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EEG_eyestate_sklearn_NOPASS.ipynb
EEG_eyestate_sklearn_NOPASS.ipynb
 
 
H2O_chicago_crimes.ipynb
H2O_chicago_crimes.ipynb
 
 
H2O_tutorial_breast_cancer_classification.ipynb
H2O_tutorial_breast_cancer_classification.ipynb
 
 
H2O_tutorial_eeg_eyestate.ipynb
H2O_tutorial_eeg_eyestate.ipynb
 
 
H2O_tutorial_gbm_monotonicity.ipynb
H2O_tutorial_gbm_monotonicity.ipynb
 
 
H2O_tutorial_medium_NOPASS.ipynb
H2O_tutorial_medium_NOPASS.ipynb
 
 
Predict_w_Unseen_Categorical_Levels.ipynb
Predict_w_Unseen_Categorical_Levels.ipynb
 
 
README.md
README.md
 
 
airlines_demo_small.ipynb
airlines_demo_small.ipynb
 
 
citi_bike_large.ipynb
citi_bike_large.ipynb
 
 
citi_bike_small.ipynb
citi_bike_small.ipynb
 
 
cm_roc.ipynb
cm_roc.ipynb
 
 
confusion_matrices_binomial.ipynb
confusion_matrices_binomial.ipynb
 
 
constrained_kmeans_demo_chicago.ipynb
constrained_kmeans_demo_chicago.ipynb
 
 
constrained_kmeans_demo_cluto.ipynb
constrained_kmeans_demo_cluto.ipynb
 
 
custom_loss_function_demo.ipynb
custom_loss_function_demo.ipynb
 
 
deeplearning.ipynb
deeplearning.ipynb
 
 
extended-isolation-forest-introduction.ipynb
extended-isolation-forest-introduction.ipynb
 
 
glrm_census_large.ipynb
glrm_census_large.ipynb
 
 
imputation.ipynb
imputation.ipynb
 
 
isax2.ipynb
isax2.ipynb
 
 
isolationforest_valid.ipynb
isolationforest_valid.ipynb
 
 
kmeans_aic_bic_diagnostics.ipynb
kmeans_aic_bic_diagnostics.ipynb
 
 
mutlinomial_auc_aucpr_demo.ipynb
mutlinomial_auc_aucpr_demo.ipynb
 
 
not_equal_factor.ipynb
not_equal_factor.ipynb
 
 
pdp_multiclass.ipynb
pdp_multiclass.ipynb
 
 
permutation_var_imp.ipynb
permutation_var_imp.ipynb
 
 
predict_contributionsShap.ipynb
predict_contributionsShap.ipynb
 
 
prep_airlines.ipynb
prep_airlines.ipynb
 
 
prostate_gbm.ipynb
prostate_gbm.ipynb
 
 
quantile_monotone_constraints.ipynb
quantile_monotone_constraints.ipynb
 
 
rf_balance_classes.ipynb
rf_balance_classes.ipynb
 
 
shap_values_drf.ipynb
shap_values_drf.ipynb
 
 
shap_values_gbm.ipynb
shap_values_gbm.ipynb
 
 
tf-idf.ipynb
tf-idf.ipynb
 
 
tree_demo.ipynb
tree_demo.ipynb
 
 
turbofan_NOPASS_phm.ipynb
turbofan_NOPASS_phm.ipynb
 
 
turbofan_phm_gtkerror_NOPASS.ipynb
turbofan_phm_gtkerror_NOPASS.ipynb
 
 
walking_gait.ipynb
walking_gait.ipynb
 
 
word2vec_craigslistjobtitles.ipynb
word2vec_craigslistjobtitles.ipynb
 
 
xgboost_feature_interactions_demo.ipynb
xgboost_feature_interactions_demo.ipynb
 
 

README.md

Launching iPython Examples

Prerequisites:

  • Python 2.7

Install iPython Notebook

  1. Download pip, a Python package manager (if it's not already installed):

    $ sudo easy_install pip

  2. Install iPython using pip install:

    $ sudo pip install "ipython[notebook]"

Install dependencies

This module uses requests and tabulate modules, both of which are available on pypi, the Python package index.

$ sudo pip install requests
$ sudo pip install tabulate

Install and Launch H2O

To use H2O in Python, follow the instructions on the Install in Python tab after selecting the H2O version on the H2O Downloads page.

Launch H2O outside of the iPython notebook. You can do this in the top directory of your H2O build download. The version of H2O running must match the version of the H2O Python module for Python to connect to H2O. To access the H2O Web UI, go to https://localhost:54321 in your web browser.

Open Demos Notebook

Open the prostate_gbm.ipynb file. The notebook contains a demo that starts H2O, imports a prostate dataset into H2O, builds a GBM model, and predicts on the training set with the recently built model. Use Shift+Return to execute each cell and proceed to the next cell in the notebook .

$ ipython notebook prostate_gbm.ipynb

All demos are available here:

Running Python Examples

To set up your Python environment to run these examples, download and install H2O from Python using the instructions above.

Available Demos

  • Predict Airline Delays - Uses historical airlines flight data to build multiple classification models to label any flight as either delayed or not delayed.
  • Chicago Crime Rate - Uses weather and city statistics to compare arrest rates with the total crimes for each category.
  • NYC Citibike Demand with Weather - Takes monthly bike ride data (~10 million rows) for the past two years to predict bike demand at each bike share station. Weather data is also incorporated to better predict bike usage.
  • NYC Citibike Demand with Weather - smaller dataset - Takes monthly bike ride data (~1 million rows) for the past two years to predict bike demand at each bike share station. Weather data is also incorporated to better predict bike usage.
  • Confusion Matrix & ROC - Creates a GBM and GLM model using the airlines dataset, including confusion matrices, ROCs, and scoring histories.
  • Imputation - Substitutes values for missing data (imputes) the airlines dataset.
  • Not Equal Factor - Try to slice the airlines dataset using != factor_level.
  • Airline Confusion Matrices - Uses the airlines dataset to generate confusion matrices for algorithm performance analysis.
  • Deep Learning for Prostate Cancer Analysis - Uses the prostate dataset to build a Deep Learning model.
  • Airlines Prep - Condition the airline dataset by filtering out NAs if the departure delay in the input dataset is unknown. Anything longer than minutesOfDelayWeTolerate is treated as delayed.
  • GBM model using prostate dataset - Creates a GBM model using the prostate dataset.
  • Balance Classes - Imports the airlines dataset, parses it, displays a summary, and runs GLM with a binomial link function.
  • Clustering with KMeans - Demonstrates kmeans clusters and different diagnostics for selecting the number of clusters. Link to data is provided in the notebook.
  • EEG Eye State - Uses EEG data collected from an Emotiv Neuroheadset and classifies eye state (open vs closed) with a GBM.
  • Tree fetch demo - Trains a basic GBM model based on Airlines dataset & fetches the tree behind the model. Exploration of the tree fetched is explained.

Corresponding Datasets

Airlines Datasets

Chicago Crime

Citibike Data

Prostate Data