def binary_report(predictions, col_true='CLASS'):
print('Binary classification results:')
y_true = (predictions[col_true] == 'QSO')
y_pred_proba = predictions['QSO_PHOTO']
y_pred_binary = (predictions['CLASS_PHOTO'] == 'QSO')
n_pos = y_pred_binary.sum()
n_all = len(y_pred_binary)
print('Predicted positives: {}/{} ({:.2f}%)'.format(
n_pos, n_all, n_pos / n_all * 100))
logloss, logloss_err = bootstrap_metric(log_loss, y_true, y_pred_proba)
print('Logloss = {:.4f} ({:.4f})'.format(logloss, logloss_err))
binary_metrics = OrderedDict([
('Accuracy', partial(bootstrap_metric, accuracy_score)),
('F1', partial(bootstrap_metric, f1_score)),
('Precision', partial(bootstrap_metric, precision_score)),
('Recall', partial(bootstrap_metric, recall_score)),
])
for metric_name, metric_func in binary_metrics.items():
score, score_err = metric_func(y_true, y_pred_binary)
print('{} = {:.4f} ({:.4f})'.format(metric_name, score, score_err))
# ROC AUC
fpr, tpr, _ = roc_curve(y_true, y_pred_proba)
roc_auc = auc(fpr, tpr)
print('ROC AUC = {:.4f}'.format(roc_auc))
plot_roc_curve(fpr, tpr, roc_auc)
# Precision - recall curve
average_precision = average_precision_score(y_true, y_pred_proba)
precision, recall = precision_score(y_true, y_pred_binary), recall_score(
y_true, y_pred_binary)
precisions, recalls, thresholds = precision_recall_curve(
y_true, y_pred_proba)
plot_precision_recall_curve(precisions, recalls, average_precision,
precision, recall)
def convert_to_layer_nodes(root):
"""
At each level in the SPN rooted in the 'root' node, model all the nodes
as a single layer-node.
Args:
root (Node): The root of the SPN graph.
Returns:
root (Node): The root of the SPN graph, with each layer modelled as a
single layer-node.
"""
parents = defaultdict(list)
depths = defaultdict(list)
node_to_depth = OrderedDict()
node_to_depth[root] = 1
def get_parents(node):
# Add to Parents dict
if node.is_op:
for i in node.inputs:
if (i and # Input not empty
not (i.is_param or i.is_var)):
parents[i.node].append(node)
node_to_depth[i.node] = node_to_depth[node] + 1
def permute_inputs(input_values, input_sizes):
# For a given list of inputs and their corresponding sizes, create a
# nested-list of (input, index) pairs.
# E.g: input_values = [(A, [2, 5]), (B, None)]
# input_sizes = [2, 3]
# inputs = [[('A', 2), ('A', 5)],
# [('B', 0), ('B', 1), ('B', 2)]]
inputs = [
list(product([inp.node], inp.indices)) if inp and inp.indices else
list(product([inp.node], list(range(inp_size))))
for inp, inp_size in zip(input_values, input_sizes)
]
# For a given nested-list of (input, index) pairs, permute over the inputs
# E.g: permuted_inputs = [('A', 2), ('B', 0),
# ('A', 2), ('B', 1),
# ('A', 2), ('B', 2),
# ('A', 5), ('B', 0),
# ('A', 5), ('B', 1),
# ('A', 5), ('B', 2)]
permuted_inputs = list(product(*[inps for inps in inputs]))
return list(chain(*permuted_inputs))
# Create a parents dictionary of the SPN graph
traverse_graph(root, fun=get_parents, skip_params=True)
# Create a depth dictionary of the SPN graph
for key, value in node_to_depth.items():
depths[value].append(key)
spn_depth = len(depths)
# Iterate through each depth of the SPN, starting from the deepest layer,
# moving up to the root node
for depth in range(spn_depth, 1, -1):
if isinstance(depths[depth][0], (Sum, ParallelSums)): # A Sums Layer
# Create a default SumsLayer node
with tf.name_scope("Layer%s" % depth):
sums_layer = SumsLayer(name="SumsLayer-%s.%s" % (depth, 1))
# Initialize a counter for keeping track of number of sums
# modelled in the layer node
layer_num_sums = 0
# Initialize an empty list for storing sum-input-sizes of sums
# modelled in the layer node
num_or_size_sums = []
# Iterate through each node at the current depth of the SPN
for node in depths[depth]:
# TODO: To be replaced with node.num_sums once AbstractSums
# class is introduced
# No. of sums modelled by the current node
node_num_sums = (1 if isinstance(node, Sum) else node.num_sums)
# Add Input values of the current node to the SumsLayer node
sums_layer.add_values(*node.values * node_num_sums)
# Add sum-input-size, of each sum modelled in the current node,
# to the list
num_or_size_sums += [sum(node.get_input_sizes()[2:])
] * node_num_sums
# Visit each parent of the current node
for parent in parents[node]:
try:
# 'Values' in case parent is an Op node
values = list(parent.values)
except AttributeError:
# 'Inputs' in case parent is a Concat node
values = list(parent.inputs)
# Iterate through each input value of the current parent node
for i, value in enumerate(values):
# If the value is the current node
if value.node == node:
# Check if it has indices
if value.indices is not None:
# If so, then just add the num-sums of the
# layer-op as offset
indices = (np.asarray(value.indices) +
layer_num_sums).tolist()
else:
# If not, then create a list accrodingly
indices = list(
range(layer_num_sums,
(layer_num_sums + node_num_sums)))
# Replace previous (node) Input value in the
# current parent node, with the new layer-node value
values[i] = (sums_layer, indices)
break # Once child-node found, don't have to search further
# Reset values of the current parent node, by including
# the new child (Layer-node)
try:
# set 'values' in case parent is an Op node
parent.set_values(*values)
except AttributeError:
# set 'inputs' in case parent is a Concat node
parent.set_inputs(*values)
# Increment num-sums-counter of the layer-node
layer_num_sums += node_num_sums
# Disconnect
node.disconnect_inputs()
# After all nodes at a certain depth are modelled into a Layer-node,
# set num-sums parameter accordingly
sums_layer.set_sum_sizes(num_or_size_sums)
elif isinstance(depths[depth][0],
(Product, PermuteProducts)): # A Products Layer
with tf.name_scope("Layer%s" % depth):
prods_layer = ProductsLayer(name="ProductsLayer-%s.%s" %
(depth, 1))
# Initialize a counter for keeping track of number of prods
# modelled in the layer node
layer_num_prods = 0
# Initialize an empty list for storing prod-input-sizes of prods
# modelled in the layer node
num_or_size_prods = []
# Iterate through each node at the current depth of the SPN
for node in depths[depth]:
# Get input values and sizes of the product node
input_values = list(node.values)
input_sizes = list(node.get_input_sizes())
if isinstance(node, PermuteProducts):
# Permute over input-values to model permuted products
input_values = permute_inputs(input_values, input_sizes)
node_num_prods = node.num_prods
prod_input_size = len(input_values) // node_num_prods
elif isinstance(node, Product):
node_num_prods = 1
prod_input_size = int(sum(input_sizes))
# Add Input values of the current node to the ProductsLayer node
prods_layer.add_values(*input_values)
# Add prod-input-size, of each product modelled in the current
# node, to the list
num_or_size_prods += [prod_input_size] * node_num_prods
# Visit each parent of the current node
for parent in parents[node]:
values = list(parent.values)
# Iterate through each input value of the current parent node
for i, value in enumerate(values):
# If the value is the current node
if value.node == node:
# Check if it has indices
if value.indices is not None:
# If so, then just add the num-prods of the
# layer-op as offset
indices = value.indices + layer_num_prods
else:
# If not, then create a list accrodingly
indices = list(
range(layer_num_prods,
(layer_num_prods + node_num_prods)))
# Replace previous (node) Input value in the
# current parent node, with the new layer-node value
values[i] = (prods_layer, indices)
# Reset values of the current parent node, by including
# the new child (Layer-node)
parent.set_values(*values)
# Increment num-prods-counter of the layer node
layer_num_prods += node_num_prods
# Disconnect
node.disconnect_inputs()
# After all nodes at a certain depth are modelled into a Layer-node,
# set num-prods parameter accordingly
prods_layer.set_prod_sizes(num_or_size_prods)
elif isinstance(depths[depth][0],
(SumsLayer, ProductsLayer, Concat)): # A Concat node
pass
else:
raise StructureError("Unknown node-type: {}".format(
depths[depth][0]))
return root
class _ProcedureWorker(Worker):
def __init__(self, cl_environment, compile_flags, cl_function, kernel_data,
double_precision, use_local_reduction):
super().__init__(cl_environment)
self._cl_function = cl_function
self._kernel_data = OrderedDict(sorted(kernel_data.items()))
self._double_precision = double_precision
self._use_local_reduction = use_local_reduction
self._mot_float_dtype = np.float32
if double_precision:
self._mot_float_dtype = np.float64
for data in self._kernel_data.values():
data.set_mot_float_dtype(self._mot_float_dtype)
self._kernel = self._build_kernel(self._get_kernel_source(),
compile_flags)
self._workgroup_size = self._kernel.run_procedure.get_work_group_info(
cl.kernel_work_group_info.PREFERRED_WORK_GROUP_SIZE_MULTIPLE,
self._cl_environment.device)
if not self._use_local_reduction:
self._workgroup_size = 1
self._kernel_inputs = {
name: data.get_kernel_inputs(self._cl_context,
self._workgroup_size)
for name, data in self._kernel_data.items()
}
def calculate(self, range_start, range_end):
nmr_problems = range_end - range_start
func = self._kernel.run_procedure
func.set_scalar_arg_dtypes(self.get_scalar_arg_dtypes())
kernel_inputs_list = []
for inputs in [
self._kernel_inputs[name] for name in self._kernel_data
]:
kernel_inputs_list.extend(inputs)
func(self._cl_queue, (int(nmr_problems * self._workgroup_size), ),
(int(self._workgroup_size), ),
*kernel_inputs_list,
global_offset=(int(range_start * self._workgroup_size), ))
for name, data in self._kernel_data.items():
data.enqueue_readouts(self._cl_queue, self._kernel_inputs[name],
range_start, range_end)
def _build_kernel(self, kernel_source, compile_flags=()):
"""Convenience function for building the kernel for this worker.
Args:
kernel_source (str): the kernel source to use for building the kernel
Returns:
cl.Program: a compiled CL kernel
"""
from mot import configuration
if configuration.should_ignore_kernel_compile_warnings():
warnings.simplefilter("ignore")
return cl.Program(self._cl_context,
kernel_source).build(' '.join(compile_flags))
def _get_kernel_source(self):
assignment = ''
if self._cl_function.get_return_type() != 'void':
assignment = '__results[gid] = '
variable_inits = []
function_call_inputs = []
post_function_callbacks = []
for parameter in self._cl_function.get_parameters():
data = self._kernel_data[parameter.name]
call_args = (parameter.name, '_' + parameter.name, 'gid',
parameter.data_type.address_space)
variable_inits.append(data.initialize_variable(*call_args))
function_call_inputs.append(
data.get_function_call_input(*call_args))
post_function_callbacks.append(
data.post_function_callback(*call_args))
kernel_source = ''
kernel_source += get_float_type_def(self._double_precision)
kernel_source += '\n'.join(data.get_type_definitions()
for data in self._kernel_data.values())
kernel_source += self._cl_function.get_cl_code()
kernel_source += '''
__kernel void run_procedure(''' + ",\n".join(self._get_kernel_arguments()) + '''){
ulong gid = (ulong)(get_global_id(0) / get_local_size(0));
''' + '\n'.join(variable_inits) + '''
''' + assignment + ' ' + self._cl_function.get_cl_function_name() + '(' + \
', '.join(function_call_inputs) + ''');
''' + '\n'.join(post_function_callbacks) + '''
}
'''
return kernel_source
def _get_kernel_arguments(self):
"""Get the list of kernel arguments for loading the kernel data elements into the kernel.
This will use the sorted keys for looping through the kernel input items.
Returns:
list of str: the list of parameter definitions
"""
declarations = []
for name, data in self._kernel_data.items():
declarations.extend(data.get_kernel_parameters('_' + name))
return declarations
def get_scalar_arg_dtypes(self):
"""Get the location and types of the input scalars.
Returns:
list: for every kernel input element either None if the data is a buffer or the numpy data type if
if is a scalar.
"""
dtypes = []
for name, data in self._kernel_data.items():
dtypes.extend(data.get_scalar_arg_dtypes())
return dtypes
def get_entitiesdata(self, datatype, since, sf):
now = datetime.now(pytz.UTC)
entities = []
end = datetime.now(
pytz.UTC) # we need to use UTC as salesforce API requires this
if since is None:
result = []
created_date_stmt = ""
while True:
query = "SELECT Id, CreatedDate FROM {} {} ORDER BY CreatedDate".format(
datatype, created_date_stmt)
records = sf.query(query)["records"]
temp_result = [x['Id'] for x in records]
result.extend(temp_result)
if records:
created_date_stmt = "WHERE CreatedDate > {}".format(
records[-1]['CreatedDate'])
if len(temp_result) < 2000: # salesforce limit 2000 rows
break
else:
start = iso8601.parse_date(since)
logging.info("Since datetime presented: %s", start)
logging.info("End -30 days delta: %s", (end - timedelta(days=30)))
if start < (end - timedelta(days=30)
): # salesforce replicates only last 30 days
logging.warning(
"Salesforce replicates only last 30 days but since is set to {}"
.format(start))
start = datetime.now(
pytz.UTC) - timedelta(days=30) + timedelta(seconds=60)
logging.warning("Changed since to {}".format(start))
if getattr(sf, datatype):
if end > (start + timedelta(seconds=60)):
result = getattr(sf, datatype).updated(start, end)["ids"]
deleted = getattr(sf,
datatype).deleted(start,
end)["deletedRecords"]
for e in deleted:
c = OrderedDict({"_id": e["id"]})
c.update({"_updated": "%s" % e["deletedDate"]})
c.update({"_deleted": True})
entities.append(c)
if result:
for e in result:
c = getattr(sf, datatype).get(e)
c.update({"_id": e})
c.update({"_updated": "%s" % c["LastModifiedDate"]})
for property, value in c.items():
schema = [
item for item in self._entities[datatype]
if item["name"] == property
]
if value and len(schema) > 0 and "type" in schema[
0] and schema[0]["type"] == "datetime":
c[property] = to_transit_datetime(parse(value))
entities.append(c)
return entities
