How to load multiple data logs

[stmicroelectronics]

How to load multiple data logs

[michelefer]

You could separate data logs in different folders, to have one folder per class, and then modify the Step 1 of the Jupyter Notebook (MLC.ipynb) to read the class name from the folder name, and all the data logs for the specific class reading all the files inside the folder. Here a code example:

## Step 1: Load log files, label data, define decision tree results
# In this step data are loaded and results are assigned. 
# Decision tree results can be configured by associating result names to result values.

result_names  = [] # leave empty here
result_values = [] # leave empty here
results = [] # leave empty here

# Load class names (folder names) from Logs folder
class_names = os.listdir("../Logs/")
print ("available classes = ", class_names)

# For each class (folder), load all data (files in the folder)
datalogs = []
datalogs_split_by_class = []
for class_name in class_names:
    datalogs_i = os.listdir("../Logs/" + class_name +"/")
    print(class_name, " --> data logs: ", datalogs_i)
    datalogs_split_by_class.append(datalogs_i)
    for datalog_i in datalogs_i:
        datalogs.append("../Logs/" + class_name + "/" + datalog_i)
        results.append(class_name);
print("All data logs: ", datalogs)

# Assign results and values for decision tree 1:
result_names.append(  class_names )
result_values.append( list(range(0, len(class_names), 1)) )
# Assign results and values for decision tree 2:
result_names.append(  [] )
result_values.append( [] )
# Assign results and values for decision tree 3:
result_names.append(  [] )
result_values.append( [] )
# Assign results and values for decision tree 4:
result_names.append(  [] )
result_values.append( [] )
# Assign results and values for decision tree 5:
result_names.append(  [] )
result_values.append( [] )
# Assign results and values for decision tree 6:
result_names.append(  [] )
result_values.append( [] )
# Assign results and values for decision tree 7:
result_names.append(  [] )
result_values.append( [] )
# Assign results and values for decision tree 8:
result_names.append(  [] )
result_values.append( [] )

dectree_filenames = []
for i in range(0,8): 
    if not result_names[i]:
        break
    else: 
        dectree_filenames.append(os.path.join(current_directory, "dectree{}.txt".format(i+1)))
n_decision_trees = i