fix: spelling errors, broken dependencies, and broken doc link

Planter.py

@@ -27,7 +27,7 @@
 warnings.filterwarnings('ignore')
 
 # =================== import Planter src ===================
-# funtions
+# functions
 from src.functions.figure_to_ASCII import *
 from src.functions.json_encoder import *
 from src.functions.project_tree_graph import *
@@ -45,12 +45,12 @@ def Planter(iteration = 0):
 
     # =================== set argparse for Planter ===================
 
-    parser = argparse.ArgumentParser(prog="Please use commend '-h' or '--help' for further information",
+    parser = argparse.ArgumentParser(prog="Please use command '-h' or '--help' for further information",
                                 usage='Planter can embed several ML algorithms to programmable network devices',
                                 description='It is a on going work, if you find any bugs, please feel free to contact changgang.zheng@eng.ox.ac.uk, it is really important to us. Thank you.',
                                 epilog='Play happy with Planter ~')
     parser.add_argument("-m", "--manually_config", help="Manually config Planter or not? If not set, please config the following file <src/config/Planter_config.json>. If set, the Planter will ask you to input all the necessary configs.", action="store_true")
-    parser.add_argument("-o", "--auto_optimisation", help="Use auto hyperparameters tunning or not? If set, the Planter will trigger auto optimisation process. Please do not use this mode with other modes, Planter will handle everything.", action="store_true")
+    parser.add_argument("-o", "--auto_optimisation", help="Use auto hyperparameters tuning or not? If set, the Planter will trigger auto optimisation process. Please do not use this mode with other modes, Planter will handle everything.", action="store_true")
     parser.add_argument("-t", "--testing_mode", help="Use testing mode or not? If set, the testing mode will only use at most 20000 input data and 5000 testing data.", action="store_true")
     parser.add_argument("-d", "--draw_project_tree", help="Draw the project tree or not? If set, this mode will output a tree graph of the Planter project.", action="store_true")
     parser.add_argument("-f", "--draw_function_call_graph", help="Draw function calling dependency graph or not, if set, after running Planter, a function calling dependency graph will be generated under <src/logs>.", action="store_true")
@@ -147,19 +147,19 @@ def Planter(iteration = 0):
         round = 0
         while True:
             round += 1
-            # call the function to find best paraemters and generate the config file
+            # call the function to find best parameters and generate the config file
             main_functions.config_generator(args, round)
-            # call Plantrer to verify the generated configs
-            print('= Begin varifcation for the generated configs. Rounds', round)
+            # call Planter to verify the generated configs
+            print('= Begin verification for the generated configs. Rounds', round)
             args.manually_config = False
             Planter(round)
             # reload the config file
             Planter_config = reload_config('src/configs/Planter_config.json')
             if Planter_config['optimisation config']['compile result'] == 'y':
-                print('= Verification seccess. Data plane and control plane codes are generated with best hyperparameters.')
+                print('= Verification success. Data plane and control plane codes are generated with best hyperparameters.')
                 break
             else:
-                print('= Verification fales. Regenerate configs with higher constrained configs')
+                print('= Verification fails. Regenerate configs with higher constrained configs')
         exit()
     # =================== auto config mode config and operations finished ===================
     # print('= args:', args)

src/configs/requirements_pip3.txt

@@ -2,18 +2,16 @@ scipy==1.5.2
 joblib==1.0.0
 pydotplus==2.0.2
 pandas==1.1.3
-xgboost==0.90
+xgboost>=1.5.0
 matplotlib==3.3.2
 torch==1.4.0
-numpy==1.19.2
+numpy>=1.21.0
 category_encoders==2.2.2
-scikit_learn
+scikit-learn
 packaging==21.0
 jsonschema==3.2.0
 scapy==2.4.3
 seaborn==0.11.2
-category_encoders==2.2.2
 wget==3.2
-pytorch
 ipython
 tqdm

src/configs/requirements_sudo_pip3.txt

@@ -1,8 +1,8 @@
- numpy==1.19.2 
-scikit_learn==1.0 
+numpy>=1.21.0
+scikit-learn==1.0
 scapy==2.4.3
 seaborn==0.11.2
 category_encoders==2.2.2
 wget==1.20.1
-pytorch==0.4.0
-multiprocess=0.70.12
+torch
+multiprocess==0.70.12

src/functions/Range_to_LPM.py

@@ -193,11 +193,11 @@ def Table_to_sep_val(table):
             lasat_lable = table[dict]
         if dict == 0:
             Table[dict] = table[dict] # When you get to the end of the for loop, the last value in table and final key is 0
-    #This for-loop selects all feature values at bounderies and puts them in Table
+    #This for-loop selects all feature values at boundaries and puts them in Table
     #For instance if the first 20 actions of the exact match table were 112111112111211121111121 then Table would be 1121212121 in reverse order
     #Selects the value before the value change
 
-    separation_value = list(Table.keys()) #Selects index of seperation values (starting from highest e.g 99,98,86,77,56,29,6,1)
+    separation_value = list(Table.keys()) #Selects index of separation values (starting from highest e.g 99,98,86,77,56,29,6,1)
 
     return separation_value, separation_value_input
 

src/help/Adding_Your_Own_Design/Adding_model.md

@@ -58,7 +58,7 @@ In ```table_generator.py```, there are two compulsory functions to be written, `
    ``` 
     sklearn_test_y # data frame/ndarray/list
                    # sklearn (baseline) inference result (output labels)
-                   # generated by funtion test_tables(*)
+                   # generated by function test_tables(*)
 
     test_X # data frame/ndarray/list
            # testing data

src/help/Common_Issues/Envronment_Issues.md

@@ -25,7 +25,7 @@
 
 If it turns out that some packages were not installed (even though you have set up the environment based on ```requirements.txt```), install the missing packages manually.
 
-💡 To set up Planter on P4Pi-enabled BMv2, follow the wiki page in [this link](./src/help/Throughput_Test/Planter_throughput_test_P4Pi-BMv2.md).
+💡 To set up Planter on P4Pi-enabled BMv2, follow the wiki page in [this link](../Throughput_Test/Planter_throughput_test_P4Pi-BMv2.md).
 
 
 ## Common Running Issues:

src/help/Planter_Supports/Planter_Supported_Datasets.md

@@ -16,7 +16,7 @@ Planter currently supports 7 datasets.
 
 **4. AWID3** [Link](https://icsdweb.aegean.gr/awid/awid3) - The AWID3 dataset is a network intrusion detection dataset with 13 different attacks. The data is collected from 16 different physical devices and VMs.
 
-**5. Requet** [Link](https://github.com/Wimnet/RequetDataSet) - Requet is a Quality of Experience (QoE) metric detection dataset collected from video streaming services to give the records of video playback information.
+**5. Request** [Link](https://github.com/Wimnet/RequetDataSet) - Request is a Quality of Experience (QoE) metric detection dataset collected from video streaming services to give the records of video playback information.
 
 **6. Jane Street Market** [Link](https://www.kaggle.com/c/jane-street-market-prediction) - Jane Street Markets is a high-frequency trading dataset with 500 days of historical data. Specifically, it contains 130 features.
 

src/models/Autoencoder/Type_1/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Autoencoder. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Autoencoder. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/Autoencoder/Type_LB/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Autoencoder. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Autoencoder. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/Bayes/Type_1/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/Bayes/Type_2/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/Bayes/Type_3/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/Bayes/Type_LB/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/Bayes/Type_LB_Bernoulli/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for Bayes. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/DT/Type_1/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for DT. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for DT. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.

src/models/DT/Type_1/table_generator.py

@@ -81,7 +81,7 @@ def recurse(left, right, child, lineage=None):
             else:
                 sign = g
             clause = clause + i[3] + sign + str(i[2]) + ' and '
-        # wirte the node information into text file
+        # write the node information into text file
         a = list(value[node][0])
         ind = a.index(np.max(a))
         clause = clause[:-4] + ' then ' + str(ind)
@@ -164,11 +164,11 @@ def generate_feature_tables(split, num_features,feature_max, table):
     for i in range(num_features):
         table["feature "+str(i)] = {}
         count_code = 0
-        nife = sorted(split["feature "+str(i)])
+        knife = sorted(split["feature "+str(i)])
         for j in range(feature_max[i]+1):
-            if nife !=[] :
-                if len(nife) > count_code:
-                    if j-1 == nife[count_code]:
+            if knife !=[] :
+                if len(knife) > count_code:
+                    if j-1 == knife[count_code]:
                         count_code+=1
             table["feature " + str(i)][j] = count_code
     return table
@@ -195,7 +195,7 @@ def find_classification(textfile, feature_split, num_features):
             sign.append(re.findall(r"(<=|>)", line))
             num.append(re.findall(r"\d+\.?\d*", line))
     f.close()
-    classfication = []
+    classification = []
     featuren = {}
     for i in range(len(fea)):
         for l in range(num_features):
@@ -220,16 +220,16 @@ def find_classification(textfile, feature_split, num_features):
         for l in range(num_features):
             feature_n[l].append(featuren[l])
         a = len(num[i])
-        classfication.append(num[i][a - 1])
+        classification.append(num[i][a - 1])
 
-    return feature_n, classfication
+    return feature_n, classification
 
 
-def find_path_for_leaf_nodes(feature_n, classfication, num_features):
+def find_path_for_leaf_nodes(feature_n, classification, num_features):
     path_to_leaf = {}
-    for i in range(len(classfication)):
+    for i in range(len(classification)):
         path_to_leaf["path "+str(i)] = {}
-        path_to_leaf["path " + str(i)]["leaf"] = classfication[i]
+        path_to_leaf["path " + str(i)]["leaf"] = classification[i]
         for j in range(num_features):
             path_to_leaf["path " + str(i)]["feature "+str(j)] = feature_n[j][i]
     return path_to_leaf
@@ -266,8 +266,8 @@ def generate_table(model, tree_index, num_features, g_table, feature_max):
     g_table[tree_index] = {}
     g_table[tree_index] = generate_feature_tables(feature_split, num_features, feature_max, g_table[tree_index])
 
-    feature_n, classfication = find_classification(textfile, feature_split , num_features)
-    path_to_leaf = find_path_for_leaf_nodes(feature_n, classfication, num_features)
+    feature_n, classification = find_classification(textfile, feature_split , num_features)
+    path_to_leaf = find_path_for_leaf_nodes(feature_n, classification, num_features)
     code_width_for_feature = np.zeros(num_features)
     for i in range(num_features):
         code_width_for_feature[i] = int(np.ceil(math.log(g_table[tree_index]['feature ' + str(i)][np.max(list(g_table[tree_index]['feature ' + str(i)].keys()))]+1,2))) or 1
@@ -383,7 +383,7 @@ def test_tables(sklearn_test_y, test_X, test_y):
     Ternary_Table = json.load(open('Tables/Ternary_Table.json', 'r'))
     Exact_Table = json.load(open('Tables/Exact_Table.json', 'r'))
 
-    print('Test the exact feature table, extact code and decision table (feel free if the acc to sklearn is slightly lower than 1)')
+    print('Test the exact feature table, exact code and decision table (feel free if the acc to sklearn is slightly lower than 1)')
     same = 0
     correct = 0
     error = 0

src/models/DT/Type_1_xsa/readme.md

@@ -1 +1 @@
-This folder contains Planter-supported ML modules (training, algortihm mapping, and ML-related P4 generation) for DT. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.
+This folder contains Planter-supported ML modules (training, algorithm mapping, and ML-related P4 generation) for DT. ```dedicated_p4.py``` generates the P4 code dedicated to this ML model and ```table_generator.py``` generate ML model parameters in the format of table enries. Please refer to ```./Docs/Planter_User_Document.pdf```for further information.