ProcessorsAPITests.test_bionlp() - Code Metrics - Inspection of "Version bump (2.7 -> 2.8)" - myedibleenso/py-processors - Measure and Improve Code Quality continuously with Scrutinizer

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by Gus

created 2016-06-24 23:22 UTC

ProcessorsAPITests.test_bionlp() A

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cc	1
c	2
b	0
f	0
dl	0
loc	10
rs	9.4285

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import unittest
from processors import *


port = 8886
# initialize the server
API = ProcessorsAPI(port=port, timeout=180, keep_alive=True)

class ProcessorsAPITests(unittest.TestCase):

    def test_api(self):
        "ProcessorsAPI instance should remember its port"

        self.assertEqual(API.port, port, "Port was not {}".format(port))

    # annotate tests
    def test_annotate(self):
        "API.annotate should produce a Document when given text"

        text = "This is sentence 1.  This is sentence 2."
        # .annotate should be successful
        doc = API.annotate(text)
        self.assertNotEqual(doc, None, ".annotate failed to produce a Document")
        # should have two sentences
        num_sentences = 2
        self.assertEqual(len(doc.sentences), num_sentences, ".annotate did not produce a Document with {} Sentences for text \"{}\"".format(num_sentences, text))

    def test_unicode(self):
        "API.annotate should produce a Document when given text containg unicode"
        # the server will do a poor job with non-English text, but it should still produce something...
        text = "頑張らなきゃならい"
        doc = API.annotate(text)
        self.assertNotEqual(doc, None, ".annotate failed to produce a Document")

    # annotate_from_sentences tests
    def test_annotate_from_sentences(self):
        "API.annotate_from_sentences should produce a Document that preserves the provided sentence segmentation"

        sentences = ["This is sentence 1.", "This is sentence 2."]
        # .annotate should be successful
        doc = API.annotate_from_sentences(sentences)
        self.assertNotEqual(doc, None, ".annotate_from_sentences failed to produce a Document")
        # should have two sentences
        self.assertEqual(len(doc.sentences), len(sentences), ".annotate_from_sentences did not produce a Document with the correct number of sentences")

    def test_fastnlp(self):
        "API.fastnlp.annotate should produce a Document when given text"

        text = "This is sentence 1.  This is sentence 2."
        # .annotate should be successful
        doc = API.fastnlp.annotate(text)
        self.assertNotEqual(doc, None, "fastnlp.annotate failed to produce a Document")
        # should have two sentences
        num_sentences = 2
        self.assertEqual(len(doc.sentences), num_sentences, "fastnlp.annotate did not produce a Document with {} Sentences for text \"{}\"".format(num_sentences, text))

    def test_bionlp(self):
        "API.bionlp.annotate should produce a Document when given text"

        text = "Ras phosphorylated Mek."
        # .annotate should be successful
        doc = API.bionlp.annotate(text)
        self.assertNotEqual(doc, None, "bionlp.annotate failed to produce a Document")
        # should have two sentences
        num_sentences = 1
        self.assertEqual(len(doc.sentences), num_sentences, "bionlp.annotate did not produce a Document with {} Sentences for text \"{}\"".format(num_sentences, text))

    # sentiment analysis tests
    def test_sentiment_analysis_of_text(self):
        "API.sentiment.corenlp.score_text should return scores for text"

        scores = API.sentiment.corenlp.score_text("This is a very sad sentence.")
        self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the text")

    def test_sentiment_analysis_of_document(self):
        "API.sentiment.corenlp.score_document should return scores for Document"

        text = "This is a terribly sad sentence."
        doc = API.annotate(text)
        scores = API.sentiment.corenlp.score_document(doc)
        self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the Document")

    def test_sentiment_analysis_of_sentence(self):
        "API.sentiment.corenlp.score_sentence should return a score for a Sentence"

        text = "This is a terribly sad sentence."
        doc = API.annotate(text)
        s = doc.sentences[0]
        score = API.sentiment.corenlp.score_sentence(s)
        self.assertIsInstance(score, int, "score for Sentence should be of type int, but was of type {}".format(type(score)))

    def test_sentiment_analysis_of_segemented_text(self):
        "API.sentiment.corenlp.score_segemented_text should return a score for each sentence its provided"

        sentences = ["This is a terribly sad sentence.", "I'm pretty happy, though :) !"]
        scores = API.sentiment.corenlp.score_segmented_text(sentences)
        self.assertTrue(len(scores) == len(sentences), "there should be {} scores, but only {} were produced :(".format(len(sentences), len(scores)))

    def test_sentiment_analysis_score_method(self):
        "API.sentiment.corenlp.score should be able to determine the appropriate API endpoint for the given parameter"
        # test with text
        text = "This is a terribly sad sentence."
        scores = API.sentiment.corenlp.score(text)
        self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the text")
        # test with Document
        doc = API.annotate(text)
        scores = API.sentiment.corenlp.score(doc)
        self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the Document")

        # test with Sentence
        s = doc.sentences[0]
        score = API.sentiment.corenlp.score(s)
        self.assertIsInstance(score, int, "score for Sentence should be of type int, but was of type {}".format(type(score)))

    # Odin tests
    def test_odin_extract_from_text_method(self):
        "API.odin.extract_from_text should return mentions whenever rules match the text"
        rules = """
        - name: "ner-person"
          label: [Person, PossiblePerson, Entity]
          priority: 1
          type: token
          pattern: |
           [entity="PERSON"]+
           |
           [tag=/^N/]* [tag=/^N/ & outgoing="cop"] [tag=/^N/]*
        """
        text = 'Inigo Montoya should be flagged as a Person.'
        mentions = API.odin.extract_from_text(text, rules)
        self.assertTrue(len(mentions) == 1, "More than one mention found for text.")
        m = mentions[0]
        self.assertIsInstance(m, Mention, "m wasn't a Mention")
        self.assertEqual(m.label, "Person", "Label of Mention was not \"Person\"")

    def test_odin_extract_from_text_method2(self):
        "API.odin.extract_from_text should be capable of handling a URL pointing to a yaml (rules) file"
        rules_url = "https://raw.githubusercontent.com/clulab/reach/master/src/main/resources/edu/arizona/sista/demo/open/grammars/rules.yml"
        text = 'Inigo Montoya should be flagged as a Person.'
        mentions = API.odin.extract_from_text(text, rules_url)
        self.assertTrue(len(mentions) != 0, "No mentions were found")

        m = mentions[0]
        self.assertIsInstance(m, Mention, "m wasn't a Mention")
        person_mentions = [m for m in mentions if m.label == "Person"]
        self.assertTrue(len(person_mentions) == 1, "{} \"Person\" Mentions found, but 1 expected.".format(len(person_mentions)))

    def test_odin_extract_from_document_method(self):
        "API.odin.extract_from_document should return mentions whenever rules match the text"
        rules = """
        - name: "ner-person"
          label: [Person, PossiblePerson, Entity]
          priority: 1
          type: token
          pattern: |
           [entity="PERSON"]+
           |
           [tag=/^N/]* [tag=/^N/ & outgoing="cop"] [tag=/^N/]*
        """
        text = 'Inigo Montoya should be flagged as a Person.'
        doc = API.annotate(text)
        mentions = API.odin.extract_from_document(doc, rules)
        self.assertTrue(len(mentions) == 1, "More than one mention found for text.")
        m = mentions[0]
        self.assertIsInstance(m, Mention, "m wasn't a Mention")
        self.assertEqual(m.label, "Person", "Label of Mention was not \"Person\"")

    def test_odin_extract_from_document_method2(self):
        "API.odin.extract_from_document should be capable of handling a URL pointing to a yaml (rules) file"
        rules_url = "https://raw.githubusercontent.com/clulab/reach/master/src/main/resources/edu/arizona/sista/demo/open/grammars/rules.yml"
        text = 'Inigo Montoya should be flagged as a Person.'
        doc = API.annotate(text)
        mentions = API.odin.extract_from_document(doc, rules_url)
        self.assertTrue(len(mentions) != 0, "No mentions were found")
        m = mentions[0]
        self.assertIsInstance(m, Mention,  "m wasn't a Mention")
        person_mentions = [m for m in mentions if m.label == "Person"]
        self.assertTrue(len(person_mentions) == 1, "{} \"Person\" Mentions found, but 1 expected.".format(len(person_mentions)))

    def test_shutdown(self):
        "api.stop_server() should stop processors-server.jar"

        self.assertTrue(API.stop_server(), "Failed to shut down processors-server.jar")

if __name__ == "__main__":
    unittest.main()


1		#!/usr/bin/env python
2		# -- coding: utf-8 --
3		from __future__ import unicode_literals
4		import unittest
5		from processors import *
6
7
8		port = 8886
9		# initialize the server
10		API = ProcessorsAPI(port=port, timeout=180, keep_alive=True)
11
12		class ProcessorsAPITests(unittest.TestCase):
13
14		def test_api(self):
15		"ProcessorsAPI instance should remember its port"
16
17		self.assertEqual(API.port, port, "Port was not {}".format(port))
18
19		# annotate tests
20		def test_annotate(self):
21		"API.annotate should produce a Document when given text"
22
23		text = "This is sentence 1. This is sentence 2."
24		# .annotate should be successful
25		doc = API.annotate(text)
26		self.assertNotEqual(doc, None, ".annotate failed to produce a Document")
27		# should have two sentences
28		num_sentences = 2
29		self.assertEqual(len(doc.sentences), num_sentences, ".annotate did not produce a Document with {} Sentences for text \"{}\"".format(num_sentences, text))
30
31		def test_unicode(self):
32		"API.annotate should produce a Document when given text containg unicode"
33		# the server will do a poor job with non-English text, but it should still produce something...
34		text = "頑張らなきゃならい"
35		doc = API.annotate(text)
36		self.assertNotEqual(doc, None, ".annotate failed to produce a Document")
37
38		# annotate_from_sentences tests
39		def test_annotate_from_sentences(self):
40		"API.annotate_from_sentences should produce a Document that preserves the provided sentence segmentation"
41
42		sentences = ["This is sentence 1.", "This is sentence 2."]
43		# .annotate should be successful
44		doc = API.annotate_from_sentences(sentences)
45		self.assertNotEqual(doc, None, ".annotate_from_sentences failed to produce a Document")
46		# should have two sentences
47		self.assertEqual(len(doc.sentences), len(sentences), ".annotate_from_sentences did not produce a Document with the correct number of sentences")
48
49		def test_fastnlp(self):
50		"API.fastnlp.annotate should produce a Document when given text"
51
52		text = "This is sentence 1. This is sentence 2."
53		# .annotate should be successful
54		doc = API.fastnlp.annotate(text)
55		self.assertNotEqual(doc, None, "fastnlp.annotate failed to produce a Document")
56		# should have two sentences
57		num_sentences = 2
58		self.assertEqual(len(doc.sentences), num_sentences, "fastnlp.annotate did not produce a Document with {} Sentences for text \"{}\"".format(num_sentences, text))
59
60		def test_bionlp(self):
61		"API.bionlp.annotate should produce a Document when given text"
62
63		text = "Ras phosphorylated Mek."
64		# .annotate should be successful
65		doc = API.bionlp.annotate(text)
66		self.assertNotEqual(doc, None, "bionlp.annotate failed to produce a Document")
67		# should have two sentences
68		num_sentences = 1
69		self.assertEqual(len(doc.sentences), num_sentences, "bionlp.annotate did not produce a Document with {} Sentences for text \"{}\"".format(num_sentences, text))
70
71		# sentiment analysis tests
72		def test_sentiment_analysis_of_text(self):
73		"API.sentiment.corenlp.score_text should return scores for text"
74
75		scores = API.sentiment.corenlp.score_text("This is a very sad sentence.")
76		self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the text")
77
78		def test_sentiment_analysis_of_document(self):
79		"API.sentiment.corenlp.score_document should return scores for Document"
80
81		text = "This is a terribly sad sentence."
82		doc = API.annotate(text)
83		scores = API.sentiment.corenlp.score_document(doc)
84		self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the Document")
85
86		def test_sentiment_analysis_of_sentence(self):
87		"API.sentiment.corenlp.score_sentence should return a score for a Sentence"
88
89		text = "This is a terribly sad sentence."
90		doc = API.annotate(text)
91		s = doc.sentences[0]
92		score = API.sentiment.corenlp.score_sentence(s)
93		self.assertIsInstance(score, int, "score for Sentence should be of type int, but was of type {}".format(type(score)))
94
95		def test_sentiment_analysis_of_segemented_text(self):
96		"API.sentiment.corenlp.score_segemented_text should return a score for each sentence its provided"
97
98		sentences = ["This is a terribly sad sentence.", "I'm pretty happy, though :) !"]
99		scores = API.sentiment.corenlp.score_segmented_text(sentences)
100		self.assertTrue(len(scores) == len(sentences), "there should be {} scores, but only {} were produced :(".format(len(sentences), len(scores)))
101
102		def test_sentiment_analysis_score_method(self):
103		"API.sentiment.corenlp.score should be able to determine the appropriate API endpoint for the given parameter"
104		# test with text
105		text = "This is a terribly sad sentence."
106		scores = API.sentiment.corenlp.score(text)
107		self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the text")
108		# test with Document
109		doc = API.annotate(text)
110		scores = API.sentiment.corenlp.score(doc)
111	View Code Duplication	self.assertTrue(len(scores) > 0, "there were no sentiment scores returned for the Document")
		0 ignored issues – show Duplication introduced 2016-06-19 06:22 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
112		# test with Sentence
113		s = doc.sentences[0]
114		score = API.sentiment.corenlp.score(s)
115		self.assertIsInstance(score, int, "score for Sentence should be of type int, but was of type {}".format(type(score)))
116
117		# Odin tests
118		def test_odin_extract_from_text_method(self):
119		"API.odin.extract_from_text should return mentions whenever rules match the text"
120		rules = """
121		- name: "ner-person"
122		label: [Person, PossiblePerson, Entity]
123		priority: 1
124		type: token
125		pattern: \|
126		[entity="PERSON"]+
127		\|
128		[tag=/^N/]* [tag=/^N/ & outgoing="cop"] [tag=/^N/]*
129		"""
130		text = 'Inigo Montoya should be flagged as a Person.'
131		mentions = API.odin.extract_from_text(text, rules)
132		self.assertTrue(len(mentions) == 1, "More than one mention found for text.")
133		m = mentions[0]
134		self.assertIsInstance(m, Mention, "m wasn't a Mention")
135		self.assertEqual(m.label, "Person", "Label of Mention was not \"Person\"")
136
137		def test_odin_extract_from_text_method2(self):
138		"API.odin.extract_from_text should be capable of handling a URL pointing to a yaml (rules) file"
139		rules_url = "https://raw.githubusercontent.com/clulab/reach/master/src/main/resources/edu/arizona/sista/demo/open/grammars/rules.yml"
140		text = 'Inigo Montoya should be flagged as a Person.'
141		mentions = API.odin.extract_from_text(text, rules_url)
142	View Code Duplication	self.assertTrue(len(mentions) != 0, "No mentions were found")
		0 ignored issues – show Duplication introduced 2016-06-19 06:22 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
143		m = mentions[0]
144		self.assertIsInstance(m, Mention, "m wasn't a Mention")
145		person_mentions = [m for m in mentions if m.label == "Person"]
146		self.assertTrue(len(person_mentions) == 1, "{} \"Person\" Mentions found, but 1 expected.".format(len(person_mentions)))
147
148		def test_odin_extract_from_document_method(self):
149		"API.odin.extract_from_document should return mentions whenever rules match the text"
150		rules = """
151		- name: "ner-person"
152		label: [Person, PossiblePerson, Entity]
153		priority: 1
154		type: token
155		pattern: \|
156		[entity="PERSON"]+
157		\|
158		[tag=/^N/]* [tag=/^N/ & outgoing="cop"] [tag=/^N/]*
159		"""
160		text = 'Inigo Montoya should be flagged as a Person.'
161		doc = API.annotate(text)
162		mentions = API.odin.extract_from_document(doc, rules)
163		self.assertTrue(len(mentions) == 1, "More than one mention found for text.")
164		m = mentions[0]
165		self.assertIsInstance(m, Mention, "m wasn't a Mention")
166		self.assertEqual(m.label, "Person", "Label of Mention was not \"Person\"")
167
168		def test_odin_extract_from_document_method2(self):
169		"API.odin.extract_from_document should be capable of handling a URL pointing to a yaml (rules) file"
170		rules_url = "https://raw.githubusercontent.com/clulab/reach/master/src/main/resources/edu/arizona/sista/demo/open/grammars/rules.yml"
171		text = 'Inigo Montoya should be flagged as a Person.'
172		doc = API.annotate(text)
173		mentions = API.odin.extract_from_document(doc, rules_url)
174		self.assertTrue(len(mentions) != 0, "No mentions were found")
175		m = mentions[0]
176		self.assertIsInstance(m, Mention, "m wasn't a Mention")
177		person_mentions = [m for m in mentions if m.label == "Person"]
178		self.assertTrue(len(person_mentions) == 1, "{} \"Person\" Mentions found, but 1 expected.".format(len(person_mentions)))
179
180		def test_shutdown(self):
181		"api.stop_server() should stop processors-server.jar"
182
183		self.assertTrue(API.stop_server(), "Failed to shut down processors-server.jar")
184
185		if __name__ == "__main__":
186		unittest.main()
187

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ProcessorsAPITests.test_bionlp() A

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