Merge pull request #12 from nickderobertis/labels

Add ability to pass labels for inputs and have them show up in styled dfs and plots

conf.py

@@ -17,7 +17,7 @@
 ]
 
 # Package version in the format (major, minor, release)
-PACKAGE_VERSION_TUPLE = (0, 2, 4)
+PACKAGE_VERSION_TUPLE = (0, 2, 5)
 
 # Short description of the package
 PACKAGE_SHORT_DESCRIPTION = "Python Sensitivity Analysis - Gradient DataFrames and Hex-Bin Plots"

sensitivity/df.py

@@ -14,7 +14,9 @@
 
 
 def sensitivity_df(sensitivity_values: Dict[str, Any], func: Callable,
-                   result_name: str = 'Result', **func_kwargs) -> pd.DataFrame:
+                   result_name: str = 'Result',
+                   labels: Optional[Dict[str, str]] = None,
+                   **func_kwargs) -> pd.DataFrame:
     """
     Creates a DataFrame containing the results of sensitivity analysis.
 
@@ -26,6 +28,8 @@ def sensitivity_df(sensitivity_values: Dict[str, Any], func: Callable,
     :param func: Function that accepts arguments with names matching the keys of sensitivity_values, and outputs a
         scalar value.
     :param result_name: Name for result shown in graph color bar label
+    :param labels: Optional dictionary where keys are arguments of the function and values are the displayed names
+        for these arguments in the styled DataFrames and plots
     :param func_kwargs: Additional arguments to pass to func, regardless of the sensitivity values picked
     :return: a DataFrame containing the results from sensitivity analysis on func
     """
@@ -41,6 +45,8 @@ def sensitivity_df(sensitivity_values: Dict[str, Any], func: Callable,
         df = df.append(pd.DataFrame(pd.Series(base_param_dict)).T)
     df.reset_index(drop=True, inplace=True)
     df = df.convert_dtypes()
+    if labels:
+        df.rename(columns=labels, inplace=True)
 
     return df
 

sensitivity/main.py

@@ -36,6 +36,8 @@ class SensitivityAnalyzer:
         same style as would be passed to df.style.format, e.g. '${:,.2f}' for USD formatting
     :param color_map: matplotlib color map, default is RdYlGn (red, yellow, green). See
         https://matplotlib.org/3.3.2/tutorials/colors/colormaps.html
+    :param labels: Optional dictionary where keys are arguments of the function and values are the displayed names
+        for these arguments in the styled DataFrames and plots
     :return: Sensitivity analysis hex bin sub plot figure
 
     Examples:
@@ -75,6 +77,7 @@ class SensitivityAnalyzer:
     func_kwargs_dict: Optional[Dict[str, Any]] = None
     num_fmt: Optional[str] = None
     color_map: str = 'RdYlGn'
+    labels: Optional[Dict[str, str]] = None
 
     def __post_init__(self):
         if self.func_kwargs_dict is None:
@@ -83,6 +86,7 @@ def __post_init__(self):
             self.sensitivity_values,
             self.func,
             result_name=self.result_name,
+            labels=self.labels,
             **self.func_kwargs_dict
         )
 
@@ -100,7 +104,7 @@ def plot(self, **kwargs) -> plt.Figure:
             color_map=self.color_map,
         )
         config_dict.update(**kwargs)
-        sensitivity_cols = list(self.sensitivity_values.keys())
+        sensitivity_cols = self.sensitivity_cols
         return _hex_figure_from_sensitivity_df(
             self.df,
             sensitivity_cols,
@@ -125,7 +129,7 @@ def styled_dfs(self, disp: bool = True, **kwargs) -> Union[Styler, Dict[Sequence
         )
         config_dict.update(**kwargs)
         # Output a single Styler if only one or two variables
-        sensitivity_cols = list(self.sensitivity_values.keys())
+        sensitivity_cols = self.sensitivity_cols
         if len(sensitivity_cols) == 1:
             output[tuple(sensitivity_cols)] = _style_sensitivity_df(
                 self.df,
@@ -188,6 +192,19 @@ def styled_dfs(self, disp: bool = True, **kwargs) -> Union[Styler, Dict[Sequence
 
         return output
 
+    @property
+    def sensitivity_cols(self) -> List[str]:
+        sensitivity_cols = list(self.sensitivity_values.keys())
+        if self.labels:
+            new_sensitivity_cols: List[str] = []
+            for col in sensitivity_cols:
+                if col in self.labels:
+                    new_sensitivity_cols.append(self.labels[col])
+                else:
+                    new_sensitivity_cols.append(col)
+            sensitivity_cols = new_sensitivity_cols
+        return sensitivity_cols
+
 
 def _display_header(text: str):
     html_str = f'<h2>{text}</h2>'

tests/base.py

@@ -9,12 +9,19 @@
 
 INPUT_FILES_FOLDER = Path(os.path.join('tests', 'input_data'))
 DF_STYLED_PATH = os.path.join(INPUT_FILES_FOLDER, 'df_styled.html')
+DF_LABELED_PATH = os.path.join(INPUT_FILES_FOLDER, 'df_labeled.html')
 DF_STYLED_NUM_FMT_PATH = os.path.join(INPUT_FILES_FOLDER, 'df_styled_num_fmt.html')
 DF_STYLE_UUID = '1ee5ad65-4cac-42e3-8133-7ae800cb23ad'
 DEFAULT_PLOT_PATH = INPUT_FILES_FOLDER / 'default_plot.png'
 PLOT_THREE_PATH = INPUT_FILES_FOLDER / 'plot_three.png'
 PLOT_OPTIONS_PATH = INPUT_FILES_FOLDER / 'plot_options.png'
 RESULT_NAME = 'my_res'
+TWO_VALUE_LABELS = {
+    'value1': 'Formatted 1',
+    'value2': 'Formatted 2'
+}
+THREE_VALUE_LABELS = deepcopy(TWO_VALUE_LABELS)
+THREE_VALUE_LABELS['value3'] = 'Formatted 3'
 EXPECT_DF_TWO_VALUE = pd.DataFrame(
     [
         (1, 4, 10),
@@ -24,6 +31,7 @@
     ],
     columns=['value1', 'value2', RESULT_NAME]
 )
+EXPECT_DF_TWO_VALUE_LABELS = EXPECT_DF_TWO_VALUE.rename(columns=TWO_VALUE_LABELS)
 
 EXPECT_DF_THREE_VALUE = pd.DataFrame(
     [
@@ -47,6 +55,7 @@
 SENSITIVITY_VALUES_THREE_VALUE['value3'] = [6, 7]
 
 
+
 def add_5_to_values(value1, value2):
     return value1 + value2 + 5
 
@@ -55,11 +64,15 @@ def add_10_to_values(value1, value2, value3=5):
     return value1 + value2 + value3 + 10
 
 
-def assert_styled_matches(styler: Styler, file_path: str = DF_STYLED_PATH):
+def assert_styled_matches(styler: Styler, file_path: str = DF_STYLED_PATH, generate: bool = False):
+    compare_html = styler.set_uuid(DF_STYLE_UUID).render()
+
+    if generate:
+        Path(file_path).write_text(compare_html)
+
     with open(file_path, 'r') as f:
         expect_html = f.read()
 
-    compare_html = styler.set_uuid(DF_STYLE_UUID).render()
     assert compare_html == expect_html
 
 

tests/input_data/df_labeled.html

@@ -0,0 +1,28 @@
+<style  type="text/css" >
+#T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow0_col0{
+            background-color:  #a50026;
+            color:  #f1f1f1;
+            background-color:  #a50026;
+            color:  #f1f1f1;
+        }#T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow0_col1,#T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow1_col0{
+            background-color:  #feffbe;
+            color:  #000000;
+            background-color:  #feffbe;
+            color:  #000000;
+        }#T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow1_col1{
+            background-color:  #006837;
+            color:  #f1f1f1;
+            background-color:  #006837;
+            color:  #f1f1f1;
+        }</style><table id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23ad" ><caption>my_res - Formatted 1 vs. Formatted 2</caption><thead>    <tr>        <th class="blank level0" ></th>        <th class="col_heading level0 col0" >4</th>        <th class="col_heading level0 col1" >5</th>    </tr>    <tr>        <th class="index_name level0" >Formatted 1</th>        <th class="blank" ></th>        <th class="blank" ></th>    </tr></thead><tbody>
+                <tr>
+                        <th id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23adlevel0_row0" class="row_heading level0 row0" >1</th>
+                        <td id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow0_col0" class="data row0 col0" >10.000000</td>
+                        <td id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow0_col1" class="data row0 col1" >11.000000</td>
+            </tr>
+            <tr>
+                        <th id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23adlevel0_row1" class="row_heading level0 row1" >2</th>
+                        <td id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow1_col0" class="data row1 col0" >11.000000</td>
+                        <td id="T_1ee5ad65-4cac-42e3-8133-7ae800cb23adrow1_col1" class="data row1 col1" >12.000000</td>
+            </tr>
+    </tbody></table>

tests/test_df.py

@@ -2,7 +2,8 @@
 from pandas.testing import assert_frame_equal
 
 from sensitivity.df import sensitivity_df
-from tests.base import EXPECT_DF_TWO_VALUE, SENSITIVITY_VALUES_TWO_VALUE, add_5_to_values, RESULT_NAME
+from tests.base import EXPECT_DF_TWO_VALUE, SENSITIVITY_VALUES_TWO_VALUE, add_5_to_values, RESULT_NAME, \
+    TWO_VALUE_LABELS, EXPECT_DF_TWO_VALUE_LABELS
 
 
 def test_create_sensitivity_df():
@@ -13,3 +14,14 @@ def test_create_sensitivity_df():
     )
 
     assert_frame_equal(df, EXPECT_DF_TWO_VALUE, check_dtype=False)
+
+
+def test_labeled_sensitivity_df():
+    df = sensitivity_df(
+        SENSITIVITY_VALUES_TWO_VALUE,
+        add_5_to_values,
+        result_name=RESULT_NAME,
+        labels=TWO_VALUE_LABELS
+    )
+
+    assert_frame_equal(df, EXPECT_DF_TWO_VALUE_LABELS, check_dtype=False)

tests/test_sensitivity_analyzer.py

@@ -5,7 +5,7 @@
 from sensitivity import SensitivityAnalyzer
 from tests.base import EXPECT_DF_TWO_VALUE, SENSITIVITY_VALUES_TWO_VALUE, add_5_to_values, RESULT_NAME, \
     SENSITIVITY_VALUES_THREE_VALUE, add_10_to_values, EXPECT_DF_THREE_VALUE, assert_styled_matches, \
-    DF_STYLED_NUM_FMT_PATH, assert_graph_matches, PLOT_THREE_PATH, PLOT_OPTIONS_PATH
+    DF_STYLED_NUM_FMT_PATH, assert_graph_matches, PLOT_THREE_PATH, PLOT_OPTIONS_PATH, TWO_VALUE_LABELS, DF_LABELED_PATH
 
 
 class TestSensitivityAnalyzer:
@@ -47,6 +47,11 @@ def test_create_styled_dfs_with_num_fmt(self):
         assert_styled_matches(result, DF_STYLED_NUM_FMT_PATH)
         assert_styled_matches(result2, DF_STYLED_NUM_FMT_PATH)
 
+    def test_create_styled_dfs_with_labels(self):
+        sa = self.create_sa(labels=TWO_VALUE_LABELS)
+        result = sa.styled_dfs()
+        assert_styled_matches(result, DF_LABELED_PATH)
+
     def test_create_styled_dfs_three_values(self):
         sa = self.create_sa(
             sensitivity_values=SENSITIVITY_VALUES_THREE_VALUE,
@@ -71,9 +76,9 @@ def test_create_plot_with_options(self):
         options = dict(
             grid_size=2, color_map='viridis', reverse_colors=True
         )
-        sa = self.create_sa(**options)
+        sa = self.create_sa(labels=TWO_VALUE_LABELS, **options)
         result = sa.plot()
         assert_graph_matches(result, file_path=PLOT_OPTIONS_PATH)
-        sa = self.create_sa()
+        sa = self.create_sa(labels=TWO_VALUE_LABELS)
         result = sa.plot(**options)
         assert_graph_matches(result, file_path=PLOT_OPTIONS_PATH)