Challenger Bratislava 2 stats & predictions

Economic Impact on Local Community

The economic benefits of hosting such a prestigious event are significant:

• Tourism Boost**: The influx of international visitors boosts local businesses, including hotels, restaurants, and shops.
1: msg = ( "Peptides are not all " "of same length." ) raise ValueError(msg) if not np.all(np.isfinite(self.X)): msg = ( "X contains infinite values." ) raise ValueError(msg) self.peptide_lengths = self._get_peptide_lengths() if not isinstance(self.y, (np.ndarray)): msg = ( "y should be numpy array." ) raise ValueError(msg) y_shape = np.shape(self.y) if len(y_shape) != 2: msg = ( "y should be two-dimensional." ) raise ValueError(msg) if y_shape[-1] != self.n_classes: msg = ( "y should have shape (n_samples," "{}).".format(self.n_classes) ) raise ValueError(msg) if not np.all(np.isfinite(self.y)): msg = ( "y contains infinite values." ) raise ValueError(msg) y_summed_axis_zero = np.sum(self.y, axis=0) if not np.all(y_summed_axis_zero > 0): msg = ( "Each class must have at least one positive sample." ) raise ValueError(msg) y_summed_axis_one = np.sum(self.y, axis=1) if not np.all(np.logical_or(y_summed_axis_one == 0, y_summed_axis_one == self.n_classes)): msg = ( "Each sample must belong exactly " "to one class or no class at all." ) raise ValueError(msg) def _check_alleles_are_valid_and_unique(alleles): check_alleles_are_valid(alleles) check_alleles_are_unique(alleles) class _Splitter(object): def __init__(self, ): self.n_splits = n_splits self.shuffle = shuffle self.random_state = random_state assert isinstance(random_state, (type(None), int)) assert isinstance(n_splits, int) assert n_splits >=2 assert isinstance(shuffle, bool) def split(self, ): X_indices = np.arange(X.shape[ ]) y_indices_by_class = [] for i in range(y.shape[ ]): class_indices_bool_array = y[:, i] class_indices_bool_array = class_indices_bool_array.astype( bool ) class_indices = X_indices[class_indices_bool_array] y_indices_by_class.append(class_indices) indices_by_class_and_split_by_train_test_by_split_index_list_of_lists_of_lists_of_lists_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers_list_of_integers ***** Tag Data ***** ID: 1 description: Complex validation logic for dataset properties including peptide length, finite checks on arrays X and y, ensuring correct dimensions of y, ensuring all classes have at least one positive sample, etc. start line: 22 end line: 103 dependencies: - type: Class name: _BaseDataset start line: 14 end line: 20 - type: Method name: __init__ start line: 17 end line: 20 - type: Method name: _get_peptide_lengths start line: 83 end line: (assumed end after 'return lengths') context description: This snippet validates various properties of dataset attributes. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: N ************ ## Challenging aspects ### Challenging aspects in above code: 1. **Property Decorators:** The use of property decorators (`@property`) adds complexity because these properties are computed dynamically based on current object state rather than being simple attributes. 2. **Validation Logic:** The `_validate_X` method contains intricate validation logic that ensures `X` meets several criteria including shape consistency, valid peptide lengths (using `_VALID_PEPTIDE_LENGTHS`), finite values only (no NaNs or infinities), among others. 3. **Class Consistency:** Ensuring that `y` is two-dimensional with consistent class membership across samples adds another layer of complexity. 4. **Class Balance Check:** Ensuring that each class has at least one positive sample introduces logical complexity since it requires checking sums across specific axes. 5. **Dynamic Error Handling:** Each validation step involves detailed error messages which must be carefully crafted to ensure clarity when exceptions are raised. ### Extension: 1. **Dynamic Updates:** Introduce functionality where `X` or `y` can be dynamically updated after initial validation while maintaining all constraints. 2. **Cross-validation Support:** Extend validation logic to support cross-validation scenarios where data might be split into training/validation sets multiple times. 3. **Feature Scaling:** Implement feature scaling checks or transformations within `_validate_X`. 4. **Handling Missing Data:** Extend `_validate_X` to handle missing data points intelligently rather than simply raising an error. 5. **Multi-class Support:** Extend validation logic to support multi-class classification scenarios more robustly. 6. **Logging Enhancements:** Improve logging within `_validate_X` to provide more granular insights during debugging. ## Exercise: ### Problem Statement: You are required to extend the functionality provided by [SNIPPET] by implementing additional features that handle dynamic updates and cross-validation scenarios while maintaining all existing constraints. #### Requirements: 1. **Dynamic Updates Handling**: * Implement methods `update_X(new_X)` and `update_y(new_y)` which allow updating `X` or `y` respectively after initial validation. * Ensure that these updates maintain all existing constraints (e.g., shape consistency, valid peptide lengths). 2. **Cross-validation Support**: * Implement cross-validation support by adding methods `split_for_cross_validation