From 2b04b63d8581e86107648fd9c9819b644cdd006d Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 30 Nov 2021 18:32:25 +0300 Subject: [PATCH 01/69] Added initial paragraphs --- content/docs/start/visualization.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 content/docs/start/visualization.md diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md new file mode 100644 index 0000000000..bd9ad46d36 --- /dev/null +++ b/content/docs/start/visualization.md @@ -0,0 +1,17 @@ +# Visualization with DVC Plots + +In parallel with versioning parameters and metrics by associating them with +experiments, DVC allows to generate plots from multivariate outputs. There are +several options to generate and keep track of the plots: + +- DVC can generate HTML files that includes interactive Vega-Lite plots, from + the data series in JSON, YAML, CSV, or TSV. + +- DVC can keep track of image files produced as plot outputs from the + training/evaluation scripts. + +- DVC, with its DVCLive integration can produce plots automatically during + training. + +In this document, we'll focus on the first option and briefly describe the other +two. Please see `dvc plots` and the DVCLive documentation for details. From e865161838c592687df31ec03dbd03e5559c6fad Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 30 Nov 2021 18:35:42 +0300 Subject: [PATCH 02/69] added three tentative tasks --- content/docs/start/visualization.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index bd9ad46d36..07c45ae7dc 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -15,3 +15,7 @@ several options to generate and keep track of the plots: In this document, we'll focus on the first option and briefly describe the other two. Please see `dvc plots` and the DVCLive documentation for details. + +- [ ] Configure the plots to use dvclive output +- [ ] Generate a plot file and open it in the browser +- [ ] Generate a plot image and use `dvc plot` on it From f6bc44ef3d03c1d5134cbc4b79c4ddb55913ebff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Tue, 14 Dec 2021 15:13:38 +0300 Subject: [PATCH 03/69] added visualization to the sidebar --- content/docs/sidebar.json | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 2460ec586c..f5a1231982 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -59,6 +59,13 @@ "katacoda": "https://katacoda.com/dvc/courses/get-started/params-metrics-plots" } }, + { + "label": "Visualization with Plots", + "slug": "visualization", + "tutorials": { + "katacoda": "https://katacoda.com/dvc/courses/get-started/visualization" + } + }, { "slug": "experiments", "label": "Experiments", From b9013dc3bcbd5f721d275ec3938924bbed4c5147 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Tue, 14 Dec 2021 15:14:15 +0300 Subject: [PATCH 04/69] moved visualization below the experiments in the sidebar --- content/docs/sidebar.json | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index f5a1231982..8045e5a16f 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -60,17 +60,17 @@ } }, { - "label": "Visualization with Plots", - "slug": "visualization", + "slug": "experiments", + "label": "Experiments", "tutorials": { - "katacoda": "https://katacoda.com/dvc/courses/get-started/visualization" + "katacoda": "https://katacoda.com/dvc/courses/get-started/experiments" } }, { - "slug": "experiments", - "label": "Experiments", + "label": "Visualization with Plots", + "slug": "visualization", "tutorials": { - "katacoda": "https://katacoda.com/dvc/courses/get-started/experiments" + "katacoda": "https://katacoda.com/dvc/courses/get-started/visualization" } } ] From 07086b9ed2925d7d018bc9f349ab8448bd82da53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Tue, 14 Dec 2021 15:25:56 +0300 Subject: [PATCH 05/69] added first two ways to visualize --- content/docs/start/visualization.md | 64 +++++++++++++++++++++++++++-- 1 file changed, 60 insertions(+), 4 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 07c45ae7dc..807c0d3358 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -4,17 +4,73 @@ In parallel with versioning parameters and metrics by associating them with experiments, DVC allows to generate plots from multivariate outputs. There are several options to generate and keep track of the plots: +- DVC, with its DVCLive integration can produce plots automatically during + training. + - DVC can generate HTML files that includes interactive Vega-Lite plots, from the data series in JSON, YAML, CSV, or TSV. - DVC can keep track of image files produced as plot outputs from the training/evaluation scripts. -- DVC, with its DVCLive integration can produce plots automatically during - training. +We'll add visualization to [`example-dvc-experiments`][ede] project. You may +refer to the [earlier section] to install it. + +One of the important plots that shows the classification performance is the +confusion matrix. DVC uses Vega-Lite to plot the text files. In order to produce +a confusion matrix, Vega expects a CSV file in the form: + +```csv +actual,predicted +0,0 +0,2 +... +``` + +We add the following loop at the end of the training function: + +```python + y_prob = m.predict(x_valid) + y_pred = y_prob.argmax(axis=-1) + os.makedirs("plots") + with open("plots/confusion.csv", "w") as f: + f.write("actual,predicted\n") + sx = y_valid.shape[0] + for i in range(sx): + f.write(f"{y_valid.argmax()},{y_pred[i]}\n") +``` + +After running the experiment, with `dvc exp run`, DVC will produce +`plots/confusion.csv` file that can be shown in the web browser: + +```dvc +$ dvc plots show plots/confusion.csv --template confusion +file:///Users/.../example-dvc-experiments/plots/confusion.json.html +``` + +The file contains the following plot that shows the performance of the CNN +classifier: + +<> + +Now, let's produce another plot to see misclassified examples from each class. +This procedure is a bit more complex than creating a CSV file, and skipped here, +but you can see the code in [Github][misclassified-example-code] + +We can see the misclassification examples in the browser: + +```dvc +$ dvc plots show plots/misclassified.png --open +``` + +DVC automatically shows the image in an HTML page. + +<> + +A major requirement for deep learning projects is to see in which epoch training loss and validation loss differs. DVC helps in that regard with its integrations to major deep learning libraries via DVCLive. + +We use the following code snippet to see the training loss -In this document, we'll focus on the first option and briefly describe the other -two. Please see `dvc plots` and the DVCLive documentation for details. - [ ] Configure the plots to use dvclive output - [ ] Generate a plot file and open it in the browser From 7175d17f2028b4b9bfc6fbcf7719d7ed83483ff1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 15 Dec 2021 18:37:49 +0300 Subject: [PATCH 06/69] a paragraph for dvclive & keras --- content/docs/start/visualization.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 807c0d3358..3927253eaa 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -67,7 +67,9 @@ DVC automatically shows the image in an HTML page. <> -A major requirement for deep learning projects is to see in which epoch training loss and validation loss differs. DVC helps in that regard with its integrations to major deep learning libraries via DVCLive. +A major requirement for deep learning projects is to see in which epoch training +loss and validation loss differs. DVC helps in that regard with its integrations +to major deep learning libraries via DVCLive. We use the following code snippet to see the training loss From ff0009426d1fb6528efad6bcffe464fb0c13dd5f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 15 Dec 2021 18:39:41 +0300 Subject: [PATCH 07/69] changes required in the code for dvclive --- content/docs/start/visualization.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 3927253eaa..29b39cae04 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -71,7 +71,23 @@ A major requirement for deep learning projects is to see in which epoch training loss and validation loss differs. DVC helps in that regard with its integrations to major deep learning libraries via DVCLive. -We use the following code snippet to see the training loss +The example project uses Keras to train a classifier, and fortunately we have a DVCLive callback that visualizes the training and validation loss for each epoch. We first import the callback from DVCLive. + +```python +from dvclive.keras import DvcLiveCallback +``` + +Then we add this callback to `fit` callbacks. + +```python +model.fit( + ... + callbacks=[DvcLiveCallback(model_file=f"{OUTPUT_DIR}/model.h5")], + ...) +``` + +With these two changes, we can see the plots showing the defined metrics in our model. + - [ ] Configure the plots to use dvclive output From 677117831ab7d109918388ee644fbca5ec323185 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 15 Dec 2021 18:42:29 +0300 Subject: [PATCH 08/69] simplified the callback --- content/docs/start/visualization.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 29b39cae04..91a5bfa6f0 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -82,11 +82,13 @@ Then we add this callback to `fit` callbacks. ```python model.fit( ... - callbacks=[DvcLiveCallback(model_file=f"{OUTPUT_DIR}/model.h5")], + callbacks=[DvcLiveCallback()], ...) ``` -With these two changes, we can see the plots showing the defined metrics in our model. +With these two changes, we can see the plots showing the defined metrics in our +model. + From 77dbdf50a2453288ca6e9da5feae4b4bc9e6aaee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 15 Dec 2021 18:47:13 +0300 Subject: [PATCH 09/69] added dvclive info and reformatted --- content/docs/start/visualization.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 91a5bfa6f0..139f3f56f3 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -71,13 +71,15 @@ A major requirement for deep learning projects is to see in which epoch training loss and validation loss differs. DVC helps in that regard with its integrations to major deep learning libraries via DVCLive. -The example project uses Keras to train a classifier, and fortunately we have a DVCLive callback that visualizes the training and validation loss for each epoch. We first import the callback from DVCLive. +The example project uses Keras to train a classifier, and fortunately we have a +DVCLive callback that visualizes the training and validation loss for each +epoch. We first import the callback from DVCLive. ```python from dvclive.keras import DvcLiveCallback ``` -Then we add this callback to `fit` callbacks. +Then we add this callback to `fit` callbacks. ```python model.fit( @@ -87,11 +89,12 @@ model.fit( ``` With these two changes, we can see the plots showing the defined metrics in our -model. - +model. You can see the HTML report with your browser in +`training_metrics/index.html` file. +<> +DVCLive has other capabilities, like saving the model every epoch or modifying +these default values. Please refer to [DVClive documentation] for details. -- [ ] Configure the plots to use dvclive output -- [ ] Generate a plot file and open it in the browser -- [ ] Generate a plot image and use `dvc plot` on it +[dvclive documentation]: /doc/dvclive/dvclive-with-dvc From 42bf550d0b736ed1d1d5a26f4a0dfe52c9a37ce2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Fri, 17 Dec 2021 20:22:16 +0300 Subject: [PATCH 10/69] added install reference to README --- content/docs/start/visualization.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 139f3f56f3..c8c6e25fb0 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -13,8 +13,9 @@ several options to generate and keep track of the plots: - DVC can keep track of image files produced as plot outputs from the training/evaluation scripts. -We'll add visualization to [`example-dvc-experiments`][ede] project. You may -refer to the [earlier section] to install it. +We'll add visualization to [`example-dvc-experiments`][ede] project. If you +would like to try these yourself, please refer to the project [README] about how +to install. One of the important plots that shows the classification performance is the confusion matrix. DVC uses Vega-Lite to plot the text files. 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zz}0RseT2#GKw96*iZ1l@8JxlORcBknKXaT=whe^=C-t*%qF@epZ?^g~uKsi2pQ_R` K#hOQ!q5lJQzMaJY literal 0 HcmV?d00001 From b6c8b3e22595349db0d220c91ba04a8a17003ac7 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 28 Dec 2021 19:27:10 +0300 Subject: [PATCH 12/69] linked dvclive image --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index c8c6e25fb0..3d9bf232c7 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -93,7 +93,7 @@ With these two changes, we can see the plots showing the defined metrics in our model. You can see the HTML report with your browser in `training_metrics/index.html` file. -<> +![dvclive](/img/start_visualization_dvclive.png) DVCLive has other capabilities, like saving the model every epoch or modifying these default values. Please refer to [DVClive documentation] for details. From 1952a9b95ca2d8b414b32b66c856940a26414123 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 28 Dec 2021 19:47:05 +0300 Subject: [PATCH 13/69] fixed dvc plots command --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 3d9bf232c7..1fcadf6407 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -45,7 +45,7 @@ After running the experiment, with `dvc exp run`, DVC will produce `plots/confusion.csv` file that can be shown in the web browser: ```dvc -$ dvc plots show plots/confusion.csv --template confusion +$ dvc plots show plots/confusion.csv --template confusion -x actual -y predicted file:///Users/.../example-dvc-experiments/plots/confusion.json.html ``` From 653ffaad5b4dc2c13ca17a4374294184d9112716 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 28 Dec 2021 19:47:30 +0300 Subject: [PATCH 14/69] Added confusion image link --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 1fcadf6407..948a4b9a08 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -52,7 +52,7 @@ file:///Users/.../example-dvc-experiments/plots/confusion.json.html The file contains the following plot that shows the performance of the CNN classifier: -<> +![confusion matrix](/img/start_visualization_confusion1.png) Now, let's produce another plot to see misclassified examples from each class. This procedure is a bit more complex than creating a CSV file, and skipped here, From 219a59c146de4d6bffcf079d529e7c43cc1812e9 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 28 Dec 2021 19:47:48 +0300 Subject: [PATCH 15/69] fixed dvc plots command --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 948a4b9a08..6405da5b1d 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -61,7 +61,7 @@ but you can see the code in [Github][misclassified-example-code] We can see the misclassification examples in the browser: ```dvc -$ dvc plots show plots/misclassified.png --open +$ dvc plots show plots/confusion.png ``` DVC automatically shows the image in an HTML page. From f4df2132b62d192b0d2ffb791f5112e297cec431 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 28 Dec 2021 19:48:04 +0300 Subject: [PATCH 16/69] added confusion image --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 6405da5b1d..9ff7569980 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -66,7 +66,7 @@ $ dvc plots show plots/confusion.png DVC automatically shows the image in an HTML page. -<> +![confusion image](/img/start_visualization_confusion2.png) A major requirement for deep learning projects is to see in which epoch training loss and validation loss differs. 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zZ)48tq=F-67S}Fgxrv8_uX$D4KD3)8Sd>w)o$HLl$MeXa`D%xo@{Y-EGs$c%=lZO# zC`;B)Kw;Y5fcNkrH;VW~#mAFaK2z05kt;0ZBj5ifOY&7$k*bP!lX6J<;1IpkoytI2 zJd-rdeOwJzvwQC!2D>i&+-g<*Ul09=1X;73%f&ZH9d9VaJ=ZuKw)*RSfL7NFGs_@53cv|Iv<%X-*V)@bTN_!JUJ?EWU9V56csN#(5P z)ndFVQ(KyZ5h8e(PcoUKTTO76#K;tVUT0r#y4f^)-58sOgTO)~>l(@Lfd{n1Dc3DE z3C4`WsQm^XgN4*k_)8#EYc^GIjf3fM$r!U7W%Q6e|FGNtYx%C;F1RFcD3-&=o6)6k z_L|UDD!Wm@90JPjjgkq;xB$qfChWTqyb@}sNDsR}wJUQS0AM#JePJ@ER=l4Z-4XB7 zQWVdO$lH>gOcD_*({jJ$9(d52W^(f<_5a=zZ}&!!)Eqf18qpYjf5_r5Lupw!9(?1( zjg(U}3TS<}=buJABpXMxKOlB_P$o}n0BQI|E)&9?K=aE7Ef1ESA_NkN+H<#WwM@N@ zW$ELYB0k#4hB&`c2N!5IElY%V{FcGGVmfGlE$4qAoi0(8x9aEeT%IH%S1s=&lF+7m z?H|uj0S|sS|1tA*U3+Z(sf$lz=tJz%Jb~fxM!o34rktz)rwz{0a}>c})7%qI6oX}! zqXm2#@oeT+8xMbGwhOCrPgjs9Q-?I-QHJJJhb8COT@RM;1v*gS z=v25{_EX#rdLa(Qc?wwUx5M%trIh}MTzn+-va&z)+qU8 From b343d349e55022605b7e855aefa9aac65c135e64 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 9 Feb 2022 17:42:19 +0300 Subject: [PATCH 20/69] revised --- content/docs/start/visualization.md | 42 ++++++++++++----------------- 1 file changed, 17 insertions(+), 25 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 9ff7569980..8af0723eb8 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -1,21 +1,23 @@ # Visualization with DVC Plots -In parallel with versioning parameters and metrics by associating them with -experiments, DVC allows to generate plots from multivariate outputs. There are -several options to generate and keep track of the plots: +As we discussed in [experiments] section, DVC versions the experiments by +associating parameters and metrics to them. On top of that, DVC can generate +plots and tracks plot images you generated from the code. As in other aspects of +the experiments, DVC is flexible and provides more than one option to fit +visualization to your workflow: -- DVC, with its DVCLive integration can produce plots automatically during - training. - -- DVC can generate HTML files that includes interactive Vega-Lite plots, from +- DVC can generate HTML files that includes interactive [Vega-Lite] plots, from the data series in JSON, YAML, CSV, or TSV. - DVC can keep track of image files produced as plot outputs from the training/evaluation scripts. -We'll add visualization to [`example-dvc-experiments`][ede] project. If you -would like to try these yourself, please refer to the project [README] about how -to install. +- DVC, with its [DVCLive] integration can produce plots automatically during + training. + +In this sectin, we'll add visualization to [`example-dvc-experiments`][ede] +project. If you would like to try these yourself, please refer to the project +[README] about how to install. One of the important plots that shows the classification performance is the confusion matrix. DVC uses Vega-Lite to plot the text files. In order to produce @@ -28,21 +30,11 @@ actual,predicted ... ``` -We add the following loop at the end of the training function: - -```python - y_prob = m.predict(x_valid) - y_pred = y_prob.argmax(axis=-1) - os.makedirs("plots") - with open("plots/confusion.csv", "w") as f: - f.write("actual,predicted\n") - sx = y_valid.shape[0] - for i in range(sx): - f.write(f"{y_valid.argmax()},{y_pred[i]}\n") -``` +We added a [straightforward loop] to the project to generate this file from the +predictions. After running the experiment, with `dvc exp run`, DVC will produce -`plots/confusion.csv` file that can be shown in the web browser: +`plots/confusion.csv` file that can immediately be shown in the browser: ```dvc $ dvc plots show plots/confusion.csv --template confusion -x actual -y predicted @@ -55,8 +47,8 @@ classifier: ![confusion matrix](/img/start_visualization_confusion1.png) Now, let's produce another plot to see misclassified examples from each class. -This procedure is a bit more complex than creating a CSV file, and skipped here, -but you can see the code in [Github][misclassified-example-code] +This procedure is a bit more complex than creating a CSV file but you can see +the code in [Github][misclassified-example-code] We can see the misclassification examples in the browser: From dc6a2eded2b8516ab011d9494fb3905c7350031a Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 9 Feb 2022 17:48:56 +0300 Subject: [PATCH 21/69] added links --- content/docs/start/visualization.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 8af0723eb8..6ef708ff75 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -15,7 +15,9 @@ visualization to your workflow: - DVC, with its [DVCLive] integration can produce plots automatically during training. -In this sectin, we'll add visualization to [`example-dvc-experiments`][ede] +[experiments]: /doc/start/experiments + +In this section, we'll add visualization to [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project [README] about how to install. @@ -33,6 +35,9 @@ actual,predicted We added a [straightforward loop] to the project to generate this file from the predictions. +[straightforward loop]: + https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L213 + After running the experiment, with `dvc exp run`, DVC will produce `plots/confusion.csv` file that can immediately be shown in the browser: @@ -50,6 +55,9 @@ Now, let's produce another plot to see misclassified examples from each class. This procedure is a bit more complex than creating a CSV file but you can see the code in [Github][misclassified-example-code] +[misclassified-example-code]: + https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L58 + We can see the misclassification examples in the browser: ```dvc From 51b9820a5bff2e9dfce1d43ef66aaf803cf1c0b7 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 13:27:36 +0300 Subject: [PATCH 22/69] updated the initial sentences --- content/docs/start/visualization.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 6ef708ff75..6f8a1e9465 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -1,10 +1,10 @@ # Visualization with DVC Plots As we discussed in [experiments] section, DVC versions the experiments by -associating parameters and metrics to them. On top of that, DVC can generate -plots and tracks plot images you generated from the code. As in other aspects of -the experiments, DVC is flexible and provides more than one option to fit -visualization to your workflow: +associating parameters and metrics to them. DVC can also generate plots and +tracks plot images you generated from the code. + +DVC provides more than one option to fit visualization to your workflow: - DVC can generate HTML files that includes interactive [Vega-Lite] plots, from the data series in JSON, YAML, CSV, or TSV. From 7dd96754a809a42e7933a4bd9eedf86662d66703 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 13:28:18 +0300 Subject: [PATCH 23/69] fixed vega-lite link --- content/docs/start/visualization.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 6f8a1e9465..d1397cd2af 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -16,6 +16,7 @@ DVC provides more than one option to fit visualization to your workflow: training. [experiments]: /doc/start/experiments +[vega-lite]: https://vega.github.io/vega-lite/ In this section, we'll add visualization to [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project From 07dcd5130b2e11c2272ed595100495ebeb25362d Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 13:31:47 +0300 Subject: [PATCH 24/69] fixed dvclive link --- content/docs/start/visualization.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index d1397cd2af..6f41f2a259 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -17,6 +17,7 @@ DVC provides more than one option to fit visualization to your workflow: [experiments]: /doc/start/experiments [vega-lite]: https://vega.github.io/vega-lite/ +[dvclive]: https://dvc.org/doc/dvclive In this section, we'll add visualization to [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project From 4f3b0f1b8acd422a1452a7fff4231b935901ea73 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 13:33:53 +0300 Subject: [PATCH 25/69] fixed readme link --- content/docs/start/visualization.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 6f41f2a259..3da1fb2f90 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -23,6 +23,10 @@ In this section, we'll add visualization to [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project [README] about how to install. +[ede]: https://github.com/iterative/example-dvc-experiments +[readme]: + https://github.com/iterative/example-dvc-experiments/blob/main/README.md + One of the important plots that shows the classification performance is the confusion matrix. DVC uses Vega-Lite to plot the text files. In order to produce a confusion matrix, Vega expects a CSV file in the form: From c90918db6912878d15e45e8b2fc22b85b157e9d7 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 13:34:30 +0300 Subject: [PATCH 26/69] comma --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 3da1fb2f90..f7b9636f2c 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -44,7 +44,7 @@ predictions. [straightforward loop]: https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L213 -After running the experiment, with `dvc exp run`, DVC will produce +After running the experiment with `dvc exp run`, DVC will produce `plots/confusion.csv` file that can immediately be shown in the browser: ```dvc From 9a7ab68af1143926da5126089b64c21234aa6363 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 13:35:56 +0300 Subject: [PATCH 27/69] removed "now" --- content/docs/start/visualization.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index f7b9636f2c..a4d60e6d6b 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -57,9 +57,9 @@ classifier: ![confusion matrix](/img/start_visualization_confusion1.png) -Now, let's produce another plot to see misclassified examples from each class. -This procedure is a bit more complex than creating a CSV file but you can see -the code in [Github][misclassified-example-code] +Let's produce another plot to see misclassified examples from each class. This +procedure is a bit more complex than creating a CSV file but you can see the +code in [Github][misclassified-example-code] [misclassified-example-code]: https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L58 From 5ff8661d9744df3271955bcc8e77f2e9fb346415 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 14:49:25 +0300 Subject: [PATCH 28/69] removed older metrics, plots, vis from the sidebar --- content/docs/sidebar.json | 7 ------- 1 file changed, 7 deletions(-) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 8045e5a16f..5791fc2730 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -52,13 +52,6 @@ "katacoda": "https://katacoda.com/dvc/courses/get-started/stages" } }, - { - "label": "Metrics, Parameters, and Plots", - "slug": "metrics-parameters-plots", - "tutorials": { - "katacoda": "https://katacoda.com/dvc/courses/get-started/params-metrics-plots" - } - }, { "slug": "experiments", "label": "Experiments", From 6b3f6609935a4db4f3dd9466264d1af7365a894a Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Mon, 14 Feb 2022 19:49:48 +0300 Subject: [PATCH 29/69] rephrase --- content/docs/start/visualization.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index a4d60e6d6b..ea8f1c3935 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -74,13 +74,13 @@ DVC automatically shows the image in an HTML page. ![confusion image](/img/start_visualization_confusion2.png) -A major requirement for deep learning projects is to see in which epoch training -loss and validation loss differs. DVC helps in that regard with its integrations -to major deep learning libraries via DVCLive. +An important issue for deep learning projects is to observe in which epoch +training loss and validation loss differs. DVC helps in that regard with its +integrations to deep learning libraries via DVCLive. -The example project uses Keras to train a classifier, and fortunately we have a -DVCLive callback that visualizes the training and validation loss for each -epoch. We first import the callback from DVCLive. +The example project uses Keras to train a classifier, and we have a DVCLive +callback that visualizes the training and validation loss for each epoch. We +first import the callback from DVCLive. ```python from dvclive.keras import DvcLiveCallback From 8c9f95711ea8700c29e74c414566432eadb274f3 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 15 Feb 2022 18:48:19 +0300 Subject: [PATCH 30/69] Moved the initial text to the end as summary --- content/docs/start/visualization.md | 35 +++++++++++++---------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index ea8f1c3935..911f7748aa 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -1,24 +1,5 @@ # Visualization with DVC Plots -As we discussed in [experiments] section, DVC versions the experiments by -associating parameters and metrics to them. DVC can also generate plots and -tracks plot images you generated from the code. - -DVC provides more than one option to fit visualization to your workflow: - -- DVC can generate HTML files that includes interactive [Vega-Lite] plots, from - the data series in JSON, YAML, CSV, or TSV. - -- DVC can keep track of image files produced as plot outputs from the - training/evaluation scripts. - -- DVC, with its [DVCLive] integration can produce plots automatically during - training. - -[experiments]: /doc/start/experiments -[vega-lite]: https://vega.github.io/vega-lite/ -[dvclive]: https://dvc.org/doc/dvclive - In this section, we'll add visualization to [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project [README] about how to install. @@ -105,3 +86,19 @@ DVCLive has other capabilities, like saving the model every epoch or modifying these default values. Please refer to [DVClive documentation] for details. [dvclive documentation]: /doc/dvclive/dvclive-with-dvc + +In summary, DVC provides more than one option to fit visualization to your +workflow: + +- DVC can generate HTML files that includes interactive [Vega-Lite] plots, from + the data series in JSON, YAML, CSV, or TSV. + +- DVC can keep track of image files produced as plot outputs from the + training/evaluation scripts. + +- DVC, with its [DVCLive] integration can produce plots automatically during + training. + +[experiments]: /doc/start/experiments +[vega-lite]: https://vega.github.io/vega-lite/ +[dvclive]: https://dvc.org/doc/dvclive From 29ceb7998ce70b57ff33f88331837a9ec89453b2 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Tue, 15 Feb 2022 18:49:34 +0300 Subject: [PATCH 31/69] added space for highlighters --- content/docs/start/visualization.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 911f7748aa..fa0c18f286 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -64,16 +64,20 @@ callback that visualizes the training and validation loss for each epoch. We first import the callback from DVCLive. ```python + from dvclive.keras import DvcLiveCallback + ``` Then we add this callback to `fit` callbacks. ```python + model.fit( ... callbacks=[DvcLiveCallback()], ...) + ``` With these two changes, we can see the plots showing the defined metrics in our From 99b4487b97a0d93cda13474b43c4e109877c179d Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Thu, 17 Feb 2022 19:04:07 +0300 Subject: [PATCH 32/69] enlarged the confusion image --- static/img/start_visualization_confusion2.png | Bin 63152 -> 231968 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/static/img/start_visualization_confusion2.png b/static/img/start_visualization_confusion2.png index 97fd20e4c92684025dd0efab797693f0ad2b46be..832417407351f5412bfa20d25ed002001f0e74c2 100644 GIT binary patch literal 231968 zcmX_n2T+qu)GnRS6KZH70h9pJ6$m{cRF!H0rG+9YNDDo50)!p}M3fE+DpCdMNDqiK zl`g$0y?3~L_y6ym*`0TGXWuz(_nh6c&qf;==rGaq(36poG3n`|O~}Y7SN^L2nya3> 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--- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -1,6 +1,6 @@ -# Visualization with DVC Plots +# Visualization with Plots -In this section, we'll add visualization to [`example-dvc-experiments`][ede] +In this section, we'll add visualization to the [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project [README] about how to install. @@ -8,9 +8,8 @@ project. If you would like to try these yourself, please refer to the project [readme]: https://github.com/iterative/example-dvc-experiments/blob/main/README.md -One of the important plots that shows the classification performance is the -confusion matrix. DVC uses Vega-Lite to plot the text files. In order to produce -a confusion matrix, Vega expects a CSV file in the form: +A useful plot to show the classification performance is the confusion matrix. In +order to produce it, DVC expects a CSV **plot file** in the form: ```csv actual,predicted @@ -19,10 +18,10 @@ actual,predicted ... ``` -We added a [straightforward loop] to the project to generate this file from the -predictions. +> We added a [loop] comparing the results to generate this file from the +> predictions. -[straightforward loop]: +[loop]: https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L213 After running the experiment with `dvc exp run`, DVC will produce From ca6ae919c228f052dc2c3e9461e7000d673826c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Mon, 14 Mar 2022 17:46:03 +0300 Subject: [PATCH 34/69] revision after review --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 67ce3deff0..6dee277858 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -25,7 +25,7 @@ actual,predicted https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L213 After running the experiment with `dvc exp run`, DVC will produce -`plots/confusion.csv` file that can immediately be shown in the browser: +`plots/confusion.csv` file which is immediately shown in the browser: ```dvc $ dvc plots show plots/confusion.csv --template confusion -x actual -y predicted From fc49d4802daae2a96ab26d8b248169b6f725cf7e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Mon, 14 Mar 2022 17:47:56 +0300 Subject: [PATCH 35/69] revise after review --- content/docs/start/visualization.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 6dee277858..43dd0c1d5b 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -28,7 +28,8 @@ After running the experiment with `dvc exp run`, DVC will produce `plots/confusion.csv` file which is immediately shown in the browser: ```dvc -$ dvc plots show plots/confusion.csv --template confusion -x actual -y predicted +$ dvc plots show plots/confusion.csv --template confusion \ + -x actual -y predicted file:///Users/.../example-dvc-experiments/plots/confusion.json.html ``` From 2eb2e6aa4adcec001b87b77c01f656ffc774a900 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Mon, 21 Mar 2022 17:44:24 +0300 Subject: [PATCH 36/69] moved the description of image --- content/docs/start/visualization.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 43dd0c1d5b..c7632beab1 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -25,7 +25,8 @@ actual,predicted https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L213 After running the experiment with `dvc exp run`, DVC will produce -`plots/confusion.csv` file which is immediately shown in the browser: +`plots/confusion.csv` file presenting the classifier performance and open it in +the browser: ```dvc $ dvc plots show plots/confusion.csv --template confusion \ @@ -33,9 +34,6 @@ $ dvc plots show plots/confusion.csv --template confusion \ file:///Users/.../example-dvc-experiments/plots/confusion.json.html ``` -The file contains the following plot that shows the performance of the CNN -classifier: - ![confusion matrix](/img/start_visualization_confusion1.png) Let's produce another plot to see misclassified examples from each class. This From 4e6cf300f502e7dc83b34e0608f36a12ae1ee73e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Mon, 21 Mar 2022 17:52:17 +0300 Subject: [PATCH 37/69] updated misclassification code link and added description --- content/docs/start/visualization.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index c7632beab1..5b108a045f 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -37,11 +37,14 @@ file:///Users/.../example-dvc-experiments/plots/confusion.json.html ![confusion matrix](/img/start_visualization_confusion1.png) Let's produce another plot to see misclassified examples from each class. This -procedure is a bit more complex than creating a CSV file but you can see the -code in [Github][misclassified-example-code] +procedure generates the misclassification examples from the validation data and +arranges them into a _confusion table_ that shows the correct label, and +misclassification sample. The code to generate an image from a set of training +images is omitted here but you can find the code in +[Github][misclassified-example-code] [misclassified-example-code]: - https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L58 + https://github.com/iterative/example-dvc-experiments/blob/48b1e5078c957f71674c00f416290eaa3b20b559/src/util.py#L49 We can see the misclassification examples in the browser: From a11b4c52495a986e8c301a3a3387c406cc251f71 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Mon, 21 Mar 2022 18:02:40 +0300 Subject: [PATCH 38/69] added section titles --- content/docs/start/visualization.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 5b108a045f..8f5f0055bc 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -8,6 +8,8 @@ project. If you would like to try these yourself, please refer to the project [readme]: https://github.com/iterative/example-dvc-experiments/blob/main/README.md +### Creating plots from text with DVC + A useful plot to show the classification performance is the confusion matrix. In order to produce it, DVC expects a CSV **plot file** in the form: @@ -36,26 +38,28 @@ file:///Users/.../example-dvc-experiments/plots/confusion.json.html ![confusion matrix](/img/start_visualization_confusion1.png) +### Using plots generated by scripts + Let's produce another plot to see misclassified examples from each class. This procedure generates the misclassification examples from the validation data and arranges them into a _confusion table_ that shows the correct label, and misclassification sample. The code to generate an image from a set of training -images is omitted here but you can find the code in -[Github][misclassified-example-code] +images is omitted here but you can find the code in [the example +project.][misclassified-example-code] [misclassified-example-code]: https://github.com/iterative/example-dvc-experiments/blob/48b1e5078c957f71674c00f416290eaa3b20b559/src/util.py#L49 -We can see the misclassification examples in the browser: +DVC automatically shows the image in an HTML page with the command: ```dvc $ dvc plots show plots/confusion.png ``` -DVC automatically shows the image in an HTML page. - ![confusion image](/img/start_visualization_confusion2.png) +### Autogenerating plots with integrations + An important issue for deep learning projects is to observe in which epoch training loss and validation loss differs. DVC helps in that regard with its integrations to deep learning libraries via DVCLive. From 3af2c422b92ecf534a9d26ef6d6e698c8c410384 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Sun, 10 Apr 2022 23:30:56 -0500 Subject: [PATCH 39/69] Update content/docs/start/visualization.md --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 8f5f0055bc..49b98ac2ca 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -24,7 +24,7 @@ actual,predicted > predictions. [loop]: - https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L213 + https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L123 After running the experiment with `dvc exp run`, DVC will produce `plots/confusion.csv` file presenting the classifier performance and open it in From b4ee07cc339d74652f43250da19c7a1ed59c3432 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Mon, 11 Apr 2022 00:45:58 -0500 Subject: [PATCH 40/69] Update content/docs/start/visualization.md --- content/docs/start/visualization.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 49b98ac2ca..9f126e4f4a 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -69,9 +69,7 @@ callback that visualizes the training and validation loss for each epoch. We first import the callback from DVCLive. ```python - from dvclive.keras import DvcLiveCallback - ``` Then we add this callback to `fit` callbacks. From ad9b4749b787ee12b9ea3ff8288af2c35e2b322c Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Mon, 11 Apr 2022 00:47:49 -0500 Subject: [PATCH 41/69] Update content/docs/start/visualization.md --- content/docs/start/visualization.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 9f126e4f4a..10bc4d88ce 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -75,12 +75,10 @@ from dvclive.keras import DvcLiveCallback Then we add this callback to `fit` callbacks. ```python - model.fit( ... callbacks=[DvcLiveCallback()], ...) - ``` With these two changes, we can see the plots showing the defined metrics in our From e894d0baf85b1a7465e0daf7241c3cf45c170934 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Mon, 11 Apr 2022 20:16:45 -0500 Subject: [PATCH 42/69] Update content/docs/start/visualization.md --- content/docs/start/visualization.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 10bc4d88ce..23bc143806 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -1,4 +1,8 @@ -# Visualization with Plots +--- +title: 'Get Started: Visualization with Plots' +--- + +# Get Started: Visualization with Plots In this section, we'll add visualization to the [`example-dvc-experiments`][ede] project. If you would like to try these yourself, please refer to the project From 18c45f011be6d0724479054b93bea2b29c129372 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:06:06 +0300 Subject: [PATCH 43/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 23bc143806..dff16ce382 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -5,7 +5,8 @@ title: 'Get Started: Visualization with Plots' # Get Started: Visualization with Plots In this section, we'll add visualization to the [`example-dvc-experiments`][ede] -project. If you would like to try these yourself, please refer to the project +project (explored [previously](/doc/start/experiments)). If you would like to try +these yourself, please refer to the project. [README] about how to install. [ede]: https://github.com/iterative/example-dvc-experiments From 4ca91960774a7f0bed3d7a3049deeb942b581a4d Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:06:21 +0300 Subject: [PATCH 44/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index dff16ce382..5d806d51a6 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -13,7 +13,7 @@ these yourself, please refer to the project. [readme]: https://github.com/iterative/example-dvc-experiments/blob/main/README.md -### Creating plots from text with DVC +### Creating plots from tabular data A useful plot to show the classification performance is the confusion matrix. In order to produce it, DVC expects a CSV **plot file** in the form: From 987105fd8cd171fe294da5638e5439139537fde9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 13 Apr 2022 18:10:59 +0300 Subject: [PATCH 45/69] added confusion matrix link --- content/docs/start/visualization.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 5d806d51a6..f81706b136 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -5,9 +5,8 @@ title: 'Get Started: Visualization with Plots' # Get Started: Visualization with Plots In this section, we'll add visualization to the [`example-dvc-experiments`][ede] -project (explored [previously](/doc/start/experiments)). If you would like to try -these yourself, please refer to the project. -[README] about how to install. +project (explored [previously](/doc/start/experiments)). If you would like to +try these yourself, please refer to the project. [README] about how to install. [ede]: https://github.com/iterative/example-dvc-experiments [readme]: @@ -15,8 +14,8 @@ these yourself, please refer to the project. ### Creating plots from tabular data -A useful plot to show the classification performance is the confusion matrix. In -order to produce it, DVC expects a CSV **plot file** in the form: +A useful plot to show the classification performance is the [confusion matrix]. +In order to produce it, DVC expects a CSV **plot file** in the form: ```csv actual,predicted @@ -30,6 +29,7 @@ actual,predicted [loop]: https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L123 +[confusion matrix]: https://en.wikipedia.org/wiki/Confusion_matrix After running the experiment with `dvc exp run`, DVC will produce `plots/confusion.csv` file presenting the classifier performance and open it in From 314791d812c9c61957613d1a5530306d73e64e96 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 13 Apr 2022 18:11:39 +0300 Subject: [PATCH 46/69] plots file --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index f81706b136..38a758521d 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -15,7 +15,7 @@ try these yourself, please refer to the project. [README] about how to install. ### Creating plots from tabular data A useful plot to show the classification performance is the [confusion matrix]. -In order to produce it, DVC expects a CSV **plot file** in the form: +In order to produce it, DVC expects a CSV **plots file** in the form: ```csv actual,predicted From f34c59fd9aff9159a827c30c0cc7d8e68a050127 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:21:38 +0300 Subject: [PATCH 47/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 38a758521d..cd98f31b70 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -31,9 +31,9 @@ actual,predicted https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L123 [confusion matrix]: https://en.wikipedia.org/wiki/Confusion_matrix -After running the experiment with `dvc exp run`, DVC will produce -`plots/confusion.csv` file presenting the classifier performance and open it in -the browser: +Running the experiment with `dvc exp run` will produce +`plots/confusion.csv`. Use `dvc plots show` to present it as an HTML +file, and open it in the browser: ```dvc $ dvc plots show plots/confusion.csv --template confusion \ From 3b285724fa52e7e5b86ba466b471da655e938085 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:21:48 +0300 Subject: [PATCH 48/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index cd98f31b70..acb099e02f 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -43,7 +43,7 @@ file:///Users/.../example-dvc-experiments/plots/confusion.json.html ![confusion matrix](/img/start_visualization_confusion1.png) -### Using plots generated by scripts +## Displaying user-generated plot images Let's produce another plot to see misclassified examples from each class. This procedure generates the misclassification examples from the validation data and From a153b7096b171a3fb25cf709930de6daa4badb6f Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:22:54 +0300 Subject: [PATCH 49/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index acb099e02f..13b2871981 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -55,8 +55,6 @@ project.][misclassified-example-code] [misclassified-example-code]: https://github.com/iterative/example-dvc-experiments/blob/48b1e5078c957f71674c00f416290eaa3b20b559/src/util.py#L49 -DVC automatically shows the image in an HTML page with the command: - ```dvc $ dvc plots show plots/confusion.png ``` From 603674a278246097fe5eed106d9cd50a0ca2cded Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:31:29 +0300 Subject: [PATCH 50/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 13b2871981..2d53e24f2d 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -61,7 +61,7 @@ $ dvc plots show plots/confusion.png ![confusion image](/img/start_visualization_confusion2.png) -### Autogenerating plots with integrations +## Autogenerating plots from deep learning code An important issue for deep learning projects is to observe in which epoch training loss and validation loss differs. DVC helps in that regard with its From 63f9dc0a25e4e95e62b67d8fee5405904c9dab79 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:31:38 +0300 Subject: [PATCH 51/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 2d53e24f2d..b6f5cf1715 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -64,8 +64,8 @@ $ dvc plots show plots/confusion.png ## Autogenerating plots from deep learning code An important issue for deep learning projects is to observe in which epoch -training loss and validation loss differs. DVC helps in that regard with its -integrations to deep learning libraries via DVCLive. +do training and validation loss differ. DVC helps in that regard with its +Python integrations to deep learning libraries via [DVCLive]. The example project uses Keras to train a classifier, and we have a DVCLive callback that visualizes the training and validation loss for each epoch. We From 4427ff3031a7ff7240207f38b13a1d045c0b7725 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:32:25 +0300 Subject: [PATCH 52/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index b6f5cf1715..09d98f2215 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -109,4 +109,4 @@ workflow: [experiments]: /doc/start/experiments [vega-lite]: https://vega.github.io/vega-lite/ -[dvclive]: https://dvc.org/doc/dvclive +[dvclive]: https://dvc.org/doc/dvclive/dvclive-with-dvc From 1f70192704a998d8ef1387cd06b71d6e55a389ef Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:32:53 +0300 Subject: [PATCH 53/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 09d98f2215..54598bcffc 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -104,8 +104,7 @@ workflow: - DVC can keep track of image files produced as plot outputs from the training/evaluation scripts. -- DVC, with its [DVCLive] integration can produce plots automatically during - training. +- [DVCLive] integrations can produce plots automatically during training. [experiments]: /doc/start/experiments [vega-lite]: https://vega.github.io/vega-lite/ From eb1853a33b4422b709df52f644e4d946603d9f1d Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:33:06 +0300 Subject: [PATCH 54/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 54598bcffc..24a7b6c54d 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -106,6 +106,6 @@ workflow: - [DVCLive] integrations can produce plots automatically during training. -[experiments]: /doc/start/experiments -[vega-lite]: https://vega.github.io/vega-lite/ +[plot outputs]: + /doc/user-guide/project-structure/pipelines-files#metrics-and-plots-outputs [dvclive]: https://dvc.org/doc/dvclive/dvclive-with-dvc From 79f2bf329e3c059fa91fed7411e44c8694d5913a Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:33:22 +0300 Subject: [PATCH 55/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 24a7b6c54d..d041ccdc62 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -98,8 +98,8 @@ these default values. Please refer to [DVClive documentation] for details. In summary, DVC provides more than one option to fit visualization to your workflow: -- DVC can generate HTML files that includes interactive [Vega-Lite] plots, from - the data series in JSON, YAML, CSV, or TSV. +- DVC can generate HTML files that includes interactive plots from + data series in JSON, YAML, CSV, or TSV format. - DVC can keep track of image files produced as plot outputs from the training/evaluation scripts. From 4cacc6be57f2ab6987af196f916db89a59ea69fc Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:33:43 +0300 Subject: [PATCH 56/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index d041ccdc62..2d7308848a 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -85,8 +85,8 @@ model.fit( ``` With these two changes, we can see the plots showing the defined metrics in our -model. You can see the HTML report with your browser in -`training_metrics/index.html` file. +model. You can see the report in `training_metrics/index.html` +with your browser: ![dvclive](/img/start_visualization_dvclive.png) From 80af0717cced3b2a29dc1134182e15465591b6bc Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:33:57 +0300 Subject: [PATCH 57/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 2d7308848a..e9e5ef8f7d 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -101,7 +101,7 @@ workflow: - DVC can generate HTML files that includes interactive plots from data series in JSON, YAML, CSV, or TSV format. -- DVC can keep track of image files produced as plot outputs from the +- DVC can keep track of image files produced as [plot outputs] from the training/evaluation scripts. - [DVCLive] integrations can produce plots automatically during training. From debec8753ae72c797072dc35218652484ec68fba Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 13 Apr 2022 18:34:14 +0300 Subject: [PATCH 58/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index e9e5ef8f7d..f7ea062065 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -91,9 +91,7 @@ with your browser: ![dvclive](/img/start_visualization_dvclive.png) DVCLive has other capabilities, like saving the model every epoch or modifying -these default values. Please refer to [DVClive documentation] for details. - -[dvclive documentation]: /doc/dvclive/dvclive-with-dvc +these default values. In summary, DVC provides more than one option to fit visualization to your workflow: From fcd0cd52b099dce174f8e9281127facb3a9a60fc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 13 Apr 2022 18:39:23 +0300 Subject: [PATCH 59/69] restyle and minor fix --- content/docs/start/visualization.md | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index f7ea062065..eb3525d41e 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -31,9 +31,8 @@ actual,predicted https://github.com/iterative/example-dvc-experiments/blob/main/src/train.py#L123 [confusion matrix]: https://en.wikipedia.org/wiki/Confusion_matrix -Running the experiment with `dvc exp run` will produce -`plots/confusion.csv`. Use `dvc plots show` to present it as an HTML -file, and open it in the browser: +Running the experiment with `dvc exp run` will produce `plots/confusion.csv`. +Use `dvc plots show` to present it as an HTML file, and open it in the browser: ```dvc $ dvc plots show plots/confusion.csv --template confusion \ @@ -63,9 +62,9 @@ $ dvc plots show plots/confusion.png ## Autogenerating plots from deep learning code -An important issue for deep learning projects is to observe in which epoch -do training and validation loss differ. DVC helps in that regard with its -Python integrations to deep learning libraries via [DVCLive]. +An important issue for deep learning projects is to observe in which epoch do +training and validation loss differ. DVC helps in that regard with its Python +integrations to deep learning libraries via [DVCLive]. The example project uses Keras to train a classifier, and we have a DVCLive callback that visualizes the training and validation loss for each epoch. We @@ -75,7 +74,7 @@ first import the callback from DVCLive. from dvclive.keras import DvcLiveCallback ``` -Then we add this callback to `fit` callbacks. +Then we add this callback to the `fit()` call. ```python model.fit( @@ -85,8 +84,8 @@ model.fit( ``` With these two changes, we can see the plots showing the defined metrics in our -model. You can see the report in `training_metrics/index.html` -with your browser: +model. You can see the report in `training_metrics/index.html` with your +browser: ![dvclive](/img/start_visualization_dvclive.png) @@ -96,8 +95,8 @@ these default values. In summary, DVC provides more than one option to fit visualization to your workflow: -- DVC can generate HTML files that includes interactive plots from - data series in JSON, YAML, CSV, or TSV format. +- DVC can generate HTML files that includes interactive plots from data series + in JSON, YAML, CSV, or TSV format. - DVC can keep track of image files produced as [plot outputs] from the training/evaluation scripts. From 18274b3920bc4fb8a26c78405d0e18d4c5447cd7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 13 Apr 2022 18:40:54 +0300 Subject: [PATCH 60/69] fit ... to ... -> use .. in .. --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index eb3525d41e..4ac0ce4ee2 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -92,7 +92,7 @@ browser: DVCLive has other capabilities, like saving the model every epoch or modifying these default values. -In summary, DVC provides more than one option to fit visualization to your +In summary, DVC provides more than one option to use visualization in your workflow: - DVC can generate HTML files that includes interactive plots from data series From 2ae5379d4ae3c32d9e8f791debc7b6edd8123a90 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 13 Apr 2022 18:42:04 +0300 Subject: [PATCH 61/69] deleted the older file --- .../docs/start/metrics-parameters-plots.md | 278 ------------------ 1 file changed, 278 deletions(-) delete mode 100644 content/docs/start/metrics-parameters-plots.md diff --git a/content/docs/start/metrics-parameters-plots.md b/content/docs/start/metrics-parameters-plots.md deleted file mode 100644 index fc6cd0927f..0000000000 --- a/content/docs/start/metrics-parameters-plots.md +++ /dev/null @@ -1,278 +0,0 @@ ---- -title: 'Get Started: Metrics, Parameters, and Plots' ---- - -# Get Started: Metrics, Parameters, and Plots - -DVC makes it easy to track [metrics](/doc/command-reference/metrics), update -parameters, and visualize performance with -[plots](/doc/command-reference/plots). These concepts are introduced below. - -> All of the above can be combined into experiments to run and -> compare many iterations of your ML project. - -https://youtu.be/bu3l75eQlQo - -## Collecting metrics - -First, let's see what is the mechanism to capture values for these ML -attributes. Let's add a final evaluation stage to our -[pipeline from before](/doc/start/data-pipelines): - -```dvc -$ dvc run -n evaluate \ - -d src/evaluate.py -d model.pkl -d data/features \ - -M scores.json \ - --plots-no-cache prc.json \ - --plots-no-cache roc.json \ - python src/evaluate.py model.pkl \ - data/features scores.json prc.json roc.json -``` - -

- -### 💡 Expand to see what happens under the hood. - -The `-M` option here specifies a metrics file, while `--plots-no-cache` -specifies a plots file (produced by this stage) which will not be -cached by DVC. `dvc run` generates a new stage in the `dvc.yaml` -file: - -```yaml -evaluate: - cmd: python src/evaluate.py model.pkl data/features ... - deps: - - data/features - - model.pkl - - src/evaluate.py - metrics: - - scores.json: - cache: false - plots: - - prc.json: - cache: false - - roc.json: - cache: false -``` - -The biggest difference to previous stages in our pipeline is in two new -sections: `metrics` and `plots`. These are used to mark certain files containing -ML "telemetry". Metrics files contain scalar values (e.g. `AUC`) and plots files -contain matrices and data series (e.g. `ROC curves` or model loss plots) meant -to be visualized and compared. - -> With `cache: false`, DVC skips caching the output, as we want `scores.json`, -> `prc.json`, and `roc.json` to be versioned by Git. - -
- -[`evaluate.py`](https://github.com/iterative/example-get-started/blob/master/src/evaluate.py) -writes the model's -[ROC-AUC](https://scikit-learn.org/stable/modules/model_evaluation.html#receiver-operating-characteristic-roc) -and -[average precision](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures) -to `scores.json`, which in turn is marked as a `metrics` file with `-M`. Its -contents are: - -```json -{ "avg_prec": 0.5204838673030754, "roc_auc": 0.9032012604172255 } -``` - -`evaluate.py` also writes `precision`, `recall`, and `thresholds` arrays -(obtained using -[`precision_recall_curve`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html)) -into the plots file `prc.json`: - -```json -{ - "prc": [ - { "precision": 0.021473008227975116, "recall": 1.0, "threshold": 0.0 }, - ..., - { "precision": 1.0, "recall": 0.009345794392523364, "threshold": 0.6 } - ] -} -``` - -Similarly, it writes arrays for the -[roc_curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html) -into `roc.json` for an additional plot. - -> DVC doesn't force you to use any specific file names, nor does it enforce a -> format or structure of a metrics or plots file. It's completely -> user/case-defined. Refer to `dvc metrics` and `dvc plots` for more details. - -You can view tracked metrics and plots with DVC. Let's start with the metrics: - -```dvc -$ dvc metrics show -Path avg_prec roc_auc -scores.json 0.52048 0.9032 -``` - -To view plots, first specify which arrays to use as the plot axes. We only need -to do this once, and DVC will save our plot configurations. - -```dvc -$ dvc plots modify prc.json -x recall -y precision -Modifying stage 'evaluate' in 'dvc.yaml' -$ dvc plots modify roc.json -x fpr -y tpr -Modifying stage 'evaluate' in 'dvc.yaml' -``` - -Now let's view the plots: - -```dvc -$ dvc plots show -file:///Users/dvc/example-get-started/plots.html -``` - -![](/img/plots_prc_get_started_show.svg) -![](/img/plots_roc_get_started_show.svg) - -Let's save this iteration, so we can compare it later: - -```dvc -$ git add scores.json prc.json roc.json -$ git commit -a -m "Create evaluation stage" -``` - -Later we will see how to -[compare and visualize different pipeline iterations](#comparing-iterations). -For now, let's see how can we capture another important piece of information -which will be useful for comparison: parameters. - -## Defining stage parameters - -It's pretty common for data science pipelines to include configuration files -that define adjustable parameters to train a model, do pre-processing, etc. DVC -provides a mechanism for stages to depend on the values of specific sections of -such a config file (YAML, JSON, TOML, and Python formats are supported). - -Luckily, we should already have a stage with -[parameters](/doc/command-reference/params) in `dvc.yaml`: - -```yaml -featurize: - cmd: python src/featurization.py data/prepared data/features - deps: - - data/prepared - - src/featurization.py - params: - - featurize.max_features - - featurize.ngrams - outs: - - data/features -``` - -
- -### ⚙️ Expand to recall how it was generated. - -The `featurize` stage -[was created](/doc/start/data-pipelines#dependency-graphs-dags) with this -`dvc run` command. Notice the argument sent to the `-p` option (short for -`--params`): - -```dvc -$ dvc run -n featurize \ - -p featurize.max_features,featurize.ngrams \ - -d src/featurization.py -d data/prepared \ - -o data/features \ - python src/featurization.py data/prepared data/features -``` - -
- -The `params` section defines the parameter dependencies of the `featurize` -stage. By default, DVC reads those values (`featurize.max_features` and -`featurize.ngrams`) from a `params.yaml` file. But as with metrics and plots, -parameter file names and structure can also be user- and case-defined. - -Here's the contents of our `params.yaml` file: - -```yaml -prepare: - split: 0.20 - seed: 20170428 - -featurize: - max_features: 500 - ngrams: 1 - -train: - seed: 20170428 - n_est: 50 - min_split: 2 -``` - -## Updating params and iterating - -We are definitely not happy with the AUC value we got so far! Let's edit the -`params.yaml` file to use bigrams and increase the number of features: - -```git - featurize: -- max_features: 500 -- ngrams: 1 -+ max_features: 1500 -+ ngrams: 2 -``` - -The beauty of `dvc.yaml` is that all you need to do now is run: - -```dvc -$ dvc repro -``` - -It'll analyze the changes, use existing results from the run-cache, -and execute only the commands needed to produce new results (model, metrics, -plots). - -The same logic applies to other possible adjustments — edit source code, update -datasets — you do the changes, use `dvc repro`, and DVC runs what needs to be. - -## Comparing iterations - -Finally, let's see how the updates improved performance. DVC has a few commands -to see changes in and visualize metrics, parameters, and plots. These commands -can work for one or across multiple pipeline iteration(s). Let's compare the -current "bigrams" run with the last committed "baseline" iteration: - -```dvc -$ dvc params diff -Path Param HEAD workspace -params.yaml featurize.max_features 500 1500 -params.yaml featurize.ngrams 1 2 -``` - -`dvc params diff` can show how params in the workspace differ vs. the last -commit. - -`dvc metrics diff` does the same for metrics: - -```dvc -$ dvc metrics diff -Path Metric HEAD workspace Change -scores.json avg_prec 0.52048 0.55259 0.03211 -scores.json roc_auc 0.9032 0.91536 0.01216 -``` - -And finally, we can compare both `precision recall` and `roc` curves with a -single command! - -```dvc -$ dvc plots diff -file:///Users/dvc/example-get-started/plots.html -``` - -![](/img/plots_prc_get_started_diff.svg) -![](/img/plots_roc_get_started_diff.svg) - -> See `dvc plots diff` for more info on its options. - -> All these commands also accept -> [Git revisions](https://git-scm.com/docs/gitrevisions) (commits, tags, branch -> names) to compare. - -On the next page, you can learn advanced ways to track, organize, and compare -more experiment iterations. From b4681fe86b6c7f9a095b3e8aed69e32659ac82a3 Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Thu, 14 Apr 2022 10:00:01 -0500 Subject: [PATCH 62/69] Apply suggestions from code review --- content/docs/start/visualization.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 4ac0ce4ee2..59f41cb957 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -12,7 +12,7 @@ try these yourself, please refer to the project. [README] about how to install. [readme]: https://github.com/iterative/example-dvc-experiments/blob/main/README.md -### Creating plots from tabular data +## Creating plots from tabular data A useful plot to show the classification performance is the [confusion matrix]. In order to produce it, DVC expects a CSV **plots file** in the form: @@ -37,7 +37,7 @@ Use `dvc plots show` to present it as an HTML file, and open it in the browser: ```dvc $ dvc plots show plots/confusion.csv --template confusion \ -x actual -y predicted -file:///Users/.../example-dvc-experiments/plots/confusion.json.html +file:///.../example-dvc-experiments/plots/confusion.json.html ``` ![confusion matrix](/img/start_visualization_confusion1.png) @@ -105,4 +105,4 @@ workflow: [plot outputs]: /doc/user-guide/project-structure/pipelines-files#metrics-and-plots-outputs -[dvclive]: https://dvc.org/doc/dvclive/dvclive-with-dvc +[dvclive]: /doc/dvclive/dvclive-with-dvc From 6f5264de6458b13b43370614dbb6cc4405c5c8f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 20 Apr 2022 17:50:55 +0300 Subject: [PATCH 63/69] renamed confusion.png to misclassified.png in the example --- content/docs/start/visualization.md | 4 ++-- ...ng => start_visualization_misclassification.png} | Bin 2 files changed, 2 insertions(+), 2 deletions(-) rename static/img/{start_visualization_confusion2.png => start_visualization_misclassification.png} (100%) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 59f41cb957..8be404f4d7 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -55,10 +55,10 @@ project.][misclassified-example-code] https://github.com/iterative/example-dvc-experiments/blob/48b1e5078c957f71674c00f416290eaa3b20b559/src/util.py#L49 ```dvc -$ dvc plots show plots/confusion.png +$ dvc plots show plots/misclassified.png ``` -![confusion image](/img/start_visualization_confusion2.png) +![confusion image](/img/start_visualization_misclassification.png) ## Autogenerating plots from deep learning code diff --git a/static/img/start_visualization_confusion2.png b/static/img/start_visualization_misclassification.png similarity index 100% rename from static/img/start_visualization_confusion2.png rename to static/img/start_visualization_misclassification.png From f26f36606b6730b9809b25df4952bcdcc6b193e8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Emre=20=C5=9Eahin?= Date: Wed, 20 Apr 2022 17:53:57 +0300 Subject: [PATCH 64/69] rename one more --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 8be404f4d7..75b063df33 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -58,7 +58,7 @@ project.][misclassified-example-code] $ dvc plots show plots/misclassified.png ``` -![confusion image](/img/start_visualization_misclassification.png) +![misclassification image](/img/start_visualization_misclassification.png) ## Autogenerating plots from deep learning code From c1f49613864cab297709016e420c735129c50afe Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 20 Apr 2022 18:03:01 +0300 Subject: [PATCH 65/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 75b063df33..f8f37bff1c 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -83,9 +83,8 @@ model.fit( ...) ``` -With these two changes, we can see the plots showing the defined metrics in our -model. You can see the report in `training_metrics/index.html` with your -browser: +With these two changes, the model metrics are automatically logged to +`dvclive.json` and plotted in `training_metrics/index.html`: ![dvclive](/img/start_visualization_dvclive.png) From b0ed669bbf73cffe83b7f9004d0662e8680d9821 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 27 Apr 2022 16:58:45 +0300 Subject: [PATCH 66/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index f8f37bff1c..09c14a3b8e 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -74,7 +74,9 @@ first import the callback from DVCLive. from dvclive.keras import DvcLiveCallback ``` -Then we add this callback to the `fit()` call. +Then we add this callback to the +[`fit` method](https://keras.io/api/models/model_training_apis/#fit-method) +call. ```python model.fit( From 7e411e2bb115868e76f7029e8be945b64cb91fc9 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 27 Apr 2022 17:05:25 +0300 Subject: [PATCH 67/69] Update content/docs/start/visualization.md Co-authored-by: Jorge Orpinel --- content/docs/start/visualization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/docs/start/visualization.md b/content/docs/start/visualization.md index 09c14a3b8e..d3d8d52764 100644 --- a/content/docs/start/visualization.md +++ b/content/docs/start/visualization.md @@ -58,7 +58,7 @@ project.][misclassified-example-code] $ dvc plots show plots/misclassified.png ``` -![misclassification image](/img/start_visualization_misclassification.png) +![Misclassification table](/img/start_visualization_misclassification.png) ## Autogenerating plots from deep learning code From cf02e81c7eb21f5dff6c8f1728a7dd62f74c96f7 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 27 Apr 2022 17:11:33 +0300 Subject: [PATCH 68/69] Revert "removed older metrics, plots, vis from the sidebar" This reverts commit 5ff8661d9744df3271955bcc8e77f2e9fb346415. --- content/docs/sidebar.json | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/content/docs/sidebar.json b/content/docs/sidebar.json index 5791fc2730..8045e5a16f 100644 --- a/content/docs/sidebar.json +++ b/content/docs/sidebar.json @@ -52,6 +52,13 @@ "katacoda": "https://katacoda.com/dvc/courses/get-started/stages" } }, + { + "label": "Metrics, Parameters, and Plots", + "slug": "metrics-parameters-plots", + "tutorials": { + "katacoda": "https://katacoda.com/dvc/courses/get-started/params-metrics-plots" + } + }, { "slug": "experiments", "label": "Experiments", From b2a832e944427580f2ffbead764173d696e3c522 Mon Sep 17 00:00:00 2001 From: Emre Sahin Date: Wed, 27 Apr 2022 17:12:54 +0300 Subject: [PATCH 69/69] readd metric-params-plots --- .../docs/start/metrics-parameters-plots.md | 278 ++++++++++++++++++ 1 file changed, 278 insertions(+) create mode 100644 content/docs/start/metrics-parameters-plots.md diff --git a/content/docs/start/metrics-parameters-plots.md b/content/docs/start/metrics-parameters-plots.md new file mode 100644 index 0000000000..fc6cd0927f --- /dev/null +++ b/content/docs/start/metrics-parameters-plots.md @@ -0,0 +1,278 @@ +--- +title: 'Get Started: Metrics, Parameters, and Plots' +--- + +# Get Started: Metrics, Parameters, and Plots + +DVC makes it easy to track [metrics](/doc/command-reference/metrics), update +parameters, and visualize performance with +[plots](/doc/command-reference/plots). These concepts are introduced below. + +> All of the above can be combined into experiments to run and +> compare many iterations of your ML project. + +https://youtu.be/bu3l75eQlQo + +## Collecting metrics + +First, let's see what is the mechanism to capture values for these ML +attributes. Let's add a final evaluation stage to our +[pipeline from before](/doc/start/data-pipelines): + +```dvc +$ dvc run -n evaluate \ + -d src/evaluate.py -d model.pkl -d data/features \ + -M scores.json \ + --plots-no-cache prc.json \ + --plots-no-cache roc.json \ + python src/evaluate.py model.pkl \ + data/features scores.json prc.json roc.json +``` + +
+ +### 💡 Expand to see what happens under the hood. + +The `-M` option here specifies a metrics file, while `--plots-no-cache` +specifies a plots file (produced by this stage) which will not be +cached by DVC. `dvc run` generates a new stage in the `dvc.yaml` +file: + +```yaml +evaluate: + cmd: python src/evaluate.py model.pkl data/features ... + deps: + - data/features + - model.pkl + - src/evaluate.py + metrics: + - scores.json: + cache: false + plots: + - prc.json: + cache: false + - roc.json: + cache: false +``` + +The biggest difference to previous stages in our pipeline is in two new +sections: `metrics` and `plots`. These are used to mark certain files containing +ML "telemetry". Metrics files contain scalar values (e.g. `AUC`) and plots files +contain matrices and data series (e.g. `ROC curves` or model loss plots) meant +to be visualized and compared. + +> With `cache: false`, DVC skips caching the output, as we want `scores.json`, +> `prc.json`, and `roc.json` to be versioned by Git. + +
+ +[`evaluate.py`](https://github.com/iterative/example-get-started/blob/master/src/evaluate.py) +writes the model's +[ROC-AUC](https://scikit-learn.org/stable/modules/model_evaluation.html#receiver-operating-characteristic-roc) +and +[average precision](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures) +to `scores.json`, which in turn is marked as a `metrics` file with `-M`. Its +contents are: + +```json +{ "avg_prec": 0.5204838673030754, "roc_auc": 0.9032012604172255 } +``` + +`evaluate.py` also writes `precision`, `recall`, and `thresholds` arrays +(obtained using +[`precision_recall_curve`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html)) +into the plots file `prc.json`: + +```json +{ + "prc": [ + { "precision": 0.021473008227975116, "recall": 1.0, "threshold": 0.0 }, + ..., + { "precision": 1.0, "recall": 0.009345794392523364, "threshold": 0.6 } + ] +} +``` + +Similarly, it writes arrays for the +[roc_curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html) +into `roc.json` for an additional plot. + +> DVC doesn't force you to use any specific file names, nor does it enforce a +> format or structure of a metrics or plots file. It's completely +> user/case-defined. Refer to `dvc metrics` and `dvc plots` for more details. + +You can view tracked metrics and plots with DVC. Let's start with the metrics: + +```dvc +$ dvc metrics show +Path avg_prec roc_auc +scores.json 0.52048 0.9032 +``` + +To view plots, first specify which arrays to use as the plot axes. We only need +to do this once, and DVC will save our plot configurations. + +```dvc +$ dvc plots modify prc.json -x recall -y precision +Modifying stage 'evaluate' in 'dvc.yaml' +$ dvc plots modify roc.json -x fpr -y tpr +Modifying stage 'evaluate' in 'dvc.yaml' +``` + +Now let's view the plots: + +```dvc +$ dvc plots show +file:///Users/dvc/example-get-started/plots.html +``` + +![](/img/plots_prc_get_started_show.svg) +![](/img/plots_roc_get_started_show.svg) + +Let's save this iteration, so we can compare it later: + +```dvc +$ git add scores.json prc.json roc.json +$ git commit -a -m "Create evaluation stage" +``` + +Later we will see how to +[compare and visualize different pipeline iterations](#comparing-iterations). +For now, let's see how can we capture another important piece of information +which will be useful for comparison: parameters. + +## Defining stage parameters + +It's pretty common for data science pipelines to include configuration files +that define adjustable parameters to train a model, do pre-processing, etc. DVC +provides a mechanism for stages to depend on the values of specific sections of +such a config file (YAML, JSON, TOML, and Python formats are supported). + +Luckily, we should already have a stage with +[parameters](/doc/command-reference/params) in `dvc.yaml`: + +```yaml +featurize: + cmd: python src/featurization.py data/prepared data/features + deps: + - data/prepared + - src/featurization.py + params: + - featurize.max_features + - featurize.ngrams + outs: + - data/features +``` + +
+ +### ⚙️ Expand to recall how it was generated. + +The `featurize` stage +[was created](/doc/start/data-pipelines#dependency-graphs-dags) with this +`dvc run` command. Notice the argument sent to the `-p` option (short for +`--params`): + +```dvc +$ dvc run -n featurize \ + -p featurize.max_features,featurize.ngrams \ + -d src/featurization.py -d data/prepared \ + -o data/features \ + python src/featurization.py data/prepared data/features +``` + +
+ +The `params` section defines the parameter dependencies of the `featurize` +stage. By default, DVC reads those values (`featurize.max_features` and +`featurize.ngrams`) from a `params.yaml` file. But as with metrics and plots, +parameter file names and structure can also be user- and case-defined. + +Here's the contents of our `params.yaml` file: + +```yaml +prepare: + split: 0.20 + seed: 20170428 + +featurize: + max_features: 500 + ngrams: 1 + +train: + seed: 20170428 + n_est: 50 + min_split: 2 +``` + +## Updating params and iterating + +We are definitely not happy with the AUC value we got so far! Let's edit the +`params.yaml` file to use bigrams and increase the number of features: + +```git + featurize: +- max_features: 500 +- ngrams: 1 ++ max_features: 1500 ++ ngrams: 2 +``` + +The beauty of `dvc.yaml` is that all you need to do now is run: + +```dvc +$ dvc repro +``` + +It'll analyze the changes, use existing results from the run-cache, +and execute only the commands needed to produce new results (model, metrics, +plots). + +The same logic applies to other possible adjustments — edit source code, update +datasets — you do the changes, use `dvc repro`, and DVC runs what needs to be. + +## Comparing iterations + +Finally, let's see how the updates improved performance. DVC has a few commands +to see changes in and visualize metrics, parameters, and plots. These commands +can work for one or across multiple pipeline iteration(s). Let's compare the +current "bigrams" run with the last committed "baseline" iteration: + +```dvc +$ dvc params diff +Path Param HEAD workspace +params.yaml featurize.max_features 500 1500 +params.yaml featurize.ngrams 1 2 +``` + +`dvc params diff` can show how params in the workspace differ vs. the last +commit. + +`dvc metrics diff` does the same for metrics: + +```dvc +$ dvc metrics diff +Path Metric HEAD workspace Change +scores.json avg_prec 0.52048 0.55259 0.03211 +scores.json roc_auc 0.9032 0.91536 0.01216 +``` + +And finally, we can compare both `precision recall` and `roc` curves with a +single command! + +```dvc +$ dvc plots diff +file:///Users/dvc/example-get-started/plots.html +``` + +![](/img/plots_prc_get_started_diff.svg) +![](/img/plots_roc_get_started_diff.svg) + +> See `dvc plots diff` for more info on its options. + +> All these commands also accept +> [Git revisions](https://git-scm.com/docs/gitrevisions) (commits, tags, branch +> names) to compare. + +On the next page, you can learn advanced ways to track, organize, and compare +more experiment iterations.

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