Debugging a pipeline¶
River encourages users to make use of pipelines. The biggest pain point of pipelines is that it can be hard to understand what's happening to the data, especially when the pipeline is complex. Fortunately the Pipeline class has a debug_one method that can help out.
Let's look at a fairly complex pipeline for predicting the number of bikes in 5 bike stations from the city of Toulouse. It doesn't matter if you understand the pipeline or not; the point of this notebook is to learn how to introspect a pipeline.
import datetime as dt
from river import compose
from river import datasets
from river import feature_extraction
from river import linear_model
from river import metrics
from river import preprocessing
from river import stats
from river import stream
X_y = datasets.Bikes()
X_y = stream.simulate_qa(X_y, moment='moment', delay=dt.timedelta(minutes=30))
def add_time_features(x):
return {
**x,
'hour': x['moment'].hour,
'day': x['moment'].weekday()
}
model = add_time_features
model |= (
compose.Select('clouds', 'humidity', 'pressure', 'temperature', 'wind') +
feature_extraction.TargetAgg(by=['station', 'hour'], how=stats.Mean()) +
feature_extraction.TargetAgg(by='station', how=stats.EWMean())
)
model |= preprocessing.StandardScaler()
model |= linear_model.LinearRegression()
metric = metrics.MAE()
questions = {}
for i, x, y in X_y:
# Question
is_question = y is None
if is_question:
y_pred = model.predict_one(x)
questions[i] = y_pred
# Answer
else:
metric.update(y, questions[i])
model = model.learn_one(x, y)
if i >= 30000 and i % 30000 == 0:
print(i, metric)
30000 MAE: 13.328051
60000 MAE: 7.824087
90000 MAE: 6.003909
120000 MAE: 5.052855
150000 MAE: 4.496826
180000 MAE: 4.140702
Let's start by looking at the pipeline. You can click each cell to display the current state for each step of the pipeline.
model
add_time_features
def add_time_features(x):
return {
**x,
'hour': x['moment'].hour,
'day': x['moment'].weekday()
}
['clouds', [...]
Select (
clouds
humidity
pressure
temperature
wind
)
y_mean_by_station_and_hour
TargetAgg (
by=['station', 'hour']
how=Mean ()
target_name="y"
)
y_ewm_0.5_by_station
TargetAgg (
by=['station']
how=EWMean (
fading_factor=0.5
)
target_name="y"
)
StandardScaler
StandardScaler (
with_std=True
)
LinearRegression
LinearRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.01
)
)
loss=Squared ()
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
clip_gradient=1e+12
initializer=Zeros ()
)
As mentioned above the Pipeline class has a debug_one method. You can use this at any point you want to visualize what happen to an input x. For example, let's see what happens to the last seen x.
print(model.debug_one(x))
0. Input
--------
clouds: 88 (int)
description: overcast clouds (str)
humidity: 84 (int)
moment: 2016-10-05 09:57:18 (datetime)
pressure: 1,017.34000 (float)
station: pomme (str)
temperature: 17.45000 (float)
wind: 1.95000 (float)
1. add_time_features
--------------------
clouds: 88 (int)
day: 2 (int)
description: overcast clouds (str)
hour: 9 (int)
humidity: 84 (int)
moment: 2016-10-05 09:57:18 (datetime)
pressure: 1,017.34000 (float)
station: pomme (str)
temperature: 17.45000 (float)
wind: 1.95000 (float)
2. Transformer union
--------------------
2.0 Select
----------
clouds: 88 (int)
humidity: 84 (int)
pressure: 1,017.34000 (float)
temperature: 17.45000 (float)
wind: 1.95000 (float)
2.1 TargetAgg
-------------
y_mean_by_station_and_hour: 7.89396 (float)
2.2 TargetAgg1
--------------
y_ewm_0.5_by_station: 11.80372 (float)
clouds: 88 (int)
humidity: 84 (int)
pressure: 1,017.34000 (float)
temperature: 17.45000 (float)
wind: 1.95000 (float)
y_ewm_0.5_by_station: 11.80372 (float)
y_mean_by_station_and_hour: 7.89396 (float)
3. StandardScaler
-----------------
clouds: 1.54778 (float)
humidity: 1.16366 (float)
pressure: 0.04916 (float)
temperature: -0.51938 (float)
wind: -0.69426 (float)
y_ewm_0.5_by_station: 0.19640 (float)
y_mean_by_station_and_hour: -0.27110 (float)
4. LinearRegression
-------------------
Name Value Weight Contribution
Intercept 1.00000 9.19960 9.19960
y_ewm_0.5_by_station 0.19640 9.19349 1.80562
humidity 1.16366 1.01680 1.18320
temperature -0.51938 -0.41575 0.21593
wind -0.69426 -0.03810 0.02645
pressure 0.04916 0.18321 0.00901
y_mean_by_station_and_hour -0.27110 0.19553 -0.05301
clouds 1.54778 -0.32838 -0.50827
Prediction: 11.87854
The pipeline does quite a few things, but using debug_one shows what happens step by step. This is really useful for checking that the pipeline is behaving as you're expecting it too. Remember that you can debug_one whenever you wish, be it before, during, or after training a model.