charts.py

from graphviz import Digraph
import matplotlib.pyplot as plt
import numpy as np
from micrograd import Value

def _recursively_build_graph(v, nodes, edges):
    if v not in nodes:
        nodes.add(v)
        for child in v._prev:
            edges.add((child, v))
            _recursively_build_graph(child, nodes, edges)


def build_graph(root):
    """builds a set of all nodes and edges in a graph"""

    nodes, edges = set(), set()
    _recursively_build_graph(root, nodes, edges)
    return nodes, edges


def draw_dot(root:Value):
    dot = Digraph(format="svg", graph_attr={'rankdir': 'LR'})

    nodes, edges = build_graph(root)
    for n in nodes:
        uid = str(id(n))
        # for any value in the graph, create a rectangular ('record') node for it
        dot.node(name=uid, label="{ %s | data %.4f | grad %.4f }" % (n.label, n.data, n.grad), shape='record')
        if n._op:
            # if this value is a result of some operation, create an op node for it
            dot.node(name = uid + n._op, label = n._op)
            # and connect this node to it
            dot.edge(uid + n._op, uid)

    for n1, n2 in edges:
        # connect n1 to the op node of n2
        dot.edge(str(id(n1)), str(id(n2)) + n2._op)

    return dot



def display_data(X, y):
    plt.figure(figsize=(5,5))
    plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')


def display_decision_boundary(model, X, y):
    h = 0.25
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Xmesh = np.c_[xx.ravel(), yy.ravel()]
    inputs = [list(map(Value, xrow)) for xrow in Xmesh]
    scores = list(map(model, inputs))
    Z = np.array([s.data > 0 for s in scores])
    Z = Z.reshape(xx.shape)

    fig = plt.figure()
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())