5 Jul 2017

Pandas/scikit-learn: get_dummies test/train sets - ValueError: shapes not aligned

I’ve been using panda’s https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html function to generate dummy columns for categorical variables to use with scikit-learn, but noticed that it sometimes doesn’t work as I expect.

Prerequisites

import pandas as pd
import numpy as np
from sklearn import linear_model

Let’s say we have the following training and test sets:

Training set

train = pd.DataFrame({"letter":["A", "B", "C", "D"], "value": [1, 2, 3, 4]})
X_train = train.drop(["value"], axis=1)
X_train = pd.get_dummies(X_train)
y_train = train["value"]~~~

<h3>Test set</h3>


~~~python

test = pd.DataFrame({"letter":["D", "D", "B", "E"], "value": [4, 5, 7, 19]})
X_test = test.drop(["value"], axis=1)
X_test = pd.get_dummies(X_test)
y_test = test["value"]

Now say we want to train a linear model on our training set and then use it to predict the values in our test set:

Train the model

lr = linear_model.LinearRegression()
model = lr.fit(X_train, y_train)

Test the model

model.score(X_test, y_test)

ValueError: shapes (4,3) and (4,) not aligned: 3 (dim 1) != 4 (dim 0)

Hmmm that didn’t go to plan. If we print X_train and X_test it might help shed some light:

Checking the train/test datasets

print(X_train)

   letter_A  letter_B  letter_C  letter_D
0         1         0         0         0
1         0         1         0         0
2         0         0         1         0
3         0         0         0         1

print(X_test)

   letter_B  letter_D  letter_E
0         0         1         0
1         0         1         0
2         1         0         0
3         0         0         1

They do indeed have different shapes and some different column names because the test set contained some values that weren’t present in the training set.

We can fix this by making the 'letter' field categorical before we run the get_dummies method over the dataframe. At the moment the field is of type 'object':

Column types

print(train.info)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
letter    4 non-null object
value     4 non-null int64
dtypes: int64(1), object(1)
memory usage: 144.0+ bytes

We can fix this by converting the 'letter' field to the type 'category' and setting the list of allowed values to be the unique set of values in the train/test sets.

All allowed values

all_data = pd.concat((train,test))
for column in all_data.select_dtypes(include=[np.object]).columns:
    print(column, all_data[column].unique())

letter ['A' 'B' 'C' 'D' 'E']

Now let’s update the type of our 'letter' field in the train and test dataframes.

Type: 'category'

all_data = pd.concat((train,test))

for column in all_data.select_dtypes(include=[np.object]).columns:
    train[column] = train[column].astype('category', categories = all_data[column].unique())
    test[column] = test[column].astype('category', categories = all_data[column].unique())

And now if we call get_dummies on either dataframe we’ll get the same set of columns:

get_dummies: Take 2

X_train = train.drop(["value"], axis=1)
X_train = pd.get_dummies(X_train)
print(X_train)

   letter_A  letter_B  letter_C  letter_D  letter_E
0         1         0         0         0         0
1         0         1         0         0         0
2         0         0         1         0         0
3         0         0         0         1         0

X_test = test.drop(["value"], axis=1)
X_test = pd.get_dummies(X_test)
print(X_train)

   letter_A  letter_B  letter_C  letter_D  letter_E
0         0         0         0         1         0
1         0         0         0         1         0
2         0         1         0         0         0
3         0         0         0         0         1

Great! Now we should be able to train our model and use it against the test set:

Train the model: Take 2

lr = linear_model.LinearRegression()
model = lr.fit(X_train, y_train)

Test the model: Take 2

model.score(X_test, y_test)

-1.0604490500863557

And we’re done!

About the author

I'm currently working on short form content at ClickHouse. I publish short 5 minute videos showing how to solve data problems on YouTube @LearnDataWithMark. I previously worked on graph analytics at Neo4j, where I also co-authored the O'Reilly Graph Algorithms Book with Amy Hodler.