def add(self, *columns):
for column in as_list(columns):
if column is None: # pragma: no cover
continue
_match = self._pattern.match(column)
_exclude_tag = _match.group("exclude_tag")
_regex_tag = _match.group("regex_tag")
_pattern_str = _match.group("pattern_str")
if not _regex_tag:
if _exclude_tag:
self.exact_exclude_columns.add(_pattern_str)
else:
self.exact_include_columns.add(_pattern_str)
else:
if _exclude_tag:
self.regex_exclude_columns.append(re.compile(_pattern_str))
else:
self.regex_include_columns.append(re.compile(_pattern_str))
assert not (
(self.exact_include_columns or self.regex_include_columns
) and (self.exact_exclude_columns or self.regex_exclude_columns)
), "include mode and exclude mode are exclusive"
self._mode = "include" if self.exact_include_columns or self.regex_include_columns else "exclude"
def _target_names(*files, target_names=None, suffix, prefix=""):
"""
Examples
--------
>>> files = ["x.txt"]
>>> _target_names(*files, suffix=[".train", ".test"])
[['x.train.txt', 'x.test.txt']]
>>> _target_names(*files, suffix=[".train", ".test"], prefix="data/")
[['data/x.train.txt', 'data/x.test.txt']]
>>> _target_names(*files, suffix=[".train", ".test"], target_names=[["train.txt", "test.txt"]])
[['train.txt', 'test.txt']]
"""
if target_names is None:
if not prefix:
return [[
"%s%s" %
(PurePath(_file).with_suffix(_suffix), type_from_name(_file))
for _suffix in suffix
] for _file in files]
else:
return [[
"%s%s%s" % (prefix, PurePath(_file).with_suffix(_suffix).name,
type_from_name(_file)) for _suffix in suffix
] for _file in files]
else:
return as_list(target_names)
def auto_types(df: pd.DataFrame, excluded: (str, Iterable) = None, verbose=False, pattern_mode=False, **kwargs):
"""
Only infer the type of object
Parameters
----------
df
excluded
verbose
pattern_mode: bool
When pattern mode is set as True,
matching columns will be inferred using regex pattern, which is time consuming
Returns
-------
Examples
--------
>>> import pandas as pd
>>> df = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [0.1, 0.2, 0.3, 0.4, 0.5], "c": ["a", "b", "c", "d", "e"]})
>>> df = auto_types(df)
>>> df.dtypes
a int64
b float64
c category
dtype: object
>>> df = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [0.1, 0.2, 0.3, 0.4, 0.5], "c": ["a", "b", "c", "d", "e"]})
>>> df = auto_types(df, excluded=["c"])
>>> df.dtypes
a int64
b float64
c object
dtype: object
"""
__log = _get_log_f(verbose=verbose, **kwargs)
if excluded:
excluded = set(
as_list(excluded) if not pattern_mode
else _filter_columns([e for e in excluded], df.columns)
)
else:
excluded = set()
if excluded:
__log("Auto typing: excluded columns: %s" % ", ".join(excluded))
for column in tqdm(_get_columns_by_dtype(df, "object"), "auto typing", disable=not verbose):
if column in excluded:
continue
numeric_column = pd.to_numeric(df[column].copy(), errors="coerce")
if numeric_column.count() > 0:
df[column] = numeric_column
else:
df[column] = df[column].astype(
"category",
)
return df
def sample(self, query: (int, str, list), n=1, excluded_key=None, neg=True, *args, **kwargs):
candidates = self.df.loc[query][self.neg_field] if neg else self.df.loc[query][self.pos_field]
if excluded_key is not None:
candidates = list(set(candidates) - set(as_list(excluded_key)))
sampled = self.random_state.choice(candidates, min(n, len(candidates)), replace=False).tolist()
# rs = default_rng(10)
# sampled = rs.choice(candidates, min(n, len(candidates)), replace=False).tolist()
return sampled
def __init__(self, columns: Iterable = None):
self._pattern = re.compile(
r"^(?P<exclude_tag>\[!\])*(?P<regex_tag>\$regex:)*(?P<pattern_str>.*)"
)
self.exact_include_columns = set()
self.exact_exclude_columns = set()
self.regex_include_columns = []
self.regex_exclude_columns = []
self._mode = None
if columns is not None:
self.add(*as_list(columns))
def eval_f(_net, test_data, ctx=mx.cpu()):
k = test_data[1]["k"]
k = as_list(k) if k is not None else []
max_k = max(k) if k else None
top_k_ground_truth = []
top_k_prediction = []
ground_truth = []
prediction = []
for batch_data in tqdm(test_data[0], "evaluating"):
ctx_data = split_and_load(ctx, *batch_data, even_split=False)
for (user, item, label) in ctx_data:
output = _net(user, item)
pred = output
label = label.asnumpy().astype("int")
pred = pred.asnumpy()
ground_truth.append(label.tolist())
prediction.append(pred.tolist())
if max_k:
top_k_indices = np.argsort(pred)[::-1]
_top_k_indices = top_k_indices[:max_k]
padding = [0] * (max_k - len(_top_k_indices)) if len(
_top_k_indices) < max_k else []
top_k_prediction.append(pred[_top_k_indices].tolist() +
padding)
top_k_ground_truth.append(label[_top_k_indices].tolist() +
padding)
chained_ground_truth = list(chain(*ground_truth))
chained_prediction = list(chain(*prediction))
metrics = {
"rmse": mean_squared_error(chained_ground_truth, chained_prediction),
"mae": median_absolute_error(chained_ground_truth, chained_prediction),
}
metrics.update(
classification_report(
chained_ground_truth,
[0 if v < 0.5 else 1
for v in chained_prediction], chained_prediction))
if k:
metrics_k = {"ndcg": {}, "HR": {}}
for _k in k:
metrics_k["ndcg"][_k] = ndcg_score(top_k_ground_truth,
top_k_prediction,
k=_k)
metrics_k["HR"][_k] = _hit_rate(top_k_ground_truth, k=_k)
metrics.update(metrics_k)
return metrics
def extract_params_combinations(candidates: dict, external=None):
"""
>>> candidates = {'b': [1, 2], 'c': [0, 3], 'd': '$b'}
>>> list(extract_params_combinations(candidates))
[{'b': 1, 'c': 0, 'd': 1}, {'b': 1, 'c': 3, 'd': 1}, {'b': 2, 'c': 0, 'd': 2}, {'b': 2, 'c': 3, 'd': 2}]
>>> candidates = {'a': [1, 2], 'b': '$c'}
>>> external = {'c': 3}
>>> list(extract_params_combinations(candidates, external))
[{'a': 1, 'b': 3}, {'a': 2, 'b': 3}]
"""
external = {} if external is None else external
params_paths, params_values = dict2pv(candidates)
params_values = [as_list(value) for value in params_values]
for params in itertools.product(*params_values):
_params = {}
for p, v in zip(params_paths, params):
list2dict(p, v, _params)
for p, v in zip(params_paths, params):
if isinstance(v, str) and v[0] == '$':
map_key_path = v.lstrip('$').split(":")
_dict_obj = get_dict_by_path(_params, p[:-1])
for map_dict in [_params, external]:
try:
_map_dict_obj = get_dict_by_path(
map_dict, map_key_path)
_dict_obj[p[-1]] = _map_dict_obj
break
except KeyError:
try:
_map_dict_obj = get_dict_by_path(
map_dict, p[:-1] + map_key_path)
_dict_obj[p[-1]] = _map_dict_obj
break
except KeyError:
pass
else:
raise KeyError(
"The mapped key should be in either candidates or external, but cannot find %s"
% v)
yield _params
def implicit_sample(self, query: (int, str, list), n=1, excluded_key=None, fast_mode=False, samples=None,
*args, fast_max_try=100, **kwargs):
exclude = set(self.df.loc[query][self.pos_field]) | set(self.df.loc[query][self.neg_field])
if excluded_key is not None:
exclude |= set(as_list(excluded_key))
if samples is not None:
exclude |= set(samples)
if fast_mode is False:
candidates = list(set(range(*self.key_range)) - exclude)
sampled = self.random_state.choice(candidates, min(n, len(candidates)), replace=False).tolist()
return sampled
else:
sampled = set()
try_cnt = 0
while len(sampled) < n and try_cnt < n + fast_max_try:
_sample = self.random_state.integers(*self.key_range)
if _sample not in exclude and _sample not in sampled:
sampled.add(_sample)
try_cnt += 1
return list(sampled)
def as_array(obj):
if isinstance(obj, np.ndarray):
return obj
else:
return np.asarray(as_list(obj))
def ranking_report(y_true,
y_pred,
k: (int, list) = None,
continuous=False,
coerce="ignore",
pad_pred=-100,
metrics=None,
bottom=False,
verbose=True) -> POrderedDict:
r"""
Parameters
----------
y_true
y_pred
k
continuous
coerce
pad_pred
metrics
bottom
verbose
Returns
-------
Examples
--------
>>> y_true = [[1, 0, 0], [0, 0, 1]]
>>> y_pred = [[0.75, 0.5, 1], [1, 0.2, 0.1]]
>>> ranking_report(y_true, y_pred) # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k
1 1.000000 0.000000 0.0 0.0 1.0 2
3 0.565465 0.333333 1.0 0.5 3.0 2
5 0.565465 0.333333 1.0 0.5 3.0 2
10 0.565465 0.333333 1.0 0.5 3.0 2
auc: 0.250000 map: 0.416667 mrr: 0.416667 coverage_error: 2.500000 ranking_loss: 0.750000 len: 3.000000
support: 2
>>> ranking_report(y_true, y_pred, k=[1, 3, 5]) # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k
1 1.000000 0.000000 0.0 0.0 1.0 2
3 0.565465 0.333333 1.0 0.5 3.0 2
5 0.565465 0.333333 1.0 0.5 3.0 2
auc: 0.250000 map: 0.416667 mrr: 0.416667 coverage_error: 2.500000 ranking_loss: 0.750000 len: 3.000000
support: 2
>>> ranking_report(y_true, y_pred, bottom=True) # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k ndcg@k(B) \
1 1.000000 0.000000 0.0 0.0 1.0 2 1.000000
3 0.565465 0.333333 1.0 0.5 3.0 2 0.806574
5 0.565465 0.333333 1.0 0.5 3.0 2 0.806574
10 0.565465 0.333333 1.0 0.5 3.0 2 0.806574
<BLANKLINE>
precision@k(B) recall@k(B) f1@k(B) len@k(B) support@k(B)
1 0.500000 0.25 0.333333 1.0 2
3 0.666667 1.00 0.800000 3.0 2
5 0.666667 1.00 0.800000 3.0 2
10 0.666667 1.00 0.800000 3.0 2
auc: 0.250000 map: 0.416667 mrr: 0.416667 coverage_error: 2.500000 ranking_loss: 0.750000 len: 3.000000
support: 2 map(B): 0.708333 mrr(B): 0.750000
>>> ranking_report(y_true, y_pred, bottom=True, metrics=["auc"]) # doctest: +NORMALIZE_WHITESPACE
auc: 0.250000 len: 3.000000 support: 2
>>> y_true = [[0.9, 0.7, 0.1], [0, 0.5, 1]]
>>> y_pred = [[0.75, 0.5, 1], [1, 0.2, 0.1]]
>>> ranking_report(y_true, y_pred, continuous=True) # doctest: +NORMALIZE_WHITESPACE
ndcg@k len@k support@k
3 0.675647 3.0 2
5 0.675647 3.0 2
10 0.675647 3.0 2
mrr: 0.750000 len: 3.000000 support: 2
>>> y_true = [[1, 0], [0, 0, 1]]
>>> y_pred = [[0.75, 0.5], [1, 0.2, 0.1]]
>>> ranking_report(y_true, y_pred) # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k
1 1.00 0.500000 0.5 0.500000 1.0 2
3 0.75 0.416667 1.0 0.583333 2.5 2
5 0.75 0.416667 1.0 0.583333 2.5 2
10 0.75 0.416667 1.0 0.583333 2.5 2
auc: 0.500000 map: 0.666667 mrr: 0.666667 coverage_error: 2.000000 ranking_loss: 0.500000 len: 2.500000
support: 2
>>> ranking_report(y_true, y_pred, coerce="abandon") # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k
1 1.0 0.500000 0.5 0.5 1.0 2
3 0.5 0.333333 1.0 0.5 3.0 1
auc: 0.500000 map: 0.666667 mrr: 0.666667 coverage_error: 2.000000 ranking_loss: 0.500000 len: 2.500000
support: 2
>>> ranking_report(y_true, y_pred, coerce="padding") # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k
1 1.00 0.500000 0.5 0.500000 1.0 2
3 0.75 0.416667 1.0 0.583333 2.5 2
5 0.75 0.416667 1.0 0.583333 2.5 2
10 0.75 0.416667 1.0 0.583333 2.5 2
auc: 0.500000 map: 0.666667 mrr: 0.666667 coverage_error: 2.000000 ranking_loss: 0.500000 len: 2.500000
support: 2
>>> ranking_report(y_true, y_pred, bottom=True) # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k ndcg@k(B) \
1 1.00 0.500000 0.5 0.500000 1.0 2 1.000000
3 0.75 0.416667 1.0 0.583333 2.5 2 0.846713
5 0.75 0.416667 1.0 0.583333 2.5 2 0.846713
10 0.75 0.416667 1.0 0.583333 2.5 2 0.846713
<BLANKLINE>
precision@k(B) recall@k(B) f1@k(B) len@k(B) support@k(B)
1 0.500000 0.5 0.500000 1.0 2
3 0.583333 1.0 0.733333 2.5 2
5 0.583333 1.0 0.733333 2.5 2
10 0.583333 1.0 0.733333 2.5 2
auc: 0.500000 map: 0.666667 mrr: 0.666667 coverage_error: 2.000000 ranking_loss: 0.500000 len: 2.500000
support: 2 map(B): 0.791667 mrr(B): 0.750000
>>> ranking_report(y_true, y_pred, bottom=True, coerce="abandon") # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k ndcg@k(B) \
1 1.0 0.500000 0.5 0.5 1.0 2 1.000000
3 0.5 0.333333 1.0 0.5 3.0 1 0.693426
<BLANKLINE>
precision@k(B) recall@k(B) f1@k(B) len@k(B) support@k(B)
1 0.500000 0.5 0.5 1.0 2
3 0.666667 1.0 0.8 3.0 1
auc: 0.500000 map: 0.666667 mrr: 0.666667 coverage_error: 2.000000 ranking_loss: 0.500000
len: 2.500000 support: 2 map(B): 0.791667 mrr(B): 0.750000
>>> ranking_report(y_true, y_pred, bottom=True, coerce="padding") # doctest: +NORMALIZE_WHITESPACE
ndcg@k precision@k recall@k f1@k len@k support@k ndcg@k(B) \
1 1.00 0.500000 0.5 0.500000 1.0 2 1.000000
3 0.75 0.416667 1.0 0.583333 2.5 2 0.846713
5 0.75 0.416667 1.0 0.583333 2.5 2 0.846713
10 0.75 0.416667 1.0 0.583333 2.5 2 0.846713
<BLANKLINE>
precision@k(B) recall@k(B) f1@k(B) len@k(B) support@k(B)
1 0.50 0.5 0.500000 1.0 2
3 0.50 1.0 0.650000 3.0 2
5 0.30 1.0 0.452381 5.0 2
10 0.15 1.0 0.257576 10.0 2
auc: 0.500000 map: 0.666667 mrr: 0.666667 coverage_error: 2.000000 ranking_loss: 0.500000 len: 2.500000
support: 2 map(B): 0.791667 mrr(B): 0.750000
"""
import numpy as np
from collections import OrderedDict
from sklearn.metrics import (label_ranking_average_precision_score,
ndcg_score, label_ranking_loss,
coverage_error)
assert coerce in {"ignore", "abandon", "raise", "padding"}
if metrics is None:
metrics = ["mrr", "ndcg"]
if continuous is False:
metrics.extend([
"auc", "map", "coverage_error", "ranking_loss", "precision",
"recall", "f1"
])
metrics = set(metrics)
if k is not None:
k = as_list(k)
else:
if continuous is True:
k = [3, 5, 10]
else:
k = [1, 3, 5, 10]
results = {
"auc": [],
"map": [],
"mrr": [],
"coverage_error": [],
"ranking_loss": [],
"len": [],
"support": [],
}
if bottom:
results.update({
"map(B)": [],
"mrr(B)": [],
})
k_results = {}
for _k in k:
k_results[_k] = {
"ndcg@k": [],
"precision@k": [],
"recall@k": [],
"f1@k": [],
"len@k": [],
"support@k": [],
}
if bottom:
k_results[_k].update({
"ndcg@k(B)": [],
"precision@k(B)": [],
"recall@k(B)": [],
"f1@k(B)": [],
"len@k(B)": [],
"support@k(B)": [],
})
suffix = [""]
if bottom:
suffix += ["(B)"]
for label, pred in tqdm(zip(y_true, y_pred),
"ranking metrics",
disable=not verbose):
if continuous is False and "map" in metrics:
results["map"].append(
label_ranking_average_precision_score([label], [pred]))
if bottom:
results["map(B)"].append(
label_ranking_average_precision_score(
[(1 - np.asarray(label)).tolist()],
[(-np.asarray(pred)).tolist()]))
if len(label) > 1 and continuous is False:
if "coverage_error" in metrics:
results["coverage_error"].append(
coverage_error([label], [pred]))
if "ranking_loss" in metrics:
results["ranking_loss"].append(
label_ranking_loss([label], [pred]))
results["len"].append(len(label))
results["support"].append(1)
label_pred = list(
sorted(zip(label, pred), key=lambda x: x[1], reverse=True))
sorted_label = list(zip(*label_pred))[0]
if "auc" in metrics:
results["auc"].append(ranking_auc(sorted_label))
if "mrr" in metrics:
try:
results["mrr"].append(
1 / (np.asarray(sorted_label).nonzero()[0][0] + 1))
except IndexError: # pragma: no cover
pass
try:
if bottom:
results["mrr(B)"].append(
1 /
(np.asarray(sorted_label[::-1]).nonzero()[0][0] + 1))
except IndexError: # pragma: no cover
pass
if metrics & {"ndcg", "precision", "recall", "f1"}:
for _k in k:
for _suffix in suffix:
if _suffix == "":
_label_pred = deepcopy(label_pred)
if len(_label_pred) < _k:
if coerce == "ignore":
pass
elif coerce == "abandon":
continue
elif coerce == "raise":
raise ValueError(
"Not enough value: %s vs target %s" %
(len(_label_pred), _k))
elif coerce == "padding": # pragma: no cover
_label_pred += [(0, pad_pred)
] * (_k - len(_label_pred))
k_label_pred = label_pred[:_k]
total_label = sum(label)
else:
inv_label_pred = [(1 - _l, -p)
for _l, p in label_pred][::-1]
if len(inv_label_pred) < _k:
if coerce == "ignore":
pass
elif coerce == "abandon":
continue
elif coerce == "raise": # pragma: no cover
raise ValueError(
"Not enough value: %s vs target %s" %
(len(inv_label_pred), _k))
elif coerce == "padding":
inv_label_pred += [
(0, pad_pred)
] * (_k - len(inv_label_pred))
k_label_pred = inv_label_pred[:_k]
total_label = len(label) - sum(label)
if not k_label_pred: # pragma: no cover
continue
k_label, k_pred = list(zip(*k_label_pred))
if "ndcg" in metrics:
if len(k_label) == 1 and "ndcg" in metrics:
k_results[_k]["ndcg@k%s" % _suffix].append(1)
else:
k_results[_k]["ndcg@k%s" % _suffix].append(
ndcg_score([k_label], [k_pred]))
p = sum(k_label) / len(k_label)
r = sum(k_label) / total_label if total_label else 0
if "precision" in metrics:
k_results[_k]["precision@k%s" % _suffix].append(p)
if "recall" in metrics:
k_results[_k]["recall@k%s" % _suffix].append(r)
if "f1" in metrics:
k_results[_k]["f1@k%s" %
_suffix].append(2 * p * r /
(p + r) if p + r else 0)
k_results[_k]["len@k%s" % _suffix].append(len(k_label))
k_results[_k]["support@k%s" % _suffix].append(1)
ret = POrderedDict()
for key, value in results.items():
if value:
if key == "support":
ret[key] = np.sum(value).item()
else:
ret[key] = np.mean(value).item()
if metrics & {"ndcg", "precision", "recall", "f1"}:
for k, key_value in k_results.items():
ret[k] = OrderedDict()
for key, value in key_value.items():
if value:
if key in {"support@k", "support@k(B)"}:
ret[k][key] = np.sum(value).item()
else:
ret[k][key] = np.mean(value).item()
return ret
def loss_dict2tmt_loss(loss_dict,
loss2value=lambda x: x,
exclude=None,
include=None,
as_loss=as_tmt_loss):
"""
Parameters
----------
loss_dict
loss2value
exclude
include
as_loss
Returns
-------
Examples
--------
>>> def mse(v):
... return v ** 2
>>> losses = loss_dict2tmt_loss({"mse": mse, "rmse": lambda x: x})
>>> losses.keys()
dict_keys(['mse', 'rmse'])
>>> ema = EMAValue(losses)
>>> losses["mse"](2)
4
>>> losses["rmse"](2)
2
>>> ema.items()
dict_items([('mse', 4), ('rmse', 2)])
>>> losses = loss_dict2tmt_loss({"mse": mse, "rmse": lambda x: x}, include="mse")
>>> losses.keys()
dict_keys(['mse', 'rmse'])
>>> ema = EMAValue(losses, auto="ignore")
>>> losses["mse"](2)
4
>>> losses["rmse"](2)
2
>>> ema.items()
dict_items([('mse', 4), ('rmse', nan)])
>>> losses = loss_dict2tmt_loss({"mse": mse, "rmse": lambda x: x}, exclude="mse")
>>> losses.keys()
dict_keys(['mse', 'rmse'])
>>> ema = EMAValue(losses, auto="ignore")
>>> losses["mse"](2)
4
>>> losses["rmse"](2)
2
>>> ema.items()
dict_items([('mse', nan), ('rmse', 2)])
"""
exclude = set() if exclude is None else set(as_list(exclude))
if include is not None:
include = set(as_list(include))
return {
name: as_loss(func, loss2value) if name in include else func
for name, func in loss_dict.items()
}
return {
name: as_loss(func, loss2value) if name not in exclude else func
for name, func in loss_dict.items()
}
def regression_report(
y_true,
y_pred,
metrics=None,
sample_weight=None,
multioutput="uniform_average",
average_options=None,
key_prefix="",
key_suffix="",
verbose=True,
):
"""
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
metrics: list of str,
Support: evar(explained_variance), mse, rmse, mae, r2
sample_weight : array-like of shape (n_samples,), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average', 'variance_weighted'], list
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Disabled when verbose is True.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Alias: "macro"
'variance_weighted':
Only support in evar and r2.
Scores of all outputs are averaged, weighted by the variances of each individual output.
Alias: "vw"
average_options: str or list
default to macro, choices (one or many): "macro", "vw"
key_prefix: str
key_suffix: str
verbose: bool
Returns
-------
evar: explained variance
mse: mean squared error
rmse: root mean squared error
mae: mean absolute error
r2: r2 score
Examples
---------
>>> y_true = [[0.5, 1, 1], [-1, 1, 1], [7, -6, 1]]
>>> y_pred = [[0, 2, 1], [-1, 2, 1], [8, -5, 1]]
>>> regression_report(y_true, y_pred) # doctest: +NORMALIZE_WHITESPACE
evar mse rmse mae r2
0 0.967742 0.416667 0.645497 0.5 0.965438
1 1.000000 1.000000 1.000000 1.0 0.908163
2 1.000000 0.000000 0.000000 0.0 1.000000
uniform_average 0.989247 0.472222 0.548499 0.5 0.957867
variance_weighted 0.983051 0.472222 0.548499 0.5 0.938257
>>> regression_report(y_true, y_pred, verbose=False) # doctest: +NORMALIZE_WHITESPACE
evar: 0.989247 mse: 0.472222 rmse: 0.548499 mae: 0.500000 r2: 0.957867
>>> regression_report(
... y_true, y_pred, multioutput="variance_weighted", verbose=False
... ) # doctest: +NORMALIZE_WHITESPACE
evar: 0.983051 mse: 0.472222 rmse: 0.548499 mae: 0.500000 r2: 0.938257
>>> regression_report(y_true, y_pred, multioutput=[0.3, 0.6, 0.1], verbose=False) # doctest: +NORMALIZE_WHITESPACE
evar: 0.990323 mse: 0.725000 rmse: 0.793649 mae: 0.750000 r2: 0.934529
>>> regression_report(y_true, y_pred, verbose=True) # doctest: +NORMALIZE_WHITESPACE
evar mse rmse mae r2
0 0.967742 0.416667 0.645497 0.5 0.965438
1 1.000000 1.000000 1.000000 1.0 0.908163
2 1.000000 0.000000 0.000000 0.0 1.000000
uniform_average 0.989247 0.472222 0.548499 0.5 0.957867
variance_weighted 0.983051 0.472222 0.548499 0.5 0.938257
>>> regression_report(
... y_true, y_pred, verbose=True, average_options=["macro", "vw", [0.3, 0.6, 0.1]]
... ) # doctest: +NORMALIZE_WHITESPACE
evar mse rmse mae r2
0 0.967742 0.416667 0.645497 0.50 0.965438
1 1.000000 1.000000 1.000000 1.00 0.908163
2 1.000000 0.000000 0.000000 0.00 1.000000
uniform_average 0.989247 0.472222 0.548499 0.50 0.957867
variance_weighted 0.983051 0.472222 0.548499 0.50 0.938257
weighted 0.990323 0.725000 0.793649 0.75 0.934529
"""
legal_metrics = ["evar", "rmse", "mse", "mae", "r2"]
if not metrics:
metrics = legal_metrics
_metrics = set(metrics)
assert not _metrics - set(legal_metrics)
y_true = np.asarray(y_true)
y_pred = np.asarray(y_pred)
average_options = as_list(average_options) if average_options else [
"macro", "vw"
]
alias_dict = {
"macro": "uniform_average",
"vw": "variance_weighted",
}
ret = POrderedDict()
if len(y_true.shape) > 1 and verbose:
_ret = regression_report(
y_true,
y_pred,
sample_weight=sample_weight,
metrics=_metrics,
multioutput="raw_values",
key_prefix=key_prefix,
key_suffix=key_suffix,
verbose=False,
)
for i in range(y_true.shape[1]):
ret[i] = {}
for _metric in _ret.keys():
ret[i][_metric] = _ret[_metric][i]
for _multioutput in average_options:
__multioutput = _multioutput if isinstance(
_multioutput, list) else alias_dict.get(
_multioutput, _multioutput)
_ret = regression_report(y_true,
y_pred,
metrics=_metrics,
sample_weight=sample_weight,
multioutput=__multioutput,
key_prefix=key_prefix,
key_suffix=key_suffix,
verbose=False)
_name = "weighted" if isinstance(_multioutput,
list) else __multioutput
ret[_name] = {}
for _metric in _ret:
ret[_name][_metric] = _ret[_metric]
else:
if "evar" in _metrics:
ret[key_prefix + "evar" + key_suffix] = explained_variance_score(
y_true,
y_pred,
sample_weight=sample_weight,
multioutput=multioutput)
if "mse" in _metrics:
_multioutput = "uniform_average" if multioutput == "variance_weighted" else multioutput
ret[key_prefix + "mse" + key_suffix] = mean_squared_error(
y_true,
y_pred,
sample_weight=sample_weight,
multioutput=_multioutput)
if "rmse" in _metrics:
_multioutput = "uniform_average" if multioutput == "variance_weighted" else multioutput
ret[key_prefix + "rmse" + key_suffix] = mean_squared_error(
y_true,
y_pred,
sample_weight=sample_weight,
squared=False,
multioutput=_multioutput)
if "mae" in _metrics:
_multioutput = "uniform_average" if multioutput == "variance_weighted" else multioutput
ret[key_prefix + "mae" + key_suffix] = mean_absolute_error(
y_true,
y_pred,
sample_weight=sample_weight,
multioutput=_multioutput)
if "r2" in metrics:
ret[key_prefix + "r2" + key_suffix] = r2_score(
y_true,
y_pred,
sample_weight=sample_weight,
multioutput=multioutput)
return ret
def category2codes(
df: pd.DataFrame, offset: (int, list, dict) = 1, columns: (str, Iterable) = None,
pattern_mode=True, verbose=False, inplace=True,
**kwargs):
"""
numerically encoding the categorical columns
Parameters
----------
df: pd.DataFrame
offset: int, list, dict
0 or 1, default to 1 for the situation where exception or nan exists.
columns: str or Iterable
categorical column to transfer to codes
pattern_mode: bool
When pattern mode is set as True,
matching columns will be inferred using regex pattern, which is time consuming
verbose
inplace
Returns
-------
Examples
--------
>>> import pandas as pd
>>> df = pd.DataFrame({"a1": [1, 2, 300], "a2": [0.1, 0.2, 0.3], "b": ["a", "c", "d"]})
>>> df.dtypes
a1 int64
a2 float64
b object
dtype: object
>>> df = columns_to_category(df, ["a1", "a2", "b"])
>>> df.dtypes
a1 category
a2 category
b category
dtype: object
>>> category2codes(df, offset=[0, 1, 2], inplace=False)
a1 a2 b
0 0 1 2
1 1 2 3
2 2 3 4
>>> category2codes(df, offset=0, columns=["$regex:^a.*$"], pattern_mode=True, inplace=False)
a1 a2 b
0 0 0 a
1 1 1 c
2 2 2 d
>>> category2codes(df, offset={"a1": 0, "a2": 1, "b": 0}, inplace=False)
a1 a2 b
0 0 1 0
1 1 2 1
2 2 3 2
>>> from collections import defaultdict
>>> d_offset = defaultdict(int)
>>> d_offset.update({"a1": 2, "a2": 1})
>>> category2codes(df, offset=d_offset, inplace=False)
a1 a2 b
0 2 1 0
1 3 2 1
2 4 3 2
"""
df = df if inplace else df.copy()
__log = _get_log_f(verbose=verbose, **kwargs)
columns = as_list(columns) if columns else df.select_dtypes(include=["category"]).columns.values.tolist()
if isinstance(offset, (int, list)):
columns = _filter_columns(columns, df.columns) if pattern_mode is True else columns
if isinstance(offset, list):
assert len(columns) == len(offset), "columns[%s] vs offset[%s]" % (
len(columns), len(offset)
)
__log("categorical columns to be coded: %s" % list(zip(columns, offset)))
else:
__log("categorical columns to be coded: %s" % ", ".join(columns))
__log("offset is %s" % offset)
offset = [offset] * len(columns)
elif isinstance(offset, dict):
# assert pattern_mode is True, "set pattern mode as True when offset is dict"
columns = _filter_columns(columns, df.columns)
offset = [
offset[column] for column in columns
]
__log("categorical columns to be coded: %s" % list(zip(columns, offset)))
else:
raise TypeError("Cannot handle %s type offset" % type(offset))
for column, _offset in tqdm(zip(columns, offset), "encoding categorical columns", disable=not verbose):
df[column] = df[column].cat.codes + _offset
return df
def columns_to_category(df: pd.DataFrame,
columns: list, to_codes: bool = False, columns_to_codes: list = None,
pattern_mode=True,
code_pattern_mode=None,
verbose=False,
inplace=True,
*args, **kwargs):
"""
transfer the specified columns into category type
Parameters
----------
df
columns
to_codes
columns_to_codes
pattern_mode: bool
When pattern mode is set as True,
matching columns will be inferred using regex pattern, which is time consuming
code_pattern_mode: bool or None
When pattern mode is set as True,
matching columns will be inferred using regex pattern, which is time consuming
verbose
inplace
args
kwargs
Returns
-------
Examples
--------
>>> import pandas as pd
>>> df = pd.DataFrame({"a": [1, 2, 300, 4, 5], "b": [0.1, 0.2, 0.3, 0.4, 0.5], "a2": ["a", "b", "c", "d", "e"]})
>>> df.dtypes
a int64
b float64
a2 object
dtype: object
>>> df = columns_to_category(df, ["a2"])
>>> df.dtypes
a int64
b float64
a2 category
dtype: object
>>> columns_to_category(df, ["a2"], to_codes=True, columns_to_codes=["a2"])
a b a2
0 1 0.1 1
1 2 0.2 2
2 300 0.3 3
3 4 0.4 4
4 5 0.5 5
>>> columns_to_category(df, ["a", "b"], to_codes=True, offset=0, inplace=False)
a b a2
0 0 0 1
1 1 1 2
2 4 2 3
3 2 3 4
4 3 4 5
>>> columns_to_category(df, ["a"], to_codes=True, offset=2, inplace=False)
a b a2
0 2 0.1 1
1 3 0.2 2
2 6 0.3 3
3 4 0.4 4
4 5 0.5 5
"""
df = df if inplace else df.copy()
__log = _get_log_f(verbose=verbose, **kwargs)
columns = as_list(columns)
columns = _filter_columns(columns, df.columns) if pattern_mode else columns
__log("columns to be categorical: %s" % columns)
for column in tqdm(columns, "columns to be categorical", disable=not verbose):
if df[column].dtype.name == "category":
__log("column[%s] has been categorical, ignored" % column)
continue
df[column] = df[column].astype(
"category",
)
if to_codes:
columns_to_codes = columns if not columns_to_codes else columns_to_codes
code_pattern_mode = pattern_mode if code_pattern_mode is None else code_pattern_mode
columns = _filter_columns(columns_to_codes, columns) if code_pattern_mode else as_list(columns_to_codes)
category2codes(df, columns=columns, verbose=verbose, pattern_mode=False, **kwargs)
return df
def columns_to_datetime(
df: pd.DataFrame,
columns: (str, list),
datetime_format: (str, list) = None,
pattern_mode=False,
verbose=False,
*args, **kwargs):
"""
Parameters
----------
df
columns: str or list
The columns to be interpreted as datetime
datetime_format
pattern_mode: bool
When pattern mode is set as True,
matching columns will be inferred using regex pattern, which is time consuming
verbose
Returns
-------
Examples
--------
>>> import pandas as pd
>>> df = pd.DataFrame({"id": [1, 2, 3], "t": ["1931-09-18", "1949-10-01", "2020-10-12"]})
>>> df = columns_to_datetime(df, "t")
>>> df.dtypes
id int64
t datetime64[ns]
dtype: object
>>> df = pd.DataFrame({"t1": ["1931-09-18", "1949-10-01", "unknown"], "t2": ["20201012", "20201013", "unknown"]})
>>> df = columns_to_datetime(df, ["t1", "t2"])
>>> df.dtypes
t1 datetime64[ns]
t2 datetime64[ns]
dtype: object
>>> df
t1 t2
0 1931-09-18 2020-10-12
1 1949-10-01 2020-10-13
2 NaT NaT
>>> df = pd.DataFrame({
... "t1": ["1931-09-18:023358", "1949-10-01:040909"],
... "t2": ["20201012-011203", "20201013-070301"]
... })
>>> df = columns_to_datetime(
... df, ["t1", "t2"],
... datetime_format=["%Y-%m-%d:%H%M%S", "%Y%m%d-%H%M%S"]
... )
>>> df
t1 t2
0 1931-09-18 02:33:58 2020-10-12 01:12:03
1 1949-10-01 04:09:09 2020-10-13 07:03:01
>>> df = pd.DataFrame({
... "t1": ["1931-09-18:023358", "1949-10-01:040909"],
... "t2": ["20201012-011203", "20201013-070301"]
... })
>>> df = columns_to_datetime(
... df, ["t1", "t2"],
... datetime_format={"t1": "%Y-%m-%d:%H%M%S", "t2": "%Y%m%d-%H%M%S"}
... )
>>> df
t1 t2
0 1931-09-18 02:33:58 2020-10-12 01:12:03
1 1949-10-01 04:09:09 2020-10-13 07:03:01
"""
__log = _get_log_f(verbose=verbose, **kwargs)
columns = as_list(columns)
if datetime_format is None or isinstance(datetime_format, (str, list)):
columns = _filter_columns(columns, df.columns) if pattern_mode is True else columns
if isinstance(datetime_format, list):
assert len(columns) == len(datetime_format), "columns[%s] vs datetime_format[%s]" % (
len(columns), len(datetime_format)
)
__log("columns to be datetime: %s" % list(zip(columns, datetime_format)))
else:
__log("columns to be datetime: %s" % ", ".join(columns))
__log("datetime format is %s" % datetime_format)
datetime_format = [datetime_format] * len(columns)
elif isinstance(datetime_format, dict):
# assert pattern_mode is True, "set pattern mode as True when datetime_format is dict"
columns = _filter_columns(columns, df.columns)
datetime_format = [
datetime_format[column] for column in columns
]
__log("columns to be datetime: %s" % list(zip(columns, datetime_format)))
else:
raise TypeError("Cannot handle %s type datetime_format" % type(datetime_format))
for column, datetime_format in tqdm(zip(columns, datetime_format), "columns to datetime", disable=not verbose):
df[column] = pd.to_datetime(df[column], format=datetime_format, errors="coerce", *args, **kwargs)
return df
def begin_states(shapes, prefix, func=mx.nd.zeros):
states = []
for i, shape in enumerate(as_list(shapes)):
state = func(name='%sbegin_state_%d' % (prefix, i), shape=shape)
states.append(state)
return states
def as_array(array):
if isinstance(array, nd.NDArray):
return array
else:
return nd.array(as_list(array))
def learn(self, learning_item: (int, str, list)):
for concept in as_list(learning_item):
self.state[concept] = max(
self.state[concept] + self.capacity[concept], 1)
def _get_columns_by_dtype(df: pd.DataFrame, dtype: (str, list)):
dtype = as_list(dtype)
return df.select_dtypes(include=dtype).columns
