sklearn-docs/Precision-Recall

1import numpy as np2import plotly.graph_objects as go3from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve, average_precision_score4 5def plot_multi_label_pr_curve(clf, X_test: np.ndarray, Y_test: np.ndarray):6    n_classes = Y_test.shape[1]7    y_score = clf.decision_function(X_test)8 9    # For each class10    precision = dict()11    recall = dict()12    average_precision = dict()13    for i in range(n_classes):14        precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i], y_score[:, i])15        average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i])16 17    # A "micro-average": quantifying score on all classes jointly18    precision["micro"], recall["micro"], _ = precision_recall_curve(19        Y_test.ravel(), y_score.ravel()20    )21    average_precision["micro"] = average_precision_score(Y_test, y_score, average="micro")22    23    # Plotting24    fig = go.Figure()25 26    27    # Plottin Precision-Recall Curves for each class28    colors = ["navy", "turquoise", "darkorange", "gold"]29    keys = list(precision.keys())30 31    for color, key in zip(colors, keys):32        if key=="micro":33            name = f"Micro-average(AP={average_precision[key]:.2f})"34        else:35            name = f"Class {key} (AP={average_precision[key]:.2f})"36        fig.add_trace(37            go.Scatter(38                x=recall[key],39                y=precision[key],40                mode="lines",41                name=name,42                line=dict(color=color),43                showlegend=True,44                line_shape="hv"45            )46        )47 48    # Creating Iso-F1 Curves49    f_scores = np.linspace(0.2, 0.8, num=4)50    for idx, f_score in enumerate(f_scores):51        if idx==0:52            name = "Iso-F1 Curves"53            showlegend = True54        else:55            name = ""56            showlegend = False57        x = np.linspace(0.01, 1, 1001)58        y = f_score * x / (2 * x - f_score)59        mask = y >= 060        fig.add_trace(go.Scatter(x=x[mask], y=y[mask], mode='lines', line_color='gray', name=name, showlegend=showlegend))61        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))62 63 64    fig.update_yaxes(range=[0, 1.05])65 66    fig.update_layout(67        title='Extension of Precision-Recall Curve to Multi-Class', 68        xaxis_title='Recall', 69        yaxis_title='Precision'70    )71 72    return fig73 74 75def plot_binary_pr_curve(clf, X_test: np.ndarray, y_test:np.array):76    # make predictions on the test data77    y_pred = clf.decision_function(X_test)78 79    # calculate precision and recall for different probability thresholds80    precision, recall, _ = precision_recall_curve(y_test, y_pred)81 82    # calculate the average precision83    ap = average_precision_score(y_test, y_pred)84 85    # Plotting86    fig = go.Figure()87 88    fig.add_trace(89        go.Scatter(90            x=recall,91            y=precision,92            mode="lines",93            name=f"LinearSVC (AP={ap:.2f})",94            line=dict(color="blue"),95            showlegend=True,96            line_shape="hv"97        )98    )99 100    # Make x-range slightly larger than max value101    fig.update_xaxes(range=[-0.05, 1.05])102    # Make Legend text size larger103    fig.update_layout(104        title='2-Class Precision-Recall Curve',105        xaxis_title='Recall (Positive label: 1)',106        yaxis_title='Precision (Positive label: 1)',107        legend=dict(108            x=0.009,109            y=0.05,110            font=dict(111                size=12,112            ),113        )114    )115 116    return fig117