Fixed some warnings and deprecations.

This includes the warning from #6, although the underlying task of
refactoring the inheritance stuff still needs to be tackled (#7).

delta/cluster.py

@@ -193,7 +193,7 @@ def evaluate(self):
         Returns:
             pandas.Series: All scores for the current clustering
         """
-        result = pd.Series()
+        result = pd.Series(dtype='float64')
         result["Cluster Errors"] = self.cluster_errors()
         result["Adjusted Rand Index"] = self.adjusted_rand_index()
         result["Homogeneity"], result["Completeness"], result["V Measure"] = \

delta/corpus.py

@@ -10,6 +10,8 @@ class which represents the feature matrix. Also contained are default
 import os
 import glob
 from fnmatch import fnmatch
+from typing import Type
+
 import regex as re
 import pandas as pd
 import collections
@@ -249,6 +251,8 @@ def __init__(self, operation):
 
 class Corpus(pd.DataFrame):
 
+    _metadata = ['metadata']
+
     def __init__(self, subdir=None, file=None, corpus=None,
                  feature_generator=FeatureGenerator(),
                  document_describer=DefaultDocumentDescriber(),
@@ -444,7 +448,7 @@ def _load_wordlist(self, filename, **kwargs):
         Yields:
             Features from the given file
         """
-        ENTRY = re.compile('\s*([^#]+)')
+        ENTRY = re.compile(r'\s*([^#]+)')
         with open(filename, 'r', **kwargs) as f:
             for line in f:
                 match = ENTRY.match(line)

delta/deltas.py

@@ -600,6 +600,9 @@ def __call__(self, corpus):
 
 
 class DistanceMatrix(pd.DataFrame):
+
+    _metadata = ['metadata']
+
     """
     A distance matrix is the result of applying a :class:`DeltaFunction` to a
     :class:`Corpus`.
@@ -781,7 +784,7 @@ def evaluate(self):
         Returns:
             pandas.Series: All scores implemented for distance matrixes
         """
-        result = pd.Series()
+        result = pd.Series(dtype='float64')
         result["F-Ratio"] = self.f_ratio()
         result["Fisher's LD"] = self.fisher_ld()
         result["Simple Score"] = self.simple_score()