def create_net(self):
net = core.Net("feature_extractor")
init_net = core.Net("feature_extractor_init")
missing_scalar = self.create_const(init_net, "MISSING_SCALAR",
MISSING_VALUE)
input_schema = schema.Struct((
"float_features",
schema.Map(
keys=core.BlobReference("input/float_features.keys"),
values=core.BlobReference("input/float_features.values"),
lengths_blob=core.BlobReference(
"input/float_features.lengths"),
),
))
input_record = net.set_input_record(input_schema)
state = self.extract_float_features(
net,
"state",
input_record.float_features,
self.sorted_state_features,
missing_scalar,
)
if self.sorted_action_features:
action = self.extract_float_features(
net,
"action",
input_record.float_features,
self.sorted_action_features,
missing_scalar,
)
if self.normalize:
C2.set_net_and_init_net(net, init_net)
state, _ = PreprocessorNet().normalize_dense_matrix(
state,
self.sorted_state_features,
self.state_normalization_parameters,
blobname_prefix="state",
split_expensive_feature_groups=True,
)
if self.sorted_action_features:
action, _ = PreprocessorNet().normalize_dense_matrix(
action,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="action",
split_expensive_feature_groups=True,
)
C2.set_net_and_init_net(None, None)
output_record = schema.Struct(("state", state))
if self.sorted_action_features:
output_record += schema.Struct(("action", action))
net.set_output_record(output_record)
return FeatureExtractorNet(net, init_net)
def create_net(self):
net = core.Net("feature_extractor")
init_net = core.Net("feature_extractor_init")
missing_scalar = self.create_const(init_net, "MISSING_SCALAR", MISSING_VALUE)
input_schema = schema.Struct(
(
"float_features",
schema.Map(
keys=core.BlobReference("input/float_features.keys"),
values=core.BlobReference("input/float_features.values"),
lengths_blob=core.BlobReference("input/float_features.lengths"),
),
)
)
input_record = net.set_input_record(input_schema)
state = self.extract_float_features(
net,
"state",
input_record.float_features,
self.sorted_state_features,
missing_scalar,
)
if self.sorted_action_features:
action = self.extract_float_features(
net,
"action",
input_record.float_features,
self.sorted_action_features,
missing_scalar,
)
if self.normalize:
C2.set_net_and_init_net(net, init_net)
state, _ = PreprocessorNet().normalize_dense_matrix(
state,
self.sorted_state_features,
self.state_normalization_parameters,
blobname_prefix="state",
split_expensive_feature_groups=True,
)
if self.sorted_action_features:
action, _ = PreprocessorNet().normalize_dense_matrix(
action,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="action",
split_expensive_feature_groups=True,
)
C2.set_net_and_init_net(None, None)
output_record = schema.Struct(("state", state))
if self.sorted_action_features:
output_record += schema.Struct(("action", action))
net.set_output_record(output_record)
return FeatureExtractorNet(net, init_net)
def create_net(self):
net = core.Net("feature_extractor")
init_net = core.Net("feature_extractor_init")
missing_scalar = self.create_const(init_net, "MISSING_SCALAR",
MISSING_VALUE)
action_schema = map_schema(
) if self.sorted_action_features else schema.Scalar()
input_schema = schema.Struct(
(InputColumn.STATE_FEATURES, map_schema()),
(InputColumn.NEXT_STATE_FEATURES, map_schema()),
(InputColumn.ACTION, action_schema),
(InputColumn.NEXT_ACTION, action_schema),
(InputColumn.NOT_TERMINAL, schema.Scalar()),
(InputColumn.TIME_DIFF, schema.Scalar()),
)
if self.include_possible_actions:
input_schema += schema.Struct(
(InputColumn.POSSIBLE_ACTIONS_MASK, schema.List(
schema.Scalar())),
(InputColumn.POSSIBLE_NEXT_ACTIONS_MASK,
schema.List(schema.Scalar())),
)
if self.sorted_action_features is not None:
input_schema += schema.Struct(
(InputColumn.POSSIBLE_ACTIONS, schema.List(map_schema())),
(InputColumn.POSSIBLE_NEXT_ACTIONS,
schema.List(map_schema())),
)
input_record = net.set_input_record(input_schema)
state = self.extract_float_features(
net,
"state",
input_record[InputColumn.STATE_FEATURES],
self.sorted_state_features,
missing_scalar,
)
next_state = self.extract_float_features(
net,
"next_state",
input_record[InputColumn.NEXT_STATE_FEATURES],
self.sorted_state_features,
missing_scalar,
)
if self.sorted_action_features:
action = self.extract_float_features(
net,
InputColumn.ACTION,
input_record[InputColumn.ACTION],
self.sorted_action_features,
missing_scalar,
)
next_action = self.extract_float_features(
net,
InputColumn.NEXT_ACTION,
input_record[InputColumn.NEXT_ACTION],
self.sorted_action_features,
missing_scalar,
)
if self.include_possible_actions:
possible_action_features = self.extract_float_features(
net,
InputColumn.POSSIBLE_ACTIONS,
input_record[InputColumn.POSSIBLE_ACTIONS]["values"],
self.sorted_action_features,
missing_scalar,
)
possible_next_action_features = self.extract_float_features(
net,
InputColumn.POSSIBLE_NEXT_ACTIONS,
input_record[InputColumn.POSSIBLE_NEXT_ACTIONS]["values"],
self.sorted_action_features,
missing_scalar,
)
else:
action = input_record[InputColumn.ACTION]
next_action = input_record[InputColumn.NEXT_ACTION]
if self.normalize:
C2.set_net_and_init_net(net, init_net)
state, _ = PreprocessorNet().normalize_dense_matrix(
state,
self.sorted_state_features,
self.state_normalization_parameters,
blobname_prefix="state",
split_expensive_feature_groups=True,
)
next_state, _ = PreprocessorNet().normalize_dense_matrix(
next_state,
self.sorted_state_features,
self.state_normalization_parameters,
blobname_prefix="next_state",
split_expensive_feature_groups=True,
)
if self.sorted_action_features is not None:
action, _ = PreprocessorNet().normalize_dense_matrix(
action,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="action",
split_expensive_feature_groups=True,
)
next_action, _ = PreprocessorNet().normalize_dense_matrix(
next_action,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="next_action",
split_expensive_feature_groups=True,
)
if self.include_possible_actions:
possible_action_features, _ = PreprocessorNet(
).normalize_dense_matrix(
possible_action_features,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="possible_action",
split_expensive_feature_groups=True,
)
possible_next_action_features, _ = PreprocessorNet(
).normalize_dense_matrix(
possible_next_action_features,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="possible_next_action",
split_expensive_feature_groups=True,
)
C2.set_net_and_init_net(None, None)
output_schema = schema.Struct(
(InputColumn.STATE_FEATURES, state),
(InputColumn.NEXT_STATE_FEATURES, next_state),
(InputColumn.ACTION, action),
(InputColumn.NEXT_ACTION, next_action),
(InputColumn.NOT_TERMINAL, input_record[InputColumn.NOT_TERMINAL]),
(InputColumn.TIME_DIFF, input_record[InputColumn.TIME_DIFF]),
)
if self.include_possible_actions:
# Drop the "lengths" blob from possible_actions_mask since we know
# it's just a list of [max_num_actions, max_num_actions, ...]
output_schema += schema.Struct(
(
InputColumn.POSSIBLE_ACTIONS_MASK,
input_record[InputColumn.POSSIBLE_ACTIONS_MASK]["values"],
),
(
InputColumn.POSSIBLE_NEXT_ACTIONS_MASK,
input_record[InputColumn.POSSIBLE_NEXT_ACTIONS_MASK]
["values"],
),
)
if self.sorted_action_features is not None:
output_schema += schema.Struct(
(InputColumn.POSSIBLE_ACTIONS, possible_action_features),
(InputColumn.POSSIBLE_NEXT_ACTIONS,
possible_next_action_features),
)
net.set_output_record(output_schema)
return FeatureExtractorNet(net, init_net)
def create_net(self):
net = core.Net("feature_extractor")
init_net = core.Net("feature_extractor_init")
missing_scalar = self.create_const(init_net, "MISSING_SCALAR", MISSING_VALUE)
action_schema = map_schema() if self.sorted_action_features else schema.Scalar()
input_schema = schema.Struct(
(InputColumn.STATE_FEATURES, map_schema()),
(InputColumn.NEXT_STATE_FEATURES, map_schema()),
(InputColumn.ACTION, action_schema),
(InputColumn.NEXT_ACTION, action_schema),
(InputColumn.NOT_TERMINAL, schema.Scalar()),
)
if self.include_possible_actions:
input_schema += schema.Struct(
(InputColumn.POSSIBLE_ACTIONS_MASK, schema.List(schema.Scalar())),
(InputColumn.POSSIBLE_NEXT_ACTIONS_MASK, schema.List(schema.Scalar())),
)
if self.sorted_action_features is not None:
input_schema += schema.Struct(
(InputColumn.POSSIBLE_ACTIONS, schema.List(map_schema())),
(InputColumn.POSSIBLE_NEXT_ACTIONS, schema.List(map_schema())),
)
input_record = net.set_input_record(input_schema)
state = self.extract_float_features(
net,
"state",
input_record[InputColumn.STATE_FEATURES],
self.sorted_state_features,
missing_scalar,
)
next_state = self.extract_float_features(
net,
"next_state",
input_record[InputColumn.NEXT_STATE_FEATURES],
self.sorted_state_features,
missing_scalar,
)
if self.sorted_action_features:
action = self.extract_float_features(
net,
InputColumn.ACTION,
input_record[InputColumn.ACTION],
self.sorted_action_features,
missing_scalar,
)
next_action = self.extract_float_features(
net,
InputColumn.NEXT_ACTION,
input_record[InputColumn.NEXT_ACTION],
self.sorted_action_features,
missing_scalar,
)
if self.include_possible_actions:
possible_action_features = self.extract_float_features(
net,
InputColumn.POSSIBLE_ACTIONS,
input_record[InputColumn.POSSIBLE_ACTIONS]["values"],
self.sorted_action_features,
missing_scalar,
)
possible_next_action_features = self.extract_float_features(
net,
InputColumn.POSSIBLE_NEXT_ACTIONS,
input_record[InputColumn.POSSIBLE_NEXT_ACTIONS]["values"],
self.sorted_action_features,
missing_scalar,
)
else:
action = input_record[InputColumn.ACTION]
next_action = input_record[InputColumn.NEXT_ACTION]
if self.normalize:
C2.set_net_and_init_net(net, init_net)
state, _ = PreprocessorNet().normalize_dense_matrix(
state,
self.sorted_state_features,
self.state_normalization_parameters,
blobname_prefix="state",
split_expensive_feature_groups=True,
)
next_state, _ = PreprocessorNet().normalize_dense_matrix(
next_state,
self.sorted_state_features,
self.state_normalization_parameters,
blobname_prefix="next_state",
split_expensive_feature_groups=True,
)
if self.sorted_action_features is not None:
action, _ = PreprocessorNet().normalize_dense_matrix(
action,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="action",
split_expensive_feature_groups=True,
)
next_action, _ = PreprocessorNet().normalize_dense_matrix(
next_action,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="next_action",
split_expensive_feature_groups=True,
)
if self.include_possible_actions:
possible_action_features, _ = PreprocessorNet().normalize_dense_matrix(
possible_action_features,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="possible_action",
split_expensive_feature_groups=True,
)
possible_next_action_features, _ = PreprocessorNet().normalize_dense_matrix(
possible_next_action_features,
self.sorted_action_features,
self.action_normalization_parameters,
blobname_prefix="possible_next_action",
split_expensive_feature_groups=True,
)
C2.set_net_and_init_net(None, None)
output_schema = schema.Struct(
(InputColumn.STATE_FEATURES, state),
(InputColumn.NEXT_STATE_FEATURES, next_state),
(InputColumn.ACTION, action),
(InputColumn.NEXT_ACTION, next_action),
(InputColumn.NOT_TERMINAL, input_record[InputColumn.NOT_TERMINAL]),
)
if self.include_possible_actions:
# Drop the "lengths" blob from possible_actions_mask since we know
# it's just a list of [max_num_actions, max_num_actions, ...]
output_schema += schema.Struct(
(
InputColumn.POSSIBLE_ACTIONS_MASK,
input_record[InputColumn.POSSIBLE_ACTIONS_MASK]["values"],
),
(
InputColumn.POSSIBLE_NEXT_ACTIONS_MASK,
input_record[InputColumn.POSSIBLE_NEXT_ACTIONS_MASK]["values"],
),
)
if self.sorted_action_features is not None:
output_schema += schema.Struct(
(InputColumn.POSSIBLE_ACTIONS, possible_action_features),
(InputColumn.POSSIBLE_NEXT_ACTIONS, possible_next_action_features),
)
net.set_output_record(output_schema)
return FeatureExtractorNet(net, init_net)
