def Train_A_Model_Direct(tag, model, vocab1,vocab2,df_Train,df_Test): #Train the actual model: trained_model,trained_vocab,tagged_vocab = modelmaker.model_trainer(df_Train,model,vocab1,vocab2) #Get predictions result = modelmaker.model_tester(df_Test,trained_model,trained_vocab,tagged_vocab) return trained_model, trained_vocab, tagged_vocab, result, df_Test
def Train_A_Model_Direct(tag, model, vocab1, vocab2, df_Train, df_Test): #Train the actual model: trained_model, trained_vocab, tagged_vocab = modelmaker.model_trainer( df_Train, model, vocab1, vocab2) #Get predictions result = modelmaker.model_tester(df_Test, trained_model, trained_vocab, tagged_vocab) return trained_model, trained_vocab, tagged_vocab, result, df_Test
def Train_A_Model(tag, model, vocab1,vocab2,myconfig,downsample=True): df_all = dfmaker.get_train_dfs(tag,myconfig) df_Train, df_Test = dfmaker.GenerateTestTrain(df_all) #Train the actual model: trained_model,trained_vocab,tagged_vocab = modelmaker.model_trainer(df_Train,model,vocab1,vocab2,downsample) #Get predictions result = modelmaker.model_tester(df_Test,trained_model,trained_vocab,tagged_vocab) return trained_model, trained_vocab, tagged_vocab, result, df_Test
def Train_Final_Model(tag, model, vocab1,vocab2,myconfig,downsample=True): df_all = dfmaker.get_train_dfs(tag,myconfig) #Check that CV looks fine: print "Checking CV (nfold=2):" modelmaker.model_cv(df_all,model,vocab1,vocab2,2,downsample) #Train the actual model: print "Training final model:" trained_model,trained_vocab1,trained_vocab2 = modelmaker.model_trainer(df_all,model,vocab1,vocab2,downsample) return trained_model, trained_vocab1, trained_vocab2
def Train_Final_Model(tag, model, vocab1, vocab2, myconfig, downsample=True): df_all = dfmaker.get_train_dfs(tag, myconfig) #Check that CV looks fine: print "Checking CV (nfold=2):" modelmaker.model_cv(df_all, model, vocab1, vocab2, 2, downsample) #Train the actual model: print "Training final model:" trained_model, trained_vocab1, trained_vocab2 = modelmaker.model_trainer( df_all, model, vocab1, vocab2, downsample) return trained_model, trained_vocab1, trained_vocab2
def Train_A_Model(tag, model, vocab1, vocab2, myconfig, downsample=True): df_all = dfmaker.get_train_dfs(tag, myconfig) df_Train, df_Test = dfmaker.GenerateTestTrain(df_all) #Train the actual model: trained_model, trained_vocab, tagged_vocab = modelmaker.model_trainer( df_Train, model, vocab1, vocab2, downsample) #Get predictions result = modelmaker.model_tester(df_Test, trained_model, trained_vocab, tagged_vocab) return trained_model, trained_vocab, tagged_vocab, result, df_Test
def Train_A_Model_Direct(tag, model, vocab1,vocab2,df_Train,df_Test): """Given a testing and training dataframe, train a model tag = tag name model = model object (sklearn) to train to vocab1 = Body vocab vocab2 = Tag vocab df_Train = Train dataframe df_Test = Test dataframe """ #Train the actual model: trained_model,trained_vocab,tagged_vocab = modelmaker.model_trainer(df_Train,model,vocab1,vocab2) #Get predictions result = modelmaker.model_tester(df_Test,trained_model,trained_vocab,tagged_vocab) #Return trained model, vocab, prediction: return trained_model, trained_vocab, tagged_vocab, result, df_Test
def Train_Final_Model(tag, model, vocab1,vocab2,myconfig,downsample=True): """Given a tag name, train a model WITH CROSS-VALIDATION tag = tag name model = model object (sklearn) to train to vocab1 = Body vocab vocab2 = Tag vocab myconfig = config file for accessing MySQL database downsample = force spoiler and non-spoiler sets to have same size """ df_all = dfmaker.get_train_dfs(tag,myconfig) #Check that CV looks fine: print "Checking CV (nfold=2):" modelmaker.model_cv(df_all,model,vocab1,vocab2,2,downsample) #Train the actual model: print "Training final model:" trained_model,trained_vocab1,trained_vocab2 = modelmaker.model_trainer(df_all,model,vocab1,vocab2,downsample) return trained_model, trained_vocab1, trained_vocab2
def Train_A_Model(tag, model, vocab1,vocab2,myconfig,downsample=True): """Given a tag name, train a model tag = tag name model = model object (sklearn) to train to vocab1 = Body vocab vocab2 = Tag vocab myconfig = config file for accessing MySQL database downsample = force spoiler and non-spoiler sets to have same size """ #Get the data and make Test/Train frames: df_all = dfmaker.get_train_dfs(tag,myconfig) df_Train, df_Test = dfmaker.GenerateTestTrain(df_all) #Train the actual model: trained_model,trained_vocab,tagged_vocab = modelmaker.model_trainer(df_Train,model,vocab1,vocab2,downsample) #Get predictions result = modelmaker.model_tester(df_Test,trained_model,trained_vocab,tagged_vocab) #Return trained model, vocab, prediction: return trained_model, trained_vocab, tagged_vocab, result, df_Test