def show_exercises(parameter_file):
"""Display a sigmoid for each exercise."""
plt.figure(1)
data = mirt_util.json_to_data(parameter_file)
parameters = data['params']
exercise_ind_dict = parameters.exercise_ind_dict
def eval_conditional_probability(x, parameters, exercise_ind):
"""Evaluate the conditional probability of answering each question
accurately for a student with ability x
"""
return mirt_util.conditional_probability_correct(
np.ones((parameters.num_abilities, 1)) * x, parameters,
exercise_ind)
abilities_to_plot = np.arange(-3, 3, .01)
exercises, indices = exercise_ind_dict.keys(), exercise_ind_dict.values()
exercise_plots = defaultdict(list)
for ability in abilities_to_plot:
conditional_probs = eval_conditional_probability(
ability, parameters, exercise_ind_dict.values())
for exercise in exercises:
exercise_plots[exercise].append(
conditional_probs[exercises.index(exercise)])
for exercise in exercises:
plt.plot(abilities_to_plot, exercise_plots[exercise], label=exercise)
plt.xlabel('Student Ability')
plt.ylabel('P(Answer Correctly)')
plt.title('Two parameter IRT model')
plt.legend(loc='best', prop={'size': 6})
plt.show()
def show_exercises(parameter_file):
"""Display a sigmoid for each exercise."""
plt.figure(1)
data = mirt_util.json_to_data(parameter_file)
parameters = data['params']
exercise_ind_dict = parameters.exercise_ind_dict
def eval_conditional_probability(x, parameters, exercise_ind):
"""Evaluate the conditional probability of answering each question
accurately for a student with ability x
"""
return mirt_util.conditional_probability_correct(
np.ones((parameters.num_abilities, 1)) * x,
parameters,
exercise_ind)
abilities_to_plot = np.arange(-3, 3, .01)
exercises, indices = exercise_ind_dict.keys(), exercise_ind_dict.values()
exercise_plots = defaultdict(list)
for ability in abilities_to_plot:
conditional_probs = eval_conditional_probability(
ability,
parameters,
exercise_ind_dict.values())
for exercise in exercises:
exercise_plots[exercise].append(conditional_probs[
exercises.index(exercise)])
for exercise in exercises:
plt.plot(abilities_to_plot, exercise_plots[exercise], label=exercise)
plt.xlabel('Student Ability')
plt.ylabel('P(Answer Correctly)')
plt.title('Two parameter IRT model')
plt.legend(loc='best', prop={'size': 6})
plt.show()
def show_exercises(parameter_file):
"""Display a sigmoid for each exercise."""
plt.figure(1)
data = mirt_util.json_to_data(parameter_file)
parameters = data['params']
exercise_ind_dict = parameters.exercise_ind_dict
def eval_conditional_probability(x, parameters, exercise_ind):
"""Evaluate the conditional probability of answering each question
accurately for a student with ability x
"""
return mirt_util.conditional_probability_correct(
np.ones((parameters.num_abilities, 1)) * x,
parameters,
exercise_ind)
abilities_to_plot = np.arange(-3, 3, .01)
exercises, indices = exercise_ind_dict.keys(), exercise_ind_dict.values()
exercise_plots = defaultdict(list)
for ability in abilities_to_plot:
conditional_probs = eval_conditional_probability(
ability,
parameters,
exercise_ind_dict.values())
for exercise in exercises:
exercise_plots[exercise].append(conditional_probs[
exercises.index(exercise)])
# to make colors/style different for each curve
# all_matplotlib_colors = []
# for c_name, c_hex in matplotlib.colors.cnames.iteritems():
# all_matplotlib_colors.append(c_name)
cmap = plt.get_cmap('jet')
colors = cmap(np.linspace(0, 1, len(exercises)))
line_styles = ['-', '--', '-.', ':', ]
# plt.style.use('ggplot')
for idx, exercise in enumerate(exercises):
plt.plot(abilities_to_plot, exercise_plots[exercise],
# color=all_matplotlib_colors[idx],
color=colors[idx],
linestyle=line_styles[idx % len(line_styles)],
label=exercise)
plt.xlabel('Student Ability')
plt.ylabel('P(Answer Correctly)')
plt.title('Two parameter IRT model')
plt.legend(loc='best', prop={'size': 6})
plt.show()
def print_report(parameter_file):
"""Print interpretable results given a json file"""
data = mirt_util.json_to_data(parameter_file)
parameters = data['params']
print 'Generating Report for %s' % parameter_file
print "%50s\t%s\t\t" % ('Exercise', 'Bias'),
for i in range(parameters.num_abilities):
print 'Dim. %s\t' % (i + 1),
print
exercises = parameters.exercise_ind_dict.keys()
exercises_to_parameters = [(ex, parameters.get_params_for_exercise(ex))
for ex in exercises]
# Sort by the difficulty bias
exercises_to_parameters.sort(key=lambda x: x[-1][-1])
for ex, param in exercises_to_parameters:
print "%50s\t%.4f\t" % (ex, param[-1]),
for p in param[:-1]:
print "\t%.4f\t" % p,
print
def print_report(parameter_file):
"""Print interpretable results given a json file"""
data = mirt_util.json_to_data(parameter_file)
parameters = data['params']
print 'Generating Report for %s' % parameter_file
print "%50s\t%s\t\t" % ('Exercise', 'Bias'),
for i in range(parameters.num_abilities):
print 'Dim. %s\t' % (i + 1),
print
exercises = parameters.exercise_ind_dict.keys()
exercises_to_parameters = [(ex, parameters.get_params_for_exercise(ex))
for ex in exercises]
# Sort by the difficulty bias
exercises_to_parameters.sort(key=lambda x: x[-1][-1])
for ex, param in exercises_to_parameters:
print "%50s\t%.4f\t" % (ex, param[-1]),
for p in param[:-1]:
print "\t%.4f\t" % p,
print
