# coding: utf-8

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import seaborn as sns


# In[2]:


import numpy as np
import pandas as pd
from operator import xor
from pprint import pprint


# In[3]:


pokemons = pd.read_csv('https://raw.githubusercontent.com/veekun/pokedex/master/pokedex/data/csv/pokemon.csv').loc[:, ['id', 'identifier']]
id2name = {pokemon_id: pokemon_name for pokemon_id, pokemon_name in zip(pokemons['id'], pokemons['identifier']) if pokemon_id <= 250}
name2id = {pokemon_name:pokemon_id for pokemon_id, pokemon_name in id2name.items()}


# In[4]:


# Print first 10 id-name pairs
pprint({k:v for k, v in id2name.items() if k <= 10})


# ### (Original) Pokemon DataFrame

# In[5]:


original_poke = pd.read_csv('pokemon.csv', low_memory=False).set_index('_id') # https://www.kaggle.com/semioniy/predictemall


# In[6]:


original_poke.head()


# ### Pokemon DataFrame

# In[7]:


poke = original_poke.copy()


# ### Filter by Country/Continent

# In[8]:


print('No. of samples from Asia:', poke.continent.value_counts()['Asia'])
print('No. of samples from Singapore:', poke.city.value_counts()['Singapore'])


# In[9]:


# We select Pokemon samples from Asia
poke = poke[poke.continent == 'Asia']
poke.shape


# ### Filtered Pokemon DataFrame - Only allow columns starting with `cooc` (i.e. the co-occurrence with other pokemons) or `pokemonId`

# In[10]:


filtered_cols = [col for col in poke.columns if (col.startswith('cooc') or col.startswith('pokemonId'))]
filtered_poke = poke[filtered_cols]


# In[11]:


filtered_poke.shape


# In[12]:


filtered_poke.head()


# ### Convert Booleans to Integers

# In[13]:


filtered_poke_numeric = filtered_poke.astype(int)
filtered_poke_numeric.head()


# ### Groupby by `pokemonId`

# In[14]:


corr_poke = filtered_poke_numeric.groupby('pokemonId').mean()
corr_poke.head()


# In[15]:


corr_poke.shape


# In[16]:


corr_poke.columns = [int(col.replace('cooc_', '')) for col in corr_poke.columns]
corr_poke.drop([150, 151], axis=1, inplace=True)  # Drop Pokemons 150 and 151 (i.e. Mewtwo and Mew)
corr_poke.head()


# In[17]:


corr_poke.shape


# In[18]:


corr_poke.index = [id2name[col] for col in corr_poke.index]
corr_poke.columns = [id2name[col] for col in corr_poke.columns]
corr_poke.head()


# In[19]:


from sklearn.preprocessing import StandardScaler, normalize

corr_poke_matrix_norm = normalize(corr_poke.as_matrix(), norm='l2')  # Alternatively, try: "norm='l1'"
corr_poke_matrix_std = StandardScaler().fit_transform(corr_poke.as_matrix())


# In[20]:


# Using Normalize
corr_poke_matrix_norm


# In[21]:


# Using StandardScaler
corr_poke_matrix_std


# # Plot using Seaborn's `heatmap` (on L1/L2 normalize)

# In[22]:


X = pd.DataFrame(corr_poke_matrix_norm, index=corr_poke.index, columns=corr_poke.columns)
X.head(3)


# [All about colormaps](http://matplotlib.org/examples/color/colormaps_reference.htm)

# In[23]:


plt.figure(figsize = (40, 25))
sns_plot = sns.heatmap(X, annot=False, cmap='afmhot', linewidths=.05, square=True) # Note that I used "cmap='afmhot"
# colormaps (cmap): http://matplotlib.org/examples/color/colormaps_reference.html


# # Plot using Seaborn's `heatmap` (on StandardScalar) and save the figure

# In[24]:


X = pd.DataFrame(corr_poke_matrix_std, index=corr_poke.index, columns=corr_poke.columns)
X.head(3)


# [All about colormaps](http://matplotlib.org/examples/color/colormaps_reference.htm)

# In[25]:


plt.figure(figsize = (40, 25))
sns_plot = sns.heatmap(X, annot=False, cmap='RdBu_r', linewidths=.05, square=True)

plt.savefig("pokemon_output.png")


# # Link to `pokemon_output.png`
# https://www.dropbox.com/s/0qlyj9xc9v45gne/pokemon_output.png?dl=0

# By: Jovian Lin ([jovianlin.com](http://jovianlin.com))