Merge pull request #43 from flatironinstitute/dev

New testing & CI

.codecov.yml

@@ -1,2 +1,4 @@
 codecov:
-  branch: release
+  branch: release
+ignore:
+  - "*/tests/*”

docs/conf.py

@@ -23,7 +23,7 @@
 author = 'Chris Jackson'
 
 # The full version, including alpha/beta/rc tags
-release = 'v0.5.5'
+release = 'v0.5.6'
 
 
 # -- General configuration ---------------------------------------------------

inferelator/postprocessing/f1_score.py

@@ -4,8 +4,6 @@
 from inferelator.postprocessing import (TARGET_COLUMN, REGULATOR_COLUMN, CONFIDENCE_COLUMN,
                                         F1_COLUMN, PRECISION_COLUMN, RECALL_COLUMN)
 
-import matplotlib
-matplotlib.use('pdf')
 import matplotlib.pyplot as plt
 
 

inferelator/postprocessing/matthews_correlation.py

@@ -5,8 +5,6 @@
 from inferelator.postprocessing import (TARGET_COLUMN, REGULATOR_COLUMN, CONFIDENCE_COLUMN, GOLD_STANDARD_COLUMN,
                                         MCC_COLUMN, TP, FP, TN, FN)
 
-import matplotlib
-matplotlib.use('pdf')
 import matplotlib.pyplot as plt
 
 

inferelator/postprocessing/model_metrics.py

@@ -8,8 +8,6 @@
 from inferelator.utils import is_string
 import os
 
-import matplotlib
-matplotlib.use('pdf')
 import matplotlib.pyplot as plt
 
 
@@ -84,36 +82,39 @@ def auc(self):
     def curve_dataframe(self):
         return self.filtered_data.loc[:, [CONFIDENCE_COLUMN, PRECISION_COLUMN, RECALL_COLUMN, MCC_COLUMN, F1_COLUMN]]
 
-    def output_curve_pdf(self, output_dir, file_name=None, dpi=300, figsize=(8, 10)):
+    def output_curve_pdf(self, output_dir, file_name=None, dpi=300, figsize=(8, 10), style_label='default'):
 
         file_name = self.curve_file_name if file_name is None else file_name
 
         # Create a figure
-        fig, axes = plt.subplots(nrows=2, ncols=2, figsize=figsize, constrained_layout=True)
+        with plt.style.context(style_label):
+            fig, axes = plt.subplots(nrows=2, ncols=2, figsize=figsize, constrained_layout=True)
 
-        # Draw the PR curve
-        RankSummaryPR.output_curve(self, ax=axes[0, 0])
+            # Draw the PR curve
+            RankSummaryPR.output_curve(self, ax=axes[0, 0])
 
-        # Add the cutoff at optimal MCC to the curve
-        _ocr = self.filtered_data.loc[self.filtered_data[CONFIDENCE_COLUMN] >= self.optconfmcc, RECALL_COLUMN].max()
-        _ocp = self.filtered_data.loc[self.filtered_data[CONFIDENCE_COLUMN] >= self.optconfmcc, PRECISION_COLUMN].min()
-        axes[0, 0].vlines(_ocr, 0, _ocp, colors='r', linestyles='dashed')
+            # Add the cutoff at optimal MCC to the curve
+            _ocr = self.filtered_data.loc[self.filtered_data[CONFIDENCE_COLUMN] >= self.optconfmcc, RECALL_COLUMN].max()
+            _ocp = self.filtered_data.loc[self.filtered_data[CONFIDENCE_COLUMN] >= self.optconfmcc, PRECISION_COLUMN].min()
+            axes[0, 0].vlines(_ocr, 0, _ocp, colors='r', linestyles='dashed')
 
-        # Draw the MCC curve
-        RankSummaryMCC.output_curve(self, ax=axes[0, 1])
+            # Draw the MCC curve
+            RankSummaryMCC.output_curve(self, ax=axes[0, 1])
 
-        # Draw the F1 curve
-        RankSummaryF1.output_curve(self, ax=axes[1, 0])
+            # Draw the F1 curve
+            RankSummaryF1.output_curve(self, ax=axes[1, 0])
 
-        # Draw the histogram
-        self.output_histogram_edges_conf(self.filtered_data[CONFIDENCE_COLUMN].values, ax=axes[1, 1])
+            # Draw the histogram
+            self.output_histogram_edges_conf(self.filtered_data[CONFIDENCE_COLUMN].values, ax=axes[1, 1])
 
         # If there's a file name set, make the output file
         if file_name is not None and output_dir is not None:
             # Save the plot and close
             self.save_figure(os.path.join(output_dir, file_name), fig, dpi=dpi)
+            plt.close(fig)
+            return None, None
 
-        plt.close(fig)
+        return fig, axes
 
     @staticmethod
     def output_histogram_edges_conf(conf, ax):

inferelator/postprocessing/precision_recall.py

@@ -8,8 +8,6 @@
 from inferelator.postprocessing import (TARGET_COLUMN, REGULATOR_COLUMN, PRECISION_COLUMN, RECALL_COLUMN,
                                         CONFIDENCE_COLUMN, GOLD_STANDARD_COLUMN)
 
-import matplotlib
-matplotlib.use('pdf')
 import matplotlib.pyplot as plt
 
 
@@ -168,4 +166,4 @@ def calculate_aupr(data):
         # using midpoint integration to calculate the area under the curve
         d_recall = np.diff(recall)
         m_precision = precision[:-1] + np.diff(precision) / 2
-        return sum(d_recall * m_precision)
+        return sum(d_recall * m_precision)

requirements-test.txt

@@ -1,4 +1,4 @@
 coverage
-nose
+pytest
 bio-test-artifacts
 tables

setup.py

@@ -2,7 +2,7 @@
 from setuptools import setup, find_packages
 
 # Current Inferelator Version Number
-version = "0.5.5"
+version = "0.5.6"
 
 
 # Description from README.md
@@ -37,8 +37,7 @@
     zip_safe=False,
     install_requires=requires,
     python_requires=">=3.5",
-    tests_require=["coverage", "nose", "bio-test-artifacts", "tables"],
-    test_suite="nose.collector",
+    tests_require=["coverage", "pytest", "bio-test-artifacts", "tables"],
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: BSD License",