This post shows how to use %run command for importing Jupyter Notebook (ipynb) files from another folders. It is essentially to run the imported ipynb files before running a main file. It can be used to split code blocks such as importing library, declaring user-defined functions, data and its preprocessing.
This post is similar to the previous post but easily extended to ipynb files from other folders. So I introduce it to you. It is so easy.
To import ipynb files in another folders, we can use %run command.
As an example, I split one file into three files: a0_load_lib_func.ipynb, a1_read_data.ipynb, and a2_run_rnn.ipynb. The first two files are saved at D:/blog_temp/ for an illustration.
1) Common code block : a1_load_lib_func.ipynb
a1_load_lib_func.ipynb contains package libraries, some user-defined functions.
# Import Library # In: _________________________________________________________________ import pandas as pd import numpy as np import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, SimpleRNN get_ipython().run_line_magic('matplotlib', 'inline') from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" _________________________________________________________________ # Functions # In: _________________________________________________________________ # convert into dataset matrix def convertToMatrix(data, step): X, Y =,  for i in range(len(data)-step): d=i+step; X.append(data[i:d,]); Y.append(data[d,]) return np.array(X), np.array(Y) def draw_plot1(df,predicted): index = df.index.values plt.figure(figsize=(5, 2.5)) plt.plot(index,df); plt.plot(index,predicted) plt.show() return plt
2) Reading data : a2_read_data.ipynb
a2_read_data.ipynb is used to read data.
# Read Dataset # In: _________________________________________________________________ step = 4; N = 1000; Tp = 800 t=np.arange(0,N) x=np.sin(0.02*t)+2*np.random.rand(N) df = pd.DataFrame(x) # df.head(); plt.plot(df); plt.show() train=df.values train = np.append(train,np.repeat(train[-1,],step)) trainX,trainY = convertToMatrix(train,step) trainX = np.reshape(trainX, (trainX.shape, 1, trainX.shape))
3) Main file : a3_run_rnn.ipynb
The following main file (a3_run_rnn.ipynb) imports two above files and run a SimpleRNN tensorflow function.
# Import other folder's ipynb files # In: _________________________________________________________________ # the same folder #%run "a1_load_lib_func.ipynb" #%run "a2_read_data.ipynb" # different folder %run "D:/blog_temp/a1_load_lib_func.ipynb" %run "D:/blog_temp/a2_read_data.ipynb" _________________________________________________________________ # ## Building Model # In: _________________________________________________________________ model = Sequential() model.add(SimpleRNN(units=32, input_shape=(1,step), activation="relu")) model.add(Dense(8, activation="relu")) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='rmsprop') model.summary() _________________________________________________________________ Model: "sequential" ----------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================= simple_rnn (SimpleRNN) (None, 32) 1184 dense (Dense) (None, 8) 264 dense_1 (Dense) (None, 1) 9 ================================================================= Total params: 1,457 Trainable params: 1,457 Non-trainable params: 0 ----------------------------------------------------------------- # ## Training Model # In: _________________________________________________________________ model.fit(trainX, trainY, epochs=100, batch_size=16, verbose=0) _________________________________________________________________ <keras.callbacks.History at 0x22e182f8c70> # In: _________________________________________________________________ trainPredict = model.predict(trainX) trainScore = model.evaluate(trainX, trainY, verbose=0) print(trainScore) draw_plot1(df,trainPredict) _________________________________________________________________ 0.3513454794883728
Visit SH Fintech Modeling for additional insight on this topic: https://kiandlee.blogspot.com/2023/01/python-run-to-import-another-folders.html
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