/
dataset.py
68 lines (58 loc) · 2.21 KB
/
dataset.py
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from ..dataset import SequenceDataset
from ..imports import *
class TabularDataset(SequenceDataset):
def __init__(
self, df, cat_columns, cont_columns, label_columns, batch_size=32, shuffle=False
):
# error checks
if not isinstance(df, pd.DataFrame):
raise ValueError("df must be pandas Dataframe")
all_columns = cat_columns + cont_columns + label_columns
missing_columns = []
for col in df.columns.values:
if col not in all_columns:
missing_columns.append(col)
if len(missing_columns) > 0:
raise ValueError("df is missing these columns: %s" % (missing_columns))
# set variables
super().__init__(batch_size=batch_size)
self.indices = np.arange(df.shape[0])
self.df = df
self.cat_columns = cat_columns
self.cont_columns = cont_columns
self.label_columns = label_columns
self.shuffle = shuffle
def __len__(self):
return math.ceil(self.df.shape[0] / self.batch_size)
def __getitem__(self, idx):
inds = self.indices[idx * self.batch_size : (idx + 1) * self.batch_size]
batch_x = []
df = self.df[self.cat_columns + self.cont_columns].iloc[inds]
for cat_name in self.cat_columns:
codes = (
np.stack(
[c.cat.codes.values for n, c in df[[cat_name]].items()], 1
).astype(np.int64)
+ 1
)
batch_x.append(codes)
if len(self.cont_columns) > 0:
conts = np.stack(
[c.astype("float32").values for n, c in df[self.cont_columns].items()],
1,
)
batch_x.append(conts)
batch_y = self.df[self.label_columns].iloc[inds].values
batch_x = batch_x[0] if len(batch_x) == 1 else tuple(batch_x)
return batch_x, batch_y
def nsamples(self):
return self.df.shape[0]
def get_y(self):
return self.df[self.label_columns].values
def on_epoch_end(self):
if self.shuffle:
np.random.shuffle(self.indices)
def xshape(self):
return self.df.shape
def nclasses(self):
return self.get_y().shape[1]