").addClass(errClass).css("position", "absolute")
- .css("top", el.offsetTop)
- .css("left", el.offsetLeft)
- // setting width can push out the page size, forcing otherwise
- // unnecessary scrollbars to appear and making it impossible for
- // the element to shrink; so use max-width instead
- .css("maxWidth", el.offsetWidth)
- .css("height", el.offsetHeight);
- errorDiv.text(err.message);
- $el.after(errorDiv);
-
- // Really dumb way to keep the size/position of the error in sync with
- // the parent element as the window is resized or whatever.
- var intId = setInterval(function() {
- if (!errorDiv[0].parentElement) {
- clearInterval(intId);
- return;
- }
- errorDiv
- .css("top", el.offsetTop)
- .css("left", el.offsetLeft)
- .css("maxWidth", el.offsetWidth)
- .css("height", el.offsetHeight);
- }, 500);
- }
- }
- },
- clearError: function(el) {
- var $el = $(el);
- var display = $el.data("restore-display-mode");
- $el.data("restore-display-mode", null);
-
- if (display === "inline" || display === "inline-block") {
- if (display)
- $el.css("display", display);
- $(el.nextSibling).filter(".htmlwidgets-error").remove();
- } else if (display === "block"){
- $el.css("visibility", "inherit");
- $(el.nextSibling).filter(".htmlwidgets-error").remove();
- }
- },
- sizing: {}
- };
-
- // Called by widget bindings to register a new type of widget. The definition
- // object can contain the following properties:
- // - name (required) - A string indicating the binding name, which will be
- // used by default as the CSS classname to look for.
- // - initialize (optional) - A function(el) that will be called once per
- // widget element; if a value is returned, it will be passed as the third
- // value to renderValue.
- // - renderValue (required) - A function(el, data, initValue) that will be
- // called with data. Static contexts will cause this to be called once per
- // element; Shiny apps will cause this to be called multiple times per
- // element, as the data changes.
- window.HTMLWidgets.widget = function(definition) {
- if (!definition.name) {
- throw new Error("Widget must have a name");
- }
- if (!definition.type) {
- throw new Error("Widget must have a type");
- }
- // Currently we only support output widgets
- if (definition.type !== "output") {
- throw new Error("Unrecognized widget type '" + definition.type + "'");
- }
- // TODO: Verify that .name is a valid CSS classname
-
- // Support new-style instance-bound definitions. Old-style class-bound
- // definitions have one widget "object" per widget per type/class of
- // widget; the renderValue and resize methods on such widget objects
- // take el and instance arguments, because the widget object can't
- // store them. New-style instance-bound definitions have one widget
- // object per widget instance; the definition that's passed in doesn't
- // provide renderValue or resize methods at all, just the single method
- // factory(el, width, height)
- // which returns an object that has renderValue(x) and resize(w, h).
- // This enables a far more natural programming style for the widget
- // author, who can store per-instance state using either OO-style
- // instance fields or functional-style closure variables (I guess this
- // is in contrast to what can only be called C-style pseudo-OO which is
- // what we required before).
- if (definition.factory) {
- definition = createLegacyDefinitionAdapter(definition);
- }
-
- if (!definition.renderValue) {
- throw new Error("Widget must have a renderValue function");
- }
-
- // For static rendering (non-Shiny), use a simple widget registration
- // scheme. We also use this scheme for Shiny apps/documents that also
- // contain static widgets.
- window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
- // Merge defaults into the definition; don't mutate the original definition.
- var staticBinding = extend({}, defaults, definition);
- overrideMethod(staticBinding, "find", function(superfunc) {
- return function(scope) {
- var results = superfunc(scope);
- // Filter out Shiny outputs, we only want the static kind
- return filterByClass(results, "html-widget-output", false);
- };
- });
- window.HTMLWidgets.widgets.push(staticBinding);
-
- if (shinyMode) {
- // Shiny is running. Register the definition with an output binding.
- // The definition itself will not be the output binding, instead
- // we will make an output binding object that delegates to the
- // definition. This is because we foolishly used the same method
- // name (renderValue) for htmlwidgets definition and Shiny bindings
- // but they actually have quite different semantics (the Shiny
- // bindings receive data that includes lots of metadata that it
- // strips off before calling htmlwidgets renderValue). We can't
- // just ignore the difference because in some widgets it's helpful
- // to call this.renderValue() from inside of resize(), and if
- // we're not delegating, then that call will go to the Shiny
- // version instead of the htmlwidgets version.
-
- // Merge defaults with definition, without mutating either.
- var bindingDef = extend({}, defaults, definition);
-
- // This object will be our actual Shiny binding.
- var shinyBinding = new Shiny.OutputBinding();
-
- // With a few exceptions, we'll want to simply use the bindingDef's
- // version of methods if they are available, otherwise fall back to
- // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
- // methods in the future, and we want them to be overrideable by
- // HTMLWidget binding definitions, then we'll need to add them to this
- // list.
- delegateMethod(shinyBinding, bindingDef, "getId");
- delegateMethod(shinyBinding, bindingDef, "onValueChange");
- delegateMethod(shinyBinding, bindingDef, "onValueError");
- delegateMethod(shinyBinding, bindingDef, "renderError");
- delegateMethod(shinyBinding, bindingDef, "clearError");
- delegateMethod(shinyBinding, bindingDef, "showProgress");
-
- // The find, renderValue, and resize are handled differently, because we
- // want to actually decorate the behavior of the bindingDef methods.
-
- shinyBinding.find = function(scope) {
- var results = bindingDef.find(scope);
-
- // Only return elements that are Shiny outputs, not static ones
- var dynamicResults = results.filter(".html-widget-output");
-
- // It's possible that whatever caused Shiny to think there might be
- // new dynamic outputs, also caused there to be new static outputs.
- // Since there might be lots of different htmlwidgets bindings, we
- // schedule execution for later--no need to staticRender multiple
- // times.
- if (results.length !== dynamicResults.length)
- scheduleStaticRender();
-
- return dynamicResults;
- };
-
- // Wrap renderValue to handle initialization, which unfortunately isn't
- // supported natively by Shiny at the time of this writing.
-
- shinyBinding.renderValue = function(el, data) {
- Shiny.renderDependencies(data.deps);
- // Resolve strings marked as javascript literals to objects
- if (!(data.evals instanceof Array)) data.evals = [data.evals];
- for (var i = 0; data.evals && i < data.evals.length; i++) {
- window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
- }
- if (!bindingDef.renderOnNullValue) {
- if (data.x === null) {
- el.style.visibility = "hidden";
- return;
- } else {
- el.style.visibility = "inherit";
- }
- }
- if (!elementData(el, "initialized")) {
- initSizing(el);
-
- elementData(el, "initialized", true);
- if (bindingDef.initialize) {
- var result = bindingDef.initialize(el, el.offsetWidth,
- el.offsetHeight);
- elementData(el, "init_result", result);
- }
- }
- bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
- evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
- };
-
- // Only override resize if bindingDef implements it
- if (bindingDef.resize) {
- shinyBinding.resize = function(el, width, height) {
- // Shiny can call resize before initialize/renderValue have been
- // called, which doesn't make sense for widgets.
- if (elementData(el, "initialized")) {
- bindingDef.resize(el, width, height, elementData(el, "init_result"));
- }
- };
- }
-
- Shiny.outputBindings.register(shinyBinding, bindingDef.name);
- }
- };
-
- var scheduleStaticRenderTimerId = null;
- function scheduleStaticRender() {
- if (!scheduleStaticRenderTimerId) {
- scheduleStaticRenderTimerId = setTimeout(function() {
- scheduleStaticRenderTimerId = null;
- window.HTMLWidgets.staticRender();
- }, 1);
- }
- }
-
- // Render static widgets after the document finishes loading
- // Statically render all elements that are of this widget's class
- window.HTMLWidgets.staticRender = function() {
- var bindings = window.HTMLWidgets.widgets || [];
- forEach(bindings, function(binding) {
- var matches = binding.find(document.documentElement);
- forEach(matches, function(el) {
- var sizeObj = initSizing(el, binding);
-
- if (hasClass(el, "html-widget-static-bound"))
- return;
- el.className = el.className + " html-widget-static-bound";
-
- var initResult;
- if (binding.initialize) {
- initResult = binding.initialize(el,
- sizeObj ? sizeObj.getWidth() : el.offsetWidth,
- sizeObj ? sizeObj.getHeight() : el.offsetHeight
- );
- elementData(el, "init_result", initResult);
- }
-
- if (binding.resize) {
- var lastSize = {
- w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
- h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
- };
- var resizeHandler = function(e) {
- var size = {
- w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
- h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
- };
- if (size.w === 0 && size.h === 0)
- return;
- if (size.w === lastSize.w && size.h === lastSize.h)
- return;
- lastSize = size;
- binding.resize(el, size.w, size.h, initResult);
- };
-
- on(window, "resize", resizeHandler);
-
- // This is needed for cases where we're running in a Shiny
- // app, but the widget itself is not a Shiny output, but
- // rather a simple static widget. One example of this is
- // an rmarkdown document that has runtime:shiny and widget
- // that isn't in a render function. Shiny only knows to
- // call resize handlers for Shiny outputs, not for static
- // widgets, so we do it ourselves.
- if (window.jQuery) {
- window.jQuery(document).on(
- "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
- resizeHandler
- );
- window.jQuery(document).on(
- "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
- resizeHandler
- );
- }
-
- // This is needed for the specific case of ioslides, which
- // flips slides between display:none and display:block.
- // Ideally we would not have to have ioslide-specific code
- // here, but rather have ioslides raise a generic event,
- // but the rmarkdown package just went to CRAN so the
- // window to getting that fixed may be long.
- if (window.addEventListener) {
- // It's OK to limit this to window.addEventListener
- // browsers because ioslides itself only supports
- // such browsers.
- on(document, "slideenter", resizeHandler);
- on(document, "slideleave", resizeHandler);
- }
- }
-
- var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
- if (scriptData) {
- var data = JSON.parse(scriptData.textContent || scriptData.text);
- // Resolve strings marked as javascript literals to objects
- if (!(data.evals instanceof Array)) data.evals = [data.evals];
- for (var k = 0; data.evals && k < data.evals.length; k++) {
- window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
- }
- binding.renderValue(el, data.x, initResult);
- evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
- }
- });
- });
-
- invokePostRenderHandlers();
- }
-
-
- function has_jQuery3() {
- if (!window.jQuery) {
- return false;
- }
- var $version = window.jQuery.fn.jquery;
- var $major_version = parseInt($version.split(".")[0]);
- return $major_version >= 3;
- }
-
- /*
- / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
- / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
- / really means $(setTimeout(fn)).
- / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
- /
- / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
- / one tick later than it did before, which means staticRender() is
- / called renderValue() earlier than (advanced) widget authors might be expecting.
- / https://github.com/rstudio/shiny/issues/2630
- /
- / For a concrete example, leaflet has some methods (e.g., updateBounds)
- / which reference Shiny methods registered in initShiny (e.g., setInputValue).
- / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
- / delay execution of those methods (until Shiny methods are ready)
- / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
- /
- / Ideally widget authors wouldn't need to use this setTimeout() hack that
- / leaflet uses to call Shiny methods on a staticRender(). In the long run,
- / the logic initShiny should be broken up so that method registration happens
- / right away, but binding happens later.
- */
- function maybeStaticRenderLater() {
- if (shinyMode && has_jQuery3()) {
- window.jQuery(window.HTMLWidgets.staticRender);
- } else {
- window.HTMLWidgets.staticRender();
- }
- }
-
- if (document.addEventListener) {
- document.addEventListener("DOMContentLoaded", function() {
- document.removeEventListener("DOMContentLoaded", arguments.callee, false);
- maybeStaticRenderLater();
- }, false);
- } else if (document.attachEvent) {
- document.attachEvent("onreadystatechange", function() {
- if (document.readyState === "complete") {
- document.detachEvent("onreadystatechange", arguments.callee);
- maybeStaticRenderLater();
- }
- });
- }
-
-
- window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
- // If no key, default to the first item
- if (typeof(key) === "undefined")
- key = 1;
-
- var link = document.getElementById(depname + "-" + key + "-attachment");
- if (!link) {
- throw new Error("Attachment " + depname + "/" + key + " not found in document");
- }
- return link.getAttribute("href");
- };
-
- window.HTMLWidgets.dataframeToD3 = function(df) {
- var names = [];
- var length;
- for (var name in df) {
- if (df.hasOwnProperty(name))
- names.push(name);
- if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
- throw new Error("All fields must be arrays");
- } else if (typeof(length) !== "undefined" && length !== df[name].length) {
- throw new Error("All fields must be arrays of the same length");
- }
- length = df[name].length;
- }
- var results = [];
- var item;
- for (var row = 0; row < length; row++) {
- item = {};
- for (var col = 0; col < names.length; col++) {
- item[names[col]] = df[names[col]][row];
- }
- results.push(item);
- }
- return results;
- };
-
- window.HTMLWidgets.transposeArray2D = function(array) {
- if (array.length === 0) return array;
- var newArray = array[0].map(function(col, i) {
- return array.map(function(row) {
- return row[i]
- })
- });
- return newArray;
- };
- // Split value at splitChar, but allow splitChar to be escaped
- // using escapeChar. Any other characters escaped by escapeChar
- // will be included as usual (including escapeChar itself).
- function splitWithEscape(value, splitChar, escapeChar) {
- var results = [];
- var escapeMode = false;
- var currentResult = "";
- for (var pos = 0; pos < value.length; pos++) {
- if (!escapeMode) {
- if (value[pos] === splitChar) {
- results.push(currentResult);
- currentResult = "";
- } else if (value[pos] === escapeChar) {
- escapeMode = true;
- } else {
- currentResult += value[pos];
- }
- } else {
- currentResult += value[pos];
- escapeMode = false;
- }
- }
- if (currentResult !== "") {
- results.push(currentResult);
- }
- return results;
- }
- // Function authored by Yihui/JJ Allaire
- window.HTMLWidgets.evaluateStringMember = function(o, member) {
- var parts = splitWithEscape(member, '.', '\\');
- for (var i = 0, l = parts.length; i < l; i++) {
- var part = parts[i];
- // part may be a character or 'numeric' member name
- if (o !== null && typeof o === "object" && part in o) {
- if (i == (l - 1)) { // if we are at the end of the line then evalulate
- if (typeof o[part] === "string")
- o[part] = tryEval(o[part]);
- } else { // otherwise continue to next embedded object
- o = o[part];
- }
- }
- }
- };
-
- // Retrieve the HTMLWidget instance (i.e. the return value of an
- // HTMLWidget binding's initialize() or factory() function)
- // associated with an element, or null if none.
- window.HTMLWidgets.getInstance = function(el) {
- return elementData(el, "init_result");
- };
-
- // Finds the first element in the scope that matches the selector,
- // and returns the HTMLWidget instance (i.e. the return value of
- // an HTMLWidget binding's initialize() or factory() function)
- // associated with that element, if any. If no element matches the
- // selector, or the first matching element has no HTMLWidget
- // instance associated with it, then null is returned.
- //
- // The scope argument is optional, and defaults to window.document.
- window.HTMLWidgets.find = function(scope, selector) {
- if (arguments.length == 1) {
- selector = scope;
- scope = document;
- }
-
- var el = scope.querySelector(selector);
- if (el === null) {
- return null;
- } else {
- return window.HTMLWidgets.getInstance(el);
- }
- };
-
- // Finds all elements in the scope that match the selector, and
- // returns the HTMLWidget instances (i.e. the return values of
- // an HTMLWidget binding's initialize() or factory() function)
- // associated with the elements, in an array. If elements that
- // match the selector don't have an associated HTMLWidget
- // instance, the returned array will contain nulls.
- //
- // The scope argument is optional, and defaults to window.document.
- window.HTMLWidgets.findAll = function(scope, selector) {
- if (arguments.length == 1) {
- selector = scope;
- scope = document;
- }
-
- var nodes = scope.querySelectorAll(selector);
- var results = [];
- for (var i = 0; i < nodes.length; i++) {
- results.push(window.HTMLWidgets.getInstance(nodes[i]));
- }
- return results;
- };
-
- var postRenderHandlers = [];
- function invokePostRenderHandlers() {
- while (postRenderHandlers.length) {
- var handler = postRenderHandlers.shift();
- if (handler) {
- handler();
- }
- }
- }
-
- // Register the given callback function to be invoked after the
- // next time static widgets are rendered.
- window.HTMLWidgets.addPostRenderHandler = function(callback) {
- postRenderHandlers.push(callback);
- };
-
- // Takes a new-style instance-bound definition, and returns an
- // old-style class-bound definition. This saves us from having
- // to rewrite all the logic in this file to accomodate both
- // types of definitions.
- function createLegacyDefinitionAdapter(defn) {
- var result = {
- name: defn.name,
- type: defn.type,
- initialize: function(el, width, height) {
- return defn.factory(el, width, height);
- },
- renderValue: function(el, x, instance) {
- return instance.renderValue(x);
- },
- resize: function(el, width, height, instance) {
- return instance.resize(width, height);
- }
- };
-
- if (defn.find)
- result.find = defn.find;
- if (defn.renderError)
- result.renderError = defn.renderError;
- if (defn.clearError)
- result.clearError = defn.clearError;
-
- return result;
- }
-})();
-
diff --git a/docs/dev/articles/articles/Models_files/jquery-1.12.4/LICENSE.txt b/docs/dev/articles/articles/Models_files/jquery-1.12.4/LICENSE.txt
deleted file mode 100644
index 5bf4f5e68..000000000
--- a/docs/dev/articles/articles/Models_files/jquery-1.12.4/LICENSE.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-Copyright 2005, 2014 jQuery Foundation and other contributors,
-https://jquery.org/
-
-This software consists of voluntary contributions made by many
-individuals. For exact contribution history, see the revision history
-available at https://github.com/jquery/jquery
-
-The following license applies to all parts of this software except as
-documented below:
-
-====
-
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of this software and associated documentation files (the
-"Software"), to deal in the Software without restriction, including
-without limitation the rights to use, copy, modify, merge, publish,
-distribute, sublicense, and/or sell copies of the Software, and to
-permit persons to whom the Software is furnished to do so, subject to
-the following conditions:
-
-The above copyright notice and this permission notice shall be
-included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
-LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
-WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
-====
-
-All files located in the node_modules and external directories are
-externally maintained libraries used by this software which have their
-own licenses; we recommend you read them, as their terms may differ from
-the terms above.
diff --git a/docs/dev/articles/articles/Models_files/jquery-1.12.4/jquery.min.js b/docs/dev/articles/articles/Models_files/jquery-1.12.4/jquery.min.js
deleted file mode 100644
index e83647587..000000000
--- a/docs/dev/articles/articles/Models_files/jquery-1.12.4/jquery.min.js
+++ /dev/null
@@ -1,5 +0,0 @@
-/*! jQuery v1.12.4 | (c) jQuery Foundation | jquery.org/license */
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deleted file mode 100644
index e6fbbfc44..000000000
--- a/docs/dev/articles/articles/Models_files/kePrint-0.0.1/kePrint.js
+++ /dev/null
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deleted file mode 100644
index 3b7014ba2..000000000
--- a/docs/dev/articles/articles/Models_files/nouislider-7.0.10/jquery.nouislider.min.css
+++ /dev/null
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diff --git a/docs/dev/articles/articles/Models_files/nouislider-7.0.10/jquery.nouislider.min.js b/docs/dev/articles/articles/Models_files/nouislider-7.0.10/jquery.nouislider.min.js
deleted file mode 100644
index 03b5244ab..000000000
--- a/docs/dev/articles/articles/Models_files/nouislider-7.0.10/jquery.nouislider.min.js
+++ /dev/null
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diff --git a/docs/dev/articles/articles/Models_files/selectize-0.12.0/selectize.bootstrap3.css b/docs/dev/articles/articles/Models_files/selectize-0.12.0/selectize.bootstrap3.css
deleted file mode 100644
index 6779260d7..000000000
--- a/docs/dev/articles/articles/Models_files/selectize-0.12.0/selectize.bootstrap3.css
+++ /dev/null
@@ -1,401 +0,0 @@
-/**
- * selectize.bootstrap3.css (v0.12.0) - Bootstrap 3 Theme
- * Copyright (c) 2013–2015 Brian Reavis & contributors
- *
- * Licensed under the Apache License, Version 2.0 (the "License"); you may not use this
- * file except in compliance with the License. You may obtain a copy of the License at:
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software distributed under
- * the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
- * ANY KIND, either express or implied. See the License for the specific language
- * governing permissions and limitations under the License.
- *
- * @author Brian Reavis
- */
-.selectize-control.plugin-drag_drop.multi > .selectize-input > div.ui-sortable-placeholder {
- visibility: visible !important;
- background: #f2f2f2 !important;
- background: rgba(0, 0, 0, 0.06) !important;
- border: 0 none !important;
- -webkit-box-shadow: inset 0 0 12px 4px #ffffff;
- box-shadow: inset 0 0 12px 4px #ffffff;
-}
-.selectize-control.plugin-drag_drop .ui-sortable-placeholder::after {
- content: '!';
- visibility: hidden;
-}
-.selectize-control.plugin-drag_drop .ui-sortable-helper {
- -webkit-box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
- box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
-}
-.selectize-dropdown-header {
- position: relative;
- padding: 3px 12px;
- border-bottom: 1px solid #d0d0d0;
- background: #f8f8f8;
- -webkit-border-radius: 4px 4px 0 0;
- -moz-border-radius: 4px 4px 0 0;
- border-radius: 4px 4px 0 0;
-}
-.selectize-dropdown-header-close {
- position: absolute;
- right: 12px;
- top: 50%;
- color: #333333;
- opacity: 0.4;
- margin-top: -12px;
- line-height: 20px;
- font-size: 20px !important;
-}
-.selectize-dropdown-header-close:hover {
- color: #000000;
-}
-.selectize-dropdown.plugin-optgroup_columns .optgroup {
- border-right: 1px solid #f2f2f2;
- border-top: 0 none;
- float: left;
- -webkit-box-sizing: border-box;
- -moz-box-sizing: border-box;
- box-sizing: border-box;
-}
-.selectize-dropdown.plugin-optgroup_columns .optgroup:last-child {
- border-right: 0 none;
-}
-.selectize-dropdown.plugin-optgroup_columns .optgroup:before {
- display: none;
-}
-.selectize-dropdown.plugin-optgroup_columns .optgroup-header {
- border-top: 0 none;
-}
-.selectize-control.plugin-remove_button [data-value] {
- position: relative;
- padding-right: 24px !important;
-}
-.selectize-control.plugin-remove_button [data-value] .remove {
- z-index: 1;
- /* fixes ie bug (see #392) */
- position: absolute;
- top: 0;
- right: 0;
- bottom: 0;
- width: 17px;
- text-align: center;
- font-weight: bold;
- font-size: 12px;
- color: inherit;
- text-decoration: none;
- vertical-align: middle;
- display: inline-block;
- padding: 1px 0 0 0;
- border-left: 1px solid rgba(0, 0, 0, 0);
- -webkit-border-radius: 0 2px 2px 0;
- -moz-border-radius: 0 2px 2px 0;
- border-radius: 0 2px 2px 0;
- -webkit-box-sizing: border-box;
- -moz-box-sizing: border-box;
- box-sizing: border-box;
-}
-.selectize-control.plugin-remove_button [data-value] .remove:hover {
- background: rgba(0, 0, 0, 0.05);
-}
-.selectize-control.plugin-remove_button [data-value].active .remove {
- border-left-color: rgba(0, 0, 0, 0);
-}
-.selectize-control.plugin-remove_button .disabled [data-value] .remove:hover {
- background: none;
-}
-.selectize-control.plugin-remove_button .disabled [data-value] .remove {
- border-left-color: rgba(77, 77, 77, 0);
-}
-.selectize-control {
- position: relative;
-}
-.selectize-dropdown,
-.selectize-input,
-.selectize-input input {
- color: #333333;
- font-family: inherit;
- font-size: inherit;
- line-height: 20px;
- -webkit-font-smoothing: inherit;
-}
-.selectize-input,
-.selectize-control.single .selectize-input.input-active {
- background: #ffffff;
- cursor: text;
- display: inline-block;
-}
-.selectize-input {
- border: 1px solid #cccccc;
- padding: 6px 12px;
- display: inline-block;
- width: 100%;
- overflow: hidden;
- position: relative;
- z-index: 1;
- -webkit-box-sizing: border-box;
- -moz-box-sizing: border-box;
- box-sizing: border-box;
- -webkit-box-shadow: none;
- box-shadow: none;
- -webkit-border-radius: 4px;
- -moz-border-radius: 4px;
- border-radius: 4px;
-}
-.selectize-control.multi .selectize-input.has-items {
- padding: 5px 12px 2px;
-}
-.selectize-input.full {
- background-color: #ffffff;
-}
-.selectize-input.disabled,
-.selectize-input.disabled * {
- cursor: default !important;
-}
-.selectize-input.focus {
- -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.15);
- box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.15);
-}
-.selectize-input.dropdown-active {
- -webkit-border-radius: 4px 4px 0 0;
- -moz-border-radius: 4px 4px 0 0;
- border-radius: 4px 4px 0 0;
-}
-.selectize-input > * {
- vertical-align: baseline;
- display: -moz-inline-stack;
- display: inline-block;
- zoom: 1;
- *display: inline;
-}
-.selectize-control.multi .selectize-input > div {
- cursor: pointer;
- margin: 0 3px 3px 0;
- padding: 1px 3px;
- background: #efefef;
- color: #333333;
- border: 0 solid rgba(0, 0, 0, 0);
-}
-.selectize-control.multi .selectize-input > div.active {
- background: #428bca;
- color: #ffffff;
- border: 0 solid rgba(0, 0, 0, 0);
-}
-.selectize-control.multi .selectize-input.disabled > div,
-.selectize-control.multi .selectize-input.disabled > div.active {
- color: #808080;
- background: #ffffff;
- border: 0 solid rgba(77, 77, 77, 0);
-}
-.selectize-input > input {
- display: inline-block !important;
- padding: 0 !important;
- min-height: 0 !important;
- max-height: none !important;
- max-width: 100% !important;
- margin: 0 !important;
- text-indent: 0 !important;
- border: 0 none !important;
- background: none !important;
- line-height: inherit !important;
- -webkit-user-select: auto !important;
- -webkit-box-shadow: none !important;
- box-shadow: none !important;
-}
-.selectize-input > input::-ms-clear {
- display: none;
-}
-.selectize-input > input:focus {
- outline: none !important;
-}
-.selectize-input::after {
- content: ' ';
- display: block;
- clear: left;
-}
-.selectize-input.dropdown-active::before {
- content: ' ';
- display: block;
- position: absolute;
- background: #ffffff;
- height: 1px;
- bottom: 0;
- left: 0;
- right: 0;
-}
-.selectize-dropdown {
- position: absolute;
- z-index: 10;
- border: 1px solid #d0d0d0;
- background: #ffffff;
- margin: -1px 0 0 0;
- border-top: 0 none;
- -webkit-box-sizing: border-box;
- -moz-box-sizing: border-box;
- box-sizing: border-box;
- -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
- box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
- -webkit-border-radius: 0 0 4px 4px;
- -moz-border-radius: 0 0 4px 4px;
- border-radius: 0 0 4px 4px;
-}
-.selectize-dropdown [data-selectable] {
- cursor: pointer;
- overflow: hidden;
-}
-.selectize-dropdown [data-selectable] .highlight {
- background: rgba(255, 237, 40, 0.4);
- -webkit-border-radius: 1px;
- -moz-border-radius: 1px;
- border-radius: 1px;
-}
-.selectize-dropdown [data-selectable],
-.selectize-dropdown .optgroup-header {
- padding: 3px 12px;
-}
-.selectize-dropdown .optgroup:first-child .optgroup-header {
- border-top: 0 none;
-}
-.selectize-dropdown .optgroup-header {
- color: #777777;
- background: #ffffff;
- cursor: default;
-}
-.selectize-dropdown .active {
- background-color: #f5f5f5;
- color: #262626;
-}
-.selectize-dropdown .active.create {
- color: #262626;
-}
-.selectize-dropdown .create {
- color: rgba(51, 51, 51, 0.5);
-}
-.selectize-dropdown-content {
- overflow-y: auto;
- overflow-x: hidden;
- max-height: 200px;
-}
-.selectize-control.single .selectize-input,
-.selectize-control.single .selectize-input input {
- cursor: pointer;
-}
-.selectize-control.single .selectize-input.input-active,
-.selectize-control.single .selectize-input.input-active input {
- cursor: text;
-}
-.selectize-control.single .selectize-input:after {
- content: ' ';
- display: block;
- position: absolute;
- top: 50%;
- right: 17px;
- margin-top: -3px;
- width: 0;
- height: 0;
- border-style: solid;
- border-width: 5px 5px 0 5px;
- border-color: #333333 transparent transparent transparent;
-}
-.selectize-control.single .selectize-input.dropdown-active:after {
- margin-top: -4px;
- border-width: 0 5px 5px 5px;
- border-color: transparent transparent #333333 transparent;
-}
-.selectize-control.rtl.single .selectize-input:after {
- left: 17px;
- right: auto;
-}
-.selectize-control.rtl .selectize-input > input {
- margin: 0 4px 0 -2px !important;
-}
-.selectize-control .selectize-input.disabled {
- opacity: 0.5;
- background-color: #ffffff;
-}
-.selectize-dropdown,
-.selectize-dropdown.form-control {
- height: auto;
- padding: 0;
- margin: 2px 0 0 0;
- z-index: 1000;
- background: #ffffff;
- border: 1px solid #cccccc;
- border: 1px solid rgba(0, 0, 0, 0.15);
- -webkit-border-radius: 4px;
- -moz-border-radius: 4px;
- border-radius: 4px;
- -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
- box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
-}
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diff --git a/docs/dev/articles/articles/Models_files/selectize-0.12.0/selectize.min.js b/docs/dev/articles/articles/Models_files/selectize-0.12.0/selectize.min.js
deleted file mode 100644
index fe955bc00..000000000
--- a/docs/dev/articles/articles/Models_files/selectize-0.12.0/selectize.min.js
+++ /dev/null
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-Regression Example • parsnip
-Making a parsnip model from scratch • parsnip
-Evaluating Submodels with the Same Model Object • parsnip
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Some R packages can create predictions from models that are different than the one that was fit. For example, if a boosted tree is fit with 10 iterations of boosting, the model can usually make predictions on submodels that have less than 10 trees (all other parameters being static). This is helpful for model tuning since you can cheap evaluate tuning parameter combinations and can often results in a large speed-up in the computations.
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In parsnip, there is a method called multi_predict()
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## [1] multi_predict._C5.0* multi_predict._earth*
-## [3] multi_predict._elnet* multi_predict._lognet*
-## [5] multi_predict._multnet* multi_predict._train.kknn*
-## [7] multi_predict._xgb.Booster* multi_predict.default*
-## see '?methods' for accessing help and source code
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We’ll use the attrition data in rsample to illustrate:
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## ── Attaching packages ───────────────────────────── tidymodels 0.1.0.9000 ──
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## ✓ broom 0.5.6 ✓ rsample 0.0.6.9000
-## ✓ dials 0.0.6.9000 ✓ tibble 3.0.1.9000
-## ✓ dplyr 0.8.5 ✓ tune 0.1.0.9000
-## ✓ infer 0.5.1 ✓ workflows 0.1.1.9000
-## ✓ purrr 0.3.4 ✓ yardstick 0.0.6
-## ✓ recipes 0.1.12
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## ── Conflicts ───────────────────────────────────── tidymodels_conflicts() ──
-## x purrr::discard() masks scales::discard()
-## x dplyr::filter() masks stats::filter()
-## x dplyr::lag() masks stats::lag()
-## x dials::margin() masks ggplot2::margin()
-## x recipes::step() masks stats::step()
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data attrition )
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-set.seed 4595 )
-data_split <- initial_split (attrition , strata = "Attrition" )
-attrition_train <- training (data_split )
-attrition_test <- testing (data_split )
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A boosted classification tree is one of the most low-maintenance approaches that we could take to these data:
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# requires the xgboost package
-attrition_boost <-
- boost_tree mode = "classification" , trees = 100 ) %>%
- set_engine "C5.0" )
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Suppose that 10-fold cross-validation was being used to tune the model over the number of trees:
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set.seed 616 )
-folds <- vfold_cv (attrition_train )
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The process would fit a model on 90% of the data and predict on the remaining 10%. Using rsample:
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model_data <- analysis (folds $splits [[ 1 ]])
-pred_data <- assessment (folds $splits [[ 1 ]])
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-fold_1_model <-
- attrition_boost %>%
- fit_xy x = model_data %>% dplyr :: select Attrition ), y = model_data $Attrition )
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For multi_predict() predict() trees. Candidate submodel values can be passed in with trees:
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fold_1_pred <-
- multi_predict fold_1_model ,
- new_data = pred_data %>% dplyr :: select Attrition ),
- trees = 1 :100 ,
- type = "prob"
- )
-fold_1_pred
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## # A tibble: 111 x 1
-## .pred
-## <list>
-## 1 <tibble [100 × 3]>
-## 2 <tibble [100 × 3]>
-## 3 <tibble [100 × 3]>
-## 4 <tibble [100 × 3]>
-## 5 <tibble [100 × 3]>
-## 6 <tibble [100 × 3]>
-## 7 <tibble [100 × 3]>
-## 8 <tibble [100 × 3]>
-## 9 <tibble [100 × 3]>
-## 10 <tibble [100 × 3]>
-## # … with 101 more rows
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The results is a tibble that has as many rows as the data being predicted (n = 111). The .pred column contains a list of tibbles and each has the predictions across the different number of trees:
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## # A tibble: 100 x 3
-## trees .pred_No .pred_Yes
-## <int> <dbl> <dbl>
-## 1 1 0.691 0.309
-## 2 2 1 0
-## 3 3 1 0
-## 4 4 1 0
-## 5 5 1 0
-## 6 6 0.862 0.138
-## 7 7 0.882 0.118
-## 8 8 0.770 0.230
-## 9 9 0.689 0.311
-## 10 10 0.724 0.276
-## # … with 90 more rows
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To get this into a format that is more usable, we can use tidyr::unnest()
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fold_1_df <-
- fold_1_pred %>%
- bind_cols (pred_data %>% dplyr :: select Attrition )) %>%
- add_rowindex %>%
- tidyr :: unnest .pred )
-fold_1_df
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## # A tibble: 11,100 x 5
-## trees .pred_No .pred_Yes Attrition .row
-## <int> <dbl> <dbl> <fct> <int>
-## 1 1 0.691 0.309 Yes 1
-## 2 2 1 0 Yes 1
-## 3 3 1 0 Yes 1
-## 4 4 1 0 Yes 1
-## 5 5 1 0 Yes 1
-## 6 6 0.862 0.138 Yes 1
-## 7 7 0.882 0.118 Yes 1
-## 8 8 0.770 0.230 Yes 1
-## 9 9 0.689 0.311 Yes 1
-## 10 10 0.724 0.276 Yes 1
-## # … with 11,090 more rows
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For two samples, what do these look like over trees?
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fold_1_df %>%
- dplyr :: filter .row %in% c 1 , 88 )) %>%
- ggplot (aes (x = trees , y = .pred_No , col = Attrition , group = .row )) +
- geom_step () +
- ylim 0 :1 ) +
- theme (legend.position = "top" )
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What does performance look like over trees (using the area under the ROC curve)?
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fold_1_df %>%
- group_by (trees ) %>%
- roc_auc (truth = Attrition , .pred_No ) %>%
- ggplot (aes (x = trees , y = .estimate )) +
- geom_step ()
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Articles • parsnip
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-parsnip Basics • parsnip
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This package provides functions and methods to create and manipulate functions commonly used during modeling (e.g. fitting the model, making predictions, etc). It allows the user to manipulate how the same type of model can be created from different sources. It also contains a basic framework for model parameter tuning.
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Modeling functions across different R packages can have very different interfaces. If you would like to try different approaches, there is a lot of syntactical minutiae to remember. The problem worsens when you move in-between platforms (e.g. doing a logistic regression in R’s glm versus Spark’s implementation).
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parsnip tries to solve this by providing similar interfaces to models. For example, if you are fitting a random forest model and would like to adjust the number of trees in the forest there are different argument names to remember:
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Rather than remembering these values, a common interface to these models can be used with
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The package makes the translation between trees and the real names in each of the implementations.
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Some terminology:
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-The model type differentiates models. Example types are: random forests, logistic regression, linear support vector machines, etc.
-The mode of the model denotes how it will be used. Two common modes are classification and regression . Others would include “censored regression” and “risk regression” (parametric and Cox PH models for censored data, respectively), as well as unsupervised models (e.g. “clustering”).
-The computational engine indicates how the actual model might be fit. These are often R packages (such as randomForest or ranger) but might also be methods outside of R (e.g. Stan, Spark, and others).
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parsnip, similar to ggplot2, dplyr and recipes, separates the specification of what you want to do from the actual doing. This allows us to create broader functionality for modeling.
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There are times where you would like to change a parameter from its default but you are not sure what the final value will be. This is the basis for model tuning . Since the model is not executing when created, these types of parameters can be changed using the varying()
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tune_mtry <- rand_forest trees = 2000 , mtry = varying tune_mtry
-#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = varying()
-#> trees = 2000
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This will come in handy later when we fit the model over different values of mtry.
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Commonly used arguments to the modeling functions have their parameters exposed in the function. For example, rand_forest has arguments for:
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-mtry: The number of predictors that will be randomly sampled at each split when creating the tree models.
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-trees: The number of trees contained in the ensemble.
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-min_n: The minimum number of data points in a node that are required for the node to be split further.
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The arguments to the default function are:
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args rand_forest )
-#> function (mode = "unknown", mtry = NULL, trees = NULL, min_n = NULL)
-#> NULL
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However, there might be other arguments that you would like to change or allow to vary. These are accessible using set_engine. For example, ranger has an option to set the internal random number seed. To set this to a specific value:
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rf_with_seed <-
- rand_forest trees = 2000 , mtry = varying mode = "regression" ) %>%
- set_engine "ranger" , seed = 63233 )
-rf_with_seed
-#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = varying()
-#> trees = 2000
-#>
-#> Engine-Specific Arguments:
-#> seed = 63233
-#>
-#> Computational engine: ranger
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To fit the model, you must:
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-have a defined model, including the mode ,
-have no varying()
-specify a computational engine.
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For example, rf_with_seed above is not ready for fitting due the varying()
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#> parsnip model object
-#>
-#> Ranger result
-#>
-#> Call:
-#> ranger::ranger(formula = formula, data = data, mtry = ~4, num.trees = ~2000, seed = sample.int(10^5, 1), num.threads = 1, verbose = FALSE)
-#>
-#> Type: Regression
-#> Number of trees: 2000
-#> Sample size: 32
-#> Number of independent variables: 10
-#> Mtry: 4
-#> Target node size: 5
-#> Variable importance mode: none
-#> Splitrule: variance
-#> OOB prediction error (MSE): 5.57
-#> R squared (OOB): 0.847
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Or, using the randomForest package:
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#> parsnip model object
-#>
-#>
-#> Call:
-#> randomForest(x = as.data.frame(x), y = y, ntree = ~2000, mtry = ~4)
-#> Type of random forest: regression
-#> Number of trees: 2000
-#> No. of variables tried at each split: 4
-#>
-#> Mean of squared residuals: 5.52
-#> % Var explained: 84.3
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Note that the call objects show num.trees = ~2000. The tilde is the consequence of parsnip using quosures to process the model specification’s arguments.
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Normally, when a function is executed, the function’s arguments are immediately evaluated. In the case of parsnip, the model specification’s arguments are not ; the expression is captured along with the environment where it should be evaluated. That is what a quosure does.
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parsnip uses these expressions to make a model fit call that is evaluated. The tilde in the call above reflects that the argument was captured using a quosure.
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Authors • parsnip
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- getTopLevel: function($scope) {
- for (var i = 1; i <= 6; i++) {
- var $headings = this.findOrFilter($scope, 'h' + i);
- if ($headings.length > 1) {
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- },
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- getNavLevel: function(el) {
- return parseInt(el.tagName.charAt(1), 10);
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- // use top level
- $context = $topContext;
- } else if ($prevNav && $context === $topContext) {
- // create a new level of the tree and switch to it
- $context = helpers.createChildNavList($prevNav);
- } // else use the current $context
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- $context.append($newNav);
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- $prevNav = $newNav;
- });
- },
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- parseOps: function(arg) {
- var opts;
- if (arg.jquery) {
- opts = {
- $nav: arg
- };
- } else {
- opts = arg;
- }
- opts.$scope = opts.$scope || $(document.body);
- return opts;
- }
- },
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- // accepts a jQuery object, or an options object
- init: function(opts) {
- opts = this.helpers.parseOps(opts);
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- // ensure that the data attribute is in place for styling
- opts.$nav.attr('data-toggle', 'toc');
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- var $topContext = this.helpers.createChildNavList(opts.$nav);
- var topLevel = this.helpers.getTopLevel(opts.$scope);
- var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
- this.helpers.populateNav($topContext, topLevel, $headings);
- }
- };
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- $(function() {
- $('nav[data-toggle="toc"]').each(function(i, el) {
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-})();
diff --git a/docs/dev/docsearch.css b/docs/dev/docsearch.css
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-/*
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-.algolia-autocomplete {
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-.algolia-autocomplete .ds-dropdown-menu {
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-.algolia-autocomplete .ds-dropdown-menu::before {
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- background-image: url("data:image/svg+xml;utf8,A Common API to Modeling and Analysis Functions • parsnip
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The goal of parsnip is to provide a tidy, unified interface to models that can be used to try a range of models without getting bogged down in the syntactical minutiae of the underlying packages.
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# The easiest way to get parsnip is to install all of tidymodels:
-install.packages "tidymodels" )
-
-# Alternatively, install just parsnip:
-install.packages "parsnip" )
-
-# Or the development version from GitHub:
-# install.packages("devtools")
-devtools :: install_github "tidymodels/parsnip" )
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One challenge with different modeling functions available in R that do the same thing is that they can have different interfaces and arguments. For example, to fit a random forest regression model, we might have:
-
# From randomForest
-rf_1 <- randomForest (
- y ~ . ,
- data = . ,
- mtry = 10 ,
- ntree = 2000 ,
- importance = TRUE
-)
-
-# From ranger
-rf_2 <- ranger (
- y ~ . ,
- data = dat ,
- mtry = 10 ,
- num.trees = 2000 ,
- importance = "impurity"
-)
-
-# From sparklyr
-rf_3 <- ml_random_forest (
- dat ,
- intercept = FALSE ,
- response = "y" ,
- features = names dat )[names dat ) != "y" ],
- col.sample.rate = 10 ,
- num.trees = 2000
-)
-
Note that the model syntax can be very different and that the argument names (and formats) are also different. This is a pain if you switch between implementations.
-
In this example:
-
-the type of model is “random forest”,
-the mode of the model is “regression” (as opposed to classification, etc), and
-the computational engine is the name of the R package.
-
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The goals of parsnip are to:
-
-Separate the definition of a model from its evaluation.
-Decouple the model specification from the implementation (whether the implementation is in R, spark, or something else). For example, the user would call rand_forest instead of ranger::ranger
-Harmonize argument names (e.g. n.trees, ntrees, trees) so that users only need to remember a single name. This will help across model types too so that trees will be the same argument across random forest as well as boosting or bagging.
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Using the example above, the parsnip approach would be:
-
library parsnip )
-
-rand_forest mtry = 10 , trees = 2000 ) %>%
- set_engine "ranger" , importance = "impurity" ) %>%
- set_mode "regression" )
-#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> trees = 2000
-#>
-#> Engine-Specific Arguments:
-#> importance = impurity
-#>
-#> Computational engine: ranger
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The engine can be easily changed. To use Spark, the change is straightforward:
-
rand_forest mtry = 10 , trees = 2000 ) %>%
- set_engine "spark" ) %>%
- set_mode "regression" )
-#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> trees = 2000
-#>
-#> Computational engine: spark
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Either one of these model specifications can be fit in the same way:
-
rand_forest mtry = 10 , trees = 2000 ) %>%
- set_engine "ranger" , importance = "impurity" ) %>%
- set_mode "regression" ) %>%
- fit mpg ~ . , data = mtcars )
-#> parsnip model object
-#>
-#> Fit time: 69ms
-#> Ranger result
-#>
-#> Call:
-#> ranger::ranger(formula = formula, data = data, mtry = ~10, num.trees = ~2000, importance = ~"impurity", num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1))
-#>
-#> Type: Regression
-#> Number of trees: 2000
-#> Sample size: 32
-#> Number of independent variables: 10
-#> Mtry: 10
-#> Target node size: 5
-#> Variable importance mode: impurity
-#> Splitrule: variance
-#> OOB prediction error (MSE): 5.815633
-#> R squared (OOB): 0.839896
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A list of all parsnip models across different CRAN packages can be found at tidymodels.org
-
Data sets previously found in parsnip are now find in the modeldata package.
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If you encounter a bug, please file a minimal reproducible example on GitHub . For questions and other discussion, please use community.rstudio.com .
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Please note that the parsnip project is released with a Contributor Code of Conduct . By contributing to this project, you agree to abide by its terms.
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PLEASE READ: Making a new issue for parsnip • parsnip
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name: Bug report or feature request about: Describe a bug you’ve seen or make a case for a new feature —
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Please follow the template below.
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If the question is related at all to a specific data analysis, please include a minimal reprex (reproducible example). If you’ve never heard of a reprex before, start by reading “What is a reprex ”, and follow the advice further down that page.
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Tips:
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When are ready to file the issue, please delete the parts above this line: < – ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ –>
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I’m having trouble with … or
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Have you considered …
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Copy your code to the clipboard and run:
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diff --git a/docs/dev/logo.png b/docs/dev/logo.png
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diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html
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- Changelog • parsnip
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-tidyr >= 1.0.0 is now required.
SVM models produced by kernlab now use the formula method. This change was due to how ksvm() made indicator variables for factor predictors (with one-hot encodings). Since the ordinary formula method did not do this, the data are passed as-is to ksvm() so that the results are closer to what one would get if ksmv() were called directly.
MARS models produced by earth now use the formula method.
Under-the-hood changes were made so that non-standard data arguments in the modeling packages can be accomodated. (#315 )
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-A new main argument was added to boost_tree() stop_iter for early stopping. The xgb_train()
If fit() actual formula is pass to the model (instead of a placeholder). This makes the model call better.
A function named repair_call() call object to better reflect what they would have obtained if the model function had been used directly (instead of via parsnip). This is only useful when the user chooses a formula interface and the model uses a formula interface. It will also be of limited use when a recipes is used to construct the feature set in workflows or tune.
The predict() #249 ) (#308 ) (tidymodels/workflows#45 )
The function req_pkgs() #308 )
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-The error message for missing packages was fixed (#289 and #292 )
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-S3 dispatch for tidy()
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-A bug (#206 and #234 ) was fixed that caused an error when predicting with a multinomial glmnet model.
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-glmnet was removed as a dependency since the new version depends on 3.6.0 or greater. Keeping it would constrain parsnip to that same requirement. All glmnet tests are run locally.
A set of internal functions are now exported. These are helpful when creating a new package that registers new model specifications.
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-There were some mis-mapped parameters (going between parsnip and the underlying model function) for spark boosted trees and some keras models. See 897c927 .
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-The time elapsed during model fitting is stored in the $elapsed slot of the parsnip model object, and is printed when the model object is printed.
Some default parameter ranges were updated for SVM, KNN, and MARS models.
The model udpate() methods gained a parameters argument for cases when the parameters are contained in a tibble or list.
fit_control() control_parsnip()
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-A bug was fixed standardizing the output column types of multi_predict and predict for multinom_reg.
A bug was fixed related to using data descriptors and fit_xy()
A bug was fixed related to the column names generated by multi_predict() .pred and this list column contains tibbles across sub-models. The column names for these sub-model tibbles will have names consistent with predict() 43c15db .
A bug was fixed standardizing the column names of nnet class probability predictions.
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Test case update due to CRAN running extra tests ( #202 )
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Unplanned release based on CRAN requirements for Solaris.
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-The method that parsnip stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in ?get_model_env package vignette for adding models .
The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation.
For surv_reg() survival package is now called survival instead of survreg.
For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Previously, the model was fit with passing penalty to glmnet’s lambda argument and the model could only make predictions at those specific values. ( #195 )
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-add_rowindex() .row to a data frame.
If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.
nearest_neighbor() multi_predict method. The multi_predict()
A suite of internal functions were added to help with upcoming model tuning features.
A parsnip object always saved the name(s) of the outcome variable(s) for proper naming of the predicted values.
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Small release driven by changes in sample()
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fit_xy() y without error
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-varying_args() full argument to control whether the full set of possible varying arguments is returned (as opposed to only the arguments that are actually varying).
fit_control()
For classification models, an error occurs if the outcome data are not encoded as factors (#115 ).
The prediction modules (e.g. predict_class, predict_numeric, etc) were de-exported. These were internal functions that were not to be used by the users and the users were using them.
An event time data set (check_times) was included that is the time (in seconds) to run R CMD check using the "r-devel-windows-ix86+x86_64` flavor. Packages that errored are censored.
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-varying_args() generics package. This means that the first argument, x, has been renamed to object to align with generics.
For the recipes step method of varying_args() cannot be varying as varying (for example, the id) (#132 ).
find_varying(), the internal function for detecting varying arguments, now returns correct results when a size 0 argument is provided. It can also now detect varying arguments nested deeply into a call (#131 , #134 ).
For multinomial regression, the .pred_ prefix is now only added to prediction column names once (#107 ).
For multinomial regression using glmnet, multi_predict() #108 ).
Confidence and prediction intervals for logistic regression were only computed the intervals for a single level. Both are now computed. (#156 )
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-The engine, and any associated arguments, are now specified using set_engine() engine argument
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-Arguments to modeling functions are now captured as quosures.
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-Data descriptor names have beemn changed and are now functions. The descriptor definitions for “cols” and “preds” have been switched.
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-regularization was changed to penalty in a few models to be consistent with this change .
-If a mode is not chosen in the model specification, it is assigned at the time of fit. 51
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-To be consistent with snake_case, newdata was changed to new_data.
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- }
- });
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- function paths(pathname) {
- var pieces = pathname.split("/");
- pieces.shift(); // always starts with /
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- if (end === "index.html" || end === "")
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- var copyButton = "Boosted trees via C5.0 — C5.0_train • parsnip
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C5.0_train is a wrapper for the C5.0() function in the
-C50 package that fits tree-based models
-where all of the model arguments are in the main function.
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C5.0_train (x , y , weights = NULL , trials = 15 , minCases = 2 , sample = 0 , ... )
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- x
- A data frame or matrix of predictors.
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- y
- A factor vector with 2 or more levels
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- weights
- An optional numeric vector of case weights. Note
-that the data used for the case weights will not be used as a
-splitting variable in the model (see
-http://www.rulequest.com/see5-win.html#CASEWEIGHT for
-Quinlan's notes on case weights).
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- trials
- An integer specifying the number of boosting
-iterations. A value of one indicates that a single model is
-used.
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- minCases
- An integer for the smallest number of samples
-that must be put in at least two of the splits.
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- sample
- A value between (0, .999) that specifies the
-random proportion of the data should be used to train the model.
-By default, all the samples are used for model training. Samples
-not used for training are used to evaluate the accuracy of the
-model in the printed output.
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A fitted C5.0 model.
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Functions required for parsnip-adjacent packages — null_value • parsnip
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These functions are helpful when creating new packages that will register
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null_value (x )
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-show_fit (model , eng )
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-update_dot_check (... )
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-new_model_spec (cls , args , eng_args , mode , method , engine )
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-check_final_param (x )
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-update_main_parameters (args , param )
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Add a column of row numbers to a data frame — add_rowindex • parsnip
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Add a column of row numbers to a data frame
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add_rowindex (x )
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The same data frame with a column of 1-based integers named .row.
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mtcars %>% add_rowindex ()
#> mpg cyl disp hp drat wt qsec vs am gear carb .row
-#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1
-#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
-#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 3
-#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4
-#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
-#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6
-#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7
-#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 8
-#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 9
-#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 10
-#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 11
-#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 12
-#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 13
-#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14
-#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 15
-#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 16
-#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 17
-#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 18
-#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 19
-#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 20
-#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 21
-#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 22
-#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 23
-#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 24
-#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 25
-#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 26
-#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 27
-#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 28
-#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 29
-#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 30
-#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 31
-#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 32
-
-
-
-
-
-
-
General Interface for Boosted Trees — boost_tree • parsnip
-
-
-
-
-
-
-
-
-
-
-
boost_tree() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-mtry: The number of predictors that will be
-randomly sampled at each split when creating the tree models.
trees: The number of trees contained in the ensemble.
min_n: The minimum number of data points in a node
-that are required for the node to be split further.
tree_depth: The maximum depth of the tree (i.e. number of
-splits).
learn_rate: The rate at which the boosting algorithm adapts
-from iteration-to-iteration.
loss_reduction: The reduction in the loss function required
-to split further.
sample_size: The amount of data exposed to the fitting routine.
stop_iter: The number of iterations without improvement before
-stopping.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using the set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
boost_tree (
- mode = "unknown" ,
- mtry = NULL ,
- trees = NULL ,
- min_n = NULL ,
- tree_depth = NULL ,
- learn_rate = NULL ,
- loss_reduction = NULL ,
- sample_size = NULL ,
- stop_iter = NULL
-)
-
-# S3 method for boost_tree
-update object ,
- parameters = NULL ,
- mtry = NULL ,
- trees = NULL ,
- min_n = NULL ,
- tree_depth = NULL ,
- learn_rate = NULL ,
- loss_reduction = NULL ,
- sample_size = NULL ,
- stop_iter = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- mtry
- A number for the number (or proportion) of predictors that will
-be randomly sampled at each split when creating the tree models (xgboost
-only).
-
- trees
- An integer for the number of trees contained in
-the ensemble.
-
- min_n
- An integer for the minimum number of data points
-in a node that are required for the node to be split further.
-
- tree_depth
- An integer for the maximum depth of the tree (i.e. number
-of splits) (xgboost only).
-
- learn_rate
- A number for the rate at which the boosting algorithm adapts
-from iteration-to-iteration (xgboost only).
-
- loss_reduction
- A number for the reduction in the loss function required
-to split further (xgboost only).
-
- sample_size
- A number for the number (or proportion) of data that is
-exposed to the fitting routine. For xgboost, the sampling is done at at
-each iteration while C5.0 samples once during training.
-
- stop_iter
- The number of iterations without improvement before
-stopping (xgboost only).
-
- object
- A boosted tree model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
An updated model specification.
-
-
-
The data given to the function are not saved and are only used
-to determine the mode of the model. For boost_tree(), the
-possible modes are "regression" and "classification".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
xgboost
-
## Boosted Tree Model Specification (regression)
-##
-## Computational engine: xgboost
-##
-## Model fit template:
-## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
-## verbose = 0)
-
## Boosted Tree Model Specification (classification)
-##
-## Computational engine: xgboost
-##
-## Model fit template:
-## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
-## verbose = 0)
-
-
-
-
C5.0
-
## Boosted Tree Model Specification (classification)
-##
-## Computational engine: C5.0
-##
-## Model fit template:
-## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg())
-
-
-
Note that C50::C5.0()
-
-
spark
-
## Boosted Tree Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(),
-## type = "regression", seed = sample.int(10^5, 1))
-
## Boosted Tree Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(),
-## type = "classification", seed = sample.int(10^5, 1))
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip xgboost C5.0 spark tree_depth max_depth NA max_depth trees nrounds trials max_iter learn_rate eta NA step_size mtry colsample_bytree NA feature_subset_strategy min_n min_child_weight minCases min_instances_per_node loss_reduction gamma NA NA sample_size subsample sample subsampling_rate stop_iter early_stop NA NA min_info_gain NA NA loss_reduction
-
-
-
-
-
-
-
-
-
-
boost_tree (mode = "classification" , trees = 20 )
#> Boosted Tree Model Specification (classification)
-#>
-#> Main Arguments:
-#> trees = 20
-#>
#> Boosted Tree Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = varying()
-#>
model <- boost_tree (mtry = 10 , min_n = 3 )
-model
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#> min_n = 3
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#> tree_depth = 5
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#> tree_depth = 5
-#>
-param_values $verbose <- 0
-# Fails due to engine argument
-# model %>% update(param_values)
-
-
-
-
-
-
-
Check to ensure that ellipses are empty — check_empty_ellipse • parsnip
-
-
-
-
-
-
-
-
-
-
-
Check to ensure that ellipses are empty
-
-
-
check_empty_ellipse (... )
-
-
-
-
- ...
- Extra arguments.
-
-
-
-
-
If an error is not thrown (from non-empty ellipses), a NULL list.
-
-
-
-
-
-
-
-
Tools to Register Models — pred_types • parsnip
-
-
-
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-
-
-
-
-
-
-
-
-
-
-
These functions are similar to constructors and can be used to validate
-that there are no conflicts with the underlying model structures used by the
-package.
-
-
-
-
pred_types
-
-check_mod_val (model , new = FALSE , existence = FALSE )
-
-get_model_env ()
-
-check_mode_val (mode )
-
-check_engine_val (eng )
-
-check_arg_val (arg )
-
-check_submodels_val (has_submodel )
-
-check_func_val (func )
-
-check_fit_info (fit_obj )
-
-check_pred_info (pred_obj , type )
-
-check_pkg_val (pkg )
-
-set_new_model (model )
-
-set_model_mode (model , mode )
-
-set_model_engine (model , mode , eng )
-
-set_model_arg (model , eng , parsnip , original , func , has_submodel )
-
-set_dependency (model , eng , pkg )
-
-get_dependency (model )
-
-set_fit (model , mode , eng , value )
-
-get_fit (model )
-
-set_pred (model , mode , eng , type , value )
-
-get_pred_type (model , type )
-
-show_model_info (model )
-
-get_from_env (items )
-
-
-
-
- model
- A single character string for the model type (e.g.
-"rand_forest", etc).
-
- new
- A single logical to check to see if the model that you are check
-has not already been registered.
-
- existence
- A single logical to check to see if the model has already
-been registered.
-
- mode
- A single character string for the model mode (e.g. "regression").
-
- eng
- A single character string for the model engine.
-
- arg
- A single character string for the model argument name.
-
- has_submodel
- A single logical for whether the argument
-can make predictions on mutiple submodels at once.
-
- func
- A named character vector that describes how to call
-a function. func should have elements pkg and fun. The
-former is optional but is recommended and the latter is
-required. For example, c(pkg = "stats", fun = "lm") c(fun = "predict") predict method.
-
- fit_obj
- A list with elements interface, protect,
-func and defaults. See the package vignette "Making a
-parsnip model from scratch".
-
- pred_obj
- A list with elements pre, post, func, and args.
-See the package vignette "Making a parsnip model from scratch".
-
- type
- A single character value for the type of prediction. Possible
-values are:
-'raw', 'numeric', 'class', 'link', 'prob', 'conf_int', 'pred_int', 'quantile'
-.
-
- pkg
- An options character string for a package name.
-
- parsnip
- A single character string for the "harmonized" argument name
-that parsnip exposes.
-
- original
- A single character string for the argument name that
-underlying model function uses.
-
- value
- A list that conforms to the fit_obj or pred_obj description
-above, depending on context.
-
- items
- A character string of objects in the model environment.
-
-
-
-
-
An object of class character of length 8.
-
-
-
-
These functions are available for users to add their
-own models or engines (in package or otherwise) so that they can
-be accessed using parsnip. This are more thoroughly documented
-on the package web site (see references below).
-
In short, parsnip stores an environment object that contains
-all of the information and code about how models are used (e.g.
-fitting, predicting, etc). These functions can be used to add
-models to that environment as well as helper functions that can
-be used to makes sure that the model data is in the right
-format.
-
-
-
-
"Making a parsnip model from scratch"
-https://tidymodels.github.io/parsnip/articles/articles/Scratch.html
-
-
-
-
# Show the information about a model:
-show_model_info ("rand_forest" )
#> Information for `rand_forest`
-#> modes: unknown, classification, regression
-#>
-#> engines:
-#> classification: randomForest, ranger, spark
-#> regression: randomForest, ranger, spark
-#>
-#> arguments:
-#> ranger:
-#> mtry --> mtry
-#> trees --> num.trees
-#> min_n --> min.node.size
-#> randomForest:
-#> mtry --> mtry
-#> trees --> ntree
-#> min_n --> nodesize
-#> spark:
-#> mtry --> feature_subset_strategy
-#> trees --> num_trees
-#> min_n --> min_instances_per_node
-#>
-#> fit modules:
-#> engine mode
-#> ranger classification
-#> ranger regression
-#> randomForest classification
-#> randomForest regression
-#> spark classification
-#> spark regression
-#>
-#> prediction modules:
-#> mode engine methods
-#> classification randomForest class, prob, raw
-#> classification ranger class, conf_int, prob, raw
-#> classification spark class, prob
-#> regression randomForest numeric, raw
-#> regression ranger conf_int, numeric, raw
-#> regression spark numeric
-#>
#> [1] "boost_tree" "boost_tree_args"
-#> [3] "boost_tree_fit" "boost_tree_modes"
-#> [5] "boost_tree_pkgs" "boost_tree_predict"
-#> [7] "decision_tree" "decision_tree_args"
-#> [9] "decision_tree_fit" "decision_tree_modes"
-#> [11] "decision_tree_pkgs" "decision_tree_predict"
-#> [13] "linear_reg" "linear_reg_args"
-#> [15] "linear_reg_fit" "linear_reg_modes"
-#> [17] "linear_reg_pkgs" "linear_reg_predict"
-#> [19] "logistic_reg" "logistic_reg_args"
-#> [21] "logistic_reg_fit" "logistic_reg_modes"
-#> [23] "logistic_reg_pkgs" "logistic_reg_predict"
-#> [25] "mars" "mars_args"
-#> [27] "mars_fit" "mars_modes"
-#> [29] "mars_pkgs" "mars_predict"
-#> [31] "mlp" "mlp_args"
-#> [33] "mlp_fit" "mlp_modes"
-#> [35] "mlp_pkgs" "mlp_predict"
-#> [37] "models" "modes"
-#> [39] "multinom_reg" "multinom_reg_args"
-#> [41] "multinom_reg_fit" "multinom_reg_modes"
-#> [43] "multinom_reg_pkgs" "multinom_reg_predict"
-#> [45] "nearest_neighbor" "nearest_neighbor_args"
-#> [47] "nearest_neighbor_fit" "nearest_neighbor_modes"
-#> [49] "nearest_neighbor_pkgs" "nearest_neighbor_predict"
-#> [51] "null_model" "null_model_args"
-#> [53] "null_model_fit" "null_model_modes"
-#> [55] "null_model_pkgs" "null_model_predict"
-#> [57] "rand_forest" "rand_forest_args"
-#> [59] "rand_forest_fit" "rand_forest_modes"
-#> [61] "rand_forest_pkgs" "rand_forest_predict"
-#> [63] "surv_reg" "surv_reg_args"
-#> [65] "surv_reg_fit" "surv_reg_modes"
-#> [67] "surv_reg_pkgs" "surv_reg_predict"
-#> [69] "svm_poly" "svm_poly_args"
-#> [71] "svm_poly_fit" "svm_poly_modes"
-#> [73] "svm_poly_pkgs" "svm_poly_predict"
-#> [75] "svm_rbf" "svm_rbf_args"
-#> [77] "svm_rbf_fit" "svm_rbf_modes"
-#> [79] "svm_rbf_pkgs" "svm_rbf_predict"
-
-
-
-
-
-
-
Execution Time Data — check_times • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
These data were collected from the CRAN web page for 13,626 R
-packages. The time to complete the standard package checking
-routine was collected In some cases, the package checking
-process is stopped due to errors and these data are treated as
-censored. It is less than 1 percent.
-
-
-
-
-
-
-
CRAN
-
-
-
-
-
check_times a data frame
-
-
-
-
As predictors, the associated package source code were
-downloaded and parsed to create predictors, including
-authors: The number of authors in the author field.
imports: The number of imported packages.
suggests: The number of packages suggested.
depends: The number of hard dependencies.
Roxygen: a binary indicator for whether Roxygen was used
-for documentation.
gh: a binary indicator for whether the URL field contained
-a GitHub link.
rforge: a binary indicator for whether the URL field
-contained a link to R-forge.
descr: The number of characters (or, in some cases, bytes)
-in the description field.
r_count: The number of R files in the R directory.
r_size: The total disk size of the R files.
ns_import: Estimated number of imported functions or methods.
ns_export: Estimated number of exported functions or methods.
s3_methods: Estimated number of S3 methods.
s4_methods: Estimated number of S4 methods.
doc_count: How many Rmd or Rnw files in the vignettes
-directory.
doc_size: The disk size of the Rmd or Rnw files.
src_count: The number of files in the src directory.
src_size: The size on disk of files in the src directory.
data_count The number of files in the data directory.
data_size: The size on disk of files in the data directory.
testthat_count: The number of files in the testthat
-directory.
testthat_size: The size on disk of files in the testthat
-directory.
check_time: The time (in seconds) to run R CMD check
-using the "r-devel-windows-ix86+x86_64` flavor.
status: An indicator for whether the tests completed.
-
-
Data were collected on 2019-01-20.
-
-
-
#> tibble [13,626 × 25] (S3: tbl_df/tbl/data.frame)
-#> $ package : chr [1:13626] "A3" "abbyyR" "abc" "abc.data" ...
-#> $ authors : int [1:13626] 1 1 1 1 5 3 2 1 4 6 ...
-#> $ imports : num [1:13626] 0 6 0 0 3 1 0 4 0 7 ...
-#> $ suggests : num [1:13626] 2 4 0 0 2 0 2 2 0 0 ...
-#> $ depends : num [1:13626] 3 1 6 1 1 1 5 0 1 1 ...
-#> $ Roxygen : num [1:13626] 0 1 0 0 1 0 0 1 0 0 ...
-#> $ gh : num [1:13626] 0 1 0 0 0 0 0 1 0 0 ...
-#> $ rforge : num [1:13626] 0 0 0 0 0 0 0 0 0 0 ...
-#> $ descr : int [1:13626] 217 313 269 63 223 1031 135 344 204 335 ...
-#> $ r_count : int [1:13626] 2 20 8 0 10 10 16 3 6 14 ...
-#> $ r_size : num [1:13626] 0.0291 0.0463 0.0784 0 0.0191 ...
-#> $ ns_import : num [1:13626] 3 15 6 0 4 5 0 4 2 10 ...
-#> $ ns_export : num [1:13626] 0 19 0 0 10 0 0 2 0 9 ...
-#> $ s3_methods : num [1:13626] 3 0 11 0 0 0 0 2 0 23 ...
-#> $ s4_methods : num [1:13626] 0 0 0 0 0 0 0 0 0 0 ...
-#> $ doc_count : int [1:13626] 0 3 1 0 1 0 0 0 0 0 ...
-#> $ doc_size : num [1:13626] 0 0.01976 0.03828 0 0.00787 ...
-#> $ src_count : int [1:13626] 0 0 0 0 0 0 0 2 0 5 ...
-#> $ src_size : num [1:13626] 0 0 0 0 0 ...
-#> $ data_count : int [1:13626] 2 0 0 3 3 1 10 0 4 2 ...
-#> $ data_size : num [1:13626] 0.0253 0 0 4.8859 4.5955 ...
-#> $ testthat_count: int [1:13626] 0 8 0 0 0 0 0 1 0 0 ...
-#> $ testthat_size : num [1:13626] 0 0.0025 0 0 0 ...
-#> $ check_time : num [1:13626] 49 101 292 21 103 46 78 91 47 196 ...
-#> $ status : num [1:13626] 1 1 1 1 1 1 1 1 1 1 ...
-
-
-
-
-
-
-
Control the fit function — control_parsnip • parsnip
-
-
-
-
-
-
-
-
-
-
-
Options can be passed to the fit()
-
-
-
control_parsnip (verbosity = 1L , catch = FALSE )
-
-fit_control (verbosity = 1L , catch = FALSE )
-
-
-
-
- verbosity
- An integer where a value of zero indicates
-that no messages or output should be shown when packages are
-loaded or when the model is fit. A value of 1 means that package
-loading is quiet but model fits can produce output to the screen
-(depending on if they contain their own verbose-type
-argument). A value of 2 or more indicates that any output should
-be seen.
-
- catch
- A logical where a value of TRUE will evaluate
-the model inside of try(, silent = TRUE)
-
-
-
-
-
An S3 object with class "fit_control" that is a named list with the
-results of the function call
-
-
-
fit_control() is deprecated in favor of control_parsnip().
-
-
-
-
-
-
-
-
Make a table of arguments — convert_args • parsnip
-
-
-
-
-
-
-
-
-
-
-
Make a table of arguments
-
-
-
convert_args (model_name )
-
-
-
-
- model_name
- A character string for the model
-
-
-
-
-
-
-
-
-
-
Convenience function for intervals — convert_stan_interval • parsnip
-
-
-
-
-
-
-
-
-
-
-
Convenience function for intervals
-
-
-
convert_stan_interval (x , level = 0.95 , lower = TRUE )
-
-
-
-
- x
- A fitted model object
-
- level
- Level of uncertainty for intervals
-
- lower
- Is level the lower level?
-
-
-
-
-
-
-
-
-
-
General Interface for Decision Tree Models — decision_tree • parsnip
-
-
-
-
-
-
-
-
-
-
-
decision_tree() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-cost_complexity: The cost/complexity parameter (a.k.a. Cp)
-used by CART models (rpart only).
tree_depth: The maximum depth of a tree (rpart and
-spark only).
min_n: The minimum number of data points in a node
-that are required for the node to be split further.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
decision_tree (
- mode = "unknown" ,
- cost_complexity = NULL ,
- tree_depth = NULL ,
- min_n = NULL
-)
-
-# S3 method for decision_tree
-update object ,
- parameters = NULL ,
- cost_complexity = NULL ,
- tree_depth = NULL ,
- min_n = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- cost_complexity
- A positive number for the the cost/complexity
-parameter (a.k.a. Cp) used by CART models (rpart only).
-
- tree_depth
- An integer for maximum depth of the tree.
-
- min_n
- An integer for the minimum number of data points
-in a node that are required for the node to be split further.
-
- object
- A random forest model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
Note that, for rpart models, but cost_complexity and
-tree_depth can be both be specified but the package will give
-precedence to cost_complexity. Also, tree_depth values
-greater than 30 rpart will give nonsense results on 32-bit
-machines.
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
rpart
-
## Decision Tree Model Specification (regression)
-##
-## Computational engine: rpart
-##
-## Model fit template:
-## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
## Decision Tree Model Specification (classification)
-##
-## Computational engine: rpart
-##
-## Model fit template:
-## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
-
-
Note that rpart::rpart()
-
-
C5.0
-
## Decision Tree Model Specification (classification)
-##
-## Computational engine: C5.0
-##
-## Model fit template:
-## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## trials = 1)
-
-
-
Note that C50::C5.0()
-
-
spark
-
## Decision Tree Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(),
-## seed = sample.int(10^5, 1))
-
## Decision Tree Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(),
-## seed = sample.int(10^5, 1))
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip rpart C5.0 spark tree_depth maxdepth NA max_depth min_n minsplit minCases min_instances_per_node cost_complexity cp NA NA
-
-
-
-
-
-
-
-
-
-
decision_tree (mode = "classification" , tree_depth = 5 )
#> Decision Tree Model Specification (classification)
-#>
-#> Main Arguments:
-#> tree_depth = 5
-#>
#> Decision Tree Model Specification (regression)
-#>
-#> Main Arguments:
-#> cost_complexity = varying()
-#>
model <- decision_tree (cost_complexity = 10 , min_n = 3 )
-model
#> Decision Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> cost_complexity = 10
-#> min_n = 3
-#>
#> Decision Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> cost_complexity = 1
-#> min_n = 3
-#>
#> Decision Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> cost_complexity = 1
-#>
-
-
-
-
-
-
-
Data Set Characteristics Available when Fitting Models — descriptors • parsnip
-
-
-
-
-
-
-
-
-
-
-
When using the fit() .obs()
-function can be used. See Details below.
-
-
-
.cols ()
-
-.preds ()
-
-.obs ()
-
-.lvls ()
-
-.facts ()
-
-.x ()
-
-.y ()
-
-.dat ()
-
-
-
-
-
Existing functions:
-.obs(): The current number of rows in the data set.
.preds(): The number of columns in the data set that are
-associated with the predictors prior to dummy variable creation.
.cols(): The number of predictor columns available after dummy
-variables are created (if any).
.facts(): The number of factor predictors in the dat set.
.lvls(): If the outcome is a factor, this is a table
-with the counts for each level (and NA otherwise).
.x(): The predictors returned in the format given. Either a
-data frame or a matrix.
.y(): The known outcomes returned in the format given. Either
-a vector, matrix, or data frame.
.dat(): A data frame containing all of the predictors and the
-outcomes. If fit_xy() ..y.
-
-
For example, if you use the model formula Sepal.Width ~ . with the iris
-data, the values would be
- .preds() = 4 (the 4 columns in `iris`)
- .cols() = 5 (3 numeric columns + 2 from Species dummy variables)
- .obs() = 150
- .lvls() = NA (no factor outcome)
- .facts() = 1 (the Species predictor)
- .y() = <vector> (Sepal.Width as a vector)
- .x() = <data.frame> (The other 4 columns as a data frame)
- .dat() = <data.frame> (The full data set)
-
-
-
If the formula Species ~ . where used:
- .preds() = 4 (the 4 numeric columns in `iris`)
- .cols() = 4 (same)
- .obs() = 150
- .lvls() = c(setosa = 50, versicolor = 50, virginica = 50)
- .facts() = 0
- .y() = <vector> (Species as a vector)
- .x() = <data.frame> (The other 4 columns as a data frame)
- .dat() = <data.frame> (The full data set)
-
-
-
To use these in a model fit, pass them to a model specification.
-The evaluation is delayed until the time when the
-model is run via fit()
-
-
-library modeldata )
-data "lending_club" )
-
-rand_forest mode = "classification" , mtry = .cols () - 2 )
-
-
When no descriptors are found, the computation of the descriptor values
-is not executed.
-
-
-
-
-
-
-
-
Evaluate parsnip model arguments — eval_args • parsnip
-
-
-
-
-
-
-
-
-
-
-
Evaluate parsnip model arguments
-
-
-
eval_args (spec , ... )
-
-
-
-
- spec
- A model specification
-
- ...
- Not used.
-
-
-
-
-
-
-
-
-
-
Fit a Model Specification to a Dataset — fit.model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
fit() fit_xy()
-
-
-
# S3 method for model_spec
-fit object , formula , data , control = control_parsnip ... )
-
-# S3 method for model_spec
-fit_xy object , x , y , control = control_parsnip ... )
-
-
-
-
- object
- An object of class model_spec that has a chosen engine
-(via set_engine()
-
- formula
- An object of class "formula" (or one that can
-be coerced to that class): a symbolic description of the model
-to be fitted.
-
- data
- Optional, depending on the interface (see Details
-below). A data frame containing all relevant variables (e.g.
-outcome(s), predictors, case weights, etc). Note: when needed, a
-named argument should be used.
-
- control
- A named list with elements verbosity and
-catch. See control_parsnip()
-
- ...
- Not currently used; values passed here will be
-ignored. Other options required to fit the model should be
-passed using set_engine()
-
- x
- A matrix or data frame of predictors.
-
- y
- A vector, matrix or data frame of outcome data.
-
-
-
-
-
A model_fit object that contains several elements:
-lvl: If the outcome is a factor, this contains
-the factor levels at the time of model fitting.
spec: The model specification object
-(object in the call to fit)
fit: when the model is executed without error,
-this is the model object. Otherwise, it is a try-error
-object with the error message.
preproc: any objects needed to convert between
-a formula and non-formula interface (such as the terms
-object)
The return value will also have a class related to the fitted model (e.g.
-"_glm") before the base class of "model_fit".
-
-
-
fit() fit_xy() x/y) and these functions translate
-between the interface used when fit() fit_xy()
-
When possible, these functions attempt to avoid making copies of the
-data. For example, if the underlying model uses a formula and
-fit() x/y, the formula is evaluated and
-the data are converted to the required format. In this case, any
-calls in the resulting model objects reference the temporary
-objects used to fit the model.
-
If the model engine has not been set, the model's default engine will be used
-(as discussed on each model page). If the verbosity option of
-control_parsnip()
-
-
-
-
-
-
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
-#> filter, lag
#> The following objects are masked from ‘package:base’:
-#> intersect, setdiff, setequal, union
#> parsnip model object
-#>
-#> Fit time: 25ms
-#>
-#> Call: stats::glm(formula = Class ~ funded_amnt + int_rate, family = stats::binomial,
-#> data = data)
-#>
-#> Coefficients:
-#> (Intercept) funded_amnt int_rate
-#> 5.131e+00 2.767e-06 -1.586e-01
-#>
-#> Degrees of Freedom: 9856 Total (i.e. Null); 9854 Residual
-#> Null Deviance: 4055
-#> Residual Deviance: 3698 AIC: 3704
using_xy
#> parsnip model object
-#>
-#> Fit time: 17ms
-#>
-#> Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
-#>
-#> Coefficients:
-#> (Intercept) funded_amnt int_rate
-#> 5.131e+00 2.767e-06 -1.586e-01
-#>
-#> Degrees of Freedom: 9856 Total (i.e. Null); 9854 Residual
-#> Null Deviance: 4055
-#> Residual Deviance: 3698 AIC: 3704
-
-
-
-
-
-
-
Control the fit function — fit_control • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Options can be passed to the fit()
-
-
-
fit_control (verbosity = 1L , catch = FALSE )
-
-
-
-
- verbosity
- An integer where a value of zero indicates
-that no messages or output should be shown when packages are
-loaded or when the model is fit. A value of 1 means that package
-loading is quiet but model fits can produce output to the screen
-(depending on if they contain their own verbose-type
-argument). A value of 2 or more indicates that any output should
-be seen.
-
- catch
- A logical where a value of TRUE will evaluate
-the model inside of try(, silent = TRUE)
-
-
-
-
-
An S3 object with class "fit_control" that is a named list with the
-results of the function call
-
-
-
-
-
-
-
-
Working with the parsnip model environment — get_model_env • parsnip
-
-
-
-
-
-
-
-
-
-
-
These functions read and write to the environment where the package stores
-information about model specifications.
-
-
-
get_model_env ()
-
-get_from_env (items )
-
-set_in_env (... )
-
-set_env_val (name , value )
-
-
-
-
- items
- A character string of objects in the model environment.
-
- ...
- Named values that will be assigned to the model environment.
-
- name
- A single character value for a new symbol in the model environment.
-
- value
- A single value for a new value in the model environment.
-
-
-
-
-
"Making a parsnip model from scratch"
-https://tidymodels.github.io/parsnip/articles/articles/Scratch.html
-
-
-
#> [1] "boost_tree" "boost_tree_args"
-#> [3] "boost_tree_encoding" "boost_tree_fit"
-#> [5] "boost_tree_modes" "boost_tree_pkgs"
-#> [7] "boost_tree_predict" "decision_tree"
-#> [9] "decision_tree_args" "decision_tree_encoding"
-#> [11] "decision_tree_fit" "decision_tree_modes"
-#> [13] "decision_tree_pkgs" "decision_tree_predict"
-#> [15] "linear_reg" "linear_reg_args"
-#> [17] "linear_reg_encoding" "linear_reg_fit"
-#> [19] "linear_reg_modes" "linear_reg_pkgs"
-#> [21] "linear_reg_predict" "logistic_reg"
-#> [23] "logistic_reg_args" "logistic_reg_encoding"
-#> [25] "logistic_reg_fit" "logistic_reg_modes"
-#> [27] "logistic_reg_pkgs" "logistic_reg_predict"
-#> [29] "mars" "mars_args"
-#> [31] "mars_encoding" "mars_fit"
-#> [33] "mars_modes" "mars_pkgs"
-#> [35] "mars_predict" "mlp"
-#> [37] "mlp_args" "mlp_encoding"
-#> [39] "mlp_fit" "mlp_modes"
-#> [41] "mlp_pkgs" "mlp_predict"
-#> [43] "models" "modes"
-#> [45] "multinom_reg" "multinom_reg_args"
-#> [47] "multinom_reg_encoding" "multinom_reg_fit"
-#> [49] "multinom_reg_modes" "multinom_reg_pkgs"
-#> [51] "multinom_reg_predict" "nearest_neighbor"
-#> [53] "nearest_neighbor_args" "nearest_neighbor_encoding"
-#> [55] "nearest_neighbor_fit" "nearest_neighbor_modes"
-#> [57] "nearest_neighbor_pkgs" "nearest_neighbor_predict"
-#> [59] "null_model" "null_model_args"
-#> [61] "null_model_encoding" "null_model_fit"
-#> [63] "null_model_modes" "null_model_pkgs"
-#> [65] "null_model_predict" "rand_forest"
-#> [67] "rand_forest_args" "rand_forest_encoding"
-#> [69] "rand_forest_fit" "rand_forest_modes"
-#> [71] "rand_forest_pkgs" "rand_forest_predict"
-#> [73] "surv_reg" "surv_reg_args"
-#> [75] "surv_reg_encoding" "surv_reg_fit"
-#> [77] "surv_reg_modes" "surv_reg_pkgs"
-#> [79] "surv_reg_predict" "svm_poly"
-#> [81] "svm_poly_args" "svm_poly_encoding"
-#> [83] "svm_poly_fit" "svm_poly_modes"
-#> [85] "svm_poly_pkgs" "svm_poly_predict"
-#> [87] "svm_rbf" "svm_rbf_args"
-#> [89] "svm_rbf_encoding" "svm_rbf_fit"
-#> [91] "svm_rbf_modes" "svm_rbf_pkgs"
-#> [93] "svm_rbf_predict"
-
-
-
-
-
-
-
-
Tools for models that predict on sub-models — has_multi_predict • parsnip
-
-
-
-
-
-
-
-
-
-
-
has_multi_predict() tests to see if an object can make multiple
-predictions on submodels from the same object. multi_predict_args()
-returns the names of the arguments to multi_predict()
-
-
-
has_multi_predict (object , ... )
-
-# S3 method for default
-has_multi_predict (object , ... )
-
-# S3 method for model_fit
-has_multi_predict (object , ... )
-
-# S3 method for workflow
-has_multi_predict (object , ... )
-
-multi_predict_args (object , ... )
-
-# S3 method for default
-multi_predict_args (object , ... )
-
-# S3 method for model_fit
-multi_predict_args (object , ... )
-
-# S3 method for workflow
-multi_predict_args (object , ... )
-
-
-
-
- object
- An object to test.
-
- ...
- Not currently used.
-
-
-
-
-
has_multi_predict() returns single logical value while
-multi_predict() NA
-if none exist).
-
-
-
#> [1] FALSE
#> [1] FALSE
-multi_predict_args (lm_model_fit )
#> [1] NA
#> [1] "neighbors"
#> [[1]]
-#> # A tibble: 4 x 2
-#> neighbors .pred
-#> <int> <dbl>
-#> 1 1 21
-#> 2 2 21
-#> 3 3 20.9
-#> 4 4 21.0
-#>
-
-
-
-
-
-
-
Function reference • parsnip
-
-
-Simple interface to MLP models via keras — keras_mlp • parsnip
-
-
-
-
-
-
-
-
-
-
-
Instead of building a keras model sequentially, keras_mlp can be used to
-create a feedforward network with a single hidden layer. Regularization is
-via either weight decay or dropout.
-
-
-
keras_mlp (
- x ,
- y ,
- hidden_units = 5 ,
- penalty = 0 ,
- dropout = 0 ,
- epochs = 20 ,
- activation = "softmax" ,
- seeds = sample.int 10 ^5 , size = 3 ),
- ...
-)
-
-
-
-
- x
- A data frame or matrix of predictors
-
- y
- A vector (factor or numeric) or matrix (numeric) of outcome data.
-
- hidden_units
- An integer for the number of hidden units.
-
- penalty
- A non-negative real number for the amount of weight decay. Either
-this parameter or dropout can specified.
-
- dropout
- The proportion of parameters to set to zero. Either
-this parameter or penalty can specified.
-
- epochs
- An integer for the number of passes through the data.
-
- activation
- A character string for the type of activation function between layers.
-
- seeds
- A vector of three positive integers to control randomness of the
-calculations.
-
- ...
- Currently ignored.
-
-
-
-
-
A keras model object.
-
-
-
-
-
-
-
-
Loan Data — lending_club • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Lending Club Statistics https://www.lendingclub.com/info/download-data.action
-
-
-
-
-
lending_club a data frame
-
-
-
-
These data were downloaded from the Lending Club
-access site (see below) and are from the first quarter of 2016.
-A subset of the rows and variables are included here. The
-outcome is in the variable Class and is either "good" (meaning
-that the loan was fully paid back or currently on-time) or "bad"
-(charged off, defaulted, of 21-120 days late). A data dictionary
-can be found on the source website.
-
-
-
#> tibble [9,857 × 23] (S3: tbl_df/tbl/data.frame)
-#> $ funded_amnt : int [1:9857] 16100 32000 10000 16800 3500 10000 11000 15000 6000 20000 ...
-#> $ term : Factor w/ 2 levels "term_36","term_60": 1 2 1 2 1 1 1 1 1 2 ...
-#> $ int_rate : num [1:9857] 13.99 11.99 16.29 13.67 7.39 ...
-#> $ sub_grade : Factor w/ 35 levels "A1","A2","A3",..: 14 11 16 13 4 10 1 7 8 12 ...
-#> $ addr_state : Factor w/ 50 levels "AK","AL","AR",..: 7 23 35 33 5 43 17 24 34 11 ...
-#> $ verification_status : Factor w/ 3 levels "Not_Verified",..: 1 3 2 3 2 2 1 1 2 1 ...
-#> $ annual_inc : num [1:9857] 35000 72000 72000 101000 50100 32000 65000 188000 89000 48000 ...
-#> $ emp_length : Factor w/ 12 levels "emp_lt_1","emp_1",..: 7 3 3 1 12 1 6 8 3 3 ...
-#> $ delinq_2yrs : int [1:9857] 0 0 0 0 0 0 0 2 0 1 ...
-#> $ inq_last_6mths : int [1:9857] 0 0 2 0 0 0 0 0 2 0 ...
-#> $ revol_util : num [1:9857] 67.4 82.2 74.2 64 78.3 68.1 54.8 33.3 72.3 36 ...
-#> $ acc_now_delinq : int [1:9857] 0 0 0 0 0 0 0 0 0 0 ...
-#> $ open_il_6m : int [1:9857] 1 5 3 6 0 1 2 8 1 0 ...
-#> $ open_il_12m : int [1:9857] 0 1 2 1 0 0 0 2 0 0 ...
-#> $ open_il_24m : int [1:9857] 0 3 3 2 0 0 0 5 0 0 ...
-#> $ total_bal_il : int [1:9857] 1099 49187 33378 55445 0 7574 20998 170814 19616 0 ...
-#> $ all_util : int [1:9857] 48 77 79 64 78 66 54 80 75 36 ...
-#> $ inq_fi : int [1:9857] 0 0 1 1 0 0 0 0 2 1 ...
-#> $ inq_last_12m : int [1:9857] 3 0 4 4 0 0 0 4 6 1 ...
-#> $ delinq_amnt : int [1:9857] 0 0 0 0 0 0 0 0 0 0 ...
-#> $ num_il_tl : int [1:9857] 3 9 9 10 3 9 7 21 7 1 ...
-#> $ total_il_high_credit_limit: int [1:9857] 13193 65945 39387 60188 0 12131 39486 177477 26129 0 ...
-#> $ Class : Factor w/ 2 levels "bad","good": 2 2 2 2 2 2 2 2 2 2 ...
-
-
-
-
-
-
-
General Interface for Linear Regression Models — linear_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
linear_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R, Stan, keras, or via Spark. The main
-arguments for the model are:
-penalty: The total amount of regularization
-in the model. Note that this must be zero for some engines.
mixture: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
linear_reg (mode = "regression" , penalty = NULL , mixture = NULL )
-
-# S3 method for linear_reg
-update object ,
- parameters = NULL ,
- penalty = NULL ,
- mixture = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "regression".
-
- penalty
- A non-negative number representing the total
-amount of regularization (glmnet, keras, and spark only).
-For keras models, this corresponds to purely L2 regularization
-(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of mixture).
-
- mixture
- A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (glmnet and spark only).
-
- object
- A linear regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The data given to the function are not saved and are only used
-to determine the mode of the model. For linear_reg(), the
-mode will always be "regression".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
lm
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: lm
-##
-## Model fit template:
-## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
-
-
-
glmnet
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: glmnet
-##
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## family = "gaussian")
-
-
-
For glmnet models, the full regularization path is always fit
-regardless of the value given to penalty. Also, there is the option to
-pass multiple values (or no values) to the penalty argument. When
-using the predict() penalty. When using predict() multi_predict() .pred that contains a tibble with all of the penalty
-results.
-
-
stan
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: stan
-##
-## Model fit template:
-## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), family = stats::gaussian, refresh = 0)
-
-
-
Note that the refresh default prevents logging of the estimation
-process. Change this value in set_engine()
-
For prediction, the stan engine can compute posterior intervals
-analogous to confidence and prediction intervals. In these instances,
-the units are the original outcome and when std_error = TRUE, the
-standard deviation of the posterior distribution (or posterior
-predictive distribution as appropriate) is returned.
-
-
spark
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_linear_regression(x = missing_arg(), formula = missing_arg(),
-## weight_col = missing_arg())
-
-
-
-
keras
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1,
-## act = "linear")
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip glmnet spark keras penalty lambda reg_param penalty mixture alpha elastic_net_param NA
-
-
-
-
-
-
-
-
-
-
linear_reg ()
#> Linear Regression Model Specification (regression)
-#>
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = varying()
-#>
model <- linear_reg (penalty = 10 , mixture = 0.1 )
-model
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 10
-#> mixture = 0.1
-#>
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 1
-#> mixture = 0.1
-#>
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 1
-#>
-
-
-
-
-
-
-
General Interface for Logistic Regression Models — logistic_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
logistic_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R, Stan, keras, or via Spark. The main
-arguments for the model are:
-penalty: The total amount of regularization
-in the model. Note that this must be zero for some engines.
mixture: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
logistic_reg (mode = "classification" , penalty = NULL , mixture = NULL )
-
-# S3 method for logistic_reg
-update object ,
- parameters = NULL ,
- penalty = NULL ,
- mixture = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "classification".
-
- penalty
- A non-negative number representing the total
-amount of regularization (glmnet, keras, and spark only).
-For keras models, this corresponds to purely L2 regularization
-(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of mixture).
-
- mixture
- A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (glmnet and spark only).
-
- object
- A logistic regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
For logistic_reg(), the mode will always be "classification".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
glm
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: glm
-##
-## Model fit template:
-## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## family = stats::binomial)
-
-
-
-
glmnet
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: glmnet
-##
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## family = "binomial")
-
-
-
For glmnet models, the full regularization path is always fit
-regardless of the value given to penalty. Also, there is the option to
-pass multiple values (or no values) to the penalty argument. When
-using the predict() penalty. When using predict() multi_predict() .pred that contains a tibble with all of the penalty
-results.
-
-
stan
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: stan
-##
-## Model fit template:
-## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), family = stats::binomial, refresh = 0)
-
-
-
Note that the refresh default prevents logging of the estimation
-process. Change this value in set_engine()
-
For prediction, the stan engine can compute posterior intervals
-analogous to confidence and prediction intervals. In these instances,
-the units are the original outcome and when std_error = TRUE, the
-standard deviation of the posterior distribution (or posterior
-predictive distribution as appropriate) is returned.
-
-
spark
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(),
-## weight_col = missing_arg(), family = "binomial")
-
-
-
-
keras
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1,
-## act = "linear")
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip glmnet spark keras penalty lambda reg_param penalty mixture alpha elastic_net_param NA
-
-
-
-
-
-
-
-
-
-
logistic_reg ()
#> Logistic Regression Model Specification (classification)
-#>
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = varying()
-#>
model <- logistic_reg (penalty = 10 , mixture = 0.1 )
-model
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 10
-#> mixture = 0.1
-#>
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#> mixture = 0.1
-#>
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#>
-
-
-
-
-
-
-
Prepend a new class — make_classes • parsnip
-
-
-
-
-
-
-
-
-
-
-
This adds an extra class to a base class of "model_spec".
-
-
-
make_classes (prefix )
-
-
-
-
- prefix
- A character string for a class.
-
-
-
-
-
A character vector.
-
-
-
-
-
-
-
-
General Interface for MARS — mars • parsnip
-
-
-
-
-
-
-
-
-
-
-
mars() is a way to generate a specification of a model before
-fitting and allows the model to be created using R. The main
-arguments for the
-model are:
-num_terms: The number of features that will be retained in the
-final model.
prod_degree: The highest possible degree of interaction between
-features. A value of 1 indicates and additive model while a value of 2
-allows, but does not guarantee, two-way interactions between features.
prune_method: The type of pruning. Possible values are listed
-in ?earth.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
mars (
- mode = "unknown" ,
- num_terms = NULL ,
- prod_degree = NULL ,
- prune_method = NULL
-)
-
-# S3 method for mars
-update object ,
- parameters = NULL ,
- num_terms = NULL ,
- prod_degree = NULL ,
- prune_method = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- num_terms
- The number of features that will be retained in the
-final model, including the intercept.
-
- prod_degree
- The highest possible interaction degree.
-
- prune_method
- The pruning method.
-
- object
- A MARS model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-R : "earth" (the default)
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
earth
-
## MARS Model Specification (regression)
-##
-## Computational engine: earth
-##
-## Model fit template:
-## earth::earth(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## keepxy = TRUE)
-
## MARS Model Specification (classification)
-##
-## Engine-Specific Arguments:
-## glm = list(family = stats::binomial)
-##
-## Computational engine: earth
-##
-## Model fit template:
-## earth::earth(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## glm = list(family = stats::binomial), keepxy = TRUE)
-
-
-
Note that, when the model is fit, the earth package only has its
-namespace loaded. However, if multi_predict is used, the package is
-attached.
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip earth num_terms nprune prod_degree degree prune_method pmethod
-
-
-
-
-
-
-
-
-
-
mars (mode = "regression" , num_terms = 5 )
#> MARS Model Specification (regression)
-#>
-#> Main Arguments:
-#> num_terms = 5
-#>
model <- mars (num_terms = 10 , prune_method = "none" )
-model
#> MARS Model Specification (unknown)
-#>
-#> Main Arguments:
-#> num_terms = 10
-#> prune_method = none
-#>
#> MARS Model Specification (unknown)
-#>
-#> Main Arguments:
-#> num_terms = 1
-#> prune_method = none
-#>
#> MARS Model Specification (unknown)
-#>
-#> Main Arguments:
-#> num_terms = 1
-#>
-
-
-
-
-
-
-
Determine the minimum set of model fits — min_grid • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
min_grid determines exactly what models should be fit in order to
-evaluate the entire set of tuning parameter combinations. This is for
-internal use only and the API may change in the near future.
-
-
-
-
min_grid (x , grid , ... )
-
-# S3 method for boost_tree
-min_grid (x , grid , ... )
-
-# S3 method for linear_reg
-min_grid (x , grid , ... )
-
-# S3 method for logistic_reg
-min_grid (x , grid , ... )
-
-# S3 method for mars
-min_grid (x , grid , ... )
-
-# S3 method for multinom_reg
-min_grid (x , grid , ... )
-
-# S3 method for nearest_neighbor
-min_grid (x , grid , ... )
-
-
-
-
- x
- A model specification.
-
- grid
- A tibble with tuning parameter combinations.
-
- ...
- Not currently used.
-
-
-
-
-
A tibble with the minimum tuning parameters to fit and an additional
-list column with the parameter combinations used for prediction.
-
-
-
-
-
-
-
-
-
General Interface for Single Layer Neural Network — mlp • parsnip
-
-
-
-
-
-
-
-
-
-
-
mlp(), for multilayer perceptron, is a way to generate a specification of
-a model before fitting and allows the model to be created using
-different packages in R or via keras The main arguments for the
-model are:
-hidden_units: The number of units in the hidden layer
-(default: 5).
penalty: The amount of L2 regularization (aka weight
-decay, default is zero).
dropout: The proportion of parameters randomly dropped out of
-the model (keras only, default is zero).
epochs: The number of training iterations (default: 20).
activation: The type of function that connects the hidden
-layer and the input variables (keras only, default is softmax).
-
-
If parameters need to be modified, this function can be used
-in lieu of recreating the object from scratch.
-
-
-
mlp (
- mode = "unknown" ,
- hidden_units = NULL ,
- penalty = NULL ,
- dropout = NULL ,
- epochs = NULL ,
- activation = NULL
-)
-
-# S3 method for mlp
-update object ,
- parameters = NULL ,
- hidden_units = NULL ,
- penalty = NULL ,
- dropout = NULL ,
- epochs = NULL ,
- activation = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- hidden_units
- An integer for the number of units in the hidden model.
-
- penalty
- A non-negative numeric value for the amount of weight
-decay.
-
- dropout
- A number between 0 (inclusive) and 1 denoting the proportion
-of model parameters randomly set to zero during model training.
-
- epochs
- An integer for the number of training iterations.
-
- activation
- A single character strong denoting the type of relationship
-between the original predictors and the hidden unit layer. The activation
-function between the hidden and output layers is automatically set to either
-"linear" or "softmax" depending on the type of outcome. Possible values are:
-"linear", "softmax", "relu", and "elu"
-
- object
- A random forest model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() hidden_units when nnet::nnet size argument has no default so a value of 5 units will be
-used. Also, unless otherwise specified, the linout argument to
-nnet::nnet() TRUE when a regression model is created.
-If parameters need to be modified, update()
-
The model can be created using the fit() engines :
-R : "nnet" (the default)
keras : "keras"
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
keras
-
## Single Layer Neural Network Specification (regression)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())
-
## Single Layer Neural Network Specification (classification)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())
-
-
-
An error is thrown if both penalty and dropout are specified for
-keras models.
-
-
nnet
-
## Single Layer Neural Network Specification (regression)
-##
-## Main Arguments:
-## hidden_units = 5
-##
-## Computational engine: nnet
-##
-## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## size = 5, trace = FALSE, linout = TRUE)
-
## Single Layer Neural Network Specification (classification)
-##
-## Main Arguments:
-## hidden_units = 5
-##
-## Computational engine: nnet
-##
-## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## size = 5, trace = FALSE, linout = FALSE)
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip keras nnet hidden_units hidden_units size penalty penalty decay dropout dropout NA epochs epochs maxit activation activation NA
-
-
-
-
-
-
-
-
-
-
mlp (mode = "classification" , penalty = 0.01 )
#> Single Layer Neural Network Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
#> Single Layer Neural Network Specification (regression)
-#>
-#> Main Arguments:
-#> hidden_units = varying()
-#>
model <- mlp (hidden_units = 10 , dropout = 0.30 )
-model
#> Single Layer Neural Network Specification (unknown)
-#>
-#> Main Arguments:
-#> hidden_units = 10
-#> dropout = 0.3
-#>
#> Single Layer Neural Network Specification (unknown)
-#>
-#> Main Arguments:
-#> hidden_units = 2
-#> dropout = 0.3
-#>
#> Single Layer Neural Network Specification (unknown)
-#>
-#> Main Arguments:
-#> hidden_units = 2
-#>
-
-
-
-
-
-
-
Model Fit Object Information — model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
An object with class "model_fit" is a container for
-information about a model that has been fit to the data.
-
-
-
-
-
-
-
The main elements of the object are:
-lvl: A vector of factor levels when the outcome is
-is a factor. This is NULL when the outcome is not a factor
-vector.
spec: A model_spec object.
fit: The object produced by the fitting function.
preproc: This contains any data-specific information
-required to process new a sample point for prediction. For
-example, if the underlying model function requires arguments x
-and y and the user passed a formula to fit, the preproc
-object would contain items such as the terms object and so on.
-When no information is required, this is NA.
-
-
As discussed in the documentation for model_spec model_fit object prior to fitting.
-If the resulting model object prints its call, any user-defined
-options are shown in the call preceded by a tilde (see the
-example below). This is a result of the use of quosures in the
-specification.
-
This class and structure is the basis for how parsnip
-stores model objects after to seeing the data and applying a model.
-
-
-
#> Linear Regression Model Specification (regression)
-#>
-#> Engine-Specific Arguments:
-#> x = ifelse(.obs() < 500, TRUE, FALSE)
-#>
-#> Computational engine: lm
-#>
#> parsnip model object
-#>
-#> Fit time: 2ms
-#>
-#> Call:
-#> stats::lm(formula = mpg ~ ., data = data, x = ~ifelse(.obs() <
-#> 500, TRUE, FALSE))
-#>
-#> Coefficients:
-#> (Intercept) cyl disp hp drat wt
-#> 12.30337 -0.11144 0.01334 -0.02148 0.78711 -3.71530
-#> qsec vs am gear carb
-#> 0.82104 0.31776 2.52023 0.65541 -0.19942
-#>
#> [1] 32
-
-
-
-
-
-
-
Print helper for model objects — model_printer • parsnip
-
-
-
-
-
-
-
-
-
-
-
A common format function that prints information about the model object (e.g.
-arguments, calls, packages, etc).
-
-
-
model_printer (x , ... )
-
-
-
-
- x
- A model object.
-
- ...
- Not currently used.
-
-
-
-
-
-
-
-
-
-
Model Specification Information — model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
An object with class "model_spec" is a container for
-information about a model that will be fit.
-
-
-
-
-
-
-
The main elements of the object are:
-args: A vector of the main arguments for the model. The
-names of these arguments may be different form their
-counterparts n the underlying model function. For example, for a
-glmnet model, the argument name for the amount of the penalty
-is called "penalty" instead of "lambda" to make it more general
-and usable across different types of models (and to not be
-specific to a particular model function). The elements of args
-can varying() NULL), the
-arguments will use the underlying model functions default value.
-As discussed below, the arguments in args are captured as
-quosures and are not immediately executed.
-...: Optional model-function-specific
-parameters. As with args, these will be quosures and can be
-varying()
mode: The type of model, such as "regression" or
-"classification". Other modes will be added once the package
-adds more functionality.
method: This is a slot that is filled in later by the
-model's constructor function. It generally contains lists of
-information that are used to create the fit and prediction code
-as well as required packages and similar data.
engine: This character string declares exactly what
-software will be used. It can be a package name or a technology
-type.
-
-This class and structure is the basis for how parsnip
-stores model objects prior to seeing the data.
-
-
-
-
-
-
-
An important detail to understand when creating model
-specifications is that they are intended to be functionally
-independent of the data. While it is true that some tuning
-parameters are data dependent , the model specification does
-not interact with the data at all.
-
For example, most R functions immediately evaluate their
-arguments. For example, when calling mean(dat_vec) dat_vec is immediately evaluated inside of the function.
-
parsnip model functions do not do this. For example, using
-
- rand_forest mtry = ncol iris ) - 1 )
-
-
does not execute ncol(iris) - 1 when creating the specification.
-This can be seen in the output:
-
- > rand_forest(mtry = ncol(iris) - 1)
- Random Forest Model Specification (unknown)
-
- Main Arguments:
- mtry = ncol(iris) - 1
-
-
-
The model functions save the argument expressions and their
-associated environments (a.k.a. a quosure) to be evaluated later
-when either fit() fit_xy()
-
The consequence of this strategy is that any data required to
-get the parameter values must be available when the model is
-fit. The two main ways that this can fail is if:
-
-The data have been modified between the creation of the
-model specification and when the model fit function is invoked.
If the model specification is saved and loaded into a new
-session where those same data objects do not exist.
-
-
The best way to avoid these issues is to not reference any data
-objects in the global environment but to use data descriptors
-such as .cols()
-
- rand_forest mtry = .cols 1 )
-
-
This is not dependent on any specific data object and
-is evaluated immediately before the model fitting process begins.
-
One less advantageous approach to solving this issue is to use
-quasiquotation. This would insert the actual R object into the
-model specification and might be the best idea when the data
-object is small. For example, using
-
- rand_forest mtry = ncol iris ) - 1 )
-
-
would work (and be reproducible between sessions) but embeds
-the entire iris data set into the mtry expression:
-
- > rand_forest(mtry = ncol(!!iris) - 1)
- Random Forest Model Specification (unknown)
-
- Main Arguments:
- mtry = ncol(structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, <snip>
-
-
-
However, if there were an object with the number of columns in
-it, this wouldn't be too bad:
-
- > mtry_val <- ncol(iris) - 1
- > mtry_val
- [1] 4
- > rand_forest(mtry = !!mtry_val)
- Random Forest Model Specification (unknown)
-
- Main Arguments:
- mtry = 4
-
-
-
More information on quosures and quasiquotation can be found at
-https://tidyeval.tidyverse.org .
-
-
-
-
-
-
-
-
Model predictions across many sub-models — multi_predict • parsnip
-
-
-
-
-
-
-
-
-
-
-
For some models, predictions can be made on sub-models in the model object.
-
-
-
multi_predict (object , ... )
-
-# S3 method for default
-multi_predict (object , ... )
-
-# S3 method for `_xgb.Booster`
-multi_predict (object , new_data , type = NULL , trees = NULL , ... )
-
-# S3 method for `_C5.0`
-multi_predict (object , new_data , type = NULL , trees = NULL , ... )
-
-# S3 method for `_elnet`
-multi_predict (object , new_data , type = NULL , penalty = NULL , ... )
-
-# S3 method for `_lognet`
-multi_predict (object , new_data , type = NULL , penalty = NULL , ... )
-
-# S3 method for `_earth`
-multi_predict (object , new_data , type = NULL , num_terms = NULL , ... )
-
-# S3 method for `_multnet`
-multi_predict (object , new_data , type = NULL , penalty = NULL , ... )
-
-# S3 method for `_train.kknn`
-multi_predict (object , new_data , type = NULL , neighbors = NULL , ... )
-
-
-
-
- object
- A model_fit object.
-
- ...
- Optional arguments to pass to predict.model_fit(type = "raw") type.
-
- new_data
- A rectangular data object, such as a data frame.
-
- type
- A single character value or NULL. Possible values
-are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
-or "raw". When NULL, predict()
-
- trees
- An integer vector for the number of trees in the ensemble.
-
- penalty
- A numeric vector of penalty values.
-
- num_terms
- An integer vector for the number of MARS terms to retain.
-
- neighbors
- An integer vector for the number of nearest neighbors.
-
-
-
-
-
A tibble with the same number of rows as the data being predicted.
-There is a list-column named .pred that contains tibbles with
-multiple rows per sub-model. Note that, within the tibbles, the column names
-follow the usual standard based on prediction type (i.e. .pred_class for
-type = "class" and so on).
-
-
-
-
-
-
-
-
General Interface for Multinomial Regression Models — multinom_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
multinom_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R, keras, or Spark. The main arguments for the
-model are:
-penalty: The total amount of regularization
-in the model. Note that this must be zero for some engines.
mixture: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
multinom_reg (mode = "classification" , penalty = NULL , mixture = NULL )
-
-# S3 method for multinom_reg
-update object ,
- parameters = NULL ,
- penalty = NULL ,
- mixture = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "classification".
-
- penalty
- A non-negative number representing the total
-amount of regularization (glmnet, keras, and spark only).
-For keras models, this corresponds to purely L2 regularization
-(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of mixture).
-
- mixture
- A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (glmnet only).
-
- object
- A multinomial regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
For multinom_reg(), the mode will always be "classification".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
glmnet
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: glmnet
-##
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## family = "multinomial")
-
-
-
For glmnet models, the full regularization path is always fit
-regardless of the value given to penalty. Also, there is the option to
-pass multiple values (or no values) to the penalty argument. When
-using the predict() penalty. When using predict() multi_predict() .pred that contains a tibble with all of the penalty
-results.
-
-
nnet
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: nnet
-##
-## Model fit template:
-## nnet::multinom(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), trace = FALSE)
-
-
-
-
spark
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(),
-## weight_col = missing_arg(), family = "multinomial")
-
-
-
-
keras
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1,
-## act = "linear")
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip glmnet spark keras nnet penalty lambda reg_param penalty decay mixture alpha elastic_net_param NA NA
-
-
-
-
-
-
-
-
-
-
multinom_reg ()
#> Multinomial Regression Model Specification (classification)
-#>
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = varying()
-#>
model <- multinom_reg (penalty = 10 , mixture = 0.1 )
-model
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 10
-#> mixture = 0.1
-#>
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#> mixture = 0.1
-#>
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#>
-
-
-
-
-
-
-
General Interface for K-Nearest Neighbor Models — nearest_neighbor • parsnip
-
-
-
-
-
-
-
-
-
-
-
nearest_neighbor() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R. The main arguments for the
-model are:
-neighbors: The number of neighbors considered at
-each prediction.
weight_func: The type of kernel function that weights the
-distances between samples.
dist_power: The parameter used when calculating the Minkowski
-distance. This corresponds to the Manhattan distance with dist_power = 1
-and the Euclidean distance with dist_power = 2.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
nearest_neighbor (
- mode = "unknown" ,
- neighbors = NULL ,
- weight_func = NULL ,
- dist_power = NULL
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- neighbors
- A single integer for the number of neighbors
-to consider (often called k). For kknn , a value of 5
-is used if neighbors is not specified.
-
- weight_func
- A single character for the type of kernel function used
-to weight distances between samples. Valid choices are: "rectangular",
-"triangular", "epanechnikov", "biweight", "triweight",
-"cos", "inv", "gaussian", "rank", or "optimal".
-
- dist_power
- A single number for the parameter used in
-calculating Minkowski distance.
-
-
-
-
-
The model can be created using the fit() engines :
-R : "kknn" (the default)
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
kknn
-
## K-Nearest Neighbor Model Specification (regression)
-##
-## Computational engine: kknn
-##
-## Model fit template:
-## kknn::train.kknn(formula = missing_arg(), data = missing_arg(),
-## ks = 5)
-
## K-Nearest Neighbor Model Specification (classification)
-##
-## Computational engine: kknn
-##
-## Model fit template:
-## kknn::train.kknn(formula = missing_arg(), data = missing_arg(),
-## ks = 5)
-
-
-
For kknn, the underlying modeling function used is a restricted
-version of train.kknn() and not kknn(). It is set up in this way so
-that parsnip can utilize the underlying predict.train.kknn method to
-predict on new data. This also means that a single value of that
-function’s kernel argument (a.k.a weight_func here) can be supplied
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip kknn neighbors ks weight_func kernel dist_power distance
-
-
-
-
-
-
-
-
-
-
nearest_neighbor (neighbors = 11 )
#> K-Nearest Neighbor Model Specification (unknown)
-#>
-#> Main Arguments:
-#> neighbors = 11
-#>
-
-
-
-
-
-
-
-
General Interface for null models — null_model • parsnip
-
-
-
-
-
-
-
-
-
-
-
null_model() is a way to generate a specification of a model before
-fitting and allows the model to be created using R. It doesn't have any
-main arguments.
-
-
-
null_model (mode = "classification" )
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
parsnip
-
## Model Specification (regression)
-##
-## Computational engine: parsnip
-##
-## Model fit template:
-## nullmodel(x = missing_arg(), y = missing_arg())
-
## Model Specification (classification)
-##
-## Computational engine: parsnip
-##
-## Model fit template:
-## nullmodel(x = missing_arg(), y = missing_arg())
-
-
-
-
-
-
-
-
-
null_model (mode = "regression" )
#> Model Specification (regression)
-#>
-
-
-
-
-
-
-
Fit a simple, non-informative model — nullmodel • parsnip
-
-
-
-
-
-
-
-
-
-
-
Fit a single mean or largest class model. nullmodel() is the underlying
-computational function for the null_model()
-
-
-
nullmodel (x , ... )
-
-# S3 method for default
-nullmodel (x = NULL , y , ... )
-
-# S3 method for nullmodel
-print x , ... )
-
-# S3 method for nullmodel
-predict object , new_data = NULL , type = NULL , ... )
-
-
-
-
- x
- An optional matrix or data frame of predictors. These values are
-not used in the model fit
-
- ...
- Optional arguments (not yet used)
-
- y
- A numeric vector (for regression) or factor (for classification) of
-outcomes
-
- object
- An object of class nullmodel
-
- new_data
- A matrix or data frame of predictors (only used to determine
-the number of predictions to return)
-
- type
- Either "raw" (for regression), "class" or "prob" (for
-classification)
-
-
-
-
-
The output of nullmodel() is a list of class nullmodel
-with elements
-
call the function call
value the mean of
-y or the most prevalent class
levels when y is a
-factor, a vector of levels. NULL otherwise
pct when y
-is a factor, a data frame with a column for each class (NULL
-otherwise). The column for the most prevalent class has the proportion of
-the training samples with that class (the other columns are zero).
n
- the number of elements in y
-
-predict.nullmodel() returns a either a factor or numeric vector
-depending on the class of y. All predictions are always the same.
-
-
-
-
nullmodel() emulates other model building functions, but returns the
-simplest model possible given a training set: a single mean for numeric
-outcomes and the most prevalent class for factor outcomes. When class
-probabilities are requested, the percentage of the training set samples with
-the most prevalent class is returned.
-
-
-
#> Null Regression Model
-#> Predicted Value: b
#> [1] b b b b b
-#> Levels: a b
-
-
-
-
-
-
-
-
Other predict methods. — predict_class.model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
These are internal functions not meant to be directly called by the user.
-
-
-
# S3 method for model_fit
-predict_class (object , new_data , ... )
-
-# S3 method for model_fit
-predict_classprob (object , new_data , ... )
-
-# S3 method for model_fit
-predict_confint (object , new_data , level = 0.95 , std_error = FALSE , ... )
-
-# S3 method for model_fit
-predict_numeric (object , new_data , ... )
-
-predict_numeric (object , ... )
-
-# S3 method for model_fit
-predict_quantile (object , new_data , quantile = (1 :9 )/10 , ... )
-
-
-
-
- object
- An object of class model_fit
-
- new_data
- A rectangular data object, such as a data frame.
-
- ...
- Arguments to the underlying model's prediction
-function cannot be passed here (see opts). There are some
-parsnip related options that can be passed, depending on the
-value of type. Possible arguments are:
-level: for types of "conf_int" and "pred_int" this
-is the parameter for the tail area of the intervals
-(e.g. confidence level for confidence intervals).
-Default value is 0.95.
std_error: add the standard error of fit or
-prediction for types of "conf_int" and "pred_int".
-Default value is FALSE.
quantile: the quantile(s) for quantile regression
-(not implemented yet)
time: the time(s) for hazard probability estimates
-(not implemented yet)
-
- level
- A single numeric value between zero and one for the
-interval estimates.
-
- std_error
- A single logical for wether the standard error should be
-returned (assuming that the model can compute it).
-
- quant
- A vector of numbers between 0 and 1 for the quantile being
-predicted.
-
-
-
-
-
-
-
-
-
-
Model predictions — predict.model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
Apply a model to create different types of predictions.
-predict()
-
-
-
# S3 method for model_fit
-predict object , new_data , type = NULL , opts = list ... )
-
-# S3 method for model_fit
-predict_raw (object , new_data , opts = list ... )
-
-predict_raw (object , ... )
-
-
-
-
- object
- An object of class model_fit
-
- new_data
- A rectangular data object, such as a data frame.
-
- type
- A single character value or NULL. Possible values
-are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
-or "raw". When NULL, predict()
-
- opts
- A list of optional arguments to the underlying
-predict function that will be used when type = "raw". The
-list should not include options for the model object or the
-new data being predicted.
-
- ...
- Arguments to the underlying model's prediction
-function cannot be passed here (see opts). There are some
-parsnip related options that can be passed, depending on the
-value of type. Possible arguments are:
-level: for types of "conf_int" and "pred_int" this
-is the parameter for the tail area of the intervals
-(e.g. confidence level for confidence intervals).
-Default value is 0.95.
std_error: add the standard error of fit or
-prediction for types of "conf_int" and "pred_int".
-Default value is FALSE.
quantile: the quantile(s) for quantile regression
-(not implemented yet)
time: the time(s) for hazard probability estimates
-(not implemented yet)
-
-
-
-
-
With the exception of type = "raw", the results of
-predict.model_fit() will be a tibble as many rows in the output
-as there are rows in new_data and the column names will be
-predictable.
-
For numeric results with a single outcome, the tibble will have
-a .pred column and .pred_Yname for multivariate results.
-
For hard class predictions, the column is named .pred_class
-and, when type = "prob", the columns are .pred_classlevel.
-
type = "conf_int" and type = "pred_int" return tibbles with
-columns .pred_lower and .pred_upper with an attribute for
-the confidence level. In the case where intervals can be
-produces for class probabilities (or other non-scalar outputs),
-the columns will be named .pred_lower_classlevel and so on.
-
Quantile predictions return a tibble with a column .pred, which is
-a list-column. Each list element contains a tibble with columns
-.pred and .quantile (and perhaps other columns).
-
Using type = "raw" with predict.model_fit() will return
-the unadulterated results of the prediction function.
-
In the case of Spark-based models, since table columns cannot
-contain dots, the same convention is used except 1) no dots
-appear in names and 2) vectors are never returned but
-type-specific prediction functions.
-
When the model fit failed and the error was captured, the
-predict()
-
-
-
If "type" is not supplied to predict() type = "numeric" for regression models and
-type = "class" for classification).
-
predict()
-
When using type = "conf_int" and type = "pred_int", the options
-level and std_error can be used. The latter is a logical for an
-extra column of standard error values (if available).
-
-
-
#> # A tibble: 10 x 1
-#> .pred
-#> <dbl>
-#> 1 23.4
-#> 2 23.3
-#> 3 27.6
-#> 4 21.5
-#> 5 17.6
-#> 6 21.6
-#> 7 13.9
-#> 8 21.7
-#> 9 25.6
-#> 10 17.1
#> # A tibble: 10 x 2
-#> .pred_lower .pred_upper
-#> <dbl> <dbl>
-#> 1 17.9 29.0
-#> 2 18.1 28.5
-#> 3 24.0 31.3
-#> 4 17.5 25.6
-#> 5 14.3 20.8
-#> 6 17.0 26.2
-#> 7 9.65 18.2
-#> 8 16.2 27.2
-#> 9 14.2 37.0
-#> 10 11.5 22.7
#> cyl disp hp drat wt qsec
-#> 1 -0.001433177 -0.8113275 0.6303467 -0.06120265 2.4139815 -1.567729
-#> 2 -0.001433177 -0.8113275 0.6303467 -0.06120265 1.4488706 -0.736286
-#> 3 -0.009315653 -1.3336453 0.8557288 -0.05014798 3.5494061 1.624418
-#> 4 -0.001433177 0.1730406 0.6303467 0.12009386 0.1620561 2.856736
-#> 5 0.006449298 1.1975870 -0.2314083 0.10461733 -0.6895124 -0.736286
-#> 6 -0.001433177 -0.1584303 0.6966356 0.19084372 -0.7652074 4.014817
-#> 7 0.006449298 1.1975870 -1.1594522 0.09135173 -1.1815297 -2.488255
-#> 8 -0.009315653 -0.9449204 1.2667197 -0.01477305 0.2566748 3.688179
-#> 9 -0.009315653 -1.0041833 0.8292133 -0.06562451 0.4080647 7.993866
-#> 10 -0.001433177 -0.7349888 0.4579957 -0.06562451 -0.6895124 1.164155
-#> vs am gear carb
-#> 1 0.2006406 2.88774 0.02512680 -0.2497240
-#> 2 0.2006406 2.88774 0.02512680 -0.2497240
-#> 3 -0.3511210 2.88774 0.02512680 0.4668753
-#> 4 -0.3511210 -2.40645 -0.06700481 0.4668753
-#> 5 0.2006406 -2.40645 -0.06700481 0.2280089
-#> 6 -0.3511210 -2.40645 -0.06700481 0.4668753
-#> 7 0.2006406 -2.40645 -0.06700481 -0.2497240
-#> 8 -0.3511210 -2.40645 0.02512680 0.2280089
-#> 9 -0.3511210 -2.40645 0.02512680 0.2280089
-#> 10 -0.3511210 -2.40645 0.02512680 -0.2497240
-#> attr(,"constant")
-#> [1] 19.96364
-
-
-
-
-
-
-
General Interface for Random Forest Models — rand_forest • parsnip
-
-
-
-
-
-
-
-
-
-
-
rand_forest() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-mtry: The number of predictors that will be
-randomly sampled at each split when creating the tree models.
trees: The number of trees contained in the ensemble.
min_n: The minimum number of data points in a node
-that are required for the node to be split further.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
rand_forest (mode = "unknown" , mtry = NULL , trees = NULL , min_n = NULL )
-
-# S3 method for rand_forest
-update object ,
- parameters = NULL ,
- mtry = NULL ,
- trees = NULL ,
- min_n = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- mtry
- An integer for the number of predictors that will
-be randomly sampled at each split when creating the tree models.
-
- trees
- An integer for the number of trees contained in
-the ensemble.
-
- min_n
- An integer for the minimum number of data points
-in a node that are required for the node to be split further.
-
- object
- A random forest model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save), the model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
ranger
-
## Random Forest Model Specification (regression)
-##
-## Computational engine: ranger
-##
-## Model fit template:
-## ranger::ranger(formula = missing_arg(), data = missing_arg(),
-## case.weights = missing_arg(), num.threads = 1, verbose = FALSE,
-## seed = sample.int(10^5, 1))
-
## Random Forest Model Specification (classification)
-##
-## Computational engine: ranger
-##
-## Model fit template:
-## ranger::ranger(formula = missing_arg(), data = missing_arg(),
-## case.weights = missing_arg(), num.threads = 1, verbose = FALSE,
-## seed = sample.int(10^5, 1), probability = TRUE)
-
-
-
Note that ranger::ranger()
-
For ranger confidence intervals, the intervals are constructed using
-the form estimate +/- z * std_error. For classification probabilities,
-these values can fall outside of [0, 1] and will be coerced to be in
-this range.
-
-
randomForest
-
## Random Forest Model Specification (regression)
-##
-## Computational engine: randomForest
-##
-## Model fit template:
-## randomForest::randomForest(x = missing_arg(), y = missing_arg())
-
## Random Forest Model Specification (classification)
-##
-## Computational engine: randomForest
-##
-## Model fit template:
-## randomForest::randomForest(x = missing_arg(), y = missing_arg())
-
-
-
Note that
-randomForest::randomForest()
-
-
spark
-
## Random Forest Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
-## type = "regression", seed = sample.int(10^5, 1))
-
## Random Forest Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
-## type = "classification", seed = sample.int(10^5, 1))
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip ranger randomForest spark mtry mtry mtry feature_subset_strategy trees num.trees ntree num_trees min_n min.node.size nodesize min_instances_per_node
-
-
-
-
-
-
-
-
-
-
rand_forest (mode = "classification" , trees = 2000 )
#> Random Forest Model Specification (classification)
-#>
-#> Main Arguments:
-#> trees = 2000
-#>
#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = varying()
-#>
model <- rand_forest (mtry = 10 , min_n = 3 )
-model
#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#>
#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#> min_n = 3
-#>
#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#>
-
-
-
-
-
-
-
Objects exported from other packages — reexports • parsnip
-
-
-
-
-
-
-
-
-
-
-
These objects are imported from other packages. Follow the links
-below to see their documentation.
-
- generics fit fit_xy tidy varying_args
magrittr %>%
-
-
-
-
-
-
-
-
-
-
-
Repair a model call object — repair_call • parsnip
-
-
-
-
-
-
-
-
-
-
-
When the user passes a formula to fit() and the underyling model function
-uses a formula, the call object produced by fit() data portion of the call will not correspond to the original data.
-
-
-
repair_call (x , data )
-
-
-
-
- x
- A fitted parsnip model. An error will occur if the underlying model
-does not have a call element.
-
- data
- A data object that is relavant to the call. In most cases, this
-is the data frame that was given to parsnip for the model fit (i.e., the
-training set data). The name of this data object is inserted into the call.
-
-
-
-
-
A modified parsnip fitted model.
-
-
-
repair_call() call can adjust the model objects call to be usable by other
-functions and methods.
-
-
-
#> stats::lm(formula = mpg ~ ., data = data, model = ~TRUE)
-# All better:
-repair_call (fitted_model , mtcars )$fit $call
#> stats::lm(formula = mpg ~ ., data = mtcars, model = TRUE)
-
-
-
-
-
-
-
Determine required packages for a model — req_pkgs • parsnip
-
-
-
-
-
-
-
-
-
-
-
Determine required packages for a model
-
-
-
req_pkgs (x , ... )
-
-# S3 method for model_spec
-req_pkgs (x , ... )
-
-# S3 method for model_fit
-req_pkgs (x , ... )
-
-
-
-
- x
- A model specification or fit.
-
- ...
- Not used.
-
-
-
-
-
A character string of package names (if any).
-
-
-
For a model specification, the engine must be set.
-
The list does not include the parsnip package.
-
-
-
#> [1] "Error : Please set an engine.\n"
-#> attr(,"class")
-#> [1] "try-error"
-#> attr(,"condition")
-#> <error/rlang_error>
-#> Please set an engine.
-#> Backtrace:
-#> 1. base::tryCatch(...)
-#> 13. pkgdown::build_site(...)
-#> 14. pkgdown:::build_site_local(...)
-#> 15. pkgdown::build_reference(...)
-#> 16. purrr::map(...)
-#> 17. pkgdown:::.f(.x[[i]], ...)
-#> 18. pkgdown:::data_reference_topic(...)
-#> 19. pkgdown:::run_examples(...)
-#> 20. pkgdown:::highlight_examples(code, topic, env = env)
-#> 21. evaluate::evaluate(x, child_env(env), new_device = TRUE)
-#> 22. evaluate:::evaluate_call(...)
-#> 32. [ base::eval(...) ] with 1 more call
-#> 40. parsnip:::req_pkgs.model_spec(linear_reg())
#> [1] "glmnet"
#> [1] "stats"
-
-
-
-
-
-
-
Decision trees via rpart — rpart_train • parsnip
-
-
-
-
-
-
-
-
-
-
-
rpart_train is a wrapper for rpart() tree-based models
-where all of the model arguments are in the main function.
-
-
-
rpart_train (
- formula ,
- data ,
- weights = NULL ,
- cp = 0.01 ,
- minsplit = 20 ,
- maxdepth = 30 ,
- ...
-)
-
-
-
-
- formula
- A model formula.
-
- data
- A data frame.
-
- weights
- Optional case weights.
-
- cp
- A non-negative number for complexity parameter. Any split
-that does not decrease the overall lack of fit by a factor of
-cp is not attempted. For instance, with anova splitting,
-this means that the overall R-squared must increase by cp at
-each step. The main role of this parameter is to save computing
-time by pruning off splits that are obviously not worthwhile.
-Essentially,the user informs the program that any split which
-does not improve the fit by cp will likely be pruned off by
-cross-validation, and that hence the program need not pursue it.
-
- minsplit
- An integer for the minimum number of observations
-that must exist in a node in order for a split to be attempted.
-
- maxdepth
- An integer for the maximum depth of any node
-of the final tree, with the root node counted as depth 0.
-Values greater than 30 rpart will give nonsense results on
-32-bit machines. This function will truncate maxdepth to 30 in
-those cases.
-
- ...
- Other arguments to pass to either rpart or rpart.control.
-
-
-
-
-
A fitted rpart model.
-
-
-
-
-
-
-
-
Change elements of a model specification — set_args • parsnip
-
-
-
-
-
-
-
-
-
-
-
set_args() can be used to modify the arguments of a model specification while
-set_mode() is used to change the model's mode.
-
-
-
set_args (object , ... )
-
-set_mode (object , mode )
-
-
-
-
- object
- A model specification.
-
- ...
- One or more named model arguments.
-
- mode
- A character string for the model type (e.g. "classification" or
-"regression")
-
-
-
-
-
An updated model object.
-
-
-
set_args() will replace existing values of the arguments.
-
-
-
#> Random Forest Model Specification (unknown)
-#>
#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = 3
-#>
-#> Engine-Specific Arguments:
-#> importance = TRUE
-#>
-
-
-
-
-
-
-
-
Declare a computational engine and specific arguments — set_engine • parsnip
-
-
-
-
-
-
-
-
-
-
-
set_engine() is used to specify which package or system will be used
-to fit the model, along with any arguments specific to that software.
-
-
-
set_engine (object , engine , ... )
-
-
-
-
- object
- A model specification.
-
- engine
- A character string for the software that should
-be used to fit the model. This is highly dependent on the type
-of model (e.g. linear regression, random forest, etc.).
-
- ...
- Any optional arguments associated with the chosen computational
-engine. These are captured as quosures and can be varying()
-
-
-
-
-
An updated model specification.
-
-
-
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> mixture = 1/3
-#>
-#> Engine-Specific Arguments:
-#> nlambda = 10
-#>
-#> Computational engine: glmnet
-#>
-#> Model fit template:
-#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-#> alpha = 1/3, nlambda = 10, family = "binomial")
-
-
-
-
-
-
-
Tools to Register Models — set_new_model • parsnip
-
-
-
-
-
-
-
-
-
-
-
These functions are similar to constructors and can be used to validate
-that there are no conflicts with the underlying model structures used by the
-package.
-
-
-
set_new_model (model )
-
-set_model_mode (model , mode )
-
-set_model_engine (model , mode , eng )
-
-set_model_arg (model , eng , parsnip , original , func , has_submodel )
-
-set_dependency (model , eng , pkg )
-
-get_dependency (model )
-
-set_fit (model , mode , eng , value )
-
-get_fit (model )
-
-set_pred (model , mode , eng , type , value )
-
-get_pred_type (model , type )
-
-show_model_info (model )
-
-pred_value_template (pre = NULL , post = NULL , func , ... )
-
-set_encoding (model , mode , eng , options )
-
-get_encoding (model )
-
-
-
-
- model
- A single character string for the model type (e.g.
-"rand_forest", etc).
-
- mode
- A single character string for the model mode (e.g. "regression").
-
- eng
- A single character string for the model engine.
-
- parsnip
- A single character string for the "harmonized" argument name
-that parsnip exposes.
-
- original
- A single character string for the argument name that
-underlying model function uses.
-
- func
- A named character vector that describes how to call
-a function. func should have elements pkg and fun. The
-former is optional but is recommended and the latter is
-required. For example, c(pkg = "stats", fun = "lm") c(fun = "predict") predict method.
-
- has_submodel
- A single logical for whether the argument
-can make predictions on multiple submodels at once.
-
- pkg
- An options character string for a package name.
-
- value
- A list that conforms to the fit_obj or pred_obj description
-below, depending on context.
-
- type
- A single character value for the type of prediction. Possible
-values are: class, conf_int, numeric, pred_int, prob, quantile,
-and raw.
-
- pre, post
- Optional functions for pre- and post-processing of prediction
-results.
-
- ...
- Optional arguments that should be passed into the args slot for
-prediction objects.
-
- options
- A list of options for engine-specific encodings. Currently,
-the option implemented is predictor_indicators which tells parsnip
-whether the pre-processing should make indicator/dummy variables from factor
-predictors. This only affects cases when fit.model_spec()
-
- arg
- A single character string for the model argument name.
-
- fit_obj
- A list with elements interface, protect,
-func and defaults. See the package vignette "Making a
-parsnip model from scratch".
-
- pred_obj
- A list with elements pre, post, func, and args.
-See the package vignette "Making a parsnip model from scratch".
-
-
-
-
-
These functions are available for users to add their
-own models or engines (in package or otherwise) so that they can
-be accessed using parsnip. This are more thoroughly documented
-on the package web site (see references below).
-
In short, parsnip stores an environment object that contains
-all of the information and code about how models are used (e.g.
-fitting, predicting, etc). These functions can be used to add
-models to that environment as well as helper functions that can
-be used to makes sure that the model data is in the right
-format.
-
check_model_exists() checks the model value and ensures that the model has
-already been registered. check_model_doesnt_exist() checks the model value
-and also checks to see if it is novel in the environment.
-
-
-
"Making a parsnip model from scratch"
-https://tidymodels.github.io/parsnip/articles/articles/Scratch.html
-
-
-
# set_new_model("shallow_learning_model")
-
-# Show the information about a model:
-show_model_info ("rand_forest" )
#> Information for `rand_forest`
-#> modes: unknown, classification, regression
-#>
-#> engines:
-#> classification: randomForest, ranger, spark
-#> regression: randomForest, ranger, spark
-#>
-#> arguments:
-#> ranger:
-#> mtry --> mtry
-#> trees --> num.trees
-#> min_n --> min.node.size
-#> randomForest:
-#> mtry --> mtry
-#> trees --> ntree
-#> min_n --> nodesize
-#> spark:
-#> mtry --> feature_subset_strategy
-#> trees --> num_trees
-#> min_n --> min_instances_per_node
-#>
-#> fit modules:
-#> engine mode
-#> ranger classification
-#> ranger regression
-#> randomForest classification
-#> randomForest regression
-#> spark classification
-#> spark regression
-#>
-#> prediction modules:
-#> mode engine methods
-#> classification randomForest class, prob, raw
-#> classification ranger class, conf_int, prob, raw
-#> classification spark class, prob
-#> regression randomForest numeric, raw
-#> regression ranger conf_int, numeric, raw
-#> regression spark numeric
-#>
-
-
-
-
-
-
-
Print the model call — show_call • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
show_call (object )
-
-
-
-
- x
- A "model_spec" object.
-
-
-
-
-
A character string.
-
-
-
-
-
-
-
-
General Interface for Parametric Survival Models — surv_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
surv_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-R. The main argument for the
-model is:
This argument is converted to its specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the value is taken from the underlying model
-functions.
-
If parameters need to be modified, this function can be used
-in lieu of recreating the object from scratch.
-
-
-
surv_reg (mode = "regression" , dist = NULL )
-
-# S3 method for surv_reg
-update object , parameters = NULL , dist = NULL , fresh = FALSE , ... )
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "regression".
-
- dist
- A character string for the outcome distribution. "weibull" is
-the default.
-
- object
- A survival regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The data given to the function are not saved and are only used
-to determine the mode of the model. For surv_reg(),the
-mode will always be "regression".
-
Since survival models typically involve censoring (and require the use of
-survival::Surv() fit()
-
Also, for the flexsurv::flexsurvfit engine, the typical
-strata function cannot be used. To achieve the same effect,
-the extra parameter roles can be used (as described above).
-
For surv_reg(), the mode will always be "regression".
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
flexsurv
-
## Parametric Survival Regression Model Specification (regression)
-##
-## Computational engine: flexsurv
-##
-## Model fit template:
-## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg())
-
-
-
-
survival
-
## Parametric Survival Regression Model Specification (regression)
-##
-## Computational engine: survival
-##
-## Model fit template:
-## survival::survreg(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), model = TRUE)
-
-
-
Note that model = TRUE is needed to produce quantile predictions when
-there is a stratification variable and can be overridden in other cases.
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip flexsurv survival dist dist dist
-
-
-
-
-
-
-
Jackson, C. (2016). flexsurv: A Platform for Parametric Survival
-Modeling in R. Journal of Statistical Software , 70(8), 1 - 33.
-
-
-
-
-
-
surv_reg ()
#> Parametric Survival Regression Model Specification (regression)
-#>
#> Parametric Survival Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> dist = varying()
-#>
-model <- surv_reg (dist = "weibull" )
-model
#> Parametric Survival Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> dist = weibull
-#>
#> Parametric Survival Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> dist = lnorm
-#>
-
-
-
-
-
-
-
General interface for polynomial support vector machines — svm_poly • parsnip
-
-
-
-
-
-
-
-
-
-
-
svm_poly() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-cost: The cost of predicting a sample within or on the
-wrong side of the margin.
degree: The polynomial degree.
scale_factor: A scaling factor for the kernel.
margin: The epsilon in the SVM insensitive loss function
-(regression only)
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
svm_poly (
- mode = "unknown" ,
- cost = NULL ,
- degree = NULL ,
- scale_factor = NULL ,
- margin = NULL
-)
-
-# S3 method for svm_poly
-update object ,
- parameters = NULL ,
- cost = NULL ,
- degree = NULL ,
- scale_factor = NULL ,
- margin = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- cost
- A positive number for the cost of predicting a sample within
-or on the wrong side of the margin
-
- degree
- A positive number for polynomial degree.
-
- scale_factor
- A positive number for the polynomial scaling factor.
-
- margin
- A positive number for the epsilon in the SVM insensitive
-loss function (regression only)
-
- object
- A polynomial SVM model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
kernlab
-
## Polynomial Support Vector Machine Specification (regression)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "polydot")
-
## Polynomial Support Vector Machine Specification (classification)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "polydot",
-## prob.model = TRUE)
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip kernlab cost C degree degree scale_factor scale margin epsilon
-
-
-
-
-
-
-
-
-
-
svm_poly (mode = "classification" , degree = 1.2 )
#> Polynomial Support Vector Machine Specification (classification)
-#>
-#> Main Arguments:
-#> degree = 1.2
-#>
#> Polynomial Support Vector Machine Specification (regression)
-#>
-#> Main Arguments:
-#> cost = varying()
-#>
model <- svm_poly (cost = 10 , scale_factor = 0.1 )
-model
#> Polynomial Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 10
-#> scale_factor = 0.1
-#>
#> Polynomial Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#> scale_factor = 0.1
-#>
#> Polynomial Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#>
-
-
-
-
-
-
-
General interface for radial basis function support vector machines — svm_rbf • parsnip
-
-
-
-
-
-
-
-
-
-
-
svm_rbf() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-cost: The cost of predicting a sample within or on the
-wrong side of the margin.
rbf_sigma: The precision parameter for the radial basis
-function.
margin: The epsilon in the SVM insensitive loss function
-(regression only)
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
svm_rbf (mode = "unknown" , cost = NULL , rbf_sigma = NULL , margin = NULL )
-
-# S3 method for svm_rbf
-update object ,
- parameters = NULL ,
- cost = NULL ,
- rbf_sigma = NULL ,
- margin = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- cost
- A positive number for the cost of predicting a sample within
-or on the wrong side of the margin
-
- rbf_sigma
- A positive number for radial basis function.
-
- margin
- A positive number for the epsilon in the SVM insensitive
-loss function (regression only)
-
- object
- A radial basis function SVM model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
kernlab
-
## Radial Basis Function Support Vector Machine Specification (regression)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "rbfdot")
-
## Radial Basis Function Support Vector Machine Specification (classification)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "rbfdot",
-## prob.model = TRUE)
-
-
-
-
liquidSVM
-
## Radial Basis Function Support Vector Machine Specification (regression)
-##
-## Computational engine: liquidSVM
-##
-## Model fit template:
-## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1,
-## threads = 0)
-
## Radial Basis Function Support Vector Machine Specification (classification)
-##
-## Computational engine: liquidSVM
-##
-## Model fit template:
-## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1,
-## threads = 0)
-
-
-
Note that models created using the liquidSVM engine cannot be saved
-like conventional R objects. The fit slot of the model_fit object
-has to be saved separately using the liquidSVM::write.liquidSVM() fit slot has to be replaced
-with the model that is read using the liquidSVM::read.liquidSVM()
-
liquidSVM parameterizes the kernel parameter differently than
-kernlab. To translate between engines, sigma = 1/gammas^2. Users
-will be specifying sigma and the function translates the value to
-gamma.
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip kernlab liquidSVM cost C lambdas rbf_sigma sigma gammas margin epsilon NA
-
-
-
-
-
-
-
-
-
-
svm_rbf (mode = "classification" , rbf_sigma = 0.2 )
#> Radial Basis Function Support Vector Machine Specification (classification)
-#>
-#> Main Arguments:
-#> rbf_sigma = 0.2
-#>
#> Radial Basis Function Support Vector Machine Specification (regression)
-#>
-#> Main Arguments:
-#> cost = varying()
-#>
model <- svm_rbf (cost = 10 , rbf_sigma = 0.1 )
-model
#> Radial Basis Function Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 10
-#> rbf_sigma = 0.1
-#>
#> Radial Basis Function Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#> rbf_sigma = 0.1
-#>
#> Radial Basis Function Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#>
-
-
-
-
-
-
-
Turn a parsnip model object into a tidy tibble — tidy.model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
This method tidies the model in a parsnip model object, if it exists.
-
-
-
# S3 method for model_fit
-tidy x , ... )
-
-
-
-
- x
- An object to be converted into a tidy tibble::tibble()
-
- ...
- Additional arguments to tidying method.
-
-
-
-
-
a tibble
-
-
-
-
-
-
-
-
Tidy method for null models — tidy.nullmodel • parsnip
-
-
-
-
-
-
-
-
-
-
-
Return the results of nullmodel as a tibble
-
-
-
# S3 method for nullmodel
-tidy x , ... )
-
-
-
-
- x
- A nullmodel object.
-
- ...
- Not used.
-
-
-
-
-
A tibble with column value.
-
-
-
#> # A tibble: 1 x 1
-#> value
-#> <chr>
-#> 1 setosa
#> # A tibble: 1 x 1
-#> value
-#> <dbl>
-#> 1 20.1
-
-
-
-
-
-
-
Resolve a Model Specification for a Computational Engine — translate • parsnip
-
-
-
-
-
-
-
-
-
-
-
translate() will translate a model specification into a code
-object that is specific to a particular engine (e.g. R package).
-It translates generic parameters to their counterparts.
-
-
-
translate (x , ... )
-
-# S3 method for default
-translate (x , engine = x $engine , ... )
-
-
-
-
- x
- A model specification.
-
- ...
- Not currently used.
-
- engine
- The computational engine for the model (see ?set_engine
-
-
-
-
-
translate() produces a template call that lacks the specific
-argument values (such as data, etc). These are filled in once
-fit() varying arguments if these are in
-the specification.
-
It does contain the resolved argument names that are specific to
-the model fitting function/engine.
-
This function can be useful when you need to understand how
-parsnip goes from a generic model specific to a model fitting
-function.
-
Note : this function is used internally and users should only use it
-to understand what the underlying syntax would be. It should not be used
-to modify the model specification.
-
-
-
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
-#> Computational engine: glmnet
-#>
-#> Model fit template:
-#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-#> family = "gaussian")
-# `penalty` not applicable for this model.
-translate (lm_spec , engine = "lm" )
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
-#> Computational engine: lm
-#>
-#> Model fit template:
-#> stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-# `penalty` is tranlsated to `reg_param`
-translate (lm_spec , engine = "spark" )
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
-#> Computational engine: spark
-#>
-#> Model fit template:
-#> sparklyr::ml_linear_regression(x = missing_arg(), formula = missing_arg(),
-#> weight_col = missing_arg(), reg_param = 0.01)
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> mixture = varying()
-#>
-#> Computational engine: glmnet
-#>
-#> Model fit template:
-#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-#> alpha = varying(), family = "gaussian")
-
-
-
-
-
-
-
-
Succinct summary of parsnip object — type_sum.model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
type_sum controls how objects are shown when inside tibble
-columns.
-
-
-
# S3 method for model_spec
-type_sum (x )
-
-# S3 method for model_fit
-type_sum (x )
-
-
-
-
- x
- A model_spec or model_fit object to summarise.
-
-
-
-
-
A character value.
-
-
-
For model_spec objects, the summary is "spec[?]"
-or "spec[+]". The former indicates that either the model
-mode has not been declared or that the specification has
-varying()
-
For fitted models, either "fit[x]" or "fit[+]" are used
-where the "x" implies that the model fit failed in some way.
-
-
-
-
-
-
-
-
A placeholder function for argument values — varying • parsnip
-
-
-
-
-
-
-
-
-
-
-
varying() is used when a parameter will be specified at a later date.
-
-
-
varying ()
-
-
-
-
-
-
-
-
-
-
Determine varying arguments — varying_args.model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
varying_args()
-
-
-
# S3 method for model_spec
-varying_args object , full = TRUE , ... )
-
-# S3 method for recipe
-varying_args object , full = TRUE , ... )
-
-# S3 method for step
-varying_args object , full = TRUE , ... )
-
-
-
-
- object
- A model_spec or a recipe.
-
- full
- A single logical. Should all possible varying parameters be
-returned? If FALSE, then only the parameters that
-are actually varying are returned.
-
- ...
- Not currently used.
-
-
-
-
-
A tibble with columns for the parameter name (name), whether it
-contains any varying value (varying), the id for the object (id),
-and the class that was used to call the method (type).
-
-
-
The id column is determined differently depending on whether a model_spec
-or a recipe is used. For a model_spec, the first class is used. For
-a recipe, the unique step id is used.
-
-
-
#> # A tibble: 3 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry FALSE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
#> # A tibble: 3 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry TRUE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
#> # A tibble: 4 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry FALSE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
-#> 4 sample.fraction TRUE rand_forest model_spec
#> # A tibble: 1 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 sample.fraction TRUE rand_forest model_spec
#> # A tibble: 5 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry FALSE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
-#> 4 strata FALSE rand_forest model_spec
-#> 5 sampsize TRUE rand_forest model_spec
-
-
-
-
-
-
-
-
Watson Churn Data — wa_churn • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
IBM Watson Analytics https://ibm.co/2sOvyvy
-
-
-
-
-
wa_churn a data frame
-
-
-
-
These data were downloaded from the IBM Watson site
-(see below) in September 2018. The data contain a factor for
-whether a customer churned or not. Alternatively, the tenure
-column presumably contains information on how long the customer
-has had an account. A survival analysis can be done on this
-column using the churn outcome as the censoring information. A
-data dictionary can be found on the source website.
-
-
-
#> tibble [7,043 × 20] (S3: tbl_df/tbl/data.frame)
-#> $ churn : Factor w/ 2 levels "Yes","No": 2 2 1 2 1 1 2 2 1 2 ...
-#> $ female : num [1:7043] 1 0 0 0 1 1 0 1 1 0 ...
-#> $ senior_citizen : int [1:7043] 0 0 0 0 0 0 0 0 0 0 ...
-#> $ partner : num [1:7043] 1 0 0 0 0 0 0 0 1 0 ...
-#> $ dependents : num [1:7043] 0 0 0 0 0 0 1 0 0 1 ...
-#> $ tenure : int [1:7043] 1 34 2 45 2 8 22 10 28 62 ...
-#> $ phone_service : num [1:7043] 0 1 1 0 1 1 1 0 1 1 ...
-#> $ multiple_lines : Factor w/ 3 levels "No","No phone service",..: 2 1 1 2 1 3 3 2 3 1 ...
-#> $ internet_service : Factor w/ 3 levels "DSL","Fiber optic",..: 1 1 1 1 2 2 2 1 2 1 ...
-#> $ online_security : Factor w/ 3 levels "No","No internet service",..: 1 3 3 3 1 1 1 3 1 3 ...
-#> $ online_backup : Factor w/ 3 levels "No","No internet service",..: 3 1 3 1 1 1 3 1 1 3 ...
-#> $ device_protection: Factor w/ 3 levels "No","No internet service",..: 1 3 1 3 1 3 1 1 3 1 ...
-#> $ tech_support : Factor w/ 3 levels "No","No internet service",..: 1 1 1 3 1 1 1 1 3 1 ...
-#> $ streaming_tv : Factor w/ 3 levels "No","No internet service",..: 1 1 1 1 1 3 3 1 3 1 ...
-#> $ streaming_movies : Factor w/ 3 levels "No","No internet service",..: 1 1 1 1 1 3 1 1 3 1 ...
-#> $ contract : Factor w/ 3 levels "Month-to-month",..: 1 2 1 2 1 1 1 1 1 2 ...
-#> $ paperless_billing: num [1:7043] 1 0 1 0 1 1 1 0 1 0 ...
-#> $ payment_method : Factor w/ 4 levels "Bank transfer (automatic)",..: 3 4 4 1 3 3 2 4 3 1 ...
-#> $ monthly_charges : num [1:7043] 29.9 57 53.9 42.3 70.7 ...
-#> $ total_charges : num [1:7043] 29.9 1889.5 108.2 1840.8 151.7 ...
-
-
-
-
-
-
-
Boosted trees via xgboost — xgb_train • parsnip
-
-
-
-
-
-
-
-
-
-
-
xgb_train is a wrapper for xgboost tree-based models where all of the
-model arguments are in the main function.
-
-
-
xgb_train (
- x ,
- y ,
- max_depth = 6 ,
- nrounds = 15 ,
- eta = 0.3 ,
- colsample_bytree = 1 ,
- min_child_weight = 1 ,
- gamma = 0 ,
- subsample = 1 ,
- validation = 0 ,
- early_stop = NULL ,
- ...
-)
-
-
-
-
- x
- A data frame or matrix of predictors
-
- y
- A vector (factor or numeric) or matrix (numeric) of outcome data.
-
- max_depth
- An integer for the maximum depth of the tree.
-
- nrounds
- An integer for the number of boosting iterations.
-
- eta
- A numeric value between zero and one to control the learning rate.
-
- colsample_bytree
- Subsampling proportion of columns.
-
- min_child_weight
- A numeric value for the minimum sum of instance
-weights needed in a child to continue to split.
-
- gamma
- A number for the minimum loss reduction required to make a
-further partition on a leaf node of the tree
-
- subsample
- Subsampling proportion of rows.
-
- validation
- A positive number. If on [0, 1) the value, validation
-is a random proportion of data in x and y that are used for performance
-assessment and potential early stopping. If 1 or greater, it is the number
-of training set samples use for these purposes.
-
- early_stop
- An integer or NULL. If not NULL, it is the number of
-training iterations without improvement before stopping. If validation is
-used, performance is base on the validation set; otherwise the training set
-is used.
-
- ...
- Other options to pass to xgb.train.
-
-
-
-
-
A fitted xgboost object.
-
-
-
-
-
-
-
-
-
- https://parsnip.tidymodels.org/index.html
-
-
- https://parsnip.tidymodels.org/reference/C5.0_train.html
-
-
- https://parsnip.tidymodels.org/reference/add_on_exports.html
-
-
- https://parsnip.tidymodels.org/reference/add_rowindex.html
-
-
- https://parsnip.tidymodels.org/reference/boost_tree.html
-
-
- https://parsnip.tidymodels.org/reference/check_empty_ellipse.html
-
-
- https://parsnip.tidymodels.org/reference/control_parsnip.html
-
-
- https://parsnip.tidymodels.org/reference/convert_args.html
-
-
- https://parsnip.tidymodels.org/reference/convert_stan_interval.html
-
-
- https://parsnip.tidymodels.org/reference/decision_tree.html
-
-
- https://parsnip.tidymodels.org/reference/descriptors.html
-
-
- https://parsnip.tidymodels.org/reference/eval_args.html
-
-
- https://parsnip.tidymodels.org/reference/fit.html
-
-
- https://parsnip.tidymodels.org/reference/get_model_env.html
-
-
- https://parsnip.tidymodels.org/reference/has_multi_predict.html
-
-
- https://parsnip.tidymodels.org/reference/keras_mlp.html
-
-
- https://parsnip.tidymodels.org/reference/linear_reg.html
-
-
- https://parsnip.tidymodels.org/reference/logistic_reg.html
-
-
- https://parsnip.tidymodels.org/reference/make_classes.html
-
-
- https://parsnip.tidymodels.org/reference/mars.html
-
-
- https://parsnip.tidymodels.org/reference/mlp.html
-
-
- https://parsnip.tidymodels.org/reference/model_fit.html
-
-
- https://parsnip.tidymodels.org/reference/model_printer.html
-
-
- https://parsnip.tidymodels.org/reference/model_spec.html
-
-
- https://parsnip.tidymodels.org/reference/multi_predict.html
-
-
- https://parsnip.tidymodels.org/reference/multinom_reg.html
-
-
- https://parsnip.tidymodels.org/reference/nearest_neighbor.html
-
-
- https://parsnip.tidymodels.org/reference/null_model.html
-
-
- https://parsnip.tidymodels.org/reference/nullmodel.html
-
-
- https://parsnip.tidymodels.org/reference/other_predict.html
-
-
- https://parsnip.tidymodels.org/reference/predict.model_fit.html
-
-
- https://parsnip.tidymodels.org/reference/rand_forest.html
-
-
- https://parsnip.tidymodels.org/reference/reexports.html
-
-
- https://parsnip.tidymodels.org/reference/repair_call.html
-
-
- https://parsnip.tidymodels.org/reference/req_pkgs.html
-
-
- https://parsnip.tidymodels.org/reference/rpart_train.html
-
-
- https://parsnip.tidymodels.org/reference/set_args.html
-
-
- https://parsnip.tidymodels.org/reference/set_engine.html
-
-
- https://parsnip.tidymodels.org/reference/set_new_model.html
-
-
- https://parsnip.tidymodels.org/reference/show_call.html
-
-
- https://parsnip.tidymodels.org/reference/surv_reg.html
-
-
- https://parsnip.tidymodels.org/reference/svm_poly.html
-
-
- https://parsnip.tidymodels.org/reference/svm_rbf.html
-
-
- https://parsnip.tidymodels.org/reference/tidy.model_fit.html
-
-
- https://parsnip.tidymodels.org/reference/tidy.nullmodel.html
-
-
- https://parsnip.tidymodels.org/reference/translate.html
-
-
- https://parsnip.tidymodels.org/reference/type_sum.model_spec.html
-
-
- https://parsnip.tidymodels.org/reference/varying.html
-
-
- https://parsnip.tidymodels.org/reference/varying_args.model_spec.html
-
-
- https://parsnip.tidymodels.org/reference/xgb_train.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Classification.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Models.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Regression.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Scratch.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Submodels.html
-
-
- https://parsnip.tidymodels.org/articles/parsnip_Intro.html
-
-
diff --git a/docs/dev/tidyverse-2.css b/docs/dev/tidyverse-2.css
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-.contents .page-header {
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-.navbar .info {
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-.navbar .info .partof {
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-.navbar .info .version-danger {
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-.navbar-nav li a {
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-.navbar-default .navbar-nav > .active > a,
-.navbar-default .navbar-nav > .active > a:hover,
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diff --git a/docs/dev/tidyverse.css b/docs/dev/tidyverse.css
deleted file mode 100644
index 0a23ebaed..000000000
--- a/docs/dev/tidyverse.css
+++ /dev/null
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- background-image: url("data:image/svg+xml;utf8,A Common API to Modeling and Analysis Functions • parsnip
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The goal of parsnip is to provide a tidy, unified interface to models that can be used to try a range of models without getting bogged down in the syntactical minutiae of the underlying packages.
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# The easiest way to get parsnip is to install all of tidymodels: install.packages "tidymodels" )# Alternatively, install just parsnip: install.packages "parsnip" )# Or the development version from GitHub: # install.packages("devtools") :: install_github "tidymodels/parsnip" )
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One challenge with different modeling functions available in R that do the same thing is that they can have different interfaces and arguments. For example, to fit a random forest regression model, we might have:
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# From randomForest 1 <- randomForest (~ ., data = ., mtry = 10 , ntree = 2000 , importance = TRUE # From ranger 2 <- ranger (~ ., data = dat, mtry = 10 , num.trees = 2000 , importance = "impurity" # From sparklyr 3 <- ml_random_forest (intercept = FALSE , response = "y" , features = names names != "y" ], col.sample.rate = 10 ,num.trees = 2000
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Note that the model syntax can be very different and that the argument names (and formats) are also different. This is a pain if you switch between implementations.
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In this example:
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-the type of model is “random forest”,
-the mode of the model is “regression” (as opposed to classification, etc), and
-the computational engine is the name of the R package.
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The goals of parsnip are to:
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-Separate the definition of a model from its evaluation.
-Decouple the model specification from the implementation (whether the implementation is in R, spark, or something else). For example, the user would call rand_forest instead of ranger::ranger
-Harmonize argument names (e.g. n.trees, ntrees, trees) so that users only need to remember a single name. This will help across model types too so that trees will be the same argument across random forest as well as boosting or bagging.
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Using the example above, the parsnip approach would be:
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library rand_forest mtry = 10 , trees = 2000 ) %>% set_engine "ranger" , importance = "impurity" ) %>% set_mode "regression" )#> Random Forest Model Specification (regression) #> #> Main Arguments: #> mtry = 10 #> trees = 2000 #> #> Engine-Specific Arguments: #> importance = impurity #> #> Computational engine: ranger
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The engine can be easily changed. To use Spark, the change is straightforward:
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rand_forest mtry = 10 , trees = 2000 ) %>% set_engine "spark" ) %>% set_mode "regression" )#> Random Forest Model Specification (regression) #> #> Main Arguments: #> mtry = 10 #> trees = 2000 #> #> Computational engine: spark
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Either one of these model specifications can be fit in the same way:
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rand_forest mtry = 10 , trees = 2000 ) %>% set_engine "ranger" , importance = "impurity" ) %>% set_mode "regression" ) %>% fit ~ ., data = mtcars)#> parsnip model object #> #> Fit time: 75ms #> Ranger result #> #> Call: #> ranger::ranger(formula = formula, data = data, mtry = ~10, num.trees = ~2000, importance = ~"impurity", num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1)) #> #> Type: Regression #> Number of trees: 2000 #> Sample size: 32 #> Number of independent variables: 10 #> Mtry: 10 #> Target node size: 5 #> Variable importance mode: impurity #> Splitrule: variance #> OOB prediction error (MSE): 5.779248 #> R squared (OOB): 0.8408977
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A list of all parsnip models across different CRAN packages can be found at tidymodels.org
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Data sets previously found in parsnip are now find in the modeldata package.
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If you encounter a bug, please file a minimal reproducible example on GitHub . For questions and other discussion, please use community.rstudio.com .
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Please note that the parsnip project is released with a Contributor Code of Conduct . By contributing to this project, you agree to abide by its terms.
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PLEASE READ: Making a new issue for parsnip • parsnip
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name: Bug report or feature request about: Describe a bug you’ve seen or make a case for a new feature —
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Please follow the template below.
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If the question is related at all to a specific data analysis, please include a minimal reprex (reproducible example). If you’ve never heard of a reprex before, start by reading “What is a reprex ”, and follow the advice further down that page.
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Tips:
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When are ready to file the issue, please delete the parts above this line: < – ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ –>
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I’m having trouble with … or
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Have you considered …
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Copy your code to the clipboard and run:
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"))&&h.css("position",a.css("position"));x=function(){var c,f,e;if(!G&&(I=A.height(),c=parseInt(g.css("border-top-width"),10),f=parseInt(g.css("padding-top"),10),d=parseInt(g.css("padding-bottom"),10),n=g.offset().top+c+f,C=g.height(),m&&(v=m=!1,null==p&&(a.insertAfter(h),h.detach()),a.css({position:"",top:"",width:"",bottom:""}).removeClass(t),e=!0),F=a.offset().top-(parseInt(a.css("margin-top"),10)||0)-q,
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-y),b(document.body).on("sticky_kit:recalc",y),a.on("sticky_kit:detach",H),setTimeout(l,0)}};n=0;for(K=this.length;n
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diff --git a/docs/logo.png b/docs/logo.png
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- Changelog • parsnip
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-The error message for missing packages was fixed (#289 and #292 )
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-S3 dispatch for tidy()
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-A bug (#206 and #234 ) was fixed that caused an error when predicting with a multinomial glmnet model.
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-glmnet was removed as a dependency since the new version depends on 3.6.0 or greater. Keeping it would constrain parsnip to that same requirement. All glmnet tests are run locally.
A set of internal functions are now exported. These are helpful when creating a new package that registers new model specifications.
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-There were some mis-mapped parameters (going between parsnip and the underlying model function) for spark boosted trees and some keras models. See 897c927 .
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-The time elapsed during model fitting is stored in the $elapsed slot of the parsnip model object, and is printed when the model object is printed.
Some default parameter ranges were updated for SVM, KNN, and MARS models.
The model udpate() methods gained a parameters argument for cases when the parameters are contained in a tibble or list.
fit_control() control_parsnip()
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-A bug was fixed standardizing the output column types of multi_predict and predict for multinom_reg.
A bug was fixed related to using data descriptors and fit_xy()
A bug was fixed related to the column names generated by multi_predict() .pred and this list column contains tibbles across sub-models. The column names for these sub-model tibbles will have names consistent with predict() 43c15db .
A bug was fixed standardizing the column names of nnet class probability predictions.
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Test case update due to CRAN running extra tests ( #202 )
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Unplanned release based on CRAN requirements for Solaris.
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-The method that parsnip stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in ?get_model_env package vignette for adding models .
The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation.
For surv_reg() survival package is now called survival instead of survreg.
For glmnet models, the full regularization path is always fit regardless of the value given to penalty. Previously, the model was fit with passing penalty to glmnet’s lambda argument and the model could only make predictions at those specific values. ( #195 )
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-add_rowindex() .row to a data frame.
If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.
nearest_neighbor() multi_predict method. The multi_predict()
A suite of internal functions were added to help with upcoming model tuning features.
A parsnip object always saved the name(s) of the outcome variable(s) for proper naming of the predicted values.
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Small release driven by changes in sample()
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-A “null model” is now available that fits a predictor-free model (using the mean of the outcome for regression or the mode for classification).
fit_xy() y without error
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-varying_args() full argument to control whether the full set of possible varying arguments is returned (as opposed to only the arguments that are actually varying).
fit_control()
For classification models, an error occurs if the outcome data are not encoded as factors (#115 ).
The prediction modules (e.g. predict_class, predict_numeric, etc) were de-exported. These were internal functions that were not to be used by the users and the users were using them.
An event time data set (check_times) was included that is the time (in seconds) to run R CMD check using the "r-devel-windows-ix86+x86_64` flavor. Packages that errored are censored.
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-varying_args() generics package. This means that the first argument, x, has been renamed to object to align with generics.
For the recipes step method of varying_args() cannot be varying as varying (for example, the id) (#132 ).
find_varying(), the internal function for detecting varying arguments, now returns correct results when a size 0 argument is provided. It can also now detect varying arguments nested deeply into a call (#131 , #134 ).
For multinomial regression, the .pred_ prefix is now only added to prediction column names once (#107 ).
For multinomial regression using glmnet, multi_predict() #108 ).
Confidence and prediction intervals for logistic regression were only computed the intervals for a single level. Both are now computed. (#156 )
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-The engine, and any associated arguments, are now specified using set_engine() engine argument
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-Arguments to modeling functions are now captured as quosures.
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-others has been replaced by ...
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-Data descriptor names have beemn changed and are now functions. The descriptor definitions for “cols” and “preds” have been switched.
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-regularization was changed to penalty in a few models to be consistent with this change .
-If a mode is not chosen in the model specification, it is assigned at the time of fit. 51
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-The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some predict methods, the earth package will need to be attached to be fully operational.
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-To be consistent with snake_case, newdata was changed to new_data.
-A predict_raw method was added.
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-A package dependency suffered a new change.
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, and enclosing if in dropdown
- if (pos >= 0) {
- var menu_anchor = $(links[pos]);
- menu_anchor.parent().addClass("active");
- menu_anchor.closest("li.dropdown").addClass("active");
- }
- });
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- function paths(pathname) {
- var pieces = pathname.split("/");
- pieces.shift(); // always starts with /
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- var end = pieces[pieces.length - 1];
- if (end === "index.html" || end === "")
- pieces.pop();
- return(pieces);
- }
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- // Returns -1 if not found
- function prefix_length(needle, haystack) {
- if (needle.length > haystack.length)
- return(-1);
-
- // Special case for length-0 haystack, since for loop won't run
- if (haystack.length === 0) {
- return(needle.length === 0 ? 0 : -1);
- }
-
- for (var i = 0; i < haystack.length; i++) {
- if (needle[i] != haystack[i])
- return(i);
- }
-
- return(haystack.length);
- }
-
- /* Clipboard --------------------------*/
-
- function changeTooltipMessage(element, msg) {
- var tooltipOriginalTitle=element.getAttribute('data-original-title');
- element.setAttribute('data-original-title', msg);
- $(element).tooltip('show');
- element.setAttribute('data-original-title', tooltipOriginalTitle);
- }
-
- if(ClipboardJS.isSupported()) {
- $(document).ready(function() {
- var copyButton = "Boosted trees via C5.0 — C5.0_train • parsnip
-
-
-
-
-
-
-
-
-
-
-
C5.0_train is a wrapper for the C5.0() function in the
-C50 package that fits tree-based models
-where all of the model arguments are in the main function.
-
-
-
C5.0_train (x , y , weights = NULL , trials = 15 , minCases = 2 , sample = 0 , ... )
-
-
-
-
- x
- A data frame or matrix of predictors.
-
- y
- A factor vector with 2 or more levels
-
- weights
- An optional numeric vector of case weights. Note
-that the data used for the case weights will not be used as a
-splitting variable in the model (see
-http://www.rulequest.com/see5-win.html#CASEWEIGHT for
-Quinlan's notes on case weights).
-
- trials
- An integer specifying the number of boosting
-iterations. A value of one indicates that a single model is
-used.
-
- minCases
- An integer for the smallest number of samples
-that must be put in at least two of the splits.
-
- sample
- A value between (0, .999) that specifies the
-random proportion of the data should be used to train the model.
-By default, all the samples are used for model training. Samples
-not used for training are used to evaluate the accuracy of the
-model in the printed output.
-
- ...
- Other arguments to pass.
-
-
-
-
-
A fitted C5.0 model.
-
-
-
-
-
-
-
-
Functions required for parsnip-adjacent packages — null_value • parsnip
-
-
-
-
-
-
-
-
-
-
-
These functions are helpful when creating new packages that will register
-new model specifications.
-
-
-
null_value (x )
-
-show_fit (model , eng )
-
-update_dot_check (... )
-
-new_model_spec (cls , args , eng_args , mode , method , engine )
-
-check_final_param (x )
-
-update_main_parameters (args , param )
-
-is_varying (x )
-
-
-
-
-
-
-
-
-
-
Add a column of row numbers to a data frame — add_rowindex • parsnip
-
-
-
-
-
-
-
-
-
-
-
Add a column of row numbers to a data frame
-
-
-
add_rowindex (x )
-
-
-
-
- x
- A data frame
-
-
-
-
-
The same data frame with a column of 1-based integers named .row.
-
-
-
mtcars %>% add_rowindex ()
#> mpg cyl disp hp drat wt qsec vs am gear carb .row
-#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1
-#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2
-#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 3
-#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4
-#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
-#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6
-#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7
-#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 8
-#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 9
-#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 10
-#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 11
-#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 12
-#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 13
-#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 14
-#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 15
-#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 16
-#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 17
-#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 18
-#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 19
-#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 20
-#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 21
-#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 22
-#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 23
-#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 24
-#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 25
-#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 26
-#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 27
-#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 28
-#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 29
-#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 30
-#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 31
-#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 32
-
-
-
-
-
-
-
General Interface for Boosted Trees — boost_tree • parsnip
-
-
-
-
-
-
-
-
-
-
-
boost_tree() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-mtry: The number of predictors that will be
-randomly sampled at each split when creating the tree models.
trees: The number of trees contained in the ensemble.
min_n: The minimum number of data points in a node
-that are required for the node to be split further.
tree_depth: The maximum depth of the tree (i.e. number of
-splits).
learn_rate: The rate at which the boosting algorithm adapts
-from iteration-to-iteration.
loss_reduction: The reduction in the loss function required
-to split further.
sample_size: The amount of data exposed to the fitting routine.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using the set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
boost_tree (
- mode = "unknown" ,
- mtry = NULL ,
- trees = NULL ,
- min_n = NULL ,
- tree_depth = NULL ,
- learn_rate = NULL ,
- loss_reduction = NULL ,
- sample_size = NULL
-)
-
-# S3 method for boost_tree
-update object ,
- parameters = NULL ,
- mtry = NULL ,
- trees = NULL ,
- min_n = NULL ,
- tree_depth = NULL ,
- learn_rate = NULL ,
- loss_reduction = NULL ,
- sample_size = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- mtry
- A number for the number (or proportion) of predictors that will
-be randomly sampled at each split when creating the tree models (xgboost
-only).
-
- trees
- An integer for the number of trees contained in
-the ensemble.
-
- min_n
- An integer for the minimum number of data points
-in a node that are required for the node to be split further.
-
- tree_depth
- An integer for the maximum depth of the tree (i.e. number
-of splits) (xgboost only).
-
- learn_rate
- A number for the rate at which the boosting algorithm adapts
-from iteration-to-iteration (xgboost only).
-
- loss_reduction
- A number for the reduction in the loss function required
-to split further (xgboost only).
-
- sample_size
- A number for the number (or proportion) of data that is
-exposed to the fitting routine. For xgboost, the sampling is done at at
-each iteration while C5.0 samples once during training.
-
- object
- A boosted tree model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
An updated model specification.
-
-
-
The data given to the function are not saved and are only used
-to determine the mode of the model. For boost_tree(), the
-possible modes are "regression" and "classification".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
xgboost
-
## Boosted Tree Model Specification (regression)
-##
-## Computational engine: xgboost
-##
-## Model fit template:
-## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
-## verbose = 0)
-
## Boosted Tree Model Specification (classification)
-##
-## Computational engine: xgboost
-##
-## Model fit template:
-## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
-## verbose = 0)
-
-
-
-
C5.0
-
## Boosted Tree Model Specification (classification)
-##
-## Computational engine: C5.0
-##
-## Model fit template:
-## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg())
-
-
-
Note that C50::C5.0()
-
-
spark
-
## Boosted Tree Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(),
-## type = "regression", seed = sample.int(10^5, 1))
-
## Boosted Tree Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_gradient_boosted_trees(x = missing_arg(), formula = missing_arg(),
-## type = "classification", seed = sample.int(10^5, 1))
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip xgboost C5.0 spark tree_depth max_depth NA max_depth trees nrounds trials max_iter learn_rate eta NA step_size mtry colsample_bytree NA feature_subset_strategy min_n min_child_weight minCases min_instances_per_node loss_reduction gamma NA NA sample_size subsample sample subsampling_rate min_info_gain NA NA loss_reduction
-
-
-
-
-
-
-
-
-
-
boost_tree (mode = "classification" , trees = 20 )
#> Boosted Tree Model Specification (classification)
-#>
-#> Main Arguments:
-#> trees = 20
-#>
#> Boosted Tree Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = varying()
-#>
model <- boost_tree (mtry = 10 , min_n = 3 )
-model
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#> min_n = 3
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#> tree_depth = 5
-#>
#> Boosted Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#> tree_depth = 5
-#>
-param_values $verbose <- 0
-# Fails due to engine argument
-# model %>% update(param_values)
-
-
-
-
-
-
-
Check to ensure that ellipses are empty — check_empty_ellipse • parsnip
-
-
-
-
-
-
-
-
-
-
-
Check to ensure that ellipses are empty
-
-
-
check_empty_ellipse (... )
-
-
-
-
- ...
- Extra arguments.
-
-
-
-
-
If an error is not thrown (from non-empty ellipses), a NULL list.
-
-
-
-
-
-
-
-
Execution Time Data — check_times • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
These data were collected from the CRAN web page for 13,626 R
-packages. The time to complete the standard package checking
-routine was collected In some cases, the package checking
-process is stopped due to errors and these data are treated as
-censored. It is less than 1 percent.
-
-
-
-
-
-
-
CRAN
-
-
-
-
-
check_times a data frame
-
-
-
-
As predictors, the associated package source code were
-downloaded and parsed to create predictors, including
-authors: The number of authors in the author field.
imports: The number of imported packages.
suggests: The number of packages suggested.
depends: The number of hard dependencies.
Roxygen: a binary indicator for whether Roxygen was used
-for documentation.
gh: a binary indicator for whether the URL field contained
-a GitHub link.
rforge: a binary indicator for whether the URL field
-contained a link to R-forge.
descr: The number of characters (or, in some cases, bytes)
-in the description field.
r_count: The number of R files in the R directory.
r_size: The total disk size of the R files.
ns_import: Estimated number of imported functions or methods.
ns_export: Estimated number of exported functions or methods.
s3_methods: Estimated number of S3 methods.
s4_methods: Estimated number of S4 methods.
doc_count: How many Rmd or Rnw files in the vignettes
-directory.
doc_size: The disk size of the Rmd or Rnw files.
src_count: The number of files in the src directory.
src_size: The size on disk of files in the src directory.
data_count The number of files in the data directory.
data_size: The size on disk of files in the data directory.
testthat_count: The number of files in the testthat
-directory.
testthat_size: The size on disk of files in the testthat
-directory.
check_time: The time (in seconds) to run R CMD check
-using the "r-devel-windows-ix86+x86_64` flavor.
status: An indicator for whether the tests completed.
-
-
Data were collected on 2019-01-20.
-
-
-
#> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 13626 obs. of 25 variables:
-#> $ package : chr "A3" "abbyyR" "abc" "abc.data" ...
-#> $ authors : int 1 1 1 1 5 3 2 1 4 6 ...
-#> $ imports : num 0 6 0 0 3 1 0 4 0 7 ...
-#> $ suggests : num 2 4 0 0 2 0 2 2 0 0 ...
-#> $ depends : num 3 1 6 1 1 1 5 0 1 1 ...
-#> $ Roxygen : num 0 1 0 0 1 0 0 1 0 0 ...
-#> $ gh : num 0 1 0 0 0 0 0 1 0 0 ...
-#> $ rforge : num 0 0 0 0 0 0 0 0 0 0 ...
-#> $ descr : int 217 313 269 63 223 1031 135 344 204 335 ...
-#> $ r_count : int 2 20 8 0 10 10 16 3 6 14 ...
-#> $ r_size : num 0.0291 0.0463 0.0784 0 0.0191 ...
-#> $ ns_import : num 3 15 6 0 4 5 0 4 2 10 ...
-#> $ ns_export : num 0 19 0 0 10 0 0 2 0 9 ...
-#> $ s3_methods : num 3 0 11 0 0 0 0 2 0 23 ...
-#> $ s4_methods : num 0 0 0 0 0 0 0 0 0 0 ...
-#> $ doc_count : int 0 3 1 0 1 0 0 0 0 0 ...
-#> $ doc_size : num 0 0.01976 0.03828 0 0.00787 ...
-#> $ src_count : int 0 0 0 0 0 0 0 2 0 5 ...
-#> $ src_size : num 0 0 0 0 0 ...
-#> $ data_count : int 2 0 0 3 3 1 10 0 4 2 ...
-#> $ data_size : num 0.0253 0 0 4.8859 4.5955 ...
-#> $ testthat_count: int 0 8 0 0 0 0 0 1 0 0 ...
-#> $ testthat_size : num 0 0.0025 0 0 0 ...
-#> $ check_time : num 49 101 292 21 103 46 78 91 47 196 ...
-#> $ status : num 1 1 1 1 1 1 1 1 1 1 ...
-
-
-
-
-
-
-
Control the fit function — control_parsnip • parsnip
-
-
-
-
-
-
-
-
-
-
-
Options can be passed to the fit()
-
-
-
control_parsnip (verbosity = 1L , catch = FALSE )
-
-fit_control (verbosity = 1L , catch = FALSE )
-
-
-
-
- verbosity
- An integer where a value of zero indicates
-that no messages or output should be shown when packages are
-loaded or when the model is fit. A value of 1 means that package
-loading is quiet but model fits can produce output to the screen
-(depending on if they contain their own verbose-type
-argument). A value of 2 or more indicates that any output should
-be seen.
-
- catch
- A logical where a value of TRUE will evaluate
-the model inside of try(, silent = TRUE)
-
-
-
-
-
An S3 object with class "fit_control" that is a named list with the
-results of the function call
-
-
-
fit_control() is deprecated in favor of control_parsnip().
-
-
-
-
-
-
-
-
Make a table of arguments — convert_args • parsnip
-
-
-
-
-
-
-
-
-
-
-
Make a table of arguments
-
-
-
convert_args (model_name )
-
-
-
-
- model_name
- A character string for the model
-
-
-
-
-
-
-
-
-
-
Convenience function for intervals — convert_stan_interval • parsnip
-
-
-
-
-
-
-
-
-
-
-
Convenience function for intervals
-
-
-
convert_stan_interval (x , level = 0.95 , lower = TRUE )
-
-
-
-
- x
- A fitted model object
-
- level
- Level of uncertainty for intervals
-
- lower
- Is level the lower level?
-
-
-
-
-
-
-
-
-
-
General Interface for Decision Tree Models — decision_tree • parsnip
-
-
-
-
-
-
-
-
-
-
-
decision_tree() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-cost_complexity: The cost/complexity parameter (a.k.a. Cp)
-used by CART models (rpart only).
tree_depth: The maximum depth of a tree (rpart and
-spark only).
min_n: The minimum number of data points in a node
-that are required for the node to be split further.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
decision_tree (
- mode = "unknown" ,
- cost_complexity = NULL ,
- tree_depth = NULL ,
- min_n = NULL
-)
-
-# S3 method for decision_tree
-update object ,
- parameters = NULL ,
- cost_complexity = NULL ,
- tree_depth = NULL ,
- min_n = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- cost_complexity
- A positive number for the the cost/complexity
-parameter (a.k.a. Cp) used by CART models (rpart only).
-
- tree_depth
- An integer for maximum depth of the tree.
-
- min_n
- An integer for the minimum number of data points
-in a node that are required for the node to be split further.
-
- object
- A random forest model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
Note that, for rpart models, but cost_complexity and
-tree_depth can be both be specified but the package will give
-precedence to cost_complexity. Also, tree_depth values
-greater than 30 rpart will give nonsense results on 32-bit
-machines.
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
rpart
-
## Decision Tree Model Specification (regression)
-##
-## Computational engine: rpart
-##
-## Model fit template:
-## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
## Decision Tree Model Specification (classification)
-##
-## Computational engine: rpart
-##
-## Model fit template:
-## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
-
-
Note that rpart::rpart()
-
-
C5.0
-
## Decision Tree Model Specification (classification)
-##
-## Computational engine: C5.0
-##
-## Model fit template:
-## parsnip::C5.0_train(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## trials = 1)
-
-
-
Note that C50::C5.0()
-
-
spark
-
## Decision Tree Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(),
-## seed = sample.int(10^5, 1))
-
## Decision Tree Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(),
-## seed = sample.int(10^5, 1))
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip rpart C5.0 spark tree_depth maxdepth NA max_depth min_n minsplit minCases min_instances_per_node cost_complexity cp NA NA
-
-
-
-
-
-
-
-
-
-
decision_tree (mode = "classification" , tree_depth = 5 )
#> Decision Tree Model Specification (classification)
-#>
-#> Main Arguments:
-#> tree_depth = 5
-#>
#> Decision Tree Model Specification (regression)
-#>
-#> Main Arguments:
-#> cost_complexity = varying()
-#>
model <- decision_tree (cost_complexity = 10 , min_n = 3 )
-model
#> Decision Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> cost_complexity = 10
-#> min_n = 3
-#>
#> Decision Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> cost_complexity = 1
-#> min_n = 3
-#>
#> Decision Tree Model Specification (unknown)
-#>
-#> Main Arguments:
-#> cost_complexity = 1
-#>
-
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Data Set Characteristics Available when Fitting Models — descriptors • parsnip
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When using the fit() .obs()
-function can be used. See Details below.
-
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.cols ()
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-.preds ()
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-.obs ()
-
-.lvls ()
-
-.facts ()
-
-.x ()
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-.y ()
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-.dat ()
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Existing functions:
-.obs(): The current number of rows in the data set.
.preds(): The number of columns in the data set that are
-associated with the predictors prior to dummy variable creation.
.cols(): The number of predictor columns available after dummy
-variables are created (if any).
.facts(): The number of factor predictors in the dat set.
.lvls(): If the outcome is a factor, this is a table
-with the counts for each level (and NA otherwise).
.x(): The predictors returned in the format given. Either a
-data frame or a matrix.
.y(): The known outcomes returned in the format given. Either
-a vector, matrix, or data frame.
.dat(): A data frame containing all of the predictors and the
-outcomes. If fit_xy() ..y.
-
-
For example, if you use the model formula Sepal.Width ~ . with the iris
-data, the values would be
- .preds() = 4 (the 4 columns in `iris`)
- .cols() = 5 (3 numeric columns + 2 from Species dummy variables)
- .obs() = 150
- .lvls() = NA (no factor outcome)
- .facts() = 1 (the Species predictor)
- .y() = <vector> (Sepal.Width as a vector)
- .x() = <data.frame> (The other 4 columns as a data frame)
- .dat() = <data.frame> (The full data set)
-
-
-
If the formula Species ~ . where used:
- .preds() = 4 (the 4 numeric columns in `iris`)
- .cols() = 4 (same)
- .obs() = 150
- .lvls() = c(setosa = 50, versicolor = 50, virginica = 50)
- .facts() = 0
- .y() = <vector> (Species as a vector)
- .x() = <data.frame> (The other 4 columns as a data frame)
- .dat() = <data.frame> (The full data set)
-
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-
To use these in a model fit, pass them to a model specification.
-The evaluation is delayed until the time when the
-model is run via fit()
-
-
-library modeldata )
-data "lending_club" )
-
-rand_forest mode = "classification" , mtry = .cols () - 2 )
-
-
When no descriptors are found, the computation of the descriptor values
-is not executed.
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Evaluate parsnip model arguments — eval_args • parsnip
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Evaluate parsnip model arguments
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eval_args (spec , ... )
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- spec
- A model specification
-
- ...
- Not used.
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Fit a Model Specification to a Dataset — fit.model_spec • parsnip
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fit() fit_xy()
-
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# S3 method for model_spec
-fit object , formula , data , control = control_parsnip ... )
-
-# S3 method for model_spec
-fit_xy object , x , y , control = control_parsnip ... )
-
-
-
-
- object
- An object of class model_spec that has a chosen engine
-(via set_engine()
-
- formula
- An object of class "formula" (or one that can
-be coerced to that class): a symbolic description of the model
-to be fitted.
-
- data
- Optional, depending on the interface (see Details
-below). A data frame containing all relevant variables (e.g.
-outcome(s), predictors, case weights, etc). Note: when needed, a
-named argument should be used.
-
- control
- A named list with elements verbosity and
-catch. See control_parsnip()
-
- ...
- Not currently used; values passed here will be
-ignored. Other options required to fit the model should be
-passed using set_engine()
-
- x
- A matrix or data frame of predictors.
-
- y
- A vector, matrix or data frame of outcome data.
-
-
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A model_fit object that contains several elements:
-lvl: If the outcome is a factor, this contains
-the factor levels at the time of model fitting.
spec: The model specification object
-(object in the call to fit)
fit: when the model is executed without error,
-this is the model object. Otherwise, it is a try-error
-object with the error message.
preproc: any objects needed to convert between
-a formula and non-formula interface (such as the terms
-object)
The return value will also have a class related to the fitted model (e.g.
-"_glm") before the base class of "model_fit".
-
-
-
fit() fit_xy() x/y) and these functions translate
-between the interface used when fit() fit_xy()
-
When possible, these functions attempt to avoid making copies of the
-data. For example, if the underlying model uses a formula and
-fit() x/y, the formula is evaluated and
-the data are converted to the required format. In this case, any
-calls in the resulting model objects reference the temporary
-objects used to fit the model.
-
If the model engine has not been set, the model's default engine will be used
-(as discussed on each model page). If the verbosity option of
-control_parsnip()
-
-
-
-
-
-
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
-#> filter, lag
#> The following objects are masked from ‘package:base’:
-#> intersect, setdiff, setequal, union
#> parsnip model object
-#>
-#> Fit time: 26ms
-#>
-#> Call: stats::glm(formula = formula, family = stats::binomial, data = data)
-#>
-#> Coefficients:
-#> (Intercept) funded_amnt int_rate
-#> 5.131e+00 2.767e-06 -1.586e-01
-#>
-#> Degrees of Freedom: 9856 Total (i.e. Null); 9854 Residual
-#> Null Deviance: 4055
-#> Residual Deviance: 3698 AIC: 3704
using_xy
#> parsnip model object
-#>
-#> Fit time: 15ms
-#>
-#> Call: stats::glm(formula = formula, family = stats::binomial, data = data)
-#>
-#> Coefficients:
-#> (Intercept) funded_amnt int_rate
-#> 5.131e+00 2.767e-06 -1.586e-01
-#>
-#> Degrees of Freedom: 9856 Total (i.e. Null); 9854 Residual
-#> Null Deviance: 4055
-#> Residual Deviance: 3698 AIC: 3704
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Fit a Model Specification to a Dataset — fit.model_spec • parsnip
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fit will take a model specification, translate the required
-code by substituting arguments, and execute the model fit
-routine.
-
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# S3 method for model_spec
-fit (object , formula = NULL , x = NULL , y = NULL ,
- data = NULL , engine = object $engine , control = fit_control ... )
-
-
-
-
- object
- An object of class model_spec
-
- formula
- An object of class "formula" (or one that can
-be coerced to that class): a symbolic description of the model
-to be fitted.
-
- x
- Optional, depending on the interface (see Details
-below). Can be data frame or matrix of predictors. Note: when
-needed, a named argument should be used.
-
- y
- Optional, depending on the interface (see Details
-below). Can be a vector, data frame or matrix of predictors (the
-latter two in case of multivariate outcomes). Note: when needed,
-a named argument should be used.
-
- data
- Optional, depending on the interface (see Details
-below). A data frame containing all relevant variables (e.g.
-outcome(s), predictors, case weights, etc). Note: when needed, a
-named argument should be used.
-
- engine
- A character string for the software that should
-be used to fit the model. This is highly dependent on the type
-of model (e.g. linear regression, random forest, etc.).
-
- control
- A named list with elements verbosity and
-catch. See fit_control()
-
- ...
- Not currently used; values passed here will be
-ignored. Other options required to fit the model should be
-passed using the others argument in the original model
-specification.
-
-
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-
A model_fit object that contains several elements:
-lvl: If the outcome is a factor, this contains
-the factor levels at the time of model fitting.
spec: The model specification object
-(object in the call to fit)
fit: when the model is executed without error,
-this is the model object. Otherwise, it is a try-error
-object with the error message.
preproc: any objects needed to convert between
-a formula and non-formula interface (such as the terms
-object)
-
-
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fit substitutes the current arguments in the model
-specification into the computational engine's code, checks them
-for validity, then fits the model using the data and the
-engine-specific code. Different model functions have different
-interfaces (e.g. formula or x/y) and fit translates
-between the interface used when fit was invoked and the one
-required by the underlying model.
-
When possible, fit attempts to avoid making copies of the
-data. For example, if the underlying model uses a formula and
-fit is invoked with a formula, the original data are references
-when the model is fit. However, if the underlying model uses
-something else, such as x/y, the formula is evaluated and
-the data are converted to the required format. In this case, any
-calls in the resulting model objects reference the temporary
-objects used to fit the model.
-
-
-
-
#> parsnip model object
-#>
-#>
-#> Call: stats::glm(formula = Class ~ funded_amnt + int_rate, family = binomial,
-#> data = lending_club)
-#>
-#> Coefficients:
-#> (Intercept) funded_amnt int_rate
-#> 5.131e+00 2.767e-06 -1.586e-01
-#>
-#> Degrees of Freedom: 9856 Total (i.e. Null); 9854 Residual
-#> Null Deviance: 4055
-#> Residual Deviance: 3698 AIC: 3704
using_xy
#> parsnip model object
-#>
-#>
-#> Call: stats::glm(formula = ..y ~ ., family = binomial, data = data)
-#>
-#> Coefficients:
-#> (Intercept) funded_amnt int_rate
-#> 5.131e+00 2.767e-06 -1.586e-01
-#>
-#> Degrees of Freedom: 9856 Total (i.e. Null); 9854 Residual
-#> Null Deviance: 4055
-#> Residual Deviance: 3698 AIC: 3704
-
-
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-
Developed by Max Kuhn.
-
-
Control the fit function — fit_control • parsnip
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Options can be passed to the fit()
-
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fit_control (verbosity = 1L , catch = FALSE )
-
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- verbosity
- An integer where a value of zero indicates
-that no messages or output should be shown when packages are
-loaded or when the model is fit. A value of 1 means that package
-loading is quiet but model fits can produce output to the screen
-(depending on if they contain their own verbose-type
-argument). A value of 2 or more indicates that any output should
-be seen.
-
- catch
- A logical where a value of TRUE will evaluate
-the model inside of try(, silent = TRUE)
-
-
-
-
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An S3 object with class "fit_control" that is a named list with the
-results of the function call
-
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Working with the parsnip model environment — get_model_env • parsnip
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These functions read and write to the environment where the package stores
-information about model specifications.
-
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get_model_env ()
-
-get_from_env (items )
-
-set_in_env (... )
-
-set_env_val (name , value )
-
-
-
-
- items
- A character string of objects in the model environment.
-
- ...
- Named values that will be assigned to the model environment.
-
- name
- A single character value for a new symbol in the model environment.
-
- value
- A single value for a new value in the model environment.
-
-
-
-
-
"Making a parsnip model from scratch"
-https://tidymodels.github.io/parsnip/articles/articles/Scratch.html
-
-
-
#> [1] "boost_tree" "boost_tree_args"
-#> [3] "boost_tree_fit" "boost_tree_modes"
-#> [5] "boost_tree_pkgs" "boost_tree_predict"
-#> [7] "decision_tree" "decision_tree_args"
-#> [9] "decision_tree_fit" "decision_tree_modes"
-#> [11] "decision_tree_pkgs" "decision_tree_predict"
-#> [13] "linear_reg" "linear_reg_args"
-#> [15] "linear_reg_fit" "linear_reg_modes"
-#> [17] "linear_reg_pkgs" "linear_reg_predict"
-#> [19] "logistic_reg" "logistic_reg_args"
-#> [21] "logistic_reg_fit" "logistic_reg_modes"
-#> [23] "logistic_reg_pkgs" "logistic_reg_predict"
-#> [25] "mars" "mars_args"
-#> [27] "mars_fit" "mars_modes"
-#> [29] "mars_pkgs" "mars_predict"
-#> [31] "mlp" "mlp_args"
-#> [33] "mlp_fit" "mlp_modes"
-#> [35] "mlp_pkgs" "mlp_predict"
-#> [37] "models" "modes"
-#> [39] "multinom_reg" "multinom_reg_args"
-#> [41] "multinom_reg_fit" "multinom_reg_modes"
-#> [43] "multinom_reg_pkgs" "multinom_reg_predict"
-#> [45] "nearest_neighbor" "nearest_neighbor_args"
-#> [47] "nearest_neighbor_fit" "nearest_neighbor_modes"
-#> [49] "nearest_neighbor_pkgs" "nearest_neighbor_predict"
-#> [51] "null_model" "null_model_args"
-#> [53] "null_model_fit" "null_model_modes"
-#> [55] "null_model_pkgs" "null_model_predict"
-#> [57] "rand_forest" "rand_forest_args"
-#> [59] "rand_forest_fit" "rand_forest_modes"
-#> [61] "rand_forest_pkgs" "rand_forest_predict"
-#> [63] "surv_reg" "surv_reg_args"
-#> [65] "surv_reg_fit" "surv_reg_modes"
-#> [67] "surv_reg_pkgs" "surv_reg_predict"
-#> [69] "svm_poly" "svm_poly_args"
-#> [71] "svm_poly_fit" "svm_poly_modes"
-#> [73] "svm_poly_pkgs" "svm_poly_predict"
-#> [75] "svm_rbf" "svm_rbf_args"
-#> [77] "svm_rbf_fit" "svm_rbf_modes"
-#> [79] "svm_rbf_pkgs" "svm_rbf_predict"
-
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Tools for models that predict on sub-models — has_multi_predict • parsnip
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has_multi_predict() tests to see if an object can make multiple
-predictions on submodels from the same object. multi_predict_args()
-returns the names of the arguments to multi_predict()
-
-
-
has_multi_predict (object , ... )
-
-# S3 method for default
-has_multi_predict (object , ... )
-
-# S3 method for model_fit
-has_multi_predict (object , ... )
-
-# S3 method for workflow
-has_multi_predict (object , ... )
-
-multi_predict_args (object , ... )
-
-# S3 method for default
-multi_predict_args (object , ... )
-
-# S3 method for model_fit
-multi_predict_args (object , ... )
-
-# S3 method for workflow
-multi_predict_args (object , ... )
-
-
-
-
- object
- An object to test.
-
- ...
- Not currently used.
-
-
-
-
-
has_multi_predict() returns single logical value while
-multi_predict() NA
-if none exist).
-
-
-
#> [1] FALSE
#> [1] FALSE
-multi_predict_args (lm_model_fit )
#> [1] NA
#> [1] "neighbors"
#> [[1]]
-#> # A tibble: 4 x 2
-#> neighbors .pred
-#> <int> <dbl>
-#> 1 1 21
-#> 2 2 21
-#> 3 3 20.9
-#> 4 4 21.0
-#>
-
-
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-
Function reference • parsnip
-
-
-Simple interface to MLP models via keras — keras_mlp • parsnip
-
-
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-
-
-
-
-
-
Instead of building a keras model sequentially, keras_mlp can be used to
-create a feedforward network with a single hidden layer. Regularization is
-via either weight decay or dropout.
-
-
-
keras_mlp (
- x ,
- y ,
- hidden_units = 5 ,
- penalty = 0 ,
- dropout = 0 ,
- epochs = 20 ,
- activation = "softmax" ,
- seeds = sample.int 10 ^5 , size = 3 ),
- ...
-)
-
-
-
-
- x
- A data frame or matrix of predictors
-
- y
- A vector (factor or numeric) or matrix (numeric) of outcome data.
-
- hidden_units
- An integer for the number of hidden units.
-
- penalty
- A non-negative real number for the amount of weight decay. Either
-this parameter or dropout can specified.
-
- dropout
- The proportion of parameters to set to zero. Either
-this parameter or penalty can specified.
-
- epochs
- An integer for the number of passes through the data.
-
- activation
- A character string for the type of activation function between layers.
-
- seeds
- A vector of three positive integers to control randomness of the
-calculations.
-
- ...
- Currently ignored.
-
-
-
-
-
A keras model object.
-
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-
Loan Data — lending_club • parsnip
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Lending Club Statistics https://www.lendingclub.com/info/download-data.action
-
-
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-
-
lending_club a data frame
-
-
-
-
These data were downloaded from the Lending Club
-access site (see below) and are from the first quarter of 2016.
-A subset of the rows and variables are included here. The
-outcome is in the variable Class and is either "good" (meaning
-that the loan was fully paid back or currently on-time) or "bad"
-(charged off, defaulted, of 21-120 days late). A data dictionary
-can be found on the source website.
-
-
-
#> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 9857 obs. of 23 variables:
-#> $ funded_amnt : int 16100 32000 10000 16800 3500 10000 11000 15000 6000 20000 ...
-#> $ term : Factor w/ 2 levels "term_36","term_60": 1 2 1 2 1 1 1 1 1 2 ...
-#> $ int_rate : num 13.99 11.99 16.29 13.67 7.39 ...
-#> $ sub_grade : Factor w/ 35 levels "A1","A2","A3",..: 14 11 16 13 4 10 1 7 8 12 ...
-#> $ addr_state : Factor w/ 50 levels "AK","AL","AR",..: 7 23 35 33 5 43 17 24 34 11 ...
-#> $ verification_status : Factor w/ 3 levels "Not_Verified",..: 1 3 2 3 2 2 1 1 2 1 ...
-#> $ annual_inc : num 35000 72000 72000 101000 50100 32000 65000 188000 89000 48000 ...
-#> $ emp_length : Factor w/ 12 levels "emp_lt_1","emp_1",..: 7 3 3 1 12 1 6 8 3 3 ...
-#> $ delinq_2yrs : int 0 0 0 0 0 0 0 2 0 1 ...
-#> $ inq_last_6mths : int 0 0 2 0 0 0 0 0 2 0 ...
-#> $ revol_util : num 67.4 82.2 74.2 64 78.3 68.1 54.8 33.3 72.3 36 ...
-#> $ acc_now_delinq : int 0 0 0 0 0 0 0 0 0 0 ...
-#> $ open_il_6m : int 1 5 3 6 0 1 2 8 1 0 ...
-#> $ open_il_12m : int 0 1 2 1 0 0 0 2 0 0 ...
-#> $ open_il_24m : int 0 3 3 2 0 0 0 5 0 0 ...
-#> $ total_bal_il : int 1099 49187 33378 55445 0 7574 20998 170814 19616 0 ...
-#> $ all_util : int 48 77 79 64 78 66 54 80 75 36 ...
-#> $ inq_fi : int 0 0 1 1 0 0 0 0 2 1 ...
-#> $ inq_last_12m : int 3 0 4 4 0 0 0 4 6 1 ...
-#> $ delinq_amnt : int 0 0 0 0 0 0 0 0 0 0 ...
-#> $ num_il_tl : int 3 9 9 10 3 9 7 21 7 1 ...
-#> $ total_il_high_credit_limit: int 13193 65945 39387 60188 0 12131 39486 177477 26129 0 ...
-#> $ Class : Factor w/ 2 levels "bad","good": 2 2 2 2 2 2 2 2 2 2 ...
-
-
-
-
-
-
-
General Interface for Linear Regression Models — linear_reg • parsnip
-
-
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-
-
-
-
-
-
-
linear_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R, Stan, keras, or via Spark. The main
-arguments for the model are:
-penalty: The total amount of regularization
-in the model. Note that this must be zero for some engines.
mixture: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
linear_reg (mode = "regression" , penalty = NULL , mixture = NULL )
-
-# S3 method for linear_reg
-update object ,
- parameters = NULL ,
- penalty = NULL ,
- mixture = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "regression".
-
- penalty
- A non-negative number representing the total
-amount of regularization (glmnet, keras, and spark only).
-For keras models, this corresponds to purely L2 regularization
-(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of mixture).
-
- mixture
- A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (glmnet and spark only).
-
- object
- A linear regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The data given to the function are not saved and are only used
-to determine the mode of the model. For linear_reg(), the
-mode will always be "regression".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
lm
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: lm
-##
-## Model fit template:
-## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-
-
-
-
glmnet
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: glmnet
-##
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## family = "gaussian")
-
-
-
For glmnet models, the full regularization path is always fit
-regardless of the value given to penalty. Also, there is the option to
-pass multiple values (or no values) to the penalty argument. When
-using the predict() penalty. When using predict() multi_predict() .pred that contains a tibble with all of the penalty
-results.
-
-
stan
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: stan
-##
-## Model fit template:
-## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), family = stats::gaussian, refresh = 0)
-
-
-
Note that the refresh default prevents logging of the estimation
-process. Change this value in set_engine()
-
For prediction, the stan engine can compute posterior intervals
-analogous to confidence and prediction intervals. In these instances,
-the units are the original outcome and when std_error = TRUE, the
-standard deviation of the posterior distribution (or posterior
-predictive distribution as appropriate) is returned.
-
-
spark
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_linear_regression(x = missing_arg(), formula = missing_arg(),
-## weight_col = missing_arg())
-
-
-
-
keras
-
## Linear Regression Model Specification (regression)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1,
-## act = "linear")
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip glmnet spark keras penalty lambda reg_param penalty mixture alpha elastic_net_param NA
-
-
-
-
-
-
-
-
-
-
linear_reg ()
#> Linear Regression Model Specification (regression)
-#>
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = varying()
-#>
model <- linear_reg (penalty = 10 , mixture = 0.1 )
-model
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 10
-#> mixture = 0.1
-#>
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 1
-#> mixture = 0.1
-#>
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 1
-#>
-
-
-
-
-
-
-
General Interface for Logistic Regression Models — logistic_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
logistic_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R, Stan, keras, or via Spark. The main
-arguments for the model are:
-penalty: The total amount of regularization
-in the model. Note that this must be zero for some engines.
mixture: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
logistic_reg (mode = "classification" , penalty = NULL , mixture = NULL )
-
-# S3 method for logistic_reg
-update object ,
- parameters = NULL ,
- penalty = NULL ,
- mixture = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "classification".
-
- penalty
- A non-negative number representing the total
-amount of regularization (glmnet, keras, and spark only).
-For keras models, this corresponds to purely L2 regularization
-(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of mixture).
-
- mixture
- A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (glmnet and spark only).
-
- object
- A logistic regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
For logistic_reg(), the mode will always be "classification".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
glm
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: glm
-##
-## Model fit template:
-## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## family = stats::binomial)
-
-
-
-
glmnet
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: glmnet
-##
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## family = "binomial")
-
-
-
For glmnet models, the full regularization path is always fit
-regardless of the value given to penalty. Also, there is the option to
-pass multiple values (or no values) to the penalty argument. When
-using the predict() penalty. When using predict() multi_predict() .pred that contains a tibble with all of the penalty
-results.
-
-
stan
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: stan
-##
-## Model fit template:
-## rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), family = stats::binomial, refresh = 0)
-
-
-
Note that the refresh default prevents logging of the estimation
-process. Change this value in set_engine()
-
For prediction, the stan engine can compute posterior intervals
-analogous to confidence and prediction intervals. In these instances,
-the units are the original outcome and when std_error = TRUE, the
-standard deviation of the posterior distribution (or posterior
-predictive distribution as appropriate) is returned.
-
-
spark
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(),
-## weight_col = missing_arg(), family = "binomial")
-
-
-
-
keras
-
## Logistic Regression Model Specification (classification)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1,
-## act = "linear")
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip glmnet spark keras penalty lambda reg_param penalty mixture alpha elastic_net_param NA
-
-
-
-
-
-
-
-
-
-
logistic_reg ()
#> Logistic Regression Model Specification (classification)
-#>
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = varying()
-#>
model <- logistic_reg (penalty = 10 , mixture = 0.1 )
-model
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 10
-#> mixture = 0.1
-#>
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#> mixture = 0.1
-#>
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#>
-
-
-
-
-
-
-
Prepend a new class — make_classes • parsnip
-
-
-
-
-
-
-
-
-
-
-
This adds an extra class to a base class of "model_spec".
-
-
-
make_classes (prefix )
-
-
-
-
- prefix
- A character string for a class.
-
-
-
-
-
A character vector.
-
-
-
-
-
-
-
-
General Interface for MARS — mars • parsnip
-
-
-
-
-
-
-
-
-
-
-
mars() is a way to generate a specification of a model before
-fitting and allows the model to be created using R. The main
-arguments for the
-model are:
-num_terms: The number of features that will be retained in the
-final model.
prod_degree: The highest possible degree of interaction between
-features. A value of 1 indicates and additive model while a value of 2
-allows, but does not guarantee, two-way interactions between features.
prune_method: The type of pruning. Possible values are listed
-in ?earth.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
mars (
- mode = "unknown" ,
- num_terms = NULL ,
- prod_degree = NULL ,
- prune_method = NULL
-)
-
-# S3 method for mars
-update object ,
- parameters = NULL ,
- num_terms = NULL ,
- prod_degree = NULL ,
- prune_method = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- num_terms
- The number of features that will be retained in the
-final model, including the intercept.
-
- prod_degree
- The highest possible interaction degree.
-
- prune_method
- The pruning method.
-
- object
- A MARS model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-R : "earth" (the default)
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
earth
-
## MARS Model Specification (regression)
-##
-## Computational engine: earth
-##
-## Model fit template:
-## earth::earth(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## keepxy = TRUE)
-
## MARS Model Specification (classification)
-##
-## Engine-Specific Arguments:
-## glm = list(family = stats::binomial)
-##
-## Computational engine: earth
-##
-## Model fit template:
-## earth::earth(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## glm = list(family = stats::binomial), keepxy = TRUE)
-
-
-
Note that, when the model is fit, the earth package only has its
-namespace loaded. However, if multi_predict is used, the package is
-attached.
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip earth num_terms nprune prod_degree degree prune_method pmethod
-
-
-
-
-
-
-
-
-
-
mars (mode = "regression" , num_terms = 5 )
#> MARS Model Specification (regression)
-#>
-#> Main Arguments:
-#> num_terms = 5
-#>
model <- mars (num_terms = 10 , prune_method = "none" )
-model
#> MARS Model Specification (unknown)
-#>
-#> Main Arguments:
-#> num_terms = 10
-#> prune_method = none
-#>
#> MARS Model Specification (unknown)
-#>
-#> Main Arguments:
-#> num_terms = 1
-#> prune_method = none
-#>
#> MARS Model Specification (unknown)
-#>
-#> Main Arguments:
-#> num_terms = 1
-#>
-
-
-
-
-
-
-
Determine the minimum set of model fits — min_grid • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
min_grid determines exactly what models should be fit in order to
-evaluate the entire set of tuning parameter combinations. This is for
-internal use only and the API may change in the near future.
-
-
-
-
min_grid (x , grid , ... )
-
-# S3 method for boost_tree
-min_grid (x , grid , ... )
-
-# S3 method for linear_reg
-min_grid (x , grid , ... )
-
-# S3 method for logistic_reg
-min_grid (x , grid , ... )
-
-# S3 method for mars
-min_grid (x , grid , ... )
-
-# S3 method for multinom_reg
-min_grid (x , grid , ... )
-
-# S3 method for nearest_neighbor
-min_grid (x , grid , ... )
-
-
-
-
- x
- A model specification.
-
- grid
- A tibble with tuning parameter combinations.
-
- ...
- Not currently used.
-
-
-
-
-
A tibble with the minimum tuning parameters to fit and an additional
-list column with the parameter combinations used for prediction.
-
-
-
-
-
-
-
-
-
General Interface for Single Layer Neural Network — mlp • parsnip
-
-
-
-
-
-
-
-
-
-
-
mlp(), for multilayer perceptron, is a way to generate a specification of
-a model before fitting and allows the model to be created using
-different packages in R or via keras The main arguments for the
-model are:
-hidden_units: The number of units in the hidden layer
-(default: 5).
penalty: The amount of L2 regularization (aka weight
-decay, default is zero).
dropout: The proportion of parameters randomly dropped out of
-the model (keras only, default is zero).
epochs: The number of training iterations (default: 20).
activation: The type of function that connects the hidden
-layer and the input variables (keras only, default is softmax).
-
-
If parameters need to be modified, this function can be used
-in lieu of recreating the object from scratch.
-
-
-
mlp (
- mode = "unknown" ,
- hidden_units = NULL ,
- penalty = NULL ,
- dropout = NULL ,
- epochs = NULL ,
- activation = NULL
-)
-
-# S3 method for mlp
-update object ,
- parameters = NULL ,
- hidden_units = NULL ,
- penalty = NULL ,
- dropout = NULL ,
- epochs = NULL ,
- activation = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- hidden_units
- An integer for the number of units in the hidden model.
-
- penalty
- A non-negative numeric value for the amount of weight
-decay.
-
- dropout
- A number between 0 (inclusive) and 1 denoting the proportion
-of model parameters randomly set to zero during model training.
-
- epochs
- An integer for the number of training iterations.
-
- activation
- A single character strong denoting the type of relationship
-between the original predictors and the hidden unit layer. The activation
-function between the hidden and output layers is automatically set to either
-"linear" or "softmax" depending on the type of outcome. Possible values are:
-"linear", "softmax", "relu", and "elu"
-
- object
- A random forest model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() hidden_units when nnet::nnet size argument has no default so a value of 5 units will be
-used. Also, unless otherwise specified, the linout argument to
-nnet::nnet() TRUE when a regression model is created.
-If parameters need to be modified, update()
-
The model can be created using the fit() engines :
-R : "nnet" (the default)
keras : "keras"
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
keras
-
## Single Layer Neural Network Specification (regression)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())
-
## Single Layer Neural Network Specification (classification)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg())
-
-
-
An error is thrown if both penalty and dropout are specified for
-keras models.
-
-
nnet
-
## Single Layer Neural Network Specification (regression)
-##
-## Main Arguments:
-## hidden_units = 5
-##
-## Computational engine: nnet
-##
-## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## size = 5, trace = FALSE, linout = TRUE)
-
## Single Layer Neural Network Specification (classification)
-##
-## Main Arguments:
-## hidden_units = 5
-##
-## Computational engine: nnet
-##
-## Model fit template:
-## nnet::nnet(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
-## size = 5, trace = FALSE, linout = FALSE)
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip keras nnet hidden_units hidden_units size penalty penalty decay dropout dropout NA epochs epochs maxit activation activation NA
-
-
-
-
-
-
-
-
-
-
mlp (mode = "classification" , penalty = 0.01 )
#> Single Layer Neural Network Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
#> Single Layer Neural Network Specification (regression)
-#>
-#> Main Arguments:
-#> hidden_units = varying()
-#>
model <- mlp (hidden_units = 10 , dropout = 0.30 )
-model
#> Single Layer Neural Network Specification (unknown)
-#>
-#> Main Arguments:
-#> hidden_units = 10
-#> dropout = 0.3
-#>
#> Single Layer Neural Network Specification (unknown)
-#>
-#> Main Arguments:
-#> hidden_units = 2
-#> dropout = 0.3
-#>
#> Single Layer Neural Network Specification (unknown)
-#>
-#> Main Arguments:
-#> hidden_units = 2
-#>
-
-
-
-
-
-
-
Model Fit Object Information — model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
An object with class "model_fit" is a container for
-information about a model that has been fit to the data.
-
-
-
-
-
-
-
The main elements of the object are:
-lvl: A vector of factor levels when the outcome is
-is a factor. This is NULL when the outcome is not a factor
-vector.
spec: A model_spec object.
fit: The object produced by the fitting function.
preproc: This contains any data-specific information
-required to process new a sample point for prediction. For
-example, if the underlying model function requires arguments x
-and y and the user passed a formula to fit, the preproc
-object would contain items such as the terms object and so on.
-When no information is required, this is NA.
-
-
As discussed in the documentation for model_spec model_fit object prior to fitting.
-If the resulting model object prints its call, any user-defined
-options are shown in the call preceded by a tilde (see the
-example below). This is a result of the use of quosures in the
-specification.
-
This class and structure is the basis for how parsnip
-stores model objects after to seeing the data and applying a model.
-
-
-
#> Linear Regression Model Specification (regression)
-#>
-#> Engine-Specific Arguments:
-#> x = ifelse(.obs() < 500, TRUE, FALSE)
-#>
-#> Computational engine: lm
-#>
#> parsnip model object
-#>
-#> Fit time: 2ms
-#>
-#> Call:
-#> stats::lm(formula = formula, data = data, x = ~ifelse(.obs() <
-#> 500, TRUE, FALSE))
-#>
-#> Coefficients:
-#> (Intercept) cyl disp hp drat wt
-#> 12.30337 -0.11144 0.01334 -0.02148 0.78711 -3.71530
-#> qsec vs am gear carb
-#> 0.82104 0.31776 2.52023 0.65541 -0.19942
-#>
#> [1] 32
-
-
-
-
-
-
-
Print helper for model objects — model_printer • parsnip
-
-
-
-
-
-
-
-
-
-
-
A common format function that prints information about the model object (e.g.
-arguments, calls, packages, etc).
-
-
-
model_printer (x , ... )
-
-
-
-
- x
- A model object.
-
- ...
- Not currently used.
-
-
-
-
-
-
-
-
-
-
Model Specification Information — model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
An object with class "model_spec" is a container for
-information about a model that will be fit.
-
-
-
-
-
-
-
The main elements of the object are:
-args: A vector of the main arguments for the model. The
-names of these arguments may be different form their
-counterparts n the underlying model function. For example, for a
-glmnet model, the argument name for the amount of the penalty
-is called "penalty" instead of "lambda" to make it more general
-and usable across different types of models (and to not be
-specific to a particular model function). The elements of args
-can varying() NULL), the
-arguments will use the underlying model functions default value.
-As discussed below, the arguments in args are captured as
-quosures and are not immediately executed.
-...: Optional model-function-specific
-parameters. As with args, these will be quosures and can be
-varying()
mode: The type of model, such as "regression" or
-"classification". Other modes will be added once the package
-adds more functionality.
method: This is a slot that is filled in later by the
-model's constructor function. It generally contains lists of
-information that are used to create the fit and prediction code
-as well as required packages and similar data.
engine: This character string declares exactly what
-software will be used. It can be a package name or a technology
-type.
-
-This class and structure is the basis for how parsnip
-stores model objects prior to seeing the data.
-
-
-
-
-
-
-
An important detail to understand when creating model
-specifications is that they are intended to be functionally
-independent of the data. While it is true that some tuning
-parameters are data dependent , the model specification does
-not interact with the data at all.
-
For example, most R functions immediately evaluate their
-arguments. For example, when calling mean(dat_vec) dat_vec is immediately evaluated inside of the function.
-
parsnip model functions do not do this. For example, using
-
- rand_forest mtry = ncol iris ) - 1 )
-
-
does not execute ncol(iris) - 1 when creating the specification.
-This can be seen in the output:
-
- > rand_forest(mtry = ncol(iris) - 1)
- Random Forest Model Specification (unknown)
-
- Main Arguments:
- mtry = ncol(iris) - 1
-
-
-
The model functions save the argument expressions and their
-associated environments (a.k.a. a quosure) to be evaluated later
-when either fit() fit_xy()
-
The consequence of this strategy is that any data required to
-get the parameter values must be available when the model is
-fit. The two main ways that this can fail is if:
-
-The data have been modified between the creation of the
-model specification and when the model fit function is invoked.
If the model specification is saved and loaded into a new
-session where those same data objects do not exist.
-
-
The best way to avoid these issues is to not reference any data
-objects in the global environment but to use data descriptors
-such as .cols()
-
- rand_forest mtry = .cols 1 )
-
-
This is not dependent on any specific data object and
-is evaluated immediately before the model fitting process begins.
-
One less advantageous approach to solving this issue is to use
-quasiquotation. This would insert the actual R object into the
-model specification and might be the best idea when the data
-object is small. For example, using
-
- rand_forest mtry = ncol iris ) - 1 )
-
-
would work (and be reproducible between sessions) but embeds
-the entire iris data set into the mtry expression:
-
- > rand_forest(mtry = ncol(!!iris) - 1)
- Random Forest Model Specification (unknown)
-
- Main Arguments:
- mtry = ncol(structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, <snip>
-
-
-
However, if there were an object with the number of columns in
-it, this wouldn't be too bad:
-
- > mtry_val <- ncol(iris) - 1
- > mtry_val
- [1] 4
- > rand_forest(mtry = !!mtry_val)
- Random Forest Model Specification (unknown)
-
- Main Arguments:
- mtry = 4
-
-
-
More information on quosures and quasiquotation can be found at
-https://tidyeval.tidyverse.org .
-
-
-
-
-
-
-
-
Model predictions across many sub-models — multi_predict • parsnip
-
-
-
-
-
-
-
-
-
-
-
For some models, predictions can be made on sub-models in the model object.
-
-
-
multi_predict (object , ... )
-
-# S3 method for default
-multi_predict (object , ... )
-
-# S3 method for `_xgb.Booster`
-multi_predict (object , new_data , type = NULL , trees = NULL , ... )
-
-# S3 method for `_C5.0`
-multi_predict (object , new_data , type = NULL , trees = NULL , ... )
-
-# S3 method for `_elnet`
-multi_predict (object , new_data , type = NULL , penalty = NULL , ... )
-
-# S3 method for `_lognet`
-multi_predict (object , new_data , type = NULL , penalty = NULL , ... )
-
-# S3 method for `_earth`
-multi_predict (object , new_data , type = NULL , num_terms = NULL , ... )
-
-# S3 method for `_multnet`
-multi_predict (object , new_data , type = NULL , penalty = NULL , ... )
-
-# S3 method for `_train.kknn`
-multi_predict (object , new_data , type = NULL , neighbors = NULL , ... )
-
-
-
-
- object
- A model_fit object.
-
- ...
- Optional arguments to pass to predict.model_fit(type = "raw") type.
-
- new_data
- A rectangular data object, such as a data frame.
-
- type
- A single character value or NULL. Possible values
-are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
-or "raw". When NULL, predict()
-
- trees
- An integer vector for the number of trees in the ensemble.
-
- penalty
- A numeric vector of penalty values.
-
- num_terms
- An integer vector for the number of MARS terms to retain.
-
- neighbors
- An integer vector for the number of nearest neighbors.
-
-
-
-
-
A tibble with the same number of rows as the data being predicted.
-There is a list-column named .pred that contains tibbles with
-multiple rows per sub-model. Note that, within the tibbles, the column names
-follow the usual standard based on prediction type (i.e. .pred_class for
-type = "class" and so on).
-
-
-
-
-
-
-
-
General Interface for Multinomial Regression Models — multinom_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
multinom_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R, keras, or Spark. The main arguments for the
-model are:
-penalty: The total amount of regularization
-in the model. Note that this must be zero for some engines.
mixture: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
multinom_reg (mode = "classification" , penalty = NULL , mixture = NULL )
-
-# S3 method for multinom_reg
-update object ,
- parameters = NULL ,
- penalty = NULL ,
- mixture = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "classification".
-
- penalty
- A non-negative number representing the total
-amount of regularization (glmnet, keras, and spark only).
-For keras models, this corresponds to purely L2 regularization
-(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of mixture).
-
- mixture
- A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (glmnet only).
-
- object
- A multinomial regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
For multinom_reg(), the mode will always be "classification".
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save() model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
glmnet
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: glmnet
-##
-## Model fit template:
-## glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-## family = "multinomial")
-
-
-
For glmnet models, the full regularization path is always fit
-regardless of the value given to penalty. Also, there is the option to
-pass multiple values (or no values) to the penalty argument. When
-using the predict() penalty. When using predict() multi_predict() .pred that contains a tibble with all of the penalty
-results.
-
-
nnet
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: nnet
-##
-## Model fit template:
-## nnet::multinom(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), trace = FALSE)
-
-
-
-
spark
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_logistic_regression(x = missing_arg(), formula = missing_arg(),
-## weight_col = missing_arg(), family = "multinomial")
-
-
-
-
keras
-
## Multinomial Regression Model Specification (classification)
-##
-## Computational engine: keras
-##
-## Model fit template:
-## parsnip::keras_mlp(x = missing_arg(), y = missing_arg(), hidden_units = 1,
-## act = "linear")
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip glmnet spark keras nnet penalty lambda reg_param penalty decay mixture alpha elastic_net_param NA NA
-
-
-
-
-
-
-
-
-
-
multinom_reg ()
#> Multinomial Regression Model Specification (classification)
-#>
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = varying()
-#>
model <- multinom_reg (penalty = 10 , mixture = 0.1 )
-model
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 10
-#> mixture = 0.1
-#>
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#> mixture = 0.1
-#>
#> Multinomial Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> penalty = 1
-#>
-
-
-
-
-
-
-
General Interface for K-Nearest Neighbor Models — nearest_neighbor • parsnip
-
-
-
-
-
-
-
-
-
-
-
nearest_neighbor() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R. The main arguments for the
-model are:
-neighbors: The number of neighbors considered at
-each prediction.
weight_func: The type of kernel function that weights the
-distances between samples.
dist_power: The parameter used when calculating the Minkowski
-distance. This corresponds to the Manhattan distance with dist_power = 1
-and the Euclidean distance with dist_power = 2.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
nearest_neighbor (
- mode = "unknown" ,
- neighbors = NULL ,
- weight_func = NULL ,
- dist_power = NULL
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- neighbors
- A single integer for the number of neighbors
-to consider (often called k). For kknn , a value of 5
-is used if neighbors is not specified.
-
- weight_func
- A single character for the type of kernel function used
-to weight distances between samples. Valid choices are: "rectangular",
-"triangular", "epanechnikov", "biweight", "triweight",
-"cos", "inv", "gaussian", "rank", or "optimal".
-
- dist_power
- A single number for the parameter used in
-calculating Minkowski distance.
-
-
-
-
-
The model can be created using the fit() engines :
-R : "kknn" (the default)
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
kknn
-
## K-Nearest Neighbor Model Specification (regression)
-##
-## Computational engine: kknn
-##
-## Model fit template:
-## kknn::train.kknn(formula = missing_arg(), data = missing_arg(),
-## ks = 5)
-
## K-Nearest Neighbor Model Specification (classification)
-##
-## Computational engine: kknn
-##
-## Model fit template:
-## kknn::train.kknn(formula = missing_arg(), data = missing_arg(),
-## ks = 5)
-
-
-
For kknn, the underlying modeling function used is a restricted
-version of train.kknn() and not kknn(). It is set up in this way so
-that parsnip can utilize the underlying predict.train.kknn method to
-predict on new data. This also means that a single value of that
-function’s kernel argument (a.k.a weight_func here) can be supplied
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip kknn neighbors ks weight_func kernel dist_power distance
-
-
-
-
-
-
-
-
-
-
nearest_neighbor (neighbors = 11 )
#> K-Nearest Neighbor Model Specification (unknown)
-#>
-#> Main Arguments:
-#> neighbors = 11
-#>
-
-
-
-
-
-
-
-
General Interface for null models — null_model • parsnip
-
-
-
-
-
-
-
-
-
-
-
null_model() is a way to generate a specification of a model before
-fitting and allows the model to be created using R. It doesn't have any
-main arguments.
-
-
-
null_model (mode = "classification" )
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
parsnip
-
## Model Specification (regression)
-##
-## Computational engine: parsnip
-##
-## Model fit template:
-## nullmodel(x = missing_arg(), y = missing_arg())
-
## Model Specification (classification)
-##
-## Computational engine: parsnip
-##
-## Model fit template:
-## nullmodel(x = missing_arg(), y = missing_arg())
-
-
-
-
-
-
-
-
-
null_model (mode = "regression" )
#> Model Specification (regression)
-#>
-
-
-
-
-
-
-
Fit a simple, non-informative model — nullmodel • parsnip
-
-
-
-
-
-
-
-
-
-
-
Fit a single mean or largest class model. nullmodel() is the underlying
-computational function for the null_model()
-
-
-
nullmodel (x , ... )
-
-# S3 method for default
-nullmodel (x = NULL , y , ... )
-
-# S3 method for nullmodel
-print x , ... )
-
-# S3 method for nullmodel
-predict object , new_data = NULL , type = NULL , ... )
-
-
-
-
- x
- An optional matrix or data frame of predictors. These values are
-not used in the model fit
-
- ...
- Optional arguments (not yet used)
-
- y
- A numeric vector (for regression) or factor (for classification) of
-outcomes
-
- object
- An object of class nullmodel
-
- new_data
- A matrix or data frame of predictors (only used to determine
-the number of predictions to return)
-
- type
- Either "raw" (for regression), "class" or "prob" (for
-classification)
-
-
-
-
-
The output of nullmodel() is a list of class nullmodel
-with elements
-
call the function call
value the mean of
-y or the most prevalent class
levels when y is a
-factor, a vector of levels. NULL otherwise
pct when y
-is a factor, a data frame with a column for each class (NULL
-otherwise). The column for the most prevalent class has the proportion of
-the training samples with that class (the other columns are zero).
n
- the number of elements in y
-
-predict.nullmodel() returns a either a factor or numeric vector
-depending on the class of y. All predictions are always the same.
-
-
-
-
nullmodel() emulates other model building functions, but returns the
-simplest model possible given a training set: a single mean for numeric
-outcomes and the most prevalent class for factor outcomes. When class
-probabilities are requested, the percentage of the training set samples with
-the most prevalent class is returned.
-
-
-
#> Null Regression Model
-#> Predicted Value: b
#> [1] b b b b b
-#> Levels: a b
-
-
-
-
-
-
-
-
Other predict methods. — predict_class.model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
These are internal functions not meant to be directly called by the user.
-
-
-
# S3 method for model_fit
-predict_class (object , new_data , ... )
-
-# S3 method for model_fit
-predict_classprob (object , new_data , ... )
-
-# S3 method for model_fit
-predict_confint (object , new_data , level = 0.95 , std_error = FALSE , ... )
-
-# S3 method for model_fit
-predict_numeric (object , new_data , ... )
-
-predict_numeric (object , ... )
-
-# S3 method for model_fit
-predict_quantile (object , new_data , quantile = (1 :9 )/10 , ... )
-
-
-
-
- object
- An object of class model_fit
-
- new_data
- A rectangular data object, such as a data frame.
-
- ...
- Arguments to the underlying model's prediction
-function cannot be passed here (see opts). There are some
-parsnip related options that can be passed, depending on the
-value of type. Possible arguments are:
-level: for types of "conf_int" and "pred_int" this
-is the parameter for the tail area of the intervals
-(e.g. confidence level for confidence intervals).
-Default value is 0.95.
std_error: add the standard error of fit or
-prediction for types of "conf_int" and "pred_int".
-Default value is FALSE.
quantile: the quantile(s) for quantile regression
-(not implemented yet)
time: the time(s) for hazard probability estimates
-(not implemented yet)
-
- level
- A single numeric value between zero and one for the
-interval estimates.
-
- std_error
- A single logical for wether the standard error should be
-returned (assuming that the model can compute it).
-
- quant
- A vector of numbers between 0 and 1 for the quantile being
-predicted.
-
-
-
-
-
-
-
-
-
-
Model predictions — predict.model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
Apply a model to create different types of predictions.
-predict()
-
-
-
# S3 method for model_fit
-predict object , new_data , type = NULL , opts = list ... )
-
-# S3 method for model_fit
-predict_raw (object , new_data , opts = list ... )
-
-predict_raw (object , ... )
-
-
-
-
- object
- An object of class model_fit
-
- new_data
- A rectangular data object, such as a data frame.
-
- type
- A single character value or NULL. Possible values
-are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
-or "raw". When NULL, predict()
-
- opts
- A list of optional arguments to the underlying
-predict function that will be used when type = "raw". The
-list should not include options for the model object or the
-new data being predicted.
-
- ...
- Arguments to the underlying model's prediction
-function cannot be passed here (see opts). There are some
-parsnip related options that can be passed, depending on the
-value of type. Possible arguments are:
-level: for types of "conf_int" and "pred_int" this
-is the parameter for the tail area of the intervals
-(e.g. confidence level for confidence intervals).
-Default value is 0.95.
std_error: add the standard error of fit or
-prediction for types of "conf_int" and "pred_int".
-Default value is FALSE.
quantile: the quantile(s) for quantile regression
-(not implemented yet)
time: the time(s) for hazard probability estimates
-(not implemented yet)
-
-
-
-
-
With the exception of type = "raw", the results of
-predict.model_fit() will be a tibble as many rows in the output
-as there are rows in new_data and the column names will be
-predictable.
-
For numeric results with a single outcome, the tibble will have
-a .pred column and .pred_Yname for multivariate results.
-
For hard class predictions, the column is named .pred_class
-and, when type = "prob", the columns are .pred_classlevel.
-
type = "conf_int" and type = "pred_int" return tibbles with
-columns .pred_lower and .pred_upper with an attribute for
-the confidence level. In the case where intervals can be
-produces for class probabilities (or other non-scalar outputs),
-the columns will be named .pred_lower_classlevel and so on.
-
Quantile predictions return a tibble with a column .pred, which is
-a list-column. Each list element contains a tibble with columns
-.pred and .quantile (and perhaps other columns).
-
Using type = "raw" with predict.model_fit() will return
-the unadulterated results of the prediction function.
-
In the case of Spark-based models, since table columns cannot
-contain dots, the same convention is used except 1) no dots
-appear in names and 2) vectors are never returned but
-type-specific prediction functions.
-
When the model fit failed and the error was captured, the
-predict()
-
-
-
If "type" is not supplied to predict() type = "numeric" for regression models and
-type = "class" for classification).
-
predict()
-
When using type = "conf_int" and type = "pred_int", the options
-level and std_error can be used. The latter is a logical for an
-extra column of standard error values (if available).
-
-
-
#> # A tibble: 10 x 1
-#> .pred
-#> <dbl>
-#> 1 23.4
-#> 2 23.3
-#> 3 27.6
-#> 4 21.5
-#> 5 17.6
-#> 6 21.6
-#> 7 13.9
-#> 8 21.7
-#> 9 25.6
-#> 10 17.1
#> # A tibble: 10 x 2
-#> .pred_lower .pred_upper
-#> <dbl> <dbl>
-#> 1 17.9 29.0
-#> 2 18.1 28.5
-#> 3 24.0 31.3
-#> 4 17.5 25.6
-#> 5 14.3 20.8
-#> 6 17.0 26.2
-#> 7 9.65 18.2
-#> 8 16.2 27.2
-#> 9 14.2 37.0
-#> 10 11.5 22.7
#> cyl disp hp drat wt qsec
-#> 1 -0.001433177 -0.8113275 0.6303467 -0.06120265 2.4139815 -1.567729
-#> 2 -0.001433177 -0.8113275 0.6303467 -0.06120265 1.4488706 -0.736286
-#> 3 -0.009315653 -1.3336453 0.8557288 -0.05014798 3.5494061 1.624418
-#> 4 -0.001433177 0.1730406 0.6303467 0.12009386 0.1620561 2.856736
-#> 5 0.006449298 1.1975870 -0.2314083 0.10461733 -0.6895124 -0.736286
-#> 6 -0.001433177 -0.1584303 0.6966356 0.19084372 -0.7652074 4.014817
-#> 7 0.006449298 1.1975870 -1.1594522 0.09135173 -1.1815297 -2.488255
-#> 8 -0.009315653 -0.9449204 1.2667197 -0.01477305 0.2566748 3.688179
-#> 9 -0.009315653 -1.0041833 0.8292133 -0.06562451 0.4080647 7.993866
-#> 10 -0.001433177 -0.7349888 0.4579957 -0.06562451 -0.6895124 1.164155
-#> vs am gear carb
-#> 1 0.2006406 2.88774 0.02512680 -0.2497240
-#> 2 0.2006406 2.88774 0.02512680 -0.2497240
-#> 3 -0.3511210 2.88774 0.02512680 0.4668753
-#> 4 -0.3511210 -2.40645 -0.06700481 0.4668753
-#> 5 0.2006406 -2.40645 -0.06700481 0.2280089
-#> 6 -0.3511210 -2.40645 -0.06700481 0.4668753
-#> 7 0.2006406 -2.40645 -0.06700481 -0.2497240
-#> 8 -0.3511210 -2.40645 0.02512680 0.2280089
-#> 9 -0.3511210 -2.40645 0.02512680 0.2280089
-#> 10 -0.3511210 -2.40645 0.02512680 -0.2497240
-#> attr(,"constant")
-#> [1] 19.96364
-
-
-
-
-
-
-
General Interface for Random Forest Models — rand_forest • parsnip
-
-
-
-
-
-
-
-
-
-
-
rand_forest() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-mtry: The number of predictors that will be
-randomly sampled at each split when creating the tree models.
trees: The number of trees contained in the ensemble.
min_n: The minimum number of data points in a node
-that are required for the node to be split further.
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
rand_forest (mode = "unknown" , mtry = NULL , trees = NULL , min_n = NULL )
-
-# S3 method for rand_forest
-update object ,
- parameters = NULL ,
- mtry = NULL ,
- trees = NULL ,
- min_n = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- mtry
- An integer for the number of predictors that will
-be randomly sampled at each split when creating the tree models.
-
- trees
- An integer for the number of trees contained in
-the ensemble.
-
- min_n
- An integer for the minimum number of data points
-in a node that are required for the node to be split further.
-
- object
- A random forest model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
For models created using the spark engine, there are
-several differences to consider. First, only the formula
-interface to via fit() fit_xy() save), the model$fit element of the parsnip
-object should be serialized via ml_save(object$fit) and
-separately saved to disk. In a new session, the object can be
-reloaded and reattached to the parsnip object.
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
ranger
-
## Random Forest Model Specification (regression)
-##
-## Computational engine: ranger
-##
-## Model fit template:
-## ranger::ranger(formula = missing_arg(), data = missing_arg(),
-## case.weights = missing_arg(), num.threads = 1, verbose = FALSE,
-## seed = sample.int(10^5, 1))
-
## Random Forest Model Specification (classification)
-##
-## Computational engine: ranger
-##
-## Model fit template:
-## ranger::ranger(formula = missing_arg(), data = missing_arg(),
-## case.weights = missing_arg(), num.threads = 1, verbose = FALSE,
-## seed = sample.int(10^5, 1), probability = TRUE)
-
-
-
Note that ranger::ranger()
-
For ranger confidence intervals, the intervals are constructed using
-the form estimate +/- z * std_error. For classification probabilities,
-these values can fall outside of [0, 1] and will be coerced to be in
-this range.
-
-
randomForest
-
## Random Forest Model Specification (regression)
-##
-## Computational engine: randomForest
-##
-## Model fit template:
-## randomForest::randomForest(x = missing_arg(), y = missing_arg())
-
## Random Forest Model Specification (classification)
-##
-## Computational engine: randomForest
-##
-## Model fit template:
-## randomForest::randomForest(x = missing_arg(), y = missing_arg())
-
-
-
Note that
-randomForest::randomForest()
-
-
spark
-
## Random Forest Model Specification (regression)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
-## type = "regression", seed = sample.int(10^5, 1))
-
## Random Forest Model Specification (classification)
-##
-## Computational engine: spark
-##
-## Model fit template:
-## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
-## type = "classification", seed = sample.int(10^5, 1))
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip ranger randomForest spark mtry mtry mtry feature_subset_strategy trees num.trees ntree num_trees min_n min.node.size nodesize min_instances_per_node
-
-
-
-
-
-
-
-
-
-
rand_forest (mode = "classification" , trees = 2000 )
#> Random Forest Model Specification (classification)
-#>
-#> Main Arguments:
-#> trees = 2000
-#>
#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = varying()
-#>
model <- rand_forest (mtry = 10 , min_n = 3 )
-model
#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 10
-#> min_n = 3
-#>
#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#> min_n = 3
-#>
#> Random Forest Model Specification (unknown)
-#>
-#> Main Arguments:
-#> mtry = 1
-#>
-
-
-
-
-
-
-
Objects exported from other packages — reexports • parsnip
-
-
-
-
-
-
-
-
-
-
-
These objects are imported from other packages. Follow the links
-below to see their documentation.
-
- generics fit fit_xy tidy varying_args
magrittr %>%
-
-
-
-
-
-
-
-
-
-
-
-
Decision trees via rpart — rpart_train • parsnip
-
-
-
-
-
-
-
-
-
-
-
rpart_train is a wrapper for rpart() tree-based models
-where all of the model arguments are in the main function.
-
-
-
rpart_train (
- formula ,
- data ,
- weights = NULL ,
- cp = 0.01 ,
- minsplit = 20 ,
- maxdepth = 30 ,
- ...
-)
-
-
-
-
- formula
- A model formula.
-
- data
- A data frame.
-
- weights
- Optional case weights.
-
- cp
- A non-negative number for complexity parameter. Any split
-that does not decrease the overall lack of fit by a factor of
-cp is not attempted. For instance, with anova splitting,
-this means that the overall R-squared must increase by cp at
-each step. The main role of this parameter is to save computing
-time by pruning off splits that are obviously not worthwhile.
-Essentially,the user informs the program that any split which
-does not improve the fit by cp will likely be pruned off by
-cross-validation, and that hence the program need not pursue it.
-
- minsplit
- An integer for the minimum number of observations
-that must exist in a node in order for a split to be attempted.
-
- maxdepth
- An integer for the maximum depth of any node
-of the final tree, with the root node counted as depth 0.
-Values greater than 30 rpart will give nonsense results on
-32-bit machines. This function will truncate maxdepth to 30 in
-those cases.
-
- ...
- Other arguments to pass to either rpart or rpart.control.
-
-
-
-
-
A fitted rpart model.
-
-
-
-
-
-
-
-
Change elements of a model specification — set_args • parsnip
-
-
-
-
-
-
-
-
-
-
-
set_args() can be used to modify the arguments of a model specification while
-set_mode() is used to change the model's mode.
-
-
-
set_args (object , ... )
-
-set_mode (object , mode )
-
-
-
-
- object
- A model specification.
-
- ...
- One or more named model arguments.
-
- mode
- A character string for the model type (e.g. "classification" or
-"regression")
-
-
-
-
-
An updated model object.
-
-
-
set_args() will replace existing values of the arguments.
-
-
-
#> Random Forest Model Specification (unknown)
-#>
#> Random Forest Model Specification (regression)
-#>
-#> Main Arguments:
-#> mtry = 3
-#>
-#> Engine-Specific Arguments:
-#> importance = TRUE
-#>
-
-
-
-
-
-
-
-
Declare a computational engine and specific arguments — set_engine • parsnip
-
-
-
-
-
-
-
-
-
-
-
set_engine() is used to specify which package or system will be used
-to fit the model, along with any arguments specific to that software.
-
-
-
set_engine (object , engine , ... )
-
-
-
-
- object
- A model specification.
-
- engine
- A character string for the software that should
-be used to fit the model. This is highly dependent on the type
-of model (e.g. linear regression, random forest, etc.).
-
- ...
- Any optional arguments associated with the chosen computational
-engine. These are captured as quosures and can be varying()
-
-
-
-
-
An updated model specification.
-
-
-
#> Logistic Regression Model Specification (classification)
-#>
-#> Main Arguments:
-#> mixture = 1/3
-#>
-#> Engine-Specific Arguments:
-#> nlambda = 10
-#>
-#> Computational engine: glmnet
-#>
-#> Model fit template:
-#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-#> alpha = 1/3, nlambda = 10, family = "binomial")
-
-
-
-
-
-
-
Tools to Register Models — set_new_model • parsnip
-
-
-
-
-
-
-
-
-
-
-
These functions are similar to constructors and can be used to validate
-that there are no conflicts with the underlying model structures used by the
-package.
-
-
-
set_new_model (model )
-
-set_model_mode (model , mode )
-
-set_model_engine (model , mode , eng )
-
-set_model_arg (model , eng , parsnip , original , func , has_submodel )
-
-set_dependency (model , eng , pkg )
-
-get_dependency (model )
-
-set_fit (model , mode , eng , value )
-
-get_fit (model )
-
-set_pred (model , mode , eng , type , value )
-
-get_pred_type (model , type )
-
-show_model_info (model )
-
-pred_value_template (pre = NULL , post = NULL , func , ... )
-
-
-
-
- model
- A single character string for the model type (e.g.
-"rand_forest", etc).
-
- mode
- A single character string for the model mode (e.g. "regression").
-
- eng
- A single character string for the model engine.
-
- parsnip
- A single character string for the "harmonized" argument name
-that parsnip exposes.
-
- original
- A single character string for the argument name that
-underlying model function uses.
-
- func
- A named character vector that describes how to call
-a function. func should have elements pkg and fun. The
-former is optional but is recommended and the latter is
-required. For example, c(pkg = "stats", fun = "lm") c(fun = "predict") predict method.
-
- has_submodel
- A single logical for whether the argument
-can make predictions on multiple submodels at once.
-
- pkg
- An options character string for a package name.
-
- value
- A list that conforms to the fit_obj or pred_obj description
-below, depending on context.
-
- type
- A single character value for the type of prediction. Possible
-values are: class, conf_int, numeric, pred_int, prob, quantile,
-and raw.
-
- pre, post
- Optional functions for pre- and post-processing of prediction
-results.
-
- ...
- Optional arguments that should be passed into the args slot for
-prediction objects.
-
- arg
- A single character string for the model argument name.
-
- fit_obj
- A list with elements interface, protect,
-func and defaults. See the package vignette "Making a
-parsnip model from scratch".
-
- pred_obj
- A list with elements pre, post, func, and args.
-See the package vignette "Making a parsnip model from scratch".
-
-
-
-
-
These functions are available for users to add their
-own models or engines (in package or otherwise) so that they can
-be accessed using parsnip. This are more thoroughly documented
-on the package web site (see references below).
-
In short, parsnip stores an environment object that contains
-all of the information and code about how models are used (e.g.
-fitting, predicting, etc). These functions can be used to add
-models to that environment as well as helper functions that can
-be used to makes sure that the model data is in the right
-format.
-
check_model_exists() checks the model value and ensures that the model has
-already been registered. check_model_doesnt_exist() checks the model value
-and also checks to see if it is novel in the environment.
-
-
-
"Making a parsnip model from scratch"
-https://tidymodels.github.io/parsnip/articles/articles/Scratch.html
-
-
-
# set_new_model("shallow_learning_model")
-
-# Show the information about a model:
-show_model_info ("rand_forest" )
#> Information for `rand_forest`
-#> modes: unknown, classification, regression
-#>
-#> engines:
-#> classification: randomForest, ranger, spark
-#> regression: randomForest, ranger, spark
-#>
-#> arguments:
-#> ranger:
-#> mtry --> mtry
-#> trees --> num.trees
-#> min_n --> min.node.size
-#> randomForest:
-#> mtry --> mtry
-#> trees --> ntree
-#> min_n --> nodesize
-#> spark:
-#> mtry --> feature_subset_strategy
-#> trees --> num_trees
-#> min_n --> min_instances_per_node
-#>
-#> fit modules:
-#> engine mode
-#> ranger classification
-#> ranger regression
-#> randomForest classification
-#> randomForest regression
-#> spark classification
-#> spark regression
-#>
-#> prediction modules:
-#> mode engine methods
-#> classification randomForest class, prob, raw
-#> classification ranger class, conf_int, prob, raw
-#> classification spark class, prob
-#> regression randomForest numeric, raw
-#> regression ranger conf_int, numeric, raw
-#> regression spark numeric
-#>
-
-
-
-
-
-
-
Print the model call — show_call • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
show_call (object )
-
-
-
-
- x
- A "model_spec" object.
-
-
-
-
-
A character string.
-
-
-
-
-
-
-
-
General Interface for Parametric Survival Models — surv_reg • parsnip
-
-
-
-
-
-
-
-
-
-
-
surv_reg() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-R. The main argument for the
-model is:
This argument is converted to its specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the value is taken from the underlying model
-functions.
-
If parameters need to be modified, this function can be used
-in lieu of recreating the object from scratch.
-
-
-
surv_reg (mode = "regression" , dist = NULL )
-
-# S3 method for surv_reg
-update object , parameters = NULL , dist = NULL , fresh = FALSE , ... )
-
-
-
-
- mode
- A single character string for the type of model.
-The only possible value for this model is "regression".
-
- dist
- A character string for the outcome distribution. "weibull" is
-the default.
-
- object
- A survival regression model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The data given to the function are not saved and are only used
-to determine the mode of the model. For surv_reg(),the
-mode will always be "regression".
-
Since survival models typically involve censoring (and require the use of
-survival::Surv() fit()
-
Also, for the flexsurv::flexsurvfit engine, the typical
-strata function cannot be used. To achieve the same effect,
-the extra parameter roles can be used (as described above).
-
For surv_reg(), the mode will always be "regression".
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below.
flexsurv
-
## Parametric Survival Regression Model Specification (regression)
-##
-## Computational engine: flexsurv
-##
-## Model fit template:
-## flexsurv::flexsurvreg(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg())
-
-
-
-
survival
-
## Parametric Survival Regression Model Specification (regression)
-##
-## Computational engine: survival
-##
-## Model fit template:
-## survival::survreg(formula = missing_arg(), data = missing_arg(),
-## weights = missing_arg(), model = TRUE)
-
-
-
Note that model = TRUE is needed to produce quantile predictions when
-there is a stratification variable and can be overridden in other cases.
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip flexsurv survival dist dist dist
-
-
-
-
-
-
-
Jackson, C. (2016). flexsurv: A Platform for Parametric Survival
-Modeling in R. Journal of Statistical Software , 70(8), 1 - 33.
-
-
-
-
-
-
surv_reg ()
#> Parametric Survival Regression Model Specification (regression)
-#>
#> Parametric Survival Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> dist = varying()
-#>
-model <- surv_reg (dist = "weibull" )
-model
#> Parametric Survival Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> dist = weibull
-#>
#> Parametric Survival Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> dist = lnorm
-#>
-
-
-
-
-
-
-
General interface for polynomial support vector machines — svm_poly • parsnip
-
-
-
-
-
-
-
-
-
-
-
svm_poly() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-cost: The cost of predicting a sample within or on the
-wrong side of the margin.
degree: The polynomial degree.
scale_factor: A scaling factor for the kernel.
margin: The epsilon in the SVM insensitive loss function
-(regression only)
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
svm_poly (
- mode = "unknown" ,
- cost = NULL ,
- degree = NULL ,
- scale_factor = NULL ,
- margin = NULL
-)
-
-# S3 method for svm_poly
-update object ,
- parameters = NULL ,
- cost = NULL ,
- degree = NULL ,
- scale_factor = NULL ,
- margin = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- cost
- A positive number for the cost of predicting a sample within
-or on the wrong side of the margin
-
- degree
- A positive number for polynomial degree.
-
- scale_factor
- A positive number for the polynomial scaling factor.
-
- margin
- A positive number for the epsilon in the SVM insensitive
-loss function (regression only)
-
- object
- A polynomial SVM model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
kernlab
-
## Polynomial Support Vector Machine Specification (regression)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "polydot")
-
## Polynomial Support Vector Machine Specification (classification)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "polydot",
-## prob.model = TRUE)
-
-
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip kernlab cost C degree degree scale_factor scale margin epsilon
-
-
-
-
-
-
-
-
-
-
svm_poly (mode = "classification" , degree = 1.2 )
#> Polynomial Support Vector Machine Specification (classification)
-#>
-#> Main Arguments:
-#> degree = 1.2
-#>
#> Polynomial Support Vector Machine Specification (regression)
-#>
-#> Main Arguments:
-#> cost = varying()
-#>
model <- svm_poly (cost = 10 , scale_factor = 0.1 )
-model
#> Polynomial Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 10
-#> scale_factor = 0.1
-#>
#> Polynomial Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#> scale_factor = 0.1
-#>
#> Polynomial Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#>
-
-
-
-
-
-
-
General interface for radial basis function support vector machines — svm_rbf • parsnip
-
-
-
-
-
-
-
-
-
-
-
svm_rbf() is a way to generate a specification of a model
-before fitting and allows the model to be created using
-different packages in R or via Spark. The main arguments for the
-model are:
-cost: The cost of predicting a sample within or on the
-wrong side of the margin.
rbf_sigma: The precision parameter for the radial basis
-function.
margin: The epsilon in the SVM insensitive loss function
-(regression only)
These arguments are converted to their specific names at the
-time that the model is fit. Other options and argument can be
-set using set_engine() NULL), the values are taken from the underlying model
-functions. If parameters need to be modified, update()
-
-
-
svm_rbf (mode = "unknown" , cost = NULL , rbf_sigma = NULL , margin = NULL )
-
-# S3 method for svm_rbf
-update object ,
- parameters = NULL ,
- cost = NULL ,
- rbf_sigma = NULL ,
- margin = NULL ,
- fresh = FALSE ,
- ...
-)
-
-
-
-
- mode
- A single character string for the type of model.
-Possible values for this model are "unknown", "regression", or
-"classification".
-
- cost
- A positive number for the cost of predicting a sample within
-or on the wrong side of the margin
-
- rbf_sigma
- A positive number for radial basis function.
-
- margin
- A positive number for the epsilon in the SVM insensitive
-loss function (regression only)
-
- object
- A radial basis function SVM model specification.
-
- parameters
- A 1-row tibble or named list with main
-parameters to update. If the individual arguments are used,
-these will supersede the values in parameters. Also, using
-engine arguments in this object will result in an error.
-
- fresh
- A logical for whether the arguments should be
-modified in-place of or replaced wholesale.
-
- ...
- Not used for update()
-
-
-
-
-
The model can be created using the fit() engines :
-
-
-
-
Engines may have pre-set default arguments when executing the model fit
-call. For this type of model, the template of the fit calls are below:
kernlab
-
## Radial Basis Function Support Vector Machine Specification (regression)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "rbfdot")
-
## Radial Basis Function Support Vector Machine Specification (classification)
-##
-## Computational engine: kernlab
-##
-## Model fit template:
-## kernlab::ksvm(x = missing_arg(), y = missing_arg(), kernel = "rbfdot",
-## prob.model = TRUE)
-
-
-
-
liquidSVM
-
## Radial Basis Function Support Vector Machine Specification (regression)
-##
-## Computational engine: liquidSVM
-##
-## Model fit template:
-## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1,
-## threads = 0)
-
## Radial Basis Function Support Vector Machine Specification (classification)
-##
-## Computational engine: liquidSVM
-##
-## Model fit template:
-## liquidSVM::svm(x = missing_arg(), y = missing_arg(), folds = 1,
-## threads = 0)
-
-
-
Note that models created using the liquidSVM engine cannot be saved
-like conventional R objects. The fit slot of the model_fit object
-has to be saved separately using the liquidSVM::write.liquidSVM() fit slot has to be replaced
-with the model that is read using the liquidSVM::read.liquidSVM()
-
liquidSVM parameterizes the kernel parameter differently than
-kernlab. To translate between engines, sigma = 1/gammas^2. Users
-will be specifying sigma and the function translates the value to
-gamma.
-
-
Parameter translations
-
-
-
The standardized parameter names in parsnip can be mapped to their
-original names in each engine that has main parameters:
-parsnip kernlab liquidSVM cost C lambdas rbf_sigma sigma gammas margin epsilon NA
-
-
-
-
-
-
-
-
-
-
svm_rbf (mode = "classification" , rbf_sigma = 0.2 )
#> Radial Basis Function Support Vector Machine Specification (classification)
-#>
-#> Main Arguments:
-#> rbf_sigma = 0.2
-#>
#> Radial Basis Function Support Vector Machine Specification (regression)
-#>
-#> Main Arguments:
-#> cost = varying()
-#>
model <- svm_rbf (cost = 10 , rbf_sigma = 0.1 )
-model
#> Radial Basis Function Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 10
-#> rbf_sigma = 0.1
-#>
#> Radial Basis Function Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#> rbf_sigma = 0.1
-#>
#> Radial Basis Function Support Vector Machine Specification (unknown)
-#>
-#> Main Arguments:
-#> cost = 1
-#>
-
-
-
-
-
-
-
Turn a parsnip model object into a tidy tibble — tidy.model_fit • parsnip
-
-
-
-
-
-
-
-
-
-
-
This method tidies the model in a parsnip model object, if it exists.
-
-
-
# S3 method for model_fit
-tidy x , ... )
-
-
-
-
- x
- An object to be converted into a tidy tibble::tibble()
-
- ...
- Additional arguments to tidying method.
-
-
-
-
-
a tibble
-
-
-
-
-
-
-
-
Tidy method for null models — tidy.nullmodel • parsnip
-
-
-
-
-
-
-
-
-
-
-
Return the results of nullmodel as a tibble
-
-
-
# S3 method for nullmodel
-tidy x , ... )
-
-
-
-
- x
- A nullmodel object.
-
- ...
- Not used.
-
-
-
-
-
A tibble with column value.
-
-
-
#> # A tibble: 1 x 1
-#> value
-#> <chr>
-#> 1 setosa
#> # A tibble: 1 x 1
-#> value
-#> <dbl>
-#> 1 20.1
-
-
-
-
-
-
-
Resolve a Model Specification for a Computational Engine — translate • parsnip
-
-
-
-
-
-
-
-
-
-
-
translate() will translate a model specification into a code
-object that is specific to a particular engine (e.g. R package).
-It translates generic parameters to their counterparts.
-
-
-
translate (x , ... )
-
-# S3 method for default
-translate (x , engine = x $engine , ... )
-
-
-
-
- x
- A model specification.
-
- ...
- Not currently used.
-
- engine
- The computational engine for the model (see ?set_engine
-
-
-
-
-
translate() produces a template call that lacks the specific
-argument values (such as data, etc). These are filled in once
-fit() varying arguments if these are in
-the specification.
-
It does contain the resolved argument names that are specific to
-the model fitting function/engine.
-
This function can be useful when you need to understand how
-parsnip goes from a generic model specific to a model fitting
-function.
-
Note : this function is used internally and users should only use it
-to understand what the underlying syntax would be. It should not be used
-to modify the model specification.
-
-
-
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
-#> Computational engine: glmnet
-#>
-#> Model fit template:
-#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-#> family = "gaussian")
-# `penalty` not applicable for this model.
-translate (lm_spec , engine = "lm" )
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
-#> Computational engine: lm
-#>
-#> Model fit template:
-#> stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
-# `penalty` is tranlsated to `reg_param`
-translate (lm_spec , engine = "spark" )
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> penalty = 0.01
-#>
-#> Computational engine: spark
-#>
-#> Model fit template:
-#> sparklyr::ml_linear_regression(x = missing_arg(), formula = missing_arg(),
-#> weight_col = missing_arg(), reg_param = 0.01)
#> Linear Regression Model Specification (regression)
-#>
-#> Main Arguments:
-#> mixture = varying()
-#>
-#> Computational engine: glmnet
-#>
-#> Model fit template:
-#> glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
-#> alpha = varying(), family = "gaussian")
-
-
-
-
-
-
-
-
Succinct summary of parsnip object — type_sum.model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
type_sum controls how objects are shown when inside tibble
-columns.
-
-
-
# S3 method for model_spec
-type_sum (x )
-
-# S3 method for model_fit
-type_sum (x )
-
-
-
-
- x
- A model_spec or model_fit object to summarise.
-
-
-
-
-
A character value.
-
-
-
For model_spec objects, the summary is "spec[?]"
-or "spec[+]". The former indicates that either the model
-mode has not been declared or that the specification has
-varying()
-
For fitted models, either "fit[x]" or "fit[+]" are used
-where the "x" implies that the model fit failed in some way.
-
-
-
-
-
-
-
-
A placeholder function for argument values — varying • parsnip
-
-
-
-
-
-
-
-
-
-
-
varying() is used when a parameter will be specified at a later date.
-
-
-
varying ()
-
-
-
-
-
-
-
-
-
-
Determine varying arguments — varying_args • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
varying_args takes a model specification and lists all of the arguments
-along with whether they are fully specified or not.
-
-
-
-
varying_args (x , id , ... )
-
-# S3 method for model_spec
-varying_args (x , id = NULL , ... )
-
-# S3 method for recipe
-varying_args (x , id = NULL , ... )
-
-# S3 method for step
-varying_args (x , id = NULL , ... )
-
-
-
-
- x
- An object
-
- id
- A string describing the object x.
-
- ...
- Not currently used.
-
-
-
-
-
A tibble with columns for the parameter name (name), whether is
-contains any varying value (varying), the id for the object, and the
-class that was used to call the method (type).
-
-
-
-
library (dplyr )
-library (rlang )
#> Attaching package: ‘rlang’
#> The following objects are masked from ‘package:testthat’:
-#> is_false, is_null, is_true
#> Warning: `list_len()` is soft-deprecated as of rlang 0.2.0.
-#> Please use `new_list()` instead
-#> [90mThis warning is displayed once per session.[39m
#> [38;5;246m# A tibble: 3 x 4[39m
-#> name varying id type
-#> [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<lgl>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
-#> [38;5;250m1[39m mtry FALSE plain model_spec
-#> [38;5;250m2[39m trees FALSE plain model_spec
-#> [38;5;250m3[39m min_n FALSE plain model_spec
#> [38;5;246m# A tibble: 3 x 4[39m
-#> name varying id type
-#> [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<lgl>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
-#> [38;5;250m1[39m mtry TRUE one arg model_spec
-#> [38;5;250m2[39m trees FALSE one arg model_spec
-#> [38;5;250m3[39m min_n FALSE one arg model_spec
#> [38;5;246m# A tibble: 4 x 4[39m
-#> name varying id type
-#> [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<lgl>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
-#> [38;5;250m1[39m mtry FALSE only eng_args model_spec
-#> [38;5;250m2[39m trees FALSE only eng_args model_spec
-#> [38;5;250m3[39m min_n FALSE only eng_args model_spec
-#> [38;5;250m4[39m sample.fraction TRUE only eng_args model_spec
#> [38;5;246m# A tibble: 5 x 4[39m
-#> name varying id type
-#> [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<lgl>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
-#> [38;5;250m1[39m mtry FALSE add an expr model_spec
-#> [38;5;250m2[39m trees FALSE add an expr model_spec
-#> [38;5;250m3[39m min_n FALSE add an expr model_spec
-#> [38;5;250m4[39m strata FALSE add an expr model_spec
-#> [38;5;250m5[39m sampsize TRUE add an expr model_spec
#> [38;5;246m# A tibble: 4 x 4[39m
-#> name varying id type
-#> [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<lgl>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
-#> [38;5;250m1[39m mtry FALSE list of values model_spec
-#> [38;5;250m2[39m trees FALSE list of values model_spec
-#> [38;5;250m3[39m min_n FALSE list of values model_spec
-#> [38;5;250m4[39m classwt TRUE list of values model_spec
-
-
-
-
-
-
-
Determine varying arguments — varying_args.model_spec • parsnip
-
-
-
-
-
-
-
-
-
-
-
varying_args()
-
-
-
# S3 method for model_spec
-varying_args object , full = TRUE , ... )
-
-# S3 method for recipe
-varying_args object , full = TRUE , ... )
-
-# S3 method for step
-varying_args object , full = TRUE , ... )
-
-
-
-
- object
- A model_spec or a recipe.
-
- full
- A single logical. Should all possible varying parameters be
-returned? If FALSE, then only the parameters that
-are actually varying are returned.
-
- ...
- Not currently used.
-
-
-
-
-
A tibble with columns for the parameter name (name), whether it
-contains any varying value (varying), the id for the object (id),
-and the class that was used to call the method (type).
-
-
-
The id column is determined differently depending on whether a model_spec
-or a recipe is used. For a model_spec, the first class is used. For
-a recipe, the unique step id is used.
-
-
-
#> # A tibble: 3 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry FALSE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
#> # A tibble: 3 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry TRUE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
#> # A tibble: 4 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry FALSE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
-#> 4 sample.fraction TRUE rand_forest model_spec
#> # A tibble: 1 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 sample.fraction TRUE rand_forest model_spec
#> # A tibble: 5 x 4
-#> name varying id type
-#> <chr> <lgl> <chr> <chr>
-#> 1 mtry FALSE rand_forest model_spec
-#> 2 trees FALSE rand_forest model_spec
-#> 3 min_n FALSE rand_forest model_spec
-#> 4 strata FALSE rand_forest model_spec
-#> 5 sampsize TRUE rand_forest model_spec
-
-
-
-
-
-
-
-
Watson Churn Data — wa_churn • parsnip
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
IBM Watson Analytics https://ibm.co/2sOvyvy
-
-
-
-
-
wa_churn a data frame
-
-
-
-
These data were downloaded from the IBM Watson site
-(see below) in September 2018. The data contain a factor for
-whether a customer churned or not. Alternatively, the tenure
-column presumably contains information on how long the customer
-has had an account. A survival analysis can be done on this
-column using the churn outcome as the censoring information. A
-data dictionary can be found on the source website.
-
-
-
#> Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 7043 obs. of 20 variables:
-#> $ churn : Factor w/ 2 levels "Yes","No": 2 2 1 2 1 1 2 2 1 2 ...
-#> $ female : num 1 0 0 0 1 1 0 1 1 0 ...
-#> $ senior_citizen : int 0 0 0 0 0 0 0 0 0 0 ...
-#> $ partner : num 1 0 0 0 0 0 0 0 1 0 ...
-#> $ dependents : num 0 0 0 0 0 0 1 0 0 1 ...
-#> $ tenure : int 1 34 2 45 2 8 22 10 28 62 ...
-#> $ phone_service : num 0 1 1 0 1 1 1 0 1 1 ...
-#> $ multiple_lines : Factor w/ 3 levels "No","No phone service",..: 2 1 1 2 1 3 3 2 3 1 ...
-#> $ internet_service : Factor w/ 3 levels "DSL","Fiber optic",..: 1 1 1 1 2 2 2 1 2 1 ...
-#> $ online_security : Factor w/ 3 levels "No","No internet service",..: 1 3 3 3 1 1 1 3 1 3 ...
-#> $ online_backup : Factor w/ 3 levels "No","No internet service",..: 3 1 3 1 1 1 3 1 1 3 ...
-#> $ device_protection: Factor w/ 3 levels "No","No internet service",..: 1 3 1 3 1 3 1 1 3 1 ...
-#> $ tech_support : Factor w/ 3 levels "No","No internet service",..: 1 1 1 3 1 1 1 1 3 1 ...
-#> $ streaming_tv : Factor w/ 3 levels "No","No internet service",..: 1 1 1 1 1 3 3 1 3 1 ...
-#> $ streaming_movies : Factor w/ 3 levels "No","No internet service",..: 1 1 1 1 1 3 1 1 3 1 ...
-#> $ contract : Factor w/ 3 levels "Month-to-month",..: 1 2 1 2 1 1 1 1 1 2 ...
-#> $ paperless_billing: num 1 0 1 0 1 1 1 0 1 0 ...
-#> $ payment_method : Factor w/ 4 levels "Bank transfer (automatic)",..: 3 4 4 1 3 3 2 4 3 1 ...
-#> $ monthly_charges : num 29.9 57 53.9 42.3 70.7 ...
-#> $ total_charges : num 29.9 1889.5 108.2 1840.8 151.7 ...
-
-
-
-
-
-
-
Boosted trees via xgboost — xgb_train • parsnip
-
-
-
-
-
-
-
-
-
-
-
xgb_train is a wrapper for xgboost tree-based models
-where all of the model arguments are in the main function.
-
-
-
xgb_train (
- x ,
- y ,
- max_depth = 6 ,
- nrounds = 15 ,
- eta = 0.3 ,
- colsample_bytree = 1 ,
- min_child_weight = 1 ,
- gamma = 0 ,
- subsample = 1 ,
- ...
-)
-
-
-
-
- x
- A data frame or matrix of predictors
-
- y
- A vector (factor or numeric) or matrix (numeric) of outcome data.
-
- max_depth
- An integer for the maximum depth of the tree.
-
- nrounds
- An integer for the number of boosting iterations.
-
- eta
- A numeric value between zero and one to control the learning rate.
-
- colsample_bytree
- Subsampling proportion of columns.
-
- min_child_weight
- A numeric value for the minimum sum of instance
-weights needed in a child to continue to split.
-
- gamma
- A number for the minimum loss reduction required to make a
-further partition on a leaf node of the tree
-
- subsample
- Subsampling proportion of rows.
-
- ...
- Other options to pass to xgb.train.
-
-
-
-
-
A fitted xgboost object.
-
-
-
-
-
-
-
-
-
- https://parsnip.tidymodels.org/index.html
-
-
- https://parsnip.tidymodels.org/reference/C5.0_train.html
-
-
- https://parsnip.tidymodels.org/reference/add_on_exports.html
-
-
- https://parsnip.tidymodels.org/reference/add_rowindex.html
-
-
- https://parsnip.tidymodels.org/reference/boost_tree.html
-
-
- https://parsnip.tidymodels.org/reference/check_empty_ellipse.html
-
-
- https://parsnip.tidymodels.org/reference/control_parsnip.html
-
-
- https://parsnip.tidymodels.org/reference/convert_args.html
-
-
- https://parsnip.tidymodels.org/reference/convert_stan_interval.html
-
-
- https://parsnip.tidymodels.org/reference/decision_tree.html
-
-
- https://parsnip.tidymodels.org/reference/descriptors.html
-
-
- https://parsnip.tidymodels.org/reference/eval_args.html
-
-
- https://parsnip.tidymodels.org/reference/fit.html
-
-
- https://parsnip.tidymodels.org/reference/get_model_env.html
-
-
- https://parsnip.tidymodels.org/reference/has_multi_predict.html
-
-
- https://parsnip.tidymodels.org/reference/keras_mlp.html
-
-
- https://parsnip.tidymodels.org/reference/linear_reg.html
-
-
- https://parsnip.tidymodels.org/reference/logistic_reg.html
-
-
- https://parsnip.tidymodels.org/reference/make_classes.html
-
-
- https://parsnip.tidymodels.org/reference/mars.html
-
-
- https://parsnip.tidymodels.org/reference/mlp.html
-
-
- https://parsnip.tidymodels.org/reference/model_fit.html
-
-
- https://parsnip.tidymodels.org/reference/model_printer.html
-
-
- https://parsnip.tidymodels.org/reference/model_spec.html
-
-
- https://parsnip.tidymodels.org/reference/multi_predict.html
-
-
- https://parsnip.tidymodels.org/reference/multinom_reg.html
-
-
- https://parsnip.tidymodels.org/reference/nearest_neighbor.html
-
-
- https://parsnip.tidymodels.org/reference/null_model.html
-
-
- https://parsnip.tidymodels.org/reference/nullmodel.html
-
-
- https://parsnip.tidymodels.org/reference/other_predict.html
-
-
- https://parsnip.tidymodels.org/reference/predict.model_fit.html
-
-
- https://parsnip.tidymodels.org/reference/rand_forest.html
-
-
- https://parsnip.tidymodels.org/reference/reexports.html
-
-
- https://parsnip.tidymodels.org/reference/rpart_train.html
-
-
- https://parsnip.tidymodels.org/reference/set_args.html
-
-
- https://parsnip.tidymodels.org/reference/set_engine.html
-
-
- https://parsnip.tidymodels.org/reference/set_new_model.html
-
-
- https://parsnip.tidymodels.org/reference/show_call.html
-
-
- https://parsnip.tidymodels.org/reference/surv_reg.html
-
-
- https://parsnip.tidymodels.org/reference/svm_poly.html
-
-
- https://parsnip.tidymodels.org/reference/svm_rbf.html
-
-
- https://parsnip.tidymodels.org/reference/tidy.model_fit.html
-
-
- https://parsnip.tidymodels.org/reference/tidy.nullmodel.html
-
-
- https://parsnip.tidymodels.org/reference/translate.html
-
-
- https://parsnip.tidymodels.org/reference/type_sum.model_spec.html
-
-
- https://parsnip.tidymodels.org/reference/varying.html
-
-
- https://parsnip.tidymodels.org/reference/varying_args.model_spec.html
-
-
- https://parsnip.tidymodels.org/reference/xgb_train.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Classification.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Models.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Regression.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Scratch.html
-
-
- https://parsnip.tidymodels.org/articles/articles/Submodels.html
-
-
- https://parsnip.tidymodels.org/articles/parsnip_Intro.html
-
-
diff --git a/docs/tidyverse-2.css b/docs/tidyverse-2.css
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diff --git a/docs/tidyverse.css b/docs/tidyverse.css
deleted file mode 100644
index 0a23ebaed..000000000
--- a/docs/tidyverse.css
+++ /dev/null
@@ -1,6531 +0,0 @@
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-
diff --git a/docs/tocBullet.svg b/docs/tocBullet.svg
deleted file mode 100644
index 29abb514f..000000000
--- a/docs/tocBullet.svg
+++ /dev/null
@@ -1,11 +0,0 @@
-
-
-
-
-
diff --git a/man/boost_tree.Rd b/man/boost_tree.Rd
index 4a5b4a1c9..576756125 100644
--- a/man/boost_tree.Rd
+++ b/man/boost_tree.Rd
@@ -158,6 +158,29 @@ call. For this type of model, the template of the fit calls are below:
## parsnip::xgb_train(x = missing_arg(), y = missing_arg(), nthread = 1,
## verbose = 0)
}
+
+Note that, for most engines to \code{boost_tree()}, the \code{sample_size}
+argument is in terms of the \emph{number} of training set points. The
+\code{xgboost} package parameterizes this as the \emph{proportion} of training set
+samples instead. When using the \code{tune}, this \strong{occurs automatically}.
+
+If you would like to use a custom range when tuning \code{sample_size}, the
+\code{dials::sample_prop()} function can be used in that case. For example,
+using a parameter set:\if{html}{\out{}}\preformatted{mod <-
+ boost_tree(sample_size = tune()) \%>\%
+ set_engine("xgboost") \%>\%
+ set_mode("classification")
+
+# update the parameters using the `dials` function
+mod_param <-
+ mod \%>\%
+ parameters() \%>\%
+ update(sample_size = sample_prop(c(0.4, 0.9)))
+}\if{html}{\out{
}}
+
+Finally, note that \code{xgboost} models require that non-numeric predictors
+(e.g., factors) must be converted to dummy variables or some other
+numeric representation.
}
\subsection{C5.0}{\if{html}{\out{}}\preformatted{boost_tree() \%>\%
diff --git a/man/decision_tree.Rd b/man/decision_tree.Rd
index 425ac6a36..4f6926678 100644
--- a/man/decision_tree.Rd
+++ b/man/decision_tree.Rd
@@ -35,7 +35,7 @@ parameter (a.k.a. \code{Cp}) used by CART models (\code{rpart} only).}
\item{min_n}{An integer for the minimum number of data points
in a node that are required for the node to be split further.}
-\item{object}{A random forest model specification.}
+\item{object}{A decision tree model specification.}
\item{parameters}{A 1-row tibble or named list with \emph{main}
parameters to update. If the individual arguments are used,
@@ -150,7 +150,7 @@ predictors to be converted to indicator variables.
## Computational engine: spark
##
## Model fit template:
-## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(),
+## sparklyr::ml_decision_tree_regressor(x = missing_arg(), formula = missing_arg(),
## seed = sample.int(10^5, 1))
}\if{html}{\out{
}}\preformatted{decision_tree() \%>\%
set_engine("spark") \%>\%
diff --git a/man/linear_reg.Rd b/man/linear_reg.Rd
index 04c302751..8787ac1ff 100644
--- a/man/linear_reg.Rd
+++ b/man/linear_reg.Rd
@@ -24,12 +24,12 @@ The only possible value for this model is "regression".}
amount of regularization (\code{glmnet}, \code{keras}, and \code{spark} only).
For \code{keras} models, this corresponds to purely L2 regularization
(aka weight decay) while the other models can be a combination
-of L1 and L2 (depending on the value of \code{mixture}).}
+of L1 and L2 (depending on the value of \code{mixture}; see below).}
-\item{mixture}{A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (\code{glmnet} and \code{spark} only).}
+\item{mixture}{A number between zero and one (inclusive) that is the
+proportion of L1 regularization (i.e. lasso) in the model. When
+\code{mixture = 1}, it is a pure lasso model while \code{mixture = 0} indicates that
+ridge regression is being used. (\code{glmnet} and \code{spark} only).}
\item{object}{A linear regression model specification.}
@@ -51,8 +51,8 @@ arguments for the model are:
\itemize{
\item \code{penalty}: The total amount of regularization
in the model. Note that this must be zero for some engines.
-\item \code{mixture}: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
+\item \code{mixture}: The mixture amounts of different types of
+regularization (see below). Note that this will be ignored for some engines.
}
These arguments are converted to their specific names at the
time that the model is fit. Other options and argument can be
diff --git a/man/logistic_reg.Rd b/man/logistic_reg.Rd
index a597eb81d..daba9724f 100644
--- a/man/logistic_reg.Rd
+++ b/man/logistic_reg.Rd
@@ -26,10 +26,10 @@ For \code{keras} models, this corresponds to purely L2 regularization
(aka weight decay) while the other models can be a combination
of L1 and L2 (depending on the value of \code{mixture}).}
-\item{mixture}{A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (\code{glmnet} and \code{spark} only).}
+\item{mixture}{A number between zero and one (inclusive) that is the
+proportion of L1 regularization (i.e. lasso) in the model. When
+\code{mixture = 1}, it is a pure lasso model while \code{mixture = 0} indicates that
+ridge regression is being used. (\code{glmnet} and \code{spark} only).}
\item{object}{A logistic regression model specification.}
@@ -51,8 +51,8 @@ arguments for the model are:
\itemize{
\item \code{penalty}: The total amount of regularization
in the model. Note that this must be zero for some engines.
-\item \code{mixture}: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
+\item \code{mixture}: The mixture amounts of different types of
+regularization (see below). Note that this will be ignored for some engines.
}
These arguments are converted to their specific names at the
time that the model is fit. Other options and argument can be
diff --git a/man/mlp.Rd b/man/mlp.Rd
index 76b792f8f..d53b2252a 100644
--- a/man/mlp.Rd
+++ b/man/mlp.Rd
@@ -41,13 +41,13 @@ of model parameters randomly set to zero during model training.}
\item{epochs}{An integer for the number of training iterations.}
-\item{activation}{A single character strong denoting the type of relationship
+\item{activation}{A single character string denoting the type of relationship
between the original predictors and the hidden unit layer. The activation
function between the hidden and output layers is automatically set to either
"linear" or "softmax" depending on the type of outcome. Possible values are:
"linear", "softmax", "relu", and "elu"}
-\item{object}{A random forest model specification.}
+\item{object}{A multilayer perceptron model specification.}
\item{parameters}{A 1-row tibble or named list with \emph{main}
parameters to update. If the individual arguments are used,
diff --git a/man/multinom_reg.Rd b/man/multinom_reg.Rd
index b2b4a93b8..513ebf885 100644
--- a/man/multinom_reg.Rd
+++ b/man/multinom_reg.Rd
@@ -26,10 +26,10 @@ For \code{keras} models, this corresponds to purely L2 regularization
(aka weight decay) while the other models can be a combination
of L1 and L2 (depending on the value of \code{mixture}).}
-\item{mixture}{A number between zero and one (inclusive) that
-represents the proportion of regularization that is used for the
-L2 penalty (i.e. weight decay, or ridge regression) versus L1
-(the lasso) (\code{glmnet} only).}
+\item{mixture}{A number between zero and one (inclusive) that is the
+proportion of L1 regularization (i.e. lasso) in the model. When
+\code{mixture = 1}, it is a pure lasso model while \code{mixture = 0} indicates that
+ridge regression is being used. (\code{glmnet} and \code{spark} only).}
\item{object}{A multinomial regression model specification.}
@@ -51,8 +51,8 @@ model are:
\itemize{
\item \code{penalty}: The total amount of regularization
in the model. Note that this must be zero for some engines.
-\item \code{mixture}: The proportion of L1 regularization in
-the model. Note that this will be ignored for some engines.
+\item \code{mixture}: The mixture amounts of different types of
+regularization (see below). Note that this will be ignored for some engines.
}
These arguments are converted to their specific names at the
time that the model is fit. Other options and argument can be
diff --git a/man/rmd/boost-tree.Rmd b/man/rmd/boost-tree.Rmd
index e10e2c6f5..5606f4d8a 100644
--- a/man/rmd/boost-tree.Rmd
+++ b/man/rmd/boost-tree.Rmd
@@ -18,6 +18,25 @@ boost_tree() %>%
translate()
```
+Note that, for most engines to `boost_tree()`, the `sample_size` argument is in terms of the _number_ of training set points. The `xgboost` package parameterizes this as the _proportion_ of training set samples instead. When using the `tune`, this **occurs automatically**.
+
+If you would like to use a custom range when tuning `sample_size`, the `dials::sample_prop()` function can be used in that case. For example, using a parameter set:
+
+```{r xgb-update, eval = FALSE}
+mod <-
+ boost_tree(sample_size = tune()) %>%
+ set_engine("xgboost") %>%
+ set_mode("classification")
+
+# update the parameters using the `dials` function
+mod_param <-
+ mod %>%
+ parameters() %>%
+ update(sample_size = sample_prop(c(0.4, 0.9)))
+```
+
+Finally, note that `xgboost` models require that non-numeric predictors (e.g., factors) must be converted to dummy variables or some other numeric representation.
+
## C5.0
