added variant ratios to PCA output

fractalis/analytics/tasks/pca/main.py

@@ -5,6 +5,7 @@ from functools import reduce
 import logging
 
 import pandas as pd
+import numpy as np
 from sklearn.decomposition import PCA
 from sklearn.preprocessing import Imputer
 
@@ -38,6 +39,7 @@ class PCATask(AnalyticTask):
         # make matrix of data
         df = df.pivot(index='feature', columns='id', values='value')
         df = df.T
+        feature_labels = list(df)
 
         # apply id filter
         if id_filter:
@@ -56,6 +58,14 @@ class PCATask(AnalyticTask):
         pca.fit(df)
         reduced_df = pca.transform(df)
 
+        # get explained variance ratios of components
+        variance_ratios = pca.explained_variance_ratio_
+
+        # get loadings
+        loadings = -1 * pca.components_.T * np.sqrt(pca.explained_variance_)
+        loadings = pd.DataFrame(loadings)
+        loadings['feature'] = feature_labels
+
         # re-assign ids
         reduced_df = pd.DataFrame(reduced_df)
         reduced_df['id'] = ids
@@ -66,5 +76,7 @@ class PCATask(AnalyticTask):
                                             categories=categories)
 
         return {
-            'data': reduced_df.to_json(orient='records')
+            'data': reduced_df.to_json(orient='records'),
+            'loadings': loadings.to_json(orient='records'),
+            'variance_ratios': variance_ratios.tolist()
         }
\ No newline at end of file

tests/pca/test_main.py

@@ -62,3 +62,38 @@ class TestPCATask:
                                 subsets=[])
         data = pd.read_json(result['data'])
         assert data['id'].unique().tolist() == [101, 104]
+
+    def test_correct_loadings(self):
+        features = [
+            pd.DataFrame([[101, 'foo', 5], [101, 'bar', 20],
+                          [102, 'foo', 10], [102, 'bar', 15],
+                          [103, 'foo', 15], [103, 'bar', 10],
+                          [104, 'foo', 20], [104, 'bar', 5]],
+                         columns=['id', 'feature', 'value'])
+        ]
+        result = self.task.main(features=features,
+                                categories=[],
+                                n_components=2,
+                                whiten=False,
+                                id_filter=[],
+                                subsets=[])
+        loadings = pd.read_json(result['loadings'])
+        assert loadings['0'].tolist()[0] == -loadings['0'].tolist()[1]
+        assert loadings['1'].tolist()[0] == loadings['1'].tolist()[1]
+
+    def test_correct_variance_ratios(self):
+        features = [
+            pd.DataFrame([[101, 'foo', 5], [101, 'bar', 5],
+                          [102, 'foo', 10], [102, 'bar', 5],
+                          [103, 'foo', 15], [103, 'bar', 5],
+                          [104, 'foo', 20], [104, 'bar', 5]],
+                         columns=['id', 'feature', 'value'])
+        ]
+        result = self.task.main(features=features,
+                                categories=[],
+                                n_components=2,
+                                whiten=False,
+                                id_filter=[],
+                                subsets=[])
+        variance_ratios = result['variance_ratios']
+        assert variance_ratios == [1, 0]