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import numpy as np |
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import pandas as pd |
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import gradio as gr |
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from huggingface_hub import from_pretrained_keras |
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model = from_pretrained_keras('keras-io/imbalanced_classification') |
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mean = np.array([7.9042977e+04, -6.7173101e-02, -1.3514652e-02, 1.8250896e-01, |
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4.3794613e-02, -6.3732401e-02, 3.0533234e-02, -2.6844479e-02, |
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3.9848695e-03, 2.2254344e-03, -1.7062010e-03, 7.6269522e-02, |
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-4.4995107e-02, 1.6710665e-02, 3.2869387e-02, 4.9116377e-02, |
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-5.5055786e-03, 1.5153111e-02, -2.2870189e-02, -7.2876248e-03, |
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9.9466369e-03, -6.6186422e-03, -2.2909872e-02, -9.9138934e-03, |
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1.1062564e-03, 3.8055412e-02, 2.8393818e-03, 2.2915885e-04, |
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1.9617653e-03, 9.0817749e+01]) |
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std = np.array([3.9504547e+04, 1.9434261e+00, 1.6578650e+00, 1.4903845e+00, |
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1.4112488e+00, 1.3730472e+00, 1.3213707e+00, 1.2281808e+00, |
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1.2094835e+00, 1.1233834e+00, 1.0938724e+00, 1.0334861e+00, |
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1.0558152e+00, 1.0195577e+00, 9.6568835e-01, 9.3387991e-01, |
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8.8559818e-01, 8.7412000e-01, 8.4275919e-01, 8.1998885e-01, |
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7.7898669e-01, 7.4443674e-01, 7.0863432e-01, 6.3049096e-01, |
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6.0594761e-01, 5.0777191e-01, 4.8668963e-01, 4.0041801e-01, |
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3.3410770e-01, 2.5052232e+02]) |
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fts_min_max = {'Amount': [0.0, 25691.16], |
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'Time': [0.0, 172792.0], |
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'V1': [-56.407509631329, 2.45492999121121], |
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'V10': [-24.5882624372475, 23.7451361206545], |
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'V11': [-4.79747346479757, 12.0189131816199], |
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'V12': [-18.6837146333443, 7.8483920756446], |
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'V13': [-5.79188120632084, 7.12688295859376], |
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'V14': [-19.2143254902614, 10.5267660517847], |
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'V15': [-4.49894467676621, 8.87774159774277], |
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'V16': [-14.1298545174931, 17.3151115176278], |
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'V17': [-25.1627993693248, 9.25352625047285], |
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'V18': [-9.49874592104677, 5.04106918541184], |
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'V19': [-7.21352743017759, 5.59197142733558], |
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'V2': [-72.7157275629303, 22.0577289904909], |
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'V20': [-54.497720494566, 39.4209042482199], |
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'V21': [-34.8303821448146, 27.2028391573154], |
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'V22': [-10.933143697655, 10.5030900899454], |
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'V23': [-44.8077352037913, 22.5284116897749], |
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'V24': [-2.83662691870341, 4.58454913689817], |
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'V25': [-10.2953970749851, 7.51958867870916], |
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'V26': [-2.60455055280817, 3.5173456116238], |
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'V27': [-22.5656793207827, 31.6121981061363], |
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'V28': [-15.4300839055349, 33.8478078188831], |
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'V3': [-48.3255893623954, 9.38255843282114], |
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'V4': [-5.68317119816995, 16.8753440335975], |
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'V5': [-113.743306711146, 34.8016658766686], |
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'V6': [-26.1605059358433, 73.3016255459646], |
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'V7': [-43.5572415712451, 120.589493945238], |
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'V8': [-73.2167184552674, 20.0072083651213], |
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'V9': [-13.4340663182301, 15.5949946071278]} |
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def infer(seed): |
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data = pd.DataFrame({ |
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col: round(np.random.uniform(fts_min_max[col][0], fts_min_max[col][1]), 0) |
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if col =='Time' |
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else np.random.uniform(fts_min_max[col][0], fts_min_max[col][1]) |
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for col in ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', |
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'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'] |
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}, index=[0]) |
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test_features = data.copy().values |
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test_features -= mean |
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test_features /= std |
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pred = model.predict(test_features) |
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data = data.round(decimals = 2) |
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return f"{round(pred.flatten()[0]*100, 2)}%", data.values.tolist() |
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inputs = [gr.Slider(minimum=0, maximum=3000, step=1, label='Choose a random number', value=5)] |
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output = [ |
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gr.Textbox(label='Probability of this transaction is fraudulent:'), |
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gr.Dataframe(headers = ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', |
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'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'], |
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max_rows = 1, row_count = 1, max_cols = 30, col_count = 30, |
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type='pandas', label='Display of generated data input for model') |
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] |
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title = 'Imbalanced Classification with Tensorflow' |
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description = 'Imbalanced Classifiication in predicting Credit card Fraud.' |
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article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on this <a href=\"https://keras.io/examples/structured_data/imbalanced_classification/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">fchollet.</a> HuggingFace Model <a href=\"https://huggingface.co/keras-io/imbalanced_classification\">here</a> " |
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examples = [123, 2022, 975] |
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gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never', |
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title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False) |
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