Spaces:
Running
Running
File size: 7,542 Bytes
c4ac745 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | import numpy as np
import lib
from tab_ddpm.modules import MLPDiffusion, ResNetDiffusion
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
def get_model(
model_name,
model_params,
):
print(model_name)
if model_name == 'mlp':
model = MLPDiffusion(**model_params)
elif model_name == 'resnet':
model = ResNetDiffusion(**model_params)
else:
raise "Unknown model!"
return model
def update_ema(target_params, source_params, rate=0.999):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
"""
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src.detach(), alpha=1 - rate)
def concat_y_to_X(X, y):
if X is None:
return y.reshape(-1, 1)
return np.concatenate([y.reshape(-1, 1), X], axis=1)
def make_dataset_from_df(
df,
T,
is_y_cond,
ratios=[0.7, 0.2, 0.1],
df_info=None,
std=0
):
"""
The order of the generated dataset: (y, X_num, X_cat)
is_y_cond:
concat: y is concatenated to X, the model learn a joint distribution of (y, X)
embedding: y is not concatenated to X. During computations, y is embedded
and added to the latent vector of X
none: y column is completely ignored
How does is_y_cond affect the generation of y?
is_y_cond:
concat: the model synthesizes (y, X) directly, so y is just the first column
embedding: y is first sampled using empirical distribution of y. The model only
synthesizes X. When returning the generated data, we return the generated X
and the sampled y. (y is sampled from empirical distribution, instead of being
generated by the model)
Note that in this way, y is still not independent of X, because the model has been
adding the embedding of y to the latent vector of X during computations.
none:
y is synthesized using y's empirical distribution. X is generated by the model.
In this case, y is completely independent of X.
Note: For now, n_classes has to be set to 0. This is because our matrix is the concatenation
of (X_num, X_cat). In this case, if we have is_y_cond == 'concat', we can guarantee that y
is the first column of the matrix.
However, if we have n_classes > 0, then y is not the first column of the matrix.
"""
train_val_df, test_df = train_test_split(df, test_size=ratios[2], random_state=42)
train_df, val_df = train_test_split(
train_val_df,
test_size=ratios[1] / (ratios[0] + ratios[1]), random_state=42
)
cat_column_orders = []
num_column_orders = []
index_to_column = list(df.columns)
column_to_index = {col: i for i, col in enumerate(index_to_column)}
if df_info['n_classes'] > 0:
X_cat = {} if df_info['cat_cols'] is not None or is_y_cond == 'concat' else None
X_num = {} if df_info['num_cols'] is not None else None
y = {}
cat_cols_with_y = []
if df_info['cat_cols'] is not None:
cat_cols_with_y += df_info['cat_cols']
if is_y_cond == 'concat':
cat_cols_with_y = [df_info['y_col']] + cat_cols_with_y
if len(cat_cols_with_y) > 0:
X_cat['train'] = train_df[cat_cols_with_y].to_numpy(dtype=np.str_)
X_cat['val'] = val_df[cat_cols_with_y].to_numpy(dtype=np.str_)
X_cat['test'] = test_df[cat_cols_with_y].to_numpy(dtype=np.str_)
y['train'] = train_df[df_info['y_col']].values.astype(np.float32)
y['val'] = val_df[df_info['y_col']].values.astype(np.float32)
y['test'] = test_df[df_info['y_col']].values.astype(np.float32)
if df_info['num_cols'] is not None:
X_num['train'] = train_df[df_info['num_cols']].values.astype(np.float32)
X_num['val'] = val_df[df_info['num_cols']].values.astype(np.float32)
X_num['test'] = test_df[df_info['num_cols']].values.astype(np.float32)
cat_column_orders = [column_to_index[col] for col in cat_cols_with_y]
num_column_orders = [column_to_index[col] for col in df_info['num_cols']]
else:
X_cat = {} if df_info['cat_cols'] is not None else None
X_num = {} if df_info['num_cols'] is not None or is_y_cond == 'concat' else None
y = {}
num_cols_with_y = []
if df_info['num_cols'] is not None:
num_cols_with_y += df_info['num_cols']
if is_y_cond == 'concat':
num_cols_with_y = [df_info['y_col']] + num_cols_with_y
if len(num_cols_with_y) > 0:
X_num['train'] = train_df[num_cols_with_y].values.astype(np.float32)
X_num['val'] = val_df[num_cols_with_y].values.astype(np.float32)
X_num['test'] = test_df[num_cols_with_y].values.astype(np.float32)
y['train'] = train_df[df_info['y_col']].values.astype(np.float32)
y['val'] = val_df[df_info['y_col']].values.astype(np.float32)
y['test'] = test_df[df_info['y_col']].values.astype(np.float32)
if df_info['cat_cols'] is not None:
X_cat['train'] = train_df[df_info['cat_cols']].to_numpy(dtype=np.str_)
X_cat['val'] = val_df[df_info['cat_cols']].to_numpy(dtype=np.str_)
X_cat['test'] = test_df[df_info['cat_cols']].to_numpy(dtype=np.str_)
cat_column_orders = [column_to_index[col] for col in df_info['cat_cols']]
num_column_orders = [column_to_index[col] for col in num_cols_with_y]
column_orders = num_column_orders + cat_column_orders
column_orders = [index_to_column[index] for index in column_orders]
label_encoders = {}
if X_cat is not None and len(df_info['cat_cols']) > 0:
X_cat_all = np.vstack((X_cat['train'], X_cat['val'], X_cat['test']))
X_cat_converted = []
for col_index in range(X_cat_all.shape[1]):
label_encoder = LabelEncoder()
X_cat_converted.append(label_encoder.fit_transform(X_cat_all[:, col_index]).astype(float))
if std > 0:
# add noise
X_cat_converted[-1] += np.random.normal(0, std, X_cat_converted[-1].shape)
label_encoders[col_index] = label_encoder
X_cat_converted = np.vstack(X_cat_converted).T
train_num = X_cat['train'].shape[0]
val_num = X_cat['val'].shape[0]
test_num = X_cat['test'].shape[0]
X_cat['train'] = X_cat_converted[: train_num, :]
X_cat['val'] = X_cat_converted[train_num: train_num + val_num, :]
X_cat['test'] = X_cat_converted[train_num + val_num:, :]
if len(X_num) > 0:
X_num['train'] = np.concatenate((X_num['train'], X_cat['train']), axis=1)
X_num['val'] = np.concatenate((X_num['val'], X_cat['val']), axis=1)
X_num['test'] = np.concatenate((X_num['test'], X_cat['test']), axis=1)
else:
X_num = X_cat
X_cat = None
D = lib.Dataset(
X_num,
None,
y,
y_info={},
task_type=lib.TaskType(df_info['task_type']),
n_classes=df_info['n_classes']
)
return lib.transform_dataset(D, T, None), label_encoders, column_orders
|