Merge pull request minitorch#37 from minitorch/2024

2024

minitorch/datasets.py

@@ -4,7 +4,7 @@
 from typing import List, Tuple
 
 
-def make_pts(N: int) -> List[Tuple[float, float]]:
+def make_pts(N):
     X = []
     for i in range(N):
         x_1 = random.random()
@@ -20,7 +20,7 @@ class Graph:
     y: List[int]
 
 
-def simple(N: int) -> Graph:
+def simple(N):
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -29,7 +29,7 @@ def simple(N: int) -> Graph:
     return Graph(N, X, y)
 
 
-def diag(N: int) -> Graph:
+def diag(N):
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -38,7 +38,7 @@ def diag(N: int) -> Graph:
     return Graph(N, X, y)
 
 
-def split(N: int) -> Graph:
+def split(N):
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -47,49 +47,39 @@ def split(N: int) -> Graph:
     return Graph(N, X, y)
 
 
-def xor(N: int) -> Graph:
+def xor(N):
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
-        y1 = 1 if ((x_1 < 0.5 and x_2 > 0.5) or (x_1 > 0.5 and x_2 < 0.5)) else 0
+        y1 = 1 if x_1 < 0.5 and x_2 > 0.5 or x_1 > 0.5 and x_2 < 0.5 else 0
         y.append(y1)
     return Graph(N, X, y)
 
 
-def circle(N: int) -> Graph:
+def circle(N):
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
-        x1, x2 = (x_1 - 0.5, x_2 - 0.5)
+        x1, x2 = x_1 - 0.5, x_2 - 0.5
         y1 = 1 if x1 * x1 + x2 * x2 > 0.1 else 0
         y.append(y1)
     return Graph(N, X, y)
 
 
-def spiral(N: int) -> Graph:
-    def x(t: float) -> float:
+def spiral(N):
+
+    def x(t):
         return t * math.cos(t) / 20.0
 
-    def y(t: float) -> float:
+    def y(t):
         return t * math.sin(t) / 20.0
-
-    X = [
-        (x(10.0 * (float(i) / (N // 2))) + 0.5, y(10.0 * (float(i) / (N // 2))) + 0.5)
-        for i in range(5 + 0, 5 + N // 2)
-    ]
-    X = X + [
-        (y(-10.0 * (float(i) / (N // 2))) + 0.5, x(-10.0 * (float(i) / (N // 2))) + 0.5)
-        for i in range(5 + 0, 5 + N // 2)
-    ]
+    X = [(x(10.0 * (float(i) / (N // 2))) + 0.5, y(10.0 * (float(i) / (N //
+        2))) + 0.5) for i in range(5 + 0, 5 + N // 2)]
+    X = X + [(y(-10.0 * (float(i) / (N // 2))) + 0.5, x(-10.0 * (float(i) /
+        (N // 2))) + 0.5) for i in range(5 + 0, 5 + N // 2)]
     y2 = [0] * (N // 2) + [1] * (N // 2)
     return Graph(N, X, y2)
 
 
-datasets = {
-    "Simple": simple,
-    "Diag": diag,
-    "Split": split,
-    "Xor": xor,
-    "Circle": circle,
-    "Spiral": spiral,
-}
+datasets = {'Simple': simple, 'Diag': diag, 'Split': split, 'Xor': xor,
+    'Circle': circle, 'Spiral': spiral}

minitorch/module.py

@@ -4,11 +4,11 @@
 
 
 class Module:
-    """
-    Modules form a tree that store parameters and other
+    """Modules form a tree that store parameters and other
     submodules. They make up the basis of neural network stacks.
 
-    Attributes:
+    Attributes
+    ----------
         _modules : Storage of the child modules
         _parameters : Storage of the module's parameters
         training : Whether the module is in training mode or evaluation mode
@@ -25,46 +25,48 @@ def __init__(self) -> None:
         self.training = True
 
     def modules(self) -> Sequence[Module]:
-        "Return the direct child modules of this module."
+        """Return the direct child modules of this module."""
         m: Dict[str, Module] = self.__dict__["_modules"]
         return list(m.values())
 
     def train(self) -> None:
-        "Set the mode of this module and all descendent modules to `train`."
+        """Set the mode of this module and all descendent modules to `train`."""
         # TODO: Implement for Task 0.4.
         raise NotImplementedError("Need to implement for Task 0.4")
 
     def eval(self) -> None:
-        "Set the mode of this module and all descendent modules to `eval`."
+        """Set the mode of this module and all descendent modules to `eval`."""
         # TODO: Implement for Task 0.4.
         raise NotImplementedError("Need to implement for Task 0.4")
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
-        """
-        Collect all the parameters of this module and its descendents.
+        """Collect all the parameters of this module and its descendents.
 
-
-        Returns:
+        Returns
+        -------
             The name and `Parameter` of each ancestor parameter.
+
         """
         # TODO: Implement for Task 0.4.
         raise NotImplementedError("Need to implement for Task 0.4")
 
     def parameters(self) -> Sequence[Parameter]:
-        "Enumerate over all the parameters of this module and its descendents."
+        """Enumerate over all the parameters of this module and its descendents."""
         # TODO: Implement for Task 0.4.
         raise NotImplementedError("Need to implement for Task 0.4")
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
-        """
-        Manually add a parameter. Useful helper for scalar parameters.
+        """Manually add a parameter. Useful helper for scalar parameters.
 
         Args:
+        ----
             k: Local name of the parameter.
             v: Value for the parameter.
 
         Returns:
+        -------
             Newly created parameter.
+
         """
         val = Parameter(v, k)
         self.__dict__["_parameters"][k] = val
@@ -118,8 +120,7 @@ def _addindent(s_: str, numSpaces: int) -> str:
 
 
 class Parameter:
-    """
-    A Parameter is a special container stored in a `Module`.
+    """A Parameter is a special container stored in a `Module`.
 
     It is designed to hold a `Variable`, but we allow it to hold
     any value for testing.
@@ -134,7 +135,7 @@ def __init__(self, x: Any, name: Optional[str] = None) -> None:
                 self.value.name = self.name
 
     def update(self, x: Any) -> None:
-        "Update the parameter value."
+        """Update the parameter value."""
         self.value = x
         if hasattr(x, "requires_grad_"):
             self.value.requires_grad_(True)

minitorch/operators.py

@@ -1,6 +1,4 @@
-"""
-Collection of the core mathematical operators used throughout the code base.
-"""
+"""Collection of the core mathematical operators used throughout the code base."""
 
 import math
 

project/app.py

@@ -19,7 +19,7 @@
 st.sidebar.markdown(
     """
 <h1 style="font-size:30pt; float: left; margin-right: 20px; margin-top: 1px;">MiniTorch</h1>{}
-""".format(get_img_tag("https://minitorch.github.io/_images/match.png", width="40")),
+""".format(get_img_tag("https://minitorch.github.io/logo-sm.png", width="40")),
     unsafe_allow_html=True,
 )
 

pyproject.toml

@@ -7,8 +7,16 @@ name = "minitorch"
 version = "0.5"
 
 [tool.pyright]
-include = ["minitorch","tests"] 
-exclude = ["**/docs", "**/project", "**/mt_diagrams", "**/assignments"]
+include = ["**/minitorch"]
+exclude = [
+    "**/docs",
+    "**/docs/module1/**",
+    "**/assignments",
+    "**/project",
+    "**/mt_diagrams",
+    "**/.*",
+    "*chainrule.py*",
+]
 venvPath = "."
 venv = ".venv"
 reportUnknownMemberType = "none"
@@ -21,6 +29,8 @@ reportUnusedExpression = "none"
 reportUnknownLambdaType = "none"
 reportIncompatibleMethodOverride = "none"
 reportPrivateUsage = "none"
+reportMissingParameterType = "error"
+
 
 [tool.pytest.ini_options]
 markers = [
@@ -50,7 +60,92 @@ markers = [
     "task4_3",
     "task4_4",
 ]
+[tool.ruff]
+
+exclude = [
+    ".git",
+    "__pycache__",
+    "**/docs/slides/*",
+    "old,build",
+    "dist",
+    "**/project/**/*",
+    "**/mt_diagrams/*",
+    "**/minitorch/testing.py",
+    "**/docs/**/*",
+]
+
+ignore = [
+    "ANN101",
+    "ANN401",
+    "N801",
+    "E203",
+    "E266",
+    "E501",
+    "E741",
+    "N803",
+    "N802",
+    "N806",
+    "D400",
+    "D401",
+    "D105",
+    "D415",
+    "D402",
+    "D205",
+    "D100",
+    "D101",
+    "D107",
+    "D213",
+    "ANN204",
+    "ANN102",
+]
+select = ["D", "E", "F", "N", "ANN"]
+fixable = [
+    "A",
+    "B",
+    "C",
+    "D",
+    "E",
+    "F",
+    "G",
+    "I",
+    "N",
+    "Q",
+    "S",
+    "T",
+    "W",
+    "ANN",
+    "ARG",
+    "BLE",
+    "COM",
+    "DJ",
+    "DTZ",
+    "EM",
+    "ERA",
+    "EXE",
+    "FBT",
+    "ICN",
+    "INP",
+    "ISC",
+    "NPY",
+    "PD",
+    "PGH",
+    "PIE",
+    "PL",
+    "PT",
+    "PTH",
+    "PYI",
+    "RET",
+    "RSE",
+    "RUF",
+    "SIM",
+    "SLF",
+    "TCH",
+    "TID",
+    "TRY",
+    "UP",
+    "YTT",
+]
+unfixable = []
 
-[tool.ruff.lint]
-ignore = ["N801", "E203", "E266", "E501", "E741", "N803", "N802", "N806"]
-exclude = [".git","__pycache__","docs/slides/*","old,build","dist"]
+[tool.ruff.extend-per-file-ignores]
+"tests/**/*.py" = ["D"]

requirements.extra.txt

@@ -1,10 +1,11 @@
 datasets==2.4.0
 embeddings==0.0.8
-networkx==2.4
 plotly==4.14.3
 pydot==1.4.1
 python-mnist
 streamlit==1.12.0
 streamlit-ace
 torch
 watchdog==1.0.2
+altair==4.2.2
+networkx==3.3

tests/strategies.py

@@ -14,4 +14,4 @@
 
 
 def assert_close(a: float, b: float) -> None:
-    assert minitorch.is_close(a, b), "Failure x=%f y=%f" % (a, b)
+    assert minitorch.operators.is_close(a, b), "Failure x=%f y=%f" % (a, b)

tests/test_module.py

@@ -44,7 +44,7 @@ def __init__(self) -> None:
 
 @pytest.mark.task0_4
 def test_stacked_demo() -> None:
-    "Check that each of the properties match"
+    """Check that each of the properties match"""
     mod = ModuleA1()
     np = dict(mod.named_parameters())
 
@@ -95,7 +95,7 @@ def __init__(self) -> None:
 @pytest.mark.task0_4
 @given(med_ints, med_ints)
 def test_module(size_a: int, size_b: int) -> None:
-    "Check the properties of a single module"
+    """Check the properties of a single module"""
     module = Module2()
     module.eval()
     assert not module.training
@@ -116,7 +116,7 @@ def test_module(size_a: int, size_b: int) -> None:
 @pytest.mark.task0_4
 @given(med_ints, med_ints, small_floats)
 def test_stacked_module(size_a: int, size_b: int, val: float) -> None:
-    "Check the properties of a stacked module"
+    """Check the properties of a stacked module"""
     module = Module1(size_a, size_b, val)
     module.eval()
     assert not module.training