SVM: why am I getting different results?
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Entering edit mode
10 weeks ago
lizabe ▴ 10

I am using SVM to make a prediction on bacterial genomes. The output of the program are short DNA sequences that best predict a given class.

I am using this function to get the best model parameters:

def fitmodel(X, pheno, estimator, parameters) :

#Separate data into train/test sets
#Perform a Grid search to identify the best hyperparameters
#Call predict on the estimator with the best found parameters using test dataset
#Generates statistics about the predictor

kfold = KFold(n_splits=5)
nk = 0
for train_index, test_index in kfold.split(X, pheno): 
    nk = nk + 1
    print("Running " + str(nk) + " fold...")

    X_train = X.iloc[train_index]

    y_train = pheno[train_index]
    X_test = X.iloc[test_index]
    y_test = pheno[test_index]

    print("Performing GRID search...")
    gs_clf = GridSearchCV(estimator=estimator, param_grid=parameters, cv=5, n_jobs=-1, scoring='balanced_accuracy')

    print("Fitting model...")
    gs_clf.fit(X_train, y_train) 

    print("Predicting-Train...")
    y_pred_train = gs_clf.predict(X_train) 
    y_pred_train[y_pred_train<0.5] = 0
    y_pred_train[y_pred_train>0.5] = 1

    print("Confusion matrix train for the fold " + str(nk))
    print(confusion_matrix(y_train, y_pred_train))
    print("Metrics report of training for the fold " + str(nk) +": " + classification_report(y_train, y_pred_train))

    y_pr = gs_clf.decision_function(X_train)
    auc = roc_auc_score(y_train, y_pr)
    print('AUC: %.3f' % auc)

    print("Predicting-Test...")
    y_pred = gs_clf.predict(X_test) 
    y_pred[y_pred<0.5] = 0
    y_pred[y_pred>0.5] = 1

    print("Best hyperparameters for the fold " + str(nk))
    print(gs_clf.best_params_)
    print("Confusion matrix test for the fold " + str(nk))
    print(confusion_matrix(y_test, y_pred))
    print("Metrics report of testing for the fold " + str(nk) +": " + classification_report(y_test, y_pred))

    y_pr_test = gs_clf.decision_function(X_test)
    aucTest = roc_auc_score(y_test, y_pr_test)
    print('AUC: %.3f' % aucTest)

return gs_clf

I would like to know why if I use the estimator that returned the function "fitmodel" it gives me a specific result but if I create a new estimator with the same parameters as the estimator that returned this function it gives me another result.

This is the main function:

svm = SVC(class_weight='balanced')
svm_params = {
    'C': [0.01],
    'gamma': [1e-06, 1e-05],
    'kernel': ['linear']
}

svm_model = fitmodel(X, pheno, svm, svm_params)

To know what were the parameters of the estimator returned by the "fitmodel()" function, I used this:

print(svm_model.best_estimator_.get_params())

This was the result:

{'C': 0.01, 'break_ties': False, 'cache_size': 200, 'class_weight': 'balanced', 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 1e-06, 'kernel': 'linear', 'max_iter': -1, 'probability': False, 'random_state': None, 'shrinking': True, 'tol': 0.001, 'verbose': False}

Then I created a new SVM estimator using this parameters:

 FinalModel = SVC(C=0.01, kernel='linear', gamma=1e-06, class_weight='balanced')
 FinalModell.fit(X,pheno)
 FinalModel.predict(X)

But the results are not the same if I run this:

feature_names_top, top_coefficients = plot_coefficients(svm_model, X.columns)
print("Top positive predictors SVN: ", feature_names_top[-100:])
print("Top positive predictors weigth SVN: ", top_coefficients[-100:])

Or this:

feature_names_top, top_coefficients = plot_coefficients_final(FinalModel, X.columns)
 print("Top positive predictors SVN: ", feature_names_top[-100:])
 print("Top positive predictors weigth SVN: ", top_coefficients[-100:])

Using these functions (that are the same but differs in the first sentence):

def plot_coefficients_final(classifier, feature_names, top_features=100):

    coef = classifier.coef_.ravel()
    top_positive_coefficients = np.argsort(coef)[-top_features:] #imprime los Ășltimos 20
    top_negative_coefficients = np.argsort(coef)[:top_features] #imprime los primeros 20
    top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])


    coef_top_positive = np.empty(top_features,dtype=object)
    coef_top_negative = np.empty(top_features,dtype=object)
    coef_top_coefficients = np.empty(top_features*2,dtype=object)

    m=0
    for n in top_positive_coefficients:
        coef_top_positive[m] = coef[n]
        m = m + 1

    m=0
    for n in top_negative_coefficients:
        coef_top_negative[m] = coef[n]
        m = m + 1


    coef_top_coefficients = np.hstack([coef_top_negative,coef_top_positive])
    feature_names_top = np.empty(top_features*2,dtype=object)

    j=0
    for i in top_coefficients:
        feature_names_top[j] = feature_names[i]
        j = j + 1

    return feature_names_top, coef_top_coefficients

and

def plot_coefficients(classifier, feature_names, top_features=100):

    coef = classifier.best_estimator_.coef_.ravel()
    top_positive_coefficients = np.argsort(coef)[-top_features:] #imprime los Ășltimos 20
    top_negative_coefficients = np.argsort(coef)[:top_features] #imprime los primeros 20
    top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])


    coef_top_positive = np.empty(top_features,dtype=object)
    coef_top_negative = np.empty(top_features,dtype=object)
    coef_top_coefficients = np.empty(top_features*2,dtype=object)

    m=0
    for n in top_positive_coefficients:
        coef_top_positive[m] = coef[n]
        m = m + 1

    m=0
    for n in top_negative_coefficients:
        coef_top_negative[m] = coef[n]
        m = m + 1


    coef_top_coefficients = np.hstack([coef_top_negative,coef_top_positive])
    feature_names_top = np.empty(top_features*2,dtype=object)

    j=0
    for i in top_coefficients:
        feature_names_top[j] = feature_names[i]
        j = j + 1

    return feature_names_top, coef_top_coefficients

Could someone please tell me why?

Thank you very much.

python svm • 257 views
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0
Entering edit mode
10 weeks ago
Mensur Dlakic ★ 21k

Most likely your results are different because different fold splits are selected each time you run this. The culprit is this line:

kfold = KFold(n_splits=5)

If you select a fixed random seed (the random_state parameter), then the fold splits will be the same each time. In that case the only difference in results could come from different model parameters (C, gamma, etc).

Setting a fixed random_state:

kfold = KFold(n_splits=5, shuffle=True, random_state=2022)
ADD COMMENT
0
Entering edit mode

Mensur, thanks for your answer.

I think that maybe my question was misunderstood.

My question is why if I use the "svm_model" predictor and the "FinalModel" predictor (that supposedly have both the same parameters) with the full dataset (X) in the corresponding plot_coefficients function, the results I get are not the same?

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