Intro-to-machine-learning/outlier/outlier_removal_regression.py

#!/usr/bin/python

from sklearn.model_selection import train_test_split
import random
import numpy
import matplotlib.pyplot as plt
import pickle
import sys
from outlier_cleaner import outlierCleaner

### load up some practice data with outliers in it
# sys.path.append("/Users/l-zl/Desktop/ud120-projects/outliers/")
# sys.path.append("/Users/l-zl/Desktop/ud120-projects/outliers/")
ages = pickle.load(open("practice_outliers_ages.pkl", "r"))
net_worths = pickle.load(open("practice_outliers_net_worths.pkl", "r"))

### ages and net_worths need to be reshaped into 2D numpy arrays
### second argument of reshape command is a tuple of integers: (n_rows, n_columns)
### by convention, n_rows is the number of data points
### and n_columns is the number of features
ages = numpy.reshape(numpy.array(ages), (len(ages), 1))
net_worths = numpy.reshape(numpy.array(net_worths), (len(net_worths), 1))
ages_train, ages_test, net_worths_train, net_worths_test = train_test_split(
    ages, net_worths, test_size=0.1, random_state=42)

### fill in a regression here!  Name the regression object reg so that
### the plotting code below works, and you can see what your regression looks like

from sklearn import linear_model

reg = linear_model.LinearRegression()
reg.fit(ages_train,net_worths_train)
print "The slope is", reg.coef_
print "The score is",reg.score(ages_test,net_worths_test)


try:
    plt.plot(ages, reg.predict(ages), color="blue")
except NameError:
    pass
plt.scatter(ages, net_worths)
plt.show()

### identify and remove the most outlier-y points
cleaned_data = []
try:
    predictions = reg.predict(ages_train)
    cleaned_data = outlierCleaner(predictions, ages_train, net_worths_train)
except NameError:
    print "your regression object doesn't exist, or isn't name reg"
    print "can't make predictions to use in identifying outliers"

### only run this code if cleaned_data is returning data
if len(cleaned_data) > 0:
    ages, net_worths, errors = zip(*cleaned_data)
    ages = numpy.reshape(numpy.array(ages), (len(ages), 1))
    net_worths = numpy.reshape(numpy.array(net_worths), (len(net_worths), 1))

    ### refit your cleaned data!
    try:
        reg.fit(ages, net_worths)
        print "The cleaned data slope is",reg.coef_
        print "The cleaned data score is",reg.score(ages_test,net_worths_test)
        plt.plot(ages, reg.predict(ages), color="blue")
    except NameError:
        print "you don't seem to have regression imported/created,"
        print "   or else your regression object isn't named reg"
        print "   either way, only draw the scatter plot of the cleaned data"
    plt.scatter(ages, net_worths)
    plt.xlabel("ages")
    plt.ylabel("net worths")
    plt.show()


else:
    print "outlierCleaner() is returning an empty list, no refitting to be done"