Added pca analysis

fractalis/analytics/tasks/pca/main.py

+"""Module containing analysis code for pca."""
+
+from typing import List, TypeVar
+from functools import reduce
+import logging
+
+import pandas as pd
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import Imputer
+
+from fractalis.analytics.task import AnalyticTask
+from fractalis.analytics.tasks.shared import utils
+
+
+T = TypeVar('T')
+logger = logging.getLogger(__name__)
+
+
+class PCATask(AnalyticTask):
+    """PCATask implementing AnalyticsTask. This class is a
+    submittable celery task."""
+
+    name = 'compute-pca'
+
+    def main(self,
+             features: List[pd.DataFrame],
+             categories: List[pd.DataFrame],
+             n_components: int,
+             whiten: bool,
+             id_filter: List[T],
+             subsets: List[List[T]]) -> dict:
+        # merge input data into single df
+        df = reduce(lambda a, b: a.append(b), features)
+        if not subsets:
+            # empty subsets equals all samples in one subset
+            subsets = [df['id'].unique().tolist()]
+
+        # make matrix of data
+        df = df.pivot(index='feature', columns='id', values='value')
+        df = df.T
+
+        # save ids so we can re-assign them after pca
+        ids = df.index.tolist()
+
+        # replace missing values with row medians
+        imp = Imputer(missing_values='NaN', strategy='median', axis=0)
+        imp.fit(df)
+        df = imp.transform(df)
+
+        # PCA
+        pca = PCA(n_components=n_components, whiten=whiten)
+        pca.fit(df)
+        reduced_df = pca.transform(df)
+
+        # re-assign ids
+        reduced_df = pd.DataFrame(reduced_df)
+        reduced_df['id'] = ids
+
+        # add category and subset column
+        reduced_df = utils.apply_subsets(df=reduced_df, subsets=subsets)
+        reduced_df = utils.apply_categories(df=reduced_df,
+                                            categories=categories)
+
+        return {
+            'data': reduced_df.to_json(orient='records')
+        }
\ No newline at end of file

tests/pca/test_main.py

+"""This module provides test for the pca task."""
+
+import json
+
+import pandas as pd
+
+from fractalis.analytics.tasks.pca.main import PCATask
+
+
+# noinspection PyMissingTypeHints
+class TestPCATask:
+
+    task = PCATask()
+
+    def test_correct_output(self):
+        features = [
+            pd.DataFrame([[101, 'foo', 5], [101, 'bar', 6],
+                          [102, 'foo', 10], [102, 'bar', 11],
+                          [103, 'foo', 15], [103, 'bar', 16],
+                          [104, 'foo', 20], [104, 'bar', 21]],
+                         columns=['id', 'feature', 'value']),
+            pd.DataFrame([[101, 'baz', 5],
+                          [102, 'baz', 10],
+                          [104, 'baz', 20],
+                          [105, 'baz', 100]],
+                         columns=['id', 'feature', 'value'])
+        ]
+        categories = [
+            pd.DataFrame([[101, '_', 'a'],
+                          [102, '_', 'a'],
+                          [104, '_', 'a']],
+                         columns=['id', 'feature', 'value'])
+        ]
+        result = self.task.main(features=features,
+                                categories=categories,
+                                n_components=2,
+                                whiten=False,
+                                id_filter=[],
+                                subsets=[])
+        data = pd.read_json(result['data'])
+        assert data.shape == (5, 5)
+        assert '0' in list(data)
+        assert '1' in list(data)
+        assert 'category' in list(data)
+        assert 'subset' in list(data)
+        assert 'id' in list(data)
+        assert data['id'].tolist() == [101, 102, 103, 104, 105]
+        assert data['subset'].unique().tolist() == [0]
+        assert data['category'].unique().tolist() == ['a', None]