Forums

model = pickle.load(open("./model/hr.pkl", "rb"))
  app = flask.Flask(__name__, template_folder='templates')


  @app.route('/', methods=['GET', 'POST'])
  def main():
if flask.request.method == 'GET':
    # Just render the initial form, to get input
    return (flask.render_template('main.html'))

if flask.request.method == 'POST':
    # Extract the input
    TotalWorkingYears = flask.request.form['TotalWorkingYears']
    OverTime_code = flask.request.form['OverTime_code']
    JobInvolvement = flask.request.form['JobInvolvement']
    JobRole_code = flask.request.form['JobRole_code']
    Age = flask.request.form['Age']
    WorkLifeBalance = flask.request.form['WorkLifeBalance']
    Gender_code = flask.request.form['Gender_code']
    DistanceFromHome = flask.request.form['DistanceFromHome']
    MaritalStatus_code = flask.request.form['MaritalStatus_code']
    YearsSinceLastPromotion = flask.request.form['YearsSinceLastPromotion']
    Education = flask.request.form['Education']
    PercentSalaryHike = flask.request.form['PercentSalaryHike']
    TrainingTimesLastYear = flask.request.form['TrainingTimesLastYear']
    JobLevel = flask.request.form['JobLevel']
    YearsAtCompany = flask.request.form['YearsAtCompany']
    DailyRate = flask.request.form['DailyRate']
    YearsWithCurrManager = flask.request.form['YearsWithCurrManager']
    MonthlyIncome = flask.request.form['MonthlyIncome']
    JobSatisfaction = flask.request.form['JobSatisfaction']
    EducationField_code = flask.request.form['EducationField_code']
    RelationshipSatisfaction = flask.request.form['RelationshipSatisfaction']
    MonthlyRate = flask.request.form['MonthlyRate']
    BusinessTravel_code = flask.request.form['BusinessTravel_code']

 # Make DataFrame for model
    input_variables = pd.DataFrame([[TotalWorkingYears, OverTime_code, JobInvolvement,JobRole_code, Age, WorkLifeBalance,
                                     Gender_code, DistanceFromHome, MaritalStatus_code, YearsSinceLastPromotion,
                                     Education,PercentSalaryHike, TrainingTimesLastYear, JobLevel, YearsAtCompany, DailyRate,
                                     YearsWithCurrManager, MonthlyIncome, JobSatisfaction, EducationField_code,
                                     RelationshipSatisfaction, MonthlyRate, BusinessTravel_code]],
                                   columns=['TotalWorkingYears', 'OverTime_code', 'JobInvolvement', 'JobRole_code',
                                            'Age','WorkLifeBalance', 'Gender_code', 'DistanceFromHome','MaritalStatus_code',
                                            'YearsSinceLastPromotion','Education','PercentSalaryHike','TrainingTimesLastYear','JobLevel',
                                            'YearsAtCompany','DailyRate','YearsWithCurrManager','MonthlyIncome','JobSatisfaction',
                                            'EducationField_code','RelationshipSatisfaction','MonthlyRate','BusinessTravel_code'],
                                   dtype=float,
                                   index=['input'])

    # Get the model's prediction
    prediction = model.predict(input_variables)[0]
    prediction_percentage = model.predict_proba(input_variables)[:,1]

    row_to_show = 1
    data_for_prediction = input_variables.iloc[1]  # use 1 row of data here. Could use multiple rows if desired
    data_for_prediction_array = data_for_prediction.values.reshape(1, -1)

    model.predict_proba(data_for_prediction_array)
    X_featurenames = input_variables.columns

    categorical_features = np.argwhere(np.array([len(set(input_variables.values[0]))]))


    predict_fn = lambda x: model.predict_proba(x).astype(float)

    explainer = lime.lime_tabular.LimeTabularExplainer(input_variables.values,
    feature_names=X_featurenames,
    class_names=['Yes','No'],
    categorical_features=categorical_features,
    verbose=True, mode='classification')

    exp = explainer.explain_instance(input_variables.values[0], predict_fn, num_features=5)
    fig = exp.as_pyplot_figure()






    if os.path.isfile("/home/Domemakarov2013/smart_hr/static/images/shap_graph/graph.svg"):
        os.remove("/home/Domemakarov2013/smart_hr/static/images/shap_graph/graph.svg")

          plt.savefig("/home/Domemakarov2013/smart_hr/static/images/shap_graph/graph.svg",

            dpi = 150,
            bbox_inches = 'tight')
       # plt.savefig('/home/Domemakarov2013/smart_hr/static/images/shap_graph/graph.svg')
    else:
       # plt.savefig('/home/Domemakarov2013/smart_hr/static/images/shap_graph/graph.svg')
          plt.savefig("/home/Domemakarov2013/smart_hr/static/images/shap_graph/graph.svg",

            dpi = 150,
            bbox_inches = 'tight')`