.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/miscellaneous/plot_display_object_visualization.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end <sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.py>` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py: =================================== 使用显示对象进行å¯è§†åŒ– =================================== .. currentmodule:: sklearn.metrics 在这个例åä¸ï¼Œæˆ‘们将直接从å„è‡ªçš„åº¦é‡æž„建显示对象,:class:`ConfusionMatrixDisplay` ã€:class:`RocCurveDisplay` å’Œ :class:`PrecisionRecallDisplay` 。当模型的预测结果已ç»è®¡ç®—å‡ºæ¥æˆ–计算代价较高时,这是使用相应绘图函数的替代方法。请注æ„,这是高级用法,一般情况下我们推è使用相应的绘图函数。 .. GENERATED FROM PYTHON SOURCE LINES 13-16 åŠ è½½æ•°æ®å¹¶è®ç»ƒæ¨¡åž‹ ------------------------- 在æ¤ç¤ºä¾‹ä¸ï¼Œæˆ‘们从 `OpenML <https://www.openml.org/d/1464>` åŠ è½½ä¸€ä¸ªè¡€æ¶²è¾“é€æœåŠ¡ä¸å¿ƒçš„æ•°æ®é›†ã€‚è¿™æ˜¯ä¸€ä¸ªäºŒå…ƒåˆ†ç±»é—®é¢˜ï¼Œç›®æ ‡æ˜¯åˆ¤æ–个体是å¦çŒ®è¡€ã€‚ç„¶åŽå°†æ•°æ®åˆ†ä¸ºè®ç»ƒé›†å’Œæµ‹è¯•集,并使用è®ç»ƒé›†æ‹Ÿåˆé€»è¾‘回归模型。 .. GENERATED FROM PYTHON SOURCE LINES 16-29 .. code-block:: Python from sklearn.datasets import fetch_openml from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler X, y = fetch_openml(data_id=1464, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y) clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0)) clf.fit(X_train, y_train) .. raw:: html <div class="output_subarea output_html rendered_html output_result"> <style>#sk-container-id-40 { /* Definition of color scheme common for light and dark mode */ --sklearn-color-text: black; --sklearn-color-line: gray; /* Definition of color scheme for unfitted estimators */ --sklearn-color-unfitted-level-0: #fff5e6; --sklearn-color-unfitted-level-1: #f6e4d2; --sklearn-color-unfitted-level-2: #ffe0b3; --sklearn-color-unfitted-level-3: chocolate; /* Definition of color scheme for fitted estimators */ --sklearn-color-fitted-level-0: #f0f8ff; --sklearn-color-fitted-level-1: #d4ebff; --sklearn-color-fitted-level-2: #b3dbfd; --sklearn-color-fitted-level-3: cornflowerblue; /* Specific color for light theme */ --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black))); --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white))); --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black))); --sklearn-color-icon: #696969; @media (prefers-color-scheme: dark) { /* Redefinition of color scheme for dark theme */ --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white))); --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111))); --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white))); --sklearn-color-icon: #878787; } } #sk-container-id-40 { color: var(--sklearn-color-text); } #sk-container-id-40 pre { padding: 0; } #sk-container-id-40 input.sk-hidden--visually { border: 0; clip: rect(1px 1px 1px 1px); clip: rect(1px, 1px, 1px, 1px); height: 1px; margin: -1px; overflow: hidden; padding: 0; position: absolute; width: 1px; } #sk-container-id-40 div.sk-dashed-wrapped { border: 1px dashed var(--sklearn-color-line); margin: 0 0.4em 0.5em 0.4em; box-sizing: border-box; padding-bottom: 0.4em; background-color: var(--sklearn-color-background); } #sk-container-id-40 div.sk-container { /* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */ display: inline-block !important; position: relative; } #sk-container-id-40 div.sk-text-repr-fallback { display: none; } div.sk-parallel-item, div.sk-serial, div.sk-item { /* draw centered vertical line to link estimators */ background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background)); background-size: 2px 100%; background-repeat: no-repeat; background-position: center center; } /* Parallel-specific style estimator block */ #sk-container-id-40 div.sk-parallel-item::after { content: ""; width: 100%; border-bottom: 2px solid var(--sklearn-color-text-on-default-background); flex-grow: 1; } #sk-container-id-40 div.sk-parallel { display: flex; align-items: stretch; justify-content: center; background-color: var(--sklearn-color-background); position: relative; } #sk-container-id-40 div.sk-parallel-item { display: flex; flex-direction: column; } #sk-container-id-40 div.sk-parallel-item:first-child::after { align-self: flex-end; width: 50%; } #sk-container-id-40 div.sk-parallel-item:last-child::after { align-self: flex-start; width: 50%; } #sk-container-id-40 div.sk-parallel-item:only-child::after { width: 0; } /* Serial-specific style estimator block */ #sk-container-id-40 div.sk-serial { display: flex; flex-direction: column; align-items: center; background-color: var(--sklearn-color-background); padding-right: 1em; padding-left: 1em; } /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is clickable and can be expanded/collapsed. - Pipeline and ColumnTransformer use this feature and define the default style - Estimators will overwrite some part of the style using the `sk-estimator` class */ /* Pipeline and ColumnTransformer style (default) */ #sk-container-id-40 div.sk-toggleable { /* Default theme specific background. It is overwritten whether we have a specific estimator or a Pipeline/ColumnTransformer */ background-color: var(--sklearn-color-background); } /* Toggleable label */ #sk-container-id-40 label.sk-toggleable__label { cursor: pointer; display: block; width: 100%; margin-bottom: 0; padding: 0.5em; box-sizing: border-box; text-align: center; } #sk-container-id-40 label.sk-toggleable__label-arrow:before { /* Arrow on the left of the label */ content: "â–¸"; float: left; margin-right: 0.25em; color: var(--sklearn-color-icon); } #sk-container-id-40 label.sk-toggleable__label-arrow:hover:before { color: var(--sklearn-color-text); } /* Toggleable content - dropdown */ #sk-container-id-40 div.sk-toggleable__content { max-height: 0; max-width: 0; overflow: hidden; text-align: left; /* unfitted */ background-color: var(--sklearn-color-unfitted-level-0); } #sk-container-id-40 div.sk-toggleable__content.fitted { /* fitted */ background-color: var(--sklearn-color-fitted-level-0); } #sk-container-id-40 div.sk-toggleable__content pre { margin: 0.2em; border-radius: 0.25em; color: var(--sklearn-color-text); /* unfitted */ background-color: var(--sklearn-color-unfitted-level-0); } #sk-container-id-40 div.sk-toggleable__content.fitted pre { /* unfitted */ background-color: var(--sklearn-color-fitted-level-0); } #sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content { /* Expand drop-down */ max-height: 200px; max-width: 100%; overflow: auto; } #sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before { content: "â–¾"; } /* Pipeline/ColumnTransformer-specific style */ #sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label { color: var(--sklearn-color-text); background-color: var(--sklearn-color-unfitted-level-2); } #sk-container-id-40 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: var(--sklearn-color-fitted-level-2); } /* Estimator-specific style */ /* Colorize estimator box */ #sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label { /* unfitted */ background-color: var(--sklearn-color-unfitted-level-2); } #sk-container-id-40 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label { /* fitted */ background-color: var(--sklearn-color-fitted-level-2); } #sk-container-id-40 div.sk-label label.sk-toggleable__label, #sk-container-id-40 div.sk-label label { /* The background is the default theme color */ color: var(--sklearn-color-text-on-default-background); } /* On hover, darken the color of the background */ #sk-container-id-40 div.sk-label:hover label.sk-toggleable__label { color: var(--sklearn-color-text); background-color: var(--sklearn-color-unfitted-level-2); } /* Label box, darken color on hover, fitted */ #sk-container-id-40 div.sk-label.fitted:hover label.sk-toggleable__label.fitted { color: var(--sklearn-color-text); background-color: var(--sklearn-color-fitted-level-2); } /* Estimator label */ #sk-container-id-40 div.sk-label label { font-family: monospace; font-weight: bold; display: inline-block; line-height: 1.2em; } #sk-container-id-40 div.sk-label-container { text-align: center; } /* Estimator-specific */ #sk-container-id-40 div.sk-estimator { font-family: monospace; border: 1px dotted var(--sklearn-color-border-box); border-radius: 0.25em; box-sizing: border-box; margin-bottom: 0.5em; /* unfitted */ background-color: var(--sklearn-color-unfitted-level-0); } #sk-container-id-40 div.sk-estimator.fitted { /* fitted */ background-color: var(--sklearn-color-fitted-level-0); } /* on hover */ #sk-container-id-40 div.sk-estimator:hover { /* unfitted */ background-color: var(--sklearn-color-unfitted-level-2); } #sk-container-id-40 div.sk-estimator.fitted:hover { /* fitted */ background-color: var(--sklearn-color-fitted-level-2); } /* Specification for estimator info (e.g. "i" and "?") */ /* Common style for "i" and "?" */ .sk-estimator-doc-link, a:link.sk-estimator-doc-link, a:visited.sk-estimator-doc-link { float: right; font-size: smaller; line-height: 1em; font-family: monospace; background-color: var(--sklearn-color-background); border-radius: 1em; height: 1em; width: 1em; text-decoration: none !important; margin-left: 1ex; /* unfitted */ border: var(--sklearn-color-unfitted-level-1) 1pt solid; color: var(--sklearn-color-unfitted-level-1); } .sk-estimator-doc-link.fitted, a:link.sk-estimator-doc-link.fitted, a:visited.sk-estimator-doc-link.fitted { /* fitted */ border: var(--sklearn-color-fitted-level-1) 1pt solid; color: var(--sklearn-color-fitted-level-1); } /* On hover */ div.sk-estimator:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover, div.sk-label-container:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover { /* unfitted */ background-color: var(--sklearn-color-unfitted-level-3); color: var(--sklearn-color-background); text-decoration: none; } div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover, div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover { /* fitted */ background-color: var(--sklearn-color-fitted-level-3); color: var(--sklearn-color-background); text-decoration: none; } /* Span, style for the box shown on hovering the info icon */ .sk-estimator-doc-link span { display: none; z-index: 9999; position: relative; font-weight: normal; right: .2ex; padding: .5ex; margin: .5ex; width: min-content; min-width: 20ex; max-width: 50ex; color: var(--sklearn-color-text); box-shadow: 2pt 2pt 4pt #999; /* unfitted */ background: var(--sklearn-color-unfitted-level-0); border: .5pt solid var(--sklearn-color-unfitted-level-3); } .sk-estimator-doc-link.fitted span { /* fitted */ background: var(--sklearn-color-fitted-level-0); border: var(--sklearn-color-fitted-level-3); } .sk-estimator-doc-link:hover span { display: block; } /* "?"-specific style due to the `<a>` HTML tag */ #sk-container-id-40 a.estimator_doc_link { float: right; font-size: 1rem; line-height: 1em; font-family: monospace; background-color: var(--sklearn-color-background); border-radius: 1rem; height: 1rem; width: 1rem; text-decoration: none; /* unfitted */ color: var(--sklearn-color-unfitted-level-1); border: var(--sklearn-color-unfitted-level-1) 1pt solid; } #sk-container-id-40 a.estimator_doc_link.fitted { /* fitted */ border: var(--sklearn-color-fitted-level-1) 1pt solid; color: var(--sklearn-color-fitted-level-1); } /* On hover */ #sk-container-id-40 a.estimator_doc_link:hover { /* unfitted */ background-color: var(--sklearn-color-unfitted-level-3); color: var(--sklearn-color-background); text-decoration: none; } #sk-container-id-40 a.estimator_doc_link.fitted:hover { /* fitted */ background-color: var(--sklearn-color-fitted-level-3); } </style><div id="sk-container-id-40" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('standardscaler', StandardScaler()), ('logisticregression', LogisticRegression(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-170" type="checkbox" ><label for="sk-estimator-id-170" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('standardscaler', StandardScaler()), ('logisticregression', LogisticRegression(random_state=0))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-171" type="checkbox" ><label for="sk-estimator-id-171" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-172" type="checkbox" ><label for="sk-estimator-id-172" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> LogisticRegression<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a></label><div class="sk-toggleable__content fitted"><pre>LogisticRegression(random_state=0)</pre></div> </div></div></div></div></div></div> </div> <br /> <br /> .. GENERATED FROM PYTHON SOURCE LINES 30-31 创建 :class:`ConfusionMatrixDisplay` .. GENERATED FROM PYTHON SOURCE LINES 33-34 使用拟åˆçš„æ¨¡åž‹ï¼Œæˆ‘们计算模型在测试数æ®é›†ä¸Šçš„预测。这些预测用于计算混淆矩阵,并使用 :class:`ConfusionMatrixDisplay` 绘制。 .. GENERATED FROM PYTHON SOURCE LINES 34-42 .. code-block:: Python from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix y_pred = clf.predict(X_test) cm = confusion_matrix(y_test, y_pred) cm_display = ConfusionMatrixDisplay(cm).plot() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_001.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 43-44 创建 :class:`RocCurveDisplay` .. GENERATED FROM PYTHON SOURCE LINES 46-47 ROC曲线需è¦ä¼°è®¡å™¨çš„æ¦‚率或éžé˜ˆå€¼å†³ç–值。由于逻辑回归æä¾›äº†å†³ç–函数,我们将使用它æ¥ç»˜åˆ¶ROC曲线: .. GENERATED FROM PYTHON SOURCE LINES 47-54 .. code-block:: Python from sklearn.metrics import RocCurveDisplay, roc_curve y_score = clf.decision_function(X_test) fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_[1]) roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_002.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /app/scikit-learn-main-origin/sklearn/metrics/_plot/roc_curve.py:163: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. .. GENERATED FROM PYTHON SOURCE LINES 55-56 创建 :class:`PrecisionRecallDisplay` .. GENERATED FROM PYTHON SOURCE LINES 58-59 åŒæ ·ï¼Œå¯ä»¥ä½¿ç”¨å‰é¢éƒ¨åˆ†çš„ `y_score` 绘制精确率-å¬å›žçŽ‡æ›²çº¿ã€‚ .. GENERATED FROM PYTHON SOURCE LINES 59-64 .. code-block:: Python from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1]) pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_003.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 65-66 将显示对象组åˆåˆ°ä¸€ä¸ªå•ä¸€çš„å›¾ä¸ .. GENERATED FROM PYTHON SOURCE LINES 68-69 显示对象å˜å‚¨ä½œä¸ºå‚æ•°ä¼ é€’çš„è®¡ç®—å€¼ã€‚è¿™ä½¿å¾—å¯ä»¥ä½¿ç”¨matplotlibçš„APIè½»æ¾åœ°ç»„åˆå¯è§†åŒ–。在下é¢çš„示例ä¸ï¼Œæˆ‘们将显示对象并排放置在一行ä¸ã€‚ .. GENERATED FROM PYTHON SOURCE LINES 69-77 .. code-block:: Python import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8)) roc_display.plot(ax=ax1) pr_display.plot(ax=ax2) plt.show() .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_004.png :alt: plot display object visualization :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_display_object_visualization_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /app/scikit-learn-main-origin/sklearn/metrics/_plot/roc_curve.py:163: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.164 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_display_object_visualization.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-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_display_object_visualization.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_display_object_visualization.ipynb <plot_display_object_visualization.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_display_object_visualization.py <plot_display_object_visualization.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_display_object_visualization.zip <plot_display_object_visualization.zip>` .. include:: plot_display_object_visualization.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_