Merge pull request #84 from albalu/master

consistent naming

matbench/automl/tests/test_tpot.py

@@ -6,11 +6,11 @@
 from matbench.automl.tpot_utils import TpotAutoml
 from matbench.automl.tpot_configs.classifier import classifier_config_dict_mb
 from matbench.automl.tpot_configs.regressor import regressor_config_dict_mb
-from matbench.analysis.analysis import Analysis
+from matbench.analysis.core import Analysis
 from matbench.data.load import load_double_perovskites_gap, \
     load_glass_binary
-from matbench.featurization.core import Featurize
-from matbench.preprocessing.core import Preprocess
+from matbench.featurization.core import Featurization
+from matbench.preprocessing.core import Preprocessing
 from matminer.featurizers.composition import ElementProperty, TMetalFraction, \
     Stoichiometry
 from sklearn.model_selection import train_test_split
@@ -28,12 +28,12 @@ def test_tpot_regression(self, limit=500):
         target = 'gap gllbsc'
         # load and featurize:
         df_init = load_double_perovskites_gap(return_lumo=False)[:limit]
-        featzer = Featurize(ignore_cols=['a_1', 'b_1', 'a_2', 'b_2'])
+        featzer = Featurization(ignore_cols=['a_1', 'b_1', 'a_2', 'b_2'])
         df_feats = featzer.featurize_formula(df_init, featurizers=[
             ElementProperty.from_preset(preset_name='matminer'),
             TMetalFraction()])
         # preprocessing of the data
-        prep = Preprocess()
+        prep = Preprocessing()
         df = prep.handle_na(df_feats, max_na_frac=0.1)
         feats0 = set(df.columns)
         df = prep.prune_correlated_features(df, target, R_max=0.95)
@@ -95,12 +95,12 @@ def test_tpot_classification(self, limit=500):
         target = 'gfa'
         # load and featurize:
         df_init = load_glass_binary()[:limit]
-        featzer = Featurize()
+        featzer = Featurization()
         df_feats = featzer.featurize_formula(df_init, featurizers=[
             ElementProperty.from_preset(preset_name='matminer'),
             Stoichiometry()])
         # preprocessing of the data
-        prep = Preprocess()
+        prep = Preprocessing()
         df = prep.handle_na(df_feats, max_na_frac=0.1)
         feats0 = set(df.columns)
         df = prep.prune_correlated_features(df, target, R_max=0.95)

matbench/featurization/core.py

@@ -10,7 +10,7 @@
     StructureFeaturizers, BSFeaturizers, DOSFeaturizers
 
 
-class Featurize(object):
+class Featurization(object):
     """
     Takes in a dataframe and generate features from preset columns such as
     "formula", "structure", "bandstructure", "dos", etc. One may use

matbench/featurization/tests/test_featurize.py

@@ -13,7 +13,7 @@
 
 from matbench.data.load import load_double_perovskites_gap, \
     load_castelli_perovskites
-from matbench.featurization.core import Featurize
+from matbench.featurization.core import Featurization
 from matbench.featurization.sets import AllFeaturizers
 from matbench.data.load import load_phonon_dielectric_mp
 
@@ -25,7 +25,7 @@ class TestFeaturize(unittest.TestCase):
     def test_featurize_formula(self, limit=5):
         df_init = load_double_perovskites_gap(return_lumo=False)[:limit]
         ignore_cols = ['a_1', 'a_2', 'b_1', 'b_2']
-        featurizer = Featurize(ignore_cols=ignore_cols,
+        featurizer = Featurization(ignore_cols=ignore_cols,
                                ignore_errors=False,
                                exclude=['CohesiveEnergy'],
                                multiindex=False)
@@ -83,7 +83,7 @@ def test_featurize_formula(self, limit=5):
 
     def test_featurize_structure(self, limit=5):
         df_init = load_castelli_perovskites()[:limit]
-        featurizer = Featurize(ignore_errors=False, multiindex=False)
+        featurizer = Featurization(ignore_errors=False, multiindex=False)
         df = featurizer.featurize_structure(df_init, inplace=False,
                                             featurizers="all")
 
@@ -187,7 +187,7 @@ def test_featurize_bsdos(self, refresh_df_init=False, limit=1):
         else:
             df_init = pd.read_pickle(os.path.join(test_dir, df_bsdos_pickled))
         df_init = df_init.dropna(axis=0)
-        featurizer = Featurize(ignore_errors=False, multiindex=False)
+        featurizer = Featurization(ignore_errors=False, multiindex=False)
         df = featurizer.featurize_dos(df_init, inplace=False)
 
         # sanity checks
@@ -222,7 +222,7 @@ def test_featurize_bsdos(self, refresh_df_init=False, limit=1):
     def test_auto_featurize(self, limit=5):
         df_init = load_phonon_dielectric_mp()[:limit]
         print(df_init.structure)
-        featurizer = Featurize(ignore_errors=False, multiindex=True)
+        featurizer = Featurization(ignore_errors=False, multiindex=True)
         df = featurizer.auto_featurize(df_init,
                                        input_cols=('formula', 'structure'))
 

matbench/preprocessing/core.py

@@ -1,4 +1,3 @@
-import logging
 import numpy as np
 import pandas as pd
 from matbench.utils.utils import setup_custom_logger
@@ -8,7 +7,7 @@
 from skrebate import ReliefF
 
 
-class Preprocess(object):
+class Preprocessing(object):
     """
     Clean and prepare the data for visualization and training.
 

matbench/preprocessing/tests/test_preprocess.py

@@ -2,9 +2,9 @@
 
 from matminer.datasets.dataframe_loader import load_elastic_tensor
 from matminer.featurizers.structure import GlobalSymmetryFeatures
-from matbench.featurization.core import Featurize
+from matbench.featurization.core import Featurization
 
-from matbench.preprocessing.core import Preprocess
+from matbench.preprocessing.core import Preprocessing
 
 
 class TestPreprocess(unittest.TestCase):
@@ -19,10 +19,10 @@ def test_preprocess_basic(self):
         """
         df = load_elastic_tensor()[:5][['K_VRH', 'structure']]
         df['K_VRH'] = df['K_VRH'].astype(str)
-        f = Featurize()
+        f = Featurization()
         df = f.featurize_structure(df, featurizers=[GlobalSymmetryFeatures()])
 
-        p = Preprocess()
+        p = Preprocessing()
         df = p.preprocess(df, 'K_VRH')
         self.assertAlmostEqual(df['K_VRH'].iloc[0], 194.26888435900003)
         self.assertEqual(df["crystal_system_tetragonal"].iloc[0], 1)