#48 add deep_classifier , #43 add test for deep_classifier

docs/nlpir.rst

@@ -97,6 +97,13 @@ nlpir.eye_checker module
    :undoc-members:
    :show-inheritance:
 
+nlpir.deep_classifier module
+-------------------------------
+
+.. automodule:: nlpir.deep_classifier
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
 nlpir.tools module
 --------------------

nlpir/deep_classifier.py

@@ -0,0 +1,49 @@
+#! coding=utf-8
+"""
+high-level toolbox for text classify
+"""
+import re
+import typing
+import nlpir
+from nlpir import get_instance as __get_instance__
+from nlpir import native
+
+# class and class instance
+__cls__ = native.deep_classifier.DeepClassifier
+__instance__: typing.Optional[native.deep_classifier.DeepClassifier] = None
+# Location of DLL
+__lib__ = None
+# Data directory
+__data__ = None
+# license_code
+__license_code__ = None
+# encode
+__nlpir_encode__ = native.UTF8_CODE
+
+__handler__ = None
+
+
+@__get_instance__
+def get_native_instance() -> native.deep_classifier.DeepClassifier:
+    """
+    返回原生NLPIR接口,使用更多函数
+
+    :return: The singleton instance
+    """
+    return __instance__
+
+
+@__get_instance__
+def classify(txt: str) -> str:
+    """
+    Text classify
+
+    :param txt: text
+    :return: class
+    """
+    global __handler__
+    if __handler__ is None:
+        # default model
+        __handler__ = __instance__.new_instance(800)
+        __instance__.load_train_result(__handler__)
+    return __instance__.classify(txt, handler=__handler__)

nlpir/eye_checker.py

@@ -6,12 +6,9 @@
 import os
 import re
 import typing
-from enum import Enum
-from pathlib import Path
 
 from pydantic import BaseModel
 
-import nlpir
 from nlpir import get_instance as __get_instance__
 from nlpir import native
 

nlpir/native/deep_classifier.py

@@ -89,7 +89,11 @@ def add_train(self, classname: str, text: str, handler: int = 0) -> bool:
         :param handler: classifier handler
         :return: add success or not
         """
-        return self.get_func("DeepClassifier_AddTrain", [c_char_p, c_char_p, POINTER(c_int)], c_bool)(classname, text, handler)
+        return self.get_func(
+            "DeepClassifier_AddTrain",
+            [c_char_p, c_char_p, POINTER(c_int)],
+            c_bool
+        )(classname, text, handler)
 
     @NLPIRBase.byte_str_transform
     def add_train_file(self, classname: str, filename: str, handler: int = 0) -> int:

tests/test_deep_classifier.py

@@ -0,0 +1,12 @@
+# coding=utf-8
+"""
+Tested function:
+
+- :func:`nlpir.deep_classifier.classify`
+"""
+from nlpir import deep_classifier
+
+
+def test_classify():
+    from tests.strings import test_str
+    assert deep_classifier.classify(txt=test_str) == "教育"

tests/test_eye_checker.py

@@ -1,3 +1,13 @@
+# coding=utf-8
+"""
+Tested function:
+
+- :func:`nlpir.eye_checker.import_kgb_rules`
+- :func:`nlpir.eye_checker.list_rules`
+- :func:`nlpir.eye_checker.delete_rules`
+- :func:`nlpir.eye_checker.extract_knowledge`
+"""
+
 import pytest
 
 from nlpir import eye_checker
@@ -15,7 +25,7 @@ def test_extract():
 
 @pytest.mark.run(order=1)
 def test_rule_manage():
-    from tests.strings import test_kgb_test_text, test_kgb_rules
+    from tests.strings import test_kgb_rules
     rule_set = {1, 2, 3, 4, 6, 7, 9}
     for rule in rule_set:
         assert eye_checker.import_kgb_rules(rule_text=test_kgb_rules, report_type=rule, overwrite=True)