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GP.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c7cc76670c04424d803b2a60ab5f250ae247aa0ac1da406d7ea5cf182e5bc87e
+size 21561756

app.py

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+# %%
+import gradio as gr
+from sklearn import preprocessing
+import os
+import numpy as np
+import pickle
+import pandas as pd
+import scipy.io as scio
+# %%
+
+
+def tansig(x):
+    return (2/(1+np.exp(-2*x)))-1
+
+
+def to_proba(output):
+    output = output-np.min(output)
+    ret = (output-np.min(output))/(np.max(output)-np.min(output))
+    return ret
+
+
+def softmax(x):
+    x = x-np.min(x)
+    ex = np.exp(x)
+    return ex/ex.sum()
+
+
+def load_data(path):
+
+    # df = pd.read_csv(path)
+    # return df.values
+    data = scio.loadmat(path)
+    traindata = np.double(data['trainx'])
+    # trainlabel = np.double(data['trainy'])
+
+    # testdata = np.double(data['testx'])
+    # testlabel = np.double(data['testy'])
+    return traindata[0]
+
+
+# %%
+labels = ['No MetS', 'MetS']
+
+
+def predict(NCRF):
+
+    params = np.load('params.npz')
+    N1 = params['N1']
+    N2 = params['N2']
+    Beta1OfEachWindow = params['Beta1OfEachWindow']
+    ymax = params['ymax']
+    ymin = params['ymin']
+    minOfEachWindow = params['minOfEachWindow']
+    distOfMaxAndMin = params['distOfMaxAndMin']
+    weightOfEnhanceLayer = params['weightOfEnhanceLayer']
+    parameterOfShrink = params['parameterOfShrink']
+    OutputWeight = params['OutputWeight']
+
+    # test_x = load_data(file)
+    test_x = NCRF.values
+    # print(test_x.shape)
+    with open('GP.pickle', 'rb') as f:
+        gp = pickle.load(f)
+
+    gp_features = gp.transform(test_x)
+    test_x = np.hstack((test_x, gp_features))
+    # print(test_x.shape)
+    test_x = preprocessing.scale(test_x, axis=1)
+    # test_x = np.expand_dims(test_x, axis=0)
+
+    FeatureOfInputDataWithBiasTest = np.hstack(
+        [test_x, 0.1 * np.ones((test_x.shape[0], 1))])
+    OutputOfFeatureMappingLayerTest = np.zeros(
+        [test_x.shape[0],  N2 * N1])
+
+    for i in range(N2):
+        outputOfEachWindowTest = np.dot(
+            FeatureOfInputDataWithBiasTest,  Beta1OfEachWindow[i])
+        OutputOfFeatureMappingLayerTest[:,  N1*i: N1*(i+1)] = (ymax - ymin)*(
+            outputOfEachWindowTest - minOfEachWindow[i]) / distOfMaxAndMin[i] - ymin
+
+    InputOfEnhanceLayerWithBiasTest = np.hstack(
+        [OutputOfFeatureMappingLayerTest, 0.1 * np.ones((OutputOfFeatureMappingLayerTest.shape[0], 1))])
+    tempOfOutputOfEnhanceLayerTest = np.dot(
+        InputOfEnhanceLayerWithBiasTest,  weightOfEnhanceLayer)
+
+    OutputOfEnhanceLayerTest = tansig(
+        tempOfOutputOfEnhanceLayerTest * parameterOfShrink)
+
+    InputOfOutputLayerTest = np.hstack(
+        [OutputOfFeatureMappingLayerTest, OutputOfEnhanceLayerTest])
+
+    OutputOfTest = np.dot(InputOfOutputLayerTest,  OutputWeight)
+    # print(OutputOfTest.shape)
+    OutputOfTest = np.squeeze(OutputOfTest)
+    proba = OutputOfTest  # to_proba(OutputOfTest)
+    confidences = {labels[i]: float(proba[i]) for i in range(len(labels))}
+    # np.argmax(OutputOfTest)  # np.array(OutputOfTest)
+    return confidences  # int(np.argmax(OutputOfTest)) #
+
+
+# %%
+# headers = ["性别", "年龄",	"腰臀比", "体脂率", "夜尿次数", "小便性质",
+#           "舌色", "苔色",	"眼", "证型-淤血", "渴饮",	"夏季平均每日空调时常",	"吸烟",	"饮食肥腻", "BMI"]
+headers = ["Gender", "Age",	"Waist-Hip Ratio", "Body fat rate", "Nocturia frequency", "Nature of pee",
+           "Tongue color", "Fur color",	"Eye anomaly", "Congestion", "Thirsty",	"Daily air conditioner usage time",	"Smoking",	"Fatty diet", "BMI"]
+iface = gr.Interface(fn=predict,
+                     inputs=gr.DataFrame(headers=headers,
+                                         row_count=(1, "fixed"),
+                                         col_count=(15, "fixed"), wrap=True),
+                     outputs=gr.Label()
+                     )
+iface.launch()
+
+# %%

params.npz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77d84dab7669e2060f5b3466cf0833ae7e037ee7ae45d6a025e10ca0fb137a7e
+size 1920294

requirements.txt

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+gradio==3.21.0
+numpy==1.21.6
+pandas==1.3.5
+scikit_learn==1.2.2
+scipy==1.7.3