In this assignment, we will explore the use of boosting. We will use the pre-implemented gradient boosted trees in GraphLab Create. You will:
Let's get started!
In [1]:
import graphlab
We will be using the LendingClub data. As discussed earlier, the LendingClub is a peer-to-peer leading company that directly connects borrowers and potential lenders/investors.
Just like we did in previous assignments, we will build a classification model to predict whether or not a loan provided by lending club is likely to default.
Let us start by loading the data.
In [2]:
loans = graphlab.SFrame('lending-club-data.gl/')
Let's quickly explore what the dataset looks like. First, let's print out the column names to see what features we have in this dataset. We have done this in previous assignments, so we won't belabor this here.
In [3]:
loans.head()
Out[3]:
In [4]:
loans.column_names()
Out[4]:
The target column (label column) of the dataset that we are interested in is called bad_loans. In this column 1 means a risky (bad) loan 0 means a safe loan.
As in past assignments, in order to make this more intuitive and consistent with the lectures, we reassign the target to be:
We put this in a new column called safe_loans.
In [5]:
loans['safe_loans'] = loans['bad_loans'].apply(lambda x : +1 if x==0 else -1)
loans = loans.remove_column('bad_loans')
In this assignment, we will be using a subset of features (categorical and numeric). The features we will be using are described in the code comments below. If you are a finance geek, the LendingClub website has a lot more details about these features.
The features we will be using are described in the code comments below:
In [6]:
target = 'safe_loans'
features = ['grade', # grade of the loan (categorical)
'sub_grade_num', # sub-grade of the loan as a number from 0 to 1
'short_emp', # one year or less of employment
'emp_length_num', # number of years of employment
'home_ownership', # home_ownership status: own, mortgage or rent
'dti', # debt to income ratio
'purpose', # the purpose of the loan
'payment_inc_ratio', # ratio of the monthly payment to income
'delinq_2yrs', # number of delinquincies
'delinq_2yrs_zero', # no delinquincies in last 2 years
'inq_last_6mths', # number of creditor inquiries in last 6 months
'last_delinq_none', # has borrower had a delinquincy
'last_major_derog_none', # has borrower had 90 day or worse rating
'open_acc', # number of open credit accounts
'pub_rec', # number of derogatory public records
'pub_rec_zero', # no derogatory public records
'revol_util', # percent of available credit being used
'total_rec_late_fee', # total late fees received to day
'int_rate', # interest rate of the loan
'total_rec_int', # interest received to date
'annual_inc', # annual income of borrower
'funded_amnt', # amount committed to the loan
'funded_amnt_inv', # amount committed by investors for the loan
'installment', # monthly payment owed by the borrower
]
In [7]:
loans, loans_with_na = loans[[target] + features].dropna_split()
# Count the number of rows with missing data
num_rows_with_na = loans_with_na.num_rows()
num_rows = loans.num_rows()
print 'Dropping %s observations; keeping %s ' % (num_rows_with_na, num_rows)
Fortunately, there are not too many missing values. We are retaining most of the data.
We saw in an earlier assignment that this dataset is also imbalanced. We will undersample the larger class (safe loans) in order to balance out our dataset. We used seed=1 to make sure everyone gets the same results.
In [8]:
safe_loans_raw = loans[loans[target] == 1]
risky_loans_raw = loans[loans[target] == -1]
# Undersample the safe loans.
percentage = len(risky_loans_raw)/float(len(safe_loans_raw))
safe_loans = safe_loans_raw.sample(percentage, seed = 1)
risky_loans = risky_loans_raw
loans_data = risky_loans.append(safe_loans)
print "Percentage of safe loans :", len(safe_loans) / float(len(loans_data))
print "Percentage of risky loans :", len(risky_loans) / float(len(loans_data))
print "Total number of loans in our new dataset :", len(loans_data)
Checkpoint: You should now see that the dataset is balanced (approximately 50-50 safe vs risky loans).
Note: There are many approaches for dealing with imbalanced data, including some where we modify the learning algorithm. These approaches are beyond the scope of this course, but some of them are reviewed in this paper. For this assignment, we use the simplest possible approach, where we subsample the overly represented class to get a more balanced dataset. In general, and especially when the data is highly imbalanced, we recommend using more advanced methods.
We split the data into training data and validation data. We used seed=1 to make sure everyone gets the same results. We will use the validation data to help us select model parameters.
In [9]:
train_data, validation_data = loans_data.random_split(.8, seed=1)
Gradient boosted trees are a powerful variant of boosting methods; they have been used to win many Kaggle competitions, and have been widely used in industry. We will explore the predictive power of multiple decision trees as opposed to a single decision tree.
Additional reading: If you are interested in gradient boosted trees, here is some additional reading material:
We will now train models to predict safe_loans using the features above. In this section, we will experiment with training an ensemble of 5 trees. To cap the ensemble classifier at 5 trees, we call the function with max_iterations=5 (recall that each iterations corresponds to adding a tree). We set validation_set=None to make sure everyone gets the same results.
In [10]:
model_5 = graphlab.boosted_trees_classifier.create(train_data, validation_set=None,
target = target, features = features, max_iterations = 5)
In [12]:
# Select all positive and negative examples.
validation_safe_loans = validation_data[validation_data[target] == 1]
validation_risky_loans = validation_data[validation_data[target] == -1]
# Select 2 examples from the validation set for positive & negative loans
sample_validation_data_risky = validation_risky_loans[0:2]
sample_validation_data_safe = validation_safe_loans[0:2]
# Append the 4 examples into a single dataset
sample_validation_data = sample_validation_data_safe.append(sample_validation_data_risky)
sample_validation_data
Out[12]:
In [16]:
sample_predictions = model_5.predict(sample_validation_data)
Quiz Question: What percentage of the predictions on sample_validation_data did model_5 get correct?
For each row in the sample_validation_data, what is the probability (according model_5) of a loan being classified as safe?
Hint: Set output_type='probability' to make probability predictions using model_5 on sample_validation_data:
In [17]:
sample_accuracy = (sample_predictions == sample_validation_data['safe_loans']).sum() / float(len(sample_validation_data))
sample_accuracy
Out[17]:
In [18]:
model_5.predict(sample_validation_data, output_type='probability')
Out[18]:
Quiz Question: According to model_5, which loan is the least likely to be a safe loan?
Checkpoint: Can you verify that for all the predictions with probability >= 0.5, the model predicted the label +1?
Recall that the accuracy is defined as follows: $$ \mbox{accuracy} = \frac{\mbox{# correctly classified examples}}{\mbox{# total examples}} $$
Evaluate the accuracy of the model_5 on the validation_data.
Hint: Use the .evaluate() method in the model.
In [19]:
model_5.evaluate(validation_data)
Out[19]:
Calculate the number of false positives made by the model.
In [22]:
print model_5.evaluate(validation_data)['confusion_matrix']
Quiz Question: What is the number of false positives on the validation_data?
Calculate the number of false negatives made by the model.
In [23]:
print 1618
In the earlier assignment, we saw that the prediction accuracy of the decision trees was around 0.64 (rounded). In this assignment, we saw that model_5 has an accuracy of 0.67 (rounded).
Here, we quantify the benefit of the extra 3% increase in accuracy of model_5 in comparison with a single decision tree from the original decision tree assignment.
As we explored in the earlier assignment, we calculated the cost of the mistakes made by the model. We again consider the same costs as follows:
Assume that the number of false positives and false negatives for the learned decision tree was
Using the costs defined above and the number of false positives and false negatives for the decision tree, we can calculate the total cost of the mistakes made by the decision tree model as follows:
cost = $10,000 * 1936 + $20,000 * 1503 = $49,420,000The total cost of the mistakes of the model is $49.42M. That is a lot of money!.
Quiz Question: Using the same costs of the false positives and false negatives, what is the cost of the mistakes made by the boosted tree model (model_5) as evaluated on the validation_set?
In [38]:
model_5_cm = model_5.evaluate(validation_data)['confusion_matrix']
In [40]:
fp = model_5_cm[model_5_cm.apply(lambda x: x['target_label'] == -1 and x['predicted_label'] == 1)]['count']
fn = model_5_cm[model_5_cm.apply(lambda x: x['target_label'] == 1 and x['predicted_label'] == -1)]['count']
model_5_cost = 10000 * fn + 20000 * fp
model_5_cost
Out[40]:
Reminder: Compare the cost of the mistakes made by the boosted trees model with the decision tree model. The extra 3% improvement in prediction accuracy can translate to several million dollars! And, it was so easy to get by simply boosting our decision trees.
In this section, we will find the loans that are most likely to be predicted safe. We can do this in a few steps:
Start here with Step 1 & Step 2. Make predictions using model_5 for examples in the validation_data. Use output_type = probability.
In [41]:
prob_predictions = model_5.predict(validation_data, output_type='probability')
validation_data['predictions'] = prob_predictions
Checkpoint: For each row, the probabilities should be a number in the range [0, 1]. We have provided a simple check here to make sure your answers are correct.
In [42]:
print "Your loans : %s\n" % validation_data['predictions'].head(4)
print "Expected answer : %s" % [0.4492515948736132, 0.6119100103640573,
0.3835981314851436, 0.3693306705994325]
Now, we are ready to go to Step 3. You can now use the prediction column to sort the loans in validation_data (in descending order) by prediction probability. Find the top 5 loans with the highest probability of being predicted as a safe loan.
In [54]:
validation_data.sort('predictions', ascending=False)
Out[54]:
Quiz Question: What grades are the top 5 loans?
Let us repeat this excercise to find the top 5 loans (in the validation_data) with the lowest probability of being predicted as a safe loan:
In [53]:
validation_data.sort('predictions', ascending=True)
Out[53]:
Checkpoint: You should expect to see 5 loans with the grade ['D', 'C', 'C', 'C', 'B'] or with ['D', 'C', 'B', 'C', 'C'].
In this assignment, we will train 5 different ensemble classifiers in the form of gradient boosted trees. We will train models with 10, 50, 100, 200, and 500 trees. We use the max_iterations parameter in the boosted tree module.
Let's get sarted with a model with max_iterations = 10:
In [55]:
model_10 = graphlab.boosted_trees_classifier.create(train_data, validation_set=None,
target = target, features = features, max_iterations = 10, verbose=False)
Now, train 4 models with max_iterations to be:
max_iterations = 50, max_iterations = 100max_iterations = 200max_iterations = 500. Let us call these models model_50, model_100, model_200, and model_500. You can pass in verbose=False in order to suppress the printed output.
Warning: This could take a couple of minutes to run.
In [56]:
model_50 = graphlab.boosted_trees_classifier.create(train_data, validation_set=None,
target = target, features = features, max_iterations = 50, verbose=False)
model_100 = graphlab.boosted_trees_classifier.create(train_data, validation_set=None,
target = target, features = features, max_iterations = 100, verbose=False)
model_200 = graphlab.boosted_trees_classifier.create(train_data, validation_set=None,
target = target, features = features, max_iterations = 200, verbose=False)
model_500 = graphlab.boosted_trees_classifier.create(train_data, validation_set=None,
target = target, features = features, max_iterations = 500, verbose=False)
Now we will compare the predicitve accuracy of our models on the validation set. Evaluate the accuracy of the 10, 50, 100, 200, and 500 tree models on the validation_data. Use the .evaluate method.
In [58]:
print "Accuracy max iterations 10: " + str(model_10.evaluate(validation_data)['accuracy'])
print "Accuracy max iterations 50: " + str(model_50.evaluate(validation_data)['accuracy'])
print "Accuracy max iterations 100: " + str(model_100.evaluate(validation_data)['accuracy'])
print "Accuracy max iterations 200: " + str(model_200.evaluate(validation_data)['accuracy'])
print "Accuracy max iterations 500: " + str(model_500.evaluate(validation_data)['accuracy'])
Quiz Question: Which model has the best accuracy on the validation_data?
Quiz Question: Is it always true that the model with the most trees will perform best on test data?
Recall from the lecture that the classification error is defined as
$$ \mbox{classification error} = 1 - \mbox{accuracy} $$In this section, we will plot the training and validation errors versus the number of trees to get a sense of how these models are performing. We will compare the 10, 50, 100, 200, and 500 tree models. You will need matplotlib in order to visualize the plots.
First, make sure this block of code runs on your computer.
In [59]:
import matplotlib.pyplot as plt
%matplotlib inline
def make_figure(dim, title, xlabel, ylabel, legend):
plt.rcParams['figure.figsize'] = dim
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if legend is not None:
plt.legend(loc=legend, prop={'size':15})
plt.rcParams.update({'font.size': 16})
plt.tight_layout()
In order to plot the classification errors (on the train_data and validation_data) versus the number of trees, we will need lists of these accuracies, which we get by applying the method .evaluate.
Steps to follow:
training_errors) that looks like this:
[train_err_10, train_err_50, ..., train_err_500]validation_errors) that looks like this:
[validation_err_10, validation_err_50, ..., validation_err_500]Let us start with Step 1. Write code to compute the classification error on the train_data for models model_10, model_50, model_100, model_200, and model_500.
In [61]:
def calculate_classification_error(data, model):
accuracy = (model.predict(data) == data['safe_loans']).sum() / float(len(data))
return 1 - accuracy
train_err_10 = calculate_classification_error(train_data, model_10)
train_err_50 = calculate_classification_error(train_data, model_50)
train_err_100 = calculate_classification_error(train_data, model_100)
train_err_200 = calculate_classification_error(train_data, model_200)
train_err_500 = calculate_classification_error(train_data, model_500)
Now, let us run Step 2. Save the training errors into a list called training_errors
In [62]:
training_errors = [train_err_10, train_err_50, train_err_100,
train_err_200, train_err_500]
training_errors
Out[62]:
Now, onto Step 3. Write code to compute the classification error on the validation_data for models model_10, model_50, model_100, model_200, and model_500.
In [63]:
validation_err_10 = calculate_classification_error(validation_data, model_10)
validation_err_50 = calculate_classification_error(validation_data, model_50)
validation_err_100 = calculate_classification_error(validation_data, model_100)
validation_err_200 = calculate_classification_error(validation_data, model_200)
validation_err_500 = calculate_classification_error(validation_data, model_500)
Now, let us run Step 4. Save the training errors into a list called validation_errors
In [64]:
validation_errors = [validation_err_10, validation_err_50, validation_err_100,
validation_err_200, validation_err_500]
validation_errors
Out[64]:
Now, we will plot the training_errors and validation_errors versus the number of trees. We will compare the 10, 50, 100, 200, and 500 tree models. We provide some plotting code to visualize the plots within this notebook.
Run the following code to visualize the plots.
In [65]:
plt.plot([10, 50, 100, 200, 500], training_errors, linewidth=4.0, label='Training error')
plt.plot([10, 50, 100, 200, 500], validation_errors, linewidth=4.0, label='Validation error')
make_figure(dim=(10,5), title='Error vs number of trees',
xlabel='Number of trees',
ylabel='Classification error',
legend='best')
Quiz Question: Does the training error reduce as the number of trees increases?
Quiz Question: Is it always true that the validation error will reduce as the number of trees increases?
In [ ]: