How to add three csv file into one LSTM using python












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I have three csv files with same inputs but values are different. I want to add these three csv file into one LSTM model to predict value. Hare I upload the three different csv file and my LSTM code. Can anyone suggest me an idea how to add that three csv file data into the LSTM model.
my code:



 import pandas as pd
import numpy as np
data = pd.read_csv('data1.csv')
data = pd.DataFrame(data,columns=['x','x1','x2','y'])
data.columns = ['x', 'x1', 'x2','y']
pd.options.display.float_format = '{:,.0f}'.format
data = data.dropna ()
d = ['y']
y=data['y'].astype(int)
cols=['x', 'x1', 'x2']
x=data[cols].astype(int)
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),3 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
print("row",len(y))
n = data.shape[0]
p = data.shape[1]
fill_missing(data.values)
train_start = 0
train_end = int(np.floor(0.65*n))
test_start = train_end+1
test_end = n
x_train = x[np.arange(train_start, train_end), :]
x_test = x[np.arange(test_start, test_end), :]
y_train = y[np.arange(train_start, train_end), :]
y_test = y[np.arange(test_start, test_end), :]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
seed = 20
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 10,
activation='relu',
input_shape =(3,1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
batchsize = 10
fit1.compile(loss="mean_squared_error",optimizer="adam")
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10, shuffle=True)
print(fit1.summary ())
pred1=fit1.predict(x_test)
pred1=fit1.predict(x_test)
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)


my three csv file :
data1.csv



enter image description here



data2.csv :



enter image description here



data3.csv :



enter image description here









share









$endgroup$

















    0












    $begingroup$


    I have three csv files with same inputs but values are different. I want to add these three csv file into one LSTM model to predict value. Hare I upload the three different csv file and my LSTM code. Can anyone suggest me an idea how to add that three csv file data into the LSTM model.
    my code:



     import pandas as pd
    import numpy as np
    data = pd.read_csv('data1.csv')
    data = pd.DataFrame(data,columns=['x','x1','x2','y'])
    data.columns = ['x', 'x1', 'x2','y']
    pd.options.display.float_format = '{:,.0f}'.format
    data = data.dropna ()
    d = ['y']
    y=data['y'].astype(int)
    cols=['x', 'x1', 'x2']
    x=data[cols].astype(int)
    scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
    x = np.array(x).reshape ((len(x),3 ))
    x = scaler_x.fit_transform(x)
    scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
    y = np.array(y).reshape ((len(y), 1))
    y = scaler_y.fit_transform(y)
    print("row",len(y))
    n = data.shape[0]
    p = data.shape[1]
    fill_missing(data.values)
    train_start = 0
    train_end = int(np.floor(0.65*n))
    test_start = train_end+1
    test_end = n
    x_train = x[np.arange(train_start, train_end), :]
    x_test = x[np.arange(test_start, test_end), :]
    y_train = y[np.arange(train_start, train_end), :]
    y_test = y[np.arange(test_start, test_end), :]
    x_train=x_train.reshape(x_train.shape +(1,))
    x_test=x_test.reshape(x_test.shape + (1,))
    seed = 20
    np.random.seed(seed)
    fit1 = Sequential ()
    fit1.add(LSTM(
    output_dim = 10,
    activation='relu',
    input_shape =(3,1)))
    fit1.add(Dense(output_dim =1))
    fit1.add(Activation(linear))
    batchsize = 10
    fit1.compile(loss="mean_squared_error",optimizer="adam")
    fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10, shuffle=True)
    print(fit1.summary ())
    pred1=fit1.predict(x_test)
    pred1=fit1.predict(x_test)
    real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)


    my three csv file :
    data1.csv



    enter image description here



    data2.csv :



    enter image description here



    data3.csv :



    enter image description here









    share









    $endgroup$















      0












      0








      0





      $begingroup$


      I have three csv files with same inputs but values are different. I want to add these three csv file into one LSTM model to predict value. Hare I upload the three different csv file and my LSTM code. Can anyone suggest me an idea how to add that three csv file data into the LSTM model.
      my code:



       import pandas as pd
      import numpy as np
      data = pd.read_csv('data1.csv')
      data = pd.DataFrame(data,columns=['x','x1','x2','y'])
      data.columns = ['x', 'x1', 'x2','y']
      pd.options.display.float_format = '{:,.0f}'.format
      data = data.dropna ()
      d = ['y']
      y=data['y'].astype(int)
      cols=['x', 'x1', 'x2']
      x=data[cols].astype(int)
      scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
      x = np.array(x).reshape ((len(x),3 ))
      x = scaler_x.fit_transform(x)
      scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
      y = np.array(y).reshape ((len(y), 1))
      y = scaler_y.fit_transform(y)
      print("row",len(y))
      n = data.shape[0]
      p = data.shape[1]
      fill_missing(data.values)
      train_start = 0
      train_end = int(np.floor(0.65*n))
      test_start = train_end+1
      test_end = n
      x_train = x[np.arange(train_start, train_end), :]
      x_test = x[np.arange(test_start, test_end), :]
      y_train = y[np.arange(train_start, train_end), :]
      y_test = y[np.arange(test_start, test_end), :]
      x_train=x_train.reshape(x_train.shape +(1,))
      x_test=x_test.reshape(x_test.shape + (1,))
      seed = 20
      np.random.seed(seed)
      fit1 = Sequential ()
      fit1.add(LSTM(
      output_dim = 10,
      activation='relu',
      input_shape =(3,1)))
      fit1.add(Dense(output_dim =1))
      fit1.add(Activation(linear))
      batchsize = 10
      fit1.compile(loss="mean_squared_error",optimizer="adam")
      fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10, shuffle=True)
      print(fit1.summary ())
      pred1=fit1.predict(x_test)
      pred1=fit1.predict(x_test)
      real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)


      my three csv file :
      data1.csv



      enter image description here



      data2.csv :



      enter image description here



      data3.csv :



      enter image description here









      share









      $endgroup$




      I have three csv files with same inputs but values are different. I want to add these three csv file into one LSTM model to predict value. Hare I upload the three different csv file and my LSTM code. Can anyone suggest me an idea how to add that three csv file data into the LSTM model.
      my code:



       import pandas as pd
      import numpy as np
      data = pd.read_csv('data1.csv')
      data = pd.DataFrame(data,columns=['x','x1','x2','y'])
      data.columns = ['x', 'x1', 'x2','y']
      pd.options.display.float_format = '{:,.0f}'.format
      data = data.dropna ()
      d = ['y']
      y=data['y'].astype(int)
      cols=['x', 'x1', 'x2']
      x=data[cols].astype(int)
      scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
      x = np.array(x).reshape ((len(x),3 ))
      x = scaler_x.fit_transform(x)
      scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
      y = np.array(y).reshape ((len(y), 1))
      y = scaler_y.fit_transform(y)
      print("row",len(y))
      n = data.shape[0]
      p = data.shape[1]
      fill_missing(data.values)
      train_start = 0
      train_end = int(np.floor(0.65*n))
      test_start = train_end+1
      test_end = n
      x_train = x[np.arange(train_start, train_end), :]
      x_test = x[np.arange(test_start, test_end), :]
      y_train = y[np.arange(train_start, train_end), :]
      y_test = y[np.arange(test_start, test_end), :]
      x_train=x_train.reshape(x_train.shape +(1,))
      x_test=x_test.reshape(x_test.shape + (1,))
      seed = 20
      np.random.seed(seed)
      fit1 = Sequential ()
      fit1.add(LSTM(
      output_dim = 10,
      activation='relu',
      input_shape =(3,1)))
      fit1.add(Dense(output_dim =1))
      fit1.add(Activation(linear))
      batchsize = 10
      fit1.compile(loss="mean_squared_error",optimizer="adam")
      fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =10, shuffle=True)
      print(fit1.summary ())
      pred1=fit1.predict(x_test)
      pred1=fit1.predict(x_test)
      real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)


      my three csv file :
      data1.csv



      enter image description here



      data2.csv :



      enter image description here



      data3.csv :



      enter image description here







      python keras tensorflow lstm





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