plot_evalplot_script with examples

An example showing the evalplot function used by a scikit-learn regressor.

# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause

Import scikit-plot

import scikitplot.snsx as sp

import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)  # reproducibility
import pandas as pd

from sklearn.datasets import make_classification
from sklearn.datasets import (
    load_breast_cancer as data_2_classes,
    load_iris as data_3_classes,
    load_digits as data_10_classes,
)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

def logistic_scale(scores):
    """Scale decision_function outputs to (0,1) using the logistic (sigmoid) function."""
    scores = np.asarray(scores, dtype=float)
    # Clip to avoid overflow for large |x| before exp
    # scores = np.clip(scores, -500, 500)
    return 1.0 / (1.0 + np.exp(-scores))

def minmax_scale(scores):
    """Linearly scale an array to [0,1]."""
    scores = np.asarray(scores, dtype=float)
    min_, max_ = np.min(scores), np.max(scores)
    if np.isclose(min_, max_):
        # Avoid divide-by-zero when all values identical
        return np.zeros_like(scores)
    return (scores - min_) / (max_ - min_)

Load the data X, y = data_3_classes(return_X_y=True, as_frame=False) X, y = data_2_classes(return_X_y=True, as_frame=False)

# Generate a sample dataset
X, y = make_classification(n_samples=5000, n_features=20, n_informative=15,
                          n_redundant=2, n_classes=2, n_repeated=0,
                          class_sep=1.5, flip_y=0.01, weights=[0.85, 0.15],
                          random_state=0)

X_train, X_val, y_train, y_val = train_test_split(
    X, y, stratify=y, test_size=0.2, random_state=0
)
np.unique(y)

array([0, 1])

Create an instance of the LogisticRegression

model = (
    LogisticRegression(
        # max_iter=int(1e5),
        # C=10,
        # penalty='l1',
        # solver='liblinear',
        class_weight='balanced',
        random_state=0
    )
    .fit(X_train, y_train)
)
# Perform predictions
y_val_prob = model.predict_proba(X_val)
# Create a DataFrame with predictions
df = pd.DataFrame({
    "y_true": y_val==1,  # target class (0,1,2)
    "y_score": y_val_prob[:, 1],  # target class (0,1,2)
    # np.argmax
    "y_pred": y_val_prob[:, 1] > 0.5,  # target class (0,1,2)
    # "y_true": np.random.normal(0.5, 0.1, 100).round(),
    # "y_score": np.random.normal(0.5, 0.15, 100),
    # "hue": np.random.normal(0.5, 0.4, 100).round(),
})
df

	y_true	y_score	y_pred
0	False	0.033725	False
1	True	0.860583	True
2	False	0.423101	False
3	False	0.137295	False
4	False	0.788645	True
...	...	...	...
995	False	0.228034	False
996	False	0.017187	False
997	True	0.987892	True
998	False	0.931136	True
999	False	0.128248	False

1000 rows × 3 columns

p = sp.evalplot(
    df,
    x="y_true",
    y="y_pred",
    # y="y_score",
    # allow_probs=True,  # if y_score provided
    # threshold=0.5,
    kind="all",
)

p = sp.evalplot(
    df,
    x="y_true",
    y="y_pred",
    kind="classification_report",
    text_kws={'fontsize': 16},
)

p = sp.evalplot(
    df,
    x="y_true",
    y="y_pred",
    kind="confusion_matrix",
)

fig, ax = plt.subplots(figsize=(8, 6))

p = sp.evalplot(
    df,
    x="y_true",
    # y="y_pred",
    y="y_score",
    allow_probs=True,  # if y_score provided
    threshold=0.5,
    kind="all",
)

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.datasets import (
    load_breast_cancer as data_2_classes,
    load_iris as data_3_classes,
    load_digits as data_10_classes,
)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix

Load the data X, y = data_3_classes(return_X_y=True, as_frame=False) X, y = data_2_classes(return_X_y=True, as_frame=False)

# Generate a sample dataset
X, y = make_classification(n_samples=5000, n_features=20, n_informative=15,
                          n_redundant=2, n_classes=2, n_repeated=0,
                          class_sep=1.5, flip_y=0.01, weights=[0.97, 0.03],
                          random_state=0)

X_train, X_val, y_train, y_val = train_test_split(
    X, y, stratify=y, test_size=0.2, random_state=0,
)
np.unique(y)

array([0, 1])

Initialize the Random Forest Classifier

rf_model = RandomForestClassifier(
    class_weight='balanced',
    n_estimators=100,
    max_depth=6,
    random_state=0,
)

# Train the model
rf_model.fit(X_train, y_train)

RandomForestClassifier(class_weight='balanced', max_depth=6, random_state=0)

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Make predictions on the test set

y_val_pred = rf_model.predict(X_val)
y_val_prob = rf_model.predict_proba(X_val)[:, 1]

fig, ax = plt.subplots(figsize=(8, 8))

p = sp.evalplot(
    x=y_val,
    y=y_val_pred,
    kind="all",
)

fig, ax = plt.subplots(figsize=(8, 8))

p = sp.evalplot(
    x=y_val,
    # y=y_pred,
    y=y_val_prob,
    allow_probs=True,  # if y_score provided
    threshold=0.5,
    kind="all",
)

Generate a classification report

print(classification_report(y_val, y_val_pred))

# Generate a confusion matrix
conf_matrix = confusion_matrix(y_val, y_val_pred)
print(conf_matrix)

              precision    recall  f1-score   support

           0       0.97      1.00      0.99       966
           1       0.73      0.24      0.36        34

    accuracy                           0.97      1000
   macro avg       0.85      0.62      0.67      1000
weighted avg       0.97      0.97      0.96      1000

[[963   3]
 [ 26   8]]

import seaborn as sns

# plt.figure(figsize=(12, 7))
# sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',
#             xticklabels=np.arange(15), yticklabels=np.arange(15))
# plt.ylabel('Actual')
# plt.xlabel('Predicted')
# plt.title('Confusion Matrix')
# plt.show()

Tags: model-type: classification model-workflow: model evaluation plot-type: line plot-type: eval level: beginner purpose: showcase

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