add new section

Author: Joseph Damiba

_includes/layouts/side-bar.html

@@ -39,6 +39,8 @@
 {% assign financial = true %}
 {% elsif page.display_as == "3d_charts" %}
 {% assign 3d_charts = true %}
+{% elsif page.display_as == "ai_ml" %}
+{% assign ai_ml = true %}
 {% elsif page.display_as == "advanced_charts"%}
 {% assign advanced_charts = true %}
 {% elsif page.display_as == "multiple_axes" %}
@@ -217,6 +219,12 @@
                 </li>
                 {% endif %}
 
+                {% if ai_ml == true %}
+                <li class="--sidebar-item">
+                    <a href="#ai-ml" class="js-splash--navigation-item">Artificial Intelligence and Machine Learning</a>
+                </li>
+                {% endif %}
+
                 {% if scientific == true %}
                 <li class="--sidebar-item">
                     <a href="#scientific-charts" class="js-splash--navigation-item">Scientific</a>

_includes/posts/documentation_eg.html

@@ -32,6 +32,8 @@
 {% assign chart_studio = true %}
 {% elsif page.display_as == "advanced_opt" %}
 {% assign advanced_opt = true %}
+{% elsif page.display_as == "ai_ml" %}
+{% assign ai_ml = true %}
 
 <!-- START OF GGPLOT CUSTOM LAYOUT -->
 {% elsif page.display_as == "aesthetics" %}
@@ -171,6 +173,36 @@
 </section>
 {% endif %}
 
+{% if ai_ml %}
+<section class="--tutorial-section" id="ai-ml">
+    <header class="--section-header"><a href="#ai-ml" name="ai-ml" id="basic-charts">Artifical Intelligence and Machine Learning Charts</a>
+    </header>
+    <section class="--grid">
+        <ul class="--grid-list">
+            {%- for page in languagelist -%}
+            {% if page.display_as == "ai_ml" %}
+
+            <li style="background-image: url({{site.imgurl}}{{page.thumbnail}});" class="--grid-item">
+                <a href="{% if page.permalink contains 'http' %}{{page.permalink}}{% else %}/{{page.permalink}}{% endif %}">
+
+
+                    <!--<a href="{{page.permalink}}">-->
+                    <div class="--item-meta"><span>{{page.name}}</span></div>
+                    <div class="--item-image">
+                        <span>View Tutorial</span>
+                        <img src="{{site.imgurl}}{{page.thumbnail}}" alt="{{page.name}}">
+                    </div>
+                </a>
+
+            </li>
+
+            {% endif %}
+            {%- endfor -%}
+        </ul>
+    </section>
+</section>
+{% endif %}
+
 {% if scientific %}
 <section class="--tutorial-section" id="scientific-charts">
     <header class="--section-header"><a href="#scientific-charts">Scientific Charts</a>

_posts/python/2020-02-23-ai-index.html

@@ -0,0 +1,29 @@
+---
+permalink: python/ai-ml/
+description: Plotly's Python graphing library makes interactive, publication-quality graphs online. Examples of how to make charts related to artificial intelligence and machine learning.
+name: More Artificial Intelligence and Machine Learning Charts
+layout: langindex
+language: python
+display_as: ai_ml
+thumbnail: thumbnail/mixed.jpg
+page_type: example_index
+order: 5
+---
+
+
+<header class="--welcome">
+	<div class="--welcome-body">
+		<!--div.--wrap-inner-->
+		<div class="--title">
+			<div class="--category-img"><img src="https://plot.ly/gh-pages/documentation/static/images/python-small.png" alt=""></div>
+			<div class="--body">
+				<h1>Plotly Python Open Source Graphing Library Artificial Intelligence and Machine Learning Charts</h1>
+				<p>{{page.description}}</p>
+			</div>
+		</div>
+	</div>
+</header>
+
+
+		{% assign languagelist = site.posts | where: "language","python" | where: "display_as","ai_ml" | where: "layout","base" | sort: "order" %}
+        {% include posts/documentation_eg.html %}

_posts/python/2020-02-23-ml-knn.md

@@ -0,0 +1,229 @@
+---
+description: How to visualize k-Nearest Neighbors (kNN) created using scikit-learn
+    in Python with Plotly.
+display_as: ai_ml
+language: python
+layout: base
+name: K-Nearest Neighbors (kNN) Classification
+order: 1
+page_type: example_index
+permalink: python/knn/
+redirect_from: python/machine-learning-tutorials/
+thumbnail: thumbnail/line-and-scatter.jpg
+---
+
+## Basic Binary Classification with `plotly.express`
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.datasets import make_moons
+from sklearn.neighbors import KNeighborsClassifier
+
+X, y = make_moons(noise=0.3, random_state=0)
+X_test, _ = make_moons(noise=0.3, random_state=1)
+
+clf = KNeighborsClassifier(15)
+clf.fit(X, y.astype(str))  # Fit on training set
+y_pred = clf.predict(X_test)  # Predict on new data
+
+fig = px.scatter(x=X_test[:, 0], y=X_test[:, 1], color=y_pred, labels={'color': 'predicted'})
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
+## Visualize Binary Prediction Scores
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.datasets import make_classification
+from sklearn.neighbors import KNeighborsClassifier
+
+X, y = make_classification(n_features=2, n_redundant=0, random_state=0)
+X_test, _ = make_classification(n_features=2, n_redundant=0, random_state=1)
+
+clf = KNeighborsClassifier(15)
+clf.fit(X, y)  # Fit on training set
+y_score = clf.predict_proba(X_test)[:, 1]  # Predict on new data
+
+fig = px.scatter(x=X_test[:, 0], y=X_test[:, 1], color=y_score, labels={'color': 'score'})
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
+## Probability Estimates with `go.Contour`
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.datasets import make_moons
+from sklearn.neighbors import KNeighborsClassifier
+
+mesh_size = .02
+margin = 1
+
+X, y = make_moons(noise=0.3, random_state=0)
+
+# Create a mesh grid on which we will run our model
+x_min, x_max = X[:, 0].min() - margin, X[:, 0].max() + margin
+y_min, y_max = X[:, 1].min() - margin, X[:, 1].max() + margin
+xrange = np.arange(x_min, x_max, mesh_size)
+yrange = np.arange(y_min, y_max, mesh_size)
+xx, yy = np.meshgrid(xrange, yrange)
+
+# Create classifier, run predictions on grid
+clf = KNeighborsClassifier(15, weights='uniform')
+clf.fit(X, y)
+Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
+Z = Z.reshape(xx.shape)
+
+fig = px.scatter(X, x=0, y=1, color=y.astype(str), labels={'0':'', '1':''})
+fig.update_traces(marker_size=10, marker_line_width=1)
+fig.add_trace(
+    go.Contour(
+        x=xrange, 
+        y=yrange, 
+        z=Z, 
+        showscale=False,
+        colorscale=['Blue', 'Red'],
+        opacity=0.4,
+        name='Confidence'
+    )
+)
+fig.show()
+```
+
+## Multi-class prediction confidence with `go.Heatmap`
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.neighbors import KNeighborsClassifier
+
+mesh_size = .02
+margin = 1
+
+# We will use the iris data, which is included in px
+df = px.data.iris()
+X = df[['sepal_length', 'sepal_width']]
+y = df.species_id
+
+# Create a mesh grid on which we will run our model
+l_min, l_max = df.sepal_length.min() - margin, df.sepal_length.max() + margin
+w_min, w_max = df.sepal_width.min() - margin, df.sepal_width.max() + margin
+lrange = np.arange(l_min, l_max, mesh_size)
+wrange = np.arange(w_min, w_max, mesh_size)
+ll, ww = np.meshgrid(lrange, wrange)
+
+# Create classifier, run predictions on grid
+clf = KNeighborsClassifier(15, weights='distance')
+clf.fit(X, y)
+Z = clf.predict(np.c_[ll.ravel(), ww.ravel()])
+Z = Z.reshape(ll.shape)
+proba = clf.predict_proba(np.c_[ll.ravel(), ww.ravel()])
+proba = proba.reshape(ll.shape + (3,))
+
+fig = px.scatter(df, x='sepal_length', y='sepal_width', color='species')
+fig.update_traces(marker_size=10, marker_line_width=1)
+fig.add_trace(
+    go.Heatmap(
+        x=lrange, 
+        y=wrange, 
+        z=Z, 
+        showscale=False,
+        colorscale=[[0.0, 'blue'], [0.5, 'red'], [1.0, 'green']],
+        opacity=0.25,
+        customdata=proba,
+        hovertemplate=(
+            'sepal length: %{x} <br>'
+            'sepal width: %{y} <br>'
+            'p(setosa): %{customdata[0]:.3f}<br>'
+            'p(versicolor): %{customdata[1]:.3f}<br>'
+            'p(virginica): %{customdata[2]:.3f}<extra></extra>'
+        )
+    )
+)
+fig.show()
+```
+
+## 3D Classification with `px.scatter_3d`
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+
+df = px.data.iris()
+features = ["sepal_width", "sepal_length", "petal_width"]
+
+X = df[features]
+y = df.species
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
+
+# Create classifier, run predictions on grid
+clf = KNeighborsClassifier(15, weights='distance')
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+y_score = clf.predict_proba(X_test)
+y_score = np.around(y_score.max(axis=1), 4)
+
+fig = px.scatter_3d(
+    X_test, 
+    x='sepal_length', 
+    y='sepal_width', 
+    z='petal_width', 
+    symbol=y_pred,
+    color=y_score,
+    labels={'symbol': 'prediction', 'color': 'score'}
+)
+fig.update_layout(legend=dict(x=0, y=0))
+fig.show()
+```
+
+## High Dimension Visualization with `px.scatter_matrix`
+
+If you need to visualize classifications that go beyond 3D, you can use the [scatter plot matrix](https://plot.ly/python/splom/).
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+
+df = px.data.iris()
+features = ["sepal_width", "sepal_length", "petal_width", "petal_length"]
+
+X = df[features]
+y = df.species
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
+
+# Create classifier, run predictions on grid
+clf = KNeighborsClassifier(15, weights='distance')
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+
+fig = px.scatter_matrix(X_test, dimensions=features, color=y_pred, labels={'color': 'prediction'})
+fig.show()
+```
+
+### Reference
+
+Learn more about `px`, `go.Contour`, and `go.Heatmap` here:
+* https://plot.ly/python/plotly-express/
+* https://plot.ly/python/heatmaps/
+* https://plot.ly/python/contour-plots/
+* https://plot.ly/python/3d-scatter-plots/
+* https://plot.ly/python/splom/
+
+This tutorial was inspired by amazing examples from the official scikit-learn docs:
+* https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
+* https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
+* https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html

check-or-enforce-order.py

@@ -16,7 +16,7 @@
     if sys.argv[2] == 'enforce':
         enforce = True
 
-categories = ["file_settings", "basic", "financial", "statistical", "scientific", "maps", "3d_charts", "multiple_axes"]
+categories = ["file_settings", "basic", "financial", "statistical", "scientific", "maps", "3d_charts", "multiple_axes", "ai_ml"]
 
 def get_post(path):
     return fm.load(str(path))

front-matter-ci.py

@@ -93,6 +93,7 @@ def check_no_example_index_with_order_under_5(meta_to_check):
     "maps",
     "3d_charts",
     "multiple_axes",
+    "ai_ml"
 ]
 languages = ["python", "python/v3", "plotly_js", "r"]