[Refactor] Migrate naming to AXE (#38)

* [Refactor] Move entry point and toml to AXE
* [Refactor] Move folder `endure` to `axe`
* [Refactor] Remove docker file
* [Refactor] Change README file

README.md

@@ -46,16 +46,16 @@ These are the general steps that can be used to generate data, train each model
    ```bash
    pip install -r requirements.txt
    ```
-8. **Configure the endure.toml file (More details can be found below)** \
-   The [endure.toml](#Configuration-File) file contains all required options for the experiments and jobs. Please configure all the jobs and their respective parameters  required to successfully run the project.
+8. **Configure the axe.toml file (More details can be found below)** \
+   The [axe.toml](#Configuration-File) file contains all required options for the experiments and jobs. Please configure all the jobs and their respective parameters  required to successfully run the project.
 
 9. **Run the project** \
-    The project is structured such that all experiments can be run by just running the endure.py file. Use the following command to run it:
+    The project is structured such that all experiments can be run by just running the axe.py file. Use the following command to run it:
    ```bash
-   python endure.py
+   python axe.py
    ```
 ## Configuration File
-All of our configuration is handled by the endure.toml file placed in the root directory of our project. 
+All of our configuration is handled by the axe.toml file placed in the root directory of our project. 
 The config file is divided into sections denoted by a heading within the square brackets (for example *[app]*)  
 1. To run a specific job, change the variable **run** under the *[app]* header. All the available jobs are already present in the variable and provided as commented out code. Uncomment all jobs that have to be run to run multiple jobs or a single job.   
 
@@ -76,14 +76,14 @@ The config file is divided into sections denoted by a heading within the square
 ## Project Structure
 Our project is separated into repositories to maintain a consistent structure. Below is a short description for each of the repositories to help any user of this project:
 
-1. **jobs** - This repo contains the main entry point defined in endure.py for each job. There are separate files dedicated to each job that can be identified using the filenames.
+1. **jobs** - This repo contains the main entry point defined in axe.py for each job. There are separate files dedicated to each job that can be identified using the filenames.
 
-2. **endure** - This repo contains multiple other repositories divided by use case as defined below:  
+2. **axe** - This repo contains multiple other repositories divided by use case as defined below:  
 
-    a. **endure/lcm** - This repo contains the folders and files responsible for the *Learned Cost Model (LCM)* that helps learn the cost surface for the implemented solution. Within this directory, there is a *model* folder that contains the files for each LCM model structure (Classic, KLSM, QHybrid, Doestoevsky (YZLSM)). The *util* folder contains all utilities used by the models within *lcm*. The *data* folder contains code required for generating and using the input (data) files by the models.  
+    a. **axe/lcm** - This repo contains the folders and files responsible for the *Learned Cost Model (LCM)* that helps learn the cost surface for the implemented solution. Within this directory, there is a *model* folder that contains the files for each LCM model structure (Classic, KLSM, QHybrid, Doestoevsky (YZLSM)). The *util* folder contains all utilities used by the models within *lcm*. The *data* folder contains code required for generating and using the input (data) files by the models.  
 
-    b. **endure/lsm** - This repo contains the analytical solver repository called *solver* that is used as the basis of comparison as well as files associated with the structure of the lsm. It has a *types.py* file that is used throughout our project to define each type used. The project also has cost files that use the equations from the cost model (as stated in the paper) to calculate the cost of each operation for all models. There is a also a data_generator file that is used for generation of data from a given sample space uniformly at random.  
+    b. **axe/lsm** - This repo contains the analytical solver repository called *solver* that is used as the basis of comparison as well as files associated with the structure of the lsm. It has a *types.py* file that is used throughout our project to define each type used. The project also has cost files that use the equations from the cost model (as stated in the paper) to calculate the cost of each operation for all models. There is a also a data_generator file that is used for generation of data from a given sample space uniformly at random.  
 
-    c. **endure/ltune** - This repo contains the folders and files responsible for the *Learned Tuner (LTuner)* that helps predict the best configuration for the LSM Tree as per the solution proposed in the paper. Within this directory, there is a *model* folder that contains the files for each Ltuner model structure (Classic, KLSM, QHybrid, Doestoevsky (YZLSM)). The *util* folder contains all utilities used by the models within *ltune*. The *data* folder contains code required for generating and using the input (data) files by the models.  
+    c. **axe/ltune** - This repo contains the folders and files responsible for the *Learned Tuner (LTuner)* that helps predict the best configuration for the LSM Tree as per the solution proposed in the paper. Within this directory, there is a *model* folder that contains the files for each Ltuner model structure (Classic, KLSM, QHybrid, Doestoevsky (YZLSM)). The *util* folder contains all utilities used by the models within *ltune*. The *data* folder contains code required for generating and using the input (data) files by the models.  
 
-    d. **endure/util** - Contains utility files that are used generally by all modules.  
+    d. **axe/util** - Contains utility files that are used generally by all modules.  

endure.py → axe.py

@@ -13,7 +13,7 @@
 from jobs.mlos_exp_runs import ExperimentMLOS
 
 
-class EndureDriver:
+class AxeDriver:
     def __init__(self, config: dict[str, Any]) -> None:
         self.config = config
 
@@ -53,10 +53,10 @@ def run(self):
         config_path = sys.argv[1]
     else:
         file_dir = os.path.dirname(__file__)
-        config_path = os.path.join(file_dir, "endure.toml")
+        config_path = os.path.join(file_dir, "axe.toml")
 
     with open(config_path) as fid:
         config = toml.load(fid)
 
-    driver = EndureDriver(config)
+    driver = AxeDriver(config)
     driver.run()

endure.toml → axe.toml

@@ -1,5 +1,5 @@
 # =============================================================================
-# ENDURE Configuration File
+# AXE Configuration File
 #   Following subsections are available
 #   APP
 #   LOGGER - output setting
@@ -18,7 +18,7 @@
 #   Logic of app including jobs list to run
 # =============================================================================
 [app]
-name = "ENDURE"
+name = "AXE"
 run = [
     # "DataGen",
     # "LCMTrain",
@@ -33,7 +33,7 @@ run = [
 #   Generic IO settings for experiments, saving data, etc
 # =============================================================================
 [log]
-name = 'endure-logger'
+name = 'axe-logger'
 format = "[%(levelname)s][%(asctime)-15s][%(filename)s] %(message)s"
 datefmt = '%d-%m-%y:%H:%M:%S'
 level = "DEBUG"

endure/data/io.py → axe/data/io.py

@@ -8,7 +8,7 @@
 class Writer(object):
     def __init__(self, config):
         self._config = config
-        self.log = logging.getLogger("endure")
+        self.log = logging.getLogger("axe")
 
     def export_csv_file(self, df, filename):
         """
@@ -57,7 +57,7 @@ def __init__(self, config):
         """
         self._config = config
         self.data_dir = self._config["io"]["data_dir"]
-        self.log = logging.getLogger("endure")
+        self.log = logging.getLogger("axe")
 
     @classmethod
     def read_config(cls, config_path):

endure/lcm/data/dataset.py → axe/lcm/data/dataset.py

@@ -8,9 +8,9 @@
 from torch import Tensor
 import torch.utils.data
 
-from endure.lcm.data.input_features import kINPUT_FEATS_DICT, kOUTPUT_FEATS
-from endure.lsm.types import LSMBounds, Policy
-from endure.lcm.util import one_hot_lcm, one_hot_lcm_classic
+from axe.lcm.data.input_features import kINPUT_FEATS_DICT, kOUTPUT_FEATS
+from axe.lsm.types import LSMBounds, Policy
+from axe.lcm.util import one_hot_lcm, one_hot_lcm_classic
 
 
 class LCMDataSet(torch.utils.data.IterableDataset):
@@ -102,4 +102,4 @@ def __iter__(self):
                 label, input = labels[idx], inputs[idx]
                 if self.test_mode:
                     input = self._transform_test_data(inputs[idx])
-                yield label, input
+                yield label, input

endure/lcm/data/generator.py → axe/lcm/data/generator.py

@@ -4,9 +4,9 @@
 
 import numpy as np
 
-from endure.lsm.types import LSMDesign, System, Policy, LSMBounds
-from endure.lsm.cost import EndureCost
-from endure.lcm.data.input_features import (
+from axe.lsm.types import LSMDesign, System, Policy, LSMBounds
+from axe.lsm.cost import EndureCost
+from axe.lcm.data.input_features import (
     kWORKLOAD_HEADER,
     kSYSTEM_HEADER,
     kCOST_HEADER,

endure/lcm/data/input_features.py → axe/lcm/data/input_features.py

@@ -1,4 +1,4 @@
-from endure.lsm.types import Policy
+from axe.lsm.types import Policy
 
 kSYSTEM_HEADER = [
     "entry_p_page",

endure/lcm/model/builder.py → axe/lcm/model/builder.py

@@ -3,9 +3,9 @@
 from torch import nn
 import torch
 
-from endure.lcm.data.input_features import kINPUT_FEATS_DICT
-from endure.lcm.model import KapModel, QModel, ClassicModel, YZModel
-from endure.lsm.types import Policy
+from axe.lcm.data.input_features import kINPUT_FEATS_DICT
+from axe.lcm.model import KapModel, QModel, ClassicModel, YZModel
+from axe.lsm.types import Policy
 
 
 class LearnedCostModelBuilder:
@@ -78,4 +78,4 @@ def build_model(self, policy: Policy) -> torch.nn.Module:
 
         model = model_class(**args)
 
-        return model
+        return model

endure/lcm/util/lcm_evaluation.py → axe/lcm/util/lcm_evaluation.py

@@ -1,9 +1,9 @@
 import torch
 
 from .util import eval_lcm_impl
-from endure.lcm.data.generator import LCMDataGenerator
-from endure.lsm.cost import EndureCost
-from endure.lsm.types import LSMDesign, Policy, System
+from axe.lcm.data.generator import LCMDataGenerator
+from axe.lsm.cost import EndureCost
+from axe.lsm.types import LSMDesign, Policy, System
 
 class LCMEvalUtil:
     def __init__(
@@ -67,4 +67,4 @@ def gen_random_sample(self):
         row['cost_acm'] = cost_acm
 
         return row, design, system
-
+

endure/lcm/util/util.py → axe/lcm/util/util.py

@@ -2,7 +2,7 @@
 import torch
 import torch.nn.functional as F
 
-from endure.lsm.types import LSMDesign, Policy, System
+from axe.lsm.types import LSMDesign, Policy, System
 
 def one_hot_lcm(
     data: Tensor,

endure/lsm/cost.py → axe/lsm/cost.py

@@ -1,6 +1,6 @@
 import numpy as np
-import endure.lsm.lsm_cost as Cost
-from endure.lsm.types import Policy, System, LSMDesign
+import axe.lsm.lsm_cost as Cost
+from axe.lsm.types import Policy, System, LSMDesign
 
 
 class EndureCost:

endure/lsm/data_generator.py → axe/lsm/data_generator.py

@@ -4,8 +4,8 @@
 
 import numpy as np
 
-from endure.lsm.types import LSMDesign, System, Policy, LSMBounds, Workload
-from endure.lsm.cost import EndureCost
+from axe.lsm.types import LSMDesign, System, Policy, LSMBounds, Workload
+from axe.lsm.cost import EndureCost
 
 
 class LSMDataGenerator:

endure/lsm/solver/__init__.py → axe/lsm/solver/__init__.py

@@ -1,5 +1,5 @@
 from typing import Type
-from endure.lsm.types import Policy
+from axe.lsm.types import Policy
 from .classic_solver import ClassicSolver
 from .qlsm_solver import QLSMSolver
 from .klsm_solver import KLSMSolver

endure/lsm/solver/classic_solver.py → axe/lsm/solver/classic_solver.py

@@ -1,10 +1,10 @@
-from typing import Any, Optional, Callable, Tuple, List
+from typing import Optional, Callable, Tuple, List
 
 import numpy as np
 import scipy.optimize as SciOpt
 
-from endure.lsm.cost import EndureCost
-from endure.lsm.types import LSMDesign, Policy, System, LSMBounds
+from axe.lsm.cost import EndureCost
+from axe.lsm.types import LSMDesign, Policy, System, LSMBounds
 from .util import kl_div_con
 from .util import get_bounds
 
@@ -15,11 +15,7 @@
 
 
 class ClassicSolver:
-    def __init__(
-        self,
-        bounds: LSMBounds,
-        policies: Optional[List[Policy]] = None
-    ):
+    def __init__(self, bounds: LSMBounds, policies: Optional[List[Policy]] = None):
         self.bounds = bounds
         self.cf = EndureCost(bounds.max_considered_levels)
         if policies is None:

endure/lsm/solver/klsm_solver.py → axe/lsm/solver/klsm_solver.py

@@ -1,10 +1,10 @@
-from typing import Any, Optional, Callable, Tuple
+from typing import Optional, Callable, Tuple
 
 import numpy as np
 import scipy.optimize as SciOpt
 
-from endure.lsm.cost import EndureCost
-from endure.lsm.types import LSMDesign, Policy, System, LSMBounds
+from axe.lsm.cost import EndureCost
+from axe.lsm.types import LSMDesign, Policy, System, LSMBounds
 from .util import kl_div_con, get_bounds
 from .util import H_DEFAULT, T_DEFAULT, LAMBDA_DEFAULT, ETA_DEFAULT, K_DEFAULT
 
@@ -29,10 +29,8 @@ def robust_objective(
         lamb, eta = x[-2:]
         design = LSMDesign(h=h, T=t, K=kaps, policy=Policy.KHybrid)
         query_cost = 0
-        query_cost += z0 * \
-            kl_div_con((self.cf.Z0(design, system) - eta) / lamb)
-        query_cost += z1 * \
-            kl_div_con((self.cf.Z1(design, system) - eta) / lamb)
+        query_cost += z0 * kl_div_con((self.cf.Z0(design, system) - eta) / lamb)
+        query_cost += z1 * kl_div_con((self.cf.Z1(design, system) - eta) / lamb)
         query_cost += q * kl_div_con((self.cf.Q(design, system) - eta) / lamb)
         query_cost += w * kl_div_con((self.cf.W(design, system) - eta) / lamb)
         cost = eta + (rho * lamb) + (lamb * query_cost)
@@ -69,7 +67,7 @@ def get_robust_design(
         minimizer_kwargs: dict = {},
         callback_fn: Optional[Callable] = None,
     ) -> Tuple[LSMDesign, SciOpt.OptimizeResult]:
-        raise NotImplemented
+        raise NotImplementedError
 
     def get_nominal_design(
         self,
@@ -97,8 +95,7 @@ def get_nominal_design(
         default_kwargs.update(minimizer_kwargs)
         kap_val = init_args[-1]
         init_args = np.concatenate(
-            (init_args[0:2],
-             np.array([kap_val for _ in range(max_levels)]))
+            (init_args[0:2], np.array([kap_val for _ in range(max_levels)]))
         )
 
         solution = SciOpt.minimize(
@@ -108,10 +105,7 @@ def get_nominal_design(
             **default_kwargs
         )
         design = LSMDesign(
-            h=solution.x[0],
-            T=solution.x[1],
-            K=solution.x[2:],
-            policy=Policy.KHybrid
+            h=solution.x[0], T=solution.x[1], K=solution.x[2:], policy=Policy.KHybrid
         )
 
         return design, solution

endure/lsm/solver/qlsm_solver.py → axe/lsm/solver/qlsm_solver.py

@@ -1,15 +1,16 @@
-from typing import Any, Optional, Callable, Tuple
+from typing import Optional, Callable, Tuple
 
 import numpy as np
 import scipy.optimize as SciOpt
 
-from endure.lsm.cost import EndureCost
-from endure.lsm.types import LSMDesign, Policy, System, LSMBounds
+from axe.lsm.cost import EndureCost
+from axe.lsm.types import LSMDesign, Policy, System, LSMBounds
 from .util import kl_div_con, get_bounds
 from .util import H_DEFAULT, T_DEFAULT, LAMBDA_DEFAULT, ETA_DEFAULT, Q_DEFAULT
 
+
 class QLSMSolver:
-    def __init__(self, bounds:LSMBounds):
+    def __init__(self, bounds: LSMBounds):
         self.bounds = bounds
         self.cf = EndureCost(bounds.max_considered_levels)
 
@@ -63,7 +64,7 @@ def get_robust_design(
         minimizer_kwargs: dict = {},
         callback_fn: Optional[Callable] = None,
     ) -> Tuple[LSMDesign, SciOpt.OptimizeResult]:
-        raise NotImplemented
+        raise NotImplementedError
 
     def get_nominal_design(
         self,
@@ -95,10 +96,7 @@ def get_nominal_design(
             **default_kwargs
         )
         design = LSMDesign(
-            h=solution.x[0],
-            T=solution.x[1],
-            Q=solution.x[2],
-            policy=Policy.QFixed)
+            h=solution.x[0], T=solution.x[1], Q=solution.x[2], policy=Policy.QFixed
+        )
 
         return design, solution
-