Predict_Bike_Sharing_Demand_with_AutoGluon

Utilizing the AutoGluon library to train several models for the Bike Sharing Demand competition in Kaggle. Using Tabular Prediction to fit data from CSV files provided by the competition

Report: Predict Bike Sharing Demand with AutoGluon Solution

Abdallah Ibrahim

Initial Training

What did you realize when you tried to submit your predictions? What changes were needed to the output of the predictor to submit your results?

I needed to make sure that I removed negative values so that Kaggle accepts the submission

What was the top-ranked model that performed?

WeightedEnsemble_L3

Exploratory data analysis and feature creation

What did the exploratory analysis find and how did you add additional features?

I found that I needed to change some columns data types and do some feature engineering

• No null values
• Created a histogram of all features to show the distribution of each one relative to the data.
• Changed the [season, weather, holiday, workingday] columns type to 'category', so that AutoGluon understands that these are categorical, not numerical features
• Changed the datetime column type to DateTime
• Created new features [year, month, day, hour] from the datetime feature

How much better did your model perform after adding additional features and why do you think that is?

Around 61.75% better, because the additional features were significant so they helped the model reach a better result

Hyperparameter tuning

How much better did your model perform after trying different hyperparameters?

Around 29.25% better

If you were given more time with this dataset, where do you think you would spend more time?

I would spend more time on feature engineering because it is the most important part of the workflow, and it resulted in the largest score improvement

Create a table with the models you ran, the hyperparameters modified, and the kaggle score.

model	NN	GBM	time_limit	score
initial	default	default	600	1.79775
add_features	default	default	600	0.68768
hpo	nn_options	gbm_options	900	0.48650

• NN: neural networks training hyperparameters
• GBM: LightGBM Gradient boosted trees training hyperparameters
• time_limit: the total training time for autogluon

---

nn_options: changed the no. of epochs, the learning rate, and the dropout probability gbm_options: changed the no. of boosting rounds and the no. of leaves in trees

Create a line plot showing the top model score for the three (or more) training runs during the project.

Create a line plot showing the top Kaggle score for the three (or more) prediction submissions during the project.

Summary

Intro

• Setting up Kaggle
• Downloading the Dataset
• importing AutoGluon
• Looking at the data using .info() and .describe()
• submitting predictions of a model with default hyperparameters and no feature engineering or addition of new features

EDA

• Added new features and changed the datatype of some columns

The model improved by around 61.75%, due to recognizing the categorical features as categorical and the newly introduced features that helped the model extract patterns better.

Hyperparameters Tuning

nn_options = {  # specifies non-default hyperparameter values for neural network models
    'num_epochs': 10,  # number of training epochs (controls training time of NN models)
    'learning_rate': ag.space.Real(1e-4, 1e-2, default=5e-4, log=True),  # learning rate used in training (real-valued hyperparameter searched on log-scale)
    'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1),  # dropout probability (real-valued hyperparameter)
}

gbm_options = {  # specifies non-default hyperparameter values for lightGBM gradient boosted trees
    'num_boost_round': 100,  # number of boosting rounds (controls training time of GBM models)
    'num_leaves': ag.space.Int(lower=26, upper=66, default=36),  # number of leaves in trees (integer hyperparameter)
}

hyperparameters = {  # hyperparameters of each model type
                   'GBM': gbm_options,
                   'NN': nn_options,
                  }


num_trials = 5 # no. of different configurations of hyperparameters to try
search_strategy = "auto" # to tune hyperparameters using Bayesian optimization routine with a local scheduler

• Changed some hyperparameters of the Neural Networks and lightGBM gradient boosted trees models

Results The model improved by around 29.25%

• The systematic search for the best hyperparameters within the defined search space led to improved model performance compared to using default hyperparameters

• increasing the training time from 600 seconds to 900 seceonds didn't improve the model performance, in fact it made the score slightly worse