title: Vowpal Wabbit Python Sklearn - Predict vw Format
body: <p>I was working with the python wrapper (SKLEARN) for VW but couldn't figure out how to use namespaces so I decided to bypass the tovw() and create my own formatted list. </p>
  
  <p>First I exported out a text file for the training and test files, ran with vw through the terminal and all worked well. I then tried to run the same with the python wrapper. Training the model appeared to work but am getting an error when I try to predict. Is it not possible to predict with a file already formatted as vw?</p>
  
  <p><strong>This is the code used to export the data and also to create the lists</strong></p>
  
  <pre><code>vwX=X_train["Response"].astype('str')+' |'\
  'a '+\
  'Product_Info_4:'+X_train["Product_Info_4"].astype('str')+' '\
  'Ins_Age:'+X_train["Ins_Age"].astype('str')+' '\
  'Ht:'+X_train["Ht"].astype('str')+' '\
  'Wt:'+X_train["Wt"].astype('str')+' '\
  'BMI:'+X_train["BMI"].astype('str')+' '\
  'Employment_Info_1:'+X_train["Employment_Info_1"].astype('str')+' '\
  'Employment_Info_4:'+X_train["Employment_Info_4"].astype('str')+' '\
  'Employment_Info_6:'+X_train["Employment_Info_6"].astype('str')+' '\
  'Insurance_History_5:'+X_train["Insurance_History_5"].astype('str')+' '\
  'Family_Hist_2:'+X_train["Family_Hist_2"].astype('str')+' '\
  'Family_Hist_3:'+X_train["Family_Hist_3"].astype('str')+' '\
  'Family_Hist_4:'+X_train["Family_Hist_4"].astype('str')+' '\
  'Family_Hist_5:'+X_train["Family_Hist_5"].astype('str')+' '\
  'Medical_History_1:'+X_train["Medical_History_1"].astype('str')+' '\
  'Medical_History_15:'+X_train["Medical_History_15"].astype('str')+' '\
  'Medical_History_24:'+X_train["Medical_History_24"].astype('str')+' '\
  'Medical_History_32:'+X_train["Medical_History_32"].astype('str')+' '\
  '|b '+\
  np.where(X_train["Medical_Keyword_1"] ==0,''," Medical_Keyword_1")+\
  np.where(X_train["Medical_Keyword_2"] ==0,''," Medical_Keyword_2")+\
  np.where(X_train["Medical_Keyword_3"] ==0,''," Medical_Keyword_3")+\
  np.where(X_train["Medical_Keyword_4"] ==0,''," Medical_Keyword_4")+\
  np.where(X_train["Medical_Keyword_5"] ==0,''," Medical_Keyword_5")+\
  np.where(X_train["Medical_Keyword_6"] ==0,''," Medical_Keyword_6")+\
  np.where(X_train["Medical_Keyword_7"] ==0,''," Medical_Keyword_7")+\
  np.where(X_train["Medical_Keyword_8"] ==0,''," Medical_Keyword_8")+\
  np.where(X_train["Medical_Keyword_9"] ==0,''," Medical_Keyword_9")+\
  np.where(X_train["Medical_Keyword_10"] ==0,''," Medical_Keyword_10")+\
  np.where(X_train["Medical_Keyword_11"] ==0,''," Medical_Keyword_11")+\
  np.where(X_train["Medical_Keyword_12"] ==0,''," Medical_Keyword_12")+\
  np.where(X_train["Medical_Keyword_13"] ==0,''," Medical_Keyword_13")+\
  np.where(X_train["Medical_Keyword_14"] ==0,''," Medical_Keyword_14")+\
  np.where(X_train["Medical_Keyword_15"] ==0,''," Medical_Keyword_15")+\
  np.where(X_train["Medical_Keyword_16"] ==0,''," Medical_Keyword_16")+\
  np.where(X_train["Medical_Keyword_17"] ==0,''," Medical_Keyword_17")+\
  np.where(X_train["Medical_Keyword_18"] ==0,''," Medical_Keyword_18")+\
  np.where(X_train["Medical_Keyword_19"] ==0,''," Medical_Keyword_19")+\
  np.where(X_train["Medical_Keyword_20"] ==0,''," Medical_Keyword_20")+\
  np.where(X_train["Medical_Keyword_21"] ==0,''," Medical_Keyword_21")+\
  np.where(X_train["Medical_Keyword_22"] ==0,''," Medical_Keyword_22")+\
  np.where(X_train["Medical_Keyword_23"] ==0,''," Medical_Keyword_23")+\
  np.where(X_train["Medical_Keyword_24"] ==0,''," Medical_Keyword_24")+\
  np.where(X_train["Medical_Keyword_25"] ==0,''," Medical_Keyword_25")+\
  np.where(X_train["Medical_Keyword_26"] ==0,''," Medical_Keyword_26")+\
  np.where(X_train["Medical_Keyword_27"] ==0,''," Medical_Keyword_27")+\
  np.where(X_train["Medical_Keyword_28"] ==0,''," Medical_Keyword_28")+\
  np.where(X_train["Medical_Keyword_29"] ==0,''," Medical_Keyword_29")+\
  np.where(X_train["Medical_Keyword_30"] ==0,''," Medical_Keyword_30")+\
  np.where(X_train["Medical_Keyword_31"] ==0,''," Medical_Keyword_31")+\
  np.where(X_train["Medical_Keyword_32"] ==0,''," Medical_Keyword_32")+\
  np.where(X_train["Medical_Keyword_33"] ==0,''," Medical_Keyword_33")+\
  np.where(X_train["Medical_Keyword_34"] ==0,''," Medical_Keyword_34")+\
  np.where(X_train["Medical_Keyword_35"] ==0,''," Medical_Keyword_35")+\
  np.where(X_train["Medical_Keyword_36"] ==0,''," Medical_Keyword_36")+\
  np.where(X_train["Medical_Keyword_37"] ==0,''," Medical_Keyword_37")+\
  np.where(X_train["Medical_Keyword_38"] ==0,''," Medical_Keyword_38")+\
  np.where(X_train["Medical_Keyword_39"] ==0,''," Medical_Keyword_39")+\
  np.where(X_train["Medical_Keyword_40"] ==0,''," Medical_Keyword_40")+\
  np.where(X_train["Medical_Keyword_41"] ==0,''," Medical_Keyword_41")+\
  np.where(X_train["Medical_Keyword_42"] ==0,''," Medical_Keyword_42")+\
  np.where(X_train["Medical_Keyword_43"] ==0,''," Medical_Keyword_43")+\
  np.where(X_train["Medical_Keyword_44"] ==0,''," Medical_Keyword_44")+\
  np.where(X_train["Medical_Keyword_45"] ==0,''," Medical_Keyword_45")+\
  np.where(X_train["Medical_Keyword_46"] ==0,''," Medical_Keyword_46")+\
  np.where(X_train["Medical_Keyword_47"] ==0,''," Medical_Keyword_47")+\
  np.where(X_train["Medical_Keyword_48"] ==0,''," Medical_Keyword_48")+\
  ' |c '+\
  "Product_Info_1_"+X_train["Product_Info_1"].astype('str')+' '\
  "Product_Info_2_"+X_train["Product_Info_2"].astype('str')+' '\
  "Product_Info_3_"+X_train["Product_Info_3"].astype('str')+' '\
  "Product_Info_5_"+X_train["Product_Info_5"].astype('str')+' '\
  "Product_Info_6_"+X_train["Product_Info_6"].astype('str')+' '\
  "Product_Info_7_"+X_train["Product_Info_7"].astype('str')+' '\
  "Employment_Info_2_"+X_train["Employment_Info_2"].astype('str')+' '\
  "Employment_Info_3_"+X_train["Employment_Info_3"].astype('str')+' '\
  "Employment_Info_5_"+X_train["Employment_Info_5"].astype('str')+' '\
  "InsuredInfo_1_"+X_train["InsuredInfo_1"].astype('str')+' '\
  "InsuredInfo_2_"+X_train["InsuredInfo_2"].astype('str')+' '\
  "InsuredInfo_3_"+X_train["InsuredInfo_3"].astype('str')+' '\
  "InsuredInfo_4_"+X_train["InsuredInfo_4"].astype('str')+' '\
  "InsuredInfo_5_"+X_train["InsuredInfo_5"].astype('str')+' '\
  "InsuredInfo_6_"+X_train["InsuredInfo_6"].astype('str')+' '\
  "InsuredInfo_7_"+X_train["InsuredInfo_7"].astype('str')+' '\
  "Insurance_History_1_"+X_train["Insurance_History_1"].astype('str')+' '\
  "Insurance_History_2_"+X_train["Insurance_History_2"].astype('str')+' '\
  "Insurance_History_3_"+X_train["Insurance_History_3"].astype('str')+' '\
  "Insurance_History_4_"+X_train["Insurance_History_4"].astype('str')+' '\
  "Insurance_History_7_"+X_train["Insurance_History_7"].astype('str')+' '\
  "Insurance_History_8_"+X_train["Insurance_History_8"].astype('str')+' '\
  "Insurance_History_9_"+X_train["Insurance_History_9"].astype('str')+' '\
  "Family_Hist_1_"+X_train["Family_Hist_1"].astype('str')+' '\
  "Medical_History_2_"+X_train["Medical_History_2"].astype('str')+' '\
  "Medical_History_3_"+X_train["Medical_History_3"].astype('str')+' '\
  "Medical_History_4_"+X_train["Medical_History_4"].astype('str')+' '\
  "Medical_History_5_"+X_train["Medical_History_5"].astype('str')+' '\
  "Medical_History_6_"+X_train["Medical_History_6"].astype('str')+' '\
  "Medical_History_7_"+X_train["Medical_History_7"].astype('str')+' '\
  "Medical_History_8_"+X_train["Medical_History_8"].astype('str')+' '\
  "Medical_History_9_"+X_train["Medical_History_9"].astype('str')+' '\
  "Medical_History_10_"+X_train["Medical_History_10"].astype('str')+' '\
  "Medical_History_11_"+X_train["Medical_History_11"].astype('str')+' '\
  "Medical_History_12_"+X_train["Medical_History_12"].astype('str')+' '\
  "Medical_History_13_"+X_train["Medical_History_13"].astype('str')+' '\
  "Medical_History_14_"+X_train["Medical_History_14"].astype('str')+' '\
  "Medical_History_16_"+X_train["Medical_History_16"].astype('str')+' '\
  "Medical_History_17_"+X_train["Medical_History_17"].astype('str')+' '\
  "Medical_History_18_"+X_train["Medical_History_18"].astype('str')+' '\
  "Medical_History_19_"+X_train["Medical_History_19"].astype('str')+' '\
  "Medical_History_20_"+X_train["Medical_History_20"].astype('str')+' '\
  "Medical_History_21_"+X_train["Medical_History_21"].astype('str')+' '\
  "Medical_History_22_"+X_train["Medical_History_22"].astype('str')+' '\
  "Medical_History_23_"+X_train["Medical_History_23"].astype('str')+' '\
  "Medical_History_25_"+X_train["Medical_History_25"].astype('str')+' '\
  "Medical_History_26_"+X_train["Medical_History_26"].astype('str')+' '\
  "Medical_History_27_"+X_train["Medical_History_27"].astype('str')+' '\
  "Medical_History_28_"+X_train["Medical_History_28"].astype('str')+' '\
  "Medical_History_29_"+X_train["Medical_History_29"].astype('str')+' '\
  "Medical_History_30_"+X_train["Medical_History_30"].astype('str')+' '\
  "Medical_History_31_"+X_train["Medical_History_31"].astype('str')+' '\
  "Medical_History_33_"+X_train["Medical_History_33"].astype('str')+' '\
  "Medical_History_34_"+X_train["Medical_History_34"].astype('str')+' '\
  "Medical_History_35_"+X_train["Medical_History_35"].astype('str')+' '\
  "Medical_History_36_"+X_train["Medical_History_36"].astype('str')+' '\
  "Medical_History_37_"+X_train["Medical_History_37"].astype('str')+' '\
  "Medical_History_38_"+X_train["Medical_History_38"].astype('str')+' '\
  "Medical_History_39_"+X_train["Medical_History_39"].astype('str')+' '\
  "Medical_History_40_"+X_train["Medical_History_40"].astype('str')+' '\
  "Medical_History_41_"+X_train["Medical_History_41"].astype('str')
  
  vwX.to_csv('train.vw',mode='a', header=False,index=False)
  
  
  
  vwX_T='1'+ ' |'\
  'a '+\
  'Product_Info_4:'+X_test["Product_Info_4"].astype('str')+' '\
  'Ins_Age:'+X_test["Ins_Age"].astype('str')+' '\
  'Ht:'+X_test["Ht"].astype('str')+' '\
  'Wt:'+X_test["Wt"].astype('str')+' '\
  'BMI:'+X_test["BMI"].astype('str')+' '\
  'Employment_Info_1:'+X_test["Employment_Info_1"].astype('str')+' '\
  'Employment_Info_4:'+X_test["Employment_Info_4"].astype('str')+' '\
  'Employment_Info_6:'+X_test["Employment_Info_6"].astype('str')+' '\
  'Insurance_History_5:'+X_test["Insurance_History_5"].astype('str')+' '\
  'Family_Hist_2:'+X_test["Family_Hist_2"].astype('str')+' '\
  'Family_Hist_3:'+X_test["Family_Hist_3"].astype('str')+' '\
  'Family_Hist_4:'+X_test["Family_Hist_4"].astype('str')+' '\
  'Family_Hist_5:'+X_test["Family_Hist_5"].astype('str')+' '\
  'Medical_History_1:'+X_test["Medical_History_1"].astype('str')+' '\
  'Medical_History_15:'+X_test["Medical_History_15"].astype('str')+' '\
  'Medical_History_24:'+X_test["Medical_History_24"].astype('str')+' '\
  'Medical_History_32:'+X_test["Medical_History_32"].astype('str')+' '\
  '|b '+\
  np.where(X_test["Medical_Keyword_1"] ==0,''," Medical_Keyword_1")+\
  np.where(X_test["Medical_Keyword_2"] ==0,''," Medical_Keyword_2")+\
  np.where(X_test["Medical_Keyword_3"] ==0,''," Medical_Keyword_3")+\
  np.where(X_test["Medical_Keyword_4"] ==0,''," Medical_Keyword_4")+\
  np.where(X_test["Medical_Keyword_5"] ==0,''," Medical_Keyword_5")+\
  np.where(X_test["Medical_Keyword_6"] ==0,''," Medical_Keyword_6")+\
  np.where(X_test["Medical_Keyword_7"] ==0,''," Medical_Keyword_7")+\
  np.where(X_test["Medical_Keyword_8"] ==0,''," Medical_Keyword_8")+\
  np.where(X_test["Medical_Keyword_9"] ==0,''," Medical_Keyword_9")+\
  np.where(X_test["Medical_Keyword_10"] ==0,''," Medical_Keyword_10")+\
  np.where(X_test["Medical_Keyword_11"] ==0,''," Medical_Keyword_11")+\
  np.where(X_test["Medical_Keyword_12"] ==0,''," Medical_Keyword_12")+\
  np.where(X_test["Medical_Keyword_13"] ==0,''," Medical_Keyword_13")+\
  np.where(X_test["Medical_Keyword_14"] ==0,''," Medical_Keyword_14")+\
  np.where(X_test["Medical_Keyword_15"] ==0,''," Medical_Keyword_15")+\
  np.where(X_test["Medical_Keyword_16"] ==0,''," Medical_Keyword_16")+\
  np.where(X_test["Medical_Keyword_17"] ==0,''," Medical_Keyword_17")+\
  np.where(X_test["Medical_Keyword_18"] ==0,''," Medical_Keyword_18")+\
  np.where(X_test["Medical_Keyword_19"] ==0,''," Medical_Keyword_19")+\
  np.where(X_test["Medical_Keyword_20"] ==0,''," Medical_Keyword_20")+\
  np.where(X_test["Medical_Keyword_21"] ==0,''," Medical_Keyword_21")+\
  np.where(X_test["Medical_Keyword_22"] ==0,''," Medical_Keyword_22")+\
  np.where(X_test["Medical_Keyword_23"] ==0,''," Medical_Keyword_23")+\
  np.where(X_test["Medical_Keyword_24"] ==0,''," Medical_Keyword_24")+\
  np.where(X_test["Medical_Keyword_25"] ==0,''," Medical_Keyword_25")+\
  np.where(X_test["Medical_Keyword_26"] ==0,''," Medical_Keyword_26")+\
  np.where(X_test["Medical_Keyword_27"] ==0,''," Medical_Keyword_27")+\
  np.where(X_test["Medical_Keyword_28"] ==0,''," Medical_Keyword_28")+\
  np.where(X_test["Medical_Keyword_29"] ==0,''," Medical_Keyword_29")+\
  np.where(X_test["Medical_Keyword_30"] ==0,''," Medical_Keyword_30")+\
  np.where(X_test["Medical_Keyword_31"] ==0,''," Medical_Keyword_31")+\
  np.where(X_test["Medical_Keyword_32"] ==0,''," Medical_Keyword_32")+\
  np.where(X_test["Medical_Keyword_33"] ==0,''," Medical_Keyword_33")+\
  np.where(X_test["Medical_Keyword_34"] ==0,''," Medical_Keyword_34")+\
  np.where(X_test["Medical_Keyword_35"] ==0,''," Medical_Keyword_35")+\
  np.where(X_test["Medical_Keyword_36"] ==0,''," Medical_Keyword_36")+\
  np.where(X_test["Medical_Keyword_37"] ==0,''," Medical_Keyword_37")+\
  np.where(X_test["Medical_Keyword_38"] ==0,''," Medical_Keyword_38")+\
  np.where(X_test["Medical_Keyword_39"] ==0,''," Medical_Keyword_39")+\
  np.where(X_test["Medical_Keyword_40"] ==0,''," Medical_Keyword_40")+\
  np.where(X_test["Medical_Keyword_41"] ==0,''," Medical_Keyword_41")+\
  np.where(X_test["Medical_Keyword_42"] ==0,''," Medical_Keyword_42")+\
  np.where(X_test["Medical_Keyword_43"] ==0,''," Medical_Keyword_43")+\
  np.where(X_test["Medical_Keyword_44"] ==0,''," Medical_Keyword_44")+\
  np.where(X_test["Medical_Keyword_45"] ==0,''," Medical_Keyword_45")+\
  np.where(X_test["Medical_Keyword_46"] ==0,''," Medical_Keyword_46")+\
  np.where(X_test["Medical_Keyword_47"] ==0,''," Medical_Keyword_47")+\
  np.where(X_test["Medical_Keyword_48"] ==0,''," Medical_Keyword_48")+\
  ' |c '+\
  "Product_Info_1_"+X_test["Product_Info_1"].astype('str')+' '\
  "Product_Info_2_"+X_test["Product_Info_2"].astype('str')+' '\
  "Product_Info_3_"+X_test["Product_Info_3"].astype('str')+' '\
  "Product_Info_5_"+X_test["Product_Info_5"].astype('str')+' '\
  "Product_Info_6_"+X_test["Product_Info_6"].astype('str')+' '\
  "Product_Info_7_"+X_test["Product_Info_7"].astype('str')+' '\
  "Employment_Info_2_"+X_test["Employment_Info_2"].astype('str')+' '\
  "Employment_Info_3_"+X_test["Employment_Info_3"].astype('str')+' '\
  "Employment_Info_5_"+X_test["Employment_Info_5"].astype('str')+' '\
  "InsuredInfo_1_"+X_test["InsuredInfo_1"].astype('str')+' '\
  "InsuredInfo_2_"+X_test["InsuredInfo_2"].astype('str')+' '\
  "InsuredInfo_3_"+X_test["InsuredInfo_3"].astype('str')+' '\
  "InsuredInfo_4_"+X_test["InsuredInfo_4"].astype('str')+' '\
  "InsuredInfo_5_"+X_test["InsuredInfo_5"].astype('str')+' '\
  "InsuredInfo_6_"+X_test["InsuredInfo_6"].astype('str')+' '\
  "InsuredInfo_7_"+X_test["InsuredInfo_7"].astype('str')+' '\
  "Insurance_History_1_"+X_test["Insurance_History_1"].astype('str')+' '\
  "Insurance_History_2_"+X_test["Insurance_History_2"].astype('str')+' '\
  "Insurance_History_3_"+X_test["Insurance_History_3"].astype('str')+' '\
  "Insurance_History_4_"+X_test["Insurance_History_4"].astype('str')+' '\
  "Insurance_History_7_"+X_test["Insurance_History_7"].astype('str')+' '\
  "Insurance_History_8_"+X_test["Insurance_History_8"].astype('str')+' '\
  "Insurance_History_9_"+X_test["Insurance_History_9"].astype('str')+' '\
  "Family_Hist_1_"+X_test["Family_Hist_1"].astype('str')+' '\
  "Medical_History_2_"+X_test["Medical_History_2"].astype('str')+' '\
  "Medical_History_3_"+X_test["Medical_History_3"].astype('str')+' '\
  "Medical_History_4_"+X_test["Medical_History_4"].astype('str')+' '\
  "Medical_History_5_"+X_test["Medical_History_5"].astype('str')+' '\
  "Medical_History_6_"+X_test["Medical_History_6"].astype('str')+' '\
  "Medical_History_7_"+X_test["Medical_History_7"].astype('str')+' '\
  "Medical_History_8_"+X_test["Medical_History_8"].astype('str')+' '\
  "Medical_History_9_"+X_test["Medical_History_9"].astype('str')+' '\
  "Medical_History_10_"+X_test["Medical_History_10"].astype('str')+' '\
  "Medical_History_11_"+X_test["Medical_History_11"].astype('str')+' '\
  "Medical_History_12_"+X_test["Medical_History_12"].astype('str')+' '\
  "Medical_History_13_"+X_test["Medical_History_13"].astype('str')+' '\
  "Medical_History_14_"+X_test["Medical_History_14"].astype('str')+' '\
  "Medical_History_16_"+X_test["Medical_History_16"].astype('str')+' '\
  "Medical_History_17_"+X_test["Medical_History_17"].astype('str')+' '\
  "Medical_History_18_"+X_test["Medical_History_18"].astype('str')+' '\
  "Medical_History_19_"+X_test["Medical_History_19"].astype('str')+' '\
  "Medical_History_20_"+X_test["Medical_History_20"].astype('str')+' '\
  "Medical_History_21_"+X_test["Medical_History_21"].astype('str')+' '\
  "Medical_History_22_"+X_test["Medical_History_22"].astype('str')+' '\
  "Medical_History_23_"+X_test["Medical_History_23"].astype('str')+' '\
  "Medical_History_25_"+X_test["Medical_History_25"].astype('str')+' '\
  "Medical_History_26_"+X_test["Medical_History_26"].astype('str')+' '\
  "Medical_History_27_"+X_test["Medical_History_27"].astype('str')+' '\
  "Medical_History_28_"+X_test["Medical_History_28"].astype('str')+' '\
  "Medical_History_29_"+X_test["Medical_History_29"].astype('str')+' '\
  "Medical_History_30_"+X_test["Medical_History_30"].astype('str')+' '\
  "Medical_History_31_"+X_test["Medical_History_31"].astype('str')+' '\
  "Medical_History_33_"+X_test["Medical_History_33"].astype('str')+' '\
  "Medical_History_34_"+X_test["Medical_History_34"].astype('str')+' '\
  "Medical_History_35_"+X_test["Medical_History_35"].astype('str')+' '\
  "Medical_History_36_"+X_test["Medical_History_36"].astype('str')+' '\
  "Medical_History_37_"+X_test["Medical_History_37"].astype('str')+' '\
  "Medical_History_38_"+X_test["Medical_History_38"].astype('str')+' '\
  "Medical_History_39_"+X_test["Medical_History_39"].astype('str')+' '\
  "Medical_History_40_"+X_test["Medical_History_40"].astype('str')+' '\
  "Medical_History_41_"+X_test["Medical_History_41"].astype('str')
  
  vwX_T.to_csv('test.vw',mode='a', header=False,index=False)
  
  #create a list to be used below in pyvw
  vwX_lst=[]
  vwX_T_lst=[]
  
  for j in vwX.values:
      vwX_lst.append(j)
  
  for j in vwX_T.values:
      vwX_T_lst.append(j)
  </code></pre>
  
  <p><strong>Then I trained a model, which seemed to run OK:</strong></p>
  
  <pre><code>import sys
  sys.path.append('/home/anaconda/lib/python2.7/site-packages/vowpal_wabbit/python')
  
  import pyvw
  import sklearn_vw as slvw
  
  import numpy as np
  import pandas as pd
  
  from sklearn.cross_validation import train_test_split,KFold
  from sklearn.preprocessing import OneHotEncoder
  from sklearn.preprocessing import StandardScaler
  
  import ml_metrics
  
  mod=slvw.VWRegressor(passes=5, quadratic="aa ab")
  mod.fit(X=vwX_lst,convert_to_vw=False)
  </code></pre>
  
  <p><strong>preds=mod.predict(X=vwX_T_lst,convert_to_vw=False)</strong></p>
  
  <pre><code>---------------------------------------------------------------------------
  IndexError                                Traceback (most recent call last)
  &lt;ipython-input-128-e43aa19fc8f5&gt; in &lt;module&gt;()
       16 mod=slvw.VWRegressor(passes=5, quadratic="aa ab")
       17 mod.fit(X=vwX_lst,convert_to_vw=False)
  ---&gt; 18 preds=mod.predict(X=vwX_T_lst,convert_to_vw=False)
       19 
  
  /home/anaconda/lib/python2.7/site-packages/vowpal_wabbit/python/sklearn_vw.pyc in predict(self, X, convert_to_vw)
      255             ex.set_test_only(True)
      256             ex.learn()
  --&gt; 257             y[idx] = ex.get_simplelabel_prediction()
      258             ex.finish()
      259 
  
  IndexError: index 1 is out of bounds for axis 0 with size 1
  </code></pre>