The following tutorials are available from the Wallaroo Tutorials Repository.

Stage 1: Data Exploration And Model Selection

When starting a project, the data scientist focuses on exploration and experimentation, rather than turning the process into an immediate production system. This notebook presents a simplified view of this stage.

Resources

The following resources are used as part of this tutorial:

• data
  • data/seattle_housing_col_description.txt: Describes the columns used as part data analysis.
  • data/seattle_housing.csv: Sample data of the Seattle, Washington housing market between 2014 and 2015.
• code
  • postprocess.py: Formats the data after inference by the model is complete.
  • preprocess.py: Formats the incoming data for the model.
  • simdb.py: A simulated database to demonstrate sending and receiving queries.
  • wallaroo_client.py: Additional methods used with the Wallaroo instance to create workspaces, etc.

Steps

The following steps are part of this process:

• Retrieve Training Data: Connect to the data store and retrieve the training data.
• Data Transformations: Evaluate the data and train the model.
• Model Testing: Evaluate different models and determine which is best suited for the problem.

Import Libraries

First we’ll import the libraries we’ll be using to evaluate the data and test different models.

import numpy as np
import pandas as pd

import sklearn
import sklearn.ensemble

import xgboost as xgb

import seaborn
import matplotlib
import matplotlib.pyplot as plt

import simdb # module for the purpose of this demo to simulate pulling data from a database

matplotlib.rcParams["figure.figsize"] = (12,6)

# ignoring warnings for demonstration
import warnings
warnings.filterwarnings('ignore')

Retrieve Training Data

For training, we will use the data on all houses sold in this market with the last two years. As a reminder, this data pulled from a simulated database as an example of how to pull from an existing data store.

Only a few columns will be shown for display purposes.

conn = simdb.simulate_db_connection()
tablename = simdb.tablename

query = f"select * from {tablename} where date > DATE(DATE(), '-24 month') AND sale_price is not NULL"
print(query)
# read in the data
housing_data = pd.read_sql_query(query, conn)

conn.close()
housing_data.loc[:, ["id", "date", "list_price", "bedrooms", "bathrooms", "sqft_living", "sqft_lot"]]

select * from house_listings where date > DATE(DATE(), '-24 month') AND sale_price is not NULL

20523 rows × 7 columns

Data transformations

To improve relative error performance, we will predict on log10 of the sale price.

Predict on log10 price to try to improve relative error performance

housing_data['logprice'] = np.log10(housing_data.sale_price)

From the data, we will create the following features to evaluate:

• house_age: How old the house is.
• renovated: Whether the house has been renovated or not.
• yrs_since_reno: If the house has been renovated, how long has it been.

import datetime

thisyear = datetime.datetime.now().year

housing_data['house_age'] = thisyear - housing_data['yr_built']
housing_data['renovated'] =  np.where((housing_data['yr_renovated'] > 0), 1, 0) 
housing_data['yrs_since_reno'] =  np.where(housing_data['renovated'], housing_data['yr_renovated'] - housing_data['yr_built'], 0)

housing_data.loc[:, ['yr_built', 'yr_renovated', 'house_age', 'renovated', 'yrs_since_reno']]

20523 rows × 5 columns

Now we pick variables and split training data into training and holdout (test).

vars = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view',
'condition', 'grade', 'sqft_above', 'sqft_basement', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'house_age', 'renovated', 'yrs_since_reno']

outcome = 'logprice'

runif = np.random.default_rng(2206222).uniform(0, 1, housing_data.shape[0])
gp = np.where(runif < 0.2, 'test', 'training')

hd_train = housing_data.loc[gp=='training', :].reset_index(drop=True, inplace=False)
hd_test = housing_data.loc[gp=='test', :].reset_index(drop=True, inplace=False)

# split the training into training and val for xgboost
runif = np.random.default_rng(123).uniform(0, 1, hd_train.shape[0])
xgb_gp = np.where(runif < 0.2, 'val', 'train')

# for xgboost, further split into train and val
train_features = np.array(hd_train.loc[xgb_gp=='train', vars])
train_labels = np.array(hd_train.loc[xgb_gp=='train', outcome])

val_features = np.array(hd_train.loc[xgb_gp=='val', vars])
val_labels = np.array(hd_train.loc[xgb_gp=='val', outcome])

Postprocessing

Since we are fitting a model to predict log10 price, we need to convert predictions back into price units. We also want to round to the nearest dollar.

def postprocess(log10price):
    return np.rint(np.power(10, log10price))

Model testing

For the purposes of this demo, let’s say that we require a mean absolute percent error (MAPE) of 15% or less, and the we want to try a few models to decide which model we want to use.

One could also hyperparameter tune at this stage; for brevity, we’ll omit that in this demo.

XGBoost

First we will test out using a XGBoost model.

xgb_model = xgb.XGBRegressor(
    objective = 'reg:squarederror', 
    max_depth=5, 
    base_score = np.mean(hd_train[outcome])
    )

xgb_model.fit( 
    train_features,
    train_labels,
    eval_set=[(train_features, train_labels), (val_features, val_labels)],
    verbose=False,
    early_stopping_rounds=35
)

XGBRegressor(base_score=5.666446833601829, booster='gbtree', callbacks=None,
             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
             early_stopping_rounds=None, enable_categorical=False,
             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
             importance_type=None, interaction_constraints='',
             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
             max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,
             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,
             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
             reg_lambda=1, ...)

XGBRegressor(base_score=5.666446833601829, booster='gbtree', callbacks=None,
         colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
         early_stopping_rounds=None, enable_categorical=False,
         eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
         importance_type=None, interaction_constraints='',
         learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
         max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,
         missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,
         num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,
         reg_lambda=1, ...)</pre>

print(xgb_model.best_score)
print(xgb_model.best_iteration)
print(xgb_model.best_ntree_limit)
0.07793614689092423
99
100

XGBoost Evaluate on holdout
With the sample model created, we will test it against the holdout data.  Note that we are calling the postprocess function on the data.
test_features = np.array(hd_test.loc[:, vars])
test_labels = np.array(hd_test.loc[:, outcome])

pframe = pd.DataFrame({
    'pred' : postprocess(xgb_model.predict(test_features)),
    'actual' : postprocess(test_labels)
})

ax = seaborn.scatterplot(
    data=pframe,
    x='pred',
    y='actual',
    alpha=0.2
)
matplotlib.pyplot.plot(pframe.pred, pframe.pred, color='DarkGreen')
matplotlib.pyplot.title("test")
plt.show()


     

pframe['se'] = (pframe.pred - pframe.actual)**2

pframe['pct_err'] = 100*np.abs(pframe.pred - pframe.actual)/pframe.actual
pframe.describe()

  

    

      

      
pred
      
actual
      
se
      
pct_err
    
  
  

    

      
count
      
4.094000e+03
      
4.094000e+03
      
4.094000e+03
      
4094.000000
    
    

      
mean
      
5.340824e+05
      
5.396937e+05
      
1.657722e+10
      
12.857674
    
    

      
std
      
3.413714e+05
      
3.761666e+05
      
1.276017e+11
      
13.512028
    
    

      
min
      
1.216140e+05
      
8.200000e+04
      
1.000000e+00
      
0.000500
    
    

      
25%
      
3.167628e+05
      
3.200000e+05
      
3.245312e+08
      
4.252492
    
    

      
50%
      
4.568700e+05
      
4.500000e+05
      
1.602001e+09
      
9.101485
    
    

      
75%
      
6.310372e+05
      
6.355250e+05
      
6.575385e+09
      
17.041227
    
    

      
max
      
5.126706e+06
      
7.700000e+06
      
6.637466e+12
      
252.097895
    
  

rmse = np.sqrt(np.mean(pframe.se))
mape = np.mean(pframe.pct_err)

print(f'rmse = {rmse}, mape = {mape}')
rmse = 128752.54982046234, mape = 12.857674005250548

Random Forest
The next model to test is Random Forest.
model_rf = sklearn.ensemble.RandomForestRegressor(n_estimators=100, max_depth=5, n_jobs=2, max_samples=0.8)

train_features = np.array(hd_train.loc[:, vars])
train_labels = np.array(hd_train.loc[:, outcome])

model_rf.fit(train_features, train_labels)
RandomForestRegressor(max_depth=5, max_samples=0.8, n_jobs=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.RandomForestRegressor
RandomForestRegressor(max_depth=5, max_samples=0.8, n_jobs=2)
Random Forest Evaluate on holdout
With the Random Forest sample model created, now we can test it against the holdout data.
pframe = pd.DataFrame({
    'pred' : postprocess(model_rf.predict(test_features)),
    'actual' : postprocess(test_labels)
})

ax = seaborn.scatterplot(
    data=pframe,
    x='pred',
    y='actual',
    alpha=0.2
)
matplotlib.pyplot.plot(pframe.pred, pframe.pred, color='DarkGreen')
matplotlib.pyplot.title("random forest")
plt.show()


     

pframe['se'] = (pframe.pred - pframe.actual)**2

pframe['pct_err'] = 100*np.abs(pframe.pred - pframe.actual)/pframe.actual
pframe.describe()

  

    

      

      
pred
      
actual
      
se
      
pct_err
    
  
  

    

      
count
      
4.094000e+03
      
4.094000e+03
      
4.094000e+03
      
4094.000000
    
    

      
mean
      
5.194535e+05
      
5.396937e+05
      
3.875433e+10
      
18.188652
    
    

      
std
      
2.797001e+05
      
3.761666e+05
      
4.054895e+11
      
17.634478
    
    

      
min
      
2.039200e+05
      
8.200000e+04
      
1.444000e+03
      
0.014729
    
    

      
25%
      
3.291252e+05
      
3.200000e+05
      
6.686879e+08
      
6.156760
    
    

      
50%
      
4.621880e+05
      
4.500000e+05
      
3.321332e+09
      
13.148593
    
    

      
75%
      
5.851052e+05
      
6.355250e+05
      
1.367023e+10
      
24.630187
    
    

      
max
      
2.888692e+06
      
7.700000e+06
      
2.314868e+13
      
175.444819
    
  

rmse = np.sqrt(np.mean(pframe.se))
mape = np.mean(pframe.pct_err)

print(f'rmse = {rmse}, mape = {mape}')
rmse = 196861.19318381665, mape = 18.188652142429135

Final Decision
At this stage, we decide to go with the xgboost model, with the variables/settings above.
With this stage complete, we can move on to Stage 2: Training Process Automation Setup.