from typing import Union
import tensorflow as tf
from ncem.models.layers import (Decoder, Encoder, GCNLayer, MaxLayer, get_out)
[docs]class ModelEDncem:
"""Model class for NCEM encoder-decoder with graph layer IND (MAX) or GCN."""
def __init__(
self,
input_shapes,
latent_dim: int = 10,
dropout_rate: float = 0.1,
l2_coef: float = 0.0,
l1_coef: float = 0.0,
enc_intermediate_dim: int = 128,
enc_depth: int = 2,
dec_intermediate_dim: int = 128,
dec_depth: int = 2,
cond_type: str = "gcn",
cond_depth: int = 1,
cond_dim: int = 8,
cond_dropout_rate: float = 0.1,
cond_activation: Union[str, tf.keras.layers.Layer] = "relu",
cond_l2_reg: float = 0.0,
cond_use_bias: bool = True,
use_domain: bool = False,
use_type_cond: bool = False,
scale_node_size: bool = False,
output_layer: str = "gaussian",
**kwargs,
):
"""Initialize encoder-decoder NCEM model.
Parameters
----------
input_shapes
Input shapes.
latent_dim : int
Latent dimension.
dropout_rate : float
Dropout rate.
l2_coef : float
l2 regularization coefficient.
l1_coef : float
l1 regularization coefficient.
enc_intermediate_dim : int
Encoder intermediate dimension.
enc_depth : int
Encoder depth.
dec_intermediate_dim : int
Decoder intermediate dimension.
dec_depth : int
Decoder depth.
cond_type : str
Graph conditional type.
cond_depth : int
Graph conditional depth.
cond_dim : int
Graph conditional dimension.
cond_dropout_rate : float
Graph conditional dropout rate.
cond_activation : str
Graph conditional activation.
cond_l2_reg : float
Graph conditional l2 regularization coefficient.
cond_use_bias : bool
Graph conditional use bias.
use_domain : bool
Whether to use domain information.
use_type_cond : bool
whether to use the categorical cell type label in conditional.
scale_node_size : bool
Whether to scale output layer by node sizes.
output_layer : str
Output layer.
kwargs
Arbitrary keyword arguments.
Raises
------
ValueError
If `cond_type` or `output_layer` is not recognized.
"""
super().__init__()
self.args = {
"input_shapes": input_shapes,
"latent_dim": latent_dim,
"dropout_rate": dropout_rate,
"l2_coef": l2_coef,
"l1_coef": l1_coef,
"enc_intermediate_dim": enc_intermediate_dim,
"enc_depth": enc_depth,
"dec_intermediate_dim": dec_intermediate_dim,
"dec_depth": dec_depth,
"cond_type": cond_type,
"cond_depth": cond_depth,
"cond_dim": cond_dim,
"cond_dropout_rate": cond_dropout_rate,
"cond_activation": cond_activation
if not isinstance(cond_activation, tf.keras.layers.Layer)
else cond_activation.name,
"cond_l2_reg": cond_l2_reg,
"cond_use_bias": cond_use_bias,
"use_domain": use_domain,
"use_type_cond": use_type_cond,
"scale_node_size": scale_node_size,
"output_layer": output_layer,
}
in_node_feature_dim = input_shapes[0]
out_node_feature_dim = input_shapes[1]
graph_dim = input_shapes[2]
in_node_dim = input_shapes[3]
categ_condition_dim = input_shapes[4]
domain_dim = input_shapes[5]
# node features - reconstruction: Input Tensor - shape=(None, N, F)
input_x_reconstruct = tf.keras.Input(
shape=(in_node_dim, out_node_feature_dim), name="node_features_reconstruct"
)
# node size - reconstruction: Input Tensor - shape=(None, N, 1)
input_node_size = tf.keras.Input(shape=(in_node_dim, 1), name="node_size_reconstruct")
# node features - node representation of other nodes: Input Tensor - shape=(None, N, F)
input_x_cond = tf.keras.Input(shape=(in_node_dim, in_node_feature_dim), name="node_features_cond")
# node features - node representation of other nodes: Input Tensor - shape=(None, N, F)
input_x_cond_full = tf.keras.Input(shape=(graph_dim, in_node_feature_dim), name="node_features_cond_full")
# adj_matrices - A: Input Tensor - shape=(None, N, N)
input_a = tf.keras.Input(shape=(in_node_dim, graph_dim), name="adjacency_matrix", sparse=True)
# full adj_matrices - A: Input Tensor - shape=(None, N, N)
input_afull = tf.keras.Input(shape=(graph_dim, graph_dim), name="adjacency_matrix_full", sparse=True)
# Categorical predictors: Input Tensor - shape=(None, N, P)
input_categ_condition = tf.keras.Input(shape=(in_node_dim, categ_condition_dim), name="categorical_predictor")
# domain information of graph - shape=(None, 1)
input_g = tf.keras.layers.Input(shape=(domain_dim,), name="input_da_group", dtype="int32")
# Decoder inputs:
input_latent_sampling = tf.keras.Input(shape=(in_node_dim, latent_dim), name="z_sampling")
latent_sampling_reshaped = tf.reshape(input_latent_sampling, [-1, latent_dim])
if use_domain:
categ_condition = tf.concat(
[
input_categ_condition,
tf.tile(tf.expand_dims(tf.cast(input_g, dtype="float32"), axis=-2), [1, in_node_dim, 1]),
],
axis=-1,
)
else:
categ_condition = input_categ_condition
if cond_depth > 1:
print("using multi layer graph model")
x_neighbour_embedding = input_x_cond_full
if cond_type == "gcn" or cond_type == "gcn_full":
for i in range(cond_depth - 1):
cond_layer = GCNLayer(
output_dim=cond_dim,
dropout_rate=cond_dropout_rate,
activation=cond_activation,
l2_reg=cond_l2_reg,
use_bias=cond_use_bias,
name=f"conditional_layer_stack_{i}",
)
x_neighbour_embedding = cond_layer([x_neighbour_embedding, input_afull])
# In last forward pass, only retain nodes that are to be decoded: not using the full adjacency matrix:
cond_layer = GCNLayer(
output_dim=cond_dim,
dropout_rate=cond_dropout_rate,
activation=cond_activation,
l2_reg=cond_l2_reg,
use_bias=cond_use_bias,
name=f"conditional_layer_stack_{cond_depth}",
)
x_neighbour_embedding = cond_layer([x_neighbour_embedding, input_a])
elif cond_type == "max":
for i in range(cond_depth - 1):
cond_layer = MaxLayer(name=f"conditional_layer_stack_{i}")
x_neighbour_embedding = cond_layer([x_neighbour_embedding, input_afull])
# In last forward pass, only retain nodes that are to be decoded: not using the full adjacency matrix:
cond_layer = MaxLayer(name=f"conditional_layer_stack_{cond_depth}")
x_neighbour_embedding = cond_layer([x_neighbour_embedding, input_a])
else:
raise ValueError("tried to access a non-supported conditional layer %s" % cond_type)
inputs_encoder = (x_neighbour_embedding, categ_condition)
self.encoder_model = Encoder(
latent_dim=latent_dim,
intermediate_dim=enc_intermediate_dim,
dropout_rate=dropout_rate,
n_hidden=enc_depth,
l1_coef=l1_coef,
l2_coef=l2_coef,
probabilistic=False,
use_type_cond=use_type_cond,
)
output_encoder = self.encoder_model(inputs_encoder)
z, z_mean, z_log_var = output_encoder
self.decoder_model = Decoder(
intermediate_dim=dec_intermediate_dim,
dropout_rate=dropout_rate,
n_hidden=dec_depth,
l1_coef=l1_coef,
l2_coef=l2_coef,
use_type_cond=use_type_cond,
)
output_decoder = self.decoder_model((z, categ_condition))
sampling_decoder = self.decoder_model((latent_sampling_reshaped, categ_condition))
output_decoder_layer = get_out(
output_layer=output_layer, out_feature_dim=out_node_feature_dim, scale_node_size=scale_node_size
)((output_decoder, input_node_size))
output_sampling_decoder = get_out(
output_layer=output_layer, out_feature_dim=out_node_feature_dim, scale_node_size=scale_node_size,
name='sampling'
)((sampling_decoder, input_node_size))
output_decoder_concat = tf.keras.layers.Concatenate(axis=2, name="reconstruction")(output_decoder_layer)
output_sampling_concat = tf.keras.layers.Concatenate(axis=2, name="reconstruction")(output_sampling_decoder)
self.encoder = tf.keras.Model(
inputs=[
input_x_reconstruct,
input_x_cond,
input_x_cond_full,
input_a,
input_afull,
input_categ_condition,
input_g,
],
outputs=output_encoder,
name="encoder_ncem",
)
self.decoder = tf.keras.Model(
inputs=[
input_latent_sampling,
input_node_size,
input_x_cond,
input_x_cond_full,
input_a,
input_afull,
input_categ_condition,
input_g,
],
outputs=output_sampling_concat,
name="decoder_ncem",
)
self.training_model = tf.keras.Model(
inputs=[
input_x_reconstruct,
input_node_size,
input_x_cond,
input_x_cond_full,
input_a,
input_afull,
input_categ_condition,
input_g,
],
outputs=output_decoder_concat,
name="ed_ncem",
)