ssl_self_train.py

# Initiate iteration counter
iterations = 0

# Containers to hold f1_scores and # of pseudo-labels
train_f1s = []
test_f1s = []
pseudo_labels = []

# Assign value to initiate while loop
high_prob = [1] 

# Loop will run until there are no more high-probability pseudo-labels
while len(high_prob) > 0:
        
    # Fit classifier and make train/test predictions
    clf = LogisticRegression(max_iter=1000)
    clf.fit(X_train, y_train)
    y_hat_train = clf.predict(X_train)
    y_hat_test = clf.predict(X_test)

    # Calculate and print iteration # and f1 scores, and store f1 scores
    train_f1 = f1_score(y_train, y_hat_train)
    test_f1 = f1_score(y_test, y_hat_test)
    print(f"Iteration {iterations}")
    print(f"Train f1: {train_f1}")
    print(f"Test f1: {test_f1}")
    train_f1s.append(train_f1)
    test_f1s.append(test_f1)
   
    # Generate predictions and probabilities for unlabeled data
    print(f"Now predicting labels for unlabeled data...")

    pred_probs = clf.predict_proba(X_unlabeled)
    preds = clf.predict(X_unlabeled)
    prob_0 = pred_probs[:,0]
    prob_1 = pred_probs[:,1]

    # Store predictions and probabilities in dataframe
    df_pred_prob = pd.DataFrame([])
    df_pred_prob['preds'] = preds
    df_pred_prob['prob_0'] = prob_0
    df_pred_prob['prob_1'] = prob_1
    df_pred_prob.index = X_unlabeled.index
    
    # Separate predictions with > 99% probability
    high_prob = pd.concat([df_pred_prob.loc[df_pred_prob['prob_0'] > 0.99],
                           df_pred_prob.loc[df_pred_prob['prob_1'] > 0.99]],
                          axis=0)
    
    print(f"{len(high_prob)} high-probability predictions added to training data.")
    
    pseudo_labels.append(len(high_prob))

    # Add pseudo-labeled data to training data
    X_train = pd.concat([X_train, X_unlabeled.loc[high_prob.index]], axis=0)
    y_train = pd.concat([y_train, high_prob.preds])      
    
    # Drop pseudo-labeled instances from unlabeled data
    X_unlabeled = X_unlabeled.drop(index=high_prob.index)
    print(f"{len(X_unlabeled)} unlabeled instances remaining.\n")
    
    # Update iteration counter
    iterations += 1