
+
+    def inverse_transform(self, data):
+        
+        data = data.view(-1, self.height * self.height)
+
+        return data
+
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN/model/ctabgan.py

@@ -0,0 +1,58 @@
+"""
+Generative model training algorithm based on the CTABGANSynthesiser
+
+"""
+import pandas as pd
+import time
+from model.pipeline.data_preparation import DataPrep
+from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+class CTABGAN():
+
+    def __init__(self,
+                 df,
+                 test_ratio = 0.20,
+                 categorical_columns = [ 'workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country', 'income'], 
+                 log_columns = [],
+                 mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]},
+                 integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'],
+                 problem_type= {"Classification": 'income'},
+                 batch_size = 512,
+                 class_dim = (256, 256, 256, 256),
+                 lr = 2e-4,
+                 epochs = 10,
+                 device=None):
+
+        self.__name__ = 'CTABGAN'
+              
+        self.synthesizer = CTABGANSynthesizer(lr = lr, epochs = epochs, batch_size = batch_size, class_dim = class_dim, device = device)
+        self.raw_df = df
+        print(self.raw_df.shape)
+        self.test_ratio = test_ratio
+        self.categorical_columns = categorical_columns
+        self.log_columns = log_columns
+        self.mixed_columns = mixed_columns
+        self.integer_columns = integer_columns
+        self.problem_type = problem_type
+        
+    def fit(self, no_train=False):
+        print("-"*100)
+        start_time = time.time()
+        self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.integer_columns,self.problem_type,self.test_ratio)
+        self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], 
+        mixed = self.data_prep.column_types["mixed"],type=self.problem_type, no_train=no_train)
+        end_time = time.time()
+        print('Finished training in',end_time-start_time," seconds.")
+        print("-"*100)
+
+
+    def generate_samples(self, num_samples, seed=0):
+        
+        sample = self.synthesizer.sample(num_samples, seed) 
+        sample_df = self.data_prep.inverse_prep(sample)
+        
+        return sample_df

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN/model/eval/evaluation.py

@@ -0,0 +1,191 @@
+import numpy as np
+import pandas as pd 
+from sklearn import metrics
+from sklearn import model_selection
+from sklearn.preprocessing import MinMaxScaler,StandardScaler
+from sklearn.neural_network import MLPClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn import svm,tree
+from sklearn.ensemble import RandomForestClassifier
+from dython.nominal import compute_associations
+from scipy.stats import wasserstein_distance
+from scipy.spatial import distance
+import warnings
+
+warnings.filterwarnings("ignore")
+
+def supervised_model_training(x_train, y_train, x_test, 
+                              y_test, model_name):
+  
+  if model_name == 'lr':
+    model  = LogisticRegression(random_state=42,max_iter=500) 
+  elif model_name == 'svm':
+    model  = svm.SVC(random_state=42,probability=True)
+  elif model_name == 'dt':
+    model  = tree.DecisionTreeClassifier(random_state=42)
+  elif model_name == 'rf':      
+    model = RandomForestClassifier(random_state=42)
+  elif model_name == "mlp":
+    model = MLPClassifier(random_state=42,max_iter=100)
+  
+  model.fit(x_train, y_train)
+  pred = model.predict(x_test)
+
+  if len(np.unique(y_train))>2:
+    predict = model.predict_proba(x_test)        
+    acc = metrics.accuracy_score(y_test,pred)*100
+    auc = metrics.roc_auc_score(y_test, predict,average="weighted",multi_class="ovr")
+    f1_score = metrics.precision_recall_fscore_support(y_test, pred,average="weighted")[2]
+    return [acc, auc,f1_score] 
+
+  else:
+    predict = model.predict_proba(x_test)[:,1]    
+    acc = metrics.accuracy_score(y_test,pred)*100
+    auc = metrics.roc_auc_score(y_test, predict)
+    f1_score = metrics.precision_recall_fscore_support(y_test,pred)[2].mean()
+    return [acc, auc,f1_score] 
+
+
+def get_utility_metrics(real_path,fake_paths,scaler="MinMax",classifiers=["lr","dt","rf","mlp"],test_ratio=.20):
+
+    data_real = pd.read_csv(real_path).to_numpy()
+    data_dim = data_real.shape[1]
+
+    data_real_y = data_real[:,-1]
+    data_real_X = data_real[:,:data_dim-1]
+    X_train_real, X_test_real, y_train_real, y_test_real = model_selection.train_test_split(data_real_X ,data_real_y, test_size=test_ratio, stratify=data_real_y,random_state=42) 
+
+    if scaler=="MinMax":
+        scaler_real = MinMaxScaler()
+    else:
+        scaler_real = StandardScaler()
+        
+    scaler_real.fit(X_train_real)
+    X_train_real_scaled = scaler_real.transform(X_train_real)
+    X_test_real_scaled = scaler_real.transform(X_test_real)
+
+    all_real_results = []
+    for classifier in classifiers:
+      real_results = supervised_model_training(X_train_real_scaled,y_train_real,X_test_real_scaled,y_test_real,classifier)
+      all_real_results.append(real_results)
+      
+    all_fake_results_avg = []
+    
+    for fake_path in fake_paths:
+      data_fake  = pd.read_csv(fake_path).to_numpy()
+      data_fake_y = data_fake[:,-1]
+      data_fake_X = data_fake[:,:data_dim-1]
+      X_train_fake, _ , y_train_fake, _ = model_selection.train_test_split(data_fake_X ,data_fake_y, test_size=test_ratio, stratify=data_fake_y,random_state=42) 
+
+      if scaler=="MinMax":
+        scaler_fake = MinMaxScaler()
+      else:
+        scaler_fake = StandardScaler()
+      
+      scaler_fake.fit(data_fake_X)
+      
+      X_train_fake_scaled = scaler_fake.transform(X_train_fake)
+      
+      all_fake_results = []
+      for classifier in classifiers:
+        fake_results = supervised_model_training(X_train_fake_scaled,y_train_fake,X_test_real_scaled,y_test_real,classifier)
+        all_fake_results.append(fake_results)
+
+      all_fake_results_avg.append(all_fake_results)
+    
+    diff_results = np.array(all_real_results)- np.array(all_fake_results_avg).mean(axis=0)
+
+    return diff_results
+
+def stat_sim(real_path,fake_path,cat_cols=None):
+    
+    Stat_dict={}
+    
+    real = pd.read_csv(real_path)
+    fake = pd.read_csv(fake_path)
+
+    really = real.copy()
+    fakey = fake.copy()
+
+    real_corr = compute_associations(real, nominal_columns=cat_cols)
+
+    fake_corr = compute_associations(fake, nominal_columns=cat_cols)
+
+    corr_dist = np.linalg.norm(real_corr - fake_corr)
+    
+    cat_stat = []
+    num_stat = []
+    
+    for column in real.columns:
+        
+        if column in cat_cols:
+            real_pdf=(really[column].value_counts()/really[column].value_counts().sum())
+            fake_pdf=(fakey[column].value_counts()/fakey[column].value_counts().sum())
+            categories = (fakey[column].value_counts()/fakey[column].value_counts().sum()).keys().tolist()
+            sorted_categories = sorted(categories)
+            
+            real_pdf_values = [] 
+            fake_pdf_values = []
+
+            for i in sorted_categories:
+                real_pdf_values.append(real_pdf[i])
+                fake_pdf_values.append(fake_pdf[i])
+            
+            if len(real_pdf)!=len(fake_pdf):
+                zero_cats = set(really[column].value_counts().keys())-set(fakey[column].value_counts().keys())
+                for z in zero_cats:
+                    real_pdf_values.append(real_pdf[z])
+                    fake_pdf_values.append(0)
+            Stat_dict[column]=(distance.jensenshannon(real_pdf_values,fake_pdf_values, 2.0))
+            cat_stat.append(Stat_dict[column])        
+        else:
+            scaler = MinMaxScaler()
+            scaler.fit(real[column].values.reshape(-1,1))
+            l1 = scaler.transform(real[column].values.reshape(-1,1)).flatten()
+            l2 = scaler.transform(fake[column].values.reshape(-1,1)).flatten()
+            Stat_dict[column]= (wasserstein_distance(l1,l2))
+            num_stat.append(Stat_dict[column])
+
+    return [np.mean(num_stat),np.mean(cat_stat),corr_dist]
+
+def privacy_metrics(real_path,fake_path,data_percent=15):
+    
+    real = pd.read_csv(real_path).drop_duplicates(keep=False)
+    fake = pd.read_csv(fake_path).drop_duplicates(keep=False)
+
+    real_refined = real.sample(n=int(len(real)*(.01*data_percent)), random_state=42).to_numpy()
+    fake_refined = fake.sample(n=int(len(fake)*(.01*data_percent)), random_state=42).to_numpy()
+
+    scalerR = StandardScaler()
+    scalerR.fit(real_refined)
+    scalerF = StandardScaler()
+    scalerF.fit(fake_refined)
+    df_real_scaled = scalerR.transform(real_refined)
+    df_fake_scaled = scalerF.transform(fake_refined)
+    
+    dist_rf = metrics.pairwise_distances(df_real_scaled, Y=df_fake_scaled, metric='minkowski', n_jobs=-1)
+    dist_rr = metrics.pairwise_distances(df_real_scaled, Y=None, metric='minkowski', n_jobs=-1)
+    rd_dist_rr = dist_rr[~np.eye(dist_rr.shape[0],dtype=bool)].reshape(dist_rr.shape[0],-1)
+    dist_ff = metrics.pairwise_distances(df_fake_scaled, Y=None, metric='minkowski', n_jobs=-1)
+    rd_dist_ff = dist_ff[~np.eye(dist_ff.shape[0],dtype=bool)].reshape(dist_ff.shape[0],-1) 
+    smallest_two_indexes_rf = [dist_rf[i].argsort()[:2] for i in range(len(dist_rf))]
+    smallest_two_rf = [dist_rf[i][smallest_two_indexes_rf[i]] for i in range(len(dist_rf))]       
+    smallest_two_indexes_rr = [rd_dist_rr[i].argsort()[:2] for i in range(len(rd_dist_rr))]
+    smallest_two_rr = [rd_dist_rr[i][smallest_two_indexes_rr[i]] for i in range(len(rd_dist_rr))]
+    smallest_two_indexes_ff = [rd_dist_ff[i].argsort()[:2] for i in range(len(rd_dist_ff))]
+    smallest_two_ff = [rd_dist_ff[i][smallest_two_indexes_ff[i]] for i in range(len(rd_dist_ff))]
+    nn_ratio_rr = np.array([i[0]/i[1] for i in smallest_two_rr])
+    nn_ratio_ff = np.array([i[0]/i[1] for i in smallest_two_ff])
+    nn_ratio_rf = np.array([i[0]/i[1] for i in smallest_two_rf])
+    nn_fifth_perc_rr = np.percentile(nn_ratio_rr,5)
+    nn_fifth_perc_ff = np.percentile(nn_ratio_ff,5)
+    nn_fifth_perc_rf = np.percentile(nn_ratio_rf,5)
+
+    min_dist_rf = np.array([i[0] for i in smallest_two_rf])
+    fifth_perc_rf = np.percentile(min_dist_rf,5)
+    min_dist_rr = np.array([i[0] for i in smallest_two_rr])
+    fifth_perc_rr = np.percentile(min_dist_rr,5)
+    min_dist_ff = np.array([i[0] for i in smallest_two_ff])
+    fifth_perc_ff = np.percentile(min_dist_ff,5)
+    
+    return np.array([fifth_perc_rf,fifth_perc_rr,fifth_perc_ff,nn_fifth_perc_rf,nn_fifth_perc_rr,nn_fifth_perc_ff]).reshape(1,6)    

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN/model/pipeline/data_preparation.py

@@ -0,0 +1,114 @@
+import numpy as np
+import pandas as pd
+from sklearn import preprocessing
+from sklearn import model_selection
+
+class DataPrep(object):
+  
+    def __init__(self, raw_df: pd.DataFrame, categorical: list, log:list, mixed:dict, integer:list, type:dict, test_ratio:float):
+        
+        
+        self.categorical_columns = categorical
+        self.log_columns = log
+        self.mixed_columns = mixed
+        self.integer_columns = integer
+        self.column_types = dict()
+        self.column_types["categorical"] = []
+        self.column_types["mixed"] = {}
+        self.lower_bounds = {}
+        self.label_encoder_list = []
+
+       
+        target_col = list(type.values())[0]
+        y_real = raw_df[target_col]
+        X_real = raw_df.drop(columns=[target_col])
+        # X_train_real, _, y_train_real, _ = model_selection.train_test_split(X_real ,y_real, test_size=test_ratio, stratify=y_real,random_state=42)
+        X_train_real, y_train_real = X_real, y_real
+        X_train_real[target_col]= y_train_real
+
+        self.df = X_train_real
+        
+        self.df = self.df.replace(r' ', np.nan)
+        self.df = self.df.fillna('empty')
+           
+        all_columns= set(self.df.columns)
+        irrelevant_missing_columns = set(self.categorical_columns)
+        relevant_missing_columns = list(all_columns - irrelevant_missing_columns)
+        
+        for i in relevant_missing_columns:
+            if i in self.log_columns:
+                if "empty" in list(self.df[i].values):
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x)
+                    self.mixed_columns[i] = [-9999999]
+            elif i in list(self.mixed_columns.keys()):
+                if "empty" in list(self.df[i].values):
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x )
+                    self.mixed_columns[i].append(-9999999)
+            else:
+                if "empty" in list(self.df[i].values):   
+                    self.df[i] = self.df[i].apply(lambda x: -9999999 if x=="empty" else x)
+                    self.mixed_columns[i] = [-9999999]
+        
+        if self.log_columns:
+            for log_column in self.log_columns:
+                valid_indices = []
+                for idx,val in enumerate(self.df[log_column].values):
+                    if val!=-9999999:
+                        valid_indices.append(idx)
+                eps = 1
+                lower = np.min(self.df[log_column].iloc[valid_indices].values)
+                self.lower_bounds[log_column] = lower
+                if lower>0: 
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x) if x!=-9999999 else -9999999)
+                elif lower == 0:
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x+eps) if x!=-9999999 else -9999999) 
+                else:
+                    self.df[log_column] = self.df[log_column].apply(lambda x: np.log(x-lower+eps) if x!=-9999999 else -9999999)
+
+        for column_index, column in enumerate(self.df.columns):
+            if column in self.categorical_columns:        
+                label_encoder = preprocessing.LabelEncoder()
+                self.df[column] = self.df[column].astype(str)
+                label_encoder.fit(self.df[column])
+                current_label_encoder = dict()
+                current_label_encoder['column'] = column
+                current_label_encoder['label_encoder'] = label_encoder
+                transformed_column = label_encoder.transform(self.df[column])
+                self.df[column] = transformed_column
+                self.label_encoder_list.append(current_label_encoder)
+                self.column_types["categorical"].append(column_index)
+            
+            elif column in self.mixed_columns:
+                self.column_types["mixed"][column_index] = self.mixed_columns[column]
+        
+        super().__init__()
+        
+    def inverse_prep(self, data, eps=1):
+        
+        df_sample = pd.DataFrame(data,columns=self.df.columns)
+     
+        for i in range(len(self.label_encoder_list)):
+            le = self.label_encoder_list[i]["label_encoder"]
+            df_sample[self.label_encoder_list[i]["column"]] = df_sample[self.label_encoder_list[i]["column"]].astype(int)
+            df_sample[self.label_encoder_list[i]["column"]] = le.inverse_transform(df_sample[self.label_encoder_list[i]["column"]])
+
+        if self.log_columns:
+            for i in df_sample:
+                if i in self.log_columns:
+                    lower_bound = self.lower_bounds[i]
+                    if lower_bound>0:
+                        df_sample[i].apply(lambda x: np.exp(x)) 
+                    elif lower_bound==0:
+                        df_sample[i] = df_sample[i].apply(lambda x: np.ceil(np.exp(x)-eps) if (np.exp(x)-eps) < 0 else (np.exp(x)-eps))
+                    else: 
+                        df_sample[i] = df_sample[i].apply(lambda x: np.exp(x)-eps+lower_bound)
+
+        if self.integer_columns:
+            for column in self.integer_columns:
+                df_sample[column]= (np.round(df_sample[column].values))
+                df_sample[column] = df_sample[column].astype(int)
+
+        df_sample.replace(-9999999, np.nan,inplace=True)
+        df_sample.replace('empty', np.nan,inplace=True)
+
+        return df_sample

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN/model/synthesizer/ctabgan_synthesizer.py

@@ -0,0 +1,526 @@
+import numpy as np
+import pandas as pd
+import torch
+import torch.utils.data
+import torch.optim as optim
+from torch.optim import Adam
+from torch.nn import functional as F
+from torch.nn import (Dropout, LeakyReLU, Linear, Module, ReLU, Sequential,
+Conv2d, ConvTranspose2d, BatchNorm2d, Sigmoid, init, BCELoss, CrossEntropyLoss,SmoothL1Loss)
+from model.synthesizer.transformer import ImageTransformer,DataTransformer
+from tqdm import tqdm
+
+
+class Classifier(Module):
+    def __init__(self,input_dim, dis_dims,st_ed):
+        super(Classifier,self).__init__()
+        dim = input_dim-(st_ed[1]-st_ed[0])
+        seq = []
+        self.str_end = st_ed
+        for item in list(dis_dims):
+            seq += [
+                Linear(dim, item),
+                LeakyReLU(0.2),
+                Dropout(0.5)
+            ]
+            dim = item
+        
+        if (st_ed[1]-st_ed[0])==1:
+            seq += [Linear(dim, 1)]
+        
+        elif (st_ed[1]-st_ed[0])==2:
+            seq += [Linear(dim, 1),Sigmoid()]
+        else:
+            seq += [Linear(dim,(st_ed[1]-st_ed[0]))] 
+        
+        self.seq = Sequential(*seq)
+
+    def forward(self, input):
+        
+        label=None
+        
+        if (self.str_end[1]-self.str_end[0])==1:
+            label = input[:, self.str_end[0]:self.str_end[1]]
+        else:
+            label = torch.argmax(input[:, self.str_end[0]:self.str_end[1]], axis=-1)
+        
+        new_imp = torch.cat((input[:,:self.str_end[0]],input[:,self.str_end[1]:]),1)
+        
+        if ((self.str_end[1]-self.str_end[0])==2) | ((self.str_end[1]-self.str_end[0])==1):
+            return self.seq(new_imp).view(-1), label
+        else:
+            return self.seq(new_imp), label
+
+def apply_activate(data, output_info):
+    data_t = []
+    st = 0
+    for item in output_info:
+        if item[1] == 'tanh':
+            ed = st + item[0]
+            data_t.append(torch.tanh(data[:, st:ed]))
+            st = ed
+        elif item[1] == 'softmax':
+            ed = st + item[0]
+            data_t.append(F.gumbel_softmax(data[:, st:ed], tau=0.2))
+            st = ed
+    return torch.cat(data_t, dim=1)
+
+def get_st_ed(target_col_index,output_info):
+    st = 0
+    c= 0
+    tc= 0
+    for item in output_info:
+        if c==target_col_index:
+            break
+        if item[1]=='tanh':
+            st += item[0]
+        elif item[1] == 'softmax':
+            st += item[0]
+            c+=1
+        tc+=1    
+    ed= st+output_info[tc][0] 
+    return (st,ed)
+
+def random_choice_prob_index_sampling(probs,col_idx):
+    option_list = []
+    for i in col_idx:
+        pp = probs[i]
+        option_list.append(np.random.choice(np.arange(len(probs[i])), p=pp))
+    
+    return np.array(option_list).reshape(col_idx.shape)
+
+def random_choice_prob_index(a, axis=1):
+    r = np.expand_dims(np.random.rand(a.shape[1 - axis]), axis=axis)
+    return (a.cumsum(axis=axis) > r).argmax(axis=axis)
+
+def maximum_interval(output_info):
+    max_interval = 0
+    for item in output_info:
+        max_interval = max(max_interval, item[0])
+    return max_interval
+
+class Cond(object):
+    def __init__(self, data, output_info):
+       
+        self.model = []
+        st = 0
+        counter = 0
+        for item in output_info:
+           
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':
+                ed = st + item[0]
+                counter += 1
+                self.model.append(np.argmax(data[:, st:ed], axis=-1))
+                st = ed
+            
+        self.interval = []
+        self.n_col = 0  
+        self.n_opt = 0  
+        st = 0
+        self.p = np.zeros((counter, maximum_interval(output_info)))  
+        self.p_sampling = []
+        for item in output_info:
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':            
+                ed = st + item[0]
+                tmp = np.sum(data[:, st:ed], axis=0)  
+                tmp_sampling = np.sum(data[:, st:ed], axis=0)     
+                tmp = np.log(tmp + 1)  
+                tmp = tmp / np.sum(tmp)
+                tmp_sampling = tmp_sampling / np.sum(tmp_sampling)
+                self.p_sampling.append(tmp_sampling)
+                self.p[self.n_col, :item[0]] = tmp
+                self.interval.append((self.n_opt, item[0]))
+                self.n_opt += item[0]
+                self.n_col += 1
+                st = ed
+                
+        self.interval = np.asarray(self.interval)
+        
+    def sample_train(self, batch):
+        if self.n_col == 0:
+            return None
+        batch = batch
+
+        idx = np.random.choice(np.arange(self.n_col), batch)
+
+        vec = np.zeros((batch, self.n_opt), dtype='float32')
+        mask = np.zeros((batch, self.n_col), dtype='float32')
+        mask[np.arange(batch), idx] = 1  
+        opt1prime = random_choice_prob_index(self.p[idx]) 
+        for i in np.arange(batch):
+            vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1
+            
+        return vec, mask, idx, opt1prime
+
+    def sample(self, batch):
+        if self.n_col == 0:
+            return None
+        batch = batch
+      
+        idx = np.random.choice(np.arange(self.n_col), batch)
+
+        vec = np.zeros((batch, self.n_opt), dtype='float32')
+        opt1prime = random_choice_prob_index_sampling(self.p_sampling,idx)
+        
+        for i in np.arange(batch):
+            vec[i, self.interval[idx[i], 0] + opt1prime[i]] = 1
+            
+        return vec
+
+def cond_loss(data, output_info, c, m):
+    loss = []
+    st = 0
+    st_c = 0
+    for item in output_info:
+        if item[1] == 'tanh':
+            st += item[0]
+            continue
+
+        elif item[1] == 'softmax':
+            ed = st + item[0]
+            ed_c = st_c + item[0]
+            tmp = F.cross_entropy(
+            data[:, st:ed],
+            torch.argmax(c[:, st_c:ed_c], dim=1),
+            reduction='none')
+            loss.append(tmp)
+            st = ed
+            st_c = ed_c
+
+    loss = torch.stack(loss, dim=1)
+    return (loss * m).sum() / data.size()[0]
+
+class Sampler(object):
+    def __init__(self, data, output_info):
+        super(Sampler, self).__init__()
+        self.data = data
+        self.model = []
+        self.n = len(data)
+        st = 0
+        for item in output_info:
+            if item[1] == 'tanh':
+                st += item[0]
+                continue
+            elif item[1] == 'softmax':
+                ed = st + item[0]
+                tmp = []
+                for j in range(item[0]):
+                    tmp.append(np.nonzero(data[:, st + j])[0])
+                self.model.append(tmp)
+                st = ed
+                
+    def sample(self, n, col, opt):
+        if col is None:
+            idx = np.random.choice(np.arange(self.n), n)
+            return self.data[idx]
+        idx = []
+        for c, o in zip(col, opt):
+            idx.append(np.random.choice(self.model[c][o]))
+        return self.data[idx]
+
+class Discriminator(Module):
+    def __init__(self, side, layers):
+        super(Discriminator, self).__init__()
+        self.side = side
+        info = len(layers)-2
+        self.seq = Sequential(*layers)
+        self.seq_info = Sequential(*layers[:info])
+
+    def forward(self, input):
+        return (self.seq(input)), self.seq_info(input)
+
+class Generator(Module):
+    def __init__(self, side, layers):
+        super(Generator, self).__init__()
+        self.side = side
+        self.seq = Sequential(*layers)
+
+    def forward(self, input_):
+        return self.seq(input_)
+
+def determine_layers_disc(side, num_channels):
+    assert side >= 4 and side <= 32
+
+    layer_dims = [(1, side), (num_channels, side // 2)]
+
+    while layer_dims[-1][1] > 3 and len(layer_dims) < 4:
+        layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2))
+
+    layers_D = []
+    for prev, curr in zip(layer_dims, layer_dims[1:]):
+        layers_D += [
+            Conv2d(prev[0], curr[0], 4, 2, 1, bias=False),
+            BatchNorm2d(curr[0]),
+            LeakyReLU(0.2, inplace=True)
+        ]
+    print()
+    layers_D += [
+
+        Conv2d(layer_dims[-1][0], 1, layer_dims[-1][1], 1, 0), 
+        Sigmoid() 
+    ]
+    
+    return layers_D
+
+def determine_layers_gen(side, random_dim, num_channels):
+    assert side >= 4 and side <= 32
+
+    layer_dims = [(1, side), (num_channels, side // 2)]
+
+    while layer_dims[-1][1] > 3 and len(layer_dims) < 4:
+        layer_dims.append((layer_dims[-1][0] * 2, layer_dims[-1][1] // 2))
+
+    layers_G = [
+        ConvTranspose2d(
+            random_dim, layer_dims[-1][0], layer_dims[-1][1], 1, 0, output_padding=0, bias=False)
+    ]
+
+    for prev, curr in zip(reversed(layer_dims), reversed(layer_dims[:-1])):
+        layers_G += [
+            BatchNorm2d(prev[0]),
+            ReLU(True),
+            ConvTranspose2d(prev[0], curr[0], 4, 2, 1, output_padding=0, bias=True)
+        ]
+    return layers_G
+
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    
+    if classname.find('Conv') != -1:
+        init.normal_(m.weight.data, 0.0, 0.02)
+
+    elif classname.find('BatchNorm') != -1:
+        init.normal_(m.weight.data, 1.0, 0.02)
+        init.constant_(m.bias.data, 0)
+
+class CTABGANSynthesizer:
+    def __init__(self,
+                 lr=2e-4,
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=128,
+                 num_channels=64,
+                 l2scale=1e-5,
+                 batch_size=1024,
+                 epochs=1,
+                 device=torch.device("cpu")):
+                 
+
+        self.random_dim = random_dim
+        self.class_dim = class_dim
+        self.num_channels = num_channels
+        self.dside = None
+        self.gside = None
+        self.l2scale = l2scale
+        self.lr = lr
+        self.batch_size = batch_size
+        self.epochs = epochs
+        self.device = device
+
+    def fit(self, train_data=pd.DataFrame, categorical=[], mixed={}, type={}, no_train=False):
+        print("Fit started.")
+        problem_type = None
+        target_index=None
+        if type:
+            problem_type = list(type.keys())[0]
+            if problem_type:
+                target_index = train_data.columns.get_loc(type[problem_type])
+
+        self.transformer = DataTransformer(train_data=train_data, categorical_list=categorical, mixed_dict=mixed)
+        self.transformer.fit() 
+        
+        train_data = self.transformer.transform(train_data.values)
+        
+        data_sampler = Sampler(train_data, self.transformer.output_info)
+        data_dim = self.transformer.output_dim
+        self.cond_generator = Cond(train_data, self.transformer.output_info)
+        		
+        sides = [4, 8, 16, 24, 32]
+        col_size_d = data_dim + self.cond_generator.n_opt
+        for i in sides:
+            if i * i >= col_size_d:
+                self.dside = i
+                break
+        
+        sides = [4, 8, 16, 24, 32]
+        col_size_g = data_dim
+        for i in sides:
+            if i * i >= col_size_g:
+                self.gside = i
+                break
+        
+        layers_G = determine_layers_gen(self.gside, self.random_dim+self.cond_generator.n_opt, self.num_channels)
+        layers_D = determine_layers_disc(self.dside, self.num_channels)
+        
+        self.generator = Generator(self.gside, layers_G).to(self.device)
+        discriminator = Discriminator(self.dside, layers_D).to(self.device)
+        optimizer_params = dict(lr=self.lr, betas=(0.5, 0.9), eps=1e-3, weight_decay=self.l2scale)
+        optimizerG = Adam(self.generator.parameters(), **optimizer_params)
+        optimizerD = Adam(discriminator.parameters(), **optimizer_params)
+
+        st_ed = None
+        classifier=None
+        optimizerC= None
+        if target_index != None:
+            st_ed= get_st_ed(target_index,self.transformer.output_info)
+            classifier = Classifier(data_dim,self.class_dim,st_ed).to(self.device)
+            optimizerC = optim.Adam(classifier.parameters(),**optimizer_params)
+        
+        
+        self.generator.apply(weights_init)
+        discriminator.apply(weights_init)
+
+        self.Gtransformer = ImageTransformer(self.gside)       
+        self.Dtransformer = ImageTransformer(self.dside)
+        
+        
+        if no_train: return
+
+        print("Training started.")
+        for i in range(self.epochs):
+            # for _ in range(steps_per_epoch):
+                
+            noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+            condvec = self.cond_generator.sample_train(self.batch_size)
+
+            c, m, col, opt = condvec
+            c = torch.from_numpy(c).to(self.device)
+            m = torch.from_numpy(m).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                
+            perm = np.arange(self.batch_size)
+            np.random.shuffle(perm)
+            real = data_sampler.sample(self.batch_size, col[perm], opt[perm])
+            c_perm = c[perm]
+                
+            real = torch.from_numpy(real.astype('float32')).to(self.device)
+                
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket, self.transformer.output_info)
+                
+            fake_cat = torch.cat([fakeact, c], dim=1)
+            real_cat = torch.cat([real, c_perm], dim=1)
+                
+            real_cat_d = self.Dtransformer.transform(real_cat)
+            fake_cat_d = self.Dtransformer.transform(fake_cat)
+                
+            optimizerD.zero_grad()
+            y_real,_ = discriminator(real_cat_d)
+            y_fake,_ = discriminator(fake_cat_d)
+            loss_d = (-(torch.log(y_real + 1e-4).mean()) - (torch.log(1. - y_fake + 1e-4).mean()))
+            loss_d.backward()
+            optimizerD.step()
+                
+            noisez = torch.randn(self.batch_size, self.random_dim, device=self.device)
+            
+            condvec = self.cond_generator.sample_train(self.batch_size)
+
+            c, m, col, opt = condvec
+            c = torch.from_numpy(c).to(self.device)
+            m = torch.from_numpy(m).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(self.batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+
+            optimizerG.zero_grad()
+
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket, self.transformer.output_info)
+
+            fake_cat = torch.cat([fakeact, c], dim=1) 
+            fake_cat = self.Dtransformer.transform(fake_cat)
+                
+            y_fake,info_fake = discriminator(fake_cat)
+            
+            cross_entropy = cond_loss(faket, self.transformer.output_info, c, m)
+
+            _,info_real = discriminator(real_cat_d)
+            
+            g = -(torch.log(y_fake + 1e-4).mean()) + cross_entropy
+            g.backward(retain_graph=True)
+            loss_mean = torch.norm(torch.mean(info_fake.view(self.batch_size,-1), dim=0) - torch.mean(info_real.view(self.batch_size,-1), dim=0), 1)
+            loss_std = torch.norm(torch.std(info_fake.view(self.batch_size,-1), dim=0) - torch.std(info_real.view(self.batch_size,-1), dim=0), 1)
+            loss_info = loss_mean + loss_std 
+            loss_info.backward()
+            optimizerG.step()
+
+            if (i + 1) % 500 == 0:
+                print(f"Step: {i}/{self.epochs} Loss: {loss_mean:.4f}")
+
+            if problem_type:
+                        
+                fake = self.generator(noisez)
+                
+                faket = self.Gtransformer.inverse_transform(fake)
+                
+                fakeact = apply_activate(faket, self.transformer.output_info)
+                
+                real_pre, real_label = classifier(real)
+                fake_pre, fake_label = classifier(fakeact)
+                    
+                c_loss = CrossEntropyLoss() 
+                
+                if (st_ed[1] - st_ed[0])==1:
+                    c_loss= SmoothL1Loss()
+                    real_label = real_label.type_as(real_pre)
+                    fake_label = fake_label.type_as(fake_pre)
+                    real_label = torch.reshape(real_label,real_pre.size())
+                    fake_label = torch.reshape(fake_label,fake_pre.size())
+                    
+                
+                elif (st_ed[1] - st_ed[0])==2:
+                    c_loss = BCELoss()
+                    real_label = real_label.type_as(real_pre)
+                    fake_label = fake_label.type_as(fake_pre)
+
+                loss_cc = c_loss(real_pre, real_label)
+                loss_cg = c_loss(fake_pre, fake_label)
+
+                optimizerG.zero_grad()
+                loss_cg.backward()
+                optimizerG.step()
+
+                optimizerC.zero_grad()
+                loss_cc.backward()
+                optimizerC.step()
+                                
+    @torch.no_grad()
+    def sample(self, n, seed=0):
+
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        sample_batch_size = 8092
+        self.generator.eval()
+
+        output_info = self.transformer.output_info
+        steps = n // sample_batch_size + 1
+        
+        data = []
+        
+        for i in range(steps):
+            noisez = torch.randn(sample_batch_size, self.random_dim, device=self.device)
+            condvec = self.cond_generator.sample(sample_batch_size)
+            c = condvec
+            c = torch.from_numpy(c).to(self.device)
+            noisez = torch.cat([noisez, c], dim=1)
+            noisez =  noisez.view(sample_batch_size,self.random_dim+self.cond_generator.n_opt,1,1)
+                
+            fake = self.generator(noisez)
+            faket = self.Gtransformer.inverse_transform(fake)
+            fakeact = apply_activate(faket,output_info)
+            # print(len(data))
+            data.append(fakeact.detach().cpu().numpy())
+
+        data = np.concatenate(data, axis=0)
+        result = self.transformer.inverse_transform(data)
+        
+        return result[0:n]
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTAB-GAN/model/synthesizer/transformer.py

@@ -0,0 +1,363 @@
+import numpy as np
+import pandas as pd
+import torch
+from sklearn.mixture import BayesianGaussianMixture
+
+class DataTransformer():
+    
+    def __init__(self, train_data=pd.DataFrame, categorical_list=[], mixed_dict={}, n_clusters=10, eps=0.005):
+        self.meta = None
+        self.n_clusters = n_clusters
+        self.eps = eps
+        self.train_data = train_data
+        self.categorical_columns= categorical_list
+        self.mixed_columns= mixed_dict
+        
+    def get_metadata(self):
+        
+        meta = []
+    
+        for index in range(self.train_data.shape[1]):
+            column = self.train_data.iloc[:,index]
+            if index in self.categorical_columns:
+                mapper = column.value_counts().index.tolist()
+                meta.append({
+                        "name": index,
+                        "type": "categorical",
+                        "size": len(mapper),
+                        "i2s": mapper
+                })
+            elif index in self.mixed_columns.keys():
+                meta.append({
+                    "name": index,
+                    "type": "mixed",
+                    "min": column.min(),
+                    "max": column.max(),
+                    "modal": self.mixed_columns[index]
+                })
+            else:
+                meta.append({
+                    "name": index,
+                    "type": "continuous",
+                    "min": column.min(),
+                    "max": column.max(),
+                })            
+
+        return meta
+
+    def fit(self):
+        data = self.train_data.values
+        self.meta = self.get_metadata()
+        model = []
+        self.ordering = []
+        self.output_info = []
+        self.output_dim = 0
+        self.components = []
+        self.filter_arr = []
+        for id_, info in enumerate(self.meta):
+            if info['type'] == "continuous":
+                gm = BayesianGaussianMixture(n_components=self.n_clusters, 
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, 
+                    max_iter=100,n_init=1, random_state=42)
+                gm.fit(data[:, id_].reshape([-1, 1]))
+                mode_freq = (pd.Series(gm.predict(data[:, id_].reshape([-1, 1]))).value_counts().keys())
+                model.append(gm)
+                old_comp = gm.weights_ > self.eps
+                comp = []
+                for i in range(self.n_clusters):
+                    if (i in (mode_freq)) & old_comp[i]:
+                        comp.append(True)
+                    else:
+                        comp.append(False)
+                self.components.append(comp) 
+                self.output_info += [(1, 'tanh'), (np.sum(comp), 'softmax')]
+                self.output_dim += 1 + np.sum(comp)
+                
+            elif info['type'] == "mixed":
+                
+                gm1 = BayesianGaussianMixture(n_components=self.n_clusters, 
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, max_iter=100,
+                    n_init=1,random_state=42)
+                gm2 = BayesianGaussianMixture(n_components=self.n_clusters,
+                    weight_concentration_prior_type='dirichlet_process',
+                    weight_concentration_prior=0.001, max_iter=100,
+                    n_init=1,random_state=42)
+                
+                gm1.fit(data[:, id_].reshape([-1, 1]))
+                
+                filter_arr = []
+                for element in data[:, id_]:
+                    if element not in info['modal']:
+                        filter_arr.append(True)
+                    else:
+                        filter_arr.append(False)
+               
+                gm2.fit(data[:, id_][filter_arr].reshape([-1, 1]))
+                mode_freq = (pd.Series(gm2.predict(data[:, id_][filter_arr].reshape([-1, 1]))).value_counts().keys())
+                self.filter_arr.append(filter_arr)
+                model.append((gm1,gm2))
+               
+                old_comp = gm2.weights_ > self.eps
+                  
+                comp = []
+                  
+                for i in range(self.n_clusters):
+                    if (i in (mode_freq)) & old_comp[i]:
+                        comp.append(True)
+                    else:
+                        comp.append(False)
+
+                self.components.append(comp)
+
+                self.output_info += [(1, 'tanh'), (np.sum(comp) + len(info['modal']), 'softmax')]
+                self.output_dim += 1 + np.sum(comp) + len(info['modal'])
+            
+            else:
+                model.append(None)
+                self.components.append(None)
+                self.output_info += [(info['size'], 'softmax')]
+                self.output_dim += info['size']
+        
+        self.model = model
+
+    def transform(self, data, ispositive = False, positive_list = None):
+        values = []
+        mixed_counter = 0
+        for id_, info in enumerate(self.meta):
+            current = data[:, id_]
+            if info['type'] == "continuous":
+                current = current.reshape([-1, 1])
+                means = self.model[id_].means_.reshape((1, self.n_clusters))
+                stds = np.sqrt(self.model[id_].covariances_).reshape((1, self.n_clusters))
+                features = np.empty(shape=(len(current),self.n_clusters))
+                if ispositive == True:
+                    if id_ in positive_list:
+                        features = np.abs(current - means) / (4 * stds)
+                else:
+                    features = (current - means) / (4 * stds)
+
+                probs = self.model[id_].predict_proba(current.reshape([-1, 1]))
+                n_opts = sum(self.components[id_])
+                features = features[:, self.components[id_]]
+                probs = probs[:, self.components[id_]]
+
+                opt_sel = np.zeros(len(data), dtype='int')
+                for i in range(len(data)):
+                    pp = probs[i] + 1e-6
+                    pp = pp / sum(pp)
+                    opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)
+
+                idx = np.arange((len(features)))
+                features = features[idx, opt_sel].reshape([-1, 1])
+                features = np.clip(features, -.99, .99) 
+                probs_onehot = np.zeros_like(probs)
+                probs_onehot[np.arange(len(probs)), opt_sel] = 1
+
+                re_ordered_phot = np.zeros_like(probs_onehot)
+                  
+                col_sums = probs_onehot.sum(axis=0)
+                  
+
+                n = probs_onehot.shape[1]
+                largest_indices = np.argsort(-1*col_sums)[:n]
+                self.ordering.append(largest_indices)
+                for id,val in enumerate(largest_indices):
+                    re_ordered_phot[:,id] = probs_onehot[:,val]
+                
+                  
+                values += [features, re_ordered_phot]
+                  
+            elif info['type'] == "mixed":
+                    
+                means_0 = self.model[id_][0].means_.reshape([-1])
+                stds_0 = np.sqrt(self.model[id_][0].covariances_).reshape([-1])
+
+                zero_std_list = []
+                means_needed = []
+                stds_needed = []
+
+                for mode in info['modal']:
+                    if mode!=-9999999:
+                        dist = []
+                        for idx,val in enumerate(list(means_0.flatten())):
+                            dist.append(abs(mode-val))
+                        index_min = np.argmin(np.array(dist))
+                        zero_std_list.append(index_min)
+                    else: continue
+
+                for idx in zero_std_list:
+                    means_needed.append(means_0[idx])
+                    stds_needed.append(stds_0[idx])
+                
+                
+                mode_vals = []
+
+                for i,j,k in zip(info['modal'],means_needed,stds_needed):
+                    this_val  = np.abs(i - j) / (4*k)
+                    mode_vals.append(this_val)
+                
+                if -9999999 in info["modal"]:
+                    mode_vals.append(0)
+                
+                current = current.reshape([-1, 1])
+                filter_arr = self.filter_arr[mixed_counter]
+                current = current[filter_arr]
+                
+                means = self.model[id_][1].means_.reshape((1, self.n_clusters))
+                stds = np.sqrt(self.model[id_][1].covariances_).reshape((1, self.n_clusters))
+                features = np.empty(shape=(len(current),self.n_clusters))
+                if ispositive == True:
+                    if id_ in positive_list:
+                        features = np.abs(current - means) / (4 * stds)
+                else:
+                    features = (current - means) / (4 * stds)
+
+                probs = self.model[id_][1].predict_proba(current.reshape([-1, 1]))
+
+                n_opts = sum(self.components[id_]) # 8
+                features = features[:, self.components[id_]]
+                probs = probs[:, self.components[id_]]
+                
+                opt_sel = np.zeros(len(current), dtype='int')
+                for i in range(len(current)):
+                    pp = probs[i] + 1e-6
+                    pp = pp / sum(pp)
+                    opt_sel[i] = np.random.choice(np.arange(n_opts), p=pp)
+                idx = np.arange((len(features)))
+                features = features[idx, opt_sel].reshape([-1, 1])
+                features = np.clip(features, -.99, .99)
+                probs_onehot = np.zeros_like(probs)
+                probs_onehot[np.arange(len(probs)), opt_sel] = 1
+                extra_bits = np.zeros([len(current), len(info['modal'])])
+                temp_probs_onehot = np.concatenate([extra_bits,probs_onehot], axis = 1)
+                final = np.zeros([len(data), 1 + probs_onehot.shape[1] + len(info['modal'])])
+                features_curser = 0
+                for idx, val in enumerate(data[:, id_]):
+                    if val in info['modal']:
+                        category_ = list(map(info['modal'].index, [val]))[0]
+                        final[idx, 0] = mode_vals[category_]
+                        final[idx, (category_+1)] = 1
+                    
+                    else:
+                        final[idx, 0] = features[features_curser]
+                        final[idx, (1+len(info['modal'])):] = temp_probs_onehot[features_curser][len(info['modal']):]
+                        features_curser = features_curser + 1
+               
+                just_onehot = final[:,1:]
+                re_ordered_jhot= np.zeros_like(just_onehot)
+                n = just_onehot.shape[1]
+                col_sums = just_onehot.sum(axis=0)
+                largest_indices = np.argsort(-1*col_sums)[:n]
+                self.ordering.append(largest_indices)
+                for id,val in enumerate(largest_indices):
+                      re_ordered_jhot[:,id] = just_onehot[:,val]
+                final_features = final[:,0].reshape([-1, 1])
+                values += [final_features, re_ordered_jhot]
+                mixed_counter = mixed_counter + 1
+    
+            else:
+                self.ordering.append(None)
+                col_t = np.zeros([len(data), info['size']])
+                idx = list(map(info['i2s'].index, current))
+                col_t[np.arange(len(data)), idx] = 1
+                values.append(col_t)
+                
+        return np.concatenate(values, axis=1)
+
+    def inverse_transform(self, data):
+        data_t = np.zeros([len(data), len(self.meta)])
+        st = 0
+        for id_, info in enumerate(self.meta):
+            if info['type'] == "continuous":
+                u = data[:, st]
+                v = data[:, st + 1:st + 1 + np.sum(self.components[id_])]
+                order = self.ordering[id_] 
+                v_re_ordered = np.zeros_like(v)
+
+                for id,val in enumerate(order):
+                    v_re_ordered[:,val] = v[:,id]
+                  
+                v = v_re_ordered
+
+                u = np.clip(u, -1, 1)
+                v_t = np.ones((data.shape[0], self.n_clusters)) * -100
+                v_t[:, self.components[id_]] = v
+                v = v_t
+                st += 1 + np.sum(self.components[id_])
+                means = self.model[id_].means_.reshape([-1])
+                stds = np.sqrt(self.model[id_].covariances_).reshape([-1])
+                p_argmax = np.argmax(v, axis=1)
+                std_t = stds[p_argmax]
+                mean_t = means[p_argmax]
+                tmp = u * 4 * std_t + mean_t
+                data_t[:, id_] = tmp
+
+            elif info['type'] == "mixed":
+
+                u = data[:, st]
+                full_v = data[:,(st+1):(st+1)+len(info['modal'])+np.sum(self.components[id_])]
+                order = self.ordering[id_]
+                full_v_re_ordered = np.zeros_like(full_v)
+
+                for id,val in enumerate(order):
+                    full_v_re_ordered[:,val] = full_v[:,id]
+                  
+                full_v = full_v_re_ordered                
+                mixed_v = full_v[:,:len(info['modal'])]
+                v = full_v[:,-np.sum(self.components[id_]):]
+
+                u = np.clip(u, -1, 1)
+                v_t = np.ones((data.shape[0], self.n_clusters)) * -100
+                v_t[:, self.components[id_]] = v
+                v = np.concatenate([mixed_v,v_t], axis=1)
+
+                st += 1 + np.sum(self.components[id_]) + len(info['modal'])
+                means = self.model[id_][1].means_.reshape([-1]) 
+                stds = np.sqrt(self.model[id_][1].covariances_).reshape([-1]) 
+                p_argmax = np.argmax(v, axis=1)
+
+                result = np.zeros_like(u)
+
+                for idx in range(len(data)):
+                    if p_argmax[idx] < len(info['modal']):
+                        argmax_value = p_argmax[idx]
+                        result[idx] = float(list(map(info['modal'].__getitem__, [argmax_value]))[0])
+                    else:
+                        std_t = stds[(p_argmax[idx]-len(info['modal']))]
+                        mean_t = means[(p_argmax[idx]-len(info['modal']))]
+                        result[idx] = u[idx] * 4 * std_t + mean_t
+            
+                data_t[:, id_] = result
+
+            else:
+                current = data[:, st:st + info['size']]
+                st += info['size']
+                idx = np.argmax(current, axis=1)
+                data_t[:, id_] = list(map(info['i2s'].__getitem__, idx))
+            
+        return data_t
+
+class ImageTransformer():
+
+    def __init__(self, side):
+    
+        self.height = side
+            
+    def transform(self, data):
+
+        if self.height * self.height > len(data[0]):
+            
+            padding = torch.zeros((len(data), self.height * self.height - len(data[0]))).to(data.device)
+            data = torch.cat([data, padding], axis=1)
+
+        return data.view(-1, 1, self.height, self.height)
+
+    def inverse_transform(self, data):
+        
+        data = data.view(-1, self.height * self.height)
+
+        return data
+
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.editorconfig

@@ -0,0 +1,24 @@
+# http://editorconfig.org
+
+root = true
+
+[*]
+indent_style = space
+indent_size = 4
+trim_trailing_whitespace = true
+insert_final_newline = true
+charset = utf-8
+end_of_line = lf
+
+[*.py]
+max_line_length = 99
+
+[*.bat]
+indent_style = tab
+end_of_line = crlf
+
+[LICENSE]
+insert_final_newline = false
+
+[Makefile]
+indent_style = tab

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/CODEOWNERS

@@ -0,0 +1,2 @@
+# Global rule:
+*           @sdv-dev/core-contributors

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,33 @@
+---
+name: Bug report
+about: Report an error that you found when using CTGAN
+title: ''
+labels: bug, pending review
+assignees: ''
+
+---
+
+### Environment Details
+
+Please indicate the following details about the environment in which you found the bug:
+
+* CTGAN version:
+* Python version:
+* Operating System:
+
+### Error Description
+
+<!--Replace this text with a description of what you were trying to get done.
+Tell us what happened, what went wrong, and what you expected to happen.-->
+
+### Steps to reproduce
+
+<!--Replace this text with a description of the steps that anyone can follow to
+reproduce the error. If the error happens only on a specific dataset, please
+consider attaching some example data to the issue so that others can use it
+to reproduce the error.-->
+
+```
+Paste the command(s) you ran and the output.
+If there was a crash, please include the traceback here.
+```

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,24 @@
+---
+name: Feature request
+about: Request a new feature that you would like to see implemented in CTGAN
+title: ''
+labels: new feature, pending review
+assignees: ''
+
+---
+
+### Problem Description
+
+<!--Replace this with a description of the problem that you think CTGAN should be able
+to solve and is not solving already-->
+
+### Expected behavior
+
+<!--Replace this a clear and concise description of what you would expect CTGAN with regards
+with the described problem. If possible, explain how you would like to interact with CTGAN
+and what the outcome of this interaction would be.-->
+
+### Additional context
+
+<!--Please provide any additional context that may be relevant to the issue here. If none,
+please remove this section.-->

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/ISSUE_TEMPLATE/question.md

@@ -0,0 +1,33 @@
+---
+name: Question
+about: Doubts about CTGAN usage
+title: ''
+labels: question, pending review
+assignees: ''
+
+---
+
+### Environment details
+
+If you are already running CTGAN, please indicate the following details about the environment in
+which you are running it:
+
+* CTGAN version:
+* Python version:
+* Operating System:
+
+### Problem description
+
+<!--Replace this with a description of the problem that you are trying to solve using CTGAN. If
+possible, describe the data that you are using, or consider attaching some example data
+that others can use to propose a working solution for your problem.-->
+
+### What I already tried
+
+<!--Replace with a description of what you already tried and what is the behavior that you observe.
+If possible, also add below the exact code that you are running.-->
+
+```
+Paste the command(s) you ran and the output.
+If there was a crash, please include the traceback here.
+```

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/integration.yml

@@ -0,0 +1,31 @@
+name: Integration Tests
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  unit:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+        os: [ubuntu-latest, macos-10.15, windows-latest]
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - if: matrix.os == 'windows-latest'
+      name: Install dependencies - Windows
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install 'torch>=1.8,<2' -f https://download.pytorch.org/whl/cpu/torch/
+          python -m pip install 'torchvision>=0.9.0,<1' -f https://download.pytorch.org/whl/cpu/torchvision/
+    - name: Install dependencies
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install invoke .[test]
+    - name: Run integration tests
+      run: invoke integration

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/lint.yml

@@ -0,0 +1,21 @@
+name: Style Checks
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install invoke .[dev]
+    - name: Run lint checks
+      run: invoke lint

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/minimum.yml

@@ -0,0 +1,31 @@
+name: Unit Tests Minimum Versions
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  minimum:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+        os: [ubuntu-latest, macos-10.15, windows-latest]
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - if: matrix.os == 'windows-latest'
+      name: Install dependencies - Windows
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install 'torch==1.8' -f https://download.pytorch.org/whl/cpu/torch/
+          python -m pip install 'torchvision==0.9.0' -f https://download.pytorch.org/whl/cpu/torchvision/
+    - name: Install dependencies
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install invoke .[test]
+    - name: Test with minimum versions
+      run: invoke minimum

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/readme.yml

@@ -0,0 +1,25 @@
+name: Test README
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  readme:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+        os: [ubuntu-latest, macos-10.15]   # skip windows bc rundoc fails
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install invoke rundoc .
+    - name: Run the README.md
+      run: invoke readme

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.github/workflows/unit.yml

@@ -0,0 +1,34 @@
+name: Unit Tests
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  unit:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+        os: [ubuntu-latest, macos-10.15, windows-latest]
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - if: matrix.os == 'windows-latest'
+      name: Install dependencies - Windows
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install 'torch>=1.8,<2' -f https://download.pytorch.org/whl/cpu/torch/
+          python -m pip install 'torchvision>=0.9.0,<1' -f https://download.pytorch.org/whl/cpu/torchvision/
+    - name: Install dependencies
+      run: |
+          python -m pip install --upgrade pip
+          python -m pip install invoke .[test]
+    - name: Run unit tests
+      run: invoke unit
+    - if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.8
+      name: Upload codecov report
+      uses: codecov/codecov-action@v2

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.gitignore

@@ -0,0 +1,107 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+tests/readme_test/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+docs/api/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# Vim
+.*.swp

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/.travis.yml

@@ -0,0 +1,15 @@
+# Config file for automatic testing at travis-ci.org
+dist: bionic
+language: python
+python:
+  - 3.8
+  - 3.7
+  - 3.6
+
+# Command to install dependencies
+install: pip install -U tox-travis codecov
+
+after_success: codecov
+
+# Command to run tests
+script: tox

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/AUTHORS.rst

@@ -0,0 +1,13 @@
+Credits
+=======
+
+Research and Development Lead
+-----------------------------
+
+* Lei Xu <leix@mit.edu>
+
+Contributors
+------------
+
+* Carles Sala <csala@csail.mit.edu>
+* Kevin Kuo <kevinykuo@gmail.com>

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/CONTRIBUTING.rst

@@ -0,0 +1,237 @@
+.. highlight:: shell
+
+============
+Contributing
+============
+
+Contributions are welcome, and they are greatly appreciated! Every little bit
+helps, and credit will always be given.
+
+You can contribute in many ways:
+
+Types of Contributions
+----------------------
+
+Report Bugs
+~~~~~~~~~~~
+
+Report bugs at the `GitHub Issues page`_.
+
+If you are reporting a bug, please include:
+
+* Your operating system name and version.
+* Any details about your local setup that might be helpful in troubleshooting.
+* Detailed steps to reproduce the bug.
+
+Fix Bugs
+~~~~~~~~
+
+Look through the GitHub issues for bugs. Anything tagged with "bug" and "help
+wanted" is open to whoever wants to implement it.
+
+Implement Features
+~~~~~~~~~~~~~~~~~~
+
+Look through the GitHub issues for features. Anything tagged with "enhancement"
+and "help wanted" is open to whoever wants to implement it.
+
+Write Documentation
+~~~~~~~~~~~~~~~~~~~
+
+CTGAN could always use more documentation, whether as part of the
+official CTGAN docs, in docstrings, or even on the web in blog posts,
+articles, and such.
+
+Submit Feedback
+~~~~~~~~~~~~~~~
+
+The best way to send feedback is to file an issue at the `GitHub Issues page`_.
+
+If you are proposing a feature:
+
+* Explain in detail how it would work.
+* Keep the scope as narrow as possible, to make it easier to implement.
+* Remember that this is a volunteer-driven project, and that contributions
+  are welcome :)
+
+Get Started!
+------------
+
+Ready to contribute? Here's how to set up `CTGAN` for local development.
+
+1. Fork the `CTGAN` repo on GitHub.
+2. Clone your fork locally::
+
+    $ git clone git@github.com:your_name_here/CTGAN.git
+
+3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed,
+   this is how you set up your fork for local development::
+
+    $ mkvirtualenv CTGAN
+    $ cd CTGAN/
+    $ make install-develop
+
+4. Create a branch for local development::
+
+    $ git checkout -b name-of-your-bugfix-or-feature
+
+   Try to use the naming scheme of prefixing your branch with ``gh-X`` where X is
+   the associated issue, such as ``gh-3-fix-foo-bug``. And if you are not
+   developing on your own fork, further prefix the branch with your GitHub
+   username, like ``githubusername/gh-3-fix-foo-bug``.
+
+   Now you can make your changes locally.
+
+5. While hacking your changes, make sure to cover all your developments with the required
+   unit tests, and that none of the old tests fail as a consequence of your changes.
+   For this, make sure to run the tests suite and check the code coverage::
+
+    $ make lint       # Check code styling
+    $ make test       # Run the tests
+    $ make coverage   # Get the coverage report
+
+6. When you're done making changes, check that your changes pass all the styling checks and
+   tests, including other Python supported versions, using::
+
+    $ make test-all
+
+7. Make also sure to include the necessary documentation in the code as docstrings following
+   the `Google docstrings style`_.
+   If you want to view how your documentation will look like when it is published, you can
+   generate and view the docs with this command::
+
+    $ make view-docs
+
+8. Commit your changes and push your branch to GitHub::
+
+    $ git add .
+    $ git commit -m "Your detailed description of your changes."
+    $ git push origin name-of-your-bugfix-or-feature
+
+9. Submit a pull request through the GitHub website.
+
+Pull Request Guidelines
+-----------------------
+
+Before you submit a pull request, check that it meets these guidelines:
+
+1. It resolves an open GitHub Issue and contains its reference in the title or
+   the comment. If there is no associated issue, feel free to create one.
+2. Whenever possible, it resolves only **one** issue. If your PR resolves more than
+   one issue, try to split it in more than one pull request.
+3. The pull request should include unit tests that cover all the changed code
+4. If the pull request adds functionality, the docs should be updated. Put
+   your new functionality into a function with a docstring, and add the
+   feature to the documentation in an appropriate place.
+5. The pull request should work for all the supported Python versions. Check the `Travis Build
+   Status page`_ and make sure that all the checks pass.
+
+Unit Testing Guidelines
+-----------------------
+
+All the Unit Tests should comply with the following requirements:
+
+1. Unit Tests should be based only in unittest and pytest modules.
+
+2. The tests that cover a module called ``ctgan/path/to/a_module.py``
+   should be implemented in a separated module called
+   ``tests/ctgan/path/to/test_a_module.py``.
+   Note that the module name has the ``test_`` prefix and is located in a path similar
+   to the one of the tested module, just inside the ``tests`` folder.
+
+3. Each method of the tested module should have at least one associated test method, and
+   each test method should cover only **one** use case or scenario.
+
+4. Test case methods should start with the ``test_`` prefix and have descriptive names
+   that indicate which scenario they cover.
+   Names such as ``test_some_methed_input_none``, ``test_some_method_value_error`` or
+   ``test_some_method_timeout`` are right, but names like ``test_some_method_1``,
+   ``some_method`` or ``test_error`` are not.
+
+5. Each test should validate only what the code of the method being tested does, and not
+   cover the behavior of any third party package or tool being used, which is assumed to
+   work properly as far as it is being passed the right values.
+
+6. Any third party tool that may have any kind of random behavior, such as some Machine
+   Learning models, databases or Web APIs, will be mocked using the ``mock`` library, and
+   the only thing that will be tested is that our code passes the right values to them.
+
+7. Unit tests should not use anything from outside the test and the code being tested. This
+   includes not reading or writing to any file system or database, which will be properly
+   mocked.
+
+Tips
+----
+
+To run a subset of tests::
+
+    $ python -m pytest tests.test_ctgan
+    $ python -m pytest -k 'foo'
+
+Release Workflow
+----------------
+
+The process of releasing a new version involves several steps combining both ``git`` and
+``bumpversion`` which, briefly:
+
+1. Merge what is in ``master`` branch into ``stable`` branch.
+2. Update the version in ``setup.cfg``, ``ctgan/__init__.py`` and
+   ``HISTORY.md`` files.
+3. Create a new git tag pointing at the corresponding commit in ``stable`` branch.
+4. Merge the new commit from ``stable`` into ``master``.
+5. Update the version in ``setup.cfg`` and ``ctgan/__init__.py``
+   to open the next development iteration.
+
+.. note:: Before starting the process, make sure that ``HISTORY.md`` has been updated with a new
+          entry that explains the changes that will be included in the new version.
+          Normally this is just a list of the Pull Requests that have been merged to master
+          since the last release.
+
+Once this is done, run of the following commands:
+
+1. If you are releasing a patch version::
+
+    make release
+
+2. If you are releasing a minor version::
+
+    make release-minor
+
+3. If you are releasing a major version::
+
+    make release-major
+
+Release Candidates
+~~~~~~~~~~~~~~~~~~
+
+Sometimes it is necessary or convenient to upload a release candidate to PyPi as a pre-release,
+in order to make some of the new features available for testing on other projects before they
+are included in an actual full-blown release.
+
+In order to perform such an action, you can execute::
+
+    make release-candidate
+
+This will perform the following actions:
+
+1. Build and upload the current version to PyPi as a pre-release, with the format ``X.Y.Z.devN``
+
+2. Bump the current version to the next release candidate, ``X.Y.Z.dev(N+1)``
+
+After this is done, the new pre-release can be installed by including the ``dev`` section in the
+dependency specification, either in ``setup.py``::
+
+    install_requires = [
+        ...
+        'ctgan>=X.Y.Z.dev',
+        ...
+    ]
+
+or in command line::
+
+    pip install 'ctgan>=X.Y.Z.dev'
+
+
+.. _GitHub issues page: https://github.com/sdv-dev/CTGAN/issues
+.. _Travis Build Status page: https://travis-ci.org/sdv-dev/CTGAN/pull_requests
+.. _Google docstrings style: https://google.github.io/styleguide/pyguide.html?showone=Comments#Comments

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/HISTORY.md

@@ -0,0 +1,147 @@
+# History
+
+## v0.5.1 - 2022-02-25
+
+This release fixes a bug with the decoder instantiation, and also allows users to set a random state for the model
+fitting and sampling.
+
+### Issues closed
+
+* Update self.decoder with correct variable name - Issue [#203](https://github.com/sdv-dev/CTGAN/issues/203) by @tejuafonja
+* Add random state - Issue [#204](https://github.com/sdv-dev/CTGAN/issues/204) by @katxiao
+
+## v0.5.0 - 2021-11-18
+
+This release adds support for Python 3.9 and updates dependencies to ensure compatibility with the
+rest of the SDV ecosystem, and upgrades to the latests [RDT](https://github.com/sdv-dev/RDT/releases/tag/v0.6.1)
+release.
+
+### Issues closed
+
+* Add support for Python 3.9 - Issue [#177](https://github.com/sdv-dev/CTGAN/issues/177) by @pvk-developer
+* Add pip check to CI workflows - Issue [#174](https://github.com/sdv-dev/CTGAN/issues/174) by @pvk-developer
+* Typo in `CTGAN` code - Issue [#158](https://github.com/sdv-dev/CTGAN/issues/158) by @ori-katz100 and @fealho
+
+## v0.4.3 - 2021-07-12
+
+Dependency upgrades to ensure compatibility with the rest of the SDV ecosystem.
+
+## v0.4.2 - 2021-04-27
+
+In this release, the way in which the loss function of the TVAE model was computed has been fixed.
+In addition, the default value of the `discriminator_decay` has been changed to a more optimal
+value. Also some improvements to the tests were added.
+
+### Issues closed
+
+* `TVAE`: loss function - Issue [#143](https://github.com/sdv-dev/CTGAN/issues/143) by @fealho and @DingfanChen
+* Set `discriminator_decay` to `1e-6` - Pull request [#145](https://github.com/sdv-dev/CTGAN/pull/145/) by @fealho
+* Adds unit tests - Pull requests [#140](https://github.com/sdv-dev/CTGAN/pull/140) by @fealho
+
+## v0.4.1 - 2021-03-30
+
+This release exposes all the hyperparameters which the user may find useful for both `CTGAN`
+and `TVAE`. Also `TVAE` can now be fitted on datasets that are shorter than the batch
+size and drops the last batch only if the data size is not divisible by the batch size.
+
+### Issues closed
+
+* `TVAE`: Adapt `batch_size` to data size - Issue [#135](https://github.com/sdv-dev/CTGAN/issues/135) by @fealho and @csala
+* `ValueError` from `validate_discre_columns` with `uniqueCombinationConstraint` - Issue [133](https://github.com/sdv-dev/CTGAN/issues/133) by @fealho and @MLjungg
+
+## v0.4.0 - 2021-02-24
+
+Maintenance relese to upgrade dependencies to ensure compatibility with the rest
+of the SDV libraries.
+
+Also add a validation on the CTGAN `condition_column` and `condition_value` inputs.
+
+### Improvements
+
+* Validate condition_column and condition_value - Issue [#124](https://github.com/sdv-dev/CTGAN/issues/124) by @fealho
+
+## v0.3.1 - 2021-01-27
+
+### Improvements
+
+* Check discrete_columns valid before fitting - [Issue #35](https://github.com/sdv-dev/CTGAN/issues/35) by @fealho
+
+## Bugs fixed
+
+* ValueError: max() arg is an empty sequence - [Issue #115](https://github.com/sdv-dev/CTGAN/issues/115) by @fealho
+
+## v0.3.0 - 2020-12-18
+
+In this release we add a new TVAE model which was presented in the original CTGAN paper.
+It also exposes more hyperparameters and moves epochs and log_frequency from fit to the constructor.
+
+A new verbose argument has been added to optionally disable unnecessary printing, and a new hyperparameter
+called `discriminator_steps` has been added to CTGAN to control the number of optimization steps performed
+in the discriminator for each generator epoch.
+
+The code has also been reorganized and cleaned up for better readability and interpretability.
+
+Special thanks to @Baukebrenninkmeijer @fealho @leix28 @csala for the contributions!
+
+### Improvements
+
+* Add TVAE - [Issue #111](https://github.com/sdv-dev/CTGAN/issues/111) by @fealho
+* Move `log_frequency` to `__init__` - [Issue #102](https://github.com/sdv-dev/CTGAN/issues/102) by @fealho
+* Add discriminator steps hyperparameter - [Issue #101](https://github.com/sdv-dev/CTGAN/issues/101) by @Baukebrenninkmeijer
+* Code cleanup / Expose hyperparameters - [Issue #59](https://github.com/sdv-dev/CTGAN/issues/59) by @fealho and @leix28
+* Publish to conda repo - [Issue #54](https://github.com/sdv-dev/CTGAN/issues/54) by @fealho
+
+### Bugs fixed
+
+* Fixed NaN != NaN counting bug. - [Issue #100](https://github.com/sdv-dev/CTGAN/issues/100) by @fealho
+* Update dependencies and testing - [Issue #90](https://github.com/sdv-dev/CTGAN/issues/90) by @csala
+
+## v0.2.2 - 2020-11-13
+
+In this release we introduce several minor improvements to make CTGAN more versatile and
+propertly support new types of data, such as categorical NaN values, as well as conditional
+sampling and features to save and load models.
+
+Additionally, the dependency ranges and python versions have been updated to support up
+to date runtimes.
+
+Many thanks @fealho @leix28 @csala @oregonpillow and @lurosenb for working on making this release possible!
+
+### Improvements
+
+* Drop Python 3.5 support - [Issue #79](https://github.com/sdv-dev/CTGAN/issues/79) by @fealho
+* Support NaN values in categorical variables - [Issue #78](https://github.com/sdv-dev/CTGAN/issues/78) by @fealho
+* Sample synthetic data conditioning on a discrete column - [Issue #69](https://github.com/sdv-dev/CTGAN/issues/69) by @leix28
+* Support recent versions of pandas - [Issue #57](https://github.com/sdv-dev/CTGAN/issues/57) by @csala
+* Easy solution for restoring original dtypes - [Issue #26](https://github.com/sdv-dev/CTGAN/issues/26) by @oregonpillow
+
+### Bugs fixed
+
+* Loss to nan - [Issue #73](https://github.com/sdv-dev/CTGAN/issues/73) by @fealho
+* Swapped the sklearn utils testing import statement - [Issue #53](https://github.com/sdv-dev/CTGAN/issues/53) by @lurosenb
+
+## v0.2.1 - 2020-01-27
+
+Minor version including changes to ensure the logs are properly printed and
+the option to disable the log transformation to the discrete column frequencies.
+
+Special thanks to @kevinykuo for the contributions!
+
+### Issues Resolved:
+
+* Option to sample from true data frequency instead of logged frequency - [Issue #16](https://github.com/sdv-dev/CTGAN/issues/16) by @kevinykuo
+* Flush stdout buffer for epoch updates - [Issue #14](https://github.com/sdv-dev/CTGAN/issues/14) by @kevinykuo
+
+## v0.2.0 - 2019-12-18
+
+Reorganization of the project structure with a new Python API, new Command Line Interface
+and increased data format support.
+
+### Issues Resolved:
+
+* Reorganize the project structure - [Issue #10](https://github.com/sdv-dev/CTGAN/issues/10) by @csala
+* Move epochs to the fit method - [Issue #5](https://github.com/sdv-dev/CTGAN/issues/5) by @csala
+
+## v0.1.0 - 2019-11-07
+
+First Release - NeurIPS 2019 Version.

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/LICENSE

@@ -0,0 +1,22 @@
+MIT License
+
+Copyright (c) 2019, MIT Data To AI Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/MANIFEST.in

@@ -0,0 +1,11 @@
+include AUTHORS.rst
+include CONTRIBUTING.rst
+include HISTORY.md
+include LICENSE
+include README.md
+
+recursive-include tests *
+recursive-exclude * __pycache__
+recursive-exclude * *.py[co]
+
+recursive-include docs *.md *.rst conf.py Makefile make.bat *.jpg *.png *.gif

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/Makefile

@@ -0,0 +1,240 @@
+.DEFAULT_GOAL := help
+
+define BROWSER_PYSCRIPT
+import os, webbrowser, sys
+
+try:
+	from urllib import pathname2url
+except:
+	from urllib.request import pathname2url
+
+webbrowser.open("file://" + pathname2url(os.path.abspath(sys.argv[1])))
+endef
+export BROWSER_PYSCRIPT
+
+define PRINT_HELP_PYSCRIPT
+import re, sys
+
+for line in sys.stdin:
+	match = re.match(r'^([a-zA-Z_-]+):.*?## (.*)$$', line)
+	if match:
+		target, help = match.groups()
+		print("%-20s %s" % (target, help))
+endef
+export PRINT_HELP_PYSCRIPT
+
+BROWSER := python -c "$$BROWSER_PYSCRIPT"
+
+.PHONY: help
+help:
+	@python -c "$$PRINT_HELP_PYSCRIPT" < $(MAKEFILE_LIST)
+
+
+# CLEAN TARGETS
+
+.PHONY: clean-build
+clean-build: ## remove build artifacts
+	rm -fr build/
+	rm -fr dist/
+	rm -fr .eggs/
+	find . -name '*.egg-info' -exec rm -fr {} +
+	find . -name '*.egg' -exec rm -f {} +
+
+.PHONY: clean-pyc
+clean-pyc: ## remove Python file artifacts
+	find . -name '*.pyc' -exec rm -f {} +
+	find . -name '*.pyo' -exec rm -f {} +
+	find . -name '*~' -exec rm -f {} +
+	find . -name '__pycache__' -exec rm -fr {} +
+
+.PHONY: clean-coverage
+clean-coverage: ## remove coverage artifacts
+	rm -f .coverage
+	rm -f .coverage.*
+	rm -fr htmlcov/
+
+.PHONY: clean-test
+clean-test: ## remove test artifacts
+	rm -fr .tox/
+	rm -fr .pytest_cache
+
+.PHONY: clean
+clean: clean-build clean-pyc clean-test clean-coverage ## remove all build, test, coverage and Python artifacts
+
+
+# INSTALL TARGETS
+
+.PHONY: install
+install: clean-build clean-pyc ## install the package to the active Python's site-packages
+	pip install .
+
+.PHONY: install-test
+install-test: clean-build clean-pyc ## install the package and test dependencies
+	pip install .[test]
+
+.PHONY: install-develop
+install-develop: clean-build clean-pyc ## install the package in editable mode and dependencies for development
+	pip install -e .[dev]
+
+MINIMUM := $(shell sed -n '/install_requires = \[/,/]/p' setup.py | head -n-1 | tail -n+2 | sed 's/ *\(.*\),$?$$/\1/g' | tr '>' '=')
+
+.PHONY: install-minimum
+install-minimum: ## install the minimum supported versions of the package dependencies
+	pip install $(MINIMUM)
+
+
+# LINT TARGETS
+
+
+.PHONY: lint
+lint: ## check style with flake8 and isort
+	invoke lint
+
+.PHONY: fix-lint
+fix-lint: ## fix lint issues using autoflake, autopep8, and isort
+	find ctgan tests -name '*.py' | xargs autoflake --in-place --remove-all-unused-imports --remove-unused-variables
+	autopep8 --in-place --recursive --aggressive ctgan tests
+	isort --apply --atomic --recursive ctgan tests
+
+
+# TEST TARGETS
+
+.PHONY: test-unit
+test-unit: ## run unit tests using pytest
+	invoke unit
+
+.PHONY: test-integration
+test-integration: ## run integration tests using pytest
+	invoke integration
+
+.PHONY: test-readme
+test-readme: ## run the readme snippets
+	invoke readme
+
+.PHONY: check-dependencies
+check-dependencies: ## test if there are any broken dependencies
+	pip check
+
+.PHONY: test
+test: test-unit test-integration test-readme ## test everything that needs test dependencies
+
+.PHONY: test-devel
+test-devel: lint ## test everything that needs development dependencies
+
+.PHONY: test-all
+test-all: ## run tests on every Python version with tox
+	tox -r
+
+
+.PHONY: coverage
+coverage: ## check code coverage quickly with the default Python
+	coverage run --source ctgan -m pytest
+	coverage report -m
+	coverage html
+	$(BROWSER) htmlcov/index.html
+
+
+# RELEASE TARGETS
+
+.PHONY: dist
+dist: clean ## builds source and wheel package
+	python setup.py sdist
+	python setup.py bdist_wheel
+	ls -l dist
+
+.PHONY: publish-confirm
+publish-confirm:
+	@echo "WARNING: This will irreversibly upload a new version to PyPI!"
+	@echo -n "Please type 'confirm' to proceed: " \
+		&& read answer \
+		&& [ "$${answer}" = "confirm" ]
+
+.PHONY: publish-test
+publish-test: dist publish-confirm ## package and upload a release on TestPyPI
+	twine upload --repository-url https://test.pypi.org/legacy/ dist/*
+
+.PHONY: publish
+publish: dist publish-confirm ## package and upload a release
+	twine upload dist/*
+
+.PHONY: bumpversion-release
+bumpversion-release: ## Merge master to stable and bumpversion release
+	git checkout stable || git checkout -b stable
+	git merge --no-ff master -m"make release-tag: Merge branch 'master' into stable"
+	bumpversion release
+	git push --tags origin stable
+
+.PHONY: bumpversion-release-test
+bumpversion-release-test: ## Merge master to stable and bumpversion release
+	git checkout stable || git checkout -b stable
+	git merge --no-ff master -m"make release-tag: Merge branch 'master' into stable"
+	bumpversion release --no-tag
+	@echo git push --tags origin stable
+
+.PHONY: bumpversion-patch
+bumpversion-patch: ## Merge stable to master and bumpversion patch
+	git checkout master
+	git merge stable
+	bumpversion --no-tag patch
+	git push
+
+.PHONY: bumpversion-candidate
+bumpversion-candidate: ## Bump the version to the next candidate
+	bumpversion candidate --no-tag
+
+.PHONY: bumpversion-minor
+bumpversion-minor: ## Bump the version the next minor skipping the release
+	bumpversion --no-tag minor
+
+.PHONY: bumpversion-major
+bumpversion-major: ## Bump the version the next major skipping the release
+	bumpversion --no-tag major
+
+.PHONY: bumpversion-revert
+bumpversion-revert: ## Undo a previous bumpversion-release
+	git checkout master
+	git branch -D stable
+
+CLEAN_DIR := $(shell git status --short | grep -v ??)
+CURRENT_BRANCH := $(shell git rev-parse --abbrev-ref HEAD 2>/dev/null)
+CHANGELOG_LINES := $(shell git diff HEAD..origin/stable HISTORY.md 2>&1 | wc -l)
+
+.PHONY: check-clean
+check-clean: ## Check if the directory has uncommitted changes
+ifneq ($(CLEAN_DIR),)
+	$(error There are uncommitted changes)
+endif
+
+.PHONY: check-master
+check-master: ## Check if we are in master branch
+ifneq ($(CURRENT_BRANCH),master)
+	$(error Please make the release from master branch\n)
+endif
+
+.PHONY: check-history
+check-history: ## Check if HISTORY.md has been modified
+ifeq ($(CHANGELOG_LINES),0)
+	$(error Please insert the release notes in HISTORY.md before releasing)
+endif
+
+.PHONY: check-release
+check-release: check-clean check-master check-history ## Check if the release can be made
+	@echo "A new release can be made"
+
+.PHONY: release
+release: check-release bumpversion-release publish bumpversion-patch
+
+.PHONY: release-test
+release-test: check-release bumpversion-release-test publish-test bumpversion-revert
+
+.PHONY: release-candidate
+release-candidate: check-master publish bumpversion-candidate
+
+.PHONY: release-candidate-test
+release-candidate-test: check-clean check-master publish-test
+
+.PHONY: release-minor
+release-minor: check-release bumpversion-minor release
+
+.PHONY: release-major
+release-major: check-release bumpversion-major release

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/README.md

@@ -0,0 +1,182 @@
+<div align="center">
+<br/>
+<p align="center">
+    <i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a>, a project from <a href="https://datacebo.com">DataCebo</a>.</i>
+</p>
+
+[![Development Status](https://img.shields.io/badge/Development%20Status-2%20--%20Pre--Alpha-yellow)](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
+[![PyPI Shield](https://img.shields.io/pypi/v/ctgan.svg)](https://pypi.python.org/pypi/ctgan)
+[![Unit Tests](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml/badge.svg)](https://github.com/sdv-dev/CTGAN/actions/workflows/unit.yml)
+[![Downloads](https://pepy.tech/badge/ctgan)](https://pepy.tech/project/ctgan)
+[![Coverage Status](https://codecov.io/gh/sdv-dev/CTGAN/branch/master/graph/badge.svg)](https://codecov.io/gh/sdv-dev/CTGAN)
+
+<div align="left">
+<br/>
+<p align="center">
+<a href="https://github.com/sdv-dev/CTGAN">
+<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/CTGAN-DataCebo.png"></img>
+</a>
+</p>
+</div>
+
+</div>
+
+# Overview
+
+CTGAN is a collection of Deep Learning based Synthetic Data Generators for single table data, which are able to learn from real data and generate synthetic clones with high fidelity.
+
+| Important Links                               |                                                                      |
+| --------------------------------------------- | -------------------------------------------------------------------- |
+| :computer: **[Website]**                      | Check out the SDV Website for more information about the project.    |
+| :orange_book: **[SDV Blog]**                  | Regular publshing of useful content about Synthetic Data Generation. |
+| :book: **[Documentation]**                    | Quickstarts, User and Development Guides, and API Reference.         |
+| :octocat: **[Repository]**                    | The link to the Github Repository of this library.                   |
+| :scroll: **[License]**                        | The entire ecosystem is published under the MIT License.             |
+| :keyboard: **[Development Status]**           | This software is in its Pre-Alpha stage.                             |
+| [![][Slack Logo] **Community**][Community]    | Join our Slack Workspace for announcements and discussions.          |
+| [![][MyBinder Logo] **Tutorials**][Tutorials] | Run the SDV Tutorials in a Binder environment.                       |
+
+[Website]: https://sdv.dev
+[SDV Blog]: https://sdv.dev/blog
+[Documentation]: https://sdv.dev/SDV
+[Repository]: https://github.com/sdv-dev/CTGAN
+[License]: https://github.com/sdv-dev/CTGAN/blob/master/LICENSE
+[Development Status]: https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha
+[Slack Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/slack.png
+[Community]: https://join.slack.com/t/sdv-space/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw
+[MyBinder Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/mybinder.png
+[Tutorials]: https://mybinder.org/v2/gh/sdv-dev/SDV/master?filepath=tutorials
+
+## Implemented Models
+
+Currently, this library implements the **CTGAN** and **TVAE** models proposed in the [Modeling Tabular data using Conditional GAN](https://arxiv.org/abs/1907.00503) paper. For more information about these models, please check out the respective user guides:
+* [CTGAN User Guide](https://sdv.dev/SDV/user_guides/single_table/ctgan.html).
+* [TVAE User Guide](https://sdv.dev/SDV/user_guides/single_table/tvae.html).
+
+# Install
+
+**CTGAN** is part of the **SDV** project and is automatically installed alongside it. For
+details about this process please visit the [SDV Installation Guide](
+https://sdv.dev/SDV/getting_started/install.html)
+
+Optionally, **CTGAN** can also be installed as a standalone library using the following commands:
+
+**Using `pip`:**
+
+```bash
+pip install ctgan
+```
+
+**Using `conda`:**
+
+```bash
+conda install -c pytorch -c conda-forge ctgan
+```
+
+For more installation options please visit the [CTGAN installation Guide](INSTALL.md)
+
+# Usage Example
+
+> :warning: **WARNING**: If you're just getting started with synthetic data, we recommend using the SDV library which provides user-friendly APIs for interacting with CTGAN. To learn more about using CTGAN through SDV, check out the user guide [here](https://sdv.dev/SDV/user_guides/single_table/ctgan.html).
+
+To get started with CTGAN, you should prepare your data as either a `numpy.ndarray` or a `pandas.DataFrame` object with two types of columns:
+
+* **Continuous Columns**: can contain any numerical value.
+* **Discrete Columns**: contain a finite number values, whether these are string values or not.
+
+In this example we load the [Adult Census Dataset](https://archive.ics.uci.edu/ml/datasets/adult) which is a built-in demo dataset. We then model it using the **CTGANSynthesizer** and generate a synthetic copy of it.
+
+
+```python3
+from ctgan import CTGANSynthesizer
+from ctgan import load_demo
+
+data = load_demo()
+
+# Names of the columns that are discrete
+discrete_columns = [
+    'workclass',
+    'education',
+    'marital-status',
+    'occupation',
+    'relationship',
+    'race',
+    'sex',
+    'native-country',
+    'income'
+]
+
+ctgan = CTGANSynthesizer(epochs=10)
+ctgan.fit(data, discrete_columns)
+
+# Synthetic copy
+samples = ctgan.sample(1000)
+```
+
+
+
+# Join our community
+
+
+1. Please have a look at the [Contributing Guide](https://sdv.dev/SDV/developer_guides/contributing.html) to see how you can contribute to the project.
+2. If you have any doubts, feature requests or detect an error, please [open an issue on github](https://github.com/sdv-dev/CTGAN/issues) or [join our Slack Workspace](https://sdv-space.slack.com/join/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw#/).
+3. Also, do not forget to check the [project documentation site](https://sdv.dev/SDV/)!
+
+
+# Citing TGAN
+
+If you use CTGAN, please cite the following work:
+
+- *Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni.* **Modeling Tabular data using Conditional GAN**. NeurIPS, 2019.
+
+```LaTeX
+@inproceedings{xu2019modeling,
+  title={Modeling Tabular data using Conditional GAN},
+  author={Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2019}
+}
+```
+
+# Related Projects
+Please note that these libraries are external contributions and are not maintained nor supervised by
+the MIT DAI-Lab team.
+
+## R interface for CTGAN
+
+A wrapper around **CTGAN** has been implemented by Kevin Kuo @kevinykuo, bringing the functionalities
+of **CTGAN** to **R** users.
+
+More details can be found in the corresponding repository: https://github.com/kasaai/ctgan
+
+## CTGAN Server CLI
+
+A package to easily deploy **CTGAN** onto a remote server. This package is developed by Timothy Pillow @oregonpillow.
+
+More details can be found in the corresponding repository: https://github.com/oregonpillow/ctgan-server-cli
+
+---
+
+
+<div align="center">
+<a href="https://datacebo.com"><img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/DataCebo.png"></img></a>
+</div>
+<br/>
+<br/>
+
+[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
+https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
+created [DataCebo](https://datacebo.com) in 2020 with the goal of growing the project.
+Today, DataCebo is the proud developer of SDV, the largest ecosystem for
+synthetic data generation & evaluation. It is home to multiple libraries that support synthetic
+data, including:
+
+* 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
+* 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular,
+  multi table and time series data.
+* 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data
+  generation models.
+
+[Get started using the SDV package](https://sdv.dev/SDV/getting_started/install.html) -- a fully
+integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
+for specific needs.

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/conda/README.md

@@ -0,0 +1,29 @@
+## Instructions
+
+These are instructions to deploy the latest version of **CTGAN** to [conda](https://docs.conda.io/en/latest/).
+It should be done after every new release.
+
+## Update the recipe
+Prior to making the release on PyPI, you should update the meta.yaml to reflect any changes in the dependencies.
+Note that you do not need to edit the version number as that is managed by bumpversion.
+
+## Make the PyPI release
+Follow the standard release instructions to make a PyPI release. Then, return here to make the conda release.
+
+## Build a package
+As part of the PyPI release, you will have updated the stable branch. You should now check out the stable 
+branch and build the conda package.
+
+```bash
+git checkout stable
+cd conda
+conda build -c sdv-dev -c pytorch -c conda-forge .
+```
+
+## Upload to Anaconda
+Finally, you can upload the resulting package to Anaconda.
+
+```bash
+anaconda login
+anaconda upload -u sdv-dev <PATH_TO_PACKAGE>
+```

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/conda/meta.yaml

@@ -0,0 +1,51 @@
+{% set name = 'ctgan' %}
+{% set version = '0.5.2.dev0' %}
+
+package:
+  name: "{{ name|lower }}"
+  version: "{{ version }}"
+
+source:
+  url: "https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz"
+
+build:
+  number: 0
+  noarch: python
+  entry_points:
+    - ctgan=ctgan.__main__:main
+  script: "{{ PYTHON }} -m pip install . -vv"
+
+requirements:
+  host:
+    - pip
+    - pytest-runner
+    - packaging >=20,<22
+    - python >=3.6,<3.10
+    - numpy >=1.18.0,<2
+    - pandas >=1.1.3,<2
+    - scikit-learn >=0.24,<1
+    - pytorch >=1.8.0,<2
+    - torchvision >=0.9.0,<1
+    - rdt >=0.6.2,<0.7
+  run:
+    - packaging >=20,<22
+    - python >=3.6,<3.10
+    - numpy >=1.18.0,<2
+    - pandas >=1.1.3,<2
+    - scikit-learn >=0.24,<1
+    - pytorch >=1.8.0,<2
+    - torchvision >=0.9.0,<1
+    - rdt >=0.6.2,<0.7
+
+about:
+  home: "https://github.com/sdv-dev/CTGAN"
+  license: MIT
+  license_family: MIT
+  license_file:
+  summary: "Conditional GAN for Tabular Data"
+  doc_url:
+  dev_url:
+
+extra:
+  recipe-maintainers:
+    - sdv-dev

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__init__.py

@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+
+"""Top-level package for ctgan."""
+
+__author__ = 'MIT Data To AI Lab'
+__email__ = 'dailabmit@gmail.com'
+__version__ = '0.5.2.dev0'
+
+from .demo import load_demo
+from .synthesizers.ctgan import CTGANSynthesizer
+from .synthesizers.tvae import TVAESynthesizer
+
+__all__ = (
+    'CTGANSynthesizer',
+    'TVAESynthesizer',
+    'load_demo'
+)

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/__main__.py

@@ -0,0 +1,102 @@
+"""CLI."""
+
+import argparse
+
+from ctgan.data import read_csv, read_tsv, write_tsv
+from ctgan.synthesizers.ctgan import CTGANSynthesizer
+
+
+def _parse_args():
+    parser = argparse.ArgumentParser(description='CTGAN Command Line Interface')
+    parser.add_argument('-e', '--epochs', default=300, type=int,
+                        help='Number of training epochs')
+    parser.add_argument('-t', '--tsv', action='store_true',
+                        help='Load data in TSV format instead of CSV')
+    parser.add_argument('--no-header', dest='header', action='store_false',
+                        help='The CSV file has no header. Discrete columns will be indices.')
+
+    parser.add_argument('-m', '--metadata', help='Path to the metadata')
+    parser.add_argument('-d', '--discrete',
+                        help='Comma separated list of discrete columns without whitespaces.')
+    parser.add_argument('-n', '--num-samples', type=int,
+                        help='Number of rows to sample. Defaults to the training data size')
+
+    parser.add_argument('--generator_lr', type=float, default=2e-4,
+                        help='Learning rate for the generator.')
+    parser.add_argument('--discriminator_lr', type=float, default=2e-4,
+                        help='Learning rate for the discriminator.')
+
+    parser.add_argument('--generator_decay', type=float, default=1e-6,
+                        help='Weight decay for the generator.')
+    parser.add_argument('--discriminator_decay', type=float, default=0,
+                        help='Weight decay for the discriminator.')
+
+    parser.add_argument('--embedding_dim', type=int, default=128,
+                        help='Dimension of input z to the generator.')
+    parser.add_argument('--generator_dim', type=str, default='256,256',
+                        help='Dimension of each generator layer. '
+                        'Comma separated integers with no whitespaces.')
+    parser.add_argument('--discriminator_dim', type=str, default='256,256',
+                        help='Dimension of each discriminator layer. '
+                        'Comma separated integers with no whitespaces.')
+
+    parser.add_argument('--batch_size', type=int, default=500,
+                        help='Batch size. Must be an even number.')
+    parser.add_argument('--save', default=None, type=str,
+                        help='A filename to save the trained synthesizer.')
+    parser.add_argument('--load', default=None, type=str,
+                        help='A filename to load a trained synthesizer.')
+
+    parser.add_argument('--sample_condition_column', default=None, type=str,
+                        help='Select a discrete column name.')
+    parser.add_argument('--sample_condition_column_value', default=None, type=str,
+                        help='Specify the value of the selected discrete column.')
+
+    parser.add_argument('data', help='Path to training data')
+    parser.add_argument('output', help='Path of the output file')
+
+    return parser.parse_args()
+
+
+def main():
+    """CLI."""
+    args = _parse_args()
+    if args.tsv:
+        data, discrete_columns = read_tsv(args.data, args.metadata)
+    else:
+        data, discrete_columns = read_csv(args.data, args.metadata, args.header, args.discrete)
+
+    if args.load:
+        model = CTGANSynthesizer.load(args.load)
+    else:
+        generator_dim = [int(x) for x in args.generator_dim.split(',')]
+        discriminator_dim = [int(x) for x in args.discriminator_dim.split(',')]
+        model = CTGANSynthesizer(
+            embedding_dim=args.embedding_dim, generator_dim=generator_dim,
+            discriminator_dim=discriminator_dim, generator_lr=args.generator_lr,
+            generator_decay=args.generator_decay, discriminator_lr=args.discriminator_lr,
+            discriminator_decay=args.discriminator_decay, batch_size=args.batch_size,
+            epochs=args.epochs)
+    model.fit(data, discrete_columns)
+
+    if args.save is not None:
+        model.save(args.save)
+
+    num_samples = args.num_samples or len(data)
+
+    if args.sample_condition_column is not None:
+        assert args.sample_condition_column_value is not None
+
+    sampled = model.sample(
+        num_samples,
+        args.sample_condition_column,
+        args.sample_condition_column_value)
+
+    if args.tsv:
+        write_tsv(sampled, args.metadata, args.output)
+    else:
+        sampled.to_csv(args.output, index=False)
+
+
+if __name__ == '__main__':
+    main()

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data.py

@@ -0,0 +1,94 @@
+"""Data loading."""
+
+import json
+
+import numpy as np
+import pandas as pd
+
+
+def read_csv(csv_filename, meta_filename=None, header=True, discrete=None):
+    """Read a csv file."""
+    data = pd.read_csv(csv_filename, header='infer' if header else None)
+
+    if meta_filename:
+        with open(meta_filename) as meta_file:
+            metadata = json.load(meta_file)
+
+        discrete_columns = [
+            column['name']
+            for column in metadata['columns']
+            if column['type'] != 'continuous'
+        ]
+
+    elif discrete:
+        discrete_columns = discrete.split(',')
+        if not header:
+            discrete_columns = [int(i) for i in discrete_columns]
+
+    else:
+        discrete_columns = []
+
+    return data, discrete_columns
+
+
+def read_tsv(data_filename, meta_filename):
+    """Read a tsv file."""
+    with open(meta_filename) as f:
+        column_info = f.readlines()
+
+    column_info_raw = [
+        x.replace('{', ' ').replace('}', ' ').split()
+        for x in column_info
+    ]
+
+    discrete = []
+    continuous = []
+    column_info = []
+
+    for idx, item in enumerate(column_info_raw):
+        if item[0] == 'C':
+            continuous.append(idx)
+            column_info.append((float(item[1]), float(item[2])))
+        else:
+            assert item[0] == 'D'
+            discrete.append(idx)
+            column_info.append(item[1:])
+
+    meta = {
+        'continuous_columns': continuous,
+        'discrete_columns': discrete,
+        'column_info': column_info
+    }
+
+    with open(data_filename) as f:
+        lines = f.readlines()
+
+    data = []
+    for row in lines:
+        row_raw = row.split()
+        row = []
+        for idx, col in enumerate(row_raw):
+            if idx in continuous:
+                row.append(col)
+            else:
+                assert idx in discrete
+                row.append(column_info[idx].index(col))
+
+        data.append(row)
+
+    return np.asarray(data, dtype='float32'), meta['discrete_columns']
+
+
+def write_tsv(data, meta, output_filename):
+    """Write to a tsv file."""
+    with open(output_filename, 'w') as f:
+
+        for row in data:
+            for idx, col in enumerate(row):
+                if idx in meta['continuous_columns']:
+                    print(col, end=' ', file=f)
+                else:
+                    assert idx in meta['discrete_columns']
+                    print(meta['column_info'][idx][int(col)], end=' ', file=f)
+
+            print(file=f)

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_sampler.py

@@ -0,0 +1,156 @@
+"""DataSampler module."""
+
+import numpy as np
+
+
+class DataSampler(object):
+    """DataSampler samples the conditional vector and corresponding data for CTGAN."""
+
+    def __init__(self, data, output_info, log_frequency):
+        self._data = data
+
+        def is_discrete_column(column_info):
+            return (len(column_info) == 1
+                    and column_info[0].activation_fn == 'softmax')
+
+        n_discrete_columns = sum(
+            [1 for column_info in output_info if is_discrete_column(column_info)])
+
+        self._discrete_column_matrix_st = np.zeros(
+            n_discrete_columns, dtype='int32')
+
+        # Store the row id for each category in each discrete column.
+        # For example _rid_by_cat_cols[a][b] is a list of all rows with the
+        # a-th discrete column equal value b.
+        self._rid_by_cat_cols = []
+
+        # Compute _rid_by_cat_cols
+        st = 0
+        for column_info in output_info:
+            if is_discrete_column(column_info):
+                span_info = column_info[0]
+                ed = st + span_info.dim
+
+                rid_by_cat = []
+                for j in range(span_info.dim):
+                    rid_by_cat.append(np.nonzero(data[:, st + j])[0])
+                self._rid_by_cat_cols.append(rid_by_cat)
+                st = ed
+            else:
+                st += sum([span_info.dim for span_info in column_info])
+        assert st == data.shape[1]
+
+        # Prepare an interval matrix for efficiently sample conditional vector
+        max_category = max([
+            column_info[0].dim
+            for column_info in output_info
+            if is_discrete_column(column_info)
+        ], default=0)
+
+        self._discrete_column_cond_st = np.zeros(n_discrete_columns, dtype='int32')
+        self._discrete_column_n_category = np.zeros(n_discrete_columns, dtype='int32')
+        self._discrete_column_category_prob = np.zeros((n_discrete_columns, max_category))
+        self._n_discrete_columns = n_discrete_columns
+        self._n_categories = sum([
+            column_info[0].dim
+            for column_info in output_info
+            if is_discrete_column(column_info)
+        ])
+
+        st = 0
+        current_id = 0
+        current_cond_st = 0
+        for column_info in output_info:
+            if is_discrete_column(column_info):
+                span_info = column_info[0]
+                ed = st + span_info.dim
+                category_freq = np.sum(data[:, st:ed], axis=0)
+                if log_frequency:
+                    category_freq = np.log(category_freq + 1)
+                category_prob = category_freq / np.sum(category_freq)
+                self._discrete_column_category_prob[current_id, :span_info.dim] = category_prob
+                self._discrete_column_cond_st[current_id] = current_cond_st
+                self._discrete_column_n_category[current_id] = span_info.dim
+                current_cond_st += span_info.dim
+                current_id += 1
+                st = ed
+            else:
+                st += sum([span_info.dim for span_info in column_info])
+
+    def _random_choice_prob_index(self, discrete_column_id):
+        probs = self._discrete_column_category_prob[discrete_column_id]
+        r = np.expand_dims(np.random.rand(probs.shape[0]), axis=1)
+        return (probs.cumsum(axis=1) > r).argmax(axis=1)
+
+    def sample_condvec(self, batch):
+        """Generate the conditional vector for training.
+
+        Returns:
+            cond (batch x #categories):
+                The conditional vector.
+            mask (batch x #discrete columns):
+                A one-hot vector indicating the selected discrete column.
+            discrete column id (batch):
+                Integer representation of mask.
+            category_id_in_col (batch):
+                Selected category in the selected discrete column.
+        """
+        if self._n_discrete_columns == 0:
+            return None
+
+        discrete_column_id = np.random.choice(
+            np.arange(self._n_discrete_columns), batch)
+
+        cond = np.zeros((batch, self._n_categories), dtype='float32')
+        mask = np.zeros((batch, self._n_discrete_columns), dtype='float32')
+        mask[np.arange(batch), discrete_column_id] = 1
+        category_id_in_col = self._random_choice_prob_index(discrete_column_id)
+        category_id = (self._discrete_column_cond_st[discrete_column_id] + category_id_in_col)
+        cond[np.arange(batch), category_id] = 1
+
+        return cond, mask, discrete_column_id, category_id_in_col
+
+    def sample_original_condvec(self, batch):
+        """Generate the conditional vector for generation use original frequency."""
+        if self._n_discrete_columns == 0:
+            return None
+
+        cond = np.zeros((batch, self._n_categories), dtype='float32')
+
+        for i in range(batch):
+            row_idx = np.random.randint(0, len(self._data))
+            col_idx = np.random.randint(0, self._n_discrete_columns)
+            matrix_st = self._discrete_column_matrix_st[col_idx]
+            matrix_ed = matrix_st + self._discrete_column_n_category[col_idx]
+            pick = np.argmax(self._data[row_idx, matrix_st:matrix_ed])
+            cond[i, pick + self._discrete_column_cond_st[col_idx]] = 1
+
+        return cond
+
+    def sample_data(self, n, col, opt):
+        """Sample data from original training data satisfying the sampled conditional vector.
+
+        Returns:
+            n rows of matrix data.
+        """
+        if col is None:
+            idx = np.random.randint(len(self._data), size=n)
+            return self._data[idx]
+
+        idx = []
+        for c, o in zip(col, opt):
+            idx.append(np.random.choice(self._rid_by_cat_cols[c][o]))
+
+        return self._data[idx]
+
+    def dim_cond_vec(self):
+        """Return the total number of categories."""
+        return self._n_categories
+
+    def generate_cond_from_condition_column_info(self, condition_info, batch):
+        """Generate the condition vector."""
+        vec = np.zeros((batch, self._n_categories), dtype='float32')
+        id_ = self._discrete_column_matrix_st[condition_info['discrete_column_id']]
+        id_ += condition_info['value_id']
+        vec[:, id_] = 1
+        return vec

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/data_transformer.py

@@ -0,0 +1,217 @@
+"""DataTransformer module."""
+
+from collections import namedtuple
+
+import numpy as np
+import pandas as pd
+from rdt.transformers import BayesGMMTransformer, OneHotEncodingTransformer
+
+SpanInfo = namedtuple('SpanInfo', ['dim', 'activation_fn'])
+ColumnTransformInfo = namedtuple(
+    'ColumnTransformInfo', [
+        'column_name', 'column_type', 'transform', 'output_info', 'output_dimensions'
+    ]
+)
+
+
+class DataTransformer(object):
+    """Data Transformer.
+
+    Model continuous columns with a BayesianGMM and normalized to a scalar [0, 1] and a vector.
+    Discrete columns are encoded using a scikit-learn OneHotEncoder.
+    """
+
+    def __init__(self, max_clusters=10, weight_threshold=0.005):
+        """Create a data transformer.
+
+        Args:
+            max_clusters (int):
+                Maximum number of Gaussian distributions in Bayesian GMM.
+            weight_threshold (float):
+                Weight threshold for a Gaussian distribution to be kept.
+        """
+        self._max_clusters = max_clusters
+        self._weight_threshold = weight_threshold
+
+    def _fit_continuous(self, data):
+        """Train Bayesian GMM for continuous columns.
+
+        Args:
+            data (pd.DataFrame):
+                A dataframe containing a column.
+
+        Returns:
+            namedtuple:
+                A ``ColumnTransformInfo`` object.
+        """
+        column_name = data.columns[0]
+        gm = BayesGMMTransformer(max_clusters=min(len(data), 10))
+        gm.fit(data, [column_name])
+        num_components = sum(gm.valid_component_indicator)
+
+        return ColumnTransformInfo(
+            column_name=column_name, column_type='continuous', transform=gm,
+            output_info=[SpanInfo(1, 'tanh'), SpanInfo(num_components, 'softmax')],
+            output_dimensions=1 + num_components)
+
+    def _fit_discrete(self, data):
+        """Fit one hot encoder for discrete column.
+
+        Args:
+            data (pd.DataFrame):
+                A dataframe containing a column.
+
+        Returns:
+            namedtuple:
+                A ``ColumnTransformInfo`` object.
+        """
+        column_name = data.columns[0]
+        ohe = OneHotEncodingTransformer()
+        ohe.fit(data, [column_name])
+        num_categories = len(ohe.dummies)
+
+        return ColumnTransformInfo(
+            column_name=column_name, column_type='discrete', transform=ohe,
+            output_info=[SpanInfo(num_categories, 'softmax')],
+            output_dimensions=num_categories)
+
+    def fit(self, raw_data, discrete_columns=()):
+        """Fit the ``DataTransformer``.
+
+        Fits a ``BayesGMMTransformer`` for continuous columns and a
+        ``OneHotEncodingTransformer`` for discrete columns.
+
+        This step also counts the #columns in matrix data and span information.
+        """
+        self.output_info_list = []
+        self.output_dimensions = 0
+        self.dataframe = True
+
+        if not isinstance(raw_data, pd.DataFrame):
+            self.dataframe = False
+            # work around for RDT issue #328 Fitting with numerical column names fails
+            discrete_columns = [str(column) for column in discrete_columns]
+            column_names = [str(num) for num in range(raw_data.shape[1])]
+            raw_data = pd.DataFrame(raw_data, columns=column_names)
+
+        self._column_raw_dtypes = raw_data.infer_objects().dtypes
+        self._column_transform_info_list = []
+        for column_name in raw_data.columns:
+            if column_name in discrete_columns:
+                column_transform_info = self._fit_discrete(raw_data[[column_name]])
+            else:
+                column_transform_info = self._fit_continuous(raw_data[[column_name]])
+
+            self.output_info_list.append(column_transform_info.output_info)
+            self.output_dimensions += column_transform_info.output_dimensions
+            self._column_transform_info_list.append(column_transform_info)
+
+    def _transform_continuous(self, column_transform_info, data):
+        column_name = data.columns[0]
+        data.loc[:, column_name] = data[column_name].to_numpy().flatten()
+        gm = column_transform_info.transform
+        transformed = gm.transform(data, [column_name])
+
+        #  Converts the transformed data to the appropriate output format.
+        #  The first column (ending in '.normalized') stays the same,
+        #  but the lable encoded column (ending in '.component') is one hot encoded.
+        output = np.zeros((len(transformed), column_transform_info.output_dimensions))
+        output[:, 0] = transformed[f'{column_name}.normalized'].to_numpy()
+        index = transformed[f'{column_name}.component'].to_numpy().astype(int)
+        output[np.arange(index.size), index + 1] = 1.0
+
+        return output
+
+    def _transform_discrete(self, column_transform_info, data):
+        ohe = column_transform_info.transform
+        return ohe.transform(data).to_numpy()
+
+    def transform(self, raw_data):
+        """Take raw data and output a matrix data."""
+        if not isinstance(raw_data, pd.DataFrame):
+            column_names = [str(num) for num in range(raw_data.shape[1])]
+            raw_data = pd.DataFrame(raw_data, columns=column_names)
+
+        column_data_list = []
+        for column_transform_info in self._column_transform_info_list:
+            column_name = column_transform_info.column_name
+            data = raw_data[[column_name]]
+            if column_transform_info.column_type == 'continuous':
+                column_data_list.append(self._transform_continuous(column_transform_info, data))
+            else:
+                column_data_list.append(self._transform_discrete(column_transform_info, data))
+
+        return np.concatenate(column_data_list, axis=1).astype(float)
+
+    def _inverse_transform_continuous(self, column_transform_info, column_data, sigmas, st):
+        gm = column_transform_info.transform
+        data = pd.DataFrame(column_data[:, :2], columns=list(gm.get_output_types()))
+        data.iloc[:, 1] = np.argmax(column_data[:, 1:], axis=1)
+        if sigmas is not None:
+            selected_normalized_value = np.random.normal(data.iloc[:, 0], sigmas[st])
+            data.iloc[:, 0] = selected_normalized_value
+
+        return gm.reverse_transform(data, [column_transform_info.column_name])
+
+    def _inverse_transform_discrete(self, column_transform_info, column_data):
+        ohe = column_transform_info.transform
+        data = pd.DataFrame(column_data, columns=list(ohe.get_output_types()))
+        return ohe.reverse_transform(data)[column_transform_info.column_name]
+
+    def inverse_transform(self, data, sigmas=None):
+        """Take matrix data and output raw data.
+
+        Output uses the same type as input to the transform function.
+        Either np array or pd dataframe.
+        """
+        st = 0
+        recovered_column_data_list = []
+        column_names = []
+        for column_transform_info in self._column_transform_info_list:
+            dim = column_transform_info.output_dimensions
+            column_data = data[:, st:st + dim]
+            if column_transform_info.column_type == 'continuous':
+                recovered_column_data = self._inverse_transform_continuous(
+                    column_transform_info, column_data, sigmas, st)
+            else:
+                recovered_column_data = self._inverse_transform_discrete(
+                    column_transform_info, column_data)
+
+            recovered_column_data_list.append(recovered_column_data)
+            column_names.append(column_transform_info.column_name)
+            st += dim
+
+        recovered_data = np.column_stack(recovered_column_data_list)
+        recovered_data = (pd.DataFrame(recovered_data, columns=column_names)
+                          .astype(self._column_raw_dtypes))
+        if not self.dataframe:
+            recovered_data = recovered_data.to_numpy()
+
+        return recovered_data
+
+    def convert_column_name_value_to_id(self, column_name, value):
+        """Get the ids of the given `column_name`."""
+        discrete_counter = 0
+        column_id = 0
+        for column_transform_info in self._column_transform_info_list:
+            if column_transform_info.column_name == column_name:
+                break
+            if column_transform_info.column_type == 'discrete':
+                discrete_counter += 1
+
+            column_id += 1
+
+        else:
+            raise ValueError(f"The column_name `{column_name}` doesn't exist in the data.")
+
+        ohe = column_transform_info.transform
+        data = pd.DataFrame([value], columns=[column_transform_info.column_name])
+        one_hot = ohe.transform(data).to_numpy()[0]
+        if sum(one_hot) == 0:
+            raise ValueError(f"The value `{value}` doesn't exist in the column `{column_name}`.")
+
+        return {
+            'discrete_column_id': discrete_counter,
+            'column_id': column_id,
+            'value_id': np.argmax(one_hot)
+        }

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/demo.py

@@ -0,0 +1,10 @@
+"""Demo module."""
+
+import pandas as pd
+
+DEMO_URL = 'http://ctgan-data.s3.amazonaws.com/census.csv.gz'
+
+
+def load_demo():
+    """Load the demo."""
+    return pd.read_csv(DEMO_URL, compression='gzip')

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/__init__.py

@@ -0,0 +1,16 @@
+"""Synthesizers module."""
+
+from .ctgan import CTGANSynthesizer
+from .tvae import TVAESynthesizer
+
+__all__ = (
+    'CTGANSynthesizer',
+    'TVAESynthesizer'
+)
+
+
+def get_all_synthesizers():
+    return {
+        name: globals()[name]
+        for name in __all__
+    }

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/base.py

@@ -0,0 +1,105 @@
+"""BaseSynthesizer module."""
+
+import contextlib
+
+import numpy as np
+import torch
+
+
+@contextlib.contextmanager
+def set_random_states(random_state, set_model_random_state):
+    """Context manager for managing the random state.
+
+    Args:
+        random_state (int or tuple):
+            The random seed or a tuple of (numpy.random.RandomState, torch.Generator).
+        set_model_random_state (function):
+            Function to set the random state on the model.
+    """
+    original_np_state = np.random.get_state()
+    original_torch_state = torch.get_rng_state()
+
+    random_np_state, random_torch_state = random_state
+
+    np.random.set_state(random_np_state.get_state())
+    torch.set_rng_state(random_torch_state.get_state())
+
+    try:
+        yield
+    finally:
+        current_np_state = np.random.RandomState()
+        current_np_state.set_state(np.random.get_state())
+        current_torch_state = torch.Generator()
+        current_torch_state.set_state(torch.get_rng_state())
+        set_model_random_state((current_np_state, current_torch_state))
+
+        np.random.set_state(original_np_state)
+        torch.set_rng_state(original_torch_state)
+
+
+def random_state(function):
+    """Set the random state before calling the function.
+
+    Args:
+        function (Callable):
+            The function to wrap around.
+    """
+    def wrapper(self, *args, **kwargs):
+        if self.random_states is None:
+            return function(self, *args, **kwargs)
+
+        else:
+            with set_random_states(self.random_states, self.set_random_state):
+                return function(self, *args, **kwargs)
+
+    return wrapper
+
+
+class BaseSynthesizer:
+    """Base class for all default synthesizers of ``CTGAN``.
+
+    This should contain the save/load methods.
+    """
+
+    random_states = None
+
+    def save(self, path):
+        """Save the model in the passed `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):
+        """Load the model stored in the passed `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):
+        """Set the random state.
+
+        Args:
+            random_state (int, tuple, or None):
+                Either a tuple containing the (numpy.random.RandomState, torch.Generator)
+                or an int representing the random seed to use for both random states.
+        """
+        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`)')

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/ctgan.py

@@ -0,0 +1,482 @@
+"""CTGANSynthesizer module."""
+
+import warnings
+
+import numpy as np
+import pandas as pd
+import torch
+from packaging import version
+from torch import optim
+from torch.nn import BatchNorm1d, Dropout, LeakyReLU, Linear, Module, ReLU, Sequential, functional
+
+from ..data_sampler import DataSampler
+from ..data_transformer import DataTransformer
+from .base import BaseSynthesizer, random_state
+
+
+class Discriminator(Module):
+    """Discriminator for the CTGANSynthesizer."""
+
+    def __init__(self, input_dim, discriminator_dim, pac=10):
+        super(Discriminator, self).__init__()
+        dim = input_dim * pac
+        self.pac = pac
+        self.pacdim = dim
+        seq = []
+        for item in list(discriminator_dim):
+            seq += [Linear(dim, item), LeakyReLU(0.2), Dropout(0.5)]
+            dim = item
+
+        seq += [Linear(dim, 1)]
+        self.seq = Sequential(*seq)
+
+    def calc_gradient_penalty(self, real_data, fake_data, device='cpu', pac=10, lambda_=10):
+        """Compute the gradient penalty."""
+        alpha = torch.rand(real_data.size(0) // pac, 1, 1, device=device)
+        alpha = alpha.repeat(1, pac, real_data.size(1))
+        alpha = alpha.view(-1, real_data.size(1))
+
+        interpolates = alpha * real_data + ((1 - alpha) * fake_data)
+
+        disc_interpolates = self(interpolates)
+
+        gradients = torch.autograd.grad(
+            outputs=disc_interpolates, inputs=interpolates,
+            grad_outputs=torch.ones(disc_interpolates.size(), device=device),
+            create_graph=True, retain_graph=True, only_inputs=True
+        )[0]
+
+        gradients_view = gradients.view(-1, pac * real_data.size(1)).norm(2, dim=1) - 1
+        gradient_penalty = ((gradients_view) ** 2).mean() * lambda_
+
+        return gradient_penalty
+
+    def forward(self, input_):
+        """Apply the Discriminator to the `input_`."""
+        assert input_.size()[0] % self.pac == 0
+        return self.seq(input_.view(-1, self.pacdim))
+
+
+class Residual(Module):
+    """Residual layer for the CTGANSynthesizer."""
+
+    def __init__(self, i, o):
+        super(Residual, self).__init__()
+        self.fc = Linear(i, o)
+        self.bn = BatchNorm1d(o)
+        self.relu = ReLU()
+
+    def forward(self, input_):
+        """Apply the Residual layer to the `input_`."""
+        out = self.fc(input_)
+        out = self.bn(out)
+        out = self.relu(out)
+        return torch.cat([out, input_], dim=1)
+
+
+class Generator(Module):
+    """Generator for the CTGANSynthesizer."""
+
+    def __init__(self, embedding_dim, generator_dim, data_dim):
+        super(Generator, self).__init__()
+        dim = embedding_dim
+        seq = []
+        for item in list(generator_dim):
+            seq += [Residual(dim, item)]
+            dim += item
+        seq.append(Linear(dim, data_dim))
+        self.seq = Sequential(*seq)
+
+    def forward(self, input_):
+        """Apply the Generator to the `input_`."""
+        data = self.seq(input_)
+        return data
+
+
+class CTGANSynthesizer(BaseSynthesizer):
+    """Conditional Table GAN Synthesizer.
+
+    This is the core class of the CTGAN project, where the different components
+    are orchestrated together.
+    For more details about the process, please check the [Modeling Tabular data using
+    Conditional GAN](https://arxiv.org/abs/1907.00503) paper.
+
+    Args:
+        embedding_dim (int):
+            Size of the random sample passed to the Generator. Defaults to 128.
+        generator_dim (tuple or list of ints):
+            Size of the output samples for each one of the Residuals. A Residual Layer
+            will be created for each one of the values provided. Defaults to (256, 256).
+        discriminator_dim (tuple or list of ints):
+            Size of the output samples for each one of the Discriminator Layers. A Linear Layer
+            will be created for each one of the values provided. Defaults to (256, 256).
+        generator_lr (float):
+            Learning rate for the generator. Defaults to 2e-4.
+        generator_decay (float):
+            Generator weight decay for the Adam Optimizer. Defaults to 1e-6.
+        discriminator_lr (float):
+            Learning rate for the discriminator. Defaults to 2e-4.
+        discriminator_decay (float):
+            Discriminator weight decay for the Adam Optimizer. Defaults to 1e-6.
+        batch_size (int):
+            Number of data samples to process in each step.
+        discriminator_steps (int):
+            Number of discriminator updates to do for each generator update.
+            From the WGAN paper: https://arxiv.org/abs/1701.07875. WGAN paper
+            default is 5. Default used is 1 to match original CTGAN implementation.
+        log_frequency (boolean):
+            Whether to use log frequency of categorical levels in conditional
+            sampling. Defaults to ``True``.
+        verbose (boolean):
+            Whether to have print statements for progress results. Defaults to ``False``.
+        epochs (int):
+            Number of training epochs. Defaults to 300.
+        pac (int):
+            Number of samples to group together when applying the discriminator.
+            Defaults to 10.
+        cuda (bool):
+            Whether to attempt to use cuda for GPU computation.
+            If this is False or CUDA is not available, CPU will be used.
+            Defaults to ``True``.
+    """
+
+    def __init__(self, embedding_dim=128, generator_dim=(256, 256), discriminator_dim=(256, 256),
+                 generator_lr=2e-4, generator_decay=1e-6, discriminator_lr=2e-4,
+                 discriminator_decay=1e-6, batch_size=500, discriminator_steps=1,
+                 log_frequency=True, verbose=False, epochs=300, pac=10, cuda=True):
+
+        assert batch_size % 2 == 0
+
+        self._embedding_dim = embedding_dim
+        self._generator_dim = generator_dim
+        self._discriminator_dim = discriminator_dim
+
+        self._generator_lr = generator_lr
+        self._generator_decay = generator_decay
+        self._discriminator_lr = discriminator_lr
+        self._discriminator_decay = discriminator_decay
+
+        self._batch_size = batch_size
+        self._discriminator_steps = discriminator_steps
+        self._log_frequency = log_frequency
+        self._verbose = verbose
+        self._epochs = epochs
+        self.pac = pac
+
+        if not cuda or not torch.cuda.is_available():
+            device = 'cpu'
+        elif isinstance(cuda, str):
+            device = cuda
+        else:
+            device = 'cuda'
+
+        self._device = torch.device(device)
+
+        self._transformer = None
+        self._data_sampler = None
+        self._generator = None
+
+    @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.
+
+        For more details about the issue:
+        https://drive.google.com/file/d/1AA5wPfZ1kquaRtVruCd6BiYZGcDeNxyP/view?usp=sharing
+
+        Args:
+            logits […, num_features]:
+                Unnormalized log probabilities
+            tau:
+                Non-negative scalar temperature
+            hard (bool):
+                If True, the returned samples will be discretized as one-hot vectors,
+                but will be differentiated as if it is the soft sample in autograd
+            dim (int):
+                A dimension along which softmax will be computed. Default: -1.
+
+        Returns:
+            Sampled tensor of same shape as logits from the Gumbel-Softmax distribution.
+        """
+        if version.parse(torch.__version__) < version.parse('1.2.0'):
+            for i in range(10):
+                transformed = functional.gumbel_softmax(logits, tau=tau, hard=hard,
+                                                        eps=eps, dim=dim)
+                if not torch.isnan(transformed).any():
+                    return transformed
+            raise ValueError('gumbel_softmax returning NaN.')
+
+        return functional.gumbel_softmax(logits, tau=tau, hard=hard, eps=eps, dim=dim)
+
+    def _apply_activate(self, data):
+        """Apply proper activation function to the output of the generator."""
+        data_t = []
+        st = 0
+        for column_info in self._transformer.output_info_list:
+            for span_info in column_info:
+                if span_info.activation_fn == 'tanh':
+                    ed = st + span_info.dim
+                    data_t.append(torch.tanh(data[:, st:ed]))
+                    st = ed
+                elif span_info.activation_fn == 'softmax':
+                    ed = st + span_info.dim
+                    transformed = self._gumbel_softmax(data[:, st:ed], tau=0.2)
+                    data_t.append(transformed)
+                    st = ed
+                else:
+                    raise ValueError(f'Unexpected activation function {span_info.activation_fn}.')
+
+        return torch.cat(data_t, dim=1)
+
+    def _cond_loss(self, data, c, m):
+        """Compute the cross entropy loss on the fixed discrete column."""
+        loss = []
+        st = 0
+        st_c = 0
+        for column_info in self._transformer.output_info_list:
+            for span_info in column_info:
+                if len(column_info) != 1 or span_info.activation_fn != 'softmax':
+                    # not discrete column
+                    st += span_info.dim
+                else:
+                    ed = st + span_info.dim
+                    ed_c = st_c + span_info.dim
+                    tmp = functional.cross_entropy(
+                        data[:, st:ed],
+                        torch.argmax(c[:, st_c:ed_c], dim=1),
+                        reduction='none'
+                    )
+                    loss.append(tmp)
+                    st = ed
+                    st_c = ed_c
+
+        loss = torch.stack(loss, dim=1)  # noqa: PD013
+
+        return (loss * m).sum() / data.size()[0]
+
+    def _validate_discrete_columns(self, train_data, discrete_columns):
+        """Check whether ``discrete_columns`` exists in ``train_data``.
+
+        Args:
+            train_data (numpy.ndarray or pandas.DataFrame):
+                Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame.
+            discrete_columns (list-like):
+                List of discrete columns to be used to generate the Conditional
+                Vector. If ``train_data`` is a Numpy array, this list should
+                contain the integer indices of the columns. Otherwise, if it is
+                a ``pandas.DataFrame``, this list should contain the column names.
+        """
+        if isinstance(train_data, pd.DataFrame):
+            invalid_columns = set(discrete_columns) - set(train_data.columns)
+        elif isinstance(train_data, np.ndarray):
+            invalid_columns = []
+            for column in discrete_columns:
+                if column < 0 or column >= train_data.shape[1]:
+                    invalid_columns.append(column)
+        else:
+            raise TypeError('``train_data`` should be either pd.DataFrame or np.array.')
+
+        if invalid_columns:
+            raise ValueError(f'Invalid columns found: {invalid_columns}')
+
+    @random_state
+    def fit(self, train_data, discrete_columns=(), epochs=None):
+        """Fit the CTGAN Synthesizer models to the training data.
+
+        Args:
+            train_data (numpy.ndarray or pandas.DataFrame):
+                Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame.
+            discrete_columns (list-like):
+                List of discrete columns to be used to generate the Conditional
+                Vector. If ``train_data`` is a Numpy array, this list should
+                contain the integer indices of the columns. Otherwise, if it is
+                a ``pandas.DataFrame``, this list should contain the column names.
+        """
+        self._validate_discrete_columns(train_data, discrete_columns)
+
+        if epochs is None:
+            epochs = self._epochs
+        else:
+            warnings.warn(
+                ('`epochs` argument in `fit` method has been deprecated and will be removed '
+                 'in a future version. Please pass `epochs` to the constructor instead'),
+                DeprecationWarning
+            )
+
+        self._transformer = DataTransformer()
+        self._transformer.fit(train_data, discrete_columns)
+
+        train_data = self._transformer.transform(train_data)
+
+        self._data_sampler = DataSampler(
+            train_data,
+            self._transformer.output_info_list,
+            self._log_frequency)
+
+        data_dim = self._transformer.output_dimensions
+
+        self._generator = Generator(
+            self._embedding_dim + self._data_sampler.dim_cond_vec(),
+            self._generator_dim,
+            data_dim
+        ).to(self._device)
+
+        discriminator = Discriminator(
+            data_dim + self._data_sampler.dim_cond_vec(),
+            self._discriminator_dim,
+            pac=self.pac
+        ).to(self._device)
+
+        optimizerG = optim.Adam(
+            self._generator.parameters(), lr=self._generator_lr, betas=(0.5, 0.9),
+            weight_decay=self._generator_decay
+        )
+
+        optimizerD = optim.Adam(
+            discriminator.parameters(), lr=self._discriminator_lr,
+            betas=(0.5, 0.9), weight_decay=self._discriminator_decay
+        )
+
+        mean = torch.zeros(self._batch_size, self._embedding_dim, device=self._device)
+        std = mean + 1
+
+        print('CTGAN training')
+        steps_per_epoch = max(len(train_data) // self._batch_size, 1)
+        for i in range(epochs):
+            for n in range(self._discriminator_steps):
+                fakez = torch.normal(mean=mean, std=std)
+
+                condvec = self._data_sampler.sample_condvec(self._batch_size)
+                if condvec is None:
+                    c1, m1, col, opt = None, None, None, None
+                    real = self._data_sampler.sample_data(self._batch_size, col, opt)
+                else:
+                    c1, m1, col, opt = condvec
+                    c1 = torch.from_numpy(c1).to(self._device)
+                    m1 = torch.from_numpy(m1).to(self._device)
+                    fakez = torch.cat([fakez, c1], dim=1)
+
+                    perm = np.arange(self._batch_size)
+                    np.random.shuffle(perm)
+                    real = self._data_sampler.sample_data(
+                        self._batch_size, col[perm], opt[perm])
+                    c2 = c1[perm]
+
+                fake = self._generator(fakez)
+                fakeact = self._apply_activate(fake)
+
+                real = torch.from_numpy(real.astype('float32')).to(self._device)
+
+                if c1 is not None:
+                    fake_cat = torch.cat([fakeact, c1], dim=1)
+                    real_cat = torch.cat([real, c2], dim=1)
+                else:
+                    real_cat = real
+                    fake_cat = fakeact
+
+                y_fake = discriminator(fake_cat)
+                y_real = discriminator(real_cat)
+
+                pen = discriminator.calc_gradient_penalty(
+                    real_cat, fake_cat, self._device, self.pac)
+                loss_d = -(torch.mean(y_real) - torch.mean(y_fake))
+
+                optimizerD.zero_grad()
+                pen.backward(retain_graph=True)
+                loss_d.backward()
+                optimizerD.step()
+
+            fakez = torch.normal(mean=mean, std=std)
+            condvec = self._data_sampler.sample_condvec(self._batch_size)
+
+            if condvec is None:
+                c1, m1, col, opt = None, None, None, None
+            else:
+                c1, m1, col, opt = condvec
+                c1 = torch.from_numpy(c1).to(self._device)
+                m1 = torch.from_numpy(m1).to(self._device)
+                fakez = torch.cat([fakez, c1], dim=1)
+
+            fake = self._generator(fakez)
+            fakeact = self._apply_activate(fake)
+
+            if c1 is not None:
+                y_fake = discriminator(torch.cat([fakeact, c1], dim=1))
+            else:
+                y_fake = discriminator(fakeact)
+
+            if condvec is None:
+                cross_entropy = 0
+            else:
+                cross_entropy = self._cond_loss(fake, c1, m1)
+
+            loss_g = -torch.mean(y_fake) + cross_entropy
+
+            optimizerG.zero_grad()
+            loss_g.backward()
+            optimizerG.step()
+
+            if self._verbose and (i + 1) % 1000 == 0:
+                print(f'Epoch {i+1}, Loss G: {loss_g.detach().cpu(): .4f},'  # noqa: T001
+                      f'Loss D: {loss_d.detach().cpu(): .4f}',
+                      flush=True)
+
+    @random_state
+    def sample(self, n, condition_column=None, condition_value=None):
+        """Sample data similar to the training data.
+
+        Choosing a condition_column and condition_value will increase the probability of the
+        discrete condition_value happening in the condition_column.
+
+        Args:
+            n (int):
+                Number of rows to sample.
+            condition_column (string):
+                Name of a discrete column.
+            condition_value (string):
+                Name of the category in the condition_column which we wish to increase the
+                probability of happening.
+
+        Returns:
+            numpy.ndarray or pandas.DataFrame
+        """
+        if condition_column is not None and condition_value is not None:
+            condition_info = self._transformer.convert_column_name_value_to_id(
+                condition_column, condition_value)
+            global_condition_vec = self._data_sampler.generate_cond_from_condition_column_info(
+                condition_info, self._batch_size)
+        else:
+            global_condition_vec = None
+
+        steps = n // self._batch_size + 1
+        data = []
+        for i in range(steps):
+            mean = torch.zeros(self._batch_size, self._embedding_dim)
+            std = mean + 1
+            fakez = torch.normal(mean=mean, std=std).to(self._device)
+
+            if global_condition_vec is not None:
+                condvec = global_condition_vec.copy()
+            else:
+                condvec = self._data_sampler.sample_original_condvec(self._batch_size)
+
+            if condvec is None:
+                pass
+            else:
+                c1 = condvec
+                c1 = torch.from_numpy(c1).to(self._device)
+                fakez = torch.cat([fakez, c1], dim=1)
+
+            fake = self._generator(fakez)
+            fakeact = self._apply_activate(fake)
+            data.append(fakeact.detach().cpu().numpy())
+
+        data = np.concatenate(data, axis=0)
+        data = data[:n]
+
+        return self._transformer.inverse_transform(data)
+
+    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)

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/ctgan/synthesizers/tvae.py

@@ -0,0 +1,218 @@
+"""TVAESynthesizer module."""
+
+import numpy as np
+import torch
+from torch.nn import Linear, Module, Parameter, ReLU, Sequential
+from torch.nn.functional import cross_entropy
+from torch.optim import Adam
+from torch.utils.data import DataLoader, TensorDataset
+
+from ..data_transformer import DataTransformer
+from .base import BaseSynthesizer, random_state
+
+
+class Encoder(Module):
+    """Encoder for the TVAESynthesizer.
+
+    Args:
+        data_dim (int):
+            Dimensions of the data.
+        compress_dims (tuple or list of ints):
+            Size of each hidden layer.
+        embedding_dim (int):
+            Size of the output vector.
+    """
+
+    def __init__(self, data_dim, compress_dims, embedding_dim):
+        super(Encoder, self).__init__()
+        dim = data_dim
+        seq = []
+        for item in list(compress_dims):
+            seq += [
+                Linear(dim, item),
+                ReLU()
+            ]
+            dim = item
+
+        self.seq = Sequential(*seq)
+        self.fc1 = Linear(dim, embedding_dim)
+        self.fc2 = Linear(dim, embedding_dim)
+
+    def forward(self, input_):
+        """Encode the passed `input_`."""
+        feature = self.seq(input_)
+        mu = self.fc1(feature)
+        logvar = self.fc2(feature)
+        std = torch.exp(0.5 * logvar)
+        return mu, std, logvar
+
+
+class Decoder(Module):
+    """Decoder for the TVAESynthesizer.
+
+    Args:
+        embedding_dim (int):
+            Size of the input vector.
+        decompress_dims (tuple or list of ints):
+            Size of each hidden layer.
+        data_dim (int):
+            Dimensions of the data.
+    """
+
+    def __init__(self, embedding_dim, decompress_dims, data_dim):
+        super(Decoder, self).__init__()
+        dim = embedding_dim
+        seq = []
+        for item in list(decompress_dims):
+            seq += [Linear(dim, item), ReLU()]
+            dim = item
+
+        seq.append(Linear(dim, data_dim))
+        self.seq = Sequential(*seq)
+        self.sigma = Parameter(torch.ones(data_dim) * 0.1)
+
+    def forward(self, input_):
+        """Decode the passed `input_`."""
+        return self.seq(input_), self.sigma
+
+
+def _loss_function(recon_x, x, sigmas, mu, logvar, output_info, factor):
+    st = 0
+    loss = []
+    for column_info in output_info:
+        for span_info in column_info:
+            if span_info.activation_fn != 'softmax':
+                ed = st + span_info.dim
+                std = sigmas[st]
+                eq = x[:, st] - torch.tanh(recon_x[:, st])
+                loss.append((eq ** 2 / 2 / (std ** 2)).sum())
+                loss.append(torch.log(std) * x.size()[0])
+                st = ed
+
+            else:
+                ed = st + span_info.dim
+                loss.append(cross_entropy(
+                    recon_x[:, st:ed], torch.argmax(x[:, st:ed], dim=-1), reduction='sum'))
+                st = ed
+
+    assert st == recon_x.size()[1]
+    KLD = -0.5 * torch.sum(1 + logvar - mu**2 - logvar.exp())
+    return sum(loss) * factor / x.size()[0], KLD / x.size()[0]
+
+
+class TVAESynthesizer(BaseSynthesizer):
+    """TVAESynthesizer."""
+
+    def __init__(
+        self,
+        embedding_dim=128,
+        compress_dims=(128, 128),
+        decompress_dims=(128, 128),
+        l2scale=1e-5,
+        batch_size=500,
+        epochs=300,
+        lr=1e-3,
+        loss_factor=2,
+        device="cuda:0"
+    ):
+        self.embedding_dim = embedding_dim
+        self.compress_dims = compress_dims
+        self.decompress_dims = decompress_dims
+
+        self.lr = lr
+        self.l2scale = l2scale
+        self.batch_size = batch_size
+        self.loss_factor = loss_factor
+        self.epochs = epochs
+
+        
+        self._device = torch.device(device)
+
+    @random_state
+    def fit(self, train_data, discrete_columns=()):
+        """Fit the TVAE Synthesizer models to the training data.
+
+        Args:
+            train_data (numpy.ndarray or pandas.DataFrame):
+                Training Data. It must be a 2-dimensional numpy array or a pandas.DataFrame.
+            discrete_columns (list-like):
+                List of discrete columns to be used to generate the Conditional
+                Vector. If ``train_data`` is a Numpy array, this list should
+                contain the integer indices of the columns. Otherwise, if it is
+                a ``pandas.DataFrame``, this list should contain the column names.
+        """
+        self.transformer = DataTransformer()
+        self.transformer.fit(train_data, discrete_columns)
+        train_data = self.transformer.transform(train_data)
+        dataset = TensorDataset(torch.from_numpy(train_data.astype('float32')))
+        loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, drop_last=False)
+
+        data_dim = self.transformer.output_dimensions
+        encoder = Encoder(data_dim, self.compress_dims, self.embedding_dim).to(self._device)
+        self.decoder = Decoder(self.embedding_dim, self.decompress_dims, data_dim).to(self._device)
+        optimizerAE = Adam(
+            list(encoder.parameters()) + list(self.decoder.parameters()),
+            lr=self.lr,
+            weight_decay=self.l2scale)
+        data_iter = iter(loader) 
+        print('Training:')
+        for i in range(self.epochs):
+            try:
+                data = next(data_iter)
+            except:
+                data_iter = iter(loader)
+                data = next(data_iter)
+
+            optimizerAE.zero_grad()
+            real = data[0].to(self._device)
+            mu, std, logvar = encoder(real)
+            eps = torch.randn_like(std)
+            emb = eps * std + mu
+            rec, sigmas = self.decoder(emb)
+            loss_1, loss_2 = _loss_function(
+                rec, real, sigmas, mu, logvar,
+                self.transformer.output_info_list, self.loss_factor
+            )
+            loss = loss_1 + loss_2
+            loss.backward()
+            optimizerAE.step()
+            self.decoder.sigma.data.clamp_(0.01, 1.0)
+            if (i + 1) % 1000 == 0:
+                print(f"{i + 1}/{self.epochs} {loss}", flush=True)
+
+    @random_state
+    def sample(self, samples, seed=0):
+        """Sample data similar to the training data.
+
+        Args:
+            samples (int):
+                Number of rows to sample.
+
+        Returns:
+            numpy.ndarray or pandas.DataFrame
+        """
+
+        torch.cuda.manual_seed(seed)
+        torch.manual_seed(seed)
+
+        self.decoder.eval()
+        
+        sample_batch_size = 8092
+        steps = samples // sample_batch_size + 1
+        data = []
+        for _ in range(steps):
+            mean = torch.zeros(sample_batch_size, self.embedding_dim)
+            std = mean + 1
+            noise = torch.normal(mean=mean, std=std).to(self._device)
+            fake, sigmas = self.decoder(noise)
+            fake = torch.tanh(fake)
+            data.append(fake.detach().cpu().numpy())
+
+        data = np.concatenate(data, axis=0)
+        data = data[:samples]
+        return self.transformer.inverse_transform(data, sigmas.detach().cpu().numpy())
+
+    def set_device(self, device):
+        """Set the `device` to be used ('GPU' or 'CPU)."""
+        self._device = device
+        self.decoder.to(self._device)

syntheticSuccess/c6/tabddpm/tabddpm-c6-20260510_222430/_tabddpm_runtime/CTGAN/CTGAN/setup.cfg

@@ -0,0 +1,59 @@
+[bumpversion]
+current_version = 0.5.2.dev0
+commit = True
+tag = True
+parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?
+serialize = 
+	{major}.{minor}.{patch}.{release}{candidate}
+	{major}.{minor}.{patch}
+
+[bumpversion:part:release]
+optional_value = release
+first_value = dev
+values = 
+	dev
+	release
+
+[bumpversion:part:candidate]
+
+[bumpversion:file:setup.py]
+search = version='{current_version}'
+replace = version='{new_version}'
+
+[bumpversion:file:ctgan/__init__.py]
+search = __version__ = '{current_version}'
+replace = __version__ = '{new_version}'
+
+[bumpversion:file:conda/meta.yaml]
+search = version = '{current_version}'
+replace = version = '{new_version}'
+
+[bdist_wheel]
+universal = 1
+
+[flake8]
+convention = google
+max-line-length = 99
+exclude = docs, .tox, .git, __pycache__, .ipynb_checkpoints
+extend-ignore = D107,  # Missing docstring in __init__
+	D407,  # Missing dashed underline after section
+	D417,  # Missing argument descriptions in the docstring
+	SFS3, # String literal formatting using f-string.
+	VNE001  # Single letter variable names are not allowed
+per-file-ignores = 
+	ctgan/data.py:T001
+
+[isort]
+include_trailing_comment = True
+line_length = 99
+lines_between_types = 0
+multi_line_output = 4
+not_skip = __init__.py
+use_parentheses = True
+
+[aliases]
+test = pytest
+
+[tool:pytest]
+collect_ignore = ['setup.py']
+