Add lint fixes

cyclops/evaluate/metrics/experimental/utils/types.py

@@ -1,3 +1,5 @@
+# noqa: A005
+
 """Utilities for array-API compatibility."""
 
 import builtins

cyclops/monitor/utils.py

@@ -258,12 +258,12 @@ def get_args(obj: Any, kwargs: Dict[str, Any]) -> Dict[str, Any]:
 
     """
     args = {}
-    for key in kwargs:
+    for key, value in kwargs.items():
         if (inspect.isclass(obj) and key in inspect.signature(obj).parameters) or (
             (inspect.ismethod(obj) or inspect.isfunction(obj))
             and key in inspect.getfullargspec(obj).args
         ):
-            args[key] = kwargs[key]
+            args[key] = value
     return args
 
 

cyclops/utils/profile.py

@@ -1,3 +1,5 @@
+# noqa: A005
+
 """Useful functions for timing, profiling."""
 
 import logging

docs/source/examples/metrics.ipynb

@@ -15,6 +15,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "\"\"\"Imports.\"\"\"\n",
+    "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from datasets.arrow_dataset import Dataset\n",
@@ -57,7 +59,7 @@
    "outputs": [],
    "source": [
     "df = breast_cancer_data.frame\n",
-    "df.describe().T"
+    "print(df.describe().T)"
    ]
   },
   {
@@ -222,7 +224,7 @@
    "outputs": [],
    "source": [
     "slice_spec = SliceSpec(spec_list, intersections=2)\n",
-    "slice_spec"
+    "print(slice_spec)"
    ]
   },
   {
@@ -316,15 +318,8 @@
     "    target_columns=\"target\",\n",
     "    prediction_columns=\"preds_prob\",\n",
     ")\n",
-    "fairness_result"
+    "print(fairness_result)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/source/examples/report.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report Generation for Heart Failure Prediction\n",
+    "Here's an example to demonstrate how we can generate a report as we proceed through all the steps to train and evaluate a model. For this purpose, we are going to use Kaggle's heart prediction failure dataset and gradually populate the report with information about dataset, model and results."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "\"\"\"Imports.\"\"\"\n",
+    "\n",
     "import copy\n",
     "import inspect\n",
     "import os\n",
@@ -23,21 +33,14 @@
     "from tqdm import tqdm\n",
     "\n",
     "from cyclops.data.slicer import SliceSpec\n",
+    "from cyclops.evaluate import evaluator\n",
     "from cyclops.evaluate.metrics import create_metric\n",
     "from cyclops.evaluate.metrics.experimental.metric_dict import MetricDict\n",
     "from cyclops.report import ModelCardReport\n",
     "from cyclops.report.plot.classification import ClassificationPlotter\n",
     "from cyclops.report.utils import flatten_results_dict"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report Generation for Heart Failure Prediction\n",
-    "Here's an example to demonstrate how we can generate a report as we proceed through all the steps to train and evaluate a model. For this purpose, we are going to use Kaggle's heart prediction failure dataset and gradually populate the report with information about dataset, model and results."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -105,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.describe().T"
+    "print(df.describe().T)"
    ]
   },
   {
@@ -531,9 +534,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from cyclops.evaluate import evaluator\n",
-    "\n",
-    "\n",
     "# Create Dataset object\n",
     "heart_failure_data = Dataset.from_pandas(df_test)\n",
     "\n",
@@ -557,24 +557,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_flat"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -878,7 +860,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cyclops",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -892,9 +874,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/source/tutorials/diabetes_130/readmission_prediction.ipynb

@@ -152,17 +152,6 @@
     "variables = diabetes_130_data[\"variables\"]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metadata"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

docs/source/tutorials/diabetes_130/readmission_prediction_detectron.ipynb

@@ -115,17 +115,6 @@
     "variables = diabetes_130_data[\"variables\"]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "metadata"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -749,7 +738,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

tests/cyclops/evaluate/metrics/experimental/utils/test_ops.py

@@ -189,19 +189,17 @@ def test_apply_to_nested_collections(self):
             },
         }
 
-        for k in expected_result:
+        for k, v in expected_result.items():
             assert k in result
 
-            if isinstance(expected_result[k], dict):
-                for kk in expected_result[k]:
+            if isinstance(v, dict):
+                for kk in v:
                     assert kk in result[k]
-                    assert anp.all(expected_result[k][kk] == result[k][kk])
-            elif isinstance(expected_result[k], (tuple, list)):
-                assert all(
-                    anp.all(a == b) for a, b in zip(result[k], expected_result[k])
-                )
+                    assert anp.all(v[kk] == result[k][kk])
+            elif isinstance(v, (tuple, list)):
+                assert all(anp.all(a == b) for a, b in zip(result[k], v))
             else:
-                assert anp.all(expected_result[k] == result[k])
+                assert anp.all(v == result[k])
 
 
 class TestBincount: