# Predict Bike Trips¶

In this example, we build a machine learning application to predict the number of bike trips from a station in the next biking period. This application is structured into three important steps:

• Prediction Engineering

• Feature Engineering

• Machine Learning

In the first step, we generate new labels from the data by using Compose. In the second step, we generate features for the labels by using Featuretools. In the third step, we search for the best machine learning pipeline by using EvalML. After working through these steps, you will learn how to build machine learning applications for real-world problems like forecasting demand. Let’s get started.

[1]:

from demo.chicago_bike import load_sample
from matplotlib.pyplot import subplots
import composeml as cp
import featuretools as ft
import evalml


We will use data provided by Divvy which is a bicycle sharing system in Chicago. In this dataset, we have a record of each bike trip.

[2]:

df = load_sample()

[2]:

gender starttime stoptime tripduration temperature events from_station_id dpcapacity_start to_station_id dpcapacity_end
trip_id
2331610 Female 2014-06-29 13:35:00 2014-06-29 13:56:00 20.750000 82.9 cloudy 178 15.0 76 39.0
2347603 Female 2014-06-30 12:07:00 2014-06-30 12:37:00 30.150000 82.0 cloudy 211 19.0 177 15.0
2345120 Male 2014-06-30 08:36:00 2014-06-30 08:43:00 6.516667 75.0 cloudy 340 15.0 67 15.0
2347527 Male 2014-06-30 12:00:00 2014-06-30 12:08:00 7.250000 82.0 cloudy 56 19.0 56 19.0
2344421 Male 2014-06-30 08:04:00 2014-06-30 08:11:00 7.316667 75.0 cloudy 77 23.0 37 19.0

## Prediction Engineering¶

How many trips will occur from a station in the next biking period?

We can change the length of the biking period to create different prediction problems. For example, how many bike trips will occur in the next 13 hours or in the next week? These variations can be done by simply tweaking a parameter. This helps us explore different scenarios which is crucial for making better decisions.

### Defining the Labeling Function¶

Let’s start by defining a labeling function to calculate the number of trips. Given that each observation is an individual trip, the number of trips is just the number of observations. Our labeling function will be used by a label maker to extract the training examples.

[3]:

def trip_count(ds):
return len(ds)


### Representing the Prediction Problem¶

Then, let’s represent the prediction problem by creating a label maker with the following parameters:

• target_entity as the column for station ID where each trip starts from, since we want to process trips from each station.

• labeling_function as the function to calculate the number of trips.

• time_index as the column for the starting time of a trip. The biking periods are based on this time index.

• window_size as the length of a biking period. We can easily change this parameter to create variations of the prediction problem.

[4]:

lm = cp.LabelMaker(
target_entity='from_station_id',
labeling_function=trip_count,
time_index='starttime',
window_size='13h',
)


### Finding the Training Examples¶

Now, let’s run a search to get the training examples by using the following parameters:

• The trips sorted by the start time, since the search will expect the trips to be sorted chronologically, otherwise an error will be raised.

• num_examples_per_instance to find the number of training examples per station. In this case, the search will return all existing examples.

• minimum_data as the start time of the first biking period. This is also the first cutoff time for building features.

[5]:

lt = lm.search(
df.sort_values('starttime'),
num_examples_per_instance=-1,
minimum_data='2014-06-30 08:00',
verbose=False,
)


[5]:

from_station_id time trip_count
0 5 2014-06-30 08:00:00 3
1 15 2014-06-30 08:00:00 1
2 16 2014-06-30 08:00:00 1
3 17 2014-06-30 08:00:00 2
4 19 2014-06-30 08:00:00 2

The output from the search is a label times table with three columns:

• The station ID associated to the trips. There can be many training examples generated from each station.

• The start time of the biking period. This is also the cutoff time for building features. Only data that existed beforehand is valid to use for predictions.

• The number of trips during the biking period window. This is calculated by our labeling function.

As a helpul reference, we can print out the search settings that were used to generate these labels.

[6]:

lt.describe()

Settings
--------
gap                                      None
minimum_data                 2014-06-30 08:00
num_examples_per_instance                  -1
target_column                      trip_count
target_entity                 from_station_id
target_type                        continuous
window_size                               13h

Transforms
----------
No transforms applied



We can also get a better look at the labels by plotting the distribution and cumulative count across time.

[7]:

%matplotlib inline
fig, ax = subplots(nrows=2, ncols=1, figsize=(6, 8))
lt.plot.distribution(ax=ax[0])
lt.plot.count_by_time(ax=ax[1])


## Feature Engineering¶

In the previous step, we generated the labels. The next step is to generate the features.

### Representing the Data¶

Let’s start by representing the data with an entity set. This way, we can generate features based on the relational structure of the dataset. We currently have a single table of trips where one station can have many trips. This one-to-many relationship can be represented by normalizing a station entity. The same can be done with other one-to-many relationships like weather-to-trips. We want to make predictions based on the station where the trips started from, so we will use this station entity as the target entity for generating features. Also, we will use the stop times of the trips as the time index for generating features, since data about a trip would likely be unavailable until the trip is complete.

[8]:

es = ft.EntitySet('chicago_bike')

es.entity_from_dataframe(
dataframe=df.reset_index(),
entity_id='trips',
time_index='stoptime',
index='trip_id',
)

es.normalize_entity(
base_entity_id='trips',
new_entity_id='from_station_id',
index='from_station_id',
make_time_index=False,
)

es.normalize_entity(
base_entity_id='trips',
new_entity_id='weather',
index='events',
make_time_index=False,
)

es.normalize_entity(
base_entity_id='trips',
new_entity_id='gender',
index='gender',
make_time_index=False,
)

es["trips"]["gender"].interesting_values = ['Male', 'Female']
es["trips"]["events"].interesting_values = ['tstorms']
es.plot()

[8]:


### Calculating the Features¶

Now, we can generate features by using a method called Deep Feature Synthesis (DFS). This will automatically build features by stacking and applying mathematical operations called primitives across relationships in an entity set. The more structured an entity set is, the better DFS can leverage the relationships to generate better features. Let’s run DFS using the following parameters:

• entity_set as the entity set we structured previously.

• target_entity as the station entity where the trips started from.

• cutoff_time as the label times that we generated previously. The label values are appended to the feature matrix.

[9]:

fm, fd = ft.dfs(
entityset=es,
target_entity='from_station_id',
trans_primitives=['hour', 'week', 'is_weekend'],
cutoff_time=lt,
cutoff_time_in_index=True,
include_cutoff_time=False,
verbose=False,
)


[9]:

COUNT(trips) MAX(trips.dpcapacity_end) MAX(trips.dpcapacity_start) MAX(trips.temperature) MAX(trips.to_station_id) MAX(trips.tripduration) MEAN(trips.dpcapacity_end) MEAN(trips.dpcapacity_start) MEAN(trips.temperature) MEAN(trips.to_station_id) ... MODE(trips.HOUR(stoptime)) MODE(trips.WEEK(starttime)) MODE(trips.WEEK(stoptime)) NUM_UNIQUE(trips.HOUR(starttime)) NUM_UNIQUE(trips.HOUR(stoptime)) NUM_UNIQUE(trips.WEEK(starttime)) NUM_UNIQUE(trips.WEEK(stoptime)) PERCENT_TRUE(trips.IS_WEEKEND(starttime)) PERCENT_TRUE(trips.IS_WEEKEND(stoptime)) trip_count
from_station_id time
5 2014-06-30 08:00:00 1.0 39.0 19.0 84.0 76.0 9.233333 39.000000 19.0 84.000000 76.000000 ... 14.0 26.0 26.0 1.0 1.0 1.0 1.0 1.000000 1.000000 3
15 2014-06-30 08:00:00 3.0 19.0 15.0 84.2 280.0 14.166667 16.333333 15.0 76.733333 137.333333 ... 7.0 27.0 27.0 3.0 2.0 2.0 2.0 0.333333 0.333333 1
16 2014-06-30 08:00:00 2.0 15.0 11.0 84.9 340.0 25.083333 13.000000 11.0 84.900000 324.500000 ... 17.0 26.0 26.0 1.0 2.0 1.0 1.0 1.000000 1.000000 1
17 2014-06-30 08:00:00 1.0 15.0 15.0 84.9 183.0 4.650000 15.000000 15.0 84.900000 183.000000 ... 17.0 26.0 26.0 1.0 1.0 1.0 1.0 1.000000 1.000000 2
19 2014-06-30 08:00:00 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN 0.000000 0.000000 2

5 rows × 49 columns

There are two outputs from DFS: a feature matrix and feature definitions. The feature matrix is a table that contains the feature values with the corresponding labels based on the cutoff times. Feature definitions are features in a list that can be stored and reused later to calculate the same set of features on future data.

## Machine Learning¶

In the previous steps, we generated the labels and features. The final step is to build the machine learning pipeline.

### Splitting the Data¶

Let’s start by extracting the labels from the feature matrix and splitting the data into a training set and holdout set.

[10]:

y = fm.pop('trip_count')

splits = evalml.preprocessing.split_data(
X=fm,
y=y,
test_size=0.1,
random_state=0,
regression=True,
)

X_train, X_holdout, y_train, y_holdout = splits


### Finding the Best Model¶

Then, let’s run a search on the training set for the best machine learning model. During the search process, predictions from several different pipelines are evaluated to find the best pipeline.

[11]:

automl = evalml.AutoMLSearch(
problem_type='regression',
objective='r2',
random_state=3,
allowed_model_families=['extra_trees', 'random_forest'],
max_iterations=3,
)

automl.search(
X=X_train,
y=y_train,
data_checks='disabled',
show_iteration_plot=False,
)

X passed was not a DataTable. EvalML will try to convert the input as a Woodwork DataTable and types will be inferred. To control this behavior, please pass in a Woodwork DataTable instead.
y passed was not a DataColumn. EvalML will try to convert the input as a Woodwork DataTable and types will be inferred. To control this behavior, please pass in a Woodwork DataTable instead.
Generating pipelines to search over...
*****************************
* Beginning pipeline search *
*****************************

Optimizing for R2.
Greater score is better.

Searching up to 3 pipelines.
Allowed model families: random_forest, extra_trees

Batch 1: (1/3) Mean Baseline Regression Pipeline        Elapsed:00:00
Starting cross validation
Finished cross validation - mean R2: -0.014
High coefficient of variation (cv >= 0.2) within cross validation scores. Mean Baseline Regression Pipeline may not perform as estimated on unseen data.
2/3) Extra Trees Regressor w/ Imputer + On... Elapsed:00:00
Starting cross validation
Finished cross validation - mean R2: 0.092
High coefficient of variation (cv >= 0.2) within cross validation scores. Extra Trees Regressor w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
3/3) Random Forest Regressor w/ Imputer + ... Elapsed:00:01
Starting cross validation
Finished cross validation - mean R2: 0.046
High coefficient of variation (cv >= 0.2) within cross validation scores. Random Forest Regressor w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.

Search finished after 00:02
Best pipeline: Extra Trees Regressor w/ Imputer + One Hot Encoder
Best pipeline R2: 0.092090


Once the search is complete, we can print out information about the best pipeline found, such as the parameters in each component.

[12]:

automl.best_pipeline.describe()
automl.best_pipeline.graph()

******************************************************
* Extra Trees Regressor w/ Imputer + One Hot Encoder *
******************************************************

Problem Type: regression
Model Family: Extra Trees

Pipeline Steps
==============
1. Imputer
* categorical_impute_strategy : most_frequent
* numeric_impute_strategy : mean
* categorical_fill_value : None
* numeric_fill_value : None
2. One Hot Encoder
* top_n : 10
* features_to_encode : None
* categories : None
* drop : None
* handle_unknown : ignore
* handle_missing : error
3. Extra Trees Regressor
* n_estimators : 100
* max_features : auto
* max_depth : 6
* min_samples_split : 2
* min_weight_fraction_leaf : 0.0
* n_jobs : -1

[12]:


Let’s score the model performance by evaluating predictions on the holdout set.

[13]:

best_pipeline = automl.best_pipeline.fit(X_train, y_train)

score = best_pipeline.score(
X=X_holdout,
y=y_holdout,
objectives=['r2'],
)

dict(score)

[13]:

{'R2': 0.5994369461860227}


From the pipeline, we can see which features are most important for predictions.

[14]:

feature_importance = best_pipeline.feature_importance
feature_importance = feature_importance.set_index('feature')['importance']
top_k = feature_importance.abs().sort_values().tail(20).index
feature_importance[top_k].plot.barh(figsize=(8, 8), fontsize=14, width=.7);

[14]:

<AxesSubplot:ylabel='feature'>


### Making Predictions¶

We are ready to make predictions with our trained model. First, let’s calculate the same set of features by using the feature definitions. We will use a cutoff time based on the latest information available in the dataset.

[15]:

fm = ft.calculate_feature_matrix(
features=fd,
entityset=es,
cutoff_time=ft.pd.Timestamp('2014-07-02 08:00:00'),
cutoff_time_in_index=True,
verbose=False,
)


[15]:

COUNT(trips) MAX(trips.dpcapacity_end) MAX(trips.dpcapacity_start) MAX(trips.temperature) MAX(trips.to_station_id) MAX(trips.tripduration) MEAN(trips.dpcapacity_end) MEAN(trips.dpcapacity_start) MEAN(trips.temperature) MEAN(trips.to_station_id) ... MODE(trips.HOUR(starttime)) MODE(trips.HOUR(stoptime)) MODE(trips.WEEK(starttime)) MODE(trips.WEEK(stoptime)) NUM_UNIQUE(trips.HOUR(starttime)) NUM_UNIQUE(trips.HOUR(stoptime)) NUM_UNIQUE(trips.WEEK(starttime)) NUM_UNIQUE(trips.WEEK(stoptime)) PERCENT_TRUE(trips.IS_WEEKEND(starttime)) PERCENT_TRUE(trips.IS_WEEKEND(stoptime))
from_station_id time
186 2014-07-02 08:00:00 1 19.0 15.0 82.9 56 6.483333 19.000000 15.0 82.900000 56.000000 ... 13 13 26 26 1 1 1 1 1.00000 1.00000
181 2014-07-02 08:00:00 8 27.0 31.0 84.9 233 18.350000 20.500000 31.0 83.212500 140.750000 ... 16 16 27 27 5 5 2 2 0.37500 0.37500
177 2014-07-02 08:00:00 23 31.0 15.0 84.9 334 50.233333 17.434783 15.0 83.334783 219.565217 ... 16 18 26 26 8 8 2 2 0.73913 0.73913
13 2014-07-02 08:00:00 3 19.0 19.0 82.9 165 13.916667 17.666667 19.0 82.600000 112.666667 ... 13 13 26 26 2 2 1 1 1.00000 1.00000
153 2014-07-02 08:00:00 2 23.0 19.0 82.9 324 13.850000 19.000000 19.0 77.950000 219.500000 ... 6 6 26 26 2 2 2 2 0.50000 0.50000

5 rows × 48 columns

Now, let’s predict the number of trips that will occur from a station in the next 13 hours.

[16]:

values = best_pipeline.predict(fm).values.round()

prediction = fm[[]]
prediction['trip_count (estimate)'] = values

[16]:

trip_count (estimate)
from_station_id time
186 2014-07-02 08:00:00 2.0
181 2014-07-02 08:00:00 6.0
177 2014-07-02 08:00:00 5.0
13 2014-07-02 08:00:00 3.0
153 2014-07-02 08:00:00 3.0

### Next Steps¶

At this point, we have completed the machine learning application. We can revisit each step to explore and fine-tune with different parameters until the model is ready for deployment. For more information on how to work with the features produced by Featuretools, take a look at the Featuretools documentation. For more information on how to work with the models produced by EvalML, take a look at the EvalML documentation.