Age/Net Worth Regression in sklearn

import sys
sys.path.append("C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/tools/")
sys.path.append('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/choose_your_own')
sys.path.append('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/datasets_questions')

import os
os.chdir('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/regression')


import numpy
import matplotlib
matplotlib.use('agg')

import matplotlib.pyplot as plt
from class_vis import prettyPicture, output_image

from ages_net_worths import ageNetWorthData

ages_train, ages_test, net_worths_train, net_worths_test = ageNetWorthData()


from sklearn import linear_model

def studentReg(ages_train, net_worths_train):
    reg = linear_model.LinearRegression()
    reg.fit(ages_train, net_worths_train)
    return reg


reg = studentReg(ages_train, net_worths_train)

%matplotlib inline
plt.clf()
plt.scatter(ages_train, net_worths_train, color="b", label="train data")
plt.scatter(ages_test, net_worths_test, color="r", label="test data")
plt.plot(ages_test, reg.predict(ages_test), color="black")
plt.legend(loc=2)
plt.xlabel("ages")
plt.ylabel("net worths")

<matplotlib.text.Text at 0x80034e0>

Now You Practice Extracting Information

reg = linear_model.LinearRegression()
reg.fit(ages_train, net_worths_train)

### get Katie's net worth (she's 27)
### sklearn predictions are returned in an array, so you'll want to index into
### the output to get what you want, e.g. net_worth = predict([[27]])[0][0] (not
### exact syntax, the point is the [0] at the end). In addition, make sure the
### argument to your prediction function is in the expected format - if you get
### a warning about needing a 2d array for your data, a list of lists will be
### interpreted by sklearn as such (e.g. [[27]]).
km_net_worth = reg.predict([[27]])[0][0]

### get the slope
### again, you'll get a 2-D array, so stick the [0][0] at the end
slope = reg.coef_[0][0]

### get the intercept
### here you get a 1-D array, so stick [0] on the end to access
### the info we want
intercept = reg.intercept_[0]


### get the score on test data
test_score = reg.score(ages_test, net_worths_test)


### get the score on the training data
training_score = reg.score(ages_train, net_worths_train)

Bonus Target and Features

import pickle
from feature_format import featureFormat, targetFeatureSplit

dictionary = pickle.load( open("../final_project/final_project_dataset_modified.pkl", "r") )

def finance_regression(dictionary, features_list, fit_test=False):
    data = featureFormat( dictionary, features_list, remove_any_zeroes=True)
    target, features = targetFeatureSplit( data )

    ### training-testing split needed in regression, just like classification
    from sklearn.cross_validation import train_test_split
    feature_train, feature_test, target_train, target_test = train_test_split(features, target, test_size=0.5, random_state=42)
    train_color = "b"
    test_color = "r"


    reg = linear_model.LinearRegression()
    reg.fit(feature_train, target_train)


    ### draw the scatterplot, with color-coded training and testing points
    import matplotlib.pyplot as plt
    for feature, target in zip(feature_test, target_test):
        plt.scatter( feature, target, color=test_color ) 
    for feature, target in zip(feature_train, target_train):
        plt.scatter( feature, target, color=train_color ) 

    ### labels for the legend
    plt.scatter(feature_test[0], target_test[0], color=test_color, label="test")
    plt.scatter(feature_test[0], target_test[0], color=train_color, label="train")


    # draw the regression line, once it's coded
    plt.plot( feature_test, reg.predict(feature_test) )
    plt.xlabel(features_list[1])
    plt.ylabel(features_list[0])
    plt.legend()
    
    if fit_test:
        reg.fit(feature_test, target_test)
        plt.plot(feature_train, reg.predict(feature_train), color="r") 
    
    return (reg, feature_train, target_train, feature_test, target_test)


(reg, feature_train, target_train, feature_test, target_test) = finance_regression(dictionary, ["bonus", "salary"])

Extracting Slope and Intercept

print 'slope = {0}'.format(reg.coef_[0])
print 'intercept = {0}'.format(reg.intercept_)

slope = 5.44814028881
intercept = -102360.543294

Regression Score: Training Data

print 'score on training set = {0}'.format(reg.score(feature_train, target_train))

score on training set = 0.0455091926995

Regression Score: Test Data

print 'score on test set = {0}'.format(reg.score(feature_test, target_test))

score on test set = -1.48499241737

Regressing Bonus Against LTI

(reg, feature_train, target_train, feature_test, target_test) = finance_regression(dictionary, ['bonus', 'long_term_incentive'])

print 'score on test set = {0}'.format(reg.score(feature_test, target_test))

score on test set = -0.59271289995

Sneak Peek: Outliers Break Regressions

(reg, feature_train, target_train, feature_test, target_test) = finance_regression(dictionary, ["bonus", "salary"], fit_test=True)
print 'slope = {0}'.format(reg.coef_[0])

slope = 2.27410114127