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Browse files- LICENSE +201 -0
- Procfile +1 -0
- app.py +334 -0
- requirements.txt +0 -0
- runtime.txt +1 -0
- setup.sh +9 -0
LICENSE
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Procfile
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web: sh setup.sh && streamlit run Fatal_Health.py
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app.py
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import streamlit as st
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st.set_page_config(layout="wide", page_icon=":hospital:")
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4 |
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st.set_option('deprecation.showPyplotGlobalUse', False)
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5 |
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import pandas as pd
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6 |
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import numpy as np
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import seaborn as sns
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import time
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import matplotlib.pyplot as plt
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plt.style.use('fivethirtyeight')
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plt.style.use('default')
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.svm import SVC
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18 |
+
from sklearn.ensemble import RandomForestClassifier
|
19 |
+
from sklearn.ensemble import GradientBoostingClassifier
|
20 |
+
from xgboost import XGBClassifier
|
21 |
+
from sklearn.model_selection import train_test_split
|
22 |
+
from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler
|
23 |
+
from sklearn.metrics import precision_recall_fscore_support as score, mean_squared_error
|
24 |
+
from sklearn.metrics import confusion_matrix, accuracy_score
|
25 |
+
from sklearn.decomposition import PCA
|
26 |
+
|
27 |
+
########################################################################################################################
|
28 |
+
start_time = time.time()
|
29 |
+
# Title for the webpage
|
30 |
+
tit1, tit2 = st.beta_columns((4, 1))
|
31 |
+
tit1.markdown("<h1 style='text-align: center;'><u>Activity/ Pain Prediction With Wearable Technology Data</u> </h1>",
|
32 |
+
unsafe_allow_html=True)
|
33 |
+
st.sidebar.title("Dataset and ML Classifiers")
|
34 |
+
|
35 |
+
dataset_select = st.sidebar.selectbox("Select Dataset: ", ('AppleWatch Data', "Fitbit Data"))
|
36 |
+
classifier_select = st.sidebar.selectbox("Select ML Classifier: ",
|
37 |
+
("Logistic Regression", "KNN", "SVM", "Decision Trees",
|
38 |
+
"Random Forest", "Gradient Boosting", "XGBoost"))
|
39 |
+
|
40 |
+
LE = LabelEncoder()
|
41 |
+
|
42 |
+
|
43 |
+
def get_dataset(dataset_select):
|
44 |
+
if dataset_select == "AppleWatch Data":
|
45 |
+
data = pd.read_csv(
|
46 |
+
"https://raw.githubusercontent.com/ajinkyalahade/PainPredictionProject/main/Data/data_applewatch.csv")
|
47 |
+
st.header("Activity Data Apple Watch")
|
48 |
+
return data
|
49 |
+
|
50 |
+
else:
|
51 |
+
data = pd.read_csv(
|
52 |
+
"https://raw.githubusercontent.com/ajinkyalahade/PainPredictionProject/main/Data/data_fitbit.csv")
|
53 |
+
st.header("Activity Data Fitbit Watch")
|
54 |
+
return data
|
55 |
+
|
56 |
+
|
57 |
+
data = get_dataset(dataset_select)
|
58 |
+
|
59 |
+
|
60 |
+
def selected_dataset(dataset_select):
|
61 |
+
if dataset_select == "AppleWatch Data":
|
62 |
+
X = data.drop(["activitytag"], axis=1)
|
63 |
+
Y = data.activitytag
|
64 |
+
return X, Y
|
65 |
+
elif dataset_select == "Fitbit Data":
|
66 |
+
X = data.drop(["tag"], axis=1)
|
67 |
+
Y = data.tag
|
68 |
+
return X, Y
|
69 |
+
|
70 |
+
|
71 |
+
X, Y = selected_dataset(dataset_select)
|
72 |
+
|
73 |
+
|
74 |
+
# Charts
|
75 |
+
def plot_op(dataset_select):
|
76 |
+
col1, col2 = st.beta_columns((1, 5))
|
77 |
+
plt.figure(figsize=(12, 3))
|
78 |
+
plt.title("Classes in 'Y'")
|
79 |
+
if dataset_select == "AppleWatch Data":
|
80 |
+
col1.write(Y)
|
81 |
+
sns.countplot(Y, palette='colorblind')
|
82 |
+
col2.pyplot()
|
83 |
+
|
84 |
+
elif dataset_select == "Fitbit Data":
|
85 |
+
col1.write(Y)
|
86 |
+
sns.countplot(Y, palette='colorblind')
|
87 |
+
col2.pyplot()
|
88 |
+
|
89 |
+
|
90 |
+
########################################################################################################################
|
91 |
+
|
92 |
+
st.write(data)
|
93 |
+
st.write("Shape of dataset: ", data.shape)
|
94 |
+
st.write("Number of classes: ", Y.nunique())
|
95 |
+
plot_op(dataset_select)
|
96 |
+
|
97 |
+
|
98 |
+
########################################################################################################################
|
99 |
+
|
100 |
+
def add_parameter_ui(clf_name):
|
101 |
+
params = {}
|
102 |
+
st.sidebar.write("Select Parameters: ")
|
103 |
+
|
104 |
+
if clf_name == "Logistic Regression":
|
105 |
+
R = st.sidebar.slider("Regularization", 0.1, 10.0, step=0.1)
|
106 |
+
MI = st.sidebar.slider("max_iter", 50, 400, step=50)
|
107 |
+
params["R"] = R
|
108 |
+
params["MI"] = MI
|
109 |
+
|
110 |
+
elif clf_name == "KNN":
|
111 |
+
K = st.sidebar.slider("n_neighbors", 1, 20)
|
112 |
+
params["K"] = K
|
113 |
+
|
114 |
+
elif clf_name == "SVM":
|
115 |
+
C = st.sidebar.slider("Regularization", 0.01, 10.0, step=0.01)
|
116 |
+
kernel = st.sidebar.selectbox("Kernel", ("linear", "poly", "rbf", "sigmoid", "precomputed"))
|
117 |
+
params["C"] = C
|
118 |
+
params["kernel"] = kernel
|
119 |
+
|
120 |
+
elif clf_name == "Decision Trees":
|
121 |
+
M = st.sidebar.slider("max_depth", 2, 20)
|
122 |
+
C = st.sidebar.selectbox("Criterion", ("gini", "entropy"))
|
123 |
+
SS = st.sidebar.slider("min_samples_split", 1, 10)
|
124 |
+
params["M"] = M
|
125 |
+
params["C"] = C
|
126 |
+
params["SS"] = SS
|
127 |
+
|
128 |
+
elif clf_name == "Random Forest":
|
129 |
+
N = st.sidebar.slider("n_estimators", 50, 500, step=50, value=100)
|
130 |
+
M = st.sidebar.slider("max_depth", 2, 20)
|
131 |
+
C = st.sidebar.selectbox("Criterion", ("gini", "entropy"))
|
132 |
+
params["N"] = N
|
133 |
+
params["M"] = M
|
134 |
+
params["C"] = C
|
135 |
+
|
136 |
+
elif clf_name == "Gradient Boosting":
|
137 |
+
N = st.sidebar.slider("n_estimators", 50, 500, step=50, value=100)
|
138 |
+
LR = st.sidebar.slider("Learning Rate", 0.01, 0.5)
|
139 |
+
L = st.sidebar.selectbox("Loss", ('deviance', 'exponential'))
|
140 |
+
M = st.sidebar.slider("max_depth", 2, 20)
|
141 |
+
params["N"] = N
|
142 |
+
params["LR"] = LR
|
143 |
+
params["L"] = L
|
144 |
+
params["M"] = M
|
145 |
+
|
146 |
+
elif clf_name == "XGBoost":
|
147 |
+
N = st.sidebar.slider("n_estimators", 50, 500, step=50, value=50)
|
148 |
+
LR = st.sidebar.slider("Learning Rate", 0.01, 0.5, value=0.1)
|
149 |
+
O = st.sidebar.selectbox("Objective", ('binary:logistic', 'reg:logistic', 'reg:squarederror', "reg:gamma"))
|
150 |
+
M = st.sidebar.slider("max_depth", 1, 20, value=6)
|
151 |
+
G = st.sidebar.slider("Gamma", 0, 10, value=5)
|
152 |
+
L = st.sidebar.slider("reg_lambda", 1.0, 5.0, step=0.1)
|
153 |
+
A = st.sidebar.slider("reg_alpha", 0.0, 5.0, step=0.1)
|
154 |
+
CS = st.sidebar.slider("colsample_bytree", 0.5, 1.0, step=0.1)
|
155 |
+
params["N"] = N
|
156 |
+
params["LR"] = LR
|
157 |
+
params["O"] = O
|
158 |
+
params["M"] = M
|
159 |
+
params["G"] = G
|
160 |
+
params["L"] = L
|
161 |
+
params["A"] = A
|
162 |
+
params["CS"] = CS
|
163 |
+
|
164 |
+
RS = st.sidebar.slider("Random State", 0, 100)
|
165 |
+
params["RS"] = RS
|
166 |
+
return params
|
167 |
+
|
168 |
+
|
169 |
+
params = add_parameter_ui(classifier_select)
|
170 |
+
|
171 |
+
|
172 |
+
# get classifier by selections above
|
173 |
+
def get_classifier(clf_name, params):
|
174 |
+
global clf
|
175 |
+
if clf_name == "Logistic Regression":
|
176 |
+
clf = LogisticRegression(C=params["R"], max_iter=params["MI"])
|
177 |
+
|
178 |
+
elif clf_name == "KNN":
|
179 |
+
clf = KNeighborsClassifier(n_neighbors=params["K"])
|
180 |
+
|
181 |
+
elif clf_name == "SVM":
|
182 |
+
clf = SVC(kernel=params["kernel"], C=params["C"])
|
183 |
+
|
184 |
+
elif clf_name == "Decision Trees":
|
185 |
+
clf = DecisionTreeClassifier(max_depth=params["M"], criterion=params["C"], min_impurity_split=params["SS"])
|
186 |
+
|
187 |
+
elif clf_name == "Random Forest":
|
188 |
+
clf = RandomForestClassifier(n_estimators=params["N"], max_depth=params["M"], criterion=params["C"])
|
189 |
+
|
190 |
+
elif clf_name == "Gradient Boosting":
|
191 |
+
clf = GradientBoostingClassifier(n_estimators=params["N"], learning_rate=params["LR"], loss=params["L"],
|
192 |
+
max_depth=params["M"])
|
193 |
+
|
194 |
+
elif clf_name == "XGBoost":
|
195 |
+
clf = XGBClassifier(booster="gbtree", n_estimators=params["N"], max_depth=params["M"],
|
196 |
+
learning_rate=params["LR"],
|
197 |
+
objective=params["O"], gamma=params["G"], reg_alpha=params["A"], reg_lambda=params["L"],
|
198 |
+
colsample_bytree=params["CS"])
|
199 |
+
|
200 |
+
return clf
|
201 |
+
|
202 |
+
|
203 |
+
clf = get_classifier(classifier_select, params)
|
204 |
+
|
205 |
+
|
206 |
+
########################################################################################################################
|
207 |
+
# get model trained
|
208 |
+
def model():
|
209 |
+
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)
|
210 |
+
|
211 |
+
# MinMax Scaling / Normalization of data
|
212 |
+
Std_scaler = StandardScaler()
|
213 |
+
X_train = Std_scaler.fit_transform(X_train)
|
214 |
+
X_test = Std_scaler.transform(X_test)
|
215 |
+
|
216 |
+
clf.fit(X_train, Y_train)
|
217 |
+
Y_pred = clf.predict(X_test)
|
218 |
+
acc = accuracy_score(Y_test, Y_pred)
|
219 |
+
|
220 |
+
return Y_pred, Y_test
|
221 |
+
|
222 |
+
|
223 |
+
Y_pred, Y_test = model()
|
224 |
+
|
225 |
+
|
226 |
+
########################################################################################################################
|
227 |
+
# Plot Output
|
228 |
+
def compute(Y_pred, Y_test):
|
229 |
+
# Plot PCA
|
230 |
+
pca = PCA(2)
|
231 |
+
X_projected = pca.fit_transform(X)
|
232 |
+
x1 = X_projected[:, 0]
|
233 |
+
x2 = X_projected[:, 1]
|
234 |
+
plt.figure(figsize=(16, 8))
|
235 |
+
plt.scatter(x1, x2, c=Y, alpha=0.8, cmap="cividis")
|
236 |
+
plt.xlabel("Principal Component 1")
|
237 |
+
plt.ylabel("Principal Component 2")
|
238 |
+
plt.colorbar()
|
239 |
+
st.pyplot()
|
240 |
+
|
241 |
+
c1, c2 = st.beta_columns((4, 3))
|
242 |
+
# Output plot
|
243 |
+
plt.figure(figsize=(12, 6))
|
244 |
+
plt.scatter(range(len(Y_pred)), Y_pred, color="blue", lw=5, label="Predictions")
|
245 |
+
plt.scatter(range(len(Y_test)), Y_test, color="red", label="Actual")
|
246 |
+
plt.title("Prediction Values vs Real Values")
|
247 |
+
plt.legend()
|
248 |
+
plt.grid(True)
|
249 |
+
c1.pyplot()
|
250 |
+
|
251 |
+
# Confusion Matrix
|
252 |
+
cm = confusion_matrix(Y_test, Y_pred)
|
253 |
+
class_label = ["High-Pain-risk", "Low-Pain-risk"]
|
254 |
+
df_cm = pd.DataFrame(cm, index=class_label, columns=class_label)
|
255 |
+
plt.figure(figsize=(12, 7.5))
|
256 |
+
sns.heatmap(df_cm, annot=True, cmap='Set1', linewidths=2, fmt='d')
|
257 |
+
plt.title("Confusion Matrix", fontsize=15)
|
258 |
+
plt.xlabel("Predicted")
|
259 |
+
plt.ylabel("True")
|
260 |
+
c2.pyplot()
|
261 |
+
|
262 |
+
# Calculate Metrics
|
263 |
+
acc = accuracy_score(Y_test, Y_pred)
|
264 |
+
mse = mean_squared_error(Y_test, Y_pred)
|
265 |
+
precision, recall, fscore, train_support = score(Y_test, Y_pred, pos_label=1, average='binary')
|
266 |
+
st.subheader("Metrics of the model: ")
|
267 |
+
st.text('Precision: {} \nRecall: {} \nF1-Score: {} \nAccuracy: {} %\nMean Squared Error: {}'.format(
|
268 |
+
round(precision, 3), round(recall, 3), round(fscore, 3), round((acc * 100), 3), round((mse), 3)))
|
269 |
+
|
270 |
+
|
271 |
+
st.markdown("<hr>", unsafe_allow_html=True)
|
272 |
+
st.header(f"1) Model for Prediction of {dataset_select}")
|
273 |
+
st.subheader(f"Classifier Used: {classifier_select}")
|
274 |
+
compute(Y_pred, Y_test)
|
275 |
+
|
276 |
+
# Execution Time
|
277 |
+
end_time = time.time()
|
278 |
+
st.info(f"Total execution time: {round((end_time - start_time), 4)} seconds")
|
279 |
+
|
280 |
+
|
281 |
+
# Get user values
|
282 |
+
def user_inputs_ui(da, data):
|
283 |
+
user_val = {}
|
284 |
+
if dataset_select == "Fitbit Data":
|
285 |
+
X = data.drop(["tag"], axis=1)
|
286 |
+
for col in X.columns:
|
287 |
+
name = col
|
288 |
+
col = st.number_input(col, abs(X[col].min() - round(X[col].std())), abs(X[col].max() + round(X[col].std())))
|
289 |
+
user_val[name] = round((col), 4)
|
290 |
+
|
291 |
+
elif dataset_select == "AppleWatch Data":
|
292 |
+
X = data.drop(["activitytag"], axis=1)
|
293 |
+
for col in X.columns:
|
294 |
+
name = col
|
295 |
+
col = st.number_input(col, abs(X[col].min() - round(X[col].std())), abs(X[col].max() + round(X[col].std())))
|
296 |
+
user_val[name] = col
|
297 |
+
|
298 |
+
return user_val
|
299 |
+
|
300 |
+
|
301 |
+
# User values
|
302 |
+
st.markdown("<hr>", unsafe_allow_html=True)
|
303 |
+
st.header("2) User Values")
|
304 |
+
with st.beta_expander("Learn More"):
|
305 |
+
st.markdown("""
|
306 |
+
Please fill in your data to see the results.<br>
|
307 |
+
<p style='color: red;'> 1 - High Risk </p> <p style='color: green;'> 0 - Low Risk </p>
|
308 |
+
""", unsafe_allow_html=True)
|
309 |
+
|
310 |
+
user_val = user_inputs_ui(dataset_select, data)
|
311 |
+
|
312 |
+
|
313 |
+
# @st.cache(suppress_st_warning=True)
|
314 |
+
def user_predict():
|
315 |
+
global U_pred
|
316 |
+
if dataset_select == "AppleWatch Data":
|
317 |
+
X = data.drop(["activitytag"], axis=1)
|
318 |
+
U_pred = clf.predict([[user_val[col] for col in X.columns]])
|
319 |
+
|
320 |
+
elif dataset_select == "Fitbit Data":
|
321 |
+
X = data.drop(["tag"], axis=1)
|
322 |
+
U_pred = clf.predict([[user_val[col] for col in X.columns]])
|
323 |
+
|
324 |
+
st.subheader("Your Status: ")
|
325 |
+
if U_pred == 0:
|
326 |
+
st.write(U_pred[0],
|
327 |
+
" - NOT A PAIN EVENT -- THIS IS NOT A PROFESSIONAL MEDICAL ADVISE - CONTACT YOUR PRIMARY CARE PROVIDER")
|
328 |
+
else:
|
329 |
+
st.write(U_pred[0],
|
330 |
+
"- POTENTIAL PAIN EVENT; PLEASE SEE YOUR DOCTOR -- THIS IS NOT A PROFESSIONAL MEDICAL ADVISE")
|
331 |
+
|
332 |
+
|
333 |
+
user_predict() # Predict the status of user.
|
334 |
+
|
requirements.txt
ADDED
Binary file (244 Bytes). View file
|
|
runtime.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
python-3.9.6
|
setup.sh
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
mkdir -p ~/.streamlit/
|
2 |
+
|
3 |
+
echo "\
|
4 |
+
[server]\n\
|
5 |
+
port = $PORT\n\
|
6 |
+
enableCORS = false\n\
|
7 |
+
headless = true\n\
|
8 |
+
\n\
|
9 |
+
" > ~/.streamlit/config.toml
|