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| import pandas as pd | |
| from .preprocess import get_dataset_from_csv | |
| from huggingface_hub import from_pretrained_keras | |
| ##Load Model | |
| model = from_pretrained_keras("keras-io/structured-data-classification-grn-vsn") | |
| def batch_predict(input_data): | |
| """ | |
| This function is used for fetching predictions corresponding to input_dataframe. | |
| It outputs another dataframe containing: | |
| 1. prediction probability for each class | |
| 2. actual expected outcome for each entry in the input dataframe | |
| """ | |
| input_data_file = "input_data.csv" | |
| labels = ['Probability of Income greater than 50000',"Probability of Income less than 50000","Actual Income"] | |
| predictions_df = pd.DataFrame(columns=labels) | |
| input_data.to_csv(input_data_file, index=None, header=None) | |
| input_dataset = get_dataset_from_csv(input_data_file, shuffle=True) | |
| pred = model.predict(input_dataset) | |
| for prediction, actual_gt in zip(pred, input_data['income_level'].values.tolist()): | |
| y_pred_prob = round(prediction.flatten()[0] * 100, 2) | |
| y_not_prob = round((1-prediction.flatten()[0]) * 100, 2) | |
| y_pred = ">50000" if prediction.flatten()[0] > 0.5 else "<50000" | |
| prob_scores = {labels[0]: str(y_pred_prob)+"%" , labels[1]: str(y_not_prob)+"%", labels[2]: y_pred} | |
| predictions_df = predictions_df.append(prob_scores,ignore_index=True) | |
| return predictions_df | |
| def user_input_predict(age, wage, cap_gains, cap_losses, dividends, num_persons, weeks_worked_in_year, | |
| class_of_worker, detailed_industry_recode,detailed_occupation_recode,education, | |
| enroll_in_edu_inst_last_wk, marital_stat, major_industry_code,major_occupation_code, | |
| race, hispanic_origin, sex, member_of_a_labor_union,reason_for_unemployment, | |
| full_or_part_time_employment_stat, tax_filer_stat,region_of_previous_residence, | |
| state_of_previous_residence,detailed_household_and_family_stat,detailed_household_summary_in_household, | |
| migration_codechange_in_msa,migration_codechange_in_reg, migration_codemove_within_reg, | |
| live_in_this_house_1_year_ago,migration_prev_res_in_sunbelt,family_members_under_18, | |
| country_of_birth_father,country_of_birth_mother,country_of_birth_self, | |
| citizenship,own_business_or_self_employed,fill_inc_questionnaire_for_veterans_admin, | |
| veterans_benefits, year): | |
| """ | |
| This function is used for fetching model predictions based on inputs given by user on demo app | |
| """ | |
| input_dict = {"age": [age], | |
| "class_of_worker": [class_of_worker], | |
| "detailed_industry_recode": [detailed_industry_recode], | |
| "detailed_occupation_recode": [detailed_occupation_recode], | |
| "education":[education], | |
| "wage_per_hour": [wage], | |
| "enroll_in_edu_inst_last_wk": [enroll_in_edu_inst_last_wk], | |
| "marital_stat": [marital_stat], | |
| "major_industry_code": [major_industry_code], | |
| "major_occupation_code": [major_occupation_code], | |
| "race": [race], | |
| "hispanic_origin": [hispanic_origin], | |
| "sex": [sex], | |
| "member_of_a_labor_union": [member_of_a_labor_union], | |
| "reason_for_unemployment": [reason_for_unemployment], | |
| "full_or_part_time_employment_stat": [full_or_part_time_employment_stat], | |
| "capital_gains": [cap_gains], | |
| "capital_losses": [cap_losses], | |
| "dividends_from_stocks": [dividends], | |
| "tax_filer_stat": [tax_filer_stat], | |
| "region_of_previous_residence": [region_of_previous_residence], | |
| "state_of_previous_residence": [state_of_previous_residence], | |
| "detailed_household_and_family_stat": [detailed_household_and_family_stat], | |
| "detailed_household_summary_in_household": [detailed_household_summary_in_household], | |
| "instance_weight": [0.0], | |
| "migration_code-change_in_msa": [migration_codechange_in_msa], | |
| "migration_code-change_in_reg": [migration_codechange_in_reg], | |
| "migration_code-move_within_reg": [migration_codemove_within_reg], | |
| "live_in_this_house_1_year_ago": [live_in_this_house_1_year_ago], | |
| "migration_prev_res_in_sunbelt": [migration_prev_res_in_sunbelt], | |
| "num_persons_worked_for_employer": [num_persons], | |
| "family_members_under_18": [family_members_under_18], | |
| "country_of_birth_father": [country_of_birth_father], | |
| "country_of_birth_mother": [country_of_birth_mother], | |
| "country_of_birth_self": [country_of_birth_self], | |
| "citizenship": [citizenship], | |
| "own_business_or_self_employed": [own_business_or_self_employed], | |
| "fill_inc_questionnaire_for_veterans_admin": [fill_inc_questionnaire_for_veterans_admin], | |
| "veterans_benefits": [veterans_benefits], | |
| "weeks_worked_in_year": [weeks_worked_in_year], | |
| "year": [year], | |
| "income_level": [0], | |
| } | |
| input_df = pd.DataFrame.from_dict(input_dict) | |
| input_data_file = "input_data.csv" | |
| input_df.to_csv(input_data_file, index=None, header=None) | |
| input_dataset = get_dataset_from_csv(input_data_file, shuffle=True) | |
| labels = ['Income greater than 50000',"Income less than 50000"] | |
| prediction = model.predict(input_dataset) | |
| y_pred_prob = round(prediction[0].flatten()[0],5) | |
| y_not_prob = round(1-prediction[0].flatten()[0],3) | |
| confidences = {labels[0]: float(y_pred_prob), labels[1]: float(y_not_prob)} | |
| return confidences | |