Spaces:
Runtime error
Runtime error
File size: 7,438 Bytes
cbc5f50 8f5efcd 35e5b86 5d4b558 bf3a8f9 c567e40 cbc5f50 bf3a8f9 db83413 23a078b cbc5f50 8f5efcd 0433697 8f5efcd 4b8158a 23a078b 4b8158a 23a078b 35e5b86 4b8158a 221622c 4b8158a 9ab4c69 fb95dc6 221622c 35e5b86 bf3a8f9 5d4b558 a459611 4be9ec7 ca9891a 8111dc3 015d55d 4be9ec7 c567e40 c81bbb9 c567e40 35e5b86 4b8158a 35e5b86 339157b 35e5b86 fb95dc6 35e5b86 bf3a8f9 35e5b86 5d4b558 bf3a8f9 e086c8c bf3a8f9 5ff3852 bd29ef5 9637b10 8e8061c 4aa5f86 6021f49 c14fc38 e80d1dd 8e8061c 6034779 bf3a8f9 5d4b558 015d55d 4a7aaea 015d55d 4a7aaea c81bbb9 c567e40 c81bbb9 221622c 4b8158a c734fd3 4b8158a c567e40 35e5b86 e681ef8 35e5b86 4b8158a c567e40 8f5efcd e681ef8 |
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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
import gradio as gr
import hopsworks
import joblib
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import shap
from sklearn.pipeline import make_pipeline
import seaborn as sns
feature_names = ["Age", "BMI", "HbA1c", "Blood Glucose"]
project = hopsworks.login(project="SonyaStern_Lab1")
fs = project.get_feature_store()
print("trying to dl model")
mr = project.get_model_registry()
model = mr.get_model("diabetes_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/diabetes_model.pkl")
print("Model downloaded")
diabetes_fg = fs.get_feature_group(name="diabetes_gan", version=1)
query = diabetes_fg.select_all()
# feature_view = fs.get_or_create_feature_view(name="diabetes",
feature_view = fs.get_or_create_feature_view(
name="diabetes_gan",
version=1,
description="Read from Diabetes dataset",
labels=["diabetes"],
query=query,
)
diabetes_df = pd.DataFrame(diabetes_fg.read())
with gr.Blocks() as demo:
with gr.Row():
gr.HTML(value="<h1 style='text-align: center;'>Diabetes prediction</h1>")
with gr.Row():
with gr.Column():
age_input = gr.Number(label="age")
bmi_input = gr.Slider(10, 100, label="bmi", info="Body Mass Index")
hba1c_input = gr.Slider(
3.5, 9, label="hba1c_level", info="Glycated Haemoglobin"
)
blood_glucose_input = gr.Slider(
80, 300, label="blood_glucose_level", info="Blood Glucose Level"
)
existent_info_input = gr.Radio(
["yes", "no", "Don't know"],
label="Do you already know if you have diabetes? (This will not be used for the prediction)",
)
consent_input = gr.Checkbox(
info="I consent that my personal data will be saved and potentially be used for the model training",
label="accept",
)
btn = gr.Button("Submit")
with gr.Column():
with gr.Row():
output = gr.Text(label="Model prediction")
with gr.Row():
mean_plot = gr.Plot()
with gr.Row():
with gr.Accordion("See model explanability", open=False):
with gr.Row():
with gr.Column():
waterfall_plot = gr.Plot()
with gr.Column():
summary_plot = gr.Plot()
with gr.Row():
with gr.Column():
importance_plot = gr.Plot()
with gr.Column():
decision_plot = gr.Plot()
def submit_inputs(
age_input,
bmi_input,
hba1c_input,
blood_glucose_input,
existent_info_input,
consent_input,
):
df = pd.DataFrame(
[[age_input, bmi_input, hba1c_input, blood_glucose_input]],
columns=["age", "bmi", "hba1c_level", "blood_glucose_level"],
)
res = model.predict(df)
mean_for_age = diabetes_df[
(diabetes_df["diabetes"] == 0) & (diabetes_df["age"] == age_input)
].mean()
print(
"your bmi is:", bmi_input, "the mean for ur age is :", mean_for_age["bmi"]
)
categories = ["BMI", "HbA1c", "Blood Level"]
fig, ax = plt.subplots()
bar_width = 0.35
indices = np.arange(len(categories))
ax.bar(
indices,
[
mean_for_age.bmi,
mean_for_age.hba1c_level,
mean_for_age.blood_glucose_level,
],
bar_width,
label="Reference",
color="b",
alpha=0.7,
)
ax.bar(
indices + bar_width,
[bmi_input, hba1c_input, blood_glucose_input],
bar_width,
label="User",
color="r",
alpha=0.7,
)
ax.legend()
ax.set_xlabel("Variables")
ax.set_ylabel("Values")
ax.set_title("Comparison with average non-diabetic values for your age")
ax.set_xticks(indices + bar_width / 2)
ax.set_xticklabels(categories)
## explainability plots
rf_classifier = model.named_steps["randomforestclassifier"]
transformer_pipeline = make_pipeline(
*[
step
for name, step in model.named_steps.items()
if name != "randomforestclassifier"
]
)
transformed_df = transformer_pipeline.transform(df)
# Generate the SHAP waterfall plot for fig2
explainer = shap.TreeExplainer(rf_classifier)
shap_values = explainer.shap_values(
transformed_df
) # Compute SHAP values directly on the DataFrame
predicted_class = rf_classifier.predict(transformed_df)[0]
shap_values_for_predicted_class = shap_values[predicted_class]
# Select the SHAP values for the first instance and the positive class
shap_explanation = shap.Explanation(
values=shap_values_for_predicted_class[0],
base_values=explainer.expected_value[predicted_class],
data=df.iloc[0],
feature_names=["age", "bmi", "hba1c", "glucose"],
)
fig2 = plt.figure(figsize=(3, 3)) # Create a new figure for SHAP plot
fig2.tight_layout()
plt.gca().set_position((0, 0, 1, 1))
plt.title("SHAP Waterfall Plot") # Optionally set a title for the SHAP plot
plt.tight_layout()
# plt.xticks(rotation=90)
# plt.yticks(rotation=45)
plt.tick_params(axis="y", labelsize=3)
shap.waterfall_plot(
shap_explanation
) # Set show=False to prevent immediate display
fig3 = plt.figure(figsize=(3, 3))
plt.title("SHAP Summary Plot")
shap.summary_plot(
shap_values,
features=transformed_df,
feature_names=["age", "bmi", "hba1c", "glucose"],
)
fig4 = plt.figure(figsize=(3, 3))
feature_importances = rf_classifier.feature_importances_
plt.title("Feature Importances")
sns.barplot(x=feature_importances, y=["age", "bmi", "hba1c", "glucose"])
fig5 = plt.figure(figsize=(3, 3))
plt.title("SHAP Interaction Plot")
shap.decision_plot(
explainer.expected_value[predicted_class],
shap_values_for_predicted_class,
df.iloc[0],
)
## save user's data in hopsworks
if consent_input == True:
user_data_fg = fs.get_or_create_feature_group(
name="user_diabetes_data",
version=1,
primary_key=["age", "bmi", "hba1c_level", "blood_glucose_level"],
description="Submitted user data",
)
user_data_df = df.copy()
user_data_df["diabetes"] = existent_info_input
user_data_fg.insert(user_data_df)
print("inserted new user data to hopsworks", user_data_df)
return res, fig, fig2, fig3, fig4, fig5
btn.click(
submit_inputs,
inputs=[
age_input,
bmi_input,
hba1c_input,
blood_glucose_input,
existent_info_input,
consent_input,
],
outputs=[output, mean_plot, waterfall_plot, importance_plot, decision_plot],
)
demo.launch()
|