def test_utils(self):
data = np.array([0, 1, 2, 3, 4, 5]).reshape(1, -1).repeat(10, axis=0)
y, x = get_YX(data, 2)
self.assertEqual(y.shape[0], 10)
self.assertEqual(x.shape[0], 10)
self.assertEqual(y.shape[1], 4)
self.assertEqual(x.shape[1], 2)
self.assertTrue(np.all(y[0, :] == [0, 1, 2, 3]))
self.assertTrue(np.all(x[0, :] == [4, 5]))
y, x = get_YX(data, 1)
self.assertEqual(y.shape[0], 10)
self.assertEqual(x.shape[0], 10)
self.assertEqual(y.shape[1], 5)
self.assertEqual(x.shape[1], 1)
self.assertTrue(np.all(y[0, :] == [0, 1, 2, 3, 4]))
self.assertTrue(np.all(x[0, :] == [5]))
y, x = get_YX(data, 5)
self.assertEqual(y.shape[0], 10)
self.assertEqual(x.shape[0], 10)
self.assertEqual(y.shape[1], 1)
self.assertEqual(x.shape[1], 5)
self.assertTrue(np.all(y[0, :] == [0]))
self.assertTrue(np.all(x[0, :] == [1, 2, 3, 4, 5]))
def test_naive_factorization(self):
np.random.seed(17)
data = np.arange(0, 1000).reshape(-1, 8)
parent = Sum()
parent.children.append(None)
ctx = Context()
ctx.feature_size = 4
scope = [1, 3, 4, 6]
data2 = np.array(data)
result = naive_factorization(data=data2,
parent=parent,
pos=0,
context=ctx,
scope=list(scope))
self.assertListEqual(data.tolist(), data2.tolist())
self.assertEqual(parent.children[0], result[0][1]['parent'])
y, x = get_YX(data, 4)
self.assertEqual(len(result), len(scope))
for i, s in enumerate(scope):
r = result[i]
self.assertEqual(len(r), 2)
self.assertEqual(r[0], SplittingOperations.CREATE_LEAF_NODE)
self.assertEqual(type(r[1]['parent']), Product)
self.assertEqual(r[1]['pos'], i)
self.assertListEqual(r[1]['scope'], [s])
self.assertListEqual(r[1]['data'].tolist(),
concatenate_yx(y[:, i], x).tolist())
def getCIGroups(local_data, ds_context=None, scope=None, families=None):
"""
:param local_data: np array
:param scope: a list of index to output variables
:param alpha: threshold
:param families: obsolete
:return: np array of clustering
This function take tuple (output, conditional) as input and returns independent groups
alpha is the cutoff parameter for connected components
BE CAREFUL WITH SPARSE DATA!
"""
# data = preproc(local_data, ds_context, None, ohe)
y, x = get_YX(local_data, ds_context.feature_size)
pvals = testRcoT(y, x) + epsilon
pvals[pvals > alpha] = 0
clusters = np.zeros(y.shape[1])
for i, c in enumerate(connected_components(from_numpy_matrix(pvals))):
clusters[list(c)] = i + 1
return split_conditional_data_by_clusters(y,
x,
clusters,
scope,
rows=False)
def ExactMPE(spn,
data,
ds_context,
node_top_down_mpe=_node_top_down_mpe,
node_bottom_up_mpe_log=_node_bottom_up_mpe_log):
y, x = get_YX(data, ds_context.feature_size)
result = np.array(y)
nodes = get_nodes_by_type(spn)
lls_per_node = np.zeros((data.shape[0], len(nodes)))
# one pass bottom up evaluating the likelihoods
# for i in range(data.shape[0]):
# result[i, :] = exact_mpe_row(spn, y[i, :], x[i, :])
# log_likelihood(spn, data, dtype=data.dtype,lls_matrix=lls_per_node)
a = likelihood(spn, data, dtype=data.dtype, lls_matrix=lls_per_node)
# a = concatenate_yx(result,x)
# instance_ids = np.arange(data.shape[0])
#
# # one pass top down to decide on the max branch until it reaches a leaf, then it fills the nan slot with the mode
# eval_spn_top_down(spn,eval_functions=node_top_down_mpe, parent_result=instance_ids, data=data, lls_per_node=lls_per_node)
# return data
return a
def naive_factorization(data=None,
node_id=0,
context=None,
scope=None,
**kwargs):
assert scope is not None, "No scope"
prod_node = Product()
prod_node.scope = scope
prod_node.id = node_id
y, x = get_YX(data, context.feature_size)
result = []
for i, rv in enumerate(scope):
prod_node.children.append(None)
data_slice = concatenate_yx(y[:, i].reshape(-1, 1), x)
result.append((
SplittingOperations.CREATE_LEAF_NODE,
{
"data": data_slice,
"parent_id": prod_node.id,
"pos": len(prod_node.children) - 1,
"scope": [rv],
},
))
return prod_node, result
def predict(self, data, y=None):
# params = self.predict_params(data, y)
if self.parametric_type == Gaussian:
y, x = get_YX(data, feature_size=self.feature_size)
# return params
label = np.ones((len(y), 1))
return self.coefficients.forward(x, smudge=0)[0]
def split_conditional_rows_KMeans(local_data, ds_context, scope):
y, x = get_YX(local_data, ds_context.feature_size)
data = preproc(y, ds_context, pre_proc, ohe)
clusters = KMeans(n_clusters=n_clusters,
random_state=seed,
precompute_distances=True).fit_predict(data)
return split_data_by_clusters(local_data, clusters, scope, rows=True)
def test_create_conditional(self):
np.random.seed(17)
data = np.arange(0, 1000).reshape(-1, 8)
parent = Sum()
parent.children.append(None)
ctx = Context()
ctx.feature_size = 4
scope = [1, 3, 4, 6]
data2 = np.array(data)
K = int(data.shape[0] * 0.25)
split_idx = np.array([0] * K + [1] * (data.shape[0] - K))
np.random.shuffle(split_idx)
y, x = get_YX(data, 4)
def label_conditional(local_y, local_x):
self.assertListEqual(local_y.tolist(), y.tolist())
self.assertListEqual(local_x.tolist(), x.tolist())
return split_idx
result = create_conditional(data=data2,
parent=parent,
pos=0,
context=ctx,
scope=list(scope),
label_conditional=label_conditional)
self.assertListEqual(data.tolist(), data2.tolist())
self.assertEqual(len(result), 2)
for i, r in enumerate(result):
self.assertEqual(r[0], SplittingOperations.GET_NEXT_OP)
self.assertIn('data', r[1])
self.assertEqual(parent.children[0], r[1]['parent'])
self.assertEqual(r[1]['pos'], i)
self.assertListEqual(scope, r[1]['scope'])
self.assertEqual(r[1]['data'].shape[1], data.shape[1])
conditional_node = result[0][1]['parent']
child_idx = conditional_supervised_likelihood(
conditional_node,
[np.zeros((data.shape[0], 1)),
np.ones((data.shape[0], 1))], data)
self.assertListEqual(result[0][1]['data'].tolist(),
data[child_idx[:, 0] == 0, :].tolist())
self.assertListEqual(result[1][1]['data'].tolist(),
data[child_idx[:, 0] == 1, :].tolist())
def predict_proba(self, data):
y, X = get_YX(data, self.feature_size)
params = self.predict_params(X, y)
if self.parametric_type == Categorical:
result = np.zeros((data.shape[0], 1))
for j, c in enumerate(self.classes_):
idx = y == c
result[idx] = params[idx[:, 0], j]
return result
return self.proba_func(y, params)
def remove_non_informative_features(data=None,
node_id=0,
scope=None,
context=0,
uninformative_features_idx=None,
**kwargs):
assert uninformative_features_idx is not None, "parameter uninformative_features_idx can't be None"
prod_node = Product()
prod_node.scope = scope
prod_node.id = node_id
y, x = get_YX(data, context.feature_size)
non_zero_variance_rvs = []
non_zero_variance_idx = []
result = []
for idx, zero_var in enumerate(uninformative_features_idx):
rv = scope[idx]
if not zero_var:
non_zero_variance_rvs.append(rv)
non_zero_variance_idx.append(idx)
continue
prod_node.children.append(None)
data_slice = concatenate_yx(y[:, idx].reshape(-1, 1), x)
result.append((
SplittingOperations.CREATE_LEAF_NODE,
{
"data": data_slice,
"parent_id": prod_node.id,
"pos": len(prod_node.children) - 1,
"scope": [rv],
},
))
assert len(result) > 0
if len(non_zero_variance_idx) > 0:
prod_node.children.append(None)
result.append((
SplittingOperations.GET_NEXT_OP,
{
"data": concatenate_yx(data[:, non_zero_variance_idx], x),
"parent_id": prod_node.id,
"pos": len(prod_node.children) - 1,
"scope": non_zero_variance_rvs,
},
))
return prod_node, result
def create_conditional_slice(local_data, feature_size, scope,
label_conditional):
cluster_labels = label_conditional(*get_YX(local_data, feature_size))
unique_labels = np.unique(cluster_labels)
if len(unique_labels) == 1:
return None, None
assert len(unique_labels) == 2
features = get_X(local_data, feature_size)
assert features.shape[0] == cluster_labels.shape[0]
node = SupervisedOr(feature_size=feature_size)
node.scope.extend(scope)
# from sklearn.linear_model import LogisticRegression
#
# node.classifier = LogisticRegression(
# C=1,
# max_iter=10000,
# fit_intercept=True,
# tol=1e-15,
# class_weight="balanced",
# solver="lbfgs",
# )
#
# # if local_data.shape[0] < 1000:
# # idx = np.random.randint(low=0, high=local_data.shape[0], size=1000)
# # node.classifier.fit(features[idx], cluster_labels[idx])
# # else:
# node.classifier.fit(features, cluster_labels)
#
# slice_idx = node.classifier.predict(features)
#
# if len(np.unique(slice_idx)) == 1:
# return None, None
#
# data_slices = []
# idx = slice_idx == 0
# data_slices.append((local_data[idx, :], scope, np.sum(idx) / len(slice_idx)))
#
# idx = slice_idx == 1
# data_slices.append((local_data[idx, :], scope, np.sum(idx) / len(slice_idx)))
return node, data_slices
def learn_cspn_structure(train_data,
ds_context,
op_lambdas=_conditional_op_lambdas,
split_rows=None,
split_cols=None,
create_leaf=None,
**kwargs):
y, x = get_YX(train_data, ds_context.feature_size)
return learn_structure(train_data,
ds_context,
compress=False,
scope=list(range(y.shape[1])),
op_lambdas=op_lambdas,
split_rows=split_rows,
split_cols=split_cols,
create_leaf=create_leaf,
**kwargs)
def supervised_leaf_likelihood(node, data=None, dtype=np.float64):
assert len(node.scope) == 1, node.scope
y, x = get_YX(data, node.feature_size)
y = y[:, node.scope]
probs = np.ones((y.shape[0], 1), dtype=dtype)
marg_ids = np.isnan(y[:, 0])
if np.sum(~marg_ids) > 0:
observations_data = concatenate_yx(y[~marg_ids], x[~marg_ids])
probs[~marg_ids] = node.predictor.predict_proba(observations_data)
probs[np.isclose(probs, 0)] = 0.000000001
return probs
def test_remove_non_informative_features(self):
np.random.seed(17)
data = np.arange(0, 1000).reshape(-1, 8)
data[:, 1] = 1
data[:, 3] = 3
parent = Sum()
parent.children.append(None)
ctx = Context()
ctx.feature_size = 4
scope = [1, 3, 4, 6]
data2 = np.array(data)
y, x = get_YX(data, 4)
uninformative_features_idx = np.var(y, 0) == 0
result = remove_non_informative_features(
data=data2,
parent=parent,
pos=0,
context=ctx,
scope=list(scope),
uninformative_features_idx=uninformative_features_idx)
self.assertListEqual(data.tolist(), data2.tolist())
self.assertEqual(len(parent.children[0].children), len(result))
resulting_scopes = [[3], [6], [1, 4]]
resulting_data_y = [y[:, 1], y[:, 3], y[:, [0, 2]]]
for i, r in enumerate(result):
self.assertEqual(len(r), 2)
self.assertEqual(type(r[1]['parent']), Product)
self.assertEqual(parent.children[0], r[1]['parent'])
self.assertListEqual(r[1]['scope'], resulting_scopes[i])
self.assertEqual(r[1]['pos'], i)
self.assertListEqual(
r[1]['data'].tolist(),
concatenate_yx(resulting_data_y[i], x).tolist())
def create_conditional_leaf(data, context, scope):
assert len(
scope
) == 1, "scope of univariate parametric for more than one variable?"
feature_size = context.feature_size
parametric_type = context.parametric_types[scope[0]]
#
predictor = CSPNLinearModel(parametric_type=parametric_type,
feature_size=feature_size) ##modefy
node = SupervisedLeaf(scope=scope,
predictor=predictor,
parametric_type=parametric_type,
feature_size=feature_size)
# node = SupervisedLeaf(data=data,scope=scope, predictor=None, parametric_type=parametric_type, feature_size=feature_size)
y, X = get_YX(data, node.feature_size)
node.predictor.fit(X, y)
return node
def split_rows_Gower(local_data, ds_context, scope):
y, x = get_YX(local_data, ds_context.feature_size)
data = preproc(y, ds_context, pre_proc, False)
feature_types = []
for s in scope:
mt = ds_context.meta_types[s]
if mt == MetaType.BINARY:
feature_types.append("categorical")
elif mt == MetaType.DISCRETE:
feature_types.append("discrete")
else:
feature_types.append("continuous")
try:
df = robjects.r["as.data.frame"](data)
clusters = robjects.r["mixedclustering"](df, feature_types,
n_clusters, seed)
clusters = np.asarray(clusters)
except Exception as e:
np.savetxt("/tmp/errordata.txt", local_data)
raise e
return split_data_by_clusters(local_data, clusters, scope, rows=True)
def next_operation(
data=None,
parent_id=0,
parent_type=None,
pos=0,
scope=None,
no_clusters=False,
no_splitting=False,
no_independencies=True,
# is_first=False,
is_first=True,
cluster_first=True,
cluster_univariate=False,
# cluster_univariate=True,
min_features_slice=1,
min_splitting_instances=500,
min_clustering_instances=500,
context=None,
allow_sum_nodes=True,
allow_conditioning_nodes=True,
remove_uninformative_features=False,
**kwargs):
y, x = get_YX(data, context.feature_size)
isMinimalFeatures = y.shape[1] <= min_features_slice
isMinimalClusteringInstances = y.shape[0] <= min_clustering_instances
isMinimalSplittingInstances = y.shape[0] <= min_splitting_instances
result_params = {
"data": data,
"parent_id": parent_id,
"pos": pos,
"scope": scope,
"no_clusters": no_clusters,
"no_independencies": no_independencies,
}
uninformative_features = np.var(y, 0) == 0
if remove_uninformative_features and np.any(uninformative_features):
result_op = SplittingOperations.REMOVE_UNINFORMATIVE_FEATURES
result_params["uninformative_features_idx"] = uninformative_features
return None, [(result_op, result_params)]
#
# if not isMinimalSplittingInstances and not no_splitting:
# #split as much as you can
# result_op = SplittingOperations.CREATE_CONDITIONAL_NODE
# # result_op = SplittingOperations.CREATE_SUM_NODE
# return None, [(result_op, result_params)]
if isMinimalFeatures:
if isMinimalClusteringInstances or no_clusters:
return None, [(SplittingOperations.CREATE_LEAF_NODE, result_params)
]
else:
if cluster_univariate:
return None, [(SplittingOperations.CREATE_SUM_NODE,
result_params)]
else:
return None, [(SplittingOperations.CREATE_LEAF_NODE,
result_params)]
if isMinimalClusteringInstances or (no_clusters and no_independencies):
return None, [(SplittingOperations.NAIVE_FACTORIZATION, result_params)]
if no_independencies:
return None, [(SplittingOperations.CREATE_SUM_NODE, result_params)]
if no_clusters:
return None, [(SplittingOperations.CREATE_PRODUCT_NODE, result_params)]
if is_first:
if cluster_first:
return None, [(SplittingOperations.CREATE_SUM_NODE, result_params)]
else:
return None, [(SplittingOperations.CREATE_PRODUCT_NODE,
result_params)]
return None, [(SplittingOperations.CREATE_PRODUCT_NODE, result_params)]
