Feature Scaling¶
Simple Example
In [1]:
from __future__ import division
def featureScaling(arr):
maxxy = max(arr)
minny = min(arr)
if maxxy == minny:
raise ValueError('min aand max same')
rescaled_arr = [float((x-minny)/(maxxy-minny)) for x in arr]
return rescaled_arr
# tests of your feature scaler--line below is input data
data = [115, 140, 175]
print featureScaling(data)
Using SK learn
Ref: http://scikit-learn.org/stable/modules/preprocessing.html
In [2]:
from sklearn.preprocessing import MinMaxScaler
import numpy
weights = numpy.array( # only numpy arrays allowed
[
[115.], [140.],[175.] # only floating point allowed
]
)
scaler = MinMaxScaler()
rescaled_weight = scaler.fit_transform(weights)
rescaled_weight
Out[2]:
Feature scaling
affects SVM, K-means etc.
does not affect Decision trees, linear regression etc
Computing Rescaled Features
Apply feature scaling to your k-means clustering code from the last lesson, on the "salary" and "exercised_stock_options" features (use only these two features).
What would be the rescaled value of a "salary" feature that had an original value of \$200,000, and an "exercised_stock_options" feature of $1 million? (Be sure to represent these numbers as floats, not integers!)
In [3]:
#k_means_cluster.py
%matplotlib inline
#!/usr/bin/python
"""
Skeleton code for k-means clustering mini-project.
"""
import pickle
import numpy
import matplotlib.pyplot as plt
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
def Draw(pred, features, poi, mark_poi=False, name="image.png", f1_name="feature 1", f2_name="feature 2"):
""" some plotting code designed to help you visualize your clusters """
### plot each cluster with a different color--add more colors for
### drawing more than five clusters
colors = ["b", "c", "k", "m", "g"]
for ii, pp in enumerate(pred):
plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]])
### if you like, place red stars over points that are POIs (just for funsies)
if mark_poi:
for ii, pp in enumerate(pred):
if poi[ii]:
plt.scatter(features[ii][0], features[ii][1], color="r", marker="*")
plt.xlabel(f1_name)
plt.ylabel(f2_name)
plt.savefig(name)
plt.show()
### load in the dict of dicts containing all the data on each person in the dataset
data_dict = pickle.load( open("../17. Final Project/final_project_dataset.pkl", "r") )
### there's an outlier--remove it!
data_dict.pop("TOTAL", 0)
### the input features we want to use
### can be any key in the person-level dictionary (salary, director_fees, etc.)
feature_1 = "salary"
feature_2 = "exercised_stock_options"
poi = "poi"
features_list = [poi, feature_1, feature_2]
data = featureFormat(data_dict, features_list )
poi, finance_features = targetFeatureSplit( data )
### FEATURE RESCALING
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
finance_features_rescaled = scaler.fit_transform(finance_features)
### in the "clustering with 3 features" part of the mini-project,
### you'll want to change this line to
### for f1, f2, _ in finance_features:
### (as it's currently written, the line below assumes 2 features)
for f1, f2 in finance_features_rescaled:
plt.scatter( f1, f2 )
plt.show()
### cluster here; create predictions of the cluster labels
### for the data and store them to a list called pred
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=2, random_state=0).fit(finance_features_rescaled)
pred = kmeans.predict(finance_features_rescaled)
print 'after k means clustering..'
### rename the "name" parameter when you change the number of features
### so that the figure gets saved to a different file
try:
Draw(pred, finance_features_rescaled, poi, mark_poi=False, name="clusters.pdf", f1_name=feature_1, f2_name=feature_2)
except NameError:
print "no predictions object named pred found, no clusters to plot"
In [4]:
import numpy as np
X_test = np.array([
[200000.0,1000000.0]
])
X_test_rescaled = scaler.transform(X_test)
X_test_rescaled
Out[4]: