add repr for genome and gene;

add ipynb test for testing whether add node or add conn will not change the output for the network.

tensorneat/algorithm/neat/ga/mutation/default.py

@@ -41,15 +41,13 @@ class DefaultMutation(BaseMutation):
         return nodes, conns
 
     def mutate_structure(self, state, randkey, genome, nodes, conns, new_node_key):
-
-        remain_node_space = jnp.isnan(nodes[:, 0]).sum()
-        remain_conn_space = jnp.isnan(conns[:, 0]).sum()
-
         def mutate_add_node(key_, nodes_, conns_):
             """
             add a node while do not influence the output of the network
             """
 
+            remain_node_space = jnp.isnan(nodes_[:, 0]).sum()
+            remain_conn_space = jnp.isnan(conns_[:, 0]).sum()
             i_key, o_key, idx = self.choose_connection_key(
                 key_, conns_
             )  # choose a connection
@@ -83,7 +81,7 @@ class DefaultMutation(BaseMutation):
                 return new_nodes, new_conns
 
             return jax.lax.cond(
-                (idx == I_INF) & (remain_node_space < 1) & (remain_conn_space < 2),
+                (idx == I_INF) | (remain_node_space < 1) | (remain_conn_space < 2),
                 lambda: (nodes_, conns_),  # do nothing
                 successful_add_node,
             )
@@ -92,7 +90,6 @@ class DefaultMutation(BaseMutation):
             """
             delete a node
             """
-
             # randomly choose a node
             key, idx = self.choose_node_key(
                 key_,
@@ -127,6 +124,8 @@ class DefaultMutation(BaseMutation):
             add a connection while do not influence the output of the network
             """
 
+            remain_conn_space = jnp.isnan(conns_[:, 0]).sum()
+
             # randomly choose two nodes
             k1_, k2_ = jax.random.split(key_, num=2)
 
@@ -164,7 +163,7 @@ class DefaultMutation(BaseMutation):
 
             if genome.network_type == "feedforward":
                 u_conns = unflatten_conns(nodes_, conns_)
-                conns_exist = (u_conns != I_INF)
+                conns_exist = u_conns != I_INF
                 is_cycle = check_cycles(nodes_, conns_exist, from_idx, to_idx)
 
                 return jax.lax.cond(

tensorneat/algorithm/neat/gene/base.py

@@ -40,3 +40,6 @@ class BaseGene(StatefulBaseClass):
     @property
     def length(self):
         return len(self.fixed_attrs) + len(self.custom_attrs)
+
+    def repr(self, state, gene, precision=2):
+        raise NotImplementedError

tensorneat/algorithm/neat/gene/conn/base.py

@@ -22,3 +22,12 @@ class BaseConnGene(BaseGene):
             jax.vmap(self.forward, in_axes=(None, None, 0))(state, attrs, batch_inputs),
             attrs,
         )
+
+    def repr(self, state, conn, precision=2, idx_width=3, func_width=8):
+        in_idx, out_idx = conn[:2]
+        in_idx = int(in_idx)
+        out_idx = int(out_idx)
+
+        return "{}(in: {:<{idx_width}}, out: {:<{idx_width}})".format(
+            self.__class__.__name__, in_idx, out_idx, idx_width=idx_width
+        )

tensorneat/algorithm/neat/gene/conn/default.py

@@ -60,3 +60,19 @@ class DefaultConnGene(BaseConnGene):
     def forward(self, state, attrs, inputs):
         weight = attrs[0]
         return inputs * weight
+
+    def repr(self, state, conn, precision=2, idx_width=3, func_width=8):
+        in_idx, out_idx, weight = conn
+
+        in_idx = int(in_idx)
+        out_idx = int(out_idx)
+        weight = round(float(weight), precision)
+
+        return "{}(in: {:<{idx_width}}, out: {:<{idx_width}}, weight: {:<{float_width}})".format(
+            self.__class__.__name__,
+            in_idx,
+            out_idx,
+            weight,
+            idx_width=idx_width,
+            float_width=precision + 3,
+        )

tensorneat/algorithm/neat/gene/node/base.py

@@ -39,3 +39,11 @@ class BaseNodeGene(BaseGene):
             ),
             attrs,
         )
+
+    def repr(self, state, node, precision=2, idx_width=3, func_width=8):
+        idx = node[0]
+
+        idx = int(idx)
+        return "{}(idx={:<{idx_width}})".format(
+            self.__class__.__name__, idx, idx_width=idx_width
+        )

tensorneat/algorithm/neat/gene/node/default.py

@@ -122,3 +122,28 @@ class DefaultNodeGene(BaseNodeGene):
         )
 
         return z
+
+    def repr(self, state, node, precision=2, idx_width=3, func_width=8):
+        idx, bias, res, agg, act = node
+
+        idx = int(idx)
+        bias = round(float(bias), precision)
+        res = round(float(res), precision)
+        agg = int(agg)
+        act = int(act)
+
+        if act == -1:
+            act_func = Act.identity
+        else:
+            act_func = self.activation_options[act]
+        return "{}(idx={:<{idx_width}}, bias={:<{float_width}}, response={:<{float_width}}, aggregation={:<{func_width}}, activation={:<{func_width}})".format(
+            self.__class__.__name__,
+            idx,
+            bias,
+            res,
+            self.aggregation_options[agg].__name__,
+            act_func.__name__,
+            idx_width=idx_width,
+            float_width=precision + 3,
+            func_width=func_width
+        )

tensorneat/algorithm/neat/gene/node/default_without_response.py

@@ -98,3 +98,26 @@ class NodeGeneWithoutResponse(BaseNodeGene):
         )
 
         return z
+
+    def repr(self, state, node, precision=2, idx_width=3, func_width=8):
+        idx, bias, agg, act = node
+
+        idx = int(idx)
+        bias = round(float(bias), precision)
+        agg = int(agg)
+        act = int(act)
+
+        if act == -1:
+            act_func = Act.identity
+        else:
+            act_func = self.activation_options[act]
+        return "{}(idx={:<{idx_width}}, bias={:<{float_width}}, aggregation={:<{func_width}}, activation={:<{func_width}})".format(
+            self.__class__.__name__,
+            idx,
+            bias,
+            self.aggregation_options[agg].__name__,
+            act_func.__name__,
+            idx_width=idx_width,
+            float_width=precision + 3,
+            func_width=func_width,
+        )

tensorneat/algorithm/neat/gene/node/kan_node.py

@@ -25,3 +25,8 @@ class KANNode(BaseNodeGene):
 
     def forward(self, state, attrs, inputs, is_output_node=False):
         return Agg.sum(inputs)
+
+    def repr(self, state, node, precision=2):
+        idx = node[0]
+        idx = int(idx)
+        return "{}(idx: {})".format(self.__class__.__name__, idx)

tensorneat/algorithm/neat/genome/base.py

@@ -1,3 +1,4 @@
+import numpy as np
 import jax, jax.numpy as jnp
 from ..gene import BaseNodeGene, BaseConnGene
 from ..ga import BaseMutation, BaseCrossover
@@ -20,8 +21,8 @@ class BaseGenome(StatefulBaseClass):
     ):
         self.num_inputs = num_inputs
         self.num_outputs = num_outputs
-        self.input_idx = jnp.arange(num_inputs)
+        self.input_idx = np.arange(num_inputs)
-        self.output_idx = jnp.arange(num_inputs, num_inputs + num_outputs)
+        self.output_idx = np.arange(num_inputs, num_inputs + num_outputs)
         self.max_nodes = max_nodes
         self.max_conns = max_conns
         self.node_gene = node_gene
@@ -127,3 +128,30 @@ class BaseGenome(StatefulBaseClass):
         Update the genome by a batch of data.
         """
         raise NotImplementedError
+
+    def repr(self, state, nodes, conns, precision=2):
+        nodes, conns = jax.device_get([nodes, conns])
+        nodes_cnt, conns_cnt = self.valid_cnt(nodes), self.valid_cnt(conns)
+        s = f"{self.__class__.__name__}(nodes={nodes_cnt}, conns={conns_cnt}):\n"
+        s += f"\tNodes:\n"
+        for node in nodes:
+            if np.isnan(node[0]):
+                break
+            s += f"\t\t{self.node_gene.repr(state, node, precision=precision)}"
+            node_idx = int(node[0])
+            if np.isin(node_idx, self.input_idx):
+                s += " (input)"
+            elif np.isin(node_idx, self.output_idx):
+                s += " (output)"
+            s += "\n"
+
+        s += f"\tConns:\n"
+        for conn in conns:
+            if np.isnan(conn[0]):
+                break
+            s += f"\t\t{self.conn_gene.repr(state, conn, precision=precision)}\n"
+        return s
+
+    @classmethod
+    def valid_cnt(cls, arr):
+        return jnp.sum(~jnp.isnan(arr[:, 0]))

tensorneat/examples/func_fit/xor.py

@@ -47,3 +47,4 @@ if __name__ == "__main__":
     state, best = pipeline.auto_run(state)
     # show result
     pipeline.show(state, best)
+    pipeline.save(state=state)

tensorneat/utils/stateful_class.py

@@ -37,7 +37,7 @@ class StatefulBaseClass:
             if "aux_for_state" in obj.__dict__:
                 if warning:
                     warnings.warn(
-                        "This object state to load, ignore it",
+                        "This object has state to load, ignore it",
                         category=UserWarning,
                     )
                 del obj.__dict__["aux_for_state"]