使用sklearn的“预测”功能

Question

If I trained a model in sklearn using dummy variables for categorical values, what is best practice for feeding a single row of features into this model to get the prediction result? For all input data set I am trying to get scores. If I have less columns than the data set I used to train/fit the model, will it throw an error.?

Just to clarify: I took a data set that had 5 columns and created over 118 dummy columns before I built my model. Now I have a single row of data with 5 columns that I would like to use in predict function. How can I do this?

Any help here would be greatly appreciated.

Answer 1

It's an error to extend features depending on table state, cause you can't repeat it with another data. If you want to create features this way, you should use a constructor that will remember structure of features. Since you gave no example of data, here is the main idea how you can make a constructor:

import pandas as pd

data = pd.DataFrame([['Missouri', 'center', 'Jan', 55, 11],
                     ['Kansas', 'center', 'Mar', 54, 31],
                     ['Georgia', 'east', 'Jan', 37, 18]],
                     columns=('state', 'pos', 'month', 'High Temp', 'Low Temp'))


test =  pd.DataFrame([['Missouri', 'center', 'Feb', 44, 23], 
                      ['Missouri', 'center', 'Mar', 55, 33]],
                      columns=('state', 'pos', 'month', 'High Temp', 'Low Temp'))  


class DummyColumns():
    def __init__(self, data):
        # Columns constructor
        self.empty = pd.DataFrame(columns=(list(data.columns) +
                                           list(data.state.unique()) +
                                           list(data.pos.unique()) +
                                           ['Winter', 'Not winter']))
    def __call__(self, data):
        # Initializing with zeros
        self.df = pd.DataFrame(data=0, columns=self.empty.columns, index=data.index)        
        for row in data.itertuples():
            self.df.loc[row.Index, :5] = row[1:]
            self.df.loc[row.Index, row.state] = 1
            self.df.loc[row.Index, row.pos] = 1
            if row.month in ['Dec', 'Jan', 'Feb']:
                self.df.loc[row.Index, 'Winter'] = 1
            else:
                self.df.loc[row.Index, 'Not winter'] = 1
        return self.df       

add_dummy = DummyColumns(data)
dummy_test = add_dummy(test)
print dummy_test

      state     pos month  High Temp  Low Temp  Missouri  Kansas  Georgia  \
0  Missouri  center   Feb         44        23         1       0        0   
1  Missouri  center   Mar         55        33         1       0        0   

   center  east  Winter  Not winter  
0       1     0       1           0  
1       1     0       0           1