支持向量机或SPARK中任意分类器的增量分类

from sklearn.svm import SVC
from sklearn.linear_model import SGDClassifier

# 初始化模型
svm_model = SGDClassifier(loss='hinge', warm_start=True)

# 增量训练
for i in range(num_batches):
    X_batch, y_batch = get_next_batch()  # 获取下一批数据
    svm_model.partial_fit(X_batch, y_batch, classes=np.unique(y))

# 评估模型
score = svm_model.score(X_test, y_test)

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.sql import SparkSession

# 初始化SparkSession
spark = SparkSession.builder.appName("IncrementalClassification").getOrCreate()

# 初始化模型
lr = LogisticRegression(maxIter=1, regParam=0.01)

# 增量训练
for i in range(num_batches):
    data_batch = spark.read.format("libsvm").load(f"data_batch_{i}.libsvm")
    lr.partialFit(data_batch)

# 评估模型
evaluator = BinaryClassificationEvaluator()
score = evaluator.evaluate(lr.transform(test_data))