M6_SVC_gitnub.py

# https://github.com/anarayanan86/Microsoft_DAT210x/blob/master/Module-6---Data-Modeling-II/Module6SVC/assignment1.py

import matplotlib as mpl
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np 
import time


# 
# INFO: Your Parameters.
# You can adjust them after completing the lab
C = 1
kernel = 'linear'
iterations = 5000   # TODO: Change to 200000 once you get to Question#2

#
# INFO: You can set this to false if you want to
# draw the full square matrix
FAST_DRAW = True


def drawPlots(model, wintitle='Figure 1'):
  # INFO: A convenience function for you
  # You can use this to break any higher-dimensional space down
  # And view cross sections of it.

  # If this line throws an error, use plt.style.use('ggplot') instead
  mpl.style.use('ggplot') # Look Pretty

  padding = 3
  resolution = 0.5
  max_2d_score = 0

  y_colors = ['#ff0000', '#00ff00', '#0000ff']
  my_cmap = mpl.colors.ListedColormap(['#ffaaaa', '#aaffaa', '#aaaaff'])
  colors = [y_colors[i] for i in y_train]
  num_columns = len(X_train.columns)

  fig = plt.figure()
  fig.canvas.set_window_title(wintitle)
  
  cnt = 0
  for col in range(num_columns):
    for row in range(num_columns):
      # Easy out
      if FAST_DRAW and col > row:
        cnt += 1
        continue

      ax = plt.subplot(num_columns, num_columns, cnt + 1)
      plt.xticks(())
      plt.yticks(())

      # Intersection:
      if col == row:
        plt.text(0.5, 0.5, X_train.columns[row], verticalalignment='center', horizontalalignment='center', fontsize=12)
        cnt += 1
        continue


      # Only select two features to display, then train the model
      X_train_bag = X_train.ix[:, [row,col]]
      X_test_bag = X_test.ix[:, [row,col]]
      model.fit(X_train_bag, y_train)

      # Create a mesh to plot in
      x_min, x_max = X_train_bag.ix[:, 0].min() - padding, X_train_bag.ix[:, 0].max() + padding
      y_min, y_max = X_train_bag.ix[:, 1].min() - padding, X_train_bag.ix[:, 1].max() + padding
      xx, yy = np.meshgrid(np.arange(x_min, x_max, resolution),
                           np.arange(y_min, y_max, resolution))

      # Plot Boundaries
      plt.xlim(xx.min(), xx.max())
      plt.ylim(yy.min(), yy.max())

      # Prepare the contour
      Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
      plt.contourf(xx, yy, Z, cmap=my_cmap, alpha=0.8)
      plt.scatter(X_train_bag.ix[:, 0], X_train_bag.ix[:, 1], c=colors, alpha=0.5)


      score = round(model.score(X_test_bag, y_test) * 100, 3)
      plt.text(0.5, 0, "Score: {0}".format(score), transform = ax.transAxes, horizontalalignment='center', fontsize=8)
      max_2d_score = score if score > max_2d_score else max_2d_score

      cnt += 1

  print "Max 2D Score: ", max_2d_score
  fig.set_tight_layout(True)


def benchmark(model, wintitle='Figure 1'):
  print '\n\n' + wintitle + ' Results'
  s = time.time()
  for i in range(iterations):    
    #
    # TODO: train the classifier on the training data / labels:
    #
    # .. your code here ..
    a = model.fit(X_train, y_train)
  print "{0} Iterations Training Time: ".format(iterations), time.time() - s


  s = time.time()
  for i in range(iterations):
    #
    # TODO: score the classifier on the testing data / labels:
    #
    # .. your code here ..
    score = a.score(X_test, y_test)
  print "{0} Iterations Scoring Time: ".format(iterations), time.time() - s
  print "High-Dimensionality Score: ", round((score*100), 3)



# 
# TODO: Load up the wheat dataset into dataframe 'X'
# Verify you did it properly.
# Indices shouldn't be doubled, nor weird headers...
#
# .. your code here ..
X = pd.read_csv('C:/Users/mckinns/Documents/GitHub/DAT210x/Module6/Datasets/wheat.data', 
                sep=',', header=0,  index_col = 0)

print X.head()

# INFO: An easy way to show which rows have nans in them
print X[pd.isnull(X).any(axis=1)]

# 
# TODO: Go ahead and drop any row with a nan
#
# .. your code here ..
X.dropna(axis = 0, how = 'any', inplace = True)

# 
# INFO: # In the future, you might try setting the nan values to the
# mean value of that column, the mean should only be calculated for
# the specific class rather than across all classes, now that you
# have the labels

#
# TODO: Copy the labels out of the dset into variable 'y' then Remove
# them from X. Encode the labels, using the .map() trick we showed
# you in Module 5 -- canadian:0, kama:1, and rosa:2
#
# .. your code here ..
y = X.wheat_type
X.drop('wheat_type', axis = 1, inplace = True)
print X.head(6)
y = y.map({'canadian': 0, 'kama': 1, 'rosa': 2})

# 
# TODO: Split your data into test / train sets
# Your test size can be 30% with random_state 7.
# Use variable names: X_train, X_test, y_train, y_test
#
# .. your code here ..
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 7)

#
# TODO: Create an SVC classifier named svc
# Use a linear kernel, and set the C value to C
#
# .. your code here ..
from sklearn.svm import SVC
svc = SVC(C = C, kernel = kernel)

#
# TODO: Create an KNeighbors classifier named knn
# Set the neighbor count to 5
#
# .. your code here ..
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5)

benchmark(knn, 'KNeighbors')
drawPlots(knn, 'KNeighbors')

benchmark(svc, 'SVC')
drawPlots(svc, 'SVC')

plt.show()