随机森林warm_start = True 在运行评分函数时给出值错误 - 操作数无法一起广播

Question

I am implementing a random forest forecast as baseline for my ml model. Since my X_train_split_xgb has shape (48195, 300), i need to do batchtraining (memory). To do that i set up randomforest with warm_start=True, but when i enable this i get an error in rf.predict(X_train_split_xgb line, namely: ValueError: operands could not be broadcast together with shapes (48195,210) (48195,187) (48195,210). If warm_start = False i do not get this error and the code runs through. Does anybody know why i get this valuerror and how to fix it? I tried lots of stuff already. Appreciate your help!

X_batch has shape (1000,300)

y_batch has shape 1000

X_train_split_xgb has shape (48195, 300)

y_train_split_xgb_encoded has shape 48195

i dont even know how it tries to broadcast (48195,210) (48195,187) (48195,210)together, where is 210 and 187 coming from?

from sklearn.ensemble import RandomForestClassifier

errors = []
rf = RandomForestClassifier(n_estimators=5,  
                                     random_state=0,warm_start=True)


for X_batch, y_batch in get_batches(X_train_split_xgb,        y_train_split_xgb_encoded, 1000):

        # Run training and evaluate accuracy
        rf.fit(X_batch, y_batch)# warm_start=True
        print(X_batch.shape)
        print(rf.predict(X_train_split_xgb))
        print(rf.score(X_train_split_xgb, y_train_split_xgb_encoded))
        #pred = rf.predict(X_batch)
        #errors.append(MSE(y_batch, rf.predict(X_batch)))
        rf.n_estimators += 1

Error:

 ValueError: operands could not be broadcast together with shapes (48195,210) (48195,187) (48195,210)

Expected: code runs through and gives the scores at each iteration. Actual: code stops running in loop2, thus, when the prediction/scoring needs to be done the second time. stops in rf.predict()

Answer 1

Very late to answer, but leaving a response in case someone else runs into the same issue.

The reason this is happening is that the class labels are not stratified between different calls to the fit method.

I performed a simple test where I fed the same X and y to the fit method in a loop, and that seems to work.

rf = RandomForestClassifer(warm_start=True)

for _ in range(10):
    X = df.head(100).drop(columns='class')
    y = df.tail(100)['class'].values
    rf.fit(X, y)
    rf.score(X_test, y_test)

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