Rainfall Predictor

Sep 2017 - Dez 2017
Language: Python3

Designed and developed supervised Machine Learning models for predicting rainfall at a particular location. The project was carried out by 2 awesome teammates and myself, in hopes of getting a good grade on Prof. Sameer Singh’s course of Machine Learning.

The data

Courtesy of the UC Irvine Center for Hydrometeorology and Remote Sensing, our data consisted of satellite-based measurements of temperature at particular locations across the globe (infrared imaging) and information about clouds (such as area and average temperature). Each data point corresponded to a location on the globe (identified by its latitude, longitude and elevation) and was labeled with CHRS’s belief of whether that particular location will admit rain.

Approaches

We tried the following models:

• Neural Networks and Deep Learning
• Random Forests
• Support Vector Machines

Random Forests turned out to be the model scoring highest in validation AUC, with scores above 0.79. We used hold-out validation with a training fraction of 80% of the data.

Experiments were carried out using scikit-learn’s implementations of Random Forests (both RandomForestRegressor and ExtraTreesRegressor) and we experimented different configurations of the following parameters (see code at the end):

• max_depth. The maximum depth on all the decision trees. If None, the depth is unrestricted
• min_samples_split. The minimum number of samples to split a node (e.g. minParent)
• min_samples_leaf. The minimum number of samples to form a leaf
• max_features. The maximum size of the subsample of features considered in splitting
• n_estimators. The number of decision trees generated
• bootstrap. Either True or False. In scikit-learn, the subsample of the data (drawn with or without replacement) will always have the size as the data itself

Random Forest Results

In the end, an unlimited max_depth together with small sizes of feature subsampling (max_features=2) and a large number of decision trees (n_estimators=300) turned out to be a very good configuration of params. The remaining parameters revealed little influence in validation AUC. See below for a plot of training and validation AUC’s varying max_features and n_estimators, with and without bootstrap.

RandomForestRegressor with bootstrap, min_samples_split=min_samples_leaf=5 and max_depth=None.

RandomForestRegressor without bootstrap, min_samples_split=min_samples_leaf=5 and max_depth=None.

$~$

The following plots are similar to the ones below, but using ExtraTreesRegressor instead.

ExtraTreesRegressor with bootstrap, min_samples_split=min_samples_leaf=5 and max_depth=None.

ExtraTreesRegressor without bootstrap, min_samples_split=min_samples_leaf=5 and max_depth=None.

Random Forests code

# Consistent behavior
np.random.seed(0)

def loadData(filename):
  """ Load data from binary cache if possible for efficiency. """
  f = os.path.splitext(filename)[0] + '.npy'
  if os.path.isfile(f):
    D = np.load(f)   # faster than genfromtxt
  else:
    D = np.genfromtxt(filename, delimiter = None)
    np.save(f, D)
  return D

def gen_params(**params_ranges):
  params_ranges = {k: [(k, v) for v in params_ranges[k]] for k in params_ranges}
  return map(dict, itertools.product(*params_ranges.values()))


if __name__ == '__main__':
  timestamp = str(int(time.time()))

  # Prepare output folder for results
  date = datetime.fromtimestamp(time.time()).strftime('%m-%d_%H-%M-%S')

  # Data Loading
  X = loadData('data/X_train.txt')
  Y = loadData('data/Y_train.txt')
  X, Y = ml.shuffleData(X,Y)
  m, n = X.shape

  Xtr, Xva, Ytr, Yva = ml.splitData(X, Y)
  Xt, Yt = Xtr, Ytr

  max_depth = [None]
  min_samples_split = [10]
  min_samples_leaf = [10]
  max_features = [2]
  n_estimators = [100]
  bootstrap = [True]
  type = ['RF']

  params_ranges = {p: eval(p) for p in ['max_depth',
                                        'min_samples_split',
                                        'min_samples_leaf',
                                        'max_features',
                                        'n_estimators',
                                        'bootstrap',
                                        'type']}

  results = []
  for params in gen_params(**params_ranges):
    t = params.pop('type')
    if t == 'RF':
      RF = RandomForestRegressor(n_jobs = -1, random_state = 0, **params)
    else:
      RF = ExtraTreesRegressor(n_jobs = -1, random_state = 0, **params)
    RF.fit(Xt, Yt)

    params['AUCt'] = roc_auc_score(Yt, RF.predict(Xt))
    params['AUCv'] = roc_auc_score(Yva, RF.predict(Xva))
    params['type'] = t
    results.append(params)
    if saveResults:
      with open('experiments/' + timestamp + '.json', 'w') as f:
        json.dump(results, f, indent = 2)