🌐 Mailman 3 December 2016 - scikit-learn - python.org

Scikit Learn Random Classifier - TPR and FPR plotted on matplotlib
by Debabrata Ghosh Dec. 14, 2016

Dec. 14, 2016

Hi All, I have run scikit-learn Random Forest Classifier algorithm against a dataset and here is my TPR and FPR against various thresholds: [image: Inline image 1] Further I have plotted the above values in matplotlib and am getting a very low AUC. Here is my matplotlib code. Can I understand the interpretation of the graph from you please.Is my model Ok or is there something wrong ? Appreciate for a quick response please. import matplotlib.pyplot as plt import numpy as np from sklearn import metrics plt.title('Receiver Operating Characteristic') plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') fpr = [0.0002337345394340,0.0001924870472260,0.0001626973851550,0.0000950977673794, 0.0000721826427097,0.0000538505429739,0.0000389557119386,0.0000263523933702, 0.0000137490748018] tpr = [0.19673638244100000000,0.18984141576600000000,0.18122270742400000000, 0.17055510860800000000,0.16434892541100000000,0.15789473684200000000, 0.15134451850100000000,0.14410480349300000000,0.13238336014700000000] roc_auc = metrics.auc(fpr, tpr) plt.plot([0, 1], [0, 1],'r--') plt.plot(fpr, tpr, 'bo-', label = 'AUC = %0.9f' % roc_auc) plt.legend(loc = 'lower right') plt.show() [image: Inline image 2]

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Renaming subject lines if you get a digest
by Dale T Smith Dec. 14, 2016

Dec. 14, 2016

Please rename subjects if you use the digest – now the thread is not complete in the archive. Others will have a harder time benefitting from answers. __________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith(a)macys.com From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org] On Behalf Of Graham Arthur Mackenzie Sent: Tuesday, December 13, 2016 5:02 PM To: scikit-learn(a)python.org Subject: Re: [scikit-learn] scikit-learn Digest, Vol 9, Issue 42 ⚠ EXT MSG: Thanks for the speedy and helpful responses! Actually, the thrust of my question was, "I'm assuming the fit() method for all three modules work the same way, so how come the example code for DTs differs from NB, SVMs?" Since you seem to be saying that it'll work either way, I'm assuming there's no real reason behind it, which was my suspicion, but just wanted to have it confirmed, as the inconsistency was conspicuous. Thanks! GAM ps, My apologies if this is the improper way to respond to responses. I am receiving the Digest rather than individual messages, so this was the best I could think to do... On Tue, Dec 13, 2016 at 12:38 PM, <scikit-learn-request(a)python.org<mailto:scikit-learn-request@python.org>> wrote: Send scikit-learn mailing list submissions to scikit-learn(a)python.org<mailto:scikit-learn@python.org> To subscribe or unsubscribe via the World Wide Web, visit https://mail.python.org/mailman/listinfo/scikit-learn or, via email, send a message with subject or body 'help' to scikit-learn-request(a)python.org<mailto:scikit-learn-request@python.org> You can reach the person managing the list at scikit-learn-owner(a)python.org<mailto:scikit-learn-owner@python.org> When replying, please edit your Subject line so it is more specific than "Re: Contents of scikit-learn digest..." Today's Topics: 1. Why do DTs have a different fit protocol than NB and SVMs? (Graham Arthur Mackenzie) 2. Re: Why do DTs have a different fit protocol than NB and SVMs? (Jacob Schreiber) 3. Re: Why do DTs have a different fit protocol than NB and SVMs? (Stuart Reynolds) 4. Re: Why do DTs have a different fit protocol than NB and SVMs? (Vlad Niculae) ---------------------------------------------------------------------- Message: 1 Date: Tue, 13 Dec 2016 12:14:43 -0800 From: Graham Arthur Mackenzie <graham.arthur.mackenzie(a)gmail.com<mailto:graham.arthur.mackenzie@gmail.com>> To: scikit-learn(a)python.org<mailto:scikit-learn@python.org> Subject: [scikit-learn] Why do DTs have a different fit protocol than NB and SVMs? Message-ID: <CAGUvBb72XoZgcuiUAxucmZ9FckirZLLnv3Zm4aw5tCSF-6GHtw(a)mail.gmail.com<mailto:CAGUvBb72XoZgcuiUAxucmZ9FckirZLLnv3Zm4aw5tCSF-6GHtw@mail.gmail.com>> Content-Type: text/plain; charset="utf-8" Hello All, I hope this is the right way to ask a question about documentation. In the doc for Decision Trees <http://scikit-learn.org/stable/modules/tree.html#tree>, the fit statement is assigned back to the classifier: clf = clf.fit(X, Y) Whereas, for Naive Bayes <http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.Gaussi…> and Support Vector Machines <http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>, it's just: clf.fit(X, Y) I assumed this was a typo, but thought I should try and verify such before proceeding under that assumption. I appreciate any feedback you can provide. Thank You and Be Well, Graham -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161213/8bbeacdb…> ------------------------------ Message: 2 Date: Tue, 13 Dec 2016 12:23:00 -0800 From: Jacob Schreiber <jmschreiber91(a)gmail.com<mailto:jmschreiber91@gmail.com>> To: Scikit-learn user and developer mailing list <scikit-learn(a)python.org<mailto:scikit-learn@python.org>> Subject: Re: [scikit-learn] Why do DTs have a different fit protocol than NB and SVMs? Message-ID: <CA+ad8Ev87kFsDfkX8xUsoLFpa9x=W3Kr_wG3ue47WosO=JoZtw(a)mail.gmail.com<mailto:JoZtw@mail.gmail.com>> Content-Type: text/plain; charset="utf-8" The fit method returns the object itself, so regardless of which way you do it, it will work. The reason the fit method returns itself is so that you can chain methods, like "preds = clf.fit(X, y).predict(X)" On Tue, Dec 13, 2016 at 12:14 PM, Graham Arthur Mackenzie < graham.arthur.mackenzie(a)gmail.com<mailto:graham.arthur.mackenzie@gmail.com>> wrote: > Hello All, > > I hope this is the right way to ask a question about documentation. > > In the doc for Decision Trees > <http://scikit-learn.org/stable/modules/tree.html#tree>, the fit > statement is assigned back to the classifier: > > clf = clf.fit(X, Y) > > Whereas, for Naive Bayes > <http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.Gaussi…> > and Support Vector Machines > <http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>, > it's just: > > clf.fit(X, Y) > > I assumed this was a typo, but thought I should try and verify such before > proceeding under that assumption. I appreciate any feedback you can provide. > > Thank You and Be Well, > Graham > > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org<mailto:scikit-learn@python.org> > https://mail.python.org/mailman/listinfo/scikit-learn > > -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161213/08e2e7c2…> ------------------------------ Message: 3 Date: Tue, 13 Dec 2016 12:33:48 -0800 From: Stuart Reynolds <stuart(a)stuartreynolds.net<mailto:stuart@stuartreynolds.net>> To: Scikit-learn user and developer mailing list <scikit-learn(a)python.org<mailto:scikit-learn@python.org>> Subject: Re: [scikit-learn] Why do DTs have a different fit protocol than NB and SVMs? Message-ID: <CAAy-kdnQ3t3gH=BbjwD_n7Zr+9gnV-X34Eke=fc=ysiW6zidrw(a)mail.gmail.com<mailto:ysiW6zidrw@mail.gmail.com>> Content-Type: text/plain; charset="utf-8" I think he's asking whether returning the model is part of the API (i.e. is it a bug that SVM and NB don't return self?). On Tue, Dec 13, 2016 at 12:23 PM, Jacob Schreiber <jmschreiber91(a)gmail.com<mailto:jmschreiber91@gmail.com>> wrote: > The fit method returns the object itself, so regardless of which way you > do it, it will work. The reason the fit method returns itself is so that > you can chain methods, like "preds = clf.fit(X, y).predict(X)" > > On Tue, Dec 13, 2016 at 12:14 PM, Graham Arthur Mackenzie < > graham.arthur.mackenzie(a)gmail.com<mailto:graham.arthur.mackenzie@gmail.com>> wrote: > >> Hello All, >> >> I hope this is the right way to ask a question about documentation. >> >> In the doc for Decision Trees >> <http://scikit-learn.org/stable/modules/tree.html#tree>, the fit >> statement is assigned back to the classifier: >> >> clf = clf.fit(X, Y) >> >> Whereas, for Naive Bayes >> <http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.Gaussi…> >> and Support Vector Machines >> <http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>, >> it's just: >> >> clf.fit(X, Y) >> >> I assumed this was a typo, but thought I should try and verify such >> before proceeding under that assumption. I appreciate any feedback you can >> provide. >> >> Thank You and Be Well, >> Graham >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn(a)python.org<mailto:scikit-learn@python.org> >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org<mailto:scikit-learn@python.org> > https://mail.python.org/mailman/listinfo/scikit-learn > > -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161213/2d6dd8e7…> ------------------------------ Message: 4 Date: Tue, 13 Dec 2016 15:38:42 -0500 From: Vlad Niculae <zephyr14(a)gmail.com<mailto:zephyr14@gmail.com>> To: Scikit-learn user and developer mailing list <scikit-learn(a)python.org<mailto:scikit-learn@python.org>>, Stuart Reynolds <stuart(a)stuartreynolds.net<mailto:stuart@stuartreynolds.net>> Subject: Re: [scikit-learn] Why do DTs have a different fit protocol than NB and SVMs? Message-ID: <94FAD0C4-CE95-4A1E-9079-418962D2B9C0(a)gmail.com<mailto:94FAD0C4-CE95-4A1E-9079-418962D2B9C0@gmail.com>> Content-Type: text/plain; charset="utf-8" It is part of the API and enforced with tests, if I'm not mistaken. So you could use either form with all sklearn estimators. Vlad On December 13, 2016 3:33:48 PM EST, Stuart Reynolds <stuart(a)stuartreynolds.net<mailto:stuart@stuartreynolds.net>> wrote: >I think he's asking whether returning the model is part of the API >(i.e. is >it a bug that SVM and NB don't return self?). > >On Tue, Dec 13, 2016 at 12:23 PM, Jacob Schreiber ><jmschreiber91(a)gmail.com<mailto:jmschreiber91@gmail.com>> >wrote: > >> The fit method returns the object itself, so regardless of which way >you >> do it, it will work. The reason the fit method returns itself is so >that >> you can chain methods, like "preds = clf.fit(X, y).predict(X)" >> >> On Tue, Dec 13, 2016 at 12:14 PM, Graham Arthur Mackenzie < >> graham.arthur.mackenzie(a)gmail.com<mailto:graham.arthur.mackenzie@gmail.com>> wrote: >> >>> Hello All, >>> >>> I hope this is the right way to ask a question about documentation. >>> >>> In the doc for Decision Trees >>> <http://scikit-learn.org/stable/modules/tree.html#tree>, the fit >>> statement is assigned back to the classifier: >>> >>> clf = clf.fit(X, Y) >>> >>> Whereas, for Naive Bayes >>> ><http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.Gaussi…> >>> and Support Vector Machines >>> ><http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>, >>> it's just: >>> >>> clf.fit(X, Y) >>> >>> I assumed this was a typo, but thought I should try and verify such >>> before proceeding under that assumption. I appreciate any feedback >you can >>> provide. >>> >>> Thank You and Be Well, >>> Graham >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn(a)python.org<mailto:scikit-learn@python.org> >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn(a)python.org<mailto:scikit-learn@python.org> >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > >------------------------------------------------------------------------ > >_______________________________________________ >scikit-learn mailing list >scikit-learn(a)python.org<mailto:scikit-learn@python.org> >https://mail.python.org/mailman/listinfo/scikit-learn -- Sent from my Android device with K-9 Mail. Please excuse my brevity. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161213/45217ea3…> ------------------------------ Subject: Digest Footer _______________________________________________ scikit-learn mailing list scikit-learn(a)python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn ------------------------------ End of scikit-learn Digest, Vol 9, Issue 42 ******************************************* * This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments.

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Re: [scikit-learn] scikit-learn Digest, Vol 9, Issue 42
by Graham Arthur Mackenzie Dec. 13, 2016

Dec. 13, 2016

Thanks for the speedy and helpful responses! Actually, the thrust of my question was, "I'm assuming the fit() method for all three modules work the same way, so how come the example code for DTs differs from NB, SVMs?" Since you seem to be saying that it'll work either way, I'm assuming there's no real reason behind it, which was my suspicion, but just wanted to have it confirmed, as the inconsistency was conspicuous. Thanks! GAM ps, My apologies if this is the improper way to respond to responses. I am receiving the Digest rather than individual messages, so this was the best I could think to do... On Tue, Dec 13, 2016 at 12:38 PM, <scikit-learn-request(a)python.org> wrote: > Send scikit-learn mailing list submissions to > scikit-learn(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-request(a)python.org > > You can reach the person managing the list at > scikit-learn-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. Why do DTs have a different fit protocol than NB and SVMs? > (Graham Arthur Mackenzie) > 2. Re: Why do DTs have a different fit protocol than NB and > SVMs? (Jacob Schreiber) > 3. Re: Why do DTs have a different fit protocol than NB and > SVMs? (Stuart Reynolds) > 4. Re: Why do DTs have a different fit protocol than NB and > SVMs? (Vlad Niculae) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 13 Dec 2016 12:14:43 -0800 > From: Graham Arthur Mackenzie <graham.arthur.mackenzie(a)gmail.com> > To: scikit-learn(a)python.org > Subject: [scikit-learn] Why do DTs have a different fit protocol than > NB and SVMs? > Message-ID: > <CAGUvBb72XoZgcuiUAxucmZ9FckirZLLnv3Zm4aw5tCSF-6GHtw(a)mail.gm > ail.com> > Content-Type: text/plain; charset="utf-8" > > Hello All, > > I hope this is the right way to ask a question about documentation. > > In the doc for Decision Trees > <http://scikit-learn.org/stable/modules/tree.html#tree>, the fit statement > is assigned back to the classifier: > > clf = clf.fit(X, Y) > > Whereas, for Naive Bayes > <http://scikit-learn.org/stable/modules/generated/sklearn. > naive_bayes.GaussianNB.html> > and Support Vector Machines > <http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>, > it's just: > > clf.fit(X, Y) > > I assumed this was a typo, but thought I should try and verify such before > proceeding under that assumption. I appreciate any feedback you can > provide. > > Thank You and Be Well, > Graham >

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Why do DTs have a different fit protocol than NB and SVMs?
by Graham Arthur Mackenzie Dec. 13, 2016

Dec. 13, 2016

Hello All, I hope this is the right way to ask a question about documentation. In the doc for Decision Trees <http://scikit-learn.org/stable/modules/tree.html#tree>, the fit statement is assigned back to the classifier: clf = clf.fit(X, Y) Whereas, for Naive Bayes <http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.Gaussi…> and Support Vector Machines <http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>, it's just: clf.fit(X, Y) I assumed this was a typo, but thought I should try and verify such before proceeding under that assumption. I appreciate any feedback you can provide. Thank You and Be Well, Graham

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svm low-api gives bad prediction results
by fulean Dec. 13, 2016

Dec. 13, 2016

hi all, I want to export the svm parameters and apply it in a c++ svm implementation at https://github.com/yctung/AndroidLibSvm. after grid search, SVC with C=1.0,gamma =10.0 get 92% accuracy, but unfortunately, SVC model does't contains the paratmeters needed by the c++ model, which take as input the low-level svm params, ie sv_coef,probA ..., the returns of low level svm api ,'libsvm.fit', match the requirement, but the prediction result is different from SVC model: The code : model = libsvm.fit(X_data.astype(np.float64),Y_data.astype(np.float64),svm_type=0,kernel='rbf',C = 1.0,gamm= 10.0) pred = libsvm.predict(X_data.astype(np.float64), *model,kernel='rbf') print "hello mean " + sa tr(np.mean(pred == Y_data)) # result "hello mean 0.570588235294" can somebody give some suggestion on this problem,thanks!

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Re: [scikit-learn] [Scikit-learn-general] Getting involved(beginner)
by Andreas Mueller Dec. 13, 2016

Dec. 13, 2016

Hi. Sorry, IRC is pretty dead. You can try gitter, though it's also not super busy. Instruction to contributing are here: http://scikit-learn.org/dev/developers/contributing.html Andy On 12/13/2016 05:57 AM, piyush goel wrote: > Dear developer, > > I am Piyush an Electrical and Electronics Engineering student from > DTU,India. > I'm new to open source and the Python foundation as well, but I want > to get involved. I tried contacting the org through irc but wasn't > able to talk to anyone, so I'm writing this mail. > Please guide me through the steps of contributing to sci kit. I don't > have much experience in machine learning but I wish to learn while > contributing to the project, please tell me if it's a good idea. If > possible can you assign me some simple issues. > > Thanks. > > > ------------------------------------------------------------------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, SlashDot.org! http://sdm.link/slashdot > > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general

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SMOTE-ENN in Imbalanced-learn package
by Mamun Rashid Dec. 12, 2016

Dec. 12, 2016

Hi All, Not sure if questions regarding the contributory packages are answered here. Just trying my luck. I am have a seriously imbalanced classification problem. I am trying to use SMOTE+ENN oversampling and undersampling method to oversample my minority class and oversample my majority class. ======== from sklearn.datasets import make_classification from imblearn.combine import SMOTEENN sm = SMOTEENN() X, y = make_classification(n_classes=2, class_sep=2, weights=[0.2, 0.8], n_informative=1, n_redundant=1, flip_y=0, n_features=3, n_clusters_per_class=1, n_samples=50, random_state=10) X_df = pd.DataFrame(X) X_resampled, y_resampled = sm.fit_sample(X_df, y) ========= I understand that SMOTE returns a resampled data matrix i.e. X_resampled. I was wondering if there is a direct way to retrieve the indexes of the original data observations ? Thanks in advance. Best Regards and Seasons Greetings., Mamun

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ensemble method within splearn
by Debabrata Ghosh Dec. 9, 2016

Dec. 9, 2016

Hi, I have downloaded sparkit-learn from https://pypi.python.org/pypi/sparkit-learn but it doesn't have the ensemble method. Please can you suggest the solution for the same. It's urgent please. Thanks, Debu

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Clustering information from one file
by Fabio Santos Dec. 9, 2016

Dec. 9, 2016

Hi all, My name is Fábio and I'm new in scikit, and I trying to cluster information from one file with python script (i fount on web). But i saw that the output had problem with numbers...See: Script# import clickimport reimport numpyimport random from collections import defaultdict from sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.cluster import KMeans @click.command()@click.argument('filename')(a)click.option('--clusters', default=50, help='Number of clusters')(a)click.option('--sample', default=400, help='Number of samples to print')def cluster_lines(filename, clusters, sample): lines = numpy.array(list(_get_lines(filename))) doc_feat = TfidfVectorizer().fit_transform(lines) km = KMeans(clusters).fit(doc_feat) k = 0 clusters = defaultdict(list) for i in km.labels_: clusters[i].append(lines[k]) k += 1 s_clusters = sorted(clusters.values(), key=lambda l: -len(l)) for cluster in s_clusters: print 'Cluster [%s]:' % len(cluster) if len(cluster) > sample: cluster = random.sample(cluster, sample) for line in cluster: print line print '--------' def _clean_line(line): line = line.strip().lower() line = re.sub('\d+', '(N)', line) return line def _get_lines(filename): for line in open(filename).readlines(): yield _clean_line(line) if __name__ == '__main__': cluster_lines() output [root@vmcaiosyscolprod01 71001492]# python Cluster-LearnMachine.py DataSets/ospf.teste3Cluster [7]:"rjbotaa max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area""rjmteab max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area""rjmckaa max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area""rjdqcaa max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area""rjdqcab max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area""rjcenaa max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area""rjcenab max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area"--------Cluster [1]:"rjbotab max-metric router-lsa on-startup log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area"--------Cluster [1]:"rjmteaa ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area"--------Cluster [1]:"rjmckab max-metric router-lsa on-startup ispf log-adjacency-changes detail auto-cost reference-bandwidth timers throttle spf timers throttle lsa timers lsa arrival timers pacing flood passive-interface default maximum-paths mpls ldp sync mpls traffic-eng router-id loopback mpls traffic-eng area"-------- See that the output shown (N) on numbers, and i'm not fount a way to use the big cluster as a template fo fount diference between the bigger cluster and others clusters. How can i do that? Thanks

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Need Urgent help please in resolving JobLibMemoryError
by Debabrata Ghosh Dec. 9, 2016

Dec. 9, 2016

Hi All, Greetings ! I am getting JoblibMemoryError while executing a scikit-learn RandomForestClassifier code. Here is my algorithm in short: from sklearn.ensemble import RandomForestClassifier from sklearn.cross_validation import train_test_split import pandas as pd import numpy as np clf = RandomForestClassifier(n_estimators=5000, n_jobs=1000) clf.fit(p_input_features_train,p_input_labels_train) The dataframe p_input_features contain 134 columns (features) and 5 million rows (observations). The exact *error message* is given below: Executing Random Forest Classifier Traceback (most recent call last): File "/home/user/rf_fold.py", line 43, in <module> clf.fit(p_features_train,p_labels_train) File "/var/opt/ lib/python2.7/site-packages/sklearn/ensemble/forest.py", line 290, in fit for i, t in enumerate(trees)) File "/var/opt/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 810, in __call__ self.retrieve() File "/var/opt/lib /python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 757, in retrieve raise exception sklearn.externals.joblib.my_exceptions.JoblibMemoryError: JoblibMemoryError ___________________________________________________________________________ Multiprocessing exception: ........................................................................... /var/opt/lib/python2.7/site-packages/sklearn/ensemble/forest.py in fit(self=RandomForestClassifier(bootstrap=True, class_wei...te=None, verbose=0, warm_start=False), X=array([[ 0. , 0. , 0. , .... 0. , 0. ]], dtype=float32), y=array([[ 0.], [ 0.], [ 0.], ..., [ 0.], [ 0.], [ 0.]]), sample_weight=None) 285 trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, 286 backend="threading")( 287 delayed(_parallel_build_trees)( 288 t, self, X, y, sample_weight, i, len(trees), 289 verbose=self.verbose, class_weight=self.class_weight) --> 290 for i, t in enumerate(trees)) i = 4999 291 292 # Collect newly grown trees 293 self.estimators_.extend(trees) 294 ........................................................................... Please can you help me to identify a possible resolution to this. Thanks, Debu

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