def test_convert_categorical_data_single_column():
data_in = np.array([['a', 3], ['b', 2], ['c', 2], ['a', 1], ['b', 1]],
dtype=object)
dtypes = ['categorical', 'continuous']
converters = {
'col2idx': {
'x': 0,
'y': 1
},
'valmaps': {
'x': {
'val2idx': {
'a': 0,
'b': 1,
'c': 2
},
'idx2val': {
0: 'a',
1: 'b',
2: 'c'
}
}
}
}
data_out = du.convert_data(data_in, ['x', 'y'], dtypes, converters)
assert data_out.shape == data_in.shape
assert data_out[0, 0] == 0
assert data_out[1, 0] == 1
assert data_out[2, 0] == 2
assert data_out[3, 0] == 0
assert data_out[4, 0] == 1
assert data_out[0, 1] == 3
assert data_out[1, 1] == 2
assert data_out[2, 1] == 2
assert data_out[3, 1] == 1
assert data_out[4, 1] == 1
data_in_2 = du.convert_data(data_out, ['x', 'y'],
dtypes,
converters,
to_val=True)
assert data_in_2.shape == data_in.shape
n_rows, n_cols = data_in.shape
for i, j in it.product(range(n_rows), range(n_cols)):
assert data_in[i, j] == data_in_2[i, j]
def probability(self, x, cols, given=None):
""" Predictive probability of x_1, ..., x_n given y_1, ..., y_n
Parameters
----------
x : numpy.ndarray(2,)
2-D numpy array where each row is a set of observations and each
column corresponds to a feature.
cols : list
The names of each column/feature of `x`.
given : list(tuple)
List of (name, value,) conditional contraints for the probability
Returns
-------
logps : numpy.ndarray
Examples
--------
The probability that an animal is fast and agile given that it is
bulbous.
>>> engine = Engine.load('examples/zoo.bcmodels')
>>> engine.probability(np.array([[1, 1]]), ['fast', 'agile'],
... given=[('bulbous': 1]))
"""
# TODO: make sure that given goes not caontain columns rom cols
x = du.format_query_data(x)
col_idxs = [self._converters['col2idx'][col] for col in cols]
x_cnv = du.convert_data(x, cols, self._dtypes, self._converters)
if given is not None:
given = du.convert_given(given, self._dtypes, self._converters)
return mu.probability(x_cnv, self._models, col_idxs, given=given)
def probability(self, x, cols, given=None):
""" Predictive probability of x_1, ..., x_n given y_1, ..., y_n
Parameters
----------
x : numpy.ndarray(2,)
2-D numpy array where each row is a set of observations and each
column corresponds to a feature.
cols : list
The names of each column/feature of `x`.
given : list(tuple)
List of (name, value,) conditional contraints for the probability
Returns
-------
logps : numpy.ndarray
Examples
--------
The probability that an animal is fast and agile given that it is
bulbous.
>>> engine = Engine.load('examples/zoo.bcmodels')
>>> engine.probability(np.array([[1, 1]]), ['fast', 'agile'],
... given=[('bulbous': 1]))
"""
# TODO: make sure that given goes not caontain columns rom cols
x = du.format_query_data(x)
col_idxs = [self._converters['col2idx'][col] for col in cols]
x_cnv = du.convert_data(x, cols, self._dtypes, self._converters)
if given is not None:
given = du.convert_given(given, self._dtypes, self._converters)
return mu.probability(x_cnv, self._models, col_idxs, given=given)
def test_convert_categorical_data_should_change_everything():
data_in = np.array([
['a', 3],
['b', 2],
['c', 2],
['a', 1],
['b', 1]], dtype=object)
dtypes = ['categorical']*2
converters = {
'col2idx':
{'x': 0, 'y': 1},
'valmaps': {
'x': {
'val2idx': {'a': 0, 'b': 1, 'c': 2},
'idx2val': {0: 'a', 1: 'b', 2: 'c'}},
'y': {
'val2idx': {1: 0, 2: 1, 3: 2},
'idx2val': {0: 1, 1: 2, 2: 3}}}}
data_out = du.convert_data(data_in, ['x', 'y'], dtypes, converters)
assert data_out.shape == data_in.shape
assert data_out[0, 0] == 0
assert data_out[1, 0] == 1
assert data_out[2, 0] == 2
assert data_out[3, 0] == 0
assert data_out[4, 0] == 1
assert data_out[0, 1] == 2
assert data_out[1, 1] == 1
assert data_out[2, 1] == 1
assert data_out[3, 1] == 0
assert data_out[4, 1] == 0
data_in_2 = du.convert_data(data_out, ['x', 'y'], dtypes, converters,
to_val=True)
assert data_in_2.shape == data_in.shape
n_rows, n_cols = data_in.shape
for i, j in it.product(range(n_rows), range(n_cols)):
assert data_in[i, j] == data_in_2[i, j]
def test_convert_continuous_data_should_do_nothing():
data_in = np.random.rand(10, 2)
dtypes = ['continuous'] * 2
converters = {'col2idx': {'x': 0, 'y': 1}, 'valmaps': {}}
data_out = du.convert_data(data_in, ['x', 'y'], dtypes, converters)
assert data_in.shape == data_out.shape
n_rows, n_cols = data_in.shape
for i, j in it.product(range(n_rows), range(n_cols)):
assert data_in[i, j] == data_out[i, j]
data_out = du.convert_data(data_in, ['x', 'y'],
dtypes,
converters,
to_val=True)
assert data_in.shape == data_out.shape
for i, j in it.product(range(n_rows), range(n_cols)):
assert data_in[i, j] == data_out[i, j]
def test_convert_continuous_data_should_do_nothing():
data_in = np.random.rand(10, 2)
dtypes = ['continuous']*2
converters = {
'col2idx': {'x': 0, 'y': 1},
'valmaps': {}}
data_out = du.convert_data(data_in, ['x', 'y'], dtypes, converters)
assert data_in.shape == data_out.shape
n_rows, n_cols = data_in.shape
for i, j in it.product(range(n_rows), range(n_cols)):
assert data_in[i, j] == data_out[i, j]
data_out = du.convert_data(data_in, ['x', 'y'], dtypes, converters,
to_val=True)
assert data_in.shape == data_out.shape
for i, j in it.product(range(n_rows), range(n_cols)):
assert data_in[i, j] == data_out[i, j]
def sample(self, cols, given=None, n=1):
""" Draw samples from cols
Parameters
----------
cols : list(index)
List of columns from which to jointly draw.
given : list(tuple(int, value))
List of column-value tuples that specify conditions
n : int
The number of samples to draw
Examples
--------
Draw whether an animal is fast and agile given that it is bulbous.
>>> engine = Engine.load('examples/zoo.bcmodels')
>>> engine.sample(['fast', 'agile'], given=[('bulbous': 1]))
"""
# FIXME: make sure that given does not contain columns from cols
if given is not None:
given = du.convert_given(given, self._dtypes, self._converters)
col_idxs = [self._converters['col2idx'][col] for col in cols]
data_out = mu.sample(self._models, col_idxs, given=given, n=n)
x = du.convert_data(data_out,
cols,
self._dtypes,
self._converters,
to_val=True)
if x.shape == (
1,
1,
):
return x[0, 0]
elif x.shape[0] == 1:
return x[0, :]
else:
return x
def sample(self, cols, given=None, n=1):
""" Draw samples from cols
Parameters
----------
cols : list(index)
List of columns from which to jointly draw.
given : list(tuple(int, value))
List of column-value tuples that specify conditions
n : int
The number of samples to draw
Examples
--------
Draw whether an animal is fast and agile given that it is bulbous.
>>> engine = Engine.load('examples/zoo.bcmodels')
>>> engine.sample(['fast', 'agile'], given=[('bulbous': 1]))
"""
# FIXME: make sure that given does not contain columns from cols
if given is not None:
given = du.convert_given(given, self._dtypes, self._converters)
col_idxs = [self._converters['col2idx'][col] for col in cols]
data_out = mu.sample(self._models, col_idxs, given=given, n=n)
x = du.convert_data(data_out, cols, self._dtypes, self._converters,
to_val=True)
if x.shape == (1, 1,):
return x[0, 0]
elif x.shape[0] == 1:
return x[0, :]
else:
return x
