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| # Contains the logic for reading and parsing the RTF file and extracting JSON content | |
| import pandas as pd | |
| import json | |
| from striprtf.striprtf import rtf_to_text | |
| from sklearn.preprocessing import train_test_split | |
| class DataReader: | |
| def rtf_parser(self, file_path, encoding='utf-8'): | |
| # Read the RTF file | |
| with open(file_path, 'r', encoding=encoding) as file: | |
| rtf_content = file.read() | |
| # Convert the RTF content to text | |
| text_content = rtf_to_text(rtf_content) | |
| return text_content | |
| def rtf_to_json_parser(self, rtf_file_path): | |
| plain_text = self.rtf_parser(rtf_file_path) | |
| json_data = json.loads(plain_text) | |
| self.json_content = json_data | |
| return json_data | |
| def get_selected_features_and_details(self): | |
| selected_features = [] | |
| feature_details = {} | |
| design_state = self.json_content["design_state_data"] | |
| feature_handling = design_state["feature_handling"] | |
| target_variable = design_state["target"]["target"] | |
| for feature, details in feature_handling.items(): | |
| if(details["is_selected"]): | |
| name = details["feature_name"] | |
| selected_features.append(name) | |
| feature_details[name] = details | |
| selected_features.remove(target_variable) | |
| return selected_features, feature_details | |
| def get_problem_type_and_target_variable(self): | |
| design_state = self.json_content["design_state_data"] | |
| problem_type = design_state["target"]["prediction_type"] | |
| target_variable = design_state["target"]["target"] | |
| return problem_type,target_variable |