utilities

utils.py

@@ -0,0 +1,117 @@
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve, average_precision_score
+
+def plot_multi_label_pr_curve(clf, X_test: np.ndarray, Y_test: np.ndarray):
+    n_classes = Y_test.shape[1]
+    y_score = clf.decision_function(X_test)
+
+    # For each class
+    precision = dict()
+    recall = dict()
+    average_precision = dict()
+    for i in range(n_classes):
+        precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i], y_score[:, i])
+        average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])
+
+    # A "micro-average": quantifying score on all classes jointly
+    precision["micro"], recall["micro"], _ = precision_recall_curve(
+        Y_test.ravel(), y_score.ravel()
+    )
+    average_precision["micro"] = average_precision_score(Y_test, y_score, average="micro")
+    
+    # Plotting
+    fig = go.Figure()
+
+    
+    # Plottin Precision-Recall Curves for each class
+    colors = ["navy", "turquoise", "darkorange", "gold"]
+    keys = list(precision.keys())
+
+    for color, key in zip(colors, keys):
+        if key=="micro":
+            name = f"Micro-average Precision-Recall (AP={average_precision[key]:.2f})"
+        else:
+            name = f"Precision-recall for class {key} (AP={average_precision[key]:.2f})"
+        fig.add_trace(
+            go.Scatter(
+                x=recall[key],
+                y=precision[key],
+                mode="lines",
+                name=name,
+                line=dict(color=color),
+                showlegend=True,
+                line_shape="hv"
+            )
+        )
+
+    # Creating Iso-F1 Curves
+    f_scores = np.linspace(0.2, 0.8, num=4)
+    for idx, f_score in enumerate(f_scores):
+        if idx==0:
+            name = "Iso-F1 Curves"
+            showlegend = True
+        else:
+            name = ""
+            showlegend = False
+        x = np.linspace(0.01, 1, 1001)
+        y = f_score * x / (2 * x - f_score)
+        mask = y >= 0
+        fig.add_trace(go.Scatter(x=x[mask], y=y[mask], mode='lines', line_color='gray', name=name, showlegend=showlegend))
+        fig.add_annotation(x=0.9, y=y[900] + 0.02, text=f"<b>f1={f_score:0.1f}</b>", showarrow=False, font=dict(size=15))
+
+
+    fig.update_yaxes(range=[0, 1.05])
+
+    fig.update_layout(
+        title='Extension of Precision-Recall Curve to Multi-Class', 
+        xaxis_title='Recall', 
+        yaxis_title='Precision',
+    )
+
+    return fig
+
+
+def plot_binary_pr_curve(clf, X_test: np.ndarray, y_test:np.array):
+    # make predictions on the test data
+    y_pred = clf.decision_function(X_test)
+
+    # calculate precision and recall for different probability thresholds
+    precision, recall, _ = precision_recall_curve(y_test, y_pred)
+
+    # calculate the average precision
+    ap = average_precision_score(y_test, y_pred)
+
+    # Plotting
+    fig = go.Figure()
+
+    fig.add_trace(
+        go.Scatter(
+            x=recall,
+            y=precision,
+            mode="lines",
+            name=f"LinearSVC (AP={ap:.2f})",
+            line=dict(color="blue"),
+            showlegend=True,
+            line_shape="hv"
+        )
+    )
+
+    # Make x-range slightly larger than max value
+    fig.update_xaxes(range=[-0.05, 1.05])
+    # Make Legend text size larger
+    fig.update_layout(
+        title='2-Class Precision-Recall Curve',
+        xaxis_title='Recall (Positive label: 1)',
+        yaxis_title='Precision (Positive label: 1)',
+        legend=dict(
+            x=0.009,
+            y=0.05,
+            font=dict(
+                size=12,
+            ),
+        )
+    )
+
+    return fig
+