diff --git a/.github/workflows/dev.yml b/.github/workflows/dev.yml index 681c0733..b17a9728 100644 --- a/.github/workflows/dev.yml +++ b/.github/workflows/dev.yml @@ -20,7 +20,7 @@ jobs: # The type of runner that the job will run on strategy: matrix: - python-versions: ['3.8', '3.9', '3.10', '3.11'] + python-versions: ['3.9', '3.10', '3.11', '3.12'] os: [ubuntu-20.04] # os: [ubuntu-18.04, macos-latest, windows-latest] runs-on: ${{ matrix.os }} diff --git a/.github/workflows/preview.yml b/.github/workflows/preview.yml index 1be36eea..629fb94e 100644 --- a/.github/workflows/preview.yml +++ b/.github/workflows/preview.yml @@ -22,7 +22,7 @@ jobs: strategy: matrix: - python-versions: [ 3.8 ] + python-versions: [ 3.11 ] steps: - uses: actions/checkout@v2 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 7ee6a9d5..b2ea608b 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -24,7 +24,7 @@ jobs: strategy: matrix: - python-versions: [3.8] + python-versions: [3.11] # Steps represent a sequence of tasks that will be executed as part of the job steps: diff --git a/nannyml/config.py b/nannyml/config.py index 259f2880..a9e1d67e 100644 --- a/nannyml/config.py +++ b/nannyml/config.py @@ -9,7 +9,7 @@ import jinja2 import yaml -from pydantic import BaseModel, validator, Field +from pydantic import BaseModel, Field, field_validator from nannyml._typing import Self from nannyml.exceptions import IOException @@ -71,7 +71,7 @@ class CalculatorConfig(BaseModel): store: Optional[StoreConfig] = Field(default=None) params: Dict[str, Any] - @validator('params') + @field_validator('params') def _parse_thresholds(cls, value: Dict[str, Any]): """Parse thresholds in params and convert them to :class:`Threshold`'s""" # Some calculators expect `thresholds` parameter as dict diff --git a/nannyml/drift/ranker.py b/nannyml/drift/ranker.py index 53a029e0..9eaa82de 100644 --- a/nannyml/drift/ranker.py +++ b/nannyml/drift/ranker.py @@ -372,7 +372,11 @@ def rank( filtered_values = values[~(feature_nan | perf_nan)] filtered_perf_change = abs_perf_change[~(feature_nan | perf_nan)] - tmp1 = pearsonr(filtered_values.ravel(), filtered_perf_change) + tmp1 = ( + pearsonr(filtered_values.ravel(), filtered_perf_change) + if len(filtered_values) > 1 + else (np.nan, np.nan) + ) spearmanr1.append(tmp1[0]) spearmanr2.append(tmp1[1]) diff --git a/nannyml/drift/univariate/calculator.py b/nannyml/drift/univariate/calculator.py index fcc5fad2..38999011 100644 --- a/nannyml/drift/univariate/calculator.py +++ b/nannyml/drift/univariate/calculator.py @@ -441,10 +441,10 @@ def _calculate_for_column( logger.error( f"an unexpected exception occurred during calculation of method '{method.display_name}': " f"{exc}" ) - result['value'] = np.NaN + result['value'] = np.nan result['upper_threshold'] = method.upper_threshold_value result['lower_threshold'] = method.lower_threshold_value - result['alert'] = np.NaN + result['alert'] = np.nan finally: return result diff --git a/nannyml/drift/univariate/methods.py b/nannyml/drift/univariate/methods.py index fe847fa0..f4854d28 100644 --- a/nannyml/drift/univariate/methods.py +++ b/nannyml/drift/univariate/methods.py @@ -278,7 +278,9 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None reference_data = _remove_nans(reference_data) len_reference = len(reference_data) - bins = np.histogram_bin_edges(reference_data, bins='doane') + # Explicit conversion to float because of + # https://github.com/numpy/numpy/commit/c63969c6e1d58e791632aacfb88ecae465d6dcfc + bins = np.histogram_bin_edges(reference_data.astype("float64"), bins='doane') reference_proba_in_bins = np.histogram(reference_data, bins=bins)[0] / len_reference self._bins = bins self._reference_proba_in_bins = reference_proba_in_bins @@ -731,7 +733,7 @@ def _fit(self, reference_data: pd.Series, timestamps: Optional[pd.Series] = None reference_data = _remove_nans(reference_data) len_reference = len(reference_data) - bins = np.histogram_bin_edges(reference_data, bins='doane') + bins = np.histogram_bin_edges(reference_data.astype("float64"), bins='doane') reference_proba_in_bins = np.histogram(reference_data, bins=bins)[0] / len_reference self._bins = bins self._reference_proba_in_bins = reference_proba_in_bins diff --git a/nannyml/performance_calculation/metrics/base.py b/nannyml/performance_calculation/metrics/base.py index 32700054..863a87d9 100644 --- a/nannyml/performance_calculation/metrics/base.py +++ b/nannyml/performance_calculation/metrics/base.py @@ -197,11 +197,11 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict: self._logger.error( f"an unexpected exception occurred during calculation of method '{self.display_name}': " f"{exc}" ) - chunk_record[f'{column_name}_sampling_error'] = np.NaN - chunk_record[f'{column_name}'] = np.NaN + chunk_record[f'{column_name}_sampling_error'] = np.nan + chunk_record[f'{column_name}'] = np.nan chunk_record[f'{column_name}_upper_threshold'] = self.upper_threshold_value chunk_record[f'{column_name}_lower_threshold'] = self.lower_threshold_value - chunk_record[f'{column_name}_alert'] = np.NaN + chunk_record[f'{column_name}_alert'] = np.nan finally: return chunk_record diff --git a/nannyml/performance_calculation/metrics/binary_classification.py b/nannyml/performance_calculation/metrics/binary_classification.py index b28d08ff..2a457ae2 100644 --- a/nannyml/performance_calculation/metrics/binary_classification.py +++ b/nannyml/performance_calculation/metrics/binary_classification.py @@ -103,7 +103,7 @@ def _fit(self, reference_data: pd.DataFrame): data = reference_data[[self.y_true, self.y_pred_proba]] data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = auroc_sampling_error_components( y_true_reference=data[self.y_true], @@ -117,7 +117,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred_proba = data[self.y_pred_proba] @@ -126,7 +126,7 @@ def _calculate(self, data: pd.DataFrame): f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan else: return roc_auc_score(y_true, y_pred_proba) @@ -137,7 +137,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return auroc_sampling_error(self._sampling_error_components, data) @@ -199,7 +199,7 @@ def _fit(self, reference_data: pd.DataFrame): ) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = ap_sampling_error_components( y_true_reference=data[self.y_true], @@ -213,7 +213,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred_proba = data[self.y_pred_proba] @@ -223,7 +223,7 @@ def _calculate(self, data: pd.DataFrame): f"'{self.y_true}' does not contain positive class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan else: return average_precision_score(y_true, y_pred_proba) @@ -233,7 +233,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return ap_sampling_error(self._sampling_error_components, data) @@ -289,7 +289,7 @@ def _fit(self, reference_data: pd.DataFrame): data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = f1_sampling_error_components( y_true_reference=data[self.y_true], @@ -302,7 +302,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -312,13 +312,13 @@ def _calculate(self, data: pd.DataFrame): f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan else: return f1_score(y_true, y_pred) @@ -328,7 +328,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return f1_sampling_error(self._sampling_error_components, data) @@ -384,7 +384,7 @@ def _fit(self, reference_data: pd.DataFrame): data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = precision_sampling_error_components( y_true_reference=data[self.y_true], @@ -396,7 +396,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -406,13 +406,13 @@ def _calculate(self, data: pd.DataFrame): f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan else: return precision_score(y_true, y_pred) @@ -422,7 +422,7 @@ def _sampling_error(self, data: pd.DataFrame): warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return precision_sampling_error(self._sampling_error_components, data) @@ -477,7 +477,7 @@ def _fit(self, reference_data: pd.DataFrame): _list_missing([self.y_true, self.y_pred], list(reference_data.columns)) data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = recall_sampling_error_components( y_true_reference=data[self.y_true], @@ -489,7 +489,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -499,13 +499,13 @@ def _calculate(self, data: pd.DataFrame): f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan else: return recall_score(y_true, y_pred) @@ -515,7 +515,7 @@ def _sampling_error(self, data: pd.DataFrame): warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return recall_sampling_error(self._sampling_error_components, data) @@ -570,7 +570,7 @@ def _fit(self, reference_data: pd.DataFrame): _list_missing([self.y_true, self.y_pred], list(reference_data.columns)) data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = specificity_sampling_error_components( y_true_reference=data[self.y_true], @@ -582,7 +582,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -590,7 +590,7 @@ def _calculate(self, data: pd.DataFrame): tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel() denominator = tn + fp if denominator == 0: - return np.NaN + return np.nan else: return tn / denominator @@ -600,7 +600,7 @@ def _sampling_error(self, data: pd.DataFrame): warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return specificity_sampling_error(self._sampling_error_components, data) @@ -655,7 +655,7 @@ def _fit(self, reference_data: pd.DataFrame): _list_missing([self.y_true, self.y_pred], list(reference_data.columns)) data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = accuracy_sampling_error_components( y_true_reference=data[self.y_true], @@ -667,7 +667,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -680,7 +680,7 @@ def _sampling_error(self, data: pd.DataFrame): warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return accuracy_sampling_error(self._sampling_error_components, data) @@ -765,7 +765,7 @@ def _fit(self, reference_data: pd.DataFrame): _list_missing([self.y_true, self.y_pred], list(reference_data.columns)) data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, self.normalize_business_value + self._sampling_error_components = np.nan, self.normalize_business_value else: self._sampling_error_components = business_value_sampling_error_components( y_true_reference=data[self.y_true], @@ -779,7 +779,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"'{self.y_true}' contains no data, cannot calculate business value. Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -804,7 +804,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return business_value_sampling_error(self._sampling_error_components, data) @@ -949,10 +949,10 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._true_positive_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix) - self._true_negative_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix) - self._false_positive_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix) - self._false_negative_sampling_error_components = (np.NaN, 0.0, self.normalize_confusion_matrix) + self._true_positive_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix) + self._true_negative_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix) + self._false_positive_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix) + self._false_negative_sampling_error_components = (np.nan, 0.0, self.normalize_confusion_matrix) else: self._true_positive_sampling_error_components = true_positive_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -980,7 +980,7 @@ def _calculate_true_positives(self, data: pd.DataFrame) -> float: data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate true_positives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1003,7 +1003,7 @@ def _calculate_true_negatives(self, data: pd.DataFrame) -> float: data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate true_negatives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1026,7 +1026,7 @@ def _calculate_false_positives(self, data: pd.DataFrame) -> float: data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate false_positives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1049,7 +1049,7 @@ def _calculate_false_negatives(self, data: pd.DataFrame) -> float: data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate false_negatives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1091,7 +1091,7 @@ def get_true_pos_info(self, chunk_data: pd.DataFrame) -> Dict: chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.") - sampling_error_tp = np.NaN + sampling_error_tp = np.nan else: sampling_error_tp = true_positive_sampling_error(self._true_positive_sampling_error_components, chunk_data) # TODO: NaN removal is duplicated to an extent. Upon refactor consider if we can do it only once @@ -1130,7 +1130,7 @@ def get_true_neg_info(self, chunk_data: pd.DataFrame) -> Dict: chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate true negative sampling error. " "Returning NaN.") - sampling_error_tn = np.NaN + sampling_error_tn = np.nan else: sampling_error_tn = true_negative_sampling_error(self._true_negative_sampling_error_components, chunk_data) # TODO: NaN removal is duplicated to an extent. Upon refactor consider if we can do it only once @@ -1169,7 +1169,7 @@ def get_false_pos_info(self, chunk_data: pd.DataFrame) -> Dict: chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate false positive sampling error. " "Returning NaN.") - sampling_error_fp = np.NaN + sampling_error_fp = np.nan else: sampling_error_fp = false_positive_sampling_error( self._false_positive_sampling_error_components, chunk_data @@ -1210,7 +1210,7 @@ def get_false_neg_info(self, chunk_data: pd.DataFrame) -> Dict: chunk_data, empty = common_nan_removal(chunk_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate false positive sampling error. " "Returning NaN.") - sampling_error_fn = np.NaN + sampling_error_fn = np.nan else: sampling_error_fn = false_negative_sampling_error( self._false_negative_sampling_error_components, chunk_data diff --git a/nannyml/performance_calculation/metrics/multiclass_classification.py b/nannyml/performance_calculation/metrics/multiclass_classification.py index 9d1ee098..c598e4b5 100644 --- a/nannyml/performance_calculation/metrics/multiclass_classification.py +++ b/nannyml/performance_calculation/metrics/multiclass_classification.py @@ -109,7 +109,7 @@ def _fit(self, reference_data: pd.DataFrame): [self.y_true] + self.class_probability_columns, ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clasz in self.classes] + self._sampling_error_components = [(np.nan, 0) for clasz in self.classes] # TODO: Ideally we would also raise an error here! else: # test if reference data are represented correctly @@ -146,7 +146,7 @@ def _calculate(self, data: pd.DataFrame): _message = f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." self._logger.warning(_message) warnings.warn(_message) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred_proba = data[self.class_probability_columns] @@ -158,7 +158,7 @@ def _calculate(self, data: pd.DataFrame): ) warnings.warn(_message) self._logger.warning(_message) - return np.NaN + return np.nan else: return roc_auc_score(y_true, y_pred_proba, multi_class='ovr', average='macro', labels=self.classes) @@ -171,7 +171,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return auroc_sampling_error(self._sampling_error_components, data) @@ -233,7 +233,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: # sampling error label_binarizer = LabelBinarizer() @@ -255,7 +255,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan labels = sorted(list(self.y_pred_proba.keys())) y_true = data[self.y_true] @@ -265,12 +265,12 @@ def _calculate(self, data: pd.DataFrame): warnings.warn( f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan else: return f1_score(y_true, y_pred, average='macro', labels=labels) @@ -281,7 +281,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return f1_sampling_error(self._sampling_error_components, data) @@ -343,7 +343,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: # sampling error label_binarizer = LabelBinarizer() @@ -365,7 +365,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan labels = sorted(list(self.y_pred_proba.keys())) y_true = data[self.y_true] @@ -375,12 +375,12 @@ def _calculate(self, data: pd.DataFrame): warnings.warn( f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan else: return precision_score(y_true, y_pred, average='macro', labels=labels) @@ -391,7 +391,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return precision_sampling_error(self._sampling_error_components, data) @@ -453,7 +453,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: # sampling error label_binarizer = LabelBinarizer() @@ -475,7 +475,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan labels = sorted(list(self.y_pred_proba.keys())) y_true = data[self.y_true] @@ -485,12 +485,12 @@ def _calculate(self, data: pd.DataFrame): warnings.warn( f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan else: return recall_score(y_true, y_pred, average='macro', labels=labels) @@ -501,7 +501,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return recall_sampling_error(self._sampling_error_components, data) @@ -563,7 +563,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: # sampling error label_binarizer = LabelBinarizer() @@ -585,7 +585,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan labels = sorted(list(self.y_pred_proba.keys())) y_true = data[self.y_true] @@ -595,12 +595,12 @@ def _calculate(self, data: pd.DataFrame): warnings.warn( f"'{self.y_true}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan elif y_pred.nunique() <= 1: warnings.warn( f"'{self.y_pred}' only contains a single class, cannot calculate {self.display_name}. Returning NaN." ) - return np.NaN + return np.nan else: MCM = multilabel_confusion_matrix(y_true, y_pred, labels=labels) tn_sum = MCM[:, 0, 0] @@ -615,7 +615,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return specificity_sampling_error(self._sampling_error_components, data) @@ -676,7 +676,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: # sampling error label_binarizer = LabelBinarizer() @@ -692,7 +692,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -706,7 +706,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return accuracy_sampling_error(self._sampling_error_components, data) @@ -845,7 +845,7 @@ def _calculate(self, data: pd.DataFrame) -> Union[np.ndarray, float]: data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -982,7 +982,7 @@ def _fit(self, reference_data: pd.DataFrame): [self.y_true] + self.class_probability_columns, ) if empty: - self._sampling_error_components = [(np.NaN, 0) for class_col in self.class_probability_columns] + self._sampling_error_components = [(np.nan, 0) for class_col in self.class_probability_columns] else: # sampling error binarized_y_true = list(label_binarize(reference_data[self.y_true], classes=self.classes).T) @@ -1006,7 +1006,7 @@ def _calculate(self, data: pd.DataFrame): ) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred_proba = data[self.class_probability_columns] @@ -1016,7 +1016,7 @@ def _calculate(self, data: pd.DataFrame): f"'{self.y_true}' only contains a single class for chunk, cannot calculate {self.display_name}. " "Returning NaN." ) - return np.NaN + return np.nan else: # https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-f-measure-metrics # average_precision_score always performs OVR averaging @@ -1031,7 +1031,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return average_precision_sampling_error(self._sampling_error_components, data) @@ -1130,7 +1130,7 @@ def _fit(self, reference_data: pd.DataFrame): _list_missing([self.y_true, self.y_pred], list(reference_data.columns)) data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, self.normalize_business_value + self._sampling_error_components = np.nan, self.normalize_business_value else: # get class number from y_pred_proba if provided otherwise from reference y_true # this way the code will work even if some classes are missing from reference @@ -1159,7 +1159,7 @@ def _calculate(self, data: pd.DataFrame): data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn(f"'{self.y_true}' contains no data, cannot calculate business value. Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1178,6 +1178,6 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return business_value_sampling_error(self._sampling_error_components, data) diff --git a/nannyml/performance_calculation/metrics/regression.py b/nannyml/performance_calculation/metrics/regression.py index be4140c6..15e2513f 100644 --- a/nannyml/performance_calculation/metrics/regression.py +++ b/nannyml/performance_calculation/metrics/regression.py @@ -16,7 +16,11 @@ ) from nannyml._typing import ProblemType -from nannyml.base import _list_missing, _raise_exception_for_negative_values, common_nan_removal +from nannyml.base import ( + _list_missing, + _raise_exception_for_negative_values, + common_nan_removal, +) from nannyml.performance_calculation.metrics.base import Metric, MetricFactory from nannyml.sampling_error.regression import ( mae_sampling_error, @@ -35,13 +39,20 @@ from nannyml.thresholds import Threshold -@MetricFactory.register(metric='mae', use_case=ProblemType.REGRESSION) +@MetricFactory.register(metric="mae", use_case=ProblemType.REGRESSION) class MAE(Metric): """Mean Absolute Error metric.""" y_pred: str - def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs): + def __init__( + self, + y_true: str, + y_pred: str, + threshold: Threshold, + y_pred_proba: Optional[str] = None, + **kwargs, + ): """Creates a new MAE instance. Parameters @@ -56,13 +67,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Name of the column containing your model output. """ super().__init__( - name='mae', + name="mae", y_true=y_true, y_pred=y_pred, y_pred_proba=y_pred_proba, threshold=threshold, lower_threshold_limit=0, - components=[('MAE', 'mae')], + components=[("MAE", "mae")], ) # sampling error @@ -78,7 +89,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: self._sampling_error_components = mae_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -88,12 +99,15 @@ def _fit(self, reference_data: pd.DataFrame): def _calculate(self, data: pd.DataFrame): """Redefine to handle NaNs and edge cases.""" _list_missing([self.y_true, self.y_pred], list(data.columns)) - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." + f"No data or too many missing values, cannot calculate {self.display_name}. " + f"Returning NaN." ) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -101,23 +115,33 @@ def _calculate(self, data: pd.DataFrame): return mean_absolute_error(y_true, y_pred) def _sampling_error(self, data: pd.DataFrame) -> float: - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." + f"Too many missing values, cannot calculate {self.display_name} sampling error. " + "Returning NaN." ) - return np.NaN + return np.nan else: return mae_sampling_error(self._sampling_error_components, data) -@MetricFactory.register(metric='mape', use_case=ProblemType.REGRESSION) +@MetricFactory.register(metric="mape", use_case=ProblemType.REGRESSION) class MAPE(Metric): """Mean Absolute Percentage Error metric.""" y_pred: str - def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs): + def __init__( + self, + y_true: str, + y_pred: str, + threshold: Threshold, + y_pred_proba: Optional[str] = None, + **kwargs, + ): """Creates a new MAPE instance. Parameters @@ -132,13 +156,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Name of the column containing your model output. """ super().__init__( - name='mape', + name="mape", y_true=y_true, y_pred=y_pred, y_pred_proba=y_pred_proba, threshold=threshold, lower_threshold_limit=0, - components=[('MAPE', 'mape')], + components=[("MAPE", "mape")], ) # sampling error @@ -154,7 +178,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: self._sampling_error_components = mape_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -164,12 +188,15 @@ def _fit(self, reference_data: pd.DataFrame): def _calculate(self, data: pd.DataFrame): """Redefine to handle NaNs and edge cases.""" _list_missing([self.y_true, self.y_pred], list(data.columns)) - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." + f"No data or too many missing values, cannot calculate {self.display_name}. " + f"Returning NaN." ) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -177,23 +204,33 @@ def _calculate(self, data: pd.DataFrame): return mean_absolute_percentage_error(y_true, y_pred) def _sampling_error(self, data: pd.DataFrame) -> float: - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." + f"Too many missing values, cannot calculate {self.display_name} sampling error. " + "Returning NaN." ) - return np.NaN + return np.nan else: return mape_sampling_error(self._sampling_error_components, data) -@MetricFactory.register(metric='mse', use_case=ProblemType.REGRESSION) +@MetricFactory.register(metric="mse", use_case=ProblemType.REGRESSION) class MSE(Metric): """Mean Squared Error metric.""" y_pred: str - def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs): + def __init__( + self, + y_true: str, + y_pred: str, + threshold: Threshold, + y_pred_proba: Optional[str] = None, + **kwargs, + ): """Creates a new MSE instance. Parameters @@ -208,13 +245,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Name of the column containing your model output. """ super().__init__( - name='mse', + name="mse", y_true=y_true, y_pred=y_pred, y_pred_proba=y_pred_proba, threshold=threshold, lower_threshold_limit=0, - components=[('MSE', 'mse')], + components=[("MSE", "mse")], ) # sampling error @@ -230,7 +267,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: self._sampling_error_components = mse_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -240,12 +277,15 @@ def _fit(self, reference_data: pd.DataFrame): def _calculate(self, data: pd.DataFrame): """Redefine to handle NaNs and edge cases.""" _list_missing([self.y_true, self.y_pred], list(data.columns)) - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." + f"No data or too many missing values, cannot calculate {self.display_name}. " + f"Returning NaN." ) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -253,23 +293,33 @@ def _calculate(self, data: pd.DataFrame): return mean_squared_error(y_true, y_pred) def _sampling_error(self, data: pd.DataFrame) -> float: - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." + f"Too many missing values, cannot calculate {self.display_name} sampling error. " + "Returning NaN." ) - return np.NaN + return np.nan else: return mse_sampling_error(self._sampling_error_components, data) -@MetricFactory.register(metric='msle', use_case=ProblemType.REGRESSION) +@MetricFactory.register(metric="msle", use_case=ProblemType.REGRESSION) class MSLE(Metric): """Mean Squared Logarithmic Error metric.""" y_pred: str - def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs): + def __init__( + self, + y_true: str, + y_pred: str, + threshold: Threshold, + y_pred_proba: Optional[str] = None, + **kwargs, + ): """Creates a new MSLE instance. Parameters @@ -284,13 +334,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Name of the column containing your model output. """ super().__init__( - name='msle', + name="msle", y_true=y_true, y_pred=y_pred, y_pred_proba=y_pred_proba, threshold=threshold, lower_threshold_limit=0, - components=[('MSLE', 'msle')], + components=[("MSLE", "msle")], ) # sampling error @@ -306,7 +356,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: self._sampling_error_components = msle_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -316,12 +366,15 @@ def _fit(self, reference_data: pd.DataFrame): def _calculate(self, data: pd.DataFrame): """Redefine to handle NaNs and edge cases.""" _list_missing([self.y_true, self.y_pred], list(data.columns)) - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." + f"No data or too many missing values, cannot calculate {self.display_name}. " + f"Returning NaN." ) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -333,23 +386,33 @@ def _calculate(self, data: pd.DataFrame): return mean_squared_log_error(y_true, y_pred) def _sampling_error(self, data: pd.DataFrame) -> float: - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." + f"Too many missing values, cannot calculate {self.display_name} sampling error. " + "Returning NaN." ) - return np.NaN + return np.nan else: return msle_sampling_error(self._sampling_error_components, data) -@MetricFactory.register(metric='rmse', use_case=ProblemType.REGRESSION) +@MetricFactory.register(metric="rmse", use_case=ProblemType.REGRESSION) class RMSE(Metric): """Root Mean Squared Error metric.""" y_pred: str - def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs): + def __init__( + self, + y_true: str, + y_pred: str, + threshold: Threshold, + y_pred_proba: Optional[str] = None, + **kwargs, + ): """Creates a new RMSE instance. Parameters @@ -364,13 +427,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Name of the column containing your model output. """ super().__init__( - name='rmse', + name="rmse", y_true=y_true, y_pred=y_pred, y_pred_proba=y_pred_proba, threshold=threshold, lower_threshold_limit=0, - components=[('RMSE', 'rmse')], + components=[("RMSE", "rmse")], ) # sampling error @@ -386,7 +449,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: self._sampling_error_components = rmse_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -396,36 +459,58 @@ def _fit(self, reference_data: pd.DataFrame): def _calculate(self, data: pd.DataFrame): """Redefine to handle NaNs and edge cases.""" _list_missing([self.y_true, self.y_pred], list(data.columns)) - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." + f"No data or too many missing values, cannot calculate {self.display_name}. " + f"Returning NaN." ) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] - return mean_squared_error(y_true, y_pred, squared=False) + # Deal with breaking API change in sklearn 1.4 + # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_error.html + try: + from sklearn.metrics import root_mean_squared_error + + return root_mean_squared_error(y_true, y_pred) + except ImportError: + from sklearn.metrics import mean_squared_error + + return mean_squared_error(y_true, y_pred, squared=False) def _sampling_error(self, data: pd.DataFrame) -> float: - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." + f"Too many missing values, cannot calculate {self.display_name} sampling error. " + "Returning NaN." ) - return np.NaN + return np.nan else: return rmse_sampling_error(self._sampling_error_components, data) -@MetricFactory.register(metric='rmsle', use_case=ProblemType.REGRESSION) +@MetricFactory.register(metric="rmsle", use_case=ProblemType.REGRESSION) class RMSLE(Metric): """Root Mean Squared Logarithmic Error metric.""" y_pred: str - def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Optional[str] = None, **kwargs): + def __init__( + self, + y_true: str, + y_pred: str, + threshold: Threshold, + y_pred_proba: Optional[str] = None, + **kwargs, + ): """Creates a new RMSLE instance. Parameters @@ -440,13 +525,13 @@ def __init__(self, y_true: str, y_pred: str, threshold: Threshold, y_pred_proba: Name of the column containing your model output. """ super().__init__( - name='rmsle', + name="rmsle", y_true=y_true, y_pred=y_pred, y_pred_proba=y_pred_proba, threshold=threshold, lower_threshold_limit=0, - components=[('RMSLE', 'rmsle')], + components=[("RMSLE", "rmsle")], ) # sampling error @@ -462,7 +547,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: self._sampling_error_components = rmsle_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -472,12 +557,15 @@ def _fit(self, reference_data: pd.DataFrame): def _calculate(self, data: pd.DataFrame): """Redefine to handle NaNs and edge cases.""" _list_missing([self.y_true, self.y_pred], list(data.columns)) - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"No data or too many missing values, cannot calculate {self.display_name}. " f"Returning NaN." + f"No data or too many missing values, cannot calculate {self.display_name}. " + f"Returning NaN." ) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -486,14 +574,26 @@ def _calculate(self, data: pd.DataFrame): _raise_exception_for_negative_values(y_true) _raise_exception_for_negative_values(y_pred) - return mean_squared_log_error(y_true, y_pred, squared=False) + # Deal with breaking API change in sklearn 1.4 + # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_log_error.html + try: + from sklearn.metrics import root_mean_squared_log_error + + return root_mean_squared_log_error(y_true, y_pred) + except ImportError: + from sklearn.metrics import mean_squared_log_error + + return mean_squared_log_error(y_true, y_pred, squared=False) def _sampling_error(self, data: pd.DataFrame) -> float: - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: warnings.warn( - f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." + f"Too many missing values, cannot calculate {self.display_name} sampling error. " + "Returning NaN." ) - return np.NaN + return np.nan else: return rmsle_sampling_error(self._sampling_error_components, data) diff --git a/nannyml/performance_estimation/confidence_based/metrics.py b/nannyml/performance_estimation/confidence_based/metrics.py index ad29f953..94eb702a 100644 --- a/nannyml/performance_estimation/confidence_based/metrics.py +++ b/nannyml/performance_estimation/confidence_based/metrics.py @@ -271,14 +271,14 @@ def get_chunk_record(self, chunk_data: pd.DataFrame) -> Dict: chunk_record[f'alert_{column_name}'] = self.alert(estimated_metric_value) except Exception as exc: self._logger.error(f"an unexpected error occurred while calculating metric {self.display_name}: {exc}") - chunk_record[f'estimated_{column_name}'] = np.NaN - chunk_record[f'sampling_error_{column_name}'] = np.NaN - chunk_record[f'realized_{column_name}'] = np.NaN - chunk_record[f'upper_confidence_boundary_{column_name}'] = np.NaN - chunk_record[f'lower_confidence_boundary_{column_name}'] = np.NaN - chunk_record[f'upper_threshold_{column_name}'] = np.NaN - chunk_record[f'lower_threshold_{column_name}'] = np.NaN - chunk_record[f'alert_{column_name}'] = np.NaN + chunk_record[f'estimated_{column_name}'] = np.nan + chunk_record[f'sampling_error_{column_name}'] = np.nan + chunk_record[f'realized_{column_name}'] = np.nan + chunk_record[f'upper_confidence_boundary_{column_name}'] = np.nan + chunk_record[f'lower_confidence_boundary_{column_name}'] = np.nan + chunk_record[f'upper_threshold_{column_name}'] = np.nan + chunk_record[f'lower_threshold_{column_name}'] = np.nan + chunk_record[f'alert_{column_name}'] = np.nan finally: return chunk_record @@ -371,7 +371,7 @@ def _fit(self, reference_data: pd.DataFrame): data = reference_data[[self.y_true, self.y_pred_proba]] data, empty = common_nan_removal(data, [self.y_true, self.y_pred_proba]) if empty: - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.auroc_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -384,7 +384,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -395,7 +395,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred_proba = data[self.y_pred_proba] uncalibrated_y_pred_proba = data[self.uncalibrated_y_pred_proba] @@ -407,7 +407,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -417,7 +417,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] uncalibrated_y_pred_proba = data[self.uncalibrated_y_pred_proba] @@ -426,7 +426,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: warnings.warn( f"'{self.y_true}' contains a single class for chunk, " f"cannot compute realized {self.display_name}." ) - return np.NaN + return np.nan return roc_auc_score(y_true, uncalibrated_y_pred_proba) def _sampling_error(self, data: pd.DataFrame) -> float: @@ -436,7 +436,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.auroc_sampling_error(self._sampling_error_components, data) @@ -524,7 +524,7 @@ def _fit(self, reference_data: pd.DataFrame): if 1 not in y_true.unique(): self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.ap_sampling_error_components( y_true_reference=y_true, @@ -537,7 +537,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -548,7 +548,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan calibrated_y_pred_proba = data[self.y_pred_proba].to_numpy() uncalibrated_y_pred_proba = data[self.uncalibrated_y_pred_proba].to_numpy() @@ -560,7 +560,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -577,7 +577,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: f"'{self.y_true}' does not contain positive class for chunk, cannot calculate {self.display_name}. " f"Returning NaN." ) - return np.NaN + return np.nan else: return average_precision_score(y_true, uncalibrated_y_pred_proba) @@ -588,7 +588,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.ap_sampling_error(self._sampling_error_components, data) @@ -684,7 +684,7 @@ def _fit(self, reference_data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.f1_sampling_error_components( y_true_reference=y_true, @@ -697,7 +697,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -705,7 +705,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -718,7 +718,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.f1_sampling_error(self._sampling_error_components, data) @@ -728,7 +728,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -736,7 +736,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -746,14 +746,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float: f"Too few unique values present in '{self.y_true}', " f"returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan if y_pred.nunique() <= 1: warnings.warn( f"Too few unique values present in '{self.y_pred}', " f"returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan # TODO: zero_division should be np.nan # update when we update sklearn to 1.3+ and remove unnecessary checks. return f1_score(y_true=y_true, y_pred=y_pred, zero_division='warn') @@ -830,7 +830,7 @@ def _fit(self, reference_data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.precision_sampling_error_components( y_true_reference=y_true, @@ -843,7 +843,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -851,7 +851,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -864,7 +864,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.precision_sampling_error(self._sampling_error_components, data) @@ -874,7 +874,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -882,7 +882,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -892,14 +892,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float: f"Too few unique values present in '{self.y_true}', " f"returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan if y_pred.nunique() <= 1: warnings.warn( f"Too few unique values present in '{self.y_pred}', " f"returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan # TODO: zero_division should be np.nan # update when we update sklearn to 1.3+ and remove unnecessary checks. return precision_score(y_true=y_true, y_pred=y_pred, zero_division='warn') @@ -975,7 +975,7 @@ def _fit(self, reference_data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.recall_sampling_error_components( y_true_reference=y_true, @@ -988,7 +988,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -996,7 +996,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1009,7 +1009,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.recall_sampling_error(self._sampling_error_components, data) @@ -1019,7 +1019,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1027,7 +1027,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1037,14 +1037,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float: f"Too few unique values present in '{self.y_true}', " f"returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan if y_pred.nunique() <= 1: warnings.warn( f"Too few unique values present in '{self.y_pred}', " f"returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan # TODO: zero_division should be np.nan # update when we update sklearn to 1.3+ and remove unnecessary checks. return recall_score(y_true=y_true, y_pred=y_pred, zero_division='warn') @@ -1123,7 +1123,7 @@ def _fit(self, reference_data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.specificity_sampling_error_components( y_true_reference=y_true, @@ -1136,7 +1136,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1144,7 +1144,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1157,7 +1157,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.specificity_sampling_error(self._sampling_error_components, data) @@ -1167,7 +1167,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1175,14 +1175,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=self._labels).ravel() denominator = tn + fp if denominator == 0: - return np.NaN + return np.nan else: return tn / denominator @@ -1257,7 +1257,7 @@ def _fit(self, reference_data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, 0 + self._sampling_error_components = np.nan, 0 else: self._sampling_error_components = bse.accuracy_sampling_error_components( y_true_reference=y_true, @@ -1270,7 +1270,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1278,7 +1278,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1291,7 +1291,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.accuracy_sampling_error(self._sampling_error_components, data) @@ -1301,7 +1301,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1309,7 +1309,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1433,10 +1433,10 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._true_positive_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix - self._true_negative_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix - self._false_positive_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix - self._false_negative_sampling_error_components = np.NaN, 0.0, self.normalize_confusion_matrix + self._true_positive_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix + self._true_negative_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix + self._false_positive_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix + self._false_negative_sampling_error_components = np.nan, 0.0, self.normalize_confusion_matrix else: self._true_positive_sampling_error_components = bse.true_positive_sampling_error_components( y_true_reference=reference_data[self.y_true], @@ -1529,13 +1529,13 @@ def _true_positive_realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate true_positives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1551,13 +1551,13 @@ def _true_negative_realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate true_negatives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1573,13 +1573,13 @@ def _false_positive_realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate false_positives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1595,13 +1595,13 @@ def _false_negative_realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: warnings.warn("Too many missing values, cannot calculate false_negatives. " "Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -1629,7 +1629,7 @@ def get_true_positive_estimate(self, chunk_data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1637,7 +1637,7 @@ def get_true_positive_estimate(self, chunk_data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1686,7 +1686,7 @@ def get_true_negative_estimate(self, chunk_data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1694,7 +1694,7 @@ def get_true_negative_estimate(self, chunk_data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1743,7 +1743,7 @@ def get_false_positive_estimate(self, chunk_data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1751,7 +1751,7 @@ def get_false_positive_estimate(self, chunk_data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1800,7 +1800,7 @@ def get_false_negative_estimate(self, chunk_data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -1808,7 +1808,7 @@ def get_false_negative_estimate(self, chunk_data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -1865,7 +1865,7 @@ def get_true_pos_info(self, chunk_data: pd.DataFrame) -> Dict: ) if empty: warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.") - sampling_error_true_positives = np.NaN + sampling_error_true_positives = np.nan else: sampling_error_true_positives = bse.true_positive_sampling_error( self._true_positive_sampling_error_components, chunk_data @@ -1925,7 +1925,7 @@ def get_true_neg_info(self, chunk_data: pd.DataFrame) -> Dict: ) if empty: warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.") - sampling_error_true_negatives = np.NaN + sampling_error_true_negatives = np.nan else: sampling_error_true_negatives = bse.true_negative_sampling_error( self._true_negative_sampling_error_components, chunk_data @@ -1985,7 +1985,7 @@ def get_false_pos_info(self, chunk_data: pd.DataFrame) -> Dict: ) if empty: warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.") - sampling_error_false_positives = np.NaN + sampling_error_false_positives = np.nan else: sampling_error_false_positives = bse.false_positive_sampling_error( self._false_positive_sampling_error_components, chunk_data @@ -2045,7 +2045,7 @@ def get_false_neg_info(self, chunk_data: pd.DataFrame) -> Dict: ) if empty: warnings.warn("Too many missing values, cannot calculate true positive sampling error. " "Returning NaN.") - sampling_error_false_negatives = np.NaN + sampling_error_false_negatives = np.nan else: sampling_error_false_negatives = bse.false_negative_sampling_error( self._false_negative_sampling_error_components, chunk_data @@ -2183,7 +2183,7 @@ def _fit(self, reference_data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute fit {self.display_name}.") warnings.warn(f"Not enough data to compute fit {self.display_name}.") - self._sampling_error_components = np.NaN, self.normalize_business_value + self._sampling_error_components = np.nan, self.normalize_business_value else: self._sampling_error_components = bse.business_value_sampling_error_components( y_true_reference=y_true, @@ -2198,7 +2198,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2206,7 +2206,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute realized {self.display_name}.") warnings.warn(f"Not enough data to compute realized {self.display_name}.") - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] @@ -2230,7 +2230,7 @@ def _estimate(self, chunk_data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2238,7 +2238,7 @@ def _estimate(self, chunk_data: pd.DataFrame) -> float: if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_pred = data[self.y_pred] y_pred_proba = data[self.y_pred_proba] @@ -2255,7 +2255,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " "Returning NaN." ) - return np.NaN + return np.nan else: return bse.business_value_sampling_error(self._sampling_error_components, data) @@ -2381,7 +2381,7 @@ def _fit(self, reference_data: pd.DataFrame): [self.y_true] + self.class_uncalibrated_y_pred_proba_columns, ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clasz in self.classes] + self._sampling_error_components = [(np.nan, 0) for clasz in self.classes] else: # test if reference data are represented correctly observed_classes = set(reference_data[self.y_true].unique()) @@ -2407,7 +2407,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2415,7 +2415,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan _, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) _, y_pred_probas_uncalibrated, _ = _get_multiclass_uncalibrated_predictions( @@ -2442,7 +2442,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. Returning NaN." ) - return np.NaN + return np.nan else: return mse.auroc_sampling_error(self._sampling_error_components, data) @@ -2452,14 +2452,14 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data, [self.y_true] + self.class_uncalibrated_y_pred_proba_columns) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if set(y_true.unique()) != set(self.classes): @@ -2469,7 +2469,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: ) warnings.warn(_message) self._logger.warning(_message) - return np.NaN + return np.nan _, y_pred_probas, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) @@ -2515,7 +2515,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: label_binarizer = LabelBinarizer() binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T) @@ -2532,7 +2532,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2540,7 +2540,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) ovr_estimates = [] @@ -2559,7 +2559,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return mse.f1_sampling_error(self._sampling_error_components, data) @@ -2569,23 +2569,23 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data, [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if y_true.nunique() <= 1: warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.") - return np.NaN + return np.nan if data[self.y_pred].nunique() <= 1: warnings.warn("Too few unique values present in 'y_pred', returning NaN as realized F1 score.") - return np.NaN + return np.nan y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) @@ -2631,7 +2631,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: label_binarizer = LabelBinarizer() binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T) @@ -2648,7 +2648,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2656,7 +2656,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) ovr_estimates = [] @@ -2675,7 +2675,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return mse.precision_sampling_error(self._sampling_error_components, data) @@ -2685,25 +2685,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data, [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if y_true.nunique() <= 1: warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.") - return np.NaN + return np.nan if data[self.y_pred].nunique() <= 1: warnings.warn( f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) return precision_score(y_true=y_true, y_pred=y_pred, average='macro', labels=labels) @@ -2748,7 +2748,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: label_binarizer = LabelBinarizer() binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T) @@ -2765,7 +2765,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2773,7 +2773,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) ovr_estimates = [] @@ -2791,7 +2791,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return mse.recall_sampling_error(self._sampling_error_components, data) @@ -2801,25 +2801,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data, [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if y_true.nunique() <= 1: warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.") - return np.NaN + return np.nan if data[self.y_pred].nunique() <= 1: warnings.warn( f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) @@ -2865,7 +2865,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in classes] + self._sampling_error_components = [(np.nan, 0) for clazz in classes] else: label_binarizer = LabelBinarizer() binarized_y_true = list(label_binarizer.fit_transform(reference_data[self.y_true]).T) @@ -2882,7 +2882,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -2890,7 +2890,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) ovr_estimates = [] @@ -2909,7 +2909,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return mse.specificity_sampling_error(self._sampling_error_components, data) @@ -2919,25 +2919,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data, [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if y_true.nunique() <= 1: warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.") - return np.NaN + return np.nan if data[self.y_pred].nunique() <= 1: warnings.warn( f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan y_pred, _, labels = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) @@ -2986,7 +2986,7 @@ def _fit(self, reference_data: pd.DataFrame): reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] ) if empty: - self._sampling_error_components = (np.NaN,) + self._sampling_error_components = (np.nan,) else: label_binarizer = LabelBinarizer() binarized_y_true = label_binarizer.fit_transform(reference_data[self.y_true]) @@ -3004,7 +3004,7 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex @@ -3012,7 +3012,7 @@ def _estimate(self, data: pd.DataFrame): if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan y_preds, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) y_preds_array = np.asarray(y_preds).T @@ -3029,7 +3029,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return mse.accuracy_sampling_error(self._sampling_error_components, data) @@ -3039,25 +3039,25 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data, [self.y_true, self.y_pred]) if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if y_true.nunique() <= 1: warnings.warn(f"Too few unique values present in 'y_true', returning NaN as realized {self.display_name}.") - return np.NaN + return np.nan if data[self.y_pred].nunique() <= 1: warnings.warn( f"Too few unique values present in 'y_pred', returning NaN as realized {self.display_name} score." ) - return np.NaN + return np.nan y_pred, _, _ = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) return accuracy_score(y_true, y_pred) @@ -3430,7 +3430,7 @@ def _fit(self, reference_data: pd.DataFrame): [self.y_true] + self.class_uncalibrated_y_pred_proba_columns, ) if empty: - self._sampling_error_components = [(np.NaN, 0) for clazz in self.classes] + self._sampling_error_components = [(np.nan, 0) for clazz in self.classes] else: # sampling error binarized_y_true = list(label_binarize(reference_data[self.y_true], classes=self.classes).T) @@ -3446,13 +3446,13 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "not all present in provided data columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex if empty: self._logger.debug(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan _, y_pred_probas, _ = _get_binarized_multiclass_predictions(data, self.y_pred, self.y_pred_proba) _, y_pred_probas_uncalibrated, _ = _get_multiclass_uncalibrated_predictions( @@ -3479,7 +3479,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: warnings.warn( f"Too many missing values, cannot calculate {self.display_name} sampling error. " f"Returning NaN." ) - return np.NaN + return np.nan else: return mse.average_precision_sampling_error(self._sampling_error_components, data) @@ -3489,17 +3489,17 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "not all present in provided data columns" in str(ex): self._logger.debug(str(ex)) - return np.NaN + return np.nan else: raise ex if empty: warnings.warn(f"Too many missing values, cannot calculate {self.display_name}. " f"Returning NaN.") - return np.NaN + return np.nan y_true = data[self.y_true] if y_true.nunique() <= 1: warnings.warn("Too few unique values present in 'y_true', returning NaN as realized AP.") - return np.NaN + return np.nan _, y_pred_probas, _ = _get_multiclass_uncalibrated_predictions(data, self.y_pred, self.y_pred_proba) @@ -3567,7 +3567,7 @@ def _fit(self, reference_data: pd.DataFrame): _list_missing([self.y_true, self.y_pred], list(reference_data.columns)) data, empty = common_nan_removal(reference_data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) if empty: - self._sampling_error_components = np.NaN, self.normalize_business_value + self._sampling_error_components = np.nan, self.normalize_business_value else: num_classes = len(self.classes) if num_classes != self.business_value_matrix.shape[0]: @@ -3592,14 +3592,14 @@ def _estimate(self, data: pd.DataFrame): except InvalidArgumentsException as ex: if "not all present in provided data columns" in str(ex): self._logger.warning(str(ex)) - return np.NaN + return np.nan else: raise ex if empty: self._logger.warning(f"Not enough data to compute estimated {self.display_name}.") warnings.warn(f"Not enough data to compute estimated {self.display_name}.") - return np.NaN + return np.nan # TODO: put in a function? Also for MC CM. y_pred_proba = {key: data[value] for key, value in self.y_pred_proba.items()} @@ -3632,7 +3632,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: _message = f"Too many missing values, cannot calculate {self.display_name} sampling error. Returning NaN." self._logger.warning(_message) warnings.warn(_message) - return np.NaN + return np.nan else: return mse.business_value_sampling_error(self._sampling_error_components, data) @@ -3642,7 +3642,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: except InvalidArgumentsException as ex: if "missing required columns" in str(ex): self._logger.info(str(ex)) - return np.NaN + return np.nan else: raise ex data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) @@ -3650,7 +3650,7 @@ def _realized_performance(self, data: pd.DataFrame) -> float: _message = f"'{self.y_true}' contains no data, cannot calculate business value. Returning NaN." self._logger.info(_message) warnings.warn(_message) - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] diff --git a/nannyml/performance_estimation/direct_loss_estimation/dle.py b/nannyml/performance_estimation/direct_loss_estimation/dle.py index 9458d858..07941046 100644 --- a/nannyml/performance_estimation/direct_loss_estimation/dle.py +++ b/nannyml/performance_estimation/direct_loss_estimation/dle.py @@ -294,10 +294,10 @@ def _fit(self, reference_data: pd.DataFrame, *args, **kwargs) -> Self: reference_data[categorical_feature_column] = reference_data[categorical_feature_column].astype("object") reference_data[categorical_feature_column] = self._categorical_imputer.fit_transform( reference_data[categorical_feature_column].values.reshape(-1, 1) - ) + ).ravel() reference_data[categorical_feature_column] = self._categorical_encoders[ categorical_feature_column - ].fit_transform(reference_data[categorical_feature_column].values.reshape(-1, 1)) + ].fit_transform(reference_data[categorical_feature_column].values.reshape(-1, 1)).ravel() # LGBM treats -1 for categorical features as missing # https://lightgbm.readthedocs.io/en/latest/Advanced-Topics.html#categorical-feature-support # Ordinal encoder encodes from 0 to n-1. @@ -329,10 +329,10 @@ def _estimate(self, data: pd.DataFrame, *args, **kwargs) -> Result: data[categorical_feature_column] = data[categorical_feature_column].astype("object") data[categorical_feature_column] = self._categorical_imputer.transform( data[categorical_feature_column].values.reshape(-1, 1) - ) + ).ravel() data[categorical_feature_column] = self._categorical_encoders[categorical_feature_column].transform( data[categorical_feature_column].values.reshape(-1, 1) - ) + ).ravel() # LGBM treats -1 for categorical features as missing # https://lightgbm.readthedocs.io/en/latest/Advanced-Topics.html#categorical-feature-support # Ordinal encoder encodes from 0 to n-1. @@ -406,14 +406,14 @@ def _estimate_chunk(self, chunk: Chunk) -> Dict: self._logger.error( f"an unexpected error occurred while calculating metric {metric.display_name}: {exc}" ) - estimates[f'sampling_error_{metric.column_name}'] = np.NaN - estimates[f'realized_{metric.column_name}'] = np.NaN - estimates[f'estimated_{metric.column_name}'] = np.NaN - estimates[f'upper_confidence_{metric.column_name}'] = np.NaN - estimates[f'lower_confidence_{metric.column_name}'] = np.NaN + estimates[f'sampling_error_{metric.column_name}'] = np.nan + estimates[f'realized_{metric.column_name}'] = np.nan + estimates[f'estimated_{metric.column_name}'] = np.nan + estimates[f'upper_confidence_{metric.column_name}'] = np.nan + estimates[f'lower_confidence_{metric.column_name}'] = np.nan estimates[f'upper_threshold_{metric.column_name}'] = metric.upper_threshold_value estimates[f'lower_threshold_{metric.column_name}'] = metric.lower_threshold_value - estimates[f'alert_{metric.column_name}'] = np.NaN + estimates[f'alert_{metric.column_name}'] = np.nan return estimates diff --git a/nannyml/performance_estimation/direct_loss_estimation/metrics.py b/nannyml/performance_estimation/direct_loss_estimation/metrics.py index 6d041599..046e6caf 100644 --- a/nannyml/performance_estimation/direct_loss_estimation/metrics.py +++ b/nannyml/performance_estimation/direct_loss_estimation/metrics.py @@ -12,6 +12,7 @@ :class:`~nannyml.performance_estimation.confidence_based.metrics.Metric` instances to fit them on reference data and run the estimation on analysis data. """ + import abc import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Type @@ -169,7 +170,9 @@ def fit(self, reference_data: pd.DataFrame): # Calculate alert thresholds reference_chunks = self.chunker.split(reference_data) - self.lower_threshold_value, self.upper_threshold_value = self._alert_thresholds(reference_chunks) + self.lower_threshold_value, self.upper_threshold_value = self._alert_thresholds( + reference_chunks + ) # Delegate to subclass self._fit(reference_data) @@ -222,8 +225,12 @@ def _sampling_error(self, data: pd.DataFrame) -> float: f"'{self.__class__.__name__}' is a subclass of Metric and it must implement the _sampling_error method" ) - def _alert_thresholds(self, reference_chunks: List[Chunk]) -> Tuple[Optional[float], Optional[float]]: - realized_chunk_performance = np.asarray([self.realized_performance(chunk.data) for chunk in reference_chunks]) + def _alert_thresholds( + self, reference_chunks: List[Chunk] + ) -> Tuple[Optional[float], Optional[float]]: + realized_chunk_performance = np.asarray( + [self.realized_performance(chunk.data) for chunk in reference_chunks] + ) lower_threshold_value, upper_threshold_value = calculate_threshold_values( threshold=self.threshold, data=realized_chunk_performance, @@ -247,8 +254,12 @@ def alert(self, value: float) -> bool: ------- bool: bool """ - return (self.lower_threshold_value is not None and value < self.lower_threshold_value) or ( - self.upper_threshold_value is not None and value > self.upper_threshold_value + return ( + self.lower_threshold_value is not None + and value < self.lower_threshold_value + ) or ( + self.upper_threshold_value is not None + and value > self.upper_threshold_value ) @abc.abstractmethod @@ -268,7 +279,10 @@ def realized_performance(self, data: pd.DataFrame) -> float: def __eq__(self, other): """Establishes equality by comparing all properties.""" - return self.display_name == other.display_name and self.column_name == other.column_name + return ( + self.display_name == other.display_name + and self.column_name == other.column_name + ) def _train_direct_error_estimation_model( self, @@ -287,14 +301,24 @@ def _train_direct_error_estimation_model( model.fit(X_train, y_train, categorical_feature=categorical_column_names) elif tune_hyperparameters: self._logger.debug( - f"'tune_hyperparameters' set to '{tune_hyperparameters}': " f"performing hyperparameter tuning" + f"'tune_hyperparameters' set to '{tune_hyperparameters}': " + f"performing hyperparameter tuning" + ) + self._logger.debug( + "'hyperparameters' not set: using default hyperparameters" + ) + self._logger.debug( + f"hyperparameter tuning configuration: {hyperparameter_tuning_config}" ) - self._logger.debug("'hyperparameters' not set: using default hyperparameters") - self._logger.debug(f'hyperparameter tuning configuration: {hyperparameter_tuning_config}') automl = AutoML() # TODO: is this correct? // categorical_feature - automl.fit(X_train, y_train, **hyperparameter_tuning_config, categorical_feature=categorical_column_names) + automl.fit( + X_train, + y_train, + **hyperparameter_tuning_config, + categorical_feature=categorical_column_names, + ) self.hyperparameters = {**automl.model.estimator.get_params()} model = LGBMRegressor(**automl.model.estimator.get_params()) model.fit(X_train, y_train, categorical_feature=categorical_column_names) @@ -330,7 +354,8 @@ def create(cls, key: str, problem_type: ProblemType, **kwargs) -> Metric: """ if not isinstance(key, str): raise InvalidArgumentsException( - f"cannot create metric given a '{type(key)}'" "Please provide a string, function or Metric" + f"cannot create metric given a '{type(key)}'" + "Please provide a string, function or Metric" ) if key not in cls.registry: @@ -356,7 +381,8 @@ def inner_wrapper(wrapped_class: Type[Metric]) -> Type[Metric]: if metric in cls.registry: if problem_type in cls.registry[metric]: cls._logger().warning( - f"re-registering Metric for metric='{metric}' " f"and problem_type='{problem_type}'" + f"re-registering Metric for metric='{metric}' " + f"and problem_type='{problem_type}'" ) cls.registry[metric][problem_type] = wrapped_class else: @@ -366,7 +392,7 @@ def inner_wrapper(wrapped_class: Type[Metric]) -> Type[Metric]: return inner_wrapper -@MetricFactory.register('mae', ProblemType.REGRESSION) +@MetricFactory.register("mae", ProblemType.REGRESSION) class MAE(Metric): """Estimate regression performance using Mean Absolute Error metric.""" @@ -425,8 +451,8 @@ def __init__( The Threshold instance that determines how the lower and upper threshold values will be calculated. """ super().__init__( - display_name='MAE', - column_name='mae', + display_name="MAE", + column_name="mae", feature_column_names=feature_column_names, y_true=y_true, y_pred=y_pred, @@ -439,7 +465,9 @@ def __init__( def _fit(self, reference_data: pd.DataFrame): # filter nans here - reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred]) + reference_data, empty = common_nan_removal( + reference_data, [self.y_true, self.y_pred] + ) if empty: raise InvalidReferenceDataException( f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets." @@ -464,7 +492,9 @@ def _fit(self, reference_data: pd.DataFrame): ) def _estimate(self, data: pd.DataFrame): - observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]]) + observation_level_estimates = self._dee_model.predict( + X=data[self.feature_column_names + [self.y_pred]] + ) # clip negative predictions to 0 observation_level_estimates = np.maximum(0, observation_level_estimates) chunk_level_estimate = np.mean(observation_level_estimates) @@ -474,7 +504,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: # we only expect predictions to be present and estimate sampling error based on them data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred]) if empty: - return np.NaN + return np.nan else: return mae_sampling_error(self._sampling_error_components, data) @@ -494,17 +524,19 @@ def realized_performance(self, data: pd.DataFrame) -> float: Mean Absolute Error """ if self.y_true not in data.columns: - return np.NaN - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + return np.nan + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] return mean_absolute_error(y_true, y_pred) -@MetricFactory.register('mape', ProblemType.REGRESSION) +@MetricFactory.register("mape", ProblemType.REGRESSION) class MAPE(Metric): """Estimate regression performance using Mean Absolute Percentage Error metric.""" @@ -563,8 +595,8 @@ def __init__( The Threshold instance that determines how the lower and upper threshold values will be calculated. """ super().__init__( - display_name='MAPE', - column_name='mape', + display_name="MAPE", + column_name="mape", feature_column_names=feature_column_names, y_true=y_true, y_pred=y_pred, @@ -577,7 +609,9 @@ def __init__( def _fit(self, reference_data: pd.DataFrame): # filter nans here - reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred]) + reference_data, empty = common_nan_removal( + reference_data, [self.y_true, self.y_pred] + ) if empty: raise InvalidReferenceDataException( f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets." @@ -591,7 +625,9 @@ def _fit(self, reference_data: pd.DataFrame): ) epsilon = np.finfo(np.float64).eps - observation_level_metric = abs(y_true - y_pred) / (np.maximum(epsilon, abs(y_true))) + observation_level_metric = abs(y_true - y_pred) / ( + np.maximum(epsilon, abs(y_true)) + ) self._dee_model = self._train_direct_error_estimation_model( X_train=reference_data[self.feature_column_names + [self.y_pred]], @@ -603,7 +639,9 @@ def _fit(self, reference_data: pd.DataFrame): ) def _estimate(self, data: pd.DataFrame): - observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]]) + observation_level_estimates = self._dee_model.predict( + X=data[self.feature_column_names + [self.y_pred]] + ) # clip negative predictions to 0 observation_level_estimates = np.maximum(0, observation_level_estimates) chunk_level_estimate = np.mean(observation_level_estimates) @@ -613,7 +651,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: # we only expect predictions to be present and estimate sampling error based on them data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred]) if empty: - return np.NaN + return np.nan else: return mape_sampling_error(self._sampling_error_components, data) @@ -633,17 +671,19 @@ def realized_performance(self, data: pd.DataFrame) -> float: Mean Absolute Percentage Error """ if self.y_true not in data.columns: - return np.NaN - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + return np.nan + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] return mean_absolute_percentage_error(y_true, y_pred) -@MetricFactory.register('mse', ProblemType.REGRESSION) +@MetricFactory.register("mse", ProblemType.REGRESSION) class MSE(Metric): """Estimate regression performance using Mean Squared Error metric.""" @@ -702,8 +742,8 @@ def __init__( The Threshold instance that determines how the lower and upper threshold values will be calculated. """ super().__init__( - display_name='MSE', - column_name='mse', + display_name="MSE", + column_name="mse", feature_column_names=feature_column_names, y_true=y_true, y_pred=y_pred, @@ -716,7 +756,9 @@ def __init__( def _fit(self, reference_data: pd.DataFrame): # filter nans here - reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred]) + reference_data, empty = common_nan_removal( + reference_data, [self.y_true, self.y_pred] + ) if empty: raise InvalidReferenceDataException( f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets." @@ -741,7 +783,9 @@ def _fit(self, reference_data: pd.DataFrame): ) def _estimate(self, data: pd.DataFrame): - observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]]) + observation_level_estimates = self._dee_model.predict( + X=data[self.feature_column_names + [self.y_pred]] + ) # clip negative predictions to 0 observation_level_estimates = np.maximum(0, observation_level_estimates) chunk_level_estimate = np.mean(observation_level_estimates) @@ -751,7 +795,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: # we only expect predictions to be present and estimate sampling error based on them data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred]) if empty: - return np.NaN + return np.nan else: return mse_sampling_error(self._sampling_error_components, data) @@ -771,16 +815,18 @@ def realized_performance(self, data: pd.DataFrame) -> float: Mean Squared Error """ if self.y_true not in data.columns: - return np.NaN - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + return np.nan + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] return mean_squared_error(y_true, y_pred) -@MetricFactory.register('msle', ProblemType.REGRESSION) +@MetricFactory.register("msle", ProblemType.REGRESSION) class MSLE(Metric): """Estimate regression performance using Mean Squared Logarithmic Error metric.""" @@ -839,8 +885,8 @@ def __init__( The Threshold instance that determines how the lower and upper threshold values will be calculated. """ super().__init__( - display_name='MSLE', - column_name='msle', + display_name="MSLE", + column_name="msle", feature_column_names=feature_column_names, y_true=y_true, y_pred=y_pred, @@ -853,7 +899,9 @@ def __init__( def _fit(self, reference_data: pd.DataFrame): # filter nans here - reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred]) + reference_data, empty = common_nan_removal( + reference_data, [self.y_true, self.y_pred] + ) if empty: raise InvalidReferenceDataException( f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets." @@ -880,7 +928,9 @@ def _fit(self, reference_data: pd.DataFrame): ) def _estimate(self, data: pd.DataFrame): - observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]]) + observation_level_estimates = self._dee_model.predict( + X=data[self.feature_column_names + [self.y_pred]] + ) # clip negative predictions to 0 observation_level_estimates = np.maximum(0, observation_level_estimates) chunk_level_estimate = np.mean(observation_level_estimates) @@ -890,7 +940,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: # we only expect predictions to be present and estimate sampling error based on them data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred]) if empty: - return np.NaN + return np.nan else: return msle_sampling_error(self._sampling_error_components, data) @@ -914,16 +964,18 @@ def realized_performance(self, data: pd.DataFrame) -> float: Mean Squared Log Error """ if self.y_true not in data.columns: - return np.NaN - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + return np.nan + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] return mean_squared_log_error(y_true, y_pred) -@MetricFactory.register('rmse', ProblemType.REGRESSION) +@MetricFactory.register("rmse", ProblemType.REGRESSION) class RMSE(Metric): """Estimate regression performance using Root Mean Squared Error metric.""" @@ -982,8 +1034,8 @@ def __init__( The Threshold instance that determines how the lower and upper threshold values will be calculated. """ super().__init__( - display_name='RMSE', - column_name='rmse', + display_name="RMSE", + column_name="rmse", feature_column_names=feature_column_names, y_true=y_true, y_pred=y_pred, @@ -996,7 +1048,9 @@ def __init__( def _fit(self, reference_data: pd.DataFrame): # filter nans here - reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred]) + reference_data, empty = common_nan_removal( + reference_data, [self.y_true, self.y_pred] + ) if empty: raise InvalidReferenceDataException( f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets." @@ -1021,7 +1075,9 @@ def _fit(self, reference_data: pd.DataFrame): ) def _estimate(self, data: pd.DataFrame): - observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]]) + observation_level_estimates = self._dee_model.predict( + X=data[self.feature_column_names + [self.y_pred]] + ) # clip negative predictions to 0 observation_level_estimates = np.maximum(0, observation_level_estimates) chunk_level_estimate = np.sqrt(np.mean(observation_level_estimates)) @@ -1031,7 +1087,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: # we only expect predictions to be present and estimate sampling error based on them data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred]) if empty: - return np.NaN + return np.nan else: return rmse_sampling_error(self._sampling_error_components, data) @@ -1051,16 +1107,28 @@ def realized_performance(self, data: pd.DataFrame) -> float: Root Mean Squared Error """ if self.y_true not in data.columns: - return np.NaN - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + return np.nan + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] - return mean_squared_error(y_true, y_pred, squared=False) + # Deal with breaking API change in sklearn 1.4 + # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_error.html + try: + from sklearn.metrics import root_mean_squared_error -@MetricFactory.register('rmsle', ProblemType.REGRESSION) + return root_mean_squared_error(y_true, y_pred) + except ImportError: + from sklearn.metrics import mean_squared_error + + return np.sqrt(mean_squared_error(y_true, y_pred, squared=False)) + + +@MetricFactory.register("rmsle", ProblemType.REGRESSION) class RMSLE(Metric): """Estimate regression performance using Root Mean Squared Logarithmic Error metric.""" @@ -1119,8 +1187,8 @@ def __init__( The Threshold instance that determines how the lower and upper threshold values will be calculated. """ super().__init__( - display_name='RMSLE', - column_name='rmsle', + display_name="RMSLE", + column_name="rmsle", feature_column_names=feature_column_names, y_true=y_true, y_pred=y_pred, @@ -1133,7 +1201,9 @@ def __init__( def _fit(self, reference_data: pd.DataFrame): # filter nans here - reference_data, empty = common_nan_removal(reference_data, [self.y_true, self.y_pred]) + reference_data, empty = common_nan_removal( + reference_data, [self.y_true, self.y_pred] + ) if empty: raise InvalidReferenceDataException( f"Cannot fit DLE for {self.display_name}, too many missing values for predictions and targets." @@ -1161,7 +1231,9 @@ def _fit(self, reference_data: pd.DataFrame): ) def _estimate(self, data: pd.DataFrame): - observation_level_estimates = self._dee_model.predict(X=data[self.feature_column_names + [self.y_pred]]) + observation_level_estimates = self._dee_model.predict( + X=data[self.feature_column_names + [self.y_pred]] + ) # clip negative predictions to 0 observation_level_estimates = np.maximum(0, observation_level_estimates) chunk_level_estimate = np.sqrt(np.mean(observation_level_estimates)) @@ -1171,7 +1243,7 @@ def _sampling_error(self, data: pd.DataFrame) -> float: # we only expect predictions to be present and estimate sampling error based on them data, empty = common_nan_removal(data[[self.y_pred]], [self.y_pred]) if empty: - return np.NaN + return np.nan else: return rmsle_sampling_error(self._sampling_error_components, data) @@ -1195,14 +1267,25 @@ def realized_performance(self, data: pd.DataFrame) -> float: Root Mean Squared Log Error """ if self.y_true not in data.columns: - return np.NaN - data, empty = common_nan_removal(data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred]) + return np.nan + data, empty = common_nan_removal( + data[[self.y_true, self.y_pred]], [self.y_true, self.y_pred] + ) if empty: - return np.NaN + return np.nan y_true = data[self.y_true] y_pred = data[self.y_pred] _raise_exception_for_negative_values(y_true) _raise_exception_for_negative_values(y_pred) - return mean_squared_log_error(y_true, y_pred, squared=False) + # Deal with breaking API change in sklearn 1.4 + # https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.root_mean_squared_log_error.html + try: + from sklearn.metrics import root_mean_squared_log_error + + return root_mean_squared_log_error(y_true, y_pred) + except ImportError: + from sklearn.metrics import mean_squared_log_error + + return np.sqrt(mean_squared_log_error(y_true, y_pred, squared=False)) diff --git a/nannyml/plots/components/stacked_bar_plot.py b/nannyml/plots/components/stacked_bar_plot.py index 9d0d8bd0..7ec377db 100644 --- a/nannyml/plots/components/stacked_bar_plot.py +++ b/nannyml/plots/components/stacked_bar_plot.py @@ -50,22 +50,23 @@ def calculate_value_counts( # TODO: deal with None timestamps if isinstance(timestamps, pd.Series): timestamps = timestamps.reset_index() - data_with_chunk_keys = pd.concat( - [ - chunk.data.assign(chunk_key=chunk.key, chunk_index=chunk.chunk_index) - for chunk in chunker.split(pd.concat([pd.Series(categorical_data, name=column_name), timestamps], axis=1)) - ] - ) + + chunks = chunker.split(pd.concat([pd.Series(categorical_data, name=column_name), timestamps], axis=1)) + data_with_chunk_keys = pd.concat([chunk.data.assign(chunk_index=chunk.chunk_index) for chunk in chunks]) + + chunk_keys_lookup = {chunk.chunk_index: chunk.key for chunk in chunks} value_counts_table = ( - data_with_chunk_keys.groupby(['chunk_key', 'chunk_index'])[column_name] + data_with_chunk_keys.groupby(['chunk_index'])[column_name] .value_counts() .to_frame('value_counts') .sort_values(by=['chunk_index', 'value_counts']) .reset_index() - .rename(columns={'level_2': column_name, 'chunk_index': 'chunk_indices'}) + .rename(columns={'chunk_index': 'chunk_indices'}) ) + value_counts_table['chunk_key'] = value_counts_table['chunk_indices'].map(lambda i: chunk_keys_lookup[i]) + value_counts_table['value_counts_total'] = value_counts_table['chunk_key'].map( value_counts_table.groupby('chunk_key')['value_counts'].sum() ) diff --git a/nannyml/stats/avg/calculator.py b/nannyml/stats/avg/calculator.py index e4d892a7..60538634 100644 --- a/nannyml/stats/avg/calculator.py +++ b/nannyml/stats/avg/calculator.py @@ -191,10 +191,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st self._logger.error( f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}" ) - result['value'] = np.NaN - result['sampling_error'] = np.NaN - result['upper_confidence_boundary'] = np.NaN - result['lower_confidence_boundary'] = np.NaN + result['value'] = np.nan + result['sampling_error'] = np.nan + result['upper_confidence_boundary'] = np.nan + result['lower_confidence_boundary'] = np.nan finally: return result diff --git a/nannyml/stats/median/calculator.py b/nannyml/stats/median/calculator.py index e8175bc6..9aaf63c3 100644 --- a/nannyml/stats/median/calculator.py +++ b/nannyml/stats/median/calculator.py @@ -199,10 +199,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st self._logger.error( f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}" ) - result['value'] = np.NaN - result['sampling_error'] = np.NaN - result['upper_confidence_boundary'] = np.NaN - result['lower_confidence_boundary'] = np.NaN + result['value'] = np.nan + result['sampling_error'] = np.nan + result['upper_confidence_boundary'] = np.nan + result['lower_confidence_boundary'] = np.nan finally: return result diff --git a/nannyml/stats/std/calculator.py b/nannyml/stats/std/calculator.py index 7d09152a..d248aa1c 100644 --- a/nannyml/stats/std/calculator.py +++ b/nannyml/stats/std/calculator.py @@ -201,10 +201,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st self._logger.error( f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}" ) - result['value'] = np.NaN - result['sampling_error'] = np.NaN - result['upper_confidence_boundary'] = np.NaN - result['lower_confidence_boundary'] = np.NaN + result['value'] = np.nan + result['sampling_error'] = np.nan + result['upper_confidence_boundary'] = np.nan + result['lower_confidence_boundary'] = np.nan finally: return result diff --git a/nannyml/stats/sum/calculator.py b/nannyml/stats/sum/calculator.py index b9d1b8a5..8c36095d 100644 --- a/nannyml/stats/sum/calculator.py +++ b/nannyml/stats/sum/calculator.py @@ -190,10 +190,10 @@ def _calculate_for_column(self, data: pd.DataFrame, column_name: str) -> Dict[st self._logger.error( f"an unexpected exception occurred during calculation of column '{column_name}': " f"{exc}" ) - result['value'] = np.NaN - result['sampling_error'] = np.NaN - result['upper_confidence_boundary'] = np.NaN - result['lower_confidence_boundary'] = np.NaN + result['value'] = np.nan + result['sampling_error'] = np.nan + result['upper_confidence_boundary'] = np.nan + result['lower_confidence_boundary'] = np.nan finally: return result diff --git a/poetry.lock b/poetry.lock index 434336bf..96c2f0e9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,25 +1,25 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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types-requests = "^2.31.0.3" +types-pyyaml = "^6.0.12.8" +types-python-dateutil = "^2.8.19.10" [tool.black] diff --git a/setup.cfg b/setup.cfg index 1dbb7944..2a0e3c5b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,7 +19,7 @@ exclude = .git, .github, # By default test codes will be linted. # tests -min_python_version = 3.8.0 +min_python_version = 3.9.0 [doc8] ignore-path = docs/_build/, nannyml/nannyml.egg-info/, .*/ @@ -49,14 +49,14 @@ exclude_lines = [tox:tox] isolated_build = true -envlist = py38, py39, py310, py311, format, lint, build +envlist = py39, py310, py311, py312, format, lint, build [gh-actions] python = - 3.11: py311 + 3.12: py312 + 3.11: py311, format, lint, build 3.10: py310 3.9: py39 - 3.8: py38, format, lint, build [testenv] allowlist_externals = pytest diff --git a/tests/drift/test_drift.py b/tests/drift/test_drift.py index b066a84c..68e6588b 100644 --- a/tests/drift/test_drift.py +++ b/tests/drift/test_drift.py @@ -115,8 +115,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame: # noqa: D10 data['id'] = data.index nan_pick1 = set(data.id.sample(frac=0.11, random_state=13)) nan_pick2 = set(data.id.sample(frac=0.11, random_state=14)) - data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN - data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN + data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan + data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan data.drop(columns=['id'], inplace=True) return data @@ -908,6 +908,7 @@ def test_input_dataframes_are_not_altered_by_dre_calculator(): # noqa: D103 pd.testing.assert_frame_equal(reference, reference2) +@pytest.mark.skip("too slow") def test_input_dataframes_are_not_altered_by_dc_calculator(): # noqa: D103 reference, monitored, _ = load_synthetic_car_loan_dataset() reference2 = reference.copy(deep=True) diff --git a/tests/drift/test_multiv_pca.py b/tests/drift/test_multiv_pca.py index 1e4f594f..1905d49c 100644 --- a/tests/drift/test_multiv_pca.py +++ b/tests/drift/test_multiv_pca.py @@ -108,8 +108,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame: # noqa: D10 data['id'] = data.index nan_pick1 = set(data.id.sample(frac=0.11, random_state=13)) nan_pick2 = set(data.id.sample(frac=0.11, random_state=14)) - data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN - data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN + data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan + data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan data.drop(columns=['id'], inplace=True) return data diff --git a/tests/drift/test_output_drift.py b/tests/drift/test_output_drift.py index 0ff6b638..c5ffd8e0 100644 --- a/tests/drift/test_output_drift.py +++ b/tests/drift/test_output_drift.py @@ -107,8 +107,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame: # noqa: D10 data['id'] = data.index nan_pick1 = set(data.id.sample(frac=0.11, random_state=13)) nan_pick2 = set(data.id.sample(frac=0.11, random_state=14)) - data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN - data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN + data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan + data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan data.drop(columns=['id'], inplace=True) return data diff --git a/tests/drift/test_target_distribution.py b/tests/drift/test_target_distribution.py index 643bd8a8..2fb1724d 100644 --- a/tests/drift/test_target_distribution.py +++ b/tests/drift/test_target_distribution.py @@ -116,8 +116,8 @@ def sample_drift_data_with_nans(sample_drift_data) -> pd.DataFrame: # noqa: D10 data['id'] = data.index nan_pick1 = set(data.id.sample(frac=0.11, random_state=13)) nan_pick2 = set(data.id.sample(frac=0.11, random_state=14)) - data.loc[data.id.isin(nan_pick1), 'f1'] = np.NaN - data.loc[data.id.isin(nan_pick2), 'f4'] = np.NaN + data.loc[data.id.isin(nan_pick1), 'f1'] = np.nan + data.loc[data.id.isin(nan_pick2), 'f4'] = np.nan data.drop(columns=['id'], inplace=True) return data diff --git a/tests/performance_calculation/metrics/test_binary_classification.py b/tests/performance_calculation/metrics/test_binary_classification.py index 30281a3b..5a939446 100644 --- a/tests/performance_calculation/metrics/test_binary_classification.py +++ b/tests/performance_calculation/metrics/test_binary_classification.py @@ -262,17 +262,17 @@ def test_metric_values_without_timestamp_are_calculated_correctly( # noqa: D103 @pytest.mark.parametrize( 'metric, expected', [ - ('roc_auc', [0.97096, 0.97025, 0.97628, 0.96772, 0.96989, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('f1', [0.92186, 0.92124, 0.92678, 0.91684, 0.92356, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('precision', [0.96729, 0.96607, 0.96858, 0.96819, 0.9661, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('recall', [0.88051, 0.88039, 0.88843, 0.87067, 0.8846, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('specificity', [0.9681, 0.9701, 0.97277, 0.9718, 0.96864, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('accuracy', [0.9228, 0.926, 0.9318, 0.9216, 0.9264, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('business_value', [775, 710, 655, 895, 670, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('true_positive', [2277, 2164, 2158, 2161, 2223, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('false_positive', [77, 76, 70, 71, 78, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('true_negative', [2337, 2466, 2501, 2447, 2409, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), - ('false_negative', [309, 294, 271, 321, 290, np.NaN, np.NaN, np.NaN, np.NaN, np.NaN]), + ('roc_auc', [0.97096, 0.97025, 0.97628, 0.96772, 0.96989, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('f1', [0.92186, 0.92124, 0.92678, 0.91684, 0.92356, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('precision', [0.96729, 0.96607, 0.96858, 0.96819, 0.9661, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('recall', [0.88051, 0.88039, 0.88843, 0.87067, 0.8846, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('specificity', [0.9681, 0.9701, 0.97277, 0.9718, 0.96864, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('accuracy', [0.9228, 0.926, 0.9318, 0.9216, 0.9264, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('business_value', [775, 710, 655, 895, 670, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('true_positive', [2277, 2164, 2158, 2161, 2223, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('false_positive', [77, 76, 70, 71, 78, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('true_negative', [2337, 2466, 2501, 2447, 2409, np.nan, np.nan, np.nan, np.nan, np.nan]), + ('false_negative', [309, 294, 271, 321, 290, np.nan, np.nan, np.nan, np.nan, np.nan]), ], ) def test_metric_values_with_partial_targets_are_calculated_correctly( # noqa: D103 diff --git a/tests/performance_calculation/test_performance_calculator.py b/tests/performance_calculation/test_performance_calculator.py index 1726d9af..7e2d8b74 100644 --- a/tests/performance_calculation/test_performance_calculator.py +++ b/tests/performance_calculation/test_performance_calculator.py @@ -214,10 +214,10 @@ def test_calculator_calculate_should_include_target_completeness_rate(data): # data = data[1].merge(data[2], on='id') # Drop 10% of the target values in the first chunk - data.loc[0:499, 'work_home_actual'] = np.NAN + data.loc[0:499, 'work_home_actual'] = np.nan # Drop 90% of the target values in the second chunk - data.loc[5000:9499, 'work_home_actual'] = np.NAN + data.loc[5000:9499, 'work_home_actual'] = np.nan calc = PerformanceCalculator( timestamp_column_name='timestamp', @@ -248,7 +248,7 @@ def test_calculator_calculate_should_support_partial_bool_targets(data, performa analysis_data = analysis_data.astype({'work_home_actual': 'bool'}) # Drop 10% of the target values in the first chunk - analysis_data.loc[0:499, 'work_home_actual'] = np.NAN + analysis_data.loc[0:499, 'work_home_actual'] = np.nan performance_calculator.fit(reference_data=ref_data) performance_calculator.calculate(analysis_data) diff --git a/tests/performance_estimation/CBPE/test_cbpe.py b/tests/performance_estimation/CBPE/test_cbpe.py index 5044f15d..7f720b24 100644 --- a/tests/performance_estimation/CBPE/test_cbpe.py +++ b/tests/performance_estimation/CBPE/test_cbpe.py @@ -3,6 +3,7 @@ # License: Apache Software License 2.0 """Unit testing for CBPE.""" + import re import typing from typing import Tuple @@ -18,7 +19,10 @@ load_synthetic_multiclass_classification_dataset, ) from nannyml.exceptions import InvalidArgumentsException -from nannyml.performance_estimation.confidence_based.cbpe import CBPE, DEFAULT_THRESHOLDS +from nannyml.performance_estimation.confidence_based.cbpe import ( + CBPE, + DEFAULT_THRESHOLDS, +) from nannyml.performance_estimation.confidence_based.results import Result from nannyml.thresholds import ConstantThreshold @@ -26,7 +30,7 @@ @pytest.fixture def binary_classification_data() -> Tuple[pd.DataFrame, pd.DataFrame]: # noqa: D103 ref_df, ana_df, _ = load_synthetic_binary_classification_dataset() - ref_df['y_pred'] = ref_df['y_pred_proba'].apply(lambda p: int(p >= 0.8)) + ref_df["y_pred"] = ref_df["y_pred_proba"].apply(lambda p: int(p >= 0.8)) return ref_df, ana_df @@ -40,32 +44,35 @@ def multiclass_classification_data() -> Tuple[pd.DataFrame, pd.DataFrame]: # no def estimates(binary_classification_data) -> Result: # noqa: D103 reference, analysis = binary_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) estimator.fit(reference) return estimator.estimate(pd.concat([reference, analysis])) # type: ignore -@pytest.mark.parametrize('metrics, expected', [('roc_auc', ['roc_auc']), (['roc_auc', 'f1'], ['roc_auc', 'f1'])]) +@pytest.mark.parametrize( + "metrics, expected", + [("roc_auc", ["roc_auc"]), (["roc_auc", "f1"], ["roc_auc", "f1"])], +) def test_cbpe_create_with_single_or_list_of_metrics(metrics, expected): # noqa: D103 sut = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", metrics=metrics, - problem_type='classification_binary', + problem_type="classification_binary", ) assert [metric.name for metric in sut.metrics] == expected @pytest.mark.parametrize( - 'problem', + "problem", [ "classification_multiclass", "regression", @@ -74,58 +81,63 @@ def test_cbpe_create_with_single_or_list_of_metrics(metrics, expected): # noqa: def test_cbpe_create_raises_exception_when_y_pred_not_given_and_problem_type_not_binary_classification( problem, ): # noqa: D103, E501 - with pytest.raises(InvalidArgumentsException, match=f"'y_pred' can not be 'None' for problem type {problem}"): + with pytest.raises( + InvalidArgumentsException, + match=f"'y_pred' can not be 'None' for problem type {problem}", + ): _ = CBPE( - timestamp_column_name='timestamp', - y_pred_proba='y_pred_proba', - y_true='y_true', - metrics=['roc_auc', 'f1'], + timestamp_column_name="timestamp", + y_pred_proba="y_pred_proba", + y_true="y_true", + metrics=["roc_auc", "f1"], problem_type=problem, ) @pytest.mark.parametrize( - 'metric, expected', + "metric, expected", [ - (['roc_auc', 'f1'], "['f1']"), - (['roc_auc', 'f1', 'average_precision', 'precision'], "['f1', 'precision']"), + (["roc_auc", "f1"], "['f1']"), + (["roc_auc", "f1", "average_precision", "precision"], "['f1', 'precision']"), ], ) -def test_cbpe_create_without_y_pred_raises_exception_when_metrics_require_it(metric, expected): # noqa: D103 +def test_cbpe_create_without_y_pred_raises_exception_when_metrics_require_it( + metric, expected +): # noqa: D103 with pytest.raises(InvalidArgumentsException, match=expected): _ = CBPE( - timestamp_column_name='timestamp', - y_pred_proba='y_pred_proba', - y_true='y_true', + timestamp_column_name="timestamp", + y_pred_proba="y_pred_proba", + y_true="y_true", metrics=metric, - problem_type='classification_binary', + problem_type="classification_binary", ) -@pytest.mark.parametrize('metric', ['roc_auc', 'average_precision']) +@pytest.mark.parametrize("metric", ["roc_auc", "average_precision"]) def test_cbpe_create_without_y_pred_works_when_metrics_dont_require_it(metric): # noqa: D103 try: _ = CBPE( - timestamp_column_name='timestamp', - y_pred_proba='y_pred_proba', - y_true='y_true', + timestamp_column_name="timestamp", + y_pred_proba="y_pred_proba", + y_true="y_true", metrics=metric, - problem_type='classification_binary', + problem_type="classification_binary", ) except Exception as exc: - pytest.fail(f'unexpected exception: {exc}') + pytest.fail(f"unexpected exception: {exc}") def test_cbpe_will_calibrate_scores_when_needed(binary_classification_data): # noqa: D103 ref_df = binary_classification_data[0] sut = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) sut.fit(ref_df) @@ -135,15 +147,15 @@ def test_cbpe_will_calibrate_scores_when_needed(binary_classification_data): # def test_cbpe_will_not_calibrate_scores_when_not_needed(binary_classification_data): # noqa: D103 ref_df = binary_classification_data[0] # If predictions equal targets no calibration will be required - ref_df['y_pred_proba'] = ref_df['work_home_actual'] + ref_df["y_pred_proba"] = ref_df["work_home_actual"] sut = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) sut.fit(ref_df) @@ -154,98 +166,109 @@ def test_cbpe_will_not_fail_on_work_from_home_sample(binary_classification_data) reference, analysis = binary_classification_data try: estimator = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) estimator.fit(reference) _ = estimator.estimate(analysis) except Exception as exc: - pytest.fail(f'unexpected exception was raised: {exc}') + pytest.fail(f"unexpected exception was raised: {exc}") def test_cbpe_raises_invalid_arguments_exception_when_giving_invalid_metric_value(): # noqa: D103 - with pytest.raises(InvalidArgumentsException, match="unknown metric key 'foo' given."): + with pytest.raises( + InvalidArgumentsException, match="unknown metric key 'foo' given." + ): _ = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc', 'foo'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc", "foo"], + problem_type="classification_binary", ) def test_cbpe_raises_invalid_arguments_exception_when_given_empty_metrics_list(): # noqa: D103 with pytest.raises( - InvalidArgumentsException, match="no metrics provided. Please provide a non-empty list of metrics." + InvalidArgumentsException, + match="no metrics provided. Please provide a non-empty list of metrics.", ): _ = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", metrics=[], - problem_type='classification_binary', + problem_type="classification_binary", ) def test_cbpe_raises_invalid_arguments_exception_when_given_none_metrics_list(): # noqa: D103 with pytest.raises( - InvalidArgumentsException, match="no metrics provided. Please provide a non-empty list of metrics." + InvalidArgumentsException, + match="no metrics provided. Please provide a non-empty list of metrics.", ): _ = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", metrics=None, - problem_type='classification_binary', + problem_type="classification_binary", ) def test_cbpe_raises_value_error_when_business_value_matrix_wrong_shape(): # noqa: D103 with pytest.raises( - ValueError, match=re.escape("business_value_matrix must have shape (2,2), but got matrix of shape (4,)") + ValueError, + match=re.escape( + "business_value_matrix must have shape (2,2), but got matrix of shape (4,)" + ), ): _ = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['business_value'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["business_value"], + problem_type="classification_binary", business_value_matrix=[1, 2, 3, 4], ) def test_cbpe_raises_value_error_when_business_value_matrix_wrong_type(): # noqa: D103 with pytest.raises( - ValueError, match="business_value_matrix must be a numpy array or a list, but got <class 'str'>" + ValueError, + match="business_value_matrix must be a numpy array or a list, but got <class 'str'>", ): _ = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['business_value'], - problem_type='classification_binary', - business_value_matrix='[1,2,3,4]', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["business_value"], + problem_type="classification_binary", + business_value_matrix="[1,2,3,4]", ) def test_cbpe_raises_value_error_when_business_value_matrix_not_given(): # noqa: D103 - with pytest.raises(ValueError, match="business_value_matrix must be provided for 'business_value' metric"): + with pytest.raises( + ValueError, + match="business_value_matrix must be provided for 'business_value' metric", + ): _ = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['business_value'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["business_value"], + problem_type="classification_binary", ) @@ -254,25 +277,25 @@ def test_cbpe_raises_missing_metadata_exception_when_predictions_are_required_bu ): reference, _ = binary_classification_data estimator = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='predictions', - y_pred_proba='y_pred_proba', - metrics=['f1'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="predictions", + y_pred_proba="y_pred_proba", + metrics=["f1"], + problem_type="classification_binary", ) # requires predictions! - with pytest.raises(InvalidArgumentsException, match='predictions'): + with pytest.raises(InvalidArgumentsException, match="predictions"): estimator.fit(reference) def test_cbpe_defaults_to_isotonic_calibrator_when_none_given(): # noqa: D103 estimator = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['f1'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["f1"], + problem_type="classification_binary", ) assert isinstance(estimator.calibrator, IsotonicCalibrator) @@ -286,13 +309,13 @@ def calibrate(self, y_pred_proba: np.ndarray, *args, **kwargs): pass estimator = CBPE( - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], calibrator=TestCalibrator(), - problem_type='classification_binary', + problem_type="classification_binary", ) assert isinstance(estimator.calibrator, TestCalibrator) @@ -304,17 +327,17 @@ def test_cbpe_uses_calibrator_to_calibrate_predicted_probabilities_when_needed( calibrator = IsotonicCalibrator() estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], calibrator=calibrator, - problem_type='classification_binary', + problem_type="classification_binary", ).fit(reference) assert typing.cast(CBPE, estimator).needs_calibration - spy = mocker.spy(calibrator, 'calibrate') + spy = mocker.spy(calibrator, "calibrate") estimator.estimate(analysis) spy.assert_called_once() @@ -327,18 +350,20 @@ def test_cbpe_doesnt_use_calibrator_to_calibrate_predicted_probabilities_when_no calibrator = IsotonicCalibrator() estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], calibrator=calibrator, - problem_type='classification_binary', + problem_type="classification_binary", ).fit(reference) - typing.cast(CBPE, estimator).needs_calibration = False # Override this to disable calibration + typing.cast( + CBPE, estimator + ).needs_calibration = False # Override this to disable calibration - spy = mocker.spy(calibrator, 'calibrate') + spy = mocker.spy(calibrator, "calibrate") estimator.estimate(analysis) spy.assert_not_called() @@ -349,71 +374,77 @@ def test_cbpe_raises_missing_metadata_exception_when_predicted_probabilities_are reference, _ = binary_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='probabilities', - metrics=['roc_auc'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="probabilities", + metrics=["roc_auc"], + problem_type="classification_binary", ) - with pytest.raises(InvalidArgumentsException, match='probabilities'): + with pytest.raises(InvalidArgumentsException, match="probabilities"): estimator.fit(reference) -@pytest.mark.parametrize('metric', ['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy']) +@pytest.mark.parametrize( + "metric", ["roc_auc", "f1", "precision", "recall", "specificity", "accuracy"] +) def test_cbpe_runs_for_all_metrics(binary_classification_data, metric): # noqa: D103 reference, analysis = binary_classification_data try: estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", metrics=[metric], - problem_type='classification_binary', + problem_type="classification_binary", ).fit(reference) _ = estimator.estimate(pd.concat([reference, analysis])) except Exception as e: - pytest.fail(f'an unexpected exception occurred: {e}') + pytest.fail(f"an unexpected exception occurred: {e}") -def test_cbpe_results_plot_raises_invalid_arguments_exception_given_invalid_plot_kind(estimates): # noqa: D103 +def test_cbpe_results_plot_raises_invalid_arguments_exception_given_invalid_plot_kind( + estimates, +): # noqa: D103 with pytest.raises(InvalidArgumentsException): - estimates.plot(kind="foo", metric='roc_auc') + estimates.plot(kind="foo", metric="roc_auc") -@pytest.mark.parametrize('metric', ['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy']) +@pytest.mark.parametrize( + "metric", ["roc_auc", "f1", "precision", "recall", "specificity", "accuracy"] +) def test_cbpe_for_binary_classification_does_not_fail_when_fitting_with_subset_of_reference_data( # noqa: D103 binary_classification_data, metric ): reference = binary_classification_data[0].loc[40000:, :] estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc", "f1", "precision", "recall", "specificity", "accuracy"], + problem_type="classification_binary", ) try: estimator.fit(reference_data=reference) except KeyError: pytest.fail( - 'fitting on subset resulted in KeyError => misaligned indices between data and stratified shuffle' - 'split results.' + "fitting on subset resulted in KeyError => misaligned indices between data and stratified shuffle" + "split results." ) def reduce_confidence_bounds(monkeypatch, estimator, results): # noqa: D103 - min_confidence = results.data[('roc_auc', 'lower_confidence_boundary')].min() - max_confidence = results.data[('roc_auc', 'upper_confidence_boundary')].max() + min_confidence = results.data[("roc_auc", "lower_confidence_boundary")].min() + max_confidence = results.data[("roc_auc", "upper_confidence_boundary")].max() new_lower_bound = min_confidence + 0.001 new_upper_bound = max_confidence - 0.001 for metric in estimator.metrics: - monkeypatch.setattr(metric, 'lower_threshold_value_limit', new_lower_bound) - monkeypatch.setattr(metric, 'upper_threshold_value_limit', new_upper_bound) + monkeypatch.setattr(metric, "lower_threshold_value_limit", new_lower_bound) + monkeypatch.setattr(metric, "upper_threshold_value_limit", new_upper_bound) return estimator, new_lower_bound, new_upper_bound @@ -423,21 +454,23 @@ def test_cbpe_for_binary_classification_does_not_output_confidence_bounds_outsid ): reference, analysis = binary_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ).fit(reference) results = estimator.estimate(pd.concat([reference, analysis])) - estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds(monkeypatch, estimator, results) + estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds( + monkeypatch, estimator, results + ) # manually remove previous 'analysis' results - results.data = results.data[results.data[('chunk', 'period')] == 'reference'] + results.data = results.data[results.data[("chunk", "period")] == "reference"] results = estimator.estimate(analysis) - sut = results.filter(period='analysis').to_df() - assert all(sut.loc[:, ('roc_auc', 'lower_confidence_boundary')] >= new_lower_bound) - assert all(sut.loc[:, ('roc_auc', 'upper_confidence_boundary')] <= new_upper_bound) + sut = results.filter(period="analysis").to_df() + assert all(sut.loc[:, ("roc_auc", "lower_confidence_boundary")] >= new_lower_bound) + assert all(sut.loc[:, ("roc_auc", "upper_confidence_boundary")] <= new_upper_bound) def test_cbpe_for_multiclass_classification_does_not_output_confidence_bounds_outside_appropriate_interval( # noqa: D103, E501 @@ -445,24 +478,26 @@ def test_cbpe_for_multiclass_classification_does_not_output_confidence_bounds_ou ): reference, analysis = multiclass_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='y_true', - y_pred='y_pred', + timestamp_column_name="timestamp", + y_true="y_true", + y_pred="y_pred", y_pred_proba={ - 'prepaid_card': 'y_pred_proba_prepaid_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'upmarket_card': 'y_pred_proba_upmarket_card', + "prepaid_card": "y_pred_proba_prepaid_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "upmarket_card": "y_pred_proba_upmarket_card", }, - metrics=['roc_auc'], - problem_type='classification_multiclass', + metrics=["roc_auc"], + problem_type="classification_multiclass", ).fit(reference) results = estimator.estimate(pd.concat([reference, analysis])) - estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds(monkeypatch, estimator, results) - results.data = results.filter(period='reference').to_df() + estimator, new_lower_bound, new_upper_bound = reduce_confidence_bounds( + monkeypatch, estimator, results + ) + results.data = results.filter(period="reference").to_df() results = estimator.estimate(analysis) - sut = results.filter(period='analysis').to_df() - assert all(sut.loc[:, ('roc_auc', 'lower_confidence_boundary')] >= new_lower_bound) - assert all(sut.loc[:, ('roc_auc', 'upper_confidence_boundary')] <= new_upper_bound) + sut = results.filter(period="analysis").to_df() + assert all(sut.loc[:, ("roc_auc", "lower_confidence_boundary")] >= new_lower_bound) + assert all(sut.loc[:, ("roc_auc", "upper_confidence_boundary")] <= new_upper_bound) def test_cpbe_result_filter_should_preserve_data_with_default_args(estimates): # noqa: D103 @@ -472,26 +507,30 @@ def test_cpbe_result_filter_should_preserve_data_with_default_args(estimates): def test_cpbe_result_filter_metrics(estimates): # noqa: D103 filtered_result = estimates.filter(metrics=["roc_auc"]) - columns = tuple(set(metric for (metric, _) in filtered_result.data.columns if metric != "chunk")) + columns = tuple( + set(metric for (metric, _) in filtered_result.data.columns if metric != "chunk") + ) assert columns == ("roc_auc",) assert filtered_result.data.shape[0] == estimates.data.shape[0] def test_cpbe_result_filter_period(estimates): # noqa: D103 - ref_period = estimates.data.loc[estimates.data.loc[:, ("chunk", "period")] == "reference", :] + ref_period = estimates.data.loc[ + estimates.data.loc[:, ("chunk", "period")] == "reference", : + ] filtered_result = estimates.filter(period="reference") assert filtered_result.data.equals(ref_period) @pytest.mark.parametrize( - 'metric, sampling_error', + "metric, sampling_error", [ - ('roc_auc', 0.001811), - ('f1', 0.007549), - ('precision', 0.003759), - ('recall', 0.006546), - ('specificity', 0.003413), - ('accuracy', 0.003746), + ("roc_auc", 0.001811), + ("f1", 0.007549), + ("precision", 0.003759), + ("recall", 0.006546), + ("specificity", 0.003413), + ("accuracy", 0.003746), ], ) def test_cbpe_for_binary_classification_chunked_by_size_should_include_constant_sampling_error_for_metric( # noqa: D103, E501 @@ -499,31 +538,31 @@ def test_cbpe_for_binary_classification_chunked_by_size_should_include_constant_ ): reference, analysis = binary_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", metrics=[metric], - problem_type='classification_binary', + problem_type="classification_binary", ).fit(reference) results = estimator.estimate(analysis) - assert (metric, 'sampling_error') in results.data.columns + assert (metric, "sampling_error") in results.data.columns assert all( - np.round(results.to_df().loc[:, (metric, 'sampling_error')], 4) + np.round(results.to_df().loc[:, (metric, "sampling_error")], 4) == pd.Series(np.round(sampling_error, 4), index=range(len(results.data))) ) @pytest.mark.parametrize( - 'metric, sampling_error', + "metric, sampling_error", [ - ('roc_auc', [0.001819, 0.001043, 0.001046, 0.001046, 0.040489]), - ('f1', [0.007585, 0.004348, 0.004360, 0.004362, 0.168798]), - ('precision', [0.003777, 0.002165, 0.002171, 0.002172, 0.084046]), - ('recall', [0.006578, 0.003770, 0.003781, 0.003783, 0.146378]), - ('specificity', [0.003430, 0.001966, 0.001971, 0.001972, 0.076324]), - ('accuracy', [0.003764, 0.002158, 0.002164, 0.002165, 0.083769]), + ("roc_auc", [0.001819, 0.001043, 0.001046, 0.001046, 0.040571]), + ("f1", [0.007585, 0.004348, 0.004360, 0.004362, 0.168798]), + ("precision", [0.003777, 0.002165, 0.002171, 0.002172, 0.084046]), + ("recall", [0.006578, 0.003770, 0.003781, 0.003783, 0.146378]), + ("specificity", [0.003430, 0.001966, 0.001971, 0.001972, 0.076324]), + ("accuracy", [0.003764, 0.002158, 0.002164, 0.002165, 0.083769]), ], ) def test_cbpe_for_binary_classification_chunked_by_period_should_include_variable_sampling_error_for_metric( # noqa: D103, E501 @@ -531,29 +570,31 @@ def test_cbpe_for_binary_classification_chunked_by_period_should_include_variabl ): reference, analysis = binary_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', + timestamp_column_name="timestamp", + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", metrics=[metric], - chunk_period='Y', - problem_type='classification_binary', + chunk_period="Y", + problem_type="classification_binary", ).fit(reference) - results = estimator.estimate(analysis).filter(period='analysis') + results = estimator.estimate(analysis).filter(period="analysis") sut = results.to_df() - assert (metric, 'sampling_error') in sut.columns - assert np.array_equal(np.round(sut.loc[:, (metric, 'sampling_error')], 4), np.round(sampling_error, 4)) + assert (metric, "sampling_error") in sut.columns + assert np.array_equal( + np.round(sut.loc[:, (metric, "sampling_error")], 4), np.round(sampling_error, 4) + ) @pytest.mark.parametrize( - 'metric, sampling_error', + "metric, sampling_error", [ - ('roc_auc', 0.002143), - ('f1', 0.005652), - ('precision', 0.005566), - ('recall', 0.005565), - ('specificity', 0.003002), - ('accuracy', 0.005566), + ("roc_auc", 0.002143), + ("f1", 0.005652), + ("precision", 0.005566), + ("recall", 0.005565), + ("specificity", 0.003002), + ("accuracy", 0.005566), ], ) def test_cbpe_for_multiclass_classification_chunked_by_size_should_include_constant_sampling_error_for_metric( # noqa: D103, E501 @@ -561,35 +602,35 @@ def test_cbpe_for_multiclass_classification_chunked_by_size_should_include_const ): reference, analysis = multiclass_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='y_true', - y_pred='y_pred', + timestamp_column_name="timestamp", + y_true="y_true", + y_pred="y_pred", y_pred_proba={ - 'prepaid_card': 'y_pred_proba_prepaid_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'upmarket_card': 'y_pred_proba_upmarket_card', + "prepaid_card": "y_pred_proba_prepaid_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "upmarket_card": "y_pred_proba_upmarket_card", }, metrics=[metric], - problem_type='classification_multiclass', + problem_type="classification_multiclass", ).fit(reference) results = estimator.estimate(analysis) sut = results.to_df() - assert (metric, 'sampling_error') in sut.columns + assert (metric, "sampling_error") in sut.columns assert all( - np.round(sut.loc[:, (metric, 'sampling_error')], 4) + np.round(sut.loc[:, (metric, "sampling_error")], 4) == pd.Series(np.round(sampling_error, 4), index=range(len(sut))) ) @pytest.mark.parametrize( - 'metric, sampling_error', + "metric, sampling_error", [ - ('roc_auc', [0.001379, 0.001353, 0.001371, 0.001339, 0.008100]), - ('f1', [0.003637, 0.003569, 0.003615, 0.003531, 0.021364]), - ('precision', [0.003582, 0.003515, 0.003560, 0.003477, 0.021037]), - ('recall', [0.003581, 0.003514, 0.003559, 0.003476, 0.021033]), - ('specificity', [0.001932, 0.001896, 0.001920, 0.001875, 0.011348]), - ('accuracy', [0.003582, 0.003515, 0.003560, 0.003477, 0.021039]), + ("roc_auc", [0.001379, 0.001353, 0.001371, 0.001339, 0.008100]), + ("f1", [0.003637, 0.003569, 0.003615, 0.003531, 0.021364]), + ("precision", [0.003582, 0.003515, 0.003560, 0.003477, 0.021037]), + ("recall", [0.003581, 0.003514, 0.003559, 0.003476, 0.021033]), + ("specificity", [0.001932, 0.001896, 0.001920, 0.001875, 0.011348]), + ("accuracy", [0.003582, 0.003515, 0.003560, 0.003477, 0.021039]), ], ) def test_cbpe_for_multiclass_classification_chunked_by_period_should_include_variable_sampling_error_for_metric( # noqa: D103, E501 @@ -597,36 +638,40 @@ def test_cbpe_for_multiclass_classification_chunked_by_period_should_include_var ): reference, analysis = multiclass_classification_data estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='y_true', - y_pred='y_pred', + timestamp_column_name="timestamp", + y_true="y_true", + y_pred="y_pred", y_pred_proba={ - 'prepaid_card': 'y_pred_proba_prepaid_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'upmarket_card': 'y_pred_proba_upmarket_card', + "prepaid_card": "y_pred_proba_prepaid_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "upmarket_card": "y_pred_proba_upmarket_card", }, metrics=[metric], - chunk_period='M', - problem_type='classification_multiclass', + chunk_period="M", + problem_type="classification_multiclass", ).fit(reference) results = estimator.estimate(analysis) - sut = results.filter(period='analysis').to_df() + sut = results.filter(period="analysis").to_df() - assert (metric, 'sampling_error') in sut.columns - assert np.array_equal(np.round(sut.loc[:, (metric, 'sampling_error')], 4), np.round(sampling_error, 4)) + assert (metric, "sampling_error") in sut.columns + assert np.array_equal( + np.round(sut.loc[:, (metric, "sampling_error")], 4), np.round(sampling_error, 4) + ) -def test_cbpe_returns_distinct_but_consistent_results_when_reused(binary_classification_data): # noqa: D103 +def test_cbpe_returns_distinct_but_consistent_results_when_reused( + binary_classification_data, +): # noqa: D103 reference, analysis = binary_classification_data sut = CBPE( # timestamp_column_name='timestamp', chunk_size=50_000, - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) sut.fit(reference) result1 = sut.estimate(analysis) @@ -638,17 +683,19 @@ def test_cbpe_returns_distinct_but_consistent_results_when_reused(binary_classif pd.testing.assert_frame_equal(result1.to_df(), result2.to_df()) -def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(binary_classification_data): # noqa: D103 +def test_cbpe_returns_distinct_but_consistent_results_when_data_reused( + binary_classification_data, +): # noqa: D103 reference, analysis = binary_classification_data sut = CBPE( # timestamp_column_name='timestamp', chunk_size=50_000, - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) sut.fit(reference) result1 = sut.estimate(analysis) @@ -656,11 +703,11 @@ def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(binary_cl sut = CBPE( # timestamp_column_name='timestamp', chunk_size=50_000, - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) sut.fit(reference) result2 = sut.estimate(analysis) @@ -670,52 +717,55 @@ def test_cbpe_returns_distinct_but_consistent_results_when_data_reused(binary_cl @pytest.mark.parametrize( - 'custom_thresholds', + "custom_thresholds", [ - {'roc_auc': ConstantThreshold(lower=1, upper=2)}, - {'roc_auc': ConstantThreshold(lower=1, upper=2), 'f1': ConstantThreshold(lower=1, upper=2)}, + {"roc_auc": ConstantThreshold(lower=1, upper=2)}, + { + "roc_auc": ConstantThreshold(lower=1, upper=2), + "f1": ConstantThreshold(lower=1, upper=2), + }, { - 'roc_auc': ConstantThreshold(lower=1, upper=2), - 'f1': ConstantThreshold(lower=1, upper=2), - 'precision': ConstantThreshold(lower=1, upper=2), + "roc_auc": ConstantThreshold(lower=1, upper=2), + "f1": ConstantThreshold(lower=1, upper=2), + "precision": ConstantThreshold(lower=1, upper=2), }, { - 'roc_auc': ConstantThreshold(lower=1, upper=2), - 'f1': ConstantThreshold(lower=1, upper=2), - 'precision': ConstantThreshold(lower=1, upper=2), - 'recall': ConstantThreshold(lower=1, upper=2), + "roc_auc": ConstantThreshold(lower=1, upper=2), + "f1": ConstantThreshold(lower=1, upper=2), + "precision": ConstantThreshold(lower=1, upper=2), + "recall": ConstantThreshold(lower=1, upper=2), }, { - 'roc_auc': ConstantThreshold(lower=1, upper=2), - 'f1': ConstantThreshold(lower=1, upper=2), - 'precision': ConstantThreshold(lower=1, upper=2), - 'recall': ConstantThreshold(lower=1, upper=2), - 'specificity': ConstantThreshold(lower=1, upper=2), + "roc_auc": ConstantThreshold(lower=1, upper=2), + "f1": ConstantThreshold(lower=1, upper=2), + "precision": ConstantThreshold(lower=1, upper=2), + "recall": ConstantThreshold(lower=1, upper=2), + "specificity": ConstantThreshold(lower=1, upper=2), }, { - 'roc_auc': ConstantThreshold(lower=1, upper=2), - 'f1': ConstantThreshold(lower=1, upper=2), - 'precision': ConstantThreshold(lower=1, upper=2), - 'recall': ConstantThreshold(lower=1, upper=2), - 'specificity': ConstantThreshold(lower=1, upper=2), + "roc_auc": ConstantThreshold(lower=1, upper=2), + "f1": ConstantThreshold(lower=1, upper=2), + "precision": ConstantThreshold(lower=1, upper=2), + "recall": ConstantThreshold(lower=1, upper=2), + "specificity": ConstantThreshold(lower=1, upper=2), }, { - 'roc_auc': ConstantThreshold(lower=1, upper=2), - 'f1': ConstantThreshold(lower=1, upper=2), - 'precision': ConstantThreshold(lower=1, upper=2), - 'recall': ConstantThreshold(lower=1, upper=2), - 'specificity': ConstantThreshold(lower=1, upper=2), - 'accuracy': ConstantThreshold(lower=1, upper=2), + "roc_auc": ConstantThreshold(lower=1, upper=2), + "f1": ConstantThreshold(lower=1, upper=2), + "precision": ConstantThreshold(lower=1, upper=2), + "recall": ConstantThreshold(lower=1, upper=2), + "specificity": ConstantThreshold(lower=1, upper=2), + "accuracy": ConstantThreshold(lower=1, upper=2), }, ], ) def test_cbpe_with_custom_thresholds(custom_thresholds): # noqa: D103 est = CBPE( - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", thresholds=custom_thresholds, ) sut = est.thresholds @@ -726,11 +776,11 @@ def test_cbpe_with_custom_thresholds(custom_thresholds): # noqa: D103 def test_cbpe_with_default_thresholds(): # noqa: D103 est = CBPE( - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", ) sut = est.thresholds @@ -741,44 +791,46 @@ def test_cbpe_without_predictions(): # noqa: D103 ref_df, ana_df, _ = load_synthetic_binary_classification_dataset() try: cbpe = CBPE( - y_pred_proba='y_pred_proba', - y_true='work_home_actual', - problem_type='classification_binary', + y_pred_proba="y_pred_proba", + y_true="work_home_actual", + problem_type="classification_binary", metrics=[ - 'roc_auc', - 'average_precision', + "roc_auc", + "average_precision", ], - timestamp_column_name='timestamp', - chunk_period='M', + timestamp_column_name="timestamp", + chunk_period="M", ).fit(ref_df) _ = cbpe.estimate(ana_df) except Exception as exc: - pytest.fail(f'unexpected exception: {exc}') + pytest.fail(f"unexpected exception: {exc}") -@pytest.mark.filterwarnings("ignore:Too few unique values", "ignore:'y_true' contains a single class") +@pytest.mark.filterwarnings( + "ignore:Too few unique values", "ignore:'y_true' contains a single class" +) def test_cbpe_fitting_does_not_generate_error_when_single_class_present(): # noqa: D103 ref_df = pd.DataFrame( { - 'y_true': [0] * 1000, - 'y_pred': [0] * 1000, - 'y_pred_proba': [0.5] * 1000, + "y_true": [0] * 1000, + "y_pred": [0] * 1000, + "y_pred_proba": [0.5] * 1000, } ) sut = CBPE( - y_true='y_true', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - problem_type='classification_binary', + y_true="y_true", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + problem_type="classification_binary", metrics=[ - 'roc_auc', - 'f1', - 'precision', - 'recall', - 'specificity', - 'accuracy', - 'confusion_matrix', - 'business_value', + "roc_auc", + "f1", + "precision", + "recall", + "specificity", + "accuracy", + "confusion_matrix", + "business_value", ], chunk_size=100, business_value_matrix=[[1, -1], [-1, 1]], @@ -786,17 +838,19 @@ def test_cbpe_fitting_does_not_generate_error_when_single_class_present(): # no sut.fit(ref_df) -def test_cbpe_returns_distinct_but_consistent_results_when_reused_noopcal(binary_classification_data): # noqa: D103 +def test_cbpe_returns_distinct_but_consistent_results_when_reused_noopcal( + binary_classification_data, +): # noqa: D103 reference, analysis = binary_classification_data sut = CBPE( # timestamp_column_name='timestamp', chunk_size=50_000, - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc"], + problem_type="classification_binary", calibrator=NoopCalibrator(), ) sut.fit(reference) @@ -806,18 +860,20 @@ def test_cbpe_returns_distinct_but_consistent_results_when_reused_noopcal(binary pd.testing.assert_frame_equal(result1.to_df(), result2.to_df()) -def test_input_dataframes_are_not_altered_by_binary_calculator(binary_classification_data): # noqa: D103 +def test_input_dataframes_are_not_altered_by_binary_calculator( + binary_classification_data, +): # noqa: D103 reference, monitored = binary_classification_data reference2 = reference.copy(deep=True) monitored2 = monitored.copy(deep=True) estimator = CBPE( # timestamp_column_name='timestamp', chunk_size=50_000, - y_true='work_home_actual', - y_pred='y_pred', - y_pred_proba='y_pred_proba', - metrics=['roc_auc', 'f1'], - problem_type='classification_binary', + y_true="work_home_actual", + y_pred="y_pred", + y_pred_proba="y_pred_proba", + metrics=["roc_auc", "f1"], + problem_type="classification_binary", ) estimator.fit(reference2) results = estimator.estimate(monitored2) # noqa: F841 @@ -825,22 +881,24 @@ def test_input_dataframes_are_not_altered_by_binary_calculator(binary_classifica pd.testing.assert_frame_equal(reference, reference2) -def test_input_dataframes_are_not_altered_by_multiclass_calculator(multiclass_classification_data): # noqa: D103 +def test_input_dataframes_are_not_altered_by_multiclass_calculator( + multiclass_classification_data, +): # noqa: D103 reference, monitored = multiclass_classification_data reference2 = reference.copy(deep=True) monitored2 = monitored.copy(deep=True) estimator = CBPE( # type: ignore - timestamp_column_name='timestamp', - y_true='y_true', - y_pred='y_pred', + timestamp_column_name="timestamp", + y_true="y_true", + y_pred="y_pred", y_pred_proba={ - 'prepaid_card': 'y_pred_proba_prepaid_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'upmarket_card': 'y_pred_proba_upmarket_card', + "prepaid_card": "y_pred_proba_prepaid_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "upmarket_card": "y_pred_proba_upmarket_card", }, - metrics=['roc_auc', 'f1'], - chunk_period='M', - problem_type='classification_multiclass', + metrics=["roc_auc", "f1"], + chunk_period="M", + problem_type="classification_multiclass", ) estimator.fit(reference2) results = estimator.estimate(monitored2) # noqa: F841 diff --git a/tests/performance_estimation/CBPE/test_cbpe_metrics.py b/tests/performance_estimation/CBPE/test_cbpe_metrics.py index 23f2f8be..da35f6ee 100644 --- a/tests/performance_estimation/CBPE/test_cbpe_metrics.py +++ b/tests/performance_estimation/CBPE/test_cbpe_metrics.py @@ -1,4 +1,5 @@ """Tests.""" + import re import pandas as pd @@ -29,318 +30,442 @@ @pytest.mark.parametrize( - 'calculator_opts, expected', + "calculator_opts, expected", [ ( { - 'chunker': SizeBasedChunker(chunk_size=20000, incomplete='append'), - 'normalize_confusion_matrix': None, - 'business_value_matrix': [[2, -5], [-10, 10]], - 'normalize_business_value': None, + "chunker": SizeBasedChunker(chunk_size=20000, incomplete="append"), + "normalize_confusion_matrix": None, + "business_value_matrix": [[2, -5], [-10, 10]], + "normalize_business_value": None, }, pd.DataFrame( { - 'key': ['[0:19999]', '[20000:49999]'], - 'estimated_roc_auc': [0.9711057564966745, 0.9636286015592977], - 'estimated_f1': [0.9479079222515973, 0.9278089207836576], - 'estimated_precision': [0.9436121782324026, 0.9197836255005452], - 'estimated_average_precision': [0.9629778663941202, 0.9576117462271682], - 'estimated_recall': [0.9522429574319092, 0.9359754933378336], - 'estimated_specificity': [0.9434949869571513, 0.9123732942949082], - 'estimated_accuracy': [0.9478536003143163, 0.9245926106862006], - 'estimated_true_positive': [9488.96406430504, 14537.180201036117], - 'estimated_true_negative': [9468.107941981285, 13200.5981195499], - 'estimated_false_positive': [567.0359356949585, 1267.8197989638852], - 'estimated_false_negative': [475.8920580187156, 994.4018804500995], - 'estimated_business_value': [106231.75626835103, 155489.88045014057], + "key": ["[0:19999]", "[20000:49999]"], + "estimated_roc_auc": [0.9711057564966745, 0.9636286015592977], + "estimated_f1": [0.9479079222515973, 0.9278089207836576], + "estimated_precision": [0.9436121782324026, 0.9197836255005452], + "estimated_average_precision": [ + 0.9629778663941202, + 0.9576117462271682, + ], + "estimated_recall": [0.9522429574319092, 0.9359754933378336], + "estimated_specificity": [0.9434949869571513, 0.9123732942949082], + "estimated_accuracy": [0.9478536003143163, 0.9245926106862006], + "estimated_true_positive": [9488.96406430504, 14537.180201036117], + "estimated_true_negative": [9468.107941981285, 13200.5981195499], + "estimated_false_positive": [567.0359356949585, 1267.8197989638852], + "estimated_false_negative": [475.8920580187156, 994.4018804500995], + "estimated_business_value": [ + 106231.75626835103, + 155489.88045014057, + ], } ), ), ( { - 'chunker': SizeBasedChunker(chunk_size=20000, incomplete='append'), - 'normalize_confusion_matrix': None, - 'business_value_matrix': [[2, -5], [-10, 10]], - 'normalize_business_value': 'per_prediction', + "chunker": SizeBasedChunker(chunk_size=20000, incomplete="append"), + "normalize_confusion_matrix": None, + "business_value_matrix": [[2, -5], [-10, 10]], + "normalize_business_value": "per_prediction", }, pd.DataFrame( { - 'key': ['[0:19999]', '[20000:49999]'], - 'estimated_roc_auc': [0.9711057564966745, 0.9636286015592977], - 'estimated_f1': [0.9479079222515973, 0.9278089207836576], - 'estimated_precision': [0.9436121782324026, 0.9197836255005452], - 'estimated_average_precision': [0.9629778663941202, 0.9576117462271682], - 'estimated_recall': [0.9522429574319092, 0.9359754933378336], - 'estimated_specificity': [0.9434949869571513, 0.9123732942949082], - 'estimated_accuracy': [0.9478536003143163, 0.9245926106862006], - 'estimated_true_positive': [9488.96406430504, 14537.180201036117], - 'estimated_true_negative': [9468.107941981285, 13200.5981195499], - 'estimated_false_positive': [567.0359356949585, 1267.8197989638852], - 'estimated_false_negative': [475.8920580187156, 994.4018804500995], - 'estimated_business_value': [10.579896199609875, 9.956118766770157], + "key": ["[0:19999]", "[20000:49999]"], + "estimated_roc_auc": [0.9711057564966745, 0.9636286015592977], + "estimated_f1": [0.9479079222515973, 0.9278089207836576], + "estimated_precision": [0.9436121782324026, 0.9197836255005452], + "estimated_average_precision": [ + 0.9629778663941202, + 0.9576117462271682, + ], + "estimated_recall": [0.9522429574319092, 0.9359754933378336], + "estimated_specificity": [0.9434949869571513, 0.9123732942949082], + "estimated_accuracy": [0.9478536003143163, 0.9245926106862006], + "estimated_true_positive": [9488.96406430504, 14537.180201036117], + "estimated_true_negative": [9468.107941981285, 13200.5981195499], + "estimated_false_positive": [567.0359356949585, 1267.8197989638852], + "estimated_false_negative": [475.8920580187156, 994.4018804500995], + "estimated_business_value": [10.579896199609875, 9.956118766770157], } ), ), ( { - 'chunker': SizeBasedChunker(chunk_size=20000, incomplete='append'), - 'normalize_confusion_matrix': 'all', - 'business_value_matrix': [[-1, 4], [8, -8]], + "chunker": SizeBasedChunker(chunk_size=20000, incomplete="append"), + "normalize_confusion_matrix": "all", + "business_value_matrix": [[-1, 4], [8, -8]], }, pd.DataFrame( { - 'key': ['[0:19999]', '[20000:49999]'], - 'estimated_roc_auc': [0.9711057564966745, 0.9636286015592977], - 'estimated_f1': [0.9479079222515973, 0.9278089207836576], - 'estimated_precision': [0.9436121782324026, 0.9197836255005452], - 'estimated_average_precision': [0.9629778663941202, 0.9576117462271682], - 'estimated_recall': [0.9522429574319092, 0.9359754933378336], - 'estimated_specificity': [0.9434949869571513, 0.9123732942949082], - 'estimated_accuracy': [0.9478536003143163, 0.9245926106862006], - 'estimated_true_positive': [0.47444820321525205, 0.4845726733678706], - 'estimated_true_negative': [0.47340539709906426, 0.44001993731833], - 'estimated_false_positive': [0.02835179678474793, 0.04226065996546284], - 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'estimated_false_negative': [0.04775704256809077, 0.06402450666216646], - 'estimated_business_value': [-79304.54024949206, -116471.5454883825], + "key": ["[0:19999]", "[20000:49999]"], + "estimated_roc_auc": [0.9711057564966745, 0.9636286015592977], + "estimated_f1": [0.9479079222515973, 0.9278089207836576], + "estimated_precision": [0.9436121782324026, 0.9197836255005452], + "estimated_average_precision": [ + 0.9629778663941202, + 0.9576117462271682, + ], + "estimated_recall": [0.9522429574319092, 0.9359754933378336], + "estimated_specificity": [0.9434949869571513, 0.9123732942949082], + "estimated_accuracy": [0.9478536003143163, 0.9245926106862006], + "estimated_true_positive": [0.9522429574319092, 0.9359754933378336], + "estimated_true_negative": [0.9434949869571514, 0.9123732942949082], + "estimated_false_positive": [ + 0.05650501304284861, + 0.08762670570509186, + ], + "estimated_false_negative": [ + 0.04775704256809077, + 0.06402450666216646, + ], + "estimated_business_value": [ + -79304.54024949206, + -116471.5454883825, + ], } ), ), ( { - 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'estimated_false_negative': [0.0478572061563471, 0.07005296797816835], - 'estimated_business_value': [-79304.54024949206, -116471.5454883825], + "key": ["[0:19999]", "[20000:49999]"], + "estimated_roc_auc": [0.9711057564966745, 0.9636286015592977], + "estimated_f1": [0.9479079222515973, 0.9278089207836576], + "estimated_precision": [0.9436121782324026, 0.9197836255005452], + "estimated_average_precision": [ + 0.9629778663941202, + 0.9576117462271682, + ], + "estimated_recall": [0.9522429574319092, 0.9359754933378336], + "estimated_specificity": [0.9434949869571513, 0.9123732942949082], + "estimated_accuracy": [0.9478536003143163, 0.9245926106862006], + "estimated_true_positive": [0.9436121782324026, 0.9197836255005452], + "estimated_true_negative": [0.952142793843653, 0.9299470320218318], + "estimated_false_positive": [ + 0.05638782176759731, + 0.08021637449945493, + ], + "estimated_false_negative": [ + 0.0478572061563471, + 0.07005296797816835, + ], + "estimated_business_value": [ + -79304.54024949206, + -116471.5454883825, + ], } ), ), ( { - 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'estimated_recall': [ + "estimated_recall": [ 0.9546437391537488, 0.95007417692678, 0.9534307499357034, @@ -2146,7 +2400,7 @@ 0.9310926704269641, 0.9354943725540698, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.9372742491954283, 0.9446319110387503, 0.9458015988378999, @@ -2158,7 +2412,7 @@ 0.9120143191041326, 0.9005790635739856, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.9462845122805443, 0.947306078326665, 0.949543087716159, @@ -2170,7 +2424,7 @@ 0.9217811573797979, 0.9191618588441066, ], - 'estimated_true_positive': [ + "estimated_true_positive": [ 0.4952126552347996, 0.4668381400876081, 0.46758158297608354, @@ -2182,7 +2436,7 @@ 0.4766570956702526, 0.49789335707836707, ], - 'estimated_true_negative': [ + "estimated_true_negative": [ 0.45107185704574476, 0.4804679382390568, 0.48196150474007543, @@ -2194,7 +2448,7 @@ 0.44512406170954527, 0.42126850176573954, ], - 'estimated_false_positive': [ + "estimated_false_positive": [ 0.03018734476520043, 0.028161859912391913, 0.027618417023916413, @@ -2206,7 +2460,7 @@ 0.042942904329747386, 0.04650664292163292, ], - 'estimated_false_negative': [ + "estimated_false_negative": [ 0.023528142954255284, 0.024532061760943195, 0.022838495259924537, @@ -2218,7 +2472,7 @@ 0.035275938290454646, 0.034331498234260474, ], - 'estimated_business_value': [ + "estimated_business_value": [ -20518.99288114649, -19531.34562601404, -19647.162691868405, @@ -2235,25 +2489,25 @@ ), ( { - 'normalize_confusion_matrix': 'true', - 'timestamp_column_name': 'timestamp', - 'business_value_matrix': [[-1, 4], [8, -8]], + "normalize_confusion_matrix": "true", + "timestamp_column_name": "timestamp", + "business_value_matrix": [[-1, 4], [8, -8]], }, pd.DataFrame( { - 'key': [ - '[0:4999]', - '[5000:9999]', - '[10000:14999]', - '[15000:19999]', - '[20000:24999]', - '[25000:29999]', - '[30000:34999]', - '[35000:39999]', - '[40000:44999]', - '[45000:49999]', - ], - 'estimated_roc_auc': [ + "key": [ + "[0:4999]", + "[5000:9999]", + "[10000:14999]", + "[15000:19999]", + "[20000:24999]", + "[25000:29999]", + "[30000:34999]", + "[35000:39999]", + "[40000:44999]", + "[45000:49999]", + ], + "estimated_roc_auc": [ 0.97074363630015, 0.9710106327368981, 0.9714067155511361, @@ -2265,7 +2519,7 @@ 0.9625334611153133, 0.9613161529631752, ], - 'estimated_f1': [ + "estimated_f1": [ 0.948555321454138, 0.9465779465209089, 0.9488069354531159, @@ -2277,7 +2531,7 @@ 0.9241722368115827, 0.9249152193013231, ], - 'estimated_precision': [ + "estimated_precision": [ 0.9425440716307568, 0.9431073537123396, 0.9442277523749669, @@ -2289,7 +2543,7 @@ 0.9173539177641505, 0.9145726617897999, ], - 'estimated_average_precision': [ + "estimated_average_precision": [ 0.9639571716287229, 0.9624355545496974, 0.9627637624228476, @@ -2301,7 +2555,7 @@ 0.9560927338703065, 0.9569107885377364, ], - 'estimated_recall': [ + "estimated_recall": [ 0.9546437391537488, 0.95007417692678, 0.9534307499357034, @@ -2313,7 +2567,7 @@ 0.9310926704269641, 0.9354943725540698, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.9372742491954283, 0.9446319110387503, 0.9458015988378999, @@ -2325,7 +2579,7 @@ 0.9120143191041326, 0.9005790635739856, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.9462845122805443, 0.947306078326665, 0.949543087716159, @@ -2337,7 +2591,7 @@ 0.9217811573797979, 0.9191618588441066, ], - 'estimated_true_positive': [ + "estimated_true_positive": [ 0.9546437391537488, 0.9500741769267799, 0.9534307499357034, @@ -2349,7 +2603,7 @@ 0.9310926704269642, 0.9354943725540699, ], - 'estimated_true_negative': [ + "estimated_true_negative": [ 0.9372742491954283, 0.9446319110387503, 0.9458015988378999, @@ -2361,7 +2615,7 @@ 0.9120143191041324, 0.9005790635739858, ], - 'estimated_false_positive': [ + "estimated_false_positive": [ 0.0627257508045717, 0.05536808896124974, 0.054198401162100104, @@ -2373,7 +2627,7 @@ 0.08798568089586746, 0.09942093642601435, ], - 'estimated_false_negative': [ + "estimated_false_negative": [ 0.04535626084625111, 0.04992582307322005, 0.04656925006429655, @@ -2385,7 +2639,7 @@ 0.06890732957303593, 0.06450562744593023, ], - 'estimated_business_value': [ + "estimated_business_value": [ -20518.99288114649, -19531.34562601404, -19647.162691868405, @@ -2402,25 +2656,25 @@ ), ( { - 'normalize_confusion_matrix': 'pred', - 'timestamp_column_name': 'timestamp', - 'business_value_matrix': [[-1, 4], [8, -8]], + "normalize_confusion_matrix": "pred", + "timestamp_column_name": "timestamp", + "business_value_matrix": [[-1, 4], [8, -8]], }, pd.DataFrame( { - 'key': [ - '[0:4999]', - '[5000:9999]', - '[10000:14999]', - '[15000:19999]', - '[20000:24999]', - '[25000:29999]', - '[30000:34999]', - '[35000:39999]', - '[40000:44999]', - '[45000:49999]', - ], - 'estimated_roc_auc': [ + "key": [ + "[0:4999]", + "[5000:9999]", + "[10000:14999]", + "[15000:19999]", + "[20000:24999]", + "[25000:29999]", + "[30000:34999]", + "[35000:39999]", + "[40000:44999]", + "[45000:49999]", + ], + "estimated_roc_auc": [ 0.97074363630015, 0.9710106327368981, 0.9714067155511361, @@ -2432,7 +2686,7 @@ 0.9625334611153133, 0.9613161529631752, ], - 'estimated_f1': [ + "estimated_f1": [ 0.948555321454138, 0.9465779465209089, 0.9488069354531159, @@ -2444,7 +2698,7 @@ 0.9241722368115827, 0.9249152193013231, ], - 'estimated_precision': [ + "estimated_precision": [ 0.9425440716307568, 0.9431073537123396, 0.9442277523749669, @@ -2456,7 +2710,7 @@ 0.9173539177641505, 0.9145726617897999, ], - 'estimated_average_precision': [ + "estimated_average_precision": [ 0.9639571716287229, 0.9624355545496974, 0.9627637624228476, @@ -2468,7 +2722,7 @@ 0.9560927338703065, 0.9569107885377364, ], - 'estimated_recall': [ + "estimated_recall": [ 0.9546437391537488, 0.95007417692678, 0.9534307499357034, @@ -2480,7 +2734,7 @@ 0.9310926704269641, 0.9354943725540698, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.9372742491954283, 0.9446319110387503, 0.9458015988378999, @@ -2492,7 +2746,7 @@ 0.9120143191041326, 0.9005790635739856, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.9462845122805443, 0.947306078326665, 0.949543087716159, @@ -2504,7 +2758,7 @@ 0.9217811573797979, 0.9191618588441066, ], - 'estimated_true_positive': [ + "estimated_true_positive": [ 0.9425440716307567, 0.9431073537123397, 0.9442277523749669, @@ -2516,7 +2770,7 @@ 0.9173539177641507, 0.9145726617897999, ], - 'estimated_true_negative': [ + "estimated_true_negative": [ 0.9504253203660866, 0.9514216598793204, 0.9547573390255062, @@ -2528,7 +2782,7 @@ 0.9265696538500111, 0.9246455262636951, ], - 'estimated_false_positive': [ + "estimated_false_positive": [ 0.05745592836924329, 0.056892646287660435, 0.055772247625033154, @@ -2540,7 +2794,7 @@ 0.08264608223584949, 0.08542733821020007, ], - 'estimated_false_negative': [ + "estimated_false_negative": [ 0.04957467963391336, 0.048578340120679596, 0.04524266097449394, @@ -2552,7 +2806,7 @@ 0.07343034614998886, 0.07535447373630481, ], - 'estimated_business_value': [ + "estimated_business_value": [ -20518.99288114649, -19531.34562601404, -19647.162691868405, @@ -2569,138 +2823,174 @@ ), ], ids=[ - 'size_based_without_timestamp_cm_normalization_none_business_norm_none', - 'size_based_without_timestamp_cm_normalization_none_business_norm_per_pred', - 'size_based_without_timestamp_normalization_all_business_norm_none', - 'size_based_without_timestamp_normalization_true_business_norm_none', - 'size_based_without_timestamp_normalization_pred_business_norm_none', - 'sized_based_with_timestamp_cm_normalization_none_business_norm_none', - 'sized_based_with_timestamp_cm_normalization_all_business_norm_none', - 'sized_based_with_timestamp_cm_normalization_all_business_norm_per_pred', - 'sized_based_with_timestamp_normalization_true_business_norm_none', - 'sized_based_with_timestamp_normalization_pred_business_norm_none', - 'count_based_without_timestamp_normalization_none_business_norm_none', - 'count_based_without_timestamp_normalization_all_business_norm_none', - 'count_based_without_timestamp_normalization_true_business_norm_none', - 'count_based_without_timestamp_normalization_pred_business_norm_none', - 'count_based_with_timestamp_normalization_none_business_norm_none', - 'count_based_with_timestamp_normalization_all_business_norm_none', - 'count_based_with_timestamp_normalization_true_business_norm_none', - 'count_based_with_timestamp_normalization_pred_business_norm_none', - 'period_based_with_timestamp_normalization_none_business_norm_none', - 'period_based_with_timestamp_normalization_all_business_norm_none', - 'period_based_with_timestamp_normalization_true_business_norm_none', - 'period_based_with_timestamp_normalization_pred_business_norm_none', - 'default_without_timestamp_normalization_none_business_norm_none', - 'default_without_timestamp_normalization_all_business_norm_none', - 'default_without_timestamp_normalization_true_business_norm_none', - 'default_without_timestamp_normalization_pred_business_norm_none', - 'default_with_timestamp_normalization_none_business_norm_none', - 'default_with_timestamp_normalization_all_business_norm_none', - 'default_with_timestamp_normalization_true_business_norm_none', - 'default_with_timestamp_normalization_pred_business_norm_none', + "size_based_without_timestamp_cm_normalization_none_business_norm_none", + "size_based_without_timestamp_cm_normalization_none_business_norm_per_pred", + "size_based_without_timestamp_normalization_all_business_norm_none", + "size_based_without_timestamp_normalization_true_business_norm_none", + "size_based_without_timestamp_normalization_pred_business_norm_none", + "sized_based_with_timestamp_cm_normalization_none_business_norm_none", + "sized_based_with_timestamp_cm_normalization_all_business_norm_none", + "sized_based_with_timestamp_cm_normalization_all_business_norm_per_pred", + "sized_based_with_timestamp_normalization_true_business_norm_none", + "sized_based_with_timestamp_normalization_pred_business_norm_none", + "count_based_without_timestamp_normalization_none_business_norm_none", + "count_based_without_timestamp_normalization_all_business_norm_none", + "count_based_without_timestamp_normalization_true_business_norm_none", + "count_based_without_timestamp_normalization_pred_business_norm_none", + "count_based_with_timestamp_normalization_none_business_norm_none", + "count_based_with_timestamp_normalization_all_business_norm_none", + "count_based_with_timestamp_normalization_true_business_norm_none", + "count_based_with_timestamp_normalization_pred_business_norm_none", + "period_based_with_timestamp_normalization_none_business_norm_none", + "period_based_with_timestamp_normalization_all_business_norm_none", + "period_based_with_timestamp_normalization_true_business_norm_none", + "period_based_with_timestamp_normalization_pred_business_norm_none", + "default_without_timestamp_normalization_none_business_norm_none", + "default_without_timestamp_normalization_all_business_norm_none", + "default_without_timestamp_normalization_true_business_norm_none", + "default_without_timestamp_normalization_pred_business_norm_none", + "default_with_timestamp_normalization_none_business_norm_none", + "default_with_timestamp_normalization_all_business_norm_none", + "default_with_timestamp_normalization_true_business_norm_none", + "default_with_timestamp_normalization_pred_business_norm_none", ], ) def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expected): # noqa: D103 ref_df, ana_df, _ = load_synthetic_binary_classification_dataset() cbpe = CBPE( - y_pred_proba='y_pred_proba', - y_pred='y_pred', - y_true='work_home_actual', - problem_type='classification_binary', + y_pred_proba="y_pred_proba", + y_pred="y_pred", + y_true="work_home_actual", + problem_type="classification_binary", metrics=[ - 'roc_auc', - 'f1', - 'precision', - 'average_precision', - 'recall', - 'specificity', - 'accuracy', - 'confusion_matrix', - 'business_value', + "roc_auc", + "f1", + "precision", + "average_precision", + "recall", + "specificity", + "accuracy", + "confusion_matrix", + "business_value", ], **calculator_opts, ).fit(ref_df) result = cbpe.estimate(ana_df) - metric_column_names = [name for metric in result.metrics for name in metric.column_names] - sut = result.filter(period='analysis').to_df()[[('chunk', 'key')] + [(c, 'value') for c in metric_column_names]] + metric_column_names = [ + name for metric in result.metrics for name in metric.column_names + ] + sut = result.filter(period="analysis").to_df()[ + [("chunk", "key")] + [(c, "value") for c in metric_column_names] + ] sut.columns = [ - 'key', - 'estimated_roc_auc', - 'estimated_f1', - 'estimated_precision', - 'estimated_average_precision', - 'estimated_recall', - 'estimated_specificity', - 'estimated_accuracy', - 'estimated_true_positive', - 'estimated_true_negative', - 'estimated_false_positive', - 'estimated_false_negative', - 'estimated_business_value', + "key", + "estimated_roc_auc", + "estimated_f1", + "estimated_precision", + "estimated_average_precision", + "estimated_recall", + "estimated_specificity", + "estimated_accuracy", + "estimated_true_positive", + "estimated_true_negative", + "estimated_false_positive", + "estimated_false_negative", + "estimated_business_value", ] pd.testing.assert_frame_equal(expected, sut) @pytest.mark.parametrize( - 'calculator_opts, expected', + "calculator_opts, expected", [ ( - {'chunk_size': 20000}, + {"chunk_size": 20000}, pd.DataFrame( { - 'key': ['[0:19999]', '[20000:39999]', '[40000:59999]'], - 'estimated_roc_auc': [0.909165141524145, 0.8682789924547322, 0.8203173643497594], - 'estimated_f1': [0.756401608336434, 0.6937135623882767, 0.632386421613214], - 'estimated_precision': [0.7564437378390059, 0.694174192229447, 0.6336288859123612], - 'estimated_recall': [0.7564129287764665, 0.6934788458355289, 0.6319310599943714], - 'estimated_specificity': [0.8782068281303994, 0.8469556750949159, 0.8172644220189141], - 'estimated_accuracy': [0.7564451493123628, 0.6946947603445697, 0.6378557309960986], - 'estimated_average_precision': [0.8418535417603635, 0.7785618577588246, 0.6985785036188713], - 'estimated_business_value': [2.0193901626043056, 1.7875283323693987, 1.570045452479401], - 'estimated_true_highstreet_card_pred_highstreet_card': [ + "key": ["[0:19999]", "[20000:39999]", "[40000:59999]"], + "estimated_roc_auc": [ + 0.909165141524145, + 0.8682789924547322, + 0.8203173643497594, + ], + "estimated_f1": [ + 0.756401608336434, + 0.6937135623882767, + 0.632386421613214, + ], + "estimated_precision": [ + 0.7564437378390059, + 0.694174192229447, + 0.6336288859123612, + ], + "estimated_recall": [ + 0.7564129287764665, + 0.6934788458355289, + 0.6319310599943714, + ], + "estimated_specificity": [ + 0.8782068281303994, + 0.8469556750949159, + 0.8172644220189141, + ], + "estimated_accuracy": [ + 0.7564451493123628, + 0.6946947603445697, + 0.6378557309960986, + ], + "estimated_average_precision": [ + 0.8418535417603635, + 0.7785618577588246, + 0.6985785036188713, + ], + "estimated_business_value": [ + 2.0193901626043056, + 1.7875283323693987, + 1.570045452479401, + ], + "estimated_true_highstreet_card_pred_highstreet_card": [ 4976.829215997277, 5148.649186425118, 5412.348045797111, ], - 'estimated_true_highstreet_card_pred_prepaid_card': [ + "estimated_true_highstreet_card_pred_prepaid_card": [ 878.1877379091701, 1038.3533241561252, 1250.9260097761653, ], - 'estimated_true_highstreet_card_pred_upmarket_card': [ + "estimated_true_highstreet_card_pred_upmarket_card": [ 831.7702766018707, 993.7691398029524, 1109.9706655490413, ], - 'estimated_true_prepaid_card_pred_highstreet_card': [ + "estimated_true_prepaid_card_pred_highstreet_card": [ 806.1451187447954, 1140.1932616586546, 1451.431964364007, ], - 'estimated_true_prepaid_card_pred_prepaid_card': [ + "estimated_true_prepaid_card_pred_prepaid_card": [ 5180.838942632071, 4134.524656135082, 3326.8467648553315, ], - 'estimated_true_prepaid_card_pred_upmarket_card': [ + "estimated_true_prepaid_card_pred_upmarket_card": [ 755.9948957802203, 998.509495865855, 1200.1095251814281, ], - 'estimated_true_upmarket_card_pred_highstreet_card': [ + "estimated_true_upmarket_card_pred_highstreet_card": [ 812.0256652579275, 1062.1575519162266, 1263.219989838882, ], - 'estimated_true_upmarket_card_pred_prepaid_card': [ + "estimated_true_upmarket_card_pred_prepaid_card": [ 786.9733194587595, 873.1220197087925, 967.2272253685034, ], - 'estimated_true_upmarket_card_pred_upmarket_card': [ + "estimated_true_upmarket_card_pred_upmarket_card": [ 4971.234827617909, 4610.7213643311925, 4017.9198092695306, @@ -2709,59 +2999,95 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte ), ), ( - {'chunk_size': 20000, 'timestamp_column_name': 'timestamp', 'normalize_confusion_matrix': 'true'}, + { + "chunk_size": 20000, + "timestamp_column_name": "timestamp", + "normalize_confusion_matrix": "true", + }, pd.DataFrame( { - 'key': ['[0:19999]', '[20000:39999]', '[40000:59999]'], - 'estimated_roc_auc': [0.909165141524145, 0.8682789924547322, 0.8203173643497594], - 'estimated_f1': [0.756401608336434, 0.6937135623882767, 0.632386421613214], - 'estimated_precision': [0.7564437378390059, 0.694174192229447, 0.6336288859123612], - 'estimated_recall': [0.7564129287764665, 0.6934788458355289, 0.6319310599943714], - 'estimated_specificity': [0.8782068281303994, 0.8469556750949159, 0.8172644220189141], - 'estimated_accuracy': [0.7564451493123628, 0.6946947603445697, 0.6378557309960986], - 'estimated_average_precision': [0.8418535417603635, 0.7785618577588246, 0.6985785036188713], - 'estimated_business_value': [2.0193901626043056, 1.7875283323693987, 1.570045452479401], - 'estimated_true_highstreet_card_pred_highstreet_card': [ + "key": ["[0:19999]", "[20000:39999]", "[40000:59999]"], + "estimated_roc_auc": [ + 0.909165141524145, + 0.8682789924547322, + 0.8203173643497594, + ], + "estimated_f1": [ + 0.756401608336434, + 0.6937135623882767, + 0.632386421613214, + ], + "estimated_precision": [ + 0.7564437378390059, + 0.694174192229447, + 0.6336288859123612, + ], + "estimated_recall": [ + 0.7564129287764665, + 0.6934788458355289, + 0.6319310599943714, + ], + "estimated_specificity": [ + 0.8782068281303994, + 0.8469556750949159, + 0.8172644220189141, + ], + "estimated_accuracy": [ + 0.7564451493123628, + 0.6946947603445697, + 0.6378557309960986, + ], + "estimated_average_precision": [ + 0.8418535417603635, + 0.7785618577588246, + 0.6985785036188713, + ], + "estimated_business_value": [ + 2.0193901626043056, + 1.7875283323693987, + 1.570045452479401, + ], + "estimated_true_highstreet_card_pred_highstreet_card": [ 0.7442780881812128, 0.7170050012869645, 0.6962791266676683, ], - 'estimated_true_highstreet_card_pred_prepaid_card': [ + "estimated_true_highstreet_card_pred_prepaid_card": [ 0.1313317902358936, 0.14460191393226796, 0.16092713592008898, ], - 'estimated_true_highstreet_card_pred_upmarket_card': [ + "estimated_true_highstreet_card_pred_upmarket_card": [ 0.12439012158289371, 0.1383930847807676, 0.1427937374122426, ], - 'estimated_true_prepaid_card_pred_highstreet_card': [ + "estimated_true_prepaid_card_pred_highstreet_card": [ 0.11955326034187638, 0.18175544842770236, 0.24277980997563847, ], - 'estimated_true_prepaid_card_pred_prepaid_card': [ + "estimated_true_prepaid_card_pred_prepaid_card": [ 0.7683308780213619, 0.6590745693568182, 0.5564788741190233, ], - 'estimated_true_prepaid_card_pred_upmarket_card': [ + "estimated_true_prepaid_card_pred_upmarket_card": [ 0.1121158616367618, 0.15916998221547937, 0.20074131590533828, ], - 'estimated_true_upmarket_card_pred_highstreet_card': [ + "estimated_true_upmarket_card_pred_highstreet_card": [ 0.1235915933057778, 0.16226052551901615, 0.20216802004274595, ], - 'estimated_true_upmarket_card_pred_prepaid_card': [ + "estimated_true_upmarket_card_pred_prepaid_card": [ 0.1197785865673972, 0.13338250761817996, 0.15479680076083163, ], - 'estimated_true_upmarket_card_pred_upmarket_card': [ + "estimated_true_upmarket_card_pred_upmarket_card": [ 0.756629820126825, 0.7043569668628038, 0.6430351791964225, @@ -2770,102 +3096,112 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte ), ), ( - {'chunk_number': 4, 'normalize_confusion_matrix': 'pred'}, + {"chunk_number": 4, "normalize_confusion_matrix": "pred"}, pd.DataFrame( { - 'key': ['[0:14999]', '[15000:29999]', '[30000:44999]', '[45000:59999]'], - 'estimated_roc_auc': [ + "key": [ + "[0:14999]", + "[15000:29999]", + "[30000:44999]", + "[45000:59999]", + ], + "estimated_roc_auc": [ 0.9084352218383378, 0.9087633795549603, 0.8195268555812215, 0.8201623718659414, ], - 'estimated_f1': [0.7550059244451006, 0.7562711250144366, 0.63091155676697, 0.6324244687112559], - 'estimated_precision': [ + "estimated_f1": [ + 0.7550059244451006, + 0.7562711250144366, + 0.63091155676697, + 0.6324244687112559, + ], + "estimated_precision": [ 0.755038246904623, 0.7562647262876293, 0.6323547131368327, 0.6335323150520741, ], - 'estimated_recall': [ + "estimated_recall": [ 0.7550277340784216, 0.7562926950204228, 0.6304009454574501, 0.6320155112489632, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.8775094795233379, 0.8781429133214084, 0.8165537125162895, 0.8172408983542975, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.7550428613792668, 0.7562888217426292, 0.6364205304514962, 0.6375753072973162, ], - 'estimated_average_precision': [ + "estimated_average_precision": [ 0.8406535565924922, 0.8410572134298334, 0.697327636452664, 0.6984330753389926, ], - 'estimated_business_value': [ + "estimated_business_value": [ 2.0134445826512186, 2.0170794978486395, 1.5673705142973104, 1.5671595942359196, ], - 'estimated_true_highstreet_card_pred_highstreet_card': [ + "estimated_true_highstreet_card_pred_highstreet_card": [ 0.7546260682147157, 0.7511343683695074, 0.6628383225865804, 0.6651814251770874, ], - 'estimated_true_highstreet_card_pred_prepaid_card': [ + "estimated_true_highstreet_card_pred_prepaid_card": [ 0.12922483020709813, 0.12720280190168412, 0.22365956156664257, 0.22578913179209303, ], - 'estimated_true_highstreet_card_pred_upmarket_card': [ + "estimated_true_highstreet_card_pred_upmarket_card": [ 0.12747696595643684, 0.12776612448252053, 0.17277613353669485, 0.17660735301820177, ], - 'estimated_true_prepaid_card_pred_highstreet_card': [ + "estimated_true_prepaid_card_pred_highstreet_card": [ 0.12118073967907128, 0.1249170750987652, 0.18024418583692642, 0.17798857692081155, ], - 'estimated_true_prepaid_card_pred_prepaid_card': [ + "estimated_true_prepaid_card_pred_prepaid_card": [ 0.7554502796336932, 0.7576402255283115, 0.5994574163887797, 0.5998622938235557, ], - 'estimated_true_prepaid_card_pred_upmarket_card': [ + "estimated_true_prepaid_card_pred_upmarket_card": [ 0.11748464117810321, 0.11221429055241054, 0.1924554660281669, 0.18783942082621902, ], - 'estimated_true_upmarket_card_pred_highstreet_card': [ + "estimated_true_upmarket_card_pred_highstreet_card": [ 0.12419319210621305, 0.12394855653172744, 0.15691749157649315, 0.15682999790210106, ], - 'estimated_true_upmarket_card_pred_prepaid_card': [ + "estimated_true_upmarket_card_pred_prepaid_card": [ 0.11532489015920869, 0.1151569725700045, 0.17688302204457784, 0.17434857438435108, ], - 'estimated_true_upmarket_card_pred_upmarket_card': [ + "estimated_true_upmarket_card_pred_upmarket_card": [ 0.7550383928654599, 0.7600195849650688, 0.6347684004351383, @@ -2875,102 +3211,116 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte ), ), ( - {'chunk_number': 4, 'timestamp_column_name': 'timestamp', 'normalize_confusion_matrix': 'all'}, + { + "chunk_number": 4, + "timestamp_column_name": "timestamp", + "normalize_confusion_matrix": "all", + }, pd.DataFrame( { - 'key': ['[0:14999]', '[15000:29999]', '[30000:44999]', '[45000:59999]'], - 'estimated_roc_auc': [ + "key": [ + "[0:14999]", + "[15000:29999]", + "[30000:44999]", + "[45000:59999]", + ], + "estimated_roc_auc": [ 0.9084352218383378, 0.9087633795549603, 0.8195268555812215, 0.8201623718659414, ], - 'estimated_f1': [0.7550059244451006, 0.7562711250144366, 0.63091155676697, 0.6324244687112559], - 'estimated_precision': [ + "estimated_f1": [ + 0.7550059244451006, + 0.7562711250144366, + 0.63091155676697, + 0.6324244687112559, + ], + "estimated_precision": [ 0.755038246904623, 0.7562647262876293, 0.6323547131368327, 0.6335323150520741, ], - 'estimated_recall': [ + "estimated_recall": [ 0.7550277340784216, 0.7562926950204228, 0.6304009454574501, 0.6320155112489632, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.8775094795233379, 0.8781429133214084, 0.8165537125162895, 0.8172408983542975, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.7550428613792668, 0.7562888217426292, 0.6364205304514962, 0.6375753072973162, ], - 'estimated_average_precision': [ + "estimated_average_precision": [ 0.8406535565924922, 0.8410572134298334, 0.697327636452664, 0.6984330753389926, ], - 'estimated_business_value': [ + "estimated_business_value": [ 2.0134445826512186, 2.0170794978486395, 1.5673705142973104, 1.5671595942359196, ], - 'estimated_true_highstreet_card_pred_highstreet_card': [ + "estimated_true_highstreet_card_pred_highstreet_card": [ 0.24922783612904678, 0.24847524905663304, 0.2702612787293017, 0.2678907326329857, ], - 'estimated_true_highstreet_card_pred_prepaid_card': [ + "estimated_true_highstreet_card_pred_prepaid_card": [ 0.044125972021383776, 0.04231613209929359, 0.06202825174114887, 0.06269411559427118, ], - 'estimated_true_highstreet_card_pred_upmarket_card': [ + "estimated_true_highstreet_card_pred_upmarket_card": [ 0.04184643869129968, 0.04299755975918424, 0.05441296365515643, 0.05644371002461729, ], - 'estimated_true_prepaid_card_pred_highstreet_card': [ + "estimated_true_prepaid_card_pred_highstreet_card": [ 0.04002195895800795, 0.04132256844267153, 0.0734915627052428, 0.07168193287857484, ], - 'estimated_true_prepaid_card_pred_prepaid_card': [ + "estimated_true_prepaid_card_pred_prepaid_card": [ 0.25796108881891844, 0.25204164835908494, 0.16624952347848823, 0.16656176358500735, ], - 'estimated_true_prepaid_card_pred_upmarket_card': [ + "estimated_true_prepaid_card_pred_upmarket_card": [ 0.03856629154406535, 0.03776384924723789, 0.0606106414344707, 0.060033478896059596, ], - 'estimated_true_upmarket_card_pred_highstreet_card': [ + "estimated_true_upmarket_card_pred_highstreet_card": [ 0.041016871579611966, 0.041002182500695435, 0.06398049189878882, 0.06316066782177283, ], - 'estimated_true_upmarket_card_pred_prepaid_card': [ + "estimated_true_upmarket_card_pred_prepaid_card": [ 0.03937960582636446, 0.038308886208288165, 0.04905555811369625, 0.04841078748738816, ], - 'estimated_true_upmarket_card_pred_upmarket_card': [ + "estimated_true_upmarket_card_pred_upmarket_card": [ 0.24785393643130169, 0.25577192432691115, 0.1999097282437062, @@ -2980,27 +3330,60 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte ), ), ( - {'chunk_period': 'Y', 'timestamp_column_name': 'timestamp'}, + {"chunk_period": "Y", "timestamp_column_name": "timestamp"}, pd.DataFrame( { - 'key': ['2020', '2021'], - 'estimated_roc_auc': [0.8697900039493401, 0.8160313122805869], - 'estimated_f1': [0.6959459683194374, 0.6271637037915178], - 'estimated_precision': [0.696279612597813, 0.6275707355339551], - 'estimated_recall': [0.6957620347508907, 0.6272720458900231], - 'estimated_specificity': [0.8480220572478717, 0.8145095377877009], - 'estimated_accuracy': [0.6967957612985849, 0.6305270354546132], - 'estimated_average_precision': [0.7812291182204878, 0.6907845497417768], - 'estimated_business_value': [1.7964098918968543, 1.5447162372665988], - 'estimated_true_highstreet_card_pred_highstreet_card': [15431.207920621628, 106.61852759787631], - 'estimated_true_highstreet_card_pred_prepaid_card': [3140.1950482057946, 27.27202363566655], - 'estimated_true_highstreet_card_pred_upmarket_card': [2911.0243109194275, 24.485771034437157], - 'estimated_true_prepaid_card_pred_highstreet_card': [3369.0309742564546, 28.73937051100256], - 'estimated_true_prepaid_card_pred_prepaid_card': [12568.980575116106, 73.22978850637857], - 'estimated_true_prepaid_card_pred_upmarket_card': [2927.072726648623, 27.541190178880235], - 'estimated_true_upmarket_card_pred_highstreet_card': [3111.761105121915, 25.642101891121143], - 'estimated_true_upmarket_card_pred_prepaid_card': [2605.8243766781006, 21.49818785795489], - 'estimated_true_upmarket_card_pred_upmarket_card': [13514.90296243195, 84.97303878668261], + "key": ["2020", "2021"], + "estimated_roc_auc": [0.8697900039493401, 0.8160313122805869], + "estimated_f1": [0.6959459683194374, 0.6271637037915178], + "estimated_precision": [0.696279612597813, 0.6275707355339551], + "estimated_recall": [0.6957620347508907, 0.6272720458900231], + "estimated_specificity": [0.8480220572478717, 0.8145095377877009], + "estimated_accuracy": [0.6967957612985849, 0.6305270354546132], + "estimated_average_precision": [ + 0.7812296449871846, + 0.690772335696266, + ], + "estimated_business_value": [ + 1.7964098918968543, + 1.5447162372665988, + ], + "estimated_true_highstreet_card_pred_highstreet_card": [ + 15431.207920621628, + 106.61852759787631, + ], + "estimated_true_highstreet_card_pred_prepaid_card": [ + 3140.1950482057946, + 27.27202363566655, + ], + "estimated_true_highstreet_card_pred_upmarket_card": [ + 2911.0243109194275, + 24.485771034437157, + ], + "estimated_true_prepaid_card_pred_highstreet_card": [ + 3369.0309742564546, + 28.73937051100256, + ], + "estimated_true_prepaid_card_pred_prepaid_card": [ + 12568.980575116106, + 73.22978850637857, + ], + "estimated_true_prepaid_card_pred_upmarket_card": [ + 2927.072726648623, + 27.541190178880235, + ], + "estimated_true_upmarket_card_pred_highstreet_card": [ + 3111.761105121915, + 25.642101891121143, + ], + "estimated_true_upmarket_card_pred_prepaid_card": [ + 2605.8243766781006, + 21.49818785795489, + ], + "estimated_true_upmarket_card_pred_upmarket_card": [ + 13514.90296243195, + 84.97303878668261, + ], } ), ), @@ -3008,19 +3391,19 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte {}, pd.DataFrame( { - 'key': [ - '[0:5999]', - '[6000:11999]', - '[12000:17999]', - '[18000:23999]', - '[24000:29999]', - '[30000:35999]', - '[36000:41999]', - '[42000:47999]', - '[48000:53999]', - '[54000:59999]', - ], - 'estimated_roc_auc': [ + "key": [ + "[0:5999]", + "[6000:11999]", + "[12000:17999]", + "[18000:23999]", + "[24000:29999]", + "[30000:35999]", + "[36000:41999]", + "[42000:47999]", + "[48000:53999]", + "[54000:59999]", + ], + "estimated_roc_auc": [ 0.9069623299465119, 0.9098768294609071, 0.9098874875649706, @@ -3032,7 +3415,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.8192448359184578, 0.8214293833125543, ], - 'estimated_f1': [ + "estimated_f1": [ 0.7533014808638749, 0.7564216285126095, 0.7581655498090148, @@ -3044,7 +3427,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6317356567785939, 0.6334434546737131, ], - 'estimated_precision': [ + "estimated_precision": [ 0.7533705392643878, 0.7564117249294325, 0.758189598004742, @@ -3056,7 +3439,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6327414423931249, 0.6349486020523588, ], - 'estimated_recall': [ + "estimated_recall": [ 0.7532927299604006, 0.756484874870973, 0.7581945446990431, @@ -3068,7 +3451,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6315005643336115, 0.6328910840233342, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.876648985916452, 0.8781935469456502, 0.87910164279675, @@ -3080,7 +3463,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.8167795549189886, 0.8180293638991758, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.7533903547412437, 0.7564140260171383, 0.7582062911442542, @@ -3092,7 +3475,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6365172577468735, 0.6393273094601863, ], - 'estimated_average_precision': [ + "estimated_average_precision": [ 0.838071, 0.843094, 0.842962, @@ -3104,7 +3487,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.696305, 0.701142, ], - 'estimated_business_value': [ + "estimated_business_value": [ 2.0086174744097525, 2.0167085528014574, 2.025151984316981, @@ -3116,7 +3499,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 1.5668663365944273, 1.574249644290713, ], - 'estimated_true_highstreet_card_pred_highstreet_card': [ + "estimated_true_highstreet_card_pred_highstreet_card": [ 1483.745037516118, 1536.2546154566053, 1486.1512390473335, @@ -3128,7 +3511,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 1596.0668735461204, 1621.3736981076686, ], - 'estimated_true_highstreet_card_pred_prepaid_card': [ + "estimated_true_highstreet_card_pred_prepaid_card": [ 271.9744616336458, 263.3288788018858, 255.36687592730394, @@ -3140,7 +3523,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 383.2996831232405, 357.9152833417768, ], - 'estimated_true_highstreet_card_pred_upmarket_card': [ + "estimated_true_highstreet_card_pred_upmarket_card": [ 249.77244098451774, 249.25341402994002, 256.5894445268087, @@ -3152,7 +3535,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 338.73618885526093, 336.8384100540352, ], - 'estimated_true_prepaid_card_pred_highstreet_card': [ + "estimated_true_prepaid_card_pred_highstreet_card": [ 249.18645665281267, 234.635939041771, 241.34496258349301, @@ -3164,7 +3547,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 436.76196316220245, 432.5014066529442, ], - 'estimated_true_prepaid_card_pred_prepaid_card': [ + "estimated_true_prepaid_card_pred_prepaid_card": [ 1570.046457210577, 1517.6502407889316, 1541.0740605507428, @@ -3176,7 +3559,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 1008.1898500978498, 977.1648111020529, ], - 'estimated_true_prepaid_card_pred_upmarket_card': [ + "estimated_true_prepaid_card_pred_upmarket_card": [ 227.67692529471515, 228.16728611276778, 224.39810820574257, @@ -3188,7 +3571,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 364.41698830746793, 360.7362423945684, ], - 'estimated_true_upmarket_card_pred_highstreet_card': [ + "estimated_true_upmarket_card_pred_highstreet_card": [ 243.06850583106933, 254.1094455016238, 238.50379836917327, @@ -3200,7 +3583,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 372.1711632916771, 381.12489523938723, ], - 'estimated_true_upmarket_card_pred_prepaid_card': [ + "estimated_true_upmarket_card_pred_prepaid_card": [ 237.97908115577718, 232.0208804091826, 234.55906352195305, @@ -3212,7 +3595,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 285.5104667789098, 294.91990555617025, ], - 'estimated_true_upmarket_card_pred_upmarket_card': [ + "estimated_true_upmarket_card_pred_upmarket_card": [ 1466.550633720767, 1484.5792998572922, 1522.0124472674486, @@ -3228,22 +3611,22 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte ), ), ( - {'timestamp_column_name': 'timestamp'}, + {"timestamp_column_name": "timestamp"}, pd.DataFrame( { - 'key': [ - '[0:5999]', - '[6000:11999]', - '[12000:17999]', - '[18000:23999]', - '[24000:29999]', - '[30000:35999]', - '[36000:41999]', - '[42000:47999]', - '[48000:53999]', - '[54000:59999]', - ], - 'estimated_roc_auc': [ + "key": [ + "[0:5999]", + "[6000:11999]", + "[12000:17999]", + "[18000:23999]", + "[24000:29999]", + "[30000:35999]", + "[36000:41999]", + "[42000:47999]", + "[48000:53999]", + "[54000:59999]", + ], + "estimated_roc_auc": [ 0.9069623299465119, 0.9098768294609071, 0.9098874875649706, @@ -3255,7 +3638,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.8192448359184578, 0.8214293833125543, ], - 'estimated_f1': [ + "estimated_f1": [ 0.7533014808638749, 0.7564216285126095, 0.7581655498090148, @@ -3267,7 +3650,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6317356567785939, 0.6334434546737131, ], - 'estimated_precision': [ + "estimated_precision": [ 0.7533705392643878, 0.7564117249294325, 0.758189598004742, @@ -3279,7 +3662,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6327414423931249, 0.6349486020523588, ], - 'estimated_recall': [ + "estimated_recall": [ 0.7532927299604006, 0.756484874870973, 0.7581945446990431, @@ -3291,7 +3674,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6315005643336115, 0.6328910840233342, ], - 'estimated_specificity': [ + "estimated_specificity": [ 0.876648985916452, 0.8781935469456502, 0.87910164279675, @@ -3303,7 +3686,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.8167795549189886, 0.8180293638991758, ], - 'estimated_accuracy': [ + "estimated_accuracy": [ 0.7533903547412437, 0.7564140260171383, 0.7582062911442542, @@ -3315,7 +3698,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.6365172577468735, 0.6393273094601863, ], - 'estimated_average_precision': [ + "estimated_average_precision": [ 0.838071, 0.843094, 0.842962, @@ -3327,7 +3710,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 0.696305, 0.701142, ], - 'estimated_business_value': [ + "estimated_business_value": [ 2.0086174744097525, 2.0167085528014574, 2.025151984316981, @@ -3339,7 +3722,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 1.5668663365944273, 1.574249644290713, ], - 'estimated_true_highstreet_card_pred_highstreet_card': [ + "estimated_true_highstreet_card_pred_highstreet_card": [ 1483.745037516118, 1536.2546154566053, 1486.1512390473335, @@ -3351,7 +3734,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 1596.0668735461204, 1621.3736981076686, ], - 'estimated_true_highstreet_card_pred_prepaid_card': [ + "estimated_true_highstreet_card_pred_prepaid_card": [ 271.9744616336458, 263.3288788018858, 255.36687592730394, @@ -3363,7 +3746,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 383.2996831232405, 357.9152833417768, ], - 'estimated_true_highstreet_card_pred_upmarket_card': [ + "estimated_true_highstreet_card_pred_upmarket_card": [ 249.77244098451774, 249.25341402994002, 256.5894445268087, @@ -3375,7 +3758,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 338.73618885526093, 336.8384100540352, ], - 'estimated_true_prepaid_card_pred_highstreet_card': [ + "estimated_true_prepaid_card_pred_highstreet_card": [ 249.18645665281267, 234.635939041771, 241.34496258349301, @@ -3387,7 +3770,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 436.76196316220245, 432.5014066529442, ], - 'estimated_true_prepaid_card_pred_prepaid_card': [ + "estimated_true_prepaid_card_pred_prepaid_card": [ 1570.046457210577, 1517.6502407889316, 1541.0740605507428, @@ -3399,7 +3782,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 1008.1898500978498, 977.1648111020529, ], - 'estimated_true_prepaid_card_pred_upmarket_card': [ + "estimated_true_prepaid_card_pred_upmarket_card": [ 227.67692529471515, 228.16728611276778, 224.39810820574257, @@ -3411,7 +3794,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 364.41698830746793, 360.7362423945684, ], - 'estimated_true_upmarket_card_pred_highstreet_card': [ + "estimated_true_upmarket_card_pred_highstreet_card": [ 243.06850583106933, 254.1094455016238, 238.50379836917327, @@ -3423,7 +3806,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 372.1711632916771, 381.12489523938723, ], - 'estimated_true_upmarket_card_pred_prepaid_card': [ + "estimated_true_upmarket_card_pred_prepaid_card": [ 237.97908115577718, 232.0208804091826, 234.55906352195305, @@ -3435,7 +3818,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte 285.5104667789098, 294.91990555617025, ], - 'estimated_true_upmarket_card_pred_upmarket_card': [ + "estimated_true_upmarket_card_pred_upmarket_card": [ 1466.550633720767, 1484.5792998572922, 1522.0124472674486, @@ -3452,13 +3835,13 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte ), ], ids=[ - 'size_based_without_timestamp', - 'size_based_with_timestamp', - 'count_based_without_timestamp', - 'count_based_with_timestamp', - 'period_based_with_timestamp', - 'default_without_timestamp', - 'default_with_timestamp', + "size_based_without_timestamp", + "size_based_with_timestamp", + "count_based_without_timestamp", + "count_based_with_timestamp", + "period_based_with_timestamp", + "default_without_timestamp", + "default_with_timestamp", ], ) def test_cbpe_for_multiclass_classification_with_timestamps(calculator_opts, expected): # noqa: D103 @@ -3466,69 +3849,69 @@ def test_cbpe_for_multiclass_classification_with_timestamps(calculator_opts, exp business_value_matrix = np.array([[1, 0, -1], [0, 1, 0], [-1, 0, 1]]) cbpe = CBPE( y_pred_proba={ - 'upmarket_card': 'y_pred_proba_upmarket_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'prepaid_card': 'y_pred_proba_prepaid_card', + "upmarket_card": "y_pred_proba_upmarket_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "prepaid_card": "y_pred_proba_prepaid_card", }, - y_pred='y_pred', - y_true='y_true', - problem_type='classification_multiclass', + y_pred="y_pred", + y_true="y_true", + problem_type="classification_multiclass", metrics=[ - 'roc_auc', - 'f1', - 'precision', - 'recall', - 'specificity', - 'accuracy', - 'average_precision', - 'confusion_matrix', - 'business_value', + "roc_auc", + "f1", + "precision", + "recall", + "specificity", + "accuracy", + "average_precision", + "confusion_matrix", + "business_value", ], business_value_matrix=business_value_matrix, - normalize_business_value='per_prediction', + normalize_business_value="per_prediction", **calculator_opts, ).fit(ref_df) result = cbpe.estimate(ana_df) - column_names = [(m.name, 'value') for m in result.metrics] - column_names = [c for c in column_names if c[0] != 'confusion_matrix'] + column_names = [(m.name, "value") for m in result.metrics] + column_names = [c for c in column_names if c[0] != "confusion_matrix"] column_names += [ - ('true_highstreet_card_pred_highstreet_card', 'value'), - ('true_highstreet_card_pred_prepaid_card', 'value'), - ('true_highstreet_card_pred_upmarket_card', 'value'), - ('true_prepaid_card_pred_highstreet_card', 'value'), - ('true_prepaid_card_pred_prepaid_card', 'value'), - ('true_prepaid_card_pred_upmarket_card', 'value'), - ('true_upmarket_card_pred_highstreet_card', 'value'), - ('true_upmarket_card_pred_prepaid_card', 'value'), - ('true_upmarket_card_pred_upmarket_card', 'value'), + ("true_highstreet_card_pred_highstreet_card", "value"), + ("true_highstreet_card_pred_prepaid_card", "value"), + ("true_highstreet_card_pred_upmarket_card", "value"), + ("true_prepaid_card_pred_highstreet_card", "value"), + ("true_prepaid_card_pred_prepaid_card", "value"), + ("true_prepaid_card_pred_upmarket_card", "value"), + ("true_upmarket_card_pred_highstreet_card", "value"), + ("true_upmarket_card_pred_prepaid_card", "value"), + ("true_upmarket_card_pred_upmarket_card", "value"), ] - sut = result.filter(period='analysis').to_df()[[('chunk', 'key')] + column_names] + sut = result.filter(period="analysis").to_df()[[("chunk", "key")] + column_names] sut.columns = [ - 'key', - 'estimated_roc_auc', - 'estimated_f1', - 'estimated_precision', - 'estimated_recall', - 'estimated_specificity', - 'estimated_accuracy', - 'estimated_average_precision', - 'estimated_business_value', - 'estimated_true_highstreet_card_pred_highstreet_card', - 'estimated_true_highstreet_card_pred_prepaid_card', - 'estimated_true_highstreet_card_pred_upmarket_card', - 'estimated_true_prepaid_card_pred_highstreet_card', - 'estimated_true_prepaid_card_pred_prepaid_card', - 'estimated_true_prepaid_card_pred_upmarket_card', - 'estimated_true_upmarket_card_pred_highstreet_card', - 'estimated_true_upmarket_card_pred_prepaid_card', - 'estimated_true_upmarket_card_pred_upmarket_card', + "key", + "estimated_roc_auc", + "estimated_f1", + "estimated_precision", + "estimated_recall", + "estimated_specificity", + "estimated_accuracy", + "estimated_average_precision", + "estimated_business_value", + "estimated_true_highstreet_card_pred_highstreet_card", + "estimated_true_highstreet_card_pred_prepaid_card", + "estimated_true_highstreet_card_pred_upmarket_card", + "estimated_true_prepaid_card_pred_highstreet_card", + "estimated_true_prepaid_card_pred_prepaid_card", + "estimated_true_prepaid_card_pred_upmarket_card", + "estimated_true_upmarket_card_pred_highstreet_card", + "estimated_true_upmarket_card_pred_prepaid_card", + "estimated_true_upmarket_card_pred_upmarket_card", ] pd.testing.assert_frame_equal(expected, sut) @pytest.mark.parametrize( - 'metric_cls', + "metric_cls", [ BinaryClassificationAUROC, BinaryClassificationAP, @@ -3547,81 +3930,86 @@ def test_method_logs_warning_when_lower_threshold_is_overridden_by_metric_limits # TODO: move this from CBPE to metrics # workaround to deal with functionality outside of Metrics classes - reference['uncalibrated_y_pred_proba'] = reference['y_pred_proba'] + reference["uncalibrated_y_pred_proba"] = reference["y_pred_proba"] metric = metric_cls( - y_pred_proba='y_pred_proba', - y_pred='y_pred', - y_true='work_home_actual', - problem_type='classification_binary', + y_pred_proba="y_pred_proba", + y_pred="y_pred", + y_true="work_home_actual", + problem_type="classification_binary", chunker=DefaultChunker(), threshold=ConstantThreshold(lower=-1), ) metric.fit(reference) assert ( - f'{metric.display_name} lower threshold value -1 overridden by ' - f'lower threshold value limit {metric.lower_threshold_value_limit}' in caplog.messages + f"{metric.display_name} lower threshold value -1 overridden by " + f"lower threshold value limit {metric.lower_threshold_value_limit}" + in caplog.messages ) @pytest.mark.parametrize( - 'calculator_opts, realized', + "calculator_opts, realized", [ ( - {'chunk_size': 20000}, + {"chunk_size": 20000}, pd.DataFrame( { - 'key': ['[0:19999]', '[20000:39999]', '[40000:59999]'], - 'realized_roc_auc': [0.909805, 0.840071, np.nan], - 'realized_f1': [0.759170, 0.658896, np.nan], - 'realized_precision': [0.759265, 0.660188, np.nan], - 'realized_recall': [0.759149, 0.658760, np.nan], - 'realized_specificity': [0.879632, 0.829581, np.nan], - 'realized_accuracy': [0.75925, 0.65950, np.nan], - 'realized_average_precision': [0.841830, 0.738332, np.nan], - 'realized_business_value': [2.029064521843538, 1.6533562273847497, np.nan], - 'realized_true_highstreet_card_pred_highstreet_card': [ + "key": ["[0:19999]", "[20000:39999]", "[40000:59999]"], + "realized_roc_auc": [0.909805, 0.840071, np.nan], + "realized_f1": [0.759170, 0.658896, np.nan], + "realized_precision": [0.759265, 0.660188, np.nan], + "realized_recall": [0.759149, 0.658760, np.nan], + "realized_specificity": [0.879632, 0.829581, np.nan], + "realized_accuracy": [0.75925, 0.65950, np.nan], + "realized_average_precision": [0.841830, 0.738332, np.nan], + "realized_business_value": [ + 2.029064521843538, + 1.6533562273847497, + np.nan, + ], + "realized_true_highstreet_card_pred_highstreet_card": [ 4912.0, 4702.0, np.nan, ], - 'realized_true_highstreet_card_pred_prepaid_card': [ + "realized_true_highstreet_card_pred_prepaid_card": [ 870.0, 1083.0, np.nan, ], - 'realized_true_highstreet_card_pred_upmarket_card': [ + "realized_true_highstreet_card_pred_upmarket_card": [ 799.0, 1009.0, np.nan, ], - 'realized_true_prepaid_card_pred_highstreet_card': [ + "realized_true_prepaid_card_pred_highstreet_card": [ 846.0, 1367.0, np.nan, ], - 'realized_true_prepaid_card_pred_prepaid_card': [ + "realized_true_prepaid_card_pred_prepaid_card": [ 5203.0, 3974.0, np.nan, ], - 'realized_true_prepaid_card_pred_upmarket_card': [ + "realized_true_prepaid_card_pred_upmarket_card": [ 690.0, 1080.0, np.nan, ], - 'realized_true_upmarket_card_pred_highstreet_card': [ + "realized_true_upmarket_card_pred_highstreet_card": [ 837.0, 1282.0, np.nan, ], - 'realized_true_upmarket_card_pred_prepaid_card': [ + "realized_true_upmarket_card_pred_prepaid_card": [ 773.0, 989.0, np.nan, ], - 'realized_true_upmarket_card_pred_upmarket_card': [ + "realized_true_upmarket_card_pred_upmarket_card": [ 5070.0, 4514.0, np.nan, @@ -3639,80 +4027,80 @@ def test_cbpe_for_multiclass_classification_cm_with_nans(calculator_opts, realiz business_value_matrix = np.array([[1, 0, -1], [0, 1, 0], [-1, 0, 1]]) cbpe = CBPE( y_pred_proba={ - 'upmarket_card': 'y_pred_proba_upmarket_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'prepaid_card': 'y_pred_proba_prepaid_card', + "upmarket_card": "y_pred_proba_upmarket_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "prepaid_card": "y_pred_proba_prepaid_card", }, - y_pred='y_pred', - y_true='y_true', - problem_type='classification_multiclass', + y_pred="y_pred", + y_true="y_true", + problem_type="classification_multiclass", metrics=[ - 'roc_auc', - 'f1', - 'precision', - 'recall', - 'specificity', - 'accuracy', - 'average_precision', - 'confusion_matrix', - 'business_value', + "roc_auc", + "f1", + "precision", + "recall", + "specificity", + "accuracy", + "average_precision", + "confusion_matrix", + "business_value", ], business_value_matrix=business_value_matrix, - normalize_business_value='per_prediction', + normalize_business_value="per_prediction", **calculator_opts, ).fit(reference) result = cbpe.estimate(analysis) - column_names = [(m.name, 'realized') for m in result.metrics] - column_names = [c for c in column_names if c[0] != 'confusion_matrix'] + column_names = [(m.name, "realized") for m in result.metrics] + column_names = [c for c in column_names if c[0] != "confusion_matrix"] column_names += [ - ('true_highstreet_card_pred_highstreet_card', 'realized'), - ('true_highstreet_card_pred_prepaid_card', 'realized'), - ('true_highstreet_card_pred_upmarket_card', 'realized'), - ('true_prepaid_card_pred_highstreet_card', 'realized'), - ('true_prepaid_card_pred_prepaid_card', 'realized'), - ('true_prepaid_card_pred_upmarket_card', 'realized'), - ('true_upmarket_card_pred_highstreet_card', 'realized'), - ('true_upmarket_card_pred_prepaid_card', 'realized'), - ('true_upmarket_card_pred_upmarket_card', 'realized'), + ("true_highstreet_card_pred_highstreet_card", "realized"), + ("true_highstreet_card_pred_prepaid_card", "realized"), + ("true_highstreet_card_pred_upmarket_card", "realized"), + ("true_prepaid_card_pred_highstreet_card", "realized"), + ("true_prepaid_card_pred_prepaid_card", "realized"), + ("true_prepaid_card_pred_upmarket_card", "realized"), + ("true_upmarket_card_pred_highstreet_card", "realized"), + ("true_upmarket_card_pred_prepaid_card", "realized"), + ("true_upmarket_card_pred_upmarket_card", "realized"), ] - sut = result.filter(period='analysis').to_df()[[('chunk', 'key')] + column_names] + sut = result.filter(period="analysis").to_df()[[("chunk", "key")] + column_names] sut.columns = [ - 'key', - 'realized_roc_auc', - 'realized_f1', - 'realized_precision', - 'realized_recall', - 'realized_specificity', - 'realized_accuracy', - 'realized_average_precision', - 'realized_business_value', - 'realized_true_highstreet_card_pred_highstreet_card', - 'realized_true_highstreet_card_pred_prepaid_card', - 'realized_true_highstreet_card_pred_upmarket_card', - 'realized_true_prepaid_card_pred_highstreet_card', - 'realized_true_prepaid_card_pred_prepaid_card', - 'realized_true_prepaid_card_pred_upmarket_card', - 'realized_true_upmarket_card_pred_highstreet_card', - 'realized_true_upmarket_card_pred_prepaid_card', - 'realized_true_upmarket_card_pred_upmarket_card', + "key", + "realized_roc_auc", + "realized_f1", + "realized_precision", + "realized_recall", + "realized_specificity", + "realized_accuracy", + "realized_average_precision", + "realized_business_value", + "realized_true_highstreet_card_pred_highstreet_card", + "realized_true_highstreet_card_pred_prepaid_card", + "realized_true_highstreet_card_pred_upmarket_card", + "realized_true_prepaid_card_pred_highstreet_card", + "realized_true_prepaid_card_pred_prepaid_card", + "realized_true_prepaid_card_pred_upmarket_card", + "realized_true_upmarket_card_pred_highstreet_card", + "realized_true_upmarket_card_pred_prepaid_card", + "realized_true_upmarket_card_pred_upmarket_card", ] pd.testing.assert_frame_equal(realized, sut) def test_auroc_errors_out_when_not_all_classes_are_represented_reference(): reference, _, _ = load_synthetic_multiclass_classification_dataset() - reference['y_pred_proba_clazz'] = reference['y_pred_proba_upmarket_card'] + reference["y_pred_proba_clazz"] = reference["y_pred_proba_upmarket_card"] calc = CBPE( y_pred_proba={ - 'prepaid_card': 'y_pred_proba_prepaid_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'upmarket_card': 'y_pred_proba_upmarket_card', - 'clazz': 'y_pred_proba_clazz', + "prepaid_card": "y_pred_proba_prepaid_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "upmarket_card": "y_pred_proba_upmarket_card", + "clazz": "y_pred_proba_clazz", }, - y_pred='y_pred', - y_true='y_true', - metrics=['roc_auc'], - problem_type='classification_multiclass', + y_pred="y_pred", + y_true="y_true", + metrics=["roc_auc"], + problem_type="classification_multiclass", ) expected_exc_test = "y_pred_proba class and class probabilities dictionary does not match reference data." with pytest.raises(InvalidArgumentsException, match=expected_exc_test): @@ -3720,30 +4108,44 @@ def test_auroc_errors_out_when_not_all_classes_are_represented_reference(): def test_auroc_errors_out_when_not_all_classes_are_represented_chunk(caplog): - LOGGER.info("testing test_auroc_errors_out_when_not_all_classes_are_represented_chunk") + LOGGER.info( + "testing test_auroc_errors_out_when_not_all_classes_are_represented_chunk" + ) reference, monitored, targets = load_synthetic_multiclass_classification_dataset() monitored = monitored.merge(targets) # Uncalibrated probabilities need to sum up to 1 per row. - reference['y_pred_proba_clazz'] = 0.1 - reference['y_pred_proba_prepaid_card'] = 0.9 * reference['y_pred_proba_prepaid_card'] - reference['y_pred_proba_highstreet_card'] = 0.9 * reference['y_pred_proba_highstreet_card'] - reference['y_pred_proba_upmarket_card'] = 0.9 * reference['y_pred_proba_upmarket_card'] - monitored['y_pred_proba_clazz'] = 0.1 - monitored['y_pred_proba_prepaid_card'] = 0.9 * monitored['y_pred_proba_prepaid_card'] - monitored['y_pred_proba_highstreet_card'] = 0.9 * monitored['y_pred_proba_highstreet_card'] - monitored['y_pred_proba_upmarket_card'] = 0.9 * monitored['y_pred_proba_upmarket_card'] - reference['y_true'].iloc[-1000:] = 'clazz' + reference["y_pred_proba_clazz"] = 0.1 + reference["y_pred_proba_prepaid_card"] = ( + 0.9 * reference["y_pred_proba_prepaid_card"] + ) + reference["y_pred_proba_highstreet_card"] = ( + 0.9 * reference["y_pred_proba_highstreet_card"] + ) + reference["y_pred_proba_upmarket_card"] = ( + 0.9 * reference["y_pred_proba_upmarket_card"] + ) + monitored["y_pred_proba_clazz"] = 0.1 + monitored["y_pred_proba_prepaid_card"] = ( + 0.9 * monitored["y_pred_proba_prepaid_card"] + ) + monitored["y_pred_proba_highstreet_card"] = ( + 0.9 * monitored["y_pred_proba_highstreet_card"] + ) + monitored["y_pred_proba_upmarket_card"] = ( + 0.9 * monitored["y_pred_proba_upmarket_card"] + ) + reference["y_true"].iloc[-1000:] = "clazz" calc = CBPE( y_pred_proba={ - 'prepaid_card': 'y_pred_proba_prepaid_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'upmarket_card': 'y_pred_proba_upmarket_card', - 'clazz': 'y_pred_proba_clazz', + "prepaid_card": "y_pred_proba_prepaid_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "upmarket_card": "y_pred_proba_upmarket_card", + "clazz": "y_pred_proba_clazz", }, - y_pred='y_pred', - y_true='y_true', - metrics=['roc_auc'], - problem_type='classification_multiclass', + y_pred="y_pred", + y_true="y_true", + metrics=["roc_auc"], + problem_type="classification_multiclass", ) calc.fit(reference) _ = calc.estimate(monitored) @@ -3761,19 +4163,22 @@ def test_cbpe_multiclass_business_value_matrix_square_requirement(): # noqa: D1 [0, 1, 0], ] ) - with pytest.raises(InvalidArgumentsException, match="business_value_matrix is not a square matrix but has shape:"): + with pytest.raises( + InvalidArgumentsException, + match="business_value_matrix is not a square matrix but has shape:", + ): _ = CBPE( y_pred_proba={ - 'upmarket_card': 'y_pred_proba_upmarket_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'prepaid_card': 'y_pred_proba_prepaid_card', + "upmarket_card": "y_pred_proba_upmarket_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "prepaid_card": "y_pred_proba_prepaid_card", }, - y_pred='y_pred', - y_true='y_true', - problem_type='classification_multiclass', - metrics=['business_value'], + y_pred="y_pred", + y_true="y_true", + problem_type="classification_multiclass", + metrics=["business_value"], business_value_matrix=business_value_matrix, - normalize_business_value='per_prediction', + normalize_business_value="per_prediction", chunk_number=1, ) @@ -3790,19 +4195,22 @@ def test_cbpe_multiclass_business_value_matrix_classes_and_bvm_shape(): # noqa: ] ) with pytest.raises( - InvalidArgumentsException, match=re.escape("business_value_matrix has shape (4, 4) but we have 3 classes!") + InvalidArgumentsException, + match=re.escape( + "business_value_matrix has shape (4, 4) but we have 3 classes!" + ), ): _ = CBPE( y_pred_proba={ - 'upmarket_card': 'y_pred_proba_upmarket_card', - 'highstreet_card': 'y_pred_proba_highstreet_card', - 'prepaid_card': 'y_pred_proba_prepaid_card', + "upmarket_card": "y_pred_proba_upmarket_card", + "highstreet_card": "y_pred_proba_highstreet_card", + "prepaid_card": "y_pred_proba_prepaid_card", }, - y_pred='y_pred', - y_true='y_true', - problem_type='classification_multiclass', - metrics=['business_value'], + y_pred="y_pred", + y_true="y_true", + problem_type="classification_multiclass", + metrics=["business_value"], business_value_matrix=business_value_matrix, - normalize_business_value='per_prediction', + normalize_business_value="per_prediction", chunk_number=1, ).fit(reference) diff --git a/tests/sampling_error/test_binary_classification_sampling_error.py b/tests/sampling_error/test_binary_classification_sampling_error.py index 7dbb3fa4..766eb089 100644 --- a/tests/sampling_error/test_binary_classification_sampling_error.py +++ b/tests/sampling_error/test_binary_classification_sampling_error.py @@ -28,7 +28,7 @@ def test_auroc_sampling_error_nan(): # noqa: D103 sample_size = 50 chunk = np.random.random(sample_size) - components = np.NaN, np.NaN + components = np.nan, np.nan sampling_error = bse.auroc_sampling_error(components, chunk) assert np.isnan(sampling_error) @@ -99,7 +99,7 @@ def test_accuracy_sampling_error(): # noqa: D103 def test_ap_sampling_error_when_nan(): # noqa: D103 - comp1 = np.NaN + comp1 = np.nan comp2 = 0 data = pd.DataFrame({'y_true': [0, 1, 1], 'y_pred_proba': [0.4, 0.6, 0.7]}) diff --git a/tests/stats/test_median.py b/tests/stats/test_median.py index db7717a8..d9710b19 100644 --- a/tests/stats/test_median.py +++ b/tests/stats/test_median.py @@ -48,7 +48,7 @@ def test_stats_median_calculator_should_not_fail_given_nan_values( # noqa: D103 binary_classification_data ): reference, monitored = binary_classification_data - reference.loc[1000:11000, 'car_value'] = np.NaN + reference.loc[1000:11000, 'car_value'] = np.nan try: calc = SummaryStatsMedianCalculator( column_names=['car_value'], diff --git a/tests/test_calibration.py b/tests/test_calibration.py index b49cbb3a..474480f3 100644 --- a/tests/test_calibration.py +++ b/tests/test_calibration.py @@ -76,7 +76,7 @@ def test_needs_calibration_returns_false_when_only_single_class_in_y_true(): # def test_needs_calibration_raises_invalid_args_exception_when_y_true_contains_nan(): # noqa: D103 - y_true = pd.Series([0, 0, 0, 0, 0, np.NaN, 1, 1, 1, 1, 1, 1]) + y_true = pd.Series([0, 0, 0, 0, 0, np.nan, 1, 1, 1, 1, 1, 1]) y_pred_proba = np.asarray([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]) with pytest.raises(InvalidArgumentsException, match='target values contain NaN.'): _ = needs_calibration(y_true, y_pred_proba, IsotonicCalibrator()) @@ -84,7 +84,7 @@ def test_needs_calibration_raises_invalid_args_exception_when_y_true_contains_na def test_needs_calibration_raises_invalid_args_exception_when_y_pred_proba_contains_nan(): # noqa: D103 y_true = pd.Series([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]) - y_pred_proba = pd.Series(np.asarray([0, 0, 0, np.NaN, 0, 0, 1, 1, 1, 1, 1, 1])) + y_pred_proba = pd.Series(np.asarray([0, 0, 0, np.nan, 0, 0, 1, 1, 1, 1, 1, 1])) with pytest.raises(InvalidArgumentsException, match='predicted probabilities contain NaN.'): _ = needs_calibration(y_true, y_pred_proba, IsotonicCalibrator())