<link rel="stylesheet" href="css/style.css"> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css"> <link rel='stylesheet prefetch' href='https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css'> <link rel="stylesheet" href="css/style.css"> </head> <body> <section class="title container"> <div class="row"> <div class="col-md-12"> <h1>Code<fade>X</fade> Blog</h1> <div class="seperator"></div> <p style="padding-bottom:50px;"></p> </div> </div> </section> <!-- Start Blog Layout --> <div class="container"> %s </div> </body> </html>''' % '\n'.join(rows) with open('index.html', 'w') as index_file: index_file.write(index_html) console.info("Wrote index HTML.") make_sitemap([blog_post['url'] for blog_post in blog_posts]) console.success("Finished blog iteration.")
console.log("Criando TASK TRAIN ") import os data_root = os.path.join(root, filename) dataset_train = task.Dataset(data_root, classes=('motorbike', )) console.info("TRAINING DATA MODEL...") time_limits = 5 * 60 * 60 # 5 hours epochs = 30 detector = task.fit(dataset_train, num_trials=2, epochs=epochs, lr=ag.Categorical(5e-4, 1e-4), ngpus_per_trial=1, time_limits=time_limits) console.success("TRAINING DONE !") console.log("START TEST MODEL ") dataset_test = task.Dataset(data_root, index_file_name='test', classes=('motorbike', )) test_map = detector.evaluate(dataset_test) console.log("mAP on test dataset: {}".format(test_map[1][1])) console.success("SAVE MODEL") savefile = 'model.pkl' detector.save(savefile) from autogluon import Detector new_detector = Detector.load(savefile)
with open('jobs.json') as f: job_data = json.load(f) console.info("Crawling %d career pages." % len(job_data)) i = 0 for job_entry in job_data: try: url = job_entry['link'] page = requests.get(url) tree = html.fromstring(page.content) links = tree.xpath('//a') job_postings = [] for link in links: job_title = link.text_content().strip().lstrip() if 'intern' in job_title: # only test if intern position res = requests.post( 'http://127.0.0.1:8000/predict', json={'title': job_title}) prediction = res.text.strip().lstrip() if prediction in ['IT/Software Development', 'Engineering']: job_postings.append(job_title) job_entry['positions'] = job_postings except Exception as e: console.error(e) i = i + 1 if i % 20 == 0: console.log("Processed %d pages." % i) console.success("Finished crawling.") with open('jobs.json', 'w') as f: json.dump(job_data, f) console.success("Dumped data.")
def load_data(): lines = open(dataset_path, 'r', encoding='utf8').readlines() data = [{ 'emotion': line.split(',')[1][1:-1], 'raw': _clean(','.join(line.split(',')[3:])) } for line in lines] return data data_save_path = os.path.join(os.getcwd(), 'data/data.sav') if os.path.exists(data_save_path): console.log("Reading from save file...") data = pkl.load(open(data_save_path, 'rb')) console.success("Finished reading data from save.") else: console.log("Did not find a save file.") data = load_data() pkl.dump(data, open(data_save_path, 'wb')) console.success("Created save file.") console.info("First data is sentence \"%s\" with emotion \'%s\'" % (data[0]['raw'], data[0]['emotion'])) def make_wordlists(data): wordlist = set() mentions = set() uppercase = set() for datapoint in data:
import utils from classifiers import JobTitle from console_logging.console import Console console = Console() train = utils.load_dataset('features') console.info("Loaded training dataset.") test = utils.load_dataset('test') console.info("Loaded testing dataset.") pipe = JobTitle.pipe(train) console.success("Finished training pipe.") t = [_['title'] for _ in test] e = [_['categories'][0] for _ in test] accuracy = utils.evaluate(pipe, t, e) console.success("%f accuracy" % accuracy) def get_analytics(): analytics = utils.analyze(pipe, t, e, utils.categories(test)) # console.log('\n'+str(analytics)) return analytics
routing_table = dict() with open('paths.json') as f: for d in j.load(f): routing_table[d["passkey"]] = d["url"] console.info("Compiled routing table of %d routes." % len(routing_table.keys())) @app.middleware('response') async def all_cors(r, s): s.headers['Access-Control-Allow-Origin'] = '*' s.headers['Access-Control-Allow-Headers'] = '*' @app.route("/knock", methods=['POST', 'OPTIONS']) async def whos_there(r): if r.method == 'OPTIONS': return json({}, status=200) if 'name' not in r.json.keys(): return json({}, status=500) console.log("%s@%s is knocking." % (r.json['name'], r.ip)) if r.json['name'] in routing_table.keys(): p = routing_table[r.json['name']] console.log("%s is answering." % p) return json({"url": p}, status=200) return json({}, status=401) if __name__ == "__main__": console.success("Starting server.") app.run(host="0.0.0.0", port=7734)
y = tf.constant(test_set.target) return x, y ## print("How many steps should we train for?") maxsteps = int(input('> ')) # Create the classifier. Take maxsteps steps. classifier.fit(input_fn=get_train_inputs, steps=maxsteps) # Evaluate loss. results = classifier.evaluate(input_fn=get_test_inputs, steps=1) print(results) console.success('\nFinished with loss {0:f}'.format(results['loss'])) print("\nPlease provide a GPA and test score to chance.") cur_gpa = float(input('GPA: ')) print("Given " + str(cur_gpa)) test_score = int(input('Test Score: ')) def new_samples(): return np.array([[0.0, 0], [cur_gpa, test_score], [maxgpa, maxtest]], dtype=np.float32) predictions = list(classifier.predict(input_fn=new_samples)) console.success("Made predictions:")