Python: %run to Import Another Folder’s ipynb Files

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.

Python : Importing an ipynb file (Jupyter Notebook) from another ipynb file

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[1]:
_________________________________________________________________
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[2]:
_________________________________________________________________
# 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[3]:
_________________________________________________________________
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[0], 1, trainX.shape[1]))

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[1]:
_________________________________________________________________
# 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[4]:
_________________________________________________________________
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[5]:
_________________________________________________________________
model.fit(trainX, trainY, epochs=100, batch_size=16, verbose=0)
_________________________________________________________________
<keras.callbacks.History at 0x22e182f8c70>
# In[6]:
_________________________________________________________________
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