install fix

.ipynb_checkpoints/MANIFEST-checkpoint.in

@@ -0,0 +1,3 @@
+include README.md
+include LICENSE
+recursive-include selfcalibratingconformal *

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/setup-checkpoint.py

@@ -1,8 +1,8 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='SelfCalibratingConformal',
-    version='1.0',
+    name='selfcalibratingconformal',
+    version='1.11',
     packages=find_packages(),
     description='A Python implementation of Self-Calibrating Conformal Prediction',
     long_description='A Python implementation of Self-Calibrating Conformal Prediction',

MANIFEST.in

@@ -0,0 +1,3 @@
+include README.md
+include LICENSE
+recursive-include selfcalibratingconformal *

SelfCalibratingConformal.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
-Metadata-Version: 2.1
-Name: SelfCalibratingConformal
-Version: 1.0
+Metadata-Version: 2.2
+Name: selfcalibratingconformal
+Version: 1.11
 Summary: A Python implementation of Self-Calibrating Conformal Prediction
 Home-page: https://github.com/Larsvanderlaan/SelfCalibratingConformal
 Author: Lars van der Laan
@@ -16,5 +16,14 @@ Requires-Dist: scipy
 Requires-Dist: statsmodels
 Requires-Dist: matplotlib
 Requires-Dist: xgboost
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
 
 A Python implementation of Self-Calibrating Conformal Prediction

SelfCalibratingConformal.egg-info/SOURCES.txt

@@ -1,7 +1,30 @@
+MANIFEST.in
 README.md
 setup.py
-SelfCalibratingConformal.egg-info/PKG-INFO
-SelfCalibratingConformal.egg-info/SOURCES.txt
-SelfCalibratingConformal.egg-info/dependency_links.txt
-SelfCalibratingConformal.egg-info/requires.txt
-SelfCalibratingConformal.egg-info/top_level.txt
+selfcalibratingconformal/.DS_Store
+selfcalibratingconformal/SelfCalibratingConformalPredictor.py
+selfcalibratingconformal/__init__.py
+selfcalibratingconformal/calibrators.py
+selfcalibratingconformal/utils.py
+selfcalibratingconformal.egg-info/PKG-INFO
+selfcalibratingconformal.egg-info/SOURCES.txt
+selfcalibratingconformal.egg-info/dependency_links.txt
+selfcalibratingconformal.egg-info/requires.txt
+selfcalibratingconformal.egg-info/top_level.txt
+selfcalibratingconformal/.ipynb_checkpoints/SelfCalibratingConformalPredictor-checkpoint.py
+selfcalibratingconformal/.ipynb_checkpoints/__init__-checkpoint.py
+selfcalibratingconformal/.ipynb_checkpoints/calibrators-checkpoint.py
+selfcalibratingconformal/.ipynb_checkpoints/utils-checkpoint.py
+selfcalibratingconformal/__pycache__/SelfCalibratedConformalPredictor.cpython-311.pyc
+selfcalibratingconformal/__pycache__/SelfCalibratedConformalPredictor.cpython-39.pyc
+selfcalibratingconformal/__pycache__/SelfCalibratingConformalPredictor.cpython-311.pyc
+selfcalibratingconformal/__pycache__/SelfCalibratingConformalPredictor.cpython-312.pyc
+selfcalibratingconformal/__pycache__/SelfCalibratingConformalPredictor.cpython-313.pyc
+selfcalibratingconformal/__pycache__/calibrators.cpython-311.pyc
+selfcalibratingconformal/__pycache__/calibrators.cpython-312.pyc
+selfcalibratingconformal/__pycache__/calibrators.cpython-313.pyc
+selfcalibratingconformal/__pycache__/calibrators.cpython-39.pyc
+selfcalibratingconformal/__pycache__/utils.cpython-311.pyc
+selfcalibratingconformal/__pycache__/utils.cpython-312.pyc
+selfcalibratingconformal/__pycache__/utils.cpython-313.pyc
+selfcalibratingconformal/__pycache__/utils.cpython-39.pyc

SelfCalibratingConformal.egg-info/top_level.txt

@@ -1 +1 @@
-
+selfcalibratingconformal

SelfCalibratingConformal/.ipynb_checkpoints/SelfCalibratingConformalPredictor-checkpoint.py

@@ -6,8 +6,8 @@
 from statsmodels.nonparametric.kernel_regression import KernelReg
 import matplotlib.pyplot as plt
 # Package imports
-from SelfCalibratingConformal.calibrators import *
-from SelfCalibratingConformal.utils import *
+from selfcalibratingconformal.calibrators import *
+from selfcalibratingconformal.utils import *
 
 class SelfCalibratingConformalPredictor:
     def __init__(self, predictor: callable, calibrator = calibrator_isotonic, 

SelfCalibratingConformal/.ipynb_checkpoints/__init__-checkpoint.py

@@ -0,0 +1,2 @@
+from .SelfCalibratingConformalPredictor import *
+from .calibrators import *

SelfCalibratingConformal/.ipynb_checkpoints/calibrators-checkpoint.py

@@ -1,6 +1,6 @@
 import numpy as np
 import xgboost as xgb
-from SelfCalibratingConformal.utils import *
+from selfcalibratingconformal.utils import *
 
 
 def calibrator_isotonic(f: np.ndarray, y: np.ndarray, max_depth=20, min_child_weight=20):

SelfCalibratingConformal/.ipynb_checkpoints/utils-checkpoint.py

@@ -0,0 +1,61 @@
+import numpy as np
+import pandas as pd
+
+def make_grid(values, num_bin, values_to_add=None, binning_method="quantile"):
+    """
+    Generates a grid of values used for binning continuous data. This grid can be created using either
+    quantile-based binning or fixed-width binning.
+
+    Args:
+        values (array-like): The data values from which the grid is to be created.
+        num_bin (int): The number of bins to use for the grid.
+        values_to_add (list, optional): Additional specific values to include in the grid.
+        binning_method (str, optional): Method of binning ('quantile' or 'fixed'). Defaults to 'quantile'.
+
+    Returns:
+        list: A list of grid values that mark the boundaries of bins.
+
+    Note:
+        - 'quantile' binning divides the data into bins such that each bin has approximately the same number of values.
+        - 'fixed' binning divides the range of data into equally spaced segments based on the minimum and maximum values.
+    """
+    unique_values = list(set(values))
+    unique_values.sort()
+    if len(unique_values) <= num_bin:
+        return unique_values
+
+    if binning_method == "quantile":
+        qs = np.linspace(0, 1, num=num_bin + 1)
+        grid_values = set(np.quantile(values, qs, method='inverted_cdf'))
+        if values_to_add is not None:
+            grid_values = grid_values.union(set(values_to_add))
+        grid_values = list(grid_values)
+        grid_values.sort()
+    elif binning_method == "fixed":
+        grid_values = np.linspace(min(values), max(values), num=num_bin + 1)
+    return grid_values
+
+def match_grid_value(values, grid, return_index=False, all_inside=False):
+    """
+    Maps values to the nearest grid points or their indices. This function is useful for discretizing continuous values
+    or for aligning values with predefined bins.
+
+    Args:
+        values (array-like): Values to be matched to the grid.
+        grid (list): A grid of values, such as those generated by `make_grid`.
+        return_index (bool, optional): If True, returns the indices in the grid for each value instead of the grid values. Defaults to False.
+        all_inside (bool, optional): If True, adjusts the indices to ensure all values are within the grid limits. Defaults to False.
+
+    Returns:
+        list: Depending on `return_index`, either a list of grid values matching each input value or a list of indices indicating the position in the grid.
+    """
+    bin_index = np.searchsorted(grid, values, side='right')
+    if all_inside:
+        bin_index[bin_index == len(grid)] = len(grid) - 1
+    bin_index = [i if i == 0 else i-1 for i in bin_index]
+
+    if not return_index:
+        values_discrete = [grid[i] for i in bin_index]
+        return values_discrete
+    else:
+        return bin_index

SelfCalibratingConformal/SelfCalibratingConformalPredictor.py

@@ -6,8 +6,8 @@
 from statsmodels.nonparametric.kernel_regression import KernelReg
 import matplotlib.pyplot as plt
 # Package imports
-from SelfCalibratingConformal.calibrators import *
-from SelfCalibratingConformal.utils import *
+from selfcalibratingconformal.calibrators import *
+from selfcalibratingconformal.utils import *
 
 class SelfCalibratingConformalPredictor:
     def __init__(self, predictor: callable, calibrator = calibrator_isotonic, 

SelfCalibratingConformal/__init__.py

@@ -0,0 +1,2 @@
+from .SelfCalibratingConformalPredictor import *
+from .calibrators import *

SelfCalibratingConformal/calibrators.py

@@ -1,6 +1,6 @@
 import numpy as np
 import xgboost as xgb
-from SelfCalibratingConformal.utils import *
+from selfcalibratingconformal.utils import *
 
 
 def calibrator_isotonic(f: np.ndarray, y: np.ndarray, max_depth=20, min_child_weight=20):