ex_ttt_w2v_pre.py

import numpy as np
from math import ceil
from skmultiflow.trees import HoeffdingTree
from strlearn.metrics import balanced_accuracy_score as bac
from sklearn.naive_bayes import GaussianNB
from tqdm import tqdm
from strlearn.ensembles import KUE, ROSE, NIE
from utils import CDS
from strlearn.metrics import balanced_accuracy_score as bac, recall, precision, specificity, f1_score, geometric_mean_score_1, geometric_mean_score_2
from sklearn.metrics import recall_score, precision_score, balanced_accuracy_score
from sentence_transformers import SentenceTransformer
from sklearn.decomposition import PCA
import os
from gensim import downloader



os.environ["TOKENIZERS_PARALLELISM"] = "false"


X = np.load("fakeddit_stream/fakeddit_posts.npy", allow_pickle=True)
bias = np.load("fakeddit_stream/fakeddit_posts_y.npy")
# How many classes?
bias_id = 0
print(X.shape)
print(bias.shape)

# Only titles, without timestamp
# Binary problem
stream = X[:, 0]
y = np.array([1,0])[bias[:,bias_id]] if bias_id == 0 else bias[:,bias_id]

chunk_size = 250
# All chunks
n_chunks = ceil(stream.shape[0]/chunk_size)
# Select dummies
classes = np.unique(y)
n_classes = len(classes)
dummies = stream[[np.where(y==label)[0][0] for label in classes]]

metrics=(recall, recall_score, precision, precision_score, specificity, f1_score, geometric_mean_score_1, geometric_mean_score_2, bac, balanced_accuracy_score)

n_estimators = 10
methods = [
        HoeffdingTree(split_criterion="hellinger"),
        CDS(HoeffdingTree(split_criterion="hellinger"), n_estimators),
        NIE(HoeffdingTree(split_criterion="hellinger"), n_estimators),
        KUE(HoeffdingTree(split_criterion="hellinger"), n_estimators),
        ROSE(HoeffdingTree(split_criterion="hellinger"), n_estimators),
    ]

# model = SentenceTransformer('all-MiniLM-L6-v2', device="mps").to("mps")
vectors = downloader.load('word2vec-google-news-300')

# METHODS x CHUNKS x METRICS
pca = PCA(100, random_state=1410)

scores = np.zeros((5, n_chunks, 10))
for chunk_id in tqdm(range(n_chunks)):
    chunk_X = stream[chunk_id*chunk_size:chunk_id*chunk_size+chunk_size]
    chunk_y = y[chunk_id*chunk_size:chunk_id*chunk_size+chunk_size]
    
    if len(np.unique(chunk_y)) != n_classes:
        chunk_X[:n_classes] = dummies
        chunk_y[:n_classes] = classes

    embeddings = []
    for text_id, text in enumerate(chunk_X):
        words = text.split(" ")

        wordvecs = np.zeros((300,len(words)))
        for idx, word in enumerate(words):
            try:
                wordvecs[:, idx] = vectors[word]
            except KeyError:
                pass
        embeddings.append(np.mean(wordvecs, axis=1))

    embeddings = np.array(embeddings)

    
    for method_id, method in enumerate(methods):
        if chunk_id == 0:
            preproc_X = pca.fit_transform(embeddings)
            method.fit(preproc_X, chunk_y)
        else:
            preproc_X = pca.transform(embeddings)
            try:
                pred = method.predict(preproc_X)
                
                for metric_id, metric in enumerate(metrics):
                    score = metric(chunk_y, pred)
                    scores[method_id, chunk_id, metric_id] = score
                
                method.partial_fit(preproc_X, chunk_y)
            except:
                scores[method_id, chunk_id, metric_id] = np.nan

np.save("results/scores_w2v_pre_pca100", scores)