o -wi@sddlmZddlZddlmZmZmZddlZddlmZddl m Z ddl m Z m Z er4ddl mZedZ     ddddZdS) ) annotationsN) TYPE_CHECKINGIterableTypeVar)util) plot_table) test_missing test_types) CustomChartTPrecision-Recall CurveFy_trueIterable[T] | Noney_probasIterable[numbers.Number] | Nonelabelslist[str] | Noneclasses_to_plotlist[T] | None interp_sizeinttitlestr split_tableboolreturnr c s$tjdddtjddd}tdd}tdd} fd d } |}|}t||d s2d St||d s:d S|} |d urE| }i} d d|d d d} | |d }|D]S}|d ur|t | |t sut | d j r||| |}n| |}|j ||d d |f| |d\}}}| |}|d d d}|d d d}j || dd}||| |<q`|dd| Dt| | t| dd}t|tjjkrtdtjjd| j|dtjjd|ddgd}ttj|ddd d!dd"|d#d$d%|d&S)'a" Constructs a Precision-Recall (PR) curve. The Precision-Recall curve is particularly useful for evaluating classifiers on imbalanced datasets. A high area under the PR curve signifies both high precision (a low false positive rate) and high recall (a low false negative rate). The curve provides insights into the balance between false positives and false negatives at various threshold levels, aiding in the assessment of a model's performance. Args: y_true: True binary labels. The shape should be (`num_samples`,). y_probas: Predicted scores or probabilities for each class. These can be probability estimates, confidence scores, or non-thresholded decision values. The shape should be (`num_samples`, `num_classes`). labels: Optional list of class names to replace numeric values in `y_true` for easier plot interpretation. For example, `labels = ['dog', 'cat', 'owl']` will replace 0 with 'dog', 1 with 'cat', and 2 with 'owl' in the plot. If not provided, numeric values from `y_true` will be used. classes_to_plot: Optional list of unique class values from y_true to be included in the plot. If not specified, all unique classes in y_true will be plotted. interp_size: Number of points to interpolate recall values. The recall values will be fixed to `interp_size` uniformly distributed points in the range [0, 1], and the precision will be interpolated accordingly. title: Title of the plot. Defaults to "Precision-Recall Curve". split_table: Whether the table should be split into a separate section in the W&B UI. If `True`, the table will be displayed in a section named "Custom Chart Tables". Default is `False`. Returns: CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to `wandb.log()`. Raises: wandb.Error: If NumPy, pandas, or scikit-learn is not installed. Example: ```python import wandb # Example for spam detection (binary classification) y_true = [0, 1, 1, 0, 1] # 0 = not spam, 1 = spam y_probas = [ [0.9, 0.1], # Predicted probabilities for the first sample (not spam) [0.2, 0.8], # Second sample (spam), and so on [0.1, 0.9], [0.8, 0.2], [0.3, 0.7], ] labels = ["not spam", "spam"] # Optional class names for readability with wandb.init(project="spam-detection") as run: pr_curve = wandb.plot.pr_curve( y_true=y_true, y_probas=y_probas, labels=labels, title="Precision-Recall Curve for Spam Detection", ) run.log({"pr-curve": pr_curve}) ``` numpyz@roc requires the numpy library, install with `pip install numpy`)requiredpandaszBroc requires the pandas library, install with `pip install pandas`zsklearn.metricszHroc requires the scikit library, install with `pip install scikit-learn`z sklearn.utilscs<|}tdt|D]}t||||d||<q |S)N)arrayrangelenmax)xyinpP/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/wandb/plot/pr_curve.py_stepns zpr_curve.._step)rrNrr ) pos_labelleft)sidecSsg|] \}}|gt|qSr*)r#).0kvr*r*r+ szpr_curve..)class precisionrecallzTable has a limit of z rows. Resampling to fit.F*r5)replace n_samples random_statestratify)r6r7r5) dataframezwandb/area-under-curve/v0r7r6)r%r&r5Recall Precision)rz x-axis-titlez y-axis-title) data_tablevega_spec_namefields string_fieldsr)r get_moduler!rr uniquelinspacewhereisin isinstancerintegerprecision_recall_curve searchsorted DataFramehstackitemslistvaluestiler#roundwandbTableMAX_ROWStermwarnresample sort_valuesr)rrrrrrrpdsklearn_metrics sklearn_utilsr,classesr6 interp_recallindices_to_plotr' class_label cur_precision cur_recall_indicesdfr*r(r+pr_curvesK          rg)NNNNr r F)rrrrrrrrrrrrrrrr ) __future__rnumberstypingrrrrUrwandb.plot.custom_chartrwandb.plot.utilsrr r r rgr*r*r*r+s$