Merge pull request #12 from angrymeir/master

Rename SS3 initialization parameter from sn_m to sg_m
sergioburdisso · Jun 30, 2020 · 63b80d9 · 63b80d9
2 parents bc8ec0a + c2bddcb
commit 63b80d9
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Showing 2 changed files with 19 additions and 19 deletions.
diff --git a/pyss3/__init__.py b/pyss3/__init__.py
@@ -83,9 +83,9 @@ class SS3:
                  term (word or n-grams), options are:
                  "norm_gv_xai", "norm_gv" and "gv" (default: "norm_gv_xai")
     :type cv_m: str
-    :param sn_m: method used to compute the sanction (sn) function, options
+    :param sg_m: method used to compute the significance (sg) function, options
                  are: "vanilla" and "xai" (default: "xai")
-    :type sn_m: str
+    :type sg_m: str
     """
 
     __name__ = "model"
@@ -119,7 +119,7 @@ class SS3:
 
     def __init__(
         self, s=None, l=None, p=None, a=None,
-        name="", cv_m=STR_NORM_GV_XAI, sn_m=STR_XAI
+        name="", cv_m=STR_NORM_GV_XAI, sg_m=STR_XAI
     ):
         """
         Class constructor.
@@ -140,9 +140,9 @@ def __init__(
                      term (word or n-grams), options are:
                      "norm_gv_xai", "norm_gv" and "gv" (default: "norm_gv_xai")
         :type cv_m: str
-        :param sn_m: method used to compute the sanction (sn) function, options
+        :param sg_m: method used to compute the significance (sg) function, options
                      are: "vanilla" and "xai" (default: "xai")
-        :type sn_m: str
+        :type sg_m: str
         :raises: ValueError
         """
         self.__name__ = (name or self.__name__).lower()
@@ -172,13 +172,13 @@ def __init__(
         elif cv_m == STR_GV:
             self.__cv__ = self.__gv__
 
-        if sn_m == STR_XAI:
+        if sg_m == STR_XAI:
             self.__sg__ = self.__sg_xai__
-        elif sn_m == STR_VANILLA:
+        elif sg_m == STR_VANILLA:
             self.__sg__ = self.__sg_vanilla__
 
         self.__cv_mode__ = cv_m
-        self.__sn_mode__ = sn_m
+        self.__sg_mode__ = sg_m
 
         self.original_sumop_ngrams = self.summary_op_ngrams
         self.original_sumop_sentences = self.summary_op_sentences
@@ -1400,7 +1400,7 @@ def save_model(self, path=None):
             "__index_to_word__": self.__index_to_word__,
             "__word_to_index__": self.__word_to_index__,
             "__cv_mode__": self.__cv_mode__,
-            "__sn_mode__": self.__sn_mode__,
+            "__sg_mode__": self.__sg_mode__,
             "__multilabel__": self.__multilabel__
         }
 
@@ -1470,7 +1470,7 @@ def load_model(self, path=None):
         self.__index_to_word__ = jmodel["__index_to_word__"]
         self.__word_to_index__ = jmodel["__word_to_index__"]
         self.__cv_mode__ = jmodel["__cv_mode__"]
-        self.__sn_mode__ = jmodel["__sn_mode__"]
+        self.__sg_mode__ = jmodel["__sg_mode__"]
         self.__multilabel__ = jmodel["__multilabel__"] if "__multilabel__" in jmodel else False
 
         self.__zero_cv__ = (0,) * len(self.__categories__)

diff --git a/tests/test_pyss3.py b/tests/test_pyss3.py
@@ -310,7 +310,7 @@ def test_pyss3_ss3(mockers):
     # training different cases
     clf = SS3(
         s=.45, l=.5, p=1, a=0,
-        cv_m=STR_NORM_GV_XAI, sn_m=STR_XAI
+        cv_m=STR_NORM_GV_XAI, sg_m=STR_XAI
     )
     clf.set_name("test")
 
@@ -333,7 +333,7 @@ def test_pyss3_ss3(mockers):
         clf.train([], [])
 
     # train and predict/classify tests (model: terms are single words)
-    # cv_m=STR_NORM_GV_XAI, sn_m=STR_XAI
+    # cv_m=STR_NORM_GV_XAI, sg_m=STR_XAI
     clf.fit(x_train, y_train)
 
     assert clf.get_ngrams_length() == 1
@@ -347,30 +347,30 @@ def test_pyss3_ss3(mockers):
     perform_tests_on(clf.cv, 0, "video games", "science&technology")
     perform_tests_on(clf.gv, 0, "video games", "science&technology")
 
-    # cv_m=STR_NORM_GV, sn_m=STR_XAI
+    # cv_m=STR_NORM_GV, sg_m=STR_XAI
     clf = SS3(
         s=.45, l=.5, p=1, a=0, name="test-norm-gv-sn-xai",
-        cv_m=STR_NORM_GV, sn_m=STR_XAI
+        cv_m=STR_NORM_GV, sg_m=STR_XAI
     )
     clf.fit(x_train, y_train)
 
     perform_tests_with(clf, [.00114, .00294, 0, 0, 0, .00016, .01878, 8.43969])
     perform_tests_on(clf.cv, 0.4307)
 
-    # cv_m=STR_GV, sn_m=STR_XAI
+    # cv_m=STR_GV, sg_m=STR_XAI
     clf = SS3(
         s=.45, l=.5, p=1, a=0, name="test-gv-sn-xai",
-        cv_m=STR_GV, sn_m=STR_XAI
+        cv_m=STR_GV, sg_m=STR_XAI
     )
     clf.fit(x_train, y_train)
 
     perform_tests_with(clf, [.00062, .00109, 0, 0, 0, .00014, .01878, 6.31605])
     assert clf.cv("chicken", "food") == clf.gv("chicken", "food")
 
-    # cv_m=STR_NORM_GV_XAI, sn_m=STR_VANILLA
+    # cv_m=STR_NORM_GV_XAI, sg_m=STR_VANILLA
     clf = SS3(
         s=.45, l=.5, p=1, a=0, name="test-norm-gv-xai-sn-vanilla",
-        cv_m=STR_NORM_GV_XAI, sn_m=STR_VANILLA
+        cv_m=STR_NORM_GV_XAI, sg_m=STR_VANILLA
     )
     clf.fit(x_train, y_train)
 
@@ -379,7 +379,7 @@ def test_pyss3_ss3(mockers):
     # train and predict/classify tests (model: terms are word n-grams)
     clf = SS3(
         name="test-3grams",
-        cv_m=STR_NORM_GV_XAI, sn_m=STR_XAI
+        cv_m=STR_NORM_GV_XAI, sg_m=STR_XAI
     )
 
     clf.fit(x_train, y_train, n_grams=3)