Merge pull request #68 from slickml/amir/dev

Fixed save_paths in plotting and prepared for new version

CHANGELOG.md

@@ -3,12 +3,22 @@ All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
 ## Unreleased
+
+
+## Version 0.1.3 - 2021-05-15
+
 ### Fixed
 * [#66](https:/slickml/slick-ml/pull/66) fixed bugs in feature selection algorithm. 
 * [#67](https:/slickml/slick-ml/pull/67) fixed bugs in metrics. 
 
 ### Updated
 * [#66](https:/slickml/slick-ml/pull/66) updated the order of the functions inside each class.
+* [#68](https:/slickml/slick-ml/pull/68) updated `save_path` in plotting functions.
+* [#68](https:/slickml/slick-ml/pull/68) updated `bibtex` citations to software.
+
+### Added
+* [#68](https:/slickml/slick-ml/pull/68) added directories for `JOSS` and `NeurIPS` papers.
+
 
 ## Version 0.1.2 - 2021-04-17
 

README.md

@@ -178,24 +178,16 @@ let others know that you are working on it. Whether the contributions consists o
 levels. The SlickML community goals are to be helpful and effective.
 
 ## Citing SlickML
-If you use SlickML in academic work, please consider citing
-https://doi.org/10.1117/12.2304418 .
+If you use SlickML in academic work, please consider citing it.
 
 ### Bibtex Entry:
 ```bib
-@inproceedings{tahmassebi2018ideeple,
- title={ideeple: Deep learning in a flash},
- author={Tahmassebi, Amirhessam},
- booktitle={Disruptive Technologies in Information Sciences},
- volume={10652},
- pages={106520S},
- year={2018},
- organization={International Society for Optics and Photonics}
+@software{slickml2020,
+ title={SlickML: Slick Machine Learning in Python},
+ author={Tahmassebi, Amirhessam and Smith, Trace},
+ url={https:/slickml/slick-ml},
+ version={0.1.3},
+ year={2021},
 }
 ```
-### APA Entry:
-
-Tahmassebi, A. (2018, May). ideeple: Deep learning in a flash. In Disruptive
-Technologies in Information Sciences (Vol. 10652, p. 106520S). International
-Society for Optics and Photonics.
 

examples/metrics.ipynb

@@ -36,7 +36,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
  "metadata": {},
  "outputs": [
  {
@@ -61,7 +61,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
  "metadata": {},
  "outputs": [
  {
@@ -101,7 +101,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [
  {
@@ -131,7 +131,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -169,47 +169,47 @@
  },
  {
  "cell_type": "code",
- "execution_count": 6,
+ "execution_count": 10,
  "metadata": {},
  "outputs": [
  {
  "data": {
  "text/html": [
  "<style type=\"text/css\" >\n",
- " #T_b4381_ th {\n",
+ " #T_2c43c_ th {\n",
  " font-size: 12px;\n",
  " text-align: left;\n",
  " font-weight: bold;\n",
- " } #T_b4381_ td {\n",
+ " } #T_2c43c_ td {\n",
  " font-size: 12px;\n",
  " text-align: center;\n",
- " }#T_b4381_row0_col0,#T_b4381_row0_col1,#T_b4381_row0_col2,#T_b4381_row0_col3,#T_b4381_row0_col4,#T_b4381_row0_col5,#T_b4381_row0_col6,#T_b4381_row0_col7,#T_b4381_row0_col8,#T_b4381_row0_col9,#T_b4381_row0_col10,#T_b4381_row0_col11,#T_b4381_row0_col12,#T_b4381_row0_col13,#T_b4381_row0_col14{\n",
+ " }#T_2c43c_row0_col0,#T_2c43c_row0_col1,#T_2c43c_row0_col2,#T_2c43c_row0_col3,#T_2c43c_row0_col4,#T_2c43c_row0_col5,#T_2c43c_row0_col6,#T_2c43c_row0_col7,#T_2c43c_row0_col8,#T_2c43c_row0_col9,#T_2c43c_row0_col10,#T_2c43c_row0_col11,#T_2c43c_row0_col12,#T_2c43c_row0_col13,#T_2c43c_row0_col14{\n",
  " background-color: #e5e5ff;\n",
  " color: #000000;\n",
- " }</style><table id=\"T_b4381_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Accuracy</th> <th class=\"col_heading level0 col1\" >Balanced Accuracy</th> <th class=\"col_heading level0 col2\" >ROC AUC</th> <th class=\"col_heading level0 col3\" >PR AUC</th> <th class=\"col_heading level0 col4\" >Precision</th> <th class=\"col_heading level0 col5\" >Recall</th> <th class=\"col_heading level0 col6\" >Average Precision</th> <th class=\"col_heading level0 col7\" >F-1 Score</th> <th class=\"col_heading level0 col8\" >F-2 Score</th> <th class=\"col_heading level0 col9\" >F-0.50 Score</th> <th class=\"col_heading level0 col10\" >Threat Score</th> <th class=\"col_heading level0 col11\" >TP</th> <th class=\"col_heading level0 col12\" >TN</th> <th class=\"col_heading level0 col13\" >FP</th> <th class=\"col_heading level0 col14\" >FN</th> </tr></thead><tbody>\n",
+ " }</style><table id=\"T_2c43c_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Accuracy</th> <th class=\"col_heading level0 col1\" >Balanced Accuracy</th> <th class=\"col_heading level0 col2\" >ROC AUC</th> <th class=\"col_heading level0 col3\" >PR AUC</th> <th class=\"col_heading level0 col4\" >Precision</th> <th class=\"col_heading level0 col5\" >Recall</th> <th class=\"col_heading level0 col6\" >Average Precision</th> <th class=\"col_heading level0 col7\" >F-1 Score</th> <th class=\"col_heading level0 col8\" >F-2 Score</th> <th class=\"col_heading level0 col9\" >F-0.50 Score</th> <th class=\"col_heading level0 col10\" >Threat Score</th> <th class=\"col_heading level0 col11\" >TP</th> <th class=\"col_heading level0 col12\" >TN</th> <th class=\"col_heading level0 col13\" >FP</th> <th class=\"col_heading level0 col14\" >FN</th> </tr></thead><tbody>\n",
  " <tr>\n",
- " <th id=\"T_b4381_level0_row0\" class=\"row_heading level0 row0\" >Threshold = 0.500 | Average =\n",
+ " <th id=\"T_2c43c_level0_row0\" class=\"row_heading level0 row0\" >Threshold = 0.500 | Average =\n",
  " Binary</th>\n",
- " <td id=\"T_b4381_row0_col0\" class=\"data row0 col0\" >0.968000</td>\n",
- " <td id=\"T_b4381_row0_col1\" class=\"data row0 col1\" >0.957000</td>\n",
- " <td id=\"T_b4381_row0_col2\" class=\"data row0 col2\" >0.988000</td>\n",
- " <td id=\"T_b4381_row0_col3\" class=\"data row0 col3\" >0.992000</td>\n",
- " <td id=\"T_b4381_row0_col4\" class=\"data row0 col4\" >0.952000</td>\n",
- " <td id=\"T_b4381_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
- " <td id=\"T_b4381_row0_col6\" class=\"data row0 col6\" >0.991000</td>\n",
- " <td id=\"T_b4381_row0_col7\" class=\"data row0 col7\" >0.975000</td>\n",
- " <td id=\"T_b4381_row0_col8\" class=\"data row0 col8\" >0.990000</td>\n",
- " <td id=\"T_b4381_row0_col9\" class=\"data row0 col9\" >0.961000</td>\n",
- " <td id=\"T_b4381_row0_col10\" class=\"data row0 col10\" >0.952000</td>\n",
- " <td id=\"T_b4381_row0_col11\" class=\"data row0 col11\" >118</td>\n",
- " <td id=\"T_b4381_row0_col12\" class=\"data row0 col12\" >64</td>\n",
- " <td id=\"T_b4381_row0_col13\" class=\"data row0 col13\" >6</td>\n",
- " <td id=\"T_b4381_row0_col14\" class=\"data row0 col14\" >0</td>\n",
+ " <td id=\"T_2c43c_row0_col0\" class=\"data row0 col0\" >0.968000</td>\n",
+ " <td id=\"T_2c43c_row0_col1\" class=\"data row0 col1\" >0.957000</td>\n",
+ " <td id=\"T_2c43c_row0_col2\" class=\"data row0 col2\" >0.988000</td>\n",
+ " <td id=\"T_2c43c_row0_col3\" class=\"data row0 col3\" >0.992000</td>\n",
+ " <td id=\"T_2c43c_row0_col4\" class=\"data row0 col4\" >0.952000</td>\n",
+ " <td id=\"T_2c43c_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
+ " <td id=\"T_2c43c_row0_col6\" class=\"data row0 col6\" >0.991000</td>\n",
+ " <td id=\"T_2c43c_row0_col7\" class=\"data row0 col7\" >0.975000</td>\n",
+ " <td id=\"T_2c43c_row0_col8\" class=\"data row0 col8\" >0.990000</td>\n",
+ " <td id=\"T_2c43c_row0_col9\" class=\"data row0 col9\" >0.961000</td>\n",
+ " <td id=\"T_2c43c_row0_col10\" class=\"data row0 col10\" >0.952000</td>\n",
+ " <td id=\"T_2c43c_row0_col11\" class=\"data row0 col11\" >118</td>\n",
+ " <td id=\"T_2c43c_row0_col12\" class=\"data row0 col12\" >64</td>\n",
+ " <td id=\"T_2c43c_row0_col13\" class=\"data row0 col13\" >6</td>\n",
+ " <td id=\"T_2c43c_row0_col14\" class=\"data row0 col14\" >0</td>\n",
  " </tr>\n",
  " </tbody></table>"
  ],
  "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f0c94808f10>"
+ "<pandas.io.formats.style.Styler at 0x7f2f4aab8990>"
  ]
  },
  "metadata": {},
@@ -228,7 +228,8 @@
  ],
  "source": [
  "example1 = BinaryClassificationMetrics(y_true, y_pred_proba, precision_digits=3)\n",
- "example1.plot()"
+ "example1.plot(figsize=(12, 12),\n",
+ " save_path=None)"
  ]
  },
  {

slickml/__init__.py

@@ -1 +1 @@
-__version__ = "0.1.2"
+__version__ = "0.1.3"

slickml/classification.py

@@ -311,6 +311,7 @@ def plot_feature_importance(
  markerfacecolor=None,
  markeredgewidth=None,
  fontsize=None,
+ save_path=None,
  ):
 
  """Function to plot XGBoost feature importance.
@@ -347,18 +348,23 @@ def plot_feature_importance(
 
  fontsize: int or float, optional, (default=12)
  Fontsize for xlabel and ylabel, and ticks parameters
+
+ save_path: str, optional (default=None)
+ The full or relative path to save the plot including the image format.
+ For example "myplot.png" or "../../myplot.pdf"
  """
 
  plot_xgb_feature_importance(
- self.feature_importance_,
- figsize,
- color,
- marker,
- markersize,
- markeredgecolor,
- markerfacecolor,
- markeredgewidth,
- fontsize,
+ feature_importance=self.feature_importance_,
+ figsize=figsize,
+ color=color,
+ marker=marker,
+ markersize=markersize,
+ markeredgecolor=markeredgecolor,
+ markerfacecolor=markerfacecolor,
+ markeredgewidth=markeredgewidth,
+ fontsize=fontsize,
+ save_path=save_path,
  )
 
  def plot_shap_summary(
@@ -377,6 +383,7 @@ def plot_shap_summary(
  class_names=None,
  class_inds=None,
  color_bar_label=None,
+ save_path=None,
  ):
  """Function to plot shap summary plot.
  This function is a helper function to plot the shap summary plot
@@ -433,6 +440,10 @@ def plot_shap_summary(
 
  color_bar_label: str, optional, (default="Feature Value")
  Label for color bar
+
+ save_path: str, optional (default=None)
+ The full or relative path to save the plot including the image format.
+ For example "myplot.png" or "../../myplot.pdf"
  """
 
  # define tree explainer
@@ -466,6 +477,7 @@ def plot_shap_summary(
  class_names=class_names,
  class_inds=class_inds,
  color_bar_label=color_bar_label,
+ save_path=save_path,
  )
 
  def plot_shap_waterfall(
@@ -483,6 +495,7 @@ def plot_shap_waterfall(
  max_display=None,
  title=None,
  fontsize=None,
+ save_path=None,
  ):
  """Function to plot shap waterfall plot.
  This function is a helper function to plot the shap waterfall plot
@@ -536,6 +549,10 @@ def plot_shap_waterfall(
 
  fontsize: int or float, optional, (default=12)
  Fontsize for xlabel and ylabel, and ticks parameters
+
+ save_path: str, optional (default=None)
+ The full or relative path to save the plot including the image format.
+ For example "myplot.png" or "../../myplot.pdf"
  """
 
  # define tree explainer
@@ -568,6 +585,7 @@ def plot_shap_waterfall(
  max_display=max_display,
  title=title,
  fontsize=fontsize,
+ save_path=save_path,
  )
 
  def _dtrain(self, X_train, y_train):
@@ -1026,6 +1044,7 @@ def plot_cv_results(
  train_std_color=None,
  test_color=None,
  test_std_color=None,
+ save_path=None,
  ):
  """
  Function to plot the results of xgboost.cv() process and evolution
@@ -1060,18 +1079,23 @@ def plot_cv_results(
 
  test_std_color: str, optional, (default="#D0AAF3")
  Color of the edge color of the testing std bars
+
+ save_path: str, optional (default=None)
+ The full or relative path to save the plot including the image format.
+ For example "myplot.png" or "../../myplot.pdf"
  """
 
  plot_xgb_cv_results(
- self.cv_results_,
- figsize,
- linestyle,
- train_label,
- test_label,
- train_color,
- train_std_color,
- test_color,
- test_std_color,
+ cv_results=self.cv_results_,
+ figsize=figsize,
+ linestyle=linestyle,
+ train_label=train_label,
+ test_label=test_label,
+ train_color=train_color,
+ train_std_color=train_std_color,
+ test_color=test_color,
+ test_std_color=test_std_color,
+ save_path=save_path,
  )
 
  def _cv(self):

slickml/feature_selection.py

@@ -572,6 +572,7 @@ def plot_frequency(
  markerfacecolor=None,
  markeredgewidth=None,
  fontsize=None,
+ save_path=None,
  ):
 
  """Function to plot selected features frequency.
@@ -614,20 +615,27 @@ def plot_frequency(
  """
 
  plot_xfs_feature_frequency(
- self.feature_frequency_,
- figsize,
- freq_pct,
- color,
- marker,
- markersize,
- markeredgecolor,
- markerfacecolor,
- markeredgewidth,
- fontsize,
+ freq=self.feature_frequency_,
+ figsize=figsize,
+ freq_pct=freq_pct,
+ color=color,
+ marker=marker,
+ markersize=markersize,
+ markeredgecolor=markeredgecolor,
+ markerfacecolor=markerfacecolor,
+ markeredgewidth=markeredgewidth,
+ fontsize=fontsize,
+ save_path=save_path,
  )
 
  def plot_cv_results(
- self, figsize=None, int_color=None, ext_color=None, sharex=False, sharey=False
+ self,
+ figsize=None,
+ int_color=None,
+ ext_color=None,
+ sharex=False,
+ sharey=False,
+ save_path=None,
  ):
  """Function to plot the cross-validation results of
  XGBoostFeatureSelector. It visualizes the internal
@@ -655,12 +663,22 @@ def plot_cv_results(
  sharey: bool, optional, (default=False)
  Flag to share "Y" axis for each row of subplots
 
+ save_path: str, optional (default=None)
+ The full or relative path to save the plot including the image format.
+ For example "myplot.png" or "../../myplot.pdf"
+
  kwargs: dict
  Plotting object plotting_cv_
  """
 
  plot_xfs_cv_results(
- figsize, int_color, ext_color, sharex, sharey, **self.plotting_cv_
+ figsize=figsize,
+ int_color=int_color,
+ ext_color=ext_color,
+ sharex=sharex,
+ sharey=sharey,
+ save_path=save_path,
+ **self.plotting_cv_,
  )
 
  def get_xgb_params(self):