## Loaders

#### Loader

```
spektral.data.loaders.Loader(dataset, batch_size=1, epochs=None, shuffle=True)
```

Parent class for data loaders. The role of a Loader is to iterate over a Dataset and yield batches of graphs to feed your Keras Models.

This is achieved by having a generator object that produces lists of Graphs, which are then collated together and returned as Tensor-like objects.

The core of a Loader is the `collate(batch)`

method.
This takes as input a list of Graphs and returns a list of Tensors or
SparseTensors.

For instance, if all graphs have the same number of nodes and size of the attributes, a simple collation function can be:

```
def collate(self, batch):
x = np.array([g.x for g in batch])
a = np.array([g.a for g in batch)]
return x, a
```

The `load()`

method of a Loader returns an object that can be given as
input to `Model.fit()`

.
You can use it as follows:

```
model.fit(loader.load(), steps_per_epoch=loader.steps_per_epoch)
```

The `steps_per_epoch`

property represents the number of batches that are in
an epoch, and is a required keyword when calling `model.fit()`

with a Loader.

If you want to write your own training function, you can use the
`tf_signature()`

method to specify the signature of your batches using the
tf.TypeSpec system, in order to avoid unnecessary re-tracings.

For example, a simple training function can be written as:

```
@tf.function(input_signature=loader.tf_signature())
def train_step(inputs, target):
with tf.GradientTape() as tape:
predictions = model(inputs, training=True)
loss = loss_fn(target, predictions) + sum(model.losses)
gradients = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(gradients, model.trainable_variables))
```

We can then train our model in a for loop as follows:

```
for batch in loader:
train_step(*batch)
```

**Arguments**

`dataset`

: a graph Dataset;`batch_size`

: size of the mini-batches;`epochs`

: number of epochs to iterate over the dataset. By default (`None`

) iterates indefinitely;`shuffle`

: whether to shuffle the data at the start of each epoch.

#### SingleLoader

```
spektral.data.loaders.SingleLoader(dataset, epochs=None, sample_weights=None)
```

A Loader for single mode.

This loader produces Tensors representing a single graph. As such, it can
only be used with Datasets of length 1 and the `batch_size`

cannot be set.

The loader supports sample weights through the `sample_weights`

argument.
If given, then each batch will be a tuple `(inputs, labels, sample_weights)`

.

**Arguments**

`dataset`

: a graph Dataset;`epochs`

: number of epochs to iterate over the dataset. By default (`None`

) iterates indefinitely;`shuffle`

: whether to shuffle the data at the start of each epoch;`sample_weights`

: if given, these will be appended to the output automatically.

**Output**

Returns a tuple `(inputs, labels)`

or `(inputs, labels, sample_weights)`

.

`inputs`

is a tuple containing the data matrices of the graph, only if they
are not `None`

:

`x`

: same as`dataset[0].x`

;`a`

: same as`dataset[0].a`

(scipy sparse matrices are converted to SparseTensors);`e`

: same as`dataset[0].e`

;

`labels`

is the same as `dataset[0].y`

.
`sample_weights`

is the same object passed to the constructor.

#### DisjointLoader

```
spektral.data.loaders.DisjointLoader(dataset, node_level=False, batch_size=1, epochs=None, shuffle=True)
```

A Loader for disjoint mode.

This loader represents a batch of graphs via their disjoint union.

The loader automatically computes a batch index tensor, containing integer indices that map each node to its corresponding graph in the batch.

The adjacency matrix os returned as a SparseTensor, regardless of the input.

If `node_level=False`

, the labels are interpreted as graph-level labels and
are stacked along an additional dimension.
If `node_level=True`

, then the labels are stacked vertically.

**Note:** TensorFlow 2.4 or above is required to use this Loader's `load()`

method in a Keras training loop.

**Arguments**

`dataset`

: a graph Dataset;`batch_size`

: size of the mini-batches;`epochs`

: number of epochs to iterate over the dataset. By default (`None`

) iterates indefinitely;`shuffle`

: whether to shuffle the data at the start of each epoch.

**Output**

For each batch, returns a tuple `(inputs, labels)`

.

`inputs`

is a tuple containing:

`x`

: node attributes of shape`[n_nodes, n_node_features]`

;`a`

: adjacency matrices of shape`[n_nodes, n_nodes]`

;`e`

: edge attributes of shape`[n_edges, n_edge_features]`

.

`labels`

have shape `[batch, n_labels]`

if `node_level=False`

or
`[n_nodes, n_labels]`

otherwise.

#### BatchLoader

```
spektral.data.loaders.BatchLoader(dataset, batch_size=1, epochs=None, shuffle=True)
```

A Loader for batch mode.

This loader returns batches of graphs stacked along an extra dimension, with all "node" dimensions padded to be equal among all graphs.

If `n_max`

is the number of nodes of the biggest graph in the batch, then
the padding consist of adding zeros to the node features, adjacency matrix,
and edge attributes of each graph so that they have shapes
`(n_max, n_node_features)`

, `(n_max, n_max)`

, and
`(n_max, n_max, n_edge_features)`

respectively.

The zero-padding is done batch-wise, which saves up memory at the cost of
more computation. If latency is an issue but memory isn't, or if the
dataset has graphs with a similar number of nodes, you can use
the `PackedBatchLoader`

that first zero-pads all the dataset and then
iterates over it.

Note that the adjacency matrix and edge attributes are returned as dense arrays (mostly due to the lack of support for sparse tensor operations for rank >2).

Only graph-level labels are supported with this loader (i.e., labels are not zero-padded because they are assumed to have no "node" dimensions).

**Arguments**

`dataset`

: a graph Dataset;`batch_size`

: size of the mini-batches;`epochs`

: number of epochs to iterate over the dataset. By default (`None`

) iterates indefinitely;`shuffle`

: whether to shuffle the data at the start of each epoch.

**Output**

For each batch, returns a tuple `(inputs, labels)`

.

`inputs`

is a tuple containing:

`x`

: node attributes of shape`[batch, n_max, n_node_features]`

;`a`

: adjacency matrices of shape`[batch, n_max, n_max]`

;`e`

: edge attributes of shape`[batch, n_edges, n_edge_features]`

.

`labels`

have shape `[batch, n_labels]`

.

#### PackedBatchLoader

```
spektral.data.loaders.PackedBatchLoader(dataset, batch_size=1, epochs=None, shuffle=True)
```

A `BatchLoader`

that zero-pads the graphs before iterating over the dataset.
This means that `n_max`

is computed over the whole dataset and not just
a single batch.

While using more memory than `BatchLoader`

, this loader should reduce the
computational overhead of padding each batch independently.

Use this loader if:

- memory usage isn't an issue and you want to produce the batches as fast as possible;
- the graphs in the dataset have similar sizes and there are no outliers in the dataset (i.e., anomalous graphs with many more nodes than the dataset average).

**Arguments**

`dataset`

: a graph Dataset;`batch_size`

: size of the mini-batches;`epochs`

: number of epochs to iterate over the dataset. By default (`None`

) iterates indefinitely;`shuffle`

: whether to shuffle the data at the start of each epoch.

**Output**

For each batch, returns a tuple `(inputs, labels)`

.

`inputs`

is a tuple containing:

`x`

: node attributes of shape`[batch, n_max, n_node_features]`

;`a`

: adjacency matrices of shape`[batch, n_max, n_max]`

;`e`

: edge attributes of shape`[batch, n_max, n_edge_features]`

.

`labels`

have shape `[batch, ..., n_labels]`

.

#### MixedLoader

```
spektral.data.loaders.MixedLoader(dataset, batch_size=1, epochs=None, shuffle=True)
```

A Loader for mixed mode.

This loader returns batches where the node and edge attributes are stacked along an extra dimension, but the adjacency matrix is shared by all graphs.

The loader expects all node and edge features to have the same number of nodes and edges. The dataset is pre-packed like in a PackedBatchLoader.

**Arguments**

`dataset`

: a graph Dataset;`batch_size`

: size of the mini-batches;`epochs`

: number of epochs to iterate over the dataset. By default (`None`

) iterates indefinitely;`shuffle`

: whether to shuffle the data at the start of each epoch.

**Output**

For each batch, returns a tuple `(inputs, labels)`

.

`inputs`

is a tuple containing:

`x`

: node attributes of shape`[batch, n_nodes, n_node_features]`

;`a`

: adjacency matrix of shape`[n_nodes, n_nodes]`

;`e`

: edge attributes of shape`[batch, n_edges, n_edge_features]`

.

`labels`

have shape `[batch, ..., n_labels]`

.