Jul 18th, 2018

predictive_maintenance_dataset.csv is a file that contains parameters and settings for many wind turbines:

• operational_setting_1
• operational_setting_2
• sensor_measurement_1
• sensor_measurement_2 ...

There is a column called unit_number which specifies which turbine it is, and one called status, in which a value of 1 means the turbine broke down that day, and 0 means it didn't.

The task is to create a model that, when fed with operational settings and sensor measurements (unit_number and time_stamp will not be fed in), outputs 1 if the turbine will break down within the next 40 days, and 0 if not.

For a closer look at the process, please review the Jupyter Notebook

forecasting_dataset.csv is a file that contains pollution data for a city. The task is to create a model that, when fed with columns co_gt, nhmc, c6h6, s2, nox, s3, no2, s4, s5, t, rh, ah, and level, predicts the value of y six hours later.

For a closer look at the process, please review the Jupyter Notebook

A writeup explaining design decisions, potential works and the reasons for making current choices: Notebook

For log files, open using TensorBoard by typing below command in your terminal in where the log folder is:

tensorboard --logdir=logs

The model is actually a pipeline for both tasks.

Task 1 pipeline contains:

• Get dummies from categorical variable and drop 1 level
• Select only features appears in training set
• Impute with the mean
• Feed Forward Neural Network with Keras

Task 2 pipeline contains:

• Select only features appears in training set
• Get dummies from categorical variable
• Impute with the mean
• Feed Forward Neural Network with Keras

Download the model saved in pickle file in Result folder.

For task 1:

• Load in the pipeline first
• Then load the keras model in the pipeline. (use Keras 1.2 to load the model)

# Load the pipeline first:
pl_load_in = joblib.load('../../results/task1_pipeline.pkl')

# Then, load the Keras model:
pl_load_in.named_steps['model'].model = load_model('../../results/task1_keras_model.h5')

# Test the model:
# Compute and print MSE for validation
ypred = pl_load_in.predict(Xval)
mse = mean_squared_error(yval, ypred)
print("Mean squared error: %f" % (mse))

# reset index for comparison (if yval already have clean index, this step can be omitted)
yval2 = yval.reset_index(drop=True)

# assign hard label (function hard_label() is in src.task1.reform_results)
new_ypred=pd.DataFrame(ypred)[0].apply(hard_label)

# Compute the accuracy: accuracy for validation
accuracy = float(np.sum(new_ypred==yval2))/yval2.shape[0]
print("accuracy: {}%".format(round(accuracy*100, 3)))

For task 2:

Load in the pipeline. The model is included in the pipeline.

# load the model from disk
filename = 'results/task2_model.pkl' # path leads to pickle model
loaded_model = pickle.load(open(filename, 'rb'))

# Test the model
ypred = loaded_model.predict(Xtest)
print("R squared score is:", r2_score(ytest,ypred).round(3))

• numpy
• pandas
• missingno
• imbalanced-learn
• sklearn
• statsmodels
• keras 2.0 for modelling, 1.2 if just need to load model and use it.
• matplotlib
• seaborn
• scikitplot

The hierarchy of this repository is described like below:

     .
     |-- README 
     |-- LICENSE
     |-- .gitignore.py        
     |-- data
     |   -- predictive_maintenance_dataset.csv
     |   -- forecasting_dataset.csv
     |-- doc 
     |   -- notebook.md         # electronic lab notebook
     |   -- manuscript.md       
     |-- results		# storing all the result models 
     |-- src                    # source code used for both tasks
     |   -- task1               # code specific for task 1
     |   -- task2               # code specific for task 2
     |-- test			# tests for functions
     |-- assets                 # store images
     |-- bin
     |   -- # keep all the files I want to delete but not sure whether I will need it later