def gen_var_and_func_samples(self, *args):
"""
Generate a list of variable/function sampling dictionaries from the supplied arguments.
Arguments may be strings, lists of strings, or dictionaries with string values.
Does not flag any bad variables.
"""
# Make a list of all expressions to check for variables
expressions = []
for entry in args:
if isinstance(entry, six.text_type):
expressions.append(entry)
elif isinstance(entry, list):
expressions += entry
elif isinstance(entry, dict):
expressions += [v for k, v in entry.items()]
# Generate the variable list
variables, sample_from_dict = self.generate_variable_list(expressions)
# Generate the samples
var_samples = gen_symbols_samples(variables, self.config['samples'],
sample_from_dict, self.functions,
self.suffixes, self.constants)
func_samples = gen_symbols_samples(list(self.random_funcs.keys()),
self.config['samples'],
self.random_funcs, self.functions,
self.suffixes, {})
return var_samples, func_samples
def test_dependent_sampler_infers_dependence():
sampler = DependentSampler(formula="a+1")
assert sampler.config['depends'] == ['a']
sampler = DependentSampler(formula="x^2+y^2+sin(c)")
assert set(sampler.config['depends']) == set(['x', 'y', 'c'])
# Test that 'depends' is now ignored
symbols = ["x", "y"]
samples = 1
sample_from = {
'x': DependentSampler(depends=["y"], formula="1"),
'y': DependentSampler(depends=["x"], formula="1")
}
funcs, suffs, consts = {}, {}, {}
# This does NOT raise an error; the depends entry is ignored
gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)
def gen_var_and_func_samples(self, answer, student_input,
sibling_formulas):
"""
Generate a list of variable/function sampling dictionaries.
"""
comparer_params = answer['expect']['comparer_params']
# Generate samples; Include siblings to get numbered_vars from them
expressions = (comparer_params + [student_input] +
[sibling_formulas[key] for key in sibling_formulas])
variables, sample_from_dict = self.generate_variable_list(expressions)
var_samples = gen_symbols_samples(variables, self.config['samples'],
sample_from_dict, self.functions,
self.suffixes, self.constants)
func_samples = gen_symbols_samples(list(self.random_funcs.keys()),
self.config['samples'],
self.random_funcs, self.functions,
self.suffixes, {})
return var_samples, func_samples
def test_overriding_constant_with_dependent_sampling():
symbols = ['a', 'b', 'c']
samples = 1
sample_from = {
'a': RealInterval([10, 10]),
'b': DependentSampler(depends=["a"], formula="a+1"),
'c': DependentSampler(depends=["b"], formula="b+1")
}
funcs, suffs = {}, {}
consts = {'unity': 1, 'b': 3.14}
result = gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)[0]
a, b, c = [result[sym] for sym in 'abc']
assert a == 10
assert b == 11
assert c == 12
def test_dependent_sampler():
"""Tests the DependentSampler class"""
# Test basic usage and multiple samples
result = gen_symbols_samples(
["a", "b"], 2, {
'a': IntegerRange([1, 1]),
'b': DependentSampler(depends=["a"], formula="a+1")
})
assert result == [{"a": 1, "b": 2.0}, {"a": 1, "b": 2.0}]
result = gen_symbols_samples(
["a", "b", "c", "d"], 1, {
'a': RealInterval([1, 1]),
'd': DependentSampler(depends=["c"], formula="c+1"),
'c': DependentSampler(depends=["b"], formula="b+1"),
'b': DependentSampler(depends=["a"], formula="a+1")
})[0]
assert result["b"] == 2 and result["c"] == 3 and result["d"] == 4
result = gen_symbols_samples(
["x", "y", "z", "r"], 1, {
'x':
RealInterval([-5, 5]),
'y':
RealInterval([-5, 5]),
'z':
RealInterval([-5, 5]),
'r':
DependentSampler(depends=["x", "y", "z"],
formula="sqrt(x^2+y^2+z^2)")
})[0]
assert result["x"]**2 + result["y"]**2 + result["z"]**2 == approx(
result["r"]**2)
with raises(ConfigError,
match="Circularly dependent DependentSamplers detected: x, y"):
gen_symbols_samples(
["x", "y"], 1, {
'x': DependentSampler(depends=["y"], formula="1"),
'y': DependentSampler(depends=["x"], formula="1")
})
with raises(ConfigError,
match=r"Formula error in dependent sampling formula: 1\+\(2"):
gen_symbols_samples(
["x"], 1, {'x': DependentSampler(depends=[], formula="1+(2")})
with raises(Exception,
match="DependentSampler must be invoked with compute_sample."):
DependentSampler(depends=[], formula="1").gen_sample()
def raw_check(self, answer, cleaned_input):
"""Perform the numerical check of student_input vs answer"""
var_samples = gen_symbols_samples(self.config['variables'],
self.config['samples'],
self.config['sample_from'])
func_samples = gen_symbols_samples(self.random_funcs.keys(),
self.config['samples'],
self.random_funcs)
# Make a copy of the functions and variables lists
# We'll add the sampled functions/variables in
funcscope = self.functions.copy()
varscope = self.constants.copy()
num_failures = 0
for i in range(self.config['samples']):
# Update the functions and variables listings with this sample
funcscope.update(func_samples[i])
varscope.update(var_samples[i])
# Evaluate integrals. Error handling here is in two parts because
# 1. custom error messages we've added
# 2. scipy's warnings re-raised as error messages
try:
expected_re, expected_im = self.evaluate_int(
answer['integrand'],
answer['lower'],
answer['upper'],
answer['integration_variable'],
varscope=varscope,
funcscope=funcscope)
except IntegrationError as e:
msg = "Integration Error with author's stored answer: {}"
raise ConfigError(msg.format(e.message))
student_re, student_im = self.evaluate_int(
cleaned_input['integrand'],
cleaned_input['lower'],
cleaned_input['upper'],
cleaned_input['integration_variable'],
varscope=varscope,
funcscope=funcscope)
# scipy raises integration warnings when things go wrong,
# except they aren't really warnings, they're just printed to stdout
# so we use quad's full_output option to catch the messages, and then raise errors.
# The 4th component only exists when its warning message is non-empty
if len(student_re) == 4:
raise IntegrationError(student_re[3])
if len(expected_re) == 4:
raise ConfigError(expected_re[3])
if len(student_im) == 4:
raise IntegrationError(student_im[3])
if len(expected_im) == 4:
raise ConfigError(expected_im[3])
self.log(
self.debug_appendix_template.format(
samplenum=i,
variables=varscope,
student_re_eval=student_re[0],
student_re_error=student_re[1],
student_re_neval=student_re[2]['neval'],
student_im_eval=student_im[0],
student_im_error=student_im[1],
student_im_neval=student_im[2]['neval'],
author_re_eval=expected_re[0],
author_re_error=expected_re[1],
author_re_neval=expected_re[2]['neval'],
author_im_eval=expected_im[0],
author_im_error=expected_im[1],
author_im_neval=expected_im[2]['neval'],
))
# Check if expressions agree
expected = expected_re[0] + (expected_im[0] or 0) * 1j
student = student_re[0] + (student_im[0] or 0) * 1j
if not within_tolerance(expected, student,
self.config['tolerance']):
num_failures += 1
if num_failures > self.config["failable_evals"]:
return {'ok': False, 'grade_decimal': 0, 'msg': ''}
# This response appears to agree with the expected answer
return {'ok': True, 'grade_decimal': 1, 'msg': ''}
def raw_check(self, answer, student_input, **kwargs):
"""Perform the numerical check of student_input vs answer"""
comparer_params = answer['expect']['comparer_params']
siblings = kwargs.get('siblings', None)
required_siblings = self.get_used_vars(comparer_params)
# required_siblings might include some extra variable names, but no matter
sibling_formulas = self.get_sibling_formulas(siblings, required_siblings)
# Generate samples; Include siblings to get numbered_vars from them
expressions = (comparer_params
+ [student_input]
+ [sibling_formulas[key] for key in sibling_formulas])
variables, sample_from_dict = self.generate_variable_list(expressions)
var_samples = gen_symbols_samples(variables,
self.config['samples'],
sample_from_dict,
self.functions,
self.suffixes)
func_samples = gen_symbols_samples(self.random_funcs.keys(),
self.config['samples'],
self.random_funcs,
self.functions,
self.suffixes)
# Make a copy of the functions and variables lists
# We'll add the sampled functions/variables in
funclist = self.functions.copy()
varlist = self.constants.copy()
# Get the comparer function
comparer = answer['expect']['comparer']
num_failures = 0
for i in range(self.config['samples']):
# Update the functions and variables listings with this sample
funclist.update(func_samples[i])
varlist.update(var_samples[i])
def scoped_eval(expression,
variables=varlist,
functions=funclist,
suffixes=self.suffixes,
max_array_dim=self.config['max_array_dim']):
return evaluator(expression, variables, functions, suffixes, max_array_dim)
# Compute the sibling values, and add them to varlist
siblings_eval = {
key: scoped_eval(sibling_formulas[key])[0]
for key in sibling_formulas
}
varlist.update(siblings_eval)
# Compute expressions
comparer_params_eval = self.eval_and_validate_comparer_params(
scoped_eval, comparer_params, siblings_eval)
# Before performing student evaluation, scrub the siblings
# so that students can't use them
for key in siblings_eval:
del varlist[key]
student_eval, used = scoped_eval(student_input)
# Check if expressions agree
comparer_result = comparer(comparer_params_eval, student_eval, self.comparer_utils)
comparer_result = ItemGrader.standardize_cfn_return(comparer_result)
if self.config['debug']:
# Put the siblings back in for the debug output
varlist.update(siblings_eval)
self.log_sample_info(i, varlist, funclist, student_eval,
comparer, comparer_params_eval, comparer_result)
if not comparer_result['ok']:
num_failures += 1
if num_failures > self.config["failable_evals"]:
return comparer_result, used.functions_used
# This response appears to agree with the expected answer
return {
'ok': answer['ok'],
'grade_decimal': answer['grade_decimal'],
'msg': answer['msg']
}, used.functions_used
def test_dependent_sampler():
"""Tests the DependentSampler class"""
# Test basic usage and multiple samples
symbols = ["a", "b"]
samples = 2
sample_from = {
'a': IntegerRange([1, 1]),
'b': DependentSampler(depends=["a"], formula="a+1")
}
funcs, suffs, consts = {}, {}, {}
result = gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)
assert result == [{"a": 1, "b": 2.0}, {"a": 1, "b": 2.0}]
symbols = ["a", "b", "c", "d"]
samples = 1
sample_from = {
'a': RealInterval([1, 1]),
'd': DependentSampler(depends=["c"], formula="c+1"),
'c': DependentSampler(depends=["b"], formula="b+1"),
'b': DependentSampler(depends=["a"], formula="a+1")
}
funcs, suffs, consts = {}, {}, {}
result = gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)[0]
assert result["b"] == 2 and result["c"] == 3 and result["d"] == 4
symbols = ["x", "y", "z", "r"]
samples = 1
sample_from = {
'x': RealInterval([-5, 5]),
'y': RealInterval([-5, 5]),
'z': RealInterval([-5, 5]),
'r': DependentSampler(depends=["x", "y", "z"], formula="sqrt(x^2+y^2+z^2 + unity)")
}
funcs = {'sqrt': lambda x: x**0.5}
consts = {'unity': 1}
suffs = {}
result = gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)[0]
assert result["x"]**2 + result["y"]**2 + result["z"]**2 + 1 == approx(result["r"]**2)
symbols = ["x", "y"]
samples = 1
sample_from = {
'x': DependentSampler(depends=["y"], formula="y"),
'y': DependentSampler(depends=["x"], formula="x")
}
funcs, suffs, consts = {}, {}, {}
with raises(ConfigError, match="Circularly dependent DependentSamplers detected: x, y"):
gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)
with raises(ConfigError, match=r"Formula error in dependent sampling formula: 1\+\(2"):
DependentSampler(formula="1+(2")
symbols = ["x"]
samples = 1
sample_from = {'x': DependentSampler(formula="min(j, i)")}
with raises(ConfigError, match=r"Formula error in dependent sampling formula: min\(j, i\)"):
gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, {'i': 1j, 'j': 1j})
with raises(ConfigError, match=r"DependentSamplers depend on undefined quantities: i, j"):
gen_symbols_samples(symbols, samples, sample_from, funcs, suffs, consts)
with raises(Exception, match="DependentSampler must be invoked with compute_sample."):
DependentSampler(depends=[], formula="1").gen_sample()
