def parse_dsp_model(model):
"""
Parses the co2mpas model results.
:param model:
Co2mpas model after dispatching.
:type model: co2mpas.dispatcher.Dispatcher
:return:
Mapped outputs.
:rtype: dict[dict]
"""
res = {}
for k, v in model.data_output.items():
co2_utl.get_nested_dicts(res, *k.split('.'), default=co2_utl.ret_v(v))
for k, v in list(co2_utl.stack_nested_keys(res, depth=3)):
n, k = k[:-1], k[-1]
if n == ('output', 'calibration') and k in ('wltp_l', 'wltp_h'):
v = dsp_utl.selector(('co2_emission_value', ), v, allow_miss=True)
if v:
d = co2_utl.get_nested_dicts(res, 'target', 'prediction')
d[k] = dsp_utl.combine_dicts(v, d.get(k, {}))
res['pipe'] = model.pipe
return res
def get_chart_reference(report):
r, _map = {}, _map_cycle_report_graphs()
out = report.get('output', {})
it = co2_utl.stack_nested_keys(out, key=('output',), depth=3)
for k, v in sorted(it):
if k[-1] == 'ts' and 'times' in v:
label = '{}/%s'.format(_sheet_name(k))
for i, j in sorted(v.items()):
param_id = _re_params_name.match(i)['param']
m = _map.get(param_id, None)
if m:
d = {
'x': k + ('times',),
'y': k + (i,),
'label': label % i
}
n = k[2], param_id, 'series'
co2_utl.get_nested_dicts(r, *n, default=list).append(d)
for k, v in co2_utl.stack_nested_keys(r, depth=2):
m = _map[k[1]]
m.pop('label', None)
v.update(m)
return r
def compare_outputs_vs_targets(data):
"""
Compares model outputs vs targets.
:param data:
Model data.
:type data: dict
:return:
Comparison results.
:rtype: dict
"""
res = {}
metrics = {
'mean_absolute_error': mean_absolute_error,
'correlation_coefficient': _correlation_coefficient,
'accuracy_score': accuracy_score,
}
for k, t in co2_utl.stack_nested_keys(data.get('target', {}), depth=3):
if not co2_utl.are_in_nested_dicts(data, 'output', *k):
continue
o = co2_utl.get_nested_dicts(data, 'output', *k)
v = _compare(t, o, metrics=metrics)
if v:
co2_utl.get_nested_dicts(res, *k, default=co2_utl.ret_v(v))
return res
def _summary2df(data):
res = []
summary = data.get('summary', {})
if 'results' in summary:
r = {}
fun = partial(dsp_utl.map_list, [{}, 'cycle', 'stage', 'usage'])
for n, m in summary['results'].items():
gen = ((fun(v, *k),)
for k, v in co2_utl.stack_nested_keys(m, depth=3))
v = [v[0] for v in _yield_sorted_params(gen)]
co2_utl.get_nested_dicts(r, n, default=co2_utl.ret_v(v))
df = _make_summarydf(r, index=['cycle', 'stage', 'usage'], depth=1)
c = list(map(_rm_sub_parts, df.columns))
df.columns = pd.MultiIndex.from_tuples(c)
setattr(df, 'name', 'results')
res.append(df)
if 'selection' in summary:
df = pd.DataFrame(summary['selection'])
df.set_index(['model_id'], inplace=True)
setattr(df, 'name', 'selection')
res.append(df)
if 'comparison' in summary:
df = _comparison2df(summary['comparison'])
if df is not None:
setattr(df, 'name', 'comparison')
res.append(df)
if res:
return {'summary': res}
return {}
def _add_special_data2report(data, report, to_keys, *from_keys):
if from_keys[-1] != 'times' and \
co2_utl.are_in_nested_dicts(data, *from_keys):
v = co2_utl.get_nested_dicts(data, *from_keys)
n = to_keys + ('{}.{}'.format(from_keys[0], from_keys[-1]),)
co2_utl.get_nested_dicts(report, *n, default=co2_utl.ret_v(v))
return True, v
return False, None
def _format_selections(selections):
res = {}
for model_id, d in selections.items():
d = deepcopy(d)
best = d.pop('best')
for k, v in d.items():
v.update(best)
v['model_id'] = model_id
co2_utl.get_nested_dicts(res, k, default=list).append(v)
return res
def _extract_summary_from_summary(report, extracted):
n = ('summary', 'results')
if co2_utl.are_in_nested_dicts(report, *n):
for j, w in co2_utl.get_nested_dicts(report, *n).items():
if j in ('co2_emission', 'fuel_consumption'):
for k, v in co2_utl.stack_nested_keys(w, depth=3):
if v:
co2_utl.get_nested_dicts(extracted, *k).update(v)
n = ('summary', 'delta')
if co2_utl.are_in_nested_dicts(report, *n):
extracted['delta'] = co2_utl.get_nested_dicts(report, *n)
def _parse_base_data(res,
match,
sheet,
sheet_name,
re_params_name=_re_params_name):
r = {}
defaults = {'usage': 'input', 'stage': 'calibration'}
if 'type' not in match:
match['type'] = 'pa' if 'cycle' not in match else 'ts'
match = dsp_utl.combine_dicts(defaults, match)
if match['type'] == 'pa':
xl_ref = '#%s!B2:C_:["pipe", ["dict", "recurse"]]' % sheet_name
data = lasso(xl_ref, sheet=sheet)
else:
# noinspection PyBroadException
try:
xl_ref = '#%s!A2(R):.3:RD:["df", {"header": 0}]' % sheet_name
data = lasso(xl_ref, sheet=sheet)
except:
return {}
data.dropna(how='all', inplace=True)
data.dropna(axis=1, how='all', inplace=True)
mask = data.count(0) == len(data._get_axis(0))
# noinspection PyUnresolvedReferences
drop = [k for k, v in mask.items() if not v]
if drop:
msg = 'Columns {} in {} sheet contains nan.\n ' \
'Please correct the inputs!'
raise ValueError(msg.format(drop, sheet_name))
for k, v in parse_values(data, match, re_params_name):
co2_utl.get_nested_dicts(r, *k[:-1])[k[-1]] = v
n = (match['scope'], 'target')
if match['type'] == 'ts' and co2_utl.are_in_nested_dicts(r, *n):
t = co2_utl.get_nested_dicts(r, *n)
for k, v in co2_utl.stack_nested_keys(t, key=n, depth=2):
if 'times' not in v:
n = list(k + ('times', ))
n[1] = match['usage']
if co2_utl.are_in_nested_dicts(r, *n):
v['times'] = co2_utl.get_nested_dicts(r, *n)
else:
for i, j in co2_utl.stack_nested_keys(r, depth=4):
if 'times' in j:
v['times'] = j['times']
break
co2_utl.combine_nested_dicts(r, depth=5, base=res)
def filter_summary(changes, summary):
l, variations = [], {}
for k, v in changes.items():
k = tuple(k.split('.')[::-1])
l.append(k[:-1])
k = k[:-1] + ('plan.%s' % k[-1],)
co2_utl.get_nested_dicts(variations, *k).update(v)
for k, v in co2_utl.stack_nested_keys(summary, depth=3):
if k[:-1] in l:
co2_utl.get_nested_dicts(variations, *k, default=co2_utl.ret_v(v))
_add_delta2filtered_summary(variations, summary, base=variations)
return variations
def _format_scores(scores):
res = {}
for k, j in co2_utl.stack_nested_keys(scores, depth=3):
if k[-1] in ('limits', 'errors'):
model_id = k[0]
extra_field = ('score',) if k[-1] == 'errors' else ()
for i, v in co2_utl.stack_nested_keys(j):
i = (model_id, i[-1], k[1],) + i[:-1] + extra_field
co2_utl.get_nested_dicts(res, *i, default=co2_utl.ret_v(v))
sco = {}
for k, v in co2_utl.stack_nested_keys(res, depth=4):
v.update(dsp_utl.map_list(['model_id', 'param_id'], *k[:2]))
co2_utl.get_nested_dicts(sco, *k[2:], default=list).append(v)
return sco
def format_report_output(data):
res = {}
for k, v in co2_utl.stack_nested_keys(data.get('output', {}), depth=3):
_add_special_data2report(data, res, k[:-1], 'target', *k)
s, iv = _add_special_data2report(data, res, k[:-1], 'input', *k)
if not s or (s and not _is_equal(iv, v)):
co2_utl.get_nested_dicts(res, *k, default=co2_utl.ret_v(v))
output = {}
for k, v in co2_utl.stack_nested_keys(res, depth=2):
v = _split_by_data_format(v)
co2_utl.get_nested_dicts(output, *k, default=co2_utl.ret_v(v))
return output
def get_values(data, keys, tag=(), update=lambda k, v: v, base=None):
k = ('input', 'target', 'output')
data = dsp_utl.selector(k, data, allow_miss=True)
base = {} if base is None else base
for k, v in co2_utl.stack_nested_keys(data, depth=3):
k = k[::-1]
v = dsp_utl.selector(keys, v, allow_miss=True)
v = update(k, v)
if v:
k = tag + k
co2_utl.get_nested_dicts(base, *k, default=co2_utl.ret_v(v))
return base
def validate_inputs(data, soft_validation=False, read_schema=None):
res, errors, validate = {}, {}, read_schema.validate
for k, v in sorted(co2_utl.stack_nested_keys(data, depth=4)):
d = co2_utl.get_nested_dicts(res, *k[:-1])
_add_validated_input(d, validate, k, v, errors)
if not soft_validation:
for k, v in co2_utl.stack_nested_keys(res, depth=3):
for c, msg in hard_validation(v):
co2_utl.get_nested_dicts(errors, *k)[c] = SchemaError([],
[msg])
if _log_errors_msg(errors):
return {}
return res
def _add_validated_input(data, validate, keys, value, errors):
try:
k, v = next(iter(validate({keys[-1]: value}).items()))
if v is not dsp_utl.NONE:
data[k] = v
except SchemaError as ex:
co2_utl.get_nested_dicts(errors, *keys[:-1])[keys[-1]] = ex
def calculate_delta(data):
# delta
n, d = ['output', 'prediction', 'cycle', 'co2_emission_value'], {}
for k in ('%s_h', '%s_l'):
co2 = []
for c in ('nedc', 'wltp'):
n[2] = k % c
if co2_utl.are_in_nested_dicts(data, *n):
co2.append(co2_utl.get_nested_dicts(data, *n))
try:
dco2 = co2_utl.ret_v(np.diff(co2)[0])
except IndexError:
continue
co2_utl.get_nested_dicts(d, k % 'nedc', *n[2:], default=dco2)
return d
def define_new_inputs(data, base, dsp_model):
remove = []
for k, v in co2_utl.stack_nested_keys(data, depth=2):
if v is dsp_utl.EMPTY:
remove.append(k)
dsp = dsp_model.get_sub_dsp_from_workflow(data, check_inputs=False)
n = set(base) - set(dsp.data_nodes)
n.update(data)
inp = dsp_utl.selector(n, base, allow_miss=True)
d = co2_utl.combine_nested_dicts(inp, data, depth=2)
for n, k in remove:
co2_utl.get_nested_dicts(d, n).pop(k)
return d
def combine_scores(scores):
scores = {k[:-9]: v for k, v in scores.items() if v}
if not scores:
return {}
s = {}
for (k, c), v in co2_utl.stack_nested_keys(scores, depth=2):
r = {'models': v['models']} if 'models' in v else {}
r.update(v.get('score', {}))
co2_utl.get_nested_dicts(s, k, c, default=co2_utl.ret_v(r))
if not co2_utl.are_in_nested_dicts(s, k, 'best'):
keys = {'models': 'selected_models', 'success': 'status'}
best = dsp_utl.map_dict(keys, dsp_utl.selector(keys, r))
best['from'] = c
co2_utl.get_nested_dicts(s, k, 'best', default=co2_utl.ret_v(best))
return {'selections': s, 'scores': scores}
def _split_by_data_format(data):
d = {}
p = ('full_load_speeds', 'full_load_torques', 'full_load_powers')
try:
s = max(v.size for k, v in data.items()
if k not in p and isinstance(v, np.ndarray))
except ValueError:
s = None
for k, v in data.items():
if isinstance(v, np.ndarray) and s == v.size: # series
co2_utl.get_nested_dicts(d, 'ts')[k] = v
else: # params
co2_utl.get_nested_dicts(d, 'pa')[k] = v
return d
def _extract_summary_from_model_scores(report, extracted):
n = ('data', 'calibration', 'model_scores', 'selections')
if co2_utl.are_in_nested_dicts(report, *n):
sel = co2_utl.get_nested_dicts(report, *n)
n, status = ('calibration', 'output'), {}
for k, v in sel.items():
gen = ((d['model_id'], d['success']) for d in v if 'success' in d)
o = _format_dict(gen, 'status %s')
co2_utl.get_nested_dicts(extracted, k, *n).update(o)
gen = ((d['model_id'], d['status']) for d in v)
status.update(_format_dict(gen, 'status %s'))
n = ('prediction', 'output')
for k in extracted:
if co2_utl.are_in_nested_dicts(extracted, k, n[0]):
co2_utl.get_nested_dicts(extracted, k, *n).update(status)
def get_selection(data):
res = []
n = ('data', 'calibration', 'model_scores', 'selections')
if co2_utl.are_in_nested_dicts(data, *n):
for k, v in sorted(co2_utl.get_nested_dicts(data, *n).items()):
d = dsp_utl.selector(('from', 'status'), v['best'])
d['model_id'] = k
res.append(d)
return res
def _add2summary(total_summary, summary, base_keys=None):
base_keys = base_keys or {}
for k, v in co2_utl.stack_nested_keys(summary, depth=3):
d = co2_utl.get_nested_dicts(total_summary, *k, default=list)
if isinstance(v, list):
for j in v:
d.append(dsp_utl.combine_dicts(j, base_keys))
else:
d.append(dsp_utl.combine_dicts(v, base_keys))
def _extract_summary_from_output(report, extracted):
for k, v in co2_utl.stack_nested_keys(report.get('output', {}), depth=2):
k = k[::-1]
for u, i, j in _param_names_values(v.get('pa', {})):
o = {}
if i == 'co2_params_calibrated':
o = _format_dict(j.valuesdict().items(), 'co2_params %s')
elif i == 'calibration_status':
o = _format_dict(enumerate(j), 'status co2_params step %d',
lambda x: x[0])
elif i == 'willans_factors':
o = j
elif i == 'phases_willans_factors':
for n, m in enumerate(j):
o.update(_format_dict(m.items(), '%s phase {}'.format(n)))
elif i == 'has_sufficient_power':
o = {i: j}
if o:
co2_utl.get_nested_dicts(extracted, *(k + (u,))).update(o)
def re_sample_targets(data):
res = {}
for k, v in co2_utl.stack_nested_keys(data.get('target', {}), depth=2):
if co2_utl.are_in_nested_dicts(data, 'output', *k):
o = co2_utl.get_nested_dicts(data, 'output', *k)
o = _split_by_data_format(o)
t = dsp_utl.selector(o, _split_by_data_format(v), allow_miss=True)
if 'times' not in t.get('ts', {}) or 'times' not in o['ts']:
t.pop('ts', None)
else:
time_series = t['ts']
x, xp = o['ts']['times'], time_series.pop('times')
if not _is_equal(x, xp):
for i, fp in time_series.items():
time_series[i] = np.interp(x, xp, fp)
v = dsp_utl.combine_dicts(*t.values())
co2_utl.get_nested_dicts(res, *k, default=co2_utl.ret_v(v))
return res
def _comparison2df(comparison):
res = {}
it = co2_utl.stack_nested_keys(comparison, depth=3)
keys = ['usage', 'cycle', 'param']
gen = [(dsp_utl.map_list(keys, *k), k, v) for k, v in it]
for s, k, v in _yield_sorted_params(gen, keys=keys):
l = co2_utl.get_nested_dicts(res, *k[:-1], default=list)
l.append(dsp_utl.combine_dicts({'param_id': k[-1]}, v))
if res:
return _dd2df(res, 'param_id', depth=2)
def _add_delta2filtered_summary(changes, summary, base=None):
cycles = {'nedc_h', 'nedc_l', 'wltp_h', 'wltp_l'}
value = 'co2_emission_value'
ref = 'prediction', 'output', value
base = {} if base is None else base
def check(cycle):
return co2_utl.are_in_nested_dicts(changes, cycle, *ref)
for c in cycles:
if not co2_utl.are_in_nested_dicts(summary, 'delta', c):
continue
sub_cycles = cycles - {c}
if check(c) or all(check(k) for k in sub_cycles):
gen = sub_cycles
else:
gen = (k for k in sub_cycles if check(k))
for k in gen:
n = 'delta', c, k, value
if co2_utl.are_in_nested_dicts(summary, *n):
v = co2_utl.get_nested_dicts(summary, *n)
co2_utl.get_nested_dicts(base, *n, default=co2_utl.ret_v(v))
return base
def _scores2df(data):
n = ('data', 'calibration', 'model_scores')
if not co2_utl.are_in_nested_dicts(data, *n):
return {}
scores = co2_utl.get_nested_dicts(data, *n)
idx = ['model_id', 'from', 'status', 'selected_models']
df = _dd2df(scores['selections'], idx, depth=1)
setattr(df, 'name', 'selections')
idx = ['model_id', 'param_id']
edf = _dd2df(scores['scores'], idx, depth=2)
setattr(edf, 'name', 'scores')
return {'.'.join(n): (df, edf)}
def validate_plan(plan, read_schema=None):
validated_plan, errors, validate = [], {}, read_schema.validate
for i, data in plan.iterrows():
inputs = {}
data.dropna(how='all', inplace=True)
plan_id = 'plan id:{}'.format(i[0])
for k, v in data.items():
k = (plan_id, ) + tuple(k.split('.'))
d = co2_utl.get_nested_dicts(inputs, '.'.join(k[1:-1]))
_add_validated_input(d, validate, k, v, errors)
validated_plan.append((i, inputs))
if _log_errors_msg(errors):
return []
return validated_plan
def format_report_scores(data):
res = {}
scores = 'data', 'calibration', 'model_scores'
if co2_utl.are_in_nested_dicts(data, *scores):
n = scores + ('selections',)
selections = _format_selections(co2_utl.get_nested_dicts(data, *n))
if selections:
co2_utl.get_nested_dicts(res, *n, default=co2_utl.ret_v(selections))
n = scores + ('scores',)
scores = _format_scores(co2_utl.get_nested_dicts(data, *n))
if scores:
co2_utl.get_nested_dicts(res, *n, default=co2_utl.ret_v(scores))
return res
