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app.py
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app.py
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"""
This app's data and plots are heavily inspired from the scikit-learn Classifier
comparison tutorial. Part of the app's code is directly taken from it. You can
find it here:
http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
The Confusion Matrix Pie Chart format was inspired by Nicolas's Dash ROC app.
"""
import os
import time
from textwrap import dedent
import dash
import dash_core_components as dcc
import dash_html_components as html
import numpy as np
from dash.dependencies import Input, Output, State
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import datasets
from sklearn.svm import SVC
import utils.dash_reusable_components as drc
from utils.figures import (
serve_prediction_plot,
serve_roc_curve,
serve_pie_confusion_matrix,
)
app = dash.Dash(__name__)
server = app.server
def generate_data(n_samples, dataset, noise):
if dataset == "moons":
return datasets.make_moons(n_samples=n_samples, noise=noise, random_state=0)
elif dataset == "circles":
return datasets.make_circles(
n_samples=n_samples, noise=noise, factor=0.5, random_state=1
)
elif dataset == "linear":
X, y = datasets.make_classification(
n_samples=n_samples,
n_features=2,
n_redundant=0,
n_informative=2,
random_state=2,
n_clusters_per_class=1,
)
rng = np.random.RandomState(2)
X += noise * rng.uniform(size=X.shape)
linearly_separable = (X, y)
return linearly_separable
else:
raise ValueError(
"Data type incorrectly specified. Please choose an existing " "dataset."
)
app.layout = html.Div(
children=[
# .container class is fixed, .container.scalable is scalable
html.Div(
className="banner",
children=[
# Change App Name here
html.Div(
className="container scalable",
children=[
# Change App Name here
html.H2(
html.A(
"Support Vector Machine (SVM) Explorer",
href="https://github.com/plotly/dash-svm",
style={"text-decoration": "none", "color": "inherit"},
)
),
html.A(
html.Img(
src="https://s3-us-west-1.amazonaws.com/plotly-tutorials/logo/new-branding/dash-logo-by-plotly-stripe-inverted.png"
),
href="https://plot.ly/products/dash/",
),
],
)
],
),
html.Div(
id="body",
className="container scalable",
children=[
html.Div(
className="row",
children=[
html.Div(
className="three columns",
id="left-column",
style={
"max-height": "90vh",
"max-width": "20%",
"overflow-y": "auto",
"overflow-x": "hidden",
},
children=[
drc.Card(
id="first-card",
children=[
drc.NamedDropdown(
name="Select Dataset",
id="dropdown-select-dataset",
options=[
{"label": "Moons", "value": "moons"},
{
"label": "Linearly Separable",
"value": "linear",
},
{
"label": "Circles",
"value": "circles",
},
],
clearable=False,
searchable=False,
value="moons",
),
drc.NamedSlider(
name="Sample Size",
id="slider-dataset-sample-size",
min=100,
max=500,
step=100,
marks={
str(i): str(i)
for i in [100, 200, 300, 400, 500]
},
value=300,
),
drc.NamedSlider(
name="Noise Level",
id="slider-dataset-noise-level",
min=0,
max=1,
marks={
i / 10: str(i / 10)
for i in range(0, 11, 2)
},
step=0.1,
value=0.2,
),
],
),
drc.Card(
id="button-card",
children=[
drc.NamedSlider(
name="Threshold",
id="slider-threshold",
min=0,
max=1,
value=0.5,
step=0.01,
),
html.Button(
"Reset Threshold",
id="button-zero-threshold",
),
],
),
drc.Card(
id="last-card",
children=[
drc.NamedDropdown(
name="Kernel",
id="dropdown-svm-parameter-kernel",
options=[
{
"label": "Radial basis function (RBF)",
"value": "rbf",
},
{"label": "Linear", "value": "linear"},
{
"label": "Polynomial",
"value": "poly",
},
{
"label": "Sigmoid",
"value": "sigmoid",
},
],
value="rbf",
clearable=False,
searchable=False,
),
drc.NamedSlider(
name="Cost (C)",
id="slider-svm-parameter-C-power",
min=-2,
max=4,
value=0,
marks={
i: "{}".format(10 ** i)
for i in range(-2, 5)
},
),
drc.FormattedSlider(
style={"padding": "5px 10px 25px"},
id="slider-svm-parameter-C-coef",
min=1,
max=9,
value=1,
),
drc.NamedSlider(
name="Degree",
id="slider-svm-parameter-degree",
min=2,
max=10,
value=3,
step=1,
marks={
str(i): str(i) for i in range(2, 11, 2)
},
),
drc.NamedSlider(
name="Gamma",
id="slider-svm-parameter-gamma-power",
min=-5,
max=0,
value=-1,
marks={
i: "{}".format(10 ** i)
for i in range(-5, 1)
},
),
drc.FormattedSlider(
style={"padding": "5px 10px 25px"},
id="slider-svm-parameter-gamma-coef",
min=1,
max=9,
value=5,
),
html.Div(
style={"padding": "20px 10px 25px 4px"},
children=[
html.P(children="Shrinking"),
dcc.RadioItems(
id="radio-svm-parameter-shrinking",
labelStyle={
"margin-right": "7px",
"display": "inline-block",
},
options=[
{
"label": " Enabled",
"value": "True",
},
{
"label": " Disabled",
"value": "False",
},
],
value="True",
),
],
),
],
),
],
),
html.Div(
id="div-graphs",
children=dcc.Graph(
id="graph-sklearn-svm", style={"display": "none"}
),
),
],
)
],
),
]
)
@app.callback(
Output("slider-svm-parameter-gamma-coef", "marks"),
[Input("slider-svm-parameter-gamma-power", "value")],
)
def update_slider_svm_parameter_gamma_coef(power):
scale = 10 ** power
return {i: str(round(i * scale, 8)) for i in range(1, 10, 2)}
@app.callback(
Output("slider-svm-parameter-C-coef", "marks"),
[Input("slider-svm-parameter-C-power", "value")],
)
def update_slider_svm_parameter_C_coef(power):
scale = 10 ** power
return {i: str(round(i * scale, 8)) for i in range(1, 10, 2)}
@app.callback(
Output("slider-threshold", "value"),
[Input("button-zero-threshold", "n_clicks")],
[State("graph-sklearn-svm", "figure")],
)
def reset_threshold_center(n_clicks, figure):
if n_clicks:
Z = np.array(figure["data"][0]["z"])
value = -Z.min() / (Z.max() - Z.min())
else:
value = 0.4959986285375595
return value
# Disable Sliders if kernel not in the given list
@app.callback(
Output("slider-svm-parameter-degree", "disabled"),
[Input("dropdown-svm-parameter-kernel", "value")],
)
def disable_slider_param_degree(kernel):
return kernel != "poly"
@app.callback(
Output("slider-svm-parameter-gamma-coef", "disabled"),
[Input("dropdown-svm-parameter-kernel", "value")],
)
def disable_slider_param_gamma_coef(kernel):
return kernel not in ["rbf", "poly", "sigmoid"]
@app.callback(
Output("slider-svm-parameter-gamma-power", "disabled"),
[Input("dropdown-svm-parameter-kernel", "value")],
)
def disable_slider_param_gamma_power(kernel):
return kernel not in ["rbf", "poly", "sigmoid"]
@app.callback(
Output("div-graphs", "children"),
[
Input("dropdown-svm-parameter-kernel", "value"),
Input("slider-svm-parameter-degree", "value"),
Input("slider-svm-parameter-C-coef", "value"),
Input("slider-svm-parameter-C-power", "value"),
Input("slider-svm-parameter-gamma-coef", "value"),
Input("slider-svm-parameter-gamma-power", "value"),
Input("dropdown-select-dataset", "value"),
Input("slider-dataset-noise-level", "value"),
Input("radio-svm-parameter-shrinking", "value"),
Input("slider-threshold", "value"),
Input("slider-dataset-sample-size", "value"),
],
)
def update_svm_graph(
kernel,
degree,
C_coef,
C_power,
gamma_coef,
gamma_power,
dataset,
noise,
shrinking,
threshold,
sample_size,
):
t_start = time.time()
h = 0.3 # step size in the mesh
# Data Pre-processing
X, y = generate_data(n_samples=sample_size, dataset=dataset, noise=noise)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min = X[:, 0].min() - 0.5
x_max = X[:, 0].max() + 0.5
y_min = X[:, 1].min() - 0.5
y_max = X[:, 1].max() + 0.5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
C = C_coef * 10 ** C_power
gamma = gamma_coef * 10 ** gamma_power
if shrinking == "True":
flag = True
else:
flag = False
# Train SVM
clf = SVC(C=C, kernel=kernel, degree=degree, gamma=gamma, shrinking=flag)
clf.fit(X_train, y_train)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
if hasattr(clf, "decision_function"):
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
prediction_figure = serve_prediction_plot(
model=clf,
X_train=X_train,
X_test=X_test,
y_train=y_train,
y_test=y_test,
Z=Z,
xx=xx,
yy=yy,
mesh_step=h,
threshold=threshold,
)
roc_figure = serve_roc_curve(model=clf, X_test=X_test, y_test=y_test)
confusion_figure = serve_pie_confusion_matrix(
model=clf, X_test=X_test, y_test=y_test, Z=Z, threshold=threshold
)
print(f"Total Time Taken: {time.time() - t_start:.3f} sec")
return [
html.Div(
className="six columns",
style={"margin-top": "5px"},
children=[
dcc.Graph(
id="graph-sklearn-svm",
figure=prediction_figure,
style={"height": "calc(100vh - 90px)"},
)
],
),
html.Div(
className="three columns",
style={
"min-width": "24.5%",
"height": "calc(100vh - 90px)",
"margin-top": "5px",
# Remove possibility to select the text for better UX
"user-select": "none",
"-moz-user-select": "none",
"-webkit-user-select": "none",
"-ms-user-select": "none",
},
children=[
dcc.Graph(
id="graph-line-roc-curve",
style={"height": "40%"},
figure=roc_figure,
),
dcc.Graph(
id="graph-pie-confusion-matrix",
figure=confusion_figure,
style={"height": "60%"},
),
],
),
]
external_css = [
# Normalize the CSS
"https://cdnjs.cloudflare.com/ajax/libs/normalize/7.0.0/normalize.min.css",
# Fonts
"https://fonts.googleapis.com/css?family=Open+Sans|Roboto",
"https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css",
]
for css in external_css:
app.css.append_css({"external_url": css})
# Running the server
if __name__ == "__main__":
app.run_server(debug=True)