ValueError: X has 1 features, but SVC is expecting 3 features as input

Question

我正在尝试使用 Keras 和 sklearn 创建一个股票价格预测器（不要实际用它来投资，别担心），它从中获取任何时间序列Kaggle 并检查“关闭”。然后它需要一个特定长度的滚动时间 window 并预测方向精度，向上 (1) 或向下 (0)。

在尝试运行下面的代码时，出现了以下错误：

File "...", line 71, in test
    y_pred = self.model.predict(self.X_test)
ValueError: X has 1 features, but SVC is expecting 3 features as input.

有人可以指导我解决可能的问题吗？ SVC 期望我可能缺少哪些功能？

代码：Model.py

create_features 根据滚动时间 window 检查市场是更低还是更高，并设置 X 和 y:

#window_size = the set size of the rolling time window

def create_features(data, window_size):
    X = []
    y = []

    for i in range(0, len(data.index) - window_size):
        temp = [data.iloc[i + j]['Close'] for j in range(0, window_size)]
        avg = sum(temp) / len(temp)

        X.append(temp)
        y.append(0 if data.iloc[i + window_size]['Close'] < avg else 1)

    return X, y

class Model:
    def __init__(self, market: Market, training_percent: float, window_size: int):
        self.model = SVC(C=10, gamma='scale', kernel='rbf')

        X, y = create_features(market.data, window_size)
        self.X_train, self.y_train, self.X_test, self.y_test = train_test_split(X, y, shuffle=False, stratify=None, train_size=training_percent)

        self.X_train = np.array(self.X_train)
        self.y_train = np.array(self.y_test)

        #self.X_test = np.array(self.X_test).reshape(-1, 1)

    def train(self):
        self.model.fit(self.X_train, self.y_train)

    def test(self):
        y_pred = self.model.predict(self.X_test) #THE COMPLAINING LINE

        y_pred = [0 if i < 0.5 else 1 for i in y_pred]
        tn, fp, fn, tp = confusion_matrix(self.y_test, y_pred, labels=[0, 1]).ravel()
        print(tn, fp, fn, tp)
        print("Accuracy:", (tn + fp) / (tn + fp + fn + tp))

    def predict(self, input_array):
        return self.model.predict(input_array)

以上称为：

model_test = Model(markets[m], training_testing[j], window_size[i])
model_test.train()
model_test.test()

如能就此问题提供任何帮助，我们将不胜感激。提前谢谢你。

Answer 1

问题在于您如何获得 train_test_split 的输出。正如 documentation 所述，您应该按顺序获取拆分数据集：

# Notice the order of the unpacking.
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, shuffle=False, stratify=None, train_size=training_percent)

因此，测试数据集的形状不同，因为它实际上是训练标签。您也不需要 .reshape。

此外，不确定您是否要这样做：

# Assigning y_test to y_train.
self.y_train = np.array(self.y_test)

ValueError: X has 1 features, but SVC is expecting 3 features as input

ValueError: X has 1 features, but SVC is expecting 3 features as input

python

tensorflow

keras

scikit-learn

svm