使用 Keras 使用 LSTM 进行预测
Prediction with LSTM using Keras
我根据过去的值根据 X 预测 Y。我们的格式化 CSV dataset 具有三列（time_stamp、X 和 Y - 其中 Y 是实际值），其示例格式为
time,X,Y
0.000561,0,10
0.000584,0,10
0.040411,5,10
0.040437,10,10
0.041638,12,10
0.041668,14,10
0.041895,15,10
0.041906,19,10
... ... ...
在训练预测模型之前，X 和 Y 的图分别如下所示。
以下是我在 Python 中使用 Keras 解决 LSTM 递归神经网络问题的方法。
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
np.random.seed(7)
# Load data
df = pd.read_csv('test32_C_data.csv')
n_features = 100
def create_sequences(data, window=15, step=1, prediction_distance=15):
x = []
y = []
for i in range(0, len(data) - window - prediction_distance, step):
x.append(data[i:i + window])
y.append(data[i + window + prediction_distance][1])
x, y = np.asarray(x), np.asarray(y)
return x, y
# Scaling prior to splitting
scaler = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_data = scaler.fit_transform(df.loc[:, ["X", "Y"]].values)
# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)
# Create test/train split
test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]
# Initialising the RNN
model = Sequential()
# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))
# Adding the output layer
model.add(Dense(1))
# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')
# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)
# Getting the predicted values
y_pred = model.predict(x_test)
#y_pred = scaler.inverse_transform(y_pred)
plot_colors = ['#332288', '#3cb44b']
# Plot the results
pd.DataFrame({"Actual": y_test, "Predicted": np.squeeze(y_pred)}).plot(color=plot_colors)
plt.xlabel('Time [Index]')
plt.ylabel('Values')
最后，当我 运行 代码时 - 神经模型似乎很好地捕获了信号模式，如下所示。
但是，我在此输出中遇到的一个问题是 Y 的范围。如前两个图中所示，范围应为 0-400，如上所示，为了解决这个问题，我尝试使用缩放到 inverse_transform 为 y_pred = scaler.inverse_transform(y_pred) 但这会引发错误：ValueError: non-broadcastable output operand with shape (7625,1) doesn't match the broadcast shape (7625,2)。我们如何解决这个 broadcast shape 错误？
基本上，缩放器已经记住它被提供了 2 个特征（/列）。所以它期望有 2 个特征来反转转换。
这里有两个选项。
1) 你做了两个不同的缩放器：scaler_x 和 scaler_y 像这样：
# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1, 1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1, 1]))
scaled_data = np.column_stack((scaled_x, scaled_y))
那么你就可以做到:
y_pred = scaler_y.inverse_transform(y_pred)
2) 你像这样伪造输出中的 X 列：
y_pred_reshaped = np.zeros((len(y_pred), 2))
y_pred_reshaped[:,1] = y_pred
y_pred = scaler.inverse_transform(y_pred_reshaped)[:,1]
有帮助吗？
编辑
这里是需要的完整代码
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
np.random.seed(7)
# Load data
#df = pd.read_csv('test32_C_data.csv')
df = pd.DataFrame(np.random.randint(0,100, size=(100,3)), columns = ['time', 'X', 'Y'])
n_features = 100
def create_sequences(data, window=15, step=1, prediction_distance=15):
x = []
y = []
for i in range(0, len(data) - window - prediction_distance, step):
x.append(data[i:i + window])
y.append(data[i + window + prediction_distance][1])
x, y = np.asarray(x), np.asarray(y)
return x, y
# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1,1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1,1]))
scaled_data = np.column_stack((scaled_x, scaled_y))
# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)
test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]
# Initialising the RNN
model = Sequential()
# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))
# Adding the output layer
model.add(Dense(1))
# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')
# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)
# Getting the predicted values
y_pred = model.predict(x_test)
y_pred = scaler_y.inverse_transform(y_pred)
我根据过去的值根据 X 预测 Y。我们的格式化 CSV dataset 具有三列（time_stamp、X 和 Y - 其中 Y 是实际值），其示例格式为
time,X,Y
0.000561,0,10
0.000584,0,10
0.040411,5,10
0.040437,10,10
0.041638,12,10
0.041668,14,10
0.041895,15,10
0.041906,19,10
... ... ...
在训练预测模型之前，X 和 Y 的图分别如下所示。
以下是我在 Python 中使用 Keras 解决 LSTM 递归神经网络问题的方法。
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
np.random.seed(7)
# Load data
df = pd.read_csv('test32_C_data.csv')
n_features = 100
def create_sequences(data, window=15, step=1, prediction_distance=15):
x = []
y = []
for i in range(0, len(data) - window - prediction_distance, step):
x.append(data[i:i + window])
y.append(data[i + window + prediction_distance][1])
x, y = np.asarray(x), np.asarray(y)
return x, y
# Scaling prior to splitting
scaler = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_data = scaler.fit_transform(df.loc[:, ["X", "Y"]].values)
# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)
# Create test/train split
test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]
# Initialising the RNN
model = Sequential()
# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))
# Adding the output layer
model.add(Dense(1))
# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')
# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)
# Getting the predicted values
y_pred = model.predict(x_test)
#y_pred = scaler.inverse_transform(y_pred)
plot_colors = ['#332288', '#3cb44b']
# Plot the results
pd.DataFrame({"Actual": y_test, "Predicted": np.squeeze(y_pred)}).plot(color=plot_colors)
plt.xlabel('Time [Index]')
plt.ylabel('Values')
最后，当我 运行 代码时 - 神经模型似乎很好地捕获了信号模式，如下所示。
但是，我在此输出中遇到的一个问题是 Y 的范围。如前两个图中所示，范围应为 0-400，如上所示，为了解决这个问题，我尝试使用缩放到 inverse_transform 为 y_pred = scaler.inverse_transform(y_pred) 但这会引发错误：ValueError: non-broadcastable output operand with shape (7625,1) doesn't match the broadcast shape (7625,2)。我们如何解决这个 broadcast shape 错误？
基本上，缩放器已经记住它被提供了 2 个特征（/列）。所以它期望有 2 个特征来反转转换。
这里有两个选项。
1) 你做了两个不同的缩放器：scaler_x 和 scaler_y 像这样：
# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1, 1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1, 1]))
scaled_data = np.column_stack((scaled_x, scaled_y))
那么你就可以做到:
y_pred = scaler_y.inverse_transform(y_pred)
2) 你像这样伪造输出中的 X 列：
y_pred_reshaped = np.zeros((len(y_pred), 2))
y_pred_reshaped[:,1] = y_pred
y_pred = scaler.inverse_transform(y_pred_reshaped)[:,1]
有帮助吗？
编辑
这里是需要的完整代码
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
np.random.seed(7)
# Load data
#df = pd.read_csv('test32_C_data.csv')
df = pd.DataFrame(np.random.randint(0,100, size=(100,3)), columns = ['time', 'X', 'Y'])
n_features = 100
def create_sequences(data, window=15, step=1, prediction_distance=15):
x = []
y = []
for i in range(0, len(data) - window - prediction_distance, step):
x.append(data[i:i + window])
y.append(data[i + window + prediction_distance][1])
x, y = np.asarray(x), np.asarray(y)
return x, y
# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1,1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1,1]))
scaled_data = np.column_stack((scaled_x, scaled_y))
# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)
test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]
# Initialising the RNN
model = Sequential()
# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))
# Adding the output layer
model.add(Dense(1))
# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')
# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)
# Getting the predicted values
y_pred = model.predict(x_test)
y_pred = scaler_y.inverse_transform(y_pred)