Run trained Machine Learning model on a different dataset

Question

I am new to Machine Learning and am in the process of trying to run a simple classification model that I trained and saved using pickle, on another dataset of the same format. I have the following python code.

Code

#Training set
features = pd.read_csv('../Data/Train_sop_Computed.csv')
#Testing set
testFeatures = pd.read_csv('../Data/Test_sop_Computed.csv')

print(colored('\nThe shape of our features is:','green'), features.shape)
print(colored('\nThe shape of our Test features is:','green'), testFeatures.shape)

features = pd.get_dummies(features)
testFeatures = pd.get_dummies(testFeatures)

features.iloc[:,5:].head(5)
testFeatures.iloc[:,5].head(5)

labels = np.array(features['Truth'])
testlabels = np.array(testFeatures['Truth'])

features= features.drop('Truth', axis = 1)
testFeatures = testFeatures.drop('Truth', axis = 1)

feature_list = list(features.columns)
testFeature_list = list(testFeatures.columns)

def add_missing_dummy_columns(d, columns):
    missing_cols = set(columns) - set(d.columns)
    for c in missing_cols:
        d[c] = 0


def fix_columns(d, columns):
    add_missing_dummy_columns(d, columns)

    # make sure we have all the columns we need
    assert (set(columns) - set(d.columns) == set())

    extra_cols = set(d.columns) - set(columns)
    if extra_cols: print("extra columns:", extra_cols)

    d = d[columns]
    return d


testFeatures = fix_columns(testFeatures, features.columns)

features = np.array(features)
testFeatures = np.array(testFeatures)

train_samples = 100

X_train, X_test, y_train, y_test = model_selection.train_test_split(features, labels, test_size = 0.25, random_state = 42)
testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

print(colored('\n        TRAINING SET','yellow'))
print(colored('\nTraining Features Shape:','magenta'), X_train.shape)
print(colored('Training Labels Shape:','magenta'), X_test.shape)
print(colored('Testing Features Shape:','magenta'), y_train.shape)
print(colored('Testing Labels Shape:','magenta'), y_test.shape)

print(colored('\n        TESTING SETS','yellow'))
print(colored('\nTraining Features Shape:','magenta'), testX_train.shape)
print(colored('Training Labels Shape:','magenta'), textX_test.shape)
print(colored('Testing Features Shape:','magenta'), testy_train.shape)
print(colored('Testing Labels Shape:','magenta'), testy_test.shape)

from sklearn.metrics import precision_recall_fscore_support

import pickle

loaded_model_RFC = pickle.load(open('../other/SOPmodel_RFC', 'rb'))
result_RFC = loaded_model_RFC.score(textX_test, testy_test)
print(colored('Random Forest Classifier: ','magenta'),result_RFC)

loaded_model_SVC = pickle.load(open('../other/SOPmodel_SVC', 'rb'))
result_SVC = loaded_model_SVC.score(textX_test, testy_test)
print(colored('Support Vector Classifier: ','magenta'),result_SVC)

loaded_model_GPC = pickle.load(open('../other/SOPmodel_Gaussian', 'rb'))
result_GPC = loaded_model_GPC.score(textX_test, testy_test)
print(colored('Gaussian Process Classifier: ','magenta'),result_GPC)

loaded_model_SGD = pickle.load(open('../other/SOPmodel_SGD', 'rb'))
result_SGD = loaded_model_SGD.score(textX_test, testy_test)
print(colored('Stocastic Gradient Descent: ','magenta'),result_SGD)

I am able to get the results for the test set.

But the problem I am facing is that I need to run the model on the entire Test_sop_Computed.csv dataset. But it is only being run on the test dataset that I've split. I would sincerely appreciate if anyone could provide any suggestions on how I can run the loaded model on the entire dataset. I know that I'm going wrong with the following line of code.

testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

Both the train and test dataset have the Subject, Predicate, Object, Computed and Truth and the features with the Truth being the predicted class. The testing dataset has the actual values for this Truth column and I dopr it usingtestFeatures = testFeatures.drop('Truth', axis = 1) and intend on using the various loaded models of classifiers to predict this Truth as 0 or 1 for the entire dataset and then get the predictions as an array.

I have done this so far. But I think that I am splitting my test dataset as well. Is there a way to pass the entire test dataset even if it is in another file?

This test dataset is in the same format as the training set. I have checked the shape of the two and I get the following.

Confirming the Features and Shape

Shape of the Train features is: (1860, 5)
Shape of the Test features is: (1386, 5)

         TRAINING SET

Training Features Shape: (1395, 1045)
Training Labels Shape: (465, 1045)
Testing Features Shape: (1395,)
Testing Labels Shape: (465,)

          TEST SETS

Training Features Shape: (1039, 1045)
Training Labels Shape: (347, 1045)
Testing Features Shape: (1039,)
Testing Labels Shape: (347,)

Any suggestions in this regard will be highly appreciated.

Answer 1

Your question is a bit unclear but as I understand, you want to run your model on testX_train and on testX_test (which is just testFeatures splitted into two sub datasets).

So, either you can run your model on testX_train the same way you did for testX_test, e.g. :

result_RFC_train = loaded_model_RFC.score(textX_train, testy_train)

or you can just remove the following line :

testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

So you just don't split you data and run it on the full dataset :

result_RFC_train = loaded_model_RFC.score(testFeatures, testlabels)