How to use system_output method in avocado

Best Python code snippet using avocado_python

debug_lp.py

Source:debug_lp.py

1"""Evaluate label propagation on a development set.2Usage:3  debug_lp.py --algo=<algo> --sim=<func> --gamma=<val>4Options:5  -h --help     Show this screen.6  --algo=<algo> Choose the algorithm (propagate, spread, or nearest) [default: propagate]7  --sim=<func>  Choose the similarity function to test (either rbf or expander)8  --gamma=<val> Value of gamma for RBF function9"""10import os11from datetime import datetime 12from collections import defaultdict13from label_propagation import LabelPropagation, expander, RBF, NearestNeighbor,\14    LabelSpreading, NearestNeighborOfAverage15from docopt import docopt16from version import version17def score_lp(system_input, system_output, gold):18    answers = []19    lemma_pos2answers = defaultdict(list)20    for key, input_info in system_input.items():21        assert len(system_output[key]) == len(gold[key]), 'output: %s, gold: %s' % (len(system_output[key]),22                                                                                    len(gold[key]))23        print('processing', key)24        for index, input_instance in enumerate(input_info):25            #print(index, input_instance)26            if input_instance[0] is None:27                system_answer = system_output[key][index]28                gold_answer = gold[key][index][0]29                #print('system', system_answer, 'gold', gold_answer)30                correct = system_answer == gold_answer31                answers.append(correct)32                lemma_pos2answers[key].append(correct)33    accuracy = sum(answers) / len(answers)34    for lemma_pos, lemma_pos_answers in lemma_pos2answers.items():35        lemma_pos_acc = sum(lemma_pos_answers) / len(lemma_pos_answers)36        print(lemma_pos, len(lemma_pos_answers), lemma_pos_acc)37    print('total', accuracy)38 39if __name__ == '__main__':40    import pickle41    from copy import deepcopy42    arguments = docopt(__doc__)43    print(arguments)44    if arguments['--sim'] == 'expander':45        sim_func = expander46    elif arguments['--sim'] == 'rbf':47        sim_func = RBF(float(arguments['--gamma']))48    else:49        raise ValueError('Unknown similarity function: %s' %arguments['--sim'])50    model_path='/var/scratch/mcpostma/testing/model-google-65/model-google/lstm-wsd-gigaword-google'51    vocab_path='/var/scratch/mcpostma/wsd-dynamic-sense-vector/output/gigaword-lstm-wsd.index.pkl'52#     model_path='output/2017-11-24-e93fdb2/lstm-wsd-gigaword-h256p64-seed_12-best-model'53#     vocab_path='preprocessed-data/2017-11-24-a74bda6/gigaword-for-lstm-wsd.index.pkl'54    path_system='output/dev.lp'55    path_gold='output/dev.lp.gold'56    path_senses_output = os.path.join('output', version, 'debug_lp__algo-%s_sim-%s_gamma-%s.pkl' 57                                      %(arguments['--algo'], arguments['--sim'], arguments['--gamma']))58    print('Senses written to %s' %path_senses_output)59    system_input = pickle.load(open(path_system, 'rb'))60    gold = pickle.load(open(path_gold, 'rb'))61    62    old_system_input = deepcopy(system_input)63    assert os.path.exists(vocab_path) and os.path.exists(model_path + '.meta'), \64            'Please update the paths hard-coded in this file (for testing only)'65    import tensorflow as tf66    with tf.Session() as sess:67        if arguments['--algo'] in ('propagate', 'LabelPropagation'): 68            lp = LabelPropagation(sess, vocab_path, model_path, 1000, sim_func=sim_func)69        elif arguments['--algo'] in ('spread', 'LabelSpreading'):70            lp = LabelSpreading(sess, vocab_path, model_path, 1000, sim_func=sim_func)71        elif arguments['--algo'] in ('nearest', 'NearestNeighbor'):72            lp = NearestNeighbor(sess, vocab_path, model_path, 1000, sim_func=sim_func)73        elif arguments['--algo'] in ('average', 'NearestNeighborOfAverage'):74            lp = NearestNeighborOfAverage(sess, vocab_path, model_path, 1000, sim_func=sim_func)75        else:76            raise ValueError('Unknown algorithm: %s' %arguments['--algo'])77        system_output = lp.predict(system_input)78        with open(path_senses_output, 'wb') as outfile:79            pickle.dump(system_output, outfile)80        lp.print_stats()81        print('Finished predicting at %s' %datetime.now())82    # score output (if gold provided)...

telegram_bot.py

Source:telegram_bot.py

1import json2from threading import Thread3from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackQueryHandler4# class TelegramBot(Thread):5class TelegramBot():6    def __init__(self, system, json_path):7        # super(TelegramBot, self).__init__()8        self.system = system9        with open(json_path, 'rt') as f:10            setting = json.load(f)11        self.TOKEN = setting["TOKEN"]12    def start(self, update, context):13        input_dir = {'utt':None, 'sessionId':str(update.message.from_user.id)}14        system_output = self.system.initial_message(input_dir)15        update.message.reply_text(system_output['utt'], reply_markup=system_output['markup'])16    def end(self, update, context):17        input_dir = {'utt':None, 'sessionId':str(update.message.from_user.id)}18        system_output = self.system.end_message(input_dir)19        update.message.reply_text(system_output['utt'], reply_markup=system_output['markup'])20    def reset(self, update, context):21        input_dir = {'utt':None, 'sessionId':str(update.message.from_user.id)}22        system_output = self.system.reset_message(input_dir)23        update.message.reply_text(system_output['utt'], reply_markup=system_output['markup'])24    def message(self, update, context):25        input_dir = {'utt':update.message.text, 'sessionId':str(update.message.from_user.id)}26        system_output = self.system.reply(input_dir)27        update.message.reply_text(system_output['utt'], reply_markup=system_output['markup'])28    def button(self, update, context):29        query = update.callback_query30        query.answer()31        reward = query.data32        self.system.button(reward)33        query.edit_message_text(text="{}: {}".format(query.message.text, reward))34    def run(self):35        self.updater = Updater(self.TOKEN, use_context=True)36        print(self.updater.bot.get_me())37        self.updater.dispatcher.add_handler(CommandHandler('start', self.start))38        self.updater.dispatcher.add_handler(CommandHandler('end', self.end))39        self.updater.dispatcher.add_handler(CommandHandler('reset', self.reset))40        self.updater.dispatcher.add_handler(CallbackQueryHandler(self.button))41        self.updater.dispatcher.add_handler(MessageHandler(Filters.text, self.message))42        self.updater.start_polling()...

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