def split_data(dtype,
x,
y,
special_codes=None,
cat_cutoff=None,
user_splits=None,
check_input=True,
outlier_detector=None,
outlier_params=None,
fix_lb=None,
fix_ub=None,
class_weight=None,
sample_weight=None):
"""Split data into clean, missing and special values data.
Parameters
----------
dtype : str, optional (default="numerical")
The variable data type. Supported data types are "numerical" for
continuous and ordinal variables and "categorical" for categorical
and nominal variables.
x : array-like, shape = (n_samples)
Data samples, where n_samples is the number of samples.
y : array-like, shape = (n_samples)
Target vector relative to x.
special_codes : array-like or None, optional (default=None)
List of special codes. Use special codes to specify the data values
that must be treated separately.
cat_cutoff : float or None, optional (default=None)
Generate bin others with categories in which the fraction of
occurrences is below the ``cat_cutoff`` value. This option is
available when ``dtype`` is "categorical".
user_splits : array-like or None, optional (default=None)
The list of pre-binning split points when ``dtype`` is "numerical" or
the list of prebins when ``dtype`` is "categorical".
check_input : bool, (default=True)
If False, the input arrays x and y will not be checked.
outlier_detector : str or None (default=None)
The outlier detection method. Supported methods are "range" to use
the interquartile range based method or "zcore" to use the modified
Z-score method.
outlier_params : dict or None (default=None)
Dictionary of parameters to pass to the outlier detection method.
fix_lb : float or None (default=None)
Lower bound or minimum admissible value.
fix_ub : float or None (default=None)
Upper bound or maximum admissible value.
class_weight : None
sample_weight : None
Returns
-------
x_clean : array, shape = (n_clean)
Clean data samples
y_clean : array, shape = (n_clean)
Clean target samples.
x_missing : array, shape = (n_missing)
Missing data samples.
y_missing : array, shape = (n_missing)
Missing target samples.
x_special : array, shape = (n_special)
Special data samples.
y_special : array, shape = (n_special)
Special target samples.
y_others : array, shape = (n_others)
Others target samples.
categories : array, shape (n_categories)
List of categories.
others : array, shape (n_other_categories)
List of other categories.
"""
if outlier_detector is not None:
if outlier_detector not in ("range", "zscore"):
raise ValueError('Invalid value for outlier_detector. Allowed '
'string values are "range" and "zscore".')
if outlier_params is not None:
if not isinstance(outlier_params, dict):
raise TypeError("outlier_params must be a dict or None; "
"got {}.".format(outlier_params))
if fix_lb is not None:
if not isinstance(fix_lb, numbers.Number):
raise ValueError("fix_lb must be a number; got {}.".format(fix_lb))
if fix_ub is not None:
if not isinstance(fix_ub, numbers.Number):
raise ValueError("fix_ub must be a number; got {}.".format(fix_ub))
if fix_lb is not None and fix_ub is not None:
if fix_lb > fix_ub:
raise ValueError("fix_lb must be <= fix_ub; got {} <= {}.".format(
fix_lb, fix_ub))
if check_input:
x = check_array(x,
ensure_2d=False,
dtype=None,
force_all_finite='allow-nan')
y = check_array(y, ensure_2d=False, dtype=None, force_all_finite=True)
check_consistent_length(x, y)
x = np.asarray(x)
y = np.asarray(y)
sample_weight = _check_sample_weight(sample_weight, x, dtype=x.dtype)
if class_weight is not None:
classes = np.unique(y)
le = LabelEncoder()
class_weight_ = compute_class_weight(class_weight, classes, y)
sample_weight *= class_weight_[le.fit_transform(y)]
if isinstance(x.dtype, object) or isinstance(y.dtype, object):
missing_mask = pd.isnull(x) | pd.isnull(y)
else:
missing_mask = np.isinan(x) | np.isnan(y)
if special_codes is None:
clean_mask = ~missing_mask
x_clean = x[clean_mask]
y_clean = y[clean_mask]
x_missing = x[missing_mask]
y_missing = y[missing_mask]
x_special = []
y_special = []
sw_clean = sample_weight[clean_mask]
sw_missing = sample_weight[missing_mask]
sw_special = []
else:
special_mask = pd.Series(x).isin(special_codes).values
clean_mask = ~missing_mask & ~special_mask
x_clean = x[clean_mask]
y_clean = y[clean_mask]
x_missing = x[missing_mask]
y_missing = y[missing_mask]
x_special = x[special_mask]
y_special = y[special_mask]
sw_clean = sample_weight[clean_mask]
sw_missing = sample_weight[missing_mask]
sw_special = sample_weight[special_mask]
if dtype == "numerical":
if outlier_detector is not None:
if outlier_detector == "range":
detector = RangeDetector()
elif outlier_detector == "zscore":
detector = ModifiedZScoreDetector()
if outlier_params is not None:
detector.set_params(**outlier_params)
mask_outlier = detector.fit(x_clean).get_support()
x_clean = x_clean[~mask_outlier]
y_clean = y_clean[~mask_outlier]
sw_clean = sw_clean[~mask_outlier]
if fix_lb is not None or fix_ub is not None:
if fix_lb is not None:
mask = x_clean >= fix_lb
elif fix_ub is not None:
mask = x_clean <= fix_ub
else:
mask = (x_clean >= fix_lb) & (x_clean <= fix_ub)
x_clean = x_clean[mask]
y_clean = y_clean[mask]
sw_clean = sw_clean[mask]
if dtype == "categorical" and user_splits is None:
if cat_cutoff is not None:
mask_others, others = categorical_cutoff(x_clean, y_clean,
cat_cutoff)
y_others = y_clean[mask_others]
sw_others = sw_clean[mask_others]
x_clean = x_clean[~mask_others]
y_clean = y_clean[~mask_others]
sw_clean = sw_clean[~mask_others]
else:
y_others = []
others = []
sw_others = []
categories, x_clean = categorical_transform(x_clean, y_clean)
return (x_clean, y_clean, x_missing, y_missing, x_special, y_special,
y_others, categories, others, sw_clean, sw_missing, sw_special,
sw_others)
else:
return (x_clean, y_clean, x_missing, y_missing, x_special, y_special,
[], [], [], sw_clean, sw_missing, sw_special, [])
def split_data(dtype, x, y, special_codes=None, cat_cutoff=None,
user_splits=None, check_input=True):
"""Split data into clean, missing and special values data.
Parameters
----------
x : array-like, shape = (n_samples)
Data samples, where n_samples is the number of samples.
y : array-like, shape = (n_samples)
Target vector relative to x.
special_codes : array-like or None (default=None)
List of special values to be considered.
user_splits_categorical : bool
check_input : bool, (default=True)
If False, the input arrays x and y will not be checked.
Returns
-------
x_clean : array, shape = (n_clean)
Clean data samples
y_clean : array, shape = (n_clean)
Clean target samples.
x_missing : array, shape = (n_missing)
Missing data samples.
y_missing : array, shape = (n_missing)
Missing target samples.
x_special : array, shape = (n_special)
Special data samples.
y_special : array, shape = (n_special)
Special target samples.
y_others :
categories :
others :
"""
if check_input:
x = check_array(x, ensure_2d=False, dtype=None,
force_all_finite='allow-nan')
y = check_array(y, ensure_2d=False, dtype=None,
force_all_finite=True)
check_consistent_length(x, y)
x = np.asarray(x)
y = np.asarray(y)
if isinstance(x.dtype, object) or isinstance(y.dtype, object):
missing_mask = pd.isnull(x) | pd.isnull(y)
else:
missing_mask = np.isinan(x) | np.isnan(y)
if special_codes is None:
clean_mask = ~missing_mask
x_clean = x[clean_mask]
y_clean = y[clean_mask]
x_missing = x[missing_mask]
y_missing = y[missing_mask]
x_special = []
y_special = []
else:
special_mask = pd.Series(x).isin(special_codes).values
clean_mask = ~missing_mask & ~special_mask
x_clean = x[clean_mask]
y_clean = y[clean_mask]
x_missing = x[missing_mask]
y_missing = y[missing_mask]
x_special = x[special_mask]
y_special = y[special_mask]
if dtype == "categorical" and user_splits is None:
if cat_cutoff is not None:
mask_others, others = categorical_cutoff(
x_clean, y_clean, cat_cutoff)
y_others = y_clean[mask_others]
x_clean = x_clean[~mask_others]
y_clean = y_clean[~mask_others]
else:
y_others = []
others = []
categories, x_clean = categorical_transform(x_clean, y_clean)
return (x_clean, y_clean, x_missing, y_missing, x_special, y_special,
y_others, categories, others)
else:
return (x_clean, y_clean, x_missing, y_missing, x_special, y_special,
[], [], [])
def transform_continuous_target(splits, dtype, x, n_records, sums,
special_codes, categories, cat_others,
metric_special, metric_missing, user_splits,
check_input):
_check_metric_special_missing(metric_special, metric_missing)
if check_input:
x = check_array(x,
ensure_2d=False,
dtype=None,
force_all_finite='allow-nan')
x = np.asarray(x)
if isinstance(x.dtype, object):
missing_mask = pd.isnull(x)
else:
missing_mask = np.isinan(x)
if special_codes is None:
clean_mask = ~missing_mask
else:
special_mask = pd.Series(x).isin(special_codes).values
clean_mask = ~missing_mask & ~special_mask
x_clean = x[clean_mask]
if dtype == "numerical":
if len(splits):
indices = np.digitize(x_clean, splits, right=False)
else:
indices = np.zeros(x_clean.shape)
n_bins = len(splits) + 1
else:
bins = bin_categorical(splits, categories, cat_others, user_splits)
n_bins = len(bins)
if "empirical" not in (metric_special, metric_missing):
n_records = n_records[:n_bins]
sums = sums[:n_bins]
metric_value = sums / n_records
x_transform = np.zeros(x.shape)
if dtype == "numerical":
x_clean_transform = np.zeros(x_clean.shape)
for i in range(n_bins):
mask = (indices == i)
x_clean_transform[mask] = metric_value[i]
x_transform[clean_mask] = x_clean_transform
else:
x_p = pd.Series(x)
for i in range(n_bins):
mask = x_p.isin(bins[i])
x_transform[mask] = metric_value[i]
if special_codes:
if metric_special == "empirical":
x_transform[special_mask] = metric_value[n_bins]
else:
x_transform[special_mask] = metric_special
if metric_missing == "empirical":
x_transform[missing_mask] = metric_value[n_bins + 1]
else:
x_transform[missing_mask] = metric_missing
return x_transform
def transform_binary_target(splits,
dtype,
x,
n_nonevent,
n_event,
special_codes,
categories,
cat_others,
metric,
metric_special,
metric_missing,
user_splits,
check_input=False):
if metric not in ("event_rate", "woe"):
raise ValueError('Invalid value for metric. Allowed string '
'values are "event_rate" and "woe".')
_check_metric_special_missing(metric_special, metric_missing)
if check_input:
x = check_array(x,
ensure_2d=False,
dtype=None,
force_all_finite='allow-nan')
x = np.asarray(x)
if isinstance(x.dtype, object):
missing_mask = pd.isnull(x)
else:
missing_mask = np.isinan(x)
if special_codes is None:
clean_mask = ~missing_mask
else:
special_mask = pd.Series(x).isin(special_codes).values
clean_mask = ~missing_mask & ~special_mask
x_clean = x[clean_mask]
if dtype == "numerical":
if len(splits):
indices = np.digitize(x_clean, splits, right=False)
else:
indices = np.zeros(x_clean.shape)
n_bins = len(splits) + 1
else:
bins = bin_categorical(splits, categories, cat_others, user_splits)
n_bins = len(bins)
# Compute event rate and WoE
n_records = n_event + n_nonevent
t_n_nonevent = n_nonevent.sum()
t_n_event = n_event.sum()
if "empirical" not in (metric_special, metric_missing):
n_event = n_event[:n_bins]
n_nonevent = n_nonevent[:n_bins]
n_records = n_records[:n_bins]
# default woe and event rate is 0
mask = (n_event > 0) & (n_nonevent > 0)
event_rate = np.zeros(len(n_records))
woe = np.zeros(len(n_records))
event_rate[mask] = n_event[mask] / n_records[mask]
constant = np.log(t_n_event / t_n_nonevent)
woe[mask] = np.log(1 / event_rate[mask] - 1) + constant
if metric == "woe":
metric_value = woe
else:
metric_value = event_rate
x_transform = np.zeros(x.shape)
if dtype == "numerical":
x_clean_transform = np.zeros(x_clean.shape)
for i in range(n_bins):
mask = (indices == i)
x_clean_transform[mask] = metric_value[i]
x_transform[clean_mask] = x_clean_transform
else:
x_p = pd.Series(x)
for i in range(n_bins):
mask = x_p.isin(bins[i])
x_transform[mask] = metric_value[i]
if special_codes:
if metric_special == "empirical":
x_transform[special_mask] = metric_value[n_bins]
else:
x_transform[special_mask] = metric_special
if metric_missing == "empirical":
x_transform[missing_mask] = metric_value[n_bins + 1]
else:
x_transform[missing_mask] = metric_missing
return x_transform
def transform_multiclass_target(splits,
x,
n_event,
special_codes,
metric,
metric_special,
metric_missing,
check_input=False):
if metric not in ("mean_woe", "weighted_mean_woe"):
raise ValueError('Invalid value for metric. Allowed string '
'values are "mean_woe" and "weighted_mean_woe".')
_check_metric_special_missing(metric_special, metric_missing)
if check_input:
x = check_array(x,
ensure_2d=False,
dtype=None,
force_all_finite='allow-nan')
x = np.asarray(x)
if isinstance(x.dtype, object):
missing_mask = pd.isnull(x)
else:
missing_mask = np.isinan(x)
if special_codes is None:
clean_mask = ~missing_mask
else:
special_mask = pd.Series(x).isin(special_codes).values
clean_mask = ~missing_mask & ~special_mask
x_clean = x[clean_mask]
if len(splits):
indices = np.digitize(x_clean, splits, right=False)
else:
indices = np.zeros(x_clean.shape)
n_bins = len(splits) + 1
# Build non-event to compute one-vs-all WoE
n_classes = n_event.shape[1]
n_records = np.tile(n_event.sum(axis=1), (n_classes, 1)).T
n_nonevent = n_records - n_event
t_n_nonevent = n_nonevent.sum(axis=0)
t_n_event = n_event.sum(axis=0)
if "empirical" not in (metric_special, metric_missing):
n_event = n_event[:n_bins, :]
n_nonevent = n_nonevent[:n_bins, :]
n_records = n_records[:n_bins, :]
event_rate = n_event / n_records
woe = np.zeros(n_event.shape)
for i in range(n_classes):
woe[:, i] = transform_event_rate_to_woe(event_rate[:, i],
t_n_nonevent[i], t_n_event[i])
if metric == "mean_woe":
metric_value = woe.mean(axis=1)
elif metric == "weighted_mean_woe":
metric_value = np.average(woe, weights=t_n_event, axis=1)
x_transform = np.zeros(x.shape)
x_clean_transform = np.zeros(x_clean.shape)
for i in range(n_bins):
mask = (indices == i)
x_clean_transform[mask] = metric_value[i]
x_transform[clean_mask] = x_clean_transform
if special_codes:
if metric_special == "empirical":
x_transform[special_mask] = metric_value[n_bins]
else:
x_transform[special_mask] = metric_special
if metric_missing == "empirical":
x_transform[missing_mask] = metric_value[n_bins + 1]
else:
x_transform[missing_mask] = metric_missing
return x_transform
