update readme & linting

README.md

@@ -43,7 +43,7 @@ Furthermore, this library presents other useful visualizations, such as **compar
     - Classification Report
     - Confusion Matrix
     - ROC curve (AUROC)
-    - y_prob histogram
+    - y_score histogram
 
 - *multi-class classifier*
 
@@ -61,7 +61,7 @@ Furthermore, this library presents other useful visualizations, such as **compar
 
 | <img src="https://github.com/joshuawe/plots_and_graphs/blob/main/images/roc_curve_bootstrap.png?raw=true" width="300" alt="Your Image">        | <img src="https://github.com/joshuawe/plots_and_graphs/blob/main/images/pr_curve.png?raw=true" width="300" alt="Your Image">        | <img src="https://github.com/joshuawe/plots_and_graphs/blob/main/images/y_prob_histogram.png?raw=true" width="300" alt="Your Image">  |
 |:--------------------------------------------------:|:----------------------------------------------------------:|:-------------------------------------------------:|
-|                    ROC Curve (AUROC) with bootstrapping             |                 Precision-Recall Curve                          |                  y_prob histogram                                 |
+|                    ROC Curve (AUROC) with bootstrapping             |                 Precision-Recall Curve                          |                  y_score histogram                                 |
 
 
 | <img src="https://github.com/joshuawe/plots_and_graphs/blob/main/images/multiclass/histogram_4_classes.png?raw=true" width="300" alt="Your Image">        |  <img src="https://github.com/joshuawe/plots_and_graphs/blob/main/images/multiclass/roc_curves_multiclass.png?raw=true" width="300" alt=""> | <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" width="300" height="300" alt=""> |
@@ -95,34 +95,28 @@ pip install -e .
 
 # Usage
 
-Example usage of results from a binary classifier for a calibration curve.
+Get all classification metrics with **ONE** line of code. Here, for a binary classifier:
 
 ```python
-import matplotlib.pyplot as plt
-import numpy as np
 import plotsandgraphs as pandg
+# ...
+pandg.pipeline.binary_classifier(y_true, y_score)
+```
 
-# create some predictions of a hypothetical binary classifier
-n_samples = 1000
-y_true = np.random.choice([0,1], n_samples, p=[0.4, 0.6])   # the true class labels 0 or 1, with class imbalance 40:60
-
-y_prob = np.zeros(y_true.shape)   # a model's probability of class 1 predictions
-y_prob[y_true==1] = np.random.beta(1, 0.6, y_prob[y_true==1].shape)
-y_prob[y_true==0] = np.random.beta(0.5, 1, y_prob[y_true==0].shape)
-
-# show prob distribution
-fig_hist = pandg.binary_classifier.plot_y_prob_histogram(y_prob, y_true, save_fig_path=None)
-
-# create calibration curve
-fig_auroc = pandg.binary_classifier.plot_calibration_curve(y_prob, y_true, save_fig_path=None)
+Or with some more configs:
+```Python
+configs = {
+  'roc': {'n_bootstraps': 10000},
+  'pr': {'figsize': (8,10)}
+}
+pandg.pipeline.binary_classifier(y_true, y_score, save_fig_path='results/metrics', file_type='png', plot_kwargs=configs)
+```
 
+For multiclass classification:
 
-# --- OPTIONAL: Customize figure ---
-# get axis of figure and change title
-axes = fig_auroc.get_axes()
-ax0 = axes[0]
-ax0.set_title('New Title for Calibration Plot')
-fig_auroc.show()
+```Python
+# with multiclass data y_true (one-hot encoded) and y_score
+pandg.pipeline.multiclass_classifier(y_true, y_score)
 ```
 
 # Requirements

plotsandgraphs/binary_classifier.py

@@ -45,7 +45,9 @@ def plot_accuracy(y_true, y_pred, name="", save_fig_path=None) -> Figure:
     return fig
 
 
-def plot_confusion_matrix(y_true: np.ndarray, y_pred: np.ndarray, save_fig_path=None) -> Figure:
+def plot_confusion_matrix(
+    y_true: np.ndarray, y_pred: np.ndarray, save_fig_path=None
+) -> Figure:
     import matplotlib.colors as colors
 
     # Compute the confusion matrix
@@ -54,7 +56,9 @@ def plot_confusion_matrix(y_true: np.ndarray, y_pred: np.ndarray, save_fig_path=
     cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
 
     # Create the ConfusionMatrixDisplay instance and plot it
-    cmd = ConfusionMatrixDisplay(cm, display_labels=["class 0\nnegative", "class 1\npositive"])
+    cmd = ConfusionMatrixDisplay(
+        cm, display_labels=["class 0\nnegative", "class 1\npositive"]
+    )
     fig, ax = plt.subplots(figsize=(4, 4))
     cmd.plot(
         cmap="YlOrRd",
@@ -144,8 +148,10 @@ def plot_classification_report(
         ax : Matplotlib.pyplot.Axe
             Axe object from matplotlib
     """
-    print("Warning: plot_classification_report is not experiencing a bug and is, hence, currently skipped.")
-    return 
+    print(
+        "Warning: plot_classification_report is not experiencing a bug and is, hence, currently skipped."
+    )
+    return
 
     import matplotlib as mpl
     import matplotlib.colors as colors
@@ -156,7 +162,11 @@ def plot_classification_report(
     cmap = "YlOrRd"
 
     clf_report = classification_report(y_true, y_pred, output_dict=True, **kwargs)
-    keys_to_plot = [key for key in clf_report.keys() if key not in ("accuracy", "macro avg", "weighted avg")]
+    keys_to_plot = [
+        key
+        for key in clf_report.keys()
+        if key not in ("accuracy", "macro avg", "weighted avg")
+    ]
     df = pd.DataFrame(clf_report, columns=keys_to_plot).T
     # the following line ensures that dataframe are sorted from the majority classes to the minority classes
     df.sort_values(by=["support"], inplace=True)
@@ -325,7 +335,9 @@ def plot_roc_curve(
             auc_upper = np.quantile(bootstrap_aucs, CI_upper)
             auc_lower = np.quantile(bootstrap_aucs, CI_lower)
             label = f"{confidence_interval:.0%} CI: [{auc_lower:.2f}, {auc_upper:.2f}]"
-            plt.fill_between(base_fpr, tprs_lower, tprs_upper, alpha=0.3, label=label, zorder=2)
+            plt.fill_between(
+                base_fpr, tprs_lower, tprs_upper, alpha=0.3, label=label, zorder=2
+            )
 
         if highlight_roc_area is True:
             print(
@@ -357,7 +369,9 @@ def plot_roc_curve(
     return fig
 
 
-def plot_calibration_curve(y_true: np.ndarray, y_score: np.ndarray, n_bins=10, save_fig_path=None) -> Figure:
+def plot_calibration_curve(
+    y_true: np.ndarray, y_score: np.ndarray, n_bins=10, save_fig_path=None
+) -> Figure:
     """
     Creates calibration plot for a binary classifier and calculates the ECE.
 
@@ -379,7 +393,9 @@ def plot_calibration_curve(y_true: np.ndarray, y_score: np.ndarray, n_bins=10, s
     ece : float
         The expected calibration error.
     """
-    prob_true, prob_pred = calibration_curve(y_true, y_score, n_bins=n_bins, strategy="uniform")
+    prob_true, prob_pred = calibration_curve(
+        y_true, y_score, n_bins=n_bins, strategy="uniform"
+    )
 
     # Find the number of samples in each bin
     bin_counts = np.histogram(y_score, bins=n_bins, range=(0, 1))[0]
@@ -452,7 +468,9 @@ def plot_calibration_curve(y_true: np.ndarray, y_score: np.ndarray, n_bins=10, s
     return fig
 
 
-def plot_y_score_histogram(y_true: Optional[np.ndarray], y_score: np.ndarray = None, save_fig_path=None) -> Figure:
+def plot_y_score_histogram(
+    y_true: Optional[np.ndarray], y_score: np.ndarray = None, save_fig_path=None
+) -> Figure:
     """
     Provides a histogram for the predicted probabilities of a binary classifier. If ```y_true``` is provided, it divides the ```y_score``` values into the two classes and plots them jointly into the same plot with different colors.
 
@@ -474,7 +492,9 @@ def plot_y_score_histogram(y_true: Optional[np.ndarray], y_score: np.ndarray = N
     ax = fig.add_subplot(111)
 
     if y_true is None:
-        ax.hist(y_score, bins=10, alpha=0.9, edgecolor="midnightblue", linewidth=2, rwidth=1)
+        ax.hist(
+            y_score, bins=10, alpha=0.9, edgecolor="midnightblue", linewidth=2, rwidth=1
+        )
         # same histogram as above, but with border lines
         # ax.hist(y_prob, bins=10, alpha=0.5, edgecolor='black', linewidth=1.2)
     else:

plotsandgraphs/multiclass_classifier.py

@@ -20,7 +20,12 @@
 from sklearn.utils import resample
 from tqdm import tqdm
 
-from plotsandgraphs.utils import bootstrap, set_black_title_boxes, scale_ax_bbox, get_cmap
+from plotsandgraphs.utils import (
+    bootstrap,
+    set_black_title_boxes,
+    scale_ax_bbox,
+    get_cmap,
+)
 
 
 def plot_roc_curve(
@@ -32,7 +37,7 @@ def plot_roc_curve(
     figsize: Optional[Tuple[float, float]] = None,
     class_labels: Optional[List[str]] = None,
     split_plots: bool = False,
-    save_fig_path:Optional[Union[str, Tuple[str, str]]] = None,
+    save_fig_path: Optional[Union[str, Tuple[str, str]]] = None,
 ) -> Tuple[Figure, Union[Figure, None]]:
     """
     Creates two plots.
@@ -190,9 +195,6 @@ def roc_metric_function(y_true, y_score):
 
     # create the subplot tiles (and black boxes)
     set_black_title_boxes(axes.flat[:num_classes], class_labels)
-
-
-
 
     # ---------- AUROC overview plot comparing classes ----------
     # Make an AUROC overview plot comparing the aurocs per class and combined
@@ -268,10 +270,12 @@ def auroc_metric_function(y_true, y_score, average, multi_class):
 
 
 def plot_y_score_histogram(
-    y_true: np.ndarray, y_score: Optional[np.ndarray] = None, save_fig_path: Optional[str]=None
+    y_true: np.ndarray,
+    y_score: Optional[np.ndarray] = None,
+    save_fig_path: Optional[str] = None,
 ) -> Figure:
     """
-    Histogram plot that is intended to show the distribution of the predicted probabilities for different classes, where the the different classes (y_true==0 and y_true==1) are plotted in different colors. 
+    Histogram plot that is intended to show the distribution of the predicted probabilities for different classes, where the the different classes (y_true==0 and y_true==1) are plotted in different colors.
     Limitations: Does not work for samples, that can be part of multiple classes (e.g. multilabel classification).
 
     Parameters
@@ -288,15 +292,19 @@ def plot_y_score_histogram(
     Figure
         The figure of the histogram plot.
     """
-    
+
     num_classes = y_true.shape[-1]
     class_labels = [f"Class {i}" for i in range(num_classes)]
-    
+
     cmap, colors = get_cmap("roma", n_colors=2)  # 2 colors for y==0 and y==1 per class
-    
+
     # Aiming for a square plot
-    plot_cols = np.ceil(np.sqrt(num_classes)).astype(int)  # Number of plots in a row # noqa
-    plot_rows = np.ceil(num_classes / plot_cols).astype(int)  # Number of plots in a column # noqa
+    plot_cols = np.ceil(np.sqrt(num_classes)).astype(
+        int
+    )  # Number of plots in a row # noqa
+    plot_rows = np.ceil(num_classes / plot_cols).astype(
+        int
+    )  # Number of plots in a column # noqa
     fig, axes = plt.subplots(
         nrows=plot_rows,
         ncols=plot_cols,

plotsandgraphs/pipeline.py

@@ -6,109 +6,105 @@
 from . import multiclass_classifier as mc
 
 
+FILE_ENDINGS = Literal["pdf", "png", "jpg", "jpeg", "svg"]
 
-FILE_ENDINGS = Literal['pdf', 'png', 'jpg', 'jpeg', 'svg']
 
-
-
-def binary_classifier(y_true, y_score, save_fig_path=None, plot_kwargs={}, file_type:FILE_ENDINGS='png'):
-
-
+def binary_classifier(
+    y_true, y_score, save_fig_path=None, plot_kwargs={}, file_type: FILE_ENDINGS = "png"
+):
     # Create new tqdm instance
-    tqdm_instance = tqdm(total=6, desc='Binary classifier metrics', leave=True)
-    
+    tqdm_instance = tqdm(total=6, desc="Binary classifier metrics", leave=True)
+
     # Update tqdm instance
     tqdm_instance.update()
-    
+
     # 1) Plot ROC curve
-    roc_kwargs = plot_kwargs.get('roc', {})
-    save_path = get_file_path(save_fig_path, 'roc_curve', file_type)
+    roc_kwargs = plot_kwargs.get("roc", {})
+    save_path = get_file_path(save_fig_path, "roc_curve", file_type)
     bc.plot_roc_curve(y_true, y_score, save_fig_path=save_path, **roc_kwargs)
     tqdm_instance.update()
-    
+
     # 2) Plot precision-recall curve
-    pr_kwargs = plot_kwargs.get('pr', {})
-    save_path = get_file_path(save_fig_path, 'pr_curve', file_type)
+    pr_kwargs = plot_kwargs.get("pr", {})
+    save_path = get_file_path(save_fig_path, "pr_curve", file_type)
     bc.plot_pr_curve(y_true, y_score, save_fig_path=save_path, **pr_kwargs)
     tqdm_instance.update()
-    
+
     # 3) Plot calibration curve
-    cal_kwargs = plot_kwargs.get('cal', {})
-    save_path = get_file_path(save_fig_path, 'calibration_curve', file_type)
+    cal_kwargs = plot_kwargs.get("cal", {})
+    save_path = get_file_path(save_fig_path, "calibration_curve", file_type)
     bc.plot_calibration_curve(y_true, y_score, save_fig_path=save_path, **cal_kwargs)
     tqdm_instance.update()
-    
+
     # 3) Plot confusion matrix
-    cm_kwargs = plot_kwargs.get('cm', {})
-    save_path = get_file_path(save_fig_path, 'confusion_matrix', file_type)
+    cm_kwargs = plot_kwargs.get("cm", {})
+    save_path = get_file_path(save_fig_path, "confusion_matrix", file_type)
     bc.plot_confusion_matrix(y_true, y_score, save_fig_path=save_path, **cm_kwargs)
     tqdm_instance.update()
-    
+
     # 5) Plot classification report
-    cr_kwargs = plot_kwargs.get('cr', {})
-    save_path = get_file_path(save_fig_path, 'classification_report', file_type)
+    cr_kwargs = plot_kwargs.get("cr", {})
+    save_path = get_file_path(save_fig_path, "classification_report", file_type)
     bc.plot_classification_report(y_true, y_score, save_fig_path=save_path, **cr_kwargs)
     tqdm_instance.update()
-    
+
     # 6) Plot y_score histogram
-    hist_kwargs = plot_kwargs.get('hist', {})
-    save_path = get_file_path(save_fig_path, 'y_score_histogram', file_type)
+    hist_kwargs = plot_kwargs.get("hist", {})
+    save_path = get_file_path(save_fig_path, "y_score_histogram", file_type)
     bc.plot_y_score_histogram(y_true, y_score, save_fig_path=save_path, **hist_kwargs)
     tqdm_instance.update()
-
-    return
-
 
+    return
 
 
-def multiclass_classifier(y_true, y_score, save_fig_path=None, plot_kwargs={}, file_type:FILE_ENDINGS = 'png'):
-
+def multiclass_classifier(
+    y_true, y_score, save_fig_path=None, plot_kwargs={}, file_type: FILE_ENDINGS = "png"
+):
     # Create new tqdm instance
-    tqdm_instance = tqdm(total=6, desc='Binary classifier metrics', leave=True)
-    
+    tqdm_instance = tqdm(total=6, desc="Binary classifier metrics", leave=True)
+
     # Update tqdm instance
     tqdm_instance.update()
-    
+
     # 1) Plot ROC curve
-    roc_kwargs = plot_kwargs.get('roc', {})
-    save_path = get_file_path(save_fig_path, 'roc_curve', '')
+    roc_kwargs = plot_kwargs.get("roc", {})
+    save_path = get_file_path(save_fig_path, "roc_curve", "")
     mc.plot_roc_curve(y_true, y_score, save_fig_path=save_path, **roc_kwargs)
     tqdm_instance.update()
-    
+
     # 2) Plot precision-recall curve
     # pr_kwargs = plot_kwargs.get('pr', {})
     # mc.plot_pr_curve(y_true, y_score, save_fig_path=save_fig_path, **pr_kwargs)
     # tqdm_instance.update()
-    
+
     # 3) Plot calibration curve
     # cal_kwargs = plot_kwargs.get('cal', {})
     # mc.plot_calibration_curve(y_true, y_score, save_fig_path=save_fig_path, **cal_kwargs)
     # tqdm_instance.update()
-    
+
     # 3) Plot confusion matrix
     # cm_kwargs = plot_kwargs.get('cm', {})
     # mc.plot_confusion_matrix(y_true, y_score, save_fig_path=save_fig_path, **cm_kwargs)
     # tqdm_instance.update()
-    
+
     # 5) Plot classification report
     # cr_kwargs = plot_kwargs.get('cr', {})
     # mc.plot_classification_report(y_true, y_score, save_fig_path=save_fig_path, **cr_kwargs)
     # tqdm_instance.update()
-    
+
     # 6) Plot y_score histogram
-    hist_kwargs = plot_kwargs.get('hist', {})
-    save_path = get_file_path(save_fig_path, 'y_score_histogram', file_type)
+    hist_kwargs = plot_kwargs.get("hist", {})
+    save_path = get_file_path(save_fig_path, "y_score_histogram", file_type)
     mc.plot_y_score_histogram(y_true, y_score, save_fig_path=save_path, **hist_kwargs)
     tqdm_instance.update()
-
-    return
 
+    return
 
 
-def get_file_path(save_fig_path: Union[Path,None, str], name:str, ending:str):
+def get_file_path(save_fig_path: Union[Path, None, str], name: str, ending: str):
     if save_fig_path is None:
         return None
     else:
         result = Path(save_fig_path) / f"{name}.{ending}"
         print(result)
-        return str(result)
+        return str(result)