How to test unseen test data with cross validation and predict labels?

Question

1.The CSV that contains data(ie. text description) along with categorized labels

df = pd.read_csv('./output/csv_sanitized_16_.csv', dtype=str)
X = df['description_plus']
y = df['category_id']

2.This CSV contains unseen data(ie. text description) for which labels need to be predicted

df_2 = pd.read_csv('./output/csv_sanitized_2.csv', dtype=str)
X2 = df_2['description_plus']

Cross validation function that operates on the training data(item #1) above.

def cross_val():
    cv = KFold(n_splits=20)
    vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                     stop_words='english')
    X_train = vectorizer.fit_transform(X) 
    clf = make_pipeline(preprocessing.StandardScaler(with_mean=False), svm.SVC(C=1))
    scores = cross_val_score(clf, X_train, y, cv=cv)
    print(scores)
    print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
cross_val()

I need to know how to pass the unseen data(item #2) to the cross validation function and how to predict the labels?

Answer 1

Using scores = cross_val_score(clf, X_train, y, cv=cv) you can only get the cross-validated scores of the model. cross_val_score will internally split the data into training and testing based on the cv parameter.

So the values that you get are the cross-validated accuracy of the SVC.

To get the score on the unseen data, you can first fit the model e.g.

clf = make_pipeline(preprocessing.StandardScaler(with_mean=False), svm.SVC(C=1))
clf.fit(X_train, y) # the model is trained now

and then do clf.score(X_unseen,y)

The last will return the accuracy of the model on the unseen data.

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EDIT: The best way to do what you want is the following using a GridSearch to first find the best model using the training data and then evaluate the best model using the unseen (test) data:

from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

# load some data
iris = datasets.load_iris()
X, y = iris.data, iris.target

#split data to training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# hyperparameter tunig of the SVC model
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svc = svm.SVC()

# fit the GridSearch using the TRAINING data
grid_searcher = GridSearchCV(svc, parameters)
grid_searcher.fit(X_train, y_train)

#recover the best estimator (best parameters for the SVC, based on the GridSearch)
best_SVC_model = grid_searcher.best_estimator_

# Now, check how this best model behaves on the test set
cv_scores_on_unseen = cross_val_score(best_SVC_model, X_test, y_test, cv=5)
print(cv_scores_on_unseen.mean())