def one_branch(children, input_data):
"""Check if there's only one branch to be followed
"""
missing = split(children) in input_data
return (missing or missing_branch(children)
or none_value(children))
def predict_proportional(self, input_data, path=None):
"""Makes a prediction based on a number of field values averaging
the predictions of the leaves that fall in a subtree.
Each time a splitting field has no value assigned, we consider
both branches of the split to be true, merging their
predictions.
"""
if path is None:
path = []
final_distribution = {}
if not self.children:
return merge_distributions({}, dict((x[0], x[1])
for x in self.distribution))
if split(self.children) in input_data:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
new_rule = child.predicate.to_rule(self.fields)
if not new_rule in path:
path.append(new_rule)
return child.predict_proportional(input_data, path)
else:
for child in self.children:
final_distribution = merge_distributions(
final_distribution,
child.predict_proportional(input_data, path))
return final_distribution
def predict_proportional(self, input_data, path=None):
"""Makes a prediction based on a number of field values averaging
the predictions of the leaves that fall in a subtree.
Each time a splitting field has no value assigned, we consider
both branches of the split to be true, merging their
predictions.
"""
if path is None:
path = []
final_distribution = {}
if not self.children:
return merge_distributions({},
dict((x[0], x[1])
for x in self.distribution))
if split(self.children) in input_data:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
new_rule = child.predicate.to_rule(self.fields)
if not new_rule in path:
path.append(new_rule)
return child.predict_proportional(input_data, path)
else:
for child in self.children:
final_distribution = merge_distributions(
final_distribution,
child.predict_proportional(input_data, path))
return final_distribution
def one_branch(children, input_data):
"""Check if there's only one branch to be followed
"""
missing = split(children) in input_data
return (missing or missing_branch(children)
or none_value(children))
def predict(self, input_data, path=None):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id.
"""
def get_instances(distribution):
"""Returns the total number of instances in a distribution
"""
return sum(x[1] for x in distribution) if distribution else 0
if path is None:
path = []
if self.children and split(self.children) in input_data:
for child in self.children:
if apply(OPERATOR[child.predicate.operator],
[input_data[child.predicate.field],
child.predicate.value]):
path.append(u"%s %s %s" % (
self.fields[child.predicate.field]['name'],
child.predicate.operator,
child.predicate.value))
return child.predict(input_data, path)
return (self.output, path, self.confidence,
self.distribution, get_instances(self.distribution))
def python_body(self, depth=1, cmv=False):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. If `cmv` (control missing
values) is set to True then as soon as a value is missing to
evaluate a predicate the output at that node is returned without
further evaluation.
"""
body = ""
if self.children:
if cmv:
field = split(self.children)
body += ("%sif (%s is None):\n " %
(INDENT * depth,
self.fields[field]['slug']))
if self.fields[self.objective_field]['optype'] == 'numeric':
body += ("%s return %s\n" %
(INDENT * (depth + 1),
self.output))
else:
body += ("%s return '%s'\n" %
(INDENT * (depth + 1),
self.output))
for child in self.children:
body += ("%sif (%s %s %s):\n" %
(INDENT * depth,
self.fields[child.predicate.field]['slug'],
PYTHON_OPERATOR[child.predicate.operator],
`child.predicate.value`))
body += child.python_body(depth + 1)
def predict(self, input_data, path=None):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id.
"""
def get_instances(distribution):
"""Returns the total number of instances in a distribution
"""
return sum(x[1] for x in distribution) if distribution else 0
if path is None:
path = []
if self.children and split(self.children) in input_data:
for child in self.children:
if apply(OPERATOR[child.predicate.operator],
[input_data[child.predicate.field],
child.predicate.value]):
path.append(u"%s %s %s" % (
self.fields[child.predicate.field]['name'],
child.predicate.operator,
child.predicate.value))
return child.predict(input_data, path)
return (self.output, path, self.confidence,
self.distribution, get_instances(self.distribution))
def tableau_body(self, body=u"", conditions=None, cmv=None,
ids_path=None, subtree=True):
"""Translate the model into a set of "if" statements in Tableau syntax
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
if cmv is None:
cmv = []
if conditions is None:
conditions = []
alternate = u"IF"
else:
alternate = u"ELSEIF"
children = filter_nodes(self.children, ids=ids_path,
subtree=subtree)
if children:
field = split(children)
if not self.fields[field]['name'] in cmv:
conditions.append("ISNULL([%s])" % self.fields[field]['name'])
body += (u"%s %s THEN " %
(alternate, " AND ".join(conditions)))
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (u"%s\n" % value)
cmv.append(self.fields[field]['name'])
alternate = u"ELSEIF"
del conditions[-1]
for child in children:
optype = self.fields[child.predicate.field]['optype']
if optype == 'text':
return u""
if (optype == 'numeric'):
value = child.predicate.value
else:
value = repr(child.predicate.value)
conditions.append("[%s]%s%s" % (
self.fields[child.predicate.field]['name'],
PYTHON_OPERATOR[child.predicate.operator],
value))
body = child.tableau_body(body, conditions[:], cmv=cmv[:],
ids_path=ids_path, subtree=subtree)
del conditions[-1]
else:
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (
u"%s %s THEN" % (alternate, " AND ".join(conditions)))
body += u" %s\n" % value
return body
def predict_proportional(self, input_data, path=None,
missing_found=False, median=False, parent=None):
"""Makes a prediction based on a number of field values averaging
the predictions of the leaves that fall in a subtree.
Each time a splitting field has no value assigned, we consider
both branches of the split to be true, merging their
predictions. The function returns the merged distribution and the
last node reached by a unique path.
"""
if path is None:
path = []
final_distribution = {}
if not self.children:
distribution = self.distribution if not self.weighted else \
self.weighted_distribution
return (merge_distributions({}, dict((x[0], x[1])
for x in distribution)),
self.min, self.max, self, self.count, parent)
if one_branch(self.children, input_data) or \
self.fields[split(self.children)]["optype"] in \
["text", "items"]:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
new_rule = child.predicate.to_rule(self.fields)
if new_rule not in path and not missing_found:
path.append(new_rule)
return child.predict_proportional(input_data, path,
missing_found, median,
parent=self)
else:
# missing value found, the unique path stops
missing_found = True
minimums = []
maximums = []
population = 0
for child in self.children:
(subtree_distribution, subtree_min,
subtree_max, _, subtree_pop, _) = \
child.predict_proportional(input_data, path,
missing_found, median,
parent=self)
if subtree_min is not None:
minimums.append(subtree_min)
if subtree_max is not None:
maximums.append(subtree_max)
population += subtree_pop
final_distribution = merge_distributions(
final_distribution, subtree_distribution)
return (final_distribution,
min(minimums) if minimums else None,
max(maximums) if maximums else None, self, population,
self)
def python_body(self, depth=1, cmv=None, input_map=False):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
def map_data(field, missing=False):
"""Returns the subject of the condition in map format when
more than MAX_ARGS_LENGTH arguments are used.
"""
if input_map:
if missing:
return "not '%s' in data or data['%s']" % (field, field)
else:
return "data['%s']" % field
return field
if cmv is None:
cmv = []
body = u""
if self.children:
field = split(self.children)
if not self.fields[field]['slug'] in cmv:
body += (u"%sif (%s is None):\n" %
(INDENT * depth,
map_data(self.fields[field]['slug'], True)))
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body += (u"%sreturn %s\n" %
(INDENT * (depth + 1),
value))
cmv.append(self.fields[field]['slug'])
for child in self.children:
if self.fields[child.predicate.field]['optype'] == 'numeric':
value = child.predicate.value
else:
value = repr(child.predicate.value)
body += (u"%sif (%s %s %s):\n" %
(INDENT * depth,
map_data(self.fields[child.predicate.field]['slug'],
False),
PYTHON_OPERATOR[child.predicate.operator],
value))
body += child.python_body(depth + 1, cmv=cmv[:],
input_map=input_map)
else:
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body = u"%sreturn %s\n" % (INDENT * depth, value)
return body
def predict_proportional(self, input_data, path=None,
missing_found=False, median=False, parent=None):
"""Makes a prediction based on a number of field values averaging
the predictions of the leaves that fall in a subtree.
Each time a splitting field has no value assigned, we consider
both branches of the split to be true, merging their
predictions. The function returns the merged distribution and the
last node reached by a unique path.
"""
if path is None:
path = []
final_distribution = {}
if not self.children:
distribution = self.distribution if not self.weighted else \
self.weighted_distribution
return (merge_distributions({}, dict((x[0], x[1])
for x in distribution)),
self.min, self.max, self, self.count, parent)
if one_branch(self.children, input_data) or \
self.fields[split(self.children)]["optype"] in \
["text", "items"]:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
new_rule = child.predicate.to_rule(self.fields)
if new_rule not in path and not missing_found:
path.append(new_rule)
return child.predict_proportional(input_data, path,
missing_found, median,
parent=self)
else:
# missing value found, the unique path stops
missing_found = True
minimums = []
maximums = []
population = 0
for child in self.children:
(subtree_distribution, subtree_min,
subtree_max, _, subtree_pop, _) = \
child.predict_proportional(input_data, path,
missing_found, median,
parent=self)
if subtree_min is not None:
minimums.append(subtree_min)
if subtree_max is not None:
maximums.append(subtree_max)
population += subtree_pop
final_distribution = merge_distributions(
final_distribution, subtree_distribution)
return (final_distribution,
min(minimums) if minimums else None,
max(maximums) if maximums else None, self, population,
self)
def python_body(self, depth=1, cmv=None, input_map=False):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
def map_data(field, missing=False):
"""Returns the subject of the condition in map format when
more than MAX_ARGS_LENGTH arguments are used.
"""
if input_map:
if missing:
return "not '%s' in data or data['%s']" % (field, field)
else:
return "data['%s']" % field
return field
if cmv is None:
cmv = []
body = u""
if self.children:
field = split(self.children)
if not self.fields[field]['slug'] in cmv:
body += (u"%sif (%s is None):\n" %
(INDENT * depth,
map_data(self.fields[field]['slug'], True)))
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body += (u"%sreturn %s\n" %
(INDENT * (depth + 1),
value))
cmv.append(self.fields[field]['slug'])
for child in self.children:
if self.fields[child.predicate.field]['optype'] == 'numeric':
value = child.predicate.value
else:
value = repr(child.predicate.value)
body += (u"%sif (%s %s %s):\n" %
(INDENT * depth,
map_data(self.fields[child.predicate.field]['slug'],
False),
PYTHON_OPERATOR[child.predicate.operator],
value))
body += child.python_body(depth + 1, cmv=cmv[:],
input_map=input_map)
else:
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body = u"%sreturn %s\n" % (INDENT * depth, value)
return body
def predict(self, input_data, path=None, missing_strategy=LAST_PREDICTION):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id. There are two possible
strategies to predict when the value for the splitting field
is missing:
0 - LAST_PREDICTION: the last issued prediction is returned.
1 - PROPORTIONAL: as we cannot choose between the two branches
in the tree that stem from this split, we consider both. The
algorithm goes on until the final leaves are reached and
all their predictions are used to decide the final prediction.
"""
if path is None:
path = []
if missing_strategy == PROPORTIONAL:
final_distribution = self.predict_proportional(input_data,
path=path)
if self.regression:
# sort elements by their mean
distribution = [
list(element)
for element in sorted(final_distribution.items(),
key=lambda x: x[0])
]
distribution = merge_bins(distribution, BINS_LIMIT)
prediction = mean(distribution)
total_instances = sum(
[instances for _, instances in distribution])
confidence = regression_error(
unbiased_sample_variance(distribution, prediction),
total_instances)
return (prediction, path, confidence, distribution,
total_instances)
else:
distribution = [
list(element)
for element in sorted(final_distribution.items(),
key=lambda x: (-x[1], x[0]))
]
return (distribution[0][0], path,
ws_confidence(distribution[0][0],
final_distribution), distribution,
get_instances(distribution))
else:
if self.children and split(self.children) in input_data:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
path.append(child.predicate.to_rule(self.fields))
return child.predict(input_data, path)
return (self.output, path, self.confidence, self.distribution,
get_instances(self.distribution))
def predict(self, input_data, path=None, missing_strategy=LAST_PREDICTION):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id. There are two possible
strategies to predict when the value for the splitting field
is missing:
0 - LAST_PREDICTION: the last issued prediction is returned.
1 - PROPORTIONAL: as we cannot choose between the two branches
in the tree that stem from this split, we consider both. The
algorithm goes on until the final leaves are reached and
all their predictions are used to decide the final prediction.
"""
if path is None:
path = []
if missing_strategy == PROPORTIONAL:
final_distribution = self.predict_proportional(input_data,
path=path)
if self.regression:
# sort elements by their mean
distribution = [list(element) for element in
sorted(final_distribution.items(),
key=lambda x: x[0])]
distribution = merge_bins(distribution, BINS_LIMIT)
prediction = mean(distribution)
total_instances = sum([instances
for _, instances in distribution])
confidence = regression_error(
unbiased_sample_variance(distribution, prediction),
total_instances)
return (prediction, path, confidence,
distribution, total_instances)
else:
distribution = [list(element) for element in
sorted(final_distribution.items(),
key=lambda x: (-x[1], x[0]))]
return (distribution[0][0], path,
ws_confidence(distribution[0][0], final_distribution),
distribution, get_instances(distribution))
else:
if self.children and split(self.children) in input_data:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
path.append(child.predicate.to_rule(self.fields))
return child.predict(input_data, path)
return (self.output, path, self.confidence,
self.distribution, get_instances(self.distribution))
def predict(self, input_data, path=[]):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id.
"""
if self.children and split(self.children) in input_data:
for child in self.children:
if apply(OPERATOR[child.predicate.operator],
[input_data[child.predicate.field],
child.predicate.value]):
path.append("%s %s %s" % (
self.fields[child.predicate.field]['name'],
child.predicate.operator,
child.predicate.value))
return child.predict(input_data, path)
else:
return self.output, path
def predict_proportional(self, input_data, path=None,
missing_found=False):
"""Makes a prediction based on a number of field values considering all
the predictions of the leaves that fall in a subtree.
Each time a splitting field has no value assigned, we consider
both branches of the split to be true, merging their
predictions. The function returns the merged distribution and the
last node reached by a unique path.
"""
if path is None:
path = []
if not self.children:
return (self.g_sum, self.h_sum, self.count, path)
if one_branch(self.children, input_data) or \
self.fields[split(self.children)]["optype"] in \
["text", "items"]:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
new_rule = child.predicate.to_rule(self.fields)
if new_rule not in path and not missing_found:
path.append(new_rule)
return child.predict_proportional(input_data, path,
missing_found)
else:
# missing value found, the unique path stops
missing_found = True
g_sums = 0.0
h_sums = 0.0
population = 0
for child in self.children:
g_sum, h_sum, count, _ = \
child.predict_proportional(input_data, path,
missing_found)
g_sums += g_sum
h_sums += h_sum
population += count
return (g_sums, h_sums, population, path)
def predict_proportional(self, input_data, path=None,
missing_found=False):
"""Makes a prediction based on a number of field values considering all
the predictions of the leaves that fall in a subtree.
Each time a splitting field has no value assigned, we consider
both branches of the split to be true, merging their
predictions. The function returns the merged distribution and the
last node reached by a unique path.
"""
if path is None:
path = []
if not self.children:
return (self.g_sum, self.h_sum, self.count, path)
if one_branch(self.children, input_data) or \
self.fields[split(self.children)]["optype"] in \
["text", "items"]:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
new_rule = child.predicate.to_rule(self.fields)
if new_rule not in path and not missing_found:
path.append(new_rule)
return child.predict_proportional(input_data, path,
missing_found)
else:
# missing value found, the unique path stops
missing_found = True
g_sums = 0.0
h_sums = 0.0
population = 0
for child in self.children:
g_sum, h_sum, count, _ = \
child.predict_proportional(input_data, path,
missing_found)
g_sums += g_sum
h_sums += h_sum
population += count
return (g_sums, h_sums, population, path)
def predict(self, input_data, path=None):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id.
"""
def get_instances(distribution):
"""Returns the total number of instances in a distribution
"""
return sum(x[1] for x in distribution) if distribution else 0
if path is None:
path = []
if self.children and split(self.children) in input_data:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
path.append(child.predicate.to_rule(self.fields))
return child.predict(input_data, path)
return (self.output, path, self.confidence,
self.distribution, get_instances(self.distribution))
def predict(self, input_data, path=None):
"""Makes a prediction based on a number of field values.
The input fields must be keyed by Id.
"""
def get_instances(distribution):
"""Returns the total number of instances in a distribution
"""
return sum(x[1] for x in distribution) if distribution else 0
if path is None:
path = []
if self.children and split(self.children) in input_data:
for child in self.children:
if child.predicate.apply(input_data, self.fields):
path.append(child.predicate.to_rule(self.fields))
return child.predict(input_data, path)
return (self.output, path, self.confidence, self.distribution,
get_instances(self.distribution))
def python_body(self,
depth=1,
cmv=None,
input_map=False,
ids_path=None,
subtree=True):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
def map_data(field, missing=False):
"""Returns the subject of the condition in map format when
more than MAX_ARGS_LENGTH arguments are used.
"""
if input_map:
if missing:
return "data.get('%s')" % field
else:
return "data['%s']" % field
return field
if cmv is None:
cmv = []
body = u""
term_analysis_fields = []
children = filter_nodes(self.children, ids=ids_path, subtree=subtree)
if children:
field = split(children)
has_missing_branch = (missing_branch(children)
or none_value(children))
# the missing is singled out as a special case only when there's
# no missing branch in the children list
if (not has_missing_branch
and not self.fields[field]['slug'] in cmv):
body += (u"%sif (%s is None):\n" %
(INDENT * depth,
map_data(self.fields[field]['slug'], True)))
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body += (u"%sreturn %s\n" % (INDENT * (depth + 1), value))
cmv.append(self.fields[field]['slug'])
for child in children:
field = child.predicate.field
pre_condition = u""
if has_missing_branch and child.predicate.value is not None:
negation = u"" if child.predicate.missing else u" not"
connection = u"or" if child.predicate.missing else u"and"
pre_condition = (u"%s is%s None %s " %
(map_data(self.fields[field]['slug'],
True), negation, connection))
if not child.predicate.missing:
cmv.append(self.fields[field]['slug'])
optype = self.fields[field]['optype']
if (optype == 'numeric' or optype == 'text'
or child.predicate.value is None):
value = child.predicate.value
else:
value = repr(child.predicate.value)
if optype == 'text':
body += (
u"%sif (%sterm_matches(%s, \"%s\", %s\"%s\") %s %s):"
u"\n" %
(INDENT * depth, pre_condition,
map_data(self.fields[field]['slug'],
False), self.fields[field]['slug'],
('u' if isinstance(child.predicate.term, unicode) else
''), child.predicate.term.replace("\"", "\\\""),
PYTHON_OPERATOR[child.predicate.operator], value))
term_analysis_fields.append((field, child.predicate.term))
else:
operator = (MISSING_OPERATOR[child.predicate.operator]
if child.predicate.value is None else
PYTHON_OPERATOR[child.predicate.operator])
if child.predicate.value is None:
cmv.append(self.fields[field]['slug'])
body += (u"%sif (%s%s %s %s):\n" %
(INDENT * depth, pre_condition,
map_data(self.fields[field]['slug'],
False), operator, value))
next_level = child.python_body(depth + 1,
cmv=cmv[:],
input_map=input_map,
ids_path=ids_path,
subtree=subtree)
body += next_level[0]
term_analysis_fields.extend(next_level[1])
else:
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body = u"%sreturn %s\n" % (INDENT * depth, value)
return body, term_analysis_fields
def tableau_body(self,
body=u"",
conditions=None,
cmv=None,
ids_path=None,
subtree=True):
"""Translate the model into a set of "if" statements in Tableau syntax
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
if cmv is None:
cmv = []
if body:
alternate = u"ELSEIF"
else:
if conditions is None:
conditions = []
alternate = u"IF"
children = filter_nodes(self.children, ids=ids_path, subtree=subtree)
if children:
field = split(children)
has_missing_branch = (missing_branch(children)
or none_value(children))
# the missing is singled out as a special case only when there's
# no missing branch in the children list
if (not has_missing_branch
and not self.fields[field]['name'] in cmv):
conditions.append("ISNULL([%s])" % self.fields[field]['name'])
body += (u"%s %s THEN " %
(alternate, " AND ".join(conditions)))
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (u"%s\n" % value)
cmv.append(self.fields[field]['name'])
alternate = u"ELSEIF"
del conditions[-1]
for child in children:
pre_condition = u""
post_condition = u""
if has_missing_branch and child.predicate.value is not None:
negation = u"" if child.predicate.missing else u"NOT "
connection = u"OR" if child.predicate.missing else u"AND"
pre_condition = (
u"(%sISNULL([%s]) %s " %
(negation, self.fields[field]['name'], connection))
if not child.predicate.missing:
cmv.append(self.fields[field]['name'])
post_condition = u")"
optype = self.fields[child.predicate.field]['optype']
if child.predicate.value is None:
value = ""
elif optype == 'text':
return u""
elif optype == 'numeric':
value = child.predicate.value
else:
value = repr(child.predicate.value)
operator = ("" if child.predicate.value is None else
PYTHON_OPERATOR[child.predicate.operator])
if child.predicate.value is None:
pre_condition = (
T_MISSING_OPERATOR[child.predicate.operator])
post_condition = ")"
conditions.append(
"%s[%s]%s%s%s" %
(pre_condition, self.fields[child.predicate.field]['name'],
operator, value, post_condition))
body = child.tableau_body(body,
conditions[:],
cmv=cmv[:],
ids_path=ids_path,
subtree=subtree)
del conditions[-1]
else:
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (u"%s %s THEN" % (alternate, " AND ".join(conditions)))
body += u" %s\n" % value
return body
def tableau_body(self, body=u"", conditions=None, cmv=None,
ids_path=None, subtree=True):
"""Translate the model into a set of "if" statements in Tableau syntax
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
if cmv is None:
cmv = []
if body:
alternate = u"ELSEIF"
else:
if conditions is None:
conditions = []
alternate = u"IF"
children = filter_nodes(self.children, ids=ids_path,
subtree=subtree)
if children:
field = split(children)
has_missing_branch = (missing_branch(children) or
none_value(children))
# the missing is singled out as a special case only when there's
# no missing branch in the children list
if (not has_missing_branch and
not self.fields[field]['name'] in cmv):
conditions.append("ISNULL([%s])" % self.fields[field]['name'])
body += (u"%s %s THEN " %
(alternate, " AND ".join(conditions)))
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (u"%s\n" % value)
cmv.append(self.fields[field]['name'])
alternate = u"ELSEIF"
del conditions[-1]
for child in children:
pre_condition = u""
post_condition = u""
if has_missing_branch and child.predicate.value is not None:
negation = u"" if child.predicate.missing else u"NOT "
connection = u"OR" if child.predicate.missing else u"AND"
pre_condition = (
u"(%sISNULL([%s]) %s " % (
negation, self.fields[field]['name'], connection))
if not child.predicate.missing:
cmv.append(self.fields[field]['name'])
post_condition = u")"
optype = self.fields[child.predicate.field]['optype']
if child.predicate.value is None:
value = ""
elif optype == 'text':
return u""
elif optype == 'numeric':
value = child.predicate.value
else:
value = repr(child.predicate.value)
operator = ("" if child.predicate.value is None else
PYTHON_OPERATOR[child.predicate.operator])
if child.predicate.value is None:
pre_condition = (
T_MISSING_OPERATOR[child.predicate.operator])
post_condition = ")"
conditions.append("%s[%s]%s%s%s" % (
pre_condition,
self.fields[child.predicate.field]['name'],
operator,
value,
post_condition))
body = child.tableau_body(body, conditions[:], cmv=cmv[:],
ids_path=ids_path, subtree=subtree)
del conditions[-1]
else:
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (
u"%s %s THEN" % (alternate, " AND ".join(conditions)))
body += u" %s\n" % value
return body
def plug_in_body(self, depth=1, cmv=None, ids_path=None, subtree=True):
"""Translate the model into a set of "if" javascript statements.
`depth` controls the size of indentation. As soon as a value is missing
to evaluate a predicate the output at that node is returned without
further evaluation.
"""
metric = "error" if self.regression else "confidence"
if cmv is None:
cmv = []
body = u""
term_analysis_fields = []
item_analysis_fields = []
prefix = u""
field_obj = self.fields[self.objective_id]
if len(self.fields) > MAX_ARGS_LENGTH:
prefix = u"data."
children = filter_nodes(self.children, ids=ids_path,
subtree=subtree)
if children:
# field used in the split
field = split(children)
has_missing_branch = missing_branch(children)
# the missing is singled out as a special case only when there's
# no missing branch in the children list
one_branch = not has_missing_branch or \
self.fields[field]['optype'] in COMPOSED_FIELDS
if (one_branch and
not self.fields[field]['camelCase'] in cmv):
body += self.missing_check_code(field, depth, prefix, cmv,
metric)
for child in children:
field = child.predicate.field
pre_condition = u""
# code when missing_splits has been used
if has_missing_branch and child.predicate.value is not None:
pre_condition = self.missing_prefix_code(child, field,
prefix, cmv)
# complete split condition code
body += child.split_condition_code( \
field, depth, prefix, pre_condition,
term_analysis_fields, item_analysis_fields)
# value to be determined in next node
next_level = child.plug_in_body(depth + 1, cmv=cmv[:],
ids_path=ids_path,
subtree=subtree)
body += next_level[0]
body += u"%s}\n" % (INDENT * depth)
term_analysis_fields.extend(next_level[1])
item_analysis_fields.extend(next_level[2])
else:
value = value_to_print(self.output,
self.fields[self.objective_id]['optype'])
body = u"%sreturn {prediction: %s, %s: %s};\n" % ( \
INDENT * depth,
value,
metric,
self.confidence)
return body, term_analysis_fields, item_analysis_fields
def python_body(self, depth=1, cmv=None, input_map=False,
ids_path=None, subtree=True):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
def map_data(field, missing=False):
"""Returns the subject of the condition in map format when
more than MAX_ARGS_LENGTH arguments are used.
"""
if input_map:
if missing:
return "data.get('%s')" % field
else:
return "data['%s']" % field
return field
if cmv is None:
cmv = []
body = u""
term_analysis_fields = []
children = filter_nodes(self.children, ids=ids_path,
subtree=subtree)
if children:
field = split(children)
has_missing_branch = (missing_branch(children) or
none_value(children))
# the missing is singled out as a special case only when there's
# no missing branch in the children list
if (not has_missing_branch and
not self.fields[field]['slug'] in cmv):
body += (u"%sif (%s is None):\n" %
(INDENT * depth,
map_data(self.fields[field]['slug'], True)))
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body += (u"%sreturn %s\n" %
(INDENT * (depth + 1),
value))
cmv.append(self.fields[field]['slug'])
for child in children:
field = child.predicate.field
pre_condition = u""
if has_missing_branch and child.predicate.value is not None:
negation = u"" if child.predicate.missing else u" not"
connection = u"or" if child.predicate.missing else u"and"
pre_condition = (
u"%s is%s None %s " % (
map_data(self.fields[field]['slug'], True),
negation,
connection))
if not child.predicate.missing:
cmv.append(self.fields[field]['slug'])
optype = self.fields[field]['optype']
if (optype == 'numeric' or optype == 'text' or
child.predicate.value is None):
value = child.predicate.value
else:
value = repr(child.predicate.value)
if optype == 'text':
body += (
u"%sif (%sterm_matches(%s, \"%s\", %s\"%s\") %s %s):"
u"\n" %
(INDENT * depth, pre_condition,
map_data(self.fields[field]['slug'],
False),
self.fields[field]['slug'],
('u' if isinstance(child.predicate.term, unicode)
else ''),
child.predicate.term.replace("\"", "\\\""),
PYTHON_OPERATOR[child.predicate.operator],
value))
term_analysis_fields.append((field,
child.predicate.term))
else:
operator = (MISSING_OPERATOR[child.predicate.operator] if
child.predicate.value is None else
PYTHON_OPERATOR[child.predicate.operator])
if child.predicate.value is None:
cmv.append(self.fields[field]['slug'])
body += (
u"%sif (%s%s %s %s):\n" %
(INDENT * depth, pre_condition,
map_data(self.fields[field]['slug'],
False),
operator,
value))
next_level = child.python_body(depth + 1, cmv=cmv[:],
input_map=input_map,
ids_path=ids_path,
subtree=subtree)
body += next_level[0]
term_analysis_fields.extend(next_level[1])
else:
if self.fields[self.objective_id]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body = u"%sreturn %s\n" % (INDENT * depth, value)
return body, term_analysis_fields
def plug_in_body(self, depth=1, cmv=None, ids_path=None, subtree=True):
"""Translate the model into a set of "if" javascript statements.
`depth` controls the size of indentation. As soon as a value is missing
to evaluate a predicate the output at that node is returned without
further evaluation.
"""
metric = "error" if self.regression else "confidence"
if cmv is None:
cmv = []
body = u""
term_analysis_fields = []
item_analysis_fields = []
prefix = u""
field_obj = self.fields[self.objective_id]
if len(self.fields) > MAX_ARGS_LENGTH:
prefix = u"data."
children = filter_nodes(self.children, ids=ids_path, subtree=subtree)
if children:
# field used in the split
field = split(children)
has_missing_branch = missing_branch(children)
# the missing is singled out as a special case only when there's
# no missing branch in the children list
one_branch = not has_missing_branch or \
self.fields[field]['optype'] in COMPOSED_FIELDS
if (one_branch and not self.fields[field]['camelCase'] in cmv):
body += self.missing_check_code(field, depth, prefix, cmv,
metric)
for child in children:
field = child.predicate.field
pre_condition = u""
# code when missing_splits has been used
if has_missing_branch and child.predicate.value is not None:
pre_condition = self.missing_prefix_code(
child, field, prefix, cmv)
# complete split condition code
body += child.split_condition_code( \
field, depth, prefix, pre_condition,
term_analysis_fields, item_analysis_fields)
# value to be determined in next node
next_level = child.plug_in_body(depth + 1,
cmv=cmv[:],
ids_path=ids_path,
subtree=subtree)
body += next_level[0]
body += u"%s}\n" % (INDENT * depth)
term_analysis_fields.extend(next_level[1])
item_analysis_fields.extend(next_level[2])
else:
value = value_to_print(self.output,
self.fields[self.objective_id]['optype'])
body = u"%sreturn {prediction: %s, %s: %s};\n" % ( \
INDENT * depth,
value,
metric,
self.confidence)
return body, term_analysis_fields, item_analysis_fields
def python_body(self,
depth=1,
cmv=None,
input_map=False,
ids_path=None,
subtree=True):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
def map_data(field, missing=False):
"""Returns the subject of the condition in map format when
more than MAX_ARGS_LENGTH arguments are used.
"""
if input_map:
if missing:
return "not '%s' in data or data['%s']" % (field, field)
else:
return "data['%s']" % field
return field
if cmv is None:
cmv = []
body = u""
term_analysis_fields = []
children = filter_nodes(self.children, ids=ids_path, subtree=subtree)
if children:
field = split(children)
if not self.fields[field]['slug'] in cmv:
body += (u"%sif (%s is None):\n" %
(INDENT * depth,
map_data(self.fields[field]['slug'], True)))
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body += (u"%sreturn %s\n" % (INDENT * (depth + 1), value))
cmv.append(self.fields[field]['slug'])
for child in children:
optype = self.fields[child.predicate.field]['optype']
if (optype == 'numeric' or optype == 'text'):
value = child.predicate.value
else:
value = repr(child.predicate.value)
if optype == 'text':
body += (
u"%sif (term_matches(%s, \"%s\", %s\"%s\") %s %s):\n" %
(INDENT * depth,
map_data(self.fields[child.predicate.field]['slug'],
False),
self.fields[child.predicate.field]['slug'],
('u' if isinstance(child.predicate.term, unicode) else
''), child.predicate.term.replace("\"", "\\\""),
PYTHON_OPERATOR[child.predicate.operator], value))
term_analysis_fields.append(
(child.predicate.field, child.predicate.term))
else:
body += (
u"%sif (%s %s %s):\n" %
(INDENT * depth,
map_data(self.fields[child.predicate.field]['slug'],
False),
PYTHON_OPERATOR[child.predicate.operator], value))
next_level = child.python_body(depth + 1,
cmv=cmv[:],
input_map=input_map,
ids_path=ids_path,
subtree=subtree)
body += next_level[0]
term_analysis_fields.extend(next_level[1])
else:
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body = u"%sreturn %s\n" % (INDENT * depth, value)
return body, term_analysis_fields
def tableau_body(self,
body=u"",
conditions=None,
cmv=None,
ids_path=None,
subtree=True):
"""Translate the model into a set of "if" statements in Tableau syntax
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
if cmv is None:
cmv = []
if conditions is None:
conditions = []
alternate = u"IF"
else:
alternate = u"ELSEIF"
children = filter_nodes(self.children, ids=ids_path, subtree=subtree)
if children:
field = split(children)
if not self.fields[field]['name'] in cmv:
conditions.append("ISNULL([%s])" % self.fields[field]['name'])
body += (u"%s %s THEN " %
(alternate, " AND ".join(conditions)))
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (u"%s\n" % value)
cmv.append(self.fields[field]['name'])
alternate = u"ELSEIF"
del conditions[-1]
for child in children:
optype = self.fields[child.predicate.field]['optype']
if optype == 'text':
return u""
if (optype == 'numeric'):
value = child.predicate.value
else:
value = repr(child.predicate.value)
conditions.append(
"[%s]%s%s" %
(self.fields[child.predicate.field]['name'],
PYTHON_OPERATOR[child.predicate.operator], value))
body = child.tableau_body(body,
conditions[:],
cmv=cmv[:],
ids_path=ids_path,
subtree=subtree)
del conditions[-1]
else:
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = tableau_string(self.output)
body += (u"%s %s THEN" % (alternate, " AND ".join(conditions)))
body += u" %s\n" % value
return body
def python_body(self, depth=1, cmv=None, input_map=False,
ids_path=None, subtree=True):
"""Translate the model into a set of "if" python statements.
`depth` controls the size of indentation. As soon as a value is missing
that node is returned without further evaluation.
"""
def map_data(field, missing=False):
"""Returns the subject of the condition in map format when
more than MAX_ARGS_LENGTH arguments are used.
"""
if input_map:
if missing:
return "not '%s' in data or data['%s']" % (field, field)
else:
return "data['%s']" % field
return field
if cmv is None:
cmv = []
body = u""
term_analysis_fields = []
children = filter_nodes(self.children, ids=ids_path,
subtree=subtree)
if children:
field = split(children)
if not self.fields[field]['slug'] in cmv:
body += (u"%sif (%s is None):\n" %
(INDENT * depth,
map_data(self.fields[field]['slug'], True)))
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body += (u"%sreturn %s\n" %
(INDENT * (depth + 1),
value))
cmv.append(self.fields[field]['slug'])
for child in children:
optype = self.fields[child.predicate.field]['optype']
if (optype == 'numeric' or optype == 'text'):
value = child.predicate.value
else:
value = repr(child.predicate.value)
if optype == 'text':
body += (
u"%sif (term_matches(%s, \"%s\", %s\"%s\") %s %s):\n" %
(INDENT * depth,
map_data(self.fields[child.predicate.field]['slug'],
False),
self.fields[child.predicate.field]['slug'],
('u' if isinstance(child.predicate.term, unicode)
else ''),
child.predicate.term.replace("\"", "\\\""),
PYTHON_OPERATOR[child.predicate.operator],
value))
term_analysis_fields.append((child.predicate.field,
child.predicate.term))
else:
body += (
u"%sif (%s %s %s):\n" %
(INDENT * depth,
map_data(self.fields[child.predicate.field]['slug'],
False),
PYTHON_OPERATOR[child.predicate.operator],
value))
next_level = child.python_body(depth + 1, cmv=cmv[:],
input_map=input_map,
ids_path=ids_path,
subtree=subtree)
body += next_level[0]
term_analysis_fields.extend(next_level[1])
else:
if self.fields[self.objective_field]['optype'] == 'numeric':
value = self.output
else:
value = repr(self.output)
body = u"%sreturn %s\n" % (INDENT * depth, value)
return body, term_analysis_fields
