add lstm-time-series demo code

2_pytorch/3_RNN/time-series/lstm-time-series.py

@@ -64,6 +64,8 @@ train_Y = data_Y[:train_size]
 test_X = data_X[train_size:]
 test_Y = data_Y[train_size:]
 
+train_Y.shape
+
 # 最后，我们需要将数据改变一下形状，因为 RNN 读入的数据维度是 (seq, batch, feature)，所以要重新改变一下数据的维度，这里只有一个序列，所以 batch 是 1，而输入的 feature 就是我们希望依据的几个月份，这里我们定的是两个月份，所以 feature 就是 2.
 
 # +

demo_code/4_LSTM_timeseries.py

@@ -0,0 +1,117 @@
+ 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import torch
+from torch import nn
+from torch.autograd import Variable
+
+
+"""
+Using torch to do time series analysis by LSTM model
+"""
+
+# load data
+data_csv = pd.read_csv("./lstm_data.csv", usecols=[1])
+
+#plt.plot(data_csv)
+#plt.show()
+
+# data pre-processing
+data_csv = data_csv.dropna()
+dataset = data_csv.values
+dataset = dataset.astype("float32")
+val_max = np.max(dataset)
+val_min = np.min(dataset)
+val_scale = val_max - val_min
+dataset = (dataset - val_min) / val_scale
+
+
+# generate dataset
+def create_dataset(dataset, look_back=6):
+    dataX, dataY = [], []
+    dataset = dataset.tolist()
+    for i in range(len(dataset) - look_back):
+        a = dataset[i:(i+look_back)]
+        dataX.append(a)
+        dataY.append(dataset[i+look_back])
+
+    return np.array(dataX), np.array(dataY)
+
+look_back = 1
+data_X, data_Y = create_dataset(dataset, look_back)
+
+
+# split train/test dataset
+train_size = int(len(data_X) * 0.7)
+test_size = len(data_X) - train_size
+
+train_X = data_X[:train_size]
+train_Y = data_Y[:train_size]
+test_X  = data_X[train_size:]
+test_Y  = data_Y[train_size:]
+
+
+# convert data for torch
+train_X = train_X.reshape(-1, 1, look_back)
+train_Y = train_Y.reshape(-1, 1, 1)
+test_X  = test_X.reshape(-1, 1, look_back)
+
+train_x = torch.from_numpy(train_X).float()
+train_y = torch.from_numpy(train_Y).float()
+test_x  = torch.from_numpy(test_X).float()
+
+# define LSTM model
+class LSTM_Reg(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size=1, num_layer=2):
+        super(LSTM_Reg, self).__init__()
+
+        self.rnn = nn.LSTM(input_size, hidden_size, num_layer)
+        self.reg = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x, _ = self.rnn(x)
+        s, b, h = x.shape
+        x = x.view(s*b, h)
+        x = self.reg(x)
+        x = x.view(s, b, -1)
+        return x
+
+net = LSTM_Reg(look_back, 4, num_layer=1)
+criterion = nn.MSELoss()
+optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)
+
+
+for e in range(1000):
+    var_x = Variable(train_x)
+    var_y = Variable(train_y)
+
+    # forward
+    out = net(var_x)
+    loss = criterion(out, var_y)
+
+    # backward
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    # print progress
+    if e % 100 == 0:
+        print("epoch: %5d, loss: %.5f" % (e, loss.data[0]))
+
+# do test
+net = net.eval()
+
+data_X = data_X.reshape(-1, 1, look_back)
+data_X = torch.from_numpy(data_X).float()
+var_data = Variable(data_X)
+pred_test = net(var_data)
+
+pred_test = pred_test.view(-1).data.numpy()
+
+# plot
+plt.plot(pred_test, 'r', label="Prediction")
+plt.plot(dataset, 'b', label="Real")
+plt.legend(loc="best")
+plt.show()

demo_code/lstm_data.csv

@@ -0,0 +1,148 @@
+"Month","International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60"
+"1949-01",112
+"1949-02",118
+"1949-03",132
+"1949-04",129
+"1949-05",121
+"1949-06",135
+"1949-07",148
+"1949-08",148
+"1949-09",136
+"1949-10",119
+"1949-11",104
+"1949-12",118
+"1950-01",115
+"1950-02",126
+"1950-03",141
+"1950-04",135
+"1950-05",125
+"1950-06",149
+"1950-07",170
+"1950-08",170
+"1950-09",158
+"1950-10",133
+"1950-11",114
+"1950-12",140
+"1951-01",145
+"1951-02",150
+"1951-03",178
+"1951-04",163
+"1951-05",172
+"1951-06",178
+"1951-07",199
+"1951-08",199
+"1951-09",184
+"1951-10",162
+"1951-11",146
+"1951-12",166
+"1952-01",171
+"1952-02",180
+"1952-03",193
+"1952-04",181
+"1952-05",183
+"1952-06",218
+"1952-07",230
+"1952-08",242
+"1952-09",209
+"1952-10",191
+"1952-11",172
+"1952-12",194
+"1953-01",196
+"1953-02",196
+"1953-03",236
+"1953-04",235
+"1953-05",229
+"1953-06",243
+"1953-07",264
+"1953-08",272
+"1953-09",237
+"1953-10",211
+"1953-11",180
+"1953-12",201
+"1954-01",204
+"1954-02",188
+"1954-03",235
+"1954-04",227
+"1954-05",234
+"1954-06",264
+"1954-07",302
+"1954-08",293
+"1954-09",259
+"1954-10",229
+"1954-11",203
+"1954-12",229
+"1955-01",242
+"1955-02",233
+"1955-03",267
+"1955-04",269
+"1955-05",270
+"1955-06",315
+"1955-07",364
+"1955-08",347
+"1955-09",312
+"1955-10",274
+"1955-11",237
+"1955-12",278
+"1956-01",284
+"1956-02",277
+"1956-03",317
+"1956-04",313
+"1956-05",318
+"1956-06",374
+"1956-07",413
+"1956-08",405
+"1956-09",355
+"1956-10",306
+"1956-11",271
+"1956-12",306
+"1957-01",315
+"1957-02",301
+"1957-03",356
+"1957-04",348
+"1957-05",355
+"1957-06",422
+"1957-07",465
+"1957-08",467
+"1957-09",404
+"1957-10",347
+"1957-11",305
+"1957-12",336
+"1958-01",340
+"1958-02",318
+"1958-03",362
+"1958-04",348
+"1958-05",363
+"1958-06",435
+"1958-07",491
+"1958-08",505
+"1958-09",404
+"1958-10",359
+"1958-11",310
+"1958-12",337
+"1959-01",360
+"1959-02",342
+"1959-03",406
+"1959-04",396
+"1959-05",420
+"1959-06",472
+"1959-07",548
+"1959-08",559
+"1959-09",463
+"1959-10",407
+"1959-11",362
+"1959-12",405
+"1960-01",417
+"1960-02",391
+"1960-03",419
+"1960-04",461
+"1960-05",472
+"1960-06",535
+"1960-07",622
+"1960-08",606
+"1960-09",508
+"1960-10",461
+"1960-11",390
+"1960-12",432
+
+International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60
+