import numpy
import urllib
import scipy.optimize
import random
from sklearn import svm # Library for SVM classification
from sklearn import linear_model # Logistic Regression (among other things)
import ast
def parseData(fname):
for l in urllib.urlopen(fname):
yield eval(l)
def parseDataFromFile(fname):
for l in open(fname):
yield eval(l)
data1 = list(parseDataFromFile("C:/Users/Julian McAuley/Documents/class_files/book_descriptions_50000.json"))
data2 = list(parseDataFromFile("C:/Users/Julian McAuley/Documents/class_files/book_images_5000.json"))
data = data1
data[2]
{'asin/ID': '157120315X',
'url': 'http://www.amazon.com/dp/157120315X',
'description': 'quiltmaker magazine one of americas most prestigious quilting publications also created the books paper piecing picnic and paper piecing potpourri',
'categories': ["Children's Books", 'Crafts']}
Priors...
p(label)
prior = ["Children's Books" in d['categories'] for d in data]
prior = sum(prior) / len(prior)
prior
0.08288
prior_neg = 1 - prior
prior_neg
0.91712
p(wizard in description | Children's Book)
p1 = ["wizard" in b['description'] for b in data
if "Children's Books" in b['categories']]
p1 = sum(p1) / len(p1)
p1
0.01085907335907336
p1_neg = ["wizard" in b['description'] for b in data
if not ("Children's Books" in b['categories'])]
p1_neg = sum(p1_neg) / len(p1_neg)
p1_neg
0.004012561060711794
p(witch in description | Children's Book)
p2 = ["witch" in b['description'] for b in data if "Children's Books" in b['categories']]
p2 = sum(p2) * 1.0 / len(p2)
p2
0.02895752895752896
p2_neg = ["witch" in b['description'] for b in data if not "Children's Books" in b['categories']]
p2_neg = sum(p2_neg) * 1.0 / len(p2_neg)
p2_neg
0.018667131891137473
score = prior * p1 * p2
score_neg = prior_neg * p1_neg * p2_neg
score / score_neg
0.3793836356818883
p = ["Children's Books" in b['categories'] for b in data if
'witch' in b['description'] and 'wizard' in b['description']]
p = sum(p) * 1.0 / len(p)
p / (1 - p)
0.4615384615384615
Classification - Judging a book by its cover
data = data2
data[0]
{'asin/ID': '1573110639',
'url': 'http://www.amazon.com/dp/1573110639',
'description': 'one of the worlds great reference books newsweek',
'categories': ['New',
'Politics & Social Sciences',
'Science & Math',
'Education & Reference'],
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data[0]['image_feature']
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0.0, ...]
X = [b['image_feature'] for b in data]
y = ["Children's Books" in b['categories'] for b in data]
X_train = X[:len(X)//2]
y_train = y[:len(y)//2]
X_test = X[len(X)//2:]
y_test = y[len(y)//2:]
mod = linear_model.LogisticRegression(C=1.0)
mod.fit(X_train, y_train)
C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)
train_predictions = mod.predict(X_train)
test_predictions = mod.predict(X_test)
# accuracy
sum(test_predictions == y_test) / len(y_test)
0.9456
y_false = numpy.array([False for b in y_test])
# accuracy when predicting "false" always
sum(y_false == y_test) / len(y_test)
0.962
f = open("C:/users/Julian McAuley/Documents/class_files/bankruptcy.arff", 'r')
while not '@data' in f.readline():
pass
dataset = []
for l in f:
if '?' in l: # Missing entry
continue
l = l.split(',')
values = [1] + [float(x) for x in l]
values[-1] = values[-1] > 0 # Convert to bool
dataset.append(values)
dataset[0]
[1, 0.088238, 0.55472, 0.01134, 1.0205, -66.52, 0.34204, 0.10949, 0.57752, 1.0881, 0.32036, 0.10949, 0.1976, 0.096885, 0.10949, 1475.2, 0.24742, 1.8027, 0.10949, 0.077287, 50.199, 1.1574, 0.13523, 0.062287, 0.41949, 0.32036, 0.20912, 1.0387, 0.026093, 6.1267, 0.37788, 0.077287, 155.33, 2.3498, 0.24377, 0.13523, 1.4493, 571.37, 0.32101, 0.095457, 0.12879, 0.11189, 0.095457, 127.3, 77.096, 0.45289, 0.66883, 54.621, 0.10746, 0.075859, 1.0193, 0.55407, 0.42557, 0.73717, 0.73866, 15182.0, 0.080955, 0.27543, 0.91905, 0.002024, 7.2711, 4.7343, 142.76, 2.5568, 3.2597, False]
Data setup
X = [d[:-1] for d in dataset]
y = [d[-1] for d in dataset]
Fit model
mod = linear_model.LogisticRegression(C=1.0)
mod.fit(X,y)
C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning) C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)
pred = mod.predict(X)
sum(pred == y) / len(y)
0.9663477400197954
sum(y)
102
len(y)
3031
1 - 102/3031
0.9663477400197954
sum(pred)
6
Balanced model
mod = linear_model.LogisticRegression(C=1.0, class_weight = 'balanced')
mod.fit(X,y)
C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning) C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LogisticRegression(C=1.0, class_weight='balanced', dual=False,
fit_intercept=True, intercept_scaling=1, l1_ratio=None,
max_iter=100, multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)
pred = mod.predict(X)
sum(pred == y) / len(y)
0.7832398548333883
# How many positive predictions?
sum(pred)
717
Train/validation/test splits
Xy = list(zip(X,y))
random.shuffle(Xy)
X = [d[0] for d in Xy]
y = [d[1] for d in Xy]
N = len(y)
Ntrain = 1000
Nvalid = 1000
Ntest = 1031
Xtrain = X[:Ntrain]
Xvalid = X[Ntrain:Ntrain+Nvalid]
Xtest = X[Ntrain+Nvalid:]
ytrain = y[:Ntrain]
yvalid = y[Ntrain:Ntrain+Nvalid]
ytest = y[Ntrain+Nvalid:]
mod.fit(Xtrain, ytrain)
C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning. FutureWarning) C:\Users\Julian McAuley\Anaconda3\lib\site-packages\sklearn\svm\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. "the number of iterations.", ConvergenceWarning)
LogisticRegression(C=1.0, class_weight='balanced', dual=False,
fit_intercept=True, intercept_scaling=1, l1_ratio=None,
max_iter=100, multi_class='warn', n_jobs=None, penalty='l2',
random_state=None, solver='warn', tol=0.0001, verbose=0,
warm_start=False)
TP, TN, FP, FN, Accuracy, BER
pred = mod.predict(Xtest)
correct = pred == ytest
TP_ = numpy.logical_and(pred, ytest)
FP_ = numpy.logical_and(pred, numpy.logical_not(ytest))
TN_ = numpy.logical_and(numpy.logical_not(pred), numpy.logical_not(ytest))
FN_ = numpy.logical_and(numpy.logical_not(pred), ytest)
TP = sum(TP_)
FP = sum(FP_)
TN = sum(TN_)
FN = sum(FN_)
# accuracy
(TP + TN) / (TP + FP + TN + FN)
0.8166828322017459
sum(correct) / len(correct)
0.8166828322017459
# BER
1 - 0.5*(TP / (TP + FN) + TN / (TN + FP))
0.23573540280857364
Ranking
scores = mod.decision_function(Xtest)
scores
array([-1.02150932, -0.45331484, 0.76035815, ..., -0.41931955,
-7.00631971, -2.79365971])
scoreslabels = list(zip(scores, ytest))
scoreslabels.sort(reverse=True)
scoreslabels
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False), (-1.234189939351645, False), (-1.2393629731550413, False), (-1.2414488964157222, False), (-1.24186141728976, False), (-1.2436028696140542, False), (-1.245471123888228, False), (-1.2458076715820674, False), (-1.2486035023553743, False), (-1.2541500978974995, False), (-1.2553988872259678, False), (-1.2565779409828748, False), (-1.257156826156862, False), (-1.2618091632950428, False), (-1.2628948220787057, False), (-1.2654730462075605, False), (-1.2718630934435144, False), (-1.2738662956101903, False), (-1.2744284775500982, False), (-1.2783448973770064, False), (-1.2792472692531613, False), (-1.2815255511560708, False), (-1.2817943380936752, False), (-1.283060247027382, False), (-1.2927258589539374, False), (-1.2948038730008842, False), (-1.2977728117829217, False), (-1.3094461737700898, False), (-1.3095432352122889, False), (-1.3109047250089025, False), (-1.3150621428412859, False), (-1.3172136901155134, False), (-1.3182317858807309, False), (-1.3190525401797992, True), (-1.3219242649338923, False), (-1.326850254880909, False), (-1.3301157306800278, False), (-1.3322114506735523, False), (-1.336878977658922, False), (-1.337555047319098, False), (-1.3399943087529853, False), (-1.3427409370940726, False), (-1.3435309774524788, False), (-1.3491276733944495, False), (-1.3496163356729138, False), (-1.3503827508559814, False), (-1.3504270909604958, False), (-1.3524835615750055, False), (-1.3532867173767462, False), (-1.35662460386953, False), (-1.3643551430669216, False), (-1.3653535703151605, False), (-1.3660578366620872, False), (-1.3678596865125878, False), (-1.369491873087348, False), (-1.3710347072200662, False), (-1.3717961618738577, False), (-1.373442233812966, False), (-1.385335633413912, False), (-1.3913003419521721, False), (-1.393857006435732, False), (-1.4100931760081414, False), (-1.4101037786756963, False), (-1.410741182004027, False), (-1.4131257453433628, False), (-1.4157735579368569, False), (-1.4215116308392868, False), (-1.4247297392559684, False), (-1.427863405082101, False), (-1.4289028559038774, False), (-1.4370614591362147, False), (-1.44019084031416, False), (-1.4443032134633307, False), (-1.4480436546962772, False), (-1.4490336026829644, False), (-1.4551200394601616, False), (-1.4587005716085453, False), (-1.46001907633036, False), (-1.46001907633036, False), (-1.4714900423835062, False), (-1.4721265055306643, False), (-1.4733308367641114, False), (-1.480314687569143, False), (-1.4825988745776117, False), (-1.4851535246688188, False), (-1.486209950358279, False), (-1.4870615767539435, False), (-1.491843056462963, False), (-1.4952318003246792, False), (-1.497054928981626, False), (-1.4970890401989787, False), (-1.500332531153938, False), (-1.5049485783179666, False), (-1.5059480177470932, False), (-1.5061136439585499, False), (-1.506396643111394, False), (-1.51148440387208, False), (-1.5218120447883516, False), (-1.5252366706981133, False), (-1.526740682130096, False), (-1.5337762317028274, False), (-1.5357458266748214, False), (-1.542061560848373, False), (-1.5456684514848915, False), (-1.5484602027763545, False), (-1.5488001018807434, False), (-1.5501548630201385, False), (-1.5628188580734015, False), (-1.575799689988818, False), (-1.5759788596489548, False), (-1.5770107592189042, False), (-1.5855821461582686, False), (-1.5867829432603526, False), (-1.5889428680981166, False), (-1.5912571684372172, False), (-1.597614800090072, False), (-1.6000799338689355, False), (-1.603494609729807, False), (-1.60547069301525, False), (-1.6097620630794018, False), (-1.611037830173756, False), (-1.6144707905413922, False), (-1.62110483212946, False), (-1.625627287171236, False), (-1.6287675115079328, False), (-1.6349973688834372, False), (-1.6349973688834372, False), (-1.6367956950370859, False), (-1.6400761925278595, False), (-1.6439925368450465, False), (-1.646032644365786, False), (-1.6497939815839135, False), (-1.652304149388035, False), (-1.6572186887832145, False), (-1.6572411670070142, False), (-1.671001421209682, False), (-1.671542052233981, False), (-1.6733241029027262, False), (-1.6743646630102766, False), (-1.67572477583371, False), (-1.6797514341803677, False), (-1.6809673699498267, False), (-1.6827107095470863, False), (-1.6828108432655673, False), (-1.6837772465258103, False), (-1.6965587922956045, False), (-1.6995742081652634, False), (-1.7019918370973512, False), (-1.714052729206089, False), (-1.7146587179200816, False), (-1.717019956859522, False), (-1.7170526942217565, False), (-1.723700457104044, False), (-1.7252631112596335, False), (-1.7269493381634486, False), (-1.7280423552946345, False), (-1.730857114332327, False), (-1.734724990607189, False), (-1.738969101732358, False), (-1.7389720536793714, False), (-1.7394984021515705, False), (-1.7536660864534905, False), (-1.7619783891209644, False), (-1.7648321036670689, False), (-1.7660735015336844, False), (-1.7704547483828348, False), (-1.7734077815256275, False), (-1.7821411512020546, False), (-1.7839661768040533, False), (-1.7878423507975065, False), (-1.7929719135782665, False), (-1.805490158654243, False), (-1.806366438477443, False), (-1.8082578133637006, False), (-1.8137963335051284, False), (-1.838628011974783, False), (-1.8536121873756517, False), (-1.8768331687309887, False), (-1.8820983943572602, False), (-1.8853964420613112, False), (-1.8862401490818406, False), (-1.8995274435842806, False), (-1.905094967475676, False), (-1.9103699957572666, False), (-1.9257948159961864, False), (-1.9275889232234675, False), (-1.938933360325183, False), (-1.9456314867792175, False), (-1.9492333350673672, False), (-1.9645711895956366, False), (-1.9670295315401098, False), (-1.969229256270903, False), (-1.9721738766699994, False), (-1.973819100264709, False), (-1.9766175055575215, False), (-1.9827047402240978, False), (-1.9847165655929773, False), (-1.9896787188528613, False), (-1.990752560697795, False), (-1.991197690415773, False), (-1.9915953007328606, False), (-1.9966494517020834, False), (-1.999466820975684, False), (-2.0017453942093337, True), (-2.0135148027163727, False), (-2.015766598103091, False), (-2.0185719931725172, False), (-2.0379849947853246, False), (-2.0471319740421534, False), (-2.049345614538491, False), (-2.0521694054227586, False), (-2.066166835863394, False), (-2.0698498101618816, False), (-2.071039028439517, False), (-2.077289824792122, False), (-2.0798233280791356, False), (-2.0833470870211643, False), (-2.0885984845217855, False), (-2.1038090009780706, False), (-2.1180446043532366, False), (-2.119029285868915, False), (-2.1239640374743254, False), (-2.1256616013766805, False), (-2.1270359510783194, False), (-2.134903219357754, False), (-2.1517789253561257, False), (-2.1532900718063597, False), (-2.1622329904456747, False), (-2.165573509099502, False), (-2.1721605061122, False), (-2.1740501959146283, False), (-2.176185758062802, False), (-2.199304453571677, False), (-2.201822879555259, False), (-2.207230786574686, False), (-2.2170816665824518, False), (-2.2190645255009165, False), (-2.2222858113849835, False), (-2.2231791096292746, False), (-2.2242776080587787, False), (-2.2359043318162244, False), (-2.2392279451299695, False), (-2.243462078712813, False), (-2.2446359900560795, False), (-2.253718237315473, False), (-2.2557520395971555, False), (-2.257423720824498, False), (-2.2600823532956356, False), (-2.2755448408562833, False), (-2.282741483889501, False), (-2.283981292392956, False), (-2.2876027157732866, False), (-2.2919846395359142, False), (-2.2936638842665205, False), (-2.2976319948522135, False), (-2.3042370935192653, False), (-2.308656779669676, False), (-2.324474206063708, False), (-2.32751999798672, False), (-2.3363060862551923, False), (-2.341726025140001, False), (-2.3652436613571663, False), (-2.372244321705121, False), (-2.378019516560769, False), (-2.3826706206044683, False), (-2.400609484588313, False), (-2.402024620819736, False), (-2.4088991215669844, False), (-2.4227753331635484, False), (-2.4425585888484855, False), (-2.444054430256326, False), (-2.4500790163056663, False), (-2.4573595445859344, False), (-2.4839015970683245, False), (-2.484443893034727, False), (-2.487995540085468, False), (-2.5095935274076635, False), (-2.5121703175438763, False), (-2.512836634430942, False), (-2.526024646636899, False), (-2.5392869615424956, False), (-2.5498574098748636, False), (-2.580856431583131, False), (-2.586708380018444, False), (-2.5971356935604297, False), (-2.6095595959104148, False), (-2.617792129558355, False), (-2.6478170424856926, False), (-2.64879971596852, False), (-2.6714731192999364, False), (-2.6887077757048767, False), (-2.6906536936299204, False), (-2.6982103841020755, False), (-2.7102175654793643, False), (-2.7102175654793643, False), (-2.7201629595318413, False), (-2.735684193364576, False), (-2.7424268446190654, False), (-2.7667356941498733, False), (-2.7851477923396963, False), (-2.787145373528966, False), (-2.7893285791152196, False), (-2.7935669402502907, False), (-2.7936597080265266, False), (-2.8119998054444086, False), (-2.81862067070448, False), (-2.8210132305060047, False), (-2.8275434877465755, False), (-2.881619618024036, False), (-2.8817705809378604, False), (-2.8888790274186427, False), (-2.890885936789966, False), (-2.8986316624796724, False), (-2.941759547451118, False), (-2.9649338626062516, False), (-2.996917304123262, False), (-3.0410545357863743, False), (-3.0410881530741576, False), (-3.043895523036807, False), (-3.0659130914067414, False), (-3.200102072958202, False), (-3.278733412718966, False), (-3.309379143357375, False), (-3.311700277684237, False), (-3.3196357740600644, False), (-3.320924965669385, False), (-3.370056075319684, False), (-3.378300629126202, False), (-3.3873922930626814, False), (-3.426479354670505, False), (-3.4344318640337006, False), (-3.447156031921008, False), (-3.48311574492906, False), (-3.5056359320006494, False), (-3.5170444601065953, False), (-3.52451134450975, False), (-3.527812039542597, False), (-3.527919834916838, False), (-3.534492089719588, False), (-3.565314207362008, False), (-3.572045927145758, False), (-3.5985742808168464, False), (-3.620963387935, False), (-3.644070538318951, True), (-3.679777517963801, False), (-3.6988264157112805, False), (-3.741131493564024, False), (-3.762196216300345, False), (-3.832428126849643, False), (-3.883506394489693, False), (-3.898613346605582, False), (-3.9597134104566467, False), (-3.9752424489364206, False), (-4.070826071794286, False), (-4.092927193314985, False), (-4.188596534704666, False), (-4.215714414008602, False), (-4.280518818282889, False), (-4.358287675133794, False), (-4.506986436128561, False), (-4.507615189492087, False), (-4.513325063109716, False), (-4.557015784355493, False), (-4.760999958511076, False), (-4.851814348319181, False), (-4.898081317318941, False), (-4.936464675466847, False), (-4.936620733286103, False), (-4.969286350247987, False), (-5.15956968108973, False), (-5.275647079800404, False), (-5.281206733279072, False), (-5.313375856744507, False), (-5.53447749009124, False), (-5.643022597263824, False), (-5.7746987451715315, False), (-6.019253301748577, False), (-6.045457936220422, False), (-6.049290403033954, False), (-6.141082438407262, False), (-6.146900600594105, False), (-6.208858501042146, False), (-6.322421021476116, False), (-6.4218046427478646, False), (-6.734000608749979, False), (-7.005423296237225, False), (-7.006319710114822, False), (-7.026240294404799, False), (-7.06563815875756, False), (-7.215911848576557, True), (-7.308169392662952, False), (-7.308460325315547, False), (-7.395510153391498, False), (-7.602132329420346, False), (-8.041399799999773, False), (-8.136667280668485, False), (-8.401868506135795, False), (-8.602327381387656, False), (-9.305551264301561, False), (-9.371474153578868, False), ...]
sortedlabels = [x[1] for x in scoreslabels]
sortedlabels
[False, False, False, False, False, True, True, False, False, True, False, True, True, False, False, False, False, False, False, True, False, False, False, False, True, False, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, True, False, False, False, False, False, True, False, False, True, False, False, True, False, False, True, False, False, False, False, True, False, False, False, False, False, False, False, True, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, True, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, ...]
# precision
retrieved = sum(pred)
relevant = sum(ytest)
intersection = sum([y and p for y,p in zip(ytest,pred)])
intersection / retrieved
0.1407766990291262
# recall
intersection / relevant
0.7073170731707317
# precision at 10
sum(sortedlabels[:10]) / 10
0.3