def plug_in(self,
out=sys.stdout,
hadoop=False,
filter_id=None,
subtree=True):
"""Generates a basic javascript implementation of local predictions
`out` is file descriptor to write the javascript code.
"""
# fill the camelcase variable names with the JS_KEYWORDS restrictions
objective_field = self.tree.fields[self.tree.objective_id]
camelcase = to_camel_js(unidecode(objective_field['name']), False)
objective_field['CamelCase'] = camelcase
for field in [(key, val)
for key, val in sort_fields(self.tree.fields)]:
field_obj = self.tree.fields[field[0]]
field_obj['camelCase'] = to_camel_js(unidecode(field_obj['name']))
body, term_analysis_predicates, item_analysis_predicates = \
self.tree.plug_in_body()
terms_body = ""
items_body = ""
if term_analysis_predicates:
terms_body = self.js_term_analysis_body(term_analysis_predicates)
if item_analysis_predicates:
items_body = self.js_item_analysis_body(item_analysis_predicates)
output = self.js_pre_body()
output += terms_body + items_body + body
output += u"%sreturn null;\n}\n" % INDENT
if not PY3:
output = output.encode("utf8")
out.write(output)
out.flush()
def js_signature(self, input_map=False):
"""Returns a the javascript signature for a prediction method.
"""
objective_field = self.tree.fields[self.tree.objective_id]
if not 'CamelCase' in objective_field:
camelcase = to_camel_js(unidecode(objective_field['name']), False)
objective_field['CamelCase'] = camelcase
output = u"function predict%s(" % objective_field['CamelCase']
args = []
if len(self.tree.fields) > MAX_ARGS_LENGTH or input_map:
args.append("data")
else:
for field in [(key, val)
for key, val in sort_fields(self.tree.fields)]:
field_obj = self.tree.fields[field[0]]
if not 'camelCase' in field_obj:
field_obj['camelCase'] = to_camel_js( \
unidecode(field_obj['name']))
if field[0] != self.tree.objective_id:
args.append(u"%s" % field_obj['camelCase'])
args_string = u", ".join(args)
output += args_string + u")"
return output
def plug_in(self, out=sys.stdout, hadoop=False,
filter_id=None, subtree=True):
"""Generates a basic javascript implementation of local predictions
`out` is file descriptor to write the javascript code.
"""
# fill the camelcase variable names with the JS_KEYWORDS restrictions
objective_field = self.tree.fields[self.tree.objective_id]
camelcase = to_camel_js(unidecode(objective_field['name']), False)
objective_field['CamelCase'] = camelcase
for field in [(key, val) for key, val in
sort_fields(self.tree.fields)]:
field_obj = self.tree.fields[field[0]]
field_obj['camelCase'] = to_camel_js(unidecode(field_obj['name']))
body, term_analysis_predicates, item_analysis_predicates = \
self.tree.plug_in_body()
terms_body = ""
items_body = ""
if term_analysis_predicates:
terms_body = self.js_term_analysis_body(term_analysis_predicates)
if item_analysis_predicates:
items_body = self.js_item_analysis_body(item_analysis_predicates)
output = self.js_pre_body()
output += terms_body + items_body + body
output += u"%sreturn null;\n}\n" % INDENT
if not PY3:
output = output.encode("utf8")
out.write(output)
out.flush()
def js_signature(self, input_map=False):
"""Returns a the javascript signature for a prediction method.
"""
objective_field = self.tree.fields[self.tree.objective_id]
if not 'CamelCase' in objective_field:
camelcase = to_camel_js(unidecode(objective_field['name']), False)
objective_field['CamelCase'] = camelcase
output = u"function predict%s(" % objective_field['CamelCase']
args = []
if len(self.tree.fields) > MAX_ARGS_LENGTH or input_map:
args.append("data")
else:
for field in [(key, val) for key, val in
sort_fields(self.tree.fields)]:
field_obj = self.tree.fields[field[0]]
if not 'camelCase' in field_obj:
field_obj['camelCase'] = to_camel_js( \
unidecode(field_obj['name']))
if field[0] != self.tree.objective_id:
args.append(u"%s" % field_obj['camelCase'])
args_string = u", ".join(args)
output += args_string + u")"
return output
def plug_in(self, out=sys.stdout, filter_id=None, subtree=True):
"""Writes an R function that implements the model.
"""
# fill the dotted variable names with the R_KEYWORDS restrictions
objective_field = self.tree.fields[self.tree.objective_id]
camelcase = to_camel_js(objective_field['name'], False)
objective_field['CamelCase'] = camelcase
default = "NA"
args = []
for field in [(key, val) for key, val in
sort_fields(self.tree.fields)]:
field_obj = self.tree.fields[field[0]]
field_obj['dotted'] = dot(field_obj['name'])
args.append("%s=%s" % (field_obj['dotted'], default))
body, term_analysis_predicates, item_analysis_predicates = \
self.tree.plug_in_body()
terms_body = ""
items_body = ""
if term_analysis_predicates:
terms_body = self.r_term_analysis_body(term_analysis_predicates)
if item_analysis_predicates:
items_body = self.r_item_analysis_body(item_analysis_predicates)
predictor_definition = (u"predict%s <- function" %
camelcase)
depth = len(predictor_definition) + 1
predictor = u"%s(%s){\n" % (predictor_definition,
(",\n" + " " * depth).join(args))
join_str = "\n#"
docstring = join_str.join(self.docstring().split("\n"))
predictor_doc = (u"# " + docstring +
u"\n" + u"#\n")
output = predictor_doc + predictor
output += terms_body + items_body + body
output += u"%sreturn(NA)\n}\n" % INDENT
if not PY3:
output = output.encode("utf8")
out.write(output)
out.flush()
def plug_in(self, out=sys.stdout, filter_id=None, subtree=True):
"""Writes an R function that implements the model.
"""
# fill the dotted variable names with the R_KEYWORDS restrictions
objective_field = self.tree.fields[self.tree.objective_id]
camelcase = to_camel_js(objective_field['name'], False)
objective_field['CamelCase'] = camelcase
default = "NA"
args = []
for field in [(key, val)
for key, val in sort_fields(self.tree.fields)]:
field_obj = self.tree.fields[field[0]]
field_obj['dotted'] = dot(field_obj['name'])
args.append("%s=%s" % (field_obj['dotted'], default))
body, term_analysis_predicates, item_analysis_predicates = \
self.tree.plug_in_body()
terms_body = ""
items_body = ""
if term_analysis_predicates:
terms_body = self.r_term_analysis_body(term_analysis_predicates)
if item_analysis_predicates:
items_body = self.r_item_analysis_body(item_analysis_predicates)
predictor_definition = (u"predict%s <- function" % camelcase)
depth = len(predictor_definition) + 1
predictor = u"%s(%s){\n" % (predictor_definition,
(",\n" + " " * depth).join(args))
join_str = "\n#"
docstring = join_str.join(self.docstring().split("\n"))
predictor_doc = (u"# " + docstring + u"\n" + u"#\n")
output = predictor_doc + predictor
output += terms_body + items_body + body
output += u"%sreturn(NA)\n}\n" % INDENT
if not PY3:
output = output.encode("utf8")
out.write(output)
out.flush()