.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/snsx/plot_evalplot_script.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_snsx_plot_evalplot_script.py: plot_evalplot_script with examples ========================================== An example showing the :py:func:`~scikitplot.snsx.evalplot` function used by a scikit-learn regressor. .. GENERATED FROM PYTHON SOURCE LINES 8-12 .. code-block:: Python :lineno-start: 9 # Authors: The scikit-plots developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 13-14 Import scikit-plot .. GENERATED FROM PYTHON SOURCE LINES 14-17 .. code-block:: Python :lineno-start: 14 import scikitplot.snsx as sp .. GENERATED FROM PYTHON SOURCE LINES 18-30 .. code-block:: Python :lineno-start: 18 import matplotlib.pyplot as plt import numpy as np; np.random.seed(0) # reproducibility import pandas as pd from sklearn.datasets import ( load_breast_cancer as data_2_classes, # load_iris as data_3_classes, ) from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split .. GENERATED FROM PYTHON SOURCE LINES 31-33 Load the data X, y = data_3_classes(return_X_y=True, as_frame=False) .. GENERATED FROM PYTHON SOURCE LINES 33-37 .. code-block:: Python :lineno-start: 33 X, y = data_2_classes(return_X_y=True, as_frame=False) X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.5, random_state=0) np.unique(y) .. rst-class:: sphx-glr-script-out .. code-block:: none array([0, 1]) .. GENERATED FROM PYTHON SOURCE LINES 38-39 Create an instance of the LogisticRegression .. GENERATED FROM PYTHON SOURCE LINES 39-56 .. code-block:: Python :lineno-start: 39 model = ( LogisticRegression(max_iter=int(1e5), 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) # "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(), }) .. GENERATED FROM PYTHON SOURCE LINES 57-65 .. code-block:: Python :lineno-start: 57 p = sp.evalplot( df, x="y_true", y="y_score", kind="all", ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_001.png :alt: Classification Report, Confusion Matrix :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 66-74 .. code-block:: Python :lineno-start: 66 p = sp.evalplot( df, x="y_true", y="y_score", kind="classification_report", text_kws={'fontsize': 16}, ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_002.png :alt: Classification Report :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 75-82 .. code-block:: Python :lineno-start: 75 p = sp.evalplot( df, x="y_true", y="y_score", kind="confusion_matrix", ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_003.png :alt: Confusion Matrix :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 83-92 .. code-block:: Python :lineno-start: 83 fig, ax = plt.subplots(figsize=(8, 6)) p = sp.evalplot( df, x="y_true", y="y_score", kind="all", # legend=True, ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_004.png :alt: Classification Report, Confusion Matrix :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 93-101 .. code-block:: Python :lineno-start: 93 import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix .. GENERATED FROM PYTHON SOURCE LINES 102-103 Create a synthetic dataset with 15 classes .. GENERATED FROM PYTHON SOURCE LINES 103-113 .. code-block:: Python :lineno-start: 103 X, y = make_classification(n_samples=5000, n_features=20, n_classes=15, n_informative=15, n_redundant=5, random_state=0) # Convert to DataFrame for easier visualization (optional) data = pd.DataFrame(X) data['target'] = y print(data.head()) .. rst-class:: sphx-glr-script-out .. code-block:: none 0 1 2 ... 18 19 target 0 -3.249907 -7.609039 3.904290 ... 0.613949 -3.092426 7 1 -1.653727 3.553595 -0.281940 ... -2.332832 -4.067393 3 2 -1.057158 7.627822 0.181669 ... 0.713138 6.743130 11 3 0.734427 -2.012334 -1.798327 ... -4.232499 1.357491 11 4 6.516446 -8.325342 -1.350152 ... 1.410434 13.547467 1 [5 rows x 21 columns] .. GENERATED FROM PYTHON SOURCE LINES 114-115 Split the dataset into training and testing sets .. GENERATED FROM PYTHON SOURCE LINES 115-117 .. code-block:: Python :lineno-start: 115 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) .. GENERATED FROM PYTHON SOURCE LINES 118-119 Initialize the Random Forest Classifier .. GENERATED FROM PYTHON SOURCE LINES 119-124 .. code-block:: Python :lineno-start: 119 rf_model = RandomForestClassifier(n_estimators=100, random_state=0) # Train the model rf_model.fit(X_train, y_train) .. raw:: html 
RandomForestClassifier(random_state=0)
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.. GENERATED FROM PYTHON SOURCE LINES 125-126 Make predictions on the test set .. GENERATED FROM PYTHON SOURCE LINES 126-128 .. code-block:: Python :lineno-start: 126 y_pred = rf_model.predict(X_test) .. GENERATED FROM PYTHON SOURCE LINES 129-137 .. code-block:: Python :lineno-start: 129 fig, ax = plt.subplots(figsize=(8, 8)) p = sp.evalplot( x=y_test, y=y_pred, kind="all", # legend=True, ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_005.png :alt: Classification Report, Confusion Matrix :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 138-148 .. code-block:: Python :lineno-start: 138 import scikitplot as sp # Save the combined figure as an image file figs = sp.stack( # experimental p.figure, p.figure, orient='x', **{'figsize': (12, 8)} ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_006.png :alt: plot evalplot script :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_006.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 149-150 Generate a classification report .. GENERATED FROM PYTHON SOURCE LINES 150-156 .. code-block:: Python :lineno-start: 150 print(classification_report(y_test, y_pred)) # Generate a confusion matrix conf_matrix = confusion_matrix(y_test, y_pred) print(conf_matrix) .. rst-class:: sphx-glr-script-out .. code-block:: none precision recall f1-score support 0 0.48 0.48 0.48 64 1 0.44 0.48 0.46 61 2 0.56 0.52 0.54 65 3 0.55 0.67 0.60 61 4 0.53 0.62 0.57 76 5 0.64 0.45 0.53 80 6 0.64 0.62 0.63 72 7 0.57 0.66 0.61 56 8 0.62 0.62 0.62 65 9 0.54 0.57 0.56 68 10 0.67 0.60 0.63 70 11 0.54 0.55 0.54 69 12 0.63 0.53 0.58 77 13 0.56 0.45 0.50 67 14 0.41 0.53 0.46 49 accuracy 0.56 1000 macro avg 0.56 0.56 0.55 1000 weighted avg 0.56 0.56 0.56 1000 [[31 2 3 3 2 3 3 2 2 4 1 3 0 1 4] [ 5 29 3 3 1 2 2 2 2 2 1 3 2 3 1] [ 0 2 34 3 3 1 0 3 2 1 3 1 5 2 5] [ 3 1 1 41 1 1 0 1 2 1 0 2 1 2 4] [ 5 4 1 3 47 1 1 2 2 2 1 2 3 2 0] [ 3 5 1 3 7 36 0 6 2 5 1 9 0 1 1] [ 0 1 2 3 7 1 45 2 0 1 0 4 2 1 3] [ 1 0 1 2 3 3 3 37 1 1 0 0 1 3 0] [ 6 3 0 3 1 1 2 1 40 4 0 2 1 0 1] [ 1 1 1 4 3 2 3 0 1 39 4 1 0 1 7] [ 1 1 7 1 4 0 3 0 1 2 42 1 3 2 2] [ 1 4 3 0 2 2 4 0 2 5 1 38 3 1 3] [ 1 5 1 1 3 2 1 2 2 2 6 5 41 3 2] [ 5 4 3 0 3 1 2 5 4 3 2 0 0 30 5] [ 2 4 0 5 2 0 1 2 1 0 1 0 3 2 26]] .. GENERATED FROM PYTHON SOURCE LINES 157-158 import seaborn as sns .. GENERATED FROM PYTHON SOURCE LINES 158-168 .. code-block:: Python :lineno-start: 159 # 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() .. GENERATED FROM PYTHON SOURCE LINES 169-177 .. tags:: model-type: classification model-workflow: model evaluation plot-type: line plot-type: eval level: beginner purpose: showcase .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.563 seconds) .. _sphx_glr_download_auto_examples_snsx_plot_evalplot_script.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-plots/scikit-plots/main?urlpath=lab/tree/notebooks/auto_examples/snsx/plot_evalplot_script.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/snsx/plot_evalplot_script.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_evalplot_script.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_evalplot_script.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_evalplot_script.zip ` .. include:: plot_evalplot_script.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_