curves.py

###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
#
# Display inline matplotlib plots with IPython
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'inline')
###########################################

import matplotlib.pyplot as pl
import numpy as np
from sklearn.model_selection import learning_curve
from sklearn.model_selection import validation_curve
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import  RandomForestRegressor
from sklearn.model_selection import ShuffleSplit, train_test_split


def ModelComplexity_DT(X, y):
    """ Calculates the performance of the model as model complexity increases.
        The learning and testing errors rates are then plotted. """

    # Create 10 cross-validation sets for training and testing
    cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 42)

    # Vary the max_depth parameter from 1 to 10
    max_depth = np.arange(10,30)

    # Calculate the training and testing scores
    train_scores, test_scores = validation_curve(DecisionTreeRegressor(), X, y, \
        param_name = "max_depth", param_range = max_depth, cv = cv, scoring = 'r2')

    # Find the mean and standard deviation for smoothing
    train_mean = np.mean(train_scores, axis=1)
    train_std = np.std(train_scores, axis=1)
    test_mean = np.mean(test_scores, axis=1)
    test_std = np.std(test_scores, axis=1)

    # Plot the validation curve
    pl.figure(figsize=(7, 5))
    pl.title('Decision Tree Regressor Complexity Performance')
    pl.plot(max_depth, train_mean, 'o-', color = 'r', label = 'Training Score')
    pl.plot(max_depth, test_mean, 'o-', color = 'g', label = 'Validation Score')
    pl.fill_between(max_depth, train_mean - train_std, \
        train_mean + train_std, alpha = 0.15, color = 'r')
    pl.fill_between(max_depth, test_mean - test_std, \
        test_mean + test_std, alpha = 0.15, color = 'g')

    # Visual aesthetics
    pl.legend(loc = 'lower right')
    pl.xlabel('Maximum Depth')
    pl.ylabel('Score')
    pl.ylim([-0.05,1.05])
    pl.show()

def ModelComplexity_RF(X, y):
    """ Calculates the performance of the model as model complexity increases.
        The learning and testing errors rates are then plotted. """

    # Create 10 cross-validation sets for training and testing
    cv = ShuffleSplit(n_splits = 4, test_size = 0.2, random_state = 42)

    # Vary the max_depth parameter from 10 to 30
    max_depth = np.arange(10,30)

    # Calculate the training and testing scores
    train_scores, test_scores = validation_curve(RandomForestRegressor(), X, y, \
        param_name = "max_depth", param_range = max_depth, cv = cv, scoring = 'r2')

    # Find the mean and standard deviation for smoothing
    train_mean = np.mean(train_scores, axis=1)
    train_std = np.std(train_scores, axis=1)
    test_mean = np.mean(test_scores, axis=1)
    test_std = np.std(test_scores, axis=1)

    # Plot the validation curve
    pl.figure(figsize=(7, 5))
    pl.title('Random Forest Regressor Complexity Performance')
    pl.plot(max_depth, train_mean, 'o-', color = 'r', label = 'Training Score')
    pl.plot(max_depth, test_mean, 'o-', color = 'g', label = 'Validation Score')
    pl.fill_between(max_depth, train_mean - train_std, \
        train_mean + train_std, alpha = 0.15, color = 'r')
    pl.fill_between(max_depth, test_mean - test_std, \
        test_mean + test_std, alpha = 0.15, color = 'g')

    # Visual aesthetics
    pl.legend(loc = 'lower right')
    pl.xlabel('Maximum Depth')
    pl.ylabel('Score')
    pl.ylim([-0.05,1.05])
    pl.show()