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- from warnings import simplefilter
- import numpy as np
- import sklearn
- import wandb
- from wandb.integration.sklearn import utils
- # ignore all future warnings
- simplefilter(action="ignore", category=FutureWarning)
- def summary_metrics(model=None, X=None, y=None, X_test=None, y_test=None): # noqa: N803
- """Calculate summary metrics for both regressors and classifiers.
- Called by plot_summary_metrics to visualize metrics. Please use the function
- plot_summary_metrics() if you wish to visualize your summary metrics.
- """
- y, y_test = np.asarray(y), np.asarray(y_test)
- metrics = {}
- model_name = model.__class__.__name__
- y_pred = model.predict(X_test)
- if sklearn.base.is_classifier(model):
- accuracy_score = sklearn.metrics.accuracy_score(y_test, y_pred)
- metrics["accuracy_score"] = accuracy_score
- precision = sklearn.metrics.precision_score(y_test, y_pred, average="weighted")
- metrics["precision"] = precision
- recall = sklearn.metrics.recall_score(y_test, y_pred, average="weighted")
- metrics["recall"] = recall
- f1_score = sklearn.metrics.f1_score(y_test, y_pred, average="weighted")
- metrics["f1_score"] = f1_score
- elif sklearn.base.is_regressor(model):
- mae = sklearn.metrics.mean_absolute_error(y_test, y_pred)
- metrics["mae"] = mae
- mse = sklearn.metrics.mean_squared_error(y_test, y_pred)
- metrics["mse"] = mse
- r2_score = sklearn.metrics.r2_score(y_test, y_pred)
- metrics["r2_score"] = r2_score
- metrics = {name: utils.round_2(metric) for name, metric in metrics.items()}
- table = make_table(metrics, model_name)
- chart = wandb.visualize("wandb/metrics/v1", table)
- return chart
- def make_table(metrics, model_name):
- columns = ["metric_name", "metric_value", "model_name"]
- table_content = [[name, value, model_name] for name, value in metrics.items()]
- table = wandb.Table(columns=columns, data=table_content)
- return table
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