def main():
user_df = get_user_data("*")
user_list = list(pandas.unique(user_df[(user_df["role"]=="STUDENT")]["id"]))
zpdp = ZPDPredictor()
S_list = []
X_list = []
U_list = []
Qv_list = []
pass_list = []
atts_list = []
i_users = 0
n_users = 50
for u in user_list[0:n_users]:
i_users += 1
print(i_users)
attempts = get_attempts_from_db(u)
ts_list = list(attempts["timestamp"])
# S_list = gen_semi_static()
tX = gen_experience(u, ts_list)
X_list += tX
U_list += gen_success(u, ts_list)
Qv_list += encode_q_vectors(attempts)
S_list += [numpy.zeros(1) for t in tX]
pass_list += list(attempts["correct"]==True)
atts_list += [numpy.ones(1) for t in tX]
if (len(X_list) > 10000) or (i_users==n_users):
S_list = numpy.array(S_list)
X_list = numpy.array(X_list)
U_list = numpy.array(U_list)
Qv_list = numpy.array(Qv_list)
pass_list = numpy.array(pass_list)
atts_list = numpy.array(atts_list)
zpdp.train((S_list,X_list,U_list),Qv_list,pass_list, atts_list)
S_list = []
X_list = []
U_list = []
Qv_list = []
pass_list = []
atts_list = []
for u in user_list[n_users:2*n_users]:
attempts = get_attempts_from_db(u)
ts_list = list(attempts["timestamp"])
delta_x = gen_experience(u, ts_list)
X_list += delta_x
U_list += gen_success(u, ts_list)
Qv_list += encode_q_vectors(attempts)
S_list += [numpy.zeros(1) for t in delta_x]
pass_list += list(attempts["correct"] == True)
S_list = numpy.array(S_list)
X_list = numpy.array(X_list)
U_list = numpy.array(U_list)
Qv_list = numpy.array(Qv_list)
pass_list = numpy.array(pass_list)
metrics = zpdp.pass_model.evaluate([S_list,X_list,U_list,Qv_list], pass_list)
print(metrics)
def gen_qhist(psi, ts):
raw_attempts = get_attempts_from_db(psi)
attempts = raw_attempts[(raw_attempts["timestamp"] <= ts)]
l1 = list(attempts["question_id"])
l2 = list(attempts["timestamp"])
qhist = list( zip(l1,l2) )
return qhist
def gen_experience(psi, ts_list):
raw_attempts = get_attempts_from_db(psi)
X_list = []
# if raw_attempts.empty:
# print("student {} has no X attempts".format(psi))
# return X_list
X = numpy.zeros(len(all_qids))
for ts in sorted(ts_list):
attempts = raw_attempts[(raw_attempts["timestamp"] <= ts)]
hits = attempts["question_id"]
for qid in list(hits):
try:
qix = reverse_qid_dict[qid]
except:
print("UNK Qn ", qid)
continue
# X = numpy.max(X-.1,0)
# X -= 0.02 # reduce to zero in 50 moves
# X[X < 0] = 0.0 #bottom out at zero
# X[X > 0] += 0.01
X[qix] = 1
# print("birdvs iirdvs", numpy.median(X), numpy.sum(X))
X_list.append(numpy.copy(X))
raw_attempts = raw_attempts[(raw_attempts["timestamp"] > ts)]
return X_list
def gen_semi_static(psi, dob_cache, ts_list):
S_list = []
raw_attempts = get_attempts_from_db(psi)
# if raw_attempts.empty:
# print("student {} has no S attempts".format(psi))
# return []
for ts in sorted(ts_list):
age=None
xp_atts = 0
sx = 0
days = 1.0
attempts = raw_attempts[raw_attempts["timestamp"] <= ts]
dob = dob_cache[psi]
age = (ts - dob).days / 365.242
if (not isinstance(age,float)) or (age>=100) or (age<10):
age = 16.9
if not attempts.empty:
# print("chex...")
maxdate = (attempts["timestamp"]).max()
mindate = (attempts["timestamp"]).min()
days = (maxdate - mindate).days
if numpy.isnan(days):
input(days)
days = 1.0 if days<1.0 else days
#xp_runs = len(numpy.unique(attempts["question_id"]))
xp_atts = attempts.shape[0]
correct = attempts[attempts["correct"] == True]
sx = correct.shape[0]
# rat = sx/xp if xp>0 else 0
# print("done...")
# S_list.append( numpy.array([age, days, xp_runs, xp_atts, (xp_atts/days), (xp_runs/days), (sx/xp_atts if xp_atts else 0)]) ) #,rat,xp/days,sx/days]))
S_list.append(numpy.array([age, days, (xp_atts/days), (sx/xp_atts if xp_atts else 0)]))
return S_list
def gen_experience(psi, ts_list):
raw_attempts = get_attempts_from_db(psi)
X_list = []
X = numpy.zeros(len(all_qids))
for ts in sorted(ts_list):
attempts = raw_attempts[(raw_attempts["timestamp"] < ts)]
hits = attempts["question_id"]
for qid in list(hits):
try:
qix = reverse_qid_dict[qid]
except:
print("UNK Qn ", qid)
continue
X[qix] = 1
X_list.append(numpy.copy(X))
raw_attempts = raw_attempts[(raw_attempts["timestamp"] >= ts)]
return X_list
def gen_success(psi,ts_list):
raw_attempts = get_attempts_from_db(psi)
U_list = []
U = numpy.zeros(len(all_qids))
for ts in sorted(ts_list):
attempts = raw_attempts[(raw_attempts["timestamp"] < ts)]
hits = attempts[(attempts["correct"] == True)]
hits = hits["question_id"]
for qid in list(hits):
try:
qix = reverse_qid_dict[qid]
except:
print("UNK Qn ", qid)
continue
attct = len(attempts[attempts["question_id"]==qid])
U[qix] = (1.0/attct)
U_list.append(numpy.copy(U))
raw_attempts = raw_attempts[(raw_attempts["timestamp"] >= ts)]
return U_list
def filter_assignments(assignments, book_only):
# query = "select id, gameboard_id, group_id, owner_user_id, creation_date from assignments order by creation_date asc"
assignments["include"] = True
print(assignments.shape)
map = make_gb_question_map()
meta = get_meta_data()
for ix in range(assignments.shape[0]):
include = True
gr_id = assignments.loc[ix, "group_id"]
if book_only:
gb_id = assignments.loc[ix, "gameboard_id"]
hexes = map[gb_id]
for hx in hexes:
hx = hx.split("|")[0]
if not (hx.startswith("ch_") or hx.startswith("ch-i")):
include = False
break
if include:
students = get_student_list([gr_id])
if students.empty:
include = False
if include:
include = False
for psi in list(students["user_id"]):
# print("checking",psi)
atts = get_attempts_from_db(psi)
if not atts.empty:
# print("OK")
include = True
break
if not include:
assignments.loc[ix, "include"] = False
# assignments = assignments[assignments["include"]==True]
print(assignments.shape)
return assignments
def save_class_report_card(ts, aid, gr_id, S, X, U, A, y, y_preds, slist,
q_names_df):
N = y_preds.shape[0]
print(N)
sum_preds = numpy.sum(y_preds, axis=0)
print("sum of sums", numpy.sum(sum_preds))
sum_preds = sum_preds / N
max_sum_ix = sum_preds.argmax()
max_sum_prob = sum_preds.max()
max_probs = y_preds.max(axis=1)
max_ixs = y_preds.argmax(axis=1)
max_labs = [pid_override[m] for m in max_ixs]
vote_ct = Counter()
for label in max_labs:
vote_ct[label] += 1
max_vote_lab = vote_ct.most_common(1)[0][0]
max_sum_lab = pid_override[max_sum_ix]
print("max sum lab =", max_sum_lab, max_sum_prob)
print("votes counted:", vote_ct.most_common(5))
print("most voted =", max_vote_lab)
wb = openpyxl.Workbook()
ws = wb.active
fn_ai = str(aid)
r = 1
col_headers = [
"student", "age", "months_on_isaac", "qns_tried", "successes",
"prev_assignts", "hexes_attempted", "top_10_topics", "last_10_qns",
"ISAAC_SUGGESTS", "DIFF: (too easy 1..5 too hard)",
"TOPIC:(bad 1..3 good)"
]
col_widths = [len(ch) for ch in col_headers]
for c, cv in enumerate(col_headers):
ws.cell(r, 1 + c, cv)
ws.cell(2, 2, ts)
ws.cell(2, 1, "Classroom sugg'n 1:")
ws.cell(3, 1, "Classroom sugg'n 2:")
ws.cell(2, 10, max_sum_lab)
ws.cell(3, 10, max_vote_lab)
r = 4
months_on_list = []
for s, x, u, a, t, psi, maxlab in zip(S, X, U, A, y, slist, max_labs):
atts = get_attempts_from_db(psi)
atts = atts[atts["timestamp"] < ts]
pids = []
for qid in atts["question_id"]:
pid = qid.split("|")[0]
if pid not in pids:
pids.append(pid)
pids = "\n".join(map(str, pids))
assigned = []
for ix, el in enumerate(a):
if el > 0:
label = pid_override[ix]
page = label.split("|")[0]
if page not in assigned:
assigned.append(page)
if len(assigned) > 0:
assigned = "\n".join(map(str, assigned))
big5 = get_top_subjs(x, 10)
if len(big5) > 0:
big5 = "\n".join(map(str, big5))
natts = atts.shape[0]
nsucc = atts[atts["correct"] == True].shape[0]
last5 = list(pandas.unique(atts["question_id"])[-10:])
temp5 = []
for n in last5:
tit = q_names_df.loc[n, "title"]
if str(tit) != "nan":
temp5.append("{} ({})".format(tit, n))
else:
temp5.append(n)
last5 = temp5
last5 = "\n".join(map(str, last5))
# if len(qh) > 0:
# ql, tl = zip(*qh)
# last5 = [q for q in numpy.unique(ql)[-5:]]
# last5 = "\n".join(last5)
# last5 = '{}'.format(last5) # wrap in quotes
# else:
# last5 = []
months_on = s[1] / 30.44
months_on_list.append(months_on)
c = 1
for cv in [
psi,
int(10 * s[0]) / 10.0, "{:.1f}".format(months_on),
str(numpy.sum(x > 0)) + " (" + str(numpy.sum(x)) + ")",
numpy.sum(u), [], pids, big5, last5, maxlab
]:
if cv == []:
cv = "-"
elif len(str(cv).split("\n")[0]) > col_widths[c - 1]:
col_widths[c - 1] = len(str(cv))
ws.cell(r, c, cv)
c += 1
r += 1
# for ci, cw in enumerate(col_widths):
# ws.column_dimensions[get_column_letter(ci + 1)].width = cw
#
# for ri, rh in enumerate(row_heights):
# ws.row_dimensions[ri+2].height = rh
for col in ws.columns:
max_length = 0
column = col[0].column # Get the column name
for cell in col:
cell.alignment = Alignment(horizontal="center", vertical="top")
try: # Necessary to avoid error on empty cells
this_max = max([len(s) for s in str(cell.value).split('\n')])
if this_max > max_length:
max_length = this_max
except:
pass
adjusted_width = max_length * 1.2
ws.column_dimensions[column].width = adjusted_width
for row in ws.rows:
max_height = 0
rowname = row[0].row # Get the column name
for cell in row:
try: # Necessary to avoid error on empty cells
cell_h = len(str(cell.value).split('\n'))
print("for row {} cell value is {} at height {}".format(
rowname, cell.value, cell_h))
if cell_h > max_height:
print("{} super {}, replaceing".format(cell_h, max_height))
max_height = cell_h
except:
pass
adjusted_height = max_height * 11.5 # convert to points??
ws.row_dimensions[rowname].height = adjusted_height
months_av = mean(months_on_list)
wb.save('./report_cards/{:.1f}_{}_{}.xlsx'.format(months_av, gr_id, aid))
# print(rd)
student_static[psi] = (rd, )
l_ts = pandas.to_datetime("1970-01-01 00:00:00")
l_hexes = []
else:
l_ts = last_ts[psi]
l_hexes = last_hexes[psi]
S, X, U, A = pickle.loads(
zlib.decompress(SXUA[psi][l_ts]))
# S,X,U,A = copy(S),copy(X),copy(U),copy(A)
#make updates
# if psi ==118651:
# print("birdskoeping")
attempts = get_attempts_from_db(psi)
attempts = attempts[attempts["timestamp"] < ts]
all_wins = list(
attempts[(attempts["correct"] == True)]["question_id"])
recent_attempts = attempts[attempts["timestamp"] >= l_ts]
# qids = list(set(recent_attempts["question_id"]))
qids = list(set(recent_attempts["question_id"]))
recent_wins = list(recent_attempts[(
recent_attempts["correct"] == True)]["question_id"])
for qid in qids:
try:
qix = all_qids.index(qid)
attct = (
recent_attempts["question_id"] == qid).sum()