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How to use _build_variation method in Slash

Best Python code snippet using slash

variation_factory.py

Source:variation_factory.py

...82        if not needed_ids:83            yield Variation(self._store, {}, {})84            return85        for value_indices in itertools.product(*(range(len(p.values)) for p in parametrizations)):86            yield self._build_variation(parametrizations, value_indices)87    def _build_variation(self, parametrizations, value_indices):88        value_index_by_id = {}89        for param, param_index in zip(parametrizations, value_indices):90            value_index_by_id[param.info.id] = param_index...

FeatureImportance.py

Source:FeatureImportance.py

...35        else:36            minimum, maximum = column_data.min(), column_data.max()37            column_variants = numpy.arange(minimum, maximum, (maximum - minimum) / 100)38        return column_variants39    def _build_variation(self, column_name, variants, median_values):40        variations = []41        for variant in variants:42            variation_dict = {**median_values}43            variation_dict[column_name] = variant44            variations.append(variation_dict)45        return variations46    def _build_tensor(self, inputs):47        tensor = {48            key: convert_to_tensor([input[key] for input in inputs])49            for key in inputs[0]50        }51        return tensor52    def _continous_target_impact(self, model_output):53        return float(max(model_output) - min(model_output))54    def _discrete_target_impact(self, model_output):55        df = DataFrame(model_output)56        impact = (df.max() - df.min()).sum()57        return float(impact)58    def _get_field_importance(self, res):59        impact = (60            self._discrete_target_impact(res)61            if self.problem_type == Coltype.DISCRETE62            else self._continous_target_impact(res)63        )64        return impact65    def extract(self, df: DataFrame):66        median_values = self._get_median(df)67        impacts = {}68        dataset_input_features = list(69            filter(70                lambda x: x.columnName != self.target_col, self.dataset.datasetFields71            )72        )73        input_counts = []74        agg_inputs = []75        for field in dataset_input_features:76            field: DatasetFeature = field77            column_variants = self._generate_variants(df[field.columnName], field)78            inputs = self._build_variation(79                field.columnName, column_variants, median_values80            )81            input_counts.append(len(inputs))82            agg_inputs.extend(inputs)83        tensor = self._build_tensor(agg_inputs)84        res = self.model.predict(tensor)85        start = 086        for idx, field in enumerate(dataset_input_features):87            field_res = res[start : start + input_counts[idx]]88            start = start + input_counts[idx]89            impacts[field.columnName] = float(self._get_field_importance(field_res))...

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