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How to use test_is_present method in toolium

Best Python code snippet using toolium_python

train-l2.py

Source:train-l2.py

1import pandas as pd2import numpy as np3import sys4from util.meta import full_split, cv1_split, cv1_split_time, test_split_time5from util import gen_prediction_name, gen_submission, score_sorted6from util.sklearn_model import SklearnModel7from util.keras_model import KerasModel8from util.xgb_model import XgbModel9from sklearn.model_selection import GroupKFold10from sklearn.linear_model import LogisticRegression11from sklearn.metrics import log_loss12from scipy.special import logit13preds = [14    #'20170114-2122-ffm2-f1b-0.68827',15    #'20170114-0106-ffm2-f1b-0.68775',16    #'20170113-1506-ffm2-f1-0.68447',17    #'20170113-1213-ffm2-p1-0.68392',18    '20170110-0230-ffm2-f1-0.69220',19    '20170110-1055-ffm2-f1-2-0.69214',20    '20170110-0124-ffm2-f1-0.69175',21    '20170109-1354-ffm2-f1-0.69148',22    '20170108-2008-ffm2-f1-0.68984',23    '20170107-2248-ffm2-p1-0.68876',24    '20170108-0345-ffm2-p2-0.68762',25    '20170106-2000-ffm2-p1-0.68754',26    '20170106-2050-ffm2-p2-0.68656',27    '20170105-2113-ffm2-p1-0.68684',28    '20161230-1323-ffm-p1-0.68204',29    '20161230-1049-ffm-p2-0.68169',30    '20161231-0544-vw-p1-0.67309',31    '20161231-1927-vw-p2-0.66718',32    '20170106-1339-vw-p1-0.67829',33    '20170109-1239-vw-p2-0.67148',34]35models = {36    'lr': lambda: SklearnModel(LogisticRegression(C=0.01)),37    'nn': lambda: KerasModel(batch_size=128, layers=[40, 10], dropouts=[0.3, 0.1], n_epoch=1),38    'xgb': lambda: XgbModel(n_iter=1500, silent=1, objective='binary:logistic', eval_metric='logloss', seed=144, max_depth=4, colsample_bytree=0.5, subsample=0.25, tree_method='exact', eta=0.05)39}40model_name = sys.argv[1]41model_factory = models[model_name]42def y_hash(y):43    return hash(tuple(np.where(y[:200])[0]) + tuple(np.where(y[-200:])[0]))44def fit_present_model(events, train_X, train_y, train_event):45    print "Training present model..."46    train_is_present = train_event.isin(events[events['timestamp'] < cv1_split_time].index).values47    present_train_X = train_X[train_is_present].values48    present_train_y = train_y[train_is_present].values49    present_train_g = train_event[train_is_present].values50    folds = list(GroupKFold(3).split(present_train_X, present_train_y, present_train_g))51    ll_scores = []52    map_scores = []53    for k, (idx_train, idx_test) in enumerate(folds):54        fold_train_X = present_train_X[idx_train]55        fold_train_y = present_train_y[idx_train]56        fold_train_g = present_train_g[idx_train]57        fold_val_X = present_train_X[idx_test]58        fold_val_y = present_train_y[idx_test]59        fold_val_g = present_train_g[idx_test]60        model = model_factory()61        model.fit(fold_train_X, fold_train_y, fold_train_g, fold_val_X, fold_val_y, fold_val_g)62        pred = model.predict(fold_val_X)63        ll_scores.append(log_loss(fold_val_y, pred, eps=1e-7))64        map_scores.append(score_sorted(fold_val_y, pred, fold_val_g))65        print "    Fold %d logloss: %.7f, map score: %.7f" % (k+1, ll_scores[-1], map_scores[-1])66    print "  Present map score: %.7f +- %.7f" % (np.mean(map_scores), np.std(map_scores))67    return model_factory().fit(present_train_X, present_train_y, fold_train_g), np.mean(map_scores)68def fit_future_model(events, train_X, train_y, train_event):69    print "Training future model..."70    val2_split_time = 107866777971    train_is_future_all = train_event.isin(events[events['timestamp'] >= cv1_split_time].index.values)72    train_is_future_train = train_event.isin(events[(events['timestamp'] >= cv1_split_time) & (events['timestamp'] < val2_split_time)].index.values)73    train_is_future_val = train_event.isin(events[(events['timestamp'] >= val2_split_time) & (events['timestamp'] < test_split_time)].index.values)74    future_train_X = train_X[train_is_future_train].values75    future_train_y = train_y[train_is_future_train].values76    future_train_g = train_event[train_is_future_train].values77    future_val_X = train_X[train_is_future_val].values78    future_val_y = train_y[train_is_future_val].values79    future_val_g = train_event[train_is_future_val].values80    model = model_factory()81    model.fit(future_train_X, future_train_y, future_train_g, future_val_X, future_val_y, future_val_g)82    pred = model.predict(future_val_X)83    ll_score = log_loss(future_val_y, pred, eps=1e-7)84    map_score = score_sorted(future_val_y, pred, future_val_g)85    print "  Future logloss: %.7f, map score: %.7f" % (ll_score, map_score)86    future_all_X = train_X[train_is_future_all].values87    future_all_y = train_y[train_is_future_all].values88    future_all_g = train_event[train_is_future_all].values89    return model_factory().fit(future_all_X, future_all_y, future_all_g), map_score90def load_x(ds):91    if ds == 'train':92        feature_ds = 'cv1_test'93        pred_ds = 'cv1'94    elif ds == 'test':95        feature_ds = 'full_test'96        pred_ds = 'test'97    else:98        raise ValueError()99    X = []100    X.append((pd.read_csv('cache/leak_%s.csv.gz' % feature_ds, dtype=np.uint8) > 0).astype(np.uint8))101    for pi, p in enumerate(preds):102        X.append(logit(pd.read_csv('preds/%s-%s.csv.gz' % (p, pred_ds), dtype=np.float32)[['pred']].rename(columns={'pred': 'p%d' % pi}).clip(lower=1e-7, upper=1-1e-7)))103    return pd.concat(X, axis=1)104def load_train_data():105    print "Loading train data..."106    d = pd.read_csv(cv1_split[1], dtype=np.uint32, usecols=['display_id', 'clicked'])107    return load_x('train'), d['clicked'], d['display_id']108## Main part109print "Loading events..."110events = pd.read_csv("../input/events.csv.gz", dtype=np.int32, index_col=0, usecols=[0, 3])  # Load events111## Training models112train_data = load_train_data()113present_model, present_score = fit_present_model(events, *train_data)114future_model, future_score = fit_future_model(events, *train_data)115score = present_score * 0.47671335657020786 + future_score * 0.5232866434297921116print "Estimated score: %.7f" % score117del train_data118## Predicting119print "Predicting on test..."120print "  Loading data..."121test_X = load_x('test').values122test_p = pd.read_csv(full_split[1], dtype=np.uint32)123test_p['pred'] = np.nan124test_is_present = test_p['display_id'].isin(events[events['timestamp'] < test_split_time].index).values125test_is_future = test_p['display_id'].isin(events[events['timestamp'] >= test_split_time].index).values126del events127print "  Predicting..."128name = gen_prediction_name('l2-%s' % model_name, score)129test_p.loc[test_is_present, 'pred'] = present_model.predict(test_X[test_is_present])130test_p.loc[test_is_future, 'pred'] = future_model.predict(test_X[test_is_future])131test_p[['pred']].to_csv('preds/%s-test.csv.gz' % name, index=False, compression='gzip')132del test_X, test_is_future, test_is_present133print "  Generating submission..."134subm = gen_submission(test_p)135subm.to_csv('subm/%s.csv.gz' % name, index=False, compression='gzip')136print "  File name: %s" % name...

tests.py

Source:tests.py

...38        self.assertEqual(account.makerCommission, 1)39        self.assertEquals(account.balances.all().filter(asset='BNB').get().free, 1000)40        self.assertEquals(account.balances.all().filter(asset='ETH').get().free, 105)41class MainIndexViewTests(TestCase):42    def test_is_present(self):43        """44        Something responds at the index url45        """46        response = self.client.get(reverse('main:index'))47        self.assertEqual(response.status_code, 200)48class GetAccountViewTests(TestCase):49    def test_is_present(self):50        """51        Something responds at the index url52        """53        response = self.client.get(reverse('main:get_account'))...

SampleTest2.py

Source:SampleTest2.py

1# -*- coding:utf-8 -*-2__author__ = "Zeal Zhang/zealzhangz@gmail.com"3__version__ = "0.0.1"4import unittest5from selenium.webdriver.common.by import By6from selenium.webdriver.support import expected_conditions as EC7from CommonUtil.WebdriverUtil import ChromeWebdriver8from CommonUtil.setttings import Settings9class SampleTestCase2(unittest.TestCase, ChromeWebdriver):10    def __init__(self, *args, **kwargs):11        super(SampleTestCase2, self).__init__(*args, **kwargs)12        ChromeWebdriver.__init__(self)13    def setUp(self):14        # login15        self.login()16    def test_html_exist_string2(self):17        self.wait.until(EC.element_to_be_clickable((By.XPATH, Settings.START_PROJECT_XPATH))).click()18        test_is_present = self.wait.until(19            EC.text_to_be_present_in_element((By.XPATH, Settings.SUBHEAD_HEADING), "Create a new repository"))20        self.assertTrue(test_is_present)21        pass22    def tearDown(self):23        self.driver.close()24if __name__ == '__main__':...

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