prrao87/fine-grained-sentiment-app

A Flask LIME explainer app for fine-grained sentiment classification.

fine-grained-sentiment-app's Issues

Converting lime explainer output to json

is it possible to convert the lime explainer output to json? or suggest any method to predict the final output without using lime framework

Lime explainer return empty graph

Hi I have create ML web app for taking input from user and show result to web app.User will fill my form on web app page and then flask will use model to predicted and then model will send result to web app page.Text result working but graph doesn't. Now i want to show explainer using LIME but when i save LIME graph it return empty graph,I already checked my error log.There are no error details

`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')`