上传文件至 'Include'

Include/Gantt1.py

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+# -*- coding: utf-8 -*-
+
+"""Main module."""
+
+from collections import OrderedDict
+
+import matplotlib.dates as m_dates
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+def gantt(task=None, start=None, finish=None, **kwargs):
+    """ Plot a gantt chart.
+    """
+
+    if 'task_type' in kwargs:
+        task_type = kwargs['task_type']
+    if 'color' in kwargs:
+        color = kwargs['color']
+
+    USES_DATES = False
+    if np.issubdtype(start.dtype, np.datetime64):
+        start = m_dates.date2num(start)
+        USES_DATES = True
+    if np.issubdtype(finish.dtype, np.datetime64):
+        finish = m_dates.date2num(finish)
+
+    delta = finish - start
+
+    ax = plt.gca()
+
+    labels = []
+
+    # TODO: refactor?    
+    encoded_tasks = OrderedDict()
+    k = 0
+    for n in task:
+        if not n in encoded_tasks:
+            encoded_tasks[n] = k
+            k += 1
+
+    labels = list(encoded_tasks)
+    for i, task in enumerate(task):
+        j = encoded_tasks[task]
+        if color:
+            c = color[task_type[i]]
+        else:
+            c = None
+        ax.broken_barh([(start[i], delta[i])], (j - 0.4, 0.8), color=c)
+
+    # Set yticks
+    ax.set_yticks(range(len(labels)))
+    ax.set_yticklabels(labels)
+
+    # Set xticks formatting
+    # TODO: use matplotlib.dates.AutoDateFormatter
+    if USES_DATES:
+        ax.xaxis.set_major_formatter(m_dates.DateFormatter('%Y-%m-%d %H:%M'))
+        fig = plt.gcf()
+        fig.autofmt_xdate()
+
+    ax.invert_yaxis()
+
+
+def my_gantt(show_pic=False):
+    _data = pd.read_csv('Bin\\Data\\Result.csv')[['FLIGHTNO', 'DES_REAL_LANDING', 'DEP_REAL_TAKEOFF', 'Seat']]
+    _data['DES_REAL_LANDING'] = pd.to_datetime(_data['DES_REAL_LANDING'])
+    _data['DEP_REAL_TAKEOFF'] = pd.to_datetime(_data['DEP_REAL_TAKEOFF'])
+    _data['Type'] = "main"
+    _data.iloc[1::2, -1] = "sub"
+    _data = _data.sort_values('Seat')
+    fig = plt.figure(figsize=(30, 20))
+    gantt(task=_data['Seat'], start=_data['DES_REAL_LANDING'],
+          finish=_data['DEP_REAL_TAKEOFF'], task_type=_data['Type'],
+          color={"main": "steelblue", "sub": "lightgreen"})
+    plt.title('Gantt', {'fontsize': 14, 'fontweight': 'heavy'})
+    if show_pic:
+        plt.show()
+    fig.savefig('Bin\\Data\\Gantt.png')

Include/Individual.py

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+import sys
+from copy import deepcopy
+from functools import reduce
+from random import choice
+
+import numpy as np
+import pandas as pd
+
+
+class GeneticAlgorithmConnect(object):
+    def __init__(self, min_ini=pd.Timedelta('0 days 00:15:00')):
+        self.flight_info = pd.DataFrame()
+        self.min_ini = min_ini
+
+    def initialize_flight_info(self):
+        signal_day_ = pd.read_csv('Bin\\Data\\Signal_day.csv')
+        signal_day_['DES_REAL_LANDING'] = pd.to_datetime(signal_day_['DES_REAL_LANDING'])
+        signal_day_['DEP_REAL_TAKEOFF'] = pd.to_datetime(signal_day_['DEP_REAL_TAKEOFF']) + self.min_ini
+        self.flight_info = signal_day_.sort_values('DES_REAL_LANDING')
+
+    def marshaling_result(self, seat):
+        self.flight_info['Seat'] = seat
+        self.flight_info['DEP_REAL_TAKEOFF'] -= self.min_ini
+        self.flight_info.to_csv('Bin\\Data\\Result.csv', index=False)
+
+
+class GeneticAlgorithm:
+    def __init__(self,
+                 dna_length=50,
+                 population_number=100,
+                 cross_probability=0.3,
+                 mutation_probability=0,
+                 generate_number=20,
+                 target='max'
+                 ):
+        self.dna_length = dna_length
+        self.population_number = population_number
+        self.cross_probability = cross_probability
+        self.mutation_probability = mutation_probability
+        self.generate_number = generate_number
+        self.target = 1 if target == 'max' else -1
+
+        self.connect_data = GeneticAlgorithmConnect()
+
+        self.boys = pd.DataFrame([], columns=['name', 'fitness', 'actual_fitness'])
+
+    def initialize_connect_class(self):
+        self.connect_data.initialize_flight_info()
+
+    def decimal_individual(self):
+        fi = self.connect_data.flight_info
+        pork = list(range(self.dna_length))
+        boy = []
+        for index, row in fi.iterrows():
+            valid = self.valid_pork(boy, row) + list(set(pork) ^ set(list(set(boy)))) if boy else pork
+            if valid:
+                boy.append(choice(valid))
+            else:
+                print('初代无解,程序退出')
+                sys.exit()
+        return boy
+
+    def valid_pork(self, boy, row):
+        assigned_position = self.connect_data.flight_info.iloc[:len(boy)].copy()
+        assigned_position['Seat'] = boy
+        takeoff_time = assigned_position.groupby('Seat').apply(lambda x: x['DEP_REAL_TAKEOFF'].max())
+        return takeoff_time[takeoff_time < row['DES_REAL_LANDING']].index.tolist()
+
+    @staticmethod
+    def duplicate_removal(some_list):
+        return reduce(lambda x, y: x if y in x else x + [y], [[], ] + some_list)
+
+    def get_boys(self):
+        boys = []
+        while len(boys) < self.population_number:
+            boys.append(self.decimal_individual())
+            boys = self.duplicate_removal(boys)
+        self.boys = pd.DataFrame([], columns=['name', 'fitness'])  # Collecting fitness in a moment
+        self.boys['name'] = boys
+
+    def get_fitness(self):
+        fi = self.connect_data.flight_info.copy()
+        fitness = []
+        for boy in self.boys['name'].values:
+            fi['Seat'] = boy
+            fitness.append(fi.groupby('Seat').apply(self.get_diff).sum())
+        self.make_up(fitness)  # never go out without make-up
+        self.sort_values()
+
+    def make_up(self, fitness):
+        self.boys['actual_fitness'] = fitness
+        self.boys['fitness'] = fitness
+        self.boys = self.boys.fillna(max(fitness) if self.target > 0 else min(fitness))
+        self.boys['fitness'] *= self.target
+        self.boys['fitness'] = self.boys['fitness'] - self.boys['fitness'].min() + 1e-5
+        self.boys['fitness'] = self.boys['fitness'].ffill()
+
+    def sort_values(self):  # It's always a ascending order
+        self.boys = self.boys.sort_values('fitness').reset_index(drop=True)
+
+    @staticmethod
+    def get_diff(x):
+        x = x.sort_values('DES_REAL_LANDING')
+        if (x['DES_REAL_LANDING'] < x['DEP_REAL_TAKEOFF'].shift()).any():
+            return np.nan
+        return (x['DES_REAL_LANDING'] - x['DEP_REAL_TAKEOFF'].shift()).sum().total_seconds()
+
+    def selection(self):
+        idx = np.random.choice(self.population_number, size=self.population_number, replace=True,
+                               p=self.boys['fitness'] / self.boys['fitness'].sum())
+        self.boys = self.boys.iloc[idx].copy()
+        self.sort_values()
+
+    def crossover(self):
+        boys = self.boys['name'].values
+        girls = deepcopy(boys)
+
+        def should_recovery(index_):
+            if not self.is_the_boy_healthy(boy):
+                boy[:] = girls[index_]
+
+        for index, boy in enumerate(boys):  # some children is unattractive
+            if index == 0:
+                boy[:] = girls[-1]
+            elif np.random.rand() < self.cross_probability:  # the boy should change
+                girl = choice(girls)
+                exchange_of_chromosomes = np.random.randint(self.dna_length, size=self.population_number)
+                for chromosome in list(set(exchange_of_chromosomes.tolist())):
+                    boy[chromosome] = girl[chromosome]
+                should_recovery(index)
+
+                if self.mutation_probability > 0:
+                    for chromosome in range(self.dna_length):
+                        if np.random.rand() < self.mutation_probability:
+                            boy[chromosome] = np.random.randint(self.dna_length)
+                    should_recovery(index)
+
+    def is_the_boy_healthy(self, boy):
+        fi = self.connect_data.flight_info.copy()
+        fi['Seat'] = boy
+        return fi.groupby('Seat').apply(self.physical_examination).all()
+
+    @staticmethod
+    def physical_examination(x):
+        if x.shape[0] > 1:
+            x = x.sort_values('DES_REAL_LANDING')
+            return not (x['DEP_REAL_TAKEOFF'].shift() > x['DES_REAL_LANDING']).any()
+        return True
+
+    def marshaling_result(self):
+        self.connect_data.marshaling_result(self.boys.iloc[0]['name'])