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How to use frame_element method in Playwright Python

Best Python code snippet using playwright-python

generate_scores.py

Source:generate_scores.py

1import os2import argparse3import itertools4import numpy as np5import pandas as pd6import config7import entailment8import utils9__author__ = 'chetannaik'10def get_question_group_key(question_group, question):11    for q in question_group.groups.keys():12        if question.strip() in q.strip():13            return q14    return None15def get_question_frames(question, question_frames, experiment):16    """Question frame extractor.17    Args:18        question: A string representing the question without options.19        question_frames: Contents of question_frames.tsv file.20    Returns: A list of python dictionaries containing the the frames for the21        question extracted from question_frames.22    """23    q_group = question_frames.groupby('QUESTION')24    if len(filter(bool, set(question.strip().split('.')))) > 1:25        q_sentences = set(question.strip().split('.'))26        q_sentences = filter(bool, map(lambda x: x.strip(), q_sentences))27    else:28        q_sentences = []29    q_sentences.append(question.strip())30    dfs = []31    for q in q_sentences:32        key = get_question_group_key(q_group, q)33        if key:34            df = q_group.get_group(key)35            dfs.append(df)36    ret_df = pd.concat(dfs)37    ret_df = ret_df[config.ROLES[experiment]]38    ret_df.drop_duplicates(inplace=True)39    return ret_df.to_dict(orient='records')40def get_answer_group_key(process_group, process):41    for group in process_group.groups.keys():42        names = group.split(" | ")43        if utils.get_lemma(process) in names:44            return group45    return None46def get_answer_frames(answer, process_db, experiment):47    """Answer frame extractor.48    Args:49        answer: A string representing the answer choice.50        process_db: Contents of process_frames.tsv file.51    Returns: a list of python dictionaries, containing frames for the answer52        with frame elements extracted from process_db.53    """54    p_group = process_db.groupby('PROCESS')55    key = get_answer_group_key(p_group, answer)56    if key:57        afs = p_group.get_group(key)58        afs = afs[config.ROLES[experiment]]59        afs.drop_duplicates(inplace=True)60        return afs.to_dict(orient='records')61    else:62        return []63def get_alignment_data(question_frames, answer_choices, process_db, experiment):64    """Gets alignment scores by calling aligner.65    Args:66        question_frames: A list of python dictionaries containing question67            frames.68        answer_choices: A python list containing answer choices.69        process_db:  Contents of process_frames.tsv file.70    Returns: A dictionary for each answer choice containing a list of71        dictionaries frame roles, role elements and their entailment scores.72    """73    answer_data = dict()74    for answer in answer_choices:75        answer_frames = get_answer_frames(answer, process_db, experiment)76        answer_scores = aligner(question_frames, answer_frames, experiment)77        answer_data[answer] = answer_scores78    return answer_data79def get_frame_directional_score(question_frame, answer_frame, experiment):80    frame_scores = dict()81    temp_scores = dict()82    temp_scores["FORWARD"] = dict()83    for frame_element in config.ROLES[experiment]:84        q_element = question_frame[frame_element]85        a_element = answer_frame[frame_element]86        if pd.isnull(q_element) or pd.isnull(a_element):87            score = np.NaN88        else:89            ret = entailment.get_ai2_textual_entailment(a_element, q_element)90            a_scores = map(lambda x: x['score'], ret['alignments'])91            if len(a_scores):92                mean_a_score = np.mean(a_scores)93            else:94                mean_a_score = 095            confidence = ret['confidence'] if ret['confidence'] else 096            score = mean_a_score * confidence97        temp_scores["FORWARD"][frame_element] = (q_element, a_element, score)98    temp_scores["BACKWARD"] = dict()99    for frame_element in config.ROLES[experiment]:100        q_element = question_frame[frame_element]101        a_element = answer_frame[frame_element]102        if pd.isnull(q_element) or pd.isnull(a_element):103            score = np.NaN104        else:105            ret = entailment.get_ai2_textual_entailment(q_element, a_element)106            a_scores = map(lambda x: x['score'], ret['alignments'])107            if len(a_scores):108                mean_a_score = np.mean(a_scores)109            else:110                mean_a_score = 0111            confidence = ret['confidence'] if ret['confidence'] else 0112            score = mean_a_score * confidence113        temp_scores["BACKWARD"][frame_element] = (q_element, a_element, score)114    best_score = 0115    best_direction = None116    for k, v in temp_scores.iteritems():117        direction = k118        score = sum(map(lambda x: x[2], v.values()))119        if score >= best_score:120            best_score = score121            best_direction = direction122    for frame_element in config.ROLES[experiment]:123        q_element = question_frame[frame_element]124        a_element = answer_frame[frame_element]125        if pd.notnull(q_element) and pd.notnull(a_element):126            q_list = map(lambda x: x.strip(), q_element.split("|"))127            a_list = map(lambda x: x.strip(), a_element.split("|"))128            if utils.has_filter_keyword(q_list):129                frame_scores[frame_element] = ("", a_element, np.NaN)130            elif utils.has_filter_keyword(a_list):131                frame_scores[frame_element] = (q_element, "", np.NaN)132            else:133                frame_scores[frame_element] = \134                    temp_scores[best_direction][frame_element]135        elif pd.notnull(q_element):136            frame_scores[frame_element] = (q_element, "", score)137        elif pd.notnull(a_element):138            frame_scores[frame_element] = ("", a_element, score)139        else:140            frame_scores[frame_element] = ("", "", np.NaN)141    # frame_scores[utils.DEFINITION] = answer_frame[utils.DEFINITION]142    return frame_scores143def get_role_directional_score(question_frame, answer_frame, experiment):144    frame_scores = dict()145    for frame_element in config.ROLES[experiment]:146        q_element = question_frame[frame_element]147        a_element = answer_frame[frame_element]148        if config.ENTAILMENT_TYPE == 'BEST_TEXT_SPAN':149            if pd.notnull(q_element):150                q_spans = q_element.split(" | ")151            else:152                q_spans = [pd.NaT]153            if pd.notnull(a_element):154                a_spans = a_element.split(" | ")155            else:156                a_spans = [pd.NaT]157            all_combinations = itertools.product(q_spans, a_spans)158            best_combination = (None, None, 0)159            for q_span, a_span in all_combinations:160                if pd.isnull(q_span) or pd.isnull(a_span):161                    score = np.NaN162                    best_combination = (q_span, a_span, score)163                else:164                    ret = entailment.get_ai2_textual_entailment(165                        utils.remove_filter_words(a_span),166                        utils.remove_filter_words(q_span))167                    a_scores = map(lambda x: x['score'], ret['alignments'])168                    if len(a_scores):169                        mean_a_score = np.mean(a_scores)170                    else:171                        mean_a_score = 0172                    confidence = ret['confidence'] if ret['confidence'] else 0173                    score1 = mean_a_score * confidence174                    ret = entailment.get_ai2_textual_entailment(175                        utils.remove_filter_words(q_span),176                        utils.remove_filter_words(a_span))177                    a_scores = map(lambda x: x['score'], ret['alignments'])178                    if len(a_scores):179                        mean_a_score = np.mean(a_scores)180                    else:181                        mean_a_score = 0182                    confidence = ret['confidence'] if ret['confidence'] else 0183                    score2 = mean_a_score * confidence184                    score = max(score1, score2)185                    if score > best_combination[2]:186                        best_combination = (q_span, a_span, score)187            if pd.notnull(best_combination[0]) and pd.notnull(best_combination[1]):188                frame_scores[frame_element] = best_combination189            elif pd.notnull(best_combination[0]):190                frame_scores[frame_element] = (best_combination[0], "", best_combination[2])191            elif pd.notnull(best_combination[1]):192                frame_scores[frame_element] = ("", best_combination[1], best_combination[2])193            else:194                frame_scores[frame_element] = ("", "", np.NaN)195        else:196            if pd.isnull(q_element) or pd.isnull(a_element):197                score = np.NaN198            else:199                ret = entailment.get_ai2_textual_entailment(200                    utils.remove_filter_words(a_element),201                    utils.remove_filter_words(q_element))202                a_scores = map(lambda x: x['score'], ret['alignments'])203                if len(a_scores):204                    mean_a_score = np.mean(a_scores)205                else:206                    mean_a_score = 0207                confidence = ret['confidence'] if ret['confidence'] else 0208                score1 = mean_a_score * confidence209                ret = entailment.get_ai2_textual_entailment(210                    utils.remove_filter_words(q_element),211                    utils.remove_filter_words(a_element))212                a_scores = map(lambda x: x['score'], ret['alignments'])213                if len(a_scores):214                    mean_a_score = np.mean(a_scores)215                else:216                    mean_a_score = 0217                confidence = ret['confidence'] if ret['confidence'] else 0218                score2 = mean_a_score * confidence219                score = max(score1, score2)220            if pd.notnull(q_element) and pd.notnull(a_element):221                if q_element in ["process", "processes"]:222                    frame_scores[frame_element] = ("", a_element, np.NaN)223                else:224                    frame_scores[frame_element] = (q_element, a_element, score)225            elif pd.notnull(q_element):226                frame_scores[frame_element] = (q_element, "", score)227            elif pd.notnull(a_element):228                frame_scores[frame_element] = ("", a_element, score)229            else:230                frame_scores[frame_element] = ("", "", np.NaN)231                #     frame_scores[utils.DEFINITION] = answer_frame[utils.DEFINITION]232    return frame_scores233def aligner(question_frames, answer_frames, experiment):234    """Aligns a question frame with a answer frame and calls entailment service235    to get a match score.236    Args:237        question_frames: A list of python dictionaries containing question238            frames.239        answer_frames: A list of python dictionaries containg answer frame240            elements.241    Returns: A number representing the match score of question frame with all242        the answer frames.243    """244    answer_scores = []245    for question_frame in question_frames:246        for answer_frame in answer_frames:247            if config.SCORE_DIRECTION_ABSTRACTION == "FRAME":248                frame_scores = get_frame_directional_score(question_frame,249                                                           answer_frame,250                                                           experiment)251            else:252                frame_scores = get_role_directional_score(question_frame,253                                                          answer_frame,254                                                          experiment)255            answer_scores.append(frame_scores)256    return answer_scores257def process_shard(shard_experiment):258    shard_num, experiment = shard_experiment259    process_db = pd.read_csv(260        "data/" + experiment + "/frames.cv." + str(shard_num) + ".tsv",261        sep='\t')262    process_db.PROCESS = process_db.PROCESS.str.lower()263    questions = pd.read_csv(264        "data/" + experiment + "/question.list.cv." + str(shard_num) + ".tsv",265        sep='\t')266    question_frames = pd.read_csv(267        "data/" + experiment + "/question.framepredict.cv." + str(268            shard_num) + ".tsv", sep='\t')269    ret_list = []270    for qid, row in questions.iterrows():271        if qid > 0 and (qid + 1) % 60 == 0:272            print "."273        else:274            print ".",275        question = row.QUESTION276        q_frames = get_question_frames(question, question_frames, experiment)277        answer_choices = filter(pd.notnull, row[config.OPTIONS].tolist())278        answer_data = get_alignment_data(q_frames, answer_choices, process_db,279                                         experiment)280        correct_answer = row[row.ANSWER]281        for answer, data in answer_data.iteritems():282            for roles in data:283                out_row = [question, answer]  # add roles[DEFINITION] here284                contents = map(lambda x: list(roles[x]),285                               config.ROLES[experiment])286                contents = list(itertools.chain.from_iterable(contents))287                out_row.extend(contents)288                out_row.append(correct_answer)289                ret_list.append(out_row)290    return ret_list291def main(num_shards, experiment):292    utils.get_filter_words()293    if not os.path.exists("output/" + experiment):294        os.makedirs("output/" + experiment)295    result = []296    for shard_num in range(num_shards):297        result.extend(process_shard((shard_num, experiment)))298    result_df = pd.DataFrame(result)299    col_list = ['QUESTION', 'ANSWER_CHOICE']  # add 'ANSWER_SENTENCE' here300    contents = map(lambda r: ['Q_' + r, 'A_' + r, r + '_SCORE'],301                   config.ROLES[experiment])302    contents = list(itertools.chain.from_iterable(contents))303    col_list.extend(contents)304    col_list.append('CORRECT_ANSWER')305    result_df.columns = col_list306    result_df.to_csv("output/" + experiment + "/features.tsv", sep="\t")307if __name__ == '__main__':308    parser = argparse.ArgumentParser(description='Frame alignment for QA.')309    parser.add_argument('--num_shards', type=int,310                        help='number of shards/folds')311    parser.add_argument('--experiment', help='experiment to run')312    args = parser.parse_args()...

framenet.py

Source:framenet.py

1#! /usr/bin/env python2# Author: Kapil Thadani (kapil@cs.columbia.edu)3from __future__ import division, with_statement4from nltk.corpus import framenet5###############################################################################6# Names of all frames in Framenet (1019 total)7frames = sorted(frame.name for frame in framenet.frames())8# Names of all possible FEs (1170 total)9fes = sorted(set(fe for frame in framenet.frames() for fe in frame.FE.keys()))10# Names of all possible frames and FEs (9633 total)11frame_fes = sorted([(frame.name, fe)12                        for frame in framenet.frames()13                        for fe in frame.FE.iterkeys()],14                   key=lambda x: x[0] + x[1])15###############################################################################16# Core types of FEs17coretypes = ['Core', 'Peripheral', 'Extra-Thematic']18# Names of all possible FEs and coretypes (1491 total)19fe_coretypes = sorted(set((fe, frame_element.coreType)20                            for frame in framenet.frames()21                            for fe, frame_element in frame.FE.iteritems()),22                        key=lambda x: x[0] + x[1])23# Names of all possible frames and FEs and coretypes (9633 total)24frame_fe_coretypes = sorted([(frame.name, fe, frame_element.coreType)25                                for frame in framenet.frames()26                                for fe, frame_element in frame.FE.iteritems()],27                            key=lambda x: x[0] + x[1] + x[2])28###############################################################################29# Names of core FEs that are specific to a frame (857 total)30core_fes = sorted(set(fe for frame in framenet.frames()31                    for fe, frame_element in frame.FE.iteritems()32                    if frame_element.coreType == 'Core'))33# Names of extra-thematic FEs that belong to larger frames (236 total)34# - intersected with core: 9035extrathematic_fes = sorted(set(fe for frame in framenet.frames()36                    for fe, frame_element in frame.FE.iteritems()37                    if frame_element.coreType == 'Extra-Thematic'))38# Names of peripheral FEs that aren't specific to a frame (349 total)39# - intersected with core: 15740# - intersected with extra-thematic: 10241# - intersected with both: 6342peripheral_fes = sorted(set(fe for frame in framenet.frames()43                    for fe, frame_element in frame.FE.iteritems()44                    if frame_element.coreType == 'Peripheral'))45###############################################################################46def get_frame_ancestors(frame_name, limit=None):47    """Return names of all ancestors of the given frame in the Framenet48    taxonomy up to a specified distance.49    """50    traversed = []51    successors = [relation.Parent.name52                  for relation in framenet.frame_relations(frame_name)53                  if relation.type.name == 'Inheritance'54                  and relation.Parent.name != frame_name]55    if limit is not None:56        limit -= 157    if limit is None or limit >= 0:58        for successor in successors:59            traversed.append(successor)60            traversed.extend(get_frame_ancestors(successor, limit=limit))61    return traversed62def get_coretype(frame_name, fe):63    """Return the core type of the given FE from the set of string types64    {'Core', 'Peripheral', 'Extra-Thematic'}65    """...

test_frames.py

Source:test_frames.py

1import unittest, sys2from lxml.tests.common_imports import make_doctest, doctest3import lxml.html4from lxml.html import html_parser, XHTML_NAMESPACE5class FrameTest(unittest.TestCase):6    def test_parse_fragments_fromstring(self):7        parser = lxml.html.HTMLParser(encoding='utf-8', remove_comments=True)8        html = """<frameset>9            <frame src="main.php" name="srcpg" id="srcpg" frameborder="0" rolling="Auto" marginwidth="" marginheight="0">10        </frameset>"""11        etree_document = lxml.html.fragments_fromstring(html, parser=parser)12        self.assertEqual(len(etree_document), 1)13        root = etree_document[0]14        self.assertEqual(root.tag, "frameset")15        frame_element = root[0]16        self.assertEqual(frame_element.tag, 'frame')17    def test_parse_fromstring(self):18        parser = lxml.html.HTMLParser(encoding='utf-8', remove_comments=True)19        html = """<html><frameset>20            <frame src="main.php" name="srcpg" id="srcpg" frameborder="0" rolling="Auto" marginwidth="" marginheight="0">21        </frameset></html>"""22        etree_document = lxml.html.fromstring(html, parser=parser)23        self.assertEqual(etree_document.tag, 'html')24        self.assertEqual(len(etree_document), 1)25        frameset_element = etree_document[0]26        self.assertEqual(len(frameset_element), 1)27        frame_element = frameset_element[0]28        self.assertEqual(frame_element.tag, 'frame')29def test_suite():30    loader = unittest.TestLoader()...

switch.py

Source:switch.py

1import pytest2from webdriver import StaleElementReferenceException3from tests.support.asserts import assert_error, assert_success4from tests.support.inline import inline, iframe5def switch_to_parent_frame(session):6    return session.transport.send(7        "POST", "session/{session_id}/frame/parent".format(**vars(session)))8def test_null_response_value(session):9    session.url = inline(iframe("<p>foo"))10    frame_element = session.find.css("iframe", all=False)11    session.switch_frame(frame_element)12    response = switch_to_parent_frame(session)13    value = assert_success(response)14    assert value is None15def test_no_browsing_context(session, closed_window):16    response = switch_to_parent_frame(session)17    assert_error(response, "no such window")18def test_stale_element_from_iframe(session):19    session.url = inline(iframe("<p>foo"))20    frame_element = session.find.css("iframe", all=False)21    session.switch_frame(frame_element)22    stale_element = session.find.css("p", all=False)23    result = switch_to_parent_frame(session)24    assert_success(result)25    with pytest.raises(StaleElementReferenceException):...

Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.