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notebooks/acro_demo.py

@@ -5,6 +5,7 @@
 
 # import libraries
 import os
+
 import pandas as pd
 from scipy.io.arff import loadarff
 
@@ -19,12 +20,12 @@
 acro = ACRO(suppress=False)
 
 # Load test data
-# The dataset used in this notebook is the nursery dataset from OpenML.  
-# - In this version, the data can be read directly from the local machine after it has been downloaded. 
+# The dataset used in this notebook is the nursery dataset from OpenML.
+# - In this version, the data can be read directly from the local machine after it has been downloaded.
 # - The code below reads the data from a folder called "data" which we assume is at the same level as the folder where you are working.
 # - The path might need to be changed if the data has been downloaded and stored elsewhere.
-#  - for example use:  
-#     path = os.path.join("data", "nursery.arff")  
+#  - for example use:
+#     path = os.path.join("data", "nursery.arff")
 #     if the data is in a sub-folder of your work folder
 
 path = os.path.join("../data", "nursery.arff")
@@ -36,12 +37,12 @@
 df.head()
 
 # Examples of producing tabular output
-# We rely on the industry-standard package **pandas** for tabulating data.  
+# We rely on the industry-standard package **pandas** for tabulating data.
 # In the next few examples we show:
 # - first, how a researcher would normally make a call in pandas, saving the results in a variable that they can view on screen (or save to file?)
 # - then how the call is identical in SACRO, except that:
 #   - "pd" is replaced by "acro"
-#   - the researcher immediately sees a copy of what the TRE output checker will see.  
+#   - the researcher immediately sees a copy of what the TRE output checker will see.
 
 print(
     "\nThese examples show acro wrappers around "
@@ -50,28 +51,26 @@
 
 
 # Pandas crosstab
-# This is an example of crosstab using pandas.  
+# This is an example of crosstab using pandas.
 # We first make the call, then the second line print the outputs to screen.
 
 print("\nCalling crosstab of recommendation by parents using pandas")
 table = pd.crosstab(df.recommend, df.parents)
 print(table)
 
 # ACRO crosstab
-# - This is an example of crosstab using ACRO.  
+# - This is an example of crosstab using ACRO.
 # - The INFO lines show the researcher what will be reported to the output checkers.
 # - Then the (suppressed as necessary) table is shown via the print command as before.
 
 print("\nNow the same crosstab call using the ACRO interface")
-safe_table = acro.crosstab(
-    df.recommend, df.parents
-)
+safe_table = acro.crosstab(df.recommend, df.parents)
 print("\nand this is the researchers output")
 print(safe_table)
 
 # ACRO crosstab with suppression
 # - This is an example of crosstab with suppressing the cells that violate the disclosure tests.
-# - Note that you need to change the value of the suppress variable in the acro object to True. Then run the crosstab command.  
+# - Note that you need to change the value of the suppress variable in the acro object to True. Then run the crosstab command.
 # - If you wish to continue the research while suppressing the outputs, leave the suppress variable as it is, otherwise turn it off.
 
 print("\nTurn on the suppression variable")
@@ -84,7 +83,7 @@
 acro.suppress = False
 
 # ACRO functionality to let users manage their outputs
-# 
+#
 # 1: List current ACRO outputs
 # This is an example of using the print_output function to list all the outputs created so far
 
@@ -95,10 +94,10 @@
 )
 acro.print_outputs()
 
-# 2: Remove some ACRO outputs before finalising 
-# This is an example of deleting some of the ACRO outputs.  
-# The name of the output that needs to be removed should be passed to the function remove_output.  
-# - The output name can be taken from the outputs listed by the print_outputs function, 
+# 2: Remove some ACRO outputs before finalising
+# This is an example of deleting some of the ACRO outputs.
+# The name of the output that needs to be removed should be passed to the function remove_output.
+# - The output name can be taken from the outputs listed by the print_outputs function,
 # - or by listing the results and choosing the specific output that needs to be removed
 
 print("\nNow removing the first output")
@@ -111,22 +110,20 @@
 acro.rename_output("output_1", "cross_tabulation")
 
 # 4: Add a comment to output
-# This is an example to add a comment to outputs.  
+# This is an example to add a comment to outputs.
 # It can be used to provide a description or to pass additional information to the output checkers.
 
 print("\nUsers can add comments which the output checkers will see.")
 acro.add_comments("cross_tabulation", "Please let me have this data.")
 
 # 5: (the big one) Finalise ACRO
-# This is an example of the function _finalise()_ which the users must call at the end of each session.  
-# - It takes each output and saves it to a CSV file.    
-# - It also saves the SDC analysis for each output to a json file or Excel file  
+# This is an example of the function _finalise()_ which the users must call at the end of each session.
+# - It takes each output and saves it to a CSV file.
+# - It also saves the SDC analysis for each output to a json file or Excel file
 #   (depending on the extension of the name of the file provided as an input to the function)
 
 print(
     "\nUsers MUST call finalise to send their outputs to the checkers"
     " If they don't, the SDC analysis, and their outputs, are lost."
 )
 output = acro.finalise("Examples", "json")
-
-