#!/usr/bin/env python
# coding: utf-8

# In[1]:


import networkx
import math
import scipy.optimize
import numpy
from lib.time_graph import *
from lib.vis import *
from lib.syn import *
from lib.io import *
from lib.datasets import *
from lib.time_graph import *
from lib.experiments import *
from IPython.display import Image


# # Synthetic data

# In[51]:


algos = [TemporalCuts("FSTC-16", "fast-sparse", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-sparse", eps=1e-5, k=64), 
             TemporalCuts("STC", "diff-sparse"), 
             TemporalCuts("LAP", "laplacian-sparse"),
             TemporalCuts("UNION", "union-sparse"), 
             TemporalCuts("SINGLE", "indep-sparse")]

grid = slide_diagonal_grids(10, 5, 10.)
grid = add_gaussian_noise(grid, 2.8)
G, values = time_graph_from_grid(grid, 2)

swap_costs = numpy.array([0., .5, 1., 1.5, 2.]) 
#swap_costs = numpy.array([0.]) * math.exp(2.)

res = sparsity_experiments(G, algos, swap_costs, 10)


# In[52]:


output_file_name = "figs/syn_sparsity.png"
plot_sparsity_experiments(res, algos, swap_costs, output_file_name, 0, 600, 1e-4, 1e-1)
Image(filename=output_file_name)


# In[50]:


algos = [TemporalCuts("FSTC-16", "fast-norm", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-norm", eps=1e-5, k=64), 
             TemporalCuts("STC", "diff-norm"), 
             TemporalCuts("LAP", "laplacian-norm"),
             TemporalCuts("UNION", "union-norm"), 
             TemporalCuts("SINGLE", "indep-norm")]

grid = grow_diagonal_grids(10, 5, 10.)
grid = add_gaussian_noise(grid, 2.8)
G, values = time_graph_from_grid(grid, 2)

swap_costs = numpy.array([0., .5, 1., 1.5, 2.0]) 
#swap_costs = numpy.array([0.]) * math.exp(2.)

res = sparsity_experiments(G, algos, swap_costs, 10)


# In[51]:


output_file_name = "figs/syn_norm_sparsity.png"
plot_sparsity_experiments(res, algos, swap_costs, output_file_name, 0, 600, 1e-5, 1e-2)
Image(filename=output_file_name)


# In[2]:


algos = [TemporalCuts("FSTC-16", "fast-sparse", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-sparse", eps=1e-5, k=64), 
             TemporalCuts("STC", "diff-sparse", eps=0.),
             TemporalCuts("LAP", "laplacian-sparse"),
             TemporalCuts("UNION", "union-sparse"), 
             TemporalCuts("SINGLE", "indep-sparse")]


swap_costs = numpy.array([0., 0.01, .02, 0.03, .04]) 
#swap_costs = numpy.array([0.]) * math.exp(2.)

G = read_time_graph(primary_school["graph"], .1)

res = sparsity_experiments(G, algos, swap_costs, 10)


# In[24]:


output_file_name = "figs/school_sparsity.png"
plot_sparsity_experiments(res, algos, swap_costs, output_file_name, 0, 600, 1e-6, 1e-3)
Image(filename=output_file_name)


# In[56]:


algos = [TemporalCuts("FSTC-16", "fast-norm", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-norm", eps=1e-5, k=64), 
             TemporalCuts("STC", "diff-norm", eps=0.),
             TemporalCuts("LAP", "laplacian-norm"),
             TemporalCuts("UNION", "union-norm"), 
             TemporalCuts("SINGLE", "indep-norm")]

swap_costs = numpy.array([0., 0.01, .02, 0.03, .04]) 
#swap_costs = numpy.array([0.]) * math.exp(2.)

G = read_time_graph(primary_school["graph"], .1)

res2 = sparsity_experiments(G, algos, swap_costs, 10)


# In[30]:


output_file_name = "figs/school_norm_sparsity.png"
plot_sparsity_experiments(res, algos, swap_costs, output_file_name, 0, 600, 1e-4, 2e-1)
Image(filename=output_file_name)


# In[36]:


res


# In[35]:


algos = [TemporalCuts("FSTC-16", "fast-sparse", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-sparse", eps=1e-5, k=64), 
             TemporalCuts("STC", "diff-sparse", eps=2e-5),
             TemporalCuts("LAP", "laplacian-sparse"),
             TemporalCuts("UNION", "union-sparse"), 
             TemporalCuts("SINGLE", "indep-sparse")]

swap_costs = numpy.array([0., 0.1, 0.2, 0.3, 0.4]) 

G = read_time_graph(stock["graph"], .1)

res = sparsity_experiments(G, algos, swap_costs, 10)


# In[28]:


output_file_name = "figs/stock_sparsity.png"
plot_sparsity_experiments(res, algos, [0., 0.1, 0.2, 0.3, 0.4], output_file_name, 0, 600, 1e-6,6e-3)
Image(filename=output_file_name)


# In[5]:


res


# In[37]:


algos = [TemporalCuts("FSTC-16", "fast-norm", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-norm", eps=1e-5, k=64), 
             TemporalCuts("STC", "diff-norm", eps=2e-5),
             TemporalCuts("LAP", "laplacian-norm"),
             TemporalCuts("UNION", "union-norm"), 
             TemporalCuts("SINGLE", "indep-norm")]


swap_costs = numpy.array([0., 0.1, 0.2, 0.3, 0.4]) 
#swap_costs = numpy.array([0.]) * math.exp(2.)

G = read_time_graph(stock["graph"], .1)

res2 = sparsity_experiments(G, algos, swap_costs, 10)


# In[49]:


output_file_name = "figs/stock_norm_sparsity.png"
plot_sparsity_experiments(res2, algos, [0., 0.1, 0.2, 0.3, 0.4], output_file_name, 0, 600, 1e-4, 2)
Image(filename=output_file_name)


# In[10]:


algos = [TemporalCuts("FSTC-16", "fast-sparse", eps=1e-5, k=16), 
             TemporalCuts("FSTC-64", "fast-sparse", eps=1e-5, k=64), 
             TemporalCuts("UNION", "union-sparse"), 
             TemporalCuts("SINGLE", "indep-sparse")]

#algos = [TemporalCuts("FSTC-10", "fast-sparse", eps=1e-5, k=10)]

swap_costs = numpy.array([0., .002, .004, .006, .008]) 

G = read_time_graph(dblp["graph"], .1)

res = sparsity_experiments(G, algos, swap_costs, 10)


# In[ ]:


res


# In[13]:


output_file_name = "figs/dblp_sparsity.png"
plot_sparsity_experiments(res, algos, [0., .02, .04, .06, .08], output_file_name, 0, 600, 1e-7, 1e-4)
Image(filename=output_file_name)