ValueError: X有1个特征，但SVC期望3个特征作为输入

Question

我正在尝试使用Keras和sklearn创建一个股票价格预测器(不是实际使用它来投资，不要担心)，它从Kaggle获取任何时间序列并检查"close"。然后，它需要一个特定长度的滚动时间窗口，并预测方向精度，向上(1)或向下(0)。

当尝试运行下面的代码时，出现以下错误:

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.

谁能告诉我可能是什么问题?哪些是SVC所期望的，而我可能遗漏的特性?

代码:Model.py

create_features根据滚动时间窗口检查市场是低还是高，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)

以上被称为:

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

对于这个问题，任何帮助都将是非常感激的。提前谢谢你。

Answer 1

问题是您如何获得train_test_split的输出。正如文档所述，您应该按顺序获得拆分的数据集:

# 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。你也不需要.reshape。

另外，我不确定你是否想这样做:

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