|
| 1 | +"""Base class for all objectives.""" |
| 2 | +from abc import ABC, abstractmethod |
| 3 | + |
| 4 | +import numpy as np |
| 5 | +import pandas as pd |
| 6 | + |
| 7 | +from checkmates.problem_types import handle_problem_types |
| 8 | +from checkmates.utils import classproperty |
| 9 | + |
| 10 | + |
| 11 | +class ObjectiveBase(ABC): |
| 12 | + """Base class for all objectives.""" |
| 13 | + |
| 14 | + problem_types = None |
| 15 | + |
| 16 | + @property |
| 17 | + @classmethod |
| 18 | + @abstractmethod |
| 19 | + def name(cls): |
| 20 | + """Returns a name describing the objective.""" |
| 21 | + |
| 22 | + @property |
| 23 | + @classmethod |
| 24 | + @abstractmethod |
| 25 | + def greater_is_better(cls): |
| 26 | + """Returns a boolean determining if a greater score indicates better model performance.""" |
| 27 | + |
| 28 | + @property |
| 29 | + @classmethod |
| 30 | + @abstractmethod |
| 31 | + def score_needs_proba(cls): |
| 32 | + """Returns a boolean determining if the score() method needs probability estimates. |
| 33 | +
|
| 34 | + This should be true for objectives which work with predicted |
| 35 | + probabilities, like log loss or AUC, and false for objectives |
| 36 | + which compare predicted class labels to the actual labels, like |
| 37 | + F1 or correlation. |
| 38 | + """ |
| 39 | + |
| 40 | + @property |
| 41 | + @classmethod |
| 42 | + @abstractmethod |
| 43 | + def perfect_score(cls): |
| 44 | + """Returns the score obtained by evaluating this objective on a perfect model.""" |
| 45 | + |
| 46 | + @property |
| 47 | + @classmethod |
| 48 | + @abstractmethod |
| 49 | + def is_bounded_like_percentage(cls): |
| 50 | + """Returns whether this objective is bounded between 0 and 1, inclusive.""" |
| 51 | + |
| 52 | + @property |
| 53 | + @classmethod |
| 54 | + @abstractmethod |
| 55 | + def expected_range(cls): |
| 56 | + """Returns the expected range of the objective, which is not necessarily the possible ranges. |
| 57 | +
|
| 58 | + For example, our expected R2 range is from [-1, 1], although the |
| 59 | + actual range is (-inf, 1]. |
| 60 | + """ |
| 61 | + |
| 62 | + @classmethod |
| 63 | + @abstractmethod |
| 64 | + def objective_function( |
| 65 | + cls, |
| 66 | + y_true, |
| 67 | + y_predicted, |
| 68 | + y_train=None, |
| 69 | + X=None, |
| 70 | + sample_weight=None, |
| 71 | + ): |
| 72 | + """Computes the relative value of the provided predictions compared to the actual labels, according a specified metric. |
| 73 | +
|
| 74 | + Args: |
| 75 | + y_predicted (pd.Series): Predicted values of length [n_samples] |
| 76 | + y_true (pd.Series): Actual class labels of length [n_samples] |
| 77 | + y_train (pd.Series): Observed training values of length [n_samples] |
| 78 | + X (pd.DataFrame or np.ndarray): Extra data of shape [n_samples, n_features] necessary to calculate score |
| 79 | + sample_weight (pd.DataFrame or np.ndarray): Sample weights used in computing objective value result |
| 80 | +
|
| 81 | + Returns: |
| 82 | + Numerical value used to calculate score |
| 83 | + """ |
| 84 | + |
| 85 | + @classproperty |
| 86 | + def positive_only(cls): |
| 87 | + """If True, this objective is only valid for positive data. Defaults to False.""" |
| 88 | + return False |
| 89 | + |
| 90 | + def score(self, y_true, y_predicted, y_train=None, X=None, sample_weight=None): |
| 91 | + """Returns a numerical score indicating performance based on the differences between the predicted and actual values. |
| 92 | +
|
| 93 | + Args: |
| 94 | + y_predicted (pd.Series): Predicted values of length [n_samples] |
| 95 | + y_true (pd.Series): Actual class labels of length [n_samples] |
| 96 | + y_train (pd.Series): Observed training values of length [n_samples] |
| 97 | + X (pd.DataFrame or np.ndarray): Extra data of shape [n_samples, n_features] necessary to calculate score |
| 98 | + sample_weight (pd.DataFrame or np.ndarray): Sample weights used in computing objective value result |
| 99 | +
|
| 100 | + Returns: |
| 101 | + score |
| 102 | + """ |
| 103 | + if X is not None: |
| 104 | + X = self._standardize_input_type(X) |
| 105 | + if y_train is not None: |
| 106 | + y_train = self._standardize_input_type(y_train) |
| 107 | + y_true = self._standardize_input_type(y_true) |
| 108 | + y_predicted = self._standardize_input_type(y_predicted) |
| 109 | + self.validate_inputs(y_true, y_predicted) |
| 110 | + return self.objective_function( |
| 111 | + y_true, |
| 112 | + y_predicted, |
| 113 | + y_train=y_train, |
| 114 | + X=X, |
| 115 | + sample_weight=sample_weight, |
| 116 | + ) |
| 117 | + |
| 118 | + @staticmethod |
| 119 | + def _standardize_input_type(input_data): |
| 120 | + """Standardize input to pandas for scoring. |
| 121 | +
|
| 122 | + Args: |
| 123 | + input_data (list, pd.DataFrame, pd.Series, or np.ndarray): A matrix of predictions or predicted probabilities |
| 124 | +
|
| 125 | + Returns: |
| 126 | + pd.DataFrame or pd.Series: a pd.Series, or pd.DataFrame object if predicted probabilities were provided. |
| 127 | + """ |
| 128 | + if isinstance(input_data, (pd.Series, pd.DataFrame)): |
| 129 | + return input_data |
| 130 | + if isinstance(input_data, list): |
| 131 | + if isinstance(input_data[0], list): |
| 132 | + return pd.DataFrame(input_data) |
| 133 | + return pd.Series(input_data) |
| 134 | + if isinstance(input_data, np.ndarray): |
| 135 | + if len(input_data.shape) == 1: |
| 136 | + return pd.Series(input_data) |
| 137 | + return pd.DataFrame(input_data) |
| 138 | + |
| 139 | + def validate_inputs(self, y_true, y_predicted): |
| 140 | + """Validates the input based on a few simple checks. |
| 141 | +
|
| 142 | + Args: |
| 143 | + y_predicted (pd.Series, or pd.DataFrame): Predicted values of length [n_samples]. |
| 144 | + y_true (pd.Series): Actual class labels of length [n_samples]. |
| 145 | +
|
| 146 | + Raises: |
| 147 | + ValueError: If the inputs are malformed. |
| 148 | + """ |
| 149 | + if y_predicted.shape[0] != y_true.shape[0]: |
| 150 | + raise ValueError( |
| 151 | + "Inputs have mismatched dimensions: y_predicted has shape {}, y_true has shape {}".format( |
| 152 | + len(y_predicted), |
| 153 | + len(y_true), |
| 154 | + ), |
| 155 | + ) |
| 156 | + if len(y_true) == 0: |
| 157 | + raise ValueError("Length of inputs is 0") |
| 158 | + |
| 159 | + if isinstance(y_true, pd.DataFrame): |
| 160 | + y_true = y_true.to_numpy().flatten() |
| 161 | + if np.isnan(y_true).any() or np.isinf(y_true).any(): |
| 162 | + raise ValueError("y_true contains NaN or infinity") |
| 163 | + |
| 164 | + if isinstance(y_predicted, pd.DataFrame): |
| 165 | + y_predicted = y_predicted.to_numpy().flatten() |
| 166 | + if np.isnan(y_predicted).any() or np.isinf(y_predicted).any(): |
| 167 | + raise ValueError("y_predicted contains NaN or infinity") |
| 168 | + if self.score_needs_proba and np.any([(y_predicted < 0) | (y_predicted > 1)]): |
| 169 | + raise ValueError( |
| 170 | + "y_predicted contains probability estimates not within [0, 1]", |
| 171 | + ) |
| 172 | + |
| 173 | + @classmethod |
| 174 | + def calculate_percent_difference(cls, score, baseline_score): |
| 175 | + """Calculate the percent difference between scores. |
| 176 | +
|
| 177 | + Args: |
| 178 | + score (float): A score. Output of the score method of this objective. |
| 179 | + baseline_score (float): A score. Output of the score method of this objective. In practice, |
| 180 | + this is the score achieved on this objective with a baseline estimator. |
| 181 | +
|
| 182 | + Returns: |
| 183 | + float: The percent difference between the scores. Note that for objectives that can be interpreted |
| 184 | + as percentages, this will be the difference between the reference score and score. For all other |
| 185 | + objectives, the difference will be normalized by the reference score. |
| 186 | + """ |
| 187 | + if pd.isna(score) or pd.isna(baseline_score): |
| 188 | + return np.nan |
| 189 | + |
| 190 | + if np.isclose(baseline_score - score, 0, atol=1e-10): |
| 191 | + return 0 |
| 192 | + |
| 193 | + # Return inf when dividing by 0 |
| 194 | + if ( |
| 195 | + np.isclose(baseline_score, 0, atol=1e-10) |
| 196 | + and not cls.is_bounded_like_percentage |
| 197 | + ): |
| 198 | + return np.inf |
| 199 | + |
| 200 | + decrease = False |
| 201 | + if (baseline_score > score and cls.greater_is_better) or ( |
| 202 | + baseline_score < score and not cls.greater_is_better |
| 203 | + ): |
| 204 | + decrease = True |
| 205 | + |
| 206 | + difference = baseline_score - score |
| 207 | + change = ( |
| 208 | + difference |
| 209 | + if cls.is_bounded_like_percentage |
| 210 | + else difference / baseline_score |
| 211 | + ) |
| 212 | + return 100 * (-1) ** (decrease) * np.abs(change) |
| 213 | + |
| 214 | + @classmethod |
| 215 | + def is_defined_for_problem_type(cls, problem_type): |
| 216 | + """Returns whether or not an objective is defined for a problem type.""" |
| 217 | + return handle_problem_types(problem_type) in cls.problem_types |
0 commit comments