Added graphing capablities. It only graphs total fitness of generation.

setup.py

@@ -26,6 +26,8 @@ setup(
         "License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)",
         "Operating System :: OS Independent",
         ],
+    install_requires = ["matplotlib ~= 3.3.2",
+                        ],
     extra_require = {
         "dev": [
             "pytest>=3.7",

src/EasyGA.py

@@ -23,6 +23,9 @@ from attributes import Attributes
 from database import database
 from sqlite3 import Error
 
+# Graphing package
+import matplotlib.pyplot as plt
+
 
 class GA(Attributes):
     """GA is the main class in EasyGA. Everything is run through the ga
@@ -154,3 +157,34 @@ class GA(Attributes):
         """Prints the best chromosome and its fitness"""
         print(f"Best Chromosome \t: {self.population.get_chromosome(0)}")
         print(f"Best Fitness    \t: {self.population.get_chromosome(0).get_fitness()}")
+
+    def graph_scatter(self):
+        """Show a scatter plot of the database information."""
+
+        # Query the X data
+        generations = self.database.query_one_item("SELECT COUNT(DISTINCT generation) FROM data;")
+
+        # Create the generations array
+        X = list(range(0, generations))
+
+        #Query the Y data
+        Y_data = self.database.query_all("SELECT SUM(fitness) FROM data GROUP BY generation;")
+
+        # Format the Y data so we can use it to plot
+        Y = [i[0] for i in Y_data]
+
+        # Set the plot size
+        plt.figure(figsize=[5, 5])
+
+        plt.scatter(X,Y)
+        # x and y labels
+        plt.xlabel('Generation')
+        plt.ylabel('Generation Total Fitness')
+        plt.title('Relationship Between Generations and Generation Total Fitness')
+
+        # Show the plot
+        plt.show()
+
+
+    def graph_line(self):
+        pass

src/attributes.py

@@ -24,6 +24,7 @@ from crossover import Crossover_Methods
 from database import database
 from sqlite3 import Error
 
+
 class Attributes:
     """Default GA attributes can be found here. If any attributes have not
     been set then they will fall back onto the default attribute. All
@@ -60,7 +61,7 @@ class Attributes:
             termination_impl            = Termination_Methods.fitness_and_generation_based,
             database                    = None,
             database_name               = 'database.db',
-            sql_create_data_structure = """CREATE TABLE IF NOT EXISTS data (
+            sql_create_data_structure   = """CREATE TABLE IF NOT EXISTS data (
                                                 id integer PRIMARY KEY,
                                                 generation integer NOT NULL,
                                                 fitness DOUBLE,

src/database/database.py

@@ -5,7 +5,7 @@ import os
 
 class database:
     """Main database class that controls all the functionality for input /
-        out of the database."""
+    out of the database."""
 
 
     def __init__(self):
@@ -13,7 +13,7 @@ class database:
 
 
     def create_connection(self, db_file):
-        """ create a database connection to the SQLite database
+        """Create a database connection to the SQLite database
          specified by db_file."""
 
         conn = None
@@ -27,11 +27,8 @@ class database:
 
 
     def create_table(self, create_table_sql):
-        """ create a table from the create_table_sql statement
-        :param conn: Connection object
-        :param create_table_sql: a CREATE TABLE statement
-        :return:
-        """
+        """Create a table from the create_table_sql statement."""
+
         try:
             c = self.conn.cursor()
             c.execute(create_table_sql)
@@ -39,20 +36,15 @@ class database:
             print(e)
 
     def insert_chromosome(self, generation, chromosome):
-        """
-        Insert a new chromosome
-        :param conn:
-        :param generation:
-        :param chromosome:
-        :return:
-        """
+        """ """
 
         # Structure the insert data
         db_chromosome = (generation, chromosome.fitness, '[chromosome]')
 
-
+        # Create sql query structure
         sql = ''' INSERT INTO data(generation,fitness,chromosome)
-                  VALUES(?,?,?) '''
+         VALUES(?,?,?) '''
+
         cur = self.conn.cursor()
         cur.execute(sql, db_chromosome)
         self.conn.commit()
@@ -61,16 +53,20 @@ class database:
     def insert_current_population(self, ga):
         """ Insert current generations population """
 
+        # For each chromosome in the population
         for chromosome in ga.population.chromosome_list:
+            # Insert the chromosome into the database
             self.insert_chromosome(ga.current_generation, chromosome)
 
 
     def create_data_table(self, ga):
+        """Create the data table that store generation data."""
 
         try:
-            # Remove old database if there
+            # Remove old database file if it exists.
             os.remove(ga.database_name)
         except:
+            # If the database does not exist continue
             pass
 
         # create a database connection
@@ -81,7 +77,23 @@ class database:
             # create projects table
             self.create_table(ga.sql_create_data_structure)
 
-            # create tasks table
-            # create_table(conn, sql_create_tasks_table)
         else:
             print("Error! cannot create the database connection.")
+
+
+    def query_all(self, query):
+        """Query for muliple rows of data"""
+
+        cur = self.conn.cursor()
+        cur.execute(query)
+
+        return cur.fetchall()
+
+    def query_one_item(self, query):
+        """Query for single data point"""
+
+        cur = self.conn.cursor()
+        cur.execute(query)
+        query_data = cur.fetchone()
+
+        return query_data[0]

src/run_testing.py

@@ -1,14 +1,14 @@
-import random
 import EasyGA
+import random
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-ga.population_size = 3
-ga.generation_goal = 10
-# Evolve the genetic algorithm
+ga.generation_goal = 200
+ga.population_size = 50
+
 ga.evolve()
 
-# Print your default genetic algorithm
-ga.print_generation()
 ga.print_population()
+
+ga.graph_scatter()