Usage

Basic usecase: "What are the physical characteristics that most distinguish men from women?"

1. Load Python dependencies:

   ###
   ### Python dependencies.
   ###
   
   from cognitivefactory.features_maximization_metric.fmc import FeaturesMaximizationMetric
   from scipy.sparse import csr_matrix
   from typing import List
   

2. Define problem data:

   ###
   ### Data.
   ###
   
   # Define people characteristics that will be studied.
   characteristics_studied: List[str] = [
       "Shoes size",
       "Hair size",
       "Nose size",
   ]
   
   # Get people characteristics.
   people_characteristics: csr_matrix = csr_matrix(
       [
           [9, 5, 5],
           [9, 10, 5],
           [9, 20, 6],
           [5, 15, 5],
           [6, 25, 6],
           [5, 25, 5],
       ]
   )
   
   # Get people genders.
   people_genders: List[str] = [
       "Man",
       "Man",
       "Man",
       "Woman",
       "Woman",
       "Woman",
   ]
   

3. Modelize the problem:

   ###
   ### Feature Maximization Metrics.
   ###
   
   # Main computation.
   fmc_computer: FeaturesMaximizationMetric = FeaturesMaximizationMetric(
       data_vectors=people_characteristics,
       data_classes=people_genders,
       list_of_possible_features=characteristics_studied,
       amplification_factor=1,
   )
   

4. Determine relevant characteristics:

   ###
   ### Analysis 1: Delete characteristics that aren't relevant.
   ###
   
   print(
       "\n",
       "1. Which characteristic seems not relevant to distinguish men from women ?",
   )
   for characteristic in characteristics_studied:
       if not fmc_computer.features_selection[characteristic]:
           print(
               "    - '{0}' seems not relevant.".format(characteristic)
           )
   

   1. Which characteristic seems not relevant to distinguish men from women ?
       - 'Nose size' seems not relevant.
   

5. Describe gender by relevant characteristics.:

   ###
   ### Analysis 2: Describe gender by relevant characteristics.
   ###
   
   print(
       "\n",
       "2. According to remaining characteristics:",
   )
   for gender in sorted(set(people_genders)):
       print(
           "    - Which characteristic seems important to recognize a '{0}' ?".format(gender)
       )
   
       for characteristic in fmc_computer.get_most_active_features_by_a_classe(
           classe=gender,
       ):
           print(
               "        - '{0}' seems important (fmeasure of '{1:.2f}', contrast of '{2:.2f}').".format(
                   characteristic,
                   fmc_computer.features_fmeasure[characteristic][gender],
                   fmc_computer.features_contrast[characteristic][gender],
               )
           )
   

   2. According to remaining characteristics:
       - Which characteristic seems important to recognize a 'Man' ?
           - 'Shoes size' seems important (fmeasure of '0.45', contrast of '1.32').
       - Which characteristic seems important to recognize a 'Woman' ?
           - 'Hair size' seems important (fmeasure of '0.66', contrast of '1.25').