Add isort precommit

precommit에 isort도 추가하였습니다.

.pre-commit-config.yaml

@@ -4,3 +4,8 @@ repos:
     hooks:
       - id: black
         language_version: python3
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.6.4
+    hooks:
+      - id: isort
+        language_version: python3

app/api/router/predict.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 from typing import List
 
-
 import numpy as np
 from fastapi import APIRouter
 from starlette.concurrency import run_in_threadpool
@@ -12,12 +11,13 @@
 from app.utils import ScikitLearnModel, my_model
 from logger import L
 
-
 models.Base.metadata.create_all(bind=engine)
 
 
 router = APIRouter(
-    prefix="/predict", tags=["predict"], responses={404: {"description": "Not Found"}}
+    prefix="/predict",
+    tags=["predict"],
+    responses={404: {"description": "Not Found"}},
 )
 
 

app/api/router/train.py

@@ -3,14 +3,21 @@
 import re
 import subprocess
 
-
 from fastapi import APIRouter
 
-from app.utils import NniWatcher, ExperimentOwl, base_dir, get_free_port, write_yml
+from app.utils import (
+    ExperimentOwl,
+    NniWatcher,
+    base_dir,
+    get_free_port,
+    write_yml,
+)
 from logger import L
 
 router = APIRouter(
-    prefix="/train", tags=["train"], responses={404: {"description": "Not Found"}}
+    prefix="/train",
+    tags=["train"],
+    responses={404: {"description": "Not Found"}},
 )
 
 
@@ -44,14 +51,18 @@ def train_insurance(
     try:
         write_yml(path, experiment_name, experimenter, model_name, version)
         nni_create_result = subprocess.getoutput(
-            "nnictl create --port {} --config {}/{}.yml".format(PORT, path, model_name)
+            "nnictl create --port {} --config {}/{}.yml".format(
+                PORT, path, model_name
+            )
         )
         sucs_msg = "Successfully started experiment!"
 
         if sucs_msg in nni_create_result:
             p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n")
             expr_id = p.findall(nni_create_result)[0]
-            nni_watcher = NniWatcher(expr_id, experiment_name, experimenter, version)
+            nni_watcher = NniWatcher(
+                expr_id, experiment_name, experimenter, version
+            )
             m_process = multiprocessing.Process(target=nni_watcher.excute)
             m_process.start()
 
@@ -68,6 +79,7 @@ def train_atmos(expr_name: str):
     """
     온도 시계열과 관련된 학습을 실행하기 위한 API입니다.
 
+
     Args:
         expr_name(str): NNI가 실행할 실험의 이름 입니다. 이 파라미터를 기반으로 project_dir/experiments/[expr_name] 경로로 찾아가 config.yml을 이용하여 NNI를 실행합니다.
 
@@ -83,7 +95,9 @@ def train_atmos(expr_name: str):
 
     try:
         nni_create_result = subprocess.getoutput(
-            "nnictl create --port {} --config {}/config.yml".format(nni_port, expr_path)
+            "nnictl create --port {} --config {}/config.yml".format(
+                nni_port, expr_path
+            )
         )
         sucs_msg = "Successfully started experiment!"
 

app/database.py

@@ -1,10 +1,9 @@
 import os
 
-
 from dotenv import load_dotenv
 from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker
 from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
 
 load_dotenv(verbose=True)
 

app/models.py

@@ -1,10 +1,17 @@
 # -*- coding: utf-8 -*-
 import datetime
 
-
-from sqlalchemy import Column, Integer, String, FLOAT, DateTime, ForeignKey, LargeBinary
-from sqlalchemy.sql.functions import now
+from sqlalchemy import (
+    FLOAT,
+    Column,
+    DateTime,
+    ForeignKey,
+    Integer,
+    LargeBinary,
+    String,
+)
 from sqlalchemy.orm import relationship
+from sqlalchemy.sql.functions import now
 
 from app.database import Base
 

app/query.py

@@ -99,4 +99,4 @@
 WHERE NOT EXISTS (SELECT 1 
 				 FROM atmos_model_metadata as amm 
 				 WHERE amm.model_name = '{mn}');
-"""
+"""

app/utils.py

@@ -11,7 +11,6 @@
 import time
 import zipfile
 
-
 import tensorflow as tf
 import yaml
 
@@ -239,7 +238,9 @@ def watch_process(self):
         if self.is_kill:
             while True:
                 self.get_running_experiment()
-                if self._running_experiment and ("DONE" in self._running_experiment[0]):
+                if self._running_experiment and (
+                    "DONE" in self._running_experiment[0]
+                ):
                     _stop_expr = subprocess.getoutput(
                         "nnictl stop {}".format(self.experiment_id)
                     )
@@ -284,7 +285,9 @@ def model_final_update(self):
 
         if saved_result is None:
             engine.execute(
-                INSERT_MODEL_CORE.format(final_result.model_name, pickled_model)
+                INSERT_MODEL_CORE.format(
+                    final_result.model_name, pickled_model
+                )
             )
             engine.execute(
                 INSERT_MODEL_METADATA.format(
@@ -303,7 +306,9 @@ def model_final_update(self):
             > final_result[self.evaluation_criteria]
         ):
             engine.execute(
-                UPDATE_MODEL_CORE.format(pickled_model, saved_result.model_name)
+                UPDATE_MODEL_CORE.format(
+                    pickled_model, saved_result.model_name
+                )
             )
             engine.execute(
                 UPDATE_MODEL_METADATA.format(
@@ -315,7 +320,9 @@ def model_final_update(self):
                 )
             )
 
-        engine.execute(DELETE_ALL_EXPERIMENTS_BY_EXPR_NAME.format(self.experiment_name))
+        engine.execute(
+            DELETE_ALL_EXPERIMENTS_BY_EXPR_NAME.format(self.experiment_name)
+        )
 
 
 def zip_model(model_path):
@@ -401,7 +408,12 @@ class ExperimentOwl:
     """
 
     def __init__(
-        self, experiment_id, experiment_name, experiment_path, mfile_manage=True, time=5
+        self,
+        experiment_id,
+        experiment_name,
+        experiment_path,
+        mfile_manage=True,
+        time=5,
     ):
         self.__minute = 60
         self.time = time * self.__minute
@@ -434,7 +446,9 @@ def main(self):
             expr_list = subprocess.getoutput("nnictl experiment list")
 
             running_expr = [
-                expr for expr in expr_list.split("\n") if self.experiment_id in expr
+                expr
+                for expr in expr_list.split("\n")
+                if self.experiment_id in expr
             ]
             print(running_expr)
             if running_expr and ("DONE" in running_expr[0]):
@@ -486,17 +500,23 @@ def update_tfmodeldb(self):
 
         if not saved_score or (metrics[0] < saved_score[0]):
             winner_model = os.path.join(
-                os.path.join(self.experiment_path, "temp", self.experiment_name)
+                os.path.join(
+                    self.experiment_path, "temp", self.experiment_name
+                )
             )
             if os.path.exists:
                 shutil.rmtree(winner_model)
             os.rename(exprs, winner_model)
 
             m_buffer = zip_model(winner_model)
-            encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode()
+            encode_model = codecs.encode(
+                pickle.dumps(m_buffer), "base64"
+            ).decode()
 
             engine.execute(
-                INSERT_OR_UPDATE_MODEL.format(mn=self.experiment_name, mf=encode_model)
+                INSERT_OR_UPDATE_MODEL.format(
+                    mn=self.experiment_name, mf=encode_model
+                )
             )
             engine.execute(
                 INSERT_OR_UPDATE_SCORE.format(
@@ -506,7 +526,10 @@ def update_tfmodeldb(self):
                     score2=metrics[1],
                 )
             )
-            L.info("saved model %s %s" % (self.experiment_id, self.experiment_name))
+            L.info(
+                "saved model %s %s"
+                % (self.experiment_id, self.experiment_name)
+            )
 
     def modelfile_cleaner(self):
         """

experiments/atmos_tmp_01/train.py

@@ -1,23 +1,21 @@
 import os
 import sys
 import time
+
 from preprocessing import preprocess
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-import numpy as np
 import nni
+import numpy as np
 import pandas as pd
 import tensorflow as tf
+from expr_db import connect
+from sklearn.metrics import mean_absolute_error, mean_squared_error
 from tensorflow import keras
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense
 from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
-from tensorflow.keras.layers import GRU
-from sklearn.metrics import mean_absolute_error, mean_squared_error
-
-
-from expr_db import connect
+from tensorflow.keras.layers import GRU, Dense
+from tensorflow.keras.models import Sequential
 
 physical_devices = tf.config.list_physical_devices("GPU")
 if physical_devices:
@@ -32,7 +30,9 @@ def make_dataset(data, label, window_size=365, predsize=None):
         for i in range(len(data) - (window_size + predsize)):
             feature_list.append(np.array(data.iloc[i : i + window_size]))
             label_list.append(
-                np.array(label.iloc[i + window_size : i + window_size + predsize])
+                np.array(
+                    label.iloc[i + window_size : i + window_size + predsize]
+                )
             )
     else:
         for i in range(len(data) - window_size):

experiments/expr_db.py

@@ -1,6 +1,7 @@
 import os
-from dotenv import load_dotenv
+
 import sqlalchemy
+from dotenv import load_dotenv
 
 
 def connect(db="postgres"):

experiments/insurance/trial.py

@@ -4,7 +4,6 @@
 import pickle
 import sys
 
-
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 
@@ -41,7 +40,9 @@ def preprocess(x_train, x_valid, col_list):
     encoder = LabelEncoder()
 
     for col in col_list:
-        tmp_x_train.loc[:, col] = encoder.fit_transform(tmp_x_train.loc[:, col])
+        tmp_x_train.loc[:, col] = encoder.fit_transform(
+            tmp_x_train.loc[:, col]
+        )
         tmp_x_valid.loc[:, col] = encoder.transform(tmp_x_valid.loc[:, col])
 
     return tmp_x_train.values, tmp_x_valid.values
@@ -87,7 +88,12 @@ def main(params, engine, experiment_info, connection):
         model = XGBRegressor(**params)
 
         # 모델 학습 및 Early Stopping 적용
-        model.fit(x_tra, y_train, eval_set=[(x_val, y_valid)], early_stopping_rounds=10)
+        model.fit(
+            x_tra,
+            y_train,
+            eval_set=[(x_val, y_valid)],
+            early_stopping_rounds=10,
+        )
 
         y_train_pred = model.predict(x_tra)
         y_valid_pred = model.predict(x_val)

logger.py

@@ -1,7 +1,7 @@
 import logging
 import logging.handlers
-from colorlog import ColoredFormatter
 
+from colorlog import ColoredFormatter
 
 L = logging.getLogger("snowdeer_log")
 L.setLevel(logging.DEBUG)

main.py

@@ -1,7 +1,6 @@
+import uvicorn
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-import uvicorn
-
 
 from app.api.router import predict, train
 
@@ -28,5 +27,9 @@ def hello_world():
 
 if __name__ == "__main__":
     uvicorn.run(
-        "main:app", host="0.0.0.0", port=8000, reload=True, reload_dirs=["app/"]
+        "main:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=True,
+        reload_dirs=["app/"],
     )

pyproject.toml

@@ -0,0 +1,10 @@
+[tool.isort]
+multi_line_output=3
+include_trailing_comma=true
+force_grid_wrap=0
+use_parentheses=true
+line_length=79
+
+[tool.black]
+line-length = 79
+target-version = ['py38']