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Set max_length_line to 88 manually for all docstrings
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@sidchaini
sidchaini committed May 12, 2024
1 parent 9114296 commit 674514d
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3 changes: 2 additions & 1 deletion distclassipy/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,8 @@
"""A module for using distance metrics for classification.
Classes:
DistanceMetricClassifier - A classifier that uses a specified distance metric for classification.
DistanceMetricClassifier - A classifier that uses a specified distance metric for
classification.
Distance - A class that provides various distance metrics for use in classification.
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85 changes: 61 additions & 24 deletions distclassipy/classifier.py
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Expand Up @@ -43,18 +43,29 @@ class DistanceMetricClassifier(BaseEstimator, ClassifierMixin):
"""A distance-based classifier that supports the use of various distance
metrics.
The distance metric classifier determines the similarity between features in a dataset by leveraging the use of different distance metrics to. A specified distance metric is used to compute the distance between a given object and a centroid for every training class in the feature space. The classifier supports the use of different statistical measures for constructing the centroid and scaling the computed distance. Additionally, the distance metric classifier also optionally provides an estimate of the confidence of the classifier's predictions.
The distance metric classifier determines the similarity between features in a
dataset by leveraging the use of different distance metrics to. A specified
distance metric is used to compute the distance between a given object and a
centroid for every training class in the feature space. The classifier supports
the use of different statistical measures for constructing the centroid and scaling
the computed distance. Additionally, the distance metric classifier also
optionally provides an estimate of the confidence of the classifier's predictions.
Parameters
----------
metric : str or callable, default="euclidean"
The distance metric to use for calculating the distance between features.
scale : bool, default=True
Whether to scale the distance between the test object and the centroid for a class in the feature space. If True, the data will be scaled based on the specified dispersion statistic.
Whether to scale the distance between the test object and the centroid for a
class in the feature space. If True, the data will be scaled based on the
specified dispersion statistic.
central_stat : {"mean", "median"}, default="median"
The statistic used to calculate the central tendency of the data to construct the feature-space centroid. Supported statistics are "mean" and "median".
The statistic used to calculate the central tendency of the data to construct
the feature-space centroid. Supported statistics are "mean" and "median".
dispersion_stat : {"std", "iqr"}, default="std"
The statistic used to calculate the dispersion of the data for scaling the distance. Supported statistics are "std" for standard deviation and "iqr" for inter-quartile range.
The statistic used to calculate the dispersion of the data for scaling the
distance. Supported statistics are "std" for standard deviation and "iqr"
for inter-quartile range.
.. versionadded:: 0.1.0
Expand Down Expand Up @@ -85,11 +96,14 @@ class DistanceMetricClassifier(BaseEstimator, ClassifierMixin):
Notes
-----
If using distance metrics supported by SciPy, it is desirable to pass a string, which allows SciPy to use an optimized C version of the code instead of the slower Python version.
If using distance metrics supported by SciPy, it is desirable to pass a string,
which allows SciPy to use an optimized C version of the code instead of the slower
Python version.
References
----------
.. [1] "Light Curve Classification with DistClassiPy: a new distance-based classifier"
.. [1] "Light Curve Classification with DistClassiPy: a new distance-based
classifier"
Examples
--------
Expand Down Expand Up @@ -122,7 +136,7 @@ def __init__(
self.calculate_kde = calculate_kde
self.calculate_1d_dist = calculate_1d_dist

def set_metric_fn_(self):
def initialize_metric_function(self):
"""Set the metric function based on the provided metric.
If the metric is a string, the function will look for a
Expand All @@ -140,7 +154,8 @@ def set_metric_fn_(self):
metric_found = False
for package_str, source in METRIC_SOURCES_.items():

# Don't use scipy for jaccard as their implementation only works with booleans - use custom jaccard instead
# Don't use scipy for jaccard as their implementation only works with
# booleans - use custom jaccard instead
if (
package_str == "scipy.spatial.distance"
and metric_str_lowercase == "jaccard"
Expand All @@ -151,7 +166,8 @@ def set_metric_fn_(self):
self.metric_fn_ = getattr(source, metric_str_lowercase)
metric_found = True

# Use the string as an argument if it belongs to scipy as it is optimized
# Use the string as an argument if it belongs to scipy as it is
# optimized
self.metric_arg_ = (
self.metric
if package_str == "scipy.spatial.distance"
Expand All @@ -160,15 +176,21 @@ def set_metric_fn_(self):
break
if not metric_found:
raise ValueError(
f"{self.metric} metric not found. Please pass a string of the name of a metric in scipy.spatial.distance or distances.Distance, or pass a metric function directly. For a list of available metrics, see: https://sidchaini.github.io/DistClassiPy/distances.html or https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
f"{self.metric} metric not found. Please pass a string of the "
"name of a metric in scipy.spatial.distance or "
"distances.Distance, or pass a metric function directly. For a "
"list of available metrics, see: "
"https://sidchaini.github.io/DistClassiPy/distances.html or "
"https://docs.scipy.org/doc/scipy/reference/spatial.distance.html"
)

def fit(self, X: np.array, y: np.array, feat_labels: list[str] = None):
"""Calculate the feature space centroid for all classes in the training
set (X,y) using the central statistic. If scaling is enabled, also
calculate the appropriate dispersion statistic.
This involves computing the centroid for every class in the feature space and optionally calculating the kernel density estimate and 1-dimensional distance.
This involves computing the centroid for every class in the feature space and
optionally calculating the kernel density estimate and 1-dimensional distance.
Parameters
----------
Expand All @@ -177,7 +199,8 @@ def fit(self, X: np.array, y: np.array, feat_labels: list[str] = None):
y : array-like of shape (n_samples,)
The target values (class labels).
feat_labels : list of str, optional, default=None
The feature labels. If not provided, default labels representing feature number will be used.
The feature labels. If not provided, default labels representing feature
number will be used.
Returns
-------
Expand All @@ -188,7 +211,7 @@ def fit(self, X: np.array, y: np.array, feat_labels: list[str] = None):
self.classes_ = unique_labels(y)
self.n_features_in_ = X.shape[1]

self.set_metric_fn_()
self.initialize_metric_function()

if feat_labels is None:
feat_labels = [f"Feature_{x}" for x in range(X.shape[1])]
Expand All @@ -209,7 +232,8 @@ def fit(self, X: np.array, y: np.array, feat_labels: list[str] = None):
std_list = []
for cur_class in self.classes_:
cur_X = X[y == cur_class]
# Note we're using ddof=1 because we're dealing with a sample. See more: https://stackoverflow.com/a/46083501/10743245
# Note we're using ddof=1 because we're dealing with a sample.
# See more: https://stackoverflow.com/a/46083501/10743245
std_list.append(np.std(cur_X, axis=0, ddof=1).ravel())
df_std = pd.DataFrame(
data=np.array(std_list), index=self.classes_, columns=feat_labels
Expand All @@ -221,7 +245,8 @@ def fit(self, X: np.array, y: np.array, feat_labels: list[str] = None):

for cur_class in self.classes_:
cur_X = X[y == cur_class]
# Note we're using ddof=1 because we're dealing with a sample. See more: https://stackoverflow.com/a/46083501/10743245
# Note we're using ddof=1 because we're dealing with a sample.
# See more: https://stackoverflow.com/a/46083501/10743245
iqr_list.append(
np.quantile(cur_X, q=0.75, axis=0).ravel()
- np.quantile(cur_X, q=0.25, axis=0).ravel()
Expand Down Expand Up @@ -254,7 +279,9 @@ def fit(self, X: np.array, y: np.array, feat_labels: list[str] = None):
def predict(self, X: np.array):
"""Predict the class labels for the provided X.
The prediction is based on the distance of each data point in the input sample to the centroid for each class in the feature space. The predicted class is the one whose centroid is the closest to the input sample.
The prediction is based on the distance of each data point in the input sample
to the centroid for each class in the feature space. The predicted class is the
one whose centroid is the closest to the input sample.
Parameters
----------
Expand Down Expand Up @@ -300,9 +327,12 @@ def predict(self, X: np.array):
def predict_and_analyse(self, X: np.array):
"""Predict the class labels for the provided X and perform analysis.
The prediction is based on the distance of each data point in the input sample to the centroid for each class in the feature space. The predicted class is the one whose centroid is the closest to the input sample.
The prediction is based on the distance of each data point in the input sample
to the centroid for each class in the feature space. The predicted class is the
one whose centroid is the closest to the input sample.
The analysis involves saving all calculated distances and confidences as an attribute for inspection and analysis later.
The analysis involves saving all calculated distances and confidences as an
attribute for inspection and analysis later.
Parameters
----------
Expand Down Expand Up @@ -403,17 +433,22 @@ def predict_and_analyse(self, X: np.array):
def calculate_confidence(self, method: str = "distance_inverse"):
"""Calculate the confidence for each prediction.
The confidence is calculated based on either the distance of each data point to the centroids of the training data, optionally the kernel density estimate or 1-dimensional distance.
The confidence is calculated based on either the distance of each data point to
the centroids of the training data, optionally the kernel density estimate or
1-dimensional distance.
Parameters
----------
method : {"distance_inverse", "1d_distance_inverse", "kde_likelihood"}, default="distance_inverse"
The method to use for calculating confidence. Default is 'distance_inverse'.
method : {"distance_inverse", "1d_distance_inverse","kde_likelihood"},
default="distance_inverse"
The method to use for calculating confidence. Default is
'distance_inverse'.
"""
check_is_fitted(self, "is_fitted_")
if not hasattr(self, "analyis_"):
raise ValueError(
"Use predict_and_analyse() instead of predict() for confidence calculation."
"Use predict_and_analyse() instead of predict() for "
"confidence calculation."
)

# Calculate confidence for each prediction
Expand All @@ -428,7 +463,8 @@ def calculate_confidence(self, method: str = "distance_inverse"):
elif method == "1d_distance_inverse":
if not self.calculate_1d_dist:
raise ValueError(
"method='1d_distance_inverse' is only valid if calculate_1d_dist is set to True"
"method='1d_distance_inverse' is only valid if calculate_1d_dist "
"is set to True"
)
self.confidence_df_ = pd.DataFrame(
data=self.conf_cl_.T, columns=[f"{x}_conf" for x in self.classes_]
Expand All @@ -437,7 +473,8 @@ def calculate_confidence(self, method: str = "distance_inverse"):
elif method == "kde_likelihood":
if not self.calculate_kde:
raise ValueError(
"method='kde_likelihood' is only valid if calculate_kde is set to True"
"method='kde_likelihood' is only valid if calculate_kde is set "
"to True"
)

self.confidence_df_ = pd.DataFrame(
Expand Down
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