chore: commit local changes from pre-commit hooks

AutoResearch · Sep 20, 2023 · bc8fcab · bc8fcab
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -22,7 +22,7 @@ repos:
     rev: "v1.5.1"
     hooks:
       - id: mypy
-        additional_dependencies: [types-requests,scipy,pytest]
+        additional_dependencies: [types-requests,types-tqdm,autora-core,scipy,pytest]
         language_version: python3.8
         args:
           - "--namespace-packages"

diff --git a/docs/Basic Usage.ipynb b/docs/Basic Usage.ipynb
@@ -3,19 +3,15 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "# Basic Usage"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Uncomment the following line when running on Google Colab\n",
@@ -24,20 +20,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "Let's generate a simple data set with two features $x_1, x_2 \\in [0, 1]$ and a target $y$. We will use the following generative model:\n",
     "$y = 2 x_1 - e^{(5 x_2)}$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -51,19 +43,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "Now let us define the search space, that is, the space of operations to consider when searching over the space of computation graphs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "primitives = [\n",
@@ -79,9 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "## Set Up The DARTS Regressor\n",
     "\n",
@@ -101,10 +87,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from autora.theorist.darts import DARTSRegressor\n",
@@ -123,19 +107,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "Now we have everything to run differentiable architecture search and visualize the model resulting from the highest architecture weights. Note that the current model corresponds to the model with the highest architecture weights."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -216,7 +196,7 @@
        "<graphviz.graphs.Digraph at 0x288b726d0>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,19 +208,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "We can refine the fit by running the `fit` method again, after changing the parameters. This allows us to keep the same architecture but refit the parameters in the final sampled model, for example:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -321,7 +297,7 @@
        "<graphviz.graphs.Digraph at 0x174355df0>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -337,19 +313,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "We can also change how the model is sampled from the architecture weight distribution:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -430,7 +402,7 @@
        "<graphviz.graphs.Digraph at 0x1746b7dc0>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,19 +419,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "To recover the initial model, we need to return the sampling strategy to the default `\"max\"`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -540,7 +508,7 @@
        "<graphviz.graphs.Digraph at 0x119bba670>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -557,9 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "source": [
     "As long as the architecture has not been refitted in the meantime, the architecture should be identical to the initial result, as the `sampling_strategy=\"max\"` is deterministic. The coefficients of the architecture functions may, however, be different, as they have different starting values compared to when they were initially set.\n"
    ]
@@ -580,8 +546,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython2"
   }
  },
  "nbformat": 4,

diff --git a/docs/Weber Fechner Example.ipynb b/docs/Weber Fechner Example.ipynb
diff --git a/pyproject.toml b/pyproject.toml
@@ -25,6 +25,16 @@ dev = [
     "autora-synthetic"
 ]
 
+[tool.isort]
+profile = "black"
+
+[tool.mypy]
+mypy_path="./src"
+
+[[tool.mypy.overrides]]
+module = ["matplotlib.*", "autora.*", "sklearn.*", "torch.*", "graphviz.*", "mkdocs_gen_files.*", "pandas.*", "numpy.*","scipy.*"]
+ignore_missing_imports=true
+
 [project.urls]
 homepage = "http://www.empiricalresearch.ai"
 repository = "https://github.com/AutoResearch/autora-theorist-darts"

diff --git a/src/autora/theorist/darts/__init__.py b/src/autora/theorist/darts/__init__.py
@@ -1,3 +1,3 @@
 from .model_search import DARTSType
 from .operations import PRIMITIVES
-from .regressor import DARTSRegressor, DARTSExecutionMonitor
+from .regressor import DARTSExecutionMonitor, DARTSRegressor
diff --git a/src/autora/theorist/darts/model_search.py b/src/autora/theorist/darts/model_search.py
@@ -505,6 +505,7 @@ def genotype(self, sample: bool = False) -> Genotype:
         Returns:
             genotype: genotype describing the current (sampled) architecture
         """
+
         # this function uses the architecture weights to retrieve the
         # operations with the highest weights
         def _parse(weights):
@@ -613,9 +614,7 @@ def count_parameters(self, print_parameters: bool = False) -> Tuple[int, int, li
 
             tmp_param_list = list()
             if isiterable(op._ops[maxIdx[0].item(0)]):  # Zero is not iterable
-
                 for subop in op._ops[maxIdx[0].item(0)]:
-
                     for parameter in subop.parameters():
                         tmp_param_list.append(parameter.data.numpy().squeeze())
                         if parameter.requires_grad is True:

diff --git a/src/autora/theorist/darts/operations.py b/src/autora/theorist/darts/operations.py
@@ -88,15 +88,13 @@ def get_operation_label(
         else:
             classifier_str = input_var + " .* ("
         for param_idx, param in enumerate(params):
-
             if param_idx > 0:
                 if output_format == "latex":
                     classifier_str += " + \\left("
                 else:
                     classifier_str += " .+ ("
 
             if isiterable(param.tolist()):
-
                 param_formatted = list()
                 for value in param.tolist():
                     param_formatted.append(format_string.format(value))
@@ -541,7 +539,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 def operation_factory(name):
-
     if name == "none":
         return Zero(1)
     elif name == "add":

diff --git a/src/autora/theorist/darts/regressor.py b/src/autora/theorist/darts/regressor.py
@@ -17,12 +17,17 @@
 
 from autora.variable import ValueType
 
-from .operations import PRIMITIVES
 from .architect import Architect
 from .dataset import darts_dataset_from_ndarray
-from .model_search import Network, DARTSType
-from .utils import get_loss_function, get_output_format, AvgrageMeter, \
-    format_input_target, get_output_str
+from .model_search import DARTSType, Network
+from .operations import PRIMITIVES
+from .utils import (
+    AvgrageMeter,
+    format_input_target,
+    get_loss_function,
+    get_output_format,
+    get_output_str,
+)
 from .visualize import darts_model_plot
 
 _logger = logging.getLogger(__name__)
@@ -161,7 +166,6 @@ def _general_darts(
     network.train()
 
     for epoch in _progress_indicator(range(max_epochs)):
-
         _logger.debug(f"Running fit, epoch {epoch}")
 
         data_iterator = _get_data_iterator(data_loader)
@@ -842,7 +846,7 @@ def display(self):
 
         arch_fig.suptitle("Architecture Weights", fontsize=10)
 
-        for (edge_i, ax) in zip(range(num_edges), arch_axes.flat):
+        for edge_i, ax in zip(range(num_edges), arch_axes.flat):
             for primitive_i in range(num_primitives):
                 print(f"{edge_i}, {primitive_i}, {ax}")
                 ax.plot(

diff --git a/src/autora/theorist/darts/utils.py b/src/autora/theorist/darts/utils.py
@@ -134,7 +134,6 @@ def compute_BIC(
     elif output_type == ValueType.PROBABILITY_SAMPLE:
         llik = 0
         for idx in range(len(target)):
-
             # fail safe if model doesn't produce probabilities
             if prediction[idx] > 1:
                 prediction[idx] = 1
@@ -166,7 +165,7 @@ def compute_BIC(
 
 
 def compute_BIC_AIC(
-    soft_targets: np.array, soft_prediction: np.array, model: Network
+    soft_targets: np.ndarray, soft_prediction: np.ndarray, model: Network
 ) -> Tuple:
     """
     Returns the Bayesian information criterion (BIC) as well as the
@@ -269,7 +268,7 @@ def count_parameters_in_MB(model: Network) -> int:
         model: model to count the parameters for
     """
     return (
-        np.sum(
+        np.sum(  # type: ignore
             np.prod(v.size())
             for name, v in model.named_parameters()
             if "auxiliary" not in name
@@ -363,7 +362,6 @@ def read_log_files(results_path: str, winning_architecture_only: bool = False) -
 
     print("Reading log files... ")
     for file in filelist:
-
         with open(file) as csvfile:
             readCSV = csv.reader(csvfile, delimiter=",")
             for row in readCSV: