fixing issue #156

README.md

@@ -30,7 +30,7 @@ If you wish to install from source:
 pip install git+https://github.com/shakedzy/dython.git
 ```
 
-**Dependencies:** `numpy`, `pandas`, `seaborn`, `scipy`, `matplotlib`, `sklearn`, `scikit-plot`
+**Dependencies:** `numpy`, `pandas`, `seaborn`, `scipy`, `matplotlib`, `sklearn`
 
 ## Contributing:
 Contributions are always welcomed - if you found something you can fix, or have an idea for a new feature, feel free to write it and open a pull request. Please make sure to go over the [contributions guidelines](https://github.com/shakedzy/dython/blob/master/CONTRIBUTING.md).

dython/model_utils.py

@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import roc_curve, precision_recall_curve, auc
-from scikitplot.helpers import binary_ks_curve
+from sklearn.preprocessing import LabelEncoder
 from typing import List, Union, Optional, Tuple, Dict, Any, Iterable
 from numpy.typing import NDArray
 from .typing import Number, OneDimArray
@@ -536,8 +536,8 @@ def ks_abc(
             )
         )
 
-    thresholds, nr, pr, ks_statistic, max_distance_at, _ = binary_ks_curve(
-        y_t, y_p
+    thresholds, nr, pr, ks_statistic, max_distance_at, _ = _binary_ks_curve(
+        y_t, y_p  # type: ignore
     )
     if ax is None:
         plt.figure(figsize=figsize)
@@ -584,3 +584,111 @@ def ks_abc(
         "eopt": max_distance_at,
         "ax": axis,
     }
+
+
+def _binary_ks_curve(
+    y_true: OneDimArray, y_probas: OneDimArray
+) -> Tuple[NDArray, NDArray, NDArray, Number, Number, NDArray]:
+    """Copied from scikit-plot: https://github.com/reiinakano/scikit-plot/blob/master/scikitplot/helpers.py
+
+    This function generates the points necessary to calculate the KS
+    Statistic curve.
+
+    Args:
+        y_true (array-like, shape (n_samples)): True labels of the data.
+
+        y_probas (array-like, shape (n_samples)): Probability predictions of
+            the positive class.
+
+    Returns:
+        thresholds (numpy.ndarray): An array containing the X-axis values for
+            plotting the KS Statistic plot.
+
+        pct1 (numpy.ndarray): An array containing the Y-axis values for one
+            curve of the KS Statistic plot.
+
+        pct2 (numpy.ndarray): An array containing the Y-axis values for one
+            curve of the KS Statistic plot.
+
+        ks_statistic (float): The KS Statistic, or the maximum vertical
+            distance between the two curves.
+
+        max_distance_at (float): The X-axis value at which the maximum vertical
+            distance between the two curves is seen.
+
+        classes (np.ndarray, shape (2)): An array containing the labels of the
+            two classes making up `y_true`.
+
+    Raises:
+        ValueError: If `y_true` is not composed of 2 classes. The KS Statistic
+            is only relevant in binary classification.
+    """
+    y_true, y_probas = np.asarray(y_true), np.asarray(y_probas)
+    lb = LabelEncoder()
+    encoded_labels = lb.fit_transform(y_true)
+    if len(lb.classes_) != 2:
+        raise ValueError(
+            "Cannot calculate KS statistic for data with "
+            "{} category/ies".format(len(lb.classes_))
+        )
+    idx = encoded_labels == 0
+    data1 = np.sort(y_probas[idx])
+    data2 = np.sort(y_probas[np.logical_not(idx)])
+
+    ctr1, ctr2 = 0, 0
+    thresholds, pct1, pct2 = [], [], []
+    while ctr1 < len(data1) or ctr2 < len(data2):
+        # Check if data1 has no more elements
+        if ctr1 >= len(data1):
+            current = data2[ctr2]
+            while ctr2 < len(data2) and current == data2[ctr2]:
+                ctr2 += 1
+
+        # Check if data2 has no more elements
+        elif ctr2 >= len(data2):
+            current = data1[ctr1]
+            while ctr1 < len(data1) and current == data1[ctr1]:
+                ctr1 += 1
+
+        else:
+            if data1[ctr1] > data2[ctr2]:
+                current = data2[ctr2]
+                while ctr2 < len(data2) and current == data2[ctr2]:
+                    ctr2 += 1
+
+            elif data1[ctr1] < data2[ctr2]:
+                current = data1[ctr1]
+                while ctr1 < len(data1) and current == data1[ctr1]:
+                    ctr1 += 1
+
+            else:
+                current = data2[ctr2]
+                while ctr2 < len(data2) and current == data2[ctr2]:
+                    ctr2 += 1
+                while ctr1 < len(data1) and current == data1[ctr1]:
+                    ctr1 += 1
+
+        thresholds.append(current)
+        pct1.append(ctr1)
+        pct2.append(ctr2)
+
+    thresholds = np.asarray(thresholds)
+    pct1 = np.asarray(pct1) / float(len(data1))
+    pct2 = np.asarray(pct2) / float(len(data2))
+
+    if thresholds[0] != 0:
+        thresholds = np.insert(thresholds, 0, [0.0])  # type: ignore
+        pct1 = np.insert(pct1, 0, [0.0])  # type: ignore
+        pct2 = np.insert(pct2, 0, [0.0])  # type: ignore
+    if thresholds[-1] != 1:
+        thresholds = np.append(thresholds, [1.0])  # type: ignore
+        pct1 = np.append(pct1, [1.0])  # type: ignore
+        pct2 = np.append(pct2, [1.0])  # type: ignore
+
+    differences = pct1 - pct2
+    ks_statistic, max_distance_at = (
+        np.max(differences),
+        thresholds[np.argmax(differences)],
+    )
+
+    return thresholds, pct1, pct2, ks_statistic, max_distance_at, lb.classes_  # type: ignore

requirements.txt

@@ -4,5 +4,4 @@ seaborn>=0.12.0
 scipy>=1.7.1
 matplotlib>=3.6.0
 scikit-learn>=0.24.2
-scikit-plot>=0.3.7
 psutil>=5.9.1