python - 日期格式的 LSTM 错误

Question

这是我对深度学习的第一次尝试，这段代码的目的是预测外汇市场的走向。

代码如下:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential

column_names = ['Date', 'Time', 'Open', 'High', 'Low','Close', 'Volume']

data = pd.read_csv(r"E:\Tutorial\EURUSD60.csv", header=None, names=column_names)

data['DateTime'] = pd.to_datetime(data.Date + ' ' + data.Time)
del data['Date']
del data['Time']

sequence_length = 21
n_features = len(data.columns)
val_ratio = 0.1
n_epochs = 300
batch_size = 512

data = data.as_matrix()
data_processed = []
for index in range(len(data) - sequence_length):
    data_processed.append(data[index: index + sequence_length])
data_processed = np.array(data_processed)

val_split = round((1 - val_ratio) * data_processed.shape[0])
train = data_processed[: int(val_split), :]
val = data_processed[int(val_split):, :]

print('Training data: {}'.format(train.shape))
print('Validation data: {}'.format(val.shape))

train_samples, train_nx, train_ny = train.shape
val_samples, val_nx, val_ny = val.shape

train = train.reshape((train_samples, train_nx * train_ny))
val = val.reshape((val_samples, val_nx * val_ny))

preprocessor = MinMaxScaler().fit(train)
train = preprocessor.transform(train)
val = preprocessor.transform(val)

train = train.reshape((train_samples, train_nx, train_ny))
val = val.reshape((val_samples, val_nx, val_ny))

X_train = train[:, : -1]
y_train = train[:, -1][:, -1]
X_val = val[:, : -1]
y_val = val[:, -1][:, -1]

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], n_features))
X_val = np.reshape(X_val, (X_val.shape[0], X_val.shape[1], n_features))

model = Sequential()
model.add(LSTM(input_shape=(X_train.shape[1:]), units=128, return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(128, return_sequences=False))
model.add(Dropout(0.25))
model.add(Dense(units=1))
model.add(Activation("linear"))

model.compile(loss="mse", optimizer="adam")

history = model.fit(
    X_train,
    y_train,
    batch_size=batch_size,
    epochs=n_epochs,
    verbose=2)

preds_val = model.predict(X_val)
diff = []
for i in range(len(y_val)):
    pred = preds_val[i][0]
    diff.append(y_val[i] - pred)

real_min = preprocessor.data_min_[104]
real_max = preprocessor.data_max_[104]
print(preprocessor.data_min_[104])
print(preprocessor.data_max_[104])

preds_real = preds_val * (real_max - real_min) + real_min
y_val_real = y_val * (real_max - real_min) + real_min

plt.plot(preds_real, label='Predictions')
plt.plot(y_val_real, label='Actual values')
plt.xlabel('test')
plt.legend(loc=0)
plt.show()

这里是错误:

Using TensorFlow backend. 2017-12-03 13:26:44.494199: W
C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but
these are available on your machine and could speed up CPU
computations. 2017-12-03 13:26:44.494660: W
C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but
these are available on your machine and could speed up CPU
computations. Training data: (1824, 21, 6) Validation data: (203, 21,
6) Traceback (most recent call last): File "E:/Tutorial/Deep
Learning.py", line 42, in preprocessor = MinMaxScaler().fit(train) File "C:\Users\sydgo\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py",
line 308, in fit
return self.partial_fit(X, y) File "C:\Users\sydgo\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py",
line 334, in partial_fit
estimator=self, dtype=FLOAT_DTYPES) File "C:\Users\sydgo\Anaconda3\lib\site-packages\sklearn\utils\validation.py",
line 433, in check_array array = np.array(array, dtype=dtype, order=order, copy=copy) TypeError: float() argument must be a string or a number, not
'Timestamp'

Answer 1

这是修复错误后的代码

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential

column_names = ['Date', 'Time', 'Open', 'High', 'Low','Close', 'Volume']

df = pd.read_csv(r"E:\Tutorial\EURUSD60.csv", header=None, names=column_names)

df['DateTime'] = pd.to_datetime(df.Date + ' ' + df.Time)
del df['Date']
del df['Time']

df.rename(columns={'DateTime': 'timestamp', 'Open': 'open',
                   'High': 'high', 'Low': 'low', 'Close': 'close', 'Volume': 'volume'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'], infer_datetime_format=True)
df.set_index('timestamp', inplace=True)
df = df.astype(float)
df['hour'] = df.index.hour
df['day'] = df.index.weekday
df['week'] = df.index.week


sequence_length = 21
n_features = len(df.columns)
val_ratio = 0.1
n_epochs = 300
batch_size = 512

data = df.as_matrix()
data_processed = []
for index in range(len(data) - sequence_length):
    data_processed.append(data[index: index + sequence_length])
data_processed = np.array(data_processed)

val_split = round((1 - val_ratio) * data_processed.shape[0])
train = data_processed[: int(val_split), :]
val = data_processed[int(val_split):, :]

print('Training data: {}'.format(train.shape))
print('Validation data: {}'.format(val.shape))

train_samples, train_nx, train_ny = train.shape
val_samples, val_nx, val_ny = val.shape

train = train.reshape((train_samples, train_nx * train_ny))
val = val.reshape((val_samples, val_nx * val_ny))

preprocessor = MinMaxScaler().fit(train)
train = preprocessor.transform(train)
val = preprocessor.transform(val)

train = train.reshape((train_samples, train_nx, train_ny))
val = val.reshape((val_samples, val_nx, val_ny))

X_train = train[:, : -1]
y_train = train[:, -1][:, -1]
X_val = val[:, : -1]
y_val = val[:, -1][:, -1]

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], n_features))
X_val = np.reshape(X_val, (X_val.shape[0], X_val.shape[1], n_features))

model = Sequential()
model.add(LSTM(input_shape=(X_train.shape[1:]), units=128, return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(128, return_sequences=False))
model.add(Dropout(0.25))
model.add(Dense(units=1))
model.add(Activation("linear"))

model.compile(loss="mse", optimizer="adam")

history = model.fit(
    X_train,
    y_train,
    batch_size=batch_size,
    epochs=n_epochs,
    verbose=2)

preds_val = model.predict(X_val)
diff = []
for i in range(len(y_val)):
    pred = preds_val[i][0]
    diff.append(y_val[i] - pred)

real_min = preprocessor.data_min_[104]
real_max = preprocessor.data_max_[104]
print(preprocessor.data_min_[:120])
print(preprocessor.data_max_[:120])

preds_real = preds_val * (real_max - real_min) + real_min
y_val_real = y_val * (real_max - real_min) + real_min

plt.plot(preds_real, label='Predictions')
plt.plot(y_val_real, label='Actual values')
plt.xlabel('test')
plt.legend(loc=0)
plt.show()