update: setuptools and m1 support

Author: Secbone

.github/workflows/linux.yml

@@ -21,7 +21,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          architecture: x64
       - run: make build_deps
       - run: pip install -r requirements-nn.txt
       - run: pip install .[all]

.github/workflows/macos.yml

@@ -7,32 +7,33 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
-        macos-version: ['macos-12', 'macos-latest']
+        macos-version: ['macos-13', 'macos-latest']
         include:
           - experimental: false
           - macos-version: 'macos-latest'
             experimental: true
+          - python-version: '3.9'
+            experimental: true
           - python-version: '3.13'
             experimental: true
       fail-fast: false
     runs-on: ${{ matrix.macos-version }}
     continue-on-error: ${{ matrix.experimental }}
-    name: Test py ${{ matrix.python-version }}
+    name: Test py ${{ matrix.python-version }} ${{ matrix.macos-version }}
     steps:
       - uses: actions/checkout@master
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          architecture: x64
       - run: make build_deps
       - run: pip install -r requirements-nn.txt
       - run: pip install .[all]
       - run: make test
       - run: make dist_wheel
       - uses: actions/upload-artifact@v4
         with:
-          name: wheel-${{ matrix.python-version }}
+          name: wheel-${{ matrix.python-version }}-${{ matrix.macos-version }}
           path: dist/*.whl
   release:
     needs: [test]

.github/workflows/windows.yml

@@ -21,7 +21,6 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          architecture: x64
       - run: make build_deps
       - run: pip install -r requirements-nn.txt
       - run: pip install .[all]

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Add
+- Added `ax` support for `plot`
+- Added `Apple M1` support
+
 ## [0.1.4] - 2024-11-03
 
 ### Add

pyproject.toml

@@ -1,14 +1,13 @@
 [project]
 name = "toad"
 description = "Toad is dedicated to facilitating model development process, especially for a scorecard."
-requires-python = ">=3.7"
+requires-python = ">=3.9"
 license = {file = "LICENSE"}
 authors = [{name = "Secbone", email = "secbone@gmail.com"}]
 classifiers = [
     'Operating System :: POSIX',
     'Operating System :: Microsoft :: Windows',
     'Operating System :: MacOS :: MacOS X',
-    'Programming Language :: Python :: 3.8',
     'Programming Language :: Python :: 3.9',
     'Programming Language :: Python :: 3.10',
     'Programming Language :: Python :: 3.11',

requirements.txt

@@ -1,7 +1,10 @@
-Cython >= 0.29.15
-numpy >= 1.20
+Cython <= 0.29.15 ; python_version < "3.10"
+Cython >= 0.29.15 ; python_version >= "3.10"
+numpy <= 1.24 ; python_version < "3.10"
+numpy >= 1.24 ; python_version >= "3.10"
 pandas >= 1.5
 scipy
 joblib >= 0.12
 scikit-learn >= 0.21
 seaborn >= 0.10.0
+setuptools

setup.cfg

@@ -6,7 +6,3 @@ universal=1
 
 [aliases]
 test=pytest
-
-[metadata]
-description-file = README.md
-license_file = LICENSE

toad/plot.py

@@ -107,7 +107,7 @@ def badrate_plot(frame, x = None, target = 'target', by = None,
     return unpack_tuple(res)
 
 
-def corr_plot(frame, figure_size = (20, 15)):
+def corr_plot(frame, figure_size = (20, 15), ax = None):
     """plot for correlation
 
     Args:
@@ -133,12 +133,13 @@ def corr_plot(frame, figure_size = (20, 15)):
         annot = True,
         fmt = '.2f',
         figure_size = figure_size,
+        ax = ax,
     )
 
     return map_plot
 
 
-def proportion_plot(x = None, keys = None):
+def proportion_plot(x = None, keys = None, ax = None):
     """plot for comparing proportion in different dataset
 
     Args:
@@ -175,12 +176,13 @@ def proportion_plot(x = None, keys = None):
         y = 'proportion',
         hue = 'keys',
         data = prop_data,
+        ax = ax,
     )
 
     return prop_plot
 
 
-def roc_plot(score, target, compare = None, figsize = (14, 10)):
+def roc_plot(score, target, compare = None, figsize = (14, 10), ax = None):
     """plot for roc
 
     Args:
@@ -193,7 +195,9 @@ def roc_plot(score, target, compare = None, figsize = (14, 10)):
     """
     auc, fpr, tpr, thresholds = AUC(score, target, return_curve = True)
 
-    fig, ax = plt.subplots(1, 1, figsize = figsize)
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize = figsize)
+    
     ax.plot(fpr, tpr, label = 'ROC curve (area = %0.5f)' % auc)
     ax.fill_between(fpr, tpr, alpha = 0.3)
     if compare is not None:
@@ -206,7 +210,7 @@ def roc_plot(score, target, compare = None, figsize = (14, 10)):
 
     return ax
 
-def ks_plot(score, target, figsize = (14, 10)):
+def ks_plot(score, target, figsize = (14, 10), ax = None):
     """plot for ks
 
     Args:
@@ -219,7 +223,9 @@ def ks_plot(score, target, figsize = (14, 10)):
     """
     fpr, tpr, thresholds = roc_curve(target, score)
 
-    fig, ax = plt.subplots(1, 1, figsize = figsize)
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize = figsize)
+    
     ax.plot(thresholds[1 : ], tpr[1 : ], label = 'tpr')
     ax.plot(thresholds[1 : ], fpr[1 : ], label = 'fpr')
     ax.plot(thresholds[1 : ], (tpr - fpr)[1 : ], label = 'ks')
@@ -235,7 +241,8 @@ def ks_plot(score, target, figsize = (14, 10)):
 
     return ax
 
-def bin_plot(frame, x = None, target = 'target', iv = True, annotate_format = ".2f", return_frame = False, figsize = (12, 6)):
+def bin_plot(frame, x = None, target = 'target', iv = True, annotate_format = ".2f", 
+            return_frame = False, figsize = (12, 6), ax = None):
     """plot for bins
 
     Args:
@@ -258,18 +265,21 @@ def bin_plot(frame, x = None, target = 'target', iv = True, annotate_format = ".
         target = temp_name
 
     table = feature_bin_stats(frame, x, target)
-    fig, prop_ax = plt.subplots(figsize=figsize)
-    prop_ax = tadpole.barplot(
+
+    if ax is None:
+        fig, ax = plt.subplots(figsize=figsize)
+    
+    ax = tadpole.barplot(
         x = x,
         y = 'prop',
         data = table,
         color = '#82C6E2',
-        ax = prop_ax,
+        ax = ax,
     )
 
-    prop_ax = add_annotate(prop_ax, format = annotate_format)
+    ax = add_annotate(ax, format = annotate_format)
 
-    badrate_ax = prop_ax.twinx()
+    badrate_ax = ax.twinx()
     badrate_ax.grid(False)
 
     badrate_ax = tadpole.lineplot(
@@ -284,10 +294,10 @@ def bin_plot(frame, x = None, target = 'target', iv = True, annotate_format = ".
     badrate_ax = add_annotate(badrate_ax, format = annotate_format)
 
     if iv:
-        prop_ax = reset_ylim(prop_ax)
-        prop_ax = add_text(prop_ax, 'IV: {:.5f}'.format(table['iv'].sum()))
+        ax = reset_ylim(ax)
+        ax = add_text(ax, 'IV: {:.5f}'.format(table['iv'].sum()))
 
-    res = (prop_ax,)
+    res = (ax,)
 
     if return_frame:
         res += (table,)

toad/utils/decorator.py

@@ -221,7 +221,7 @@ class xgb_loss(Decorator):
     >>> model = xgb.XGBClassifier(objective = xgb_func)
     """
     def wrapper(self, pred, label):
-        from scipy.misc import derivative
+        from .func import derivative
 
         def partial_func(x):
             return self.call(x, label, **self.kwargs)

toad/utils/func.py

@@ -408,3 +408,26 @@ def get_dummies(dataframe, exclude = None, binary_drop = False, **kwargs):
 
     data = pd.get_dummies(dataframe, columns = columns, **kwargs)
     return data
+
+
+
+def derivative(func, x0, dx=1.0, n=1, args=()):
+    """calculate derivative of function at x0 for order 3
+    """
+    from numpy import array, prod
+
+    order = 3
+    
+    if n == 1:
+        weights = array([-1, 0, 1]) / 2.0
+    elif n == 2:
+        weights = array([1, -2.0, 1])
+    else:
+        raise ValueError('n must be 1 or 2')
+    
+    val = 0.0
+    ho = order >> 1
+    for k in range(order):
+        val += weights[k] * func(x0 + (k - ho) * dx, *args)
+    return val / prod((dx,) * n, axis=0)
+