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| import numpy as np | |
| import pandas as pd | |
| from catboost import CatBoostClassifier, Pool | |
| model = CatBoostClassifier() | |
| model.load_model('model_v0.cbm') | |
| target_col = 'FINAL_CALL_TYPE' | |
| feature_cols = ['INITIAL_CALL_TYPE', 'INITIAL_SEVERITY_LEVEL_CODE', 'DAY_OF_WEEK', 'INCIDENT_HOUR', 'INCIDENT_DURATION', 'POLICEPRECINCT', 'ZIPCODE'] | |
| categorical_features = ['INITIAL_CALL_TYPE', 'DAY_OF_WEEK', 'POLICEPRECINCT', 'ZIPCODE'] | |
| def encode(data): | |
| params = data.copy() | |
| params['INCIDENT_DATETIME'] = pd.to_datetime(params['INCIDENT_DATETIME']) | |
| params['INCIDENT_CLOSE_DATETIME'] = pd.to_datetime(params['INCIDENT_CLOSE_DATETIME']) | |
| params['DAY_OF_WEEK'] = params['INCIDENT_DATETIME'].dayofweek | |
| params['INCIDENT_HOUR'] = params['INCIDENT_DATETIME'].hour | |
| params['INCIDENT_DURATION'] = (params['INCIDENT_CLOSE_DATETIME'] - params['INCIDENT_DATETIME']).total_seconds() | |
| # params['POLICEPRECINCT'] = params['POLICEPRECINCT'].astype(str) | |
| # params['ZIPCODE'] = params['ZIPCODE'].astype(str) | |
| return params[feature_cols] | |
| def predict(params): | |
| try: | |
| print(params) | |
| res = model.predict(params) | |
| return res[0] | |
| except Exception as e: | |
| print(e) | |
| return "error" |