Revert to old dimensionality_reduction.py (per Noah's request)

ark/analysis/dimensionality_reduction.py

@@ -82,37 +82,37 @@ def visualize_dimensionality_reduction(cell_data, columns, category, color_map="
         save_dir (str):
             Directory to save plots, default is None
     """
-
     cell_data = cell_data.dropna()
 
-    # check if the algorithm is valid
     if algorithm not in ["UMAP", "PCA", "tSNE"]:
         raise ValueError(f"The algorithm specified must be one of the following: "
                          f"{['UMAP', 'PCA', 'tSNE']}")
 
-    # set the title of the graph
     graph_title = "%s projection of data" % algorithm
 
-    # create a function mapping from each algorithm name
-    # to their respective Python algorithm calls
-    dim_red_map = {
-        'UMAP': umap.UMAP,
-        'PCA': PCA,
-        'tSNE': TSNE
-    }
-
-    # just map cell_data[columns].values to fit_transform of the dimensionality reduction
-    # function if not UMAP, otherwise we will need to run an additional StandardScalar
-    # transform beforehand
-    dim_red_reducer = dim_red_map[algorithm]()
-    dim_red_results = dim_red_reducer.fit_transform(
-        cell_data[columns].values if algorithm != 'UMAP'
-        else StandardScaler().fit_transform(cell_data[columns].values)
-    )
-
-    # now plot the results of the dimensionality reduction
-    plot_dim_reduced_data(dim_red_results[:, 0], dim_red_results[:, 1],
-                          fig_id=list(dim_red_map.keys()).index(algorithm),
-                          hue=cell_data[category], cell_data=cell_data,
-                          title=graph_title, save_dir=save_dir,
-                          save_file=f"{algorithm}Visualization.png")
+    if algorithm == "UMAP":
+        reducer = umap.UMAP()
+
+        column_data = cell_data[columns].values
+        scaled_column_data = StandardScaler().fit_transform(column_data)
+        embedding = reducer.fit_transform(scaled_column_data)
+
+        plot_dim_reduced_data(embedding[:, 0], embedding[:, 1], fig_id=1,
+                              hue=cell_data[category], cell_data=cell_data, title=graph_title,
+                              save_dir=save_dir, save_file="UMAPVisualization.png")
+
+    elif algorithm == "PCA":
+        pca = PCA()
+        pca_result = pca.fit_transform(cell_data[columns].values)
+
+        plot_dim_reduced_data(pca_result[:, 0], pca_result[:, 1], fig_id=2,
+                              hue=cell_data[category], cell_data=cell_data, title=graph_title,
+                              save_dir=save_dir, save_file="PCAVisualization.png")
+
+    elif algorithm == "tSNE":
+        tsne = TSNE()
+        tsne_results = tsne.fit_transform(cell_data[columns].values)
+
+        plot_dim_reduced_data(tsne_results[:, 0], tsne_results[:, 1], fig_id=3,
+                              hue=cell_data[category], cell_data=cell_data, title=graph_title,
+                              save_dir=save_dir, save_file="tSNEVisualization.png")