[TOOL] Model Selector update.

This patch adds stability updates to Model Selector

Signed-off-by: Sridhar K. N. Rao <srao@linuxfoundation.org>
Change-Id: I74b4e9a1b1837d85dac7f0d091019a23b1abd7b5

1 files changed, 365 insertions, 138 deletions

diff --git a/tools/modelselector/modelselector.py b/tools/modelselector/modelselector.py
index 90b289c..a3a0b0b 100644
--- a/tools/modelselector/modelselector.py
+++ b/tools/modelselector/modelselector.py
@@ -1,5 +1,5 @@
-# Copyright 2021 Spirent Communications.
-# sridhar.rao@spirent.com
+# Copyright 2022 Linux Foundation.
+# srao@linuxfoundation.org
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,10 +18,8 @@ Tool to suggest which ML approach is more applicable for
 a particular data and usecase.
 TODO:
 1. Minimize code.
-a. Reduce returns.
-b. Optimize loops.
-
 2. Add Informative data to the user.
+3. Check for Size Entry - 1G/K ..
 """
 
 from __future__ import print_function
@@ -54,6 +52,7 @@ class AlgoSelectorWizard():
         Perform Initialization.
         """
         self.shell = Shell()
+        # Set of all values from the user
         self.main_values = {}
         self.main_l1_values = {}
         self.main_l2a_values = {}
@@ -63,6 +62,16 @@ class AlgoSelectorWizard():
         self.unsup_values = {}
         self.ri_values = {}
         self.gen_values = {}
+        self.gen_choice_values = {}
+        self.gen_metrics_values = {}
+        self.gen_data_main_values = {}
+        self.gen_data_text_values = {}
+        self.gen_data_features_values = {}
+        self.gen_data_signal_values = {}
+        self.gen_about_data_basic_values = {}
+        self.gen_about_data_adv_values = {}
+        self.gen_about_data_output_values = {}
+        # Set of Wizards.
         self.wiz_main = None
         self.wiz_main_l1 = None
         self.wiz_main_l2_a = None
@@ -70,8 +79,18 @@ class AlgoSelectorWizard():
         self.wiz_main_l3 = None
         self.wiz_main_l4 = None
         self.wiz_generic = None
+        self.wiz_generic_choice = None
+        self.wiz_geneirc_metric = None
+        self.wiz_generic_data_main = None
+        self.wiz_generic_data_signal = None
+        self.wiz_generic_data_features = None
+        self.wiz_generic_data_text = None
+        self.wiz_generic_data_basic = None
+        self.wiz_generic_data_adv = None
+        self.wiz_generic_data_output = None
         self.wiz_unsupervised = None
         self.wiz_reinforcement = None
+        # Some Inferences
         self.ml_needed = False
         self.supervised = False
         self.unsupervised = False
@@ -101,8 +120,8 @@ class AlgoSelectorWizard():
                     # Display name
                     name=Bcolors.HEADER+"Do you have access to data about different situations, or that describes a lot of examples of situations"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U - Yes/No/Unknown",
-                    validators=(wiz.required_validator),
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y',
                 ),
             )
@@ -123,8 +142,8 @@ class AlgoSelectorWizard():
                     # Display name
                     name=Bcolors.HEADER+"Will a system be able to gather a lot of data by trying sequences of actions in many different situations and seeing the results"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U - Yes/No/Unknown",
-                    validators=(wiz.required_validator),
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y',
                 ),
             )
@@ -144,10 +163,10 @@ class AlgoSelectorWizard():
                     # ID where the value will be stored
                     id="data_label",
                     # Display name
-                    name=Bcolors.HEADER+" Do you have Labelled data? (Type Y/N/U - Yes/No/Unknown). Type help for description of label. "+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" Do you have Labelled data? (Type Y/N - Yes/No). Type help for description of label. "+Bcolors.ENDC,
                     # Help message
                     help=label,
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y',
                 ),
                 wiz.WizardStep(
@@ -156,9 +175,9 @@ class AlgoSelectorWizard():
                     # Display name
                     name=Bcolors.HEADER+"Can a program or set of rules decide what actions to take based on the data you have about the situations"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U - Yes/No/Unknown",
-                    validators=(wiz.required_validator),
-                    default='Y',
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='N',
                 ),
             )
         )
@@ -179,8 +198,8 @@ class AlgoSelectorWizard():
                     # Display name
                     name=Bcolors.HEADER+"Could a knowledgeable human decide what actions to take based on the data you have about the situations"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U - Yes/No/Unknown",
-                    validators=(wiz.required_validator),
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y',
                 ),
             )
@@ -201,19 +220,19 @@ class AlgoSelectorWizard():
                     # Display name
                     name=Bcolors.HEADER+"Could there be patterns in these situations that the humans haven't recognized before"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U - Yes/No/Unknown",
-                    validators=(wiz.required_validator),
+                    help="Y/N - Yes/No.",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y'
                 ),
             )
         )
     ### GENERIC Wizards - GOAL, METRICS, DATA ##############################
-    def gen_wizard(self):
+    def gen_choice_wizard(self):
         """
         Generic Wizard - Goal, metrics, data
         """
-        self.wiz_generic = wiz.PromptWizard(
-            name=Bcolors.OKBLUE+"Understanding Goal, Metrics, Data and Output Type"+Bcolors.ENDC,
+        self.wiz_generic_choice = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Goal, and Preferences"+Bcolors.ENDC,
             description="",
             steps=(
                 # The list of input prompts to ask the user.
@@ -224,17 +243,84 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" What is your goal with the data? Predict, Describe or Explore"+Bcolors.ENDC,
                     # Help message
                     help="Enter one of Predict/Describe/Explore",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Predict',
+                    	                                                      'predict',
+                    	                                                      'Describe',
+                    	                                                      'describe',
+                    	                                                      'Explore',
+                    	                                                      'explore'])),
                     default='Explore'
                 ),
                 wiz.WizardStep(
                     # ID where the value will be stored
+                    id="data_metrics_pref",
+                    # Display name
+                    name=Bcolors.HEADER+" Do you know which metrics (speed, accuracy, etc.) are more important for you? "+Bcolors.ENDC,
+                    # Help message
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
+                ),
+                wiz.WizardStep(
+                    # ID where the value will be stored
+                    id="data_main",
+                    # Display name
+                    name=Bcolors.HEADER+" Do you know about the input data type (If its signal/features/text)  ?  "+Bcolors.ENDC,
+                    # Help message
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
+                ),
+                wiz.WizardStep(
+                    # ID where the value will be stored
+                    id="data_databasic_pref",
+                    # Display name
+                    name=Bcolors.HEADER+" Do you have basic information (size, count, etc.) about the input data? "+Bcolors.ENDC,
+                    # Help message
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
+                ),
+                wiz.WizardStep(
+                    # ID where the value will be stored
+                    id="data_dataadv_pref",
+                    # Display name
+                    name=Bcolors.HEADER+" Do you have advanced information (distribution, relation, independency, etc.) about the input data? "+Bcolors.ENDC,
+                    # Help message
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
+                ),
+                wiz.WizardStep(
+                    # ID where the value will be stored
+                    id="data_dataoutput_pref",
+                    # Display name
+                    name=Bcolors.HEADER+" Do you have basic information (size, count, etc.) about the output? "+Bcolors.ENDC,
+                    # Help message
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
+                ),
+            )
+        )
+
+    def gen_metrics_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_metrics = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Goal, Metrics, Data and Output Type"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
+                wiz.WizardStep(
+                    # ID where the value will be stored
                     id="metric_accuracy",
                     # Display name
                     name=Bcolors.HEADER+" How important the metric 'Accuracy' is for you? 1-5: 1- Least important 5- Most Important"+Bcolors.ENDC,
                     # Help message
                     help="Enter 1-5: 1 being least important, and 5 being most important",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='1'
                 ),
                 wiz.WizardStep(
@@ -244,7 +330,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" How important the metric 'Speed' is for you? 1-5: 1- Least important 5- Most Important"+Bcolors.ENDC,
                     # Help message
                     help="Enter 1-5: 1 being least important, and 5 being most important",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='1'
                 ),
                 wiz.WizardStep(
@@ -254,7 +340,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" How important the metric 'Interpretability' is for you? 1-5: 1- Least important 5- Most Important"+Bcolors.ENDC,
                     # Help message
                     help="Enter 1-5: 1 being least important, and 5 being most important",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='1'
                 ),
                 wiz.WizardStep(
@@ -264,7 +350,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" How important the metric 'Reproducibility' is for you? 1-5: 1- Least important 5- Most Important"+Bcolors.ENDC,
                     # Help message
                     help="Enter 1-5: 1 being least important, and 5 being most important",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='1'
                 ),
                 wiz.WizardStep(
@@ -274,47 +360,73 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" How important the metric 'Ease of Implementation and Maintenance' is for you? 1-5: 1- Least important 5- Most Important"+Bcolors.ENDC,
                     # Help message
                     help="Enter 1-5: 1 being least important, and 5 being most important",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='1'
                 ),
+            )
+        )
+
+    def gen_data_main_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_main = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Goal, and Preferences"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
                 wiz.WizardStep(
                     # ID where the value will be stored
                     id="data_column",
                     # Display name
-                    name=Bcolors.HEADER+" What does the data (columns) represent? well defined 'Features', 'signals' (Timeseries, pixels, etc) or Text - (Please type the associated number)"+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" What does the data (columns) represent? Please type help and select the associated number"+Bcolors.ENDC,
                     # Help message
-                    help="1. Well Defined Features\n 2. Signals\n 3. Text - Unstructured\n 4. None of the above\n",
-                    validators=(wiz.required_validator),
-                    default='Features'
+                    help="1. Well Defined Features\n 2. Signals - Timeseries, pixels, etc\n 3. Text - Unstructured\n 4. None of the above\n",
+                    validators=(wiz.required_validator, wiz.int_validator(1, 4)),
+                    default='1'
                 ),
+            )
+        )
+
+    def gen_data_signal_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_signal = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Goal, and Preferences"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
                 wiz.WizardStep(
                     # ID where the value will be stored
                     id="data_signal_type",
                     # Display name
-                    name=Bcolors.HEADER+" If Signals, can you choose any one from the below list? "+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" If Signals, can you choose any one from the below list? Please type help for list "+Bcolors.ENDC,
                     # Help message
                     help="1. Image\n 2. Audio\n 3. Timeseries\n 4. None of the above\n 5. Not Applicable\n  ",
-                    validators=(wiz.required_validator),
-                    default='3'
-                ),
-                wiz.WizardStep(
-                    # ID where the value will be stored
-                    id="data_text_type",
-                    # Display name
-                    name=Bcolors.HEADER+" If Text, can you choose any one from the below list? "+Bcolors.ENDC,
-                    # Help message
-                    help="1. Webpages\n 2. Emails\n 3. Social-Media Posts\n 4. Books\n 5. Formal Articles\n 6. Speech converted to text\n 7. None of the above\n 8. Not Applicable\n  ",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='3'
                 ),
+            )
+        )
+
+    def gen_data_features_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_features = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Goal, and Preferences"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
                 wiz.WizardStep(
                     # ID where the value will be stored
                     id="data_features",
                     # Display name
                     name=Bcolors.HEADER+" If features, are they well defined? i.e., are all the variables well understood? "+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    help="Y/N",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y'
                 ),
                 wiz.WizardStep(
@@ -323,80 +435,139 @@ class AlgoSelectorWizard():
                     # Display name
                     name=Bcolors.HEADER+" If features, How many are there? "+Bcolors.ENDC,
                     # Help message
-                    help="Number or NA",
-                    validators=(wiz.required_validator),
+                    help="Number only - Approximate should be OK.",
+                    validators=(wiz.required_validator, wiz.int_validator(1, 100000)),
                     default='10'
                 ),
+            )
+        )
+
+    def gen_data_text_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_text = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Goal, and Preferences"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_distribution",
+                    id="data_text_type",
                     # Display name
-                    name=Bcolors.HEADER+" Are you aware of any 'Distribution' that is inherent to the data, we can take advantage of?"+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" If Text, can you choose any one from the below list? Please type help for list"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U",
-                    validators=(wiz.required_validator),
-                    default='Y'
+                    help="1. Webpages\n 2. Emails\n 3. Social-Media Posts\n 4. Books\n 5. Formal Articles\n 6. Speech converted to text\n 7. None of the above\n 8. Not Applicable\n  ",
+                    validators=(wiz.required_validator, wiz.int_validator(1, 8)),
+                    default='3'
                 ),
+
+            )
+        )
+
+    def gen_about_data_basic_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_basic = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Basic Input Data Information"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_io_relation",
+                    id="data_missing",
                     # Display name
-                    name=Bcolors.HEADER+" Is the probability of 'Linear Relation' between input and the output is high?"+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" Are there any missing values in the data? "+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U",
-                    validators=(wiz.required_validator),
-                    default='Y'
+                    help="Y/N",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='N'
                 ),
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_correlation",
+                    id="data_size_bytes",
                     # Display name
-                    name=Bcolors.HEADER+" Are you confident that there is NO high correlation among the independent variables in your day?"+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" How big is the data in terms of size? (Use K/M/G Bytes unit) "+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U. Change in one  ",
+                    help="Number(integer) and unit: K for Kilo, M for Mega and G for Giga. Ex: 10G for 10 Giga bytes",
                     validators=(wiz.required_validator),
-                    default='Y'
+                    default='1G'
                 ),
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_cond_indep",
+                    id="data_size_samples",
                     # Display name
-                    name=Bcolors.HEADER+" Are you confident that the variables are conditionally independent?"+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" How big is the data in terms of samples? (Use T/M/B Samples) "+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U. If probability that it rains given lightining and thunder is same as probability that it rains given lightining, then rain and thunder are conditionally independent",
+                    help="Number(integer) and unit: T for Thousand, M for Million and B for Billion. Ex: 1M for 1 Million Samples",
                     validators=(wiz.required_validator),
+                    default='1M'
+                ),
+            )
+        )
+
+    def gen_about_data_advanced_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_adv = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Advanced Input Data Information"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.
+                wiz.WizardStep(
+                    # ID where the value will be stored
+                    id="data_distribution",
+                    # Display name
+                    name=Bcolors.HEADER+" Are you aware of any 'Distribution' that is inherent to the data, we can take advantage of?"+Bcolors.ENDC,
+                    # Help message
+                    help="Y/N - Yes",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='Y'
                 ),
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_missing",
+                    id="data_io_relation",
                     # Display name
-                    name=Bcolors.HEADER+" Are there any missing values in the data? "+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" Is the probability of 'Linear Relation' between input and the output is high?"+Bcolors.ENDC,
                     # Help message
-                    help="Y/N/U",
-                    validators=(wiz.required_validator),
-                    default='N'
+                    help="Y/N - Yes/No",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
                 ),
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_size_bytes",
+                    id="data_correlation",
                     # Display name
-                    name=Bcolors.HEADER+" How big is the data in terms of size? (Use K/M/G Bytes unit) "+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" Are you confident that there is NO high correlation among the independent variables in your day?"+Bcolors.ENDC,
                     # Help message
-                    help="Number(integer) and unit: K for Kilo, M for Mega and G for Giga. Ex: 10G for 10 Giga bytes",
-                    validators=(wiz.required_validator),
-                    default='1G'
+                    help="Y/N/ - Yes/No ",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
                 ),
                 wiz.WizardStep(
                     # ID where the value will be stored
-                    id="data_size_samples",
+                    id="data_cond_indep",
                     # Display name
-                    name=Bcolors.HEADER+" How big is the data in terms of samples? (Use T/M/B Samples) "+Bcolors.ENDC,
+                    name=Bcolors.HEADER+" Are you confident that the variables are conditionally independent?"+Bcolors.ENDC,
                     # Help message
-                    help="Number(integer) and unit: T for Thousand, M for Million and B for Billion. Ex: 1M for 1 Million Samples",
-                    validators=(wiz.required_validator),
-                    default='1M'
+                    help="Y/N/. If probability that it rains given lightining and thunder is same as probability that it rains given lightining, then rain and thunder are conditionally independent",
+                    validators=(wiz.required_validator, wiz.boolean_validator),
+                    default='Y'
                 ),
+            )
+        )
+
+    def gen_about_output_wizard(self):
+        """
+        Generic Wizard - Goal, metrics, data
+        """
+        self.wiz_generic_data_output = wiz.PromptWizard(
+            name=Bcolors.OKBLUE+"Understanding Data Output"+Bcolors.ENDC,
+            description="",
+            steps=(
+                # The list of input prompts to ask the user.        
                 wiz.WizardStep(
                     # ID where the value will be stored
                     id="data_type_output",
@@ -404,7 +575,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" What is the expected output data type ? (Please type number associated with type in 'help') "+Bcolors.ENDC,
                     # Help message
                     help=" 1:Numerical-Discrete\n 2:Numerical-Continuous\n 3:Ordinal\n 4:Categorical-Binary\n 5:Categorical-Multiclass",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 5)),
                     default='1'
                 ),
                 wiz.WizardStep(
@@ -414,7 +585,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" Is the expected output data a probability value ? "+Bcolors.ENDC,
                     # Help message
                     help="Y/N",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.boolean_validator),
                     default='N'
                 ),
             )
@@ -425,7 +596,7 @@ class AlgoSelectorWizard():
         """
         The Un-Supervized Learning Wizard
         """
-        self.wiz_generic = wiz.PromptWizard(
+        self.wiz_unsupervised = wiz.PromptWizard(
             name=Bcolors.OKBLUE+"Understanding Goal, Metrics, Data and Output Type"+Bcolors.ENDC,
             description="",
             steps=(
@@ -437,7 +608,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" What is the main goal? (Please type number associated with type in 'help')"+Bcolors.ENDC,
                     # Help message
                     help="1: Explore Similar Groups (clustering) \n 2: Perform Dimensionality Reduction\n 3: Others\n",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 3)),
                     default='1'
                 ),
                 wiz.WizardStep(
@@ -447,7 +618,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" If dimensionality reduction, do you prefer topic modelling ? (Please type NA is you are not sure)"+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='NA'
                 ),
                 wiz.WizardStep(
@@ -457,7 +629,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" Are you aware of density variations in your data ? (Please type NA is you are not sure)"+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='NA'
                 ),
                 wiz.WizardStep(
@@ -467,7 +640,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" Are there too many outliers in your data ? (Please type NA is you are not sure)"+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='NA'
                 ),
                 wiz.WizardStep(
@@ -477,7 +651,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" If clustering, do you know how many groups to form? (Please type NA is you are not sure)"+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='NA'
                 ),
 
@@ -522,7 +697,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" Do you prefer model-based approach? (Type NA if you are not sure) "+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='Y'
                 ),
                 wiz.WizardStep(
@@ -532,7 +708,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" Do you have a model for model-based approach? (Type NA if not applicable) "+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='Y'
                 ),
                 wiz.WizardStep(
@@ -542,7 +719,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" In Model-Free approach, do you prefer value-based approach? (Type NA if not applicable) "+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='Y'
                 ),
                 wiz.WizardStep(
@@ -552,7 +730,8 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" In Model-Free Value-Based approach, do you prefer state-only model? (Type NA if not applicable) "+Bcolors.ENDC,
                     # Help message
                     help="Y/N/NA",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.choice_validator(['Y','N','NA','Na',
+                    	                                                      'y','n','na','nA'])),
                     default='Y'
                 ),
                 wiz.WizardStep(
@@ -562,7 +741,7 @@ class AlgoSelectorWizard():
                     name=Bcolors.HEADER+" What is the application domain ? (Please type number associated with type in 'help') "+Bcolors.ENDC,
                     # Help message
                     help=" 1:Computer Resource Mgmt.\n 2:Robotics\n 3:Traffic-Control\n 4:Reccommenders\n 5:Autonomous Vehicles\n 6:Games\n 7:Chemistry\n 8:Others\n",
-                    validators=(wiz.required_validator),
+                    validators=(wiz.required_validator, wiz.int_validator(1, 8)),
                     default='1'
                 ),
             )
@@ -575,25 +754,26 @@ class AlgoSelectorWizard():
         """
         self.main_wizard_l1()
         self.main_l1_values = self.wiz_main_l1.run(self.shell)
-        if self.main_l1_values['data_availability'].lower() == 'y':
+        if self.main_l1_values['data_availability']:
+            print("OK-1")
             self.main_wizard_l2_b()
             self.main_l2b_values = self.wiz_main_l2_b.run(self.shell)
-            if self.main_l2b_values['data_labe'].lower() == 'y':
+            if self.main_l2b_values['data_label']:
                 self.supervised = True
             else:
                 self.unsupervised = True
-            if self.main_l2b_values['data_programmability'].lower() == 'y':
+            if self.main_l2b_values['data_programmability']:
                 print(Bcolors.FAIL+"ML is not required - Please consider alternate approaches\n"+Bcolors.ENDC)
             else:
                 self.main_wizard_l3()
                 self.main_l3_values = self.wiz_main_l3.run(self.shell)
-                if self.main_l3_values['data_knowledge'].lower() == 'y':
+                if self.main_l3_values['data_knowledge']:
                     print(Bcolors.OKGREEN+"Looks like you need ML, let's continue"+Bcolors.ENDC)
                     self.ml_needed = True
                 else:
                     self.main_wizard_l4()
                     self.main_l4_values = self.wiz_main_l4.run(self.shell)
-                    if self.main_l4_values['data_pattern'].lower() == 'y':
+                    if self.main_l4_values['data_pattern']:
                         print(Bcolors.OKGREEN+"Looks like you need ML, let's continue"+Bcolors.ENDC)
                         self.ml_needed = True
                     else:
@@ -601,7 +781,7 @@ class AlgoSelectorWizard():
         else:
             self.main_wizard_l2_a()
             self.main_l2a_values = self.wiz_main_l2_a.run(self.shell)
-            if self.main_l2a_values['data_creativity'].lower() == 'y':
+            if self.main_l2a_values['data_creativity']:
                 print(Bcolors.OKGREEN+"Looks like you need ML, let's continue"+Bcolors.ENDC)
                 self.ml_needed = True
                 self.reinforcement = True
@@ -612,8 +792,55 @@ class AlgoSelectorWizard():
         """
         Run Generic Wizard
         """
-        self.gen_wizard()
-        self.gen_values = self.wiz_generic.run(self.shell)
+        self.gen_choice_wizard()
+        self.gen_choice_values = self.wiz_generic_choice.run(self.shell)
+        if self.gen_choice_values['data_metrics_pref']:
+            self.gen_metrics_wizard()
+            self.gen_metrics_values = self.wiz_generic_metrics.run(self.shell)
+        if self.gen_choice_values['data_main']:
+            self.gen_data_main_wizard()
+            self.gen_data_main_values = self.wiz_generic_data_main.run(self.shell)
+            if int(self.gen_data_main_values['data_column']) == 3:
+                self.gen_data_text_wizard()
+                self.gen_data_text_values = self.wiz_generic_data_text.run(self.shell)
+            else:
+                self.gen_data_text_values = {'data_text_type': '3'}
+            if int(self.gen_data_main_values['data_column']) == 1:
+                self.gen_data_features_wizard()
+                self.gen_data_features_values = self.wiz_generic_data_features.run(self.shell)
+            else:
+                self.gen_data_features_values = {'data_features': 'Y',
+                                                 'data_features_count': '10'}
+            if int(self.gen_data_main_values['data_column']) == 2:
+                self.gen_data_signal_wizard()
+                self.gen_data_signal_values = self.wiz_generic_data_signal.run(self.shell)
+            else:
+                self.gen_data_signal_values = {'data_signal_type': '1'}
+        else:
+            self.gen_data_main_values = {'data_column': '1'}
+            print("Unknown Data Type")
+        if self.gen_choice_values['data_databasic_pref']:
+            self.gen_about_data_basic_wizard()
+            self.gen_about_data_basic_values = self.wiz_generic_data_basic.run(self.shell)
+        else:
+            self.gen_about_data_basic_values = {'data_missing':'N',
+                                                'data_size_bytes': '1G',
+                                                'data_size_samples': '1M'}
+        if self.gen_choice_values['data_dataadv_pref']:
+            self.gen_about_data_advanced_wizard()
+            self.gen_about_data_adv_values = self.wiz_generic_data_adv.run(self.shell)
+        else:
+            self.gen_about_data_adv_values = {'data_distribution': 'N',
+                                              'data_io_relation': 'N',
+                                              'data_correlation': 'N',
+                                              'data_cond_indep': 'N'}
+        if self.gen_choice_values['data_dataoutput_pref']:
+            self.gen_about_output_wizard()
+            self.gen_about_data_output_values = self.wiz_generic_data_output.run(self.shell)
+        else:
+            self.gen_about_data_output_values = {'data_type_output': '1',
+                                                 'data_output_prob': 'N'}
+
 
     def run_unsupervised_wizard(self):
         """
@@ -643,8 +870,8 @@ class AlgoSelectorWizard():
                 else:
                     repro = True
             else:
-                if 'y' in self.unsup_values['unsup_clus_dv'].tolower():
-                    if 'y' in self.unsup_values['unsup_clus_groups'].tolower():
+                if 'y' in self.unsup_values['unsup_clus_dv'].lower():
+                    if 'y' in self.unsup_values['unsup_clus_groups'].lower():
                         clus_prob = True
                     else:
                         print("Unsupervised Learning model to consider: Hierarchical Clustering")
@@ -652,21 +879,21 @@ class AlgoSelectorWizard():
                 else:
                     repro = True
             if repro:
-                if 'y' in self.unsup_values['unsup_clus_outliers'].tolower():
+                if 'y' in self.unsup_values['unsup_clus_outliers'].lower():
                     print("Unsupervised Learning model to consider: Hierarchical Clustering")
                 else:
                     print("Unsupervised Learning model to consider: DBSCAN")
                 return
             if clus_prob:
-                if 'y' in self.gen_values['data_output_prob'].tolower():
+                if 'y' in self.gen_about_data_output_values['data_output_prob'].lower():
                     print("Unsupervised Learning model to consider: Gaussian Mixture")
                 else:
                     print("Unsupervised Learning model to consider: KMeans")
                 return
         elif int(self.unsup_values['unsup_goal']) == 2:
             # Dimensionality Reduction
-            if 'y' in self.unsup_values['unsup_dr_topic_mod'].tolower():
-                if 'y' in self.gen_values['data_output_prob'].tolower():
+            if 'y' in self.unsup_values['unsup_dr_topic_mod'].lower():
+                if 'y' in self.gen_about_data_output_values['data_output_prob'].lower():
                     print("Unsupervised Learning model to consider: SVD")
                 else:
                     print("Unsupervised Learning model to consider: LDA")
@@ -679,25 +906,25 @@ class AlgoSelectorWizard():
         """
         Decide which reinforement learning to use.
         """
-        if (int(self.gen_values['data_type_output']) == 2 or
-                'y' in self.ri_values['ri_model_preference'].tolower()):
+        if (int(self.gen_about_data_output_values['data_type_output']) == 2 or
+                'y' in self.ri_values['ri_model_preference'].lower()):
             # Model Bsaed
-            if 'y' in self.ri_values['ri_model_availability'].tolower():
+            if 'y' in self.ri_values['ri_model_availability'].lower():
                 print("Reinforcement Learning model to consider - AlphaZero")
             else:
                 print("Reinforcement Learning models to consider - World Models, I2A, MBMF, and MBVE")
-        elif 'n' in self.ri_values['ri_model_preference'].tolower():
+        elif 'n' in self.ri_values['ri_model_preference'].lower():
             # Model-Free based approach.
-            if 'y' not in self.ri_values['ri_modelfree_value'].tolower():
+            if 'y' not in self.ri_values['ri_modelfree_value'].lower():
                 print("Reinforcement Learning models to consider: Policy Gradient and Actor Critic")
             else:
-                if 'y' in self.ri_values['ri_modelfree_value_state'].tolower():
+                if 'y' in self.ri_values['ri_modelfree_value_state'].lower():
                     print("Reinforcement Learning models to consider - Monte Carlo, TD(0), and TD(Lambda)")
                 else:
                     print("Reinforcement Learning models to consider - SARSA, QLearning, Deep Queue Nets")
         else:
             # Default
-            print("Sorry. We need to discuss, please connect with Anuket Thoth Project <sridhar.rao@spirent.com>")
+            print("Sorry. We need to discuss, please connect with Anuket Thoth Project <srao@linuxfoundation.org>")
 
     def perform_inference(self):
         """
@@ -705,28 +932,28 @@ class AlgoSelectorWizard():
         """
         # Decide whether data is Low or High
         self.data_size = 'unknown'
-        if ('k' in self.gen_values['data_size_bytes'].lower() or
-                't' in self.gen_values['data_size_samples']):
+        if ('k' in self.gen_about_data_basic_values['data_size_bytes'].lower() or
+                't' in self.gen_about_data_basic_values['data_size_samples']):
             self.data_size = 'low'
 
-        if int(self.gen_values['metric_interpretability']) >= 3 :
+        if int(self.gen_metrics_values['metric_interpretability']) >= 3 :
             self.interpretability = True
-        if int(self.gen_values['metric_speed']) >= 3 :
+        if int(self.gen_metrics_values['metric_speed']) >= 3 :
             self.faster = True
-        if int(self.gen_values['metric_reproducibility']) >= 3 :
+        if int(self.gen_metrics_values['metric_reproducibility']) >= 3 :
             self.reproducibility = True
 
         # Decide Features relative to Data (ftod_ratio) - high/low
-        if ('k' in self.gen_values['data_size_bytes'].lower() or
-                't' in self.gen_values['data_size_samples']):
-            if int(self.gen_values['data_features_count']) > 50:
+        if ('k' in self.gen_about_data_basic_values['data_size_bytes'].lower() or
+                't' in self.gen_about_data_basic_values['data_size_samples']):
+            if int(self.gen_data_features_values['data_features_count']) > 50:
                 self.ftod_ratio = 'high'
-        elif ('m' in self.gen_values['data_size_bytes'].lower() or
-                'm' in self.gen_values['data_size_samples']):
-            if int(self.gen_values['data_features_count']) > 5000:
+        elif ('m' in self.gen_about_data_basic_values['data_size_bytes'].lower() or
+                'm' in self.gen_about_data_basic_values['data_size_samples']):
+            if int(self.gen_data_features_values['data_features_count']) > 5000:
                 self.ftod_ratio = 'high'
         else:
-            if int(self.gen_values['data_features_count']) > 500000:
+            if int(self.gen_data_features_values['data_features_count']) > 500000:
                 self.ftod_ratio = 'high'
 
 
@@ -742,15 +969,15 @@ class AlgoSelectorWizard():
                 else:
                     print("Supervised Learning model to consider  - Random Forest")
             else:
-                if int(self.gen_values['data_column']) == 3:
+                if int(self.gen_data_main_values['data_column']) == 3:
                     print("Supervised Learning model to consider  - RNN")
-                elif (int(self.gen_values['data_column']) == 2 and
-                        int(self.gen_values['data_signal_type']) == 1):
+                elif (int(self.gen_data_main_values['data_column']) == 2 and
+                        int(self.gen_data_signal_values['data_signal_type']) == 1):
                     print("Supervised Learning model to consider  - CNN")
-                elif (int(self.gen_values['data_column']) == 2 and
-                        (int(self.gen_values['data_signal_type']) == 2 or
-                            int(self.gen_values['data_signal_type']) == 3)):
-                    if 'y' in self.gen_values['data_output_prob'].tolower():
+                elif (int(self.gen_data_main_values['data_column']) == 2 and
+                        (int(self.gen_data_signal_values['data_signal_type']) == 2 or
+                            int(self.gen_data_signal_values['data_signal_type']) == 3)):
+                    if 'y' in self.gen_about_data_output_values['data_output_prob'].lower():
                         print("Supervised Learning model to consider  - Naive Bayes")
                     else:
                         print("Supervised Learning model to consider  - ANN")
@@ -764,21 +991,21 @@ class AlgoSelectorWizard():
             else:
                 print("Supervised Learning model to consider  - SVN with Gaussian Kernel")
                 return
-            if int(self.gen_values['data_type_output']) != 2:
+            if int(self.gen_about_data_output_values['data_type_output']) != 2:
                 from_b = True
             else:
-                if 'y' in self.gen_values['data_io_relation'].tolower():
+                if 'y' in self.gen_about_data_adv_values['data_io_relation'].lower():
                     print("Supervised Learning model to consider  - Linear Regression or Linear SVM")
                 else:
                     print("Supervised Learning model to consider  - Polynomial Regression or nonLinear SVM")
                 return
             if from_b:
-                if int(self.gen_values['data_output_type']) == 4:
-                    if 'y' in self.gen_values['data_output_prob'].tolower():
-                        if 'y' in self.gen_values['data_cond_indep'].tolower():
+                if int(self.gen_about_data_output_values['data_output_type']) == 4:
+                    if 'y' in self.gen_about_data_output_values['data_output_prob'].lower():
+                        if 'y' in self.gen_about_data_adv_values['data_cond_indep'].lower():
                             print("Supervised Learning model to consider  - Naive Bayes")
                         else:
-                            if 'y' in self.gen_values['data_correlation'].tolower():
+                            if 'y' in self.gen_about_data_adv_values['data_correlation'].lower():
                                 print("Supervised Learning model to consider  - LASSO or Ridge Regression")
                             else:
                                 print("Supervised Learning model to consider  - Logistic Regression")