AmmarRashed · AmmarRashed · Jul 19, 2020
diff --git a/TrElections.npz b/TrElections.npz
diff --git a/demo.ipynb b/demo.ipynb
@@ -777,7 +777,7 @@
     "# this calculates the micro f1 score for all umap configurations in the grid search\n",
     "# and plots the result of each configuration\n",
     "# then returns the results matrix and a heatmap plot of it\n",
-    "results, hm = cluster_projection_grid_search(\n",
+    "results, hm = Clusterer.cluster_projection_grid_search(\n",
     "    \"trials\", users=labels.username, labels=labels.label,\n",
     "    # this means multiple clusters can be assigned the same label\n",
     "    allow_multiple_clusters=True\n",

diff --git a/src/__pycache__/clustering.cpython-36.pyc b/src/__pycache__/clustering.cpython-36.pyc
diff --git a/src/__pycache__/projection.cpython-36.pyc b/src/__pycache__/projection.cpython-36.pyc
diff --git a/src/clustering.py b/src/clustering.py
@@ -55,6 +55,7 @@ def plot_tree(path):
         return pickle.load(open(path, 'rb')).plot()
 
     def plot(self, labels_col="clusters"):
+        sns.set(context='notebook', style='white', rc={'figure.figsize': (15, 15)})
         return Projector.plot(embeddings=self._params["umap"], labels=self._params[labels_col])
 
     def inject_labels(self, users, labels):
@@ -70,7 +71,7 @@ def align_clusters_with_labels(self, allow_multiple_clusters=True):
         labels = labels[ind]
 
         df = pd.DataFrame(
-            {"username": users, "labels": labels}
+            {"username": users, "label": labels}
         ).merge(
             pd.DataFrame({"username": self._params["users"], "clusters": self._params["clusters"]})
         )
@@ -108,7 +109,7 @@ def evaluate(self, metric=f1_score, report=True):
         y = y[ind]
         p = p[ind]
 
-        s = set(y)
+        s = list(set(y))
         if report:
             return pd.DataFrame(classification_report(y, p, labels=s, output_dict=True))
 
@@ -132,7 +133,8 @@ def cluster_projection_grid_search(trials_dir, users=None, labels=None, allow_mu
             plt.savefig(plot_path, bbox_inches='tight')
             plt.close()
 
-            score = c.evaluate()
+            score = c.evaluate(report=False)
             results.setdefault(min_dist, dict())
             results[min_dist][n_neighbors] = score
-        return results
+        return results, Projector.plot_grid_search_heatmap(results,
+                                                           heatmap_destination=os.path.join(trials_dir, 'hm.png'))
diff --git a/src/run.py b/src/run.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+from clustering import Clusterer, pickle
+from projection import Projector, os
+
+f = np.load("../TrElections.npz")
+
+results, hm = Clusterer.cluster_projection_grid_search(
+    f"../umap4_trials", users=f["users"], labels=f["labels"],
+    # this means multiple clusters can be assigned the same label
+    allow_multiple_clusters=True
+)
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