def get_difficulties(answers, data=None, model=None, force=False, name="difficulty"):
if data and model:
runner = Runner(data, model)
file_name = "../cache/difficulties_{}.pd".format(runner._hash)
else:
data = d.Data("../data/matmat/2016-11-28/answers.pd")
model = EloPriorCurrentModel(KC=2, KI=0.5)
runner = Runner(data, model)
file_name = "../cache/difficulties_matmat.pd"
if os.path.exists(file_name) and not force:
difficulties = pd.read_pickle(file_name)
else:
items = answers["item"].unique()
runner.run(force=True)
difficulties = pd.Series(data=model.get_difficulties(items), index=items, name=name)
difficulties.to_pickle(file_name)
return difficulties
"beta": np.arange(0., 0.2, 0.02),
# "KC": np.arange(1.5, 5.0, 0.25),
# "KI": np.arange(0, 2.5, 0.25),
# }, plot_axes=["KC", "KI"])
}, plot_axes=["alpha", "beta"])
plt.show()
items = data.get_items_df()
items = items[(items["skill_lvl_2"] == 210) & ~items["skill_lvl_3"].isnull()].loc[:, ("question", "answer", "visualization")]
items = items[items["visualization"] == "free_answer"]
answers[answers["item"].isin(items.index)].to_pickle("../../data/matmat/2016-01-04/answers-multiplication.pd")
data_multiplication = Data("../../data/matmat/2016-01-04/answers-multiplication.pd")
model = EloPriorCurrentModel(alpha=1.4, beta=0.1, KC=3, KI=0.5)
items = items.join(pd.Series(answers.groupby("item").size(), name="answer_count"))
items = items.join(pd.Series(answers.groupby("item").apply(lambda i: i["correct"].sum() / len(i)), name="success_rate"))
items = items.join(pd.Series(answers.groupby("item")["response_time"].median(), name="response_time"))
Evaluator(data_multiplication, model).get_report(force_run=True)
items["model_difficulty"] = model.get_difficulties(items.index)
items["model_difficulty"] -= items["model_difficulty"].mean()
skills = items.groupby("question").agg({
"answer_count": "sum",
"success_rate": "mean",
"response_time": "mean",
"model_difficulty": "mean",
})
