Module ktrain.tabular.dataset
Expand source code
from ..imports import *
from ..dataset import SequenceDataset
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]
Classes
class TabularDataset (df, cat_columns, cont_columns, label_columns, batch_size=32, shuffle=False)
-
Base class for custom datasets in ktrain. If subclass of Dataset implements a method to to_tfdataset that converts the data to a tf.Dataset, then this will be invoked by Learner instances just prior to training so fit() will train using a tf.Dataset representation of your data. Sequence methods such as __get_item__ and __len__ must still be implemented. The signature of to_tfdataset is as follows: def to_tfdataset(self, training=True) See ktrain.text.preprocess.TransformerDataset as an example.
Expand source code
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]
Ancestors
- SequenceDataset
- Dataset
- tensorflow.python.keras.utils.data_utils.Sequence
Methods
def get_y(self)
-
Expand source code
def get_y(self): return self.df[self.label_columns].values
def nsamples(self)
-
Expand source code
def nsamples(self): return self.df.shape[0]
def on_epoch_end(self)
-
Method called at the end of every epoch.
Expand source code
def on_epoch_end(self): if self.shuffle: np.random.shuffle(self.indices)
Inherited members