From 8477b81deec04d4c4e87cef5668f6160d6a8fa1b Mon Sep 17 00:00:00 2001
From: Abdullah Shah <abdullahshah2018@gmail.com>
Date: Sun, 24 Apr 2022 15:32:18 -0400
Subject: [PATCH] made edit to allow visual for numpy arrays
---
.DS_Store | Bin 0 -> 6148 bytes
examples/.DS_Store | Bin 0 -> 6148 bytes
examples/decision_tree/Iris.csv | 151 ++++++++++++++++++
examples/decision_tree/main.html | 86 ++++++++++
examples/decision_tree/main.py | 69 ++++++--
src/.DS_Store | Bin 0 -> 6148 bytes
src/Iris2.csv | 151 ++++++++++++++++++
src/WHY.egg-info/PKG-INFO | 85 ++++++++++
src/WHY.egg-info/SOURCES.txt | 8 +
src/WHY.egg-info/dependency_links.txt | 1 +
src/WHY.egg-info/not-zip-safe | 1 +
src/WHY.egg-info/requires.txt | 32 ++++
src/WHY.egg-info/top_level.txt | 1 +
src/__pycache__/config.cpython-38.pyc | Bin 0 -> 1237 bytes
src/__pycache__/data_vis.cpython-38.pyc | Bin 0 -> 1551 bytes
.../decisionTreeVisuals.cpython-38.pyc | Bin 0 -> 2857 bytes
src/__pycache__/synthetic.cpython-38.pyc | Bin 0 -> 5305 bytes
src/config.py | 20 ++-
src/data_vis.py | 93 ++++++++++-
src/decisionTreeVisuals.py | 135 ++++++++++++++++
20 files changed, 821 insertions(+), 12 deletions(-)
create mode 100644 .DS_Store
create mode 100644 examples/.DS_Store
create mode 100644 examples/decision_tree/Iris.csv
create mode 100644 examples/decision_tree/main.html
create mode 100644 src/.DS_Store
create mode 100644 src/Iris2.csv
create mode 100644 src/WHY.egg-info/PKG-INFO
create mode 100644 src/WHY.egg-info/SOURCES.txt
create mode 100644 src/WHY.egg-info/dependency_links.txt
create mode 100644 src/WHY.egg-info/not-zip-safe
create mode 100644 src/WHY.egg-info/requires.txt
create mode 100644 src/WHY.egg-info/top_level.txt
create mode 100644 src/__pycache__/config.cpython-38.pyc
create mode 100644 src/__pycache__/data_vis.cpython-38.pyc
create mode 100644 src/__pycache__/decisionTreeVisuals.cpython-38.pyc
create mode 100644 src/__pycache__/synthetic.cpython-38.pyc
create mode 100644 src/decisionTreeVisuals.py
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new file mode 100644
index 0000000..1bf42f2
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+2,4.9,3.0,1.4,0.2,Iris-setosa
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+4,4.6,3.1,1.5,0.2,Iris-setosa
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+7,4.6,3.4,1.4,0.3,Iris-setosa
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+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
+81,5.5,2.4,3.8,1.1,Iris-versicolor
+82,5.5,2.4,3.7,1.0,Iris-versicolor
+83,5.8,2.7,3.9,1.2,Iris-versicolor
+84,6.0,2.7,5.1,1.6,Iris-versicolor
+85,5.4,3.0,4.5,1.5,Iris-versicolor
+86,6.0,3.4,4.5,1.6,Iris-versicolor
+87,6.7,3.1,4.7,1.5,Iris-versicolor
+88,6.3,2.3,4.4,1.3,Iris-versicolor
+89,5.6,3.0,4.1,1.3,Iris-versicolor
+90,5.5,2.5,4.0,1.3,Iris-versicolor
+91,5.5,2.6,4.4,1.2,Iris-versicolor
+92,6.1,3.0,4.6,1.4,Iris-versicolor
+93,5.8,2.6,4.0,1.2,Iris-versicolor
+94,5.0,2.3,3.3,1.0,Iris-versicolor
+95,5.6,2.7,4.2,1.3,Iris-versicolor
+96,5.7,3.0,4.2,1.2,Iris-versicolor
+97,5.7,2.9,4.2,1.3,Iris-versicolor
+98,6.2,2.9,4.3,1.3,Iris-versicolor
+99,5.1,2.5,3.0,1.1,Iris-versicolor
+100,5.7,2.8,4.1,1.3,Iris-versicolor
+101,6.3,3.3,6.0,2.5,Iris-virginica
+102,5.8,2.7,5.1,1.9,Iris-virginica
+103,7.1,3.0,5.9,2.1,Iris-virginica
+104,6.3,2.9,5.6,1.8,Iris-virginica
+105,6.5,3.0,5.8,2.2,Iris-virginica
+106,7.6,3.0,6.6,2.1,Iris-virginica
+107,4.9,2.5,4.5,1.7,Iris-virginica
+108,7.3,2.9,6.3,1.8,Iris-virginica
+109,6.7,2.5,5.8,1.8,Iris-virginica
+110,7.2,3.6,6.1,2.5,Iris-virginica
+111,6.5,3.2,5.1,2.0,Iris-virginica
+112,6.4,2.7,5.3,1.9,Iris-virginica
+113,6.8,3.0,5.5,2.1,Iris-virginica
+114,5.7,2.5,5.0,2.0,Iris-virginica
+115,5.8,2.8,5.1,2.4,Iris-virginica
+116,6.4,3.2,5.3,2.3,Iris-virginica
+117,6.5,3.0,5.5,1.8,Iris-virginica
+118,7.7,3.8,6.7,2.2,Iris-virginica
+119,7.7,2.6,6.9,2.3,Iris-virginica
+120,6.0,2.2,5.0,1.5,Iris-virginica
+121,6.9,3.2,5.7,2.3,Iris-virginica
+122,5.6,2.8,4.9,2.0,Iris-virginica
+123,7.7,2.8,6.7,2.0,Iris-virginica
+124,6.3,2.7,4.9,1.8,Iris-virginica
+125,6.7,3.3,5.7,2.1,Iris-virginica
+126,7.2,3.2,6.0,1.8,Iris-virginica
+127,6.2,2.8,4.8,1.8,Iris-virginica
+128,6.1,3.0,4.9,1.8,Iris-virginica
+129,6.4,2.8,5.6,2.1,Iris-virginica
+130,7.2,3.0,5.8,1.6,Iris-virginica
+131,7.4,2.8,6.1,1.9,Iris-virginica
+132,7.9,3.8,6.4,2.0,Iris-virginica
+133,6.4,2.8,5.6,2.2,Iris-virginica
+134,6.3,2.8,5.1,1.5,Iris-virginica
+135,6.1,2.6,5.6,1.4,Iris-virginica
+136,7.7,3.0,6.1,2.3,Iris-virginica
+137,6.3,3.4,5.6,2.4,Iris-virginica
+138,6.4,3.1,5.5,1.8,Iris-virginica
+139,6.0,3.0,4.8,1.8,Iris-virginica
+140,6.9,3.1,5.4,2.1,Iris-virginica
+141,6.7,3.1,5.6,2.4,Iris-virginica
+142,6.9,3.1,5.1,2.3,Iris-virginica
+143,5.8,2.7,5.1,1.9,Iris-virginica
+144,6.8,3.2,5.9,2.3,Iris-virginica
+145,6.7,3.3,5.7,2.5,Iris-virginica
+146,6.7,3.0,5.2,2.3,Iris-virginica
+147,6.3,2.5,5.0,1.9,Iris-virginica
+148,6.5,3.0,5.2,2.0,Iris-virginica
+149,6.2,3.4,5.4,2.3,Iris-virginica
+150,5.9,3.0,5.1,1.8,Iris-virginica
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+ </script>
+ <script type="text/javascript">
+ (function() {
+ const fn = function() {
+ Bokeh.safely(function() {
+ (function(root) {
+ function embed_document(root) {
+
+ const docs_json = document.getElementById('1268').textContent;
+ const render_items = [{"docid":"74e96afd-87a4-44e0-9b4d-be3f8e2c99a0","root_ids":["1083"],"roots":{"1083":"beff73e3-3f8e-418c-883d-a74c6af6e68b"}}];
+ root.Bokeh.embed.embed_items(docs_json, render_items);
+
+ }
+ if (root.Bokeh !== undefined) {
+ embed_document(root);
+ } else {
+ let attempts = 0;
+ const timer = setInterval(function(root) {
+ if (root.Bokeh !== undefined) {
+ clearInterval(timer);
+ embed_document(root);
+ } else {
+ attempts++;
+ if (attempts > 100) {
+ clearInterval(timer);
+ console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
+ }
+ }
+ }, 10, root)
+ }
+ })(window);
+ });
+ };
+ if (document.readyState != "loading") fn();
+ else document.addEventListener("DOMContentLoaded", fn);
+ })();
+ </script>
+
+ </body>
+
+</html>
\ No newline at end of file
diff --git a/examples/decision_tree/main.py b/examples/decision_tree/main.py
index 3ca1536..9b8f366 100644
--- a/examples/decision_tree/main.py
+++ b/examples/decision_tree/main.py
@@ -1,22 +1,33 @@
import numpy as np
+import pandas as pd
from sklearn.tree import export_graphviz
from subprocess import call
from synthetic import *
from data_vis import *
+#from data_vis import decisionTreemodel
import config as config
+from decisionTreeVisuals import *
from bokeh.io import curdoc
from bokeh.models import ColumnDataSource, Select, Slider, Plot, Scatter, Row, Column
from bokeh.palettes import Spectral6
+import pandas_bokeh
+pandas_bokeh.output_notebook()
+pd.set_option('plotting.backend', 'pandas_bokeh')
+# Create Bokeh-Table with DataFrame:
+from bokeh.models.widgets import DataTable, TableColumn
+from bokeh.models import ColumnDataSource
+
np.random.seed(0)
data = SyntheticData()
config.x, config.y = data.generator()
+
config.spectral = np.hstack([Spectral6] * 20)
colors = [config.spectral[i] for i in config.y]
@@ -25,17 +36,57 @@ config.source = ColumnDataSource(dict(x=config.x[:,0], y=config.x[:,1], colors=c
b = vis_synthetic()
-clf_algorithms = [
- 'Decision Tree'
-]
+#decisionTreemodel(config.x, config.y)
+'''
+df = pd.read_csv("/Users/abdullahshah/documents/Spring Term 2021-2022/ci493/NewProject/why-senior-project/examples/decision_tree/Iris.csv")
+
+# data split to features matrix and target vector
+# df stands for the dataframe
+
+# feature matrix
+X = df.iloc[:,0:-1]
+# target vector
+Y = df.iloc[:,-1:]
+'''
+test = decisionTreemodel(config.x, config.y)
+#text_output2 = Paragraph(text=test, width=200, height=100)
+
+
+
+#show(p_bar)
+#plots = layout([p_bar])
-algorithm_select = Select(value = 'Decision Tree',
- title='Select Algorithm:',
- width=200,
- options=clf_algorithms
- )
+fig = figure(x_range=test[0], height=250, title='Feature Importance Scores',
+ toolbar_location=None, tools="")
+# define some data
+x = [1,2,3,4,5]
+y = [4,6,3,7,9]
+# plot a line graph
+fig.vbar( x = test[0], top=test[1], width=0.9)
+
+#plot = gridplot(p3, ncols=2, sizing_mode='stretch_both')
+
+#tupleTest = decisionTreemodel()
+# add to document
+
+#tupleTest = decisionTreemodel()
# add to document
-curdoc().add_root(Row(config.inputs, b))
+curdoc().add_root(Row(config.inputs, b, fig)) #, text_output2))
curdoc().title = "Decision Tree"
+#print(config.x)
+#print(config.y)
+print(test[0])
+print(test[1])
+
+'''
+
+p_bar = test.plot_bokeh.bar(
+ y = 'FeatureNumbers',
+ x = 'Features',
+ ylabel="Feature Importance Score",
+ title="Feature Importance Chart",
+ alpha=0.6)
+p_bar.xaxis.major_label_orientation = np.pi / 4
+'''
\ No newline at end of file
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diff --git a/src/Iris2.csv b/src/Iris2.csv
new file mode 100644
index 0000000..1bf42f2
--- /dev/null
+++ b/src/Iris2.csv
@@ -0,0 +1,151 @@
+Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+1,5.1,3.5,1.4,0.2,Iris-setosa
+2,4.9,3.0,1.4,0.2,Iris-setosa
+3,4.7,3.2,1.3,0.2,Iris-setosa
+4,4.6,3.1,1.5,0.2,Iris-setosa
+5,5.0,3.6,1.4,0.2,Iris-setosa
+6,5.4,3.9,1.7,0.4,Iris-setosa
+7,4.6,3.4,1.4,0.3,Iris-setosa
+8,5.0,3.4,1.5,0.2,Iris-setosa
+9,4.4,2.9,1.4,0.2,Iris-setosa
+10,4.9,3.1,1.5,0.1,Iris-setosa
+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
+81,5.5,2.4,3.8,1.1,Iris-versicolor
+82,5.5,2.4,3.7,1.0,Iris-versicolor
+83,5.8,2.7,3.9,1.2,Iris-versicolor
+84,6.0,2.7,5.1,1.6,Iris-versicolor
+85,5.4,3.0,4.5,1.5,Iris-versicolor
+86,6.0,3.4,4.5,1.6,Iris-versicolor
+87,6.7,3.1,4.7,1.5,Iris-versicolor
+88,6.3,2.3,4.4,1.3,Iris-versicolor
+89,5.6,3.0,4.1,1.3,Iris-versicolor
+90,5.5,2.5,4.0,1.3,Iris-versicolor
+91,5.5,2.6,4.4,1.2,Iris-versicolor
+92,6.1,3.0,4.6,1.4,Iris-versicolor
+93,5.8,2.6,4.0,1.2,Iris-versicolor
+94,5.0,2.3,3.3,1.0,Iris-versicolor
+95,5.6,2.7,4.2,1.3,Iris-versicolor
+96,5.7,3.0,4.2,1.2,Iris-versicolor
+97,5.7,2.9,4.2,1.3,Iris-versicolor
+98,6.2,2.9,4.3,1.3,Iris-versicolor
+99,5.1,2.5,3.0,1.1,Iris-versicolor
+100,5.7,2.8,4.1,1.3,Iris-versicolor
+101,6.3,3.3,6.0,2.5,Iris-virginica
+102,5.8,2.7,5.1,1.9,Iris-virginica
+103,7.1,3.0,5.9,2.1,Iris-virginica
+104,6.3,2.9,5.6,1.8,Iris-virginica
+105,6.5,3.0,5.8,2.2,Iris-virginica
+106,7.6,3.0,6.6,2.1,Iris-virginica
+107,4.9,2.5,4.5,1.7,Iris-virginica
+108,7.3,2.9,6.3,1.8,Iris-virginica
+109,6.7,2.5,5.8,1.8,Iris-virginica
+110,7.2,3.6,6.1,2.5,Iris-virginica
+111,6.5,3.2,5.1,2.0,Iris-virginica
+112,6.4,2.7,5.3,1.9,Iris-virginica
+113,6.8,3.0,5.5,2.1,Iris-virginica
+114,5.7,2.5,5.0,2.0,Iris-virginica
+115,5.8,2.8,5.1,2.4,Iris-virginica
+116,6.4,3.2,5.3,2.3,Iris-virginica
+117,6.5,3.0,5.5,1.8,Iris-virginica
+118,7.7,3.8,6.7,2.2,Iris-virginica
+119,7.7,2.6,6.9,2.3,Iris-virginica
+120,6.0,2.2,5.0,1.5,Iris-virginica
+121,6.9,3.2,5.7,2.3,Iris-virginica
+122,5.6,2.8,4.9,2.0,Iris-virginica
+123,7.7,2.8,6.7,2.0,Iris-virginica
+124,6.3,2.7,4.9,1.8,Iris-virginica
+125,6.7,3.3,5.7,2.1,Iris-virginica
+126,7.2,3.2,6.0,1.8,Iris-virginica
+127,6.2,2.8,4.8,1.8,Iris-virginica
+128,6.1,3.0,4.9,1.8,Iris-virginica
+129,6.4,2.8,5.6,2.1,Iris-virginica
+130,7.2,3.0,5.8,1.6,Iris-virginica
+131,7.4,2.8,6.1,1.9,Iris-virginica
+132,7.9,3.8,6.4,2.0,Iris-virginica
+133,6.4,2.8,5.6,2.2,Iris-virginica
+134,6.3,2.8,5.1,1.5,Iris-virginica
+135,6.1,2.6,5.6,1.4,Iris-virginica
+136,7.7,3.0,6.1,2.3,Iris-virginica
+137,6.3,3.4,5.6,2.4,Iris-virginica
+138,6.4,3.1,5.5,1.8,Iris-virginica
+139,6.0,3.0,4.8,1.8,Iris-virginica
+140,6.9,3.1,5.4,2.1,Iris-virginica
+141,6.7,3.1,5.6,2.4,Iris-virginica
+142,6.9,3.1,5.1,2.3,Iris-virginica
+143,5.8,2.7,5.1,1.9,Iris-virginica
+144,6.8,3.2,5.9,2.3,Iris-virginica
+145,6.7,3.3,5.7,2.5,Iris-virginica
+146,6.7,3.0,5.2,2.3,Iris-virginica
+147,6.3,2.5,5.0,1.9,Iris-virginica
+148,6.5,3.0,5.2,2.0,Iris-virginica
+149,6.2,3.4,5.4,2.3,Iris-virginica
+150,5.9,3.0,5.1,1.8,Iris-virginica
diff --git a/src/WHY.egg-info/PKG-INFO b/src/WHY.egg-info/PKG-INFO
new file mode 100644
index 0000000..966ca3b
--- /dev/null
+++ b/src/WHY.egg-info/PKG-INFO
@@ -0,0 +1,85 @@
+Metadata-Version: 2.1
+Name: WHY
+Version: 0.0.1.dev0+a83d626
+Summary: Explainable AI system
+Home-page: https://gitlab.cci.drexel.edu/pjm363/why-senior-project
+Author: Philip Monaco, Abdullah Shah, Ibrahim Elsaid, Jashanpreet Singh, William Lu, Songheng Li
+License: LICENSE.md
+Platform: UNKNOWN
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+Provides-Extra: lint
+Provides-Extra: docs
+Provides-Extra: all
+
+# WHY Senior Project
+
+## Installing WHY
+
+There are two way in which the `WHY` package can be installed.
+First, follow the [prerequisite](#prerequisites) instructions.
+IF you do not need to rebuild documentation or make modifications to the library, follow the instructions under [User Installation](#user-installation).
+Otherwise, follow the instructions under [Developer Installation](#developer-installation).
+
+### Prerequisites
+
+The `WHY` requires at least `python3.8` and makes use of a number of third-party libraries. The bare minimum packages are automatically installed when you install `WHY` using `pip`. Additional dependencies for developers are contained in `requirements.txt` file. See the
+
+It's recommended that you use `WHY` within a python virtual environment.
+Virtual environments are now included natively with Python 3 using `venv`. Instructions to create a virtual environment can be found [here](https://docs.python.org/3/library/venv.html).
+
+API documentation requires the use of `sphinx` which will require installing `Cmake`. Installation instructions can be found [here](https://cmake.org/install/).
+
+### User Installation
+
+First you must clone the repository using the following bash command.
+```bash
+pip clone git@gitlab.cci.drexel.edu:pjm363/why-senior-project.git
+```
+
+From the root path of the repository (the folder where `setup.py` is located) `WHY` can be installed using `pip` using the following command.
+
+```bash
+pip install .
+```
+
+### Developer Installation
+Developers need an additional tool, `clang-format` in order to run the precommit script.
+
+Install via Ubuntu.
+```bash
+apt-get install clang-format
+```
+Install via MacOSX with homebrew.
+```bash
+brew install clang-format
+```
+Install via [installer](https://llvm.org/builds/) or using chocolatey via.
+```bash
+choco install llvm
+```
+Install WHY using pip:
+```bash
+pip install -e .
+```
+## Running examples
+From the `examples` directory
+```bash
+bokeh serve --show <name of app folder>
+```
+For a developer option the `--dev` flag can be used to auto refresh server from IDE.
+```bash
+bokeh serve --show <name of app folder> --dev
+```
+## Building API Documentation
+
+To build the API Documentation in HTML format for local browsing, execute the following from the root of the repository.
+```
+cd docs/
+make html
+```
+
+This will also cause any examples contained in `examples` to be generated in the example gallery.
+All documentation is also built automatically when the `./precommit.sh` script is run.
+
+
diff --git a/src/WHY.egg-info/SOURCES.txt b/src/WHY.egg-info/SOURCES.txt
new file mode 100644
index 0000000..5983488
--- /dev/null
+++ b/src/WHY.egg-info/SOURCES.txt
@@ -0,0 +1,8 @@
+README.md
+setup.py
+src/WHY.egg-info/PKG-INFO
+src/WHY.egg-info/SOURCES.txt
+src/WHY.egg-info/dependency_links.txt
+src/WHY.egg-info/not-zip-safe
+src/WHY.egg-info/requires.txt
+src/WHY.egg-info/top_level.txt
\ No newline at end of file
diff --git a/src/WHY.egg-info/dependency_links.txt b/src/WHY.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/src/WHY.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/src/WHY.egg-info/not-zip-safe b/src/WHY.egg-info/not-zip-safe
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/src/WHY.egg-info/not-zip-safe
@@ -0,0 +1 @@
+
diff --git a/src/WHY.egg-info/requires.txt b/src/WHY.egg-info/requires.txt
new file mode 100644
index 0000000..e0c87a2
--- /dev/null
+++ b/src/WHY.egg-info/requires.txt
@@ -0,0 +1,32 @@
+numpy>=1.21
+pandas>=1.3.5
+bokeh>=2.4.2
+matplotlib>=3.5.0
+scikit-learn>=1.0.2
+
+[all]
+numpy>=1.21
+pandas>=1.3.5
+bokeh>=2.4.2
+matplotlib>=3.5.0
+scikit-learn>=1.0.2
+black==21.12b0
+isort==5.10.1
+flake8==4.0.1
+mypy
+Sphinx
+sphinx-gallery
+sphinx-rtd-theme
+m2r2
+
+[docs]
+Sphinx
+sphinx-gallery
+sphinx-rtd-theme
+m2r2
+
+[lint]
+black==21.12b0
+isort==5.10.1
+flake8==4.0.1
+mypy
diff --git a/src/WHY.egg-info/top_level.txt b/src/WHY.egg-info/top_level.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/src/WHY.egg-info/top_level.txt
@@ -0,0 +1 @@
+
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diff --git a/src/config.py b/src/config.py
index d31dec3..6ced33f 100644
--- a/src/config.py
+++ b/src/config.py
@@ -1,8 +1,9 @@
"""
This is a docstring for config.
"""
-from bokeh.models import Select, Slider, Row, Column
+from bokeh.models import Select, Slider, Row, Column, Dropdown, Paragraph
from bokeh.layouts import column, row
+#from src.data_vis import *t
x = 0
y = 0
@@ -52,5 +53,22 @@ inf_slider = Slider(title='Informative Classes',
width=400)
+#tupleTest = decisionTreemodel()
+
+myMessage = 'You have entered nothing yet: (none)'
+text_output = Paragraph(text=myMessage, width=200, height=100)
+
+
selects = Row(dataset_select, width=420)
inputs = Column(selects, samples_slider, classes_slider, inf_slider, features_slider)
+
+
+#menu = [("Item 1", "item_1"), ("Item 2", "item_2"), None, ("Item 3", "item_3")]
+
+#clf_algorithms = [
+ # 'Decision Tree'
+#]
+
+#algorithm_select = Dropdown(label="Dropdown button", button_type="warning", menu=menu)
+
+
\ No newline at end of file
diff --git a/src/data_vis.py b/src/data_vis.py
index c8ac781..771bac5 100644
--- a/src/data_vis.py
+++ b/src/data_vis.py
@@ -3,7 +3,33 @@ This is a docstrings for datavis
"""
from bokeh.models import ColumnDataSource, Select, Slider, Plot, Scatter, Row, Column
from bokeh.plotting import figure
-import config as config
+import config as config
+
+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# sklearn ML libraries/modules
+from sklearn import preprocessing
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+# bokeh libraries/modules
+from bokeh.io import output_file, show
+from bokeh.layouts import widgetbox
+#from bokeh.models.Column import widgetbox
+from bokeh.models.widgets import Div
+from bokeh.models.widgets import Paragraph
+from bokeh.models.widgets import PreText
+
+# lime modules
+from lime import submodular_pick
+import lime
+from lime.lime_tabular import LimeTabularExplainer
+from lime import submodular_pick
def vis_synthetic():
"""This is a docstring for datavis
@@ -18,4 +44,67 @@ def vis_synthetic():
b.add_glyph(config.source, glyph)
- return b
\ No newline at end of file
+ return b
+
+'''
+def decisionTreemodel():
+ """This is a docstring for decisionTreeVisuals
+
+ Returns:
+ _type_: _description_
+ """
+ # loading data
+ df = pd.read_csv("src/Iris.csv")
+
+ # data split to features matrix and target vector
+ # df stands for the dataframe
+ # feature matrix
+ X = df.iloc[:,0:-1]
+ # target vector
+ Y = df.iloc[:,-1:]
+
+ # splitting data into training and test sets
+ X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=7)
+
+ # instantiating model
+ model_logreg = DecisionTreeClassifier(max_depth=8, random_state=0)
+
+ # fit model on training set
+ model_logreg.fit(X_train, Y_train)
+
+ # grabbing unique class names
+ class_names=model_logreg.classes_
+
+ # grabbing specific row for model to use to make prediction
+ ex_specie = np.array(X_test.iloc[3]).reshape(1,-1)
+
+ # lime_tabular is a module that contains functions that explain classifiers which use tabular data (matrices).
+ # LimeTabularExplainer is a function that explains predictions of tabular (matrix) data.
+
+ explainer = lime.lime_tabular.LimeTabularExplainer(X_train.values, feature_names=X_train.columns,
+ class_names=class_names, discretize_continuous=True)
+
+ # grab count of columns from feature matrix
+ featureCount = len(X.columns)
+
+ # explain_instance is a function that generates explanations for a prediction after using LimeTabularExplainer.
+ exp = explainer.explain_instance(X_test.iloc[3],model_logreg.predict_proba,num_features=featureCount,top_labels=1)
+
+ # converting explainations as list
+ tupleTest = exp.as_list()
+
+ # converting tuples to list
+ NewList = [list(x) for x in tupleTest]
+ # converting all elements in list to strings
+ doubleStrList = [[str(s) for s in sublist] for sublist in NewList]
+
+
+ # print(doubleStrList[0][0])
+ return doubleStrList[0][0]
+
+
+
+#df = pd.read_csv("src/Iris.csv")
+decisionTreemodel()
+
+'''
diff --git a/src/decisionTreeVisuals.py b/src/decisionTreeVisuals.py
new file mode 100644
index 0000000..b3114ed
--- /dev/null
+++ b/src/decisionTreeVisuals.py
@@ -0,0 +1,135 @@
+"""
+This is a docstrings for decisionTreeVisuals
+"""
+
+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# sklearn ML libraries/modules
+from sklearn import preprocessing
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+# bokeh libraries/modules
+from bokeh.io import output_file, show
+from bokeh.layouts import widgetbox
+#from bokeh.models.Column import widgetbox
+from bokeh.models.widgets import Div
+from bokeh.models.widgets import Paragraph
+from bokeh.models.widgets import PreText
+
+# lime modules
+from lime import submodular_pick
+import lime
+from lime.lime_tabular import LimeTabularExplainer
+from lime import submodular_pick
+
+import config as config
+
+def decisionTreemodel(X, Y):
+ """This is a docstring for decisionTreeVisuals
+
+ Returns:
+ _type_: _description_
+ """
+ # loading data
+ #df = pd.read_csv("Iris.csv")
+
+ # data split to features matrix and target vector
+ # df stands for the dataframe
+ # feature matrix
+ #X = df.iloc[:,0:-1]
+ # target vector
+ #Y = df.iloc[:,-1:]
+
+ # splitting data into training and test sets
+ X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=7)
+
+ # instantiating model
+ model_logreg = DecisionTreeClassifier(max_depth=8, random_state=0)
+
+ # fit model on training set
+ model_logreg.fit(X_train, Y_train)
+
+ # grabbing unique class names
+ class_names=model_logreg.classes_
+
+ # grabbing specific row for model to use to make prediction
+ #ex_specie = X_test[3, ]
+
+ # lime_tabular is a module that contains functions that explain classifiers which use tabular data (matrices).
+ # LimeTabularExplainer is a function that explains predictions of tabular (matrix) data.
+
+ explainer = lime.lime_tabular.LimeTabularExplainer(X_train, feature_names=Y,
+ class_names=class_names, discretize_continuous=True)
+
+ # grab count of columns from feature matrix
+ featureCount = len(X)
+
+ # explain_instance is a function that generates explanations for a prediction after using LimeTabularExplainer.
+ exp = explainer.explain_instance(X_test[3],model_logreg.predict_proba,num_features=featureCount)
+
+ # converting explainations as list
+ tupleTest = exp.as_list()
+
+ # converting tuples to list
+ NewList = [list(x) for x in tupleTest]
+ # converting all elements in list to strings
+ doubleStrList = [[str(s) for s in sublist] for sublist in NewList]
+
+ doublestring = ",\n ".join([' '.join([str(c) for c in lst]) for lst in NewList])
+
+ # separating comparisons from feature scores
+ Labels1 = [item[0] for item in NewList]
+ # outlst = " ".join([' '.join([str(c) for c in lst]) for lst in doubleStrList])
+
+ # separating feature scores from comparisons
+ featureNums = [item[1] for item in NewList]
+
+ # grabbing count of number of features to determing number of x axis ticks in the chart
+ count = 0
+ newList = []
+ for i in featureNums:
+ count += 1
+ newList.append(count)
+ #p = figure(width=400, height=400)
+
+
+ #fig = plt.bar(newList, featureNums, align='center')
+ #plt.xticks(newList, Labels1)
+ #plt.xticks(rotation=60, ha='right')
+ #plt.title("Feature Importance graph")
+ #plt.show()
+
+ #print(doubleStrList[0][0])
+ #return Labels1#, doubleStrList[0][1], doubleStrList[0][2], doubleStrList[0][3]
+ #test = ', \n'.join([i for i in Labels1[0:]])
+
+ dfBokehChart = pd.DataFrame(list(zip(Labels1, featureNums)), columns =['Features', 'FeatureNumbers'])
+
+ return (Labels1, featureNums)
+ #return dfBokehChart
+ # return p
+
+
+
+#df = pd.read_csv("src/Iris.csv")
+#decisionTreemodel()
+
+# put the below in config
+'''
+clf_algorithms = [
+ 'Decision Tree'
+]
+
+algorithm_select = Select(value = 'Decision Tree',
+ title='Select Algorithm:',
+ width=200,
+ options=clf_algorithms
+ )
+
+'''
\ No newline at end of file
--
GitLab