forked from sagnikmitra/streamlit-event
-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
87 lines (70 loc) · 2.63 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
import streamlit as st
import numpy as np
import sklearn
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
st.write("""
# Explore different classifier
""")
dataset_name = st.sidebar.selectbox("Select Dataset",("Iris","Breast Cancer","Wine Dataset"))
st.write(f"## Name of the Dataset: {dataset_name}")
classifier_name = st.sidebar.selectbox("Select Classifier",("KNN","SVM","Random Forest"))
def get_dataset(dataset_name):
if dataset_name == "Iris":
data = datasets.load_iris()
elif dataset_name == "Breast Cancer":
data = datasets.load_breast_cancer()
else:
data = datasets.load_wine()
X = data.data
y = data.target
return X,y
X, y = get_dataset(dataset_name)
st.write("## Shape of Dataset:",X.shape)
st.write("## Number of classes:",len(np.unique(y)))
def add_parameter_ui(classifier_name):
params = dict()
if classifier_name == "KNN":
K = st.sidebar.slider("K",1,15)
params["K"] = K
elif classifier_name == "SVM":
C = st.sidebar.slider("C",0.01,10.0)
params["C"] = C
elif classifier_name == "Random Forest":
max_depth = st.sidebar.slider("max_depth",2,15)
n_estimators = st.sidebar.slider("n_estimators",1,100)
params["max_depth"] = max_depth
params["n_estimators"] = n_estimators
return params
params = add_parameter_ui(classifier_name)
def get_classifier(classifier_name, params):
if classifier_name == "KNN":
classifier = KNeighborsClassifier(n_neighbors=params["K"])
elif classifier_name == "SVM":
classifier = SVC(C=params["C"])
elif classifier_name == "Random Forest":
classifier = RandomForestClassifier(n_estimators=params["n_estimators"],max_depth=params["max_depth"],random_state=1234)
return classifier
classifier = get_classifier(classifier_name,params)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1234)
classifier.fit(X_train,y_train)
y_pred = classifier.predict(X_test)
accuracy_score = accuracy_score(y_test, y_pred)
st.write(f"## Classifier = {classifier_name}")
st.write(f"## Accuracy = {accuracy_score}")
pca = PCA(2)
X_projected = pca.fit_transform(X)
x1 = X_projected[:,0]
x2 = X_projected[:,1]
fig = plt.figure()
plt.scatter(x1,x2,c=y,alpha=0.8,cmap = "viridis")
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.colorbar()
st.pyplot(fig)