[3/N] FFX Moderization: adopt Click (#40)

ffx/cli.py

@@ -0,0 +1,151 @@
+import os
+
+import click
+import numpy as np
+import pandas as pd
+from ffx.api import run
+
+from .time_utils import time_execution_scope
+
+
+@click.group()
+def main():
+    '''Fast Function Extraction (FFX) toolkit.
+    '''
+
+
+@main.command()
+@click.argument('x_file', type=click.Path(exists=True))
+@click.argument('y_file', type=click.Path(exists=True))
+def splitdata(x_file, y_file):
+    '''Usage: ffx splitdata INPUTS_FILE[.csv/.txt] OUTPUTS_FILE[.csv/.txt]
+
+    Given csv-formatted inputs and outputs files, splits them into training and testing data files
+    of the form INPUTS_FILE_train.csv, OUTPUTS_FILE_train.csv, INPUTS_FILE_test.csv,
+    OUTPUTS_FILE_test.csv.
+
+    Sorts the data in ascending y.  Assigns every fourth value to test data; and rest to train data.
+
+    In the csv files, there is one column for each sample point.  The inputs files have one row for
+    each input variable.  The outputs files have just one row total, because the output is scalar.
+    Values in a given row are separated by spaces.
+    '''
+    if not (x_file.endswith('.csv') or x_file.endswith('.txt')):
+        print('INPUTS_FILE file \'%s\' needs to end with .csv or .txt.' % x_file)
+        return
+
+    if not (y_file.endswith('.csv') or y_file.endswith('.txt')):
+        print('OUTPUTS_FILE file \'%s\' needs to end with .csv or .txt.' % y_file)
+        return
+
+    # create the target output filenames, and ensure they don't exist
+    join = lambda n, prefix: os.path.join(os.path.dirname(n), prefix + os.path.basename(n))
+    train_X_file = join(x_file, 'train_')
+    train_y_file = join(y_file, 'train_')
+    test_X_file = join(x_file, 'test_')
+    test_y_file = join(y_file, 'test_')
+
+    for newfile in [train_X_file, train_y_file, test_X_file, test_y_file]:
+        if os.path.exists(newfile):
+            print('New file \'%s\' exists, and should not. Early exit.' % newfile)
+            return
+
+    print('Begin ffx splitdata. INPUTS_FILE.csv=%s, OUTPUTS_FILE.csv=%s' % (x_file, y_file))
+
+    X = pd.read_csv(x_file)  # [sample_i][var_i] : float
+    y = pd.read_csv(y_file)  # [sample_i] : float
+
+    if X.shape[0] != y.shape[0]:
+        X = X.T
+    assert X.shape[0] == y.shape[0], 'Error: X shape and y shape do not match. Early exit.'
+
+    # create train/test data from X,y
+    I = np.argsort(y)
+    test_I, train_I = [], []
+    for (loc, i) in enumerate(I):
+        if loc % 4 == 0:
+            test_I.append(i)
+        else:
+            train_I.append(i)
+
+    train_X = np.take(X, train_I, 0)
+    train_y = np.take(y, train_I)
+    test_X = np.take(X, test_I, 0)
+    test_y = np.take(y, test_I)
+
+    print(
+        'There will be %d samples in training data, and %d samples in test data'
+        % (len(train_y), len(test_y))
+    )
+
+    delimiter = ',' if x_file.endswith('.csv') else '\t'
+    np.savetxt(train_X_file, train_X, delimiter=delimiter)
+    np.savetxt(train_y_file, train_y, delimiter=delimiter)
+    np.savetxt(test_X_file, test_X, delimiter=delimiter)
+    np.savetxt(test_y_file, test_y, delimiter=delimiter)
+
+    print('Created these files:')
+    print('  Training inputs:  %s' % train_X_file)
+    print('  Training outputs: %s' % train_y_file)
+    print('  Testing inputs:   %s' % test_X_file)
+    print('  Testing outputs:  %s' % test_y_file)
+
+
+@main.command()
+@click.argument('samples-file', type=click.Path(exists=True))
+def aboutdata(samples_file):
+    '''Simply prints the number of variables and number of samples for the given file
+    '''
+    d = pd.read_csv(samples_file)
+    print('Data file: %s' % samples_file)
+    print('Number of input variables: %d' % d.shape[1])
+    print('Number of input samples: %d' % d.shape[0])
+
+
+@main.command()
+@click.argument('train-x', type=click.Path(exists=True))
+@click.argument('train-y', type=click.Path(exists=True))
+@click.argument('test-x', type=click.Path(exists=True))
+@click.argument('test-y', type=click.Path(exists=True))
+@click.argument('varnames', type=click.Path())
+def testffx(train_x, train_y, test_x, test_y, varnames):
+    '''Usage: runffx test TRAIN_IN.csv TRAIN_OUT.csv TEST_IN.csv TEST_OUT.csv [VARNAMES.csv]
+
+    - Builds a model from training data TRAIN_IN.csv and TRAIN_OUT.csv.
+    - Computes & prints test nmse using test data TEST_IN.csv TEST_OUT.csv.
+    - Also outputs the whole pareto optimal set of # bases vs. error in a .csv
+
+    Arguments:
+    TRAIN_IN.csv -- model input values for training data
+    TRAIN_OUT.csv -- model output values for training data
+    TEST_IN.csv -- model input values for testing data
+    TEST_OUT.csv -- model output values for testing data
+    VARNAMES.csv (optional) -- variable names.  One string for each variable name.
+
+    In the training and test files, there is one column for each sample point.  The inputs
+    files have one row for each input variable.  The outputs files have just one row total,
+    because the output is scalar.  Values in a given row are separated by spaces.
+    '''
+    print('Begin ffx test.')
+
+    # get X/y
+    train_X, train_y, test_X, test_y = [pd.read_csv(f) for f in (train_x, train_y, test_x, test_y)]
+
+    # get varnames
+    varnames = pd.read_csv(varnames) if varnames else ['x%d' % i for i in range(train_X.shape[1])]
+
+    # build models
+    with time_execution_scope() as timer_result:
+        models = run(train_X, train_y, test_X, test_y, varnames)
+
+        output_csv = 'pareto_front_%s.csv' % str(int(timer_result.start_time))
+        pd.DataFrame(
+            [[model.numBases(), (model.test_nmse * 100.0), model] for model in models],
+            columns=['Num Bases', 'Test error (%)', 'Model'],
+        ).to_csv(output_csv, encoding='utf-8')
+
+    print('Done.  Runtime: %.1f seconds.  Results are in: %s' % (timer_result.seconds, output_csv))
+
+
+if __name__ == '__main__':
+    main()  # pylint:disable=no-value-for-parameter