Create base configuration files loadable from the command line

[lucventurini]

At the moment, creating the configuration files is still a patchy process, by copying them manually from the sample_data location.

Mikado need to be able to do a "mikado.py configure" as a first step to create the necessary files.

Ideally, it would have two options:

• copying the YAML from a predefined folder
• configuring it on the fly using raw_input

Possible inspiration: sklearn.datasets.load_iris(); here is the code:

def load_iris():
module_path = dirname(file)
with open(join(module_path, 'data', 'iris.csv')) as csv_file:
data_file = csv.reader(csv_file)
temp = next(data_file)
n_samples = int(temp[0])
n_features = int(temp[1])
target_names = np.array(temp[2:])
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=np.int)

      for i, ir in enumerate(data_file):
          data[i] = np.asarray(ir[:-1], dtype=np.float)
          target[i] = np.asarray(ir[-1], dtype=np.int)

  with open(join(module_path, 'descr', 'iris.rst')) as rst_file:
      fdescr = rst_file.read()

  return Bunch(data=data, target=target,
               target_names=target_names,
               DESCR=fdescr,
               feature_names=['sepal length (cm)', 'sepal width (cm)',
                              'petal length (cm)', 'petal width (cm)'])

[lucventurini]

Progress in 0c2c04b. Switched to JSON schema, progress on the migration is good but still ongoing.

[lucventurini]

Solved in 5b972be.