Working on jupyter notebooks, I came across this problem:

在研究Jupyter笔记本电脑时，我遇到了这个问题：

TypeError:unsupported format string passed to function .__format__

This is where the code starts:

这是代码开始的地方：

def evaluate_preds(y_true, y_preds):
    """
    Performs Evaluation comp on y_true labels and y_preds labels on a classification
    """
    accuracy = accuracy_score(y_true,y_preds)
    precision = precision_score(y_true,y_preds)
    recall = recall_score(y_true,y_preds)
    f1 = f1_score(y_true,y_preds)
    metric_dict = {"accuracy": round(accuracy,2),
                  "precision": round(precision,2),
                  "recall": round(recall,2),
                  "f1": round(f1,2)}

    print(f"Accuracy :{ accuracy_score*100:.2f}%")
    print(f"Precision :{ precision_score:.2f}")
    print(f"Recall :{ recall_score:.2f}")
    print(f"F1 :{ f1_score:.2f}")

    return metric_dict

This below code is being run on a different cell in jupyter notebook

下面的代码是在jupyter笔记本的另一个单元上运行的

from sklearn.ensemble import RandomForestClassifier

np.random.seed(42)

heart_disease_shuffled= heart_disease.sample(frac=1)

X= heart_disease_shuffled.drop("target",axis =1)
y = heart_disease_shuffled["target"]

train_split = round(0.7 * len(heart_disease_shuffled))
valid_split = round(train_split + 0.15 * len(heart_disease_shuffled))

X_train,y_train = X[:train_split], y[:train_split]
X_valid,y_valid = X[train_split:valid_split], y[train_split:valid_split]

X_test,y_test = X[valid_split:], y[:valid_split]

clf = RandomForestClassifier()
clf.fit(X_train,y_train)


# Make Baseline preds

y_preds = clf.predict(X_valid)

# Evaluate classifier on validation set

baseline_metrics = evaluate_preds(y_valid, y_preds) 
baseline_metrics

How can I resolve it?

我怎么才能解决它呢？

Tried changing the parameters and a bunch of other things but all of them popped up some errors like the one listed above

尝试更改参数和一系列其他内容，但所有这些都弹出了一些类似上面列出的错误

You are trying to print functions instead of the values they return. For instance, you assign

您正在尝试打印函数，而不是它们返回的值。例如，您可以将

accuracy = accuracy_score(y_true,y_preds)

but then try to format the function, not the result

但然后尝试格式化函数，而不是结果

f"Accuracy :{ accuracy_score*100:.2f}%"

Instead, you should use the calculated value

相反，您应该使用计算出的值

f"Accuracy :{ accuracy*100:.2f}%"

But you've also created a dictionary with rounded values. You could use that dictionary with an f-string or use the .format method instead

但您还创建了一个四舍五入值的词典。您可以将该词典与f字符串一起使用，也可以改用.Format方法

f"Accuracy :{ metric_dict['accuracy']:.2f}"
# -- or --
"Accuracy : {accuracy:.2f}".format(**metric_dict)

Here's the updated/ working version,

以下是更新/工作版本，

import pandas as pd

def evaluate_preds(y_true, y_preds):
    """
    Performs Evaluation comp on y_true labels and y_preds labels on a classification
    """
    accuracy = accuracy_score(y_true,y_preds)
    precision = precision_score(y_true,y_preds)
    recall = recall_score(y_true,y_preds)
    f1 = f1_score(y_true,y_preds)
    metric_dict = {"accuracy": round(accuracy,2),
                      "precision": round(precision,2),
                      "recall": round(recall,2),
                      "f1": round(f1,2)}
    
    print(f"Accuracy :{ accuracy *100:.2f}%")
    print(f"Precision :{ precision:.2f}")
    print(f"Recall :{ recall:.2f}")
    print(f"F1 :{ f1:.2f}")
    return metric_dict

from sklearn.ensemble import RandomForestClassifier

np.random.seed(42)

heart_disease_shuffled= heart_disease.sample(frac=1)

X= heart_disease_shuffled.drop("target",axis =1)
y = heart_disease_shuffled["target"]

train_split = round(0.7 * len(heart_disease_shuffled))
valid_split = round(train_split + 0.15 * len(heart_disease_shuffled))

X_train,y_train = X[:train_split], y[:train_split]
X_valid,y_valid = X[train_split:valid_split], y[train_split:valid_split]

X_test,y_test = X[valid_split:], y[:valid_split]

clf = RandomForestClassifier()
clf.fit(X_train,y_train)

# Make Baseline preds
y_preds = clf.predict(X_valid)
# Evaluate classifier on validation set
baseline_metrics = evaluate_preds(y_valid, y_preds) 
baseline_metrics