def test_init(self):
# let's add some uncertainties to this
uncs = [RealParameter("a", 0, 1),
RealParameter("b", 0, 1)]
outcomes = [TimeSeriesOutcome("test")]
constraints = []
callback = DefaultCallback(uncs, [], outcomes, constraints,
nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, [o.name for o in outcomes])
names = rf.get_names(callback.cases.dtype)
names = set(names)
self.assertEqual(names, {'a', 'b', 'policy', 'model', 'scenario_id'})
self.assertEqual(callback.results, {})
# with levers
levers = [RealParameter('c', 0, 10)]
callback = DefaultCallback(uncs, levers, outcomes, constraints,
nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, [o.name for o in outcomes])
names = rf.get_names(callback.cases.dtype)
names = set(names)
self.assertEqual(names, {'a', 'b', 'c','policy', 'model', 'scenario_id'})
self.assertEqual(callback.results, {})
def test_init(self):
# let's add some uncertainties to this
uncs = [RealParameter("a", 0, 1), RealParameter("b", 0, 1)]
outcomes = [TimeSeriesOutcome("test")]
callback = DefaultCallback(uncs, [], outcomes, nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, [o.name for o in outcomes])
names = rf.get_names(callback.cases.dtype)
names = set(names)
self.assertEqual(names, {'a', 'b', 'policy', 'model'})
self.assertEqual(callback.results, {})
# with levers
levers = [RealParameter('c', 0, 10)]
callback = DefaultCallback(uncs, levers, outcomes, nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, [o.name for o in outcomes])
names = rf.get_names(callback.cases.dtype)
names = set(names)
self.assertEqual(names, {'a', 'b', 'c', 'policy', 'model'})
self.assertEqual(callback.results, {})
def _make_box(x):
'''
Make a box that encompasses all the data
Parameters
----------
x : structured numpy array
'''
box = np.zeros((2, ), x.dtype)
names = recfunctions.get_names(x.dtype)
for name in names:
dtype = x.dtype.fields.get(name)[0]
mask = np.ma.getmaskarray(x[name])
values = x[name][mask==False]
if dtype == 'object':
try:
values = set(values)
box[name][:] = values
except TypeError as e:
ema_logging.warning("{} has unhashable values".format(name))
raise e
else:
box[name][0] = np.min(values, axis=0)
box[name][1] = np.max(values, axis=0)
return box
def test_drop_restriction(self):
x = np.array([(0, 1, 2), (2, 5, 6), (3, 2, 1)],
dtype=[('a', np.float), ('b', np.float), ('c', np.float)])
y = {'y': np.array([1, 1, 0])}
results = (x, y)
prim_obj = prim.setup_prim(results, 'y', threshold=0.8)
box = PrimBox(prim_obj, prim_obj.box_init, prim_obj.yi)
new_box_lim = np.array([(0, 1, 1), (2, 2, 6)],
dtype=[('a', np.float), ('b', np.float),
('c', np.float)])
indices = np.array([0, 1], dtype=np.int)
box.update(new_box_lim, indices)
box.drop_restriction('b')
correct_box_lims = np.array([(0, 1, 1), (2, 5, 6)],
dtype=[('a', np.float), ('b', np.float),
('c', np.float)])
box_lims = box.box_lims[-1]
names = recfunctions.get_names(correct_box_lims.dtype)
for entry in names:
lim_correct = correct_box_lims[entry]
lim_box = box_lims[entry]
for i in range(len(lim_correct)):
self.assertEqual(lim_correct[i], lim_box[i])
self.assertEqual(box.peeling_trajectory['mean'][2], 1)
self.assertEqual(box.peeling_trajectory['coverage'][2], 1)
self.assertEqual(box.peeling_trajectory['density'][2], 1)
self.assertEqual(box.peeling_trajectory['res dim'][2], 1)
self.assertEqual(box.peeling_trajectory['mass'][2], 2 / 3)
def _prepare_experiments(experiments):
'''
transform the experiments structured array into a numpy array.
Parameters
----------
experiments : structured array
Returns
-------
ndarray
'''
uncs = recfunctions.get_names(experiments.dtype)
temp_experiments = np.zeros((experiments.shape[0], len(uncs)))
for i, u in enumerate(uncs):
try:
temp_experiments[:,i] = experiments[u].astype(np.float)
except ValueError:
data = experiments[u]
entries = sorted(list(set(data)))
for j, entry in enumerate(entries):
temp_experiments[data==entry,i] = j
return temp_experiments
def test_store_cases(self):
nr_experiments = 3
uncs = [ParameterUncertainty((0,1), "a"),
ParameterUncertainty((0,1), "b"),
CategoricalUncertainty([0, 1, 2], "c"),
ParameterUncertainty((0,1), "d", integer=True),]
outcomes = [Outcome("test", time=True)]
case = {unc.name:random.random() for unc in uncs}
case["c"] = int(round(case["c"]*2))
case["d"] = int(round(case["d"]))
policy = {'name':'none'}
name = "test"
callback = DefaultCallback(uncs,
[outcome.name for outcome in outcomes],
nr_experiments=nr_experiments,
reporting_interval=1)
result = {outcomes[0].name: 1}
callback(0, case, policy, name, result)
experiments, _ = callback.get_results()
design = case
design['policy'] = policy['name']
design['model'] = name
names = rf.get_names(experiments.dtype)
for name in names:
self.assertEqual(experiments[name][0], design[name])
def test_prim_init_select(self):
self.results = test_utilities.load_flu_data()
self.classify = flu_classify
experiments, outcomes = self.results
unc = recfunctions.get_names(experiments.dtype)
# test initialization, including t_coi calculation in case of searching
# for results equal to or higher than the threshold
outcomes['death toll'] = outcomes['deceased population region 1'][:, -1]
results = experiments, outcomes
threshold = 10000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.ABOVE, threshold=threshold,
incl_unc=unc)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] >= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
# test initialization, including t_coi calculation in case of searching
# for results equal to or lower than the threshold
threshold = 1000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.BELOW,
threshold=threshold)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] <= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
prim.setup_prim(self.results, self.classify, threshold=prim.ABOVE)
def setup_cart(results, classify, incl_unc=[], mass_min=0.05):
"""helper function for performing cart
Parameters
----------
results : tuple of structured array and dict with numpy arrays
the return from :meth:`perform_experiments`.
classify : string, function or callable
either a string denoting the outcome of interest to
use or a function.
incl_unc : list of strings
mass_min : float
Raises
------
TypeError
if classify is not a string or a callable.
"""
if not incl_unc:
x = np.ma.array(results[0])
else:
drop_names = set(recfunctions.get_names(results[0].dtype))-set(incl_unc)
x = recfunctions.drop_fields(results[0], drop_names, asrecarray = True)
if type(classify)==types.StringType:
y = results[1][classify]
elif callable(classify):
y = classify(results[1])
else:
raise TypeError("unknown type for classify")
return CART(x, y, mass_min)
def _rotate_subset(self, value, orig_experiments, logical):
'''
rotate a subset
Parameters
----------
value : list of strings
orig_experiment : numpy structured array
logical : boolean array
'''
list_dtypes = [(name, "<f8") for name in value]
#cast everything to float
drop_names = set(rf.get_names(orig_experiments.dtype)) - set(value)
orig_subset = rf.drop_fields(orig_experiments, drop_names,
asrecarray=True)
subset_experiments = orig_subset.astype(list_dtypes).view('<f8').reshape(orig_experiments.shape[0], len(value))
#normalize the data
mean = np.mean(subset_experiments,axis=0)
std = np.std(subset_experiments, axis=0)
std[std==0] = 1 #in order to avoid a devision by zero
subset_experiments = (subset_experiments - mean)/std
#get the experiments of interest
experiments_of_interest = subset_experiments[logical]
#determine the rotation
rotation_matrix = self._determine_rotation(experiments_of_interest)
#apply the rotation
subset_experiments = np.dot(subset_experiments,rotation_matrix)
return rotation_matrix, subset_experiments
def _make_box(x):
'''
Make a box that encompasses all the data
Parameters
----------
x : structured numpy array
'''
box = np.zeros((2, ), x.dtype)
names = recfunctions.get_names(x.dtype)
for name in names:
dtype = x.dtype.fields.get(name)[0]
mask = np.ma.getmaskarray(x[name])
values = x[name][mask == False]
if dtype == 'object':
try:
values = set(values)
except TypeError as e:
ema_logging.warning("{} has unhashable values".format(name))
raise e
else:
box[name][:] = values
else:
box[name][0] = np.min(values, axis=0)
box[name][1] = np.max(values, axis=0)
return box
def test_prim_init_select(self):
self.results = util.load_flu_data()
self.classify = flu_classify
experiments, outcomes = self.results
unc = recfunctions.get_names(experiments.dtype)
# test initialization, including t_coi calculation in case of searching
# for results equal to or higher than the threshold
outcomes['death toll'] = outcomes['deceased population region 1'][:, -1]
results = experiments, outcomes
threshold = 10000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.ABOVE, threshold=threshold,
incl_unc=unc)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] >= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
# test initialization, including t_coi calculation in case of searching
# for results equal to or lower than the threshold
threshold = 1000
prim_obj = prim.setup_prim(results, classify='death toll',
threshold_type=prim.BELOW,
threshold=threshold)
value = np.ones((experiments.shape[0],))
value = value[outcomes['death toll'] <= threshold].shape[0]
self.assertTrue(prim_obj.t_coi==value)
prim.setup_prim(self.results, self.classify, threshold=prim.ABOVE)
def get_univariate_feature_scores(x,y, score_func=F_CLASSIFICATION):
'''
calculate feature scores using univariate statistical tests. In case of
categorical data, chi square or the Anova F value is used. In case of
continuous data the Anova F value is used.
Parameters
----------
x : structured array
y : 1D nd.array
score_func : {F_CLASSIFICATION, F_REGRESSION, CHI2}
the score function to use, one of f_regression (regression), or
f_classification or chi2 (classification).
Returns
-------
list of tuples
sorted in descending order of tuples with uncertainty and feature
scores (i.e. p values in this case).
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
pvalues = score_func(x, y)[1]
pvalues = np.asarray(pvalues)
pvalues = zip(uncs, pvalues)
pvalues = list(pvalues)
pvalues.sort(key=itemgetter(1))
return pvalues
def _prepare_experiments(experiments):
'''
transform the experiments structured array into a numpy array.
Parameters
----------
experiments : structured array
Returns
-------
ndarray
'''
uncs = recfunctions.get_names(experiments.dtype)
temp_experiments = np.zeros((experiments.shape[0], len(uncs)))
for i, u in enumerate(uncs):
try:
temp_experiments[:,i] = experiments[u].astype(np.float)
except ValueError:
data = experiments[u]
entries = sorted(list(set(data)))
for j, entry in enumerate(entries):
temp_experiments[data==entry,i] = j
return temp_experiments
def test_store_cases(self):
nr_experiments = 3
uncs = [
ParameterUncertainty((0, 1), "a"),
ParameterUncertainty((0, 1), "b"),
CategoricalUncertainty([0, 1, 2], "c"),
ParameterUncertainty((0, 1), "d", integer=True),
]
outcomes = [Outcome("test", time=True)]
case = {unc.name: random.random() for unc in uncs}
case["c"] = int(round(case["c"] * 2))
case["d"] = int(round(case["d"]))
policy = {'name': 'none'}
name = "test"
callback = DefaultCallback(uncs,
[outcome.name for outcome in outcomes],
nr_experiments=nr_experiments,
reporting_interval=1)
result = {outcomes[0].name: 1}
callback(0, case, policy, name, result)
experiments, _ = callback.get_results()
design = case
design['policy'] = policy['name']
design['model'] = name
names = rf.get_names(experiments.dtype)
for name in names:
self.assertEqual(experiments[name][0], design[name])
def plot_cdfs(x, y, ccdf=False):
'''plot cumulative density functions for each column in x, based on
the classification specified in y.
Parameters
----------
x : recarray
the experiments to use in the cdfs
y : ndaray
the categorization for the data
ccdf : bool, optional
if true, plot a complementary cdf instead of a normal cdf.
Returns
-------
a matplotlib Figure instance
'''
x = rf.drop_fields(x, "scenario_id", asrecarray=True)
uncs = rf.get_names(x.dtype)
cp = sns.color_palette()
n_col = 4
n_row = math.ceil(len(uncs) / n_col)
size = 3
aspect = 1
figsize = n_col * size * aspect, n_row * size
fig, axes = plt.subplots(n_row, n_col, figsize=figsize, squeeze=False)
for i, unc in enumerate(uncs):
discrete = False
i_col = i % n_col
i_row = i // n_col
ax = axes[i_row, i_col]
data = x[unc]
if x.dtype[unc] == np.dtype('O'):
discrete = True
plot_individual_cdf(ax, unc, data, y, discrete, ccdf=ccdf)
# last row might contain empty axis,
# let's make them disappear
for j_col in range(i_col + 1, n_col):
ax = axes[i_row, j_col]
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticklabels([])
ax.set_yticks([])
sns.despine(ax=ax, top=True, right=True, left=True, bottom=True)
proxies, labels = build_legend(x, y)
fig.legend(proxies, labels, "upper center")
return fig
def plot_cdfs(x, y, ccdf=False):
'''plot cumulative density functions for each column in x, based on the
classification specified in y.
Parameters
----------
x : recarray
the experiments to use in the cdfs
y : ndaray
tthe categorization for the data
ccdf : bool, optional
if true, plot a complementary cdf
instead of a normal cdf.
'''
uncs = rf.get_names(x.dtype)
cp = sns.color_palette()
n_col = 4
n_row = len(uncs)//n_col +1
size = 3
aspect = 1
figsize = n_col * size * aspect, n_row * size
fig, axes = plt.subplots(n_row, n_col,
figsize=figsize,
squeeze=False)
for i, unc in enumerate(uncs):
discrete = False
i_col = i % n_col
i_row = i // n_col
ax = axes[i_row, i_col]
data = x[unc]
if x.dtype[unc] == np.dtype('O'):
discrete = True
plot_cdf(ax, unc, data, y, discrete, ccdf=ccdf)
# last row might contain empty axis,
# let's make them disappear
i_row = len(uncs) // n_col
i_col = len(uncs) % n_col
for i_col in range(i_col, n_col):
ax = axes[i_row, i_col]
ax.set_xticklabels([])
ax.set_xticks([])
ax.set_yticklabels([])
ax.set_yticks([])
sns.despine(ax=ax, top=True, right=True, left=True, bottom=True)
proxies, labels = build_legend(x, y)
fig.legend(proxies, labels, "upper center")
return fig
def _in_box(x, boxlim):
'''
returns the indices of the data points that are within the
box_lims.
Parameters
----------
x : numpy structured array
boxlim : numpy structured array
Returns
-------
ndarray
valid numpy indices on x
'''
logical = np.ones(x.shape[0], dtype=np.bool)
dims = recfunctions.get_names(boxlim.dtype)
for name in dims:
value = x.dtype.fields.get(name)[0]
if value == 'object':
entries = boxlim[name][0]
l = np.ones( (x.shape[0], len(entries)), dtype=np.bool)
for i,entry in enumerate(entries):
if type(list(entries)[0]) not in (str, float, int):
bools = []
for element in list(x[name]):
if element == entry:
bools.append(True)
else:
bools.append(False)
l[:, i] = np.asarray(bools, dtype=bool)
else:
l[:, i] = x[name] == entry
l = np.any(l, axis=1)
logical = logical & l
else:
logical = logical & (boxlim[name][0] <= x[name] )&\
(x[name] <= boxlim[name][1])
indices = np.where(logical==True)
assert len(indices)==1
indices = indices[0]
return indices
def _in_box(x, boxlim):
'''
returns the indices of the data points that are within the
box_lims.
Parameters
----------
x : numpy structured array
boxlim : numpy structured array
Returns
-------
ndarray
valid numpy indices on x
'''
logical = np.ones(x.shape[0], dtype=np.bool)
dims = recfunctions.get_names(boxlim.dtype)
for name in dims:
value = x.dtype.fields.get(name)[0]
if value == 'object':
entries = boxlim[name][0]
l = np.ones((x.shape[0], len(entries)), dtype=np.bool)
for i, entry in enumerate(entries):
if type(list(entries)[0]) not in (str, float, int):
bools = []
for element in list(x[name]):
if element == entry:
bools.append(True)
else:
bools.append(False)
l[:, i] = np.asarray(bools, dtype=bool)
else:
l[:, i] = x[name] == entry
l = np.any(l, axis=1)
logical = logical & l
else:
logical = logical & (boxlim[name][0] <= x[name] )&\
(x[name] <= boxlim[name][1])
indices = np.where(logical == True)
assert len(indices) == 1
indices = indices[0]
return indices
def get_univariate_feature_scores(results,
classify,
score_func='f_classification'):
'''
calculate feature scores using univariate statistical tests. In case of
categorical data, chi square or the Anova F value is used. In case of
continuous data the Anova F value is used.
Parameters
----------
results : tuple
classify : str
score_func : {'f_classification', 'chi2', 'f_regression'}
the score function to use, one of f_regression (regression), or
f_classification or chi2 (classification).
Returns
-------
list of tuples
sorted in descending order of tuples with uncertainty and feature
scores (i.e. p values in this case).
'''
score_funcs = {
'f_regression': f_regression,
'f_classification': f_classif,
'chi2': chi2
}
experiments, outcomes = results
uncs = recfunctions.get_names(experiments.dtype)
x = _prepare_experiments(experiments)
y, categorical = _prepare_outcomes(outcomes, classify)
if categorical:
score_func = score_funcs[score_func]
else:
score_func = f_regression
pvalues = score_func(x, y)[1]
pvalues = np.asarray(pvalues)
pvalues = zip(uncs, pvalues)
pvalues = list(pvalues)
pvalues.sort(key=itemgetter(1))
return pvalues
def determine_restricted_dims(self, box_lims):
'''
determine which dimensions of the given box are restricted compared
to compared to the initial box that contains all the data
:param box_lims:
'''
logical = self.compare(self.box_init, box_lims)
u = np.asarray(recfunctions.get_names(box_lims.dtype),
dtype=object)
dims = u[logical==False]
return dims
def test_init(self):
# let's add some uncertainties to this
uncs = [ParameterUncertainty((0,1), "a"),
ParameterUncertainty((0,1), "b")]
outcomes = [Outcome("test", time=True)]
callback = DefaultCallback(uncs, outcomes, nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, outcomes)
names = rf.get_names(callback.cases.dtype)
names = set(names)
self.assertEqual(names, {'a', 'b', 'policy', 'model'})
self.assertEqual(callback.results, {})
def test_init(self):
# let's add some uncertainties to this
uncs = [ParameterUncertainty((0,1), "a"),
ParameterUncertainty((0,1), "b")]
outcomes = [Outcome("test", time=True)]
callback = DefaultCallback(uncs, outcomes, nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, outcomes)
names = rf.get_names(callback.cases.dtype)
names = set(names)
self.assertEqual(names, {'a', 'b', 'policy', 'model'})
self.assertEqual(callback.results, {})
def setup_prim(results, classify, threshold, incl_unc=[], **kwargs):
"""Helper function for setting up the prim algorithm
Parameters
----------
results : tuple
tuple of structured array and dict with numpy arrays
the return from :meth:`perform_experiments`.
classify : str or callable
either a string denoting the outcome of interest to
use or a function.
threshold : double
the minimum score on the objective function of the last box
on the peeling trajectory. In case of a binary classification,
this should be between 0 and 1.
incl_unc : list of str, optional
list of uncertainties to include in prim analysis
kwargs : dict
valid keyword arguments for prim.Prim
Returns
-------
a Prim instance
Raises
------
PrimException
if data resulting from classify is not a 1-d array.
TypeError
if classify is not a string or a callable.
"""
if not incl_unc:
x = np.ma.array(results[0])
else:
drop_names = set(rf.get_names(results[0].dtype)) - set(incl_unc)
x = rf.drop_fields(results[0], drop_names, asrecarray=True)
if isinstance(classify, six.string_types):
y = results[1][classify]
elif callable(classify):
y = classify(results[1])
else:
raise TypeError("unknown type for classify")
return Prim(x, y, threshold=threshold, **kwargs)
def _determine_restricted_dims(box_lims, box_init):
'''
determine which dimensions of the given box are restricted compared
to compared to the initial box that contains all the data
Parameters
----------
box_lims : structured numpy array
a specific box limit
box_init : structured numpy array
the initial box containing all data points
'''
logical = _compare(box_init, box_lims)
u = np.asarray(recfunctions.get_names(box_lims.dtype), dtype=object)
dims = u[logical == False]
return dims
def test_drop_restriction(self):
x = np.array([(0,1,2),
(2,5,6),
(3,2,1)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
y = np.array([1,1,0])
prim_obj = Prim(x, y, threshold=0.8)
box = PrimBox(prim_obj, prim_obj._box_init, prim_obj.yi)
new_box_lim = np.array([(0,1,1),
(2,2,6)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
indices = np.array([0,1], dtype=np.int)
box.update(new_box_lim, indices)
box.drop_restriction('b')
correct_box_lims = np.array([(0,1,1),
(2,5,6)],
dtype=[('a', np.float),
('b', np.float),
('c', np.float)])
box_lims = box._box_lims[-1]
names = recfunctions.get_names(correct_box_lims.dtype)
for entry in names:
lim_correct = correct_box_lims[entry]
lim_box = box_lims[entry]
for i in range(len(lim_correct)):
self.assertEqual(lim_correct[i], lim_box[i])
self.assertEqual(box.peeling_trajectory['mean'][2], 1)
self.assertEqual(box.peeling_trajectory['coverage'][2], 1)
self.assertEqual(box.peeling_trajectory['density'][2], 1)
self.assertEqual(box.peeling_trajectory['res dim'][2], 1)
self.assertEqual(box.peeling_trajectory['mass'][2], 2/3)
def _determine_restricted_dims(box_lims, box_init):
'''
determine which dimensions of the given box are restricted compared
to compared to the initial box that contains all the data
Parameters
----------
box_lims : structured numpy array
a specific box limit
box_init : structured numpy array
the initial box containing all data points
'''
logical = _compare(box_init, box_lims)
u = np.asarray(recfunctions.get_names(box_lims.dtype),
dtype=object)
dims = u[logical==False]
return dims
def make_box(x):
'''
Make a box that encompasses all the data
Parameters
----------
x : structured numpy array
'''
# get the types in the order they appear in the numpy array
types = [(v[1], k, v[0].name) for k, v in six.iteritems(x.dtype.fields)]
types = sorted(types)
# convert any bool types to object to store set(False, True)
ntypes = [(k, 'object' if t == 'bool' else t) for (_, k, t) in types]
# create box limits
box = np.zeros((2, ), ntypes)
names = recfunctions.get_names(x.dtype)
for name in names:
dtype = box.dtype.fields.get(name)[0]
values = x[name]
if isinstance(values, np.ma.MaskedArray):
values = values.compressed()
if dtype == 'object':
try:
values = set(values)
box[name][:] = values
except TypeError as e:
logging.getLogger(__name__).warning(
"{} has unhashable values".format(name))
raise e
else:
box[name][0] = np.min(values, axis=0)
box[name][1] = np.max(values, axis=0)
return box
def setup_prim(results, classify, incl_unc=[], **kwargs):
"""Helper function for setting up the prim algorithm
Parameters
----------
results : tuple of structured array and dict with numpy arrays
the return from :meth:`perform_experiments`.
classify : string, function or callable
either a string denoting the outcome of interest to
use or a function.
kwargs : valid keyword arguments for prim.Prim
Returns
-------
a Prim instance
Raises
------
PrimException
if data resulting from classify is not a 1-d array.
TypeError
if classify is not a string or a callable.
"""
if not incl_unc:
x = np.ma.array(results[0])
else:
drop_names = set(rf.get_names(results[0].dtype))-set(incl_unc)
x = rf.drop_fields(results[0], drop_names, asrecarray = True)
if type(classify)==StringType:
y = results[1][classify]
elif callable(classify):
y = classify(results[1])
else:
raise TypeError("unknown type for classify")
return Prim(x,y, **kwargs)
def setup_cart(results, classify, incl_unc=[], mass_min=0.05):
"""helper function for performing cart in combination with data
generated by the workbench.
Parameters
----------
results : tuple of structured array and dict with numpy arrays
the return from :meth:`perform_experiments`.
classify : string, function or callable
either a string denoting the outcome of interest to
use or a function.
incl_unc : list of strings
mass_min : float
Raises
------
TypeError
if classify is not a string or a callable.
"""
if not incl_unc:
x = np.ma.array(results[0])
else:
drop_names = set(recfunctions.get_names(
results[0].dtype)) - set(incl_unc)
x = recfunctions.drop_fields(results[0], drop_names, asrecarray=True)
if isinstance(classify, six.string_types):
y = results[1][classify]
mode = sdutil.REGRESSION
elif callable(classify):
y = classify(results[1])
mode = sdutil.BINARY
else:
raise TypeError("unknown type for classify")
return CART(x, y, mass_min, mode=mode)
def setup_cart(results, classify, incl_unc=[], mass_min=0.05):
"""helper function for performing cart in combination with data
generated by the workbench.
Parameters
----------
results : tuple of structured array and dict with numpy arrays
the return from :meth:`perform_experiments`.
classify : string, function or callable
either a string denoting the outcome of interest to
use or a function.
incl_unc : list of strings
mass_min : float
Raises
------
TypeError
if classify is not a string or a callable.
"""
if not incl_unc:
x = np.ma.array(results[0])
else:
drop_names = set(recfunctions.get_names(
results[0].dtype))-set(incl_unc)
x = recfunctions.drop_fields(results[0], drop_names, asrecarray=True)
if isinstance(classify, six.string_types):
y = results[1][classify]
mode = sdutil.REGRESSION
elif callable(classify):
y = classify(results[1])
mode = sdutil.BINARY
else:
raise TypeError("unknown type for classify")
return CART(x, y, mass_min, mode=mode)
def make_box(x):
'''
Make a box that encompasses all the data
Parameters
----------
x : structured numpy array
'''
# get the types in the order they appear in the numpy array
types = [(v[1], k, v[0].name) for k, v in six.iteritems(x.dtype.fields)]
types = sorted(types)
# convert any bool types to object to store set(False, True)
ntypes = [(k, 'object' if t == 'bool' else t) for (_, k, t) in types]
# create box limits
box = np.zeros((2, ), ntypes)
names = recfunctions.get_names(x.dtype)
for name in names:
dtype = box.dtype.fields.get(name)[0]
values = x[name]
if isinstance(values, np.ma.MaskedArray):
values = values.compressed()
if dtype == 'object':
try:
values = set(values)
box[name][:] = values
except TypeError as e:
logging.getLogger(__name__).warning("{} has unhashable values".format(name))
raise e
else:
box[name][0] = np.min(values, axis=0)
box[name][1] = np.max(values, axis=0)
return box
def get_univariate_feature_scores(x,y, score_func=F_CLASSIFICATION):
'''
calculate feature scores using univariate statistical tests. In case of
categorical data, chi square or the Anova F value is used. In case of
continuous data the Anova F value is used.
Parameters
----------
x : structured array
y : 1D nd.array
score_func : {F_CLASSIFICATION, F_REGRESSION, CHI2}
the score function to use, one of f_regression (regression), or
f_classification or chi2 (classification).
Returns
-------
pandas DataFrame
sorted in descending order of tuples with uncertainty and feature
scores (i.e. p values in this case).
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
pvalues = score_func(x, y)[1]
pvalues = np.asarray(pvalues)
pvalues = zip(uncs, pvalues)
pvalues = list(pvalues)
pvalues.sort(key=itemgetter(1))
pvalues = pd.DataFrame(pvalues)
return pvalues
def _rotate_subset(self, value, orig_experiments, logical):
'''
rotate a subset
Parameters
----------
value : list of str
orig_experiment : numpy structured array
logical : boolean array
'''
list_dtypes = [(name, "<f8") for name in value]
#cast everything to float
drop_names = set(rf.get_names(orig_experiments.dtype)) - set(value)
orig_subset = rf.drop_fields(orig_experiments,
drop_names,
asrecarray=True)
subset_experiments = orig_subset.astype(list_dtypes).view(
'<f8').reshape(orig_experiments.shape[0], len(value))
#normalize the data
mean = np.mean(subset_experiments, axis=0)
std = np.std(subset_experiments, axis=0)
std[std == 0] = 1 #in order to avoid a devision by zero
subset_experiments = (subset_experiments - mean) / std
#get the experiments of interest
experiments_of_interest = subset_experiments[logical]
#determine the rotation
rotation_matrix = self._determine_rotation(experiments_of_interest)
#apply the rotation
subset_experiments = np.dot(subset_experiments, rotation_matrix)
return rotation_matrix, subset_experiments
def __init__(self,
x,
y,
threshold=None,
threshold_type=">",
include=None,
exclude=None,
**kwargs):
"""Generates a decision tree for classification.
Parameters
----------
x : a matrix-like object (pandas.DataFrame, numpy.recarray, etc.)
the independent variables
y : a list-like object, the column name (str), or callable
the dependent variable either provided as a list-like object
classifying the data into cases of interest (e.g., False/True),
a list-like object storing the raw variable value (in which case
a threshold must be given), a string identifying the dependent
variable in x, or a function called on each row of x to compute the
dependent variable
threshold : float
threshold for identifying cases of interest
threshold_type : str
comparison operator used when identifying cases of interest
include : list of str
the names of variables included in the PRIM analysis
exclude : list of str
the names of variables excluded from the PRIM analysis
"""
super(Cart, self).__init__()
# Ensure the input x is a numpy matrix/array
if isinstance(x, pd.DataFrame):
x = x.to_records(index=False)
elif isinstance(x, np.ma.MaskedArray):
pass
else:
x = pd.DataFrame(x).to_records(index=False)
# if y is a string or function, compute the actual response value
# otherwise, ensure y is a numpy matrix/array
if isinstance(y, six.string_types):
key = y
y = x[key]
if exclude:
exclude = list(exclude) + [key]
else:
exclude = [key]
elif six.callable(y):
fun = y
y = np.apply_along_axis(fun, 0, x)
elif isinstance(y, pd.DataFrame) or isinstance(y, pd.Series):
y = y.values
elif isinstance(y, np.ma.MaskedArray):
pass
else:
y = np.asarray(y)
# convert include/exclude arguments to lists if they are strings
if include and isinstance(include, six.string_types):
include = [include]
if exclude and isinstance(exclude, six.string_types):
exclude = [exclude]
# include or exclude columns from the analysis
if include:
if isinstance(include, six.string_types):
include = [include]
drop_names = set(rf.get_names(x.dtype)) - set(include)
x = rf.drop_fields(x, drop_names, asrecarray=True)
if exclude:
if isinstance(exclude, six.string_types):
exclude = [exclude]
drop_names = set(exclude)
x = rf.drop_fields(x, drop_names, asrecarray=True)
# apply the threshold if
if threshold:
if six.callable(threshold):
y = np.apply_along_axis(threshold, 0, y)
else:
# The syntax for threshold_type is "x <op> <threshold>", e.g.,
# "x > 0.5". However, partial only supports positional
# arguments for built-in operators. Thus, we must assign the
# threshold to the first position and use a different operator.
# For example, "x > 0.5" must be evaluated as "0.5 < x".
OPERATORS = {
"<": operator.ge,
">": operator.le,
"<=": operator.gt,
">=": operator.lt,
"=": operator.eq
}
op = OPERATORS[threshold_type]
y = np.apply_along_axis(functools.partial(op, threshold), 0, y)
# validate inputs
if len(y.shape) > 1:
raise ValueError("y is not a 1-d array")
# extract feature names
feature_names = rf.get_names(x.dtype)
# ensure x is formatted as a 2D matrix
x = x.view("<f8").reshape(x.shape + (-1, ))
clf = tree.DecisionTreeClassifier(**kwargs)
clf = clf.fit(x, y)
# add our custom metadata to the classifier
self._feature_names = feature_names
self._x = x
self._y = y
self._clf = clf
def __init__(self,
x,
y,
threshold = None,
threshold_type = ">",
obj_func = lenient1,
peel_alpha = 0.05,
paste_alpha = 0.05,
mass_min = 0.05,
include = None,
exclude = None,
coi = None):
"""Creates a new PRIM object.
The PRIM object maintains the current state of the PRIM algorithm,
recording the PRIM boxes found thus far, the remaining (uncaptured)
cases of interest in the dataset, and provides methods for finding the
next PRIM box and viewing statistics.
Parameters
----------
x : a matrix-like object (pandas.DataFrame, numpy.recarray, etc.)
the independent variables
y : a list-like object, the column name (str), or callable
the dependent variable either provided as a list-like object
classifying the data into cases of interest (e.g., False/True),
a list-like object storing the raw variable value (in which case
a threshold must be given), a string identifying the dependent
variable in x, or a function called on each row of x to compute the
dependent variable
threshold : float
threshold for identifying cases of interest
threshold_type : str
comparison operator used whwen identifying cases of interest
obj_func : callable (default: lenient1)
a function that computes the objective function (peeling criteria)
peel_alpha : float (default: 0.05)
parameter controlling the peeling stage
paste_alpha : float (default: 0.05)
parameter controlling the pasting stage
mass_min : float (default: 0.05)
minimum mass of a box
include : list of str
the names of variables included in the PRIM analysis
exclude : list of str
the names of variables excluded from the PRIM analysis
coi : str or list of str
if y contains strings, coi identifies which string is the case of
interest
"""
# Ensure the input x is a numpy matrix/array
if isinstance(x, pd.DataFrame):
x = x.to_records(index=False)
elif isinstance(x, np.ma.MaskedArray):
pass
else:
x = pd.DataFrame(x).to_records(index=False)
# if y is a string or function, compute the actual response value
# otherwise, ensure y is a numpy matrix/array
if isinstance(y, six.string_types):
key = y
y = x[key]
if exclude:
exclude = list(exclude) + [key]
else:
exclude = [key]
elif six.callable(y):
fun = y
y = np.apply_along_axis(fun, 0, x)
elif isinstance(y, pd.DataFrame) or isinstance(y, pd.Series):
y = y.values
elif isinstance(y, np.ma.MaskedArray):
pass
else:
y = np.asarray(y)
# convert include/exclude arguments to lists if they are strings
if include and isinstance(include, six.string_types):
include = [include]
if exclude and isinstance(exclude, six.string_types):
exclude = [exclude]
# include or exclude columns from the analysis
if include:
if isinstance(include, six.string_types):
include = [include]
drop_names = set(rf.get_names(x.dtype))-set(include)
x = rf.drop_fields(x, drop_names, asrecarray=True)
if exclude:
if isinstance(exclude, six.string_types):
exclude = [exclude]
drop_names = set(exclude)
x = rf.drop_fields(x, drop_names, asrecarray=True)
# apply the threshold if
if threshold:
if six.callable(threshold):
y = np.apply_along_axis(threshold, 0, y)
else:
# The syntax for threshold_type is "x <op> <threshold>", e.g.,
# "x > 0.5". However, partial only supports positional
# arguments for built-in operators. Thus, we must assign the
# threshold to the first position and use a different operator.
# For example, "x > 0.5" must be evaluated as "0.5 < x".
OPERATORS = {"<=" : operator.ge,
">=" : operator.le,
"<" : operator.gt,
">" : operator.lt,
"=" : operator.eq}
op = OPERATORS[threshold_type]
y = np.apply_along_axis(functools.partial(op, threshold), 0, y)
# validate inputs
if len(y.shape) > 1:
raise PrimError("y is not a 1-d array")
unique_y = np.unique(y)
if unique_y.shape[0] > 2:
raise PrimError("y must contain only two values (0/1 or False/True)")
if ((unique_y.shape[0] == 2 and (False not in unique_y or True not in unique_y)) or
(False not in unique_y and True not in unique_y)):
if coi is None:
raise PrimError("y must contain only two values (0/1 or False/True)")
else:
if not hasattr(coi, "__iter__") and not isinstance(coi, six.string_types):
coi = [coi]
y = np.asarray([1 if yi in coi else 0 for yi in y])
# store the parameters
self.x = x
self.y = y
self.paste_alpha = paste_alpha
self.peel_alpha = peel_alpha
self.mass_min = mass_min
self.threshold = threshold
self.threshold_type = threshold_type
self.obj_func = obj_func
# set the indices
self.yi = np.arange(0, self.y.shape[0])
# how many data points do we have
self.n = self.y.shape[0]
# how many cases of interest do we have?
self.t_coi = self.determine_coi(self.yi)
# initial box that contains all data
self._box_init = make_box(self.x)
# make a list in which the identified boxes can be put
self._boxes = []
# set yi_remaining to all y values
self._update_yi_remaining()
def test_store_cases(self):
nr_experiments = 3
uncs = [RealParameter("a", 0, 1),
RealParameter("b", 0, 1),
CategoricalParameter('c', [0, 1, 2]),
IntegerParameter("d", 0, 1)]
outcomes = [TimeSeriesOutcome("test")]
constraints = []
case = {unc.name:random.random() for unc in uncs}
case["c"] = int(round(case["c"]*2))
case["d"] = int(round(case["d"]))
model = NamedObject('test')
policy = Policy('policy')
scenario = Scenario(**case)
experiment = Case(0, model.name, policy, scenario, 0)
callback = DefaultCallback(uncs, [],outcomes, constraints,
nr_experiments=nr_experiments,
reporting_interval=1)
model_outcomes = {outcomes[0].name: 1}
model_constraints = {}
callback(experiment, model_outcomes, model_constraints)
experiments, _ = callback.get_results()
design = case
design['policy'] = policy.name
design['model'] = model.name
design['scenario_id'] = scenario.name
names = rf.get_names(experiments.dtype)
for name in names:
entry_a = experiments[name][0]
entry_b = design[name]
self.assertEqual(entry_a, entry_b, "failed for "+name)
# with levers
nr_experiments = 3
uncs = [RealParameter("a", 0, 1),
RealParameter("b", 0, 1)]
levers = [RealParameter("c", 0, 1),
RealParameter("d", 0, 1)]
outcomes = [TimeSeriesOutcome("test")]
case = {unc.name:random.random() for unc in uncs}
model = NamedObject('test')
policy = Policy('policy', c=1, d=1)
scenario = Scenario(**case)
experiment = Case(0, model.name, policy, scenario, 0)
callback = DefaultCallback(uncs, levers,outcomes,constraints,
nr_experiments=nr_experiments,
reporting_interval=1)
model_outcomes = {outcomes[0].name: 1}
model_constraints = {}
callback(experiment, model_outcomes, model_constraints)
experiments, _ = callback.get_results()
design = case
design['c'] = 1
design['d'] = 1
design['policy'] = policy.name
design['model'] = model.name
design['scenario_id'] = scenario.name
names = rf.get_names(experiments.dtype)
for name in names:
self.assertEqual(experiments[name][0], design[name])
def get_ex_feature_scores(x, y, mode=CLASSIFICATION, nr_trees=250,
max_features='auto', max_depth=None,
min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0, max_leaf_nodes=None,
bootstrap=True, oob_score=True, random_state=None):
'''
Get feature scores using extra trees
Parameters
----------
x : structured array
y : 1D nd.array
mode : {CLASSIFICATION, REGRESSION}
nr_trees : int, optional
nr. of trees in forest (default=250)
max_features : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
max_depth : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
min_samples_split : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
min_samples_leaf : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
min_weight_fraction_leaf : float, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
max_leaf_nodes: int or None, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
bootstrap : bool, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
oob_score : bool, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
random_state : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
Returns
-------
pandas DataFrame
sorted in descending order of tuples with uncertainty and feature
scores
object
either ExtraTreesClassifier or ExtraTreesRegressor
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
if mode==CLASSIFICATION:
etc = ExtraTreesClassifier
criterion='gini'
elif mode==REGRESSION:
etc = ExtraTreesRegressor
criterion = 'mse'
else:
raise ValueError('{} not valid for mode'.format(mode))
extra_trees = etc(n_estimators=nr_trees,
criterion=criterion,
max_features=max_features,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_leaf_nodes=max_leaf_nodes,
bootstrap=bootstrap,
oob_score=oob_score,
random_state=random_state)
extra_trees.fit(x,y)
importances = extra_trees.feature_importances_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
importances = pd.DataFrame(importances)
return importances, extra_trees
def get_lasso_feature_scores(x, y, mode=CLASSIFICATION, scaling=0.5,
sample_fraction=0.75, n_resampling=200,
random_state=None):
'''
Calculate features scores using a randomized lasso (regression) or
randomized logistic regression (classification). This is also known as
stability selection.
see http://scikit-learn.org/stable/modules/feature_selection.html for
details.
Parameters
----------
x : structured array
y : 1D nd.array
mode : {CLASSIFICATION, REGRESSION}
scaling : float, optional
scaling parameter, should be between 0 and 1
sample_fraction : float, optional
the fraction of samples to used in each randomized
dataset
n_resmpling : int, optional
the number of times the model is trained on a random subset
of the data
random_state : int, optional
if it is an int, it specifies the seed to use, defaults to
None.
Returns
-------
pandas DataFrame
sorted in descending order of tuples with uncertainty and feature
scores
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
if mode==CLASSIFICATION:
lfs = RandomizedLogisticRegression(scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x,y)
elif mode==REGRESSION:
# we use LassoLarsCV to determine alpha see
# http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_recovery.html
lars_cv = LassoLarsCV(cv=6).fit(x, y,)
alphas = np.linspace(lars_cv.alphas_[0], .1 * lars_cv.alphas_[0], 6)
# fit the randomized lasso
lfs = RandomizedLasso(alpha=alphas,scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
else:
raise ValueError('{} invalid value for mode'.format(mode))
importances = lfs.scores_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
importances = pd.DataFrame(importances)
return importances
def test_store_cases(self):
nr_experiments = 3
uncs = [
RealParameter("a", 0, 1),
RealParameter("b", 0, 1),
CategoricalParameter('c', [0, 1, 2]),
IntegerParameter("d", 0, 1)
]
outcomes = [TimeSeriesOutcome("test")]
case = {unc.name: random.random() for unc in uncs}
case["c"] = int(round(case["c"] * 2))
case["d"] = int(round(case["d"]))
model = NamedObject('test')
policy = Policy('policy')
scenario = Scenario(**case)
experiment = Experiment(0, model, policy, scenario, 0)
callback = DefaultCallback(uncs, [],
outcomes,
nr_experiments=nr_experiments,
reporting_interval=1)
result = {outcomes[0].name: 1}
callback(experiment, result)
experiments, _ = callback.get_results()
design = case
design['policy'] = policy.name
design['model'] = model.name
names = rf.get_names(experiments.dtype)
for name in names:
self.assertEqual(experiments[name][0], design[name])
# with levers
nr_experiments = 3
uncs = [RealParameter("a", 0, 1), RealParameter("b", 0, 1)]
levers = [RealParameter("c", 0, 1), RealParameter("d", 0, 1)]
outcomes = [TimeSeriesOutcome("test")]
case = {unc.name: random.random() for unc in uncs}
model = NamedObject('test')
policy = Policy('policy', c=1, d=1)
scenario = Scenario(**case)
experiment = Experiment(0, model, policy, scenario, 0)
callback = DefaultCallback(uncs,
levers,
outcomes,
nr_experiments=nr_experiments,
reporting_interval=1)
result = {outcomes[0].name: 1}
callback(experiment, result)
experiments, _ = callback.get_results()
design = case
design['c'] = 1
design['d'] = 1
design['policy'] = policy.name
design['model'] = model.name
names = rf.get_names(experiments.dtype)
print(experiments[0])
for name in names:
self.assertEqual(experiments[name][0], design[name])
def get_lasso_feature_scores(x, y, mode=CLASSIFICATION, scaling=0.5,
sample_fraction=0.75, n_resampling=200,
random_state=None):
'''
Calculate features scores using a randomized lasso (regression) or
randomized logistic regression (classification). This is also known as
stability selection.
see http://scikit-learn.org/stable/modules/feature_selection.html for
details.
Parameters
----------
x : structured array
y : 1D nd.array
mode : {CLASSIFICATION, REGRESSION}
scaling : float, optional
scaling parameter, should be between 0 and 1
sample_fraction : float, optional
the fraction of samples to used in each randomized
dataset
n_resmpling : int, optional
the number of times the model is trained on a random subset
of the data
random_state : int, optional
if it is an int, it specifies the seed to use, defaults to
None.
Returns
-------
pandas DataFrame
sorted in descending order of tuples with uncertainty and feature
scores
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
if mode==CLASSIFICATION:
lfs = RandomizedLogisticRegression(scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x,y)
elif mode==REGRESSION:
# we use LassoLarsCV to determine alpha see
# http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_recovery.html
lars_cv = LassoLarsCV(cv=6).fit(x, y,)
alphas = np.linspace(lars_cv.alphas_[0], .1 * lars_cv.alphas_[0], 6)
# fit the randomized lasso
lfs = RandomizedLasso(alpha=alphas,scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
else:
raise ValueError('{} invalid value for mode'.format(mode))
importances = lfs.scores_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
importances = pd.DataFrame(importances)
return importances
def __init__(self,
x,
y,
threshold=None,
threshold_type=">",
obj_func=lenient1,
peel_alpha=0.05,
paste_alpha=0.05,
mass_min=0.05,
include=None,
exclude=None,
coi=None):
"""Creates a new PRIM object.
The PRIM object maintains the current state of the PRIM algorithm,
recording the PRIM boxes found thus far, the remaining (uncaptured)
cases of interest in the dataset, and provides methods for finding the
next PRIM box and viewing statistics.
Parameters
----------
x : a matrix-like object (pandas.DataFrame, numpy.recarray, etc.)
the independent variables
y : a list-like object, the column name (str), or callable
the dependent variable either provided as a list-like object
classifying the data into cases of interest (e.g., False/True),
a list-like object storing the raw variable value (in which case
a threshold must be given), a string identifying the dependent
variable in x, or a function called on each row of x to compute the
dependent variable
threshold : float
threshold for identifying cases of interest
threshold_type : str
comparison operator used whwen identifying cases of interest
obj_func : callable (default: lenient1)
a function that computes the objective function (peeling criteria)
peel_alpha : float (default: 0.05)
parameter controlling the peeling stage
paste_alpha : float (default: 0.05)
parameter controlling the pasting stage
mass_min : float (default: 0.05)
minimum mass of a box
include : list of str
the names of variables included in the PRIM analysis
exclude : list of str
the names of variables excluded from the PRIM analysis
coi : str or list of str
if y contains strings, coi identifies which string is the case of
interest
"""
# Ensure the input x is a numpy matrix/array
if isinstance(x, pd.DataFrame):
x = x.to_records(index=False)
elif isinstance(x, np.ma.MaskedArray):
pass
else:
x = pd.DataFrame(x).to_records(index=False)
# if y is a string or function, compute the actual response value
# otherwise, ensure y is a numpy matrix/array
if isinstance(y, six.string_types):
key = y
y = x[key]
if exclude:
exclude = list(exclude) + [key]
else:
exclude = [key]
elif six.callable(y):
fun = y
y = np.apply_along_axis(fun, 0, x)
elif isinstance(y, pd.DataFrame) or isinstance(y, pd.Series):
y = y.values
elif isinstance(y, np.ma.MaskedArray):
pass
else:
y = np.asarray(y)
# convert include/exclude arguments to lists if they are strings
if include and isinstance(include, six.string_types):
include = [include]
if exclude and isinstance(exclude, six.string_types):
exclude = [exclude]
# include or exclude columns from the analysis
if include:
if isinstance(include, six.string_types):
include = [include]
drop_names = set(rf.get_names(x.dtype)) - set(include)
x = rf.drop_fields(x, drop_names, asrecarray=True)
if exclude:
if isinstance(exclude, six.string_types):
exclude = [exclude]
drop_names = set(exclude)
x = rf.drop_fields(x, drop_names, asrecarray=True)
# apply the threshold if
if threshold:
if six.callable(threshold):
y = np.apply_along_axis(threshold, 0, y)
else:
# The syntax for threshold_type is "x <op> <threshold>", e.g.,
# "x > 0.5". However, partial only supports positional
# arguments for built-in operators. Thus, we must assign the
# threshold to the first position and use a different operator.
# For example, "x > 0.5" must be evaluated as "0.5 < x".
OPERATORS = {
"<=": operator.ge,
">=": operator.le,
"<": operator.gt,
">": operator.lt,
"=": operator.eq
}
op = OPERATORS[threshold_type]
y = np.apply_along_axis(functools.partial(op, threshold), 0, y)
# validate inputs
if len(y.shape) > 1:
raise PrimError("y is not a 1-d array")
unique_y = np.unique(y)
if unique_y.shape[0] > 2:
raise PrimError(
"y must contain only two values (0/1 or False/True)")
if ((unique_y.shape[0] == 2 and
(False not in unique_y or True not in unique_y))
or (False not in unique_y and True not in unique_y)):
if coi is None:
raise PrimError(
"y must contain only two values (0/1 or False/True)")
else:
if not hasattr(coi, "__iter__") and not isinstance(
coi, six.string_types):
coi = [coi]
y = np.asarray([1 if yi in coi else 0 for yi in y])
# store the parameters
self.x = x
self.y = y
self.paste_alpha = paste_alpha
self.peel_alpha = peel_alpha
self.mass_min = mass_min
self.threshold = threshold
self.threshold_type = threshold_type
self.obj_func = obj_func
# set the indices
self.yi = np.arange(0, self.y.shape[0])
# how many data points do we have
self.n = self.y.shape[0]
# how many cases of interest do we have?
self.t_coi = self.determine_coi(self.yi)
# initial box that contains all data
self._box_init = make_box(self.x)
# make a list in which the identified boxes can be put
self._boxes = []
# set yi_remaining to all y values
self._update_yi_remaining()
def get_rf_feature_scores(results,
classify,
nr_trees=250,
criterion='gini',
max_features='auto',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
bootstrap=True,
oob_score=True,
random_state=None):
'''
Get feature scores using a random forest
Parameters
----------
results : tuple
results tuple
classify : callable or str
a classify function or variable analogous to PRIM
nr_trees : int, optional
nr. of trees in forest (default=250)
criterion : str, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
max_features : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
max_depth : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
min_samples : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
min_samples_leaf : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
bootstrap : bool, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
oob_score : bool, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
random_state : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
Returns
-------
list of tuples
sorted in descending order of tuples with uncertainty and feature
scores
object
either RandomForestClassifier or RandomForestRegressor
'''
experiments, outcomes = results
uncs = recfunctions.get_names(experiments.dtype)
x = _prepare_experiments(experiments)
y, categorical = _prepare_outcomes(outcomes, classify)
if categorical:
rfc = RandomForestClassifier
else:
rfc = RandomForestRegressor
criterion = 'mse'
forest = rfc(n_estimators=nr_trees,
criterion=criterion,
max_features=max_features,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
bootstrap=bootstrap,
oob_score=oob_score,
random_state=random_state)
forest.fit(x, y)
importances = forest.feature_importances_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
return importances, forest
def get_lasso_feature_scores(results,
classify,
scaling=0.5,
sample_fraction=0.75,
n_resampling=200,
random_state=None):
'''
Calculate features scores using a randomized lasso (regression) or
randomized logistic regression (classification). This is also known as
stability selection.
see http://scikit-learn.org/stable/modules/feature_selection.html for
details.
Parameters
----------
results : tuple
classify : callable or str
a classify function or variable analogous to PRIM
scaling : float, optional
scaling parameter, should be between 0 and 1
sample_fraction : float, optional
the fraction of samples to used in each randomized
dataset
n_resmpling : int, optional
the number of times the model is trained on a random subset
of the data
random_state : int, optional
if it is an int, it specifies the seed to use, defaults to
None.
Returns
-------
list of tuples
sorted in descending order of tuples with uncertainty and feature
scores
'''
experiments, outcomes = results
uncs = recfunctions.get_names(experiments.dtype)
x = _prepare_experiments(experiments)
y, categorical = _prepare_outcomes(outcomes, classify)
if categorical:
lfs = RandomizedLogisticRegression(scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
else:
# we use LassoLarsCV to determine alpha see
# http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_recovery.html
lars_cv = LassoLarsCV(cv=6).fit(
x,
y,
)
alphas = np.linspace(lars_cv.alphas_[0], .1 * lars_cv.alphas_[0], 6)
# fit the randomized lasso
lfs = RandomizedLasso(alpha=alphas,
scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
importances = lfs.scores_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
return importances
def __init__(self,
x,
y,
threshold = None,
threshold_type = ">",
include = None,
exclude = None,
**kwargs):
"""Generates a decision tree for classification.
Parameters
----------
x : a matrix-like object (pandas.DataFrame, numpy.recarray, etc.)
the independent variables
y : a list-like object, the column name (str), or callable
the dependent variable either provided as a list-like object
classifying the data into cases of interest (e.g., False/True),
a list-like object storing the raw variable value (in which case
a threshold must be given), a string identifying the dependent
variable in x, or a function called on each row of x to compute the
dependent variable
threshold : float
threshold for identifying cases of interest
threshold_type : str
comparison operator used when identifying cases of interest
include : list of str
the names of variables included in the PRIM analysis
exclude : list of str
the names of variables excluded from the PRIM analysis
"""
super(Cart, self).__init__()
# Ensure the input x is a numpy matrix/array
if isinstance(x, pd.DataFrame):
x = x.to_records(index=False)
elif isinstance(x, np.ma.MaskedArray):
pass
else:
x = pd.DataFrame(x).to_records(index=False)
# if y is a string or function, compute the actual response value
# otherwise, ensure y is a numpy matrix/array
if isinstance(y, six.string_types):
key = y
y = x[key]
if exclude:
exclude = list(exclude) + [key]
else:
exclude = [key]
elif six.callable(y):
fun = y
y = np.apply_along_axis(fun, 0, x)
elif isinstance(y, pd.DataFrame) or isinstance(y, pd.Series):
y = y.values
elif isinstance(y, np.ma.MaskedArray):
pass
else:
y = np.asarray(y)
# convert include/exclude arguments to lists if they are strings
if include and isinstance(include, six.string_types):
include = [include]
if exclude and isinstance(exclude, six.string_types):
exclude = [exclude]
# include or exclude columns from the analysis
if include:
if isinstance(include, six.string_types):
include = [include]
drop_names = set(rf.get_names(x.dtype))-set(include)
x = rf.drop_fields(x, drop_names, asrecarray=True)
if exclude:
if isinstance(exclude, six.string_types):
exclude = [exclude]
drop_names = set(exclude)
x = rf.drop_fields(x, drop_names, asrecarray=True)
# apply the threshold if
if threshold:
if six.callable(threshold):
y = np.apply_along_axis(threshold, 0, y)
else:
# The syntax for threshold_type is "x <op> <threshold>", e.g.,
# "x > 0.5". However, partial only supports positional
# arguments for built-in operators. Thus, we must assign the
# threshold to the first position and use a different operator.
# For example, "x > 0.5" must be evaluated as "0.5 < x".
OPERATORS = {"<" : operator.ge,
">" : operator.le,
"<=" : operator.gt,
">=" : operator.lt,
"=" : operator.eq}
op = OPERATORS[threshold_type]
y = np.apply_along_axis(functools.partial(op, threshold), 0, y)
# validate inputs
if len(y.shape) > 1:
raise ValueError("y is not a 1-d array")
# extract feature names
feature_names = rf.get_names(x.dtype)
# ensure x is formatted as a 2D matrix
x = x.view("<f8").reshape(x.shape + (-1,))
clf = tree.DecisionTreeClassifier(**kwargs)
clf = clf.fit(x, y)
# add our custom metadata to the classifier
self._feature_names = feature_names
self._x = x
self._y = y
self._clf = clf
def __init__(self,
results,
classify,
obj_function=DEFAULT,
peel_alpha = 0.05,
paste_alpha = 0.05,
mass_min = 0.05,
threshold = None,
threshold_type=ABOVE,
incl_unc=[]):
'''
:param results: the return from :meth:`perform_experiments`.
:param classify: either a string denoting the outcome of interest to
use or a function.
:param peel_alpha: parameter controlling the peeling stage (default = 0.05).
:param paste_alpha: parameter controlling the pasting stage (default = 0.05).
:param mass_min: minimum mass of a box (default = 0.05).
:param threshold: the threshold of the output space that boxes should meet.
:param threshold_type: If 1, the boxes should go above the threshold, if -1
the boxes should go below the threshold, if 0, the
algorithm looks for both +1 and -1.
:param obj_func: The objective function to use. Default is
:func:`def_obj_func`
:param incl_unc: optional argument, should be a list of uncertainties
that are to be included in the prim analysis.
:raises: PrimException if data resulting from classify is not a
1-d array.
:raises: TypeError if classify is not a string or a callable.
'''
assert threshold!=None
if not incl_unc:
self.x = results[0]
else:
drop_names = set(recfunctions.get_names(results[0].dtype))-set(incl_unc)
self.x = recfunctions.drop_fields(results[0], drop_names, asrecarray = True)
if type(classify)==StringType:
self.y = results[1][classify]
elif callable(classify):
self.y = classify(results[1])
else:
raise TypeError("unknown type for classify")
if len(self.y.shape) > 1:
raise PrimException("y is not a 1-d array")
# store the remainder of the parameters
self.paste_alpha = paste_alpha
self.peel_alpha = peel_alpha
self.mass_min = mass_min
self.threshold = threshold
self.threshold_type = threshold_type
self.obj_func = self._obj_functions[obj_function]
# set the indices
self.yi = np.arange(0, self.y.shape[0])
# how many data points do we have
self.n = self.y.shape[0]
# how many cases of interest do we have?
self.t_coi = self.determine_coi(self.yi)
# initial box that contains all data
self.box_init = self.make_box(self.x)
# make a list in which the identified boxes can be put
self.boxes = []
self._update_yi_remaining()
def get_ex_feature_scores(x, y, mode=CLASSIFICATION, nr_trees=250,
max_features='auto', max_depth=None,
min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0, max_leaf_nodes=None,
bootstrap=True, oob_score=True, random_state=None):
'''
Get feature scores using extra trees
Parameters
----------
x : structured array
y : 1D nd.array
mode : {CLASSIFICATION, REGRESSION}
nr_trees : int, optional
nr. of trees in forest (default=250)
max_features : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
max_depth : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
min_samples_split : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
min_samples_leaf : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
min_weight_fraction_leaf : float, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
max_leaf_nodes: int or None, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
bootstrap : bool, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
oob_score : bool, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
random_state : int, optional
see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
Returns
-------
pandas DataFrame
sorted in descending order of tuples with uncertainty and feature
scores
object
either ExtraTreesClassifier or ExtraTreesRegressor
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
if mode==CLASSIFICATION:
etc = ExtraTreesClassifier
criterion='gini'
elif mode==REGRESSION:
etc = ExtraTreesRegressor
criterion = 'mse'
else:
raise ValueError('{} not valid for mode'.format(mode))
extra_trees = etc(n_estimators=nr_trees,
criterion=criterion,
max_features=max_features,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_leaf_nodes=max_leaf_nodes,
bootstrap=bootstrap,
oob_score=oob_score,
random_state=random_state)
extra_trees.fit(x,y)
importances = extra_trees.feature_importances_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
importances = pd.DataFrame(importances)
return importances, extra_trees
