How to use test_index method in locust
Best Python code snippet using locust
cross_val.py
Source:cross_val.py 
1"""2Utilities for cross validation.3taken from scikits.learn4# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,5# Gael Varoquaux <gael.varoquaux@normalesup.org>6# License: BSD Style.7# $Id$8changes to code by josef-pktd:9 - docstring formatting: underlines of headers10"""11from statsmodels.compat.python import range, lrange12import numpy as np13from itertools import combinations14################################################################################15class LeaveOneOut(object):16 """17 Leave-One-Out cross validation iterator:18 Provides train/test indexes to split data in train test sets19 """20 def __init__(self, n):21 """22 Leave-One-Out cross validation iterator:23 Provides train/test indexes to split data in train test sets24 Parameters25 ----------26 n: int27 Total number of elements28 Examples29 --------30 >>> from scikits.learn import cross_val31 >>> X = [[1, 2], [3, 4]]32 >>> y = [1, 2]33 >>> loo = cross_val.LeaveOneOut(2)34 >>> for train_index, test_index in loo:35 ... print "TRAIN:", train_index, "TEST:", test_index36 ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y)37 ... print X_train, X_test, y_train, y_test38 TRAIN: [False True] TEST: [ True False]39 [[3 4]] [[1 2]] [2] [1]40 TRAIN: [ True False] TEST: [False True]41 [[1 2]] [[3 4]] [1] [2]42 """43 self.n = n44 def __iter__(self):45 n = self.n46 for i in range(n):47 test_index = np.zeros(n, dtype=np.bool)48 test_index[i] = True49 train_index = np.logical_not(test_index)50 yield train_index, test_index51 def __repr__(self):52 return '%s.%s(n=%i)' % (self.__class__.__module__,53 self.__class__.__name__,54 self.n,55 )56################################################################################57class LeavePOut(object):58 """59 Leave-P-Out cross validation iterator:60 Provides train/test indexes to split data in train test sets61 """62 def __init__(self, n, p):63 """64 Leave-P-Out cross validation iterator:65 Provides train/test indexes to split data in train test sets66 Parameters67 ----------68 n: int69 Total number of elements70 p: int71 Size test sets72 Examples73 --------74 >>> from scikits.learn import cross_val75 >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]76 >>> y = [1, 2, 3, 4]77 >>> lpo = cross_val.LeavePOut(4, 2)78 >>> for train_index, test_index in lpo:79 ... print "TRAIN:", train_index, "TEST:", test_index80 ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y)81 TRAIN: [False False True True] TEST: [ True True False False]82 TRAIN: [False True False True] TEST: [ True False True False]83 TRAIN: [False True True False] TEST: [ True False False True]84 TRAIN: [ True False False True] TEST: [False True True False]85 TRAIN: [ True False True False] TEST: [False True False True]86 TRAIN: [ True True False False] TEST: [False False True True]87 """88 self.n = n89 self.p = p90 def __iter__(self):91 n = self.n92 p = self.p93 comb = combinations(lrange(n), p)94 for idx in comb:95 test_index = np.zeros(n, dtype=np.bool)96 test_index[np.array(idx)] = True97 train_index = np.logical_not(test_index)98 yield train_index, test_index99 def __repr__(self):100 return '%s.%s(n=%i, p=%i)' % (101 self.__class__.__module__,102 self.__class__.__name__,103 self.n,104 self.p,105 )106################################################################################107class KFold(object):108 """109 K-Folds cross validation iterator:110 Provides train/test indexes to split data in train test sets111 """112 def __init__(self, n, k):113 """114 K-Folds cross validation iterator:115 Provides train/test indexes to split data in train test sets116 Parameters117 ----------118 n: int119 Total number of elements120 k: int121 number of folds122 Examples123 --------124 >>> from scikits.learn import cross_val125 >>> X = [[1, 2], [3, 4], [1, 2], [3, 4]]126 >>> y = [1, 2, 3, 4]127 >>> kf = cross_val.KFold(4, k=2)128 >>> for train_index, test_index in kf:129 ... print "TRAIN:", train_index, "TEST:", test_index130 ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y)131 TRAIN: [False False True True] TEST: [ True True False False]132 TRAIN: [ True True False False] TEST: [False False True True]133 Notes134 -----135 All the folds have size trunc(n/k), the last one has the complementary136 """137 assert k>0, ValueError('cannot have k below 1')138 assert k<n, ValueError('cannot have k=%d greater than %d'% (k, n))139 self.n = n140 self.k = k141 def __iter__(self):142 n = self.n143 k = self.k144 j = int(np.ceil(n/k))145 for i in range(k):146 test_index = np.zeros(n, dtype=np.bool)147 if i<k-1:148 test_index[i*j:(i+1)*j] = True149 else:150 test_index[i*j:] = True151 train_index = np.logical_not(test_index)152 yield train_index, test_index153 def __repr__(self):154 return '%s.%s(n=%i, k=%i)' % (155 self.__class__.__module__,156 self.__class__.__name__,157 self.n,158 self.k,159 )160################################################################################161class LeaveOneLabelOut(object):162 """163 Leave-One-Label_Out cross-validation iterator:164 Provides train/test indexes to split data in train test sets165 """166 def __init__(self, labels):167 """168 Leave-One-Label_Out cross validation:169 Provides train/test indexes to split data in train test sets170 Parameters171 ----------172 labels : list173 List of labels174 Examples175 --------176 >>> from scikits.learn import cross_val177 >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]178 >>> y = [1, 2, 1, 2]179 >>> labels = [1, 1, 2, 2]180 >>> lol = cross_val.LeaveOneLabelOut(labels)181 >>> for train_index, test_index in lol:182 ... print "TRAIN:", train_index, "TEST:", test_index183 ... X_train, X_test, y_train, y_test = cross_val.split(train_index, \184 test_index, X, y)185 ... print X_train, X_test, y_train, y_test186 TRAIN: [False False True True] TEST: [ True True False False]187 [[5 6]188 [7 8]] [[1 2]189 [3 4]] [1 2] [1 2]190 TRAIN: [ True True False False] TEST: [False False True True]191 [[1 2]192 [3 4]] [[5 6]193 [7 8]] [1 2] [1 2]194 """195 self.labels = labels196 def __iter__(self):197 # We make a copy here to avoid side-effects during iteration198 labels = np.array(self.labels, copy=True)199 for i in np.unique(labels):200 test_index = np.zeros(len(labels), dtype=np.bool)201 test_index[labels==i] = True202 train_index = np.logical_not(test_index)203 yield train_index, test_index204 def __repr__(self):205 return '%s.%s(labels=%s)' % (206 self.__class__.__module__,207 self.__class__.__name__,208 self.labels,209 )210def split(train_indexes, test_indexes, *args):211 """212 For each arg return a train and test subsets defined by indexes provided213 in train_indexes and test_indexes214 """215 ret = []216 for arg in args:217 arg = np.asanyarray(arg)218 arg_train = arg[train_indexes]219 arg_test = arg[test_indexes]220 ret.append(arg_train)221 ret.append(arg_test)222 return ret223'''224 >>> cv = cross_val.LeaveOneLabelOut(X, y) # y making y optional and225possible to add other arrays of the same shape[0] too226 >>> for X_train, y_train, X_test, y_test in cv:227 ... print np.sqrt((model.fit(X_train, y_train).predict(X_test)228- y_test) ** 2).mean())229'''230################################################################################231#below: Author: josef-pktd232class KStepAhead(object):233 """234 KStepAhead cross validation iterator:235 Provides fit/test indexes to split data in sequential sets236 """237 def __init__(self, n, k=1, start=None, kall=True, return_slice=True):238 """239 KStepAhead cross validation iterator:240 Provides train/test indexes to split data in train test sets241 Parameters242 ----------243 n: int244 Total number of elements245 k : int246 number of steps ahead247 start : int248 initial size of data for fitting249 kall : boolean250 if true. all values for up to k-step ahead are included in the test index.251 If false, then only the k-th step ahead value is returnd252 Notes253 -----254 I don't think this is really useful, because it can be done with255 a very simple loop instead.256 Useful as a plugin, but it could return slices instead for faster array access.257 Examples258 --------259 >>> from scikits.learn import cross_val260 >>> X = [[1, 2], [3, 4]]261 >>> y = [1, 2]262 >>> loo = cross_val.LeaveOneOut(2)263 >>> for train_index, test_index in loo:264 ... print "TRAIN:", train_index, "TEST:", test_index265 ... X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y)266 ... print X_train, X_test, y_train, y_test267 TRAIN: [False True] TEST: [ True False]268 [[3 4]] [[1 2]] [2] [1]269 TRAIN: [ True False] TEST: [False True]270 [[1 2]] [[3 4]] [1] [2]271 """272 self.n = n273 self.k = k274 if start is None:275 start = int(np.trunc(n*0.25)) # pick something arbitrary276 self.start = start277 self.kall = kall278 self.return_slice = return_slice279 def __iter__(self):280 n = self.n281 k = self.k282 start = self.start283 if self.return_slice:284 for i in range(start, n-k):285 train_slice = slice(None, i, None)286 if self.kall:287 test_slice = slice(i, i+k)288 else:289 test_slice = slice(i+k-1, i+k)290 yield train_slice, test_slice291 else: #for compatibility with other iterators292 for i in range(start, n-k):293 train_index = np.zeros(n, dtype=np.bool)294 train_index[:i] = True295 test_index = np.zeros(n, dtype=np.bool)296 if self.kall:297 test_index[i:i+k] = True # np.logical_not(test_index)298 else:299 test_index[i+k-1:i+k] = True300 #or faster to return np.arange(i,i+k) ?301 #returning slice should be faster in this case302 yield train_index, test_index303 def __repr__(self):304 return '%s.%s(n=%i)' % (self.__class__.__module__,305 self.__class__.__name__,306 self.n,...
entrenar.py
Source:entrenar.py
1from utilities import *2from scipy.sparse import coo_matrix, hstack3from sklearn.tree import DecisionTreeClassifier4from sklearn.cross_validation import cross_val_score, KFold5from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB6from sklearn.neighbors import KNeighborsClassifier7from sklearn import svm8from sklearn import ensemble9from sklearn.grid_search import GridSearchCV10from sklearn.metrics import fbeta_score, make_scorer11import pickle12# Leo los mails (poner los paths correctos).13ham_txt= json.load(open('../../dataset_json/ham_dev.json'))14spam_txt= json.load(open('../../dataset_json/spam_dev.json'))15# Pongo todos los mails en minusculas16ham_txt = map(lambda x: x.lower(), ham_txt)17spam_txt = map(lambda x: x.lower(), spam_txt)18# Armo el data frame19df = pd.DataFrame(ham_txt+spam_txt, columns=['text'])20# 0 = ham, 1 = spam (es necesario binarizar para poder usar el score f_0.5)21df['class'] = [0 for _ in range(len(ham_txt))]+[1 for _ in range(len(spam_txt))]22print "Lei json y arme data frame"23# Extraigo atributos simples24# Agrego feature que clasifica los mails segun tienen o no html25HTML = coo_matrix(map(hasHTML, df.text)).transpose()26#) Agrego feature que clasifica los mails segun tienen o no subject27SUBJ = coo_matrix(map(hasSubject, df.text)).transpose()28# Longitud del mail.29LEN = coo_matrix(map(len, df.text)).transpose()30# Cantidad de espacios en el mail.31SPACES = coo_matrix(map(count_spaces, df.text)).transpose()32print "Clasifique por atributos simples"33vectorizer = obtenerVectorizer()34word_freq_matrix = vectorizer.transform(df.text)35print "Arme matriz"36X = hstack([HTML, SUBJ, LEN, SPACES, word_freq_matrix]).toarray()37y = df['class']38# Definimos F-Beta score con Beta=0.539# (favorecemos precision sobre recall)40f05_scorer = make_scorer(fbeta_score, beta=0.5)41print "Defino clasificadores"42# Decision Tree43'''44print "Decision Tree"45clf = DecisionTreeClassifier(max_features = None, max_leaf_nodes = 100, min_samples_split = 2, criterion = 'gini')46kf = KFold(72000, n_folds=10, shuffle=True)47best_score = 048best_clf = 049for train_index, test_index in kf:50 print("TRAIN:", train_index, "TEST:", test_index)51 X_train, X_test = X[train_index], X[test_index]52 y_train, y_test = y[train_index], y[test_index]53 clf.fit(X_train, y_train)54 score = f05_scorer(clf, X_test, y_test)55 if score > best_score:56 best_clf = clf57 best_score = score58fout = open('dectree.pickle','w')59pickle.dump(best_clf,fout)60fout.close()61# Random Forest62print "Random Forest"63clf = ensemble.RandomForestClassifier(max_features = 0.5, max_leaf_nodes = None, min_samples_split = 4, criterion = 'gini', n_estimators = 20, n_jobs=4)64kf = KFold(72000, n_folds=10, shuffle=True)65best_score = 066best_clf = 067for train_index, test_index in kf:68 print("TRAIN:", train_index, "TEST:", test_index)69 X_train, X_test = X[train_index], X[test_index]70 y_train, y_test = y[train_index], y[test_index]71 clf.fit(X_train, y_train)72 score = f05_scorer(clf, X_test, y_test)73 if score > best_score:74 best_clf = clf75 best_score = score76fout = open('ranfor.pickle','w')77pickle.dump(clf,fout)78fout.close()79'''80# SVM81print "SVM"82clf = svm.SVC(kernel = 'rbf', C = 1, gamma = 1.0)83kf = KFold(72000, n_folds=3, shuffle=True)84best_score = 085best_clf = 086for train_index, test_index in kf:87 print("TRAIN:", train_index, "TEST:", test_index)88 X_train, X_test = X[train_index], X[test_index]89 y_train, y_test = y[train_index], y[test_index]90 clf.fit(X_train, y_train)91 score = f05_scorer(clf, X_test, y_test)92 if score > best_score:93 best_clf = clf94 best_score = score95fout = open('svm.pickle','w')96pickle.dump(clf,fout)97fout.close()98'''99# Naive Bayes100print "Gaussian NB"101clf = GaussianNB()102kf = KFold(72000, n_folds=10, shuffle=True)103best_score = 0104best_clf = 0105for train_index, test_index in kf:106 print("TRAIN:", train_index, "TEST:", test_index)107 X_train, X_test = X[train_index], X[test_index]108 y_train, y_test = y[train_index], y[test_index]109 clf.fit(X_train, y_train)110 score = f05_scorer(clf, X_test, y_test)111 if score > best_score:112 best_clf = clf113 best_score = score114fout = open('gaussianNB.pickle','w')115pickle.dump(clf,fout)116fout.close()117#########################118print "Multinomial NB"119clf = MultinomialNB(alpha = 0.25, fit_prior = False)120kf = KFold(72000, n_folds=10, shuffle=True)121best_score = 0122best_clf = 0123for train_index, test_index in kf:124 print("TRAIN:", train_index, "TEST:", test_index)125 X_train, X_test = X[train_index], X[test_index]126 y_train, y_test = y[train_index], y[test_index]127 clf.fit(X_train, y_train)128 score = f05_scorer(clf, X_test, y_test)129 if score > best_score:130 best_clf = clf131 best_score = score132fout = open('multinomialNB.pickle','w')133pickle.dump(clf,fout)134fout.close()135#######################136print "Bernoulli NB"137clf = BernoulliNB(binarize = 0.0, alpha = 0.25, fit_prior = False)138kf = KFold(72000, n_folds=10, shuffle=True)139best_score = 0140best_clf = 0141for train_index, test_index in kf:142 print("TRAIN:", train_index, "TEST:", test_index)143 X_train, X_test = X[train_index], X[test_index]144 y_train, y_test = y[train_index], y[test_index]145 clf.fit(X_train, y_train)146 score = f05_scorer(clf, X_test, y_test)147 if score > best_score:148 best_clf = clf149 best_score = score150fout = open('bernoulliNB.pickle','w')151pickle.dump(clf,fout)152fout.close()153# KNN154print "KNN"155clf = KNeighborsClassifier(n_neighbors = 1, weights = 'uniform', leaf_size = 15, algorithm = 'kd_tree')156kf = KFold(72000, n_folds=10, shuffle=True)157best_score = 0158best_clf = 0159for train_index, test_index in kf:160 print("TRAIN:", train_index, "TEST:", test_index)161 X_train, X_test = X[train_index], X[test_index]162 y_train, y_test = y[train_index], y[test_index]163 clf.fit(X_train, y_train)164 score = f05_scorer(clf, X_test, y_test)165 if score > best_score:166 best_clf = clf167 best_score = score168fout = open('knn.pickle','w')169pickle.dump(clf,fout)170fout.close()...
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