remove conda. add requirements.txt. add tests

.gitignore

@@ -1,4 +1,163 @@
-storage/
+data/
+
+outputs
+# Byte-compiled / optimized / DLL files
 __pycache__/
-*.swp
+*.py[cod]
-*.tmp
+*$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
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__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/

Makefile

@@ -1,15 +1,30 @@
-CONDA_ENV=ml_pipeline
+PYTHON=.venv/bin/python3
+.PHONY: help test
 
 all: run
 
-run:
+init:
-	python src/pipeline.py train
+	python3.9 -m virtualenv .venv
 
-data:
+run: ## run the pipeline (train)
-	python src/data.py
+	$(PYTHON) src/train.py \
+		debug=false
 
-batch:
+debug: ## run the pipeline (train) with debugging enabled
-	python src/batch.py
+	$(PYTHON) src/train.py \
+		debug=true
+
+data: ## download the mnist data
+	wget https://pjreddie.com/media/files/mnist_train.csv -O data/mnist_train.csv
+	wget https://pjreddie.com/media/files/mnist_test.csv -O data/mnist_test.csv
+test:
+	find . -iname "*.py" | entr -c pytest
+
+install:
+	$(PYTHON) -m pip install -r requirements.txt
+
+help: ## display this help message
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
 
 install:
 	conda env updates -n ${CONDA_ENV} --file environment.yml

requirements.txt

@@ -0,0 +1,12 @@
+black
+click
+einops
+hydra-core
+matplotlib
+numpy
+wandb
+pytest
+python-dotenv
+torch
+requests
+tqdm

src/data/dataset.py

@@ -0,0 +1,66 @@
+from torch.utils.data import Dataset
+import numpy as np
+import einops
+import csv
+import torch
+from pathlib import Path
+from typing import Tuple
+import os
+
+
+class MnistDataset(Dataset):
+    """
+    The MNIST database of handwritten digits.
+    Training set is 60k labeled examples, test is 10k examples.
+    The b/w images normalized to 20x20, preserving aspect ratio.
+
+    It's the defacto standard image training set to learn about classification in DL
+    """
+
+    def __init__(self, path: Path):
+        """
+        give a path to a dir that contains the following csv files:
+        https://pjreddie.com/projects/mnist-in-csv/
+        """
+        self.path = path
+        self.features, self.labels = self._load()
+
+    def __getitem__(self, idx):
+        return (self.features[idx], self.labels[idx])
+
+    def __len__(self):
+        return len(self.features)
+
+    def _load(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        # opening the CSV file
+        with open(self.path, mode="r") as file:
+            images, labels = [], []
+            csvFile = csv.reader(file)
+            examples = int(os.getenv("TRAINING_EXAMPLES", 1000))
+            for line, content in enumerate(csvFile):
+                if line == examples:
+                    break
+                labels.append(int(content[0]))
+                image = [int(x) for x in content[1:]]
+                images.append(image)
+            labels = torch.tensor(labels, dtype=torch.long)
+            images = torch.tensor(images, dtype=torch.float32)
+            images = einops.rearrange(images, "n (w h) -> n w h", w=28, h=28)
+            images = einops.repeat(
+                images, "n w h -> n c (w r_w) (h r_h)", c=1, r_w=8, r_h=8
+            )
+            return (images, labels)
+
+
+def main():
+    path = Path("storage/mnist_train.csv")
+    dataset = MnistDataset(path=path)
+    print(f"len: {len(dataset)}")
+    print(f"first shape: {dataset[0][0].shape}")
+    mean = einops.reduce(dataset[:10][0], "n w h -> w h", "mean")
+    print(f"mean shape: {mean.shape}")
+    print(f"mean image: {mean}")
+
+
+if __name__ == "__main__":
+    main()

src/data/spark.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+from sys import stdout
+import csv
+
+# 'pip install pyspark' for these
+from pyspark import SparkFiles
+from pyspark.sql import SparkSession
+
+# make a spark "session".  this creates a local hadoop cluster by default (!)
+spark = SparkSession.builder.getOrCreate()
+# put the input file in the cluster's filesystem:
+spark.sparkContext.addFile("https://csvbase.com/meripaterson/stock-exchanges.csv")
+# the following is much like for pandas
+df = (
+    spark.read.csv(f"file://{SparkFiles.get('stock-exchanges.csv')}", header=True)
+    .select("MIC")
+    .na.drop()
+    .sort("MIC")
+)
+# pyspark has no easy way to write csv to stdout - use python's csv lib
+csv.writer(stdout).writerows(df.collect())

src/model/cnn.py

@@ -37,10 +37,10 @@ class VGG11(nn.Module):
         self.linear_layers = nn.Sequential(
             nn.Linear(in_features=512 * 7 * 7, out_features=4096),
             nn.ReLU(),
-            nn.Dropout2d(0.5),
+            nn.Dropout(0.5),
             nn.Linear(in_features=4096, out_features=4096),
             nn.ReLU(),
-            nn.Dropout2d(0.5),
+            nn.Dropout(0.5),
             nn.Linear(in_features=4096, out_features=self.num_classes),
         )
 

test/.env.test

@@ -0,0 +1,2 @@
+TRAIN_PATH=${HOME}/Dev/ml/data/mnist_train.csv
+INPUT_FEATURES=40

test/conftest.py

@@ -0,0 +1,20 @@
+# conftest.py
+import pytest
+import os
+from dotenv import load_dotenv
+from pathlib import Path
+
+
+@pytest.fixture(autouse=True)
+def load_env():
+    # Set up your environment variables here
+    env = Path(__file__).parent / ".env.test"
+    if not load_dotenv(env):
+        raise RuntimeError(".env not loaded")
+    # os.environ['MY_ENV_VAR'] = 'some_value'
+    # You can add more setup code here if needed
+
+    yield
+
+    # Optional: Cleanup code after test (if needed)
+    # e.g., unset environment variables if they should not persist after test

test/test_pipeline.py

@@ -0,0 +1,17 @@
+from src.model.linear import DNN
+from src.data.dataset import MnistDataset
+import os
+
+
+def test_size_of_dataset():
+    examples = 500
+    os.environ["TRAINING_EXAMPLES"] = str(examples)
+    channels = 1
+    width, height = 224, 224
+    dataset = MnistDataset(os.getenv("TRAIN_PATH"))
+    # label = dataset[0][1].item()
+    image = dataset[0][0].shape
+    assert channels == image[0]
+    assert width == image[1]
+    assert height == image[2]
+    assert len(dataset) == examples