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updated readme

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Hubert Sokołowski
2024-03-15 00:08:04 +01:00
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151
public/sets/iris_dataset.csv Normal file
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sepal length (cm),sepal width (cm),petal length (cm),petal width (cm),variety
5.1,3.5,1.4,0.2,setosa
4.9,3,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3,1.4,0.1,setosa
4.3,3,1.1,0.1,setosa
5.8,4,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5,3,1.6,0.2,setosa
5,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.2,setosa
5,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.6,1.4,0.1,setosa
4.4,3,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5,3.3,1.4,0.2,setosa
7,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5,2,3.5,1,versicolor
5.9,3,4.2,1.5,versicolor
6,2.2,4,1,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3,4.5,1.5,versicolor
5.8,2.7,4.1,1,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3,5,1.7,versicolor
6,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1,versicolor
5.8,2.7,3.9,1.2,versicolor
6,2.7,5.1,1.6,versicolor
5.4,3,4.5,1.5,versicolor
6,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3,4.1,1.3,versicolor
5.5,2.5,4,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3,4.6,1.4,versicolor
5.8,2.6,4,1.2,versicolor
5,2.3,3.3,1,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3,5.8,2.2,virginica
7.6,3,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3,5.5,2.1,virginica
5.7,2.5,5,2,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6,2.2,5,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2,virginica
7.7,2.8,6.7,2,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6,3,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3,5.2,2.3,virginica
6.3,2.5,5,1.9,virginica
6.5,3,5.2,2,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3,5.1,1.8,virginica
1 sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) variety
2 5.1 3.5 1.4 0.2 setosa
3 4.9 3 1.4 0.2 setosa
4 4.7 3.2 1.3 0.2 setosa
5 4.6 3.1 1.5 0.2 setosa
6 5 3.6 1.4 0.2 setosa
7 5.4 3.9 1.7 0.4 setosa
8 4.6 3.4 1.4 0.3 setosa
9 5 3.4 1.5 0.2 setosa
10 4.4 2.9 1.4 0.2 setosa
11 4.9 3.1 1.5 0.1 setosa
12 5.4 3.7 1.5 0.2 setosa
13 4.8 3.4 1.6 0.2 setosa
14 4.8 3 1.4 0.1 setosa
15 4.3 3 1.1 0.1 setosa
16 5.8 4 1.2 0.2 setosa
17 5.7 4.4 1.5 0.4 setosa
18 5.4 3.9 1.3 0.4 setosa
19 5.1 3.5 1.4 0.3 setosa
20 5.7 3.8 1.7 0.3 setosa
21 5.1 3.8 1.5 0.3 setosa
22 5.4 3.4 1.7 0.2 setosa
23 5.1 3.7 1.5 0.4 setosa
24 4.6 3.6 1 0.2 setosa
25 5.1 3.3 1.7 0.5 setosa
26 4.8 3.4 1.9 0.2 setosa
27 5 3 1.6 0.2 setosa
28 5 3.4 1.6 0.4 setosa
29 5.2 3.5 1.5 0.2 setosa
30 5.2 3.4 1.4 0.2 setosa
31 4.7 3.2 1.6 0.2 setosa
32 4.8 3.1 1.6 0.2 setosa
33 5.4 3.4 1.5 0.4 setosa
34 5.2 4.1 1.5 0.1 setosa
35 5.5 4.2 1.4 0.2 setosa
36 4.9 3.1 1.5 0.2 setosa
37 5 3.2 1.2 0.2 setosa
38 5.5 3.5 1.3 0.2 setosa
39 4.9 3.6 1.4 0.1 setosa
40 4.4 3 1.3 0.2 setosa
41 5.1 3.4 1.5 0.2 setosa
42 5 3.5 1.3 0.3 setosa
43 4.5 2.3 1.3 0.3 setosa
44 4.4 3.2 1.3 0.2 setosa
45 5 3.5 1.6 0.6 setosa
46 5.1 3.8 1.9 0.4 setosa
47 4.8 3 1.4 0.3 setosa
48 5.1 3.8 1.6 0.2 setosa
49 4.6 3.2 1.4 0.2 setosa
50 5.3 3.7 1.5 0.2 setosa
51 5 3.3 1.4 0.2 setosa
52 7 3.2 4.7 1.4 versicolor
53 6.4 3.2 4.5 1.5 versicolor
54 6.9 3.1 4.9 1.5 versicolor
55 5.5 2.3 4 1.3 versicolor
56 6.5 2.8 4.6 1.5 versicolor
57 5.7 2.8 4.5 1.3 versicolor
58 6.3 3.3 4.7 1.6 versicolor
59 4.9 2.4 3.3 1 versicolor
60 6.6 2.9 4.6 1.3 versicolor
61 5.2 2.7 3.9 1.4 versicolor
62 5 2 3.5 1 versicolor
63 5.9 3 4.2 1.5 versicolor
64 6 2.2 4 1 versicolor
65 6.1 2.9 4.7 1.4 versicolor
66 5.6 2.9 3.6 1.3 versicolor
67 6.7 3.1 4.4 1.4 versicolor
68 5.6 3 4.5 1.5 versicolor
69 5.8 2.7 4.1 1 versicolor
70 6.2 2.2 4.5 1.5 versicolor
71 5.6 2.5 3.9 1.1 versicolor
72 5.9 3.2 4.8 1.8 versicolor
73 6.1 2.8 4 1.3 versicolor
74 6.3 2.5 4.9 1.5 versicolor
75 6.1 2.8 4.7 1.2 versicolor
76 6.4 2.9 4.3 1.3 versicolor
77 6.6 3 4.4 1.4 versicolor
78 6.8 2.8 4.8 1.4 versicolor
79 6.7 3 5 1.7 versicolor
80 6 2.9 4.5 1.5 versicolor
81 5.7 2.6 3.5 1 versicolor
82 5.5 2.4 3.8 1.1 versicolor
83 5.5 2.4 3.7 1 versicolor
84 5.8 2.7 3.9 1.2 versicolor
85 6 2.7 5.1 1.6 versicolor
86 5.4 3 4.5 1.5 versicolor
87 6 3.4 4.5 1.6 versicolor
88 6.7 3.1 4.7 1.5 versicolor
89 6.3 2.3 4.4 1.3 versicolor
90 5.6 3 4.1 1.3 versicolor
91 5.5 2.5 4 1.3 versicolor
92 5.5 2.6 4.4 1.2 versicolor
93 6.1 3 4.6 1.4 versicolor
94 5.8 2.6 4 1.2 versicolor
95 5 2.3 3.3 1 versicolor
96 5.6 2.7 4.2 1.3 versicolor
97 5.7 3 4.2 1.2 versicolor
98 5.7 2.9 4.2 1.3 versicolor
99 6.2 2.9 4.3 1.3 versicolor
100 5.1 2.5 3 1.1 versicolor
101 5.7 2.8 4.1 1.3 versicolor
102 6.3 3.3 6 2.5 virginica
103 5.8 2.7 5.1 1.9 virginica
104 7.1 3 5.9 2.1 virginica
105 6.3 2.9 5.6 1.8 virginica
106 6.5 3 5.8 2.2 virginica
107 7.6 3 6.6 2.1 virginica
108 4.9 2.5 4.5 1.7 virginica
109 7.3 2.9 6.3 1.8 virginica
110 6.7 2.5 5.8 1.8 virginica
111 7.2 3.6 6.1 2.5 virginica
112 6.5 3.2 5.1 2 virginica
113 6.4 2.7 5.3 1.9 virginica
114 6.8 3 5.5 2.1 virginica
115 5.7 2.5 5 2 virginica
116 5.8 2.8 5.1 2.4 virginica
117 6.4 3.2 5.3 2.3 virginica
118 6.5 3 5.5 1.8 virginica
119 7.7 3.8 6.7 2.2 virginica
120 7.7 2.6 6.9 2.3 virginica
121 6 2.2 5 1.5 virginica
122 6.9 3.2 5.7 2.3 virginica
123 5.6 2.8 4.9 2 virginica
124 7.7 2.8 6.7 2 virginica
125 6.3 2.7 4.9 1.8 virginica
126 6.7 3.3 5.7 2.1 virginica
127 7.2 3.2 6 1.8 virginica
128 6.2 2.8 4.8 1.8 virginica
129 6.1 3 4.9 1.8 virginica
130 6.4 2.8 5.6 2.1 virginica
131 7.2 3 5.8 1.6 virginica
132 7.4 2.8 6.1 1.9 virginica
133 7.9 3.8 6.4 2 virginica
134 6.4 2.8 5.6 2.2 virginica
135 6.3 2.8 5.1 1.5 virginica
136 6.1 2.6 5.6 1.4 virginica
137 7.7 3 6.1 2.3 virginica
138 6.3 3.4 5.6 2.4 virginica
139 6.4 3.1 5.5 1.8 virginica
140 6 3 4.8 1.8 virginica
141 6.9 3.1 5.4 2.1 virginica
142 6.7 3.1 5.6 2.4 virginica
143 6.9 3.1 5.1 2.3 virginica
144 5.8 2.7 5.1 1.9 virginica
145 6.8 3.2 5.9 2.3 virginica
146 6.7 3.3 5.7 2.5 virginica
147 6.7 3 5.2 2.3 virginica
148 6.3 2.5 5 1.9 virginica
149 6.5 3 5.2 2 virginica
150 6.2 3.4 5.4 2.3 virginica
151 5.9 3 5.1 1.8 virginica

75
public/sets/iris_skeleton.json Normal file
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@@ -0,0 +1,75 @@
{
"attr2": "petal width (cm)",
"pivot": 0.8,
"predicateName": ">=",
"weight": null,
"match": {
"attr2": "petal width (cm)",
"pivot": 1.75,
"predicateName": ">=",
"weight": null,
"match": {
"attr2": "petal length (cm)",
"pivot": 4.85,
"predicateName": ">=",
"weight": null,
"match": {
"category": "virginica"
},
"notMatch": {
"attr2": "sepal length (cm)",
"pivot": 5.95,
"predicateName": ">=",
"weight": null,
"match": {
"category": "virginica"
},
"notMatch": {
"category": "versicolor"
}
}
},
"notMatch": {
"attr2": "petal length (cm)",
"pivot": 4.95,
"predicateName": ">=",
"weight": null,
"match": {
"attr2": "petal width (cm)",
"pivot": 1.55,
"predicateName": ">=",
"weight": null,
"match": {
"attr2": "sepal length (cm)",
"pivot": 6.95,
"predicateName": ">=",
"weight": null,
"match": {
"category": "virginica"
},
"notMatch": {
"category": "versicolor"
}
},
"notMatch": {
"category": "virginica"
}
},
"notMatch": {
"attr2": "petal width (cm)",
"pivot": 1.65,
"predicateName": ">=",
"weight": null,
"match": {
"category": "virginica"
},
"notMatch": {
"category": "versicolor"
}
}
}
},
"notMatch": {
"category": "setosa"
}
}

132
src/components/Readme.jsx
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@@ -161,7 +161,7 @@ export const Readme = () => (
<Box marginTop={5}>
Steps: <br />
<OrderedList ml={'2em'} p={2}>
<ListItem>Download and upload training set</ListItem>
<ListItem>Download and upload training set&sup1;</ListItem>
<ListItem>Choose algorithm/s, you can take all if You want</ListItem>
<ListItem>Set Decision attribute (for examples it will be - Class)</ListItem>
<ListItem>
@@ -179,6 +179,10 @@ export const Readme = () => (
</ListItem>
<ListItem>Use Upload test set button to compare result with your set</ListItem>
</OrderedList>
<Box marginTop={2}>
Ad. 1) You can upload json skeleton of decision tree based on our model of node and leaf and
modify it. For more information, please take a look at <b>PYTHON</b> section below &#x1F61C;
</Box>
</Box>
</Code>
<Divider marginBottom={3} />
@@ -222,6 +226,132 @@ export const Readme = () => (
page.
</Text>
</Box>
<Divider margin={10} />
<Box p={4}>
<Box border={'1px solid'}>
<Heading textAlign={'center'} textTransform={'uppercase'} size="lg" color={'grey'} mt={5}>
PYTHON
</Heading>
<Box m={5} textAlign={'left'}>
If You would like to play with own tree which you generated in Python, please use our script to
generate json file with a structure of Your decision tree. In example we used well-known iris set,
function is based on 'clf' object so here you can assing Your model.
<br />
<br />
When you upload csv file with your data and json file with your skeleton then aplication will show
your tree and will spread the samples over the tree.
<br />
<br /> You can check this feature using our prepared files:
<UnorderedList>
<Link href="/sets/iris_dataset.csv" isExternal>
<ListItem>Iris dataset CSV</ListItem>
</Link>
<Link href="/sets/iris_skeleton.json" isExternal>
<ListItem>Skeleton of tree JSON</ListItem>
</Link>
</UnorderedList>
<Code
mt={5}
colorScheme="red"
children="REMINDER: Please be aware that both files must have the same attribute names. In other way distribution won't work."
/>
<Code w="100%" mt={5}>
<Box>
<UnorderedList styleType="none">
<ListItem>import matplotlib.pyplot as plt</ListItem>
<ListItem>from sklearn import tree</ListItem>
<ListItem>from sklearn.datasets import load_iris</ListItem>
<ListItem>from sklearn.tree import DecisionTreeClassifier</ListItem>
<ListItem>import json</ListItem>
<ListItem>
<br />
</ListItem>
<ListItem color="grey"># Load data</ListItem>
<ListItem>
iris = load_iris()
<br /> X = iris.data
<br /> y = iris.target
</ListItem>
<ListItem>
<br />
</ListItem>
<ListItem color="grey"># Create tree model</ListItem>
<ListItem>
clf = DecisionTreeClassifier() <br />
clf.fit(X, y) <br />
</ListItem>
<ListItem>
<br />
</ListItem>
<ListItem color="grey"># Function for generating json </ListItem>
<ListItem>
def node_to_dict(node, feature_names, target_names): <br />
<UnorderedList styleType="none">
<ListItem>
result = &#123;&#125; <br />
<ListItem color="grey"># Leaf</ListItem>
if clf.tree_.children_left[node] == -1:
<UnorderedList styleType="none">
<ListItem>
result['category'] = target_names[clf.tree_.value[node].argmax()]
</ListItem>
</UnorderedList>
<ListItem color="grey"># Node</ListItem>
else:
<UnorderedList styleType="none">
<ListItem>feature = feature_names[clf.tree_.feature[node]]</ListItem>
<ListItem>threshold = round(clf.tree_.threshold[node], 3)</ListItem>
<ListItem>predicate = "==" if isinstance(threshold, str) else "&gt;="</ListItem>
<ListItem>
weight = clf.tree_.weight[node] if hasattr(clf.tree_, 'weight') else None
</ListItem>
<ListItem> result = &#123;</ListItem>
<UnorderedList styleType="none">
<ListItem>
'attr2': feature,
<br /> 'pivot': threshold,
<br /> 'predicateName': predicate,
<br /> 'weight': weight,
<br /> 'match': node_to_dict(clf.tree_.children_right[node], feature_names,
target_names),
<br /> 'notMatch': node_to_dict(clf.tree_.children_left[node], feature_names,
target_names)
</ListItem>
</UnorderedList>
<ListItem>&#125;</ListItem>
</UnorderedList>
return result
</ListItem>
</UnorderedList>
</ListItem>
<ListItem>
<br />
</ListItem>
<ListItem color="grey"># Convert root of tree to json</ListItem>
<ListItem>tree_json = node_to_dict(0, iris.feature_names, iris.target_names)</ListItem>
<ListItem>
<br />
</ListItem>
<ListItem color="grey"># Save structure of tree to json file</ListItem>
<ListItem>
with open('decision_tree.json', 'w') as json_file: json.dump(tree_json, json_file,
indent=2)
</ListItem>
<ListItem>
<br />
</ListItem>
<ListItem color="grey"># Show plot with tree</ListItem>
<ListItem>
fig = plt.figure(figsize=(25,20)) <br />_ = tree.plot_tree(clf,
feature_names=iris.feature_names, class_names=iris.target_names, filled=True) <br />{' '}
plt.show()
</ListItem>
</UnorderedList>
</Box>
</Code>
</Box>
</Box>
</Box>
</Box>
<Box padding={30} mt={10} width={'100%'}>

17
src/components/Tree.jsx
View File

@@ -186,12 +186,6 @@ const Tree = ({ options, headers, jsonTreeFromFile = null }) => {
<Stack spacing={2} direction="row">
<Box>
<a
leftIcon={<GrTechnology />}
bg={'#ddd'}
color="#black"
_hover={{ bg: '#aaa' }}
onClick={() => logTree(root)}
size="sm"
href={`data:text/json;charset=utf-8,${encodeURIComponent(
JSON.stringify(
root,
@@ -202,7 +196,16 @@ const Tree = ({ options, headers, jsonTreeFromFile = null }) => {
)}`}
download={'iTree_decisionTree_test_' + new Date().toJSON().slice(0, 10) + '.json'}
>
Log tree
<Button
leftIcon={<GrTechnology />}
bg={'#ddd'}
color="#black"
_hover={{ bg: '#aaa' }}
onClick={() => logTree(root)}
size="sm"
>
Export Tree
</Button>
</a>
</Box>