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

# Taking `examples/examples.ipynb` as a starting point.  

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


# In[2]:


import os
import sys

sys.path.append("..")
sys.path.append("../..")

import numpy as np 
import pandas as pd
import yellowbrick as yb


# In[3]:


from yellowbrick.features import (ParallelCoordinates,
                                  parallel_coordinates)


# ### Dataset

# In[4]:


from download import download_all 

## The path to the test data sets
FIXTURES  = os.path.join(os.getcwd(), "data")

## Dataset loading mechanisms
datasets = {
    "credit": os.path.join(FIXTURES, "credit", "credit.csv"),
    "concrete": os.path.join(FIXTURES, "concrete", "concrete.csv"),
    "occupancy": os.path.join(FIXTURES, "occupancy", "occupancy.csv"),
    "mushroom": os.path.join(FIXTURES, "mushroom", "mushroom.csv"),
}

def load_data(name, download=True):
    """
    Loads and wrangles the passed in dataset by name.
    If download is specified, this method will download any missing files. 
    """
    # Get the path from the datasets 
    path = datasets[name]
    
    # Check if the data exists, otherwise download or raise 
    if not os.path.exists(path):
        if download:
            download_all() 
        else:
            raise ValueError((
                "'{}' dataset has not been downloaded, "
                "use the download.py module to fetch datasets"
            ).format(name))
    
    # Return the data frame
    return pd.read_csv(path)


# In[5]:


# Load the classification data set
data = load_data('occupancy') 
print(len(data))
data.head()


# In[6]:


# Specify the features of interest and the classes of the target 
features = ["temperature", "relative humidity", "light", "C02", "humidity"]
classes = ['unoccupied', 'occupied']

# Extract the numpy arrays from the data frame 
X = data.head(1000)[features]
y = data.head(1000).occupancy


# ### Parallel Coordinates
# * add dataframe compatibility

# In[7]:


# numpy inputs
visualizer = ParallelCoordinates(features=features, classes=classes)
visualizer.fit_transform_poof(X.values, y.values);


# In[8]:


# numpy inputs, no labels
visualizer = ParallelCoordinates(classes=classes)
visualizer.fit_transform_poof(X.values, y.values);


# In[9]:


# dataframe inputs
visualizer = ParallelCoordinates(classes=classes)
visualizer.fit_transform_poof(X, y);


# In[10]:


# quick method
parallel_coordinates(X, y);


# ### `normalize` argument

# In[11]:


visualizer = ParallelCoordinates(normalize='minmax', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[12]:


visualizer = ParallelCoordinates(normalize='maxabs', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[13]:


visualizer = ParallelCoordinates(normalize='standard', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[14]:


visualizer = ParallelCoordinates(normalize='l1', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[15]:


visualizer = ParallelCoordinates(normalize='l2', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[16]:


visualizer = ParallelCoordinates(normalize='l2', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[17]:


# should raise YellowbrickValueError
visualizer = ParallelCoordinates(normalize='bad', classes=classes)
visualizer.fit_transform_poof(X, y);


# In[18]:


# quick method
parallel_coordinates(X, y, normalize='standard');


# ### `sample` argument

# In[19]:


visualizer = ParallelCoordinates(classes=classes, sample=200)
visualizer.fit_transform_poof(X, y);


# In[20]:


visualizer = ParallelCoordinates(classes=classes, sample=0.2)
visualizer.fit_transform_poof(X, y);


# In[21]:


# quick method
parallel_coordinates(X, y, sample=0.2);


# In[22]:


# should raise YellowbrickTypeError
visualizer = ParallelCoordinates(classes=classes, sample='bad')
visualizer.fit_transform_poof(X, y);


# In[23]:


# should raise YellowbrickValueError
visualizer = ParallelCoordinates(classes=classes, sample=-1)
visualizer.fit_transform_poof(X, y);


# In[24]:


# should raise YellowbrickValueError
visualizer = ParallelCoordinates(classes=classes, sample=1.1)
visualizer.fit_transform_poof(X, y);


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