Fixes tests after moving run to Ubuntu

Author: rigaml

poetry.lock

@@ -20,6 +20,7 @@ mlxtend = "^0.23.1"
 
 [tool.poetry.group.dev.dependencies]
 nbstripout = "^0.7.1"
+pytest = "^8.3.3"
 
 [build-system]
 requires = ["poetry-core"]

pyproject.toml

@@ -0,0 +1,2 @@
+[tool:pytest]
+pythonpath = src

setup.cfg

@@ -3,6 +3,8 @@
 
 If a function gets defined once and could be used over and over, it'll go in here.
 """
+import torchvision
+from typing import List
 import torch
 import matplotlib.pyplot as plt
 import numpy as np
@@ -18,7 +20,7 @@
 
 # Walk through an image classification directory and find out how many files (images)
 # are in each subdirectory.
-import os
+
 
 def walk_through_dir(dir_path):
     """
@@ -35,6 +37,7 @@ def walk_through_dir(dir_path):
     for dirpath, dirnames, filenames in os.walk(dir_path):
         print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
 
+
 def plot_decision_boundary(model: torch.nn.Module, X: torch.Tensor, y: torch.Tensor):
     """Plots decision boundaries of model predicting on X in comparison to y.
 
@@ -166,8 +169,6 @@ def plot_loss_curves(results):
 
 # Pred and plot image function from notebook 04
 # See creation: https://www.learnpytorch.io/04_pytorch_custom_datasets/#113-putting-custom-image-prediction-together-building-a-function
-from typing import List
-import torchvision
 
 
 def pred_and_plot_image(
@@ -185,7 +186,7 @@ def pred_and_plot_image(
         class_names (List[str], optional): different class names for target image. Defaults to None.
         transform (_type_, optional): transform of target image. Defaults to None.
         device (torch.device, optional): target device to compute on. Defaults to "cuda" if torch.cuda.is_available() else "cpu".
-    
+
     Returns:
         Matplotlib plot of target image and model prediction as title.
 
@@ -236,7 +237,8 @@ def pred_and_plot_image(
     plt.title(title)
     plt.axis(False)
 
-def set_seeds(seed: int=42):
+
+def set_seeds(seed: int = 42):
     """Sets random sets for torch operations.
 
     Args:
@@ -247,7 +249,8 @@ def set_seeds(seed: int=42):
     # Set the seed for CUDA torch operations (ones that happen on the GPU)
     torch.cuda.manual_seed(seed)
 
-def download_data(source: str, 
+
+def download_data(source: str,
                   destination: str,
                   remove_source: bool = True) -> Path:
     """Downloads a zipped dataset from source and unzips to destination.
@@ -256,10 +259,10 @@ def download_data(source: str,
         source (str): A link to a zipped file containing data.
         destination (str): A target directory to unzip data to.
         remove_source (bool): Whether to remove the source after downloading and extracting.
-    
+
     Returns:
         pathlib.Path to downloaded data.
-    
+
     Example usage:
         download_data(source="https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip",
                       destination="pizza_steak_sushi")
@@ -268,13 +271,13 @@ def download_data(source: str,
     data_path = Path("data/")
     image_path = data_path / destination
 
-    # If the image folder doesn't exist, download it and prepare it... 
+    # If the image folder doesn't exist, download it and prepare it...
     if image_path.is_dir():
         print(f"[INFO] {image_path} directory exists, skipping download.")
     else:
         print(f"[INFO] Did not find {image_path} directory, creating one...")
         image_path.mkdir(parents=True, exist_ok=True)
-        
+
         # Download pizza, steak, sushi data
         target_file = Path(source).name
         with open(data_path / target_file, "wb") as f:
@@ -284,11 +287,11 @@ def download_data(source: str,
 
         # Unzip pizza, steak, sushi data
         with zipfile.ZipFile(data_path / target_file, "r") as zip_ref:
-            print(f"[INFO] Unzipping {target_file} data...") 
+            print(f"[INFO] Unzipping {target_file} data...")
             zip_ref.extractall(image_path)
 
         # Remove .zip file
         if remove_source:
             os.remove(data_path / target_file)
-    
+
     return image_path

src/notebooks/freecodecamp/helper_functions.py

@@ -203,7 +203,7 @@
    "source": [
     "\n",
     "\n",
-    "from src.classificators.proportions_calc import calculate_proportions\n",
+    "from classificators.proportions_calc import calculate_proportions\n",
     "\n",
     "\n",
     "proportions= calculate_proportions(close_list, windows_rolling_avg)\n"

src/notebooks/stock_up_down/multiple_classes.ipynb

@@ -1,5 +1,6 @@
 # pytest.ini
 [pytest]
+pythonpath = .
 minversion = 6.0
 addopts = -ra -q
 testpaths =

src/pytest.ini

@@ -1,5 +1,4 @@
 # File config.json added to Keepass entry for Tiingo
-import os
 import json
 
 from utils.file_utils import get_project_root

src/settings.py

@@ -0,0 +1,5 @@
+import sys
+import os
+
+# Add the project root to the Python path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))

src/tests/__init__.py

@@ -1,11 +1,11 @@
-from typing import List
 import pytest
 from unittest import mock
+
+from typing import List
 import pandas as pd
 from datetime import datetime, timedelta
 
-# import src.tests.context
-from src.data_sources import tiingo_repo as tr
+from data_sources import tiingo_repo as tr
 
 DAYS_DOWNLOAD = 30
 

src/tests/conftest.py

@@ -1,10 +1,8 @@
-import unittest
 import pytest
 
-from math import nan, isclose
+from math import isclose
 
-# import context
-from src.classifiers import ewa_classifier as ec
+from classifiers import ewa_classifier as ec
 
 
 def test_ewa_classifier_when_window_less_than_2_raises_exception():

src/tests/data_sources/test_tiingo_repo.py

@@ -4,7 +4,7 @@
 from datetime import datetime
 
 import requests
-from src.apis.tiingo_api import TiingoAPI
+from apis.tiingo_api import TiingoAPI
 
 
 class TestTiingoAPI(unittest.TestCase):