Sorry, no update, I'm on vacation till October. But later I return to your question, it's not trivial on preprocessing stage.
вт, 15 сент. 2020 г., 9:21 Priya Yadav <[email protected]>: > Hi, > > > > If there in any update, Please let me know. > > > > Thanks > > > > *From:* Priya Yadav > *Sent:* Sunday, September 6, 2020 8:14 PM > *To:* Alexey Zinoviev <[email protected]> > *Cc:* [email protected] > *Subject:* RE: Preprocessing of data to use in Naive-Bayes > > > > Hi Alexey, > > > > I am stuck on the preprocessing step itself as I am not able to find any > api which takes the sentence , reads the tokens and calculate their count > whereas scikit-learn provides the apis out of the box. > > > > I am attaching the sample data that I need to categorize on the basis of > user experience. Please find the python code snippet below: > > > > from sklearn.naive_bayes import MultinomialNB > > from sklearn.feature_extraction.text import CountVectorizer > > classifier = MultinomialNB(); > > vect=CountVectorizer(); > > counts=vect.fit_transform(["pizza was soft, very nice"," good ambience and > excellent service","tool a long time, service needs improvement","toppings > were very less, but bread was excellent"]) ; > > counts=vect.fit_transform(comment); > > targets = ['Good Experience','Good Experience','Bad Experience','Good > Experience']; > > classifier.fit(counts,targets); > > predictComments = [“soft bread, nice toppings”] > > predictData=vect.transform(predictComments); > > predictions = classifier.predict(predictData) > > print(predictions); > > > > > > Thanks, > > Priya > > > > > > *From:* Alexey Zinoviev <[email protected]> > *Sent:* Sunday, September 6, 2020 6:41 PM > *To:* Igor Belyakov <[email protected]> > *Cc:* user <[email protected]> > *Subject:* Re: Preprocessing of data to use in Naive-Bayes > > > > Very interesting case! > > > > We have 3 different implementations for NaiveBayes algorithm > > https://apacheignite.readme.io/docs/naive-bayes > <https://urldefense.proofpoint.com/v2/url?u=https-3A__apacheignite.readme.io_docs_naive-2Dbayes&d=DwMFaQ&c=ObqWq9831a7badpzAhIKIA&r=qixDeHnSzhtciDY_pRHc4x12Ip0suDtJCZ5Ce1zlWfQ&m=s_IECR0VZUJ9ds7ehfpq8i3L0GTFiHRJ3ghViHS6dE8&s=oCy265A-SLfh0-HlWoiLAaoxQoXI4w6qOJ_BgZh66Dg&e=> > > > > I suppose that this is the best for this task > https://apacheignite.readme.io/docs/naive-bayes#discrete-bernoulli-naive-bayes > <https://urldefense.proofpoint.com/v2/url?u=https-3A__apacheignite.readme.io_docs_naive-2Dbayes-23discrete-2Dbernoulli-2Dnaive-2Dbayes&d=DwMFaQ&c=ObqWq9831a7badpzAhIKIA&r=qixDeHnSzhtciDY_pRHc4x12Ip0suDtJCZ5Ce1zlWfQ&m=s_IECR0VZUJ9ds7ehfpq8i3L0GTFiHRJ3ghViHS6dE8&s=S0CrU7joi3OwZA5W7BunClUM8cv-m2HtQziDPhuDtlg&e=> > > Data should be prepared as Vectors in Ignite Cache to start training. > > > > Dear Priya Yadav, could you please provide code or pseudocode with how you > populate your Ignite cache with sentences data, a few sentences will be > useful too. > > Also will be useful, how could you solve this task in scikit-learn, I'll > try to help with the preprocessing code for this case. > > > > Sincerely yours, > > Alexey > > > > пт, 4 сент. 2020 г. в 19:40, Igor Belyakov <[email protected]>: > > Alexey, > > > > Do you have any thoughts regarding that? > > > > Igor > > > > On Fri, Sep 4, 2020 at 10:03 AM Priya Yadav <[email protected]> wrote: > > Hi, > > > > Problem Statement: I have a feedback sentences having words separated by > spaces like normal English sentences. Using these sentences I need to > classify into categories based on some keywords. How should I preprocess my > data in order to use it in Naive-Bayes. > > Any leads would be helpful. > > Thanks in advance. > > > > This email and any files transmitted with it are confidential, proprietary > and intended solely for the individual or entity to whom they are > addressed. If you have received this email in error please delete it > immediately. > > This email and any files transmitted with it are confidential, proprietary > and intended solely for the individual or entity to whom they are > addressed. If you have received this email in error please delete it > immediately. >
