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user_tools/src/spark_rapids_tools/tools/speedup_category.py
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| # Copyright (c) 2024, NVIDIA CORPORATION. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """Implementation class for Speedup Category logic.""" | ||
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| from dataclasses import dataclass, field | ||
| from typing import Optional | ||
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| import pandas as pd | ||
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| @dataclass | ||
| class SpeedupCategory: | ||
| """ | ||
| Encapsulates the logic to categorize the speedup values based on the range values. | ||
| """ | ||
| props: dict = field(default=None, init=True) | ||
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| def __build_category_column(self, all_apps: pd.DataFrame) -> pd.DataFrame: | ||
| """ | ||
| Build the category column based on the range values of the speedup column. | ||
| Example: | ||
| props['categories'] = [ | ||
| {'title': 'Not Recommended', 'lowerBound': -100000, 'upperBound': 1.3}, | ||
| {'title': 'Small', 'lowerBound': 1.3, 'upperBound': 2}, | ||
| {'title': 'Medium', 'lowerBound': 2, 'upperBound': 3}, | ||
| {'title': 'Large', 'lowerBound': 3, 'upperBound': 100000} | ||
| ] | ||
| 1. input: row_1 = pd.Series({'speedup': 1.8}) | ||
| output: row_1 = pd.Series({'speedup': 1.8, 'speedup category': 'Small'}) | ||
| reason: Speedup Category will be 'Small' because the speedup is within the range (1.3-2). | ||
| 2. input: row_2 = pd.Series({'speedup': 3.5}) | ||
| output: row_2 = pd.Series({'speedup': 3.5, 'speedup category': 'Large'}) | ||
| reason: Speedup Category will be 'Large' because the speedup is within the range (3-100000). | ||
| """ | ||
| categories = self.props.get('categories') | ||
| category_col_name = self.props.get('categoryColumnName') | ||
| speedup_col_name = self.props.get('speedupColumnName') | ||
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| # Calculate the category based on the speedup value | ||
| def calculate_category(col_value) -> Optional[str]: | ||
| for category in categories: | ||
| if category.get('lowerBound') <= col_value < category.get('upperBound'): | ||
| return category.get('title') | ||
| return None | ||
| all_apps[category_col_name] = all_apps[speedup_col_name].apply(calculate_category) | ||
| return all_apps | ||
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| def __process_category(self, all_apps: pd.DataFrame) -> pd.DataFrame: | ||
| """ | ||
| Process the speedup category column based on the eligibility criteria. If the row does not match | ||
| the criteria, the category column will be set to the `Not Recommended` category. | ||
| Example: | ||
| self.props['eligibilityConditions'] = [ | ||
| {'columnName': 'criteriaCol1', 'lowerBound': 18, 'upperBound': 30}, | ||
| {'columnName': 'criteriaCol2', 'lowerBound': 70, 'upperBound': 100} | ||
| ] | ||
| 1. input: row_1 = pd.Series({'criteriaCol1': 25, 'criteriaCol2': 85, 'speedup category': 'Large'}) | ||
| output: row_1 = pd.Series({'criteriaCol1': 25, 'criteriaCol2': 85, 'speedup category': 'Large'}) | ||
| reason: Category will remain 'Large' because the criteriaCol1 is within the range (18-30) and | ||
| the criteriaCol2 (85) is within the range (70-100). | ||
| 2. input: row_2 = pd.Series({'criteriaCol1': 15, 'criteriaCol2': 85, 'speedup category': 'Medium'}) | ||
| output: row_2 = pd.Series({'criteriaCol1': 15, 'criteriaCol2': 85, 'speedup category': 'Not Recommended'}) | ||
| reason: Category will be set to 'Not Recommended' because the criteriaCol1 is not within the range (18-30) | ||
| """ | ||
| category_col_name = self.props.get('categoryColumnName') | ||
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| def process_row(single_row: pd.Series) -> str: | ||
| for entry in self.props.get('eligibilityConditions'): | ||
| col_value = single_row[entry.get('columnName')] | ||
| # If the value is not within the range, set the category to default category (Not Recommended) | ||
| if not entry.get('lowerBound') <= col_value <= entry.get('upperBound'): | ||
| return self.props.get('defaultCategory') | ||
| return single_row.get(category_col_name) | ||
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| all_apps[category_col_name] = all_apps.apply(process_row, axis=1) | ||
| return all_apps | ||
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| def build_category_column(self, all_apps: pd.DataFrame) -> pd.DataFrame: | ||
| apps_with_category = self.__build_category_column(all_apps) | ||
| processed_apps = self.__process_category(apps_with_category) | ||
| return processed_apps |
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