.. 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 18-48 .. 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 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_) .. GENERATED FROM PYTHON SOURCE LINES 49-52 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) .. GENERATED FROM PYTHON SOURCE LINES 52-59 .. code-block:: Python :lineno-start: 53 # 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) .. GENERATED FROM PYTHON SOURCE LINES 60-65 .. code-block:: Python :lineno-start: 60 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) .. rst-class:: sphx-glr-script-out .. code-block:: none array([0, 1]) .. GENERATED FROM PYTHON SOURCE LINES 66-67 Create an instance of the LogisticRegression .. GENERATED FROM PYTHON SOURCE LINES 67-94 .. code-block:: Python :lineno-start: 67 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 .. raw:: html
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

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.. GENERATED FROM PYTHON SOURCE LINES 95-106 .. code-block:: Python :lineno-start: 95 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", ) .. 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 107-115 .. code-block:: Python :lineno-start: 107 p = sp.evalplot( df, x="y_true", y="y_pred", 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 116-123 .. code-block:: Python :lineno-start: 116 p = sp.evalplot( df, x="y_true", y="y_pred", 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 124-125 fig, ax = plt.subplots(figsize=(8, 6)) .. GENERATED FROM PYTHON SOURCE LINES 125-135 .. code-block:: Python :lineno-start: 125 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", ) .. 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 136-149 .. code-block:: Python :lineno-start: 136 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 .. GENERATED FROM PYTHON SOURCE LINES 150-153 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) .. GENERATED FROM PYTHON SOURCE LINES 153-160 .. code-block:: Python :lineno-start: 154 # 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) .. GENERATED FROM PYTHON SOURCE LINES 161-166 .. code-block:: Python :lineno-start: 161 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) .. rst-class:: sphx-glr-script-out .. code-block:: none array([0, 1]) .. GENERATED FROM PYTHON SOURCE LINES 167-168 Initialize the Random Forest Classifier .. GENERATED FROM PYTHON SOURCE LINES 168-178 .. code-block:: Python :lineno-start: 168 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) .. raw:: html 
RandomForestClassifier(class_weight='balanced', max_depth=6, random_state=0)
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.. GENERATED FROM PYTHON SOURCE LINES 179-180 Make predictions on the test set .. GENERATED FROM PYTHON SOURCE LINES 180-183 .. code-block:: Python :lineno-start: 180 y_val_pred = rf_model.predict(X_val) y_val_prob = rf_model.predict_proba(X_val)[:, 1] .. GENERATED FROM PYTHON SOURCE LINES 184-185 fig, ax = plt.subplots(figsize=(8, 8)) .. GENERATED FROM PYTHON SOURCE LINES 185-191 .. code-block:: Python :lineno-start: 185 p = sp.evalplot( x=y_val, y=y_val_pred, kind="all", ) .. 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 192-193 fig, ax = plt.subplots(figsize=(8, 8)) .. GENERATED FROM PYTHON SOURCE LINES 193-202 .. code-block:: Python :lineno-start: 193 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", ) .. image-sg:: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_006.png :alt: Classification Report, Confusion Matrix :srcset: /auto_examples/snsx/images/sphx_glr_plot_evalplot_script_006.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 203-204 Generate a classification report .. GENERATED FROM PYTHON SOURCE LINES 204-210 .. code-block:: Python :lineno-start: 204 print(classification_report(y_val, y_val_pred)) # Generate a confusion matrix conf_matrix = confusion_matrix(y_val, y_val_pred) print(conf_matrix) .. rst-class:: sphx-glr-script-out .. code-block:: none 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]] .. GENERATED FROM PYTHON SOURCE LINES 211-212 import seaborn as sns .. GENERATED FROM PYTHON SOURCE LINES 212-222 .. code-block:: Python :lineno-start: 213 # 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 223-231 .. 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 1.437 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/maintenance/0.4.X?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 `_