update DataGenerator & New Version 0.2.2

ml4co_kit/__init__.py

@@ -87,5 +87,5 @@
     from .learning.utils import points_to_distmat, sparse_points
 
 
-__version__ = "0.2.1"
+__version__ = "0.2.2"
 __author__ = "SJTU-ReThinkLab"

ml4co_kit/generator/atsp_data.py

@@ -7,7 +7,6 @@
 import pathlib
 from tqdm import tqdm
 from typing import Union
-from multiprocessing import Pool
 from ml4co_kit.utils.type_utils import SOLVER_TYPE
 from ml4co_kit.solver import ATSPSolver, ATSPLKHSolver
 
@@ -18,6 +17,7 @@
 class ATSPDataGenerator:
     def __init__(
         self,
+        only_instance_for_us: bool = False,
         num_threads: int = 1,
         nodes_num: int = 55,
         data_type: str = "sat",
@@ -70,12 +70,17 @@ def __init__(
         if self.data_type == "sat":
             self.nodes_num = 2 * sat_clauses_nums * sat_vars_nums + sat_clauses_nums
 
-        # check the input variables
-        self.sample_types = ["train", "val", "test"]
-        self.check_num_threads()
+        # only instance for us
+        self.only_instance_for_us = only_instance_for_us
         self.check_data_type()
-        self.check_solver()
-        self.get_filename()
+
+        # generate and solve
+        if only_instance_for_us == False:
+            # check the input variables
+            self.sample_types = ["train", "val", "test"]
+            self.check_num_threads()    
+            self.check_solver()
+            self.get_filename()
 
     def check_num_threads(self):
         self.samples_num = 0
@@ -177,6 +182,12 @@ def get_filename(self):
         if not os.path.exists(self.save_path):
             os.makedirs(self.save_path)
 
+    def generate_only_instance_for_us(self, samples: int) -> np.ndarray:
+        self.num_threads = samples
+        dists = self.generate_func()[0]
+        self.solver.from_data(dists=dists)
+        return self.solver.dists
+
     def generate(self):
         start_time = time.time()
         for _ in tqdm(

ml4co_kit/generator/cvrp_data.py

@@ -5,8 +5,7 @@
 import numpy as np
 import pathlib
 from tqdm import tqdm
-from typing import Union
-from multiprocessing import Pool
+from typing import Union, Sequence
 from ml4co_kit.utils.type_utils import SOLVER_TYPE
 from ml4co_kit.solver import (
     CVRPSolver, CVRPPyVRPSolver, CVRPLKHSolver, CVRPHGSSolver
@@ -16,6 +15,7 @@
 class CVRPDataGenerator:
     def __init__(
         self,
+        only_instance_for_us: bool = False,
         num_threads: int = 1,
         nodes_num: int = 50,
         data_type: str = "uniform",
@@ -73,21 +73,29 @@ def __init__(
         self.test_samples_num = test_samples_num
         self.save_path = save_path
         self.filename = filename
+
         # special for demand and capacity
         self.min_demand = min_demand
         self.max_demand = max_demand
         self.min_capacity = min_capacity
         self.max_capacity = max_capacity
+
         # special for gaussian
         self.gaussian_mean_x = gaussian_mean_x
         self.gaussian_mean_y = gaussian_mean_y
         self.gaussian_std = gaussian_std
-        # check the input variables
-        self.sample_types = ["train", "val", "test"]
-        self.check_num_threads()
+
+        # only instance for us
+        self.only_instance_for_us = only_instance_for_us
         self.check_data_type()
-        self.check_solver()
-        self.get_filename()
+
+        # generate and solve
+        if only_instance_for_us == False:
+            # check the input variables
+            self.sample_types = ["train", "val", "test"]
+            self.check_num_threads()    
+            self.check_solver()
+            self.get_filename()
 
     def check_num_threads(self):
         self.samples_num = 0
@@ -195,6 +203,22 @@ def get_filename(self):
         if not os.path.exists(self.save_path):
             os.makedirs(self.save_path)
 
+    def generate_only_instance_for_us(self, samples: int) -> Sequence[np.ndarray]:
+        self.num_threads = samples
+        batch_depots_coord, batch_nodes_coord = self.generate_func()
+        batch_demands = self.generate_demands()
+        batch_capacities = self.generate_capacities()
+        self.solver.from_data(
+            depots=batch_depots_coord,
+            points=batch_nodes_coord,
+            demands=batch_demands,
+            capacities=batch_capacities
+        )
+        return (
+            self.solver.depots, self.solver.points, 
+            self.solver.demands, self.solver.capacities
+        )
+
     def generate(self):
         start_time = time.time()
         for _ in tqdm(

ml4co_kit/generator/mcl_data.py

@@ -6,7 +6,7 @@
 import numpy as np
 import networkx as nx
 from tqdm import tqdm
-from typing import Union
+from typing import Union, List
 from ml4co_kit.utils.graph.mcl import MClGraphData
 from ml4co_kit.utils.type_utils import SOLVER_TYPE
 from ml4co_kit.solver import MClSolver, MClGurobiSolver
@@ -15,6 +15,7 @@
 class MClDataGenerator:
     def __init__(
         self,
+        only_instance_for_us: bool = False,
         num_threads: int = 1,
         nodes_num_min: int = 700,
         nodes_num_max: int = 800,
@@ -92,13 +93,18 @@ def __init__(
         self.ws_prob = ws_prob
         self.ws_ring_neighbors = ws_ring_neighbors
 
-        # check the input variables
-        self.sample_types = ["train", "val", "test"]
-        self.check_num_threads()
+        # only instance for us
+        self.only_instance_for_us = only_instance_for_us
         self.check_data_type()
-        self.check_solver()
-        self.check_save_path()
-        self.get_filename()
+
+        # generate and solve
+        if only_instance_for_us == False:
+            # check the input variables
+            self.sample_types = ["train", "val", "test"]
+            self.check_num_threads()    
+            self.check_solver()
+            self.check_save_path()
+            self.get_filename()
 
     def check_num_threads(self):
         self.samples_num = 0
@@ -187,6 +193,11 @@ def check_free(self):
     def random_weight(self, n, mu=1, sigma=0.1):
         return np.around(np.random.normal(mu, sigma, n)).astype(int).clip(min=0)
 
+    def generate_only_instance_for_us(self, samples: int) -> List[MClGraphData]:
+        nx_graphs = [self.generate_func() for _ in range(samples)]
+        self.solver.from_nx_graph(nx_graphs=nx_graphs)
+        return self.solver.graph_data
+
     def generate(self):
         start_time = time.time()
         for _ in tqdm(

ml4co_kit/generator/mcut_data.py

@@ -1,6 +1,5 @@
 import os
 import time
-import pickle
 import pathlib
 import random
 import numpy as np

ml4co_kit/generator/mis_data.py

@@ -6,7 +6,7 @@
 import numpy as np
 import networkx as nx
 from tqdm import tqdm
-from typing import Union
+from typing import Union, List
 from ml4co_kit.utils.graph.mis import MISGraphData
 from ml4co_kit.utils.type_utils import SOLVER_TYPE
 from ml4co_kit.solver import MISSolver, KaMISSolver, MISGurobiSolver
@@ -15,6 +15,7 @@
 class MISDataGenerator:
     def __init__(
         self,
+        only_instance_for_us: bool = False,
         num_threads: int = 1,
         nodes_num_min: int = 700,
         nodes_num_max: int = 800,
@@ -92,14 +93,19 @@ def __init__(
         self.ws_prob = ws_prob
         self.ws_ring_neighbors = ws_ring_neighbors
 
-        # check the input variables
-        self.sample_types = ["train", "val", "test"]
-        self.check_num_threads()
+        # only instance for us
+        self.only_instance_for_us = only_instance_for_us
         self.check_data_type()
-        self.check_solver()
-        self.check_save_path()
-        self.get_filename()
-
+
+        # generate and solve
+        if only_instance_for_us == False:
+            # check the input variables
+            self.sample_types = ["train", "val", "test"]
+            self.check_num_threads()    
+            self.check_solver()
+            self.check_save_path()
+            self.get_filename()
+
     def check_num_threads(self):
         self.samples_num = 0
         for sample_type in self.sample_types:
@@ -194,6 +200,11 @@ def check_free(self):
     def random_weight(self, n, mu=1, sigma=0.1):
         return np.around(np.random.normal(mu, sigma, n)).astype(int).clip(min=0)
 
+    def generate_only_instance_for_us(self, samples: int) -> List[MISGraphData]:
+        nx_graphs = [self.generate_func() for _ in range(samples)]
+        self.solver.from_nx_graph(nx_graphs=nx_graphs)
+        return self.solver.graph_data
+
     def generate(self):
         if self.solver_type == SOLVER_TYPE.KAMIS:
             # check

ml4co_kit/generator/mvc_data.py

@@ -1,12 +1,11 @@
 import os
 import time
-import pickle
 import pathlib
 import random
 import numpy as np
 import networkx as nx
 from tqdm import tqdm
-from typing import Union
+from typing import Union, List
 from ml4co_kit.utils.graph.mvc import MVCGraphData
 from ml4co_kit.utils.type_utils import SOLVER_TYPE
 from ml4co_kit.solver import MVCSolver, MVCGurobiSolver
@@ -15,6 +14,7 @@
 class MVCDataGenerator:
     def __init__(
         self,
+        only_instance_for_us: bool = False,
         num_threads: int = 1,
         nodes_num_min: int = 700,
         nodes_num_max: int = 800,
@@ -92,13 +92,18 @@ def __init__(
         self.ws_prob = ws_prob
         self.ws_ring_neighbors = ws_ring_neighbors
 
-        # check the input variables
-        self.sample_types = ["train", "val", "test"]
-        self.check_num_threads()
+        # only instance for us
+        self.only_instance_for_us = only_instance_for_us
         self.check_data_type()
-        self.check_solver()
-        self.check_save_path()
-        self.get_filename()
+
+        # generate and solve
+        if only_instance_for_us == False:
+            # check the input variables
+            self.sample_types = ["train", "val", "test"]
+            self.check_num_threads()    
+            self.check_solver()
+            self.check_save_path()
+            self.get_filename()
 
     def check_num_threads(self):
         self.samples_num = 0
@@ -187,6 +192,11 @@ def check_free(self):
     def random_weight(self, n, mu=1, sigma=0.1):
         return np.around(np.random.normal(mu, sigma, n)).astype(int).clip(min=0)
 
+    def generate_only_instance_for_us(self, samples: int) -> List[MVCGraphData]:
+        nx_graphs = [self.generate_func() for _ in range(samples)]
+        self.solver.from_nx_graph(nx_graphs=nx_graphs)
+        return self.solver.graph_data
+
     def generate(self):
         start_time = time.time()
         for _ in tqdm(

ml4co_kit/generator/tsp_data.py

@@ -22,6 +22,7 @@
 class TSPDataGenerator:
     def __init__(
         self,
+        only_instance_for_us: bool = False,
         num_threads: int = 1,
         nodes_num: int = 50,
         data_type: str = "uniform",
@@ -96,13 +97,18 @@ def __init__(
         self.regret_save_path = regret_save_path
         self.regret_solver = regret_solver
 
-        # check the input variables
-        self.sample_types = ["train", "val", "test"]
-        self.check_num_threads()
+        # only instance for us
+        self.only_instance_for_us = only_instance_for_us
         self.check_data_type()
-        self.check_solver()
-        self.get_filename()
-        self.check_regret()
+
+        # generate and solve
+        if only_instance_for_us == False:
+            # check the input variables
+            self.sample_types = ["train", "val", "test"]
+            self.check_num_threads()    
+            self.check_solver()
+            self.get_filename()
+            self.check_regret()
 
     def check_num_threads(self):
         self.samples_num = 0
@@ -239,6 +245,12 @@ def check_regret(self):
         if not os.path.exists(self.regret_save_path):
             os.makedirs(self.regret_save_path)
 
+    def generate_only_instance_for_us(self, samples: int) -> np.ndarray:
+        self.num_threads = samples
+        points = self.generate_func()
+        self.solver.from_data(points=points)
+        return self.solver.points
+
     def generate(self):
         start_time = time.time()
         cnt = 0