How to use show_test method in Lemoncheesecake
Best Python code snippet using lemoncheesecake
test_autoplot.py
Source:test_autoplot.py 
...43# @viz_reg_test44# def test_autoplot_{n}():45# return ar.autoplot(data.head({nrows}), columns={vars})46#47# show_test(test_autoplot_{n})48# """.format(49# nrows=nrows, vars=vars, n=n50# )51# )52#' <h3> Test autoplot #1</h3>53@viz_reg_test54def test_autoplot_1():55 return ar.autoplot(data.head(10), columns=["x"])56show_test(test_autoplot_1)57#' <h3> Test autoplot #2</h3>58@viz_reg_test59def test_autoplot_2():60 return ar.autoplot(data.head(50), columns=["x"])61show_test(test_autoplot_2)62#' <h3> Test autoplot #3</h3>63@viz_reg_test64def test_autoplot_3():65 return ar.autoplot(data.head(250), columns=["x"])66show_test(test_autoplot_3)67#' <h3> Test autoplot #4</h3>68@viz_reg_test69def test_autoplot_4():70 return ar.autoplot(data.head(1000), columns=["x"])71show_test(test_autoplot_4)72#' <h3> Test autoplot #5</h3>73@viz_reg_test74def test_autoplot_5():75 return ar.autoplot(data.head(5000), columns=["x"])76show_test(test_autoplot_5)77#' <h3> Test autoplot #6</h3>78@viz_reg_test79def test_autoplot_6():80 return ar.autoplot(data.head(10), columns=["x_cat"])81show_test(test_autoplot_6)82#' <h3> Test autoplot #7</h3>83@viz_reg_test84def test_autoplot_7():85 return ar.autoplot(data.head(50), columns=["x_cat"])86show_test(test_autoplot_7)87#' <h3> Test autoplot #8</h3>88@viz_reg_test89def test_autoplot_8():90 return ar.autoplot(data.head(250), columns=["x_cat"])91show_test(test_autoplot_8)92#' <h3> Test autoplot #9</h3>93@viz_reg_test94def test_autoplot_9():95 return ar.autoplot(data.head(1000), columns=["x_cat"])96show_test(test_autoplot_9)97#' <h3> Test autoplot #10</h3>98@viz_reg_test99def test_autoplot_10():100 return ar.autoplot(data.head(5000), columns=["x_cat"])101show_test(test_autoplot_10)102#' <h3> Test autoplot #11</h3>103@viz_reg_test104def test_autoplot_11():105 return ar.autoplot(data.head(10), columns=["x", "y"])106show_test(test_autoplot_11)107#' <h3> Test autoplot #12</h3>108@viz_reg_test109def test_autoplot_12():110 return ar.autoplot(data.head(50), columns=["x", "y"])111show_test(test_autoplot_12)112#' <h3> Test autoplot #13</h3>113@viz_reg_test114def test_autoplot_13():115 return ar.autoplot(data.head(250), columns=["x", "y"])116show_test(test_autoplot_13)117#' <h3> Test autoplot #14</h3>118@viz_reg_test119def test_autoplot_14():120 return ar.autoplot(data.head(1000), columns=["x", "y"])121show_test(test_autoplot_14)122#' <h3> Test autoplot #15</h3>123@viz_reg_test124def test_autoplot_15():125 return ar.autoplot(data.head(5000), columns=["x", "y"])126show_test(test_autoplot_15)127#' <h3> Test autoplot #16</h3>128@viz_reg_test129def test_autoplot_16():130 return ar.autoplot(data.head(10), columns=["x_cat", "y"])131show_test(test_autoplot_16)132#' <h3> Test autoplot #17</h3>133@viz_reg_test134def test_autoplot_17():135 return ar.autoplot(data.head(50), columns=["x_cat", "y"])136show_test(test_autoplot_17)137#' <h3> Test autoplot #18</h3>138@viz_reg_test139def test_autoplot_18():140 return ar.autoplot(data.head(250), columns=["x_cat", "y"])141show_test(test_autoplot_18)142#' <h3> Test autoplot #19</h3>143@viz_reg_test144def test_autoplot_19():145 return ar.autoplot(data.head(1000), columns=["x_cat", "y"])146show_test(test_autoplot_19)147#' <h3> Test autoplot #20</h3>148@viz_reg_test149def test_autoplot_20():150 return ar.autoplot(data.head(5000), columns=["x_cat", "y"])151show_test(test_autoplot_20)152#' <h3> Test autoplot #21</h3>153@viz_reg_test154def test_autoplot_21():155 return ar.autoplot(data.head(10), columns=["x_cat", "y_cat"])156show_test(test_autoplot_21)157#' <h3> Test autoplot #22</h3>158@viz_reg_test159def test_autoplot_22():160 return ar.autoplot(data.head(50), columns=["x_cat", "y_cat"])161show_test(test_autoplot_22)162#' <h3> Test autoplot #23</h3>163@viz_reg_test164def test_autoplot_23():165 return ar.autoplot(data.head(250), columns=["x_cat", "y_cat"])166show_test(test_autoplot_23)167#' <h3> Test autoplot #24</h3>168@viz_reg_test169def test_autoplot_24():170 return ar.autoplot(data.head(1000), columns=["x_cat", "y_cat"])171show_test(test_autoplot_24)172#' <h3> Test autoplot #25</h3>173@viz_reg_test174def test_autoplot_25():175 return ar.autoplot(data.head(5000), columns=["x_cat", "y_cat"])176show_test(test_autoplot_25)177#' <h3> Test autoplot #26</h3>178@viz_reg_test179def test_autoplot_26():180 return ar.autoplot(data.head(10), columns=["x", "y", "z"])181show_test(test_autoplot_26)182#' <h3> Test autoplot #27</h3>183@viz_reg_test184def test_autoplot_27():185 return ar.autoplot(data.head(50), columns=["x", "y", "z"])186show_test(test_autoplot_27)187#' <h3> Test autoplot #28</h3>188@viz_reg_test189def test_autoplot_28():190 return ar.autoplot(data.head(250), columns=["x", "y", "z"])191show_test(test_autoplot_28)192#' <h3> Test autoplot #29</h3>193@viz_reg_test194def test_autoplot_29():195 return ar.autoplot(data.head(1000), columns=["x", "y", "z"])196show_test(test_autoplot_29)197#' <h3> Test autoplot #30</h3>198@viz_reg_test199def test_autoplot_30():200 return ar.autoplot(data.head(5000), columns=["x", "y", "z"])201show_test(test_autoplot_30)202#' <h3> Test autoplot #31</h3>203@viz_reg_test204def test_autoplot_31():205 return ar.autoplot(data.head(10), columns=["x_cat", "y", "z"])206show_test(test_autoplot_31)207#' <h3> Test autoplot #32</h3>208@viz_reg_test209def test_autoplot_32():210 return ar.autoplot(data.head(50), columns=["x_cat", "y", "z"])211show_test(test_autoplot_32)212#' <h3> Test autoplot #33</h3>213@viz_reg_test214def test_autoplot_33():215 return ar.autoplot(data.head(250), columns=["x_cat", "y", "z"])216show_test(test_autoplot_33)217#' <h3> Test autoplot #34</h3>218@viz_reg_test219def test_autoplot_34():220 return ar.autoplot(data.head(1000), columns=["x_cat", "y", "z"])221show_test(test_autoplot_34)222#' <h3> Test autoplot #35</h3>223@viz_reg_test224def test_autoplot_35():225 return ar.autoplot(data.head(5000), columns=["x_cat", "y", "z"])226show_test(test_autoplot_35)227#' <h3> Test autoplot #36</h3>228@viz_reg_test229def test_autoplot_36():230 return ar.autoplot(data.head(10), columns=["x_cat", "y_cat", "z"])231show_test(test_autoplot_36)232#' <h3> Test autoplot #37</h3>233@viz_reg_test234def test_autoplot_37():235 return ar.autoplot(data.head(50), columns=["x_cat", "y_cat", "z"])236show_test(test_autoplot_37)237#' <h3> Test autoplot #38</h3>238@viz_reg_test239def test_autoplot_38():240 return ar.autoplot(data.head(250), columns=["x_cat", "y_cat", "z"])241show_test(test_autoplot_38)242#' <h3> Test autoplot #39</h3>243@viz_reg_test244def test_autoplot_39():245 return ar.autoplot(data.head(1000), columns=["x_cat", "y_cat", "z"])246show_test(test_autoplot_39)247#' <h3> Test autoplot #40</h3>248@viz_reg_test249def test_autoplot_40():250 return ar.autoplot(data.head(5000), columns=["x_cat", "y_cat", "z"])251show_test(test_autoplot_40)252#' <h3> Test autoplot #41</h3>253@viz_reg_test254def test_autoplot_41():255 return ar.autoplot(data.head(10), columns=["x_cat", "y_cat", "z_cat"])256show_test(test_autoplot_41)257#' <h3> Test autoplot #42</h3>258@viz_reg_test259def test_autoplot_42():260 return ar.autoplot(data.head(50), columns=["x_cat", "y_cat", "z_cat"])261show_test(test_autoplot_42)262#' <h3> Test autoplot #43</h3>263@viz_reg_test264def test_autoplot_43():265 return ar.autoplot(data.head(250), columns=["x_cat", "y_cat", "z_cat"])266show_test(test_autoplot_43)267#' <h3> Test autoplot #44</h3>268@viz_reg_test269def test_autoplot_44():270 return ar.autoplot(data.head(1000), columns=["x_cat", "y_cat", "z_cat"])271show_test(test_autoplot_44)272#' <h3> Test autoplot #45</h3>273@viz_reg_test274def test_autoplot_45():275 return ar.autoplot(data.head(5000), columns=["x_cat", "y_cat", "z_cat"])276show_test(test_autoplot_45)277#' To finish three corner cases when there's no overlap with categorical vars only278w = pd.Series(range(10))279no_overlap_data = pd.DataFrame(280 dict(281 x=pd.concat([w, w - 3]).astype(str),282 y=pd.concat([w, w]).astype(str),283 z=pd.concat([w, w]).astype(str),284 )285)286@viz_reg_test287def test_autoplot_CCC():288 return ar.autoplot(no_overlap_data)289show_test(test_autoplot_CCC)290@viz_reg_test291def test_autoplot_CC():292 return ar.autoplot(no_overlap_data, columns=["x", "y"])293show_test(test_autoplot_CC)294@viz_reg_test295def test_autoplot_C():296 return ar.autoplot(no_overlap_data.head(10), columns=["x"])...
kernel_utils.py
Source:kernel_utils.py
1import logging2import numpy as np3from eval_pd import get_evaluation_results4from itertools import combinations5from global_constants import *6from global_utils import *7def run_config(c, dataset, resulting_matrices, labels,show_test=True, probability=False,8 skip_all_positives_and_all_negatives=True):9 cs = set(c)10 label = " + ".join(cs)11 matrix = dict()12 for mode in ALL_MODES:13 matrix[mode] = sum([v[mode] for k, v in resulting_matrices.items() if k in cs])14 predictions = train_and_predict(matrix, labels, kernel='precomputed', probability=probability)15 dev_results = get_evaluation_results(dataset[DEV], predictions[DEV],16 skip_all_positives_and_all_negatives=skip_all_positives_and_all_negatives)17 test_results = get_evaluation_results(dataset[TEST], predictions[TEST],18 skip_all_positives_and_all_negatives=skip_all_positives_and_all_negatives)19 message = "%s\t%s\t%s" % (label, "\t".join(["%.2f" % (x) for x in list(dev_results)]),20 "\t".join(["%.2f" % (x) for x in list(test_results)]))21 logging.info("DEV:\t%s\t%s" % (label, "\t".join(["%.2f" % (x) for x in list(dev_results)])))22 if show_test:23 logging.info("TEST:\t%s\t%s" % (label, "\t".join(["%.2f" % (x) for x in list(test_results)])))24 return message25def run_experiments(dataset, configurations, resulting_matrices, labels, predefined=False, show_test=True, min_length=1,26 max_length=-1, probability=False, skip_all_positives_and_all_negatives=True):27 messages = []28 if predefined:29 for c in configurations:30 message = run_config(c, dataset, resulting_matrices, labels, show_test=show_test, probability=probability,31 skip_all_positives_and_all_negatives= skip_all_positives_and_all_negatives)32 messages.append(message)33 else:34 start = min_length - 135 end = max_length if max_length > 0 else len(configurations)36 for l in range(start, end):37 for c in combinations(configurations, l + 1):38 message = run_config(c, dataset, resulting_matrices, labels, show_test=show_test,39 probability=probability,40 skip_all_positives_and_all_negatives= skip_all_positives_and_all_negatives)41 messages.append(message)42 return messages43def convert_svm_light_vector_to_np_array(x,array_size,zero_vector):44 aa = zero_vector45# print type(x),x46 if not isinstance(x,str):47 logging.debug("Substituting %s with zero vector" % (str(x)))48 return aa49# if isinstance(x,np.ndarray):50# return x51 aa = np.zeros(shape=(1,array_size))52 if len(x.strip())==0:53 return aa54 for tuplesa in x.strip().split(" "):55 if not ":" in tuplesa:56 logging.error("Wrong format: %s" % (str(x)))57 indexa,valuea=tuplesa.strip().split(":")58 if int(indexa)-1>=array_size:59 print x60 aa[0,int(indexa)-1]=np.float64(valuea)61# print aa[0]...
urls.py
Source:urls.py
1from django.conf.urls import url2from booktest import views3urlpatterns = [4 url(r'^$', views.index, name='index'),5 url(r'^show_test', views.show_test, name='show_test'),6 url(r'^index2/$', views.index2, name='index2'),7 url(r'^user1/$', views.user1, name='user1'),8 url(r'^user2/$', views.user2, name='user2'),9 url(r'^zhuanyi/$', views.zhuanyi, name='zhuanyi'),10 url(r'^csrf1/$', views.csrf1, name='csrf1'),11 url(r'^csrf2/$', views.csrf2, name='csrf2'),12 url(r'^verify1/$', views.verify1, name='verify1'),13 url(r'^verify2/$', views.verify2, name='verify2'),14 url(r'^show_verify/$', views.show_verify, name='show_verify'),...
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