Merge branch 'release/tok'

.github/FUNDING.yml

@@ -0,0 +1,15 @@
+
+# These are supported funding model platforms
+
+github: [cnheider]
+patreon: cnheider
+open_collective: cnheider
+ko_fi: cnheider
+custom: # Replace with a single custom sponsorship URL
+
+
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username

.github/workflows/disabled/stale.yml

@@ -0,0 +1,27 @@
+name: Mark stale issues and pull requests
+
+on:
+  workflow_dispatch:
+    #inputs:
+    #  logLevel:
+    #    description: 'Log level'
+    #    required:    true
+    #    default:     'warning'
+    #  tags:
+    #    description: 'Test scenario tags'
+  #schedule:
+  #  - cron: "0 0 * * *"
+
+jobs:
+  stale:
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/stale@v1
+        with:
+          repo-token:          ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: 'Stale issue message'
+          stale-pr-message:    'Stale pull request message'
+          stale-issue-label:   'no-issue-activity'
+          stale-pr-label:      'no-pr-activity'

MANIFEST.in

@@ -0,0 +1,17 @@
+include requirements*.txt # Include requirements
+
+#include pyproject.toml
+
+# Include the EMDS
+#include *.md
+#recursive-include . *.md
+global-include *.md
+
+
+
+
+# Include the license file
+#include LICENSE.txt
+
+# Include the data files
+#recursive-include data *

munin/README.md

@@ -0,0 +1 @@
+# Munin

munin/__init__.py

@@ -22,6 +22,11 @@
 @author: cnheider
 """
 
+import pathlib
+
+with open(pathlib.Path(__file__).parent / "README.md", "r") as this_init_file:
+    __doc__ += this_init_file.read()
+
 import pkg_resources
 
 # __all__ = ['PROJECT_APP_PATH', 'PROJECT_NAME', 'PROJECT_VERSION', 'get_version']

munin/generate_report.py

@@ -3,13 +3,13 @@
 import pathlib
 from collections import namedtuple
 
+import draugr
 import numpy
+from apppath import ensure_existence
+from munin.utilities.html_embeddings import MetricEntry, generate_metric_table, plt_html, plt_html_svg
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.preprocessing import LabelBinarizer
 
-import draugr
-from munin.utilities.html_embeddings import generate_metrics, plt_html, plt_html_svg
-
 ReportEntry = namedtuple("ReportEntry", ("name", "figure", "prediction", "truth", "outcome", "explanation"))
 
 __author__ = "Christian Heider Nielsen"
@@ -21,37 +21,41 @@
 
 
 def generate_html(
-    file_name, template_page="classification_report_template.html", template_path=None, **kwargs
-):
-    if not template_path:
-        template_path = pathlib.Path(__file__).parent / "templates"
+    file_name,
+    template_page: str = "classification_report_template.html",
+    template_path: pathlib.Path = None,
+    **kwargs
+    ):
+  if not template_path:
+    template_path = pathlib.Path(__file__).parent / "templates"
 
-    from jinja2 import Environment, select_autoescape, FileSystemLoader
+  from jinja2 import Environment, select_autoescape, FileSystemLoader
 
-    env = Environment(
-        loader=FileSystemLoader(str(template_path)), autoescape=select_autoescape(["html", "xml"])
-    )
-    template = env.get_template(template_page)
-    with open(f"{file_name}.html", "w") as f:
-        f.writelines(template.render())
+  with open(f"{file_name}.html", "w", encoding="utf-8") as f:
+    f.writelines(Environment(loader=FileSystemLoader(str(template_path)),
+                             autoescape=select_autoescape(["html", "xml"])
+                             ).get_template(template_page).render(**kwargs))
 
 
 def generate_pdf(file_name):
-    import pdfkit
+  import pdfkit
 
-    pdfkit.from_file(f"{file_name}.html", f"{file_name}.pdf")
+  pdfkit.from_file(f"{file_name}.html", f"{file_name}.pdf")
 
 
 if __name__ == "__main__":
+  def a(title: str = "Classification Report", out_path=pathlib.Path.cwd() / 'exclude', num_classes=3):
 
     from matplotlib import pyplot
 
     do_generate_pdf = False
     pyplot.rcParams["figure.figsize"] = (3, 3)
     from warg.named_ordered_dictionary import NOD
 
-    data_path = pathlib.Path.home()
-    num_classes = 3
+    ensure_existence(out_path)
+
+    file_name = out_path / title.lower().replace(" ", "_")
+
     cell_width = (800 / num_classes) - 6 - 6 * 2
 
     pyplot.plot(numpy.random.random((3, 3)))
@@ -63,7 +67,7 @@ def generate_pdf(file_name):
         truth="b",
         outcome="fp",
         explanation=None,
-    )
+        )
 
     pyplot.plot(numpy.ones((9, 3)))
 
@@ -74,7 +78,7 @@ def generate_pdf(file_name):
         truth="c",
         outcome="fp",
         explanation=None,
-    )
+        )
 
     pyplot.plot(numpy.ones((5, 6)))
 
@@ -85,7 +89,7 @@ def generate_pdf(file_name):
         truth="a",
         outcome="tp",
         explanation=None,
-    )
+        )
 
     d = ReportEntry(
         name="fas3",
@@ -94,7 +98,7 @@ def generate_pdf(file_name):
         truth="a",
         outcome="tp",
         explanation=None,
-    )
+        )
 
     e = ReportEntry(
         name="fas3",
@@ -103,7 +107,7 @@ def generate_pdf(file_name):
         truth="c",
         outcome="tn",
         explanation=plt_html(format="svg", size=[cell_width, cell_width]),
-    )
+        )
 
     from sklearn import svm, datasets
     from sklearn.model_selection import train_test_split
@@ -126,22 +130,34 @@ def generate_pdf(file_name):
     y_p_max = y_pred.argmax(axis=-1)
     y_t_max = y_test.argmax(axis=-1)
 
-    draugr.plot_confusion_matrix(y_t_max, y_p_max, class_names=class_names)
-
-    title = "Classification Report"
-    confusion_matrix = plt_html(format="png", size=[800, 800])
+    confusion_matrix = plt_html(draugr.confusion_matrix_plot(y_t_max,
+                                                             y_p_max, category_names=class_names),
+                                format="png",
+                                size=[800, 800])
     predictions = [[GPU_STATS, b, d], [GPU_STATS, c, d], [GPU_STATS, c, b], [c, b, e]]
 
-    metric_fields, metrics = generate_metrics(y_t_max, y_p_max, class_names)
-
-    draugr.roc_plot(y_pred, y_test, n_classes)
-
-    roc_figure = plt_html(format="png", size=[800, 800])
-
-    bundle = NOD.nod_of(title, confusion_matrix, metric_fields, metrics, predictions, roc_figure)
-
-    file_name = title.lower().replace(" ", "_")
+    metrics = generate_metric_table(y_t_max,
+                                    y_p_max,
+                                    class_names)
+    metric_fields = ('Metric', *MetricEntry._fields)
+
+    roc_figure = plt_html(draugr.roc_plot(y_pred,
+                                          y_test,
+                                          n_classes),
+                          format="png",
+                          size=[800, 800])
+
+    bundle = NOD.nod_of(title,
+                        confusion_matrix,
+                        metric_fields,
+                        metrics,
+                        predictions,
+                        roc_figure
+                        )
 
     generate_html(file_name, **bundle)
     if do_generate_pdf:
-        generate_pdf(file_name)
+      generate_pdf(file_name)
+
+
+  a()