Python API

[csala]

Implement a clean API to use ATM from Python.

The usage should follow this pattern:

>>> from atm import data
>>> demo_datasets = data.get_demos()
>>> demo_datasets
{
    'iris': 'demos/iris.csv',
    'pollution': 'demos/pollution.csv',
    'pitchfork_genres': 'demos/pitchfork_genres.csv'
}
>>> from atm import ATM
>>> atm = ATM()    # Additional DB connection arguments can be passed here
>>> path_to_csv = demo_datasets['pollution']
>>> results = atm.run(train_path=path_to_csv)    # Additional dataset and datarun config an be passed here
Processing dataset demos/pollution.csv
100%|##########################| 100/100 [00:10<00:00,  6.09it/s]
>>> results.describe()
Datarun 1 summary:
    Dataset: 'demos/pollution.csv'
    Column Name: 'class'
    Judgment Metric: 'f1'
    Classifiers Tested: 100
    Elapsed Time: 0:00:07.638668
>>> results.get_best_classifier()
Classifier id: 94
Classifier type: knn
Params chosen:
    n_neighbors: 13
    leaf_size: 38
    weights: uniform
    algorithm: kd_tree
    metric: manhattan
    _scale: True
Cross Validation Score: 0.858 +- 0.096
Test Score: 0.714
>>> scores = results.get_scores()
>>> scores.head()
  cv_judgment_metric cv_judgment_metric_stdev  id test_judgment_metric  rank
0       0.8584126984             0.0960095737  94         0.7142857143   1.0
1       0.8222222222             0.0623609564  12         0.6250000000   2.0
2       0.8147619048             0.1117618135  64         0.8750000000   3.0
3       0.8139393939             0.0588721670  68         0.6086956522   4.0
4       0.8067754468             0.0875180564  50         0.6250000000   5.0
>>> results.export_best_classifier('path/to/model.pkl')
Classifier 94 saved as path/to/model.pkl
>>> from atm import Model
>>> model = Model.load('path/to/model.pkl')
>>> import pandas as pd
>>> data = pd.read_csv(demo_datasets['pollution'])
>>> predictions = model.predict(data.head())

[csala]

Closed via #141