xIssuer]
, csec.SecType as [CoraxSecType]
, csec.InvType as [CoraxInvType]
, csec.PriceCurrency as [CoraxPriceCurrency]
, csec.Region as [CoraxRegion]
, csec.IssuerCountry as [CoraxIssuerCountry]
, csec.Exchange as [CoraxExchange]
, csec.IndustrySector as [CoraxIndustrySector]
, sf.ChangeType as [ChangeType]
, ssec.Ticker as [ChangeTicker]
, ssec.Issuer as [ChangeIssuer]
, ssec.SecType as [ChangeSecType]
, ssec.InvType as [ChangeInvType]
, ssec.PriceCurrency as [ChangePriceCurrency]
, ssec.Region as [ChangeRegion]
, ssec.IssuerCountry as [ChangeIssuerCountry]
, ssec.Exchange as [ChangeExchange]
, ssec.IndustrySector as [ChangeIndustrySector] .CoraxFactsTable as cfdbo.SecuritiesSource as csec on cf.SecId=csec.SecIddbo.PriceFactstable as sf on cf.EffectiveDate=sf. [DateStart] dbo.SecuritiesSource as ssec on sf.SecId=ssec.SecId.FieldType= Price
Додаток 4
Система класифікації
__ future__ import divisionsyscsv as csvnumpy as nppandas as pdpandas import DataFramesklearnsklearn.preprocessing import LabelEncodersklearn.cross_validation import train_test_splitsklearn.grid_search import GridSearchCVsklearn.metrics import classification_reportsklearn.svm import SVCsklearn.svm import LinearSVCsklearn import cross_validation, svm, treesklearn. naive_bayes import MultinomialNBsklearn.pipeline import Pipelinesklearn.feature_extraction.text import CountVectorizersklearn.neighbors import KNeighborsClassifiersklearn.feature_extraction.text import TfidfTransformersklearn.multiclass import OneVsRestClassifiersklearn import preprocessingsklearn.linear_model import SGDClassifierrandomwarningsdatetime import datetimesklearn.grid_search import GridSearchCV.filterwarnings ( ignore )={
linear raquo ;: LinearSVC (),
linearWithSGD raquo ;: SGDClassifier (),
rbf raquo ;: SVC (kernel= rbf raquo ;, probability=True),
poly raquo ;: SVC (kernel= poly raquo ;, probability=True),
sigmoid raquo ;: SVC (kernel= sigmoid raquo ;, probability=True),
bayes raquo ;: MultinomialNB ()
}={
linearWithSGD raquo ;: linear SVM with SGD training ,
linear raquo ;: linear SVM without SGD training ,
rbf raquo ;: SVM with RBF kernel ,
poly raquo ;: SVM with polynomial kernel ,
sigmoid raquo ;: SVM with sigmoid kernel ,
bayes raquo ;: Naive Bayes classifier
}replacer(text):str(str(text).replace(laquo;ulaquo;raquo;,laquo;raquo;).replace(laquo;raquo;raquo;, )) workMode (fileIn, toPredict, fileOut, classif):=pd.read_csv (fileIn, header=0, encoding= utf - 8-sig ) _ test=pd.read_csv (toPredict, header=0 , encoding= utf - 8-sig ) _ train=[] _train=[] _test=[] i in work [[i for i in list (work.columns.values) if
i.startswith(laquo;Changeraquo;)]].values:_train.append(laquo;,raquo;.join(i.T.tolist()))_train =Np.array (X_train) i in work [[i for i in list (work.columns.values) if
i.startswith ( Corax )]]. values: _train.append (list (i)) i in work_test [[i for i in list (work_test.columns.values) if
i.startswith(laquo;Changeraquo;)]].values:_test.append(laquo;,raquo;.join(i.T.tolist()))_test =Np.array (X_test)=preprocessing.MultiLabelBinarizer ()=lb.fit_transform (y_train) ( Getting results of classifier )=Pipeline ([( vectorizer raquo ;, CountVectorizer ()), ( tfidf raquo ;,
TfidfTransformer ()), ( clf raquo ;, OneVsRestClassifier (selClassifiers [classif]))]). fit (X_train, Y)=classifier.predict (X_test) _labels=lb.inverse_transform (predicted )=DataFrame.from_items ([( Change raquo ;, X_test), ( Prediction , all_labels)]). Prediction=df.Prediction.map (replacer) .to_csv (fileOut) testMode (fileIn, fileOut, classif) :=pd.read_csv (fileIn, header=0, encoding= utf - 8-sig )=random.sample (list (df.index), int (len (df) * 0.9))=df.ix [ rows] _test=df.drop (rows) _train=[] _train=[] _test=[] _test=[] i in work [[i for i in list (work.columns.values) if
i.startswith(laquo;Changeraquo;)]].values:_train.append(laquo;,raquo;.join(i.T.tolist()))_train =Np.array (X_train) i in work [[i for i in list (work.colum...
