From 27d798841333edafeba772368180ac30c5f736db Mon Sep 17 00:00:00 2001 From: "Mario G." Date: Mon, 16 Dec 2024 12:00:08 +0100 Subject: [PATCH] chore: preparing ci docs --- .github/workflows/gql-pr.yml | 15 + .github/workflows/gql-release.yml | 43 +++ docs/guide/guide.gradle | 17 - .../dataframe/dataframe/creation.adoc | 64 ---- .../dataframe/dataframe/filtering.adoc | 250 ------------- .../dataframe/dataframe/import_export.adoc | 3 - .../dataframe/import_export_csv.adoc | 212 ----------- .../asciidoc/dataframe/dataframe/index.adoc | 11 - .../asciidoc/dataframe/dataframe/mapping.adoc | 3 - .../asciidoc/dataframe/dataframe/sorting.adoc | 3 - .../src/docs/asciidoc/dataframe/index.adoc | 8 - .../asciidoc/dataframe/series/creation.adoc | 92 ----- .../docs/asciidoc/dataframe/series/index.adoc | 7 - .../docs/asciidoc/dataframe/series/intro.adoc | 36 -- .../asciidoc/dataframe/series/statistics.adoc | 3 - .../src/docs/asciidoc/dataframe/tutorial.adoc | 351 ------------------ .../src/docs/asciidoc/graphs/distances.adoc | 62 ---- .../guide/src/docs/asciidoc/graphs/index.adoc | 12 - 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- main + +jobs: + build: + runs-on: ubuntu-latest + environment: release + env: + ORG_GRADLE_PROJECT_PUBLISH_SIGN_KEY_ID: ${{ secrets.ORG_GRADLE_PROJECT_PUBLISH_SIGN_KEY_ID }} + ORG_GRADLE_PROJECT_PUBLISH_SIGN_KEY: ${{ secrets.ORG_GRADLE_PROJECT_PUBLISH_SIGN_KEY }} + ORG_GRADLE_PROJECT_PUBLISH_SIGN_SECRET: ${{ secrets.ORG_GRADLE_PROJECT_PUBLISH_SIGN_SECRET }} + ORG_GRADLE_PROJECT_PUBLISH_REPO_USERNAME: ${{ secrets.ORG_GRADLE_PROJECT_PUBLISH_REPO_USERNAME }} + ORG_GRADLE_PROJECT_PUBLISH_REPO_PASSWORD: ${{ secrets.ORG_GRADLE_PROJECT_PUBLISH_REPO_PASSWORD }} + ORG_GRADLE_PROJECT_PUBLISH_GH_TOKEN: ${{ secrets.ORG_GRADLE_PROJECT_PUBLISH_GH_TOKEN }} + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + - name: installing poetry + run: | + python -m pip install --upgrade pip + pip install poetry + - name: building docs + run: | + cd docs/guide/docs && \ + poetry install --with dev && \ + poetry shell && mkdocs build + - uses: actions/setup-java@v4 + with: + distribution: 'corretto' + java-version: '17' + - name: release + run: | + ./gradlew \ + --stacktrace \ + -x aggregateCodenarc \ + -x aggregateGroovydoc \ + build \ No newline at end of file diff --git a/docs/guide/guide.gradle b/docs/guide/guide.gradle index ef20509..d179c2d 100644 --- a/docs/guide/guide.gradle +++ b/docs/guide/guide.gradle @@ -1,6 +1,5 @@ plugins { id('groovy') - id('org.kordamp.gradle.guide') id('org.ajoberstar.git-publish') } @@ -27,22 +26,6 @@ gitPublishPush { dependsOn 'setAuthentication' } -asciidoctor { - baseDirFollowsSourceDir() - attributes( - 'underdog': version, - 'github': '.', - 'toc': 'left', - 'toclevels' : 4, - 'source-highlighter': 'rouge') -} - -asciidoctorj { - modules { - diagram.version '2.1.0' - } -} - test { useJUnitPlatform() } \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/creation.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/creation.adoc deleted file mode 100644 index 13f58e4..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/creation.adoc +++ /dev/null @@ -1,64 +0,0 @@ -==== Creating -:sources: ../../../../../src/test/groovy/underdog/guide/dataframe/dataframe - -The easiest way to create a Dataframe is using the Underdog extension method `Underdog.df()`. Here we're creating an empty DataFrame: - -[source, groovy] -.empty dataframe ----- -include::{sources}/CreationSpec.groovy[tag=empty,indent=0] ----- - -We can create a dataframe with a series of map entries representing series. In this case the key entry is the name of the series and the value is a collection which will become the content of the series. - -[source, groovy] -.dataframe from a map ----- -include::{sources}/CreationSpec.groovy[tag=from_map,indent=0] ----- - -[source, shell] -.output ----- - people-dataframe - name | age | ------------------- - John | 22 | - Laura | 34 | - Ursula | 83 | ----- - -Underdog dataframe library adds additional methods to collection types so that you can convert from collections to Dataframes. And example is invoking the `toDataFrame(...)` method from the map directly: - -[source, groovy] -.map extension ----- -include::{sources}/CreationSpec.groovy[tag=map_extension,indent=0] ----- - -You can also pass a list of maps to the `Underdog.df().from(col, name)` method: - -[source, groovy] -.collection of maps ----- -include::{sources}/CreationSpec.groovy[tag=collectionsOfMaps,indent=0] ----- - -[source, groovy] -.output ----- - people-dataframe - name | age | ------------------- - John | 22 | - Laura | 34 | - Ursula | 83 | ----- - -Here there is also an extension method for collections so that, **IF** your list complies to this structure you can call to the method `toDataFrame(name)` and create a DataFrame from that collection. - -[source, groovy] -.collection extension ----- -include::{sources}/CreationSpec.groovy[tag=collectionsOfMaps_extension,indent=0] ----- diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/filtering.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/filtering.adoc deleted file mode 100644 index 19dc3f0..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/filtering.adoc +++ /dev/null @@ -1,250 +0,0 @@ -==== Filtering -:sources: ../../../../../src/test/groovy/underdog/guide/dataframe/dataframe - -In a dataframe you can filter data by any of the Series the dataframe has. - -===== Numbers - -The following dataframe builds a theoretical increase of population in ten years: - -[source, groovy] -.numbers ----- -include::{sources}/FilteringSpec.groovy[tag=numbers,indent=0] ----- - -[source,shell] -.output ----- - population increase - years | population | ------------------------- - 1991 | 1000 | - 1992 | 2000 | - 1993 | 3000 | - 1994 | 4000 | - 1995 | 5000 | - 1996 | 6000 | - 1997 | 7000 | - 1998 | 8000 | - 1999 | 9000 | - 2000 | 10000 | ----- - -If we wanted to take the records after year 1995: - -[source, groovy] -.greater than ----- -include::{sources}/FilteringSpec.groovy[tag=greaterThanNumber,indent=0] ----- - -[source, shell] -.output ----- - population increase - years | population | ------------------------- - 1996 | 6000 | - 1997 | 7000 | - 1998 | 8000 | - 1999 | 9000 | - 2000 | 10000 | ----- - -Or getting records with population less than 4000: - -[source, groovy] -.less than ----- -include::{sources}/FilteringSpec.groovy[tag=lessThanNumber,indent=0] ----- - -[source,shell] -.output ----- - population increase - years | population | ------------------------- - 1991 | 1000 | - 1992 | 2000 | - 1993 | 3000 | ----- - -===== String - -Of course we can filter by strings. Follow up we've got a dataframe with some employee data: - -[source, groovy] -.employees ----- -include::{sources}/FilteringSpec.groovy[tag=string,indent=0] ----- - -[source, shell] -.output ----- - employees - employees | department | payroll | ----------------------------------------- - Udo | sales | 10000 | - John | it | 12000 | - Albert | sales | 11000 | - Ronda | it | 13000 | ----- - -Getting employees from sales department: - -[source, groovy] -.sales ----- -include::{sources}/FilteringSpec.groovy[tag=string_equals,indent=0] ----- - -[source,shell] -.output ----- - employees - employees | department | payroll | ----------------------------------------- - Udo | sales | 10000 | - Albert | sales | 11000 | ----- - -You can also use to filter by a list of possible choices: - -[source, groovy] -.in list ----- -include::{sources}/FilteringSpec.groovy[tag=string_in,indent=0] ----- - -[source, shell] -.output ----- - employees - employees | department | payroll | ----------------------------------------- - Udo | sales | 10000 | - Ronda | it | 13000 | ----- - -You can even try by a regular expression. Lets look for employees with an **'o'** in their name: - -[source, groovy] -.regex ----- -include::{sources}/FilteringSpec.groovy[tag=string_regex,indent=0] ----- - -[source, shell] -.output ----- - employees - employees | department | payroll | ----------------------------------------- - Udo | sales | 10000 | - John | it | 12000 | - Ronda | it | 13000 | ----- - -===== Dates - -Of course in time series is crucial to allow searches by time frame. - -[source, groovy] -.dates ----- -include::{sources}/FilteringSpec.groovy[tag=dates_df,indent=0] ----- - -<1> Using a given date as the beginning of our df dates series -<2> a dataframe containing the simulation of bicycles rented through 2000 - -[source, shell] -.output ----- - rented bicycles 2000 - dates | rented | -------------------------- - 2000-01-02 | 41 | - 2000-01-03 | 47 | - 2000-01-04 | 27 | - 2000-01-05 | 95 | - 2000-01-06 | 30 | - 2000-01-07 | 162 | - 2000-01-08 | 52 | - 2000-01-09 | 197 | - 2000-01-10 | 125 | - 2000-01-11 | 15 | - ... | ... | ----- - -What if we'd like to get only those records of december 2000 ? - -[source, groovy] -.after ----- -include::{sources}/FilteringSpec.groovy[tag=dates_after,indent=0] ----- - -[source, shell] -.output ----- - rented bicycles 2000 - dates | rented | -------------------------- - 2000-12-01 | 104 | - 2000-12-02 | 193 | - 2000-12-03 | 107 | - 2000-12-04 | 108 | - 2000-12-05 | 193 | - 2000-12-06 | 165 | - 2000-12-07 | 82 | - 2000-12-08 | 77 | - 2000-12-09 | 176 | - 2000-12-10 | 158 | - ... | ... | - 2000-12-31 | 150 | ----- - -===== Summary - -Here you have the tables with the supported operators: - -[cols="1,1,1,1,1"] -.arithmetic operators -|=== -| Left | Right | Operator | Example | Status -| Series | Series | + | df['a'] + df['b'] | Yes -| Series | Series | - | df['a'] - df['b] | Yes -| ---- | ---- | ---- | ---- | ---- -| Series | Object | + | df['a'] + 1 | Yes -| Series | Object | - | df['a'] - 1 | Yes -| Series | Object | * | df['a'] * 2 | Yes -| Series | Object | / | df['a'] / 2 | Yes - -|=== - -[cols="1,1,1,1"] -.filtering operators -|=== -|Type | Operator | Example | Status - -| String | == | df['a'] == 'x' | Yes -| String | != | df['a'] != 'x' | Yes -| String | ==~ | df['a'] ==~ /.*/ | Yes -| String | in | df['a'] in ['x'] | Yes -| ---- | ---- | ---- | ---- -| Number | == | df['a'] == 1 | Yes -| Number | != | df['a'] != 1 | Yes -| Number | > | df['a'] > 1 | Yes -| Number | >= | df['a'] >= 1 | Yes -| Number | < | df['a'] < 1 | Yes -| Number | <= | df['a'] <= 1 | Yes -| ---- | ---- | ---- | ---- -| LocalDate | > | df['a'] > date | Yes -| LocalDate | >= | df['a'] >= date | Yes -| LocalDate | < | df['a'] < date | Yes -| LocalDate | <= | df['a'] <= date | Yes -|=== \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/import_export.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/import_export.adoc deleted file mode 100644 index cc7c421..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/import_export.adoc +++ /dev/null @@ -1,3 +0,0 @@ -==== Import / Export - -include::import_export_csv.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/import_export_csv.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/import_export_csv.adoc deleted file mode 100644 index 2ab3062..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/import_export_csv.adoc +++ /dev/null @@ -1,212 +0,0 @@ -===== CSV -:sources: ../../../../../src/test/groovy/underdog/guide/dataframe/dataframe -:resources: ../../../../../src/test/resources - -====== Reading - -You can read csv files via `Underdog.df().read_csv(...)` method. Here we are importing a csv files containing tornado -incidents in USA: - -[source, groovy] -.import csv ----- -include::{sources}/CsvImportExportSpec.groovy[tag=simple_read_csv,indent=0] ----- - -[source, shell] -.output ----- - tornadoes_1950-2014.csv - Date | Time | State | State No | Scale | Injuries | ... | --------------------------------------------------------------------------------- - 1950-01-03 | 11:00:00 | MO | 1 | 3 | 3 | ... | - 1950-01-03 | 11:00:00 | MO | 1 | 3 | 3 | | - 1950-01-03 | 11:10:00 | IL | 1 | 3 | 0 | | - 1950-01-03 | 11:55:00 | IL | 2 | 3 | 3 | | - 1950-01-03 | 16:00:00 | OH | 1 | 1 | 1 | | - 1950-01-13 | 05:25:00 | AR | 1 | 3 | 1 | | - 1950-01-25 | 19:30:00 | MO | 2 | 2 | 5 | | - 1950-01-25 | 21:00:00 | IL | 3 | 2 | 0 | | - 1950-01-26 | 18:00:00 | TX | 1 | 2 | 2 | | - 1950-02-11 | 13:10:00 | TX | 2 | 2 | 0 | | - ... | ... | ... | ... | ... | ... | ... | ----- - -====== Separator - -By default the csv reader assumes the csv file is using comma (,) as the separator character, but you can provide a custom -separator. For example the following csv file content: - -[source, shell] -.csv file ----- -include::{resources}/data/dataframe/io_custom_separator.csv[indent=0] ----- - -Can be read by using the **sep** argument: - -[source, groovy] -.custom separator ----- -include::{sources}/CsvImportExportSpec.groovy[tag=custom_separator,indent=0] ----- - -====== Duplicated names - -Sometimes you can find a csv where columns are repeated, by default if you don't specify you allow repeated columns -the import process will fail. Imagine we've got the following csv: - -[source, shell] -.csv with repeated cols ----- -include::{resources}/data/dataframe/io_repeated_cols.csv[indent=0] ----- - -To allow repeated columns you should set the `allowDuplicatedNames` flag to **true**. - -[source, groovy] -.allow repeated cols ----- -include::{sources}/CsvImportExportSpec.groovy[tag=allow_repeated_cols,indent=0] ----- - -Then all repeated names will be prefixed in order with a number to avoid collisions: - -[source, shell] -.output ----- - io_repeated_cols.csv - bronze | silver | gold | summer_total | bronze-2 | silver-2 | gold-2 | winter_total| ------------------------------------------------------------------------------------------------ - 1 | 2 | 1 | 4 | 1 | 1 | 1 | 3 | ----- - -====== Missing values - -If a csv file contains values which should be considered as well as missing values, we can pass this information before -reading the csv file. - -[source, text] -.csv file with missing data ----- -include::{resources}/data/dataframe/io_custom_missing_data.csv[indent=0] ----- - -Here we're considering missing data the values **N/C** and **NONE**: - -[source, groovy] -.considering missing data ----- -include::{sources}/CsvImportExportSpec.groovy[tag=custom_missing_data,indent=0] ----- - -That will inform the reader to consider cells containing that value as missing values: - -[source, text] -.output ----- - io_custom_missing_data.csv - from | to | id | ------------------------------ - | Madrid | 123 | - Madrid | Paris | 124 | - Paris | London | 125 | - London | | 126 | ----- - -====== Date format - -If your csv files have a custom date format you can provide the date pattern as a parameter. Here we have a file -with a custom format: - -[source, text] -.custom date format ----- -include::{resources}/data/dataframe/io_custom_date_format.csv[indent=0] ----- - -Passing the pattern as parameter: - -[source, groovy] -.custom date format ----- -include::{sources}/CsvImportExportSpec.groovy[tag=custom_date_format,indent=0] ----- - -Gives the following output: - -[source, text] -.output ----- - io_custom_date_format.csv - Date | Close | -------------------------------------- - 2014-12-05 | 0.5267500281333923 | - 2014-12-08 | 0.5199999809265137 | - 2014-12-09 | 0.5182499885559082 | ----- - -====== Skip rows/footer - -If you're sure that there is data you'd like to avoid parsing, like nonsense data, you can skip parsing those rows. Check the following example: - -[%linenums,shell] -.csv file with comments ----- -include::{resources}/data/dataframe/io_skipping_rows.csv[indent=0] ----- - -There are lines we don't want to consider when creating our dataframe: - -- comments in the beginning of the file (lines 1-3) -- comments in the end of the file (line 15) -- rows we don't want to parse because they don't add any meaningful information (4-8 and 11-14) - -To avoid parsing any of these lines we can instruct the csv reader to skip lines in the header and/or in the -footer of the file: - -[source, groovy] -.skipping rows ----- -include::{sources}/CsvImportExportSpec.groovy[tag=skipping_rows,indent=0] ----- - -<1> no using first row as header -<2> skipping rows at the beginning of the file -<3> skipping rows at the end of the file -<4> renaming series names with the list passed as parameter - -[source, shell] -.output ----- -io_skipping_rows.csv - city | id | ------------------ - Madrid | 1 | - Paris | 2 | ----- - -====== Max chars x col - -You can instruct the csv reader to avoid parsing columns with more than a number of characters. - -[source, groovy] -.limiting col chars ----- -include::{sources}/CsvImportExportSpec.groovy[tag=col_char_limit,indent=0] ----- - -NOTE: If a column exceeds the number of characters the process will throw an exception - -====== Max cols - -You can instruct the csv reader to avoid parsing more than a given number of columns. - -[source, groovy] -.limiting number of cols ----- -include::{sources}/CsvImportExportSpec.groovy[tag=col_limit,indent=0] ----- - -NOTE: If the number of columns exceeds the number specified the process will throw an exception - diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/index.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/index.adoc deleted file mode 100644 index 13a664d..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/index.adoc +++ /dev/null @@ -1,11 +0,0 @@ -=== Dataframe - -include::creation.adoc[] - -include::filtering.adoc[] - -include::sorting.adoc[] - -include::mapping.adoc[] - -include::import_export.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/mapping.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/mapping.adoc deleted file mode 100644 index 5a707db..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/mapping.adoc +++ /dev/null @@ -1,3 +0,0 @@ -==== Mapping - -TODO \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/dataframe/sorting.adoc b/docs/guide/src/docs/asciidoc/dataframe/dataframe/sorting.adoc deleted file mode 100644 index 1efcd4e..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/dataframe/sorting.adoc +++ /dev/null @@ -1,3 +0,0 @@ -==== Sorting - -TODO \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/index.adoc b/docs/guide/src/docs/asciidoc/dataframe/index.adoc deleted file mode 100644 index dc4a689..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/index.adoc +++ /dev/null @@ -1,8 +0,0 @@ -[#_dataframe] -== Dataframe - -include::tutorial.adoc[] - -include::series/index.adoc[] - -include::dataframe/index.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/series/creation.adoc b/docs/guide/src/docs/asciidoc/dataframe/series/creation.adoc deleted file mode 100644 index 35521ef..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/series/creation.adoc +++ /dev/null @@ -1,92 +0,0 @@ -==== Creating -:sources: ../../../../../src/test/groovy/underdog/guide/dataframe/series - -Series are meant to be created from collections or as a transformation from another Series. - -The only way to create a **Series** from a collection is invoking the extension method `toSeries()` from a list: - -[source, groovy] -.collection extension ----- -include::{sources}/CreationSpec.groovy[tag=series_from_collection,indent=0] ----- - -Most of the time we will be dealing with a Series creation inside the scope of a Dataframe. Sometimes as the result of the transformation of another series, sometimes because we would like to fill a series from a constant value. - -Lets say we have a DataFrame with some Series: - -[source, groovy] -.sample dataframe ----- -include::{sources}/CreationSpec.groovy[tag=series_as_transformation_sample,indent=0] ----- - -[source, groovy] -.output ----- - numbers - numbers | ------------ - 1 | - 2 | - 3 | - 4 | - 5 | - 6 | - 7 | - 8 | - 9 | - 10 | ----- - -And we want to create a new series named **by_two** with the result of multiplying all numbers in the **numbers** series: - -[source, groovy] -.new series ----- -include::{sources}/CreationSpec.groovy[tag=series_as_transformation_creation,indent=0] ----- - -[source, groovy] -.output ----- - numbers - numbers | by_two | ----------------------- - 1 | 2 | - 2 | 4 | - 3 | 6 | - 4 | 8 | - 5 | 10 | - 6 | 12 | - 7 | 14 | - 8 | 16 | - 9 | 18 | - 10 | 20 | ----- - -You can also create a new Series inside a dataframe filling all rows with the same value: - -[source, groovy] -.series from value ----- -include::{sources}/CreationSpec.groovy[tag=series_from_value_creation,indent=0] ----- - -[source, groovy] -.output ----- - numbers - numbers | by_two | one | ------------------------------- - 1 | 2 | 1 | - 2 | 4 | 1 | - 3 | 6 | 1 | - 4 | 8 | 1 | - 5 | 10 | 1 | - 6 | 12 | 1 | - 7 | 14 | 1 | - 8 | 16 | 1 | - 9 | 18 | 1 | - 10 | 20 | 1 | ----- \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/series/index.adoc b/docs/guide/src/docs/asciidoc/dataframe/series/index.adoc deleted file mode 100644 index d1eb042..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/series/index.adoc +++ /dev/null @@ -1,7 +0,0 @@ -=== Series - -include::intro.adoc[] - -include::creation.adoc[] - -include::statistics.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/series/intro.adoc b/docs/guide/src/docs/asciidoc/dataframe/series/intro.adoc deleted file mode 100644 index b6dd028..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/series/intro.adoc +++ /dev/null @@ -1,36 +0,0 @@ -==== Introduction -:sources: ../../../../../src/test/groovy/underdog/guide/dataframe/series - -A **Series** object represents a named one-dimensional array. It also supports operations and statistical methods. It also has operations to deal with missing values. You can create a **Series** object from different sources: - -[source, groovy] -.create ----- -include::{sources}/IntroSpec.groovy[tag=create_series,indent=0] ----- - -<1> from a range of numbers -<2> from a range of letters -<3> from a list - -You can use operator symbols to apply simple operations over the Series object: - -[source, groovy] -.operations ----- -include::{sources}/IntroSpec.groovy[tag=operations,indent=0] ----- - -<1> multiplying a series by a number -<2> multiplying a series by another series -<3> transforming a series into another series - -Sometimes you may want to analyze a given Series object by using statistical methods: - -[source, groovy] -.statistics ----- -include::{sources}/IntroSpec.groovy[tag=statistics,indent=0] ----- - -You can find all statistical available methods in the `SeriesStatsExtensions` class. diff --git a/docs/guide/src/docs/asciidoc/dataframe/series/statistics.adoc b/docs/guide/src/docs/asciidoc/dataframe/series/statistics.adoc deleted file mode 100644 index b61cefa..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/series/statistics.adoc +++ /dev/null @@ -1,3 +0,0 @@ -==== Statistics - -TODO \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/dataframe/tutorial.adoc b/docs/guide/src/docs/asciidoc/dataframe/tutorial.adoc deleted file mode 100644 index 6050856..0000000 --- a/docs/guide/src/docs/asciidoc/dataframe/tutorial.adoc +++ /dev/null @@ -1,351 +0,0 @@ -=== Tutorial -:sources: ../../../../src/test/groovy/underdog/guide/dataframe - -==== Read a csv file - -Here we read a csv file of tornado data. Underdog infers the column types by sampling the data. - -[source, groovy] -.reading csv ----- -include::{sources}/TornadosSpec.groovy[tag=read_csv,indent=0] ----- - -Note: that the file is addressed relative to the current working directory. You may have to change it for your code. - -==== Viewing table metadata - -Often, the best way to start is to print the column names for reference: - -[source, groovy] -.list dataframe column names ----- -include::{sources}/TornadosSpec.groovy[tag=columns,indent=0] ----- - -[source, shell] -.output ----- -[Date, Time, State, State No, Scale, Injuries, Fatalities, Start Lat, Start Lon, Length, Width] ----- - -The shape() method displays the row and column counts: - -[source, groovy] -.shape of the dataframe ----- -include::{sources}/TornadosSpec.groovy[tag=shape,indent=0] ----- - -[source, groovy] -.shape of the dataframe ----- -include::{sources}/TornadosSpec.groovy[tag=shape2,indent=0] ----- - -[source] -.output ----- -59945 rows X 11 cols ----- - -structure() shows the index, name and type of each column - -[source, groovy] -.tornadoes schemas ----- -include::{sources}/TornadosSpec.groovy[tag=schema,indent=0] ----- - -[source, shell] -.output ----- - Structure of tornadoes_1950-2014.csv - Index | Column Name | Column Type | ------------------------------------------ - 0 | Date | LOCAL_DATE | - 1 | Time | LOCAL_TIME | - 2 | State | STRING | - 3 | State No | INTEGER | - 4 | Scale | INTEGER | - 5 | Injuries | INTEGER | - 6 | Fatalities | INTEGER | - 7 | Start Lat | DOUBLE | - 8 | Start Lon | DOUBLE | - 9 | Length | DOUBLE | - 10 | Width | INTEGER | ----- - -Like many Tablesaw methods, structure() returns a table. You can then produce a string representation for display. For convenience, calling toString() on a table invokes print(), which produces a string representation of the table table. To display the table then, you can simply call. - -[source, groovy] ----- -println(tornadoes) ----- - -You can also perform other table operations on it. For example, the code below removes all columns whose type isn’t DOUBLE: - -[source, groovy] -.using schema to change dataframe structure ----- -include::{sources}/TornadosSpec.groovy[tag=schema_tune,indent=0] ----- - -[source, groovy] -.output ----- - Structure of tornadoes_1950-2014.csv - Index | Column Name | Column Type | ------------------------------------------ - 7 | Start Lat | DOUBLE | - 8 | Start Lon | DOUBLE | - 9 | Length | DOUBLE | ----- - -Of course, that also returned a table. We’ll cover selecting rows in more detail later. - -==== Previewing data - -The first(n) method returns a new table containing the first n rows. - -[source, groovy] -.getting first 3 lines ----- -include::{sources}/TornadosSpec.groovy[tag=first,indent=0] ----- - -[source, shell] -.output ----- - tornadoes_1950-2014.csv - Date | Time | State | State No | Scale | Injuries | ... | --------------------------------------------------------------------------------- - 1950-01-03 | 11:00:00 | MO | 1 | 3 | 3 | ... | - 1950-01-03 | 11:00:00 | MO | 1 | 3 | 3 | ... | - 1950-01-03 | 11:10:00 | IL | 1 | 3 | 0 | ... | ----- - -==== Mapping operations - -Mapping operations in Tablesaw take one or more columns as inputs and produce a new column as output. We can map arbitrary expressions onto the table, but many common operations are built in. You can, for example, calculate the difference in days, weeks, or years between the values in two date columns. The method below extracts the Month name from the date column into a new column. - -[source, groovy] -.creating a new series to dataframe ----- -include::{sources}/TornadosSpec.groovy[tag=mapping,indent=0] ----- - -Now that you have a new column, you can add it to the table: - -[source, groovy] -.adding month series to tornadoes dataframe ----- -include::{sources}/TornadosSpec.groovy[tag=add_series,indent=0] ----- - -Of course nothing prevents you from doing everything altogether. - -You can remove columns from tables to save memory or reduce clutter: - -[source, groovy] -.remove series from dataframe ----- -include::{sources}/TornadosSpec.groovy[tag=remove_series,indent=0] ----- - -==== Sorting - -Now lets sort the table in reverse order by the id column. The negative sign before the name indicates a descending sort. - -[source, groovy] -.sort asc ----- -include::{sources}/TornadosSpec.groovy[tag=sorting_1,indent=0] ----- - -You can also sort in descending order: - -[source, groovy] -.sort desc ----- -include::{sources}/TornadosSpec.groovy[tag=sorting_2,indent=0] ----- - -and even sorting by more than one field: - -[source, groovy] -.sort by n-fields ----- -include::{sources}/TornadosSpec.groovy[tag=sorting_3,indent=0] ----- - -==== Descriptive statistics - -Descriptive statistics are calculated using the summary() method: - -[source, groovy] -.printing column insights ----- -include::{sources}/TornadosSpec.groovy[tag=column_describe,indent=0] ----- - -Showing the following output: - -[source, shell] -.column describe output ----- - Column: Fatalities - Measure | Value | ------------------------------------- - n | 59945 | - sum | 6802 | - Mean | 0.11347068145800349 | - Min | 0 | - Max | 158 | - Range | 158 | - Variance | 2.901978053261765 | - Std. Dev | 1.7035193140266314 | ----- - -==== Filtering - -You can write your own methods to filter rows, but it’s easier to use the built-in filter classes as shown below: - -[source, groovy] -.filtering dataframe by column expressions ----- -include::{sources}/TornadosSpec.groovy[tag=filtering,indent=0] ----- - -[source, shell] -.output ----- -tornadoes_1950-2014.csv -State | Date | ------------------------- - MO | 1950-01-03 | - IL | 1950-01-03 | - OH | 1950-01-03 | ----- - -The last example above returns a table containing only the columns named in select() parameters,rather than all the columns in the original. -Totals and sub-totals - -Column metrics can be calculated using methods like sum(), product(), mean(), max(), etc. - -You can apply those methods to a table, calculating results on one column, grouped by the values in another. - -[source, groovy] ----- -include::{sources}/TornadosSpec.groovy[tag=summarizing,indent=0] ----- - -This produces the following table, in which Group represents the Tornado Scale and Median the median injures for that group: - -[source, shell] ----- -Median injuries by Tornado Scale - Scale | Median [Injuries] | -------------------------------- - -9 | 0 | - 0 | 0 | - 1 | 0 | - 2 | 0 | - 3 | 1 | - 4 | 12 | - 5 | 107 | ----- - -==== Cross Tabs - -Tablesaw lets you easily produce two-dimensional cross-tabulations (“cross tabs”) of counts and proportions with row and column subtotals. Here’s a count example where we look at the interaction of tornado severity and US state: - -[source, groovy] -.crosstabs (count) ----- -include::{sources}/TornadosSpec.groovy[tag=crosstabs,indent=0] ----- - -[source, shell] ----- - Crosstab Counts: State x Scale - [labels] | -9 | 0 | 1 | 2 | 3 | 4 | 5 | total | ----------------------------------------------------------------------------- - AL | 0 | 624 | 770 | 425 | 142 | 38 | 12 | 2011 | - AR | 1 | 486 | 667 | 420 | 162 | 29 | 0 | 1765 | - AZ | 1 | 146 | 71 | 16 | 3 | 0 | 0 | 237 | - CA | 1 | 271 | 117 | 23 | 2 | 0 | 0 | 414 | - CO | 3 | 1322 | 563 | 112 | 22 | 1 | 0 | 2023 | - CT | 0 | 18 | 53 | 22 | 4 | 2 | 0 | 99 | - DC | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | - DE | 0 | 22 | 26 | 12 | 1 | 0 | 0 | 61 | - FL | 2 | 1938 | 912 | 319 | 37 | 3 | 0 | 3211 | - GA | 0 | 413 | 700 | 309 | 74 | 11 | 0 | 1507 | ----- -Putting it all together - -Now that you’ve seen the pieces, we can put them together to perform a more complex data analysis. Lets say we want to know how frequently Tornadoes occur in the summer. Here’’s one way to approach that: - -Let’s start by getting only those tornadoes that occurred in the summer. - -[source, groovy] -.tornadoes in the summer ----- -include::{sources}/TornadosSpec.groovy[tag=summer,indent=0] ----- - -To get the frequency, we calculate the difference in days between successive tornadoes. The lag() method creates a column where every value equals the previous value (the prior row) of the source column. Then we can simply get the difference in days between the two dates. DateColumn has a method daysUntil() that does this. It returns a NumberColumn that we’ll call “delta”. - -[source, groovy] -.lag and delta dates ----- -include::{sources}/TornadosSpec.groovy[tag=summer_lag,indent=0] ----- - -Now we simply calculate the mean of the delta column. Splitting on year keeps us from inadvertently including the time between the last tornado of one summer and the first tornado of the next. - -[source, groovy] -.create summary ----- -include::{sources}/TornadosSpec.groovy[tag=summary,indent=0] ----- - -Printing summary gives us the answer by year. - -[source, shell] -.summary output ----- - tornadoes_1950-2014.csv summary - Date year | Mean [Date lag(1) - Date[DAYS]] | Count [Date lag(1) - Date[DAYS]] | --------------------------------------------------------------------------------------- - 1950 | 2.0555555555555545 | 162 | - 1951 | 1.7488584474885829 | 219 | - 1952 | 1.8673469387755088 | 196 | - 1953 | 0.983870967741935 | 372 | - 1954 | 0.8617283950617302 | 405 | -... ----- - -To get a DOUBLE for the entire period, we can take the average of the annual means. - -[source, groovy] ----- -include::{sources}/TornadosSpec.groovy[tag=mean_of_series,indent=0] ----- - -[source, shell] -.Average days between tornadoes in the summer: ----- -0.5931137164104612 ----- - -==== Saving your data - -To save a table, you can write it as a CSV file: - -[source, groovy] ----- -tornadoes.write().csv("rev_tornadoes_1950-2014.csv"); ----- - -And that’s it for the introduction. Please see the User Guide for more information. \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/graphs/distances.adoc b/docs/guide/src/docs/asciidoc/graphs/distances.adoc deleted file mode 100644 index 8619fa0..0000000 --- a/docs/guide/src/docs/asciidoc/graphs/distances.adoc +++ /dev/null @@ -1,62 +0,0 @@ -=== Distances -:sources: ../../../../src/test/groovy/underdog/guide/graphs - -Sometimes comes handy to know what is the shortest path from one node to another. It could be anything: How many people do I have to meet to meet a specific person ? How many cities do I have to visit before getting to a specific location ? ...etc - -Here's a very naive example with cities. The following graph contains cities and the edges represent how many kilometers there are between them. - -image::graphs/distances_cities.png[align=center] - -We'd like to use the graph as a route planner to get from point A to B. First we need to create our graph: - -[source, groovy] -.cities distances graph ----- -include::{sources}/DistancesSpec.groovy[tag=shortest_path_graph,indent=0] ----- - -See how the weight of the edges represent the km between then. Then I'd like to know how many kilometers I'm going to drive if I'd like to go from Teruel to Madrid: - -[source, groovy] -.kms ----- -include::{sources}/DistancesSpec.groovy[tag=shortest_path_edges,indent=0] ----- - -I've taken the edges and I've added up all weights to get the whole trip in kms: - -[source, shell] -.output ----- -761.4 ----- - -To get the city names I'm asking for the shortest path getting the vertices this time: - -[source, groovy] -.cities visited ----- -include::{sources}/DistancesSpec.groovy[tag=shortest_path_vertices,indent=0] ----- - -[source, shell] -.output ----- -["Teruel", "Cuenca", "Zaragoza", "Guadalajara", "Madrid"] ----- - -If you are not sure whether you are interested in vertices or edges, or maybe you are interested in both, just use `shortestPath`: - -[source, groovy] -.shortestPath ----- -include::{sources}/DistancesSpec.groovy[tag=shortest_path,indent=0] ----- - -And access the resulting object: - -[source, groovy] -.shortestPath attributes ----- -include::{sources}/DistancesSpec.groovy[tag=shortest_path_attributes,indent=0] ----- \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/graphs/index.adoc b/docs/guide/src/docs/asciidoc/graphs/index.adoc deleted file mode 100644 index 2255e00..0000000 --- a/docs/guide/src/docs/asciidoc/graphs/index.adoc +++ /dev/null @@ -1,12 +0,0 @@ -[#_graphs] -== Graphs - -include::./tutorial.adoc[] - -include::traversal.adoc[] - -include::introspection.adoc[] - -include::distances.adoc[] - -include::operators.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/graphs/introspection.adoc b/docs/guide/src/docs/asciidoc/graphs/introspection.adoc deleted file mode 100644 index 5dfe899..0000000 --- a/docs/guide/src/docs/asciidoc/graphs/introspection.adoc +++ /dev/null @@ -1,37 +0,0 @@ -=== Introspection -:sources: ../../../../src/test/groovy/underdog/guide/graphs - -You can ask the tree for different attributes such as the degrees: - -[source, groovy] ----- -include::{sources}/IntrospectionSpec.groovy[tag=max_degree, indent=0] ----- - -Imagine a more complex example where our nodes are beans. In the following example we are repeating the graph but with people: - -[source, groovy] -.Person ----- -include::{sources}/IntrospectionSpec.groovy[tag=person, indent=0] ----- - -NOTE: You can learn more about Groovy destructuring in the link:https://docs.groovy-lang.org/latest/html/documentation/#_object_destructuring_with_multiple_assignment[Groovy documentation] - -We create the graph: - -[source, groovy] -.Graph using complex objects ----- -include::{sources}/IntrospectionSpec.groovy[tag=person_graph, indent=0] ----- - -Then look for the person with more relationship and extract that person name and age: - -[source, groovy] -.Person with more relationships ----- -include::{sources}/IntrospectionSpec.groovy[tag=person_max_degree, indent=0] ----- - -Do you remember we implemented destructuring for the Person class ? Here the `maxDegree()` function returns an instance of type Person, therefore we can access the properties name and age using destructuring knowing that the property in index 0 is the name and the property in index 1 is age. \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/graphs/operators.adoc b/docs/guide/src/docs/asciidoc/graphs/operators.adoc deleted file mode 100644 index 736bc3f..0000000 --- a/docs/guide/src/docs/asciidoc/graphs/operators.adoc +++ /dev/null @@ -1,18 +0,0 @@ -=== Operators -:sources: ../../../../src/test/groovy/underdog/guide/graphs - -You can apply arithmetic operations over graphs. Here for example you can apply a union operation over two graphs using the `+` operator: - -[source, groovy] -.merging graphs ----- -include::{sources}/OperatorsSpec.groovy[tag=operators_plus, indent=0] ----- - -You can check how the result has all the vertices from the previous merged graphs: - -[source, groovy] -.output ----- -include::{sources}/OperatorsSpec.groovy[tag=operators_plus_result, indent=0] ----- \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/graphs/traversal.adoc b/docs/guide/src/docs/asciidoc/graphs/traversal.adoc deleted file mode 100644 index 90e5088..0000000 --- a/docs/guide/src/docs/asciidoc/graphs/traversal.adoc +++ /dev/null @@ -1,32 +0,0 @@ -=== Traversal -:sources: ../../../../src/test/groovy/underdog/guide/graphs - -To traverse the grpah you can directly access the vertices or edges sets and use normal Groovy/Java mechanisms. In the following example we are looking in all vertices to find all vertices having the number two as a neighbor - -[source, groovy] -.collections (vertices) ----- -include::{sources}/TraversalSpec.groovy[tag=collections_vertices, indent=0] ----- - -[source, shell] -.output ----- -1 ----- - -Now in the next example we are exploring boss-employee relationships and we'd like to find all the bosses names: - -[source, groovy] -.collections (edges) ----- -include::{sources}/TraversalSpec.groovy[tag=collections_edges, indent=0] ----- - -Sometimes the graph could be bigger and more complex and we could benefit from using breadthFirst (\*) or depthFirst (**) algorithms: - -[source, groovy] -.depthFirst ----- -include::{sources}/TraversalSpec.groovy[tag=depth_first, indent=0] ----- \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/graphs/tutorial.adoc b/docs/guide/src/docs/asciidoc/graphs/tutorial.adoc deleted file mode 100644 index 1631a7f..0000000 --- a/docs/guide/src/docs/asciidoc/graphs/tutorial.adoc +++ /dev/null @@ -1,215 +0,0 @@ -=== Tutorial -:sources: ../../../../src/test/groovy/underdog/guide/graphs - -==== Creating a graph - -Creating an empty graph without vertices and edges: - - -[source, groovy] -.create a graph ----- -include::{sources}/TutorialSpec.groovy[tag=import,indent=0] - -include::{sources}/TutorialSpec.groovy[tag=create,indent=0] ----- - -==== Vertices / Nodes - -Lets create a graph with two nodes without any edge between them: - -image::graphs/tutorial_adding_vertices.png[align=center] - -There are a couple of ways of adding more vertices to a graph. One is when creating the graph: - -[source, groovy] -.adding vertices at creation time ----- -include::{sources}/TutorialSpec.groovy[tag=add_vertices_at_creation_time,indent=0] ----- - -You can also add more vertices after the graph has been created: - -[source, groovy] -.adding vertices after creation ----- -include::{sources}/TutorialSpec.groovy[tag=add_vertices_after_creation,indent=0] ----- - -You can add simple type vertices but you can also add more complex objects. Imagine we can add the relationships between employees in a given company. First lets define the `Employee` class: - -[source, groovy] -.Employee ----- -include::{sources}/TutorialSpec.groovy[tag=employee,indent=0] ----- - -<1> `@Canonical` implements very useful methods, the most important are equals and hashcode. These methods will help the graph to id each node in the graph. - -Now we can create a Graph and add relationships between employees: - -[source, groovy] -.Adding employees ----- -include::{sources}/TutorialSpec.groovy[tag=add_employees,indent=0] ----- - -==== Edges - -Graphs normally are not very useful without setting edges between nodes. Lets add an edge between two nodes: - -image::graphs/tutorial_adding_edges.png[align=center] - -We can add vertices at creation time: - -[source, groovy] -.Adding edges at creation time ----- -include::{sources}/TutorialSpec.groovy[tag=adding_edges_at_creation,indent=0] ----- - -You can also add several edges at once using `edges(...)` - -image::graphs/tutorial_adding_edges_II.png[align=center] - -[source, groovy] -.adding several edges ----- -include::{sources}/TutorialSpec.groovy[tag=adding_several_at_once,indent=0] ----- - -But it is also possible to add edges after the graph has been created: - -[source, groovy] -.Adding edges after creation time ----- -include::{sources}/TutorialSpec.groovy[tag=adding_edges_after_creation,indent=0] ----- - -We can at any point ask about how many vertices and edges there are in the graph: - -[source, groovy] -.shape ----- -include::{sources}/TutorialSpec.groovy[tag=shape,indent=0] ----- - -which prints: - -[source, shell] -.output ----- -4 vertices X 3 edges ----- - -==== Elements of a graph - -- vertices -- edges -- adjacent -- degree - -Lets create a graph first and then ask for its elements: - -[source, groovy] -.graph ----- -include::{sources}/TutorialSpec.groovy[tag=elements_graph,indent=0] ----- - -The lets ask for its vertices, edges, neighbors. - -[source, groovy] -.graph elements ----- -include::{sources}/TutorialSpec.groovy[tag=elements,indent=0] ----- -<1> getting graph vertices -<2> getting graph edges -<3> getting neighbors of a specific vertex - -==== Removing elements - -Here there are some example on how to remove vertices and edges from a given graph. When using -the operator minus (`-`) the result return the graph minus the element removed whereas the `removeXXX(,,,)` functions only return a boolean value: true if the element was successfully removed, or false -if it couldn't be removed from graph. - -[source, groovy] -.removing elements ----- -include::{sources}/TutorialSpec.groovy[tag=removing_elements,indent=0] ----- - -==== Graph types - -You can create different types of graphs. There are a couple of methods you can use: - -[source, groovy] -.graph types ----- -include::{sources}/TutorialSpec.groovy[tag=graph_types,indent=0] ----- - -NOTE: Because graphs in underdog are using JGraphT underneath you can always create an instance of any type of graph directly using JGraphT api. - -==== What to use as vertices - -You can use almost anything as a vertex. The only mandatory condition is that the graph must be able to distinguish between vertices. For that your vertex should implement both equals and hashcode methods. In Groovy you can use the `@Canonical` annotation to get that. - -==== Analyzing graphs - -The structure of the graph can be analyzed by using various functions. - -[source, groovy] -.graph ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph,indent=0] ----- - -What is the shape of the graph ? - -[source, groovy] -.shape ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph_shape,indent=0] ----- - -What is the clustering of the graph ? - -[source, groovy] -.clustering of the graph ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph_clustering_global,indent=0] ----- - -What is the clustering avg ? - -[source, groovy] -.clustering average ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph_clustering_avg,indent=0] ----- - -And what about the clustering of a given vertex ? - -[source, groovy] -.clustering of a vertex ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph_clustering_vertex,indent=0] ----- - -What is the vertex with max degree ? - -[source, groovy] -.max degree ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph_max_degree,indent=0] ----- - -Lets say we want to sort vertices by degree in descending order: - -[source, groovy] -.sort vertices by degree (desc) ----- -include::{sources}/TutorialSpec.groovy[tag=analyzing_graph_sort_by_degree,indent=0] ----- \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/index.adoc b/docs/guide/src/docs/asciidoc/index.adoc deleted file mode 100644 index 568dc4a..0000000 --- a/docs/guide/src/docs/asciidoc/index.adoc +++ /dev/null @@ -1,109 +0,0 @@ -= Underdog -:revnumber: {underdog} -:numbered: -:sources_df: ../../../src/test/groovy/underdog/guide/dataframe -:sources_gr: ../../../src/test/groovy/underdog/guide/graphs -:sources_ml: ../../../src/test/groovy/underdog/guide/ml - -.What is an Underdog ? -**** -"An underdog is a person or group in a competition, usually in sports and creative works, who is largely expected to lose." -**** - -== About - -**Underdog** is a set of Groovy libraries for data analysis. - -It doesn't expect to be the best data analysis tool, but it combines Groovy's expressiveness, and some of the best Java data analysis libraries to at least, make data analysis fun. - -=== Modules - -Underdog project covers several data analysis fields with the following subprojects: - -- **underdog-dataframe**: combines tools for working with dataframes and series. - -[source, groovy] -.gradle ----- -implementation "com.github.grooviter:underdog-dataframe:VERSION" ----- - -[source, groovy] -.example ----- -include::{sources_df}/GettingStartedSpec.groovy[tag=getting_started_simple_imports, indent=0] - -include::{sources_df}/GettingStartedSpec.groovy[tag=getting_started_simple, indent=0] ----- - -image::intro_dataframe.png[align=center, width="60%"] - -More information on the link:#_dataframe[Dataframe section] - -- **underdog-graphs**: library of graph theory data structures and algorithms - -[source, groovy] -.gradle ----- -implementation "com.github.grooviter:undeHere is a snippet:rdog-graphs:VERSION" ----- - -[source, groovy] -.example ----- -include::{sources_gr}/GettingStartedSpec.groovy[tag=getting_started_simple_imports, indent=0] - -include::{sources_gr}/GettingStartedSpec.groovy[tag=getting_started_simple, indent=0] ----- - -image::intro_graphs.png[align=center, width="60%"] - -More information in the link:#_graphs[Graphs section] - -- **underdog-ml**: contains machine learning algorithms and evaluation mechanisms - -[source, groovy] -.gradle ----- -implementation "com.github.grooviter:underdog-ml:VERSION" ----- - -[source, groovy] -.example ----- -include::{sources_ml}/GettingStartedSpec.groovy[tag=getting_started_simple_import, indent=0] - -include::{sources_ml}/GettingStartedSpec.groovy[tag=getting_started_simple, indent=0] ----- - -image::intro_ml.png[align=center, width="60%"] - -More information in the link:#_ml[ML section] - -- **underdog-plots**: creates different types of charts using the https://echarts.apache.org[Apache Echart] library underneath - -[source, groovy] -.gradle ----- -implementation "com.github.grooviter:underdog-plots:VERSION" ----- - -[source, groovy] -.example ----- -include::{sources_df}/GettingStartedSpec.groovy[tag=show_plot, indent=0] ----- - -image::intro_dataframe.png[align=center, width="60%"] - -More information in the link:#_plots[Plots section] - -include::dataframe/index.adoc[] - -include::graphs/index.adoc[] - -include::ml/index.adoc[] - -include::plots/index.adoc[] - -include::ta/index.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/ml/extensions.adoc b/docs/guide/src/docs/asciidoc/ml/extensions.adoc deleted file mode 100644 index 6f9bf50..0000000 --- a/docs/guide/src/docs/asciidoc/ml/extensions.adoc +++ /dev/null @@ -1,3 +0,0 @@ -=== Extensions - -TODO \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/ml/index.adoc b/docs/guide/src/docs/asciidoc/ml/index.adoc deleted file mode 100644 index 4f6918f..0000000 --- a/docs/guide/src/docs/asciidoc/ml/index.adoc +++ /dev/null @@ -1,6 +0,0 @@ -[#_ml] -== ML - -include::tutorial.adoc[] - -include::extensions.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/ml/tutorial.adoc b/docs/guide/src/docs/asciidoc/ml/tutorial.adoc deleted file mode 100644 index a739f45..0000000 --- a/docs/guide/src/docs/asciidoc/ml/tutorial.adoc +++ /dev/null @@ -1,288 +0,0 @@ -## Tutorial - -The typical phases in machine learning flows are: - -- Data analysis and preparation -- Model training -- Evaluation -- Deploy and/or Iterate - -.Note -**** -The current tutorial follows the link:https://grooviter.github.io/tablesaw/#_moneyball[Tablesaw Moneyball tutorial] but using Underdog's dataframe and ml modules. -**** - -In baseball, you make the playoffs by winning more games than your rivals, but you can’t control the number of games your rivals win. How should you proceed? The A’s needed to find controllable variables that affected their likelihood of making the playoffs. - -Specifically, they wanted to know how to spend their salary dollars to produce the most wins. Statistics like "Batting Average" are available for individual players so if you knew Batting Average had the greatest impact, you can trade for players with high batting averages, and thus improve your odds of success. - -To connect player stats to making the playoffs, they systematically decomposed their high-level goal. They started by asking how many wins they’d need to make the playoffs. They decided that 95 wins would give them a strong chance. Here’s how we might check that assumption using Underdog. - -### Underdog modules - -The modules required to follow this tutorial are the `ml` and `plots` modules: - -[source, groovy] -.gradle ----- -implementation 'com.github.grooviter:underdog-ml:VERSION' -implementation 'com.github.grooviter:underdog-plots:VERSION' ----- - -### Analyzing data - -To connect player stats to making the playoffs, they systematically decomposed their high-level goal. They started by asking how many wins they’d need to make the playoffs. They decided that 95 wins would give them a strong chance. Here’s how we might check that assumption using Underdog. First lets load the data: - -[source, groovy] -.loading data ----- -include::{sources}/TutorialSpec.groovy[tag=loading_data, indent=0] ----- - -Lets take only data before 2002: - -[source, groovy] -.filtering ----- -include::{sources}/TutorialSpec.groovy[tag=only_before_2002, indent=0] ----- - -We can check the assumption visually by plotting wins per year in a way that separates the teams who make the playoffs from those who don’t. This code produces the chart below: - -[source, groovy] -.playoffs ----- -include::{sources}/TutorialSpec.groovy[tag=show_playoffs, indent=0] ----- - -image::moneyball_playoffs.png[] - -### Preparing data - -Unfortunately, you can’t directly control the number of games you win. We need to go deeper. At the next level, we hypothesize that the number of wins can be predicted by the number of Runs Scored during the season, combined with the number of Runs Allowed. - -To check this assumption we compute Run Difference (RD) as Runs Scored (RS) - Runs Allowed (RA) - -[source, groovy] -.difference ----- -include::{sources}/TutorialSpec.groovy[tag=show_difference, indent=0] ----- - -Now lets see if Run Difference is correlated with Wins. We use a scatter plot again: - -[source, groovy] -.correlation ----- -include::{sources}/TutorialSpec.groovy[tag=show_correlation, indent=0] ----- - -image::moneyball_correlation.png[] - -### Model training - -Let’s create our first predictive model using linear regression, with runDifference as the sole explanatory variable. Here we use Smile’s OLS (Ordinary Least Squares) regression model. - -[source, groovy] -.Ordinary least square (OLS) ----- -include::{sources}/TutorialSpec.groovy[tag=ols, indent=0] ----- - -If we print our “winsModel”, it produces the output below: - -[source, shell] -.output ----- -Residuals: - Min 1Q Median 3Q Max - -14.4535 -2.5195 0.2255 2.9513 11.5651 - -Coefficients: - Estimate Std. Error t value Pr(>|t|) -Intercept 80.9257 0.1838 440.3014 0.0000 *** -X0 0.1038 0.0019 54.6362 0.0000 *** ---------------------------------------------------------------------- -Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 - -Residual standard error: 3.9032 on 449 degrees of freedom -Multiple R-squared: 0.8693, Adjusted R-squared: 0.8690 -F-statistic: 2985.1152 on 2 and 449 DF, p-value: 1.757e-200 ----- - -If you’re new to regression, here are some take-aways from the output: - -- The R-squared of .88 can be interpreted to mean that roughly 88% of the variance in Wins can be explained by the Run Difference variable. The rest is determined by some combination of other variables and pure chance. -- The estimate for the Intercept is the average wins independent of Run Difference. In baseball, we have a 162 game season so we expect this value to be about 81, as it is. -- The estimate for the RD variable of .1, suggests that an increase of 10 in Run Difference, should produce about 1 additional win over the course of the season. - -Of course, this model is not simply descriptive. We can use it to make predictions. In the code below, we predict how many games we will win if we score 135 more runs than our opponents. To do this, we pass an array of doubles, one for each explanatory variable in our model, to the predict() method. In this case, there’s just one variable: run difference. - -[source, groovy] -.using prediction ----- -include::{sources}/TutorialSpec.groovy[tag=ols_sample_prediction, indent=0] ----- - -[source, shell] -.output ----- -94.93591869149651 ----- - -We’d expect almost 95 wins when we outscore opponents by 135 runs. - -### Scoring with test data - -If we want to check how the model is performing overall we can pick the testing datasets we kept aside from the model training and use them to get a measure on how well the model is predicting a new case. - -In this example we are using the https://en.wikipedia.org/wiki/Coefficient_of_determination[R2 square metric]. In regression, the R2 score is a statistical measure of how well the regression predictions approximate the real data points. - -We're passing to the model the testing features (xTest) and comparing the model predictions with the test labels we have (yTest): - -[source, groovy] -.r2score ----- -include::{sources}/TutorialSpec.groovy[tag=score, indent=0] ----- - -Getting a result of: - -[source, shell] -.output ----- -0.8895307770897616 ----- - -### Modeling Runs Scored - -It’s time to go deeper again and see how we can model Runs Scored and Runs Allowed. The approach the A’s took was to model Runs Scored using team On-base percent (OBP) and team Slugging Average (SLG). In Underdog, we write: - -[source, groovy] ----- -include::{sources}/TutorialSpec.groovy[tag=runs_scored_model, indent=0] ----- - -Once again the first parameter takes a Underdog column containing the values we want to predict (Runs scored). The next two parameters take the explanatory variables OBP and SLG. - -[source, shell] -.output ----- -Residuals: - Min 1Q Median 3Q Max - -67.7289 -18.0586 -1.5988 16.8863 68.9436 - -Coefficients: - Estimate Std. Error t value Pr(>|t|) -Intercept -846.1069 29.6301 -28.5556 0.0000 *** -X0 2937.5751 141.7312 20.7264 0.0000 *** -X1 1524.8355 65.4379 23.3020 0.0000 *** ---------------------------------------------------------------------- -Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 - -Residual standard error: 25.4627 on 448 degrees of freedom -Multiple R-squared: 0.9204, Adjusted R-squared: 0.9201 -F-statistic: 2590.2693 on 3 and 448 DF, p-value: 6.276e-247 ----- - -Again we have a model with excellent explanatory power with an R-squared of 92. Now we’ll check the model visually to see if it violates any assumptions. Our residuals should be normally distributed. We can use a histogram to verify: - -[source, groovy] -.histogram ----- -include::{sources}/TutorialSpec.groovy[tag=runs_scored_histogram, indent=0] ----- - -image::moneyball_runs_distribution.png[] - -It looks great. It’s also important to plot the predicted (or “fitted”) values against the residuals. We want to see if the model fits some values better than others, which will influence whether we can trust its predictions or not. Ideally, we want to see a cloud of random dots around zero on the y axis. - -Our Scatter class can create this plot directly from the model: - -[source, groovy] -.fitted vs residuals ----- -include::{sources}/TutorialSpec.groovy[tag=fitted_vs_residuals, indent=0] ----- - -image::moneyball_fitted_vs_residuals.png[] - -Again, the plot looks good. - -Let’s review. We’ve created a model of baseball that predicts entry into the playoffs based on batting stats, with the influence of the variables as: - -SLG & OBP -> Runs Scored -> Run Difference -> Regular Season Wins - -### Modeling Runs Allowed - -Of course, we haven’t modeled the Runs Allowed side of Run Difference. We could use pitching and field stats to do this, but the A’s cleverly used the same two variables (SLG and OBP), but now looked at how their opponent’s performed against the A’s. We could do the same as these data are encoded in the dataset as OOBP and OSLG. - -[source, groovy] -.SLG & OBP ----- -include::{sources}/TutorialSpec.groovy[tag=modeling_runs_allowed_model, indent=0] ----- - -[source, shell] -.println(runsAllowed) ----- -Residuals: - Min 1Q Median 3Q Max - -82.1479 -8.9954 0.7291 15.7773 46.4004 - -Coefficients: - Estimate Std. Error t value Pr(>|t|) -Intercept -822.7172 97.7824 -8.4138 0.0000 *** -X0 2844.9158 524.5965 5.4231 0.0000 *** -X1 1532.0857 286.8341 5.3414 0.0000 *** ---------------------------------------------------------------------- -Significance codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 - -Residual standard error: 27.7020 on 42 degrees of freedom -Multiple R-squared: 0.8985, Adjusted R-squared: 0.8936 -F-statistic: 185.8286 on 3 and 42 DF, p-value: 1.377e-21 ----- - -This model also looks good, but you’d want to look at the plots again, and do other checking as well. Checking the predictive variables for collinearity is always good. - -Finally, we can tie this all together and see how well wins is predicted when we consider both offensive and defensive stats. - -[source, groovy] ----- -include::{sources}/TutorialSpec.groovy[tag=offensive_plus_defensive_model, indent=0] ----- - -### The A's in 2001 - -For fun, I decided to see what the model predicts for the 2001 A’s. First, I got the independent variables for the A’s in that year. - -[source, groovy] -.A's in 2001 ----- -include::{sources}/TutorialSpec.groovy[tag=as_in_2001, indent=0] ----- - -[output, shell] -.output ----- - baseball.csv - Year | OOBP | OBP | OSLG | SLG | ------------------------------------------------ - 2001 | 0.308 | 0.345 | 0.38 | 0.439 | ----- - -Now we get the prediction: - -[source, groovy] -.A's in 2001 ----- -include::{sources}/TutorialSpec.groovy[tag=as_in_2001_prediction, indent=0] ----- - -[source, shell] -.output ----- -102.22837899017894 ----- - -The model predicted that the 2001 A’s would win 102 games given their slugging and On-Base stats. They won 103. \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/bar.adoc b/docs/guide/src/docs/asciidoc/plots/bar.adoc deleted file mode 100644 index bf37ffa..0000000 --- a/docs/guide/src/docs/asciidoc/plots/bar.adoc +++ /dev/null @@ -1,22 +0,0 @@ -=== Bar -:sources: ../../../../src/test/groovy/underdog/guide/plots - -==== Simple - -[source, groovy] -.simple bar ----- -include::{sources}/BarSpec.groovy[tag=simple, indent=0] ----- - -image::plots/bar_simple.png[width="75%", align=center] - -==== Histogram - -[source, groovy] -.simple histogram ----- -include::{sources}/BarSpec.groovy[tag=histogram_simple, indent=0] ----- - -image::plots/histogram_simple.png[width="75%", align=center] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/extensions.adoc b/docs/guide/src/docs/asciidoc/plots/extensions.adoc deleted file mode 100644 index 12c37e4..0000000 --- a/docs/guide/src/docs/asciidoc/plots/extensions.adoc +++ /dev/null @@ -1,18 +0,0 @@ -=== Extensions -:sources: ../../../../src/test/groovy/underdog/guide/plots - -==== Dataframe - -[source, groovy] -.dataframe extensions ----- -include::{sources}/ExtensionsSpec.groovy[tag=dataframe, indent=0] ----- - -<1> Dataframe created -<2> Plots extensions for dataframe add plots methods such as `scatter()` - - -==== Graphs - -TODO \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/graphs.adoc b/docs/guide/src/docs/asciidoc/plots/graphs.adoc deleted file mode 100644 index bd6a477..0000000 --- a/docs/guide/src/docs/asciidoc/plots/graphs.adoc +++ /dev/null @@ -1,61 +0,0 @@ -=== Graphs -:sources: ../../../../src/test/groovy/underdog/guide/plots - -==== Simple - -image::plots/graphs_simple.png[width="50%", align=center] - -[source, groovy] -.simple graph ----- -include::{sources}/GraphSpec.groovy[tag=simple, indent=0] ----- - -==== Directed graph - -image::plots/graphs_directed.png[width="50%", align=center] - -[source, groovy] -.directed graph ----- -include::{sources}/GraphSpec.groovy[tag=directed, indent=0] ----- - -==== Edge labels - -image::plots/graphs_edge_labels.png[width="50%", align=center] - -[source, groovy] -.edge labels ----- -include::{sources}/GraphSpec.groovy[tag=show_labels, indent=0] ----- - -==== Showing paths - -image::plots/graphs_paths.png[width="50%", align=center] - -[source, groovy] -.show paths ----- -include::{sources}/GraphSpec.groovy[tag=graph_path, indent=0] ----- - -==== Graph domain - -image::plots/graphs_domain.png[width="40%", align=center] - -[source, groovy] -.domain classes ----- -include::{sources}/GraphSpec.groovy[tag=graph_domain, indent=0] ----- - - -==== Customize - -[source, groovy] -.customization ----- -include::{sources}/GraphSpec.groovy[tag=customize, indent=0] ----- \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/guidelines.adoc b/docs/guide/src/docs/asciidoc/plots/guidelines.adoc deleted file mode 100644 index 409e223..0000000 --- a/docs/guide/src/docs/asciidoc/plots/guidelines.adoc +++ /dev/null @@ -1,37 +0,0 @@ -=== Guidelines - -==== Echarts DSL - -Underdog plots uses the https://echarts.apache.org[Apache Echarts] under the hood, so the idea is to try to be able to render whatever is possible in Echarts. To accomplish that this project creates a Groovy DSL mimicking the Echarts Option object. You can access the DSL when link:#_customizing_chart[customizing the chart] - -At the moment the support of the Echarts Option object is limited but we aim to improve that overtime. - -==== Basic properties -**For every chart** we must provide methods containing the following properties: - -- data entry as list of numbers -- data entry as Series -- chart title -- chart subtitle - -Apart from that every plot has a `customize(Closure)` method to be able to customize the chart following Echarts documentation using a Groovy DSL. - -When **the method is receiving data as List** instances: - -- X coordinate label (by default is X) -- Y coordinate label (by default is Y) - -In **methods receiving Series objects** the name of the X and Y coordinate will be taken from the Series' name passed as parameter. - -In general we'd like to be able to use the plots library for anyone, that's why any chart should be able to receive its data from a plain java list. - -Apart from those mandatory properties, there could be extra properties added to specific charts depending on how practical these properties are when dealing which that type of charts. - -==== Return Options - -All plotting methods return a **memento.plots.charts.Options** instance which represents the Echarts Options object. - -[#_customizing_chart] -==== Customizing chart - -All default methods provide a limited setup of the chart via the mandatory attributes we saw previously.To access the full Groovy Echarts DSL we can always access the **Options#customize(Closure)** method rendering the chart calling the **Options#show()**. \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/index.adoc b/docs/guide/src/docs/asciidoc/plots/index.adoc deleted file mode 100644 index 594c2ff..0000000 --- a/docs/guide/src/docs/asciidoc/plots/index.adoc +++ /dev/null @@ -1,34 +0,0 @@ -[#_plots] -== Plots - -The Underdog plots module renders different types of charts in order to visualize the data analysis done with the rest of Underdog's modules. - -.Underdog plots -[cols="1a,1a,1a,1a", frame="none"] -|=== -| image::plots/line_n_lines_share_x.png[align=center] -| image::plots/scatter_simple.png[align=center] -| image::plots/graphs_paths.png[align=center] -| image::plots/bar_simple.png[align=center] - -| image::plots/pie_simple.png[align=center] -| image::plots/radar_simple.png[align=center] -| -| -|=== - -include::guidelines.adoc[] - -include::line.adoc[] - -include::bar.adoc[] - -include::scatter.adoc[] - -include::graphs.adoc[] - -include::pie.adoc[] - -include::radar.adoc[] - -include::extensions.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/line.adoc b/docs/guide/src/docs/asciidoc/plots/line.adoc deleted file mode 100644 index 8f546be..0000000 --- a/docs/guide/src/docs/asciidoc/plots/line.adoc +++ /dev/null @@ -1,63 +0,0 @@ -=== Line -:sources: ../../../../src/test/groovy/underdog/guide/plots - -==== Simple - -Here is a simple line chart: - -[source, groovy] -.simple line ----- -include::{sources}/LineSpec.groovy[tag=simple, indent=0] ----- - -<1> You can use a **range or a list** for X axis -<2> You can use a **range or a list** for the Y axis -<3> Optional attributes - -image::plots/line_simple.png[width="75%", align=center] - -There are methods adapted for using Underdog's Series objects: - -[source, groovy] -.Series ----- -include::{sources}/LineSpec.groovy[tag=line_from_series, indent=0] ----- - -<1> using **year** series for X axis -<2> renaming series to **Wins X Years** and using it for Y axis - -image::plots/line_series.png[width="75%", align=center] - -In any method using Underdog's Series there is no attribute for changing the xLabel or the yLabel as it takes the Series' name. To change the label you can rename the Series' name as mentioned in the example. - -==== N-lines - -[source, groovy] -.lines using collections ----- -include::{sources}/LineSpec.groovy[tag=n_lines, indent=0] ----- - -<1> A list of lists of 2 elements `\[[x1, y1], [x2, y2],..., [xn, yn]]` -<2> Using `[listX, listY]transpose()` == `\[[x1, y1], [x2, y2],..., [xn, yn]]` - -image::plots/line_n_lines.png[width="75%", align=center] - -==== Customize - -As in any chart once we've created our Options object and before calling `show()` we can use the `Options#customize()` method to customize the chart using Groovy's Echart DSL. - -[source, groovy] -.Customize ----- -include::{sources}/LineSpec.groovy[tag=customize, indent=0] ----- - -<1> Adding legend in the top right corner -<2> Adding tooltip of type `axis` - -In this occasion we are adding the chart legend and positioning it to the top-right side of the chart. - -image::plots/line_customize.png[width="75%", align=center] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/pie.adoc b/docs/guide/src/docs/asciidoc/plots/pie.adoc deleted file mode 100644 index 3d1a52a..0000000 --- a/docs/guide/src/docs/asciidoc/plots/pie.adoc +++ /dev/null @@ -1,59 +0,0 @@ -=== Pie -:sources: ../../../../src/test/groovy/underdog/guide/plots - -According to Wikipedia: - -"A pie chart (or a circle chart) is a circular statistical graphic which is divided into slices to illustrate numerical proportion." --- Wikipedia, https://en.wikipedia.org/wiki/Pie_chart - -In a pie chart, the arc length of each slice (and consequently its central angle and area) is proportional to the quantity it represents. - -==== Simple - -[source, groovy] -.building pie ----- -include::{sources}/PieSpec.groovy[tag=simple, indent=0] ----- - -<1> slice labels -<2> slice values - -image::plots/pie_simple.png[align=center, width="50%"] - -==== Color mapping - -[source, groovy] -.color mapping ----- -include::{sources}/PieSpec.groovy[tag=color_mapping, indent=0] ----- - -<1> Colors matching the labels -<2> Labels -<3> Values -<4> Passing color mappings - -image::plots/pie_color_mapping.png[align=center, width="60%"] - -==== Dataframe - -[source, groovy] -.dataframe ----- -include::{sources}/PieSpec.groovy[tag=dataframe, indent=0] ----- - -image::plots/pie_dataframe.png[align=center, width="50%"] - -[source, groovy] -.series ----- -include::{sources}/PieSpec.groovy[tag=series, indent=0] ----- - -<1> using series **A** and renaming it to **names** -<2> using series **B** and renaming it to **values** -<3> using series **C** and renaming it to **colors** - -image::plots/pie_series.png[align=center, width="50%"] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/radar.adoc b/docs/guide/src/docs/asciidoc/plots/radar.adoc deleted file mode 100644 index 80410f1..0000000 --- a/docs/guide/src/docs/asciidoc/plots/radar.adoc +++ /dev/null @@ -1,19 +0,0 @@ -=== Radar -:sources: ../../../../src/test/groovy/underdog/guide/plots - -"A radar chart is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point" --- https://en.wikipedia.org/wiki/Radar_chart - -==== Simple - -[source, groovy] -.simple ----- -include::{sources}/RadarSpec.groovy[tag=simple, indent=0] ----- - -<1> Name of the categories -<2> Maximum values for each category -<3> Actual value for each category - -image::plots/radar_simple.png[width="50%", align=center] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/plots/scatter.adoc b/docs/guide/src/docs/asciidoc/plots/scatter.adoc deleted file mode 100644 index b189705..0000000 --- a/docs/guide/src/docs/asciidoc/plots/scatter.adoc +++ /dev/null @@ -1,27 +0,0 @@ -=== Scatter -:sources: ../../../../src/test/groovy/underdog/guide/plots - -==== Simple - -[source, groovy] -.simple line ----- -include::{sources}/ScatterSpec.groovy[tag=simple, indent=0] ----- - -<1> You can use a **range or a list** for X axis -<2> You can use a **range or a list** for the Y axis -<3> Optional attributes - -image::plots/scatter_simple.png[width="75%", align=center] - -Here's the same example but using Underdog's series for X and Y axes. Given the dataframe instance `df` and series `xs` and `ys`: - -[source, groovy] -.simple line ----- -include::{sources}/ScatterSpec.groovy[tag=simple_series, indent=0] ----- - -<1> using a series for x axis and renaming it to X -<2> using another series for y axis and renaming it to Y \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/ta/index.adoc b/docs/guide/src/docs/asciidoc/ta/index.adoc deleted file mode 100644 index a77983a..0000000 --- a/docs/guide/src/docs/asciidoc/ta/index.adoc +++ /dev/null @@ -1,5 +0,0 @@ -== Technical Analysis (TA) - -The technical analysis module is a wrapper over the link:https://ta4j.github.io/ta4j-wiki/[ta4j] java library. It adds some extension modules to the existent classes so that it makes easier to play with technical indicators and rules and **integrate with Underdog's dataframes and series**. - -include::tutorial.adoc[] \ No newline at end of file diff --git a/docs/guide/src/docs/asciidoc/ta/tutorial.adoc b/docs/guide/src/docs/asciidoc/ta/tutorial.adoc deleted file mode 100644 index b74c684..0000000 --- a/docs/guide/src/docs/asciidoc/ta/tutorial.adoc +++ /dev/null @@ -1,201 +0,0 @@ -=== Tutorial -:sources: ../../../../src/test/groovy/underdog/guide/ta - -.Note -**** -This getting started section mimics the link:https://ta4j.github.io/ta4j-wiki/Getting-started.html[getting started] section steps from link:https://ta4j.github.io/ta4j-wiki/[Ta4j wiki] but using underdog-ta module. You can compare both entries to see the differences. -**** - -==== Modules - -To be able to follow the tutorial you should add the following modules to your gradle project: - -[source, groovy] -.gradle ----- -implementation 'com.github.grooviter:underdog-ta:VERSION' -implementation 'com.github.grooviter:underdog-plots:VERSION' ----- - -==== BarSeries vs DataFrame - -It's important to start defining some concepts. - -- BarSeries: Ta4j's BarSeries is like a dataframe containing series (columns) such as open price, lower price, close price, and volume data. - -- DataFrame: Underdog's dataframe which is composed of different Series (columns), Each Series or column is like an array of objects. - -As this module integrates Ta4j with Underdog there will be methods which converts from BarSeries to DataFrame and vice versa. - -==== Loading data - -In this example we are using Underdog to load stock quotes from a csv file and create a DataFrame: - -[source, groovy] -.stock quotes from csv ----- -include::{sources}/GettingStartedSpec.groovy[tag=loading_data, indent=0] ----- - -<1> The file path where the csv file can be found -<2> The format of the dates included in the file. This will help the csv reader to parse those dates and create instances of **java.time.LocalDate**. - -IMPORTANT: If dates are not treated as dates the csv reader will consider them as strings. That will cause you problems when converting an Underdog's DataFrame to Ta4j's BarSeries. - -Which outputs something like the following (the prices and volume are truncated here to make it look good). - -[source, shell] -.output ----- - stock_quotes_10_years.csv - Date | Adj Close | Close | High | Low | Open | Volume | ------------------------------------------------------------------------------------------ - 2014-12-05 00:00:00+00:00 | 0.50 | 0.52 | 0.52 | 0.52 | 0.52 | 165680 | ----- - -In order to successfully convert the dataframe to a bar series the name of the series (columns) should match to the expected ones which are: DATE, CLOSE, HIGH, LOW, OPEN, VOLUME. - -[source, groovy] -.renaming ----- -include::{sources}/GettingStartedSpec.groovy[tag=renaming_cols, indent=0] ----- - -<1> Removing the **Adj Close** column as it doesn't matches any of the bar series columns -<2> Renaming all remaining columns to upper case to match the expected column names in the bar series. - -[source, shell] -.output ----- - stock_quotes_10_years.csv - DATE | CLOSE | HIGH | LOW | OPEN | VOLUME | ---------------------------------------------------------------------------- - 2014-12-05 00:00:00+00:00 | 0.52 | 0.52 | 0.52 | 0.52 | 165680 | ----- - -==== DataFrame to BarSeries - -Because we need to convert the dataframe to a BarSeries in order to create technical analysis rules: - -[source, groovy] -.to bar series ----- -include::{sources}/GettingStartedSpec.groovy[tag=convert_to_bar_series, indent=0] ----- - -Now we can operate with the bar series. - -==== Indicators - -Now we can start creating some indicators and metrics. This time we are getting metrics based on the closing price indicator: - -[source, groovy] -.indicators ----- -include::{sources}/GettingStartedSpec.groovy[tag=building_indicators,indent=0] ----- - -[source, shell] -.output ----- -5-bars-SMA value at the 42nd index: 0.503899997472763 ----- - -==== Building a trading strategy - -Now that we've got a couple of indicators ready lets build a strategy. Strategies are made of two trading rules: one for entry (buying), the other for exit (selling). - -[source,groovy] -.strategy base ----- -include::{sources}/GettingStartedSpec.groovy[tag=strategy_base,indent=0] ----- - -We can use Groovy's syntax to refactor the rules a little bit: - -[source,groovy] -.strategy using operators ----- -include::{sources}/GettingStartedSpec.groovy[tag=strategy_or,indent=0] ----- - -In Groovy **|** and **&** represent calls to methods **or** and **and** of any object (any object having those methods implemented). So if your object has these methods, you can substitute your method call by the operators. - -==== Backtesting - -What is backtesting ? According to Investopedia: - -"Backtesting is the general method for seeing how well a strategy or model would have done after the fact. It assesses the viability of a trading strategy by discovering how it would play out using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward" --- Investopedia, https://www.investopedia.com/terms/b/backtesting.asp - -The backtest step is pretty simple: - -[source, groovy] -.backtesting ----- -include::{sources}/GettingStartedSpec.groovy[tag=backtesting_base, indent=0] ----- - -[source, shell] -.output ----- -Number of positions (trades) for our strategy: 57 ----- - -For many scenarios we can run a base strategy backtesting by just executing the **run** method in the BarSeries object and pass directly the entry (buying) rule and the exit (selling) rule: - -[source, groovy] -.backtesting ----- -include::{sources}/GettingStartedSpec.groovy[tag=backtesting_extension, indent=0] ----- - -We can see this visually. Follow-up showing only trades from 2024-04-01: - -[source, groovy] -.plotting trades ----- -include::{sources}/GettingStartedSpec.groovy[tag=show_trades,indent=0] ----- - -image::ta/getting_started_trades.png[align=center] - -We can also visualize winning vs losing positions. - -[source, groovy] -.winning vs losing ----- -include::{sources}/GettingStartedSpec.groovy[tag=show_positions,indent=0] ----- - -image::ta/getting_started_winners.png[align=center] - -==== Analyzing our results - -Here is how we can analyze the results of our backtest: - -[source, groovy] -.analysis ----- -include::{sources}/GettingStartedSpec.groovy[tag=analysis_base, indent=0] ----- - -[source, shell] -.output ----- -Winning positions ratio: 0.54385964912280701754385964912281 -Return over Max Drawdown: 3.4519153297405649777237484258582 -Our return vs buy-and-hold return: 0.0040904249296843023287166775087440 ----- - -Showing these metrics in a chart: - -[source, groovy] -.radar ----- -include::{sources}/GettingStartedSpec.groovy[tag=analysis_radar,indent=0] ----- - -Which displays: - -image::ta/getting_started_radar.png[align=center, width="60%"] diff --git a/docs/guide/src/docs/resources/images/graphs/distances_cities.png b/docs/guide/src/docs/resources/images/graphs/distances_cities.png deleted file mode 100644 index 276ba68ed8a54fc48d6cd20d43687417f4870b5b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 23805 zcmce8g8PUKX?a>xar05BID1iXb8_=iaXuB~ekRECluAvRN>W zONndwyx(2$_BXQ4=C~cz8n*lNvGiLS>%3bk>wwDYOhT$fGCUR=w_76kbIUF}N1CzT z^1d^Np`O{}Z{3T==hYEku(y@vlENg>(kumrr7OQ!E2d3;(|Ymx<7iV5Q(9rkJdgWF z|HV7A7bC)>y06D%M+H=qP(rDKqobLy4sB*Afik1>x zP4U+jQThYa`Gs*{!OhRn!gLUl)lqAzVSJhh3al(iPZ(&19$|DSO@|K5-WB0Pt(ACP4*9~hV2DPVcGG7yAHfP8d`6T zPh+Cpa3neVR#6ee{MsowYU6206+g6g;KrhLoT)t}og&d(B*Iazw?z49 zi&@Ra?8ypX%6hWr%Vz>&3AmXLVAyO(lAMDkLhHSFY&S0PQGM*z1`}RSL{HmO#|Yg{ z?HEvGSM7whddIyO1)JOCor5GRD1;uf&D2eXBGTWTQyZX2a>jsn&RrF&N*bPT{JZrV 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