[Deprecated] Starting AI/ML section in python docs

doc/python/ml-knn.md

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+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    notebook_metadata_filter: all
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.1'
+      jupytext_version: 1.1.1
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
+      version: 3
+    file_extension: .py
+    mimetype: text/x-python
+    name: python
+    nbconvert_exporter: python
+    pygments_lexer: ipython3
+    version: 3.7.6
+  plotly:
+    description: How to visualize k-Nearest Neighbors (kNN) created using scikit-learn
+      in Python with Plotly.
+    display_as: basic
+    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

doc/python/ml-regression.md

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+# Regression
+
+
+### Visualizing kNN Regression
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.neighbors import KNeighborsRegressor
+
+df = px.data.tips()
+X = df.total_bill.values.reshape(-1, 1)
+
+knn_dist = KNeighborsRegressor(10, weights='distance')
+knn_uni = KNeighborsRegressor(10, weights='uniform')
+knn_dist.fit(X, df.tip)
+knn_uni.fit(X, df.tip)
+
+x_range = np.linspace(X.min(), X.max(), 100)
+y_dist = knn_dist.predict(x_range.reshape(-1, 1))
+y_uni = knn_uni.predict(x_range.reshape(-1, 1))
+
+fig = px.scatter(df, x='total_bill', y='tip', color='sex', opacity=0.65)
+fig.add_traces(go.Scatter(x=x_range, y=y_uni, name='Weights: Uniform'))
+fig.add_traces(go.Scatter(x=x_range, y=y_dist, name='Weights: Distance'))
+fig.show()
+```
+
+### Reference
+
+Learn more about `px` here:
+* https://plot.ly/python/plotly-express/
+
+This tutorial was inspired by amazing examples from the official scikit-learn docs:
+* https://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html

doc/requirements.txt

@@ -17,6 +17,7 @@ requests
 networkx
 squarify
 scikit-image
+scikit-learn
 sphinx
 sphinx_bootstrap_theme
 recommonmark