--- /dev/null
+# Copyright 2021 Spirent Communications.
+# sridhar.rao@spirent.com
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+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.
+"""
+
+from __future__ import print_function
+import signal
+import sys
+from pypsi import wizard as wiz
+from pypsi.shell import Shell
+
+# pylint: disable=line-too-long,too-few-public-methods,too-many-instance-attributes, too-many-nested-blocks, too-many-return-statements, too-many-branches
+
+class Bcolors:
+ """
+ For Coloring
+ """
+ HEADER = '\033[95m'
+ OKBLUE = '\033[94m'
+ OKGREEN = '\033[92m'
+ WARNING = '\033[93m'
+ FAIL = '\033[91m'
+ ENDC = '\033[0m'
+ BOLD = '\033[1m'
+ UNDERLINE = '\033[4m'
+
+class AlgoSelectorWizard():
+ """
+ Class to create wizards
+ """
+ def __init__(self):
+ """
+ Perform Initialization.
+ """
+ self.shell = Shell()
+ self.main_values = {}
+ self.main_l1_values = {}
+ self.main_l2a_values = {}
+ self.main_l2b_values = {}
+ self.main_l3_values = {}
+ self.main_l4_values = {}
+ self.unsup_values = {}
+ self.ri_values = {}
+ self.gen_values = {}
+ self.wiz_main = None
+ self.wiz_main_l1 = None
+ self.wiz_main_l2_a = None
+ self.wiz_main_l2_b = None
+ self.wiz_main_l3 = None
+ self.wiz_main_l4 = None
+ self.wiz_generic = None
+ self.wiz_unsupervised = None
+ self.wiz_reinforcement = None
+ self.ml_needed = False
+ self.supervised = False
+ self.unsupervised = False
+ self.reinforcement = False
+ self.data_size = 'high'
+ self.interpretability = False
+ self.faster = False
+ self.ftod_ratio = 'low'
+ self.reproducibility = False
+
+
+ ############# All the Wizards ##################################
+
+ ### GENERIC Wizards - Need for ML ##############################
+ def main_wizard_l1(self):
+ """
+ The Main Wizard L1
+ """
+ self.wiz_main_l1 = wiz.PromptWizard(
+ name=Bcolors.OKBLUE+"Do you Need ML - Data Availability"+Bcolors.ENDC,
+ description="",
+ steps=(
+ # The list of input prompts to ask the user.
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_availability",
+ # 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),
+ default='Y',
+ ),
+ )
+ )
+
+ def main_wizard_l2_a(self):
+ """
+ The Main Wizard L2-A
+ """
+ self.wiz_main_l2_a = wiz.PromptWizard(
+ name=Bcolors.OKBLUE+"Do you Need ML - Data Creation"+Bcolors.ENDC,
+ description="",
+ steps=(
+ # The list of input prompts to ask the user.
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_creativity",
+ # 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),
+ default='Y',
+ ),
+ )
+ )
+
+ def main_wizard_l2_b(self):
+ """
+ The Main Wizard L2-B
+ """
+ label = """ One or more meaningful and informative 'tag' to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor. Data labeling is required for a variety of use cases including computer vision, natural language processing, and speech recognition."""
+ self.wiz_main_l2_b = wiz.PromptWizard(
+ name=Bcolors.OKBLUE+"Do you Need ML - Data Programmability"+Bcolors.ENDC,
+ description="",
+ steps=(
+ # The list of input prompts to ask the user.
+ wiz.WizardStep(
+ # 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,
+ # Help message
+ help=label,
+ validators=(wiz.required_validator),
+ default='Y',
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_programmability",
+ # 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',
+ ),
+ )
+ )
+
+
+ def main_wizard_l3(self):
+ """
+ The Main Wizard L3
+ """
+ self.wiz_main_l3 = wiz.PromptWizard(
+ name=Bcolors.OKBLUE+"Do you Need ML - Data Knowledge"+Bcolors.ENDC,
+ description="",
+ steps=(
+ # The list of input prompts to ask the user.
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_knowledge",
+ # 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),
+ default='Y',
+ ),
+ )
+ )
+
+ def main_wizard_l4(self):
+ """
+ The Main Wizard - L4
+ """
+ self.wiz_main_l4 = wiz.PromptWizard(
+ name=Bcolors.OKBLUE+"Do you Need ML - Data Pattern"+Bcolors.ENDC,
+ description="",
+ steps=(
+ # The list of input prompts to ask the user.
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_pattern",
+ # 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),
+ default='Y'
+ ),
+ )
+ )
+ ### GENERIC Wizards - GOAL, METRICS, DATA ##############################
+ def gen_wizard(self):
+ """
+ Generic Wizard - Goal, metrics, data
+ """
+ self.wiz_generic = 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="data_goal",
+ # Display name
+ 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),
+ default='Explore'
+ ),
+ 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),
+ default='1'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="metric_speed",
+ # Display name
+ 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),
+ default='1'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="metric_interpretability",
+ # Display name
+ 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),
+ default='1'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="metric_reproducibility",
+ # Display name
+ 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),
+ default='1'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="metric_implementation",
+ # Display name
+ 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),
+ default='1'
+ ),
+ 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,
+ # 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'
+ ),
+ 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,
+ # 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),
+ default='3'
+ ),
+ 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),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_features_count",
+ # Display name
+ name=Bcolors.HEADER+" If features, How many are there? "+Bcolors.ENDC,
+ # Help message
+ help="Number or NA",
+ validators=(wiz.required_validator),
+ default='10'
+ ),
+ 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/U",
+ validators=(wiz.required_validator),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_io_relation",
+ # Display name
+ 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='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_correlation",
+ # Display name
+ name=Bcolors.HEADER+" Are you confident that there is NO high correlation among the independent variables in your day?"+Bcolors.ENDC,
+ # Help message
+ help="Y/N/U. Change in one ",
+ validators=(wiz.required_validator),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_cond_indep",
+ # Display name
+ name=Bcolors.HEADER+" Are you confident that the variables are conditionally independent?"+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",
+ validators=(wiz.required_validator),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_missing",
+ # Display name
+ name=Bcolors.HEADER+" Are there any missing values in the data? "+Bcolors.ENDC,
+ # Help message
+ help="Y/N/U",
+ validators=(wiz.required_validator),
+ default='N'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_size_bytes",
+ # Display name
+ name=Bcolors.HEADER+" How big is the data in terms of size? (Use K/M/G Bytes unit) "+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'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_size_samples",
+ # Display name
+ name=Bcolors.HEADER+" How big is the data in terms of samples? (Use T/M/B Samples) "+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'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_type_output",
+ # Display name
+ 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),
+ default='1'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="data_output_prob",
+ # Display name
+ name=Bcolors.HEADER+" Is the expected output data a probability value ? "+Bcolors.ENDC,
+ # Help message
+ help="Y/N",
+ validators=(wiz.required_validator),
+ default='N'
+ ),
+ )
+ )
+
+
+ def unsupervised_wizard(self):
+ """
+ The Un-Supervized Learning Wizard
+ """
+ self.wiz_generic = 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="unsup_goal",
+ # Display name
+ 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),
+ default='1'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="unsup_dr_topic_mod",
+ # Display name
+ 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),
+ default='NA'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="unsup_clus_dv",
+ # Display name
+ 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),
+ default='NA'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="unsup_clus_outliers",
+ # Display name
+ 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),
+ default='NA'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="unsup_clus_groups",
+ # Display name
+ 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),
+ default='NA'
+ ),
+
+ )
+ )
+
+ def reinforcement_wizard(self):
+ """
+ The Reinforced Learning Wizard
+ """
+ message = """
+ Reward |--------|
+ |-------| Agent | Action
+ | |-----| |-------|
+ | | |--------| |
+ | |state |
+ | | |
+ | | |-----------| |
+ | |----|Environment| |
+ |------| |-----|
+ |-----------|
+ """
+ self.wiz_reinforcement = wiz.PromptWizard(
+ name=Bcolors.OKBLUE+"Reinforcement Specific"+Bcolors.ENDC,
+ description="",
+ steps=(
+ # The list of input prompts to ask the user.
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="ri_info",
+ # Display name
+ name=Bcolors.HEADER+" Type help for reference diagram for reinforcement-learning"+Bcolors.ENDC,
+ # Help message
+ help=message,
+ validators=(wiz.required_validator),
+ default='Type Help or Press Enter'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="ri_model_preference",
+ # Display name
+ 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),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="ri_model_availability",
+ # Display name
+ 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),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="ri_modelfree_value",
+ # Display name
+ 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),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="ri_modelfree_value_state",
+ # Display name
+ 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),
+ default='Y'
+ ),
+ wiz.WizardStep(
+ # ID where the value will be stored
+ id="ri_app_domain",
+ # Display name
+ 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),
+ default='1'
+ ),
+ )
+ )
+
+ ############### All the Run Operations ######################
+ def run_mainwiz(self):
+ """
+ Run the Main Wizard
+ """
+ self.main_wizard_l1()
+ self.main_l1_values = self.wiz_main_l1.run(self.shell)
+ if self.main_l1_values['data_availability'].lower() == 'y':
+ 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':
+ self.supervised = True
+ else:
+ self.unsupervised = True
+ if self.main_l2b_values['data_programmability'].lower() == 'y':
+ 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':
+ 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':
+ print(Bcolors.OKGREEN+"Looks like you need ML, let's continue"+Bcolors.ENDC)
+ self.ml_needed = True
+ else:
+ print(Bcolors.FAIL+"ML is not required - Please consider alternate approaches\n"+Bcolors.ENDC)
+ 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':
+ print(Bcolors.OKGREEN+"Looks like you need ML, let's continue"+Bcolors.ENDC)
+ self.ml_needed = True
+ self.reinforcement = True
+ else:
+ print(Bcolors.FAIL+"ML is not required - Please consider alternate approaches\n"+Bcolors.ENDC)
+
+ def run_generic_wizard(self):
+ """
+ Run Generic Wizard
+ """
+ self.gen_wizard()
+ self.gen_values = self.wiz_generic.run(self.shell)
+
+ def run_unsupervised_wizard(self):
+ """
+ Run UnSupervised Learning Wizard.
+ """
+ self.unsupervised_wizard()
+ self.unsup_values = self.wiz_unsupervised.run(self.shell)
+
+ def run_reinforcement_wizard(self):
+ """
+ Run Reinforced Learning Wizard
+ """
+ self.reinforcement_wizard()
+ self.ri_values = self.wiz_reinforcement.run(self.shell)
+
+ def decide_unsupervised(self):
+ """
+ Decide which Unsupervised-learning to use
+ """
+ repro = False
+ clus_prob = False
+ if int(self.unsup_values['unsup_goal']) == 1:
+ # Clustering
+ if 'high' in self.data_size:
+ if not self.reproducibility:
+ clus_prob = True
+ else:
+ repro = True
+ else:
+ if 'y' in self.unsup_values['unsup_clus_dv'].tolower():
+ if 'y' in self.unsup_values['unsup_clus_groups'].tolower():
+ clus_prob = True
+ else:
+ print("Unsupervised Learning model to consider: Hierarchical Clustering")
+ return
+ else:
+ repro = True
+ if repro:
+ if 'y' in self.unsup_values['unsup_clus_outliers'].tolower():
+ 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():
+ 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():
+ print("Unsupervised Learning model to consider: SVD")
+ else:
+ print("Unsupervised Learning model to consider: LDA")
+ else:
+ print("Unsupervised Learning model to consider: PCA")
+ else:
+ print("Sorry. We need to discuss, please connect with Anuket Thoth Project <sridhar.rao@spirent.com>")
+
+ def decide_reinforcement(self):
+ """
+ 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()):
+ # Model Bsaed
+ if 'y' in self.ri_values['ri_model_availability'].tolower():
+ 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():
+ # Model-Free based approach.
+ if 'y' not in self.ri_values['ri_modelfree_value'].tolower():
+ print("Reinforcement Learning models to consider: Policy Gradient and Actor Critic")
+ else:
+ if 'y' in self.ri_values['ri_modelfree_value_state'].tolower():
+ 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>")
+
+ def perform_inference(self):
+ """
+ Perform Inferences. Used across all 3 types.
+ """
+ # 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']):
+ self.data_size = 'low'
+
+ if int(self.gen_values['metric_interpretability']) >= 3 :
+ self.interpretability = True
+ if int(self.gen_values['metric_speed']) >= 3 :
+ self.faster = True
+ if int(self.gen_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:
+ 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:
+ self.ftod_ratio = 'high'
+ else:
+ if int(self.gen_values['data_features_count']) > 500000:
+ self.ftod_ratio = 'high'
+
+
+ def decide_supervised(self):
+ """
+ Decide which Supervised learning to use.
+ """
+ if 'high' in self.data_size:
+ # Cover: DT, RF, RNN, CNN, ANN and Naive Bayes
+ if self.interpretability:
+ if self.faster:
+ print("Supervised Learning model to consider - Decision Tree")
+ else:
+ print("Supervised Learning model to consider - Random Forest")
+ else:
+ if int(self.gen_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):
+ 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():
+ print("Supervised Learning model to consider - Naive Bayes")
+ else:
+ print("Supervised Learning model to consider - ANN")
+ else:
+ print("Supervised model to consider Learning - ANN")
+ elif 'low' in self.data_size:
+ from_b = False
+ # Cover: Regressions
+ if 'high' in self.ftod_ratio:
+ from_b = True
+ else:
+ print("Supervised Learning model to consider - SVN with Gaussian Kernel")
+ return
+ if int(self.gen_values['data_type_output']) != 2:
+ from_b = True
+ else:
+ if 'y' in self.gen_values['data_io_relation'].tolower():
+ 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():
+ print("Supervised Learning model to consider - Naive Bayes")
+ else:
+ if 'y' in self.gen_values['data_correlation'].tolower():
+ print("Supervised Learning model to consider - LASSO or Ridge Regression")
+ else:
+ print("Supervised Learning model to consider - Logistic Regression")
+ else:
+ print("Supervised Learning model to consider - Polynomial Regression or nonLinear SVM")
+
+ else:
+ print("Supervised Learning model to consider - KNN")
+ else:
+ # Default
+ print("Sorry. We need to discuss, please connect with Anuket Thoth Project <sridhar.rao@spirent.com>")
+
+ def ask_and_decide(self):
+ """
+ THe Main Engine
+ """
+ self.run_mainwiz()
+ if self.ml_needed:
+ self.run_generic_wizard()
+ if self.supervised:
+ self.decide_supervised()
+ elif self.unsupervised:
+ self.run_unsupervised_wizard()
+ self.decide_unsupervised()
+ elif self.reinforcement:
+ self.run_reinforcement_wizard()
+ self.decide_reinforcement()
+
+
+def signal_handler(signum, frame):
+ """
+ Signal Handler
+ """
+ print("\n You interrupted, No Suggestion will be provided!")
+ print(signum, frame)
+ sys.exit(0)
+
+def main():
+ """
+ The Main Function
+ """
+ try:
+ algowiz = AlgoSelectorWizard()
+ algowiz.ask_and_decide()
+ except(KeyboardInterrupt, MemoryError):
+ print("Some Error Occured - No Suggestion can be provided")
+
+ print("Thanks for using the Algoselector-Wizard, " +
+ "Hope our suggestion will be useful")
+
+if __name__ == "__main__":
+ signal.signal(signal.SIGINT, signal_handler)
+ main()
Code Review / thoth.git / commitdiff
