This tutorial explains how to calculate Compute Area Under the Curve (AUC) from scikit-learn on a classification model from catboost.
During this tutorial you will build and evaluate a model to predict arrival delay for flights in and out of NYC in 2013.
Packages
This tutorial uses:
Open a new Jupyter notebook and import the following:
import statsmodels.api as sm
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from catboost import CatBoostClassifier, Pool
Reading the data
The data is from rdatasets imported using the Python package statsmodels.
df = sm.datasets.get_rdataset('flights', 'nycflights13').data
df.info()
RangeIndex: 336776 entries, 0 to 336775
Data columns (total 19 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 year 336776 non-null int64
1 month 336776 non-null int64
2 day 336776 non-null int64
3 dep_time 328521 non-null float64
4 sched_dep_time 336776 non-null int64
5 dep_delay 328521 non-null float64
6 arr_time 328063 non-null float64
7 sched_arr_time 336776 non-null int64
8 arr_delay 327346 non-null float64
9 carrier 336776 non-null object
10 flight 336776 non-null int64
11 tailnum 334264 non-null object
12 origin 336776 non-null object
13 dest 336776 non-null object
14 air_time 327346 non-null float64
15 distance 336776 non-null int64
16 hour 336776 non-null int64
17 minute 336776 non-null int64
18 time_hour 336776 non-null object
dtypes: float64(5), int64(9), object(5)
memory usage: 48.8+ MB
Feature Engineering
Handle null values
year 0
month 0
day 0
dep_time 8255
sched_dep_time 0
dep_delay 8255
arr_time 8713
sched_arr_time 0
arr_delay 9430
carrier 0
flight 0
tailnum 2512
origin 0
dest 0
air_time 9430
distance 0
hour 0
minute 0
time_hour 0
dtype: int64
As this model will predict arrival delay, the Null values are caused by flights did were cancelled or diverted. These can be excluded from this analysis.
Convert the times from floats or ints to hour and minutes
df['arr_hour'] = df.arr_time.apply(lambda x: int(np.floor(x/100)))
df['arr_minute'] = df.arr_time.apply(lambda x: int(x - np.floor(x/100)*100))
df['sched_arr_hour'] = df.sched_arr_time.apply(lambda x: int(np.floor(x/100)))
df['sched_arr_minute'] = df.sched_arr_time.apply(lambda x: int(x - np.floor(x/100)*100))
df['sched_dep_hour'] = df.sched_dep_time.apply(lambda x: int(np.floor(x/100)))
df['sched_dep_minute'] = df.sched_dep_time.apply(lambda x: int(x - np.floor(x/100)*100))
df.rename(columns={'hour': 'dep_hour',
'minute': 'dep_minute'}, inplace=True)
Prepare data for modeling
Create binary target
df['delayed'] = (df['arr_delay'] > 30)
Set up train-test split
target = 'delayed'
y = df[target]
X = df.drop(columns=[target, 'arr_delay', 'flight', 'tailnum', 'time_hour', 'year', 'dep_time', 'sched_dep_time', 'arr_time', 'sched_arr_time', 'dep_delay'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=1066)
Fit the model
Build the classification model
categorical_features = X_train.select_dtypes(exclude=[np.number])
train_pool = Pool(X_train, y_train, categorical_features)
test_pool = Pool(X_test, y_test, categorical_features)
model = CatBoostClassifier(iterations=500, max_depth=5, learning_rate=0.05, random_seed=1066, logging_level='Silent')
model.fit(X_train, y_train, eval_set=test_pool, cat_features=categorical_features, use_best_model=True, early_stopping_rounds=10)
Using log_loss from scikit-learn, calculate the log loss.
We use predict_proba to return the probability of being in the positive class for our test set
auc = roc_auc_score(y_test, model.predict_proba(X_test)[:,1])
auc
