arima error package corrected

deployment/arima/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

deployment/arima/.~lock.demo.csv#

-,awarno,bulls-ThinkPad-T480,28.09.2021 09:11,file:///home/awarno/.config/libreoffice/4;
\ No newline at end of file

deployment/arima/Dockerfile

@@ -2,9 +2,13 @@ FROM python:3.8-slim-buster
 
 # Install Python dependencies.
 WORKDIR /wd
-# COPY deployment/arima/requirements.txt .
-COPY deployment/arima/requirements.txt .
-RUN pip3 install --no-cache-dir -r requirements.txt && mkdir models
+
+COPY deployment/arima/poetry.lock deployment/arima/pyproject.toml /wd/
+
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir poetry \
+    \
+    && poetry install --no-dev && mkdir models
 
 ADD https://gitlab.ow2.org/melodic/morphemic-preprocessor/-/archive/morphemic-rc1.5/morphemic-preprocessor-morphemic-rc1.5.tar.gz /var/lib/morphemic/
 
@@ -19,6 +23,6 @@ RUN cd /var/lib/morphemic/ \
     && cp -R /var/lib/morphemic/morphemic-preprocessor-morphemic-rc1.5/amq-message-python-library /wd/amq_message_python_library \
     && rm -rf /var/lib/morphemic
 
-CMD ["python3",  "main.py"]
+CMD ["poetry", "run" ,"python3",  "main.py"]
 
 

deployment/arima/arima/__init__.py

+__version__ = '0.1.0'

deployment/arima/src/dataset_maker.py → deployment/arima/arima/dataset_maker.py

-from morphemic.dataset import DatasetMaker
+"""Script for preparing csv data downloaded form InfluxDB database, data"""
+
 import os
-from filelock import FileLock
 
-"""Script for preparing csv data downloaded form InfluxDB database, data"""
+from filelock import FileLock
+from typing import Optional, Union
+from morphemic.dataset import DatasetMaker
 
 
 class CSVData(object):
-    def __init__(self, name, start_collection=None):
+    def __init__(
+        self, name: str, start_collection: Optional[Union[str, int]] = None
+    ) -> None:
+        """Create csv data downloaded from influx
+
+        Args:
+        ------
+            name (str): dataset name, downloaded dataset will be saved
+            under : <SELF NAME>.csv
+            start_collection (Optional[Union[str, int]], optional): start timestamp
+            if None then the whole dataset is downloaded. Defaults to None.
+        """
         self.name = name
         self.config = {
             "hostname": os.environ.get("INFLUXDB_HOSTNAME", "localhost"),
@@ -18,7 +31,8 @@ class CSVData(object):
         }
         self.start_collection = start_collection
 
-    def prepare_csv(self):
+    def prepare_csv(self) -> None:
+        """Download latest data"""
         lockfile = os.path.join(self.config["path_dataset"], f"{self.name}.csv")
         lock = FileLock(lockfile + ".lock")
 

deployment/arima/src/influxdb_predictions.py → deployment/arima/arima/influxdb_predictions.py

-from influxdb import *
-import os
 import datetime
+import os
 
+from influxdb import *
 
 """Connects with Influxdb and sends predictet values"""
 

deployment/arima/src/model_predict.py → deployment/arima/arima/model_predict.py

-import yaml
-import pandas as pd
-import numpy as np
-import time
+import logging
 import os
-import statsmodels.api as sm
-from filelock import FileLock
-from src.preprocess_dataset import Dataset
 import time
-import logging
-import pytz
 from datetime import datetime
 
+import numpy as np
+import pandas as pd
+import pytz
+import statsmodels.api as sm
+import yaml
+from filelock import FileLock
+from arima.preprocess_dataset import Dataset
+
 pd.options.mode.chained_assignment = None
 
 """Script for temporal fusion transformer prediction"""

deployment/arima/src/model_train.py → deployment/arima/arima/model_train.py

-import time
 import os
+import time
 
 
 def train(target_column, prediction_length, yaml_file="model.yaml"):

deployment/arima/src/preprocess_dataset.py → deployment/arima/arima/preprocess_dataset.py

-import pandas as pd
-import numpy as np
 import logging
 import time
 
+import numpy as np
+import pandas as pd
+
 pd.options.mode.chained_assignment = None
 
 """Script for preparing time series dataset from pythorch-forecasting package 

deployment/arima/main.py

-import os
-import stomp
 import json
-from amq_message_python_library import *  # python amq-message-python-library
 import logging
+import os
 import time
 from datetime import datetime
-from pytz import timezone
-import time
-import setproctitle
 
-# from src.log import logger
+import setproctitle
+import stomp
+from amq_message_python_library import *  # python amq-message-python-library
+from pytz import timezone
 
 AMQ_USER = os.environ.get("AMQ_USER", "admin")
 AMQ_PASSWORD = os.environ.get("AMQ_PASSWORD", "admin")

deployment/arima/predict.py

-import time
-import os
-import stomp
-import threading
-from src.model_predict import predict
-from amq_message_python_library import *
-from src.influxdb_predictions import InfluxdbPredictionsSender
 import json
-import sys
-import pandas as pd
 import logging
+import os
+import sys
+import threading
+import time
 from datetime import datetime
-from src.dataset_maker import CSVData
-from pytz import timezone
+
 import pytz
-from datetime import datetime
-import random
+import stomp
+from amq_message_python_library import *
+from pytz import timezone
+from arima.dataset_maker import CSVData
+from arima.influxdb_predictions import InfluxdbPredictionsSender
+from arima.model_predict import predict
 
 METHOD = os.environ.get("METHOD", "nbeats")
 START_TOPIC = f"start_forecasting.{METHOD}"

deployment/arima/pyproject.toml

+[tool.poetry]
+name = "arima"
+version = "0.1.0"
+description = ""
+authors = ["Anna Warno <awarno@7bulls.com>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+"stomp.py" = "^8.0.0"
+pandas = "1.1.3"
+statsmodels = "0.13.2"
+filelock = "3.0.12"
+PyYAML = "^6.0"
+influxdb = "^5.3.1"
+python-slugify = "^6.1.1"
+setproctitle = "^1.2.2"
+
+[tool.poetry.dev-dependencies]
+pytest = "^5.2"
+isort = "^5.10.1"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

deployment/arima/requirements.txt

-stomp.py
-pandas==1.1.3
-statsmodels==0.12.2
-filelock==3.0.12
-pyyaml
-influxdb
-python-slugify
-setproctitle

deployment/arima/retrain.py

-import os
-import sys
 import json
 import logging
-import time
-from src.model_train import train
-from amq_message_python_library import *
-from src.dataset_maker import CSVData
-import pytz
+import os
+import sys
 import time
 from datetime import datetime
 
+import pytz
+from amq_message_python_library import *
+from arima.dataset_maker import CSVData
+from arima.model_train import train
+
 TOPIC_NAME = "training_models"
 RETRAIN_CYCLE = 10  # minutes
 AMQ_USER = os.environ.get("AMQ_USER", "admin")

deployment/arima/test/model_predict_test.py → deployment/arima/tests/model_predict_test.py

@@ -2,12 +2,13 @@ import sys
 
 sys.path.append(".")
 
-import pytest
-from src.model_predict import predict
-import pandas as pd
-import numpy as np
 import random
 
+import numpy as np
+import pandas as pd
+import pytest
+from arima.model_predict import predict
+
 
 @pytest.fixture
 def df_1():

deployment/arima/test/model_train_test.py → deployment/arima/tests/model_train_test.py

@@ -2,12 +2,13 @@ import sys
 
 sys.path.append(".")
 
-import pytest
-from src.model_train import train
-import pandas as pd
-import numpy as np
 import random
 
+import numpy as np
+import pandas as pd
+import pytest
+from arima.model_train import train
+
 
 @pytest.fixture
 def df_1():

deployment/arima/test/preprocess_dataset_test.py → deployment/arima/tests/preprocess_dataset_test.py

@@ -2,12 +2,13 @@ import sys
 
 sys.path.append(".")
 
-import pytest
-from src.preprocess_dataset import Dataset
-import pandas as pd
-import numpy as np
 import random
 
+import numpy as np
+import pandas as pd
+import pytest
+from arima.preprocess_dataset import Dataset
+
 
 @pytest.fixture
 def df_1():