Python scikit svm "ValueError: X has 62 features per sample; expecting 337"

Question

Playing around with Python's scikit SVM Linear Support Vector Classification and I'm running into an error when I attempt to make predictions:

ten_percent = len(raw_routes_data) / 10

# Training
training_label = all_labels[ten_percent:]
training_raw_data = raw_routes_data[ten_percent:]
training_data = DictVectorizer().fit_transform(training_raw_data).toarray()


learner = svm.LinearSVC()
learner.fit(training_data, training_label)

# Predicting
testing_label = all_labels[:ten_percent]
testing_raw_data = raw_routes_data[:ten_percent]
testing_data = DictVectorizer().fit_transform(testing_raw_data).toarray()

testing_predictions = learner.predict(testing_data)


m = metrics.classification_report(testing_label, testing_predictions)

The raw_data is represented as a Python dictionary with categories of arrival times for various travel options and categories for weather data:

{'72_bus': '6.0 to 11.0', 'uber_eta': '2.0 to 3.5', 'tweet_delay': '0', 'c_train': '1.0 to 4.0', 'weather': 'Overcast', '52_bus': '16.0 to 21.0', 'uber_surging': '1.0 to 1.15', 'd_train': '17.6666666667 to 21.8333333333', 'feels_like': '27.6666666667 to 32.5'}

When I train and fit the training data I use a Dictionary Vectorizer on 90% of the data and turning it into an array.

The provided testing_labels are represented as:

[1,2,3,3,1,2,3, ... ]

It's when I attempt to use the LinearSVC to predict that I'm informed:

ValueError: X has 27 features per sample; expecting 46

What am I missing here? Obviously it is the way I fit and transform the data.

Answer 1

The problem is that you creating and fitting different DictVectorizer for train and for test.

You should create and fit only one DictVectorizer using train data and use transform method of this object on your testing data to create feature representation of your test data.