Exemple #1
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 def test_is_nd(self):
     nd = np.array([[1, 2, 3], [4, 5, 6]], dtype=int)
     dtype = np.dtype({'names': map('f{}'.format, xrange(3)),
                       'formats': [float] * 3})
     sa = np.array([(-1.0, 2.0, -1.0), (0.0, -1.0, 2.0)], dtype=dtype)
     self.assertTrue(utils.is_nd(nd))
     self.assertTrue(utils.is_nd(sa))
Exemple #2
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def plot_on_timeline(col, verbose=True):
    """Plots points on a timeline
    
    Parameters
    ----------
    col : np.array
    verbose : boolean
        iff True, display the graph

    Returns
    -------
    matplotlib.figure.Figure
        Figure containing plot

    
    Returns
    -------
    matplotlib.figure.Figure
    """
    col = utils.check_col(col)
    # http://stackoverflow.com/questions/1574088/plotting-time-in-python-with-matplotlib
    if is_nd(col):
        col = col.astype(datetime)
    dates = matplotlib.dates.date2num(col)
    fig = plt.figure()
    plt.plot_date(dates, [0] * len(dates))
    if verbose:
        plt.show()
    return fig
Exemple #3
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def plot_on_timeline(col, verbose=True):
    """Plots points on a timeline
    
    Parameters
    ----------
    col : np.array
    verbose : boolean
        iff True, display the graph

    Returns
    -------
    matplotlib.figure.Figure
        Figure containing plot

    
    Returns
    -------
    matplotlib.figure.Figure
    """
    col = utils.check_col(col)
    # http://stackoverflow.com/questions/1574088/plotting-time-in-python-with-matplotlib
    if is_nd(col):
        col = col.astype(datetime)
    dates = matplotlib.dates.date2num(col)
    fig = plt.figure()
    plt.plot_date(dates, [0] * len(dates))
    if verbose:
        plt.show()
    return fig
Exemple #4
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 def __init__(self,
              M,
              labels,
              clfs=[{
                  'clf': RandomForestClassifier
              }],
              subsets=[{
                  'subset': s_i.SubsetNoSubset
              }],
              cvs=[{
                  'cv': KFold
              }],
              trials=None):
     if M is not None:
         if utils.is_nd(M) and not utils.is_sa(M):
             # nd_array, short circuit the usual type checking and coersion
             if M.ndim != 2:
                 raise ValueError('Expected 2-dimensional array for M')
             self.M = M
             self.col_names = ['f{}'.format(i) for i in xrange(M.shape[1])]
             self.labels = utils.check_col(labels,
                                           n_rows=M.shape[0],
                                           argument_name='labels')
         else:
             # M is either a structured array or something that should
             # be converted
             (M, self.labels) = utils.check_consistent(
                 M, labels, col_argument_name='labels')
             self.col_names = M.dtype.names
             self.M = utils.cast_np_sa_to_nd(M)
     else:
         self.col_names = None
     if trials is None:
         clfs = utils.check_arguments(
             clfs, {'clf': lambda clf: issubclass(clf, BaseEstimator)},
             optional_keys_take_lists=True,
             argument_name='clfs')
         subsets = utils.check_arguments(subsets, {
             'subset':
             lambda subset: issubclass(subset, s_i.BaseSubsetIter)
         },
                                         optional_keys_take_lists=True,
                                         argument_name='subsets')
         cvs = utils.check_arguments(
             cvs, {'cv': lambda cv: issubclass(cv, _PartitionIterator)},
             optional_keys_take_lists=True,
             argument_name='cvs')
     self.clfs = clfs
     self.subsets = subsets
     self.cvs = cvs
     self.trials = trials
Exemple #5
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 def __init__(
         self, 
         M, 
         labels, 
         clfs=[{'clf': RandomForestClassifier}], 
         subsets=[{'subset': s_i.SubsetNoSubset}], 
         cvs=[{'cv': KFold}],
         trials=None):
     if M is not None:
         if utils.is_nd(M) and not utils.is_sa(M):
             # nd_array, short circuit the usual type checking and coersion
             if M.ndim != 2:
                 raise ValueError('Expected 2-dimensional array for M')
             self.M = M
             self.col_names = ['f{}'.format(i) for i in xrange(M.shape[1])]
             self.labels = utils.check_col(
                     labels, 
                     n_rows=M.shape[0], 
                     argument_name='labels')
         else:    
             # M is either a structured array or something that should
             # be converted
             (M, self.labels) = utils.check_consistent(
                     M, 
                     labels, 
                     col_argument_name='labels')
             self.col_names = M.dtype.names
             self.M = utils.cast_np_sa_to_nd(M)
     else:
         self.col_names = None
     if trials is None:
         clfs = utils.check_arguments(
                 clfs, 
                 {'clf': lambda clf: issubclass(clf, BaseEstimator)},
                 optional_keys_take_lists=True,
                 argument_name='clfs')
         subsets = utils.check_arguments(
                 subsets,
                 {'subset': lambda subset: issubclass(subset, s_i.BaseSubsetIter)},
                 optional_keys_take_lists=True,
                 argument_name='subsets')
         cvs = utils.check_arguments(
                 cvs,
                 {'cv': lambda cv: issubclass(cv, _PartitionIterator)},
                 optional_keys_take_lists=True,
                 argument_name='cvs')
     self.clfs = clfs
     self.subsets = subsets
     self.cvs = cvs
     self.trials = trials
Exemple #6
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    def test_check_col(self):
        valid1 = np.array([1, 2, 3, 4])
        valid2 = np.array([[1.0], [2], [3], [4]])
        valid3 = [3.0, 2.0, 1.8]
        valid4 = pd.Series(valid1)
        for valid in (valid1, valid2, valid3, valid4):
            self.assertTrue(utils.is_nd(utils.check_col(valid)))

        self.assertRaises(ValueError, utils.check_col, None)
        self.assertRaises(ValueError, utils.check_col, "lalala")
        self.assertRaises(ValueError, utils.check_col, np.array(
            [[1, 2], [3, 4]]))

        utils.check_col(valid1, n_rows=4)
        self.assertRaises(ValueError, utils.check_col, valid1, n_rows=5)